Reviewerly/exHarmony huggingface dataset:
RETRACTED: Strength fitness control system and motor balance based on FPGA and wireless sensors

The Good quality ready for balance, adjustment and pose more helpful. According to a study, due to poor practice and a high personal risk of falling (and a ton of damage), the contrast, the quality of the preparatory work reduces the risk of falls. It is of high quality—no preparation movement of people. The latest trends in the sensor frame are looking for a method to provide higher accuracy and target while reducing the size and the use of force. Using a Field Programmable Gate Array (FPGA) brings explicit innovative devices that can be reprogrammed; correcting these innovations may be used for the sensor frame reconfigurable. This conversion capability allows the use of low power consumption and an incomplete configuration of complex applications. Since the FPGA's adaptive structure (parallel processing, on-chip memory, etc.) and the configurable properties genius performing the computationally brings high productivity, they are used in very high demand tasks. It has been supported. Improved display of sensor frame FPGA is now available for new uses. Spain is emerging another FPGA rely more sensitive, reconfigurable on, low-power sensor of the times. This white paper provided an overview of these improvements, an overview of the FPGA's innovative use in a unique exploration of the meeting, and outlined the future considering the field.
Digital Supply Chain Management Ecosystem Powered by Blockchain Technology

Most of the leading companies of the world invest heavily in a computerized system to maintain their supply chain management. From collecting manufacturing equipment to final delivery, products are being tracked by supply chain management software. Despite this huge investment in digitizing the supply chain system, complete visibility of a product at any given time is not fully achieved. Protecting authentic information throughout the supply chain management becomes a challenging task along with maintaining the quality of products. As all parties of a supply chain system have their standalone software, real-time communication amongst this software developed in different platforms using different technologies is not quite feasible. Other illegal parties can inject counterfeit products into the supply chain by taking advantage of this situation. This can pose a significant threat not only to the company&#x2019;s revenue but also to its public image as well. In this paper, we propose a framework to implement a unique decentralized ecosystem powered by blockchain for the supply chain system.
Korean-Chinese Bilingual Sentence Alignment Method Based on Character Length

Sentence-level aligned bilingual parallel corpus is an indispensable and valuable resource for machine translation, translation knowledge acquisition, and bilingual dictionary compilation. Based on the Korean-Chinese parallel corpus, this paper uses a Korean-Chinese sentence alignment algorithm based on character length to realize automatic Korean-Chinese sentence alignment, and proposes a sentence alignment evaluation method. Firstly, preprocess and segment the Korean-Chinese corpus; Secondly, calculate the mean and variance of the corpus distribution based on the Korean Chinese corpus, and use the probability score to find the maximum likelihood probability of the sentence under the framework of dynamic programming; Finally, proposed a sentence alignment judgment method, with the help of Hanjaja tool to convert Sino-Korean words in Korean sentences into Chinese words to form Korean sentences containing Chinese words (abbreviated as C-K sentences), then calculate the Jaccard coefficient between the C-K sentence and the Chinese sentence, by determining the appropriate threshold to automatically determine the alignment or not. Experiments show that the length-based sentence alignment method has a good effect on automatic alignment of Korean-Chinese sentences, and the accuracy of sentence alignment reaches 88.61%. The proposed sentence alignment judgment method is simple and effective.
Group affect Recognition: Facial Feature Extraction via Color Inverted Points

The paper proposes a new approach to extract facial features via color inverted points. This approach has been incorporated in GRAFFER, a system that we have been developing towards GRoup AFFect Recognition in educational, entertaining and other events. As facial features and especially the eyes, the mouth and the eyebrows offer a lot of information about human emotional states, we investigated existing facial component extraction methods and developed feature extraction through color-inversion of frames-images with facial components. The proposed approach is implemented and tested on the extensive datasets that we have collected with GRAFFER.
Model-Parallel Learning of Generative Adversarial Networks and Its Implementation

In this paper, we propose a novel model-parallel training approach for generative adversarial networks (GANs). We show that our method achieves faster training than the conventional method by implementing our method on an embedded single board computer. Code is available at https://github.com/hjdw2/GAN_model_parallel.
Reliability of Decision Support in Cross-spectral Biometric-enabled Systems

