Reviewerly/exHarmony huggingface dataset:
The Enhancement of OSH Training with an Augmented Reality-Based App

Recent advances in training approaches and technology have identified the need to improve training in the field of occupational safety and health (OSH). While many researchers have examined the use of augmented reality (AR) and technology-based classrooms to enhance conventional training practices, quantitative evidence for their effectiveness and motivational im-pact remains scarce. To contribute to filling this research gap, an AR-based application was developed to enhance OSH training. The enhanced training session was conducted in a closed, safe environment with ten participants with no background in OSH. Pre-test, post-test, and instructional material motivation surveys (IMMS) using a five-point Likert scale were deployed to measure the effectiveness and motivational impact in accordance with the respective ARCS factors. The empirical results obtained show that partici-pants performed significantly better in the post-tests than in the pre-tests (mean = 14.60, s = 1.71, t(9) = –12.11 with p-value &lt; 0.001). The IMMS re-sults show that the perceived motivation arising from the AR-based applica-tion resulted in the highest mean score for the satisfaction factor (4.80) fol-lowed by the relevance (4.65), confidence (4.60), and attention (4.55) fac-tors. The results of the evaluation suggest that the AR-based application boosted participants’ active learning behavior, engagement, and interest dur-ing the enhanced training session and produced a better learning outcome and experience.
Pretrained models and evaluation data for the Khmer language

Trained on a large corpus, pretrained models (PTMs) can capture different levels of concepts in context and hence generate universal language representations, which greatly benefit downstream natural language processing (NLP) tasks. In recent years, PTMs have been widely used in most NLP applications, especially for high-resource languages, such as English and Chinese. However, scarce resources have discouraged the progress of PTMs for low-resource languages. Transformer-based PTMs for the Khmer language are presented in this work for the first time. We evaluate our models on two downstream tasks: Part-of-speech tagging and news categorization. The dataset for the latter task is self-constructed. Experiments demonstrate the effectiveness of the Khmer models. In addition, we find that the current Khmer word segmentation technology does not aid performance improvement. We aim to release our models and datasets to the community in hopes of facilitating the future development of Khmer NLP applications.
PyOMP: Multithreaded Parallel Programming in Python

Python is a widely used language in scientific computing. When the goal is high performance, however, Python lags far behind low-level languages such as C and Fortran. To support applications that stress performance, Python needs to access the full capabilities of modern CPUs. That means support for parallel multithreading. In this article, we describe PyOMP, a system that enables OpenMP in Python. Programmers write code in Python with OpenMP, Numba generates code that compiles to LLVM, and the resulting programs run with performance that approaches that from code written with C and OpenMP. In this article, we provide an update on the PyOMP project and explain how to install it and use it to write parallel multithreaded code in Python.
The Design of an Intelligent Robotic Wheelchair Supporting People with Special Needs, Including for Their Visual System

The paper aims to study the applicability and limitations of the solution resulting from a design process for an intelligent system supporting people with special needs who are not physically able to control a wheelchair using classical systems. The intelligent system uses information from smart sensors and offers a control system that replaces the use of a joystick. The necessary movements of the chair in the environment can be determined by an intelligent vision system analyzing the direction of the patient's gaze and point of view, as well as the actions of the head. In this approach, an important task is to detect the destination target in the 3D workspace. This solution has been evaluated, outdoor and indoor, under different lighting conditions. In order to design the intelligent wheelchair, and because sometimes people with special needs also have specific problems with their optical system (e.g., strabismus, Nystagmus) the system was tested on different subjects, some of them wearing eyeglasses. During the design process of the intelligent system, all the tests involving human subjects were performed in accordance with specific rules of medical security and ethics. In this sense, the process was supervised by a company specialized in health activities that involve people with special needs. The main results and findings are as follows: validation of the proposed solution for all indoor lightning conditions; methodology to create personal profiles, used to improve the HMI efficiency and to adapt it to each subject needs; a primary evaluation and validation for the use of personal profiles in real life, indoor conditions. The conclusion is that the proposed solution can be used for persons who are not physically able to control a wheelchair using classical systems, having with minor vision deficiencies or major vision impairment affecting one of the eyes.
Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security

