Miracle-dz/UniFAR-FTUs huggingface dataset:
Solving Maximal Stable Set Problem via Deep Reinforcement Learning

This paper provides an innovative method to approximate the optimal solution to the maximal stable set problem, a typical NP-hard combinatorial optimization problem. Different from traditional greedy or heuristic algorithms, we combine graph embedding and DQN-based reinforcement learning to make this NP-hard optimization problem trainable so that the optimal solution over new graphs can be approximated. This appears to be a new approach in solving maximal stable set problem. The learned policy is to choose a sequence of nodes incrementally to construct the stable set, with action determined by the outputs of graph embedding network over current partial solution. Our numerical experiments suggest that the proposed algorithm is promising in tackling the maximum stable independent set problem.
Web-Gorgias-B: Argumentation for All

This paper proposes the use of a web-based authoring tool for the development of applications of argumentation. It focuses on aiding people that have little, or no knowledge of logic programming, or of an argumentation framework, to develop argumentation-based decision policies. To achieve this, it proposes an implementation of the table formalism that has recently been proposed in the literature. The proposed implementation contains original features that were evaluated by experts in web-application development, students and experts in argumentation. The main feature of the proposed system is the ability to define a default preferred option in a given scenario, thus, allowing for other options to be used in further refinements of the scenario. We followed a user-centered development process using the think aloud protocol. We evaluated the usability of the system with the System Usability Scale, validating our hypothesis that even naive users can employ it to define their
Tourism Forecast with Weather, Event, and Cross-industry Data

The ability to make accurate forecasts on the number of customers is a pre-requisite for efficient planning and use of resources in various industries. It also contributes to global challenges of society such as food waste. Tourism is a domain particularly focussed on short-term forecasting for which the existing literature suggests that calendar and weather data are the most important sources for accurate prediction. We collected and make available a dataset with visitor counts over ten years from four different businesses representative for the tourism sector in Switzerland, along with nearly a thousand features comprising weather, calendar, event and lag information. Evaluation of a plethora of machine learning models revealed that even very advanced deep learning models as well as industry benchmarks show performance at most on a par with simple (piecewise) linear models. Notwithstanding the fact that weather and event features are relevant, contrary to expectations, they proved insufficient for high-quality forecasting. Moreover, and again in contradiction to the existing literature, performance could not be improved by including cross-industry data.
Scene Detection in De Boer Historical Photo Collection

This paper demonstrates how transfer learning can be used to improve scene detection applied to a historical press photo collection. After applying transfer learning to a pre-trained Places-365 ResNet-50 model, we achieve a Top-1 accuracy of . 68 and a Top-5 accuracy of . 89 on our data set, which consists of 132 categories. In addition to describing our annotation and training strategy, we also reflect on the use of transfer learning and the evaluation of computer vision models for heritage institutes.
Using Game AI to Control a Simulated Economy

We explore the use of Artificial Intelligence (AI) to manipulate a simulated economy. Towards this end, we present work in progress on a macroeconomic simulation that can be controlled by a game player. We view this simulation as a sort of serious game; it can be played as a competition, but it can also be an educational tool through which players learn both about economic principles and the behaviour of AI controllers. The main contribution of this paper is the comparative study of the effectiveness of different AI agents for the manipulation of a simulated environment. Focusing on AI approaches that are common in the gaming industry, we implement four players that use intelligent methods to control the simulation by trying to maximize the economic output. The aim of our work is to illustrate that simple methods from the game AI community can be used to control a complex economic simulation effectively. This work, therefore, supports the common position in the gaming community that simple character-based AI methods can produce competitive game play even for complex tasks. Moreover, we demonstrate that, in the case of this particular simulation, a rulebased reasoner outperforms more sophisticated AI agents.
From Exposed to Exploited: Drawing the Picture of Industrial Control Systems Security Status in the Internet Age

The number of Internet-facing industrial control system(ICS) devices has risen rapidly due to remote control demand. Going beyond benefits in maintenance, this also exposes the fragile ICS devices to cyber-attackers. To characterize the security status of Internet-facing ICS devices, we analyze the exposed ICS devices and their vulnerabilities. Considering the ethic, we design and implement ICScope, a passive vulnerability assessment system based on device search engines. Firstly, ICScope extracts the ICS device information from the banners returned by multiple search engines. Then, ICScope filters out the possible ICS honeypots to guarantee accuracy. Finally, ICScope associates ICS vulnerabilities with each ICS device. Over the past year, our measurements cover more than 466,000 IPs. We first perform a comprehensive measurement of Internet-facing ICS devices from Dec 2019 to Jan 2020. We find that there are about 49.58% of Internet-facing ICS devices that can be identified are affected by one or more vulnerabilities. We also conduct three times experiments from Jun 2020 to Dec 2020 to monitor the security status of Internet-facing ICS devices. We observe a slowly decreasing trend in the number of vulnerable ICS devices during our experiment period.
Privacy Preserving Services for Intelligent Transportation Systems with Homomorphic Encryption

