Reviewerly/exHarmony huggingface dataset:
COVID-19 Response and Its Aftermath: Experience in Introducing Online Learning Technologies to Diverse Teaching Staff

The paper is devoted to the experience of developing an online educational system at Volgograd State Technical University. We describe the process of the abrupt transition to online learning during the COVID-19 pandemic and the following process of merging online and on-site learning. The paper discusses how the university’s online educational environment was formed and how its staff adapted. We describe the technical, didactic, and organizational measures which resulted in the growing popularity of the online learning environment and other e-learning tools. The online learning environment was created by connecting the existing information system 1C.University with the online learning management system Moodle. The 1C.University software stores the organizational information about the students, their enrollments, teachers, courses, curricula, etc. The custom module that connects 1C.University and Moodle LMS allows keeping student enrollment in the electronic courses up to date and creating additional services for students and teaching staff. The developed education and information electronic environment of the university hosts more than 16 000 online courses. Many courses contain educational videos and interactive learning elements.
Mapped Variably Scaled Kernels: Applications to Solar Imaging

Variably scaled kernels and mapped bases constructed via the so-called fake nodes approach are two different strategies to provide adaptive bases for function interpolation. In this paper, we focus on kernel-based interpolation and we present what we call mapped variably scaled kernels, which take advantage of both strategies. We present some theoretical analysis and then we show their efficacy via numerical experiments. Moreover, we test such a new basis for image reconstruction tasks in the framework of hard X-ray astronomical imaging.
Tweets Sentiment Analysis of Putin’s Participation at the G20 Summit in Indonesia

The Group of Twenty (G20) is an international economic cooperation forum established to address global and regional issues. The members are countries with large economies and the ability to have a significant impact on the global, since their GDP (Gross Domestic Product) shares about 80% of the global GDP. The dispute between Russia and Ukraine drew international attention regarding Russia’s involvement in the G20. The Russian President, Vladimir Putin, played a major role and became a major source of concern in this dispute. The Western Bloc countries, such as the United States, wanted Russia to be expelled from the forum, but the Eastern Bloc believed the G20 was not a platform for discussing security matters, therefore Russia could still be included. As a result, sentiment analysis has to be conducted to determine the global community’s reaction to Putin’s presence at the G20 summit in Indonesia. Aside from that, the goal of this study is to automate the forecast of future public sentiments on the same subject. The Bidirectional Gated Recurrent Unit (BiGRU) architecture was employed in this work, using three sentiment labels: positive, negative, and neutral. The model created in this investigation has an accuracy value of 86.76% after being run for 20 epochs. This demonstrates that the model may get a reasonably accurate classification result when categorizing sentiments about Putin’s attendance at the G20 Indonesia summit.
Evolution of Applications: From Natively Installed to Web and Decentralized

Applications evolve from year to year either to fit consumer needs or to increase productivity and simplify work for developers. With the rapid development of web technologies, there has been a significant shift towards web applications as a means to distribute software instead of traditional, installable packages. This paper analyses the evolution of applications along with challenges and strategies to overcome them. More specifically, we discuss the trend of applications being shifted to online versions rather than native-oriented ones, which must be installed on the users’ devices. Comparisons for performance and speed of development have been studied, considering current limitations and advantages/disadvantages. Ultimately, we conclude that while transitioning to web applications requires careful planning and execution, the benefits of this shift make it a worthwhile endeavor for many organizations.
“SHeMeD”: An Application on Secure Computation of Medical Cloud Data Based on Homomorphic Encryption

In today's Internet-centric world, two main challenges have arisen regarding data exploration: a) the privacy of sensitive data and b) the need for users to have an efficient way to manage their data. Cloud computing offers a cost-effective way towards direct data storage and access, which can function as a catalyst to modernize and update legacy systems. Although cloud computing provides efficient dynamic storage space, data security remains a concern. To this end, we propose a novel design and implementation of an application that enables secure storage of personal data in the cloud through a complete homomorphic encryption system. The application is named "SHeMed", which stands for Secure Homomorphic encryption on Medical cloud data. We have concentrated on the secure storage of medical data for breast cancer patients to ensure patients' data privacy and prevent unauthorized access. With the introduction of homomorphic encryption in the core of this application, we achieve four significant milestones regarding the secure "sealing" of data privacy: a) the user does not share the private key with the cloud provider, b) the user can proceed with implementing mathematical operations on encrypted data, c) cloud can be easily scaled and provide a convenient way to store data, d) after the implementation of mathematical operations, the new processed data may be shared with other users, mainly doctors, without providing the original ones. Our design implies that our users will need to retrieve their patient's data just once. In this paper, we present a case study of our application that utilizes homomorphic encryption to eliminate the burden of preserving data confidentiality in the cloud and enable a convenient analysis of medical data through computations on encrypted data.
Design of an Integrated Cyber Defense Platform for Communication Network Security of Intelligent Smart Units

