Reviewerly/exHarmony huggingface dataset:
The Development of Library Sector Terminology in Latvia and Problems and Solutions for Translating RDA Standard Terms

&nbsp;&nbsp;&nbsp;&nbsp;The National Library of Latvia (NLL) is the methodological centre of Latvian libraries in data creation and ensuring the development of data standards as well as the methodical centre co-ordinating the terminology work of library field and related branches. The historical insight shows the development of terminology work and allows to trace the development of terminology work and problems. The terms are adopted and approved by the subcommittee of the Terminology Commission of the Latvian Academy of Science (LAS).&nbsp;&nbsp;&nbsp;&nbsp;The article highlights the role of the NLL in the implementation and translation of Resource Descriptions and Access (RDA) standard in Latvia. It is the standard for descriptive cataloguing, intended for use by libraries and other cultural organizations such as museums and archives. The RDA standard includes a set of data elements, guidelines and instructions that can be adapted for other information communities around the world, such as museums and archives. The translation problems of RDA Glossary are described, as well as principles and solutions for correct reproduction of RDA terms and their explanations in Latvian. An overview is provided of how the correct, appropriate terminology was followed in the translation process of the terms, so that the chosen term, in the appropriate meaning, could be used throughout the translation and related documents.Finally, the influence of RDA terms on the common data model of Latvia memory institutions for the unified discovery of cultural heritage digital resources is also outlined.
Domain Adaptation for Holistic Skin Detection

Human skin detection in images is a widely studied topic of Computer Vision for which it is commonly accepted that analysis of pixel color or local patches may suffice. However, we found that the lack of contextual information may hinder the performance of local approaches. In this paper, we present a comprehensive evaluation of holistic and local Convolutional Neural Network (CNN) approaches on in-domain and cross-domain experiments and compare them with state-of-the-art pixel-based approaches. We also propose combining inductive transfer learning and unsupervised domain adaptation methods evaluated on different domains under several amounts of labelled data availability. We show a clear superiority of CNN over pixel-based approaches even without labeled training samples on the target domain and provide experimental support for the superiority of holistic over local approaches for human skin detection.
Developing Interactive Electronic Book Based on TPACK to Increase Creative Thinking Skill

This study aims to: (1) improve the creative thinking skills of prospective teachers, (2) measure the level of validity, practicality, and effectiveness of the developed electronic book. This type of research is research and development (RD) by applying the ADDIE model. This research was conducted through 5 stages: (1) analysis, (2) design, (3) development, (4) implementation, and (5) evaluation. The subjects of this study were students (prospective teachers) in the 5th-semester consisting of 55 people in the IT-based learning media course. At the same time, the object of this research was an electronic book developed based on TPACK. Several instruments were used: observation sheets, questionnaires, validation sheets, and essay tests. The results of data analysis indicate that the electronic book has met the requirements of validity, practicality, and effectiveness. In addition, there was a significant increase in the ability to think creatively.
Performance Evaluation of Three Intelligent Optimization Algorithms for Obstacle Avoidance Path Planning

In this paper, we study an intelligent path planning problem on avoiding traffic obstacles in urban transportation networks. We apply three heuristic methods to solve the optimization model, including genetic algorithm, wolf pack algorithm, and a mixture method of genetic algorithm and wolf pack algorithm. In the numerical results, we compare the solutions and performance of these three intelligent algorithms. It shows that the optimal path lengths obtained by these three algorithms are very closed, whereas the convergence time of the genetic algorithm is the shortest among them. We also find that the mixture method is more efficient in updating the population, and its convergence speed is the fastest. Besides, it is found that the convergence time of the wolf pack algorithm is smaller than the others. By means of the analysis in our experiments, we evaluate the pros and cons of these algorithms when solving the studied optimization problem, which would be of great significance for the intelligent optimization of obstacle path planning in smart cities.
A Task Modelling Formalism for Industrial Mobile Robot Applications

Industrial mobile robots are increasingly introduced in factories and warehouses. These environments are becoming more dynamic with human co-workers and other uncertainties that may interfere with the robot&#x0027;s actions. To uphold efficient operation, the robots should be able to autonomously plan and replan the order of their tasks. On the other hand, the robot&#x0027;s actions should be predictable in an industrial process. We believe the deployment and operation of robots become more robust if the experts of the industrial processes are able to understand and modify the robot&#x0027;s behaviour. To this end, we present an intuitive novel task modelling formalism, Robot Task Scheduling Graph (RTSG). RTSG provides building blocks for the explicit definition of alternative task sequences in a compact graph format. We present how such a graph is automatically converted to a task planning problem in two different forms, i.e., a Mixed Integer Linear Program (MILP) and a Planning Domain Definition Language specification (PDDL). Converted RTSG models of a mobile kitting application are used to experimentally compare the performance of one MILP planner and two PDDL planners. Besides providing this comparison, the experiments confirm the equivalence of the converted MILP and PDDL problem formulations. Finally, a simulation experiment verifies the assumed correlation between a cost model, based on path lengths, and the makespan.
Comprehensive Saliency Fusion for Object Co-segmentation

