Reviewerly/exHarmony huggingface dataset:
The Impact of Using Smart Algorithms and Blockchain Technology on the Profits' Quality in Saudi Financial Market

The study aimed to demonstrate the impact of using smart algorithms and blockchain technology on the profits' quality in Saudi financial market. As these forms of the recent technological revolution lead to great changes in business models and affect consequently the accounting profession, study was applied on Saudi Money (Tadawul), the shares of which were traded during the time period (2018/2021), amounted to (223) companies divided into (21) sectors, financial institutions were excluded due to the nature of the study, where a sample was taken consisting of (60) companies. The banking and insurance companies sectors were also excluded due to the different nature of the activity (10 banks + 32 insurance companies), and thus the number of observations was (240) for the years covered by the study, then the proposed model was applied using data extracted from the annual reports and financial statements of companies listed in the Saudi capital market, and the study concluded that the use of smart algorithms contributes significantly to improving the quality of profits in companies listed on the Saudi capital market as it has the ability by using its forecasting models to detect the frauds in the financial statements, also the use of blockchain technology contributes significantly to improving the quality of profits in the companies listed in the Saudi stock market as the blockchain is a system that ensures against hacking and the ability to alter records, study conclude that Saudi financial market may adopt blockchain technology in accounting information systems in the future to take advantage of the advantages and opportunities that it achieves, and for future research implications. Researchers may work to shed light on smart algorithms and block chain technology effects and changes in the accounting systems, and the need for global and Saudi regulatory authorities to issue standards and guidelines governing the application of smart algorithms and block chains in accounting information systems.
How Gender Affected the Acceptance of Remote Work for Accountants During Coronavirus Lockdown in Jordan

During the coronavirus outbreak, accountants in Jordan shifted their ordinary routine to remote working under the government and health ministry regulations. This paper investigates the moderating effect of gender on the actual use of technology and other determents believed to be critical factors influencing the usage. These factors are compiled under three theoretical frameworks, namely, technology acceptance, unified theory of acceptance and use of technology, and diffusion innovation theory. Technology acceptance compiled two factors: perceived ease of use and perceived usefulness. While unified theory of acceptance and use of technology consists of the moderate factor gender. Likewise, the diffusion innovation theory consists of the system's technological features, such as the complexity used in this paper. The overall results of this paper are based on previous scholars’ findings and results from different technology acceptance and usage contexts. Researchers indicate that even with the mixed results presented by previous literature, research assumes that gender behavioral patterns toward remote working differ based on the time of risks and crises such as coronavirus and the time period.
Leveraging Artificial Intelligence for Enforcing Competition in the Digital Economy

Artificial Intelligence (AI) is a transformative technology which is rapidly changing the nature of consumers, markets, and even the economy. The digital economy is rife with various anti-competitive practices and the current competition law regimes around the world struggle to keep up with it. The competition law enforcers and regulators need to closely monitor and study the advancements in AI as it keeps changing the dynamics of the digital economy. This research paper analyzes the interface of AI and competition laws and evaluates the possibility of using AI to enforce better competition in the digital economy.
Unsupervised Hyperbolic Action Recognition

Methods based on Deep Geometric Learning allow the development of solutions with a geometric approximation in different applications. In particular, the curved feature of hyperbolic space has the ability to describe hierarchical structures in a better manner. In this paper, we aim to define an unsupervised learning model for action recognition. The curved feature space is intended to be used to describe a hierarchical relationship between the clips that compose a complete video sequence. These, in turn, are related to each other by means of a triplet loss function and a VAE (Variational Auto-Encoder) neural architecture, which establishes a similarity relationship between clips to identify actions from a set of unlabelled data.
Robot Navigation Anticipative Strategies in Deep Reinforcement Motion Planning

