Reviewerly/exHarmony huggingface dataset:
Adversarial Generation of Target Review for Rating Prediction

Recent years have witnessed a growing trend of utilizing reviews to improve the performance and interpretability of recommender systems. Almost all existing methods learn the latent representations from the user’s and the item’s historical reviews, and then combine these two representations for rating prediction. The fatal limitation in these methods is that they are unable to utilize the most predictive review of the target user for the target item since such a review is not available at test time.In this paper, we propose a novel recommendation model, called GTR, which can generate the unseen target review with adversarial training for rating prediction. To this end, we develop a unified framework to combine the rating tailored generative adversarial nets (RTGAN) for synthetic review generation and the neural latent factor module (NLFM) using the generated target review along with historical reviews for rating prediction. Extensive experiments on four real-world datasets demonstrate that our model achieves the state-of-the-art performance in both rating prediction and review generation tasks.
System-Wide Security for Offline Payment Terminals

Most self-service payment terminals require network connectivity for processing electronic payments. The necessity to maintain network connectivity increases costs, introduces cybersecurity risks, and significantly limits the number of places where the terminals can be installed. Leading payment service providers have proposed offline payment solutions that rely on algorithmically generated payment tokens. Existing payment token solutions, however, require complex mechanisms for authentication, transaction management, and most importantly, security risk management. In this paper, we present VolgaPay, a blockchain-based system that allows merchants to deploy secure offline payment terminal infrastructure that does not require collection and storage of any sensitive data. We design a novel payment protocol which mitigates security threats for all the participants of VolgaPay, such that the maximum loss from gaining full access to any component by an adversary incurs only a limited scope of harm. We achieve significant enhancements in security, operation efficiency, and cost reduction via a combination of polynomial multi-hash chain micropayment channels and blockchain grafting for off-chain channel state transition. We implement the VolgaPay payment system, and with thorough evaluation and security analysis, we demonstrate that VolgaPay is capable of delivering a fast, secure, and cost-efficient solution for offline payment terminals.
Semantic similarity on constraints datasets: A latent approach

The technological world has grown by incorporating billions of small sensing devices, collecting and sharing large amounts of diversified data over the new generation of wireless and mobile networks. Semantic similarity models have been used as a means to organize and optimize devices in constrained environments such as IoT, edge computing, and 5G and next-generation networks. In this paper, we reviewed the commonly used semantic similarity models, discussed the limitations of our previous model, and explored latent space methods (through matrix factorization) as a way to reduce noise and correct the model profiles with no additional data. Our solution was evaluated on two datasets: Miller-Charles and IoT semantic datasets. The improved model achieved a correlation of 0.62 and 0.53 respectively (which represents an improvement of 0.21 and 0.13 for each dataset).
POSTER: ReAvatar: Virtual Reality De-anonymization Attack Through Correlating Movement Signatures

Virtual reality (VR) is on the precipice of entering mainstream entertainment with devices equipped with a multitude of sensing, tracking, and internet capabilities that can reshape the current infotainment industry such as online gaming or conferences with novel features. With VR techniques, the online gamer or conference attendances could choose to keep their identity anonymous by easily altering their appearances (i.e., avatars). However, in this study, we present ReAvatar, a novel de-anonymization attack that identifies users by their virtual avatar via a correlation in specific recorded movements. Using 3D pose estimation, we train a sophisticated machine learning model with user movement data recorded while performing a set of movements in real life and then again with their avatars. We then map correlations between these two sets of movement data using a bespoke agglomerative clustering algorithm and establish relationship between the user's virtual and real-life identity. ReAvatar achieves 89.60% accuracy in detecting a unique user among multiple avatars. The security and privacy implications of this paper will be foundational for users and researchers alike that explore the realm of virtual reality.
Container manager for multiple container runtimes

Container technology has been around for a couple of decades now; however, it is only recently that containers got more attention from the tech world. As one of the first tools for managing containers, Docker played one of the major roles in popularity of containers, providing an easy and meaningful way to use them, by utilizing its advantages over traditional virtualization tools as virtual machines. As containers gained in popularity, more developers took interest in creating container runtimes and managers to facilitate the process of containerization. Today, more than a few container managers exist on the market, each aiming at a single container runtime, which makes switching to another runtime time-consuming and unintuitive, having to use a different container manager. The purpose of this paper is to present an alternative implementation of container manager, that will enable presence of multiple container runtimes, by creating a common interface and combining it with existing runtime APIs. Ideally, this container manager can be used for starting different containers using container runtime which suits the current environment better than the rest and can easily follow the development of new container runtimes.
AID: Attesting the Integrity of Deep Neural Networks

