Reviewerly/exHarmony huggingface dataset:
The Korean Sign Language Dataset for Action Recognition

Recently, the development of computer vision technologies has shown excellent performance in complex tasks such as behavioral recognition. Therefore, several studies propose datasets for behavior recognition, including sign language recognition. In many countries, researchers are carrying out studies to automatically recognize and interpret sign language to facilitate communication with deaf people. However, there is no dataset aiming at sign language recognition that is used in Korea yet, and research on this is insufficient. Since sign language varies from country to country, it is valuable to build a dataset for Korean sign language. Therefore, this paper aims to propose a dataset of videos of isolated signs from Korean sign language that can also be used for behavior recognition using deep learning. We present the Korean Sign Language (KSL) dataset. The dataset is composed of 77 words of Korean sign language video clips conducted by 20 deaf people. We train and evaluate this dataset in deep learning networks that have recently achieved excellent performance in the behavior recognition task. Also, we have confirmed through the deconvolution-based visualization method that the deep learning network fully understands the characteristics of the dataset.
An Improved PageRank Algorithm Based on Reachability to Reduce Mutual Reinforcement Effect

It is very difficult to find relevant information in this rapidly growing hyper structure. Basically web site owner creates web site to provide relevant information to cater user's need. Therefore, it is very important to retrieve web pages based on user's interest and behavior by finding the content of the web page. Link analysis is the significant parameter to find related information. PageRank and HITS are two basic algorithms which works on web structure mining. Numerous algorithms have been developed to improve performance based on these two basic algorithms. An Improved PageRank Algorithm (IPRA), is the extension of the page Rank algorithm, is introduced in this paper. IPRA takes into account the number of different domain inlink and outlink and distributes the rank score based on the reachability of the web pages. The result of our model studies shows that IPRA algorithms give better result than the standard PageRank algorithm.
Secure Content-Based Image Retrieval Using Combined Features in Cloud

Secure Content-Based Image Retrieval (SCBIR) is gaining enormous importance due to its applications involving highly sensitive images comprising of medical and personally identifiable data such as clinical decision-making, biometric-matching, and multimedia search. SCBIR on outsourced images is realized by generating secure searchable index from features like color, shape, and texture in unencrypted images. We focus on enhancing the efficiency of SCBIR by combining two visual descriptors which serve as a modified feature descriptor. To improve the efficacy of search, pre-filter tables are generated using Locality Sensitive Hashing (LSH) and resulting adjacent hash buckets are joined to enhance retrieval precision. Top-k relevant images are securely retrieved using Secure k-Nearest Neighbors (kNN) algorithm. Performance of the scheme is evaluated based on retrieval precision and search efficiency on distinct and similar image categories. Experimental results show that the proposed scheme outperforms the existing state of the art SCBIR schemes.
English as a Foreign Language (EFL) Teacher's Perceptions and Use of Mobile Devices and Applications

This paper reports on a study of how a group of tertiary level EFL teachers perceived and used mobile devices in their teaching and personal learning. One hundred and fifty teachers (66 female, 84 male) from public universities in Saudi Arabia completed an online questionnaire. Results showed that the majority of participants used mobile devices and applications in their teaching and learning. Survey data showed that the vast majority of teachers had positively perceived and frequently used mobile technologies in their teaching and personal learning. In addition, there was a correlation between teachers’ use of mobile technologies in their teaching and their use in learning. There was also a correlation between how teachers perceived the value of mobile technologies in learning, and how they use them in their teaching.
Application of numerical integration in solving a reverse osmosis model

Clean water is always needed for human living. Due to pollution, we often need to purify water. One way to do so is using the reverse osmosis system. A mathematical model for the reverse osmosis system has been obtained. In this paper, we show the importance of numerical methods in solving the reverse osmosis model. In particular, we focus on the application of numerical integration methods in the process of solving the model. We consider three types of rules in numerical integration, namely, the Riemann sums, the trapezoidal rule and the Simpson’s rule. We present our research results of these three rules relating to the solving process of our reverse osmosis model. The Simpson’s rule is the most accurate, as it has the highest order of accuracy in comparison to the Riemann sums and the trapezoidal rule. Our main point in this research is that the numerical integration has an important role in solving the reverse osmosis model.
Cycle-CNN for Colorization towards Real Monochrome-Color Camera Systems

