Reviewerly/exHarmony huggingface dataset:
Intake

OpflowVolume 48, Issue 3 p. 2-2 Departments Intake First published: 06 April 2022 https://doi.org/10.1002/opfl.1656Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume48, Issue3April 2022Pages 2-2 RelatedInformation
Multimodal Teaching of College English Based on Similarity

There are many disadvantages in traditional English teaching. In order to solve these problems, multimodal teaching mode is applied to college English teaching. This paper combines the similarity model with English multimodal teaching and puts forward corresponding teaching methods based on the current situation of students’ English learning. This paper studies the multimodal similarity learning method, defines different relation vectors according to different physical or semantic meanings contained in sample data, and transforms the calculation of single-modal similarity between samples into the calculation of multimodal similarity between samples under different relations. Based on the multimodal discourse analysis theory, this paper explores the influence of multimodal teaching mode on students’ English achievement and attitude by combining quantitative research with qualitative research, so as to verify the effectiveness of this teaching mode. The results show that this method is effective and feasible. The application of multimodal teaching mode in English classroom can have an important influence and has far-reaching research significance. The research in this paper has certain reference value for innovating English teaching mode and improving students’ English achievement.
Rethinking the practices of continuing professional development in digital environments: a systematic review

Purpose In the ever-changing circumstances, library professionals need to maintain their competencies in line with the new trends. Continuing professional development (CPD) is a mechanism out of these; social media (SM) is a major channel usually used by the professionals for CPD, more specifically the video conferencing mediums such as YouTube, Skype, Zoom and Google meet. These outreach mediums can be used by library professionals to communicate with each other irrespective of their location. This study aims to highlight the effective use of such channels by library professionals to meet the needs of CPD through a systematic review of prior studies. Design/methodology/approach The initial search was carried out in Library, Information Science and Technology Abstracts (LISTA) ( n = 94), Google Scholar ( n = 20), Search on hand ( n = 13), Scopus ( n = 1) and Web of Science ( n = 0). The keywords combinations were used as a formula, i.e. [(Information professional) (Librarians) + CPD + Continuing Professional Development + Youtube + Skype + Zoom + Google meet + Video conferencing]. This search produced a cumulative of 128 results out of the five main databases and hand searches. Moreover, for the purpose of screening and identification of paper, the preferred-reporting of items for systematic reviews and meta-analyses statement was used. Findings The review findings show that the adoption of these channels for CPD purposes has a significant potential to hone up the skills of the professionals who are well proficient in using modern technologies. The results mentioned YouTube as a leading channel of SM used for CPD, followed by Skype. This review further provided practical implications for the information professionals and suggestions for future research. Originality/value This study will not only increase the body of knowledge but also attract the researchers to know about the current scenario that how much library professionals are using SM channels for their continuing professional development as well as assists the stakeholders to devise their policies accordingly.
Implementation and Verification of Steganography IP Core Based on HLS and OpenCV

Abstract Steganography is a safe transmission method that hides secret data in public information to avoid attacks as much as possible. However, the current steganography research and application mainly stay in the software stage. Although the secure transmission of data is realized, it is difficult to realize the purpose of high-speed data transmission. This thesis solves this problem with the help of a fully parallel, high-speed and high-bandwidth FPGA. This paper uses HLS to design a steganography IP core that can be applied to FPGA, and uses OpenCV to quickly and effectively verify the IP core. The final result proves that this paper successfully designed a high-performance IP core that can realize steganography, which effectively solves the problem of difficult high-speed transmission of encrypted data. This IP core combines the AXI bus protocol, which not only facilitates data interaction with DDR3 and other devices, but also conducive to the design and optimization of the complete secret data transmission system in the future.
A Live-Coded Add-On System for Video Conferencing in Virtual Reality

Despite our increasing reliance on video conferencing, it has inherent limitations in engagement and effective communication. We explore a VR add-on system that supplements traditional video conferencing where a host live-codes data visualisations for stakeholders. It combines VR visualisation techniques with libraries present in pop-ular data analytics tools such as Python, allowing real-time changes by utilising a RESTful realtime database. An application has been built to explore pedigree node-link graphs, and its feasibility has been analysed with a few domain experts. Trials have demonstrated that our system appears to enhance engagement and communication above traditional video conferencing for data exploration.
Performance Impact of Control Flow Enforcement Technology (CET)

