Reviewerly/exHarmony huggingface dataset:
DESPRO: Decentralized business platform for student non-profit organizations

Student non-profit organizations are a specific type of small associations that congregate students from diverse backgrounds around a common purpose. These organizations are non-profit driven, autonomous, and typically have largely similar statutes and regulations. In the context of software engineering, information systems that support their business processes have similar software requirements specifications. DESPRO is a web-based platform that fully supports principal use cases of students' non-profit organizations. The platform has three uses: accounting, voting, and fundraising management. DESPRO allows such organizations to begin with their principal operations practically immediately without unnecessary overhead. Unique features that distinguish DESPRO from other similar systems are: 1) lightweight and streamlined deployment process, 2) minimal required remote maintenance, 3) pre-defined functionalities specifically suited for student non-profit organizations, 4) user anonymity customized to specific functional requirements, 5) voting without prior user registration. The platform was developed with open-source technologies Node.js, Vue.js, Vuetify, Bootstrap, Sequalize, Axios, Web3, MariaDB, and smart contracts for Ethereum. It is freely available for noncommercial use by contacting the first author.
Ongoing Advances in Jute Coloration

This purpose of this study is to rebuild a website in a major office of textile industry and be able to build a system which can be used there. The research method used in this paper was qualitative descriptive method, which is an actual or accurate systematic description or writing about facts, as well as the relation between phenomena in detail. This research method was used since the process of scientific research conducted was interpreting and explaining the data concerned with the situation that is happening in the Central Textile. The goal is to create a new system using that major office of textile industry website. And customers will find it easier and faster to perform business processes.
Stitching Dynamic Movement Primitives and Image-Based Visual Servo Control

Utilizing perception for feedback control in combination with dynamic movement primitive (DMP)-based motion generation for a robot’s end-effector control is a useful solution for many robotic manufacturing tasks. For instance, while performing an insertion task when the hole or the recipient part is not visible in the eye-in-hand camera, a learning-based movement primitive method can be used to generate the end-effector path. Once the recipient part is in the field of view (FOV), image-based visual servo (IBVS) can be used to control the motion of the robot. Inspired by such applications, this article presents a generalized control scheme that switches between motion generation using DMP and IBVS control. To facilitate the design, a common state-space representation for the DMP and the IBVS systems is first established. The stability analysis of the switched system using multiple Lyapunov functions shows that the state trajectories converge to a bound asymptotically. The developed method is validated by three real-world experiments using the eye-in-hand configuration of a Baxter research robot.
Design and Implementation of Children's Cognitive Education Software Based on IOS Platform

Children's music cognitive education plays an important role in the process of children's growth and development. In recent years, with the development of science and technology, the emergence of smart phones, pad and other electronic products that integrate intelligent systems makes music cognition more intuitive and easy to use. How to develop excellent children's music cognitive education products has become an urgent task. Starting from the analysis of the characteristics of children's music growth, this paper refines a set of cognitive characteristics of children's sensitivity to music in its wisdom growth, emotional growth, social growth, perceptual growth, physiological growth, aesthetic growth and creative growth. A set of music cognitive assistant education software based on IOS mobile platform is designed, which can accompany the healthy emotion and mental growth of preschool children. So as to systematically cultivate children's creativity, perception, coordination, concentration and observation.
Optical Digital Theremin with Audio Synthesis and Graphic Interface

he theremin is one of the first elec- tronic musical instruments and one of the few played without physical contact since it only re- quires hand and finger movements to control the amplitude and frequency of the musical note. However, the capacitive functioning of the anten- nas increases the sensitivity to electrical interfer- ence, its timbre is fixed, and the frequency an- tenna's vertical arrangement could limit the use of people with amputated fingers. Furthermore, it does not contain any help to guide the execu- tion, which makes it a very difficult instrument to play. In this paper, we present the development of a digital optical theremin with an audio syn- thesis process, intuitive graphical interface, frequency antenna in the horizontal position, and linearization of the frequency-distance relationship. These features are intended to aid learn- ing and interpretation of the instrument and ex- tend access to people with finger limitations. In order to validate the instrument's behavior and characteristics, we conducted three experiments: 1) accuracy analysis of the linearization through the mean absolute error in units of cents and the Kruskal-Wallis statistical inference test, 2) val- idation of the steps of the audio synthesis mod- ule, and 3) checking of the timbral diversity, both through the Fourier spectrum. This prototype could be used as an auxiliary tool in musical initi- ation and the development of musical perception.
DF-FSOD: A Novel Approach for Few-shot Object Detection via Distinguished Features

