Reviewerly/exHarmony huggingface dataset:
Humanistic Co-design for Specific Learning Difficulties Using Scenario-Based Personas: Tangible Arabic Alphabet Blocks for Dyslexia

In this paper, we describe the design rationale and iterative development process of a scenario-based technique for conveying the results of user analysis studies for children with dyslexia, and the first evaluation results for our scenario-based personas. Personas are well established in the user-centered design (UCD) and co-design communities, however, they are inadequately understood by practitioners not familiar with user experience (UX) design. We developed scenario-based personas for specific learning difficulties based on the main contexts of reading difficulties such as dyslexia. The method combines personas and scenarios in the artefacts for designing interactive learning products. Personas are depicted in silhouettes of children, educators or caregivers, which are overlaid in scenario contexts of learning and leisure. Scenarios were designed to describe the stories and context behind why learners and educators would consider using tangible blocks for developing phonological skills in literacy education. Methodological considerations for using scenario-based personas in user research will be discussed.
OntoJogo: An Ontology for Game Classification

Games are no longer just an entertainment activity. Various games are being adopted in different fields, such as psychology, education, and healthcare. Their usage requires professionals to be capable of categorizing games to take the most advantage out of them. Therefore, we argue that a uniform classification for games can facilitate and improve the work on numerous investigations that focus on the usage of games. Along with research work on the matter, in this paper, we present an ontology, OntoJogo, for the classification of games according to different perspectives or categorization axes. In the paper, OntoJogo is described in detail (classes and relations are explained), and examples of use are shown.
The evaluation model of reconstruction effect of ancient villages under the influence of epidemic situation based on big data

In rural construction, affected by covid-19, it leads to the collection and demand survey of basic information data of relevant interest groups. The specific situation of the transformation of ancient villages is also gradually increasing. However, due to the complexity of rural space, the dispersion of settlement space and the diversity of information demand of rural planning work, the data coverage is large, information acquisition is difficult, the use effect of data collection is not ideal, and there is no planning feedback mechanism. However, during the epidemic period, the staff could not carry out a series of reconstruction of ancient villages. At present, the data of village planning and construction and architectural design are complex, the needs of relevant interest groups are diversified, and regional planning is difficult. In this paper, the big data function is applied to the reconstruction of ancient villages in the epidemic period of covid-19.
Semantic Oriented Text Clustering Based on RDF

Text clustering is the discipline that purports to find related groups in a collection of documents. Based on text clustering the use of documents can be more salubrious. Researchers have used various methods to implement text clustering either agglomerative, divisive, or itemsets-based clustering. Most of these proposed approaches do not take into account the semantic relationships between words, in this case, the documents are considered only as bags of unrelated words. Our work aims to consider the semantics of the text phrases in the clustering task, and to get full usage and exploitation of documents. The semantic web concept is overloaded with valuable techniques allowing the significant use of documents. Our goal is to take full advantage of these techniques. Using the Resource Description Framework (RDF) to represent textual data as triplets. They provide a semantic representation of data on which the clustering process will be based, to provide a more efficient clustering system. On the other hand, and based on the clustering process, we opt on incorporating other techniques such as ontology representation using RDF, RDF Schemas (RDFS), and Web Ontology Language (OWL) to manipulate and extract meaningful information. In this paper, we propose a framework of semantic oriented text clustering based on RDF by the means of a semantic similarity measure, and we highlight the benefits of using semantic web techniques in clustering, topic modeling, and information extraction based on questioning, reasoning and inferencing processes.
Location privacy preservation through kernel transformation

Summary The frequent data leak scandals of recent years indicate that service providers who hold personal data may not be reliable as they claim. We assert that sensitive user information must be sanitized locally before it is sent to service providers if it is to be protected. The LPPK privacy‐preserving framework presented in this article is a local sanitization scheme, for location‐based services (LBSs). It applies a fog‐computing structure in which a private map is generated by the LBS server with kernel transformation for each user. A fog device then provides location services for each user according to the private map. Without colluding, neither the LBS server nor the fog device can deduce a user's real location. Experiments conducted on real‐world data sets demonstrate that LPPK delivers sufficient query accuracy at a level significantly higher than existing approaches while preserving location privacy.
Behavior-based Popularity Ranking on Amazon Video