The application flow integrity plays a critical role in proper system behavior. Unfortunately, taking over the control of the flow is still difficult to detect. We evaluate the performance of hardware support for the application flow integrity control. A solution is based on platform capabilities and is said to overcome existing limitations met in similar systems. Our results show that overhead is acceptable and with minimal effort, the solution can be successfully used in practical applications.
Inductive Latent Space Sparse and Low-rank Subspace Clustering Algorithm

Abstract Sparse subspace clustering (SSC) and low-rank representation (LRR) are the most popular algorithms for subspace clustering. However, SSC and LRR are transductive methods and cannot deal with the new data not involved in the training data. When a new data comes, SSC and LRR need to calculate over all the data again, which is a time-consuming thing. On the other hand, for high-dimensional data, dimensionality reduction is firstly performed before running SSC and LRR algorithms which isolate the dimensionality reduction and the following subspace clustering. To overcome these shortcomings, in this paper, two sparse and low-rank subspace clustering algorithms based on simultaneously dimensionality reduction and subspace clustering which can deal with out-of-sample data were proposed. The proposed algorithms divide the whole data set into in-sample data and out-of-sample data. The in-sample data are used to learn the projection matrix and the sparse or low-rank representation matrix in the low-dimensional space. The membership of in-sample data is obtained by spectral clustering. In the low dimensional embedding space, the membership of out of sample data is obtained by collaborative representation classification (CRC). Experimental results on a variety of data sets verify that our proposed algorithms can handle new data in an efficient way.
A forward and backward private oblivious RAM for storage outsourcing on edge-cloud computing

In recent years, edge-cloud computing is regarded as a promising solution to meet the requirements of mobile computing and Internet-of-Things (IoT). However, due to the limited storage resources of edge equipment, there is a security threat when users outsource their sensitive data to the cloud computing center. The users usually adopt a data-encryption approach, Oblivious RAM (ORAM), which enables a user to read/write her outsourced private data without access pattern leakage. Not all users like the fully functional ORAM all the time since the ORAM protocol is usually highly interactive or occupies large edge storage space. We show that forward-private/backward-private (FP/BP) ORAMs are good alternatives for secure storage outsourcing. We introduce the FP/BP-ORAM definitions and present LL-ORAM, the first FP/BP-ORAM that achieves near-zero edge storage, single-round-trip read/write, and worst-case sublinear access time. For any outsourced record, LL-ORAM provides both an oblivious-access interface and a nonoblivious-access interface. FP-ORAM concerns more data-write privacy than data-read privacy. BP-ORAM concerns more data-read privacy. The constructions involve a tree data structure named LL-tree, which supports fast computation in the cloud with an access-pattern-reduced leakage profile. The security analysis shows that LL-ORAM meets the proposed forward and backward security model. The experimental results demonstrate that LL-ORAM is round-efficient and can be deployed on edge-cloud computing systems. • We give the security definitions of forward-private ORAMs and backward-private ORAMs. • We propose an approach of accessing private data both obliviously and nonobliviously. • We present an FP/BP-ORAM implementation.
Learning Reaching Tasks Using an Arm Robot Equipped with MACMSA

Abstract In recent years, models that integrate multimodal information to control robots have been actively developed. Memorizing and Associating Converted Multimodal Signal Architecture (MACMSA) was proposed to integrate multimodal information obtained from robots with Hopfield networks as associators and independent feed-forward neural networks as encoders and decoders. The performance of MACMSA has thus far been investigated only using pseudo-data. Notably, MACMSA exhibits high resistance to noise. However, it cannot generate signals for robot control. The purpose of this study was to improve MACMSA to generate signals for robot control and optimize it using real data on reaching tasks. The results of the generated control signals on a real machine are presented to demonstrate that the improved model can be effectively used in a real environment. The results also show that the proposed model can perform well with real data.
An Image Saliency Detection Method Based on Combining Global and Local Information

In the field of computer vision, image saliency target detection can not only improve the accuracy of image detection but also accelerate the speed of image detection. In order to solve the existing problems of the saliency target detection algorithms at present, such as inconspicuous texture details and incomplete edge contour display, this paper proposes a saliency target detection algorithm integrating multiple information. The algorithm consists of three processes: preprocessing process, multi-information extraction process, and fusion optimization process. The frequency domain features of the image are calculated, the algorithm calculates the frequency domain features of the image, introduces power law transform and feature normalization, improves the frequency domain features of the image, saves the information of the target region, and inhibits the information of the background region. On three public MSRA, SED2, and ECSSD image datasets, the proposed algorithm is compared with other classical algorithms in subjective and objective comparison experiments. Experimental results show that the proposed algorithm can not only accurately and comprehensively extract significant target regions but also retain more texture information and complete edge information while satisfying the human visual experience. All evaluation indexes are significantly better than the comparison algorithm, showing good reliability and adaptability.
Improved Cam Shift Algorithm for Detecting Athletes' Action Targets in Sports Videos