This paper addresses the evaluation of the performance of face and facial expression biometrics through a decision support system. The evaluation criteria include capturing the risk of the system, estimating the reliability of decision, and predicting the change in the perceived operator's trust in the decision. The relevant applications include human behavior monitoring and stress detection in individuals and teams, and in situational awareness system. Using an available database of cross-spectral videos of faces and facial expressions, we conducted a series of experiments to 1) demonstrate the phenomenon of biases in biometrics that affect the evaluated measures of the performance in human-machine systems, 2) explore the overall risk of the system caused by error rates such as false match and false nonmatch rates, and 3) calculate the reliability of cross-spectral and emotion-varying face identification.
“There’s no other way when nothing comes to mind”: Internet use in L2 writing classes

Technology is changing traditional views of language teaching and learning, with generational and cultural differences influencing the ways that we interact with it. This paper describes an action research project undertaken at a university in Japan to understand how students use the Internet to prepare written assignments in L2, and the students’ and their instructors’ reactions towards this usage. Classroom observation and technology usage logs revealed that students use websites and applications to gather ideas at the start of the writing process, rather than coming up with ideas themselves. Thematic analysis of student journal entries suggests that many students disregarded the brainstorming method that they had been taught due to a lack of confidence either in their ideas or in their linguistic competence. Students did not identify this behaviour as dishonest, unlike 70% of the instructors interviewed. This mismatch in student and instructor views may lead to missed learning opportunities for the L2 writer.
Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems

In a distributed deep learning system, a parameter server and workers must communicate to exchange gradients and parameters, and the communication cost increases as the number of workers increases. This paper presents a communication data optimization scheme to mitigate the decrease in throughput due to communication performance bottlenecks in distributed deep learning. To optimize communication, we propose two methods. The first is a layer dropping scheme to reduce communication data. The layer dropping scheme we propose compares the representative values of each hidden layer with a threshold value. Furthermore, to guarantee the training accuracy, we store the gradients that are not transmitted to the parameter server in the worker’s local cache. When the value of gradients stored in the worker’s local cache is greater than the threshold, the gradients stored in the worker’s local cache are transmitted to the parameter server. The second is an efficient threshold selection method. Our threshold selection method computes the threshold by replacing the gradients with the L1 norm of each hidden layer. Our data optimization scheme reduces the communication time by about 81% and the total training time by about 70% in a 56 Gbit network environment.
Monotonicity in Intuitionistic Minimal Change Semantics Given Gärdenfors’ Triviality Result

Monotonicity is desirable for many cognitive, computational and pragmatical reasons, even to non-monotonic logics. This paper is inspired by the role a monotonicity principle (M) plays in Gärdenfors’ [5] triviality result. It is found similar to another monotonicity principle in the semantics of IVC logic [4]. Hence I give an intuitionistic minimal belief change account, or IAGM, which is immune to triviality, along with a representation theorem. Moreover, the investigation of IAGM semantics sheds new light on understanding the behavior of rational monotonicity in various non-monotonic logics (NMLs).
Social Recommendation System Using Network Embedding and Temporal Information

Recommendation systems are one of the most popular and prevalent applications of machine learning. They aim to predict user interests and recommend products or items that are most likely to be of interest to them. A social recommendation can be characterized as a recommendation with an additional input, since the online users are connected to each other over social networks and have correlations which can be used to significantly increase the efficiency of recommendations. Given a continuously evolving social network, we seek to provide accurate recommendation services to large-scale heterogeneous user networks with temporal information and perform time-based updations that capture instant interests and social evolution. For this, a model is proposed that leverages the temporal semantic effects to preserve the social relationships and user behavior sequential patterns of a heterogeneous information network. Our aim is to perform the task of recommendation in a heterogeneous user-item network setting. To capture structural and semantic correlations of a heterogeneous user-item network, we use metapath2vec which is a promising network embedding method. The results obtained from the experiments demonstrate that the system can capture both the network property and its evolution, show the role of network embedding and temporal information in improving the performance of recommendation systems to predict top-K friends and items.
The Immune Space

In the context of SARS-CoV-2 health emergency, strongly framed in the normalization of logic of risk, the authors analyze three digital platforms of contagion containment and population tracking in order to investigate, through a comparative-descriptive analysis, the relationship between different socio-political-cultural contexts and the respective responses adopted—the Chinese government tool Health Code, the South Korean app Corona100, and the Italian app, Immuni—to counter a single global emergency. The objective is to investigate the framing operations that introduced on a global scale the use of apps in bio-security and immunity regime for which individual privacy increasingly collides with collective security. The authors consider central the opening of a debate on how the logic of risk and worst case scenario are paradigmatic nowadays in the development of increasingly sophisticated systems, potentially invasive of privacy, even in function of complex threats interconnected on a global scale.
Multi-OctConv: Reducing Memory Requirements in Image Generative Adversarial Networks