Due to their crucial role in many decision-making tasks, Deep Neural Networks (DNNs) are common targets for a large array of integrity breaches. In this paper, we propose AID, a novel methodology to Attest the Integrity of DNNs. AID generates a set of test cases called edge-points that can reveal whether a model has been compromised. AID does not require access to parameters of the DNN and can work with a restricted black-box access to the model, which makes it applicable to most real life scenarios. Experimental results show that AID is highly effective and reliable. With at most four edge-points, AID is able to detect eight representative integrity breaches including backdoor, poisoning, and compression attacks, with zero false-positive.
ROSEN

A versatile non-repudiation service that can be used directly and without application-specific modifications is desirable in many realistic use-cases. Since TLS is one of the most popular current means of secure communication, several proposals have been made for augmenting it with a general, flexible and efficient non-repudiation service. However, none of them offers sufficient robustness for scenarios that require high reliability. Also, they lack flexibility by requiring the party providing non-repudiable evidence to do so for all content transmitted within a given TLS session. In this paper, we propose ROSEN, an extension for TLS that provides non-repudiation using an efficient checkpointing mechanism that minimizes loss of evidence in the presence of faults in order to increase robustness and ensure reliability. In addition, ROSEN inherits privacy-preserving properties of prior methods and introduces selective non-repudiation which allows the party providing non-repudiable evidence to selectively and efficiently redact parts of the session so as to make them repudiable.
TernGEMM: GEneral Matrix Multiply Library with Ternary Weights for Fast DNN Inference

Efficient implementation of deep neural networks on CPU-based systems is very critical because applications proliferate to embedded and Internet of Things (IoT) systems. Many CPUs for personal computers and embedded systems equip Single Instruction Multiple Data (SIMD) instructions, which can be utilized to implement an efficient GEneral Matrix Multiply (GEMM) library that is very necessary for efficient deep neural network implementation. While many deep neural networks show quite good performance even at 1-bit or 2-bit precision, the current CPU instruction and library do not efficiently support arithmetic operations below 8-bit. We propose TernGEMM, a special GEMM library using SIMD instructions for Deep Neural Network (DNN) inference with ternary weights and activations under 8-bit. TernGEMM improves the speed by replacing slow multiply-add with logical operations and also accumulating a number of multiplications without bit expansion operations. We compared the speedup of TernGEMM with tiling optimization and GEMMLowp, an 8-bit precision GEMM library. For Intel CPU, the speedup of ×2.052, ×2.973, and ×2.986 is achieved on ResNet-50, MobileNet-V2, EfficientNet-B0, respectively. For ARM CPU, TernGEMM’s speedup is ×2.143, ×1.765, and ×1.856, respectively.
Mathematical model of the evolution of operating parametersof dielectrics in the marine power supply system when forcing certain factors

Abstract The paper addresses the issue of development of a mathematical model for the evolution of operating parameters of dielectrics in the marine power supply system when forcing certain factors. The paper addresses a comprehensive problem in comparison with the classical problem of the wear theory. With regard to the given details, factors that cause a decrease in the operating time were introduced to determine the operating time before electrical wear of the power supply unit. The study employs methods of the fuzzy (uncertain) set theory. The uncertainty of quantities in the fuzzy set theory is set by the membership function, which determines the degree of its fuzziness. In accordance with fuzzy set methods, a membership function is constructed to estimate fuzziness of the function of a shrinking time resource of operating time before wear. In Mathcad , the authors have developed a computational module for estimating the fuzzy time (due to inaccuracy of the initial data) subtracted from the total operating time before electrical wear of the berthing power supply unit. To improve the reliability of power supply to seaports and reduce accidents, new technical and organizational measures are being developed. Therefore, the task is formulated to ensure serviceability of the power supply system with due regard to its interactions with various subsystems.
Pattern Mining—FTISPAM Using Hybrid Genetic Algorithm

