Reviewerly/exHarmony huggingface dataset:
Ensuring the Compatibility of Autonomous Electric Vehicles Components Through a Formal Approach Based on Interaction Protocols

In the context of automotive applications, complex tasks such as automatic driving of electric vehicles are handled through the composition of several components, each offering a different service. Such component composition is not straightforward and is often subject to bugs that might stem mainly from the incompatibility of services. In other words, in this context, which includes critical services and in which people's life is at stake, detecting and eliminating bugs early at the design stage is crucial and even mandatory. To remedy this issue, we propose in this paper a formal approach for modeling and verifying the reliability of electric self-driving vehicles that are continuously communicating with off-road infrastructures. First, for the modeling phase, SysML language is used to model the system architecture and to specify the connections between its embedded components. Second, we present a formal verification approach based on the extended interface automata formalism to verify the compatibility between the interacting components, and to check whether this set of components achieve their required tasks. This formalism allows to specify component interfaces that exhibit component protocols and system non-functional constraints. The proposed approach permits an algorithmic verification to decide whether a set of components, when assembled together, fulfill compatibility conditions. Results in this paper show, on one hand, that SysML and extended interface automata formalism are relevant to model and capture component features in the context of automotive systems, on the other hand, that our methodology allows to develop autonomous electric vehicle systems correct-by-design, regarding to component compatibility.
Unsupervised Cross-Domain Person Re-Identification Method Based on Attention Block and Refined Clustering

Most unsupervised cross-domain person re-identification methods based on clustering suffer from a lack of feature discrimination and clustering generates pseudo-labels noise, leading to a decrease in accuracy.To solve these problems, this paper proposes an unsupervised cross-domain person reidentification method based on attention block and refined clustering.Firstly, ResNet50 is selected as the backbone network, coordinate attention and triple attention are concatenated and embedded in ResNet50 to extract fine-grained features, perform feature aggregation, and mine fine-grained information.Secondly, a refined clustering strategy is proposed to achieve a coarse-to-fine clustering process by designing the measurement standards for clustering, determining its reliability, and eliminating noisy samples.Finally, the hybrid memory bank dynamically stores cluster centers and continues to update them with iterations, adapting to changes in clusters and performing invariant learning.The experimental results show that the new method designed in the paper improves the accuracy of rank-1 and mAP by 0.4% and 2.4%, respectively, on the target domain Market-1501 dataset, and improves the accuracy of rank-1 and mAP by 0.4% and 1.1%, respectively, on the target domain DukeMTMC-ReID dataset, compared with other typical methods.
RoboHapalytics: A Robot Assisted Haptic Controller for Immersive Analytics

Immersive environments offer new possibilities for exploring three-dimensional volumetric or abstract data. However, typical mid-air interaction offers little guidance to the user in interacting with the resulting visuals. Previous work has explored the use of haptic controls to give users tangible affordances for interacting with the data, but these controls have either: been limited in their range and resolution; were spatially fixed; or required users to manually align them with the data space. We explore the use of a robot arm with hand tracking to align tangible controls under the user's fingers as they reach out to interact with data affordances. We begin with a study evaluating the effectiveness of a robot-extended slider control compared to a large fixed physical slider and a purely virtual mid-air slider. We find that the robot slider has similar accuracy to the physical slider but is significantly more accurate than mid-air interaction. Further, the robot slider can be arbitrarily reoriented, opening up many new possibilities for tangible haptic interaction with immersive visualisations. We demonstrate these possibilities through three use-cases: selection in a time-series chart; interactive slicing of CT scans; and finally exploration of a scatter plot depicting time-varying socio-economic data.
A Set of Registers Supporting Semantic Interoperability in the E-governance’s Environment

The process of establishing and maintaining the Semantic Interoperability (SI) is essentially a standardization process. In order to be successfully implemented, it is necessary to develop not only the standardization requirements but also the environment in which they will be presented, as well as the means by which their implementation will be controlled. In the case of SI for e-Governance, this environment has to be also under the same requirements for SI. Therefore, the most appropriate implementation of this environment is the registry form with full support of the connectivity between data in the registries.
Token Selection is a Simple Booster for Vision Transformers

