Reviewerly/exHarmony huggingface dataset:
Modular Design in Distance Learning

The current epidemiological situation requires new distance learning techniques, and modular design seems to be a good systematic approach to the formation of standards for distance learning. Modular approach makes it possible to provide students with knowledge and develop competencies in the current challenging social conditions. The authors analyzed the distance learning technologies used by various academic organizations during the pandemic, as well as some distance learning platforms for advanced training courses. The analysis showed that modular competence-based distance learning technologies are widely introduced in the domestic academic environment. Modular academic disciplines consist of interrelated modules and presuppose a large amount of programmed self-learning. The article also contains an analysis of particular distance learning techniques and introduces new various technologies of blended learning. The materials can be used to develop new distance learning and blended courses.
CapsRec: A Capsule Graph Neural Network Model for Social Recommendation

Social recommendations, which aim at using social information to improve the quality of recommendation systems, have attracted an increasing amount of attention recently. Many of these social recommendation methods assume that social influence is fixed and determined only by users' historical behaviors. However, social influence is variable by nature as it is generally related to the item in a specific recommendation case. Besides, many of the observed rating values are due to effects associated with either users or items, independent of any interactions, known as biases. Previous studies generally consider biases as scalars, ignoring their power in enriching user/item latent factor vectors. To address these issues, we propose a new CapsRec framework based on the combination of capsule network and Graph Neural Network for social recommendation, which considers the power of biases on enriching user/item latent factor vectors and captures the diversification of social influence. Experiment results on two real datasets demonstrate the effectiveness of our proposed framework.
RealitySketch

In this Real-Time Live, we demonstrate RealitySketch, an augmented reality interface for sketching interactive graphics and visualizations [Suzuki et al. 2020]. In recent years, an increasing number of AR sketching tools enable users to draw and embed sketches in the real world. However, with the current tools, sketched contents are inherently static, floating in mid air without responding to the real world. This paper introduces a new way to embed dynamic and responsive graphics in the real world. In RealitySketch, the user draws graphical elements on a mobile AR screen and binds them with physical objects in real-time and improvisational ways, so that the sketched elements dynamically move with the corresponding physical motion. The user can also quickly visualize and analyze real-world phenomena through responsive graph plots or interactive visualizations. This paper contributes to a set of interaction techniques that enable capturing, parameterizing, and visualizing real-world motion without pre-defined programs and configurations. Finally, we demonstrate our tool with several application scenarios, including physics education, sports training, and in-situ tangible interfaces.
Exploration of the Attacking Web Vectors

Most people in the industrial world use a wide variety of web applications daily with the majority being insecure and vulnerable. This gives hackers the opportunity to steal data from the user&#x2019;s web application, which may contain sensitive information. Vulnerability detection may be conducted by a rigorous penetration test. A penetration tester&#x2019;s duty is to define and exploit the web applications&#x2019; vulnerabilities.This paper describes a technique for automatic vulnerable web application generation application. Firstly, the prepared web application is sent to the tool to create the vulnerable web application version. This tool does this by the injection of Cross Site Request Forgery (CSRF) and Cross Site Scripting (XSS) into the web application. Different variant vulnerabilities may be injected too, so different methods are needed, in order to exploit vulnerabilities dependent on the variant. One of the tool&#x2019;s tasks is to produce web applications, which will be used to train the penetration testers.
Digital model creation and image meticulous processing based on variational partial differential equation

Abstract In the application of digital model of animation scene, image restoration technology and image denoising technology are the basic tasks of practical operation, which are closely related, but there exist also essential differences. The reason is that both of them want to obtain the original image from the degraded noise image or damaged image, but generally speaking, as there is no sufficient constraint information to accurately recover the original image, both of them are unwell-posed inverse problems. Therefore, on the basis of understanding the basic content and application research status of variational partial differential equations (PDEs), this paper discusses the application value of variational PDEs in image denoising and restoration according to the image processing requirements in the digital model of animation scenes.
Span-based Neural Model for Multilingual Flat and Nested Named Entity Recognition

