Miracle-dz/UniFAR-FTUs huggingface dataset:
GREE-COCO: Green Artificial Intelligence Powered Cost Pricing Models for Congestion Control

: The objective of the proposed research is to design a system called Green Artificial Intelligence Powered Cost Pricing Models for Congestion Control (GREE-COCO) for road vehicles that address the issue of congestion control through the concept of cost pricing. The motivation is to facilitate smooth traffic flow among densely congested roads by incorporating static and dynamic cost pricing models. The other objective behind the study is to reduce pollution and fuel consumption and encourage people towards positive usage of the public transport system (e.g., bus, train, metro, and tram). The system will be implemented by charging the vehicles driven on a particular congested road during a specific time. The pricing will differ according to the location, type of vehicle, and vehicle count. The cost pricing model incorporates an incentive approach for rewarding the usage of electric/non-fuel vehicles. The system will be tested with analytics gathered from cameras installed for testing purposes in some of the Indian and Irish cities. One of the challenges that will be addressed is to develop sustainable and energy-efficient Artificial Intelligence (AI) models that use less power consumption which results in low carbon emission. The GREE-COCO model consists of three modules: vehicle detection and classification, license plate recognition, and cost pricing model. The AI models for vehicle detection and classification are implemented with You Only Look Once (YOLO) v3, Faster-Region based Convolutional Neural Network (F-RCNN), and Mask-Region based Convolutional Neural Network (Mask RCNN). The selection of the best model depends upon their performance concerning accuracy and energy efficiency. The dynamic cost pricing model is tested with both the Support Vector Machine (SVM) classifier and the Generalised Linear Regression Model (GLM). The experiments are carried out on a custom-made video dataset of 103 videos of different time duration. The initial results obtained from the experimental study indicate that YOLOv3 is best suited for the system as it has the highest accuracy and is more energy-efficient.
LongiControl: A Reinforcement Learning Environment for Longitudinal Vehicle Control

Reinforcement Learning (RL) might be very promising for solving a variety of challenges in the field of autonomous driving due to its ability to find long-term oriented solutions in complex decision scenarios. For training and validation of a RL algorithm, a simulative environment is advantageous due to risk reduction and saving of resources. This contribution presents an RL environment designed for the optimization of longitudinal control. The focus is on providing an illustrative and comprehensible example for a continuous real-world problem. The environment will be published following the OpenAI Gym interface, allowing for easy testing and comparing of novel RL algorithms. In addition to details on implementation reference is also made to areas where research is required.
A Diagnosis Support System for Veterinary Necropsy based on Bayesian Networks

Veterinary autopsy requires a high level of expertise and skills that not all veterinarians necessarily master, especially in the context of the desertification of rural areas. The development of support systems is a challenging issue, since such a tool, to be considered relevant and accepted by practitioners in their diagnosis process, must avoid any black box effect. The diagnosis support system we introduce here, IVAN (“Innovative Veterinary Assisted Necropsy”), aims to engage the user in an explicit, understandable, validable and reviewable process, able to cope with the specific issues of cattle necropsy. Besides, it provides uncertainty management to deal with approximate lesion descriptions. IVAN relies on a Bayesian network to infer relevant proposals at each step of the diagnostic process. IVAN was trained on a set of real autopsy cases from autopsy reports, and its performance was assessed using another set of reports. In addition, the tool had to provide results in short response time and be able to run the application on mobile device and web server. In addition to demonstrating the feasibility of the approach, IVAN is a first step towards other support systems in other species and in broader contexts than autopsy.
A QUBO Model to the Tail Assignment Problem

: Tail Assignment is the problem of allocating individual aircraft to a set of ﬂights subject to multiple constraints while optimising an objective function, such as operational costs. Given the enormous amount of possibilities and constraints involved, this problem has been a case study over the last decade. Many solutions have emerged using classical computing, but with limitations. Quantum Annealing (QA) is a heuristic technique to solve combinatorial optimisation problems by ﬁnding global minimum energy levels over an energy landscape using quantum mechanics. In this study, Tail Assignment Problem was framed as a Quadratic Unconstrained Binary Optimisation (QUBO) model and was solved using a classical and two hybrid solvers. The considered hybrid solvers made use of the D-Wave 2000Q quantum annealer. Tests were run based on extractions from real-world data, analysing, empirically, the performance of the implementation in terms of quality (i.e., the lowest operational costs) of the obtained solutions. We concluded that, for the considered datasets, there was a higher probability of obtaining better solutions for this problem using one of the hybrid solvers when compared with a classical heuristic algorithm such as Simulated Annealing (SA).
Who Stores the Private Key? An Exploratory Study about User Preferences of Key Management for Blockchain-based Applications

