Reviewerly/exHarmony huggingface dataset:
A Regularized Hypergraph Recommendation Algorithm with Attention Mechanism

Collaborative filtering is one of the most commonly used recommendation technologies in recommender systems. However, it has the problems of sparse score data and low recommendation accuracy. To address these problems, we propose a regularized hypergraph recommendation algorithm with attention mechanism. We can fully mine the high-order relationships in the hyperedges without loss of information. In addition, we apply users’ attributes information to calculate the attraction of an item to the attributes, and then get the similarity between items. In order to calculate the similarity more accurately, the relations of items to attributes are divided into two cases: likes and dislikes. Moreover, according to the different attention of users to different items, we introduce the attention mechanism. Finally, we build and optimize the regularization function to obtain the predictive scores and recommendation lists. Experimental results on Movielens-100K and Movielens-1M demonstrate the effectiveness of our proposed algorithm.
Teacher-Pupil Interaction and Distance Learning in Emergency Periods

There are many types of coronaviruses, but the one identified in December 2019, that is, the SARS-CoV-2, which has caused a pandemic of respiratory illness, is referred to as COVID-19. The World Health Organisation (WHO) declared COVID-19 a public health emergency of international concern on 30 January 2020 and a pandemic on 11 March 2020. The pandemic became an emergency that required emergency interventions. The United Kingdom went through more than one period of lockdowns, just to keep people away from each other as much as possible. With the lockdowns, all educational institutions were asked to close their doors, and since the end of a pandemic cannot be determined, distance learning was adopted as the alternative to physical classes. Standards may exist and work in normal circumstances, but in emergencies, proactiveness and innovation in different directions come to play. The COVID-19 pandemic has been a lesson to everyone that we must be always prepared to adapt to any situation that we find ourselves in with unity of purpose to surmount every challenge.
The Dark Side of Sentiment Analysis: An Exploratory Review Using Lexicons, Dictionaries, and a Statistical Monkey and Chimp

This article discusses the inconsistencies, inaccuracies and challenges, namely the `dark side' of sentiment analysis and then demonstrates problems with using sentiment analysis lexicons or dictionaries for estimating sentiment in textual artifacts. Sentiment analysis, an important dimension of natural language processing (NLP), has seen an exponential adoption rate across research and practitioner disciplines. Many interesting developments in NLP methods continue to improve the accuracy of sentiment analysis. However, the plethora of sentiment analysis methods, dictionaries and lexicons, tools, open source code for machine learning based sentiment analysis, and of-the-shelf sentiment analysis solutions have led to a flurry of research and applied solutions without sufficient concern for the limitations, context, and the inaccuracies of sentiment analysis, and the inherent ambiguities associated with the unaddressed sentiment analysis domain challenges. Scant attention is given, especially in applied research and industry usage, to the inherent ambiguities associated with the unanswered questions pertaining to the science of sentiment analysis. This study reviews known issues with sentiment analysis as documented by prior research and then compares the application of multiple of-the-shelf lexicon and dictionary methods to stock market and vaccine tweets. The intention is not in any way to improve the accuracy of sentiment analysis as compared to prior benchmarks but to identify and discuss critical aspects of the dark side and develop a conceptual discussion of the characteristics of the dark side of sentiment analysis. We conclude with notes on conceptual solutions for the dark side of sentiment analysis and point to future strategies that could be used to improve the accuracy of sentiment analysis and understanding. This research will also help align researcher and practitioner expectations to understanding the limits and boundaries of natural language processing based solutions for sentiment analysis and estimation.
Improving Springboard Diving Video Classification Accuracy Through Cumulated Segment Values

Automatic classification of springboard dives from video sequences is a challenging task for modern action recognition systems, still resulting in low accuracies. We evaluate a new approach aiming to increase overall classification accuracy through improved, segment cumulation based somersault recognition. Each video sequence is automatically split into its frames, which are then as- signed to one of four classes representing 90-degree segments of a rotation. In the following, the sequence of segment values is then condensed and summed up to a cumulated segment value (CSV). Based on threshold values, the CSV can finally be mapped to the number of performed somersaults. Applying this approach to a test sample, segments were recognized with an accuracy of 82.3%, resulting in correct somersault recognition for all investigated dives.
The Prevalence of Nigerian Pidgin as a Medium of Communication among Nigerian University Undergraduates: A Study of Nigerian Pidgin usages among the Undergraduates of University of Nigeria Nsukka