The key to building an intelligent smart unit promoted by military advanced countries is to strengthen security based on new technologies of the 4th Industrial Revolution. It should be manageable the security of information system and network in real time by applying new information and communication technologies such as artificial intelligence and big data. As various new technologies are applied to the information and communication networks of smart units, vulnerabilities will increase, and sophisticated cyber threats using these vulnerabilities will also increase. Artificial intelligence and big data technologies are essential for the integrated security control system of a smart city currently being built. They are also needed for the cyber security orchestration and security control system developed by a company. So, it processes, classifies, and analyzes massive data in real time, detects threats more accurately, and reduces false detection rates. In this study, we proposes an integrated cyber defense platform design that can process data, detect threats, and judge true/false detection in real time based on the functions of the existing integrated security control system and the characteristics of the information communication network of intelligent smart units.
Improving Color Image Binary Segmentation Using Nonnegative Matrix Factorization

Image segmentation is the process of dividing a digital image into multiple segments or regions, each of which represents a different object or part of the image or shares certain visual characteristics. The goal of image segmentation is to simplify the representation of an image into something that is more meaningful and easier to analyze. Typically, segmentation creates histograms indicating edges and boundaries that use local information such as color. The color space used determines the segmentation results: while RGB is the most fundamental and widely used color space, other color spaces can also be adopted for segmentation to emphasize different aspects of an image. No single color space, however, is capable of producing satisfactory results for all types of color image segmentation tasks. Nonnegative Matrix Factorization (NMF) is a powerful data reduction and exploration tool efficiently used to extract features that provide part-based representation for a wide range of data types. In this paper, we propose an NMF-based method for integrating different color space information from an image and then extracting from it an “optimal color space representation” which is used to improve the results of a threshold segmentation algorithm. The proposed approach uses NMF on histograms of different color spaces to generate a meta-histogram representation of the given image, which is then binary segmented. Experimental results showed that the features extracted from the proposed NMF-based approach are able to improve segmentation outcomes obtained by conventional segmentation algorithms like Otsu method.
Information Retrieval Using Domain Adapted Language Models: Application to Resume Documents for HR Recruitment Assistance

To attract and hire the most talented candidate who fit a company’s requirements and have the potential to drive its growth, HR professionals spend a significant amount of time sourcing candidates, reviewing their resumes, and assessing their experiences against the needs of open positions. However, with the rise of online recruitment, this process has become increasingly challenging to manage manually. Therefore, job-resume matching has emerged as a crucial technique to automate and streamline the recruitment process. By leveraging machine learning algorithms, job-resume matching allows recruiters to quickly identify the most qualified candidates by comparing their skills and experiences with the requirements of vacant positions. The challenge today is to develop an approach that can effectively encode the semantic meaning of textual information in job descriptions and candidate resumes. In this paper, we propose a novel approach that combines TSDAE and BERT models to create more specialized sentence embeddings for HR data. This approach is powered by clean data extracted using a dedicated CV parsing tool. We also present two methods to automatically create labelled data for training the model. The benchmark of our method against real data shows an improvement of up to 11 points compared to the baseline model without TSDAE, highlighting the effectiveness of our proposed approach.
A Simple Implementation of an Entangler Circuit by Using Quantum Linear Optics

The topic of quantum computing is one that is expanding quickly and has the potential to completely change how some difficult problems can be resolved. In recent years, there has been considerable advancement in the research and application of quantum computing technologies. The use of this technology to address challenging or impractical tasks for conventional computers is an area of ongoing research. By creating an optical entangler, a quantum-helpful circuit for generating pairs of intensely entangled states, which are useful in numerous applications including quantum teleportation, quantum dense coding, and quantum error correction, this work has demonstrated a potential application of these technologies. Although the Optical Dual Rail Mode has shown to be extremely helpful for scaling up optical quantum circuits, there are still many obstacles and issues that need to be resolved before it can be widely applied in quantum computing.
Building a Network Knowledge Base Based on a Belief Revision Operator

