Reviewerly/exHarmony huggingface dataset:
Development of psychological culture as a condition for effective personal self-actualization

The article examines the problem of personal self-actualization through the lens of psychological culture, approaches to the definition of psychological culture are presented, and the struсture of psychological culture and ways of its development are considered. The article reviews the methods that go beyond the activities of educational institutions: volunteer work, social and psychological training, methods of expressive psychotherapy (art therapy, movement and dance therapy, music therapy, and neuroacoustics – sound therapy), etc. Based on the ecopsychological approach, psychological culture is suggested to be defined as a set of actions to create optimal internal conditions for the development and self-modification of the personality, to cultivate a subject of activity capable of taking action, coordinating its activity with the natural, social, spiritual. and moral (value and meaning) foundations of interaction with the surrounding world and oneself. The article analyses the results of the study of the effect of psychological culture on the development of self-actualization of teachers in refresher courses. At the ascertaining and control stages, the level of self-actualization of teachers in the control and experimental groups was assessed using the Self-Actualization Test (SAT). At the formative stage, teachers of the experimental group participated in a program for the development of psychological culture. The ecopsychological approach with principles developed on its basis and the stage-by-stage nature of the development of new mental functions serves as a basis for constructing the technology for implementing the program for the development of psychological culture in specialists. The reliability of the test results and differences in the mean values are assessed using the Wilcoxon signed-rank test. The formative program shows the effective influence of psychological culture development on personal self-actualization.
Triple Linear Congruential Generator-Based Hardware-Efficient Pseudorandom Bit Generation

Many of the Internet of things applications are employed on power and resource-constrained devices. Pseudorandom bit generator (PRBG) is one of the crucial components to manage privacy and security on these devices. Among popular PRBGs, linear congruential generator (LCG)-based methods have lower hardware complexity. However, bit sequences generated using single LCG fail to meet the randomness and uniform distribution requirements. In this paper, a triple linear congruential generator (Tri-LCG)-based PRBG method is proposed which can generate pseudorandom bits at uniform clock rate. The proposed method passes 14 tests out of the 15 benchmark tests of NIST standard. Two designs of 8 bits and 24 bits of the proposed Tri-LCG are implemented and synthesized in 45 nm CMOS technology. The proposed designs showed up to 19.87, 14.60 and 38.05% reductions in energy, energy–delay product and area–delay–power product when compared with those of state-of-the-art PRBG methods.
Consumer Sentiment Analysis to E-Commerce in the Covid-19 Pandemic Era

This study aims to identify e-commerce problems during the Covid-19 pandemic as a basis for providing recommendations for improving e-commerce services. The methods used in this research are Na誰ve Bayes Classifier (NBC), Text Association, and Focus Group Discussion (FGD). The NBC method is used to classify consumer sentiment, while the Text Association is to find the relationship between words. The data source used is consumer reviews submitted on Twitter for the period January-April 2021. The FGD method is used to classify the results of the Text Association into service marketing mix elements, identify root causes using Fishbone Diagrams and develop recommendations for improvement. The respondents involved in the FGD were e-commerce experts representing practitioners and academics. The result of the improvement recommendation is: educating sellers always to provide accurate product specification information and implementing quality control; increasing mutually beneficial cooperation with logistics service providers, especially concerning service commitments and cutting shipping costs; improve information disclosure of promotional events; improve the refund procedure; optimization of multichannel e-commerce software; increase accuracy in the process of selecting and verifying prospective seller data; improve transaction process monitoring; optimizing the application of a reward and punishment system based on consumer reviews.
Exploring Out-of-Distribution Generalization in Text Classifiers Trained on Tobacco-3482 and RVL-CDIP

To be robust enough for widespread adoption, document analysis systems involving machine learning models must be able to respond correctly to inputs that fall outside of the data distribution that was used to generate the data on which the models were trained. This paper explores the ability of text classifiers trained on standard document classification datasets to generalize to out-of-distribution documents at inference time. We take the Tobacco-3482 and RVL-CDIP datasets as a starting point and generate new out-of-distribution evaluation datasets in order to analyze the generalization performance of models trained on these standard datasets. We find that models trained on the smaller Tobacco-3482 dataset perform poorly on our new out-of-distribution data, while text classification models trained on the larger RVL-CDIP exhibit smaller performance drops.
Asset allocation decision method for universities based on decision tree