The use of Nigerian pidgin, popularly known as broken or Pidgin English, is gradually but consistently gaining the upper hand of usage among university undergraduates in the southern part of Nigeria, over the English language which should be the linguistic model in institutions of higher learning. This study used the undergraduates of the University of Nigeria, Nsukka as a case study to verify the reasons why university undergraduates prefer the use of pidgin to the use of Nigerian English during conversations and the possible effect of this overt linguistic behavior on the spoken and written English of the undergraduates. In doing this two research questions were generated and data collected from fifty undergraduates from different faculties and departments of the University of Nigeria Nsukka, through a twenty-item questionnaire. This was done using a random sampling technique. The questionnaires were administered to the respondents and their responses were analyzed through Pearson Moment Correlation Formula. The result of the analysis showed that one of the reasons why the undergraduates prefer the use of pidgin is because it is easier to speak, and also makes the conversation interesting and enduring. On the effects of Nigerian pidgin on the English language, the respondents agreed that the use of Nigerian pidgin can have a negative effect on the written and spoken English of the undergraduates.
DG-Trans: Automatic Code Summarization via Dynamic Graph Attention-based Transformer

Automatic code summarization is an important topic in the software engineering field, which aims to automatically generate the description for the source code. Based on Graph Neural Networks (GNN), most existing methods apply them to Abstract Syntax Tree (AST) to achieve code summarization. However, these methods face two major challenges: 1) they can only capture limited structural information of the source code; 2) they did not effectively solve Out-Of-Vocabulary (OOV) problems by reducing vocabulary size. In order to resolve these problems, in this paper, we propose a novel code summarization model named Dynamic Graph attention-based Transformer (DG-Trans for short), which effectively captures abundant information of the code subword sequence and utilizes the fusion of dynamic graph attention mechanism and Transformer. Extensive experiments show that DG-Trans is able to outperform state-of-the-art models (such as Ast-Attendgru, Transformer, and CodeGNN) by averagely increasing 8.39% and 8.86% on BLEU scores and ROUGUE-L, respectively.
Negative Samples-enhanced Graph Convolutional Neural Networks

Graph neural networks (GNNs) have shown great success in graph representation learning by extracting high-level features from nodes and their topology. Many previous studies have used the message passing mechanism to fuse neighborhood information with an assumption that neighbors should share some similar behaviors and then have positive correlations. Therefore, the neighbor nodes can be considered as positive samples and most of existing methods can be viewed as using these positive samples to update new feature vectors. Along with these positive samples, there are also some hidden negative samples (non-neighborhood nodes) which also contains information for a certain node but has been ignored so far. In addition, existing studies on negative sampling for graphs only fuse the sampling results into the loss function for the model training. In this paper, we propose a Negative Samples-enhanced Graph Convolutional Neural Networks (NegGCNs), where the negatively sampled nodes are directly incorporated into the message passing mechanism and used to update new node feature vectors. We further investigated the effects of negative sampling rate and the number of negatively sampled nodes on the classification accuracy of graph nodes. Experiments on citation network datasets suggest that the proposed NegGCNs model can improve the accuracy of graph node classification with a specific negative rate compared to GCNs.
Research on Zhuang Brocade Culture Based on 3D Printing Technology in Digital Context

With the continuous development of science and technology, under the background of information and digital era, Zhuang Brocade culture also integrates into the great torrent of The Times, and will be widely used in business, ethnic minority regional culture research, characteristic courses and audio Zhuang Brocade database and other fields. The author will from zhuang jin general situation of the regional cultural development, protection, inheritance, development trend and delve into audio zhuang jin database creation aspect, incorporate digital zhuang jin zhuang jin in one hundred for the transmission of cultural protection in the future even more long-term plan, it will be the outstanding traditional culture protection heritage for the future development direction and trend.
Local to global Tracker: A Siamese Network for Long-term Tracking

Abstract Visual object tracking is a basic task in computer vision, which can be applied to different applications. Former methods of single object tracking problem demands to perform target state estimation according to object position of previous frame, but these methods are poorly capable of handling the fast movement of the target. In this paper, we introduce a Siamese network for single object tracking task. The network consists of two branches, one of which is the classification branch used to predict positive or negative samples, and the other is the regression branch used to predict the specific location of the object in sequence. In addition, we propose a local to global algorithm for long-term tracking to expand the search region when the target is out-of-view or full occlusion. Experiments show that our tracker achieves very promising results on VOT2018LT datasets while running at over 89 FPS.
Personalized Thread Recommendation on Thai Internet Forum