An innovative approach is elegantly launched to effectively identify the medical behavioural changes of the patients. With this end in view, the sequential change patterns are extracted at two diverse time intervals, with the help of the fuzzy time interval sequential change pattern mining employing the HGA technique. However, the pattern mining at two diverse time intervals is likely to yield further superfluous data. With an eye on averting the generation of the corresponding superfluous data, an optimized method such as the hybrid genetic algorithm (HGA) based fuzzy time interval sequential pattern mining is envisaged for the purpose of attaining the patterns. The sequential pattern detection algorithm effectively segments the located change patterns into four diverse types such as the perished patterns, added patterns, unexpected changes, and the emerging patterns. When the pattern categorization comes to an end, the changed patterns are harmonized by means of the Similarity Computation Index (SCI) values. At last, the significant patterns are estimated and employed to categorize the change in the conduct of the patient. The imaginative system is performed in the working stage of the MATLAB and its execution is surveyed and appeared differently in relation to that of the advanced strategy like the genetic algorithm.
A Switched Approach to Image-Based Stabilization for Nonholonomic Mobile Robots with Field-of-View Constraints

This paper presents a switched visual servoing strategy for maneuvering the nonholonomic mobile robot to the desired configuration while keeping the tracked image points in the vision of the camera. Firstly, a pure backward motion and a pure rotational motion are applied to the mobile robot in succession. Thus, the principle point and the scaled focal length in x direction of the camera are identified through the visual feedback from a fixed onboard camera. Secondly, the identified parameters are used to build the system model in polar-coordinate representation. Then an adaptive non-smooth controller is designed to maneuver the mobile robot to the desired configuration under the nonholonomic constraint. And a switched strategy which consists of two image-based controllers is utilized for keeping the features in the field-of-view. Simulation results are presented to validate the effectiveness of the proposed approach.
Recognizing Activities from Egocentric Images with Appearance and Motion Features

With the development of wearable cameras, recognizing activities from egocentric images has attracted the interest of many researchers. The motion of the camera wearer is an important cue for the activity recognition, and is either explicitly used by optical flow for videos or implicitly used by fusing several images for images. In this paper, based on the observation that the two consecutive images captured by the wearable camera contain the motion information of the camera wearer, we propose to use the camera wearer's rotation and translation computed from the two consecutive images as the motion features. The motion features are combined with appearance features extracted by a CNN as the activity features, and the activity is classified by a random decision forest. We test our method on two egocentric image datasets. The experimental results show that by adding the motion information, the accuracy of activity recognition has been significantly improved.
Investigating the Effects of Covid-19 on Online Shopping Cybercrime in Buffalo City

Due to the unprecedented events of Covid-19 pandemic and the lockdown regulations that were introduced by the government, online shopping became much more popular than it used to be, before the pandemic. This is because people needed to use the online shopping quite frequently for safety measures by keeping social distancing and avoiding overcrowd. However, as much as online shopping was a positive solution for shoppers, online criminals saw this as an opportunity to defraud people using crimeware such as identity theft, cloning of bank cards, and electronic stealing of money. Crimeware can spread by way of viruses, Trojan horse programs, worms, spyware or adware. Due to Covid-19, a higher number of people needed to stay indoors more, hence the need for the use of online shopping to buy groceries and other necessary goods has greatly increased. This rapid increase of online shopping has led to more people prone to cybercrime. However, the effect of Covid-19 lockdown regulation on online shopping cybercrime, especially in the developing nations, where computer security literacy level is still low, remains a research issue that needs to be addressed. Hence, this study investigates the effects of Covid-19 on cybercrime in online shopping using Buffalo City as a case study. Data was collected from 105 participants through a structured questionnaire. The data collected was analysed using statistical package. This research found that indeed Covid-19 had negatively affected people, especially online shoppers by increasing the number of fraud cases involving cybercrime. The study recommends that online shops improve the perception of their websites' trustworthiness through security certificates and secure payment facilities. It further recommends that online shops raise awareness of cybercrime to customers to help sensitize them.
Quality Evaluation in Disaster Mitigation Information System using Webqual 4.0 Method

This study aimed to measure the quality of the Disaster Mitigation Information System (Sikabi) belonging to the Regional Disaster Management Authority of Boyolali Regency. Quality tests are carried out to determine the quality and functionality of the system that functions optimally and can display accurate, easily accessible, and useful information for disaster mitigation in the region. The results of the study are useful for providing feedback to information system developers regarding the quality of the website usability aspect, the quality of the information presented, and the quality of the interactions provided as well as the user satisfaction at accessing the Disaster Information System website. This study uses the Webqual 4.0 method which has 4 assessment variables, namely in terms of usability (information quality), interaction service (interaction quality), and user satisfaction while accessing the website (overall impression). From the research results, the information quality variable has a sig value. 0, 000 <0, 05 which indicates that there is an influence of information quality on user satisfaction in using the Sikabi website. Therefore, we recommend improving the quality of the information displayed on the Sikabi website page.
Simulation-based Analysis Of Highway Trajectory Planning Using High-Order Polynomial For Highly Automated Driving Function