We present a proposal for negotiation in a network knowledge base (NKB) formed by a set of k intelligent agents, in order to decide the acceptance (or not) of a new information ϕ that is expressed in propositional logic. Each one of the agents Ai, i = 1,…,k has associated a knowledge base (KB) Ki expressed as propositional formulas in conjunctive form. In order to decide the acceptance of ϕ, the belief revision operator Ind is applied on each KB Ki and ϕ, then Si = Ind(ϕ, Ki), and each one of the literals that form Si is weighed in order to estimate the cost that is required by Ai to accept ϕ. Si expresses the required information by the agent Ai such that (Ki ∪ Si) ╞ ϕ. The NKB uses a constant value γ that is the threshold of acceptance of NKB. Thus, a piece of new information ϕ will be accepted by all the agents in NKB if the average of the weights of the requested formulas Si is greater than γ. Otherwise, ϕ is rejected by all the agents in the NKB. Only in the case that ϕ will be accepted collectively, each one of the Ki in the network NKB is updated, as Ki = (Ki ⋀ Si). Otherwise, there are no changes to any KB Ki in the NKB.
A Machine Learning Method for the Analysis of Urban Italian Mobility

The aim of this paper is to consider a method of machine learning to analyze the problem of the sustainability of the urban transport in Italian cities. First of all we recall the definition of sustainable mobility then we present some indicators considered in our analysis. The methodology used in this paper are decisional trees. We both consider classification and regression trees. We have chosen two different dependent variables one for classification trees (a categorical variable: Macroregion according to NUTS 1: North West, North East, Centre, Islands and South of Italy) and one for regression trees (a quantitative variable: PM10 maximum number of days in excess of the human health protection limit foreseen for PM10). In order to test the performance of this methodology we have applied random forest. The analysis has been performed using SAS language.
Solving 3SAT and MIS Problems with Analog Quantum Machines

This work considers the use of analog quantum machines to solve the boolean satisfiability problem 3SAT by taking Quadratic Unconstrained Binary Optimization models (QUBO) as input. With the aim of using real quantum computers instead of emulators to solve instances of the problem, we choose the D-Wave quantum machines, which have a static topology and limited connectivity. Therefore, the choice of the problem formulation must take these important constraints into account. For this reason, we propose to solve 3SAT instances through polynomial-time reduction to the Maximum Independent Set problem. This is because the resulting graph is less dense and requires lower connectivity than the one that would be produced by directly modeling 3SAT into a QUBO.
TokenFuse: A Versatile NFT Marketplace

An NFT (non-fungible token) marketplace is a platform where people can buy and sell one-of-a-kind digital items such as art work, music, and videos. These items are represented by NFTs, which are not exchangeable for other tokens or assets and are stored on a blockchain. The growing use of blockchain technology and the desire for individuals to own and trade rare digital items have fueled the growth of NFT marketplaces. The purpose of this paper is to demonstrate the creation and working of an NFT marketplace named “TokenFuse” and discuss its features as well as the background technologies that keep it running. Our NFT marketplace uses the power of Inter Planetary File System (IPFS) Technology to provide artists with a unique and secure way to sell their original works as NFTs. In contrast to traditional marketplaces that rely on centralized servers and databases, our platform stores and distributes NFTs in a decentralized and peer-to-peer network, ensuring security and redundancy. It also provides a unique and cost-effective way for authors to monetize their works by taking advantage of blockchain technology. Our platform has extremely low fees, making it one of the most affordable options on the market.
Dual Word Embedding for Robust Unsupervised Bilingual Lexicon Induction

The word embedding models such as Word2vec and FastText simultaneously learn dual representations of input vectors and output vectors. In contrast, almost all existing unsupervised bilingual lexicon induction (UBLI) methods use only input vectors without utilizing output vectors. In this paper, we propose a novel approach to making full use of both input and output vectors for more robust and strong UBLI. We discover the Common Difference Property that one orthogonal transformation can connect not only the input vectors of two languages but also the output vectors. Therefore, we can learn just one transformation to induce two different dictionaries from the input and output vectors, respectively. Between these two quite different dictionaries, a more accurate lexicon with less noise can be induced by taking the intersection of them in UBLI procedure. Extensive experiments show that our method achieves much more robust and strong results than state-of-the-art methods in distant language pairs, while reserving comparable performances in similar language pairs.
Analytical Side Channel EM Models, Extending Simulation Abilities for ICs, and Linking Physical Models to Cryptographic Metrics