Vision transformers have recently attained state-of-the-art results in visual recognition tasks. Their success is largely attributed to the self-attention component, which models the global dependencies among the image patches (tokens) and aggregates them into higher-level features. However, self-attention brings significant training difficulties to ViTs. Many recent works thus develop various new self-attention components to alleviate this issue. In this article, instead of developing complicated self-attention mechanism, we aim to explore simple approaches to fully release the potential of the vanilla self-attention. We first study the token selection behavior of self-attention and find that it suffers from a low diversity due to attention over-smoothing, which severely limits its effectiveness in learning discriminative token features. We then develop simple approaches to enhance selectivity and diversity for self-attention in token selection. The resulted token selector module can server as a drop-in module for various ViT backbones and consistently boost their performance. Significantly, they enable ViTs to achieve 84.6% top-1 classification accuracy on ImageNet with only 25M parameters. When scaled up to 81M parameters, the result can be further improved to 86.1%. In addition, we also present comprehensive experiments to demonstrate the token selector can be applied to a variety of transformer-based models to boost their performance for image classification, semantic segmentation and NLP tasks. Code is available at https://github.com/zhoudaquan/dvit_repo.
G2LP-Net: Global to Local Progressive Video Inpainting Network

The self-attention based video inpainting methods have achieved promising progress by establishing long-range correlation over the whole video. However, existing methods generally relied on the global self-attention that directly searches missing contents among all reference frames but lacks accurate matching and effective organization on contents, which often blurs the result owing to the loss of local textures. In this paper, we propose a Global-to-Local Progressive Inpainting Network (G2LP-Net) consisting of the following innovative ideas. First, we present a global to local self-attention mechanism by incorporating local self-attention into global self-attention to improve searching efficiency and accuracy, where the self-attention is implemented in multi-scale regions to fully exploit local redundancy for the texture recovery. Second, we propose a progressive video inpainting (PVI) method to organize the generated contents, which completes the target video frames from periphery to core to ensure reliable contents serve first. Last, we develop a window-sliding method for sampling reference frames to obtain rich available information for inpainting. In addition, we release a wire-removal video (WRV) dataset that consists of 150 video clips masked by wires to evaluate the video inpainting on irregularly slender regions. Both quantitative and qualitative experiments on benchmark datasets, DAVIS, YouTube-VOS and our WRV dataset have demonstrated the superiority of our proposed G2LP-Net method.
A Passive Testing Approach using a Semi-Supervised Intrusion Detection Model for SCADA Network Traffic

Worldwide cyber-attacks constantly threaten the security of available infrastructure relying on cyber-physical systems. Infrastructure companies use passive testing approaches such as anomaly-based intrusion detection systems to observe such systems and prevent attacks. However, the effectiveness of intrusion detection systems depends on the underlying models used for detecting attacks and the observations that may suffer from scarce data availability. Hence, we need research on a) passive testing methods for obtaining appropriate detection models and b) for analysing the impact of the scarceness of data for improving intrusion detection systems. In this paper, we contribute to these challenges. We build on former work on supervised intrusion detection of power grid substation SCADA network traffic where a real-world data set (APG data set) is available. In contrast to previous work, we use a semi-supervised model with recurrent neural network architectures (i.e., LSTM Autoencoders and sequence models). This model only considers samples of ordinary data traffic without attacks to learn an adequate detection model. We outline the underlying foundations regarding the machine learning approach used. Furthermore, we present and discuss the obtained experimental results and compare them with prior results on supervised machine learning approaches. The source code of this work is available at:https: //github.com/muehlburger/semi-supervised-intrusion-detection-scada
A Framework for the Analysis of Security Technology Vulnerabilities: Defeat Evaluation of an Electronic Access Control Locking System

Security literature has identified the need for product evaluation; however, there still exists limited published work in product testing and an absence of cross-disciplinary evaluation frameworks. Evaluation frameworks should consider both physical and technical vulnerabilities due to, in part, an increase in integration and connectivity of electronic security systems. Consequently, the study developed and applied a criterion-based Defeat Evaluation Framework in the evaluation of a security technology, specifically on an electronic access control locking system. The study found that the Defeat Evaluation Framework supported the identification of risks across commercial, performance and defeat categories, which included physical and technical vulnerabilities to defeat. Identification of such vulnerabilities enhances risk decision-making in the uptake of such security technologies.
Perceived severity of vulnerability in cybersecurity: cross linguistic variegation

