Dimosthenis Kyriazis

Ofer Biran
email: biran@il.ibm.com

Thanassis Bouras
email: bouras@ubitech.eu

Klaus Brisch
email: klaus.brisch@dwf.law

Armend Duzha
email: armend.duzha@maggioli.it

Rafael Del Hoyo

Athanasios Kiourtis
email: kiourtis@unipi.gr

Pavlos Kranas
email: pavlos@leanxcale.com

Ilias Maglogiannis

George Manias
email: gmanias@unipi.gr

Armend Du- Zha

Rafael Del Hoyo
email: rdelhoyo@itainnova.es

Marc Meerkamp
email: marc.meerkamp@dwf.law

Konstantinos Moutselos

Argyro Mavrogiorgou

Panayiotis Michael
email: panayiotismichael@mail.ntua.gr

Ricard Munné
email: ricard.munne@atos.net

Giuseppe La Rocca
email: giuseppe.larocca@egi.eu

Kostas Nasias
email: knasias@okys.eu

Tomas Pariente Lobo
email: tomas.parientelobo@atos.net

Vega Rodrigálvarez
email: vrodrigalvarez@itainnova.es

Nikitas M Sgouros
email: sgouros@unipi.gr

Konstantinos Theodo- Siou
email: ktheodosiou@ubitech.eu

Panagiotis Tsanakas

PolicyCLOUD: Analytics as a Service Facilitating Efficient Data-Driven Public Policy Management

Keywords: Data Analytics as a Service, Public Policies, Cloud Computing, Policy Modelling

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The ICT advances as well as the increasing use of devices and networks, and the digitalisation of several processes is leading to the generation of vast quantities of data. These technological advances have made it possible to store, transmit and process large amounts of data more effectively than before in several domains of human activity of public interest [START_REF]How can social media data be used to improve services?[END_REF]. It is undeniable that we are inundated with more data than we can possibly analyse [START_REF]Data Growth, Business Opportunities, and the IT Imperatives[END_REF]. This rich data environment affects decision and policy making: cloud environments, big data and other innovative data-driven approaches for policy making create opportunities for evidence-based policies, modernization of public sectors and assistance of local governance towards enhanced levels of trust [START_REF]Setting Data Policies, Standards, and Processes[END_REF]. During the traditional policy cycle, which is divided into 5 different stages (agenda setting, policy formulation, decision making, policy implementation and policy evaluation), data is a valuable tool for allowing policy choices to become more evidence-based and analytical [START_REF] Anderson | Cases in public policy-making[END_REF]. The discussion of data-driven approaches to support policy making can be distinguished between two main types of data. The first is the use of open data (administrative -open -data and statistics about populations, economic indicators, education, etc.) that typically contain descriptive statistics, which are used more intensively and in a linked way, shared through cloud environments [START_REF] Anderson | Cases in public policy-making[END_REF]. The second main type of data is from any source, including data related to social dynamics and behaviour that affect the engagement of citizens (e.g. online platforms, social media, crowd-sourcing, etc.). These data are analysed with novel methods such as sentiment analysis, location mapping or advanced social network mining. Furthermore, one key challenge goes beyond using and analysing big data, towards the utilization of infrastructures for shared data in the scope of ethical constraints both for the citizens and for the policy makers. These ethnical constraints include "data ownership" ones, which determine the data sharing rules, as well as data localisation constraints, which may unjustifiably interfere with the "free flow of data". As for policy makers, the dilemma, to what extent big data policy making is in accordance to values elected governments promote, is created. This is a problem deriving from the fact that it is difficult to point to the scope of the consent citizens may give to big data policy analysis [START_REF]Data for Policy: A study of big data and other innovative data-driven approaches for evidence-informed policymaking[END_REF]. Furthermore, such policies provide a broad framework for how decisions should be made regarding data, meaning that they are high-level statements and need more detail before they can be operationalized [START_REF]Big Data: Basics and Dilemmas of Big Data Use in Policy-making[END_REF].

Big data enabled policy making should answer modern democratic challenges, considering facts about inequality and transparency both on national and local level, involving multi-disciplinary and multi-sectoral teams.

In this context, this paper presents the main research challenges and proposes an architecture of an overall integrated cloud-based environment for data-driven policy management. The proposed environment (namely PolicyCLOUD) provides decision support to public authorities for policy modelling, implementation and simulation through identified populations, as well as for policy enforcement and adaptation. Additionally, a number of technologies are introduced that aim at optimizing policies across public sectors by utilizing the analysed inter-linked datasets and assessing the impact of policies, while considering different properties (i.e. area, regional, local, national) and population segmentations. One of the key aspects of the environment is its ability to trigger the execution of various analytics and machine learning models as a service. Thus, the implemented and integrated service can be executed over different datasets, in order to obtain the results and compile the corresponding policies.

Related work

In terms of managing diverse data sources, the evolution of varieties of data stores (i.e. SQL and NoSQL), where each variety has different strengths and usage models, is linked with the notion of "polyglot persistence" [START_REF]What is Polyglot Persistence?[END_REF]. The latter emphasizes that each application and workload may need a different type of data store, tailored for its needs (e.g. graph, time series). Moreover, the field of data warehousing addresses creating snapshots of Online Transactional Processing (OLTP) databases for the purposes of Online Analytical Processing (OLAP). This often requires copying the data and preparing it for analytics by transforming its structure (i.e. Extract Transform Load process) and building the relevant indexes for the analytical queries. This costly process is performed in order to achieve fast query times for analytical queries of interest, and in order to support data mining. However, there is an increasing trend to adopt a "just in time data warehouse" model, where data are federated on the fly according to runtime parameters and constraints [START_REF]Building a Just-In-Time Data Warehouse Platform with Databricks[END_REF]. As a result, data analytics frameworks increasingly strive to cater for data regardless of the underlying data store. Apache Spark [START_REF]Apache Spark Homepage[END_REF] is an open source framework for analytics, which is designed to run in a distributed setting within a data centre. Spark provides a framework for cluster computing and memory management, and invents the notion of a Resilient Distributed Dataset (RDD) [START_REF] Zaharia | Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing[END_REF] that can be stored persistently using any storage framework that implements the Hadoop File System interface, including the Hadoop Distributed Filesystem (HDFS), Amazon S3 and OpenStack Swift. The Spark SQL component additionally defines a DataFrame as an RDD having a schema, and provides a SQL interface over DataFrames [START_REF] Armbrust | Spark SQL: Relational Data Processing in Spark[END_REF]. Built in support is provided for data sources with a JDBC interface, as well as for Hive, and the Avro, Parquet, ORC and JSON formats. Moreover, there is an external data sources API, where new data sources can be added by implementing a driver for the data source that implements the API. Many such drivers have been implemented, for example for Cassandra, MongoDB and Cloudant. These data sources can be queried, joining data across them and thus provide the ability to run batch queries across multiple data sources and formats. Spark also integrates batch processing with real time processing in the form of Spark Streaming [START_REF] Zaharia | Discretized streams: faulttolerant streaming computation at scale[END_REF] that allows real-time processing using the same underlying framework and programming paradigm as for batch computations. In Spark 2.0, streaming Spark SQL computations are also planned [START_REF]Structuring Spark: DataFrames, DataSets and Streaming[END_REF]. With the advent of IoT and the increasing capabilities available at the edge, applications may store and process data locally [START_REF] Bonomi | Fog computing and its role in the internet of things[END_REF]. In the PolicyCLOUD architecture, data are managed whether in flight or at rest and are federated across multiple frameworks, data sources, locations and formats.

Another key aspect is data interoperability given the diversity of the data sources. Among the main value propositions of the PolicyCLOUD environment and tools for policy development and management will be its ability to integrate, link and unify the datasets from diverse sources, while at the same time enabling analytics over the unified datasets. As a key prerequisite to providing this added-value, the interoperability of diverse datasets should be ensured. A wide array of data representation standards in various domains have emerged as a means of enabling data interoperability and data exchange between different systems. Prominent examples of such standards in different policy areas include: (i) the INSPIRE Data Specifications [START_REF]Data Specifications[END_REF] for the interoperability of spatial data sets and services, which specify common data models, code lists, map layers and additional metadata on the interoperability to be used when exchanging spatial datasets, (ii) the Common European Research Information Format (CERIF) [START_REF] Cordis | [END_REF] for representing research information and supporting research policies, (iii) the Internet of Things ontologies and schemas, such as the W3C Semantic Sensor Networks (SSN) ontology [17] and data schemas developed by the Open Geospatial Consortium (e.g., SensorML) [START_REF]Open Geospatial Standards[END_REF], (iv) the Common Reporting Standard (CRS) that specifies guidelines for obtaining information from financial institutions and automatically exchanging that information in an interoperable way, and (v) standards-based ontologies appropriate for describing social relationships between individuals or groups, such as the "The Friend Of A Friend" (FOAF) ontology [START_REF]FOAF Homepage[END_REF] and the Socially Interconnected Online Communities (SIOC) ontology [START_REF]SIOC project[END_REF]. These standards provide the means for common representation of domain specific datasets, towards data interoperability (including in several cases semantic interoperability) across diverse databases and datasets. Nevertheless, these standards are insufficient for delivering what PolicyCLOUD promises for a number of reasons. Initially, there is a lack of semantic interoperability in the given domain. For example, compliance to ontologies about IoT and sensor data fails to ensure a unified modelling of the physics and mathematics, which are at the core of any sensing task. Hence, in several cases there is a need for extending existing models with capabilities for linking/relating various quantifiable and measurable (real-world) features to define, in a user understandable and machine-readable manner the processes behind single or combined tasks in the given domain. Furthermore, there is a lack of semantic interoperability across datasets from different sectors. There is not easy way to link related information elements stemming from datasets in different sectors, which typically comprise different schemas. In this context, environmental datasets and transport datasets for instance contain many related elements, which cannot however be automatically identified and processed by a system due to the lack of common semantics. Finally, one needs to consider the lack of process interoperability. PolicyCLOUD deals with data-driven policy development and management, which entails the simulation and validation of entire processes. Especially in the case of multi-sectoral considerations (e.g., interaction and trade-offs between different policies) process interoperability is required in order to assess the impact of one policy on another. PolicyCLOUD proposes a multi-layer framework for interoperability across diverse policy related datasets, which will facilitate semantic interoperability across related datasets both within a single sector and across different policy sectors. Within a specific sector of each use case, semantic interoperability will enable adhering to existing standards-based representations for the sector data and other auxiliary data (e.g. sensor data, social media data). Across different use cases, PolicyCLOUD proposes a LinkedData approach [START_REF] Heath | Linked Data: Evolving the Web into a Global Data Space[END_REF] to enable linking of interrelated data across different ontologies.