Electromagnetic (EM) side channel analysis (SCA) attacks have evolved to a significant threat for integrated circuits (ICs). Radiation, emanated from devices which manipulate sensitive information, carries it with a sufficient signal-to-noise ratio. Security evaluation in design stages, from a model or from a simulation, is complicated and hard to argue for correctness. Extensive literature exist on attacks utilizing EM signals. However, both frontiers of simulatability and modeling of the radiation have shown rather little research effort in the past in the SCA context. On simulatability prior work investigated utilization of complete EM physical solvers, such as COMSOL, HFSS which require much effort from the user/engineer to model the electronic environment, or alternatively used simulated (SPICE) currents within IC’s design kits and from it provide analytical estimation. On the modeling side, efforts were mainly limited to either: a specific EM component (e.g., magnetic), very limited scope of the model, or a limited connection with real-life IC. The main contributions of this research are: 1) providing a fully analytical model of the radiated signal considering the ground plane of the substrate and the medium change from oxidation layers to other mediums, such as air or plastic, utilizing EM theory-of-images for both E and H fields; 2) providing methodologies to simulate EM signals on IC-technologies (by adapting Cadence EMX tool); and 3) demonstrating little error between the analytical and simulated models and linking cryptographic SCA security metrics to the analytical tools. The developed model provides significant added value for security architects, faster, premanufacturing evaluation and a verifiability framework through IC tool.
MDED-Framework: A Distributed Microservice Deep-Learning Framework for Object Detection in Edge Computing

The demand for deep learning frameworks capable of running in edge computing environments is rapidly increasing due to the exponential growth of data volume and the need for real-time processing. However, edge computing environments often have limited resources, necessitating the distribution of deep learning models. Distributing deep learning models can be challenging as it requires specifying the resource type for each process and ensuring that the models are lightweight without performance degradation. To address this issue, we propose the Microservice Deep-learning Edge Detection (MDED) framework, designed for easy deployment and distributed processing in edge computing environments. The MDED framework leverages Docker-based containers and Kubernetes orchestration to obtain a pedestrian-detection deep learning model with a speed of up to 19 FPS, satisfying the semi-real-time condition. The framework employs an ensemble of high-level feature-specific networks (HFN) and low-level feature-specific networks (LFN) trained on the MOT17Det dataset, achieving an accuracy improvement of up to AP50 and AP0.18 on MOT20Det data.
On the Capacity of the Quantum Switch With and Without Entanglement Decoherence

This letter studies the capacity of the quantum switch for two decoherence models: when link-level entanglements last (i) for a time slot, or (ii) until they are used to serve a request (i.e., there is no decoherence). The two models are important as they set lower and upper bounds on the capacity region for any other decoherence model. The letter’s contributions are to characterize the switch capacity region for both decoherence models and to propose throughput-optimal policies based on gradient descent.
Learning Attentional Communication with a Common Network for Multiagent Reinforcement Learning

For multiagent communication and cooperation tasks in partially observable environments, most of the existing works only use the information contained in hidden layers of a network at the current moment, limiting the source of information. In this paper, we propose a novel algorithm named multiagent attentional communication with the common network (MAACCN), which adds a consensus information module to expand the source of communication information. We regard the best-performing overall network in the historical moment for agents as the common network, and we extract consensus knowledge by leveraging such a network. Especially, we combine current observation information with the consensus knowledge to infer more effective information as input for decision-making through the attention mechanism. Experiments conducted on the StarCraft multiagent challenge (SMAC) demonstrate the effectiveness of MAACCN in comparison to a set of baselines and also reveal that MAACCN can improve performance by more than 20% in a super hard scenario especially.
Inference of microbial covariation networks using copula models with mixture margins

Quantification of microbial covariations from 16S rRNA and metagenomic sequencing data is difficult due to their sparse nature. In this article, we propose using copula models with mixed zero-beta margins for the estimation of taxon-taxon covariations using data of normalized microbial relative abundances. Copulas allow for separate modeling of the dependence structure from the margins, marginal covariate adjustment, and uncertainty measurement.Our method shows that a two-stage maximum-likelihood approach provides accurate estimation of model parameters. A corresponding two-stage likelihood ratio test for the dependence parameter is derived and is used for constructing covariation networks. Simulation studies show that the test is valid, robust, and more powerful than tests based upon Pearson's and rank correlations. Furthermore, we demonstrate that our method can be used to build biologically meaningful microbial networks based on a dataset from the American Gut Project.R package for implementation is available at https://github.com/rebeccadeek/CoMiCoN.
Navigating Performance-Efficiency Tradeoffs in Serverless Computing: Deduplication to the Rescue!

Navigating the performance and efficiency trade-offs is critical for serverless platforms, where the providers ideally want to give the illusion of warm function startups while maintaining low resource costs. Limited controls, provided via toggling sandboxes between warm and cold states and keepalives, force operators to sacrifice significant resources to achieve good performance.