Reviewerly/exHarmony huggingface dataset:
Exploiting Concepts of Instance Segmentation to Boost Detection in Challenging Environments

In recent years, due to the advancements in machine learning, object detection has become a mainstream task in the computer vision domain. The first phase of object detection is to find the regions where objects can exist. With the improvements in deep learning, traditional approaches, such as sliding windows and manual feature selection techniques, have been replaced with deep learning techniques. However, object detection algorithms face a problem when performed in low light, challenging weather, and crowded scenes, similar to any other task. Such an environment is termed a challenging environment. This paper exploits pixel-level information to improve detection under challenging situations. To this end, we exploit the recently proposed hybrid task cascade network. This network works collaboratively with detection and segmentation heads at different cascade levels. We evaluate the proposed methods on three complex datasets of ExDark, CURE-TSD, and RESIDE, and achieve a mAP of 0.71, 0.52, and 0.43, respectively. Our experimental results assert the efficacy of the proposed approach.
DS‐SRI: Diversity similarity measure against scaling, rotation, and illumination change for robust template matching

This paper presents a novel multi-scale template matching method that can be applied in unconstrained environments. The key component behind this is a general similarity measure is referred to as the diversity similarity measure against scaling, rotation, and illumination (DS-SRI). Specifically, DS-SRI exploits bidirectional diversity calculated from the nearest neighbour matches between two sets of points. Scaling and rotation changes are taken into consideration by introducing normalisation term on the scale change, and geometric consistency term with respect to the polar coordinate system. Moreover, in order to deal with the illumination change and further deformation, illumination-corrected local appearance and rank information are jointly exploited during the nearest neighbour search. All the features of DS-SRI are statistically assessed, and the extensive visual and quantitative results on both synthetic and real-world data show that DS-SRI can significantly outperform state-of-the-art methods.
PREPARING STUDENTS FOR THE USE OF THEATER ACTIVITIES FOR CHILDREN'S DEVELOPMENT SOFT SKILLS: EUROPEAN CONTEXT

The article substantiates the importance of introducing European experience in the training of future teachers in Ukrainian higher education institutions. Features of preparation of future teachers for realization of theatrical activity in work with children are opened, attention is focused on social and educational value of theater for development of the general and special abilities of children. The functions realized by theatrical activity in the process of soft skills development of children of primary school age are singled out. The article emphasizes the various forms and types of theatrical activities and ways of organizing them with children (from creating a theatrical character or a specific image to its interpretation and display on the stage).The purpose of the publication is to present the experience of the higher education institution of Ukraine in preparing future primary school teachers for the use of theater activities for children`s development soft skills taking into account the European context.In the research the results of diagnostics of primary school teachers on the use of theatrical activities in their own pedagogical practice are presented. The results of monitoring the awareness of future primary school teachers with the theoretical and methodological principles of using theatrical activities as a means of developing soft skills in primary school children and their level of readiness to organize theatrical activities with children are demonstrated. The experience of teachers of Ukrainian pedagogical universities in the implementation of European innovative technologies and practices in lectures and practical classes is demonstrated. The article reveals the content of preparing students for the use of European experience in the organization of theatrical activities in the education of children, offers guidelines.
A Classifier to Detect Profit and Non Profit Websites Upon Textual Metrics for Security Purposes

Currently, most organizations have a defense system to protect their digital communication network against cyberattacks. However, these defense systems deal with all network traffic regardless if it is from profit or non-profit websites. This leads to enforcing more security policies, which negatively affects network speed. Since most dangerous cyberattacks are aimed at commercial websites, because they contain more critical data such as credit card numbers, it is better to set up the defense system priorities towards actual attacks that come from profit websites. This study evaluated the effect of textual website metrics in determining the type of website as profit or nonprofit for security purposes. Classifiers were built to predict the type of website as profit or non-profit by applying machine learning techniques on a dataset. The corpus used for this research included profit and non-profit websites. Both traditional and deep machine learning techniques were applied. The results showed that J48 performed best in terms of accuracy according to its outcomes in all cases. The newly built models can be a significant tool for defense systems of organizations, as they will help them to implement the necessary security policies associated with attacks that come from both profit and non-profit websites. This will have a positive impact on the security and efficiency of the network.
Mobile Robot Path Planning Optimization Based on Integration of Firefly Algorithm and Cubic Polynomial Equation

