Reviewerly/exHarmony huggingface dataset:
Modern aspects of the implementation of interactive technologies in a multidisciplinary university

The article discusses modern aspects of the implementation of interactive technologies in a multidisciplinary university. The characteristic features of interactive technologies are described, the feasibility of their use in the educational process is substantiated. Particular attention is paid to the consideration of the didactic potential of interactive technologies in a higher educational institution. The importance of interactive technologies in comparison with traditional teaching methods is discussed, which allows organizing effective interaction of all participants in the educational process, planning joint work, correctly allocating resources, and providing the necessary tools for solving educational problems. In conclusion, it is determined that interactive technologies contain great potential for their application in the educational process of a multidisciplinary university, which requires further study, including the development of software and methodological support.
A Hardware Perspective on the ChaCha Ciphers: Scalable Chacha8/12/20 Implementations Ranging from 476 Slices to Bitrates of 175 Gbit/s

AES (Advanced Encryption Standard) accelerators are commonly used in high-throughput applications, but they have notable resource requirements. We investigate replacing the AES cipher with ChaCha ciphers and propose the first ChaCha FPGA implementations optimized for data throughput. In consequence, we compare implementations of three different system architectures and analyze which aspects dominate the performance of those.Our experimental results indicate that a bandwidth of 175 Gbit/s can be reached with as little as 2982 slices, whereas comparable state of the art AES accelerators require 10 times as many slices [1]. Taking advantage of the flexibility inherent in the ChaCha cipher, we also demonstrate how our implementation scales to even higher throughputs or lower resource usage (down to 476 slices), benefiting applications which previously could not employ cryptography because of resource limitations.
Data Set For Sentiment Analysis On Bengali News Comments And Its Baseline Evaluation

The biggest challenge of Bengali language processing is creating a strong data set to do research on. The main focus of this paper is to introduce an authentic and credible data set and this dataset is open for all to be used for educational purposes <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> for Bengali sentiment analysis where the data was extracted from a well known online news portal’s user comments. Here comments on various news were scraped, and for detecting the true sentiments of the sentences, five labels of sentiments were used. An online crowd sourcing platform was used for data annotation. To ensure the credibility and validity of the data set, every entry of the data set was tagged three times. Three models of text classification were used for baseline evaluation to check the validity of the data set. This data set might be of valuable help for future works and researches on Bengali sentiment analysis.
Leveraging Behavioral Heterogeneity Across Markets for Cross-Market Training of Recommender Systems

Modern recommender systems are optimised to deliver personalised recommendations to millions of users spread across different geographic regions exhibiting various forms of heterogeneity, including behavioural-, content- and trend specific heterogeneity. System designers often face the challenge of deploying either a single global model across all markets, or developing custom models for different markets. In this work, we focus on the specific case of music recommendation across 21 different markets, and consider the trade-off between developing global model versus market specific models. We begin by investigating behavioural differences across users of different markets, and motivate the need for considering market as an important factor when training models. We propose five different training styles, covering the entire spectrum of models: from a single global model to individual market specific models, and in the process, propose ways to identify and leverage users abroad, and data from similar markets. Based on a large scale experimentation with data for 100M users across 21 different markets, we present insights which highlight that markets play a key role, and describe models that leverage market specific data in serving personalised recommendations.
A Medical Image Encryption Algorithm Based on Hermite Chaotic Neural Network

To ensure the safety and reliability of medical image during information transmission, a medical image encryption algorithm based on Hermite chaotic neural network is proposed. Firstly, the medical image encryption algorithm uses chaotic sequences generated by the logistic map. Secondly, this chaotic sequence is used to train a Hermite chaotic neural network. Finally, the medical image is encrypted by two key streams generated by the trained Hermite chaotic neural network. Experimental results show that the encryption algorithm is very effective. The security analysis shows that the encryption algorithm can effectively resist statistical analysis, has strong key sensitivity, large keyspace, and greatly improves the security of medical images.
A maturity model for secure requirements engineering