Applications based on blockchain technology have become popular. While these applications have clear benefits, users are not yet familiar with their usage, which could hinder further applications of this technology. In this paper, an online survey with 110 potential users, as a representative of an average citizen, was conducted. The focus of this survey is to explore their preferences concerning the interaction with blockchain-based applications by mainly focusing on how to handle private keys. To best of our knowledge this is the first study where average citizens are asked about the preferred management of a private key, which is necessary when interacting with blockchain-based applications. One of the main results was that about 80% of the participants would like to have the benefit of data sovereignty despite the cost of being fully responsible to backup their
Quantum Control for Error Correction using Mother Tee Optimization

Quantum Control Problem (QCP) for Error Correction (EC) is a significant issue that helps in producing an efficient quantum computer. The QCP for EC can be tackled using Stochastic Local Search (SLS) methods. However, these techniques might produce low quality results for large dimensional quantum systems. Lately, Nature-Inspired (NI) algorithms including different variants of Particle Swarm Optimization (PSO) and Deferential Evolution (DE) were implemented in several studies to tackle the QCP for EC, but the results were not promising. In this paper, we propose a quantum model that is built on our NI algorithm, called Mother Tree Optimization for QCP (MTO-QCP), to overcome the stagnation issue that the other methods suffer from. In order to assess the performance of MTO-QCP, we conducted several preliminary experiments to adjust our MTO parameters. In this regard, our MTO-QCP achieves high-fidelity (> 99.99%) for a Single-Shot (SS) three-qubit gate control at gate operation time of 26 ns. This recommended fidelity is an acceptable threshold fidelity for fault-tolerant Quantum Computing (QC) problems.
Time-First Tracking: An Efficient Multiple-Object Tracking Architecture for Dynamic Surveillance Environments

Given the countless hours of video that are generated in surveillance environments, real-time for multi-object tracking (MOT) is vastly insufficient. Current MOT methods prioritize tracking accuracy in crowded environments, with little concern for total computational expense, which has led to a reliance on expensive object detectors to perform tracking. Indiscriminate use of object detectors is not scalable for surveillance problems and ignores the inherent spatio-temporal variation in scene complexity in many real-world environments. A novel MOT method is proposed, termed “Time-First Tracking”, which relies on “shallowly” processed motion with a new tracking method, leaving the use of expensive object detection methods to an “as-needed” basis. The resulting vast reduction in pixels-processed may yield orders of magnitude in cost savings, making MOT more tractable. Time-First Tracking is adaptable to spatio-temporal changes in tracking difficulty; videos are divided into spatio-temporal sub-volumes, rated with different tracking difficulties, that are subsequently processed with different object localization methods. New MOT metrics are proposed to account for cost along with code to create a synthetic MOT dataset for motion-based tracking.
ML-based Decision Support for CSP Modelling with Regular Membership and Table Constraints

The regular membership and the table constraints are very powerful constraints which allow it to substitute every other constraint in a constraint satisfaction problem. Both constraints can be used very flexible in a huge amount of problems. The main question we want to answer with this paper is, when is it faster to use the regular membership constraint, and when the table constraint. We use a machine learning approach for such a prediction based on propagation times. As learning input it takes randomly generated constraint problems, each containing exactly one table resp. one regular membership constraint. The evaluation of the resulting decision tool with specific but randomly generated CSPs shows the usefulness of our approach.
Decentralized Multi-agent Formation Control via Deep Reinforcement Learning

Multi-agent formation control has been a much-researched topic and while several methods from control theory exist, they require astute expertise to tune properly which is highly resource-intensive and often fails to adapt properly to slight changes in the environment. This paper presents an end-to-end decentralized approach towards multi-agent formation control with the information available from onboard sensors by using a Deep Reinforcement learning framework. The proposed method directly utilizes the raw sensor readings to calculate the agent’s movement velocity using a Deep Neural Network. The approach utilizes Policy gradient methods to generalize efficiently on various simulation scenarios and is trained over a large number of agents. We validate the performance of the learned policy using numerous simulated scenarios and a comprehensive evaluation. Finally, the performance of the learned policy is demonstrated in new scenarios with noncooperative agents that were not introduced during the training process.
Recipe Enrichment: Knowledge Required for a Cooking Assistant