Few-shot object detection (FSOD) is a challenging task in which detectors are trained to recognize unseen objects with limited training data. The majority of existing methods are evaluated on the benchmarks built with a fixed quantity of base and novel classes categories. To be specific, the number of base classes is larger than the novel ones. This positively affects the performance evaluated on novel data. However, there are not many works focusing on the effect of such dominated categories on the performance of FSOD models. In this paper, we investigate the efficiency of the detectors in different ratios of base and novel categories in the novel phase. Based on our findings of the affection between base and novel classes, we present a new approach: Distinguished Features for FSOD (DF-FSOD), which encourages the detector to learn distinguished features to capture novel objects via base-class expansion better. In the end, our proposed method outperforms average 4% AP@50 on PASCAL VOC compared to the previous works on the unseen classes when extremely scare labeled data.
Network computer security and protection measures based on information security risk in cloud computing environment

Cloud computing based on Internet is considered as the development direction of Internet nowadays, which has aroused widespread concern in recent years. How to build a secure cloud computing environment has become one of the hot issues in computer science research. In the development of network information, security will take the leading position. With the continuous development of computer technology, cloud computing has been gradually applied to the Internet, bringing great convenience to the production and operation activities of different industries. However, due to the virtual and open nature of the Internet, information security in the cloud computing environment has attracted people's attention. As a product of technological development in the new era, cloud computing technology has greater advantages in data storage and processing, and therefore brings greater convenience to traditional data storage and processing technologies in current industries. On the basis of analyzing some of the security problems that the current computer faces under the network environment, different security protection measures are proposed for different situations.
Analysis Method of Life Counseling Features of French Majors Based on Big Data Analysis

With the advancement of our country’s economic system reform and the reform and opening up of the socialist market economic system, the people’s daily life and mental conditions are rapidly undergoing tremendous changes. The younger generation of college students is no exception as the leader of the culture of the times. The changes in their lifestyles directly reflect a change in the spirit of the times. This article aims to study the analysis methods of the life counseling characteristics of French majors based on big data analysis. On the basis of analyzing the classification of college students’ lifestyles and the characteristics of college students’ lifestyles, in order to further understand the current living conditions of French majors, French majors in colleges and universities conducted a questionnaire survey, and finally put forward life counseling methods based on the survey results. The survey results show that 52.3% and 51.6% of French majors in the two universities choose to listen to music for self-entertainment in their leisure time, 27.1% and 29.5% of students choose sports and fitness as their leisure activities after school, 9.4% and 10.7% Students choose to chat with friends, 6.2% and 5.6% of students choose to go shopping, 5% and 2.6% of students do not have a casual lifestyle.
Performance-Driven Reinforcement Learning Approach for Abstractive Text Summarization

Recently, the use of Reinforcement Learning with Neural Networks in Abstractive Summarization is getting more popular, but still currently restricted. In this paper, we propose PEARL as a novel framework to expand the proficiency of Reinforcement Learning approach in Abstractive Text Summarization. PEARL consists of two out-of-the-box Reinforcement Learning algorithms: \(F_{Rouge}\) and \(D_{Threshold}\), where \(F_{Rouge}\) reconstructs the training objective, and \(D_{Threshold}\) helps to improve the flexibility for the arbitrary data. We evaluate PEARL in the large-scale CNN/DailyMail and the medium-scale VNTC-Abs datasets. Results show that our PEARL produces significantly greater Rouge scores than baselines as well as achieves the new state-of-the-art model without either pre-trained models or extra training data. This research provides proof of validity based on data analysis.
A Class Imbalance Monitoring Model for Fetal Heart Contractions Based on Gradient Boosting Decision Tree Ensemble Learning

Aiming at the imbalance and cost-sensitive problem of sample categories in actual fetal monitoring, as well as actual needs, we proposed a category imbalance fetal contraction monitoring model based on GBDT (Gradient Boosting Decision Tree) combined learning. Subsets with balanced category were generated by random under-sampling and applied to train several GBDT base classifiers using the method of feature selection. We integrated the base classifiers by the simple average method and calculated the final prediction probability. In this study, AUC and cost-sensitive error rate were used as evaluation indicators to compare with the commonly used single learning models such as Decision Tree, Logistic Regression and combined learning models like Random Forest to verify the effectiveness of the model.
Enhancing Latent Features for Unsupervised Video Anomaly Detection

