Reviewerly/exHarmony huggingface dataset:
New Method of Prime Factorisation-Based Attacks on RSA Authentication in IoT

The potential benefits of the Internet of Things (IoT) are hampered by malicious interventions of attackers when the fundamental security requirements such as authentication and authorization are not sufficiently met and existing measures are unable to protect the IoT environment from data breaches. With the spectrum of IoT application domains increasing to include mobile health, smart homes and smart cities in everyday life, the consequences of an attack in the IoT network connecting billions of devices will become critical. Due to the challenges in applying existing cryptographic standards to resource constrained IoT devices, new security solutions being proposed come with a tradeoff between security and performance. While much research has focused on developing lightweight cryptographic solutions that predominantly adopt RSA (Rivest–Shamir–Adleman) authentication methods, there is a need to identify the limitations in the usage of such measures. This research paper discusses the importance of a better understanding of RSA-based lightweight cryptography and the associated vulnerabilities of the cryptographic keys that are generated using semi-primes. In this paper, we employ mathematical operations on the sum of four squares to obtain one of the prime factors of a semi-prime that could lead to the attack of the RSA keys. We consider the even sum of squares and show how a modified binary greatest common divisor (GCD) can be used to quickly recover one of the factors of a semi-prime. The method presented in this paper only uses binary arithmetic shifts that are more suitable for the resource-constrained IoT landscape. This is a further improvement on previous work based on Euler’s method which is demonstrated using an illustration that allows for the faster testing of multiple sums of squares solutions more quickly.
Exposing Points of Interest as Linked Geospatial Data

Point of Interest (POI) data is widely used in many modern applications and services related to navigation, tourism, social networking, logistics, and many more. In this paper, we propose a comprehensive and vendor-agnostic data model to represent multi-faceted and enriched POI profiles. Harnessing the versatility of Linked Data technologies, this semantically rich ontology accommodates and extends existing POI formats for assembling and managing POI data from heterogeneous sources. Furthermore, we have developed the open-source software TripleGeo, which can effectively transform POI data from diverse sources and formats (geographical files, databases, and semi-structured data) to their RDF representations and vice versa. Thus, it is possible to import POI data from various existing systems and products, transfer and address the data integration challenges in the Linked Data domain, and export back the results. Our empirical study confirms the validity and efficiency of this framework for a variety of real-world POI assets and formats, underscoring its robustness to cope with scalable data volumes.
Effectiveness of Model-Based Defenses for Digitally Controlled Industrial Systems: Nuclear Reactor Case Study

Model-based defenses have been promoted over the past decade as essential defenses against intrusion and data deception attacks into the control network used to digitally regulate the operation of critical industrial systems such as nuclear reactors. The idea is that physics-based models could differentiate between genuine, i.e., unaltered by adversaries, and malicious network engineering data, e.g., flowrates, temperatures, etc. Machine learning techniques have also been proposed to further improve the differentiating power of model-based defenses by constantly monitoring the engineering data for any possible deviations that are not consistent with the physics. While this is a sound premise, critical systems, such as nuclear reactors, chemical plants, oil and gas plants, etc., share a common disadvantage: almost any information about them can be obtained by determined adversaries, such as state-sponsored attackers. Thus, one must question whether model-based defenses would be resilient under these extreme adversarial conditions. This paper represents a first step toward answering this question. Specifically, we introduce self-learning techniques, including both pure data-driven, e.g., deep neural networks, and physics-based techniques able to predict dynamic behavior for a nuclear reactor model. The results indicate that if attackers are technically capable, they can learn very accurate models for reactor behavior, which raises concerns about the effectiveness of model-based defenses.
Issues relating to trust in care agents for the elderly

There is increasing academic interest in and commercial development of care agents to assist with the care of the elderly in the home. This paper defines some of the under-explored questions and issues relating to trust. It raises specific questions to instigate debate and recommends directions for future research in the domain.
Combining Dynamic Field of View Modification with Physical Obstacle Avoidance

Motion sickness is a major cause of discomfort for users of virtual reality (VR) systems. Over the past several years, several techniques have been proposed to mitigate motion sickness, such as high-quality “room-scale” tracking systems, dynamic field of view modification, and displaying static or dynamic rest frames. At the same time, an absence of real world spatial cues may cause trouble during movement in virtual reality, and users may collide with physical obstacles. To address both of these problems, we propose a novel technique that combines dynamic field of view modification with rest frames generated from 3D scans of the physical environment. As the users moves, either physically and/or virtually, the displayed field of view can be artificially reduced to reveal a wireframe visualization of the real world geometry in the periphery, rendered in the same reference frame as the user. Although empirical studies have not yet been conducted, informal testing suggests that this approach is a promising method for reducing motion sickness and improving user safety at the same time.
Privacy-Protected Blockchain System

