Reviewerly/exHarmony huggingface dataset:
Ways of Enriching MOOCs for Higher Education: A Philosophy Course

Using Massive Open Online Courses (MOOC) in higher education poses new challenges that include the following: an increase in the number of students taught by one teacher, which makes it difficult to individualize the tasks, the easy access to other students' answers due to the social media, the lower engagement of students when studying online. In the situation of the complete transition to e-education due to the 2020 pandemic, teachers were forced to solve these problems in the electronic environment. This article presents the case of enriching the online Philosophy course given to students of Peter the Great St. Petersburg Polytechnic University by creating new assignments in various forms: video blogging, multimedia projects and in-depth study of philosophical texts. In this respect, the current situation becomes advantageous and allows the lecturers to achieve better results due to the availability of all ideas and experiences and the collaborative activities of the students. The variety of approaches used in studying the Philosophy course confirms the important role of the teacher in e-learning.
TriGAN: image-to-image translation for multi-source domain adaptation

Most domain adaptation methods consider the problem of transferring knowledge to the target domain from a single source dataset. However, in practical applications, we typically have access to multiple sources. In this paper we propose the first approach for Multi-Source Domain Adaptation (MSDA) based on Generative Adversarial Networks. Our method is inspired by the observation that the appearance of a given image depends on three factors: the domain, the style (characterized in terms of low-level features variations) and the content. For this reason we propose to project the image features onto a space where only the dependence from the content is kept, and then re-project this invariant representation onto the pixel space using the target domain and style. In this way, new labeled images can be generated which are used to train a final target classifier. We test our approach using common MSDA benchmarks, showing that it outperforms state-of-the-art methods.
WITHDRAWN: Word Embedding Generation for Urdu Language using Word2vec model

Word embedding generation is the task of distributed representation of words on vector space. The word embeddings captures both the syntactic and semantic information to perform natural language processing tasks. The word embeddings has attained considerable attention from last few years giving their inherent applicability to Natural Language Processing like part of speech tagging, sentiment analyzing or dependency parsing etc. despite recent advancement in word embedding mappings, very little research has devoted for Urdu word embeddings where word with similar meaning will align to similar vector space and different far apart. In this research paper Word2vec model has been used for Urdu word embedding generation. The model will help to make the dense word vector representation of Urdu words which could be used for pre-trained word vectors. The results showed the proposed method can be used to improve conventional word embedding methods.
Forecasting diversion type hydropower plant generations using an artificial bee colony based extreme learning machine method

(2021). Forecasting diversion type hydropower plant generations using an artificial bee colony based extreme learning machine method. Energy Sources, Part B: Economics, Planning, and Policy: Vol. 16, No. 2, pp. 216-234.
A spatial–temporal graph neural network framework for automated software bug triaging

The bug triaging process, an essential process of assigning bug reports to the most appropriate developers, is related closely to the quality and costs of software development. As manual bug assignment is a labor-intensive task, especially for large-scale software projects, many machine-learning-based approaches have been proposed to automatically triage bug reports. Although developer collaboration networks (DCNs) are dynamic and evolving in the real-world, most automated bug triaging approaches focus on static tossing graphs at a single time slice. Also, none of the previous studies consider periodic interactions among developers. To address the problems mentioned above, in this article, we propose a novel spatial-temporal dynamic graph neural network (ST-DGNN) framework, including a joint random walk (JRWalk) mechanism and a graph recurrent convolutional neural network (GRCNN) model. In particular, JRWalk aims to sample local topological structures in a graph with two sampling strategies by considering both node importance and edge importance. GRCNN has three components with the same structure, i.e., hourly-periodic, daily-periodic, and weekly-periodic components, to learn the spatial-temporal features of dynamic DCNs. We evaluated our approach's effectiveness by comparing it with several state-of-the-art graph representation learning methods in two domain-specific tasks that belong to node classification. In the two tasks, experiments on two real-world, large-scale developer collaboration networks collected from the Eclipse and Mozilla projects indicate that the proposed approach outperforms all the baseline methods.
Improving Opportunities for New Value of Open Data: Assessing and Certifying Research Data Repositories

