Reviewerly/exHarmony huggingface dataset:
Crimes of Electronic Defamation, Libel, and Slander under Jordanian Cybercrimes Law

This article aims to examine how the Jordanian Cybercrimes Law (No. 27 of 2015) addresses the crimes of electronic defamation, libel, and slander, to develop a better understanding of how these crimes can be combatted, as they are the most widespread cybercrimes committed in Jordan. This article uses a mixed-methods approach using descriptive and analytical methods. The descriptive method is used to outline the special nature of the crimes of electronic defamation, libel and slander, as well as related substantive and procedural rules. Meanwhile, the analytical method is used to determine how these crimes are considered in the existing literature, including both primary and secondary sources. This article has several results, the most important being that it is irrational to rely solely on the Jordanian Cybercrimes Law to address electronic defamation, libel, and slander; instead, other legislation should be followed as well, such as the substantive and procedural rules within the Jordanian Penal Code and the Jordanian Code of Criminal Procedures. This article is a piece of original research that paves the way for future research on the adoption of a comprehensive legal framework tackling cybercrimes in Jordon.
An Intelligent Semi-Honest System for Secret Matching against Malicious Adversaries

With natural language processing as an important research direction in deep learning, the problems of text similarity calculation, natural language inference, question and answer systems, and information retrieval can be regarded as text matching applications for different data and scenarios. Secure matching computation of text string patterns can solve the privacy protection problem in the fields of biological sequence analysis, keyword search, and database query. In this paper, we propose an Intelligent Semi-Honest System (ISHS) for secret matching against malicious adversaries. Firstly, a secure computation protocol based on the semi-honest model is designed for the secret matching of text strings, which adopts a new digital encoding method and an ECC encryption algorithm and can provide a solution for honest participants. The text string matching protocol under the malicious model which uses the cut-and-choose method and zero-knowledge proof is designed for resisting malicious behaviors that may be committed by malicious participants in the semi-honest protocol. The correctness and security of the protocol are analyzed, which is more efficient and has practical value compared with the existing algorithms. The secure text matching has important engineering applications.
An Efficient Personal Key Recovery in Self-Sovereign Identity Environments

An efficient method for personal key recovery in self-sovereign identity environments, combining online and offline infrastructure for personal identification is proposed in this paper. This new concept, self-sovereign identity, that emerged in recent years supports various applications like online banking, digital assets, and digital identity management. Public key encryption is fundamental in self-sovereign identity and the management and recovery of private keys is of paramount importance. The proposed method uses a fragmentation and combination strategy for private keys, augmented by bio-metric recognition utilizing encryption algorithms such as AES, SEED, and SSS and minutiae points. A private key storage and recovery approach utilizing a combination of online and offline environments through a hash graph is also proposed.
Automatic Business Location Selection through Particle Swarm Optimization and Neural Network

Location selection is an important part of running a business. A good business location can greatly increase customer flow, reduce operating costs, and increase business revenue. Therefore, it is necessary to select an appropriate location for business premises. In this research, we propose a method combining machine learning and particle swarm optimization for the business location selection. In the proposed method, we first apply the particle swarm algorithm to optimize the proportional coefficient of the influence of different surrounding commercial places on the site selection of the research site. Then we use a machine learning method to perform the binary classification on the sites to judge whether a geographic location is a good site selection point. The proposed method is compared with several common location selection methods such as random selection method and k-nearest neighbor method. The experimental results have validated the effectiveness of our proposed method for business location selection.
MVDet: multi-view multi-class object detection without ground plane assumption

Abstract Although many state-of-the-art methods of object detection in a single image have achieved great success in the last few years, they still suffer from the false positives in crowd scenes of the real-world applications like automatic checkout. In order to address the limitations of single-view object detection in complex scenes, we propose MVDet, an end-to-end learnable approach that can detect and re-identify multi-class objects in multiple images captured by multiple cameras (multi-view). Our approach is based on the premise that incorrect detection results in a specific view can be eliminated using precise cues from other views, given the availability of multi-view images. Unlike most existing multi-view detection algorithms, which assume that objects belong to a single class on the ground plane, our approach can classify multi-class objects without such assumptions and is thus more practical. To classify multi-class objects, we propose an integrated architecture for region proposal, re-identification, and classification. Additionally, we utilize the epipolar geometry constraint to devise a novel re-identification algorithm that does not require assumptions about ground plane assumption. Our model demonstrates competitive performance compared to several baselines on the challenging MessyTable dataset.
Secure and Decentralized Anonymous E-Voting Scheme