With the advent of intelligent transportation systems, vehicles will connect continuously to the Internet via the vehicular core network or the cellular network. Opening vehicles systems to the Internet aims at improving vehicles safety and comfort via the development of remote services for drivers assistance. Such services are for example infotainment applications, software update over the air, remote diagnostics and adaptive insurance. However, some of these services come with an inherent problem of privacy as they require as inputs the private data from the vehicles. In this work, we investigate the use of homomorphic encryption for ensuring the confidentiality of vehicles private data. We study the confidentiality of data, which are treated by external service providers such as cars manufacturers, their stakeholders and insurances. Our protocol ensures, by design, the private treatment of vehicles data thanks to homomorphic encryption properties. We validate our proposal by studying drivers behaviour using a simple neural network that takes as input drivers pictures and tells whether a driver is concentrated or distracted. Indeed, we rely on a 3 layers network for classifying drivers behavior in 10 different classes from normal to dangerous. We use a quadratic activation function for intermediate layers which contain 20 and 10 units, respectively. Meanwhile, we use a sigmoid activation function for the last layer which contains 10 units, one per label. Our classification takes 11 seconds with a classification accuracy of 86% and 25 seconds with a classification accuracy of 92%.
On the Optimal Allocation of Resources for a Marketing Campaign

Many companies and institutions, such as banks, typically have a wide range of products that they make available to customers. However, such products must be marketed to their customers, especially when the product is new. Phone calls, emails, postal mail, and online advertisements are among the ways companies can market products to specific customers. However, the cost incurred during marketing increases with every contact made. Phone calls are the most personal means of targeted marketing but also the most costly. In telemarketing, a company can make multiple calls to a single customer with each call incurring a human resource cost. Such calls may or may not be able to persuade a customer to subscribe to the service or product. Some customers might subscribe after the first call. Some customers might require several calls to convince them. Other customers might never be persuaded. In light of limited resources, to maximize return, a company would need to determine which customers to contact and how many attempts to make for a customer. In this paper, we present a mathematical model for this problem in which, given a marketing budget of calls, one can determine a policy for selecting customers to target along with the optimal number of calls to use for each selected customer. We illustrate our model using a Portuguese banking dataset and show that our model can achieve significantly higher levels of success performance.
Protecting Privacy during a Pandemic Outbreak

Respecting privacy is a major challenge when sharing data among enterprises. Because of the high stakes and complexity, enterprises need expressive and clear methods for defining tailored data sharing so that they can share the right information with the right partners with confidence. This paper describes a privacy-oriented declarative policy framework together with an intuitive user interface to manage data sharing policies. It introduces a use case about a pandemic outbreak, in which the system can be used to share relevant information with partners to ensure that help can be coordinated quickly and effectively, while at the same time ensuring that the privacy of individuals remains protected and not sharing overly widely. A set of privacy policies is introduced to describe how the policy system can meet these requirements.
Multimodal Sentiment Analysis on Video Streams using Lightweight Deep Neural Networks

: Real-time sentiment analysis on video streams involves classifying a subject’s emotional expressions over time based on visual and/or audio information in the data stream. Sentiment can be analyzed using various modalities such as speech, mouth motion, and facial expression. This paper proposes a deep learning approach based on multiple modalities in which extracted features of an audiovisual data stream are fused in real time for sentiment classiﬁcation. The proposed system comprises four small deep neural network models that analyze visual features and audio features concurrently. We fuse the visual and audio sentiment features into a single stream and accumulate evidence over time using an exponentially-weighted moving average to make a ﬁnal prediction. Our work provides a promising solution to the problem of building real-time sentiment analysis systems that have constrained software or hardware capabilities. Experiments on the Ryerson audio-video database of emotional speech (RAVDESS) show that deep audiovisual feature fusion yields substantial improvements over analysis of either single modality. We obtain an accuracy of 90.74%, which is better than baselines of 11.11% – 31.48% on a challenging test dataset.
Profiling and Discriminating of Containerized ML Applications in Digital Data Marketplaces (DDM)