The blockchain uses a decentralized consensus mechanism to maintain the books in an immutable way, which ensures the blockchain smart contract system highly secure. In existing blockchain systems, all user information is disclosed in the blockchain. However, currently users begin to pay more and more attention to personal privacy, therefore the future blockchain smart contract system needs not only to keep immutability but also to protect user privacy. To achieve this goal, in this paper we propose a privacy-encrypted blockchain system, where all data is encrypted within a controllable period of time. Although the data is visible from a historical perspective, our design can effectively protect user privacy and against deceivers, making the system more secure and healthy.
A Class of Optimal Cyclic Codes With Two Zeros

Let m > 2 be an integer and p be an odd prime. We explore the minimum distance of p-ary cyclic codes of length n = 2(p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> - 1)/(p - 1) with two zeros. A sufficient condition for such cyclic codes with minimum distance at least three is obtained. A class of optimal p-ary cyclic codes with minimum distance four are presented. Four explicit constructions for such optimal cyclic codes are provided. The weight distribution of the dual of the cyclic code in the first construction is given.
Ontology-Based Security Tool for Critical Cyber-Physical Systems

Industry 4.0 considers as a new advancement concept of the industrial revolution, which introduces a full utilization of Internet technologies. This concept aims to combine diverse technological resources into the industry field, which enables the communication between two worlds: the physical and the cyber one. Cyber-physical Systems are one of the special forces that integrate and build a variety of existing technologies and components. The diversity of components and technologies creates new security threats that can exploit vulnerabilities to attack a critical system. This work introduces an ontology-based security tool-chain able to be integrated with the initial stages of the development process of critical systems. The tool detects the potential threats, and apply the suitable security requirements which can address these threats. Eventually, it uses the ontology approach to ensure that the security requirements are fulfilled.
Formal Foundations for Analyzing and Refactoring Delta-Oriented Model-Based Software Product Lines

Model-Based Software Product Line (MBSPL) Engineering combines Model-Based Software Engineering (MBSE) and Software Product Line (SPL) Engineering by specifying variability in models and generating model variants as products of an MBSPL. Delta Modeling (DM) is a transformational approach for implementing MBSPLs by adding, removing or modifying model elements through delta modules to activate individual features. To date, the applicability of DM to real-world MBSPLs is severely hindered due to the resulting complex network of interrelated delta modules in which errors are hard to identify and fix without unintentionally harming overall consistency. To address this challenge, we present a set of analyses to identify problems in a network of delta modules as well as a construction kit to assemble refactorings to remedy these problems and simplify the network. We give a modeling-language independent formalization of delta modules based on graph transformation concepts. This is the basis for our analyses which, in turn, build the basis for our refactorings to prevent unintended side-effects.
NUQSGD: Improved Communication Efficiency for Data-parallel SGD via Nonuniform Quantization.

As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed on clusters to perform model fitting in parallel. Alistarh et al. (2017) describe two variants of data-parallel SGD that quantize and encode gradients to lessen communication costs. For the first variant, QSGD, they provide strong theoretical guarantees. For the second variant, which we call QSGDinf, they demonstrate impressive empirical gains for distributed training of large neural networks. Building on their work, we propose an alternative scheme for quantizing gradients and show that it yields stronger theoretical guarantees than exist for QSGD while matching the empirical performance of QSGDinf.
Comparison-Based Feature Location in ArgoUML Variants

Identifying and extracting parts of a system's implementation for reuse is an important task for re-engineering system variants into Software Product Lines (SPLs). An SPL is an approach that enables systematic reuse of existing assets across related product variants. The re-engineering process to adopt an SPL from a set of individual variants starts with the location of features and their implementation, to be extracted and migrated into an SPL and reused in new variants. Therefore, feature location is of fundamental importance to the success in the adoption of SPLs. Despite its importance, existing feature location techniques struggle with huge, complex, and numerous system artifacts. This is the scenario of ArgoUML-SPL, which stands out as the most used case study for the validation of feature location approaches. In this paper we use an automated feature location technique and apply it to the ArgoUML feature location challenge posed.
Arabic Speech Synthesis System Based on HMM