The navigation of robots in dynamic urban environments, requires elaborated anticipative strategies for the robot to avoid collisions with dynamic objects, like bicycles or pedestrians, and to be human aware. We have developed and analyzed three anticipative strategies in motion planning taking into account the future motion of the mobile objects that can move up to 18 km/h. First, we have used our hybrid policy resulting from a Deep Deterministic Policy Gradient (DDPG) training and the Social Force Model (SFM), and we have tested it in simulation in four complex map scenarios with many pedestrians. Second, we have used these anticipative strategies in real-life experiments using the hybrid motion planning method and the ROS Navigation Stack with Dynamic Windows Approach (NS-DWA). The results in simulations and real-life experiments show very good results in open environments and also in mixed scenarios with narrow spaces.
SAFaD: A System for Automatic Fall Detection on Surveillance Imagery

In this work we introduce SAFaD: A System for Automatic Fall Detection on Surveillance Imagery. Our system heavily relies in an intermediate representation that allows us to accurately and optimally encode the motion of a person. An ensemble of different approaches use this volume as input to predict whether a fall event has happened. We tested out system with a state-of-the-art fall detection dataset reaching a 62.86% accuracy.
The Use of Artificial Intelligence in Combating Crimes in the UAE: Critical Review

The current study discusses artificial intelligence as one of the most important outcomes of the modern technological revolution, as it has multiple uses in all areas of security, economics, military and civil matters. According to the literature, research neglects to review studies regarding the detection of crimes in the security field. Nevertheless, Artificial intelligence enables police agencies to use resources effectively and achieve societal security and the speed of crime detection in the UAE. Furthermore, the synthesis of the research problems, research methods, and findings of the surveyed studies will assist the scholars in pursuing their future studies. Further discussions and implications are also presented in the study.
Investigation of the Key Internal Factors Influencing Building a Risk Management Culture that Supports the Diffusion of Innovation

This paper has presented qualitative research examining the impact of internal organizational factors on Risk Management and the diffusion of Innovation in the Ajman Educational Zone. The internal factors were organizational culture, organization structure, organization strategy, knowledge sharing, and information technology. A qualitative approach was used to collect the study’s primary data by conducting personal interviews with upper and middle managers working for the Ajman Educational Zone. The analysis of the primary data went through many steps. The study commenced by conducting interviews and transcribing the participant’s interview discussions. Then, codes were created for the study constructs, coding the discussion output by meaning, allocating the elicited items to the study themes, and finally calculating the frequencies of the chosen expressions. The study found that organizational strategy and structure, culture, knowledge sharing, and information technology positively influenced knowledge management employment. Besides, the result showed that knowledge management positively affected the performance of organizations. Many managerial implications and limitations have been listed, and several future research venues have been proposed.
Horn rule discovery with batched caching and rule identifier for proficient compressor of knowledge data

Abstract Knowledge data has been widely applied to artificial intelligence applications for interpretable and complex reasoning. Modern knowledge bases are constructed via automatic knowledge extraction from open‐accessible sources. Thus the sizes of KBs are continuously growing, heavily burdening the maintenance and application of the knowledge data. Besides the grammatical redundancies, semantically repeated information also frequently appears in knowledge bases but is still under‐explored. Existing semantic compressors fail to efficiently discover expressive patterns and thus perform unsatisfyingly on knowledge data. This article proposes SInC , a semantic inductive compressor, to efficiently induce first‐order Horn rules and semantically compress knowledge bases. SInC improves the scalability of top‐down rule mining by batching correlated records in the cache and further optimizes the pruning of duplication and specialization via an identifier structure of Horn rules. SInC was evaluated on real‐world and synthetic datasets and compared against the state‐of‐the‐art. The results show that the batched caching speed up the rule mining procedure by more than two orders while consuming fewer than three times memory space. The identifier technique speeds up the duplication and specialization pruning by orders of magnitude with less than 5‰ and 15% error rates, respectively. SInC outperforms the state‐of‐the‐art from the perspective of overall compression on both scalability and compression effect.
The Impact of Hris Usage on Organizational Efficiency and Employee Performance: A Research in Higher Education Sector in Jordan