Secure electronic voting (e-voting) schemes are an imperative topic in today's society as we transition to an increasingly online world. Conventional e-voting processes are centralized that heavily rely on a central aggregator which can raise privacy concerns. Hence, this paper proposes a novel decentralized e-voting scheme that uses the BGW protocol, secure multi-party computation (MPC), and secret sharing as part of a distributed computation mechanism among all the voters to generate a fair and safe e-voting system. Additionally, the proposed scheme provides fully decentralized vote counting, ensures anonymity to further enhance the security of the protocol, and delivers the results of the vote to users. The security of the proposed e-voting scheme is evaluated using prominent security theorems, where the analysis shows that the scheme satisfies several security attributes of secure e-voting, and is more secure and reliable than conventional e-voting protocols.
Visual Analytics in Software Maintenance: A Systematic Literature Review

The research on visual analytics for software maintenance has noticeabily advanced in the past few years. For many software projects, software maintenance needs an effective and efficient path from data to decision. Visual analytics (VA) creates such a path that enables the user to extract insights by interacting with the relevant information. This paper focuses on VA in software maintenance and has the following goals: investigate the VA adoption and suggest important implications for practice, and identify current research trends, open problems, and areas for improvement. To achieve these goals we conducted a systematic literature review with three research questions and assessed 51 studies published in the past two decades. The results indicate that there is a lack of collaboration between academic researchers and industry practitioners. This impedes the credibility of the proposed tools and methods due to lack of confidence in industry adoption. Furthermore, in this study we identified the need to expand VA support to other programming languages and software maintenance tasks.
Context-enhanced motion coherence modeling for global outlier rejection

Feature matching is an essential step in a wide range of photogrammetry and computer vision tasks but limited by the ambiguities of local descriptors. Hence, numerous false feature matches (outliers) will inevitably be generated, especially in complex scenarios. Motion coherence can establish the statistical relationships among sparse motions to remove outliers, assuming that the true matches are coherent, and the false matches are randomly scattered. However, existing methods model motion coherence either in a local spatial context or without considering rich matching priors, thereby leading to numerous matching failures when outlier rates are high. In this study, we propose a context-enhanced motion coherence modeling (CoMo) method to distinguish consistent correct motions from erroneous matches for robust outlier rejection. The CoMo method deploys a consistency-aware motion descriptor to encode the consistency-related matching priors of feature matches as a high-dimensional representation, which can provide a rich context for differentiating heterogeneous motions. Based on this discriminative descriptor, we further introduce deformable affine transformation (DAT) as a proxy for motion and fit the coherent motions from the candidate matches with a globally smooth function under the truncated least squares estimation framework. Extensive experiments on multiple large datasets (including the Image Matching Challenge at CVPR 2020) demonstrate that the CoMo method can effectively model motion coherence from noisy candidate matches and outperform other state-of-the-art methods in outlier rejection and relative camera pose estimation. The code is available at https://github.com/geovsion/CoMo.
Regulation-Friendly Privacy-Preserving Blockchain Based on zk-SNARK

Recently, blockchain has attracted much attention from industries, due to its good characteristics such as decentralization and tamper proofing. To ensure that sensitive transaction data are not disclosed to the public, many privacy protection methods have been proposed for blockchain, which generally conflicts with regulatory requirements. To resolve such a conflict, in this paper we propose a privacy-preserving account-based blockchain system which supports auditing on transactions. The proposed system protects the privacy of a transaction via homomorphic encryption. The validity of the transaction is guaranteed via zero-knowledge proof. Especially, details of a transaction are presented in form of ciphertexts on the public ledger, which can be decrypted by regulatory authorities. We have implemented a demo of the proposed system using the Substrate framework. Simulation results show that the system has acceptable performance.
Utility–Privacy Trade-Offs with Limited Leakage for Encoder

The utilization of databases such as IoT has progressed, and understanding how to protect the privacy of data is an important issue. As pioneering work, in 1983, Yamamoto assumed the source (database), which consists of public information and private information, and found theoretical limits (first-order rate analysis) among the coding rate, utility and privacy for the decoder in two special cases. In this paper, we consider a more general case based on the work by Shinohara and Yagi in 2022. Introducing a measure of privacy for the encoder, we investigate the following two problems: The first problem is the first-order rate analysis among the coding rate, utility, privacy for the decoder, and privacy for the encoder, in which utility is measured by the expected distortion or the excess-distortion probability. The second task is establishing the strong converse theorem for utility-privacy trade-offs, in which utility is measured by the excess-distortion probability. These results may lead to a more refined analysis such as the second-order rate analysis.
KAJIAN STATISTIK DALAM INDEKS PEMBANGUNAN KEBUDAYAAN DI INDONESIA TAHUN 2018 – 2021

