Reviewerly/exHarmony huggingface dataset:
Time-Series Physiological Data Balancing for Regression

Many studies have shown the effectiveness of machine learning in estimating psychological or physiological states using physiological data as input. However, it is ethically and physically difficult to collect a large amount of data without bias in an uncontrolled environment. Specifically, the amount of data in rare cases is especially small compared to common data. Therefore, the distribution bias may cause overfitting in machine learning. In this paper, we propose a SMOTE-based method to alleviate the distribution bias by data augmentation in the regression problem using a dataset containing time-series physiological data. The effectiveness of the proposed method was confirmed for datasets of thermal sensation and core body temperature collected in uncontrolled environments. The results show that our method improves the performance of regression models for minor cases with a bit of decline in the mean average error.
Secured and Blockchained IoT

This work examines the architectures, protocols and SW platforms for IoT devices as well as the important issue for data security and safety. Secured and Blockchained IoT can be provided only under use of the combination of the existed approaches possible like crypto-protocols, FW, IDS/ IPS and CIDN. As advanced approach, the Blockchain technology can be step-by-step deployed with the aim to compulsoriness, accountability and liability for IoT solutions. As one of the known BC problems is use of existing computing and resources in a responsible manner. The trade-off between energy-efficient IoT and BC must be found as well as the BC deployment must be appropriate and acceptable in terms of energy consumption. The case studies on the Secured and Blockchained IoT devices are discussed.
Effects of Augmented Reality Markers for Networked Robot Navigation

The authors have studied augmented reality techniques applied to a support robot navigation system. It helps care recipients to remotely control robots for themselves with simple commands through a communication network. This paper focuses on the identification and geometrical measurement of planar markers with mosaic patterns. Experiments reveal that the identification rate as well as measurement accuracy is related to feature points of the pattern. They also clarify that there is a positive correlation between the number of feature points and the accuracy of marker recognition. This research project also proposes the optimum AR markers for networked welfare robot navigation.
An improved hybrid recommendation algorithm based on feature preference

Abstract In order to solve the problem of data matrix sparsity and cold start in the era of big data. This paper designs a collaborative filtering algorithm, which is an improved hybrid recommendation algorithm based on feature preference analysis. It combines the analysis of user feature preference and item feature, and then uses the traditional collaborative filtering idea to recommend the best scoring object to users. The experimental results show that the accuracy of the proposed algorithm is improved obviously.
A generic flexible and scalable framework for hierarchical parallelization of population-based metaheuristics

Parallelizing population-based metaheuristics – such as Evolutionary Algorithms (EAs) – on a cluster computing environment is a viable approach for enhancing the computational performance of metaheuristics. Generally, there are three basic models to parallelize EAs, namely the Global, the Coarse-Grained and the Fine-Grained models whereby each one has advantages and disadvantages. Combining two or more of them in a hierarchical form reduces the disadvantages of each approach and allows users to fully utilize the advantages. In this article, a generic, scalable and flexible software architecture which supports users in combining the basic parallelization models of EAs with minimum adaptation effort is presented. This architecture based on microservices, container virtualization and the publish/subscribe messaging paradigm decouples the basic building blocks of the algorithms by encapsulating them in separate services. This allows users to easily combine the building blocks to form combinations of the parallelization models of EAs. An extensive evaluation exploring the scalability and applicability of the software architecture is performed by combining the Coarse-Grained Model with the Global Model for solving the unit commitment optimization problem. Four communication topologies among the islands with homogeneous/heterogeneous EA configurations and synchronous/asynchronous migration policies are applied and compared. For evaluation, the EA GLEAM (General Learning Evolutionary Algorithm and Method) is used. This article discusses the architectural concepts of our approach and presents main results of the evaluation. The evaluation results show that the new software architecture has high potential for scaling up the optimization speed of complex optimization problems when metaheuristics are used.
Fuzzy Software Analyzer (FSA): A New Approach for Interpreting Source Code Versioning Repositories

Source code quality plays a key role in software quality mainly due to its impact on software maintainability. Software engineers have been using source code metrics to support them to assess source code quality. Source code metrics quantify different source code characteristics. However, source code metric analysis still involves subjectivity. For instance, it is not trivial to decide whether a metric value is high or low. To reduce the eventual subjectivity of source code metrics analysis, several researchers are using Machine Learning algorithms. Therefore, in this paper, we designed a Fuzzy-based approach to extract characteristics and patterns present in source code versioning repositories in order to: i) assist the specialist in the interpretation of releases, especially when working with large volumes of source code; ii) from the release interpretation, specialists can improve the quality of the source code; and iii) monitor the evolution of the software as new releases are submitted to the repositories. We evaluated the proposed approach with the Linux Test Project repository, emphasizing the interpretability of large source code versioning repositories.
Effects of Immersive Spherical Video-based Virtual Reality on Cognition and Affect Outcomes of Learning: A Meta-analysis