Investments in research that produce scientific and scholarly data can be leveraged by enabling the resulting research data products and services to be used by broader communities and for new purposes, extending reuse beyond the initial users and purposes for which the data were originally collected. Submitting research data to a data repository offers opportunities for the data to be used in the future, providing ways for new benefits to be realized from data reuse. Improvements to data repositories that facilitate new uses of data increase the potential for data reuse and for gains in the value of open data products and services that are associated with such reuse. Assessing and certifying the capabilities and services offered by data repositories provides opportunities for improving the repositories and for realizing the value to be attained from new uses of data. The evolution of data repository certification instruments is described and discussed in terms of the implications for the curation and continuing use of research data.
Comparing Collaborative Filtering and Hybrid based Approaches for Movie Recommendation

This paper analyzes and compares the performance of the Collaborative Filtering and Hybrid based approaches in generating movie recommendations. The Collaborative Filtering approach uses the rating data consisting of two main phases: (1) movie similarity and (2) movie rating prediction. Meanwhile, the Hybrid based approach adds the benefit of a Content-based to the Collaborative Filtering based approach. Thus, it uses both the rating and movie data and is consisting of four main phases: (1) text preprocessing, (2) term weighting, (3) movie clustering, and (4) Collaborative Filtering based approach. Empirical results show that the recommendation performances of both approaches are linear to the size of the movie neighbourhood. However, the Hybridbased approach's required neighbourhood size is naturally a lot smaller than that of the Collaborative Filtering since the former employs a clustering technique. The performance comparisons show that the Collaborative Filtering based approach always outperforms the Hybrid based at any top-N position in Precision and NDCG metrics. These findings conjecture that the Hybrid approach does not always improve the Collaborative Filtering approach in movie recommendation.
Balancing Quality of Experience and Traffic Volume in Adaptive Bitrate Streaming

Adaptive bitrate (ABR) streaming services have spread with advances in the codec, video streaming, and network technologies. For smooth video playback in ABR streaming services, a video player runs an ABR algorithm, which dynamically adjusts the bitrate of video data on the basis of the statuses of the network and player. Existing ABR algorithms calculate a suitable bitrate to maximize the quality of experience (QoE). However, providing a high-QoE video increases network investment costs and content delivery network (CDN) usage fees. According to a survey conducted by the Streaming Video Alliance, mobile users prefer low-traffic videos to high-QoE videos. To reduce traffic volume, commercial video-streaming services enable users to set an upper limit of the bitrate. However, this cannot always achieve the required QoE because they cannot select a high bitrate even when the communication environment improves during viewing. In this paper, we propose BANQUET, a novel ABR algorithm that can reduce the traffic volume while maintaining QoE above the target QoE. The target QoE can be set by users or streaming providers considering user's preferences or CDN budget. BANQUET selects a suitable bitrate by estimating QoE and traffic volume that will be experienced by all the bitrate patterns for the next several chunks on the basis of future throughput and a buffer transition calculation. The trace-based simulation showed that BANQUET reduces traffic volume 18.3%-51.2% on average in the mobile environment and 1.2%-38.3% in the broadband environment while maintaining QoE the same as or better than existing algorithms.
Real-time Traffic Signs Detection Based on YOLO Network Model

Recently, real-time traffic sign detection has been widely applied in autonomous and assisted car driving. At the same time, the research on traffic signs detection based on YOLO (You Only Look Once) has attracted intensive attention. However, the accuracy and precision of small targets detection based on YOLO need to be further improved. We trained and tested the latest YOLOv4 and YOLOv3 on the same data set to comparatively study the detection results. The data set consists of 4000 Chinese traffic signs, which were manually labeled by ourselves. Comparing the detection results, the detection accuracy of the V4 is significantly higher than that of the V3.
HARDenseNet: A 1D DenseNet Inspired Convolutional Neural Network for Human Activity Recognition with Inertial Sensors

Human Activity Recognition (HAR) is currently one of the active research areas considering its applications in fields such as sports, healthcare, Social interaction, fitness, entertainment and the list goes on. Traditionally Computer Vision (CV) strategies were used for HAR which has many provocations including portability, environmental conditions, occlusion, greater cost and most of all privacy. But recently a variety of sensors such as gyroscope, accelerometer and heart rate sensor etc are used for HAR. There are many benefits of using sensor data as an alternative to usual computer vision techniques. Their usage is said to have removed almost all the limitations of computer vision strategies. The use of Machine Learning (ML) and Deep Neural Networks (DNN) using inertial sensor data for Human Activity Recognition can be extensively found in literature. In this paper, we have proposed a novel 1 dimensional neural network which is inspired by DenseNet neural network which has 1 dimensional convolutional layers for processing 1 dimensional signal data of inertial sensors.
A quasi-affine transformation artificial bee colony algorithm for global optimization