Over the past couple of years, machine learning methods—especially the outlier detection ones—have anchored in the cybersecurity field to detect network-based anomalies rooted in novel attack patterns. However, the ubiquity of massive continuously generated data streams poses an enormous challenge to efficient detection schemes and demands fast, memory-constrained online algorithms that are capable to deal with concept drifts. Feature selection plays an important role when it comes to improve outlier detection in terms of identifying noisy data that contain irrelevant or redundant features. State-of-the-art work either focuses on unsupervised feature selection for data streams or (offline) outlier detection. Substantial requirements to combine both fields are derived and compared with existing approaches. The comprehensive review reveals a research gap in unsupervised feature selection for the improvement of outlier detection methods in data streams. Thus, a novel algorithm for Unsupervised Feature Selection for Streaming Outlier Detection, denoted as UFSSOD, will be proposed, which is able to perform unsupervised feature selection for the purpose of outlier detection on streaming data. Furthermore, it is able to determine the amount of top-performing features by clustering their score values. A generic concept that shows two application scenarios of UFSSOD in conjunction with off-the-shell online outlier detection algorithms has been derived. Extensive experiments have shown that a promising feature selection mechanism for streaming data is not applicable in the field of outlier detection. Moreover, UFSSOD, as an online capable algorithm, yields comparable results to a state-of-the-art offline method trimmed for outlier detection.
Research Trends in Media Pedagogy: Between the Paradigm of Risk and the Paradigm of Opportunity

The use of computers, internet, and smartphones in the learning and teaching process has become an irreversible fact. Information and communication technologies (ICT) are now one of the fundamental teaching resources and even one of the principal teaching environments. The widespread use of ICT stands in positive correlation to the growing number of studies on educational aspects of the use of new media in schooling. The dynamically growing number of publications in this field requires reflection on the directions of research in the intensely developing sub-discipline of education science, i.e. media pedagogy. The aim of the article is to explore the two dominant directions of research on didactic and upbringing aspects of ICT use in education. The text presents the assumptions and processes assigned to both the opportunity paradigm and the risk paradigm of media pedagogy. These paradigms clash, giving rise to research directed at positive or negative phenomena related to the digitalization of schooling and educational processes. The text is an attempt to draw attention not only to the development of media pedagogy, but also to methodological errors resulting from anchoring research to only one trend.
Survey on deep learning applications in digital image security

In the digital era, sharing pictures on social media has become a common privacy issue. To prevent private images from being eavesdropped on and destroyed, developing secure and efficient image steganography, image cryptography, and image authentication has been difficult. Deep learning provides a solution for digital image security. First, we make an overall conclusion on deep learning applications in image steganography to generate five aspects: the cover image, stego-image, embedding change probabilities, coverless steganography, and steganalysis. Second, we also combine and compare deep learning methods used in six aspects: image cryptography from image compression, image resolution improvement, image object detection and classification, key generation, end-to-end image encryption, and image cryptoanalysis. Third, we collect deep learning methods in image authentication from five perspectives: image forgery detection, watermarked image generation, image watermark extraction and detection, image watermarking attack, and image watermark removal. Finally, we summarize future research directions of deep learning utilization in image steganography, image cryptography, and image authentication.
Studying human-to-computer bias transference

Abstract It is generally agreed that one origin of machine bias is resulting from characteristics within the dataset on which the algorithms are trained, i.e., the data does not warrant a generalized inference. We, however, hypothesize that a different ‘mechanism’ may also be responsible for machine bias, namely that biases may originate from (i) the programmers’ cultural background, including education or line of work, or (ii) the contextual programming environment, including software requirements or developer tools. Combining an experimental and comparative design, we study the effects of cultural and contextual metaphors, and test whether each of these are ‘transferred’ from the programmer to the program, thus constituting a machine bias. Our results show that (i) cultural metaphors influence the programmer’s choices and (ii) contextual metaphors induced through priming can be used to moderate or exacerbate the effects of the cultural metaphors. Our studies are purposely performed with users of varying educational backgrounds and programming skills stretching from novice to proficient.
Network Course Recommendation System Based on Double-Layer Attention Mechanism

In view of the lack of accurate recommendation and selection of courses on the network teaching platform in the new form of higher education, a network course recommendation system based on the double-layer attention mechanism is proposed. First of all, the collected data are preprocessed, while the data of students and course information are normalized and classified. Then, the dual attention mechanism is introduced into the parallel neural network recommendation model so as to improve the model’s ability to mine important features. TF-IDF (term frequency-inverse document frequency) based on the student score and course category is improved. The recommendation results are classified according to the weight of course categories, so as to construct different types of course groups and complete the recommendation. The experimental results show that the proposed algorithm can effectively improve the model recommendation accuracy compared with other algorithms.
Expression Recognition Method Using Improved VGG16 Network Model in Robot Interaction