The rise of user-generated content on the Internet today has led to the problem of data overload. Therefore, recommender systems have been introduced in various social platforms to automatically serve interesting content to users. Pantip.com is the most popular Thai Internet forum where people can discuss ideas, tips, and news on a variety of topics. Although Pantip has many recommendation services, these are not specific for individual users. In this paper, we proposed a personalized thread recommender system that is applicable to the Pantip site. The approach finds out appropriate threads for each user based on three aspects: user interests, thread trends, and thread freshness along with the analysis in changing of user behavior over time. We conducted experiments on the Pantip clickstream dataset and evaluated the performance by real users. Experimental results show that the proposed approach recommends threads that are significantly more satisfying for users than the baseline approaches.
A beginner's guide to how robots work

This chapter makes the reader aware of the complexity of building intelligent robots with the ambition of clarifying common misconceptions about Artificial Intelligence (AI). Starting from the basics of computer programming explained simply, the chapter shows that AI is nothing more than a computer program designed in such a way to address the diversity of situations that a robot may encounter when interacting with people and the environment. Specifically, the concept of “robustness” of a program is introduced, clarifying why robotic researchers aim to design robust AI programs: that is, robot programs that work well in almost all real-world situations and not only under well-specified and favorable conditions. Next, two popular approaches in the literature to enable robots to deal with the complexity of the environment are described and commented on. Finally, the chapter discusses why making a robot capable of operating reliably in a world that is inherently noisy and dynamic is so hard, and any attempt to build such a robot might incur the risk of frustrating people’s expectations.
Adult Students Become Professionals Teaching or Learning – What’s in a Name?

We provide our experience-based vision on teaching Interaction Design-related courses in multiple cultures. We explain how students should be treated as adults with their own learning goals and their own competence of assessing the level of professionalism reached. We analyse our approach in relation to the ACM-IEEE CC2020 Curriculum Guidelines. Finally, we provide an example of our teaching approach during lock-down.
Image Processing and ArcSoft Based Data Acquisition and Extraction System

Characteristic data acquisition and extraction are one of the core realizations of artificial intelligence application development, which is completed through the data output by traditional hardware equipment as the basis, coupled with efficient artificial intelligence applications, and then provide accurate and reliable data support for the data analysis stage. Focusing on image filtering and recognition technology, and framework development applying to portable monitoring systems, here is a realization of data acquisition and extraction system. OpenCV graphics library is used to complete the data acquisition of monitoring terminal and a bilateral Gaussian filter algorithm with configurable parameters adapted to the Raspiberry hardware is designed to filter out noise and smooth the image while saving/protecting the edges. The filtering output as the image sample data basis, a rapid development framework (V1.0) with the ArcSoft artificial intelligence engine as the core is realized to filter out redundant data and extract facial feature data on demand (what users care about) with the conversion and storage in proper format, and such extraction tool is suitable for face data processing.
Research on Japanese Listening Teaching System for Autonomous Learning Supported by Multimedia Resources

Multimedia expresses the teaching content more intuitively and vividly in the form of pictures, text, animation and video. Japanese listening teaching supported by multimedia resources can create the real language environment, expand listening content input, and increase students' interest and enthusiasm. Japanese listening teaching system supported by multimedia resources is constructed in this paper. The network architecture consists of three parts: “Integrated service platform, Routing and switching system, User operation terminal”, and the functional architecture consists of “Resources management, Listening teaching, Listening entertainment, System management”. It is composed of four modules, and each module includes five sub-modules. In order to improve the effect of autonomous learning, it is also necessary to realize the transformation of teachers' and students' roles, cultivate students' ability to pose and solve problems, and use formative evaluation to promote autonomous learning of Japanese listening.
Undersampling and Oversampling Strategies for Convolutional Neural Networks Classifier

A deep learning network requires a large number of training data to optimize the complex feature representation. However, it is hard to gather a lot of samples in certain cases due to limited occurrence data or a costly sampling process. In medical applications, some of the patients refused to be part of the study, coupled with the limited occurrence of certain diseases has prevented a large-scale data collection. Besides that, data availability is also limited for a new disease such as COVID-19 x-ray images during the breakout of the pandemic in 2020. Thus, it is important to explore several sampling strategies, so that the network can be trained better with uniformly distributed samples among the classes. In this paper, EfficienNet-B1 is proposed to detect cancer cells for acute lymphoblast leukemia. It is a lightweight model with just 6,577,801 parameters even though it consists of 116 convolutional layers. Oversampling and undersampling strategies are explored to produce a balanced training dataset. Oversampling strategy is executed by duplicating samples in the class with a fewer total number of samples, while undersampling strategy is executed by deleting samples in the class with a more total number of samples. The results proved that oversampling improved the accuracy from 0.7754 to 0.7807. For future work, the effect of the data augmentation can be experimented together with various sampling strategies.
FPGA Implementation of Convolutional Neural Networks Based on Resource Sharing Techniques