Mobile Robot is an extremely essential technology in the industrial world. Optimal path planning is essential for the navigation of mobile robots. The firefly algorithm is a very promising tool of Swarm Intelligence, which is used in various optimization areas. This study used the firefly algorithm to solve the mobile robot path-planning problem and achieve optimal trajectory planning. The objective of the proposed method is to find the free-collision-free points in the mobile robot environment and then generate the optimal path based on the firefly algorithm. It uses the A∗ algorithm to find the shortest path. The essential function of use the firefly algorithm is applied to specify the optimal control points for the corresponding shortest smooth trajectory of the mobile robot. Cubic Polynomial equation is applied to generate a smooth path from the initial point to the goal point during a specified period. The results of computer simulation demonstrate the efficiency of the firefly algorithm in generating optimal trajectory of mobile robot in a variable degree of mobile robot environment complexity.
Research on social recommendation algorithm based on fuzzy subjective trust

In order to resist malicious attacks and improve recommendation accuracy, this paper proposes a collaborative filtering recommendation algorithm based on fuzzy subjective trust based on the subjectivity and fuzziness of trust. Firstly, the trust matrix is constructed by calculating the trust degree between users based on fuzzy subjective trust model and set of trusted users. Then, based on the traditional collaborative filtering recommendation algorithm, the user hybrid recommendation matrix is obtained through user similarity matrix and trust matrix to form recommendations for target users. Experimental results show that this method can effectively improve the resistance to malicious attacks and improve the accuracy of the recommendation system.
Investigating the Efficiency of Bitcoin Futures in Price Discovery

The present study investigates the efficiency of the Bitcoin futures in the price discovery process by assessing the lead-lag relationship between the futures and spot prices of Bitcoin. The study tests whether the Bitcoin futures market is leading the price discovery mechanism for the Bitcoin spot market. The study considers daily closing prices of both Bitcoin spot and future indices from December 12, 2017 to December 31, 2020. The stationarity of the two time-series variables is tested using Augmented Dickey-Fuller test while the long-run co-integrating relationship is tested using Johansen Co-integration test. To test the long-run causality, the Error Correction Mechanism framework (ECM) is used while the Wald test is applied to assess the short-run causality between the Bitcoin future and spot prices. The results of trace and max-eigen statistics indicate that there is long term co-integrating relationship between Bitcoin futures and Bitcoin spot markets. The negative significant coefficient of error correction term indicates that there is long-run causality from the Bitcoin futures towards the Bitcoin spot market. The significant Chi-square test statistics of the Wald test suggest that there is short-run causality from the Bitcoin futures towards the Bitcoin spot market. This shows that the Bitcoin futures market is acting as a leading indicator and the Bitcoin spot market as a lagging indicator. Thus, it is concluded that the price discovery is taking place between Bitcoin futures and the Bitcoin spot market. With the entrance of the new information in the cryptocurrency market, it is first observed in the Bitcoin futures followed by the Bitcoin spot prices.
Text data mining of power based on natural language processing technology

Abstract This paper first introduces the development of text mining technology, points out the difficulties of text mining technology, and then designs the power text mining based on natural language processing technology Then it introduces the typical application of text data mining technology in 95598 customer service complaints. Finally, it puts forward the application effect and challenge of power text mining.
Cultivation Path for Innovation Ability of Sci-Tech Talents in the Background of Big Data

China mainly relies on higher education to cultivate sci-tech talents. The decision makers and workers of higher education face the important task of cultivating high-quality sci-tech talents that benefits the society. However, the current cultivation system for the innovation ability of high-quality sci-tech talents has some defects, and the practical experience is severely lacking for the cultivation of big data ability of high-quality sci-tech talents. Therefore, this paper explores the cultivation path for innovation ability of sci-tech talents in the background of big data. Firstly, a pre-survey was carried out on the factors affecting the innovation ability of sci-tech talents in the background of big data, an evaluation index system was established for the said ability, and the cultivation path was given for that ability. Next, the gradient boosted decision tree (GBDT) was combined with neural network (NN) into a hybrid approach, which integrates the merits of both methods. The hybrid approach was adopted to analyze and evaluate the factors affecting the innovation ability of sci-tech talents. Then, the authors further explored whether the basic ability, technology ability, and management ability of big data analysis promotes the optimization of the cultivation path for innovation ability of sci-tech talents. Through experiments, the authors obtained the regression analysis results on the innovation ability of sci-tech talents, and put forward suggestions on how to optimize the cultivation path for innovation ability of sci-tech talents.
Hyperspectral Remote Sensing Image Processing and Information Extraction Technology Research in Geological Recognition Application

