Reviewerly/exHarmony huggingface dataset:
Application of recommendation algorithm combined with expert knowledge in apparel field

A personalized clothing recommendation method based on expert knowledge is proposed to address the lack of comprehensive consideration of users' appearance characteristics and dressing occasions in current garment recommendation methods. An apparel recommendation knowledge base is designed to store complex heterogeneous data information in the apparel domain in a structured manner and establish semantic relationships between user characteristics and apparel characteristics labels. A forward chain knowledge reasoning method based on the certainty factor (CF) model is used to realize the intelligent recommendation of apparel in combination with expert rules. The experimental results show that the F-measure of the proposed recommendation method in this paper is significantly improved and has a high accuracy and practical value compared with the traditional recommendation method based on collaborative filtering.
M-LEARNING IN HIGHER EDUCATION: TECHNOLOGY OWNERSHIP AND COMMON ATTRIBUTES AMONG MILLENNIALS IN SOUTH AFRICA

This research investigates the mobile technology ownership concerning M-learning and identifies Tapscott's (2009) common traits of Millennials in a South African context.Technology growth and adoption is impacting the education sector by changing teaching and learning through an innovative form of learning commonly known as M-learning.Along with the growth in adoption of technology, students learning profiles have also changed over time, evolving toward another generation of learners referred to as Millennials.A total of 103 Millennial student respondents in a higher education institution in South Africa were surveyed to collect primary data and descriptive statistics were used to summarize the results according to which the entire sample population owned at least one type of mobile devices.The findings also indicated that technology acquaintance and innovation are the most relatable attributes shared among Millennials.
TRACING THE VIEWS OF GREEK SECONDARY SCHOOL TEACHERS ON MOBILE LEARNING

Teachers' attitudes and views towards mobile learning are essential since they affect their actual use of mobile technology in the classrooms.This study investigated secondary school teachers' mobile learning perceptions, by using the Mobile Learning Perception Scale.An online questionnaire was administered to 208 teachers in Greece and three factors were revealed, mTech Fit for teaching and learning, Specialization, and Communication.Teachers' views were, in general, positive, with female teachers expressing more positive views for the Specialization factor.No significant differences were revealed with regard to age, years of teaching experience, and specialization/discipline.
UAV Target Detection Algorithm Based on Improved YOLOv4

In recent years, UAV (Unmanned Aerial Vehicle) has developed rapidly and has been widely used in various fields, which has also posed a great threat to the security of military bases, government departments and other sensitive areas. Considering the problem of low accuracy and insufficient datasets of current algorithm in the target detection of “low, and slow small” UAV, and in order to improve the real-time detection performance of “low, and slow small” UAV, this paper proposes an improvement strategy of anchor, and obtains the anchor size suitable for UAV dataset through K-means++ clustering algorithm, making the size of anchor more targeted. Meanwhile, the different loss functions and IoU related threshold parameters for research, analysis of different thresholds, and the influence of different loss functions for UAV detection precision, thus by selecting the best loss function and the threshold is set to YOLOv4 target detection algorithm is improved, further, enhancing the YOLOv4 for “low, and slow small” UAV detection accuracy.
Cooperative Search-Track Mission Planning for Multi-UAV Based on a Distributed Approach in Uncertain Environment

The problem of multiple Unmanned Aerial Vehicles (multi-UAV) cooperative searching and tracking moving targets in uncertain dynamic environment is considered. A Distributed Particle Swarm Optimization (DPSO) method is presented. The cooperative search-track mission is achieved by coupling task assignment and path planning. An uncertain map is designed to represent the dynamic environment. In the search-track mission environment of UAV swarm, the information exchange among UAVs is realized by broadcasting to update the information of uncertain dynamic map. Simulations are carried out to highlight the advantages of high search efficiency and excellent task completion which contribute to the target search and tracking task of UAV swarm in complex time-sensitive environment.
An Improved Jaya Optimization Algorithm with Hybrid Logistic-Sine-Cosine Chaotic Map

Jaya optimization algorithm is a simple but powerful intelligence optimization method which has several outstanding characteristics of both population-based algorithms and swarm intelligence-based algorithms. It has shown great potentials to solve various hard and complex optimization problems, but there still has much room to improve its performance, especially for solving high-dimensional and non-convex problems. Hence, this paper proposes an improved Jaya optimization algorithm with a novel hybrid logistic-sine-cosine chaotic map, which is named IJaya for short. The hybrid logisticsine-cosine chaotic map is applied to balance the exploration and the exploitation processes of Jaya optimization algorithm. Seven benchmark testing functions with different scale settings are used to evaluate the performance of our improved algorithm. Computational results indicate that our improved Jaya optimization algorithm outperforms greatly its original version on most testing functions with high-dimensions.
Deep Flow Rendering: View Synthesis via Layer‐aware Reflection Flow

