Reviewerly/exHarmony huggingface dataset:
Path Planning for Moving Robots in an Unknown Dynamic Area Using RND-Based Deep Reinforcement Learning

Robots can be used for various purposes in environments humans cannot reach. Dynamic environments with static and dynamic obstacles are challenging areas in robotic implementations. In this proposed study, robotic exploration is presented in an unknown dynamic environment using Random Network Distillation(RND) algorithm, which was introduced for Atari games. RND algorithm is used here for robotic navigation depending on obstacle location and environmental features. The proposed route planning robot is implemented using the Robot Operating System(ROS) and the Gazebo simulator. In previous works, Deep Reinforcement Learning based agents have shown promising outcomes. However, the reward system needs to be more robust for this path-planning task. In order to unravel the sparse reward issue, a revised RND approach is suggested. In order to dynamically alter intrinsic and extrinsic rewards, this algorithm determines the variation of the training steps count in each training episode. Laser inputs from Gazebo are fed to the RND algorithm to get the actions. Actions will define by avoiding walls or obstacles. The robotic path and map are visualized on the Gazebo. The results show the robot's actions and combine intrinsic and extrinsic rewards from RND on the application. Further, employing the RND-trained agent learns to cover environments presented as a novel part of the research. Four simulations are conducted based on the environment to validate the algorithm, including without obstacles, static obstacles, dynamic obstacles, and both. The experimental results indicate that RND agents trained in different environments can reach the goal accurately without crashes.
Educational chatbot on Data Structures and Algorithms for the visually impaired

Visually impaired people find it difficult to study from educational websites, even though they have a lot of knowledge to provide. They are unable to feel confident about using online tools to learn because of their lack of independence. This becomes a problem for almost every student when they have to search through different websites for answers to their doubts. A chatbot is a conversational AI technology that simulates human interaction. It is more comfortable for users to interact with a chatbot if it is voice-enabled. Our proposed solution is a chatbot that is voice-enabled as well as multilingual. It can interact with more than 180 global languages, although, in this paper, we have focused on the Hindi language. It can help visually impaired people as well as normal students, as they can learn by just having a conversation with the voice bot. Our voice bot can teach students many concepts regarding data structures and algorithms. It gives the user a speech-based response received from a generated .mp3 file. For the voice-bot system, which has been trained with more than 70 intents, we have utilized the RASA NLU principle. It has been deployed onto our e-learning web application. The chatbot is compatible with all the languages mentioned in the python translate module. This chatbot recognizes and converts the speech received from the microphone into text and matches with the trained intents information, and accordingly gives the most appropriate answer and converts text-to-speech and presents the text and speaks in the language selected along with multi-intent classifications. We have made a custom pipeline and also applied various policies to it to improve pipeline performance. Nearly all of the queries are correctly answered by the voice bot, which makes communication more effective.
DECC: Achieving Low Latency in Data Center Networks with Deep Reinforcement Learning

Data Center Networks (DCNs) suffer from synchronized bursts for segmentation and aggregation modes, leading to buffer overflows at switches and increasing network delay. To overcome this problem, some congestion control algorithms like DCTCP use Explicit Congestion Notification (ECN) to notify in-network congestion and reduce switch buffer occupancy. However, the traditional Additive Increase Multiplicative Decrease (AIMD) method causes high fluctuation of round-trip time (RTT) in DCNs. Some intelligent congestion control algorithms designed for Internet can achieve great flexibility, but are not applicable in DCNs for a lack of accurate congestion feedback. In this paper, we analyze the deficiencies of utilizing RTT as congestion signals and the applicability of learning algorithms in DCNs. Then, we propose DECC, a smart TCP congestion control algorithm for DCNs, which combines Deep Reinforcement Learning (DRL) with ECN to achieve high bandwidth utilization as well as low queuing delay. DECC fully utilizes precise in-network feedback and formulates several QoS requirements to a multi-objective function. Meanwhile, it decouples cwnd adjustment with DRL decision making to gradually learn the optimal congestion control policy in real-time. We evaluate the performance of DECC in various scenarios. Simulation results show that DECC can reduce the queue length at bottleneck switches by more than 50% compared to DCTCP, while maintaining high bandwidth utilization and reducing Flow Completion Time (FCTs) under burst traffic.
The Impact of Adversarial Attacks on Interpretable Semantic Segmentation in Cyber–Physical Systems