Recently, memory-augmented autoencoder has played a vital role in unsupervised video anomaly detection. The memory module is used for recording the prototypes of normal data and suppressing the feature representation capacity for anomalies. However, the feature representation capacity for normal data may also be suppressed due to the limited memory ability of the memory module. This may bring a serious problem to normal data being judged as anomaly. To this end, we propose a simple but effective method called Enhancing Latent Features, i.e. ELF, which integrates two modules Single Grid Feature Enhancement (SGFE) and Local Context Feature Enhancement (LCFE). They are performed sequentially to enhance the latent feature of normal instance based on single grid information and local context information, respectively. To capture the meaningful local context information, a learnable Local Sampling Location Prediction Network (LSLPN) is embedded into LCFE to predict the valuable sampling locations of local context information. Experimental results on standard benchmarks demonstrate the effectiveness of our method.
Development of Virtual Reality Training module for Maithari of Martial Art Kalari

Virtual Reality (VR) is an innovative technology that provides a unique platform to create an imaginary world in the real world that can be addressed by physical presence. This enveloping technology develops a computer-based imaginary environment platform and the advancements give unique solutions. The project aims to create a Virtual Reality training module for Maithari, the first stage of kalarippayattu (Kalari) which includes postures, exercises and sequences practised during the Kalari with the help of Unity Engine. There are four stages of kalarippayattu i.e. First stage- Meithari (Physical exercises), Second stage- Kolthari (Wooden Weapon Training), Third stage-Angathari (Metal Weapon Training), Fourth stage- Verumkai (Bare-Hand Technique). Here we are focusing on the first stage, Meithari. It builds the basics steps for the entire training and exercises that include jumps, leaps, kicks, circular sequences, leaps, postures, leg swings, and various movement technique. The physical training performed at this stage prepares the practitioner by developing several attributes such as strength, stamina, flexibility, agility, and speed. The aim is also to develop patience, self-control, focus, and awareness. In this project, we are using the Unity Engine as a tool to develop the training module as it offers support for multiple platforms, including PC, mobile and major consoles. With Unity, we just need to have a one-click process to have multiple platforms with ease.
Testing Machine Learning at Classical Electrodynamics

Like physics or another laws-based basic science, machine learning might also be a firm methodology to solve physics problems by the which a kind of optimization and minimization of energy are needed. Expressed at the Mitchell's principles, machine learning can be seen as a strategy that allows to improve physical actions such as observation and measurement. In the classical territory, one can project the well-known electrodynamics over the steps: (i) task, (ii) performance, and (iii) experience. With this one might to guarantee a kind of learning to face a next similar situation and so on. This paper try to solve the problem of a charged particle inside a cylindrical volume but emphasizing its energy and its measurement. Simulations have shown that machine learning can also be an alternative tool to solve physics problems that require of minimization of energy.
The Development of Financial Science and Technology Based on Matrix Algorithm and the Digital Strategic Transformation of Commercial Banks

Traditional banking financial institutions actively use financial technology to promote business process and product innovation, which has become an important direction of banking development in the current situation in our country. Faced with the dilemma in the digital transformation of credit business, commercial banks need to improve from strengthening data governance, building a strong team of financial science and technology talents, establishing an effective fault-tolerant and error-correcting mechanism, and continuously optimizing credit risk control model to realize the development and transformation of credit business. Based on the matrix algorithm, this paper designs two dimensions to analyze the direction of corporate strategic transformation of commercial banks driven by financial technology, and constructs a strategic selection model for commercial banks. On this basis, the paper gives the feasible strategies for the digital transformation of commercial banks based on financial technology.
Role of Digital Technologies in the Conceptual Transforming Foundations of Artistic Creativity

Technology in this article refers the transformation of substance, energy and information. Particular attention is paid to digital technologies, which were preceded by analog technologies. If analog technologies were at the heart of socialist realism, then digital technologies became the foundation for postmodernism. In socialist realism, the dominant positions were occupied by communist ideology and propaganda, and technologies in artistic creation performed mainly an auxiliary function. Their main task was to replicate artistic creations. Innovation and creativity in artistic creation were strictly limited by the party and political structures of the Soviet state. In postmodernism, in contrast to socialist realism, external restrictions are insignificant. The state allows the pluralism of opinions and the variety of artistic approaches, and digital technologies make art accessible to everyone. For artistic creation, there are ample opportunities to demonstrate innovation and creativity. The main thing here is the subject of creativity, the artist. This article defends that the thesis that the transition from socialist realism to postmodernism was primarily due to the development of technologies from analog to digital. The decisive role was played by digital technologies, and not by the socio-political changes that took place in the Soviet Union and other socialist countries at the turn of the 80s–90s of the twentieth century. The study employs methods drawn from conceptual analysis, hermeneutics, phenomenology, observation, comparison, as well as visual methods.
Contour-Aware Panoptic Segmentation Network