: The preparation of a meal consisting of multiple components with different timing and critical synchronization points is a complex task. An automated system assisting a human in the preparation process needs to track the progress state, prompt the next recipe steps, control kitchen devices, monitor the ﬁnal preparation time, and deal with process deviations. This requires a detailed process representation including knowledge about states with critical timing, control signals of devices, preparation steps and cooking times of ingredients, and necessary user attention. At the same time, the system should be ﬂexible enough to allow the free combination of component recipes and kitchen devices to support the preparation of complete menus independent of a speciﬁc kitchen setup. To meet these requirements, we propose a method to automatically enrich simple component recipes with process-speciﬁc information. The resulting detailed process description can be processed by stan-dard scheduling algorithms to sequence the preparation steps of complex meals. The control information for kitchen devices is already included in the process description, so that monitoring of the progress becomes possible. The reasoning process is driven by so-called action templates that allow to decouple knowledge on recipes, ingredients, and kitchen devices in seperate re-usable knowledge bases.
State Tracking in the Presence of Heavy-tailed Observations

In this paper, we define a state-space model with discrete latent states and a multivariate heavy-tailed observation density for applications in tracking the state of a system with observations including extreme deviations from the median. We use a Gaussian distribution with an unknown variance parameter which has a Gamma distribution prior depending on the state of the system to model the observation density. The key contribution of the paper is the theoretical formulation of such a state-space model which makes use of scale mixtures of Gaussians to yield an exact inference method. We derive the framework for estimation of the states and how to estimate the parameters of the model. We demonstrate the performance of the model on synthetically generated data sets.
MADLIRA: A Tool for Android Malware Detection

: Today, there are more threats to Android users since malware writers are changing their target to explore the weakness of Android devices, in order to generate malicious behaviors. Thus, detecting Android malwares is becoming crucial. We present in this paper a tool, called MADLIRA (MAlware Detection using Learning and Information Retrieval for Android). This tool implements two static approaches: (1) apply Information Retrieval techniques to automatically extract malicious behaviors from a set of malicious and benign applications, (2) apply learning techniques to automatically learn malicious applications. Then, in both cases, MADLIRA can classify a new Android application as malicious or benign.
Securing Orchestrated Containers with BSI Module SYS.1.6

Orchestrated container virtualization, such as Docker/Kubernetes, is an attractive option to transfer complex IT ecosystems into the cloud. However, this is associated with new challenges for IT security. A prominent option to secure IT infrastructures is to use security guidelines from agencies, such as Germany’s Federal Office for Information Security. In this work, we analyze the module ”SYS.1. 6 Container” from this agency. We want to find out how suitable this module is to secure a typical Kubernetes scenario. Our scenario is a classical 3-tier architecture with front end, business logic and database-back end. We show that with orchestration, the protection needs for the entire Kubernetes cluster in terms of confidentiality, integrity and availability automatically become ”high” as soon as a sensitive data object is processed or stored in any container. Our analysis has shown that the SYS.1. 6 module is generally suitable. However, we have identified three additional threats. Two of them could be exploited automatically, as soon as a respective vulnerability appears.
Towards Exploring User Perception of a Privacy Sensitive Information Detection Tool

Users reveal privacy sensitive information when they post on social media, which can have negative consequences. To help these users make informed decisions, different tools have been developed that detect privacy sensitive information (PSI) and provide alerts. However, how users would perceive this type of tool has not yet been evaluated. In this position paper, we take the first steps to address this gap, by exploring user intention, perceived usefulness and attitude towards a PSI detection tool. We designed an experiment and showed participants examples of the PSI detection tool alerts, and quantitatively and qualitatively evaluated their response. The results showed that participants perceived the PSI detection tool as useful, had positive interest, and a low level of concern about it, although they had a neutral level of intention of using the tool. The participants’ open-ended responses revealed that they considered the PSI detection tool useful, but mostly for other people and not for themselves. In addition, they were concerned about the privacy risks of using the tool and about its effectiveness. The findings reveal the challenges that PSI detection tools have to overcome to gain acceptance among users that would benefit from this type of privacy protection.
Multi-level Feature Selection for Oriented Object Detection

Horizontal object detection has made significant progress, but the representation of horizontal bounding box still has application limitations for oriented objects. In this paper, we propose an end-to-end rotation detector to localize and classify oriented targets precisely. Firstly, we introduce the path aggregation module, to shorten the path of feature propagation. To distribute region proposals to the most suitable feature map, we propose the feature selection module instead of using selection mechanism based on the size of region proposals. What’s more, for rotation detection, we adopt eight-parameter representation method to parametrize the oriented bounding box and we add a novel loss to handle the boundary problems resulting from the representation way. Our experiments are evaluated on DOTA and HRSC2016 datasets.
CNN-LSTM-CRF for Aspect-Based Sentiment Analysis: A Joint Method Applied to French Reviews

