Reviewerly/exHarmony huggingface dataset:
Impact of digitalization trends on the development of the Russian system of advanced training and methodological support for teachers

The challenges of the digital transformation of education require continuous renewal and development of teachers' professional competencies. The effectiveness of teacher training depends on how advanced the development of the teacher training system will be. The purpose of the study was to identify the interrelationship of the processes of digitalization of education and digitalization of the system for improving the qualifications and methodological support of teachers. During the study, the stages and drivers of digitalization of the system of additional professional education of teachers in Russia were highlighted. The dynamics of the goals and configuration of the system, the main technological, organizational and pedagogical trends at each stage of the digitalization of the teacher training system are traced. Conclusions have been drawn about the advanced nature of digitalization and digital transformation of the system of advanced training of teachers in relation to the general education system. The analysis of the identified trends and risks associated with both the expansion of the number and increase in the activity of significant subject digitization of the processes of professional development of teachers, as well as with the circumstances of the pandemic, made it possible to formulate proposals for the further development of the digital transformation processes of the system of advanced training and methodological support for teachers as an ecosystem and platform.
Privacy-Preserving Verifiable Asynchronous Federated Learning

Federated learning (FL) is a recently proposed technique to cope with growing data and break the barriers among datasets, which enables nodes to train machine learning models without sharing their local datasets. However, the data privacy and model performance concerns in asynchronous federated learning hinder its deployment in practical applications, especially in dynamic scenarios. To address these problems, we propose a verifiable asynchronous federated learning with a peer-to-peer network based on local dataset test and cosine value examination to improve the model performance. We also design a privacy-preserving scheme by using the local differential privacy (LDP) to protect data privacy. We evaluate our scheme on the model accuracy and convergence performance. Numerical results show the high accuracy and efficiency of our proposed scheme while protecting privacy.
Speeding up <i>L</i><sub>2</sub>-loss support vector regression by random Fourier features

To avoid the expensive quadratic programming in the L2-loss support vector regression (SVR) model, smooth approximation and iteratively reweighted least square (IRLS) techniques were introduced in literature, resulting in smoothed SVR (SSVR) and IRLS-SVR. However, for nonlinear models, SSVR and IRLS-SVR both involve operations with matrices and vectors of the same size as the training set. Thus, as the training set becomes large, nonlinear SSVR and IRLS-SVR both need long training time and large memory. To further alleviate the training cost, this paper projects the original data into a low dimensional space via random Fourier feature. The inner product of the random Fourier features of two data points is approximately the same as the kernel function evaluated at these two data points. Hence, it is possible to use a linear model in the new low dimensional space to approximate the original nonlinear model, and consequently the time/memory efficient linear training algorithms could be applied. This paper applies the idea of random Fourier features to nonlinear SSVR and IRLS-SVR, and our testing results on real-world datasets show that, the introduction of random Fourier features makes SSVR and IRLS-SVR achieve similar prediction accuracy as the original nonlinear version with substantially higher time efficiency.
Steps and traces

Abstract In the theory of coalgebras, trace semantics can be defined in various distinct ways, including through algebraic logics, the Kleisli category of a monad or its Eilenberg–Moore category. This paper elaborates two new unifying ideas: (i) coalgebraic,draftrules trace semantics is naturally presented in terms of corecursive algebras, and (ii) all three approaches arise as instances of the same abstract setting. Our perspective puts the different approaches under a common roof and allows to derive conditions under which some of them coincide.
IC design of optimal four-bit absolute value detector based on CMOS process

Design and implement a high-performance absolute-value detector through the CMOS process, and realize the function of taking the absolute value of the input data and then comparing it, which can be applied in a variety of circuits, such as A/D conversion. The circuit structure is simple, which adopts the structure of the mirror adder and the multiplexer. This paper also looks for the critical path and uses logical effort theory to find the minimum delay. The next work is to adjust the gate size and determine the optimal size of the device and the optimal power supply voltage. Finally, this paper attempts to achieve the minimum energy consumption.
Automation of active reconnaissance phase