Named entity recognition (NER) is an important task of finding entities with specific semantic types such as Protein, Cell, and RNA in text by addressing mostly with sequence labeling-based approach. In this paper, we present a bidirectional encoder representations from transformers (BERT)-based neural exhaustive approach that addresses flat and nested entities by reasoning over all the spans within a specified maximum span length. We evaluate the BERT-based span representation approach over the multilingual data sets of different domains including biomedical and general to address flat and nested entities. Experiment results show that span-aware-based approach is very effective to leverage NER.
Application of Kendall’s W Coefficient to Identify Groups of Statistically Related Variables

Original algorithm for identifying groups of statistically interrelated variables based on Kendall’s W statistics is considered. A formal description of the algorithm and assessing of the computational complexity are presented. The results of implementation of proposed algorithm for data of manufacturing process are shown. Comparison of the results obtained with the results of calculating Pearson and Spearman correlations for the same data is carried out.
A neural network model for tracking marker objects in a video

Marker objects tracking is a key component required for understanding state and position of objects in a video. At present time the most popular approaches for marker objects tracking are those based on neural networks. Despite their excellent performance, it is quite often that they skip or confuse some of the groups of marker objects. In this work we propose a neural network based model that corrects tracking errors of the main model via an analysis of marker objects’ relative positions. The network is lightweight as it takes only 1.6 ms to run on a consumer level GPU making it well suited for real-time applications. It also reduces marker objects tracking error significantly.
The Use of Video Project Assignment (VPA) To Enhance Students’ Achievement in Communicative English Subject

Speaking skills is a crucial element which all students in Malaysian polytechnic should master. The new English syllabus introduced in Polytechnic is aimed at building confidence and fluency in English for students as well as enabling them to function effectively at the workplace. Majority of the Communicative English assessment requires students to communicate in English either as individuals or in groups, but many students are still reluctant to speak in front of the classroom and have a very low level of confidence. Thus, Video Project Assignment (VPA) is implemented to boost the achievement of students in the field of Communicative English. The aims of this study are 1) to analyse the student’s perception on the use of Video Project Assignment (VPA) in Communicative English subject, 2) to examine students’ engagement level towards the use of Video Project Assignment (VPA) based on emotional engagement and cognitive engagement and 3) to identify students’ achievement in DUE3012 Communicative English 2 subject. A set of questionnaire was used to gather data from thirty-three respondents from one of the polytechnics in Johor. Descriptive analysis technique was used to analyzed the data gathered from questionnaire. Result of the study indicates that by generating Video Project Assignment (VPA), students demonstrate positive perception, engagement, and achievement. This study also provides the students, lecturers, and potential researcher with a good inference.
Hyperspherical Variational Co-embedding for Attributed Networks

Network-based information has been widely explored and exploited in the information retrieval literature. Attributed networks, consisting of nodes, edges as well as attributes describing properties of nodes, are a basic type of network-based data, and are especially useful for many applications. Examples include user profiling in social networks and item recommendation in user-item purchase networks. Learning useful and expressive representations of entities in attributed networks can provide more effective building blocks to down-stream network-based tasks such as link prediction and attribute inference. Practically, input features of attributed networks are normalized as unit directional vectors. However, most network embedding techniques ignore the spherical nature of inputs and focus on learning representations in a Gaussian or Euclidean space, which, we hypothesize, might lead to less effective representations. To obtain more effective representations of attributed networks, we investigate the problem of mapping an attributed network with unit normalized directional features into a non-Gaussian and non-Euclidean space. Specifically, we propose a hyperspherical variational co-embedding for attributed networks (HCAN), which is based on generalized variational auto-encoders for heterogeneous data with multiple types of entities. HCAN jointly learns latent embeddings for both nodes and attributes in a unified hyperspherical space such that the affinities between nodes and attributes can be captured effectively. We argue that this is a crucial feature in many real-world applications of attributed networks. Previous Gaussian network embedding algorithms break the assumption of uninformative prior, which leads to unstable results and poor performance. In contrast, HCAN embeds nodes and attributes as von Mises-Fisher distributions, and allows one to capture the uncertainty of the inferred representations. Experimental results on eight datasets show that HCAN yields better performance in a number of applications compared with nine state-of-the-art baselines.
Provectories: Embedding-based Analysis of Interaction Provenance Data