Aiming at the problems of poor representation ability and less feature data when traditional expression recognition methods are applied to intelligent applications, an expression recognition method based on improved VGG16 network is proposed. Firstly, the VGG16 network is improved by using large convolution kernel instead of small convolution kernel and reducing some fully connected layers to reduce the complexity and parameters of the model. Then, the high-dimensional abstract feature data output by the improved VGG16 is input into the convolution neural network (CNN) for training, so as to output the expression types with high accuracy. Finally, the expression recognition method combined with the improved VGG16 and CNN model is applied to the human-computer interaction of the NAO robot. The robot makes different interactive actions according to different expressions. The experimental results based on CK + dataset show that the improved VGG16 network has strong supervised learning ability. It can extract features well for different expression types, and its overall recognition accuracy is close to 90%. Through multiple tests, the interactive results show that the robot can stably recognize emotions and make corresponding action interactions.
Rethinking the Two-Stage Framework for Grounded Situation Recognition

Grounded Situation Recognition (GSR), i.e., recognizing the salient activity (or verb) category in an image (e.g.,buying) and detecting all corresponding semantic roles (e.g.,agent and goods), is an essential step towards “human-like” event understanding. Since each verb is associated with a specific set of semantic roles, all existing GSR methods resort to a two-stage framework: predicting the verb in the first stage and detecting the semantic roles in the second stage. However, there are obvious drawbacks in both stages: 1) The widely-used cross-entropy (XE) loss for object recognition is insufficient in verb classification due to the large intra-class variation and high inter-class similarity among daily activities. 2) All semantic roles are detected in an autoregressive manner, which fails to model the complex semantic relations between different roles. To this end, we propose a novel SituFormerfor GSR which consists of a Coarse-to-Fine Verb Model (CFVM) and a Transformer-based Noun Model (TNM). CFVM is a two-step verb prediction model: a coarse-grained model trained with XE loss first proposes a set of verb candidates, and then a fine-grained model trained with triplet loss re-ranks these candidates with enhanced verb features (not only separable but also discriminative). TNM is a transformer-based semantic role detection model, which detects all roles parallelly. Owing to the global relation modeling ability and flexibility of the transformer decoder, TNM can fully explore the statistical dependency of the roles. Extensive validations on the challenging SWiG benchmark show that SituFormer achieves a new state-of-the-art performance with significant gains under various metrics. Code is available at https://github.com/kellyiss/SituFormer.
Analysis and Research on Digital Reading Platform of Multimedia Library by Big Data Computing in Internet Era

Digital reading promotion service is a service way for libraries to provide readers with a series of digital resources, enjoy the service functions, and share the experience of using them in various digital reading platforms, which is to meet the reading interests and reading needs of more readers, and is also the focus of the current library work. In the era of new media, the characteristics of digital reading are subtly changing the readers’ needs for reading environment, reading content, and reading style. Libraries should keep pace with the development of the times and provide readers with diversified, intelligent, and targeted digital reading platforms. The digital reading platform should continuously improve the digital reading service functions, broaden the service scope and dissemination channels, and finally realize the diversification, interest, and intelligence of digital reading service. This paper takes the digital reading platform of libraries in the region as the research theme and carries out research work on libraries. The province is divided into three regions according to the geographical map: southeastern region, central region, and northwestern region. The digital reading platforms of 14 prefecture-level public libraries and 58 libraries of higher education institutions in each region were accessed. Firstly, we check the construction of digital resources within the library websites, secondly, we count the opening of digital reading platform functions, and finally, we check the opening of digital reading platforms. Through the research, it is found that there are problems of unbalanced distribution of digital reading resources in regional libraries; unattractive design of readers’ interface and inadequate reading service functions; lack of continuous training of readers’ guidance; insufficient publicity and promotion; low efficiency of staff in responding to consultation; and low degree of platform openness and weak awareness of sharing. Finally, the problems found in the research are summarized, and the solution measures for the regional digital reading platform are proposed. Libraries in the digital era should give priority to systems that can manage all library resources comprehensively and effectively, adapt to more flexible library workflows, and enable libraries to provide better services to users.
Compositional Generalization via Semantic Tagging