The work presented in this paper is about Text-to-Speech (TTS) synthesis and, more particularly, about statistical speech synthesis using the Hidden Markov Models (HMM). The main objective of this work is to study the functioning of the HMM-based speech synthesis system (HTS) and the implementation of this method to create a system that produces understandable speech output for a given Arabic text. We have done a brief description of the statistical parametric speech synthesis based on HMM, the steps followed to implement this method for Arabic language. Finally, for the evaluations of the system are based on subjective mean opinion score and objective tests. Regarding the intelligibility, naturalness aspects (listening) and the quality (Perceptual Evaluation of Speech Quality (PESQ)).
Visualization of Feature Locations with the Tool FeatureDashboard

Modern development processes and issue trackers often use the notion of features to manage a software system. Features allow communicating system characteristics across stakeholders and keeping an overview understanding---especially important for systems that exist in many different variants. However, maintaining, evolving or reusing features (e.g., propagating across variants, or integrating into a platform) requires knowing their locations to prevent extensive feature-location recovery. We advocate the use of embedded annotations, added directly into software assets by the developers during development. To support this process and provide immediate benefits to developers when using such annotations, we present the open-source tool FeatureDashboard. It extracts and visualizes features and their locations using different views and metrics. As such, it encourages developers recording features and their locations early, to prevent feature identification and location efforts, as well as it supports system comprehension.
Collaborative Data Analytics Using Virtual Reality

Immersive analytics allows a large amount of data and complex structures to be concurrently investigated. We propose a collaborative analytics system that benefits from new advances in immersive technologies for collaborators working in the early stages of data exploration. We implemented a combination of Star Coordinates and Star Plot visualisation techniques to support the visualisation of multidimensional data and the encoding of datasets using simple and compact visual representations. To support data analytics tasks, we propose tools and interaction techniques for users to build decision trees for visualising and analysing data in a top-down method.
Vehicle Re-Identification with Refined Part Model

Vehicle re-identification (re-ID) is a challenging task in computer vision. Different vehicle identities belonging to the same model may have a similar appearance with little inter-instance discrepancy, while one vehicle may have large intra-instance differences under different viewpoint and illumination. In this paper, we propose a refined part model to learn an efficient feature embedding to solve this problem. Different from other methods, which directly obtain region part for vehicle re-ID. The refined part model is formulated through a Grid Spatial Transformer Network (GSTN) to automatically locate the vehicle and perform division for regional features. Residual attention is also conducted to give an additional refinement for a fine-grained identification. Finally, the refined part features are fused to form an efficient feature embedding. Extensive experimental results show that our approach significantly outperforms state-of-the-art on two large scale datasets: VehicleID and Vehicle-1M.
Semantic Information G Theory and Logical Bayesian Inference for Machine Learning

An important problem with machine learning is that when label number n>2, it is very difficult to construct and optimize a group of learning functions, and we wish that optimized learning functions are still useful when prior distribution P(x) (where x is an instance) is changed. To resolve this problem, the semantic information G theory, Logical Bayesian Inference (LBI), and a group of Channel Matching (CM) algorithms together form a systematic solution. A semantic channel in the G theory consists of a group of truth functions or membership functions. In comparison with likelihood functions, Bayesian posteriors, and Logistic functions used by popular methods, membership functions can be more conveniently used as learning functions without the above problem. In Logical Bayesian Inference (LBI), every label's learning is independent. For Multilabel learning, we can directly obtain a group of optimized membership functions from a big enough sample with labels, without preparing different samples for different labels. A group of Channel Matching (CM) algorithms is developed for machine learning. For the Maximum Mutual Information (MMI) classification of three classes with Gaussian distributions on a two-dimensional feature space, 2-3 iterations can make mutual information between three classes and three labels surpass 99% of the MMI for most initial partitions. For mixture models, the Expectation-Maximization (EM) algorithm is improved and becomes the CM-EM algorithm, which can outperform the EM algorithm when mixture ratios are imbalanced, or local convergence exists. The CM iteration algorithm needs to combine neural networks for MMI classifications on high-dimensional feature spaces. LBI needs further studies for the unification of statistics and logic.
Hand Gesture Detection Based Real-Time American Sign Language Letters Recognition using Support Vector Machine