Understanding user behavior patterns and visual analysis strategies is a long-standing challenge. Existing approaches rely largely on time-consuming manual processes such as interviews and the analysis of observational data. While it is technically possible to capture a history of user interactions and application states, it remains difficult to extract and describe analysis strategies based on interaction provenance. In this paper, we propose a novel visual approach to the meta-analysis of interaction provenance. We capture single and multiple user sessions as graphs of high-dimensional application states. Our meta-analysis is based on two different types of two-dimensional embeddings of these high-dimensional states: layouts based on (i) topology and (ii) attribute similarity. We applied these visualization approaches to synthetic and real user provenance data captured in two user studies. From our visualizations, we were able to extract patterns for data types and analytical reasoning strategies.
AddrArmor: An Address-based Runtime Code-reuse Attack Mitigation for Shared Objects at the Binary-level

The widespread adoption of DEP has made most modern attacks follow the same general steps: Attackers try to construct code-reuse attacks by using vulnerable indirect branch instructions in shared objects after successful exploits on memory vulnerabilities. In response to code-reuse attacks, researchers have proposed a large number of defenses. However, most of them require access to source code and/or specific hardware features. These limitations hinder the deployment of these defenses much.In this paper, we propose an address-based code-reuse attack mitigation for shared objects at the binary-level. We emphasize that the execution of indirect branch instruction must follow several principles we propose. More specifically, we first reconstruct function boundaries at the program’s dynamic-linking stage by combining shared object’s dynamic symbols with binary-level instruction analysis. We then leverage static instrumentation to hook vulnerable indirect branch instructions to a novel target address computation and validation routine. At runtime, AddrArmor will protect against code-reuse attacks based on the computed target address.Our experimental results show that AddrArmor provides a strong line of defense against code reuse attacks, and has an acceptable performance overhead of about 6.74% on average using SPEC CPU 2006.
Virtual Reality Implementation for University Presentation

The purpose of this study is to apply virtual reality technology for university presentation, so that evaluate if it is useful and usable. Due to the pandemic situation, the topic is also relevant to the current situation as many universities have shifted to online classes. With 3D modelling and coding, a university presentation application (only for computer engineering department) with virtual reality base has been developed that will allow the user to navigate around the school building and its surroundings. Because the developed application uses a new technology, it is designed to be convenient and simple considering that it can be difficult to use. Application allows movement in this virtual world according to constraints, as well as providing sound warnings when necessary. The application tested with a helmeted display, represented by Google Cardboard, with two lenses in it and a groove for the phone. The limited experiments by different users showed that the application was impressive, although it was not easy to use devices efficiently for an unexperienced user. On the other hand, with emerging technologies, it is possible develop such applications as web application using Unity WebVR Assests which will make it easy of use.
Relieving the Incompatibility of Network Representation and Classification for Long-Tailed Data Distribution

In the real-world scenario, data often have a long-tailed distribution and training deep neural networks on such an imbalanced dataset has become a great challenge. The main problem caused by a long-tailed data distribution is that common classes will dominate the training results and achieve a very low accuracy on the rare classes. Recent work focuses on improving the network representation ability to overcome the long-tailed problem, while it always ignores adapting the network classifier to a long-tailed case, which will cause the "incompatibility" problem of network representation and network classifier. In this paper, we use knowledge distillation to solve the long-tailed data distribution problem and fully optimize the network representation and classifier simultaneously. We propose multiexperts knowledge distillation with class-balanced sampling to jointly learn high-quality network representation and classifier. Also, a channel activation-based knowledge distillation method is also proposed to improve the performance further. State-of-the-art performance on several large-scale long-tailed classification datasets shows the superior generalization of our method.
Person search by natural language description in Vietnamese using pre-trained visual-textual attributes alignment model