3

Main challenges and proposed approach

Main challenges and objectives addressed by PolicyCLOUD

A data-driven approach for effective policies management The challenge is to provide a scalable, flexible and dependable methodology and environment for facilitating the needs of data-driven policy modelling, making and evaluation. The methodology should aim at applying the properties of policy modelling, cocreation and implementation across the complete data path, including data modelling, representation and interoperability, metadata management, heterogeneous datasets linking and aggregation, analytics for knowledge extraction, and contextualization. Moreover, the methodology should exploit the collective knowledge out of policy "collections"/ clusters combined with the immense amounts of data from several sources (e.g. sensor readings, online platforms, etc.). These collections of policies should be analysed based on specific Key Performance Indicators (KPIs) in order to enable the correlation of these KPIs with different potential determinants of policies impact within and across different sectors (e.g. environment, radicalisation, migration, goods and services, etc.).

Compilation, assessment and optimization of multi-domain policies

Another challenge refers to holistic policy modelling, making and implementation in different sectors (e.g. environment, migration, goods and services, etc.), through the analysis and linking of KPIs of different policies that may be inter-dependant and intercorrelated (e.g. environment). The goal is to identify (unexpected) patterns and relationships between policies (through their KPIs) to improve policy making. Moreover, the approach should enable evaluation and adaptation of policies by dynamically extracting information from various data sources, community knowledge out of the collections of policies, and outcomes of simulations and evidence-based approaches. Policies should be evaluated to identify both their effective KPIs to be re-used in new / other policies, and the non-effective ones (including the causes for not being effective) towards their improvement. Thus, developed policies should consider the outcomes of strategies in other cases, such as policies addressing specific city conditions.

Data management techniques across the complete data path

Data-driven policy making highlights the need for a set of mechanisms that address the data lifecycle, including data modelling, cleaning, interoperability, aggregation, incremental data analytics, opinion mining, sentiment analysis, social dynamics and behavioural data analytics. In order to address data heterogeneity from different sources, modelling and representation technologies should provide a "meta-interpretation" layer, enabling the semantic and syntactic capturing of data properties and their representation. Another key aspect refers to techniques for data cleaning in order to ensure data quality, coherence and consistency including the adaptive selection of information sources based on evolving volatility levels (i.e. changing availability or engagement level of information sources). Mechanisms to assess the precision and correctness of the data, correct errors and remove ambiguity beyond limitations for multidimensional processing should be incorporated in the overall environment, while taking into consideration legal, security and ethical aspects.

Context-aware interoperability

The specific challenge refers to the design and implementation of a semantic layer that will address data heterogeneity. To this end, the challenge is to research on techniques and semantic models for the interoperable use of data in different scenarios (and thus policies), locations and contexts. Techniques for interoperability (such as OSLC -Open Services for Lifecycle Collaboration) with different ontologies (as placeholders for the corresponding information) should be combined with semantic annotations. Semantic models for physical entities / devices (i.e. sensors related to different policy sectors), virtual entities (e.g. groupings of such physical entities according to intrinsic or extrinsic, permanent or temporary properties) and online platforms (e.g. social media, humans acting as providers) should be integrated in data-driven policy making environments. These models should be based on a set of transversal and domain-specific ontologies and could provide a foundation for high-level semantic interoperability and rich semantic annotations across policy sectors, online systems and platforms. These will be turned into rich metadata structures providing a paradigm shift towards contentbased storage and retrieval of data instead of data-based, given that stakeholders and applications target and require such content based on different high-level concepts.

Content-based networks of data objects need to be developed, allowing retrieval of semantically similar contents.

Social dynamics and incentives management

One of the main barriers to public bodies experimenting with big data to improve evidence-based policy making is citizens' participation since they are lacking awareness of the extend they may influence policy design and the ways these will be feasible. The challenge is to raise awareness about policy consultations and enable citizens to take direct action to participate, thus ensuring higher levels of acceptance and of trust. A potential solution could be to follow a living lab approach and implement an engagement strategy based on different incentives mechanisms. Furthermore, a data-driven policy management environment should allow social dynamics and behaviour to be included in the policy lifecycle (creation, adaptation, enforcement, etc.) through the respective models and analytical tools. These will allow policy makers to obtain the relevant crowd-sourcing data and the knowledge created by the closed groups (i.e. communities evaluating proposed policies) and the engaged citizens to analyse and propose social requirements that will be turned into policy requirements. On top of this, incentives management techniques will identify, declare and manage incentives for citizens' engagement, supporting different types of incentives (e.g. social, cultural, political, etc.), with respect to information exchange, contributions and collaboration aspirations. The environment should also provide strategies and techniques for the alignment participation incentives, as well as protocols enabling citizens to establish their participation.

Analytics as a service reusable on top of different datasets

Machine and deep learning techniques, such as classification, regression, clustering and frequent pattern mining algorithms, should be realized in order to infer new data and knowledge. Sentiment analysis and opinion mining techniques should determine whether the provided contributor's input is positive or negative about a policy, thus developing a "contributor graph" for the contributors of the opinions that happen to be themselves contributors to ongoing policy making projects. In the same context, social dynamics and behavioural data analytics should provide insights regarding which data needs to be collected and aggregated in a given case (e.g. time window addressed by the policy, location of populations, expected impact, etc.), taking into consideration the requirements of the engaged citizens to model the required policies. Moreover, a main challenge refers to technologies that allow analytics tasks to be decoupled from specific datasets and thus be triggered as services and applies to various cases and datasets.

Transferable methods and a unique endpoint to exploit analytics in different cases

A key challenge refers to an overall system, acting as an endpoint that will allow stakeholders (such as policy makers and public authorities) to trigger the execution of different models and analytical tools on their data (e.g. to identify trends, to mine opinion artefacts, to explore situational and context awareness information, to identify sensitives, etc.) and obtain the results. Based on these results, the modelled policies (through their KPIs) will be realized / implemented and monitored against these KPIs. Moreover, the endpoint should allow stakeholders and public administration entities to express in a declarative way their analytical tasks and thus perform / ingest any kind of data processing. Another need is for an adaptive visualization environment, enabling policy monitoring to be visualized in different ways while the visualization can be modified on the fly. The environment should also enable the specification of the assets to be visualized: which data sources and which meta-processed information. The goal is to enable the selection of sources based on the stakeholders' needs. Incremental visualization of analytics outcomes should also be feasible enabling visualization of results as they are generated.

Architecture overview

As a complete environment, the proposed architectural approach includes a set of main building blocks to realize the corresponding functionality as depicted in the following figure (Fig. 1).

Conclusions

The vast amounts of data that are being generated by different sources highlight an opportunity for public authorities and stakeholders to create, analyse, evaluate and optimize policies based on the "fresh" data, the information that can be continuously collected by citizens and other sensors. To this end, what is required refers to techniques and an overall integrated environment that will facilitate not only data collection but also assessment in terms of reliability of the data sources, homogenization of the datasets in order to make them interoperable (following the heterogeneity in terms of content and formats of the data sources), cleaning of the datasets and analytics. While several analytical models and mechanisms are being developed a key challenge relates to approaches that will enable analytics to be triggered as services and thus applies and utilized in different datasets and contexts. In this paper, we have presented the aforementioned challenges and the necessary steps to address them. We also introduced a conceptual architecture that depicts a holistic cloud-based environment integrating a set of techniques across the complete data and policy management lifecycles in order to enable data-driven policy management. It is within our next steps to implement the respective mechanisms and integrated them based on the presented architecture, thus realizing the presented environment.

Fig. 1 .

 1 Fig. 1. Proposed functional architecture.The overall flow is initiated from various data sources, as depicted in the figure through the respective Data Acquisition block. Data sources can be data stores from public authorities or external data sources (e.g. mobile devices, IoT sensors, etc.) that contribute data following the provision of incentives, facilitated through the incentives management mechanism. A set of APIs incorporated in a gateway component, enable data collection by applying techniques to identify the reliable sources exploiting the sources reliability component and for these sources obtain the data and perform the required data quality assessment and cleaning. Semantic and syntactic interoperability techniques are utilized over the cleaned data providing the respective interoperable datasets to the Policy CLOUD datastore following the required data linking and aggregation processes. The datastore is accessible from a set of machine learning models represented through the Data Analytics building block. Machine learning models incorporate opinion mining, sentiment and social dynamic analysis, behavioural analysis and situational / context knowledge acquisition. The data store and the analytics models are hosted and executed in a cloud-based environment that provides the respective services obtained from a catalogue of cloud infrastructure resources. Furthermore, all the analytics models are realized as services, thus enabling their invocation through a proposed policy development toolkitrealized in the scope of the Policies building block of the proposed architecture as a single point of entry into the PolicyCLOUD platform. The toolkit allows the compilation of policies as data models, i.e. structural representations that include key performance indicators (KPIs) as a means to set specific parameters (and their target values) and monitor the implementation of policies against these KPIs along with the list of analytical tools to be used for their computation. According to these analytics outcomes, the values of the KPIs are specified resulting to policies implementation / creation. It should be noted that PolicyCLOUD also introduces the

Acknowledgment

The research leading to the results presented in this paper has received funding from the European Union's funded project PolicyCLOUD under Grant Agreement no 870675.