A Digital Data Marketplace (DDM) facilitates secure and trustworthy data sharing among multiple parties. For instance, training a machine learning (ML) model using data from multiple parties normally contributes to higher prediction accuracy. It is crucial to enforce the data usage policies during the execution stage. In this paper, we propose a methodology to distinguish programs running inside containers by monitoring system calls sequence externally. To support container portability and the necessity of retraining ML models, we also investigate the stability of the proposed methodology in 7 typical containerized ML applications over different execution platform OSs and training data sets. The results show our proposed methodology can distinguish between applications over various configurations with an average classification accuracy of 93.85%, therefore it can be integrated as an enforcement component in DDM infrastructures.
Two Stage Anomaly Detection for Network Intrusion Detection

Network intrusion detection is one of the most import tasks in today’s cyber-security defence applications. In the field of unsupervised learning methods, variants of variational autoencoders promise good results. The fact that these methods are very computationally time-consuming is hardly considered in the literature. Therefore, we propose a new two-stage approach combining a fast preprocessing or filtering method with a variational autoencoder using reconstruction probability. We investigate several types of anomaly detection methods mainly based on autoencoders to select a pre-filtering method and to evaluate the performance of our concept on two well established datasets.
A Blended Attention-CTC Network Architecture for Amharic Text-image Recognition

In this paper, we propose a blended Attention-Connectionist Temporal Classification (CTC) network architecture for a unique script, Amharic, text-image recognition. Amharic is an indigenous Ethiopic script that uses 34 consonant characters with their 7 vowel variants of each and 50 labialized characters which are derived, with a small change, from the 34 consonant characters. The change involves modifying the structure of these characters by adding a straight line, or shortening and/or elongating one of its main legs including the addition of small diacritics to the right, left, top or bottom of the character. Such a small change affects orthographic identities of character and results in shape similarly among characters which are interesting, but challenging task, for OCR research. Motivated with the recent success of attention mechanism on neural machine translation tasks, we propose an attention-based CTC approach which is designed by blending attention mechanism directly within the CTC network. The proposed model consists of an encoder module, attention module and transcription module in a unified framework. The efficacy of the proposed model on the Amharic language shows that attention mechanism allows learning powerful representations by integrating information from different time steps. Our method outperforms state-of-the-art methods and achieves 1.04% and 0.93% of the character error rate on ADOCR test datasets.
Mobile Robots: An Overview of Data and Security

The field of mobile robotics has become the focus of several types of research for many years. The revolutionary technology of Wireless Sensor Networks (WSNs) has provided many benefits for the process of data collection and communication. On the other hand, the network is facing challenges in supporting the traffic requirements to carry on the data flow generated by the nodes. Hence, the focus of this work is to give an overview of data processes in mobile robots based on the literature review. At first, we present the definitions and the most common types of mobile robots. Then, we emphasize the role of sensors and sensor nodes in WSNs for gathering and communicating the data. In the fourth section, we extend this work by introducing the main security issues posed to data in mobile robots. Our conclusions are drawn in the end. As this paper generally describes and points out the main problems related to data in mobile robots, further analysis is planned for future work.
A Two Step Fine-tuning Approach for Text Recognition on Identity Documents

Manually extracting data from documents for digitization is a long, tedious and error-prone job. In recent years, technologies capable of automating these processes are gaining ground and managing to obtain surprising results. Research in this field is driven by the strong interest of organizations that have identified how the automation of data entry leads to a reduction in working time and a speed-up of business processes. Documents of interest are heterogeneous in format and content. These can be natively machine readable or not when they are images obtained by scanning paper. Documents in image format require pre-processing before applying information extraction. A typical pre-processing pipeline consists of two steps: text detection and text recognition. This work proposes a two step fine-tuning approach for text recognition in Italian identity documents based on Scene Text Recognition networks. Experiments show promising results.
Dancing Guide: Near Realtime Audio Classification of Live Dance Music on Smartphones