The widespread adoption of deep learning (DL) models raises concerns about their trustworthiness and reliability. Adversarial attacks are cyber-related attacks that target the DL network's prediction by adding imperceptible perturbations to its input. Their deployment against critical artificial-intelligence-based systems, such as industrial cyber–physical systems (ICPSs), can result in substantial damage. Research on their scope and limitations can provide information that would help with their detection and prevention. In this article, the interconnection of adversarial attacks and interpretable semantic segmentation is investigated for potential applications in the ICPS in order to contribute to the safe use of future intelligent systems. We first explore gradient-based interpretability extensions to semantic segmentation on two industry-related cyber–physical system datasets. Then, two types of attacks on semantic segmentation networks are discussed. First, we apply the dense adversary generation attack on different segmentation outputs and evaluate its influence on the corresponding saliency maps. We then introduce a way to visualize the similarity of attacked saliency maps to the original with respect to the targeted attack's direction. Finally, we extend the application of adversarial attacks on saliency maps to semantic segmentation.
RelCLAS: A Reliable Malicious KGC-Resistant Certificateless Aggregate Signature Protocol for Vehicular Ad Hoc Networks

Vehicular ad hoc network (VANET) enables a more efficient and secure traffic environment by integrating several emerging technologies. The technology of certificateless aggregate signature (CLAS) is widely utilized to guarantee secure communications in VANET. However, the legitimation of vehicles’ public keys cannot be verified effectively in existing CLAS protocols for VANET. Thus, a malicious key generation center (KGC) can masquerade as any vehicle and generate legitimate signatures. In this article, a reliable malicious KGC-resistant CLAS protocol, called RelCLAS, is proposed to eradicate this problem. RelCLAS solves the key escrow problem owning to the combination of registration-based encryption (RBE) and CLAS. Partial public keys selected by vehicles are stored in a key accumulator to prevent malicious KGC from forging valid signatures. Formal security proof demonstrates that RelCLAS satisfies the fundamental security requirements of VANET. Extensive simulations illustrate that RelCLAS is efficient, reliable, and feasible.
Video Streaming and Cloud Gaming Services Over 4G and 5G: A Complete Network and Service Metrics Dataset

The development of telecommunications, particularly mobile communications, has led to the emergence of numerous multimedia services. As these services become increasingly reliant on connectivity, the network has a huge impact on the user experience. However, it is difficult to assess the End-to-End (E2E) performance of the service because of the limited access to the application data and encryption used to secure and integrate the network information exchanged. In this context, this work presents a dataset based on several metrics, namely Key Quality Indicators (KQIs), representing different services' E2E conditions. In particular, video streaming and Cloud Gaming (CG) services are considered in the dataset. Furthermore, a detailed description of the testbed where the dataset has been generated is given, discussing the rationale behind the choices made for its development.
Enabling Integrity and Compliance Auditing in Blockchain-based GDPR-compliant Data Management

The General Data Protection Regulation (GDPR) is a European Union (EU) data protection and privacy law. According to the GDPR, the data on a hosting platform must meet semantic consistency and data integrity requirements. Semantic consistency means that the data operation should comply with the GDPR, while data integrity is meant to ensure that the outsourcing data should be intact. The two terms are not interchangeable. For example, if a cloud service provider migrates data to foreign storage nodes without authorization of the data owner, the data integrity requirement of the GDPR is met but the semantic consistency requirement is not. How to ensure data integrity and compliance is the main challenge for a GDPR-compliant data supervision platform. To achieve this aim, we leverage a blockchain based data management framework to check the data compliance, which can break the black box of the data hosting platform and demonstrate its logic to data owners, allowing for inspection. We propose a new provable data possession (PDP) scheme for the aforementioned framework that can check for semantic consistency and data integrity simultaneously. The verifier does not need to hold any audited data, which can reduce bandwidth usage. The verification result can be regarded as the proof for subsequent data recovery and accountability. Experimental results show higher efficiency of the PDP scheme.
Predicting Voting Behaviors and Election Results Using Digital Trace Data and Twitter

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Co-Designing for Transparency: Lessons from Building a Document Organization Tool in the Criminal Justice Domain

Investigative journalists and public defenders conduct the essential work of examining, reporting, and arguing critical cases around police use-of-force and misconduct. In an ideal world, they would have access to well-organized records they can easily navigate and search. In reality, records can come as large, disorganized data dumps, increasing the burden on the already resource-constrained teams. In a cross-disciplinary research team of stakeholders and computer scientists, we worked closely with public defenders and investigative journalists in the United States to co-design an AI-augmented tool that addresses challenges in working with such data dumps. Our Document Organization Tool (DOT) is a Python library that has data cleaning, extraction, and organization features. Our collaborative design process gave us insights into the needs of under-resourced teams who work with large data dumps, such as how some domain experts became self-taught programmers to automate their tasks. To understand what type of programming paradigm could support our target users, we conducted a user study (n=18) comparing visual, programming-by-example, and traditional text-based programming tools. From our user study, we found that once users passed the initial learning stage, they could comfortably use all three paradigms. Our work offers insights for designers working with under-resourced teams who want to consolidate cutting-edge algorithms and AI techniques into unified, expressive tools. We argue user-centered tool design can contribute to the broader fight for accountability and transparency by supporting existing practitioners in their work in domains like criminal justice.
Multi-key fully homomorphic encryption from NTRU and (R)LWE with faster bootstrapping