Petia Koprinkova-Hristova

Nadejda Bocheva

Simona Nedelcheva

Miroslava Stefanova

Bilyana Genova
email: b.genova@abv.bg

Radoslava Kraleva
email: radykraleva@swu.bg

Velin Kralev
email: velinkralev@swu.bg

Keywords: Spike timing neural model, Spike timing dependent plasticity, Visual system, Decision making, Saccade generation

We investigated age related synaptic plasticity in thalamic reticular nucleus (TRN) as a part of visual information processing system in the brain. Simulation experiments were performed using a hierarchical spike timing neural network model in NEST simulator. The model consists of multiple layers starting with retinal photoreceptors through thalamic relay, primary visual cortex layers up to the lateral intraparietal cortex (LIP) responsible for decision making and preparation of motor response. All synaptic inter-and intra-layer connections of our model are structured according to the literature information. The present work extends the model with spike timing dependent plastic (STDP) synapses within TRN as well as from visual cortex to LIP area. Synaptic strength changes were forced by teaching signal typical for three different age groups (young, middle and elderly) determined experimentally from eye movement data collected by eye tracking device from human subjects preforming a simplified simulated visual navigation task.

STDP Plasticity in TRN within Hierarchical Spike Timing Model of Visual Information Processing 1 Introduction

The visual information coming through our eyes is processed by a hierarchy of multiple consecutive brain areas having different functionality. The sensory layer (retina) consists of photo-receptive cells. It transforms the incoming light into electrical signals fed into the brain via retina ganglion cells (RGC). Next a relay structure (lateral geniculate nucleus (LGN) and thalamic reticular nucleus (TRN)) transmits the signals to the primary visual cortex (V1). Higher brain areas (middle temporal area (MT) and medial superior temporal area (MST)) are responsible for motion information processing. Based on perceived sensory information our brain makes decisions and initiates motor responses. The decisions based on processed visual information are taken in the lateral intraparietal area (LIP) that is also responsible for preparation of the eyes' motor response (change of gaze direction) called saccade. Most of the existing motion information processing models are restricted to the interactions between some of the mentioned areas like: V1 and MT in [START_REF] Simoncelli | A model of neuronal responses in visual area MT[END_REF][START_REF] Bayerl | Disambiguating visual motion through contextual feedback modulation[END_REF][START_REF] Bayerl | A Model of Visual Perception[END_REF][START_REF] Chessa | A systematic analysis of a V1-MT neural model for motion estimation[END_REF], V1, MT and MST in [START_REF] Raudies | Active gaze control improves optic flowbased segmentation and steering[END_REF]; MT and MST in [START_REF] Grossberg | A neural model of motion processing and visual navigation by cortical area MST[END_REF][START_REF] Perrone | A neural-based code for computing image velocity from small sets of middle temporal (MT/V5) neuron inputs[END_REF]. Many models consider only the feedforward interactions (e.g. [START_REF] Simoncelli | A model of neuronal responses in visual area MT[END_REF][START_REF] Solari | What can we expect from a V1-MT feedforward architecture for optical flow estimation?[END_REF]) disregarding the feedback connectivity; others employ rate-based equations (e.g. [START_REF] Raudies | A neural model of the temporal dynamics of figure-ground segregation in motion perception[END_REF][START_REF] Grossberg | Neural dynamics of motion integration and segmentation within and across apertures[END_REF]) considering an average number of spikes in a population of neurons. In our preliminary research [START_REF] Koprinkova-Hristova | Spike timing neural model of motion perception and decision making[END_REF] we have developed and implemented in NEST 2.12.0 simulator ( [16]) a spike-timing neural network model having static inter-and intra-layer synaptic connections structured according to the literature information that includes all mentioned above structures. Determination of the parameters' values for such kind of models is usually done using electrophysiological recordings directly from the brain that are rarely available for all modelled brain areas for human subjects. Our preliminary attempt to tune the synaptic connections between the last two layers (MST and LIP) using final outcome from experiments on visual information perception and decision making, i.e. recorded motor reaction (saccade generation) revealed that spike timing dependent plasticity (STDP) led to age-related changes in these synaptic weights [START_REF] Koprinkova-Hristova | STDP training of hierarchical spike timing model of visual information processing[END_REF]. Training data was collected from the human decisions during experiment with visual stimuli simulating optic flow patterns of forward self-motion on a linear trajectory to the left or to the right of the center of the visual field with a gaze in the direction of heading. The subjects had to indicate the perceived direction of heading by saccade movement. The mean latency of the eye movements for each one of the three age groups (young, middle age and elderly) was used as training signal fed into the output layer of the model structures.

In the present work we extended our model [START_REF] Koprinkova-Hristova | Spike timing neural model of motion perception and decision making[END_REF] with feedback connectivity between each pair of consecutive layers thus allowing for complete feedback propagation of training signals. We also allow STDP plasticity in the feedback / feedforward connections within thalamic relay structure in order to test whether training signal propagates deeper in the model. The thalamus provides sensory input to the cortex, but it also receives feedback from the cortex that is considered to be modulatory [START_REF] Sherman | Exploring the thalamus and its role in cortical function[END_REF]. It receives also indirect inhibitory feedback from TRN, a structure that is considered to be related to attention (e.g., [START_REF] Crick | Function of the thalamic reticular complex: The searchlight hypothesis[END_REF]) and thus, it is also expected to have a modulatory effect on the activity in the thalamus. Hence, a propagation of the effects of training to the thalamus would support the biological relevance of the proposed model. The paper is organized as follows: section 2 describes briefly the overall model structure and parameters; next we describe briefly the experimental set-up and data processing; section 4 presents results from STDP training of the model and obtained parameters typical for mean behaviour of each one of the three tested age groups; the concluding section comments obtained results and determines directions for our future work.

Model Structure

The hierarchical model organization is shown on Fig. 1. It is based on structure developed first in [START_REF] Koprinkova-Hristova | Spike timing neural model of motion perception and decision making[END_REF] based on literature information about each layer neurons' functionality, structure and connectivity according to [START_REF] Escobar | Action recognition using a bio-inspired feedforward spiking network[END_REF][START_REF] Ghodratia | Towards building a more complex view of the lateral geniculate nucleus: Recent advances in understanding its role[END_REF][START_REF] Kremkow | Push-pull receptive field organization and synaptic depression: Mechanisms for reliably encoding naturalistic stimuli in V1[END_REF][START_REF] Layton | Possible role for recurrent interactions between expansion and contraction cells in MSTd during self-motion perception in dynamic environments[END_REF][START_REF] Nedelcheva | Orientation selectivity tuning of a spike timing neural network model of the first layer of the human visual cortex[END_REF][START_REF] Sadeh | Statistics and geometry of orientation selectivity in primary visual cortex[END_REF][START_REF] Troyer | Contrast invariant orientation tuning in cat visual cortex: thalamocortical input tuning and correlationbased intracortical connectivity[END_REF]. The difference is in additional feedback connections from MST to MT as well as STDP connections within thalamic relay (to and from LGN to TRN and interneurons IN) and from MST to LIP area. Detailed description of model structure and connectivity design can be found in [START_REF] Koprinkova-Hristova | Spike timing neural model of motion perception and decision making[END_REF]. Here we briefly explain it. Each coloured rectangle on Fig. 1 represents a layer of neurons positioned on a regular two-dimensional grid. Connections between layers are denoted by arrows having color corresponding to the sign of their weights (red for positive, called excitatory and blue for negative, called inhibitory connections respectively). Connections denoted by solid lines have constant weights while those denoted by dashed lines are able to change their weights in dependence on activity of pre-and postsynaptic neurons, i.e. they have spike timing dependent plasticity (STDP) [START_REF] Rubin | Equilibrium properties of temporally asymmetric Hebbian plasticity[END_REF]. Sensory layer (RGC) as well as thalamic relay (LGN) consist of two sub-layers of neurons reacting positively (ON1 and ON2) and negatively (OFF1 and OFF2) to the increase of luminosity. Each neuron within LGN has its own interneuron (IN) and thalamic reticular nucleus (TRN) that processes feedback from the next (V1) layer. Layers V1 and MT have identical structure and connectivity adopted from [START_REF] Kremkow | Push-pull receptive field organization and synaptic depression: Mechanisms for reliably encoding naturalistic stimuli in V1[END_REF] that makes them sensitive to orientation and direction of movement of visual objects. The MST consists of two layers, sensitive to expansion (MSTe) and contraction (MSTc) movement patterns respectively, like in [START_REF] Layton | Possible role for recurrent interactions between expansion and contraction cells in MSTd during self-motion perception in dynamic environments[END_REF]. These sub-structures are represented as three groups on Fig. 1 that are able to detect expansion or contraction of moving objects from imaginary centers positioned left, right or at the center of the visual scene denoted by l, r and c on Fig. 1. Since our model aims to decide whether the expansion center of a moving dot stimulus is left or right from the stimulus center, we proposed a task-dependent design of excitatory/inhibitory connections from MST expansion/contraction layers to the two LIP sub-regions whose increased firing rate corresponds to two taken decisions for two alternative motor responses -eye movement to the left or to the right.

The reaction of RGC to light changes is simulated by a convolution with a spatio-temporal filter following model from [START_REF] Troyer | Contrast invariant orientation tuning in cat visual cortex: thalamocortical input tuning and correlationbased intracortical connectivity[END_REF]. For the neurons in LGN conductance-based leaky integrate-and-fire neuron model as in [START_REF] Casti | A simple model of retina-LGN transmission[END_REF] (iaf chxk 2008 in NEST) was adopted. For the rest of neurons, leaky integrate-and-fire model with exponential shaped postsynaptic currents according to [START_REF] Tsodyks | Synchrony generation in recurrent networks with frequency-dependent synapse[END_REF] (iaf psc exp in NEST) was used. All connection parameters are the same as in the cited literature sources.