Object co-segmentation has drawn significant attention in recent years, thanks to its clarity on the expected foreground, the shared object in a group of images. Saliency fusion has been one of the promising ways to carry it out. However, prior works either fuse saliency maps of the same image or saliency maps of different images to extract the expected foregrounds. Also, they rely on hand-crafted saliency extraction and correspondence processes in most cases. This paper revisits the problem and proposes fusing saliency maps of both the same image and different images. It also leverages advances in deep learning for the saliency extraction and correspondence processes. Hence, we call it comprehensive saliency fusion. Our experiments reveal that our approach achieves much-improved object co-segmentation results compared to prior works on important benchmark datasets such as iCoseg, MSRC, and Internet Images.
Usage of Augmented Reality for Improved Human-Machine Interaction and Real-Time Error Correction of Laboratory Units

Abstract For future innovations, complex Industry 4.0-technologies need to improve the interaction of humans and technology. Augmented Reality ( AR ) has a significant potential for this task by introducing more interactivity into modern technical assistance systems. However, AR systems are usually very expensive and thus unsuitable for small and medium-sized enterprises (SMEs). Furthermore, the machine's reliable data transfer to the AR applications and the user activity indication appear to be problematic. This work proposes a solution to these problems. A simple and scalable data transfer from industrial systems to Android applications has been developed.The suggested prototype demonstrates an AR application for troubleshooting and error correction in real-time, even on mobile or wearable devices, while working in a laboratory unit to simulate and solve various errors. The unit components (small garage doors) are equipped with sensors. The information about the state of the system is available in real-time at any given moment and is transmitted to a mobile or wearable device (tablet or smart glass) equipped with AR application. The operator is enabled to preview the required information in a graphical form (marks and cursors). Potential errors are shown and solved with an interactive manual. The system can be used for training purposes to achieve more efficient error correction and faster repairing.
Automatic Construction of Fine-Grained Paraphrase Corpora System Using Language Inference Model

Paraphrase detection and generation are important natural language processing (NLP) tasks. Yet the term paraphrase is broad enough to include many fine-grained relations. This leads to different tolerance levels of semantic divergence in the positive paraphrase class among publicly available paraphrase datasets. Such variation can affect the generalisability of paraphrase classification models. It may also impact the predictability of paraphrase generation models. This paper presents a new model which can use few corpora of fine-grained paraphrase relations to construct automatically using language inference models. The fine-grained sentence level paraphrase relations are defined based on word and phrase level counterparts. We demonstrate that the fine-grained labels from our proposed system can make it possible to generate paraphrases at desirable semantic level. The new labels could also contribute to general sentence embedding techniques.
IBM: CISOs under the spotlight

Of all the C-level executives, the chief information security officer (CISO) has one of the least enviable jobs. In too many organisations, information security either goes unseen, which means people fail to understand how much work is being done in the background, or it is regarded as a hindrance by people who just want to get on with their work, unfettered by passwords or multifactor authentication.
Performance Analysis of Gujarati Script Recognition Using Multiclass and Multilabel Classification

In the era of digital world, the paper documents are turning into digitally accessible and editable documents. It has led to the advancement of the optical character recognition system. So to take the document work into digital format, the research on optical characters for Gujarati script has been carried out in this paper. We have tried to inculcate all the Gujarati consonants, vowels and their combinations for recognition and classification, which has not been done so far. The approach of deep learning and optimization had been used to train our network using multiclass and multilabel classification methods on a systematically generated data set. The chief goal of the research is to include all the Gujarati characters and analyse the performance of these two classification methods used for the character recognition of Gujarati language and produce comparative results in terms of validation accuracy, validation loss and number of epochs. The various models used for comparison over the two methods are DenseNet201, EfficientNetB0 and ResNet50.
Stochastic sparse adversarial attacks