Scientific and effective allocation of university assets is of great significance to improve the ability of university education guarantee. Decision tree is a decision analysis method based on the known probability of occurrence of various situations, which can calculate the probability that the expected value of net present value is greater than or equal to zero, evaluate the project risk and judge its feasibility by constructing a decision tree. It is a graphic method of intuitively using probability analysis. In machine learning, decision tree is a prediction model, which represents a mapping relationship between object attributes and object values. In this paper, by quantifying the data of state-owned assets in colleges and universities to form attribute sample categories, we use Cart decision tree algorithm which build a binary tree to learn data samples, construct prediction criteria, give correct classification of asset allocation, and provide a more reasonable solution for asset allocation.
Industrial Control System Defense Decision-Making Method Based on Dynamic Attack-Defense Game

Abstract Industrial control systems are different from traditional information systems and require extremely high system availability. In the face of potential attack incidents, blind defense deployment may affect system performance and efficiency, which is counterproductive. For this reason, this paper proposes a defense decision-making method for industrial control systems based on dynamic attack-defense games. This method first analyzes the actual attack-defense confrontation scenarios in the industrial control system, and constructs a dynamic attack-defense game model based on this. After that, an industrial control system attack-defense game tree defined on this model is constructed, and the game tree is used to describe the specific attack-defense confrontation process. Then the backward induction method is used in the game tree to find the equilibrium path. The information on this path is of great reference value for defenders to choose a reasonable and efficient defense plan, maintain system stability and reduce defense costs. At the end of this paper, this method is applied to a real limestone powder preparation system for experiments. The experimental results verify the effectiveness of the method in actual scenarios.
Channel Hourglass Residual Network For Single Image Super-Resolution

Deep convolutional neural networks (CNNs) for Super-Resolution (SR) from low-resolution (LR) images have achieved remarkable reconstruction performance with the utilization of residual networks and visual attention mechanism. However, the existing single image super-resolution (SISR) methods with deeper or wider network architectures encounter module representation bottleneck and neglect module efficiency in real-world applications. To solve these issues, in this paper, we design channel hourglass residual structure (CHRS) consisted of several nested residual modules for reducing parameters and extracting more representational features. Furthermore, we integrate channel attention (CA) mechanism into CHRS to generate channel hourglass residual block (CHRB) which can be easily extended to other methods for improving performance. We also propose channel hourglass residual network (CHRN) which not only pays attention to network learning efficiency but also learns more discriminative expressions. Extensive experiments demonstrate the effectiveness of our CHRN and the generalization ability of our CHRB.
A Hybrid Routing Approach Using Two Searching Layers

Abstract This paper considers SUB_GOALs by using basic A* algorithm and Subgoal Graphs in a hybrid approach to execute optimal route. SUB_GOALs identified with pre-searching from basic A* at break points and Subgoal Graphs at corners of obstacles are added to SUB_TABLE to expedite the final searching in the hybrid approach. Map to work on is divided to subregions with decision-making process by using line-of-sight to avoid redundant searching. In the final searching layer, all feasible SUB_GOALs gained from decision-making process in the same subregion are connected to find final solutions of routes. Solutions achieved in the divided subregions are evaluated and combined to discover the final optimal route. The proposed hybrid approach is applied to three different scenarios in various dimensions of maps. In these three scenarios, the shortest route without hitting obstacles is calculated as 46.67, 57.76 and 124.7 units, respectively, and compared with other search algorithms. Simulation results of route planning are demonstrated to exhibit the effectiveness of the proposed hybrid approach.
Application Methods of Artificial Neural Network in Pattern Recognition

Artificial neural network is a non-linear dynamic system. It takes an important place in knowledge processing. This paper discusses the applications of artificial neural network in knowledge processing and pattern recognition synthetically, especially the theories and applications in adaptive filtering, feature extraction, feature selection, data fusion, function mapping, system identification and fault diagnosis. These have great importance in resolving the problems in state measurement and fault diagnosis.
An Adaptive Density-Sensitive Similarity Measure Based Spectral Clustering Algorithm and Its Parallelization