3 Experimental Set-up

Behavioral experiment and data collection

The time series data used to test the idea described above were collected by eye tracking device that recorded the human eye movements during a behavioral experiment performed with the participation of volunteer human subjects responding to series of visual stimuli. Detailed description of experimental conditions can be found in [START_REF] Bocheva | Drift diffusion modeling of response time in heading estimation based on motion and form cues[END_REF][START_REF] Koprinkova-Hristova | Features extraction from human eye movements via echo state network[END_REF].

Here we briefly remind basic experimental set-up. The visual stimulation was performed by projection on a gray screen of different patterns of 50 white moving dots in a circular aperture with radius of 7.5 cm positioned in the middle of a computer screen. The patterns of dot movements were designed to mimic dots expansion from an imaginary center positioned left or right from the screen center respectively. The subject sat at 57cm from the monitor screen. Each stimulus presentation was preceded by a warning sound signal. A red fixation point with size of 0.8cm appeared in the center of the screen for 500 ms. The stimuli were presented immediately after the disappearance of the fixation point. The Subject's task was to continue looking at the position where the fixation point was presented until he/she made a decision where the center of the pattern was and to indicate this position by a saccade (fast eye movement). The subjects also had to press the left or the right mouse button depending on the perceived position of the center -to the left or to the right from the middle of the screen. If the subject could not make a decision during the stimulus presentation (3.3 s for 100 consecutive frames), the stimulus disappeared and the screen remained gray until the subject made a response. Each experimental session consisted of consecutive presentation in random order of 10 patterns for each of the 14 possible stimulus types from chosen experimental condition, i.e. totally 140 stimuli were observed by test subjects during every session. The eye movements of the participants in the experiment were recorded by a specialized hardware -Jazz novo eye tracking system [30]. All recordings from all the sensors of the device for one session per person were collected with 1 KHz frequency and the information is stored in files. These include: the calibration information; records of horizontal and vertical eye positions in degrees of visual angle eye x and eye y ; screen sensor signal for presence/absence of a stimulus on the monitor; microphone signal recording sounds during the experiment; information about tested subjects (code) and type of the experimental trial for each particular record.

Three age groups took part in the experiment: young (between 20 and 34 years old), elderly (from 57 to 84 years old) and middle aged group (between 36 and 52 years old). All participants have given a written informed consent for participating in the study after explanation of the experimental procedure. The experiments were approved by the ethical committee of the Institute of Neurobiology, Bulgarian Academy of Sciences and are in accordance with the Declaration of Helsinki.

Data processing

The raw data was collected in a relational database [START_REF] Kraleva | Design and analysis of a relational database for behavioral experiments data processing[END_REF]. It allowed us to process all sensors data in order to extract only the records from the presentation of a stimulus on the screen to the mouse button press. The data between the stimulus presentations were excluded since it is not relevant to the eye movements during task performance. The processed eye movement data were refined by removing the outliers and a drift diffusion model of mean response time of each one of the age groups for all four experimental conditions was derived [START_REF] Bocheva | Drift diffusion modeling of response time in heading estimation based on motion and form cues[END_REF].

Based on the identified mean reaction times from [START_REF] Bocheva | Drift diffusion modeling of response time in heading estimation based on motion and form cues[END_REF] we've created training signals as generating currents I lef t and I right for the left and right LIP neurons respectively as follows:

I lef t/right = A lef t/right /(1 + exp(k lef t/right t)) (1)
Amplitude A lef t/right defines maximal input current (in pA) while k lef t/right determines settling time of the exponent that corresponds to the mean reaction time determined from experiments for each age group and experimental condition. For all three age groups amplitude values were the same: A lef t = 200 and A right = 100. In order to achieve approximately the settling time determined from experimental data, parameter k lef t/right has different values for three age groups (Y -young, M -middle, O -old) with opposite signs for left and right case of stimulus respectively as follows: k Y lef t/right = -/+0.02; k M lef t/right = -/+0.01; k O lef t/right = -/ + 0.005.

Simulations

The overall model was tested using visual stimulation simulating an observer's motion on a linear trajectory with eyes fixed in the heading direction. Example from the stimuli used in the behavioural study with a position of the imaginary center to the left was selected so our aim is to teach the model to react correctly with increased spiking activity in the left LIP area after a time interval typical for group mean reaction time for each age. Training was performed in iterations, each one consisting of presentation of visual stimulation to the model input and the teaching signal to its output layer respectively. [ area of MST are of both types (excitatory and inhibitory) in dependence on their focal points position. Our simulations revealed that STDP rule changes only the positive connection weights while the negative ones remained constant.

E -4 ] [E -2 ] [E -2 ] [E -2 ] Y M O Y M O Y M O Y M O 1 2.
For the contraction MST layer connections to both LIP areas are inhibitory and they were changed more significantly for the right (incorrect) LIP area. Hence, it appears that the activity in the LIP layers is not directly related to the weights of the connections for the MST templates that correspond to the stimulus (i.e. the MSTe), but it depends on the combined activity of all templates. Fig. 3 and Tables 1 and2 show the connection weight changes after iterative training. We observe that the clearest differentiation that remains stable during iterations is in the excitatory connections from the expansion MST layer to the right LIP area. These connections' weights became smaller for the elderly group and bigger for the group of young test subjects while the excitatory connections from MSTe to the left LIP area (corresponding to correct response in this case) as well the inhibitory connections from MSTc to right (incorrect) LIP area reach highest absolute values for elderly group and lowest for the group of young test subjects. Therefore, the reduction in LIP activity in the wrong LIP layer is achieved by different means for the young and the elderly group suggesting a re-organization and different balance between the excitatory and inhibitory connections between the different layers and areas.

We also investigated connection weight changes during iterative training in TRN and LGN. Most of the connections within thalamic relay remained constant. The only observed changes were in the inhibitory feedback connections from TRN to LGN. This result implies an indirect modulatory effect of the activity in LGN from the cortical regions. The values obtained after five training iterations are shown on Fig. 4. Mean values and variances of trained weights obtained after one, two and five iterations for teaching signals corresponding to

Y M O Y M O Y M O Y M O 1
-0.98 -0.98 -0.98 -0.97 -0.97 -0.97 -0.87 -0.87 -0.87 -0.85 -0.85 -0.85 2 -0.98 -0.98 -0.98 -1.01 -1.01 -1.01 -0.86 -0.86 -0.86 -0.84 -0.84 -0.84 5 -0.99 -0.99 -0.99 -1.01 -1.01 -1.01 -0.86 -0.86 -0.86 -0.84 -0.84 -0.84 Table 4. Variances of normalized connection weights from TRN to LGN.

TRN to LGN ON1 TRN to LGN ON2 TRN to LGN OFF1 TRN to LGN OFF2 It.

[ the three age groups are presented in Tables 3 and4 respectively. While the connections from MST to LIP for the three age groups start to diverge just after the first iteration, those in thalamic relay needed more than two iterations to differentiate.

E -3 ] [E -3 ] [E -3 ] [E -3 ] Y M O Y M O Y M O Y M O 1
Concerning the propagation of learning-induced signal to the thalamic relay, the observed greater strength of the connections from TRN for the ON responses than to the OFF ones might be considered as an improvement of the sensitivity to the luminance changes in the stimulus and improved signal to noise ratio in its encoding at initial stages of the visual information processing. The increased variability of the connections might be related to the spatial distribution of the dots in the stimulus and to the greater specificity to it induced by the inhibitory connections from TRN to LGN. In the deeper STDP connectivity in thalamic relay, age differentiation became obvious after fifth iteration. Besides, after second iteration connectivity for young and middle age groups remain identical and different from the elderly group, while after the fifth iteration it seems that the middle age group differs from both young and elderly groups who became a little bit closer. This result suggests learning-induced plasticity that can alleviate the negative effect of ageing though after longer training periods.

Conclusions

In conclusion, the attempt to train our hierarchical spike timing neural network model using training signal for only output layer that is based on human reaction time from behavioural experiments rather than on electrophysiological recordings from the brain, revealed that it is completely possible to change not only connectivity between the last two layers of the model but also to propagate the teaching signal much deeper in the hierarchical structure. Such feedback mod-ulatory propagation from the cortical areas to the thalamus is in accordance with the known physiological data and gives credit to our modeling efforts. Our preliminary results also revealed typical for aging differentiation of connection weights especially for the connections between MST and LIP areas. While for all age groups the activity in the LIP layer corresponding to the incorrect response terminates with time, this effect is achieved by different recombination of the connection weights for the different age groups. Concerning deep thalamic relay, although age-related changes propagate much slowly, they were also observed after more training iterations indicating that learning-induced activity may reduce the age-related changes induced by the imbalance of inhibitory and excitatory activity in the cortical regions.

Further direction of our investigations will be enriching our hierarchical model with more STDP connections in order to complete training of its connection weights in dependence on typical reactions of different age groups.

Fig. 1 .

 1 Fig. 1. Model structure.

Fig. 2 .

 2 Fig. 2. LIP reactions for left (blue arrows) and right (red arrows) during first (left column) and fifth (right column) iteration for three age groups (top -young, second row -middle age and bottom row -elderly people).

Fig. 2 Fig. 3 .

 23 Fig. 2 presents spikes in both left (blue) and right (red) LIP layers obtained during first and last (fifth) iteration. It shows that the frequency of spikes induced in the right LIP layer decrease with time and after a varying delay for the three age groups no more spikes occur. The frequency of spikes for the left LIP region increases with time (though this change is not evident from the figure). Fig. 3 compares the weights of connections from MST to LIP area obtained after five iterations for the three age groups. For clarity only weights that are different for the three age groups are shown. The connections from expansion template

It.Table 2 .