Colorization in monochrome-color camera systems aims to colorize the gray image IG from the monochrome camera using the color image RC from the color camera as reference. Since monochrome cameras have better imaging quality than color cameras, the colorization can help obtain higher quality color images. Related learning based methods usually simulate the monochrome-color camera systems to generate the synthesized data for training, due to the lack of ground-truth color information of the gray image in the real data. However, the methods that are trained relying on the synthesized data may get poor results when colorizing real data, because the synthesized data may deviate from the real data. We present a new CNN model, named cycle CNN, which can directly use the real data from monochrome-color camera systems for training. In detail, we use the colorization CNN model to do the colorization twice. First, we colorize IG using RC as reference to obtain the first-time colorization result IC. Second, we colorize the de-colored map of RC, i.e. RG, using the first-time colorization result IC as reference to obtain the second-time colorization result R′C. In this way, for the second-time colorization result R′C, we use the original color map RC as ground-truth and introduce the cycle consistency loss to push R′C ≈ RC. Also, for the first-time colorization result IC, we propose a structure similarity loss to encourage the luminance maps between IG and IC to have similar structures. In addition, we introduce a spatial smoothness loss within the colorization CNN model to encourage spatial smoothness of the colorization result. Combining all these losses, we could train the colorization CNN model using the real data in the absence of the ground-truth color information of IG. Experimental results show that we can outperform related methods largely for colorizing real data.
Examining the Means of Engagement (MOE) for Enterprise Resource Planning (ERP) Adoption in Indonesia: Factors and Measures

This research aims to develop a model that explain the means of engagement towards the usage of product service software of ERP by specifying the degree or intensification in term of agreement, acceptance, approval and adaption, which the latter has been focused on. Actually, by looking at the current engagement ways, there are many strategies measurement has been executed such as subscription, interaction, promotion, exploration and retention in order to maintain the loyalty of the user and developing complementary bond through understanding users' characteristics. This study focused on the implementation of a successful product adoption that applied in two companies. The analysis method uses in this study will be regression analysis with survey collection, which specified F test value, T-test and significance value. The results of this research show that an involvement of the users in adopting ERP by simultaneously is affected by a product implementation with R Square values 91.4% for user decision, user characteristics factors and user convenience. It can be break down related to the significance usage approach that associated with a successful ERP adoption namely a submission of use factor, system performance, and satisfaction of use as well process of achievement. Based on the results, it can be concluded that to increase the success of ERP adoption in both companies, the training of ERP functionality system can support the process of increasing the ease of users in term of system performance and company's business although the assessment should be done regularly.
HeteroRWR: A Novel Algorithm for Top-&lt;i&gt;k&lt;/i&gt; Co-Author Recommendation with Fusion of Citation Networks

It is of great importance to recommend collaborators for scholars in academic social networks, which can benefit more scientific research results. Facing the problem of data sparsity of co-author recommendation in academic social networks, a novel recommendation algorithm named HeteroRWR (Heterogeneous Random Walk with Restart) is proposed. Different from the basic Random Walk with Restart (RWR) model which only walks in homogeneous networks, HeteroRWR implements multiple random walks in a heterogeneous network which integrates a citation network and a co-authorship network to mine the k mostly valuable co-authors for target users. By introducing the citation network, HeteroRWR algorithm can find more suitable candidate authors when the co-authorship network is extremely sparse. Candidate recommenders will not only have high topic similarities with target users, but also have good community centralities. Analyses on the convergence and time efficiency of the proposed approach are presented. Extensive experiments have been conducted on DBLP and CiteSeerX datasets. Experimental results demonstrate that HeteroRWR outperforms state-of-the-art baseline methods in terms of precision and recall rate even in the case of incorporating an incomplete citation dataset.
Region-Based Global Reasoning Networks

Global reasoning plays a significant role in many computer vision tasks which need to capture long-distance relationships. However, most current studies on global reasoning focus on exploring the relationship between pixels and ignore the critical role of the regions. In this paper, we propose an novel approach that explores the relationship between regions which have richer semantics than pixels. Specifically, we design a region aggregation method that can gather regional features automatically into a uniform shape, and adjust theirs positions adaptively for better alignment. To achieve the best performance of global reasoning, we propose various relationship exploration methods and apply them on the regional features. Our region-based global reasoning module, named ReGr, is end-to-end and can be inserted into existing visual understanding models without extra supervision. To evaluate our approach, we apply ReGr to fine-grained classification and action recognition benchmark tasks, and the experimental results demonstrate the effectiveness of our approach.
eSRCNN: A Framework for Optimizing Super-Resolution Tasks on Diverse Embedded CNN Accelerators