Panoptic segmentation incorporating semantic segmentation and instance segmentation plays an important role in scene understanding. Although current research has made remarkable progress, it has great potential for improvement in the contour region. In this paper, we propose a Contour-Aware Panoptic Segmentation Network (CAPSNet) with a panoptic contour branch and a new structure loss. The panoptic contour branch is designed to enhance structural cues perception by guiding feature extraction. The novel structure loss is presented for associating the consistency between panoptic segmentation and contour. Experimental results on the Cityscapes dataset show that CAPSNet achieves 60.0 in PQ metric, improving by 0.9% over baseline, and the proposed panoptic contour branch and structure loss are able to realize promising boosts on panoptic segmentation task.
Are We Ready for Smart Contact Lenses?

The aim of this study was to delve into the acceptability of smart contact lenses, to understand their viability on the market. We identified that the technology is being developed but the reaction it will have on potential buyers is not yet known. To achieve this, the Unified Theory of Acceptance and Use of Technology (UTAUT) model was used and adapted to an online questionnaire that was answered by 200 people, from Portugal (106) and Colombia (94). Considering that one is from Latin America and the other from Europe, two countries in opposite continents and with different cultures and contexts, the main hypothesis is that there will be a difference in the acceptance of smart contacts lenses between Portugal and Colombia. Additionally, it was hypothesized that people would be scared of using smart contact lenses and would be focused on the negative potential of the new product, like the lack of privacy. The main results did not reveal statistically relevant differences between the two countries (significant difference was found just for the Social Influence construct). Considering the whole sample, the results show that people tend to not be ready for smart contact lenses in their everyday life yet, but they are open to the idea of using them in specific situations that could help them improve their lives and work (healthcare, information), which is a great opportunity for interaction design.
Features of the Knowledge Base of the System of Automated Construction of Logic and Linguistic Models of Text Documents

The article outlines the problem of finding meaningful units in electronic text documents and analyzes the main shortcomings of existing approaches of extracting knowledge from textual information. The article is devoted to the study of the peculiarities of the process of construction of logic and linguistic models of electronic text documents, in particular the description and research of the peculiarities of knowledge bases of the system of automated construction of logic and linguistic models of Ukrainian- language text documents. The author proposes a scheme of formalization of textual information based on the construction of a logic and linguistic model of an electronic text document. The first stage of construction is the formation of logical and linguistic models of natural language sentences, which uses a specially developed method of automated formation of logical and linguistic models. This method is based on parsing sentences of natural language, using words of natural language as a thesaurus database and using a database of rules to identify logical connections. This in turn is made possible by the author’s developed knowledge base 1, which is used to determine the role of each word in an electronic text document and serves as a production model with formalized rules of the Ukrainian language for forming phrases that can form members of sentence of natural language. The knowledge base 2 was created by the author to find connections between sentences that are part of an electronic text document and is a set of productions that reflect the principles of synthesis of logic and linguistic models of sentences of natural language, ie the rules of combining and replacing structural components of logic and linguistic models of sentences of natural language. The knowledge base 3, used to build the linguistic component of the logic and linguistic model of a text document, is a set of productions that contains the rules of forming of transition networks to interpret the thematic progression of the text. The application of the developed formalized rules was demonstrated on specific text fragments. Applying the developed knowledge bases allows to trace the process of formation of logic and linguistic models of electronic text documents.
Open Questions and Future Directions

Modern technological advancements such as mobile ubiquitous computing and decentralized p2p networks rely on the collection, processing and sharing of vast amount of personal information which—when combined with big data and machine learning techniques—pose significant challenges to the rights of privacy and data protection. The GDPR, seeking to regulate the uncontrollable dissemination of personal data across the Internet and to give control of their data back to individuals, enshrines the RtbF to allow for the retroactive and permanent erasure of their personal data from all the places to which they have been disseminated. However, the issues arising from the GDPR data protection provisions, and in particular by the RtbF, are not trivial since they overlap with other rights such as the right to information and to free speech, with organizational practices such as the backup and archiving procedures, and with novel state-of-the-art technologies such as algorithmic profiling, ubiquitous computing, and decentralized p2p architectures. Following our analyses in the preceding Chapters, hereafter we identify any remaining open research questions and we highlight potential future directions towards the effective alignment of state-of-the-art technologies and business practices with the privacy and data protection principles of the GDPR.