 2 MSTe to LIP left MSTe to LIP right MSTc to LIP left MSTc to LIP right Y 08 +0.13 +0.10 +1.48 +1.40 +1.25 -0.95 -0.95 -0.95 -0.99 -0.99 -0.99 2 +0.12 +0.10 +0.12 +1.48 +1.40 +1.25 -0.95 -0.95 -0.95 -1.05 -1.04 -0.89 5 +0.09 +0.11 +0.13 +1.48 +1.40 +1.25 -0.95 -0.95 -0.95 -0.95 -0.83 -0.84 Variances of normalized connection weights from MST to LIP. MSTe to LIP left MSTe to LIP right MSTc to LIP left MSTc to LIP right It.

Fig. 4 .

 4 Fig. 4. Connections from TRN to LGN.

Table 1 .

 1 Mean values of normalized connection weights from MST to LIP.

Table 3 .

 3 Mean values of normalized connection weights from TRN to LGN.

	It. TRN to LGN ON1 TRN to LGN ON2 TRN to LGN OFF1 TRN to LGN OFF2

This work was financially supported by the Bulgarian Science Fund, grant No DN02-3-2016 "Modeling of voluntary saccadic eye movements during decision making".


Chuan Tan

Jin Zhu

Pietro Lio'

Arbitrary Scale Super-Resolution for Brain MRI Images

Keywords: Super-Resolution, Medical Image Analysis, Meta-Learning, Image Processing Convolutions Low-Resolution Input Feature maps Upsampling High-Resolution output Network

Recent attempts at Super-Resolution for medical images used deep learning techniques such as Generative Adversarial Networks (GANs) to achieve perceptually realistic single image Super-Resolution. Yet, they are constrained by their inability to generalise to different scale factors. This involves high storage and energy costs as every integer scale factor involves a separate neural network. A recent paper has proposed a novel meta-learning technique that uses a Weight Prediction Network to enable Super-Resolution on arbitrary scale factors using only a single neural network. In this paper, we propose a new network that combines that technique with SRGAN, a state-of-the-art GAN-based architecture, to achieve arbitrary scale, high fidelity Super-Resolution for medical images. By using this network to perform arbitrary scale magnifications on images from the Multimodal Brain Tumor Segmentation Challenge (BraTS) dataset, we demonstrate that it is able to outperform traditional interpolation methods by up to 20% on SSIM scores whilst retaining generalisability on brain MRI images. We show that performance across scales is not compromised, and that it is able to achieve competitive results with other state-of-the-art methods such as EDSR whilst being fifty times smaller than them. Combining efficiency, performance, and generalisability, this can hopefully become a new foundation for tackling Super-Resolution on medical images.

Introduction

In this paper, we seek to apply elements of Meta-Learning to Generative Adversarial Networks (GANs) to tackle Super-Resolution in medical images, specifically brain MRI images. We are the first to apply such a scale-free Super-Resolution technique on these images. Super-Resolution is the task of increasing the resolution of images. It helps radiological centres located in rural areas which do not have high-fidelity instruments achieve comparable diagnostic results as their advanced counterparts in the city. The importance of Super-Resolution is Supported by University of Cambridge.

growing as cross modality analysis requires combining different types of information (such as PET and NMR scans) of varying resolution.

Traditionally, Super-Resolution has been done using interpolation, such as bicubic interpolation, but recent attempts have involved the use of deep learning methods to extract high level information from data, which can be used to supply additional information to increase the resolution of the image. Current deep learning techniques for medical images rely heavily on Generative Adversarial Networks (GANs) for they are able to generate realistic and sharper images by using a different loss function that yields high perceptual quality [START_REF] Chen | Efficient and accurate MRI super-resolution using a generative adversarial network and 3d multilevel densely connected network[END_REF]. For example, mDCSRN [START_REF] Chen | Efficient and accurate MRI super-resolution using a generative adversarial network and 3d multilevel densely connected network[END_REF], Lesion-focussed GAN [START_REF] Zhu | How can we make gan perform better in single medical image super-resolution? a lesion focused multi-scale approach[END_REF], ESRGAN [START_REF] Bing | Medical image super resolution using improved generative adversarial networks[END_REF], are all GANbased solutions tackling Super-Resolution for medical images. Most of them are based off SRGAN -a GAN-based network tackling Super-Resolution developed by Ledig et al. [START_REF] Ledig | Photo-realistic single image super-resolution using a generative adversarial network[END_REF]. We therefore use SRGAN as a foundation to which we apply our modification.

Despite their better performance, almost all the networks relied on the subpixel convolution layer introduced by Shi et al. [START_REF] Shi | Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[END_REF], which tied a particular upscaling factor to a particular network architecture. This incurs high storage and energy costs for medical professionals who may wish to conduct Super-Resolution on different scaling factors. In this paper, we combine the Meta-Upscale Module introduced by Hu et al. [START_REF] Hu | Meta-sr: A magnificationarbitrary network for super-resolution[END_REF] with SRGAN to create a novel network lovingly termed Meta-SRGAN. This breaks the constraint imposed by Shi et al's layer and is capable of tackling Super-Resolution on any scale, even non-integer ones, and hence can reduce storage, energy costs and lay the foundations for realtime Super-Resolution. We first show that Meta-SRGAN outperforms the baseline of bicubic interpolation on the BraTS dataset [START_REF] Menze | The multimodal brain tumor image segmentation benchmark (brats)[END_REF][START_REF] Bakas | Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features[END_REF][START_REF] Bakas | Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge[END_REF], and also show that Meta-SRGAN is capable of performing similarly to SRGAN, but yet is able to super-resolve images of arbitrary scales. We also compare the memory footprint and show that Meta-SRGAN is ≈ 98% smaller than EDSR, a state-of-the-art Super-Resolution technique, but yet is able to achieve similar performance.

The rest of the paper is organised as follows. Background introduces Super-Resolution formally and illustrates how Hu et al. reframes the problem. We then introduce the architecture of Meta-SRGAN in Methods. Experiments elaborates on dataset preparation and training details. Finally, we show the outcomes of the experiments in Results.

Background

Super-Resolution Deep learning-based Super-Resolution techniques are generally supervised learning algorithms. They analyse relationships between the low-resolution (LR) image and its corresponding high-resolution (HR) image, and use this relationship to obtain the super-resolved (SR) image. This SR image is then evaluated against the HR image to see how well the algorithm is performing. Any algorithm that is capable of extracting these relationships can be used to do Super-Resolution. Dong et al [START_REF] Dong | Image super-resolution using deep convolutional networks[END_REF] showed that any such algorithms can be thought of as convolutional neural networks (CNNs). Figure 1 shows a typical high level recipe for a Super-Resolution task.

Fig. 1. How a Super-Resolution task is often carried out. A low-resolution input image undergoes some convolutions to generate feature maps. Feature maps are some sort of representation that captures the features of the low-resolution image. Depending on the upscaling factor (it must be a multiple of 2 n ), there would be some number n of upscaling modules applied to the representation. This upscaling unfolds the image into the high-resolution output.

Meta-Upscale Module

The Upsampling module in the network is often the efficient sub-pixel convolution layer proposed by Shi et al. [START_REF] Shi | Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[END_REF]. Instead of explicitly enlarging feature maps, it expands the channels of the output features to store the extra points to increase resolution. It then rearranges these points to obtain the super-resolved output image. Almost every network tackling Super-Resolution uses this Upsampling module. An example is EDSR [START_REF] Lim | Enhanced deep residual networks for single image super-resolution[END_REF], a convolutional neural network tackling Super-Resolution. Hu et al. then proposed replacing the Upsampling module with a Meta-Upscale Module [START_REF] Hu | Meta-sr: A magnificationarbitrary network for super-resolution[END_REF]. This Meta-Upscale Module consists of a Weight Prediction Network and is an example of Meta-Learning as it generalises a network to tackle more than one upscaling factor. Essentially, given a few training examples (small amount of images from some scales), we train a network to tackle arbitrary scale factors. The Weight Prediction Network is able to predict weights for different upscaling factors. This extra layer of abstraction enables the underlying neural network to generalise across tasks. The framework proposed by Hu et al. for enabling arbitrary scale Super-Resolution is illustrated in Figure 2.

Hu et al. reframes Super-Resolution as a matrix multiplication problem:

Y = W x + b ( 1
)
where Y is the super-resolved image, W is some matrix of weights we learn, x is the input low-resolution image, and b is some constant. This may seem like an oversimplification and in many ways it is (for example it doesn't take account how pixels close to each other tend to exert more influence on each other), but it gives us a rather intuitive understanding for why a separate weight prediction network works for arbitrary scale Super-Resolution. Consider x as a 4 × 1 vector, and W as a 8 × 4 matrix. The output Y is therefore a 8 × 1 vector. In some sense we have doubled the size of x. We can think of W as providing an upscaling of 2.

The idea proposed by Hu et al. is to predict W, for every upscaling factor. This is done by passing in the input dimensions to an external function to create the shape of W, before using a weight prediction network to predict the values of W , as in the equation below:

W = f θ (x) (2)
where W is the matrix of weights, f θ is the weight prediction network with parameters θ, and x is the input low-resolution image.

The architecture of the Meta-Upscale Module is shown in Figure 3, and includes a weight prediction network that outputs weights which are then matrix multiplied to the input features to obtain the super-resolved image. The Weight Prediction Network consists of three layers, and is trained alongside the main network. Of significant importance is the shape of the input into the Weight Prediction Network. It must contain information relating to the shape of the high-resolution image, as that determines the size of the weight matrix that is multiplied with the input features fed into the Module. Additional details can be found in [START_REF] Hu | Meta-sr: A magnificationarbitrary network for super-resolution[END_REF].