Application of hyperspectral remote sensing technology in geological identification has highlighted its advantages, but the identification accuracy of metal mineral cations is limited. In this paper, the dominant spectrum of domestic HJ-1A data and Hyperion data are used for collaborative processing, realize multi-source hyperspectral image fusion and information extraction and recognition, mapping of mineral alteration information in the Qinghai-Tibet Plateau. The adopted method improves the identification accuracy of metal mineral cations in the visible light and near-infrared range. After comparative verification analysis and statistical evaluation of results, it is confirmed that this method has important application value for the identification of metal mineral cations in complex geological environment.
Follow Your Nose: Extended Arm Reach After Pinocchio Illusion in Virtual Reality

In this study, we recreate the Pinocchio Illusion—a bodily illusion whereby the perceived length of one’s nose is extended—in Virtual Reality. Participants ( n = 38) self-administered tapping on the tip of the nose of a virtual avatar seen from the first-person perspective (using a hand-held controller) while the nose of the avatar slowly grew with each tap. The stimulating virtual arm and the virtual nose were linked such that while the nose grew the arm extended, and then also grew up to 50%. This produced an extension of the perceived reach of the stimulating arm, and an outward drift in the participants’ real arm. A positive correlation between the extent of the outward drift of the participants’ arm and the perceived reachability of distal objects was observed. These results were found both with synchronous tactile stimulation on the participants’ real nose, and without, but not for control conditions in which the visuomotor synchrony or body schema were violated. These findings open new avenues for hand grasp interactions with virtual objects out of arm’s-reach in immersive setups and are discussed in the context of theories of body ownership, body schema, and touch perception.
A Comparative Analysis of Changes in the Learning Motivation of Russian and Foreign Medical Students during the COVID-19 Pandemic

This paper tests the hypothesis that the structure of the learning motivation of Russian and foreign medical students have undergone changes during the COVID-19 pandemic.The authors explored the learning motives of students studying the same medical specialty at the same university.The comparative analysis of changes in the principal learning motives (professional, cognitive, developmental, creative self-actualisation, communicative, social, avoidance-and prestige-driven) from February 2019 to June 2021 was made using the assessment of students' learning motivation developed by Rean and Yakunin, as modified by Badmaeva.SPSS Statistics 22.0 was used for statistical analysis.The results confirmed the research hypothesis: the structure and manifestation of medical students' learning motives have undergone changes during the COVID-19 pandemic.Before the pandemic, most of the learning motives of Russian and foreign medical students show a high level, with primarily professional motives, which evidences a professional orientation and a conscious choice of the profession.However, such important motives as cognitive, developmental and creative self-actualisation are significantly less expressed than professional ones.During the COVID-19 pandemic, there was a much higher manifestation of professional motives in Russian medical students.From the authors' point of view, this can be explained by extended practice with patients while there was an increase in creative selfactualisation and a decrease in communicative and social motives.Foreign medical students show a decrease in prestige-driven motives and some decrease in social motives.The obtained results open up prospects for improving medical education and for developing the personal and professional potential of Russian and foreign medical students.
Secondary School Teachers’ Perceptions of Mobile Technology Adoption in English as a Foreign Language Learning: Trends and Practices