CNN-based Super-Resolution (SR), the most representative of low-level vision task, is a promising solution to improve users' QoS on IoT devices that suffer from limited network bandwidth and storage capacity by effectively enhancing image/video resolution. Although prior accelerators to embed CNN show tremendous performance and energy efficiency, they are not suitable for SR tasks regarding off-chip memory accesses. In this work, we present eSRCNN, a framework that enables performing energy-efficient SR tasks on diverse embedded CNN accelerators by decreasing off-chip memory accesses. To reduce off-chip memory accesses, our framework consists of three steps: a network reformation using a cross-layer weight scaling, a precision minimization with priority-based quantization, and an activation map compression exploiting a data locality. As a result, the energy consumption of off-chip memory accesses is reduced up to 71.89% with less than 3.52% area overhead.
The Effect of Student Cultural Enculturation on Student Art Appreciation

This research investigates how student cultural enculturation affects student art appreciation. In this research, a model was proposed. Through this model, students’ art appreciation can be improved by encouraging the cultural acquisition, cultural merging, and cultural internalization of the students. Data were collected from 585 students representing grades four to six from five elementary schools in the DKI Jakarta province. The students answered cultural enculturation and art appreciation questionnaires. In this research, the structural equation model (SEM) was tested. The results was that student cultural enculturation, through student cultural acquisition, student cultural merging, and student cultural internalization, had predictive effects on student art appreciation described by the variables for The Student Aesthetic Pleasure, The Student Emotional Appreciation, The Student Cognitive Appreciation, The Student Aesthetic Fascination and The Student Art Awareness.
Social sciences, humanities and their interoperability with the European Open Science Cloud: What is SSHOC?

The EC H2020 cluster project SSHOC aims to provide a full-fledged Social Sciences and Humanities Open Cloud where data, tools, and training are available and accessible for users of SSH data. The focus of the project is determined by the goal to further the innovation of infrastructural support for digital scholarship, to stimulate multidisciplinary collaboration across the various subfields of SSH and beyond, and to increase the potential for societal impact. The intention is to create a European open cloud ecosystem for social sciences and humanities, consisting of an infrastructural and human component. SSHOC will encourage secure environments for sharing and using sensitive and confidential data. It will contribute to the Open Science agenda and realization of the European Open Science Cloud (EOSC), as well as contribute to innovations stemming from the coupling of heterogeneous data types and work on the Interoperability principle of FAIR.
Explainable Machine Learning for Modeling of Early Postoperative Mortality in Lung Cancer

In recent years we see an increasing interest in applications of complex machine learning methods to medical problems. Black box models based on deep neural networks or ensembles are more and more popular in diagnostic, personalized medicine (Hamet and Tremblay 2017) or screening studies (Scheeder et al. 2018). Partially because they are accurate and easy to train. Nevertheless such models may be hard to understand and interpret. In high stake decisions, especially in medicine, the understanding of factors that drive model decisions is crucial. Lack of model understanding creates a serious risk in applications. In our study we propose and validate new approaches to exploration and explanation of predictive models for early postoperative mortality in lung cancer patients. Models are created on the Domestic Lung Cancer Database run by the National Institute of Tuberculosis and Lung Diseases. We show how explainable machine learning techniques can be used to combine data driven signals with domain knowledge. Additionally we explore whether the insight provided by model explainers give valuable information for physicians.
Merging Weak and Active Supervision for Semantic Parsing

A semantic parser maps natural language commands (NLs) from the users to executable meaning representations (MRs), which are later executed in certain environment to obtain user-desired results. The fully-supervised training of such parser requires NL/MR pairs, annotated by domain experts, which makes them expensive to collect. However, weakly-supervised semantic parsers are learnt only from pairs of NL and expected execution results, leaving the MRs latent. While weak supervision is cheaper to acquire, learning from this input poses difficulties. It demands that parsers search a large space with a very weak learning signal and it is hard to avoid spurious MRs that achieve the correct answer in the wrong way. These factors lead to a performance gap between parsers trained in weakly- and fully-supervised setting. To bridge this gap, we examine the intersection between weak supervision and active learning, which allows the learner to actively select examples and query for manual annotations as extra supervision to improve the model trained under weak supervision. We study different active learning heuristics for selecting examples to query, and various forms of extra supervision for such queries. We evaluate the effectiveness of our method on two different datasets. Experiments on the WikiSQL show that by annotating only 1.8% of examples, we improve over a state-of-the-art weakly-supervised baseline by 6.4%, achieving an accuracy of 79.0%, which is only 1.3% away from the model trained with full supervision. Experiments on WikiTableQuestions with human annotators show that our method can improve the performance with only 100 active queries, especially for weakly-supervised parsers learnt from a cold start. 1
Cryptocurrency – Sentiment Analysis in Social Media