Generative Adversarial Networks (GANs) for image generation of human faces have provided excellent results in recent years. However, we were able to identify a common problem among them: high memory usage in their training phase due to the convolutional encoder architecture used in these models. We address this issue by replacing the traditional convolutional layers in a model by what we call a Multi-Octave Convolution (M-OctConv) without modifying its architecture. An advantage of this method is that it can be easily combined with traditional memory reduction techniques, such as pruning. We evaluate our proposition on StarGAN model achieving up to 40% of memory usage reduction without affecting the quality of the generated images.
Separation of the Latent Representations into "Identity" and "Expression" without Emotional Labels

Learning semantically disentangled representations is important for various computer vision tasks, such as image generation and classification. Although it is possible to learn an effective representation in supervised settings, there are problems requiring enormous effort focused in data collection and labeling, and the difficulty in labeling continuously changing events like facial expressions is significant. In this paper, we propose a method for separating the latent representation of facial images into identity factors and facial expression factors using the variational autoencoder (VAE) framework. In our method, we only use subject labels to control training, and we do not use information attached to facial expressions like emotional labels. The separation between extracted facial expression factors and identity features is very useful for controlling image generation and for classifying facial expressions. Using this latent representation, we also suggest a new approach for facial expression recognition with a simple clustering method, which is based on Euclidean distance. Our classification method dramatically reduces the cost of labeling. The experimental results show that our method successfully disentangles the representation of facial images and separates the latent representation into identity and facial expression factors. Moreover, in a facial expression recognition task, our approach shows advantages over the baseline method without supervision.
Improving Agile Development from Perspective of Design-Informing Model

Agile development is usually used to solve the problem of inflexibility which the other project management models, like the Waterfall model and the V model, may meet. However, conventional agile development still faces many problems under rapid iterations. Frequent iteration lacks efficient communication a large community, resources to share information, and quality of product. For better project management, this article describes a method, called the design-informing model (DIM), to improve the agile development. The design-informing model consists of four parts, user mental mode, developer mental model, system model, and environmental model. At the end of the paper, some discussions will be conducted to evaluate DIM in practice.
Decentralized Billing and Subcontracting of Application Services for Cloud Environment Providers

This paper proposes a decentralized billing and subcontracting system for regional cloud service providers. Based on blockchain technology, this system allows, on the one hand side, to collectively offer services in a distributed environment in a strict or ad-hoc federation and, on the other, to bill each user of such a services individually without a respective central service. In order to do so, it uses a blockchain-based transaction process which uses specialized tokens in order to enable a fair and secure distribution of requested cloud services. It maintains the ability to achieve consensus by validating the respective blockchain (part). In result, the proposed system is not bound to a specific technology, but rather open to any blockchain that allows arbitrary data or modeling of custom transactions.
Logical Circuits of a ΣΠ-Neuron Model

The paper proposes a schematic representation for implementing a ΣΠ-neuron model. Two types of digital and hybrid ΣΠ-neurons are presented with two layers that include multiplication and summing operations. The networks are built using digital and analog electronics. The multiplication operations in both networks are carried out identically. Summing operations in the digital network is implemented through a digital circuit - an adder, while in the hybrid framework it is implemented via an analogue-to-digital conversion and analogue voltage adder. To set up the threshold voltage in the hybrid and digital networks the hybrid and logic comparators are employed correspondingly. The results obtained can be used to construct ΣΠ-neural networks that could be embedded into hardware/software for controlling mechatronic systems.
A foreground-focused action recognition algorithm for intelligent unmanned systems

A foreground-focused action recognition algorithm based on convolutional neural network for intelligent unmanned systems is presented in this paper. The algorithm consists of the foreground extraction part and the action recognition part. For the foreground extraction part which can be considered as an object detection task, a convolutional neural network-based single-shot model with the spatial pyramid pooling structure and Mish activation function is developed to achieve a better performance. For the action recognition part, a Convolution-3D modified model is proposed which applies the 3D convolution kernel to obtain temporospatial features. To avoid the distortion of the foreground target, the padding form of the foreground extracted by the object detector is used as an input of the action recognition part. The algorithm can be applied to intelligent unmanned systems, being able to make a real-time performance of accurate action recognition.
Proposal for Visual Attention Level Based on Microsaccades after Saccades