Although neural sequence-to-sequence models have been successfully applied to semantic parsing, they fail at compositional generalization, i.e., they are unable to systematically generalize to unseen compositions of seen components.Motivated by traditional semantic parsing where compositionality is explicitly accounted for by symbolic grammars, we propose a new decoding framework that preserves the expressivity and generality of sequence-tosequence models while featuring lexicon-style alignments and disentangled information processing.Specifically, we decompose decoding into two phases where an input utterance is first tagged with semantic symbols representing the meaning of individual words, and then a sequence-to-sequence model is used to predict the final meaning representation conditioning on the utterance and the predicted tag sequence.Experimental results on three semantic parsing datasets show that the proposed approach consistently improves compositional generalization across model architectures, domains, and semantic formalisms. 1
An Auxiliary Decision-Making System for Electric Power Intelligent Customer Service Based on Hadoop

Aiming at the problems of low security, high occupancy rate, and long response time in the current power intelligent customer service assistant decision-making system, a power intelligent customer service assistant decision-making system based on the Hadoop big data framework is designed. By analyzing the Hadoop big data framework, according to the characteristics and core elements of the HDFS distributed file system, the MapReduce programming model, and the data mining algorithm, the basic process of power intelligent customer service assistance decision-making is established. We analyze the overall and functional requirements of the system, design the overall architecture and application architecture of the system, design the E-R diagram and table structure of the database according to the database design principle, and realize the design of power intelligent customer service auxiliary decision-making system based on Hadoop big data framework. The test results show that the proposed method has high system security and low system occupancy and can effectively shorten the system response time. The systems run more flawlessly as compared to the existing methods and give impressing results with lesser CPU utilization. The response time was recorded to be about 12.2 seconds for 1000 power intelligent customer servers, which is much lower than that of the competitors.
Review of studies of blockchain technology effects on the shipping industry

Abstract Decision-making is a prolific research area in the internet era, which has propelled globalization and the virtual elimination of many country border barriers. However, effective decision-making in the shipping industry is a time consuming and often complicated process. Digital evolution has provided new innovative organizational operation methods. Blockchain technology—a basic component of the Fourth Industrial Revolution—is one such innovation that promises to alter the process of decision-making. However, only a few academic studies have explored the decision-making aspect of blockchain technology. Moreover, there is a dearth of comprehensive research on how blockchain affects decisions in the shipping industry. This study explored how this novice technology can address issues, such as vast documentation and information asymmetry in the shipping industry. Specifically, grounded theory was used to qualitatively investigate extant practices and examine the potential impact of blockchain technology on decision-making in the shipping industry and the potential of using blockchain technology to emancipate decision-making. The study results indicate that the instant and reliable data-sharing capability of blockchain can significantly impact the shipping industry, while transforming its decision-making processes.
Modern Authentication Schemes in Smartphones and IoT Devices: An Empirical Survey

User authentication remains a challenging issue, despite the existence of a large number of proposed solutions, such as traditional text-based, graphical-based, biometrics-based, Web-based, and hardware-based schemes.For example, some of these schemes are not suitable for deployment in an Internet of Things (IoT) setting, partly due to the hardware and/or software constraints of IoT devices.The increasing popularity and pervasiveness of IoT equipment in a broad range of settings reinforces the importance of ensuring the security and privacy of IoT devices.Therefore, in this article, we conduct a comprehensive literature review and an empirical study to gain an in-depth understanding of the different authentication schemes as well as their vulnerabilities and deficits against various types of cyberattacks when applied in IoT-based systems.Based on the identified limitations, we recommend several mitigation strategies and discuss the practical implications of our findings.
AN EXPLORATION OF SOCIAL MEDIA MARKETING FEATURES AND BRAND LOYALTY IN THE FAST FOOD INDUSTRY

The key objective of this research was to test the role of social media marketing features (interactivity, perceived relevance, entertainment, and informativeness) in affecting brand loyalty in the fast-food industry. The required data was obtained through a quantitative survey from customers of fast-food brands in the United Arab Emirates. The findings revealed that most of the social media marketing features positively influence brand loyalty. In detail, it was found that interactivity and perceived relevance positively affect brand loyalty. However, contrary to expectations, it was found that the impact of entertainment on brand loyalty is insignificant. Finally, the outcomes confirmed that informativeness is a significant predictor of brand loyalty. This research adds to the existing marketing literature on this topic through the examination of selected social media marketing features and brand loyalty in a Middle East country as the empirical research on this topic, particularly, in this region is scarce.