Reviewerly/exHarmony huggingface dataset:
Using Bidirectional GAN with Improved Training Architecture for Imbalanced Tasks

Classification with an imbalanced dataset has been a hard task for a long time. As generative models become more and more attractive, many researchers try to use these methods to address the imbalance problem. In this paper, we employ Bidirectional Generative Adversarial Networks (BIGAN) with improved training architecture as an oversampling method for minority class. We introduce an additional feedback by making use of the encoder <tex>$E$</tex> and further enhance the training of generator <tex>$G$</tex>. We conduct the qualitative and quantitative assessment on generated samples, and it shows that under the premise of ensuring diversity, the generated data tend to have less noise. Using it to perform data augmentation in training process, our method has been proved to get a better performance than original GAN-based method on multi-class imbalanced classification task.
Sports Dance Action Recognition System Oriented to Human Motion Monitoring and Sensing

Because of its high research value, action recognition has become a very popular research direction in recent years. However, the research on the combination of motion recognition technology and dance movements is still in its infancy. At the same time, due to the high complexity of dance movements and the problems of human body self-occlusion when performing dances, research on dance video action recognition has been caused. Progress is relatively slow. This article mainly introduces the research of sports dance action recognition system oriented to human motion monitoring and sensing, fully considers the abovementioned problems, and makes in-depth research and analysis on the current excellent action recognition research content in this field. This paper proposes a research method of sports dance movement recognition for human movement monitoring and sensing, including sports dance movement classification algorithm and sports dance movement preprocessing algorithm, which is used to conduct research experiments on sports dance movement recognition for human movement monitoring and sensing. The experimental results of this article show that the average recognition accuracy of the sports dance action recognition system for human motion monitoring and sensing is 92%, which can be used in daily sports dance training and competition.
Offline Reinforcement Learning as Anti-exploration

Offline Reinforcement Learning (RL) aims at learning an optimal control from a fixed dataset, without interactions with the system. An agent in this setting should avoid selecting actions whose consequences cannot be predicted from the data. This is the converse of exploration in RL, which favors such actions. We thus take inspiration from the literature on bonus-based exploration to design a new offline RL agent. The core idea is to subtract a prediction-based exploration bonus from the reward, instead of adding it for exploration. This allows the policy to stay close to the support of the dataset. We connect this approach to a more common regularization of the learned policy towards the data. Instantiated with a bonus based on the prediction error of a variational autoencoder, we show that our agent is competitive with the state of the art on a set of continuous control locomotion and manipulation tasks.
The RESTless cloud

Cloud provider APIs have emerged as the de facto operating system interface for the warehouse scale computers that comprise the public cloud. Like single-server operating systems, they provide the resource allocation, protection, communication paths, naming, and scheduling for these large machines. Cloud provider APIs also provide all sorts of things that operating systems do not, things like big data analytics, machine learning model training, or factory automation. Somewhere, lurking within this menagerie of services, there is an operating system interface to a really big computer, the computer that today's application developers target. This computer works nothing like a single server, yet it also isn't a dispersed distributed system like the internet. It is something in-between. Now is the time to distill and refine a coherent "cloud system interface" from the multitude of cloud provider APIs, preferably a portable one. In this paper we discuss what goes in, what stays out, and the principles that inform these decisions.
Blockchain technology as a panacea for procurement corruption in digital era

Corruption in public institutions is a significant problem that stifles economic, social and environmental development worldwide. This predominates when there is a lack of transparency, inadequate record-keeping, and low public accountability. Accordingly, the questions this paper intends to provide answers to are two-fold. Firstly, what are the recurring patterns of procurement corruption in the South Africa (SA) public sector? Secondly, how can digital technology deployment assist in checking this trend? The desktop method was adopted through literature examination of studies relating to corruption, procurement, blockchain and digitization. We conclude by proposing a model/framework for adopting and using blockchain technology in public institutions to minimise corruption and the time taken for contract document preparation and acceptance. This study contributed to knowledge by evaluating the issues associated with public procurement and how blockchain and digitization can be adopted to help stern the tide of corruption in public institutions.
Set-aware Entity Synonym Discovery with Flexible Receptive Fields