Multi-Key Fully Homomorphic Encryption (MKFHE) supports homomorphic evaluation on ciphertexts encrypted under different keys, guaranteeing data privacy in the case of multiple mutually untrusted parties. In ASIACRYPT 2019, Chen et al. designed an LWE-based MKFHE scheme, which evaluates a boolean gate on ciphertexts followed by a bootstrapping procedure in the dynamic multi-key setting. In this paper, we propose a new MKFHE scheme from NTRU and (R)LWE, achieving better bootstrapping performance. To that end, we adapt Chen et al.'s bootstrapping algorithm to the NTRU setting and construct an efficient conversion from multi-key NTRU to multi-key RLWE ciphertexts. With appropriate parameter choice, our scheme avoids the sublattice attacks that broke previous NTRU-based MKFHE schemes. Finally, we implement a proof-of-concept of our construction and conduct experiments to test its performance. Our experiments indicate that gate bootstrapping in our scheme for 2 parties takes 0.18 seconds, which is faster than with Chen et al. by around 5.3 times, but unfortunately does not scale beyond that. It is however practical for uses such as oblivious neural network inference.
A methodological approach for mobile applications development: MethApp4Mob

The mobile platform has challenged the software development industry, with the consequent need to adapt commonly used methodologies. The study presents MethApp4Mob: a methodological approach based on the traditional technical phases of software development and industrial project experience. It consists of 8 phases: negotiation, planning, design, implementation and alpha testing, beta testing and feedback analysis, promotion, application publication, release and maintenance, and mechanisms to optimize the software development process. MethApp4Mobile tries to keep the balance between a simple and practical approach without losing formality. Validations results encourage its possible application to offer a robust and easy-to-apply methodology in an industrial context.
The Misuse of AUC: What High Impact Risk Assessment Gets Wrong

When determining which machine learning model best performs some high impact risk assessment task, practitioners commonly use the Area under the Curve (AUC) to defend and validate their model choices. In this paper, we argue that the current use and understanding of AUC as a model performance metric misunderstands the way the metric was intended to be used. To this end, we characterize the misuse of AUC and illustrate how this misuse negatively manifests in the real world across several risk assessment domains. We locate this disconnect in the way the original interpretation of AUC has shifted over time to the point where issues pertaining to decision thresholds, class balance, statistical uncertainty, and protected groups remain unaddressed by AUC-based model comparisons, and where model choices that should be the purview of policymakers are hidden behind the veil of mathematical rigor. We conclude that current model validation practices involving AUC are not robust, and often invalid.
Differential privacy in deep learning: Privacy and beyond

Motivated by the security risks of deep neural networks, such as various membership and attribute inference attacks, differential privacy has emerged as a promising approach for protecting the privacy of neural networks. As a result, it is crucial to investigate the frontier intersection of differential privacy and deep learning, which is the main motivation behind this survey. Most of the current research in this field focuses on developing mechanisms for combining differentially private perturbations with deep learning frameworks. We provide a detailed summary of these works and analyze potential areas for improvement in the near future. In addition to privacy protection, differential privacy can also play other critical roles in deep learning, such as fairness, robustness, and prevention of over-fitting, which have not been thoroughly explored in previous research. Accordingly, we also discuss future research directions in these areas to offer practical suggestions for future studies.
Isolator Detection in Power Transmission Lines using Lightweight Dept-wise Convolution with BottleneckCSP YOLOv5

The detection of insulators is of great importance in power transmission lines. This is because accurate detection ensures reliability and continuity of energy transmission, preventing line interruptions. The proposed method in this study utilizes the DWB-YOLOv5 (Dept-wise convolution with BottleneckCSP YOLOv5) model to effectively detect insulators, contributing to the safe and uninterrupted operation of power lines. In the suggested approach, the DWB-YOLOv5 model is employed to detect insulators. The bottleneckCSP module enhances the accuracy of targets at various scales, while the depth-wise c2onvolution module assists in reducing the model's complexity. Images undergo preprocessing steps such as automatic orientation and resizing. The preprocessed images are fed into the DWB-YOLOv5 model to extract deep features, perform object detection, and conduct classification. The insulator detection model obtained through this method exhibits a minimum of 8.53% better mean average precision (mAP) performance compared to existing methods. This study represents a significant step towards ensuring the safe and uninterrupted operation of power transmission lines. Accurate detection of insulators facilitates the smooth functioning of lines, ensuring reliability and continuity in energy transmission. The proposed method offers important advantages such as high accuracy, lightweight design, and efficiency.
A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis

Biomedical data analysis is essential in current diagnosis, treatment, and patient condition monitoring. The large volumes of data that characterize this area require simple but accurate and fast methods of intellectual analysis to improve the level of medical services. Existing machine learning (ML) methods require many resources (time, memory, energy) when processing large datasets. Or they demonstrate a level of accuracy that is insufficient for solving a specific application task. In this paper, we developed a new ensemble model of increased accuracy for solving approximation problems of large biomedical data sets. The model is based on cascading of the ML methods and response surface linearization principles. In addition, we used Ito decomposition as a means of nonlinearly expanding the inputs at each level of the model. As weak learners, Support Vector Regression (SVR) with linear kernel was used due to many significant advantages demonstrated by this method among the existing ones. The training and application procedures of the developed SVR-based cascade model are described, and a flow chart of its implementation is presented. The modeling was carried out on a real-world tabular set of biomedical data of a large volume. The task of predicting the heart rate of individuals was solved, which provides the possibility of determining the level of human stress, and is an essential indicator in various applied fields. The optimal parameters of the SVR-based cascade model operating were selected experimentally. The authors shown that the developed model provides more than 20 times higher accuracy (according to Mean Squared Error (MSE)), as well as a significant reduction in the duration of the training procedure compared to the existing method, which provided the highest accuracy of work among those considered.
Secure and Memorable Authentication Using Dynamic Combinations of 3D Objects in Virtual Reality

As Virtual Reality (VR) applications gain popularity, the need for a secure, usable, and memorable user authentication method becomes crucial. However, security and privacy in such VR applications are often ignored. Current methods are insufficient in preventing man-in-the-room (MITR) attacks, which allow attackers to observe user interactions in VR while remaining invisible, and inputted passwords can easily be stolen. In this study, we propose a dynamic combination of multi-attribute authentication methods for VR, where various 3D objects and their attributes can be created and displayed. Users must select combinations of 3D objects and their attributes provided by our designed principles for identity authentication. We explore the impact of method parameters on security and provide three specific parameter schemes to deploy the practical authentication system. We designed three user studies to evaluate the usability, security, and memorability of our authentication system. The results show that the proposed scheme can effectively resist both shoulder surfing and MITR attacks with unsuccessful attack rates of 100% and 95.83%, respectively. Furthermore, this research provides suggestions to secure VR applications while maintaining usability and enhancing the memorability of the authentication method.
Customer Capital and its Role on Promoting Entrepreneurial Marketing: The Case of Small and Medium Enterprises in Iraq

This research examines the role of the customer capital through its three dimensions (market intelligence, customer delight and customer loyalty) on entrepreneurial marketing. The empirical study of this research was used in Iraq, Baghdad, on small and medium -sized enterprises, and the questionnaires were the researchers who randomly chose 60 from small and medium enterprises to distribute 400 questionnaires to employees and about 21 of these questionnaires were invalid, after collecting the data, it was analyzed to show the relationship between the factors. The results of this study showed that there is an effect on the customer capital in the entrepreneurial marketing, and the hypotheses of this study have been supported in conducting more research in other countries and the other legal form to work to generalize the results
Enriching Enterprise Architecture Models with Healthcare Domain Knowledge

Enterprise architecture (EA) modeling gives an opportunity to have an overview of the enterprise architecture supporting business-IT alignment within the rapidly changing environment. Visual representation of enterprise architecture models is appropriate for interpretation by humans. Machines, however, cannot interpret labels associated with the model element, as well as its domain-specific concepts. To make EA models machine-interpretable, a graphical representation of models shall be connected to domain knowledge. This research demonstrates an approach to enriching the EA model of a medical institution with healthcare domain knowledge. Evaluation of the developed solution proves that a human and a machine could equally understand the ontology-based EA model.
Fuzzy Dynamic Adaptation of an Artificial Fish Swarm Algorithm for the Optimization of Benchmark Functions

An improved artificial fish swarm algorithm (AFSA) through the technique of the Type- 1 Fuzzy Logic System (T1FLS) for the optimization of a benchmark set of functions is presented in this paper. The main idea is present a proposal on adjusting the values in S and V parameters of the AFSA with T1FLS to improve the performance on the results. The efficiency of the Fuzzy AFSA (FAFSA) demonstrate that it is an excellent algorithm to solve benchmark sets of functions. The FAFSA has proven to be successful in the implementation of various benchmarking optimization problems. A comparative with the original AFSA is presented.