The detection and tracking of athletes in sports videos is of great importance, as it helps to automate the analysis of sports videos, thus providing advanced tools and instruments for sports training. The Cam Shift algorithm uses the colour information of objects to achieve tracking of moving targets, so it is very important to choose a suitable colour space when obtaining the colour information of objects. To this end, this paper improves the Cam Shift algorithm by converting the visual system mechanism to perceive the colour characteristics of the image processing, i.e., converting the video image from the RGB colour space to the HSV colour space, using the H (hue) component to model the target object. Experiments show that the improved athlete detection and tracking algorithm is more robust in practical applications. The improved tracking algorithm also has better real-time performance, with a processing speed of 20 fps during the experiments.
Comparing principally imagination and interaction versions of a play anywhere mobile AR location-based story

Augmented Reality (AR) allows virtual elements to be overlaid on the real world, providing new opportunities for location-based storytelling, by offering blended environments to more closely resemble those in a story. However, there is limited research considering how different interaction opportunities with such augmented surroundings, affect user engagement and immersion in such a story. A mobile AR app, Map Story 2, was developed to investigate this, offering a guided story-walk around a user&#x0027;s chosen location, either interacting with overlaid virtual objects to progress the story, or being asked to imagine the same events playing out at each augmented location.
Sentimental analysis on E-Learning videos using Hybrid Algorithm based on Naïve Bayes and SVM

E-learning has piqued the interest of companies, educational institutions, and people alike. E-learning systems are becoming increasingly prominent as an educational trend. It typically refers to educational attempts spread via the use of computers in an attempt to transmit information. Students can engage with other students and discuss questions about certain topics thanks to e-Learning platforms and similar technologies. Teachers, on the other hand, frequently remain outside of this process and are unaware of the learning issues that exist in their classes. Adopting a Sentiment Analysis approach for detecting the student mood throughout the learning process might be a solution for better learning method. In this paper, we used sentimental analysis on E-learning data. SVM and Na&#x00EF;ve Bayes algorithms are fused to be used as a Hybrid algorithm for better accuracy. Performance analysis shows that state-of-art methods like Na&#x00EF;ve Bayes and SVM algorithms give 90&#x0025; and 94&#x0025; respectively whereas our proposed hybrid method gives approximately 97&#x0025; of accuracy.
Optimal Resource Allocation by Reverse Stackelberg Game Approach in Blockchain

With the increase in Internet of Things (IoT) devices, data produced by these devices is also increasing. For its trusted use, security of the IoT data is very important. Nowadays, blockchain is a contemporary technology which is used in various fields for providing security. But the mining task of the blockchain is very computation intensive. So, there exist dependency on resource providers like near-by devices, edge network for providing resources to the miners for the task of computations. In our proposed framework, bidding of the resources is done by the miners from near-by devices and edge network. Reverse stackelberg game and auction mechanism are used to reach optimality of the decisions. Nash equilibrium is also achieved. Less storage is consumed by few blocks of the blockchain network as meta data is stored in some blocks. Experimental evaluation is done to compute average delay and net profit.
Reveal: Online Fake Job Advert Detection Application using Machine Learning

New technologies are rapidly emerging to fight increasing Job scams. Online Job scams are onerous to detect, thus giving the perpetrators plenty of time to flee the area in which the crime was committed, because of this fact the criminals can be in another country far away from the scene of the crime by the time it is detected. In today's digital world, we see many such instances where a particular person is targeted. The introduction of the internet and the quick access of social networking sites (including Twitter and Instagram) prepared the door for unprecedented levels of knowledge distribution in human history. Humans can be vulnerable and easily deceived making technological advances inadequate for Online Job scams. Fake recruitments are advertised to entice people to apply, so fraudsters can gain personal information such as residential address, email address, contact number, date of birth, previous job history, bank details and steal complete identify. In this paper, we developed Reveal, a machine learning-based web application, to identify fake online job advertisements such that the applicants are cautious in applying for jobs that are authentic and reliable.
An Improving Approach for Top-Rank-k Frequent Pattern Mining