Security is considered to be a critical software quality attribute. Tackling security at the requirements phase helps to avoid the need to rework secure software development issues. The aim of this paper is to develop a Requirements Engineering (RE) Security Maturity Model (RESMM) to assist software development organizations to better specify the requirements for secure software development. To achieve this objective, first, we conducted a systematic literature review (SLR) to identify the requirement practices for secure software development. Then we modified Sommerville's requirements engineering practices. We also conducted a questionnaire survey based on the identified security requirements practices. Next, the RESMM was built based on the results of the SLR, the modified Sommerville practices and feedback from the security practitioners. Finally, two case studies were conducted to assess RESMM. RESMM has 79 practices classified into 7 RE categories. The case study results show that RESMM has a clear structure and is easy to comprehend and use. In addition, the case study participants recommended that software organizations adopt RESMM. RESMM has the ability to identify the RE security maturity levels in software organizations. RESMM can also help software development organizations deliver secure software.
Translating a mineral systems model into continuous and data-driven targeting models: An example from the Dolatabad chromite district, southeastern Iran

The Dolatabad chromite district, a well-endowed mineralized tract located in SE Iran, hosts numerous podiform chromite deposits. Its favorable geological setting and known chromite endowment suggest that the district has good potential for the discovery of additional chromite mineralization. However, the processes that controlled the development and preservation of the chromite ores are poorly known. As such, a better understanding of the ore-forming processes will be critical for the success of future exploration activities in the Dolatabad chromite district. Recognition of these critical genetic processes and the mapping of their spatial expressions are the precursors to mineral prospectivity mapping (MPM) as well as any mineral exploration activities on the ground. This study adopted a mineral systems approach to MPM, which entailed the formulation of a probabilistic framework to the recognition of the critical genetic processes and translation of these processes to mappable evidence maps. Here, we developed three proxies, representing trap, deposition, and preservation processes, that were subjected to a range of weighting procedures, including continuous (i.e., fuzzy gamma, geometric average, and data-driven index overlay) and data-driven machine-learning (i.e., multilayer perceptron neural network and random forests) methods. The machine-learning procedures outperformed the continuous procedures, suggesting that the former approaches are more reliable in targeting mineralized zones. The targets delineated by random forest algorithm, the superior model generated in this study, predicted c. 71% of the known chromite mineralization in c. 6% of the study area.
Privacy-Preserving Biomedical Database Queries with Optimal Privacy-Utility Trade-Offs

Sharing data across research groups is an essential driver of biomedical research. While interactive query-answering systems for biomedical databases aim to facilitate the sharing of aggregate insights without divulging sensitive individual-level data, query answers can still leak private information about the individuals in the database. Here, we draw upon recent advances in differential privacy to introduce query-answering mechanisms that provably maximize the utility (e.g., accuracy) of the system while achieving formal privacy guarantees. We demonstrate our accuracy improvement over existing approaches for a range of use cases, including cohort discovery, variant lookup, and association testing. Our new theoretical results extend the proof of optimality of the underlying mechanism, previously known only for count queries with symmetric utility functions, to more general utility functions needed for key biomedical research workflows. Our work presents a path toward interactive biomedical databases that achieve the optimal privacy-utility trade-offs permitted by the theory of differential privacy.
A Strictly Unsupervised Deep Learning Method for HEp-2 Cell Image Classification

Classifying the images that portray the Human Epithelial cells of type 2 (HEp-2) represents one of the most important steps in the diagnosis procedure of autoimmune diseases. Performing this classification manually represents an extremely complicated task due to the heterogeneity of these cellular images. Hence, an automated classification scheme appears to be necessary. However, the majority of the available methods prefer to utilize the supervised learning approach for this problem. The need for thousands of images labelled manually can represent a difficulty with this approach. The first contribution of this work is to demonstrate that classifying HEp-2 cell images can also be done using the unsupervised learning paradigm. Unlike the majority of the existing methods, we propose here a deep learning scheme that performs both the feature extraction and the cells’ discrimination through an end-to-end unsupervised paradigm. We propose the use of a deep convolutional autoencoder (DCAE) that performs feature extraction via an encoding–decoding scheme. At the same time, we embed in the network a clustering layer whose purpose is to automatically discriminate, during the feature learning process, the latent representations produced by the DCAE. Furthermore, we investigate how the quality of the network’s reconstruction can affect the quality of the produced representations. We have investigated the effectiveness of our method on some benchmark datasets and we demonstrate here that the unsupervised learning, when done properly, performs at the same level as the actual supervised learning-based state-of-the-art methods in terms of accuracy.
Fake Document Generation for Cyber Deception by Manipulating Text Comprehensibility