With the advancement of portable head-mounted displays, interest in educational application of immersive spherical video-based virtual reality (SVVR) has been emerging. However, it remains unclear regarding the effects of immersive SVVR on cognitive and affective outcomes. In this study, we retrieved 58 learning outcomes from 16 studies. A meta-analysis was performed using the random effects model to calculate the effect size. Several important moderators were also examined such as control group treatment, learning outcome type, interaction functionality, content instruction, learning domain, and learner's stage. The results show that immersive SVVR is more effective than other instructional conditions with a medium effect size. The key findings of the moderator analysis are that immersive SVVR has a greater impact on affective outcomes, as well as under the conditions that learning system provides interaction functionality or integrates with content instruction before virtual exploratory learning.
Analysis of Internet Network Technology Online Training for Indonesian Navy Information Technology Employees Using Hierarchical Clustering

During the covid-19 pandemic, all institutions had a high dependency on information technology. Network strength is very important. Thus, an internet network technology course was conducted for information technology personnel of the Indonesian Navy. The training is conducted online. Studied learning success is associated with the initial data held, namely rank, gender, work experience in the field, previous education and learning activities, and age of participants. The data was clustered with the final test results to determine the clustering of training results and these factors. Based on the clustering it can be stated a good grouping of learning success in this pandemic period. From the 75 personnel data, we could made 5 clusters. Recommendation for internet network tools online training, especially in internet network technology, is that the participants of the course should have experience in internet and network, young and have good education level at least high school.
Self-supervised Learning of Occlusion Aware Flow Guided 3D Geometry Perception with Adaptive Cross Weighted Loss from Monocular Videos

Self-supervised deep learning-based 3D scene understanding methods can overcome the difficulty of acquiring the densely labeled ground-truth and have made a lot of advances. However, occlusions and moving objects are still some of the major limitations. In this paper, we explore the learnable occlusion aware optical flow guided self-supervised learning of depth and ego-motion with an adaptive cross weighted loss to address the above limitations. Firstly, we try to learn the self-discovered occlusion mask fused optical flow network by an occlusion aware photometric loss combined with temporally supplemental information and backward-forward consistency between adjacent views. And then, we design an adaptive cross-weighted loss of the geometric and photometric error between the cross tasks to distinguish the moving objects that violate the static scene assumption. Our method shows promising results on KITTI, Make3D and Cityscapes datasets on multiple tasks, and also shows good generalization ability under a variety of challenging scenarios.
Extended Belief Rule Base Parameter and Structure Optimization Based on Similarity Measure

The extended belief rule base (EBRB) combines the characteristics of knowledge structure and evidence reasoning, and can quantitatively describe the uncertainty and incompleteness of data. However, the original model will activate all rules whose activation weight is greater than zero, which greatly affects the inference results of the EBRB model. At the same time, the initial parameter settings also have different effects on the output of the model. For example, the accuracy of classification is affected by interval division, meanwhile the setting of rule weights and attribute weights will affect the output of EBRB. In response to the above problems, this paper proposes an EBRB structure and parameter optimization model based on similarity, and applies this model to the health assessment of lithium-ion batteries. EBRB optimized based on similarity measures uses a data-driven approach to convert rule sets into rules, uses multiple kernel maximum mean discrepancies (MKMMD) to calculate the similarity between the test data set and the corresponding rules, and selects rules with high similarity for inference output. Through comparison with traditional methods, the effectiveness of the proposed method is verified.
Unsupervised Construction of Task-Specific Datasets for Object Re-identification

In the last decade, we have seen a significant uprise of deep neural networks in image processing tasks and many other research areas. However, while various neural architectures have successfully solved numerous tasks, they constantly demand more and more processing time and training data. Moreover, the current trend of using existing pre-trained architectures just as backbones and attaching new processing branches on top not only increases this demand but diminishes the explainability of the whole model.
Supply-demand-based optimization

This chapter introduces a metaheuristic, named supply-demand-based optimization (SDO), which is inspired by the supply-demand relation found in economics. The optimization performance of the SDO is analyzed on a range of unconstrained benchmark functions, demonstrating its efficacy.
THE INFLUENCE OF QUIPPER SCHOOL LMS ON LEARNING RESULTS OF ARITHMATIC SEQUENCES AND SERIES IN SMA DR. SOETOMO IN THE TIME OF THE COVID-19 PANDEMIC