Mining frequent patterns (FPs) received a lot of research interest which is a fundamental problem in the field of data mining. It has been studied on many database types with various applications in intelligent systems. However, the traditional methods using a given threshold to mine FPsterns suffer from a crucial drawback, that is, the number of resulting patterns is uncontrollable, sometimes very large. Mining top-rank-[Formula: see text] FPs is one of the current research directions to solve the aforementioned disadvantage of mining FPs. Many methods have been proposed to mine effectively top-rank-[Formula: see text] frequent patterns. However, these methods also have many limitations regarding memory consumption and suboptimal mining time. Therefore, this study proposes a new method based on [Formula: see text]-list structure using the Priority Queue, the Hash Table structure, and some fast pruning strategies for efficient mining top-rank-[Formula: see text] FPs from binary transaction databases. The experimental results on many datasets demonstrate that the proposed method is more effective than the previous methods in the terms of runtime and memory usage.
A New Fast Algorithm for Library Circulation Data Mining Based on FUP

As the first incremental association mining algorithm, FUP can well solve the problem, but the algorithm also has the deficiencies to produce a large set of candidates and multiple iterating the database, leading to the algorithm’s low execution efficiency when dealing with some large transactions with fast updates, such as book circulation data. This study proposes an improved FUP algorithm that takes transaction identifier (TID) in the database to scan the database only once, making the computation significantly less than the FUP algorithm. Through detecting the circulation data of a university library, the experimental results show that compared with the standard FUP algorithm and SFUA algorithm, with the increase of borrowing and record transactions, the improved FUP algorithm has significantly improved the operation efficiency, which can help the library to do a good job in book recommendation scientifically.
Solving degree constrained minimum spanning tree problem based on improved fireworks algorithm

Abstract In the iterative process of fireworks algorithm, an improved algorithm is proposed to improve the local search ability of optimal fitness value and make it more suitable for solving discrete problems, so as to obtain a method to find degree constrained minimum spanning tree. Firstly, the corresponding mathematical model is constructed by analyzing the degree constrained minimum spanning tree, then the explosion operator and the corresponding fitness function are established according to the model, and finally the algorithm is improved to adapt to the discrete fireworks algorithm, so as to achieve the purpose of seeking the degree constrained minimum spanning tree. The example calculation shows that the improved fireworks algorithm can reduce the number of iterations and achieve high accuracy.
Graph neural networks for parameter estimation in micro-electro-mechanical system testing

Micro-electro-mechanical systems (MEMS) are of great importance in a broad range of applications including vehicle safety and consumer electronics. During the testing of these devices, large heterogeneous data sets containing a variety of parameters are recorded. Aiming to substitute costly measurements as well as to gain insight into the relations among the measured parameters, graph neural networks (GNNs) are investigated. Thus, the questions are addressed whether for inference of MEMS final module level test parameters, working on graph structures leads to an improvement of the predictive performance compared to the analysis via standard machine learning approaches on tabular data and how the graph structure and learning algorithm contribute to the overall performance. To evaluate this, in an empirical study different graph representations of the acquired test data were set up. On these, four different state-of-the-art GNN architectures were trained and compared on the task of raw sensitivity prediction for a MEMS gyroscope. Whereas the GNNs performed on par with a light gradient boosting machine, neural network and multivariate adaptive regression splines model used as baseline on the complete data set, in the presence of sparse data, the GNNs outperformed the baseline methods in terms of the overall root-mean-square error (RMSE) and achieved distinct improvement in the maximum error when trained on data with similar sparsity rates as observed during the validation.
A Novel Approach to Optimizing the Initial Path of Mobile Robots in Static Environments

Path planning is one of the most important tasks for autonomous mobile robots. The purpose of path planning is to find a collision-free path from a starting position to a target position due to specific criteria such as distance. This paper proposes a modified path improvement algorithm based on the former and later points. In the initial path, some points are randomly placed on the line segments that have the necessary condition. New positions of points in the path are calculated by their adjacent points. This approach explores the most optimal path between different values of angle for the different number of segments. A method is proposed in which the initial path in the line segments that contain the turns adheres to the obstacles in the way. This operation makes a shorter path. Experiments are conducted on six maps and the results indicate that the proposed method achieves a considerable optimization in the final path.
An Optimized Arabic Sarcasm Detection in Tweets using Artificial Neural Networks

This paper presents an optimized Arabic sarcasm classification model using artificial neural networks in conjunction with particle swarm optimization. Artificial Neural Networks (ANNs) are used to learn the extracted feature representation of a given text. Term frequency with inverse document frequency (TFIDF) is adapted for feature extraction and text transformation into numerical values. Particle Swarm Optimization (PSO) selects the most relevant features to optimize classification performance. Experiments show that the classification accuracy is optimized after using PSO from 82.12&#x0025; to 86.85&#x0025;.