With the growth in the number of video streaming services, providers have to strive hard to make relevant content available and keep customers engaged. A good experience would help customers discover new and popular videos to stream with ease. Customer streaming behavior tends to be a strong indicator of whether they found a video engaging. Aggregate customer behavior serves as a useful predictor of popularity. We discuss the use of past streaming behavior to learn patterns and predict a video’s popularity using tree ensembles.
Design and trajectory planning of blocking plate robot in steam generator

During nuclear power maintenance, the primary main pipe blocking plate needs to be put into the manhole of steam generator water chamber to complete the blocking plate operation. In view of the limitation of the space condition of the working site in the steam generator, this paper designs a six degree of freedom robot with a hybrid of moving and rotating joints. In order to meet the needs of blocking operation, the joint types and driving system of the robot are designed and calculated. According to the robot configuration, the kinematics model of the robot is established and the forward and inverse kinematics are solved. Matlab is used to simulate the trajectory planning of the manipulator in order to determine the motion state of each axis and provide theoretical support for practical application.
Local Ontologies Merging in Data Ponds

Today, Ontologies have a major place in knowledge representation and modeling. They are used to formalize a domain knowledge and add a semantic layer to current systems and applications. Ontologies make it possible to explicitly represent the knowledge of a domain by means a formal language so that they can be manipulated automatically and shared easily. They are widely used in various fields of research such as Knowledge Representation (KR) and Data Integration (DI). However, the effectiveness to interoperate learning objects among various learning object repositories is often decreased because of using different ontological schemes for annotating learning objects into every learning object repository. Hence, semantic heterogeneity and structural differences between ontologies need to be resolved so as to generate common ontology to expedite learning object reusability. This paper focused on automated ontology mapping and merging concept. The study significance lies in an algorithmic approach for mapping attributes of learning objects/concepts and merging them based on mapped attributes; identifying suitable threshold value for mapping and merging.
Neutrino: Efficient InfiniBand Access for Java Applications

Fast networks like InfiniBand are important for large-scale applications and big data analytics. Current InfiniBand hardware offers bandwidths of up to 200 Gbit/s with latencies of less than two microseconds. While it is mainly used in high performance computing, there are also some applications in the field of big data analytics. In addition, some cloud providers are offering instances equipped with InfiniBand hardware. Many big data applications and frameworks are written using the Java programming language, but the Java Development Kit does not provide native support for InfiniBand. To this end we propose neutrino, a network library providing comfortable and efficient access to InfiniBand hardware in Java as well as epoll based multithreaded connection management. Neutrino supports InfiniBand message passing as well as remote direct memory access, is implemented using the Java Native Interface, and can be used with any Java Virtual Machine. It also provides access to native C structures via a specially developed proxy system, which in turn enables the developer to leverage the InfiniBand hardware's full functionality. Our experiments show that efficient access to InfiniBand hardware from within a Java Virtual Machine is possible while fully utilizing the available bandwidth.
Cross-Lingual Transfer Learning for Medical Named Entity Recognition

Extensive technologies have been employed to explore a best way for cross-lingual transfer learning. In medical domain, Named Entity Recognition is pivotal for many downstream tasks, such as medical entity linking and clinical decision support systems. Nevertheless, the lack of annotation limits the applicability in many languages without enough labeled data. To alleviate this issue and make use of languages with sufficient annotated data, we find a new way to obtain medical parallel corpus from medical terminology systems and knowledge bases and propose a methodology which combines cross-lingual language model pretraining and bilingual word embedding alignment with the help of the parallel corpus. Moreover, our combined architecture which maintains the framework of pretrained model can not only be used for NER task but also other downstream NLP tasks. Experiments demonstrated that incorporating Chinese and English medical data can effectively improve the performance for an English medical NER dataset (i2b2).
Design of Efficient CNN Accelerator Based on Zynq Platform

With the rapid development of deep learning, convolution neural network is widely used in image recognition and object detection. However, CNN with extensive calculation is not suitable for the mobile terminals. In this paper, lightweight convolution neural network MobileNet is modified as backbone network for small object detection based on the Zynq platform. A method of data bypass is proposed which reserves shallow feature map information to the last convolutional layer, with which the detection accuracy increases by 8%. Additionally, 16 x 4 Wallace tree addition tree is designed instead of the original two-operand addition tree generated by Vivado HLS. The usage of hardware resources is reduced by 26%. The detection accuracy of the entire acceleration system is 73.7% and the FPS is 28. Based on the methods proposed, the accelerator achieve both high detection speed and accuracy, which are better than other MobileNet hardware acceleration works.
Neural Architecture Search with Improved Genetic Algorithm for Image Classification