A review of literature on mobile technology in EFL learning environment has indicated the growth, trends and practices of smartphones and mobile apps adoption both in classroom and outside classroom settings. However, little has been known on how EFL teachers perceived such trends and current practices and how they plan for future teaching. This study attempted to explore teachers’ responses to trends and practices of mobile technology adoption and their future teaching plan. To this end, a qualitative design was adopted with two consecutive research procedures: a systematic review on the selected trends and practices analysis studies on mobile technology for language learning practices and a survey through a semi-structured interview. Five EFL teachers (three females and two males) from secondary school level were interviewed using six open-ended questions. Findings of the study revealed that teachers perceived positively towards the trends and practices of mobile technology in and outside language learning classroom. Teachers perceived motivation as a main driver to incorporate mobile technology. Some of motivations frequently cited in the interviews with the sampled teachers revealed the current trends and practices of blended learning practices among Indonesian teachers, including the perceived benefits of mobile technology adoptions and the effect of mobile technology on teaching and learning practices and students’ characteristics. Three challenges were identified when technology was incorporated for learning namely infrastructure related issues, teacher technological knowledge and education policy.
Improving Mixed-Reality Prototyping through a Classification and Characterisation of Fidelity

Abstract Prototyping is a vital activity in product development. For reasons of time, cost and level of definition, low fidelity representations of products are used to advance understanding and progress design. With the advent of Mixed Reality prototyping, the ways in which abstractions of different fidelities can be created have multiplied, but there is no guidance on how best to specify this abstraction. In this paper, a taxonomy of the dimensions of product fidelity is proposed so that both designers and researchers can better understand how fidelity can be managed to maximise prototype value.
Character Recognition in Endangered Archives: Shui Manuscripts Dataset, Detection and Application Realization

Shui manuscripts provide a historical testimony of the national identity and spirit of the Shui people. In response to the lack of a high-quality Shui manuscripts dataset, we collected Shui manuscript images in the Shui area and used various methods to enhance them. Through our efforts, we created a well-labeled and sizable Shui manuscripts dataset, named Shuishu_T, which is the largest of its kind. Then, we applied target detection technology for Shui manuscript characters recognition. Specifically, we compared the advantages and disadvantages of Faster R-CNN, you only look once (YOLO), and single shot multibox detector (SSD), and subsequently chose Faster R-CNN to detect and recognize Shui manuscript characters. We trained and tested 111 classes of Shui manuscript characters with Faster R-CNN and achieved an average recognition rate of 87.8%. Finally, we designed a WeChat applet that can be used to quickly identify Shui manuscript characters in images obtained by scanning Shui manuscripts with a mobile phone. This work provides a basis for realizing the recognition of characters in Shui manuscripts on mobile terminals. Our research enables the intangible cultural heritage of the Shui people to be preserved, promoted, and shared, which is of great significance for the conservation and inheritance of Shui manuscripts.
Towards a Generic Description Schema for Clinical Decision Support Systems

Many clinical decision support systems (CDSS) have shown good performance in the research context, but only a few have been brought into routine care. Analysis of success or failure factors in the design of CDSS may support translation from development to routine care by guiding CDSS design and development along these factors. In this work, we propose a schema to describe CDSS designs in a consistent way. We focus on design criteria with the aim to investigate the observed translation gap in CDSS. Existing description models on different aspects relevant for CDSS are combined to a comprehensive schema that allows description and comparison of CDSS without limitation to the domain or architecture.
Primary Color Based Numerous Image Creation in Visual Cryptography with the Help of Grasshopper Algorithm, Artificial Neural Network and Elliptic Curve Cryptography

The two primary restrictions of visual cryptography methods, contract, and security are well-known.Visual cryptography is considered secure if no share reveals information about the secure picture and this image is unreachable when shares are combined.Recursion can be used in a visual cryptography method to ensure the integrity of both arguments.Sometimes, the many shares are generated illogically in such a manner that they sometimes reveal the actual image.The model proposed in the paper has been proved beneficial in reducing the computation, noise, or distortion in the recreated image and helps in providing amended contracts.The suggested scheme uses unique pixel patterns to create a contract for the decrypted image.The contract for the recursion visual cryptography scheme is alterable from the white pixel pattern to new pixel patterns.The elliptic curve cryptography approach is used to ensure the privacy and security of the image.This study has taken a four-share mechanism in which each component is divided into four shares for each color segment.The proposed algorithm uses the Grasshopper algorithm to select the bit's positions where the encryption must be done.Neural Networks have been used as a cross validator in the proposed work model.A new fitness function is designed for the Grasshopper algorithm.The proposed algorithm is compared based on Mean Square Error and Peak Signal to Noise Ratio.