This study aims to determine the effect of using LMS Quipper School in online learning on student learning outcomes. This study used samples from two high school classes of DR. Soetomo Surabaya. Each class consists of 31 students. This research was conducted to test the validity, reliability, and hypothesis. From the first result, there were 26 valid questions according to the calculation of Rcount > Rtable worth 0.367. In the hypothesis test there are three stages, namely normality test, homogeneity test, and independent t-test. in normality test, the result of significant value is more than 0.05 so that it is normally distributed. In the homogeneity test, it is known that the Fcount of the two classes is 1.397. It will be searched for Ftable with a standard significance of 5% with df numerator (Between Groups) of 1 and df of denominator (within Groups) of 60, it is known that the result of Ftable is 4.03 so it can be concluded that Fcount≤ Ftable, then H0 is accepted. In hypothesis test, the result of sig.(2-tailed) is 0.002 less than 0.05 so that the hypothesis H0 is rejected. So from this conclusion, there is a significant effect on learning outcomes
Defining “information resources” in the era of information space development

The study is novel in that the author attempts to define unequivocally the term "information resources" to be integrated into scientific, professional and regulatory discourse. The concept of information resources is one of the key ones for building information society and information space, meanwhile it has been still interpreted equivocally. The author analyzes the meaning of the term “information resources” as used in reference, legal, regulatory and scientific documents. He also reviews its historical background from the very introduction till nowadays. In considering the phenomenon of information resources, the author proceeds from the category "knowledge," which serves as a link in embracing the nature, development and use of technologies, and life activities. This approach forms the basis for the model of information resources circulation from individuals to the society using computer and communication technology. The importance of information resources in the era of information space development is emphasized. The author proposes the component structure of information resources to comprise five elements: the society, hardware, software (software), data and knowledge bases, and communication. Each element of this structure comprises a number of complementary components. The findings of the study evidence on the significant role of the term in research, library and information activities, as well on the need to include this term in law and regulatory documents.
A Novel Approach to Image Enhancement Based on Fuzzy Logic

Image filtering, which removes or reduces noises from the contaminated images, is an important task in image processing. This paper presents a novel approach to the problem of noise reduction for gray-scale images. The proposed technique is able to remove the noise component, while adapting itself to the local noise intensity. In this way, the proposed algorithm can be considered as a modification of the median filter driven by fuzzy membership functions. Experimental results are compared to static median filter by numerical measures and visual inspection. As was expected, the new filter shows better performances.
Dilemmas of Online Lessons in the Background of the Internet Era and Optimization Method for Online Lessons in the Post-pandemic Era

In 2020, the spread of COVID-19 forced online education to popularity. At present, the online lessons of some colleges and universities still have a lot of problems. They fail to give full play to the advantages of the Internet information technology. Meanwhile, dilemmas exist in the aspects of teaching, learning and teaching platform. Based on all these problems, this paper starts from the present situation of online lessons of colleges universities during the pandemic period. Through research statistics and data analysis, this paper summarizes typical problems of online lessons during the pandemic period and puts forward the main approaches of using the Internet information technology to optimize online lessons.
Modeling Cognitive Activity of the Human Brain by the Mathematical Apparatus of Quantum Mechanics

This paper discusses the possible approaches to modeling the cognitive activity of the human brain using the mathematical apparatus of quantum mechanics (primarily – potential wells and virtual particles) in terms of the theory of information representations. The article briefly describes the proposed theory of information representations, draws analogies, and identifies common features of information representations of the human mind and Feynman’s virtual particles. The human mind is represented as a one-dimensional potential well with finite walls of different sizes and internal potential barrier simulating the boundary between consciousness and subconsciousness. This creates a foundation for a mathematical apparatus that can make it possible to forecast particular cognitive functions of the human brain. The results of these studies can be used to create predictive models of various cognitive disorders (diseases) and to be used in diagnostics.
A Design of a Secured E-voting System Framework for Poll-Site Voting in Ghana