Person search by natural language description is a challenging task as it has to model and learn visual-text semantic embedding. While several works have been dedicated to person search by English descriptions, few attempts have been made for other languages. As a result, it lacks of available resource for person search in these languages. Inspired by transfer learning idea in image classification, in this paper, we propose a method for person search by natural language description in Vietnamese using a model whose weights are trained on a large scale dataset for person search in English. To this end, first, the published network architecture of ViTAA that allows to learn effectively the alignment between visual and textual attributes is employed in this work. Then, we propose to apply different techniques for Vietnamese language processing to analyze and extract relevant elements from descriptions in Vietnamese to feed to the network. Finally, to leverage the information learnt from a large scale dataset, we employ model weights trained from a large scale dataset to a dataset dedicated to person search by natural language description in Vietnamese - VnPersonSearch. The obtained accuracies at Top-1, Top-5 and Top-10 for VnPersonSearch are 61.57%, 83.93% and 90.67% respectively. This means that the proposed method can return relevant persons with very high accuracy in first ten results.
Disparity based depth estimation using light field camera

Light field cameras have a unique feature of capturing the direction of light rays along with its intensity. This additional information is used to estimate the depth of a 3-D scene. Recently a considerable amount of research has been done in depth estimation using light field data. However, these depth estimation methods heavily rely on iterative optimization techniques and do not fully exploit the inherent structured light field data. In this paper, we present a novel three-step disparity based algorithm to estimate accurate depth maps of a light field image. First, an initial depth map of scene points is estimated using principal views by estimating the disparity of segments in the central image. This initial depth helps in resolving ambiguity in depth propagation of refined depth map in Epi-Polar line Images (EPIs). Second, refined depth is estimated at lines using the disparity vector in EPIs. Finally, refined depth at lines in EPIs is propagated to other locations using the initial depth map. We also provided a synthetic data-set having inherent characteristic of a light field. We have tested our approach on a variety of real-world scenes captured with Lytro Illum camera and also on synthetic images. The proposed method outperforms several state-of-the-art algorithms.
Comparison of classical machine learning algorithms in the task of handwritten digits classification

The purpose of this paper is to compare classical machine learning algorithms for handwritten number classification. The following algorithms were chosen for comparison: Logistic Regression, SVM, Decision Tree, Random Forest and k-NN. MNIST handwritten digit database is used in the task of training and testing the above algorithms. The dataset consists of 70,000 images of numbers from 0 to 9. The algorithms are compared considering such criteria as the learning speed, prediction construction speed, host machine load, and classification accuracy. Each algorithm went through the training and testing phases 100 times, with the desired KPIs retained at each iteration. The results were averaged to reach reliable outcomes.
Solar power plant generation forecasting using NARX neural network model: A case study

New technologies have been developed and adopted to generate energy from renewable sources to satisfy the increasing demand without causing environmental damage. However, estimating the power output of inherently intermittent, weather-driven, and non-dispatchable renewable energy sources is a major scientific and societal concern. In this study, a neural network model to enable short-to-middle term forecasts of a photovoltaic (PV) power system is provided. Using historical weather and power generation data, a non-linear autoregressive network with exogenous input (NARX) model is built to forecast the non-linear photovoltaic system output. The performance of the model is then analyzed by different statistical evaluation parameters. It is shown that the PV system power output estimation method can be successfully employed.
Dynamic Distributed and Parallel Machine Learning algorithms for big data mining processing

Purpose This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies. Design/methodology/approach In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks. Findings The authors got very satisfactory classification results. Originality/value DDPML system is specially designed to smoothly handle big data mining classification.