Abstract Novel view synthesis (NVS) generates images from unseen viewpoints based on a set of input images. It is a challenge because of inaccurate lighting optimization and geometry inference. Although current neural rendering methods have made significant progress, they still struggle to reconstruct global illumination effects like reflections and exhibit ambiguous blurs in highly view‐dependent areas. This work addresses high‐quality view synthesis to emphasize reflection on non‐concave surfaces. We propose Deep Flow Rendering that optimizes direct and indirect lighting separately, leveraging texture mapping, appearance flow, and neural rendering. A learnable texture is used to predict view‐independent features, meanwhile enabling efficient reflection extraction. To accurately fit view‐dependent effects, we adopt a constrained neural flow to transfer image‐space features from nearby views to the target view in an edge‐preserving manner. Then we further implement a fusing renderer that utilizes the predictions of both layers to form the output image. The experiments demonstrate that our method outperforms the state‐of‐the‐art methods at synthesizing various scenes with challenging reflection effects.
Face Verification Component for Offline Proctoring System using One-shot learning

Historically Deep learning algorithms like Face Recognition do not work well on One-shot learning tasks. Hence, we need algorithms that can learn information about objects from one, or a few, training examples or images. In addition to that, if any new class is introduced to the model, there will be requirement to train it from beginning. We solve these problems by using the weights of a pre-trained model. The goal of this project is to use the concept of one-shot learning to create a component for an offline proctoring system. This system is foolproof against seat-swapping and it is also resilient to different lighting conditions and poses. In this project, we have used a pre-trained siamese network model and have established the feasibility of the product on a number of metrics including the accuracy of the model under different conditions like change in orientation and lighting.
Securing Web User Privacy with Steganalysis of Images using Deep Learning

Nowadays, images are extensively used in all aspects of community because of their ease of understandability, like cyber forensics and research-based exploration. Tampering digital images in special fields maliciously, may change the information contained therein, form false information, and result in damage to society. Data security is very important when sensitive data is transmitted over the internet. Whenever a website is opened, there are a lot of advertisements displayed on the page. Although they don’t intrude on our browsing experience, some images may hide vicious data inside them by using steganography. Hackers have become proficient in embedding their harmful code inside images. As soon as the user clicks on the image, the hidden code starts to affect the host machine by running in the background without the knowledge of the user. Henceforth, this research work proposes a system by using Deep Learning (DL) to detect steganography (Blind Steganalysis). Convolutional Neural Networks (CNN) is used in our system to prognosticate the presence of steganography by providing an extension in the web browser. By testing our model, it’s inferred that the accuracy in finding hidden content in images shows good results. The proposed model has achieved an accuracy of 89 percentage.
Application for tracking and forecasting human health

Currently, a large number of people are interested in their health and well-being. But not everyone knows what they had in childhood or what diseases their closest relatives had. This article discusses the possibility of using the storage, exchange and transmission of information about human health. Namely, an application in which all data on human health and possible subsequent diseases will be collected in order to prevent them. The characteristics and operation of the application are considered, which allows a person to control his well-being, and a doctor to diagnose and prescribe treatment in a short time. The way of collecting and transmitting information into one database, with their further processing, is shown. Analogs with their advantages and disadvantages are also presented, in comparison with this innovation.
Attrition and Retention

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Experimental Research on Quantitative Evaluation of Basketball Specialized Academic Achievement in College Physical Education Enrollment Physical Examination

The research process shows that: Determining the contents of test and assessment of academic results for students majoring in Basketball in each module in accordance with practical learning conditions aims to accurately assess learners' competencies and ensure the enhanced quality of training at Bac Ninh Sport University, contributing to preparation of high quality human resources for the future.
Thoughts on College English Teaching on Computer Science and Technology Majors in Private Colleges

English is a fundamental competence that computer professionals must master in the context of rapid social and economic development. English has become a crucial aspect of in-depth understanding and investigation of associated technologies in recent years, thanks to the ongoing invention and development of computer technology. However, there are other issues with college English teaching on computer science and technology majors in private colleges that must be addressed via experience. Based on this, this paper analyzes the challenges of teaching college English to students majoring in computer science and technology, and proposes several reform strategies in order to provide useful information for future research.
Pengembangan Instrumen Asesmen Autentik Ranah Afektif pada Pembelajaran dalam Jaringan (Daring) di Kelas 4 Sekolah Dasar