Entity synonym discovery (ESD) from text corpus is an essential problem in many entity-leveraging applications, e.g., web search and question answering. This paper aims to address three limitations that widely exist in the current ESD solutions: 1) the lack of effective utilization for synonym set information; 2) the feature extraction of entities from restricted receptive fields; and 3) the incapacity to capture higher-order contextual information. We propose a novel set-aware ESD model that enables a flexible receptive field for ESD by making a breakthrough in using entity synonym set information. The contextual information of entities and entity synonym sets are arranged by a two-level network from which entities and entity synonym sets can be mapped into the same embedding space to facilitate ESD by encoding the high-order contexts from flexible receptive fields. Extensive experimental results on public datasets show that our model consistently outperforms the state-of-the-art with significant improvement.
Making Sense of Algorithms: Relational Perception of Contact Tracing and Risk Assessment during COVID-19

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Implementing innovative gaze analytic methods in design for manufacturing: a study on eye movements in exploiting design guidelines

Design guidelines aim to raise awareness of possibilities and limitations in manufacturing. The guidelines intend to support engineers and students to exploit the potential of manufacturing processes and to overcome the challenges of design for manufacturing. However although there are studies which show the benefit of guidelines, it remains unclear how this benefit is achieved or why it is missing. The aim of the present paper is to analyze the visual behavior of engineers and students while working with a design guideline. Gaze stationary and transition entropy are eye tracking metrics that quantitatively describe visual behavior. These metrics have been implemented in fields such as medicine and arts, but not yet in engineering. We conducted a study with 16 engineers and 20 students, who work with a design guideline and then solved an engineering design task. Their performance was classified by the manufacturability and the manufacturing effort of the design outcome, and their scanning patterns were analyzed using innovative eye tracking metrics. The results show that high performing engineers have a significant lower stationary entropy, while high performing students tend to have both high stationary and high transition entropies. While we were not able to show clear group differences, to predict the performance only through visual scanning behavior, gaze entropy remains a feasible metric for interest and curiosity. Furthermore, we adopt a Target Based Analysis for design methods to further investigate in the benefit of design guidelines.
Keyword search over schema-less RDF datasets by SPARQL query compilation

This article introduces an algorithm to automatically translate a user-specified keyword-based query K to a SPARQL query Q so that the answers Q returns are also answers for K. The algorithm does not rely on an RDF schema, but it synthesizes SPARQL queries by exploring the similarity between the property domains and ranges, and the class instance sets observed in the RDF dataset. It estimates set similarity based on set synopses, which can be efficiently pre-computed in a single pass over the RDF dataset. The article includes two sets of experiments with an implementation of the algorithm. The first set of experiments shows that the implementation outperforms a baseline RDF keyword search tool that explores the RDF schema, while the second set of experiments indicate that the implementation performs better than the state-of-the-art TSA+BM25 and TSA+VDP keyword search systems over RDF datasets based on the “virtual documents” approach.
Tac-Valuer: Knowledge-based Stroke Evaluation in Table Tennis

Stroke evaluation is critical for coaches to evaluate players' performance in table tennis matches. However, current methods highly demand proficient knowledge in table tennis and are time-consuming. We collaborate with the Chinese national table tennis team and propose Tac-Valuer, an automatic stroke evaluation framework for analysts in table tennis teams. In particular, to integrate analysts' knowledge into the machine learning model, we employ the latest effective framework named abductive learning, showing promising performance. Based on abductive learning, Tac-Valuer combines the state-of-the-art computer vision algorithms to extract and embed stroke features for evaluation. We evaluate the design choices of the approach and present Tac-Valuer's usability through use cases that analyze the performance of the top table tennis players in world-class events.
Incorporating LDA with LSTM for followee recommendation on Twitter network

Purpose The purpose of this study is to facilitate the task of finding appropriate information to read about, and searching for people who are in the same field of interest. Knowing that more people keep up with new streaming information on Twitter micro-blogging service. With the immense number of micro-posts shared via the follower/followee network graph, Twitter users find themselves in front of millions of tweets, which makes the task crucial. Design/methodology/approach In this paper, a long short–term memory (LSTM) model that relies on the latent Dirichlet allocation (LDA) output vector for followee recommendation, the LDA model applied as a topic modeling strategy is proposed. Findings This study trains the model using a real-life data set extracted based on Twitter follower/followee architecture. It confirms the effectiveness and scalability of the proposed approach. The approach improves the state-of-the-art models average-LSTM and time-LSTM. Research limitations/implications This study improves mainly the existing followee recommendation systems. Because, unlike previous studies, it applied a non-hand-crafted method which is the LSTM neural network with LDA model for topics extraction. The main limitation of this study is the cold-start users cannot be treated, also some active fake accounts may not be detected. Practical implications The aim of this approach is to assist users seeking appropriate information to read about, by choosing appropriate profiles to follow. Social implications This approach consolidates the social relationship between users in a microblogging platform by suggesting like-minded people to each other. Thus, finding users with the same interests will be easy without spending a lot of time seeking relevant users. Originality/value Instead of classic recommendation models, the paper provides an efficient neural network searching method to make it easier to find appropriate users to follow. Therefore, affording an effective followee recommendation system.
Project scheduling and performance prediction: a fuzzy-Bayesian network approach