The unprecedented growth in technology has increased the importance of the required information security that is still hard to be reached. Recently, network and web application attacks have occurred frequently, causing confidential data to be stolen by the available vulnerabilities in the systems and the most prominent is in the form of open ports. This causes the CIA (Confidentiality Integrity and Availability) Triad Model to break. Penetration testing is one of the key techniques used in real life to accurately detect the possible threats and potential attacks against the system, and the first step for hackers to conduct attacks is information collection. In this paper, we present a useful schema for the active information-gathering phase that can be used during penetration testing and by system administrators. It will be the first feature of a security engine going to be implemented. The work involves an automated API-based IP and port scanner, service-version enumerator, and vulnerability detection system. This scheme is based on the Network Mapper (Nmap) to collect the information with high accuracy depending on the provided rules in our schema. Besides, the work has been implemented as a RESTful-API server, aiming at easy integration for real-life cases and allowing administrators to scan and secure their networks more quickly and easily. The effectiveness and efficiency of this technique has been proved by the various test cases applied considering different scenarios from the real world. The average time of scanning a server and detecting the vulnerabilities is 2.2 minutes. Regardless of the number of vulnerabilities, the increase in time for each open port is just about 12 seconds.
Error Serial Episodes Discovery from Mobile Payment Log in Distributed ETC

The Electronic Toll Collection (ETC), as a kind of distributed system, has become more and more mature in application of highways. The mobile payment system, distributed in main network links of ETC, possibly causes the functional failure of entire system if an error occurs. But the error message can be directly reflected in log. Therefore, effective analysis of mobile payment log is crucial to the stability of distributed system. In this paper, we propose a novel analysis method, which is error serial episodes discovery. This method converts mobile payment log in ETC into a long event sequence, and improves window truncation by WINEPI algorithm to explore the relevance of events in the log. We use real data from mobile payment log in ETC of a Chinese highway company. The experiments indicate that this method obtains a significant rule that multiple identical events will cause the error event to occur. This rule, which is found in actual scenarios, will provide a basis for data analysis of the log, and it has been recognized by business experts from relevant institutions.
Covering Radius of Melas Codes

We prove that the covering radius of the Melas code <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M(m,q)$ </tex-math></inline-formula> of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n=q^{m}-1$ </tex-math></inline-formula> over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{q}$ </tex-math></inline-formula> is 2 if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q &gt; 3$ </tex-math></inline-formula> . We also prove that the covering radius of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M(m,3)$ </tex-math></inline-formula> is 3 is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m \ge 3$ </tex-math></inline-formula> , the covering radius of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M(2,3)$ </tex-math></inline-formula> is 4, and the covering radii of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M(1,2)$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M(1,3)$ </tex-math></inline-formula> are 1.
Secure E-Governance System To Preserve Justice And Right Decision Making Using Cloud Computing

Nowadays in several criminal cases the progress makes us to think whether justice persists. In existing system, many news, and articles state that the victim data is manipulated, and records are changed by the officials. To address this issue, this paper proposes a secure system rendering justice with transparency which makes the court decision making easy. In the proposed Justice Secure Model, the victim voice data and records are prevented from data tampering using security policies. The criminal voice data are transformed into text using Hidden Markov Model (HMM). The voice data and records are secured by encrypting using Secured Hash Algorithm (SHA 256). To validate the access request, each victim records are embedded with private key. These private keys are securely transmitted by encrypting using modified AES algorithm. The above steps would avoid the internal data tampering and manipulating the victim case data. To address the external intrusion, our proposed methodology detects and prevent SQL injection attacks. Finally, to provide security of data at the server level, we have integrated multiple cloud storage system. The proposed system provide security at the application and server levels, thus making the unauthorized data access, intrusion, and data manipulation impossible.
Autonomy Algorithms for Stable Dynamic Linear Convoying of Autonomous Marine Vehicles

Linear convoying of multiple vehicles involves typically several vehicles in a follow-the-leader formation with prescribed separation distance between vehicles. Following vehicles are primarily concerned with following the exact path of the leader rather than maintaining a constant 180 degree relative bearing. This paper describes algorithms for the three primary phases of this type of mission, the initial order selection phase, and the initial convoying aggregation phase, and the stable state transiting phase. The algorithms described are built upon the open source MOOS-IvP marine autonomy project. They include algorithms for decentralized task allocation to address the initial ordering phase, and a new convoying behavior of the IvP Helm for implementing the initial aggregation and stable state convoying phases. The convoying behavior uses the multi-objective optimization structure of the IvP Helm to allow existing obstacle avoidance and COLREGS collision avoidance behaviors.
Comparative Analysis of Load Balancing Techniques in Cloud Computing, Based on LB Metrices