Today's students and teachers face obstacles with online learning. Teachers struggle to evaluate the learning results of their students, and the students themselves cannot find authentic tasks. The purpose of this research and development is to develop products, namely effective assessment instruments in the affective realm of online learning using the Borg &amp; Gall model, that meet valid and effective requirements. The results obtained in the development are in two aspects, namely validity and effectiveness. The results of the validation itself obtained results, namely, instrument experts obtained 75% and linguists obtained 80%. Both of these validation results show that the product can be used. Furthermore, on the effectiveness of the product, namely by questionnaires of teacher and student responses. In the questionnaire, students' responses obtained a result of 80.6%, while the teacher's questionnaire obtained a result of 90%. The results of this effectiveness test show that the product of the authentic assessment instrument is feasible, effective, and can be implemented. It can be concluded that this valid and effective authentic assessment instrument product can be a solution to the problem of assessment or assessment in the affective realm at SDN Merjosari 2 Malang City during online learning.
Examples of Corpus Data Visualization: Collocations in Chinese

The article aims to show a practical procedure that can be used in the visualization of language data. The paper freely follows our previous articles about the visualization of language data in language pedagogy. We demonstrate how to retrieve language data – in our case from corpora, how to edit data in a spreadsheet program, and then in the last step, how to visualize it on the example of Legal Chinese and partly Legal German. The Javascript library Vis.js via Pyvis is chosen for the visualization of the language data.
Super resolution image reconstruction via dual dictionary learning in sparse environment

&lt;span lang="EN-US"&gt;Patch-based super resolution is a method in which spatial features from a low-resolution (LR) patch are used as references for the reconstruction of high-resolution (HR) image patches. Sparse representation for each patch is extracted. These coefficients obtained are used to recover HR patch. One dictionary is trained for LR image patches, and another dictionary is trained for HR image patches and both dictionaries are jointly trained. In the proposed method, high frequency (HF) details required are treated as combination of main high frequency (MHF) and residual high frequency (RHF). Hence, dual-dictionary learning is proposed for main dictionary learning and residual dictionary learning. This is required to recover MHF and RHF respectively for recovering finer image details. Experiments are carried out to test the proposed technique on different test images. The results illustrate the efficacy of the proposed algorithm.&lt;/span&gt;
Dual-stream feature fusion encoding for Chinese named entity recognition

Recently, the attention mechanism and convolutional operation have been widely applied in Chinese Named Entity Recognition (NER) owing to their parallelization ability. However, in Chinese NER, the attention mechanism tends to model for global information and almost ignores local feature information between characters. On the contrary, convolutional operation can capture the local information while inability to solve patterns with discontinuous characters. In this paper, we propose a novel dual stream feature fusion encoding method. Specifically, we design a Dual-stream Temporal Network (DSTN), in which we use the advantages of both convolutional operation and self-attention mechanism while alleviate their respective drawbacks. DSTN can effectively capture the local and global feature information by encoding the characters. Besides, we also present a loss calculation method, namely Multi-loss, which can prevent the model from over-fitting. The experiment results on two NER datasets showed that our method has excellent performance and efficiency than most mainstream methods.
INNOVATIVE METHODS AT THE UNIVERSITY AS A SIGN OF ITS MODERNITY

The article deals with the requirement of transition to innovative education, which is due to today's challenges. The importance of innovative learning in the process of functioning of a modern university as a tool of integration into the world educational space is emphasized. It is emphasized that the innovative orientation of the higher education system, which is implemented through increasing the level of computerization of educational institutions, intensification of research activities, the formation of innovative structures in the Free Economic Zone is one of the strategic priorities. The characteristic differences of traditional and innovative teaching are determined, different variants of existence of innovative teaching methods are investigated, and the main approaches to the choice of innovative methods according to the educational purpose of employment, advantages and disadvantages of use of separate methods. The concepts of «educational innovation», «innovative university» are also specified, some innovative methods of teaching at the university are analyzed.
Design and implementation for intelligent conference video monitoring and management system of state grid based on Qt

With the wide application of online conference in plenty of companies and institutions, the maintainers need to monitor the real-time video stream of the main venue and branch venue to ensure the success of conference. In order to improve the maintenance efficiency of State Grid, this paper designs an intelligent conference video monitoring and management system that integrates various image processing technologies and artificial intelligence algorithms with the cooperation of software and hardware. The system adopts the Qt development framework to complete the system interaction logic and display interface design. PaddleOCR tool is used to identify baffle text in branch venue picture, and YOLOv5 algorithm is adopted to detect people in the picture. Dlib library is used for participant attendance of main venue. The test and verification show that the system realizes the user login and management function, decoration guide and exceptional malfunction detection function of branch venue, participant attendance function of main venue. The maintenance requirements of State Grid can be met properly.
Panoptic segmentation method based on pixel-level instance perception