Purpose Despite the extensive research in project risk management and availability of several techniques and tools, quantifying uncertainty in project schedules remains a challenge. Current risk analysis models suffer from several shortcomings that need to be addressed to provide more reliable and valid schedules. This paper aims to present a dynamic decision support system with the purpose of providing project managers with necessary tool for making real-time informed decisions. Design/methodology/approach The proposed approach incorporates the widely accepted critical path method (CPM) calculations in a Bayesian network (BN). BN is employed to conduct inferencing and causal analysis and provide probabilistic results, which can improve the decision-making process. Time parameters of each activity in the CPM network is modeled by a set of simulation nodes in the BN. Prior probability distribution of activities duration is extracted from experts using a fuzzy analytical solution. Findings The model proposed in this paper is able to address some key outstanding issues of current project scheduling techniques through: (1) modeling the causality among different sources of schedule uncertainty, (2) minimizing uncertainty in experts' evaluations, (3) assessing effects of unknown risk factors and (4) using actual activity data for learning the behavior of project and predicting crew productivity. Originality/value The purposed methodology provides a framework for the new generation of project schedule analysis tools that are better informed by available knowledge and data, and hence, more reliable and useful.
Modal Logic With Non-Deterministic Semantics: Part II—Quantified Case

Abstract In the first part of this paper we analyzed finite non-deterministic matrix semantics for propositional non-normal modal logics as an alternative to the standard Kripke possible world semantics. This kind of modal system characterized by finite non-deterministic matrices was originally proposed by Ju. Ivlev in the 70s. The aim of this second paper is to introduce a formal non-deterministic semantical framework for the quantified versions of some Ivlev-like non-normal modal logics. It will be shown that several well-known controversial issues of quantified modal logics, relative to the identity predicate, Barcan’s formulas and de re and de dicto modalities, can be tackled from a new angle within the present framework.
EntDetector: Entanglement Detecting Toolbox for Bipartite Quantum States

Quantum entanglement is an extremely important phenomenon in the field of quantum computing. It is the basis of many communication protocols, cryptography and other quantum algorithms. On the other hand, however, it is still an unresolved problem, especially in the area of entanglement detection methods. In this article, we present a computational toolbox which offers a set of currently known methods for detecting entanglement, as well as proposals for new tools operating on two-partite quantum systems. We propose to use the concept of combined Schmidt and spectral decomposition as well as the concept of Gramian operators to examine a structure of analysed quantum states. The presented here computational toolbox was implemented by the use of Python language. Due to popularity of Python language, and its ease of use, a proposed set of methods can be directly utilised with other packages devoted to quantum computing simulations. Our toolbox can also be easily extended.
Using parsed and annotated corpora to analyze parliamentarians' talk in Finland

Abstract We present a search system for grammatically analyzed corpora of Finnish parliamentary records and interviews with former parliamentarians, annotated with metadata of talk structure and involved parliamentarians, and discuss their use through carefully chosen digital humanities case studies. We first introduce the construction, contents, and principles of use of the corpora. Then we discuss the application of the search system and the corpora to study how politicians talk about power, how ideological terms are used in political speech, and how to identify narratives in the data. All case studies stem from questions in the humanities and the social sciences, but rely on the grammatically parsed corpora in both identifying and quantifying passages of interest. Finally, the paper discusses the role of natural language processing methods for questions in the (digital) humanities. It makes the claim that a digital humanities inquiry of parliamentary speech and interviews with politicians cannot only rely on computational humanities modeling, but needs to accommodate a range of perspectives starting with simple searches, quantitative exploration, and ending with modeling. Furthermore, the digital humanities need a more thorough discussion about how the utilization of tools from information science and technologies alter the research questions posed in the humanities.
Smart Spying Robot with IR Thermal Vision