Human Resources as a strategic resource one of an organization's human resources and primary strategic management techniques is management. Organizations must keep track of changes in their internal and external climates on a regular basis. Developing countries like Jordan, on the other hand, appeared to have difficulty implementing HRIS. The impact of HRIS on organizational performance has been investigated in previous research. The focus of this study is to investigate the influence of the use of HRIS in higher institutions in Jordan on organizational efficiency and employee performance. The research used SmartPLS 3.3.2 to model partial least square structural equations to examine 326 questionnaires completed by staff working at higher institutions in Jordan. The rate of response was 65.20 percent. The results showed that the use of HRIS had a substantial positive impact on both employee performance and organizational efficiency, while the latter served as a partial mediator in the higher institutions of Jordan between the usage of HRIS and employee performance. The results of the study are anticipated to be useful for human resource managers to further study the contribution of HRIS in implementing and recognizing the conductive effects of HRIS applications for academics as well as for organizations.
Proposing an efficient implementation method for exponentiation in digital signature scheme on ring Zn

In this paper, we propose a design method for the signature scheme based on ring structure Zn. Our signature schemes are more secure, generate signatures at a faster rate than that of the ElGamal scheme and its variants. Moreover, our approaches also overcome some disadvantages of some similar signature schemes on ring Zn. For these advantages, they are fully applicable in practice.
A reproducible experimental survey on biomedical sentence similarity: A string-based method sets the state of the art

This registered report introduces the largest, and for the first time, reproducible experimental survey on biomedical sentence similarity with the following aims: (1) to elucidate the state of the art of the problem; (2) to solve some reproducibility problems preventing the evaluation of most current methods; (3) to evaluate several unexplored sentence similarity methods; (4) to evaluate for the first time an unexplored benchmark, called Corpus-Transcriptional-Regulation (CTR); (5) to carry out a study on the impact of the pre-processing stages and Named Entity Recognition (NER) tools on the performance of the sentence similarity methods; and finally, (6) to bridge the lack of software and data reproducibility resources for methods and experiments in this line of research. Our reproducible experimental survey is based on a single software platform, which is provided with a detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of all our experiments and results. In addition, we introduce a new aggregated string-based sentence similarity method, called LiBlock, together with eight variants of current ontology-based methods, and a new pre-trained word embedding model trained on the full-text articles in the PMC-BioC corpus. Our experiments show that our novel string-based measure establishes the new state of the art in sentence similarity analysis in the biomedical domain and significantly outperforms all the methods evaluated herein, with the only exception of one ontology-based method. Likewise, our experiments confirm that the pre-processing stages, and the choice of the NER tool for ontology-based methods, have a very significant impact on the performance of the sentence similarity methods. We also detail some drawbacks and limitations of current methods, and highlight the need to refine the current benchmarks. Finally, a notable finding is that our new string-based method significantly outperforms all state-of-the-art Machine Learning (ML) models evaluated herein.
Algorithm and its application for automatic measurement of the full-field isoclinic parameter by digital phase-shifting photoelasticity

The photoelastic method is an experimental technique that combines optics and mechanics for a stress analysis. The photoelastic phase-shifting technique is different from the moiré, holography, and speckle phase-shifting techniques, which only need to measure one parameter. The photoelastic phase-shifting technique needs to assess isoclinic and isochromatic parameters, which affect each other, seriously hindering the development of the phase-shifting photoelasticity method. First, the interaction between the isoclinic and isochromatic parameters is analyzed in detail. Secondly, an algorithm is proposed to adjust the mutation and obtain the correct isoclinic parameter affected by the isochromatic parameter. This method can effectively eliminate the influence of the isochromatic parameter. The isoclinic parameter is consistent with the theoretical value, which verifies the effectiveness of this method. Finally, the photoelastic method uses the proposed algorithm to test the stress at different positions of the turbine blade root. Moreover, the bearing capacity of the turbine blade root is analyzed to provide support for the safe use and optimization design of the turbine.
Counting the Answers to a Query

Counting the answers to a query is a fundamental problem in databases, with several applications in the evaluation, optimization, and visualization of queries. Unfortunately, counting query answers is a #P-hard problem in most cases, so it is unlikely to be solvable in polynomial time. Recently, new results on approximate counting have been developed, specifically by showing that some problems in automata theory admit fully polynomial-time randomized approximation schemes. These results have several implications for the problem of counting the answers to a query; in particular, for graph and conjunctive queries. In this work, we present the main ideas of these approximation results, by using labeled DAGs instead of automata to simplify the presentation. In addition, we review how to apply these results to count query answers in different areas of databases.
MicroInfer