Between 2008 and 2014 we developed and deployed a live music classification system, Dancing Guide, to be run on Android and iPhones mobile phones in near realtime. Although internet access was needed to send feedback and classifications to the server for statistical purposes, the music classification system also worked offline without any loss in accuracy or speed. This is essential since in most discos and dancing schools, internet access is spotty at best. During the seven years of the project, the app was available both for iPhone and Android, initially in German and English, but later – thanks to volunteer translations – also in Czech, Spanish, French, Italian, Japanese, Korean, Dutch, Polish, Portuguese, Russian and Traditional Chinese. Measured by user feedback, we achieved an accuracy of roughly 73% at a coverage of 61%. While the accuracy is comparable to initial estimates using cross-validation, the coverage is much worse. Background noise – which we did not model – or the limited feature set may have been responsible. We retrained the system several times, however performance did not always improve, so we sometimes left the previously trained system in place. In the end, the limited feature set which was initially chosen prevented further improvement of coverage and accuracy, and we stopped further development.
A New Dataset for Smartphone Gesture-based Authentication

In this paper, we consider the problem of authentication on a smartphone, based on gestures. Specifically, the gestures consist of users holding a smartphone while writing their initials in the air. Accelerometer data from 80 subjects was collected and we provide a preliminary analysis of this data using machine learning techniques. The machine learning techniques considered include principal component analysis (PCA) and support vector machines (SVM). The results presented here are intended to provide a baseline for additional research based on our dataset.
Distributed Service Area Control for Ride Sharing by using Multi-Agent Deep Reinforcement Learning

We propose a decentralized system to determine where ride-sharing vehicle agents should wait for passengers using multi-agent deep reinforcement learning. Although numerous drivers have begun participating in ridesharing services as the demand for these services has increased, much of their time is idle. The result is not only inefficiency but also wasted energy and increased traffic congestion in metropolitan area, while also causing a shortage of ride-sharing vehicles in the surrounding areas. We therefore developed the distributed service area adaptation method for ride sharing (dSAAMS) to decide the areas where each agent should wait for passengers through deep reinforcement learning based on the networks of individual agents and the demand prediction data provided by an external system. We evaluated the performance and characteristics of our proposed method in a simulated environment with varied demand occurrence patterns and by using actual data obtained in the Manhattan area. We compare the performance of our method to that of other conventional methods and the centralized version of the dSAAMS. Our experiments indicate that by using the dSAAMS, agents individually wait and move more effectively around their service territory, provide better quality service, and exhibit better performance in dynamically changing environments than when using the comparison methods.
Using Machine Learning to Forecast Air and Water Quality

Environmental sustainability is one of the biggest concerns nowadays. With increasingly latent negative impacts, it is substantiated that future generations may be compromised. The research here presented addresses this topic, focusing on air quality and atmospheric pollution, in particular the Ultraviolet index and Carbon Monoxide air concentration, as well as water issues regarding Wastewater Treatment Plants, in particular the pH of water. A set of Machine Learning regressors and classifiers are conceived, tuned, and evaluated in regard to their ability to forecast several parameters of interest. The experimented models include Decision Trees, Random Forests, Multilayer Perceptrons, and Long Short-Term Memory networks. The obtained results assert the strong ability of LSTMs to forecast air pollutants, with all models presenting similar results when the subject was the pH of water.
Developing Cyber-risk Centric Courses and Training Material for Cyber Ranges: A Systematic Approach

The use of cyber ranges to train and develop cybersecurity skills and awareness is attracting more attention, both in public and private organizations. However, cyber ranges typically focus mainly on hands-on exercises and do not consider aspects such as courses, learning goals and learning objectives, specific skills to train and develop, etc. We address this gap by proposing a method for developing courses and training material based on identified roles and skills to be trained in cyber ranges. Our method has been used by people with different background grouped in academia, critical infrastructure, research, and service providers who have developed 22 courses including hands-on exercises. The developed courses have been tried out in pilot studies by SMEs. Our assessment shows that the method is feasible and that it considers learning and educational aspects by facilitating the development of courses and training material for specific cybersecurity roles and skills.
Achieving Fair Inference Using Error-Prone Outcomes

Recently, an increasing amount of research has focused on methods to assess and account for fairness criteria when predicting ground truth targets in supervised learning. However, recent literature has shown that prediction unfairness can potentially arise due to measurement error when target labels are error prone. In this study we demonstrate that existing methods to assess and calibrate fairness criteria do not extend to the true target variable of interest, when an error-prone proxy target is used. As a solution to this problem, we suggest a framework that combines two existing fields of research: fair ML methods, such as those found in the counterfactual fairness literature and measurement models found in the statistical literature. Firstly, we discuss these approaches and how they can be combined to form our framework. We also show that, in a healthcare decision problem, a latent variable model to account for measurement error removes the unfairness detected previously.