Aspect Based Sentiment Analysis (ABSA) aims to detect the different aspects addressed in a text and the sentiment associated to each of them. There exists a lot of work on this topic for the English language, but only few models are adapted for French ABSA. In this paper, we propose a new model for ABSA, named CLC, which combines CNN (Convolutional Neural Network), Bidirectional LSTM (Long Short-Term Memory) and CRF (Conditional Random Field). We demonstrate herein its great performance on the SemEval2016 French dataset. We prove that our CLC model outperforms the state-of-the-art models for French ABSA. We also prove that CLC is well adapted for other languages such as English. One main strength of CLC is its ability to detect the aspects and the associated sentiments in a joint manner, unlike the state-of-the-art models which detect them separately.
An Overview of Cryptographic Accumulators

This paper is a primer on cryptographic accumulators and how to apply them practically. A cryptographic accumulator is a space- and time-efficient data structure used for set-membership tests. Since it is possible to represent any computational problem where the answer is yes or no as a set-membership problem, cryptographic accumulators are invaluable data structures in computer science and engineering. But, to the best of our knowledge, there is neither a concise survey comparing and contrasting various types of accumulators nor a guide for how to apply the most appropriate one for a given application. Therefore, we address that gap by describing cryptographic accumulators while presenting their fundamental and so-called optional properties. We discuss the effects of each property on the given accumulator's performance in terms of space and time complexity, as well as communication overhead.
Sentence Boundary Detection in German Legal Documents

Sentence boundary detection on German legal texts is a task which standardized NLP-systems have little or no ability to handle, since they are sometimes overburdened by more complex structures such as lists, paragraph structures and citations. In this paper we evaluate the performance of these systems and adapt methods directly to the legal domain. We created an annotated dataset with over 50,000 sentences consisting of various German legal documents which can be utilized for further research within the community. Our neural networks and conditional random fields models show significantly higher performances on this data than the tested, already existing systems. Thus this paper contradicts the assumption that the problem of segmenting sentences is already solved.
Blockchain based Secured Virtual Machine Image Monitor

Blockchain technology supports data immutability. Wherea s, smart contracts are piece of self-executable codes running inside the blockchain network, responsible f or the transformation or state change of these data. Furthermore, Cloud Computing is used in the applicati on of data storage and usage. Several business enterprises use cloud for hosting their applications and da ta with a minimized effort, cost and hurdles of maintenance. However, ensuring security of client data and proper management of the Service Provider’s infrastructure remains a crucial issue. In this article, an Ethereum based blockchain network has been proposed that monitors and assures the safety of the Virtual Machine I mages (VMI) stored at the Cloud Service Provider (CSP) end. The proposed scheme tends to design a dedicated Sm art Contract which handles each and every function, starting from request of a VMI by the Cloud Service Consumer (CSC) to the usage of the same by the later. The use of blockchain technology ensures that no s ingle admin/third party can control/modify the system. This prevents unwanted modification of the VMIs by an intruder and guarantees the efficiency of the scheme to be higher than any other methodology designed for t he same purpose till date.
A Secure Network Scanner Architecture for Asset Management in Strongly Segmented ICS Networks

: Industrial Control System (ICS) are essential for process automation and control in critical infrastructures, like smart grids, water distribution and also food production, in our modern world. These industrial devices will be even more connected, due to the trend of Industry 4.0 and Internet of Things (IoT), to provide additional functionality. An example for a use case is predictive maintenance, where sensor data is required, to e.g. replace defective parts before outage. While connectivity enables easier and more efﬁcient process management, it also increases the attack surface for cyber-attacks. To provide secure operation for interconnected ICSs additional protection measures, like asset management should be applied, to observe and maintain assets within a control network. One of the ﬁrst steps to improve cyber-security with asset management is device identiﬁcation in ICS networks. A common method for device identiﬁcation is active network scanning, which adds additional network trafﬁc to the ICS network. Because of the common segmentation with ﬁrewalls of ICS networks, scanner nodes in each sub-network are necessary. The distribution of active scan nodes typically adds additional cross connections within segmented ICS networks. In this paper, we introduce a secure scanning architecture for fragile ICS networks. Our architecture is based on scanning nodes, which use the concept of hardware-based data diodes to e.g. separate the critical control network from the ofﬁce network. To ensure a gentle scan on fragile ICS networks, the scan node provide a bandwidth limitation of the scan, to reduce risk of inﬂuences within ICS networks. We implemented a Proof of Concept (PoC) system and evaluated it within our industrial testbed, to show the feasibility of our architecture.