The emergence of artificial intelligence [AI] , computer vision, and speech recognition systems have made significant growth in all areas of human life including cybersecurity. Multiple cybersecurity companies are trying to leverage AI to help combat cyberattacks as AI and machine learning can faster and cheaper monitor for any suspicious activity in the network, informing security specialists and network administrators only in case of a true emergency. As modern AI-powered systems are cooperating with human users by not only providing raw reports but also producing information based on text generation algorithms and text-to-speech functions, it is essential to ensure that this sensitive, security related information is not biased. In this pilot study, we are trying to showcase the problem of the perceived severity of a vulnerability by recipients speaking different native languages. Also, we are trying to answer the following question: how should an AI system present the information in order for the user to correctly understand the severity of the finding?
Game theory analysis of incentive distribution for prompt generation of the proof tree in zk-SNARK based sidechains

In sidechains with Latus consensus, a block forger generates a block using SNARK-proofs, created by decentralized provers and organized in a perfect binary tree (proof tree). One of the most important questions is to assign incentives for these proofs. In this paper, the game theory instruments are used to investigate incentive distribution in proof trees for SNARK-based sidechains to provide stable and efficient block generation. Two different models are considered: when only one sidechain exists, and when there exist a lot of sidechains among which provers may switch any time, trying to get a higher incentive. Utilization of Stirling numbers with non-integer arguments turns out to be very efficient for the second model.
MRASS: Dynamic Task Scheduling enabled High Multi-cluster Resource Availability in JointCloud

As the new paradigm of JointCloud Computing matures, enterprises are trying to build multiple Kubernetes clusters on different clouds to deploy tasks, with the advantages of disaster backup, low latency, and avoidance of single vendor lock-in, etc. Tasks in a JointCloud environment, always have highly diversified resource demands on CPU, memory, disk, and network. However, the mismatch between these tasks and heterogeneous clusters can easily cause many resource fragments, resulting in low resource availability. Therefore, the task scheduling strategy is the key to solving the above problem. The existing task schedule strategies for multi-clusters are always aiming at clusters’ load balancing instead of increasing the resource availability. In this paper, we propose a dynamic task scheduling framework with the design of multi-cluster resource high-availability schedule strategy (MRASS) based on historical task resource consumption. MRASS conducts a cooperation model between multiple clusters and tasks, and proposes an indicator of resource availability, which is used to optimize the proportion of remaining resources of the cluster to keep approaching the proportion of resource requirements of future tasks, thereby execute more tasks within limited resources. Extensive numerical results confirm that the strategy has stable performance and performs well with different initial cluster resource setting, task resource type and task number. Compared with the existing algorithm, MRASS can place up to 20% more tasks, and the success rate of first placement of tasks can reach over 98%.
Sentiment Analysis Algorithm for Arabic Reviews on the Movies Domain

“Sentiment analysis has become popular in recent years and has been used in many life aspects where the people reviews are very important to know their reaction on any product. Regarding movies, these impressions are important for people to choose the best film to watch. Movie reviews are a very important tool for measuring the performance of the movie, most of the sentiment analysis research in those reviews are in English language, but our goal is how to analyze the feelings and reviews using Arabic language. In this paper, an Arabic lexicon for movies was created and the lexicon is used in applying movies sentiment analysis. Movie Rating for Arabic Reviews System (MRARS) was built for sentiment analysis for Arabic movies reviews using Oracle Language. The lexicon contains all the feelings words about movies which are positive, negative and neutral sentiment. We collected about 1250 words, the number of positive words is 655, the number of negative words is 441 and the number of neutral words is 154. The proposed system showed excellent performance varied between 94 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> - 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and the accuracy can be raised after adding new words to the lexicon”.
Exploring the Potential of A.I.-Driven Opponents in Video Games

Video games can often be a medium for technological solutions and innovations that were previously unheard of, but the potential of developing or researching this for anything beyond gaming is often overlooked. This paper examines the strides made towards crafting a responsive opponent based on Artificial Intelligence principles, examining some notable examples throughout the history of the medium, and what the future of such entities may be. This research is used to determine what elements would be the best fit for a prototype game meant to demonstrate the potential of AI as an opponent, and the implementation and modification of this prototype are outlined.
ELSA: Edge Lightweight Searchable Attribute-based encryption Multi-keyword Scalability

The digitalisation of industrial manufacturing needs the support of systems technology to enhance the efficiency of manufacturing operations, product quality, and smart decisions. This digitalisation can be achieved by the industrial internet of things (IIoT). IIoT has played a powerful role in smart manufacturing by performing real-time analysis for a large volume of data. One possible approach to perform these operations in a secure and privacy-preserving manner is to utilise cryptographic solutions. In previous work, we proposed searchable encryption with an access control algorithm for IIoT based on an edge-cloud architecture, namely ELSA. This paper extends ELSA to illustrate the correlation between the number of keywords and ELSA performance. This extension supports annotating records with multiple keywords in trapdoor and record storage and allows the record to be returnable with single-keyword queries. In addition, the experiments demonstrate the scalability and efficiency of ELSA with an increasing number of keywords and complexity.
An Engineering Process Framework for Cybersecurity Incident Response Assessment