Methods

Baselines

We used Bicubic Interpolation as a baseline, and implemented EDSR as a stateof-the-art technique that we compare Meta-SRGAN to. We also wanted to investigate how well using the Meta-Upscale Module on a GAN will affect its performance, so we implemented SRGAN too. EDSR and SRGAN were trained on x2 upsampling tasks, and were trained on DIV2K for 160k updates before transferring their learning onto the BraTS dataset for an additional 160k updates. EDSR was trained with 256 number of features, 32 residual blocks, and a residual scaling of 0.1 (see [START_REF] Lim | Enhanced deep residual networks for single image super-resolution[END_REF] for more information). EDSR was trained using L1 Loss, whilst SRGAN was trained using the same combination of losses used to train Meta-SRGAN.

Meta-SRGAN

To reap the benefits of both GANs and meta-learning, we combine There are two networks being trained in a Generative Adversarial Network. There is the generator which generates an image (in this case it super-resolves an image) which is then fed into the discriminator which discriminates between a real and a generated image (in this case it determines whether the image presented is the ground truth high-resolution, or not). This feedback from the discriminator is then used to further train the generator. Both generator and discriminator are playing a game to outbid each other. The generator tries to improve its ability to generate images that can fool the discriminator whilst the discriminator tries to improve its ability to discern real images from generated images. This adversarial learning enables us to generate more realistic and detailed images. The architecture of the generator and discriminator are shown in figures 4 and 5 respectively.

Generator The generator is used to generate super-resolved images, which the discriminator will take in and output a classification that says whether it is a generated image or a real image. Residual Blocks are used in the generator to sta- bilise training by incorporating a feedback loop back onto the input. Parametric ReLU was used as it was empirically found to stabilise training [START_REF] Ledig | Photo-realistic single image super-resolution using a generative adversarial network[END_REF].

Discriminator The discriminator consists of several Leaky ReLU layers and an average pooling layer. These was empirically found to stabilise training [START_REF] Ledig | Photo-realistic single image super-resolution using a generative adversarial network[END_REF]. A sigmoid layer was used to facilitate binary classification.

L1 Loss L1 Loss is the typical loss function used by other networks that calculates pixel-wise errors. We incorporate it to train the generator in Meta-SRGAN. We denote an input image by x, and a neural network and its parameters by f θ . Letting ŷ = f θ (x), i be just an index, x i be the low-resolution input image and y i be the high-resolution target image, C × H × W be the dimensions of an image, and || . . . || be the Frobenius norm, L1 Loss is defined as

l1 (ŷ, y) = 1 CHW i ||ŷ i -y i || (3)
L1 Loss minimises the mean absolute error between pixels and helps Meta-SRGAN match its output as closely as possible to the high-resolution target.

Adversarial Loss

The generator tries to minimise the following function whilst the discriminator tries to maximise it. D(x) is the discriminator's output that the high-resolution image is high-resolution, G(z) is the super-resolved image produced by the generator, and D(G(z)) is the discriminator's output that the super-resolved image is high-resolution.

E x [log(D(x))] + E z [log(1 -D(G(z)))] (4)
The above function is termed Adversarial Loss, and it allows Meta-SRGAN to achieve realistic super-resolved images.

Perceptual Loss Meta-SRGAN also incorporates Perceptual Loss, a type of loss introduced by Johnson et al. to improve performance on Super-Resolution tasks [START_REF] Johnson | Perceptual losses for real-time style transfer and super-resolution[END_REF]. The idea was to encourage the generated image to have similar feature representations as computed by a separate network. We use a 19-layer VGG network [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF] pretrained on the ImageNet dataset [START_REF] Russakovsky | Imagenet large scale visual recognition challenge[END_REF], and we extract the features before the last layer of the network. Letting ŷ and y be the super-resolved and high-resolution images respectively, φ be the network that extracts their feature representations, and assuming that the feature representations are of shape C × H × W , we define the Perceptual Loss as the Euclidean distance between the feature representations:

p (ŷ, y) = 1 CHW ||φ(ŷ) -φ(y)|| 2 (5)
In the following experiments we combined the loss functions as total (ŷ, y) = l1 (ŷ, y) + 0.001 adversarial (ŷ, y) + 0.006 p (ŷ, y)

and use it to train Meta-SRGAN.

Experiments

We performed experiments on the Multimodal Brain Tumor Segmentation (BraTS) dataset [START_REF] Menze | The multimodal brain tumor image segmentation benchmark (brats)[END_REF][START_REF] Bakas | Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features[END_REF][START_REF] Bakas | Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge[END_REF]. We first trained Meta-SRGAN on the DIV2K dataset [START_REF] Agustsson | Ntire 2017 challenge on single image super-resolution: Dataset and study[END_REF] -a dataset curated for Super-Resolution tasks, and then transferred that learning onto the BraTS dataset.

Datasets and Data Preparation

To demonstrate how well Meta-SRGAN performs on medical images, we trained and tested Meta-SRGAN on the Multimodal Brain Tumor Segmentation (BraTS) dataset [START_REF] Menze | The multimodal brain tumor image segmentation benchmark (brats)[END_REF][START_REF] Bakas | Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features[END_REF][START_REF] Bakas | Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge[END_REF]. This dataset contains several versions, including t1, t1ce, and t2 brain MRI scans, and serves as a good proxy for medical images. We picked the 2D t1ce version as it was two dimensional and was simple to work with. There were 15140 training images and 3784 validation images. Training images were normalised using a mean and standard deviation of 0.370 and 0.117 respectively before they were passed to the network to be trained on. These values were calculated using pixel values from the training images, after the maximal informational crop was applied. The BraTS dataset is tricky to deal with because every image has sparse information. Only about 50% of each image contained useful information (i.e. the brain), the rest was black. To tackle this problem, we cropped the image with the most pixel information. This corresponded to crops of the brain. Then, from this crop, we then randomly sampled 16 96 by 96 patches to provide sufficient coverage per image. For an upscaling factor of 2, the low-resolution input image (LR) was of size 48 by 48 whilst the high-resolution target image (HR) and super-resolved output image (SR) were of size 96 by 96. This workflow is summarised in Figure 6.

Training Parameters and Experiment Settings

We first trained Meta-SRGAN on the DIV2K dataset for 360k updates, with a mini-batch size of 8. We then did transfer learning and trained Meta-SRGAN on the BraTS dataset for an additional 360k updates. We trained Meta-SRGAN on a range of scales from 1.1 to 4.0 (with 0.1 increments). Higher upscaling factors were not chosen as that would have made the input image too small to be useful. Each minibatch of images was associated with a scale, and each scale was chosen uniformly at random from 1.1 to 4.0. We optimised the network using a combination of L1 Loss, Adversarial Loss, and Perceptual Loss as described in the previous section. We used an Adam optimiser with a learning rate of 1 * 10 -4 on each of the generator and discriminator, and decreased the learning rate by 20% every 60k updates. Pixel values were clamped to between 0 and 255 to give the generator an edge. The network was trained on a NVIDIA TITAN X GPU and took three days. We calculated the PSNR and SSIM scores only on the luminance channel of the maximal information crops.

Results

The results of our experiments are tabulated in Table 1. We see that Meta-SRGAN clearly outperformed the baseline of Bicubic Interpolation, which suggests that the inclusion of the Meta-Upscale Module did not hinder the network's performance. This is further reinforced by the fact that Meta-SRGAN was able to achieve similar performance on the x2 upsampling task as SRGAN, the architecture it was based on. It also had the added benefit of a smaller number of parameters compared to SRGAN. Visual quality is indicated by a proxy metric, Structural Similarity Index Metric (SSIM), which measures the perceived image similarity between two images. The high SSIM scores achieved by the GANs bodes well for brain MRI imaging which demands accurate images. We also see that Meta-SRGAN has the lowest number of parameters, which means its memory footprint is really small (≈ 98% smaller than EDSR), which suggests that it can be deployed easily. Sampled images are shown in Figure 7. The higher SSIM Meta-SRGAN was also able to produce a sequence of images corresponding to different upscaling factors. This is shown in Figure 8. The relatively consistent PSNR and SSIM scores indicate that performance across different scales are not compromised. All these results suggests that we have a memory efficient network capable of generating super-resolved brain MRI images of high visual quality, and can be used to generate images of any scale.

With better visual quality than the baseline, lower memory footprint, and ability to tackle any upscaling factor with negligible loss in performance, Meta-SRGAN clearly can deliver comparable performance to state-of-the-art Super-Resolution techniques.

Conclusions

The implications of the results on the BraTS dataset are three-fold. The low memory footprint of Meta-SRGAN enables it to be wrapped as a helper tool to aid medical tasks such as MRI and endoscope videography. The ability to super-resolve images on any arbitrary scale allows one to easily extend this to an application of real-time zooming and enhancing of an image, which will be useful in medical screening and surgical monitoring. The fact that its performance is not hindered across multiple upscaling factors affords it the flexibility to be used in other works such segmentation, de-noising, and registration. Despite only testing it on brain MRI images, Meta-SRGAN can easily be extended to datasets of other modalities such as Ultrasound and CT scans. Its low memory footprint, high visual quality, and generalisability suggests that Meta-SRGAN can become a new foundation on which other architectures can be built to enhance the performance for medical images.

We have built a network that combines the ability to generate images of high visual quality with the ability to tackle arbitrary scales, and are the first to show that this does not compromise performance or memory footprint for brain MRI images. This means that unlike other state-of-the-art methods, our method works on arbitrary scales which means that only a single network is required to perform Super-Resolution on any upscaling factors. In future work we hope to enhance the performance of Meta-SRGAN and apply it to other modalities.

Fig. 2 .