Several studies on microsaccades have been performed; however, those studies measured microsaccades under the fixation state. For practical applications, such as cognitive science, psychology, brain science, and user interface, it is ideal to measure microsaccades from the eye-tracking data that includes saccades. In this study, we propose the visual attention level based on the eye-tracking data including microsaccades after saccades. Based on the research on the visual attention level of humans, the microsaccades and pupil diameter were simultaneously measured in three experimental tasks that required different visual attention levels. Through statistical analysis, the results of the experiment indicated that the behaviors of the microsaccades and the pupil were different between the three experimental tasks, and suggested that the visual attention level can be classified by microsaccades after saccades.
Designing a Predictive Model for Efficient Learning Outcomes in Korean Education

The present study extracted meaningful data from domestic students’ accumulated past learning outcome data utilizing the Educational Data Mining analysis technique and processed the data to design a university entrance probability prediction model and derive indicators that can be utilized by learners in the prediction of university entrance examination results. The design model presented in the present paper proposed analysis modeling to semantically classify individual learners’ training data tables based on the basic policy of Korean education for the entrance examination in order to create position models suitable for the attributes of prediction and predict university entrance examination routes.
Enhancing primary students’ vocabulary learning motivation and outcomes using the mobile user-generated-content tool

The purpose of this study was to investigate the effect of a mobile User-Generated-Content (m-UGC) tool on enhancing primary students’ vocabulary learning motivation and learning outcomes. A total of 40 primary students in Hong Kong participated in this study. The results showed using the m-UGC tool could increase primary students’ learning motivation and significantly improve their vocabulary learning.
Software Architecture

What is software architecture? A clear and simple definition is that software architecture is about making important design decisions that you want to get right early in the development of a software system because, in the future, they are costly to change. Being a good software architect is not easy. It requires not only a deep technical competency from practicing software architecture design in industry, but also an excellent understanding of the theoretical foundations of software architecture are gained from doing software architecture research. This chapter describes some significant research, development, and education activities that the author has performed during her professional trajectory path to develop knowledge, skills, and experiences around this topic.
Professional competence build-up among future teachers and teaching psychologists during professional training

This article deals with the specifics of practical professional training among future teachers and teaching psychologists. The said specialists receive professional training during practical studies. Recently the focus of attention shifted to justifying the contents of professional training which is conducive to the build-up of professional competences. Thusly, we deemed it rational to expose a number of issues and problems student cope with during work experience. We defined markers of professional competence build-up and presented the follow-up diagnostics results of the said practice.
Self-adaptive Topology-hiding Broadcast based on CRT

In the course of network communication, the exposure of network topology may seriously result in leaking sensitive information to attackers, saying social relationships or financial flows. Howbeit, balancing the functionality of routing between topology concealment is never a trivial task in consideration of efficiency, robustness, and scalability. Though a certain of topological-hiding protocols have emerged in recent years, they are faulty with third-party dependence and high latency. This paper focuses on devising a broadcast protocol exploiting the structure of ideal. We also implemented it utilizing the Chinese Remainder Theorem (CRT) and compared it with previous schemes. It is proved that our protocol is unconditionally secure under the game-based definition of topology hiding and capable of broadcasting the information with few iterations. Besides, the simulation also demonstrates its preferable computation/communication overhead in contrast with existing protocols, not to mention that any third-party would be unnecessary.
Dataset on the perceptions of ordinary people on the persistence of bribery practices in Nigeria

This dataset provides the perceptions of ordinary people on the relationship between societal acceptance of bribery, anti-bribery measures, socio-economic impacts of bribery, demographic factors and persistence of bribery practices. A multi-stage sampling technique was used to target the respondents. A total of 887 grassroots respondents participated, out of which 836 responses were used for quantitative data analyses. Statistical Packages for Social Sciences (SPSS) version 23 and SmartPLS3.0 were used to determine the reliability and validity of the data. The data is useful to the authorities in formulating strategies to deal decisively with the issues of bribery practices in Nigeria.