One of the fundamental tasks of autonomous driving is safe trajectory planning, the task of deciding where the vehicle needs to drive, while avoiding obstacles, obeying safety rules, and respecting the fundamental limits of road. Real-world application of such a method involves consideration of surrounding environment conditions and movements such as Lane Change, collision avoidance, and lane merge. The focus of the paper is to develop and implement safe collision free highway Lane Change trajectory using high order polynomial for Highly Automated Driving Function (HADF). Planning is often considered as a higher-level process than control. Behavior Planning Module (BPM) is designed that plans the high-level driving actions like Lane Change maneuver to safely achieve the functionality of transverse guidance ensuring safety of the vehicle using motion planning in a scenario including environmental situation. Based on the recommendation received from the (BPM), the function will generate a desire corresponding trajectory. The proposed planning system is situation specific with polynomial based algorithm for same direction two lane highway scenario. To support the trajectory system polynomial curve can be used to reduces overall complexity and thereby allows rapid computation. The proposed Lane Change scenario is modeled, and results has been analyzed (verified and validate) through the MATLAB simulation environment. The method proposed in this paper has achieved a significant improvement in safety and stability of Lane Changing maneuver.
Classification and Evaluation of Goal-Oriented Requirements Analysis Methods

Goal-oriented requirements analysis (GORA) is a sub-process of goal-oriented requirements engineering, which is used for the identification and analysis of the high-level objective of an organization. There are different types of GORA methods like AGORA, PRFGOREP, FAGOSRA, Tropos, etc., which have been developed to deal with different issues related to GORA like reasoning with goals, selection and prioritization of the goals and requirements, stakeholders analysis, detection of conflictions among goals, and so on. The objective of this paper is to classify and evaluate the GORA methods based on goal concepts, goal links, and soft computing techniques used in GORA methods to deal with impression and vagueness during the decision-making process. Based on the evaluation, we have also discussed the future scope in the field of GORA methods.
Two decades in the making: A scoping review on research on digital making and its potential for digital empowerment in non-formal settings

FabLabs and makerspaces offer unprecedented opportunities for digital empowerment, especially for children and adolescents. However, reviews on making with regard to empowerment are lacking. We identified n = 180 publications on digital making with children and adolescents and, after categorizing them regarding formal criteria and formality of settings, identified the main topics in publications on digital making in non-formal settings. Results revealed a great demand for empirical studies with experimental designs. Three main topics emerged: domain-specific determinants of participation, equity, and skills and competencies. Implications for further research and limitations as well as implications for digital empowerment processes are discussed.
Modeling and Performance Analysis on Federated Learning in Edge Computing

Federated Learning (FL) deployed in edge computing may achieve some advantages such as private data protection, communication cost reduction, and lower training latency compared to cloud-centric training approaches. The Anything-as-a-Service (XaaS) paradigm, as the main service provisioning model in edge computing, enables various flexible FL deployments. On the other hand, the distributed nature of FL together with the highly diverse computing and networking infrastructures in an edge environment introduce extra latency that may degrade FL performance. Therefore, delay performance evaluation on edge-based FL systems becomes an important research topic. However, XaaS-based FL deployment brings new challenges to performance analysis that cannot be well addressed by conventional analytical approaches. In this paper, we attempt to address such challenges by proposing a profile-based modeling and analysis method for evaluating delay performance of edge-based FL systems. The insights obtained from the modeling and analysis may offer useful guidelines to various aspects of FL design. Application of network calculus techniques makes the proposed method general and flexible, thus may be applied to FL systems deployed upon the heterogeneous edge infrastructures.
A Study of Crossover Operators in Genetic Algorithms

Crossover is an important operator in genetic algorithms. Although hundreds of application dependent and independent crossover operators exist in the literature, this chapter provides holistic, but by no means an exhaustive, overview of different crossover techniques used in different variants of genetic algorithms. We will review some of the commonly used crossover operators in binary-coded genetic algorithms as well as in real-coded genetic algorithms and explore the use cases and performance of different techniques for different applications to provide a better understanding of the types of bias exhibited by different crossover operators. This knowledge can be useful when designing an algorithm for a specific problem, particularly if there are known patterns or dependencies in the selected representation.