The clustering effect of the spectral clustering algorithm depends on the calculation of the similarity between samples. Although a better clustering effect of the spectral clustering algorithm can be obtained using the Gaussian kernel function to calculate the similarity between samples, it relies on the setting of the kernel parameter. Therefore, an adaptive density-sensitive similarity measure based spectral clustering (DSSC) algorithm is proposed for improving the clustering effect. Specifically, firstly, the Euclidean distances between samples are calculated to get the nearest neighbors of each sample. Secondly, the standard deviation of distances between each sample and its nearest neighbors is calculated as the density parameter. Thirdly, the density-sensitive distances between each sample and its nearest neighbors are calculated. Finally, the similarities between each sample and its nearest neighbors are calculated to construct a similarity matrix. In addition, the proposed DSSC algorithm is parallelized on Dask distributed parallel computing platform with CPU+GPU, which can improve the computational efficiency of the DSSC algorithm by taking full advantage of the CPU and GPU resources. A series of experiments are conducted to verify the effectiveness of the proposed DSSC algorithm on several synthetic datasets and UCI datasets, and the results show that the DSSC algorithm not only achieves satisfactory clustering results, but also obtains better efficiency of performing large-scale clustering analysis.
AmI-VR: An Accessible Building Information System as Case Study Towards the Applicability of Ambient Intelligence in Virtual Reality

Ambient intelligence represents a paradigm in which the user does not react to the environment, but vice versa. Accordingly, smart environments can react to the presence and activities of users and support them unobtrusively from the background. Especially in the context of accessibility, this offers great potential that has so far only been demonstrated for individual user groups. To overcome this limitation, we propose the automated, user- and context-related adaptation of the modality as well as locality of the representation of building information in the form of both an adjustable table as well as two displays on the basis of a prototype for a library information center. For being independent from material and regulatory restrictions and for better planability (especially with the ongoing COVID-19 pandemic) we used in addition to the hardware components also a Virtual Reality simulation, which proved to be very useful. Further optimization and evaluation will be needed for a more in depth understanding and dissemination in the long run, yet our prototype aims to help fostering further activities in the field of ambient intelligence, accessibility and virtual reality as a planning tool.
Design and Implementation of a Small Hand Held Camera Detector on FPGA

Nowadays, abuse of camera is high due to the rapid development technology. In a video surveillance system, wireless camera is commonly used. Even though, privacy issues related to illegal videos have recently increased. Due to accuracy limitation, conventional technique is not recommended for practical real-time implementations. In this paper, we propose A Small Hand Held Hidden Camera detector to detect the Hidden cameras in public places like trial rooms, hotels, and restrooms. This device uses a camera to scan the areas to capture the lights emitted by the components of the hidden camera that maybe the LED indicator or maybe the IR light emitted by the camera lenses. In this project, an adaptive thresholding method-based segmentation algorithm will segment the abnormal portion of the image and send it to the monitor via a Video Graphics Array (VGA) port. A hidden camera detector prototype is implemented using a Field Programmable Gate Array (FPGA) and Raspberry Pi Board. Around 98% of classification accuracy is obtained using proposed method which is high when compared to the existing techniques.
Early Detection of Parkinson’s Disease Using Computer Vision

Non-invasive tests like handwriting using spirals patterns have been used over the years to study writing speed and strength of patients compromised by Parkinson’s. Handwriting tests can be administered at various times during a patient’s treatment to understand the severity of the disease, disease progression, and effect of treatment. In this paper, we study handwriting traces and analyze them using machine learning methods to understand the subject.
Maximizing Parallelism in the Cloud: A Hybrid Load Balancing Approach

When the number of processes requiring CPU is higher than the number of cores in a system, achieving parallel systems show many challenges. However, different approaches are known to work well for achieving an acceptable degree of parallelism. Load balancing operations are one of the main concerns in virtual systems and, specifically, in Cloud Computing. An optimal load balancing strategy can increase the multiprogramming rate, significantly impacting the parallelism degree of a system. There are many algorithms proposed and designed to carry out client requests at distributed servers in the cloud. All of them target maximizing the performance of the overall system. In this manuscript, various existing load balancing algorithms are discussed and compared with a new hybrid algorithm. This hybrid algorithm has been developed to handle the client requests based on the priority and availability of the cluster nodes, which are part of the virtual system. This algorithm maintains two tables with a list of node id, priority, and availability so that the load can be optimally distributed. That enables selecting an appropriate node to achieve efficient load balancing and therefore s a higher degree of overall parallelism.
Sleep Behaviour and Online Engagement in Learning Management System at Higher Education During COVID-19 Pandemic