Neural Architecture Search (NAS) is an automatic process of designing a neural architecture for solving classification problems. It is closely related to hyper-parameters such as hidden layers, neurons in each hidden layer, type of activation function (ACT), network optimizer and so on. Therefore, finding appropriate hyper-parameters to construct suitable network architecture for a particular problem is a challenging task. In this paper, an improved Genetic Algorithm (GA-NAS) is proposed to build a multi-layer feed forward architecture for image classification problem. Each chromosome of the proposed method is encoded with four hyper-parameters namely no. of hidden layers, neurons per hidden layer, activation function (ACT) and network error optimization technique. Each chromosome represents a neural network architecture for the given problem. The categorical cross-entropy or log function is considered to represent fitness function which provides performance accuracy of the architecture. The proposed methodology is experimented on two well-known benchmark image classification data sets such as CIFAR-10 and MNIST. The GA-NAS is compared with brute force algorithm and obtained results demonstrated the effectiveness for solving image classification problems.
Optimized Wavelet Filter from Genetic Algorithm, for Image Compression

This article proposes an optimized wavelet filter for image compression whose coefficients are derived from a genetic algorithm. In contrast to other existing wavelet filters like db4, bior 5.5 and rbior 4.4, the wavelet filter produced here delivers highly accurate fidelity parameters. With regard to PSNR, MSE and SSIM at different thresholds (bpp), the comparative output studies were performed with different existing wavelet filters and the proposed wavelet filters. The simulation results presented in this paper show that the proposed image compression filters have improved efficiency and performance.
Centralized Control of Account Migration at Single Sign-On in Shibboleth

Single Sign-On (SSO) is adopted to use multiple services with a single log-in in the Internet. However, when a user tries to change the identity provider (IdP) which is responsible for authenticating of the user, he needs to release the binding between the log-in account on the migration-source IdP and his service account on each service provider (SP), and needs to set a new binding between the account on the migration-destination IdP and the service account on the SP. There is no common migration system to support migration using the SSO function. In this research, we especially focus on Shibboleth's function as an SSO service. And we propose a protocol to migrate accounts of a user on multiple SPs at once using an attribute provider (AP) in SSO environment. Also we implement the mechanism as an open source software using SimpleSAMLphp.
Extraction and Portrait of Knowledge Points for Open Learning Resources

This article explores how to use the technology of text summarization and keyword extraction to automatically extract key knowledge points from massive educational resources and use open resources to generate feature portraits of relevant knowledge points. Specifically, this article takes the field of programming competitions as an example, firstly, crawl the problem solution resources of program design related issues, use data preprocessing to clean the data, then, use unsupervised extraction models based on Bert and centrality to summarize the documents of the resources, the LDA model is used to extract keywords from the generated document summary to identify relevant knowledge points in the resource. Finally, crawl and analyze resources based on knowledge points to establish relevant feature portraits for knowledge points. Unlike manual analysis of resources, this method can automatically select candidate knowledge points, greatly reduce labor costs.
DockerKG

Docker helps developers reuse software artifacts by providing a lightweight solution to the problem of operating system virtualization. A Docker image contains very rich and useful knowledge of software engineering, including the source of software packages, the correlations among software packages, the installation methods of software packages and the information on operating systems. To effectively obtain this knowledge, this paper proposes an approach to constructing a knowledge graph of Docker artifacts, named DockerKG, by analyzing a large number of Dockerfiles in Docker Hub, which contains more than 3.08 million Docker repositories (up to February 2020). Currently, DockerKG contains the domain knowledge extracted from approximately 200 thousand Dockerfiles in Docker Hub. Besides, it contains the information on Docker repositories and their semantic tags. In future work, DockerKG can be used for Docker image recommendations and online Q&A service providing software engineering domain knowledge.
Feature Selection Using Metaheuristic Algorithms: Concept, Applications and Population Based Comparison

Technologies like machine learning in the current times, have emerged as capable domains of research in Computer Science. In Machine learning, the system is trained on the basis of the available dataset; the dataset may contain many redundant and irrelevant features which may require more memory for storage and also increases the cost of computation. Selection of best features enhances the accuracy of data classification along with working on smallest amount of features is considered as an optimization problem. Metaheuristic algorithms in current times have been used far and wide unravel various optimization problems. In this context, this study aims to discuss the solution of feature selection problem using metaheuristic algorithms and presents a population based comparison of four metaheuristic algorithms for extracting smallest feature subset with utmost accuracy.
Using high-performance computing artifacts as a learning intervention