Advanced cyber attackers can penetrate enterprise networks and steal critical documents containing intellectual property despite all access control measures. Cyber deception is one of many solutions to protect critical documents after an attacker penetrates the network. It requires the generation and deployment of decoys such as fake text. The comprehensibility of a fake text document can affect the required time and effort for an attack to succeed. However, existing cybersecurity research has given limited attention to exploring the comprehensibility features of text for fake document generation. This article presents a novel method to generate believable fake text documents by measuring and manipulating the comprehensibility of legit text within a genetic algorithm (GA) framework. For measuring text comprehensibility, we adopt a set of quantitative measures based on qualitative principles of psycholinguistics and reading comprehension: connectivity, dispersion, and sequentiality. Our user-study analysis indicates that the quantitative comprehensibility measures can approximate the degree of human effort required to comprehend a fake text document in contrast to a legit text. For manipulating text comprehensibility, we develop a multiobjective, multimutation GA that modifies a legit document to Pareto-optimally alter its comprehensibility measures and generate hard-to-comprehend, believable fake documents. Our experiments show that the proposed algorithm successfully generates fake documents for a broader class of legit documents with varied text characteristics when compared to baselines from previous research. Hence, the application of our method can help improve cyber deception systems by providing more believable yet hard-to-comprehend fake documents to mislead cyber attackers.
Load Balancing in Cloud Computing using Mutation Based Particle Swarm Optimization

Cloud computing has emerged as a technology that grease tasks by the dynamic allocation of virtual machines. Users pay for resources based on their demand. A cloud provider has to face many challenges. One out of the essential problem is load balancing, which suffers from many issues like premature convergence, reduced convergence speed, at first chosen random solutions, and stuck in native optima. The proposed method considered the MakeSpan parameters to handle the problem related to existing met heuristic techniques. The proposed method focuses on the mutation-based Particle Swarm algorithm to balance load among the data centers. Here an efficient load balancing algorithm is developed to minimize performance parameters like MakeSpan time and improve the fitness function in cloud computing.
SPARKs: Succinct Parallelizable Arguments of Knowledge

We introduce the notion of a Succinct Parallelizable Argument of Knowledge (SPARK). This is an argument system with the following three properties for computing and proving a time T (non-deterministic) computation: While the third property is standard in succinct arguments, the combination of all three is desirable as it gives a way to leverage moderate parallelism in favor of near-optimal running time. We emphasize that even a factor two overhead in the prover's parallel running time is not allowed. Our main results are the following, all for non-deterministic polynomial-time RAM computation. We construct (1) an (interactive) SPARK based solely on the existence of collision-resistant hash functions, and (2) a non-interactive SPARK based on any collision-resistant hash function and any SNARK with quasi-linear overhead (as satisfied by recent SNARK constructions).
Integrated Kalman Filter of Accurate Ranging and Tracking With Wideband Radar

Accurate ranging and wideband tracking are treated as two independent and separate processes in traditional radar systems. As a result, limited by low data rate due to nonsequential processing, accurate ranging usually performs low efficiency in practical application. Similarly, without applying accurate ranging, the data after thresholding and clustering are used in wideband tracking, leading to a significant decrease in tracking accuracy. In this article, an integrated Kalman filter of accurate ranging and tracking is proposed using methods of phase-derived-ranging and Bayesian inference in wideband radar. Besides the motion state, in this integrated Kalman filter, the complex-valued high-resolution range profile (HRRP) is also introduced as a reference signal by coherent integration in a sliding window, which incorporates target's scattering distribution and phase characteristics. Corresponding kinetic equations are derived to predict the motion state and the reference signal in the next moment. A ranging process is constructed based on the received signal and the predicted reference signal in order to estimate innovation using methods of phase-derived-ranging and Bayesian inference, and a sequential update for motion state can be accomplished with the Kalman filter as well. In every recursion, the complex-valued reference signal is also updated by coherently integrating the latest pulses. The integrated Kalman filter takes full use of high range resolution and phase information, improving both efficiency and precision compared with conventional approaches of ranging and wideband tracking. Implemented in a sequential manner, the integrated Kalman filter can be applied in a real-time application, realizing simultaneous ranging with high precision and wideband tracking. Finally, simulated and real-measured experiments confirm the remarkable performance.
Lightweight image super-resolution with feature enhancement residual network