Deep learning-based panoptic segmentation technology has enabled in-depth understanding of images as well as scene descriptions and provides solutions for most computer vision tasks. The post-processing fusion of instance branches and semantic branches in the top-down approach often causes conflicts between branches. In order to solve this problem, we propose a single-stage panoptic segmentation network based on pixel-level instance perception with FCOS object detector. Specifically, the FCOS output extraction network is extended by using three branches, namely the object detection branch, the semantic branch, and the panoptic branch. The object detection branch uses the FCOS object detector to provide the instance center for the panoptic branch to generate a pixel-level instance perception mask, and the semantic branch provides semantical information for the panoptic branch. The panoptic segmentation output comes from the pixel-wise product of the semantic branch and the panoptic branch, avoiding the heuristic post-processing fusion in end-to-end learning. A good trade-off between accuracy and inference time was achieved on standard datasets Cityscapes, with PQ at 59.4%, mIoU at 76.2% and running time 94ms on Cityscapes validation dataset. Compared to other methods, our method achieves better panoptic performance within a reasonable runtime range. At the same time, the inference process is concise and can be optimized by using the deep learning inference engine to reduce the difficulty of deployment in actual application scenarios.
Implementation of industrial simulators — digital twins at enterprises and educational institutions

New functions in software tools for simulation of dynamic systems are considered. The importance of practical experience in teaching of the control for both industrial personnel and students of educational institutions is shown. Keywords modeling, simulation, industrial simulators, Raspberry Pi, SimInTech, VR- and AR-applications
Enhancing the role of authentic materials in teaching process

Teachers sometimes struggle to discover adequate learning resources while teaching reading. Students spend extended hours in the classroom without suitable reading texts, resulting in low reading comprehension outcomes. The purpose of this study is to describe pupils’ reactions to the usage of actual material. This study adopts the descriptive qualitative technique because it provides a clear explanation of the study.
Blockchain Based Software Engineering Requirements Analysis and Management

Requirements analysis and engineering is a vital phase in any software project’s lifecycle, and the success or failure during this phase mainly determines the entire project’s outcome. Recently the challenges incredibly increased in the software industry either in the technology, project management, or requirements engineering, drive-by leveraging the diversity of the tremendous tools and techniques that aim to avoid failure in requirements engineering and analysis. However, the tools and techniques will not always tackle the most significant challenge: validate, align, and confirm the needs and outcomes accordingly to both parties, the customer and the vendor, ensuring the confirmation and validation process is trustworthy. A unique process that ensures the requirements reliability is by introducing a framework that will authenticate the requirements analytics and engineering by Ethereum blockchain technology smart contract for the customers and vendor to guarantee substantial agreement on all requirements aspects through the project’s lifecycle.
Inferior and Coordinate Distillation for Object Detectors

Current distillation methods only distill between corresponding layers, and do not consider the knowledge contained in preceding layers. To solve this problem, we analyzed the guiding effect of the inferior features of a teacher model on the coordinate feature of a student model, and proposed inferior and coordinate distillation for object detectors. The proposed method utilizes the rich information contained in different layers of the teacher model; such that the student model can review the old information and learn the new information, in addition to the dark knowledge in the teacher model. Moreover, the refine module is used to align the features of different layers, distinguish the spatial and channel to extract attention, strengthen the correlation between the features of different stages, and prevent the disorder caused by merging. Exclusive experiments were conducted on different object detectors. The results for the mean average precision (mAP) obtained using Faster R-CNN, RetinaNet, and fully convolutional one-stage object detector (FCOS) with ResNet-50 as its backbone were 40.5%, 39.8%, and 42.8% with regard to the COCO dataset, respectively; which are 2.1%, 2.4%, and 4.3% higher than the benchmark, respectively.
A Branch Elimination-based Efficient Algorithm for Large-scale Multiple Longest Common Subsequence Problem (Extended Abstract)

Searching Longest Common Subsequences (LCS) of Multi-ple (i.e., three or more) sequences, denoted as MLCS for short, is a fundamental problem in computational biology, pattern recognition, file comparison and information retrieval, to name a few. Basically, an MLCS problem is more challenging than the LCS of two sequences which can be solved by a dynamic programming (DP) method whose running time and memory space are both <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(n^{2})$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> is the length of the sequences.