There is a lack of trust and transparency in the current manual voting system in Ghana, and there is the belief that e-voting technology can address the trust issues. However, an e-voting technology is perceived to have security vulnerabilities which can also affect the integrity of an election. This study aimed to design an e-voting system framework that can bring trust and transparency in the electoral process. In this paper, the researcher reviewed existing e-voting designs and their weakness. The study further examined the weakness in the manual voting system and identified the requirements a secure e-voting system must satisfy. The proposed framework was based on a two-tier architecture that was integrated with blockchain technology to provide a transparent means of processing and storing elections results. The design framework was made up of an authentication system, voting system, tallying system, and vote recording system that runs on a local intranet at polling stations. The design used two-factor authentication which includes voters ID and one-time code. The design also allowed voters to verify how their votes were recorded using a blockchain system. The proposed e-voting system was able to address the security vulnerabilities that impact on the integrity and trustworthiness of the electoral process.
Securing Multimedia Content Using Watermark and Digital Signature

With the increasing rate of multimedia usages, many processes have been introduced to ensure the safety of their contents. Digital signature and watermarking are the processes that are used in cryptography and digital image transmission respectively. These two techniques can be combined for secure multimedia transmission. Over the years, these technologies have been able to secure the data of text documents but securing multimedia contents have been a difficult task. Therefore, this research proposed to use the digital signature and watermarking to secure multimedia data. The multimedia data is embedded with another data and is transmitted after encrypting it using the cryptographic algorithms. The receiver end decrypts the data and extracts the original data from the watermark. For digital watermarking, various transformations have been suggested. In this exploration, Discrete Wavelet Transform (DWT) has been used as it shows better results in digital watermarking applications.
Software Defect Prediction Method Based on Transformer Model

Aiming at the problem of grammar and semantic information understanding of the network structure of software system, this paper proposes a method of software defect prediction, which is based on Transformer model, which is completely dependent on self-attention mechanism, it can embed key information in the code semantics of the end-to-end learning software modules. Firstly, the software module is converted into an abstract syntax tree, and later it is traversed to extract word sequence of the software module. Then, thinking of the word sequence as input, acts on the self-attention layer to perform semantic feature embedding of the software module. Finally, the Softmax neural network is used to predict the software defect. The experimental results show that the software defect prediction method based on Transformer model has better defect prediction effects in the three open source software, and it has an average increase of 3.2% than the optimal model based on the Convolutional Neural Network (CNN).
Superlinear Speedup on GPGPU Using Laplacian Algorithm with Convolution Filtering as A Case Study

In this paper, the main idea is to investigate the hypothesis that superlinear speedup occurs when the concurrent threads on General Purposes Graphic Processing Units (GPGPU) carry heavy workloads. In order to evaluate this hypothesis, Laplacian image edge detection algorithm with convolution filtering is chosen as a case study. In this work, local memories of GPGPU are utilized in order to achieve the superlinear speedup. The convolution filtering kernels of the Laplacian edge detection algorithm are invoked in these local memories. By this, the low latency of the GPGPGU local memory are deployed efficiently and this subsequently leads to a higher speedup. The results obtained presented that the superlinear speedup is achieved when the size of the convolution kernel is large. In this study, when the convolution kernel size is 7×7, superlinear speedup is observed for image dataset of sizes between 1KB-2500KB.
Non-local Graph Convolutional Network for joint Activity Recognition and Motion Prediction

3D skeleton-based motion prediction and activity recognition are two interwoven tasks in human behaviour analysis. In this work, we propose a motion context modeling methodology that provides a new way to combine the advantages of both graph convolutional neural networks and recurrent neural networks for joint human motion prediction and activity recognition. Our approach is based on using an LSTM encoder-decoder and a non-local feature extraction attention mechanism to model the spatial correlation of human skeleton data and temporal correlation among motion frames. The proposed network can easily include two output branches, one for Activity Recognition and one for Future Motion Prediction, which can be jointly trained for enhanced performance. Experimental results on Human 3.6M, CMU Mocap and NTU RGB-D datasets show that our proposed approach provides the best prediction capability among baseline LSTM-based methods, while achieving comparable performance to other state-of-the-art methods.
AVAILABILITY OF BIBLIOGRAPHIC MANUALS ON NATURAL SCIENCES IN THE PROCESS OF STUDYING NORTH ASIA IN THE XVIII-EARLY XX CENTURIES

The article considers the bibliographic equipment of scientific research of the pre-revolutionary period in the field of natural sciences in Siberia and the Far East.
Real-world String Comparison

In many languages a string comparison is a pitfall for beginners. With any Unicode string as input, a comparison often causes problems even for advanced users. The semantic equivalence of different characters in Unicode requires a normalization of the strings before comparing them. This article shows how to handle Unicode sequences correctly. The comparison of two strings for equality often raises questions concerning the difference between comparison by value, comparison of object references, strict equality, and loose equality. The most important aspect is semantic equivalence.