Sign language is an indispensable communication means for deaf-mute people because of their hearing impairment. At present, sign language is not a popular communication method among hearing people, so that the majority of the hearing is not willing to have a talk with the deaf-mute, or they have to spend much time and energy trying to figure out what the correct meaning is. Sign Language Recognition (SLR), which aims to translate sign language to people who know few about it in the form of text or speech, can be said to be a great help to deaf-mute and hearing people communicate. In this study, a real-time vision-based static hand gesture recognition system for sign language was developed. All data is collected from a USB camera connected to a computer, and no auxiliary items (such as gloves) were required. The proposed system is based on a skin color algorithm in HSV color space to find the Region of Interest (ROI), where hand gesture is. After completing all pre-processing work, 8 features were extracted from each sample using Principal Component Analysis (PCA). The recognition machine learning approach used was based on Support Vector Machine (SVM). The experimental results show that this system can distinguish B, D, F, L and U, these five American sign language hand gestures, with the success rate of about 99.4%.
The Upper Limit of Distributed Solar PV Capacity in Riyadh: A GIS-Assisted Study

Rooftop solar photovoltaic (PV) systems, commonly referred to as distributed generation (DG) solar systems, are deemed important contenders in future sustainable cities. Because deploying DG systems is associated with technical, financial, policy, and market implications that impact utilities, governments, and businesses, quantifying the potential of DG systems that could be deployed in a certain jurisdiction ex ante helps inform the decision-making process for all stakeholders. To that end, the upper limit of rooftop PV systems that could be deployed in Riyadh, the capital of Saudi Arabia, was assessed with the aid of geographic information systems (GIS). By relying on urban land lot data for different categories, i.e., zones, and the maximum allowable area that could be built within a certain lot using prevailing building codes and regulations, the rooftop area suitable for PV deployment within Riyadh Metro was quantified. The analysis was restricted to rooftops in residential, mosque, shopping mall, and health care buildings only. Following the quantification of the rooftop area, the upper limit of rooftop solar PV capacity that can be deployed in the city of Riyadh was found to be 4.34 GW. This capacity represents nearly 22% of the peak load and can satisfy approximately 9% of the energy requirement in the central region, the region in which Riyadh resides.
Determining Design Requirements for AR Physics Education Applications

While we are in the midst of a renaissance of interest in augmented reality (AR), there remain a small number of application domains which have seen significant development. Education is a domain that often drives innovation with emerging technologies. One particular subject which benefits from additional visualization capabilities is physics. In this paper, we present the results of a series of interviews with secondary school teachers about their experience with AR and the features which would be most beneficial to them from a pedagogical perspective. To gather meaningful information, a prototype application was developed and presented to the teachers. Based on the feedback collected from the teachers, we present a set of design recommendations for AR physics education tools, as well as other useful collects comments.
The impact of innovative learning environments on social competences of youth

The exponential development of learning environments supported by information and communication technology (ICT), coupled with new insights from the fields of cognitive and neuroscience and artificial intelligence (AI), is a huge challenge for both the educational system and youths and their social competences. This article defines the impact of an effective use of contemporary education technologies on young people’s engagement and their interest in collaborating with their peers and teachers on the level of interpersonal relationships. It investigates the teachers’ and parents’ perspective on youth and their cooperation when using ICT in the pedagogical process. The study confirms that innovative didactic approaches to teaching, supported by ICT, exert a positive influence on the collaboration between students, as well as between students and their teachers, and that youths, teachers and parents are all equally aware of that. Parents and teachers have a unified position in support of the idea that an effective use of ICT makes a positive contribution to collaboration between students, and thereby directly affects an increase in their social competences. ICT is becoming one of the important variables that affect the development of young people’s social competences. The 2-year study was conducted in the framework of a national project.
Privacy Preserving Intrusion Detection Via Homomorphic Encryption

In the recent years, we are assisting to an undiminished, and unlikely to stop number of cyber threats, that have increased the organizations/companies interest about security concerns. Further, the rising costs of an efficient IT security staff and environment is posing a significant challenge. These have created a new fast growing trend named Managed Security Services (MSS). Often customers turn to MSS providers to alleviate the pressures they face daily related to information security. One of the most critical aspect, related to the outsourcing of security issues, is privacy. Security monitoring and in general security services require access to as much data as possible, in order to provide an effective and reliable service. It is the well known conflict between privacy and security, a particularly evident problem in security monitoring solutions. This paper analyzes a scenario of MSS in order to provide a privacy preserving solution that allows the security monitoring without violating the privacy requirements. The basic idea relies on the usage of the Homomorphic Encryption technology. Encrypting data using homomorphic schemes, cloud computing and MSS providers can perform different computations on encrypted data without ever having access to their decryption. This solution keeps data confidential and secured, not only during exchange and storage, but also during processing. We provide an ad-hoc Intrusion Detection System architecture for privacy preserving security monitoring, considering as counter threats Code Injection attacks on homomorphically encrypted fields.