The artificial bee colony (ABC) algorithm is one of the classical bioinspired swarm-based intelligence algorithms that has strong search ability, because of its special search mechanism, but its development ability is slightly insufficient and its convergence speed is slow. In view of its weak development ability and slow convergence speed, this paper proposes the QABC algorithm in which a new search equation is based on the idea of quasi-affine transformation, which greatly improves the cooperative ability between particles and enhances its exploitability. During the process of location updating, the convergence speed is accelerated by updating multiple dimensions instead of one dimension. Finally, in the overall search framework, a collaborative search matrix is introduced to update the position of particles. The collaborative search matrix is transformed from the lower triangular matrix, which not only ensures the randomness of the search, but also ensures its balance and integrity. To evaluate the performance of the QABC algorithm, CEC2013 test set and CEC2014 test set are used in the experiment. After comparing with the conventional ABC algorithm and some famous ABC variants, QABC algorithm is proved to be superior in efficiency, development ability, and robustness.
Vehicle Make and Model Recognition using Deep Transfer Learning and Support Vector Machines

Vehicle make and model recognition (VMMR) is a vital task in applications like intelligent parking systems, driver assistance systems, and automated toll collection. Accurate implementation of VMMR becomes challenging due to the similar appearance of different vehicle models. In this paper, we proposed a VMMR framework based on deep feature extraction from VGG16 convolutional neural network (CNN) model, followed by the feature reduction and classification. First, deep features were extracted from the image of the vehicle through the FC-6 layer of VGG16 CNN. Next, the Genetic Algorithm was applied to identify best-describing features and reduce feature dimensions. Finally, a reduced feature vector was supplied to support vector machines classifier to distinguish among various make and models of vehicle images using 5-fold cross-validation. The proposed technique obtained an average accuracy of 98.22%, sensitivity of 94.57%, and specificity of 96.86%. A comparative analysis with state-of-the-art methods shows significant improvement of the proposed model.
The problem with SMS campus warning systems: An evaluation based on recipients’ spatial awareness

SMS warnings are critical to alert campus communities in case of emergency. But how effective are they in enabling users' compliance with a warning? Our survey of a student sample shows a major flaw in the design of SMS warnings: they fail to provide their recipients with adequate spatial awareness; that, in turn, hinders their ability to counteract upon an active emergency. For example, recipients of a warning who are willing to comply with it may fail doing so because the SMS does not convey enough information to them. In particular, we discovered that recipients are often unable to identify the affected area sent in the SMS. That calls for a better understanding of how to design SMS warning systems which enable recipients of a warning message to unambiguously comprehend and act upon them.
Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

This study aims to build a face recognition prototype that can recognize multiple face objects within one frame. The proposed method uses a local binary pattern histogram and Haar cascade classifier on low-resolution images. The lowest data resolution used in this study was 76 × 76 pixels and the highest was 156 × 156 pixels. The face images were preprocessed using the histogram equalization and median filtering. The face recognition prototype proposed successfully recognized four face objects in one frame. The results obtained were comparable for local and real-time stream video data for testing. The RR obtained with the local data test was 99.67%, which indicates better performance in recognizing 75 frames for each object, compared to the 92.67% RR for the real-time data stream. In comparison to the results obtained in previous works, it can be concluded that the proposed method yields the highest RR of 99.67%.
Chaotic Discrete Fractional-Order Food Chain Model and Hybrid Image Encryption Scheme Application

Using the discrete fractional calculus, a novel discrete fractional-order food chain model for the case of strong pressure on preys map is proposed. Dynamical behaviors of the model involving stability analysis of its equilibrium points, bifurcation diagrams and phase portraits are investigated. It is demonstrated that the model can exhibit a variety of dynamical behaviors including stable steady states, periodic and quasiperiodic dynamics. Then, a hybrid encryption scheme based on chaotic behavior of the model along with elliptic curve key exchange scheme is proposed for colored plain images. The hybrid scheme combines the characteristics of noise-like chaotic dynamics of the map, including high sensitivity to values of parameters, with the advantages of reliable elliptic curves-based encryption systems. Security analysis assures the efficiency of the proposed algorithm and validates its robustness and efficiency against possible types of attacks.
Speaker identification based on Radon transform and CNNs in the presence of different types of interference for Robotic Applications