Abstract The paper proposes the exploration, identification and development of a Java solution for extracting the sentiment related to the cryptocurrencies phenomenon, from the content of the posts of certain popular social networks. Detecting the positive, neutral or negative character of the sentiment is adopted as a relevant method of establishing the nature of the human perception on the topical issue defined by cryptocurrencies.
TemPEST: Soft Template-Based Personalized EDM Subject Generation through Collaborative Summarization

We address personalized Electronic Direct Mail (EDM) subject generation, which generates an attractive subject line for a product description according to user's preference on different contents or writing styles. Generating personalized EDM subjects has a few notable differences from generating text summaries. The subject has to be not only faithful to the description itself but also attractive to increase the click-through rate. Moreover, different users may have different preferences over the styles of topics. We propose a novel personalized EDM subject generation model named Soft Template-based Personalized EDM Subject Generator (TemPEST) to consider the aforementioned users' characteristics when generating subjects, which contains a soft template-based selective encoder network, a user rating encoder network, a summary decoder network and a rating decoder. Experimental results indicate that TemPEST is able to generate personalized topics and also effectively perform recommending rating reconstruction.
A Selective Review of Literature on Research Data Management in Academic Libraries

The present paper dwells on Research Data Management (RDM), its need, importance, the behaviour of the researchers in different disciplines towards data sharing and the role of libraries in extending data management services. The policies of publishers and funding bodies for sharing research data have also been described. It underlines that the library professionals should have a comprehensive understanding of the emerging issues, trends and challenges about the research data management to deploy appropriate services for the researchers. It highlights various resources on research data management, which may serve as guidelines for library professionals and researchers. The study will prove beneficial to library professionals and inspire them to extend RDM services in their organisations The paper concludes by advocating that the library professionals must update and upskill themselves with new trends, tools and techniques to provide RDM services. They should sensitise researchers to make their datasets accessible for reuse and sharing.
Cloud Annealing: A Novel Simulated Annealing Algorithm Based on Cloud Model

As one of the most popular intelligent optimization algorithms, Simulated Annealing (SA) faces two key problems, the generation of perturbation solutions and the control strategy of the outer loop (cooling schedule). In this paper, we introduce the Gaussian Cloud model to solve both problems and propose a novel cloud annealing algorithm. Its basic idea is to use the Gaussian Cloud model with decreasing numerical character He (Hyper-entropy) to generate new solutions in the inner loop, while He essentially indicates a heuristic control strategy to combine global random search of the outer loop and local tuning search of the inner loop. Experimental results in function optimization problems (i.e. single-peak, multi-peak and high dimensional functions) show that, compared with the simple SA algorithm, the proposed cloud annealing algorithm will lead to significant improvement on convergence and the average value of obtained solutions is usually closer to the optimal solution.
A doubly stochastic block Gauss-Seidel algorithm for solving linear equations

We propose a simple doubly stochastic block Gauss--Seidel algorithm for solving linear systems of equations. By varying the row partition parameter and the column partition parameter of the coefficient matrix, we recover the Landweber algorithm, the randomized Kaczmarz algorithm, the randomized Gauss--Seidel algorithm, and the doubly stochastic Gauss--Seidel algorithm. For general (consistent or inconsistent) linear systems, we show the exponential convergence of the {\it norms of the expected iterates} via exact formulas. For consistent linear systems, we prove the exponential convergence of the {\it expected norms of the error and the residual}. Numerical experiments are given to illustrate the efficiency of the proposed algorithm.
Frei-Chen bases based lossy digital image compression technique

In the big data era, image compression is of significant importance in today’s world. Importantly, compression of large sized images is required for everyday tasks; including electronic data communications and internet transactions. However, two important measures should be considered for any compression algorithm: the compression factor and the quality of the decompressed image. In this paper, we use Frei-Chen bases technique and the Modified Run Length Encoding (RLE) to compress images. The Frei-Chen bases technique is applied at the first stage in which the average subspace is applied to each 3 × 3 block. Those blocks with the highest energy are replaced by a single value that represents the average value of the pixels in the corresponding block. Even though Frei-Chen bases technique provides lossy compression, it maintains the main characteristics of the image. Additionally, the Frei-Chen bases technique enhances the compression factor, making it advantageous to use. In the second stage, RLE is applied to further increase the compression factor. The goal of using RLE is to enhance the compression factor without adding any distortion to the resultant decompressed image. Integrating RLE with Frei-Chen bases technique, as described in the proposed algorithm, ensures high quality decompressed images and high compression rate. The results of the proposed algorithms are shown to be comparable in quality and performance with other existing methods.