High-Performance Computing (HPC) artifacts provide opportunities for students to improve their understanding of parallel computing, which is important for students who study computer science. In line with that, many computing departments are integrating HPC systems into their curricula. However, there is a need to investigate HPC artifacts that have been used as learning interventions. This study has employed a systematic literature review to investigate published papers on HPC education from 1988 to 2018. The findings of our investigation of a stratified sample of 211 papers reveal the state of the practice of application of HPC artifacts in computing education in terms of the contexts, themes, nature and topics of the publications. The study revealed that a majority of publications reported the usage of Beowulf and other clusters as the pedagogical tools. Furthermore, the study discovered gaps in research on the application of HPC artifacts in ability and aptitude, teaching and learning, teaching and learning techniques, curriculum, parallel programming, and parallel processing. This study contributes to our understanding of what HPC artifacts are used in computer science education.
Design and Implementation of Primary School App Inventor Programming Mobile Learning Platform based on Wechat Applet

With the rapid development of mobile Internet and artificial intelligence, programming education has entered primary and secondary schools and started to be popularized rapidly. At the same time, the widespread use of wechat has brought great convenience for mobile learning. In this paper, for primary school programming education, with the help of wechat widget tool launched by wechat, App Inventor is selected as primary school programming tool to build a wechat widget mobile learning platform for senior primary school students, which provides online learning tools for students, so that students can make full use of fragmented time and improve learning efficiency.
Linecept

In software design, the various stakeholders generate large numbers of heterogeneous artifacts. These artifacts are often developed in, and managed by, different tools. In this paper, we present our initial prototype of Linecept, a tool that helps stakeholders organize, find, and view disparate design artifacts by organizing them on a timeline that presents a single unified view of the artifacts and who created them. We have used Linecept to retrospectively capture design artifacts for its own creation and in a software design class.
Three-Stream Convolutional Neural Network for Human Fall Detection

Lower child mortality rates, advances in medicine, and cultural changes have increased life expectancy to above 60-years old in developed countries. Some countries expect that, by 2030, 20% of their population will be over 65 years old. The quality of life at this advanced age is highly dictated by the individual’s health, which will determine whether the elderly can engage in important activities to their well-being, independence, and personal satisfaction. Old age is accompanied by health problems caused by biological limitations and muscle weakness. This weakening facilitates the occurrence of falls, which are responsible for the deaths of approximately 646,000 people worldwide and, even when a minor fall occurs, it can still cause fractures, break bones, or damage soft tissues, which will not heal completely. Injuries and damages of this nature, in turn, will consume the self-confidence of the individual, diminishing their independence. In this work, we propose a method capable of detecting human falls in video sequences using multi-channel convolutional neural networks (CNN). Our method makes use of a 3D CNN fed with features previously extracted from each frame to generate a vector for each channels. Then, the vectors are concatenated, and a support vector machine (SVM) is applied to classify the vectors and indicate whether or not there was a fall. We experiment with four types of features, namely: (i) optical flow, (ii) visual rhythm, (iii) pose estimation, and (iv) saliency map. The benchmarks used (UR Fall Detection Dataset (URFD) [] and (ii) Fall Detection Dataset (FDD) []) are publicly available and our results are compared to those in the literature. The metrics selected for evaluation are balanced accuracy, accuracy, sensitivity, and specificity. Our results are competitive with those obtained by the state of the art on both URFD and FDD datasets. To the authors’ knowledge, we are the first to perform cross-tests between the datasets in question and to report results for the balanced accuracy metric. The proposed method is able to detect falls in the selected benchmarks. Fall detection, as well as activity classification in videos, is strongly related to the network’s ability to interpret temporal information and, as expected, optical flow is the most relevant feature for detecting falls.
I/O Efficient Algorithm for c-Approximate Furthest Neighbor Search in High-Dimensional Space

Furthest Neighbor search in high-dimensional space has been widely used in many applications such as recommendation systems. Because of the "curse of dimensionality" problem, c-approximate furthest neighbor (C-AFN) is a substitute as a trade-off between result accuracy and efficiency. However, most of the current techniques for external memory are only suitable for low-dimensional space.