Both automatic speaker identification (ASI) and speech recognition can be utlized now for the control of modern robots. An ASI algorithm can be implemented at a speech interface of the robot to determine the identity of the person allowed to deal with the robot, while speech recognition can be implemented for the interpretation of the order given to the robot. Robustness of the ASI system is a challenging task in the presence of speech degradations such as noise and interference. This study presents a new approach to improve the accuracy of speaker identification in the presence of interference for robot control applications with a convolutional neural network (CNN). First, the speech signal from the speaker is divided into segments, each of which is transformed into a spectrogram, and hence Radon transformation is estimated for this spectrogram. The spectrogram resolves the speech segment into a map of power distribution with both time and frequency. Together, the spectrograms and their Radon transforms are used as inputs to a proposed CNN-based deep learning model. Necessary refinements are undertaken and the resulting optimized "Radon-Deep-Learning Model (RDLM) is compared with a benchmark model. The proposed model consists of six convolutional (CNV) layers followed by six Max. pooling layers, while the benchmark model consists of three CNV layers followed by three Max. pooling layers. Experimental results reveal that the proposed RDLM model achieves a high classification accuracy up to 97.5%, which is more than double the performance reported for some traditional methods that are used for speaker identification.
Finite-Sample Analysis of Two-Time-Scale Natural Actor–Critic Algorithm

Actor–critic style two-time-scale algorithms are one of the most popular methods in reinforcement learning, and have seen great empirical success. However, their performance is not completely understood theoretically. In this article, we characterize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">global</i> convergence of an online natural actor–critic algorithm in the tabular setting using a single trajectory of samples. Our analysis applies to very general settings, as we only assume ergodicity of the underlying Markov decision process. In order to ensure enough exploration, we employ an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -greedy sampling of the trajectory. For a fixed and small enough exploration parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> , we show that the two-time-scale natural actor–critic algorithm has a rate of convergence of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\tilde{\mathcal {O}}(1/T^{1/4})$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T$</tex-math></inline-formula> is the number of samples, and this leads to a sample complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\tilde{\mathcal {O}}(1/\delta ^{8})$</tex-math></inline-formula> samples to find a policy that is within an error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> from the global optimum. Moreover, by carefully decreasing the exploration parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> as the iterations proceed, we present an improved sample complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\tilde{\mathcal {O}}(1/\delta ^{6})$</tex-math></inline-formula> for convergence to the global optimum.
Analyzing Dynamic Code

Dynamic languages, such as JavaScript, employ string-to-code primitives to turn dynamically generated text into executable code at run-time. These features make standard static analysis extremely hard if not impossible, because its essential data structures, i.e., the control-flow graph and the system of recursive equations associated with the program to analyze, are themselves dynamically mutating objects. Nevertheless, assembling code at run-time by manipulating strings, such as by eval in JavaScript, has been always strongly discouraged, since it is often recognized that “ eval is evil ,” leading static analyzers to not consider such statements or ignoring their effects. Unfortunately, the lack of formal approaches to analyze string-to-code statements pose a perfect habitat for malicious code, that is surely evil and do not respect good practice rules, allowing them to hide malicious intents as strings to be converted to code and making static analyses blind to the real malicious aim of the code. Hence, the need to handle string-to-code statements approximating what they can execute, and therefore allowing the analysis to continue (even in the presence of dynamically generated program statements) with an acceptable degree of precision, should be clear. To reach this goal, we propose a static analysis allowing us to collect string values and to soundly over-approximate and analyze the code potentially executed by a string-to-code statement.
Multi-task adversarial autoencoder network for face alignment in the wild

Face alignment has been applied widely in the field of computer vision, which is still a very challenging task for the existence of large pose, partial occlusion, and illumination, etc. The method based on deep regression neural network has achieved the most advanced performance in the field of face alignment in recent years, and how to learn more representative facial appearance is the key to face alignment. Based on the idea of Multi-task Learning, we propose a Multi-task Adversarial Autoencoder (MTAAE) network, which can learn more representative facial appearance for heatmap regression and improve the performance of face alignment in the wild. MTAAE is composed of three tasks. The main task uses the heatmap regression method to locate the position of landmarks and introduces a discriminator on the landmark heatmaps to generate more realistic heatmaps. Facial attribute estimation tasks and face reconstruction task based on Adversarial Autoencoder respectively extract discriminative and generative representations to improve the effect of heatmap regression. At the same time, the dynamic weight network is designed to assign a weight coefficient dynamically and reasonably for each auxiliary task. Extensive experiments on 300 W, MTFL, and WFLW datasets demonstrate that our method is more robust in complex environments and outperforms state-of-the-art methods.