During COVID-19 pandemic, many higher education institutions have shifted their teaching and learning operation towards full online learning. As a result, using learning management system (LMS) become more intensive, crucial and compulsory. The issue is, there is lack of research to prove that online learning engagement may cause sleep disorder among student and instructor in higher education. Therefore, this paper aim to investigate user engagement in LMS and it associate with the risk of sleep disorder. This study adopts educational data mining (EDM) to extract the data from Moodle LMS log to discover the potential insights with business intelligence software tools. The outlier’s detection using box-plot visualization manage to determine the indicators of sleep disorder behavior pattern. The findings from this study show that there are some student and instructor that potential to have experience sleep disorder. This novel finding is critical for institution to respond by developing a policy and risk mitigation strategy associated with unhealthy sleep behavior in online learning engagement.
How to Split a Logic Program

Answer Set Programming (ASP) is a successful method for solving a range of real-world applications. Despite the availability of fast ASP solvers, computing answer sets demands a very large computational power, since the problem tackled is in the second level of the polynomial hierarchy. A speed-up in answer set computation may be attained, if the program can be split into two disjoint parts, bottom and top. Thus, the bottom part is evaluated independently of the top part, and the results of the bottom part evaluation are used to simplify the top part. Lifschitz and Turner have introduced the concept of a splitting set, i.e., a set of atoms that defines the splitting. In this paper, We show that the problem of computing a splitting set with some desirable properties can be reduced to a classic Search Problem and solved in polynomial time. This allows us to conduct experiments on the size of the splitting set in various programs and lead to an interesting discovery of a source of complication in stable model computation. We also show that for Head-Cycle-Free programs, the definition of splitting sets can be adjusted to allow splitting of a broader class of programs.
Study on information-based instructional design and methods of improving teaching ability

In order to improve the actual effect of information-based teaching and establish a more scientific and reasonable information-based teaching mode, the necessity of the information-based teaching was analyzed from the characteristics of teaching means and learning methods. The basic principles of information teaching were discussed from the perspective of the learning circumstances, cooperative learning and resource sharing, etc. In view of the problems existing in the current information-based teaching process, the methods of improving the information-based teaching ability were analyzed and some relevant countermeasures were given. Based on the above analysis, taking the civil engineering specialty of Jiangxi University of Science and Technology as an example, the classroom naming program integrating VB (Visual Basic) computer language and mobile phone WeChat scanning function is compiled. The results show that students can quickly complete the naming by using WeChat QR-code scanning, which has the advantages of saving time, efficiency and rapidity. The research can provide relevant reference for the information design and teaching practice of the correlated course.
Design of Employee Management Application for Small Medium Enterprise

Information technology has become the basis in various companies to support operations and management so that companies in various fields have begun to develop information technology within their companies to compete in their competition. By developing information technology in a company, it can increase the productivity of a company. The objectives of this research are: Designing applications for Small and Medium Enterprises such as calculating employee absences, leave, overtime, BPJS, PPh21 to assist Small and Medium Enterprises in managing employees, designing reports that can help SME owners to view employee data and monitor employee attendance/permission. The methodology used for designing this system was study literature, interviews with employees and small-medium enterprise owners, and System Development Life Cycle (SDLC). The benefit of this research is that it helps SME owners to view employee data and monitor employee attendance/permits.
Hamsi-based parametrized family of hash-functions

In this paper a new family of cryptographic hash-functions is described. The main goal was to create a such hash function, where algorithm varies depending on hash code length. Hash function Hamsi was taken as basis of a parameterized algorithm. This hash function was analyzed in a different ways. For a linear transformation, whole class of linear transformations with the same branch numbers was defined. For this class were found invariant subspaces. The second part of the analysis was a research of differential attacks on Hamsi compression function. After the analysis of published works, changes were made to compression function. With these changes a parameterized hash function Hansi- $$n$$ was described, that produces $$n$$ bit of hash code (e.g. 512, 1024, 2048). To find out complexity of different versions of algorithm, the estimation of bitwise operations needed for one compression function evaluation is described. This new hash-functions can be used in a lot of applications, where hash-codes of varying length are needed.
Applied Intelligence for Medical Diagnosing

Medical imaging is associated with different techniques and processes that are used to create visual representations of internal parts of the human body for diagnostic and treatment purposes within digital health. Machine learning plays a crucial role in the medical imaging field including analysis of various medical images, computer-aided diagnosis or detection, image retrieval, gene data analysis, image reconstruction, and organ segmentation. The machine learning algorithm framework recognizes the best combination of the medical image features for categorizing the medical images or processing some metric for the given image area. The images obtained are then processed using algorithms such as K-means, support vector machines, decision trees, neural networks, and deep learning techniques.