Indonesia’s Culture Development Index (also known as IPK) is the first index in world that can specifically measure achievements of cultural development in various regions. This index consists of 7 dimensions, which is annually calculated with coverage in national and provincial levels. We initiated a study of statistics applications for more detailed analysis of results of IPK, especially with composite scores and dimensional scores at provincial level for last 4 years (2018-2021). Our study aimed to find out more about interesting aspects to explore from provincial IPK scores, for example related to IPK score development in the last 4 years, investigated the differences in IPK scores between before and during Covid-19 pandemic, identified main factors that more simply explained IPK dimension, also simulated clustering provinces based on their IPK scores. Our main research method was analysing secondary data on provincial IPK values in period 2018-2021, as well as literature study to strengthen theory of some statistical methods. The results of our research showed that applications of such statistical methods were useful to answer various interesting aspects related to provincial IPK scores. For example, with Two-Sample Test we obtained several dimensions that have significantly different scores between before and during the Covid-19 pandemic. In addition, Factor Analysis method also found 3 main factors that simplified for explaining IPK dimensions. Moreover, from Cluster Analysis at least 4 clusters were produced, which clearly described characteristics of provinces included in each cluster based on their IPK scores.
To Improve the Identification of Images Using LBPH in Machine Learning

One of the most significant and extensively studied local texture descriptors is called Local Binary Patterns (LBP). In the studies, a comprehensive analysis of the most renowned LBP related procedures is required in order to make sense of them all. Descriptors have been created using multistage convolutional neural networks (CNN) in deep learning. With low-resolution pixels, this work presents an enhanced real-time facial identification system that integrates posture, mood and resolution variations. We produced training and classification datasets. Face detection and recognition is performed using the different algorithm, which gets the face picture. With an updated database, the training and recognition process will resume automatically. The distance between a person and camera can be used for this facial recognition technology to help security forces in system. A surveillance system for airports, bus terminals and other public locations can also be installed to reduce the risk of criminal activity. To lessen the possibility of criminal behaviour, a surveillance system for airports, bus terminals and other public venues can be implemented.
Entropy estimation via uniformization

Entropy estimation is of practical importance in information theory and statistical science. Many existing entropy estimators suffer from fast growing estimation bias with respect to dimensionality, rendering them unsuitable for high-dimensional problems. In this work we propose a transform-based method for high-dimensional entropy estimation, which consists of the following two main ingredients. Firstly, we provide a modified k-nearest neighbors (k-NN) entropy estimator that can reduce estimation bias for samples closely resembling a uniform distribution. Second we design a normalizing flow based mapping that pushes samples toward the uniform distribution, and the relation between the entropy of the original samples and the transformed ones is also derived. As a result the entropy of a given set of samples is estimated by first transforming them toward the uniform distribution and then applying the proposed estimator to the transformed samples. The performance of the proposed method is compared against several existing entropy estimators, with both mathematical examples and real-world applications.
Evaluation of Query-Based Membership Inference Attack on the Medical Data

In recent years, machine learning (ML) has achieved huge success in healthcare and medicine areas. However, recent work has demonstrated that ML is vulnerable to privacy leakage since it exhibits to overfit the training datasets. Especially, in healthcare and medical communities, there are concerns that medical images and electronic health records containing protected health information (PHI) are vulnerable to inference attacks. These PHI might be unwittingly leaked when the aforementioned data is used for training ML models to address necessary healthcare concerns. Given access to the trained ML model, the attacker is able to adopt membership inference attacks (MIA) to determine whether a specific data sample is used in the corresponding medical training dataset. In this paper, we concentrate on MIA and propose a new method to determine whether a sample was used to train the given ML model or not. Our method is based on the observation that a trained machine learning model usually is lesser sensitive to the feature value perturbations on its training samples compared with the non-training samples. The key idea of our method is to perturb a training sample's feature value in the corresponding feature space and then compute the relationship between each feature's perturbation and the corresponding prediction's change as features to train the attack model. We used publicly available medical datasets such as diabetes and heartbeat categorization data to evaluate our method. Our evaluation shows that the proposed attack can perform better than the existing membership inference attack method.
Interactive Machine Learning of Knowledge Graph-Based Explainable Process Analysis