Recently, with rising public security awareness, the demand for an efficient surveillance system has become highly significant. But, several monitoring systems developed have certain drawbacks limiting their applications. Therefore, a refined surveillance system has been proposed which could be utilized in many fields, especially in border areas for intrusion detection, to reduce the crime rates and to serve tremendous purposes. The project focuses on building a spying robot for maneuvering and monitoring in perilous environments. The module includes Raspberry Pi interfaced with DC motors, GPS, Pi camera, and IR Thermal camera sensor. Meanwhile, the motion of the robot could be controlled by a web application through Wi-fi connectivity. This robot is programmed to stream the live data over a virtual network and update the captured images along with the time stamp and location at certain instances to the cloud. Hence, it can transmit real-time videos and pictures with thermal vision capabilities and due to its unobtrusive nature, it can be utilized in war zones. Also, this can be used in observing disaster-affected regions that are beyond human reach.
Research on the English Teaching Mode under the Background of Computer Big Data

Abstract With the popularity of the Internet, the era of big data (hereinafter referred to as BD) has come. At the same time, information technology is gradually improving, which also leads to the application of AI in many fields, including English teaching (hereinafter referred to as ET). With the application of “Internet +”, colleges must change the traditional ET mode (hereinafter referred to as TM). By adapting to computer BD technology, colleges can establish a smart English education platform (hereinafter referred to as EEP), which can help students learn better. With the construction of smart EEP, colleges can carry out activities and evaluation from many aspects. Through the innovation of English TM, colleges can innovate teaching ideas, which can create a good English learning context. Through the smart EEP, colleges can enrich the teaching content (hereinafter referred to as TC), which can further improve the teaching evaluation system. Firstly, this paper analyzes the impact of BD on College ET. Then, this paper constructs a smart EEP. Finally, some suggestions are put forward.
AudioLens: Audio-Aware Video Recommendation for Mitigating New Item Problem

From the early years, the research on recommender systems has been largely focused on developing advanced recommender algorithms. These sophisticated algorithms are capable of exploiting a wide range of data, associated with video items, and build quality recommendations for users. It is true that the excellency of recommender systems can be very much boosted with the performance of their recommender algorithms. However, the most advanced algorithms may still fail to recommend video items that the system has no form of representative data associated to them (e.g., tags and ratings). This is a situation called New Item problem and it is part of a major challenge called Cold Start. This problem happens when a new item is added to the catalog of the system and no data is available for that item. This can be a serious issue in video-sharing applications where hundreds of hours of videos are uploaded in every minute, and considerable number of these videos may have no or very limited amount of associated data.In this paper, we address this problem by proposing recommendation based on novel features that do not require human-annotation, as they can be extracted completely automatic. This enables these features to be used in the cold start situation where any other source of data could be missing. Our proposed features describe audio aspects of video items (e.g., energy, tempo, and danceability, and speechiness) which can capture a different (still important) picture of user preferences. While recommendation based on such preferences could be important, very limited attention has been paid to this type of approaches.We have collected a large dataset of unique audio features (from Spotify) extracted from more than 9000 movies. We have conducted a set of experiments using this dataset and evaluated our proposed recommendation technique in terms of different metrics, i.e., Precision@K, Recall@K, RMSE, and Coverage. The results have shown the superior performance of recommendations based on audio features, used individually or combined, in the cold start evaluation scenario.
Research on the application of computer network resources in Japanese Teaching

Abstract Many students are eager to study in Japan. They want to understand the national culture of Japan. The prerequisite for studying in Japan is to pass the Japanese certificate. The policy of Japanese Teaching in China is very clear. The Ministry of education encourages college students to fully understand foreign languages and cultures[1]. With the development of computer network resource technology, experts believe that the process of Japanese teaching has become more convenient. This paper briefly describes the main requirements of Japanese teaching. On this basis, this paper puts forward the main application of network resources in Japanese teaching.
Construction of Music Teaching Effect Evaluation Model Based on 5G Technology

In this era of rapid development, informatization is the new trend of social development. With the advent of information society, the traditional music teaching concept and teaching practice are facing great challenges and new opportunities. In order to meet this challenge and meet the needs of the development of information society and the training of music normal students, 5G mobile communication system has become a hot topic in the communication industry and academia. With the advent of 5G era, 5G mobile communication will play a far-reaching role in our future education, and 5G technology will also become one of the core technologies of education. Based on the analysis of 5G technology, this paper predicts that under the support of new technology, music education will present more novel teaching scenes. The new art teaching mode of “integration of multi-source practice teaching”, “cloud space intelligent teaching” and “network collaborative City Teaching” is more conducive to the growth of music teaching and plays a promoting role in music teaching. Music classroom performance will be more diverse, students will learn more comprehensive things, is no longer limited to the knowledge of books, the invention of 5G technology, can let students in music learning reflect more fun