This paper introduces stochastic sparse adversarial attacks (SSAA), standing as simple, fast and purely noise-based targeted and untargeted attacks of neural network classifiers (NNC). SSAA offer new examples of sparse (or L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> ) attacks for which only few methods have been proposed previously. These attacks are devised by exploiting a small-time expansion idea widely used for Markov processes. Experiments on small and large datasets (CIFAR-10 and ImageNet) illustrate several advantages of SSAA in comparison with the-state-of-the-art methods. For instance, in the untargeted case, our method called Voting Folded Gaussian Attack (VFGA) scales efficiently to ImageNet and achieves a significantly lower L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> score than SparseFool (up to $\frac{2}{5}$) while being faster. Moreover, VFGA achieves better L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> scores on ImageNet than Sparse-RS when both attacks are fully successful on a large number of samples.
Images Within Images? A Multi-image Paradigm with Novel Key-Value Graph Oriented Steganography

Steganographic methods have been in the limelight of research and development for concealing secret data within a cover media without being noticed through general visualization. The Least Significant Bits (LSBs) of 8-bit color code for the RGB image arises the possibility of replacing the last two bits with the bits of the encrypted message. Several procedures have been developed to hide an image within another image however in most cases the payload image has to be within the accommodatable range of the cover image and very little literature have shown methods to hide multiple images within multiple images. This paper presents a novel approach to split the image into JSON styled dictionary of key-value pairs and using a metadata graph to locate different parts and positions of the payload images in the entire cluster of cover images. The model could be easily used in the real world scenario for privately sharing secret data over public communication channels without being noticed.
Multi-Source Domain Generalization Using Domain Attributes for Recurrent Neural Network Language Models

Most conventional multi-source domain adaptation techniques for recurrent neural network language models (RNNLMs) are domain-centric. In these approaches, each domain is considered independently and this makes it difficult to apply the models to completely unseen target domains that are unobservable during training. Instead, our study exploits domain attributes, which represent common knowledge among such different domains as dialects, types of wordings, styles, and topics, to achieve domain generalization that can robustly represent unseen target domains by combining the domain attributes. To achieve attribute-based domain generalization system in language modeling, we introduce domain attribute-based experts to a multi-stream RNNLM called recurrent adaptive mixture model (RADMM) instead of domain-based experts. In the proposed system, a long short-term memory is independently trained on each domain attribute as an expert model. Then by integrating the outputs from all the experts in response to the context-dependent weight of the domain attributes of the current input, we predict the subsequent words in the unseen target domain and exploit the specific knowledge of each domain attribute. To demonstrate the effectiveness of our proposed domain attributes-centric language model, we experimentally compared the proposed model with conventional domain-centric language model by using texts taken from multiple domains including different writing styles, topics, dialects, and types of wordings. The experimental results demonstrated that lower perplexity can be achieved using domain attributes.
Mobile-Assisted Language Learning in L2 Korean Using WeChat: A Case Study

Abstract—Learners studying Korean as a foreign language have a long-standing problem in language learning: less exposure to the target language and a lack of communicative learning opportunities. This study attempts to explore how these challenges can be solved by incorporating mobile technologies—especially WeChat—into Korean language learning. By examining the implementation of the WeChat Project’s five components, this study reports on the advantages and disadvantages of using mobile technologies for students who learn an intermediate Korean language course at a liberal arts college in China. A total of 16 students participated in a one-semester study, and they were asked to complete an end-of-semester questionnaire and attend a follow-up interview. The questionnaire’s results revealed that students generally welcomed the WeChat Project and that they positively perceived the usefulness of the five components of the WeChat Project. In the interviews, the students reported four major benefits of using WeChat in their Korean learning: increased learning time, an acquisition of linguistic knowledge, the Korean language community, and the convenience of using WeChat. The disadvantages and suggestions for improvements are discussed.
Organizational agility to counter coronavirus implications in emerging Saudi universities

Objective: The study's primary goal was to uncover the reality of organizational agility in emerging universities. It also aimed to determine the statistical variances between the mean scores of the sample used in the axes of the organizational agility questionnaire for these universities. The main variables used are gender, academic position, and years of experience. Methodology: The study utilized the descriptive method. The data collection process entailed handing out questionnaires to a sample of seven hundred and forty-seven faculty members in Jazan, Bishah, Hafar Al-Batin, Shaqraa, and Najran Universities. Results: Emerging universities regularly practice organizational agility in light of the emerging coronavirus with high flexibility at moderate costs. The study's results also indicated mathematically substantial variances at the degree of viability between the male sample's average and the female sample's average in favor of the female sample in the timing and the flexibility of practices. It also indicated statistically significant variances at the degree of significance between the mean scores of the research sample, attributed to the difference in the academic position in the timing, flexibility, and cost of practices.&#x0D; Keywords: Coronavirus, Degree of variability Emerging universities, Organizational agility, Strategy,
A Critique Empirical Evaluation of Relevance Computation for Focused Web Crawlers