The core idea behind combining knowledge graph-based AI and machine learning is to formalize explicit knowledge as a knowledge base, which does not need to be learned, and to consider it in machine learning. Interactive machine learning provides an alternative to explicit knowledge modeling to include feedback from human experts in order to adapt and improve learned models. For semantic process analysis, we have developed an explainable AI approach based on a specific knowledge graph that delivers interpretable results about weaknesses and improvement potentials in business processes. In the present paper we show how interactive machine learning can extend the knowledge graph with human implicit knowledge. The knowledge graph does not solely rely on the initially created knowledge elements and defined calculation formulas, but learns incrementally at the time of user feedback by making small adjustments in the graph.
Investigating the Optimum Tilt Angle of PV Modules in Al-Sherouk City, Cairo

Energy is a crucial element that can ensure the quality of life for citizens. The consumption of energy by citizens is increasing rapidly, and future existence also became reliant on the sufficient availability of energy and its sources. Nevertheless, energy consumption is becoming a major concern due to the increase in usage rates and the lack of sufficient renewable sources of energy. Nowadays, the technological advancement that lies in the use of Photovoltaic Panels can help in generating energy and take the advantage of the sun especially in Egypt. The usage of PV’s relies on multiple factors that can ensure the highest potential generation of energy. Therefore, several studies investigated these factors including the optimum titling angle that can be different according to the location and orientation of the panel. Modifying the titling angle can play a significant role in generating high rates of power depending on the measurements and calculations. The aim of this paper was to investigate the efficient titling angle that can be used for PV panels that are installed in Al-Sherouk City in Egypt. The methodology involved the implementation of an experimental investigation that is based on position 2 similar PV panels to estimate the power generation over a period of 2 days from 9 am to 4 pm. The findings have shown that the theoretical and experimental results were similar, and the optimum tilting angle was determined to range between 54.7 degrees and 8.16 degrees. The study demonstrated the differences between an adjusted and a fixed angle, and the variable titled angle can generate more energy than the fixed one. This paper contributes to the body of knowledge by presenting the significance of a variable tilting angle to generate more power than relying on a fixed angle as demonstrated by many previous studies.
Uncertainty Analysis in Cryptographic Key Recovery for Machine Learning-Based Power Measurements Attacks

The present work concerns side-channel attacks on cryptographic devices protected with the advanced encryption standard. In this regard, the assessment of guessing entropy and the related uncertainty is proposed for machine learning-based attacks based on power measurements. For the first time, the guessing entropy was assessed on the entire key while uncertainty was introduced in the field of side-channel attacks, thus allowing a more rigorous vulnerability test for a device. Notably, a state-of-the-art attack relying on a multilayer perceptron is exploited for classifying power traces leaked from physically accessible devices. A public database was exploited for the sake of results reproducibility. Thanks to cross-validation, the uncertainty associated with retrieving a single key byte can be quantified and then propagated to the entire key by means of the Monte Carlo method. It is thus shown that, when exploiting about 4000 attack records (traces), there is a 10% probability to retrieve the secret key as a whole with less than ten attempts. This implies that a full cryptographic key can be discovered on average ten times for every 100 similar devices by a side-channel attack. This poses security threats particularly relevant in an internet-of-things scenario and addresses the need for improved vulnerability testing and proper countermeasures.
A Histopathological Image Classification Method Based on Model Fusion in the Weight Space

Automatic classification of histopathological images plays an important role in computer-aided diagnosis systems. The automatic classification model of histopathological images based on deep neural networks has received widespread attention. However, the performance of deep models is affected by many factors, such as training hyperparameters, model structure, dataset quality, and training cost. In order to reduce the impact of the above factors on model training and reduce the training and inference costs of the model, we propose a novel method based on model fusion in the weight space, which is inspired by stochastic weight averaging and model soup. We use the cyclical learning rate (CLR) strategy to fine-tune the ingredient models and propose a ranking strategy based on accuracy and diversity for candidate model selection. Compared to the single model, the weight fusion of ingredient models can obtain a model whose performance is closer to the expected value of the error basin, which may improve the generalization ability of the model. Compared to the ensemble model with n base models, the testing cost of the proposed model is theoretically 1/n of that of the ensemble model. Experimental results on two histopathological image datasets show the effectiveness of the proposed model in comparison to baseline ones, including ResNet, VGG, DenseNet, and their ensemble versions.