Recent incidents and continuous cyber attacks highlight that most currently adopted cybersecurity solutions and processes remain highly reactive, with short-lived remedies. The critical need for more proactive and mature response processes has never been more critical. We look at engineering and borrow concepts from mature systematic processes such as the engineering design process to develop a new framework for activer cyber incident response processes. Currently, cybersecurity incident response structure and processes are outlined in multiple frameworks including the MITRE ATT&CK framework, NIST's Cybersecurity framework, and the traditional incident response (IR) lifecycle. Using a blend of those frameworks and the concepts from the engineering design process we propose O2I that is more adept to a more proactive and sustainable security response. With proper implementation, O2I's iterative processes could provide a sustainable and adaptable approach to assess and improve cyber security response processes. To validate the benefits and feasibility of the proposed framework, we utilize it to evaluate recent attacks using publicly available information.
Explaining Deep Learning Models for Per-packet Encrypted Network Traffic Classification

Machine learning is increasingly applied to network traffic analysis to aid in tasks such as quality of service management, trend monitoring, and security. Recent advances in deep learning have enabled not only the classification of encrypted transits, but classification on a per-packet level. End-to-end deep learning models are becoming increasingly ubiq-uitous given their ease of use, i.e., developers do not need to engineer features, and their apparent versatility. However, deep learning entails black-box models that hinder the capability to debug and explain classifications. Moreover, the computational complexity of deep learning can incur unnecessary latency, which is problematic for real-time classification needs. In this paper, we propose a methodology to interpret black-box, deep learning-based encrypted network traffic classification models, with an attempt to understand the dominant features a classifier is focusing on for a given task. We evaluate our approach on state-of-the-art deep learning classification techniques for encrypted per-packet classification and demonstrate how interpretability can be used to debug and improve the training pipeline while significantly reducing the size of the deep learning model. We propose future directions toward optimizing model performance while maintaining explainability.
Design and Analysis of Novel Bit-flip Attacks and Defense Strategies for DNNs

In this paper, we present novel bit-flip attack (BFA) algorithms for DNNs, along with techniques for defending against the attack. Our attack algorithms leverage information about the layer importance, such that a layer is considered important if it has high-ranked feature maps. We first present a classwise-targeted attack that degrades the accuracy of just one class in the dataset. Comparative evaluation with related works shows the effectiveness of our attack algorithm. We finally propose multiple novel defense strategies against untargeted BFAs. We comprehensively evaluate the robustness of both large-scale CNNs (VGG19, ResNext50, AlexNet and Res Net) and compact CNNs (MobileNet-v2, ShuffleNet, GoogleNet and SqueezeNet) towards BFAs. We also reveal a valuable insight that compact CNNs are highly vulnerable to not only well-crafted BFAs such as ours, but even random BFAs. Also, defense strategies are less effective on compact CNNs. This fact makes them unsuitable for use in security-critical domains. Source code is released at https://sites.google.com/view/dsc-2022-paper-bit-flip-attack.
Path Planning Simulation of a quadrotor in ROS/Gazebo using RGPPM

In this article we are interested in planning collision-free trajectories for the autonomous navigation of a quadrotor. Before planning a trajectory, its start and end points are defined. Subsequently, using Resistive Grid Path Planning Methodology (RGPPM), the trajectory is calculated by modeling the displacement space with a mesh of resistances. This methodology has been expanded to plan 3D trajectories due to the movements in the XYZ axis that the quadrotor can perform in space. The computed trajectories are executed in ROS and Gazebo. The results obtained suggest that the trajectories computed by the RGPPM methodology are suitable for the quadrotor to navigate in a collision-free 3D space.
Mobile Robot Path Planning based on Fuzzy Logic Algorithm in Dynamic Environment

Based on the idea of fuzzy control to solve the collision avoidance problem in the path planning of mobile robots, so that the robot can quickly and accurately find a collision-free path in the dynamic environment, and finally reach the target point. For the given dynamic environment, a fuzzy control is designed Based on the determination of the input and output variables of the fuzzy controller and the respective language values, the corresponding fuzzy Control rules. The research results show that the path planning of mobile robots based on fuzzy logic algorithms in the dynamic environment designed in this paper is effective.