 2 Fig. 2. The framework for tackling Super-Resolution tasks, as proposed by Hu et al. There is no constraint on the upscaling factor. Note that the Upsampling module has been replaced with a matrix multiplication and a Weight Prediction Network. The matrix multiplication and Weight Prediction Network are collectively called the Meta-Upscale Module.

Fig. 4 .

 4 Fig. 4. (Top) Architecture of Generator. Generator consists of Convolutions, Residual Blocks, Batch Normalisation, and the Meta-Upscale Module. PReLU refers to the Parametric Leaky ReLU activation function. (Bottom) Architecture of Residual Block used in Generator. ReLU refers to the ReLU activation layer. The number 64 refers to the number of kernels, and can be modified like any hyperparameter.

Fig. 5 .

 5 Fig. 5. Architecture of Discriminator. Leaky ReLU refers to the Leaky ReLU activation function. AdaptiveAveragePool2d refers to an average pooling layer, whilst Sigmoid refers to the sigmoid activation function that outputs a binary value.

Fig. 6 .

 6 Fig. 6. Workflow for handling the BraTS dataset, for a x2 upsampling task.

Fig. 7 .

 7 Fig. 7. Sample images from Bicubic Interpolation, EDSR, SRGAN and Meta-SRGAN. This is from a x2 upsampling task.

Fig. 8 .

 8 Fig. 8. (Top) Image going through various upsampling factors using Bicubic Interpolation. (Bottom) Image going through various upsampling factors using Meta-SRGAN. PSNR and SSIM scores are calculated against a target image downscaled by a factor of 4.

Table 1 .

 1 A comparison of PSNR and SSIM scores between Bicubic Interpolation, SRGAN and Meta-SRGAN on the BraTS dataset. Best results are bolded. of SRGAN and Meta-SRGAN than the baseline of Bicubic Interpolation highlights the fact that they generate perceptually better images than the baseline. The very fact that Meta-SRGAN is even able to have comparable scores to EDSR despite being trained on a whole range of scales, and with considerably less parameters, attests to not just the power of adversarial training, but also the robustness of the Meta-Upscale Module.

			x2 Upsampling Task, BraTS Testing Set
		Bicubic Interpolation	EDSR	SRGAN	Meta-SRGAN
		PSNR (dB) SSIM PSNR (dB) SSIM PSNR (dB) SSIM PSNR (dB) SSIM
	mean	17.98	0.6091	18.93	0.6885	18.42	0.7295	18.72	0.7279
	std	5.674	0.1708	4.2909	0.1814	5.4975	0.1308	5.338	0.1422
	#parameters	-		40.7M	0.565M	0.561M

scores PSNR (dB) / SSIM 20.58 dB / 0.7410 22.86 dB / 0.8697 22.07 dB / 0.8626 22.09 dB / 0.8372

6 Acknowledgements Jin Zhu's PhD research is funded by China Scholarship Council (grant No.201708060173), whilst Pietro Lio' is supported by the GO-DS21 EU grant proposal.


Konstantinos Christantonis

Christos Tjortjis
email: c.tjortjis@ihu.edu.gr

Anastassios Manos

Despina Elizabeth Filippidou

Eleni Mougiakou

Evangelos Christelis

Despina Eliza- Beth Filippidou

Εleni Mougiakou

Using Classification for Traffic Prediction in Smart Cities

Keywords: Smart Cities, Data Mining, Prediction, Classification

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The deployment of modern and smart cities increasingly gains attention, as large urban centers over time present numerous challenges for citizens. Traffic is a stressful and time-consuming factor affecting citizens. Lately, many local authorities attempt to design and create smart infrastructures and tools in order to collect data and utilize models for better decision making and citizen support. Such data are often derived from sensors collecting information about Points Of Interest (POIs) in real time. Data mining techniques and algorithms can then support getting useful insights into the problem, whilst forming appropriate strategies to counter it. This work focuses on analyzing different approaches regarding data manipulation in order to predict day-ahead traffic loads at random places around cities, based on weather conditions. Prediction efforts regard classification tasks aiming at highlighting factors that affect traffic prediction. This study utilizes weather data collected from sensor devices located in Athens and Thessaloniki, Greece. Three different day zones are introduced and compared while subsets of trimesters are also tested.

The remaining of the paper is structured as follows: Section 2 provides background information. Section 3 presents our approach for traffic prediction, including data selection as well as experimental results. Section 4 discusses and evaluates our findings while section 5 concludes the paper with suggestions for future work.

Background

Traffic prediction is not a new subject; there are numerous scientific efforts that perform both classification and regression tasks in this domain [START_REF] Theodorou | Short-Term Traffic Prediction Under both Typical and Atypical Traffic Conditions using a Pattern Transition Model[END_REF]. However, few efforts attempted to predict traffic volumes based on low level data, such as weather data. Nejad et al. examined the power of decision trees for classifying traffic loads on three levels, based only on time and temperature [START_REF] Nejad | Applying Data Mining in Prediction and Classification of Urban Traffic[END_REF]. Results were positive and motivated our work. Xu et al. compared CART with k-NN and a direct Kalmann Filter [START_REF]Short-term traffic volume prediction using classification and regression trees[END_REF]. Moreover, Wang et al. proposed the use of volume and occupancy data [START_REF] Wang | Dynamic Traffic Prediction Based on Traffic Flow Mining[END_REF]. Such data, as well as speed, can be obtained by loop detectors. Loop detectors are sensors buried underneath highways which estimate traffic by observing information related to vehicles passing above them. Loop detectors along with rain predictions were also used in order to predict crashes [START_REF]Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data[END_REF].

Tree-based algorithms are widely used and justified, however even more sophisticated algorithms were used such as Support Vector Machines (SVMs) [START_REF]Short-Term traffic condition prediction of urban road network based on improved SVM[END_REF] and Neural Networks (NNs) [START_REF] Tang | Short-Term Traffic Flow Prediction Considering Spatio-Temporal Correlation: A Hybrid Model Combing Type-2 Fuzzy C-Means and Artificial Neural Network[END_REF]. Α novel hybrid method for short-term traffic prediction under both typical and atypical traffic conditions. Theodorou et al. introduced an SVM based model that identifies atypical conditions [START_REF] Theodorou | Short-Term Traffic Prediction Under both Typical and Atypical Traffic Conditions using a Pattern Transition Model[END_REF]. We used ARIMA or k-NN regression to identify typical or atypical conditions, respectively. In addition to [START_REF] Wang | Dynamic Traffic Prediction Based on Traffic Flow Mining[END_REF], Liu et al. introduced three binary variables, other than weather conditions, for holiday, special conditions and quality of roads [START_REF] Forest | Prediction of Road Traffic Congestion Based on Random[END_REF].

However, in this work we chose not to include such features for several reasons. Firstly, holidays do not have the same effect on each season. For example, sunny holidays might result in lower levels of traffic in large urban centers whilst for winter holidays this is not the case. Special conditions, such as a major social events or protests are not easy to add to models, since most of the times these occur unexpectedly. Abnormal conditions have a negative impact on such models, for instance when attempting to estimate the traffic flow based on Expressway Operating Vehicle [START_REF] Ai | Traffic Flow Prediction Based on Expressway Operating Vehicle Data[END_REF].

Another significant decision on such predictions relates to the actual day of the week. As highlighted in [START_REF]Traffic Prediction Using Multivariate Nonparametric Regression[END_REF][START_REF]Data Mining for Smart Cities: Predicting Electricity Consumption by Classification[END_REF], weekdays tend to have different characteristics from weekends and should be carefully tested. Finally, most scenarios regard short-time prediction, meaning minutes to hours ahead. Dunne et al. utilized neurowavelet models along with the rain expectation in the next hour [START_REF] Dunne | Weather Adaptive Traffic Prediction Using Neurowavelet Models[END_REF].

Traffic Prediction

Weather data can be exploited in different smart cities problems and scenarios. Collecting accurate weather data can be beneficial for numerous daily problems which associate weather with human decisions. Traffic is affected by numerous factors, only some of which are predictable. As mentioned in Section 2, traffic is directly affected by weather conditions, however the scale differs across cities and cultures.

Problem Definition and Approach

This section tests the above intuition on traffic prediction. Traffic is a multidimensional problem; researchers focus on either predicting traffic loads on a certain time interval or selecting the optimal route based on real-time adaptations in order to minimize travel time. Accidents and social events can shortly disrupt normal traffic in specific areas, while seasonality and weather conditions can affect traffic in a larger scale. Based on that, we used weather data collected from sensors installed around carefully chosen specific city spots for predicting the day-ahead traffic volume.

To select the most appropriate locations to install sensors that either measure traffic loads or collect weather data, it is crucial to define their objective in advance. Our efforts focus on the question 'How can one exploit sensor data that are not personalized and create meaningful conclusions for the general public?' Deployment of smart city infrastructure requires a deep understanding of the traffic problem. Roads that are busier than others do not always provide more information in comparison to more isolated ones. The number of alternative roads or the location of busy buildings can affect the necessity of measuring traffic for a specific road.

Our approach, besides examining traffic predictability based on weather data, also aims to clarifying differences among locations. Moreover, we implement a series of tests regarding different monthly periods under the objective to understand which months contribute positively on the deployment of such models. The approach followed is explained in Section 3.2.

Dataset Description and Processing

For this task, our data source was the newly deployed ppcity.io, which is a set of platforms providing useful information derived from sensors to citizens and visitors of Athens and Thessaloniki, Greece. These two large urban centers host almost half the Greek population. The selected platform collects and analyses environmental, traffic and geospatial data. Those data are collected through several sensors located at central points around the two cities. In general, there are numerous city spots on both cities where the environmental and traffic conditions are measured. Initially, we chose locations about which we had information regarding both traffic and weather conditions. We focused on the ten most reliable spots that produce data, i.e. sensors with the most compact flow of data and wider range of recording.