Recently, single image super-resolution (SR) methods based on deep convolutional neural network (CNN) have demonstrated remarkable progress. The essence of most CNN-based models is to learn the non-linear mapping between low-resolution patches and corresponding high-resolution ones. However, numerous convolutions are applied to implement this mapping, which directly contributes to large model sizes and huge graphics memory consumption. In this paper, we propose a lightweight feature enhancement residual network (FERN) to achieve prominent performance by incorporating lightweight non-local operations into the residual block. By taking advantage of utilizing this non-locally enhanced residual block, the proposed model can capture long-range dependencies. For further improving performance, we design the structure-aware channel attention layer that explicitly boosts feature maps with more structural and textural details. Extensive experiments suggest that the proposed approach performs favorably against the state-of-the-art SR algorithms in terms of visual quality and inference time.
Fine-Grained Visual Categorization by Localizing Object Parts With Single Image

Fine-grained visual categorization (FGVC) refers to assigning fine-grained labels to images which belong to the same base category. Due to the high inter-class similarity, it is challenging to distinguish fine-grained images under different subcategories. Recently, researchers have proposed to firstly localize key object parts within images and then find discriminative clues on object parts. To localize object parts, existing methods train detectors for different kinds of object parts. However, due to the fact that the same kind of object part in different images often changes intensely in appearance, the existing methods face two shortages: 1) Training part detector for object parts with diverse appearance is laborious; 2) Discriminative parts with unusual appearance may be neglected by the trained part detectors. To localize the key object parts efficiently and accurately, a novel FGVC method is proposed in the paper. The main novelty is that the proposed method localizes the key object parts within each image only depending on a single image and hence avoid the influence of diversity between parts in different images. The proposed FGVC method consists of two key steps. Firstly, the proposed method localizes the key parts in each image independently. To this end, potential object parts in each image are identified and then these potential parts are merged to generate the final representative object parts. Secondly, two kinds of features are extracted for simultaneously describing the discriminative clues within each part and the relationship between object parts. In addition, a part based dropout learning technique is adopted to boost the classification performance further in the paper. The proposed method is evaluated in comparison experiments and the experiment results show that the proposed method can achieve comparable or better performance than state-of-the-art methods.
Bearing fault diagnosis using weakly supervised long short-term memory

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Recommending refactorings via commit message analysis

The purpose of software restructuring, or refactoring, is to improve software quality and developer productivity. Prior studies have relied mainly on static and dynamic analysis of code to detect and recommend refactoring opportunities, such as code smells. Once identified, these smells are fixed by applying refactorings which then improve a set of quality metrics. While this approach has value and has shown promising results, many detected refactoring opportunities may not be related to a developer's current context and intention. Recent studies have shown that while developers document their refactoring intentions, they may miss relevant refactorings aligned with their rationale. In this paper, we first identify refactoring opportunities by analyzing developer commit messages and check the quality improvements in the changed files, then we distill this knowledge into usable context-driven refactoring recommendations to complement static and dynamic analysis of code. The evaluation of our approach, based on six open source projects, shows that we outperform prior studies that apply refactorings based on static and dynamic analysis of code alone. This study provides compelling evidence of the value of using the information contained in existing commit messages to recommend future refactorings.
Outsourced privacy-preserving decision tree classification service over encrypted data

The classification is one of the important machine learning technologies, which plays an important role in the fields of medical treatment, image processing, and cyberspace security. In recent years, the popularity of cloud computing has caused many data owners to seek outsourced storage computing, which has become a new trend in the development of classification. Although it can be used in several research areas, such as medical and financial, this paradigm introduces new security and privacy problems. In response to the ciphertext classification requirements in different scenarios in outsourcing environments, this paper proposed an outsourced privacy-preserving decision tree classification model based on homomorphic encryption, which can hide sensitive inputs (model and query) and outputs (analytic result) from a counterparty. A secure exclusive OR (XOR) protocol is created; it intelligently computes the XOR of two bit-encrypted data to obtain another bit-encrypted result. The experimental results show that the proposed protocols are particularly efficient for outsourced decision trees, which are encrypted and stored in the cloud. These decision trees are typical of classification models trained from real datasets, ranging from heart disease to cancer classification.