Convolutional neural networks(CNNs) are now widely used in image processing.For large-scale CNNs,computationally intensive convolutional layers are one of the main challenges in implementing CNNs in FPGAs.In view of the limited resources of FPGA,this paper proposes a general line buffer structure and DSP resource sharing technology for the implementation of convolutional circuits,aiming to improve the parallelism of convolutional operations and increase the resource utilization,so as to maximize the performance of the accelerator.We deployed the CNN model on a Xilinx ZYNQ xc7z035 FPGA using a modified circuit, achieving 0.48GOP/S performance at 200Mhz system clock frequency with low power consumption.
An Internet of Things Framework for the Credit-Based Peer-to-Peer Energy Sharing Mechanism Under the Distributed Negotiation

Peer-to-peer (P2P) energy sharing has recently emerged as an appealing concept and is believed to be one of the essential techniques in the next-generation energy market. However, under the conventional energy sharing mechanism, the “ covenant-lite ” market enables pure consumers to enjoy the bonus brought by the sharing economy but make a very low contribution. Therefore, in the paper, we present a credit-based P2P energy sharing mechanism under the context of the Internet of Things (IoT). Compared with the current mechanism, the introduction of the credit aims at recording the contribution of each prosumer to encourage the end-users with complementary energy consumption and supply profiles to participate in the sharing scheme. Therefore, a fair and profitable energy-sharing platform can be provided to motivate all prosumers. To protect the privacy of the prosumers, an IoT-based distributed negotiation framework between prosumers and the distribution system operator (DSO), based on the Alternating Direction Method of Multipliers (ADMM) algorithm, is presented. Therefore, the sharing decision can be made in a distributed manner while ensuring the physical constraints of the electricity grid. The proposed mechanism and framework are verified in case studies. Through the simulation results, it can be concluded that the proposed method not only enhances the fairness of the sharing but also reduces the cost of the prosumers. Furthermore, more sharing behaviors occur under the credit-based mechanism.
Predicting Video Game Sales Based on Machine Learning and Hybrid Feature Selection Method

Sales prediction plays a critical role in the rapid development of the internet. Unlike the goal of predicting physical items sales, the virtual items of video games needn’t consider the relationship of inventory and demand, and it reflects consumer preferences to a certain extent. Predicting sales of the video game can pre-adjust sales strategies and development plans in advance. In the process of predicting, an excellent feature selection method can remove the features irrelevant to enhance the accuracy of the model. In this paper, we proposed a new hybrid feature selection method Pearson correlation coefficient - Random Forest Feature Selection(PCC-RFFS), and utilized 9 machine learning methods combined with PCC-RFFS to predict the sales of video games. For the filter-based stage, we used the absolute value of Pearson correlation coefficient and feature ranking technique, while the wrapper-based stage is based on Random Forest to measure the importance between features and target. The strategy of combination is in addition to both stages. Experiments on the real-world dataset from February 2006 to November 2016 show that the machine learning method combined PCC-RFFS outperforms the machine learning method combined Pearson correlation coefficient or Random Forest.
Seamless Multi-platform Tracing: Shadow Builder

Tracing software application is a mandatory step toward producing quality code and software. Many of the tracing tools exist, with their set of advantages and limitations. This variety of tools forces developers to introducing readability over-head and additional software glues. This paper presents a solution for reducing code branching to obtain a multi-platform, cleaner code base along with a comparison to the already existing tools.
New Factors Affecting Productivity of the Software Factory

Productivity is very important because it allows organizations to achieve greater efficiency and effectiveness in their activities; however, it is affected by numerous factors. While these factors have been identified for over two decades, all of the previous works limited the software factory to the programming work unit and did not analyze other work units that are also relevant. 90% of a software factory's effort is absorbed by the software production component, 85% of which is concentrated in the efforts of the analysis and design, programming, and testing work units. The present work identifies three new factors that influence the software factory, demonstrating that the use of rules and events influences analysis &amp; design, team heterogeneity negatively affects analysis and design and positively affects programming; and the osmotic communication affects programming. An empirical study on software factories in Peru, determined that 95% of the influence came from these factors, which corroborated as well that team size and trust within the team influences in software production.