Weather related attributes used for the research analysis involved the following: Humidity, Pressure, Temperature, Wind Direction, Wind Speed and Ultraviolet Radiation (UV). The platform provides additional weather data, but these were not included in the modeling process. Indicatively, such variables include measurements for ozone concentration, nitrogen dioxide etc.

The target variable (traffic load), named Jam Factor, represents the quality of travel. It ranges from 0 to 10, where 10 describes stopped traffic flow and 0 a completely empty road. Data have been collected for almost six months, a rather short period of time, but at least including August and December, two months when people tend to take holidays. August is widely considered in Greece as the month with the lowest traffic volume, since it is a period that most people go on holidays, away from large urban centers. Similarly, in December both cities face abnormalities in traffic loads, since many citizens move from and to the two large urban centers. The dataset comprises data collected between 29/7/19 and 3/2/20. Eight sensors are in Athens and two in Thessaloniki. In addition, two sensors are located near school areas. More information about the sensors is available in Section 4.

The way data are processed is conceptually simple. Regarding the data structure, we defined three distinct time intervals within a day and examined their predictability. The first interval, named Morning, includes signals from sensors between 07:00 and 10:00. The second interval, named Afternoon, includes signals between 15:00 and 18:00 and the last one, named Evening, includes signals between 19:00 and 22:00. Therefore, the average value for the given signals was computed. For example, if a sensor captures information every hour (e.g. at 07:10, 08:10, 09:10 etc.), we computed and assigned the average value for each weather metric and the traffic load for that specific day period. The above strategy resulted in creating three different datasets consisting of 185 instances on average. Fortunately, the quality of the dataset was high, thus missing values were minimal. It is worth noting that our averaging strategy does not replace missing values. If there is a missing value in a weather averaging is performed using the remaining two available values, instead of averaging and labeling three values. An important decision was to transform the problem into a classification task by categorizing the target variable into two and later into three classes. Initially, the split point was the median value for each dataset, aiming at a fully balanced task which would allow for safer conclusions. Therefore, the two classes named High and Low indicate if the traffic load on that specific day was higher than usual. In the second stage the target variable was split into three categories of equal size (High, Medium and Low). The metric used for classification evaluation was accuracy, since classes are balanced and of equal interest.

Finally, we split the dataset into three subsets consisting of data regarding different trimesters. The first one contains data for the period between 29-July and 25-October, the second for the period between 15-September and 20-December and the third one, between 25-October and 3-February. We aimed at distinguishing if any periods (seasonality) affect the models negatively.

Experiments

Different cities demonstrated different traffic patterns; however, in almost every case we observed a few common patterns. First, on weekends citizens tended to use vehicles much less than weekdays, while on holidays people tended to leave the urban centers. To further understand the case of Greece, Fig. 1 visualizes the mean value for all available sensors in order to explain phenomena beyond seasonality. The colors on all figures share the same palette and indicate the same subsets. More precisely, for Fig. 1, the blue line indicates the Afternoon values while red and green indicates Morning and Evening respectively.

Fig. 1. Mean value for all sensors for each day period

For Fig. 234567, blue indicates the subset of 29 July -3 February, red for 15 September -20 December, green for 29 July -25 October and purple for 25 October -3 February. As expected, a rapid introductory search on the data surface highlight that the volume of traffic was consistently higher during the Afternoon period, while both Morning and Evening periods seem to present similar amounts of traffic.

Regarding the predictability for each of these combinations between sensors and day periods, for all the experiments on this task, we used the Random Forest Classifier. It is an ensemble learning method that operates by constructing a multitude of decision trees. It is considered as a powerful method and stands as a top-notch solution for various problems [START_REF]3C: Improving a Decision Tree Classification Algorithm's Interval Splits on Continuous Attributes[END_REF]. On top of that, it handles both scaled and not scaled data. In order to prevent over-fitting, we used 10-fold cross-validation and for the optimal hyper-parameter tuning we used an extended grid search.

It is important to clarify that all experiments were also conducted using the Logistic Regression algorithm, however the results were not included since they were almost identical. That is to justify that our algorithm does not over-fit since our datasets are small. Further, Fig. 345show classification results for each of the described time intervals. The Y-axis indicates accuracy with a start-point at 0.5 to highlight the margin of a baseline model on a perfectly balanced classification task. Since the purpose of this work was to analyze and highlight differences between time and day periods on traffic prediction we did not focus on exact values. In most cases feeding machine learning algorithms with more data is considered a crucial step to achieve generalization and expand the margin for higher accuracy. However, in all cases above, results with the initial dataset clearly did not outperform the ones with subsets. Fig. 678show results obtained by dividing the target variables into three classes of equal instances.

Fig. 6-8 illustrate the same intuition as for the case of two classes (i.e. Higher accuracy for the 29-Jul/25-Oct period), but also highlight significant changes on the individual performance for each sensor. In Section 4 we evaluate and discuss results, whilst explaining abnormalities and unexpected behavior.

. Accuracy with 3-classes -EveningEvaluation and Discussion

The evaluation of this work examines all the essential steps on a data mining project. Firstly, data acquisition, which is a critical component on every project, revealed the importance of enough data. The data collection process should be constant and clearly defined in advance. We dealt with a well-structured database that was recording sensor signals in a nearly perfect synchronization between traffic and weather data. Regarding the pre-processing step, we did not use all the available features, instead the models contained only weather metrics tested and introduced by the literature. For weather data exploitation, it is crucial to fully understand the correlation between variables, because the same level of information might be repeatedly captured. The rest of this section further discusses our findings per case.

The general problem in terms of real-time adjustment of routes in order to achieve traffic "congestions" is still most important for many cities. However, the day-ahead prediction of the volume was based on a major assumption that there would not be any accident or abnormal conditions in general. Starting from this point, the factor of environmental conditions was essential since many employees, students and tourists decide in advance the way they travel around the city on the upcoming day. The results of the approach presented in Section 3.3 are encouraging and justify what was stated above. All three-day periods on the binary classification achieved quite satisfying accuracy levels.

More precisely, the initial model, resulted in accuracy higher than 0.7 for most sensors on Morning and Afternoon. On the other hand, for the 3-classes experiments, results do not allow to reach safe conclusions. However, the initial model demonstrated robust performance achieving on average accuracy higher than 0.5. For the Morning period we observed predictability similar with the binary case. For Afternoon and Evening periods there were not significant gaps in accuracy even though the 29-Jul/25-Oct (light green line) period shows better performance than the rest. In addi- 3 Classes -Evening tion, we observed that the 3-class experiments do not show similar patterns with the previous results. Surprisingly, the middle period 15-Sep/20-Dec (red line) was expected to achieve much better results especially for the sensors 13 and 19 which are installed close to school areas. Indeed, the range of this period was selected to cover and adjust for the periods that schools operate, undisrupted. Results were contradicting, while Sensor 19 outperforms the rest on the Morning period for the binary case, Sensor 13 resulted in the lowest accuracy.

Fig. 1 highlights the special case of August in Greece and the fact that the traffic load is highly reduced. In addition, as expected, on weekends traffic was heavily reduced while we observed that peaks on Afternoon and Evening periods for weekdays, happen mostly on Thursdays and Fridays.

Finally, it is worth noting that Sensors 15 and 17 are in Thessaloniki. For both, results were lower than the average for Athens. Based on 2, we observed that the range of traffic values for those two is amongst the highest.

Conclusions and future work

Experiments for both stages produced some clear and informative results. The main conclusion is that the systematic recording and use of weather data can support decision making. The list below summarizes the conclusions.

• Standalone weather metrics can assist in building reliable prediction models regarding traffic volumes. • Roads are busier in the afternoon and for most of the sensors even evenings have higher traffic volumes in comparison to mornings. • Mornings do not return steady results for all sensors. As discussed, the results are stable only for sensors located near schools. • For morning periods, the peaks of traffic happen in the beginning of the week. No earlier than the middle of September the load gets similar to that of Evenings and gets clearly lower again by the start of Christmas holidays. • The first data subset regarding the period 29-Jul/25-Oct outperforms both other subsets and the initial data set consisting of all available data. That indicates that winter months introduce uncertainty and volatility, thus related models underperform significantly. • Transforming the target variable into three classes resulted in admittedly good results, firmly better than the baseline model.

The above conclusions emerge from the detailed analysis of the models, weather metrics and traffic volumes. However, threats to their validity exist. We briefly summarize them in Section 5.1.

Threats to validity

The biggest threat on approaches as such, is the fact that those day-ahead models rely on weather data which also are predicted. Thus, it is crucial that we have accurate predictions of weather conditions. Moreover, including the weekends that admittedly have different loads of traffic in the same models with weekdays may affect the validity on a negative way. Another threat could be enough deseasonilising; the factor of time could be possible analyzed into more explanatory variables. Not having available data for at least one year of recordings may lead to questionable conclusions about seasonal effects; however, this is not a rule. Finally, the sensors regard roads of different volume of traffic and even though traffic usually fluctuates uniformly that may conclude to misleading results.

Fig. 2 .Fig. 3 .Fig. 4 .

 234 Fig. 2. Accuracy with 2-classes -Morning

Fig. 5 .Fig. 6 .

 56 Fig. 5. Accuracy with 3-classes -Morning

Classes -Evening

Acknowledgements

The work was implemented within the project Public Participation City (ppCity -T1ΕΔΚ-02901 and MIS 5029727) by Action Aid "Research-Create-Innovate" implemented by General Secretariat of Research and Technology, Ministry of Development and Investments, cofinanced by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH -CREATE -INNOVATE. The project (www.ppcity.eu) provides a set of tools and platforms to collect city environmental data and use these in an intelligent way in order to support informed urban planning. Key element in this process is the opinions and views of citizens which are collected in a crowd sourcing manner. The research depicted in this paper is based on Platform 3 (run from https://panel.ppcity.eu/platform3/) which provides an open data city portal from environmental data in Athens and Thessaloniki, Greece.