HIGHLIGHTS This paper presents a survey on focused web crawlers. This paper presents the challenges in focused crawling research. This paper presents the highlights and hindrances of existing focused web crawlers. This paper also presents the future scope for research in focused web crawling.
Research on rescuers’ training process in toxic / explosive / flammable environments

The paper presents the structuring of physical effort put in by rescuers during training, taking into account the activity specific psychosocial factors, structuring that aimed at streamlining the training process of intervention and rescue personnel in toxic / explosive / flammable environments. Training routes with various degrees of difficulty, allowing the simulation of intervention activities in horizontally and vertically confined spaces, low visibility, high temperature and humidity environments were analysed, for each the specific labour consumption being calculated. A dysfunctional phenomenon caused by the intervention and rescue activity is fatigue, perceived as a body reaction to readjust and restore its functions following intense or repeated body requests for energy consumption. Not only physical fatigue (which can be controlled through exercises) is specific for rescue and intervention activity but also mental fatigue. In addition to physical and technical training, psychosocial training of people who carry out intervention and rescue activities was followed in the training programs.
Vehicle Taillight Detection Based on Semantic Information Fusion

The detection of the vehicle taillights is important for predicting the driving intention of the vehicle in front. The YOLOv4 target detection algorithm has problems such as insufficient detection capabilities for small targets and inaccurate bounding box positioning. In response to this problem, a front vehicle taillight detection algorithm combining high-level semantics and low-level features is proposed. Based on the improved CSPResNeXt model to extract features, the algorithm uses the mask image containing semantic information and the corresponding feature map to fuse and enhance distinguishability of vehicle taillights and background. At the same time, BiFPN model is used for further multi-scale feature fusion to extract more detailed information. The experimental results show that our proposed algorithm can improve the mAP of object detection without affecting the real-time performance of the YOLOv4 algorithm. Our algorithm achieves 90.55% mAP (IoU = 0.5) on the test set, which is 14.42% mAP (IoU [0.5:0.95]) higher than the original YOLOv4 algorithm.
ElGamal Homomorphic Encryption-Based Privacy Preserving Association Rule Mining on Horizontally Partitioned Healthcare Data

In today’s world, life-threatening diseases have become a pre-eminent issue in healthcare due to the higher mortality rate. It is possible to lower this mortality rate by utilizing healthcare intelligence to detect diseases early. Patient’s medical data is stored in the EHR system, which is kept up to date by the healthcare provider. Data mining techniques like Association Rule Mining can detect a patient’s disease from their symptoms using digital healthcare data stored in the EHR system. Association rule mining’s efficacy can be improved by using global data from various EHR systems. It mandates that all EHR systems exchange healthcare records to a central server. When personal health information is made available on an untrusted server, several privacy laws may be violated. As a result, the challenge of privacy preserving distributed healthcare data mining has become a well-known study field in the healthcare industry. This research uses an efficient ElGamal homomorphic encryption technique to protect privacy in a distributed association rule mining. The proposed approach to discover the risk factor of most life-threatening diseases like breast cancer and heart disease with its symptoms and discuss the scope for combating COVID-19. Theoretical analysis of the proposed approach shows that it is efficient and maintains privacy in an insecure communication environment. An experimental study with a real dataset shows the proposed approach’s benefit compared to the local single EHR system results.
Near Hue-Preserving Reversible Contrast and Saturation Enhancement Using Histogram Shifting

In recent years, several reversible contrast-enhancement methods for color images using digital watermarking have been proposed. These methods can restore an original image from a contrast-enhanced image, in which the information required to recover the original image is embedded with other payloads. In these methods, the hue component after enhancement is similar to that of the original image. However, the saturation of the image after enhancement is significantly lower than that of the original image, and the obtained image exhibits a pale color tone. Herein, we propose a method for enhancing the contrast and saturation of color images and nearly preserving the hue component in a reversible manner. Our method integrates red, green, and blue histograms and preserves the median value of the integrated components. Consequently, the contrast and saturation improved, whereas the subjective image quality improved. In addition, we confirmed that the hue component of the enhanced image is similar to that of the original image. We also confirmed that the original image was perfectly restored from the enhanced image. Our method can contribute to the field of digital photography as a legal evidence. The required storage space for color images and issues pertaining to evidence management can be reduced considering our method enables the creation of color images before and after the enhancement of one image.