Cloud computing (CC) is a form of computing that uses the internet to distribute computer resources. This method has massive flaws. Load balancing is one of the most complex things. As a result of all this, the unwelcomed variable local workload would most likely be dispersed evenly over overall cloud nodes. This study covers existing cloud load balancing approaches and compares them based on various characteristics. To get the optimal result, these are all compared in the system's state load-balancing procedures. In the comparison table in this article, we primarily focused on reliability, reaction time, adaptability, performance, resource usage, and fault tolerance. These parameters have been proving the most effective for system performance.
Research on International Law Data Integrity Guarantee Based on Antiterrorism Prediction Algorithm

In order to improve the quality of international law data, this paper designs a method to ensure the integrity of international law data based on an antiterrorism prediction algorithm. On the basis of introducing random function and deep learning technology, the prediction model set is split through the trusted seed model, the model selection is completed through iterative optimization, and then new terrorism-related factors are added to obtain the new prediction model set, so as to complete the antiterrorism data prediction. Based on the antiterrorism prediction data obtained by the above process, on the basis of identifying incomplete data, the antiterrorism data integrity guarantee method in international law database is designed through the process of determining data integrity operation parameters, eliminating worthless feature items, processing noise dimension reduction, and adding feature series of data integrity. Experimental results show that the frequency range and data sensitivity of international law data can be optimized by using this method, the audit process of data integrity is less time consuming, and the data integrity after processing can reach 0.967.
Trends Matching as a Dataset Attack Detection Strategy During Machine Learning

Machine learning systems rely on the validity and integrity of the datasets provided to make correct predictions. However, datasets attacks are real. Datasets can be poisoned, spoiled, corrupted, or falsified to mislead machine learning system. Strategies for preventing datasets poisoning are, therefore, apparently needed. Models to defend datasets are scarce. In our context, datasets poisoning is about addition of malicious data into credible datasets during machine learning. We propose a strategy for preventing addition of malicious data into accepted datasets by verifying and matching the trends depicted in credible datasets to the patterns arising from the additional data. Precisely, we investigate the strength of association between the trends depicted in the two datasets before a recommendation to accept or reject the additional data into a machine learning process is made. The age detection datasets, gender classification datasets and uber pickups datasets were used for illustration purposes. These datasets are all freely available online on Kaggle. The age detection dataset was the selected credible dataset. The uber pickups was used as the malicious dataset, while the gender classification dataset was used as the valid additional data. An experimental assessment of the validity of the proposed trends matching strategy indicates that only those datasets with matching trends can be accepted as additional data into credible datasets. Malicious datasets are blocked from poisoning credible datasets. This intervention implicitly supports high quality and accurate machine learning.
Contextual Perceptions of Feminine-, Masculine- and Gender-Ambiguous-Sounding Conversational Agents

The study explored whether cultural gender stereotypes are carried into the domain of conversational agents (CAs), and examined user reactions to feminine, masculine, and gender-ambiguous voices in the context of stressful and non-stressful interactions. The user’s image of an ideal CA was also investigated. A fully virtual experiment guided participants through interactions with three differently voiced Amazon Alexa test apps, collected participants’ demographics, ratings and comments about CAs performance, voice and personality manifestations on stressful, non-stressful and personality-revealing tasks. The masculine sounding agent was most frequently associated with extraverted, sensitive and open-minded personality, the gender-ambiguous agent was perceived as organized; and the feminine sounding agent as sympathetic. Most of the participants wanted their ideal CAs to have a highly warm and competent personality, and preferred this personality in both stressful and non-stressful contexts. Nearly half of the participants identified a preference for contextually dependent voice or stated a preference for an ideal agent with a gender-ambiguous voice, though this agent received the lowest scores during experimental interactions. The study contributes to the discussion of the cultural gender stereotypes in conversational technology and user preferences for agent’s voices and personality.
Compare Machine Learning Models in Text Classification Using Steam User Reviews

Text Classification and Sentiment Analysis of game reviews are viewed as important parts in not only academic fields but also in game studies. In this paper, with more than 400 thousand game reviews on Steam platform, we preprocess the data using different libraries (sklearn, nltk, and spaCy) and use them as inputs to build three sentiment classification models based on different algorithms (Naive Bayes, SVM, and Random Forest). In contrast to previous studies that only focus on different sentiment analysis models, our paper also highlights the use of different APIs to preprocess the data and their corresponding model performance. The results show that no matter which API we choose, Random Forest models always perform the best. However, in terms of training time, Naive Bayes is the fastest. This work can be used to apply grid search for researchers to automatically find the optimum API before conducting sentiment analysis in the future.
A novel shot boundary detection technique for illumination and motion effects

Shot boundary detection is the main task in the preprocessing stage of content-based video operations. There are unforeseen illumination change and motion effects in a video due to the complexity of video content, which may lead to the detection of wrong shot boundaries. This paper proposes a novel shot boundary detection method to solve this problem. The method mainly includes two parts: abrupt and gradual transition detection. In the first stage, CIEDE2000 color-difference and adaptive threshold are used to find the possible abrupt transition frames. Then BRISK feature is utilized to extract real abrupt transition frames. In the next stage, the brightness change of video frames is utilized to detect the frame group that may be gradual. Then CIEDE2000 color-difference along with cumulative frame algorithm is used to detect actual gradual transition frames. The experiment is evaluated on the TRECVid2001 and ClipShots datasets. Experimental results show that the method proposed in this paper can improve the precision of shot segmentation.
AT-BOD: An Adversarial Attack on Fool DNN-Based Blackbox Object Detection Models

Object recognition is a fundamental concept in computer vision. Object detection models have recently played a vital role in various applications, including real-time and safety-critical systems such as camera surveillance and self-driving cars. Scientific research has proven that object detection models are prone to adversarial attacks. Although several proposed methods exist throughout the literature, they either target white-box models or specific-task black-box models and do not generalize on other detectors. In this paper, we proposed a new adversarial attack against Blackbox-based object detectors called AT-BOD. The proposed AT-BOD model can fool the single-stage and multi-stage detectors, where we used an optimization algorithm to generate adversarial examples depending only on the detector predictions. AT-BOD model works in two diverse ways, reducing the confidence score and misleading the model to make the wrong decision or hide the object detection models. Our solution achieved a fooling rate of 97% and a false negative increase of 99% on the YOLOv3 detector, and a fooling rate of 61% false-negative increase of 57% on the Faster R-CNN detector. The detection accuracy of YOLOv3 and Faster R-CNN under AT-BOD was dramatically reduced and reached ≤1% and ≤3%, respectively.
Use of Static Surrogates in Hyperparameter Optimization

Optimizing the hyperparameters and architecture of a neural network is a long yet necessary phase in most applications. This consuming process can benefit from strategies designed to discard low-quality configurations and quickly focus on more promising candidates. This work aims at enhancing HyperNOMAD, a library that adapts a direct search derivative-free optimization algorithm to tune both the architecture and the training of a neural network simultaneously. Two static surrogates are developed to trigger an early stopping during the configuration evaluation and strategically rank a pool of candidates. These additions to HyperNOMAD are shown to reduce its resource consumption by orders of magnitude without harming the quality of the proposed solutions.
Perception of the quality of virtual educational services during the COVID-19 pandemic at the National University of the Peruvian Amazon (UNAP)

This work consists of a second descriptive study on the implementation of the use of virtual education services at the National University of the Peruvian Amazon (UNAP), in the context of the COVID-19 pandemic. Iquitos, home to UNAP, is a Peruvian city located in the Amazon, with no road connection to the rest of Peru and with a series of limitations in electricity and telecommunications services, which conditions or negatively affects the quality of e-learning services at UNAP. The Peruvian government has suspended face-to-face educational activities since March 2020, motivated by the spread of the COVID-19 pandemic in the main cities of Peru, so digital tools are being used as mechanisms for the continuity of educational services. For this descriptive research, the survey method was used. The surveys have been designed based on the usefulness of virtual services using the UNE 66181:2008 Standard, considering indicators such as the levels of Information, Employability, Ease of assimilation and Accessibility. Virtual teaching is carried out at UNAP through the following tools: Zoom, Google Meet, Youtube, Whatsapp, Moodle and others. The results of this second experience, unlike the first study, show that the participating students have improved their perception of the quality of university educational services through virtual media. At the same time, the low quality of connectivity and the low penetration of the use of technologies among teachers are still limiting factors.