To address the difficult deployment, high memory overhead, and poor portability of edge deep learning frameworks in industry IoT, we propose MicroInfer which is an edge deep learning inference framework for Microprocessors. MicroInfer employs a combination of upper computer and lower computer architecture. The upper computer automatically implements model quantization, model precompilation, operator matching, and code generation, estimates in advance the memory overhead and inference acceleration scheme required on the lower computer chip, and automatically generates a customized AI plug-in. The lower computer utilize an improved memory management strategy to execute the algorithm with as little memory space as possible without affecting the inference speed. A custom XidianOS operating system for microprocessors is also designed for MicroInfer, and the AI framework is designed to support various AI inference frameworks. The proposed MicroInfer is compared with other inference frameworks, achieving significant improvements in memory usage and inference speed, accelerating the development of industry IoT.
Evaluating the Effects of Virtual Human Animation on Students in an Immersive VR Classroom Using Eye Movements

Virtual humans presented in VR learning environments have been suggested in previous research to increase immersion and further positively influence learning outcomes. However, how virtual human animations affect students’ real-time behavior during VR learning has not yet been investigated. This work examines the effects of social animations (i.e., hand raising of virtual peer learners) on students’ cognitive response and visual attention behavior during immersion in a VR classroom based on eye movement analysis. Our results show that animated peers that are designed to enhance immersion and provide companionship and social information elicit different responses in students (i.e., cognitive, visual attention, and visual search responses), as reflected in various eye movement metrics such as pupil diameter, fixations, saccades, and dwell times. Furthermore, our results show that the effects of animations on students differ significantly between conditions (20%, 35%, 65%, and 80% of virtual peer learners raising their hands). Our research provides a methodological foundation for investigating the effects of avatar animations on users, further suggesting that such effects should be considered by developers when implementing animated virtual humans in VR. Our findings have important implications for future works on the design of more effective, immersive, and authentic VR environments.
Leveraging VR Techniques for Efficient Exploration and Interaction in Large and Complex AR Space with Clipped and Small FOV AR Display

In this paper, we propose to take advantage of the digital twinned environment to interact more efficiently in the large and complex AR space in spite of the limited sized and clipped FOV of the AR display. Using the digital twin of the target environment, “magical” VR interaction techniques can be applied, as visualized and overlaid through the small window, while still maintaining the spatial association to the augmented real world. First we consider the use of amplified movement within the corresponding VR twinned space to help the user search, plan, navigate and explore efficiently by providing an effectively larger view and thereby better spatial understanding of the same AR space with less amount of physical movements. Secondly, we also apply the amplified movement and in addition, the stretchable arm to interact with relatively large objects (or largely spaced objects) which cannot be seen in their entirety at a time with the small FOV glass. The results of the experiment with the proposed methods have showed advantages with regards to the interaction performance as the scene became more complex and task more difficult. The work illustrates the concept of and potential for XR based interaction where the user can leverage the advantages of both VR and AR mode operations.
Sign Language in Immersive VR: Design, Development, and Evaluation of a Testbed Prototype

Immersive Virtual Reality (IVR) systems support several modalities such as body, finger, eye, and facial expressions tracking, thus they can support sign-language-based communication. The combined utilization of tracking technologies requires careful evaluation to ensure high-fidelity transference of body posture, gestures, and facial expressions in real-time. This paper presents the design, development and evaluation of an IVR system utilizing state-of-the-art tracking options. The system is evaluated by certified sign language teachers to detect usability issues and examine appropriate methodology for large-scale follow-up evaluation by users fluent in sign language.
Exploration of Form Factor and Bimanual 3D Manipulation Performance of Rollable In-hand VR Controller

Virtual reality (VR) environments are expected to become future workspaces. An effective bimanual 3D manipulation technique would be essential to support this vision. A ball-shaped tangible input device that can be rolled in a hand is known to be useful for 3D object manipulation because such devices allow users to utilize their finger dexterity. In this study, we further explored the potential of a rollable in-hand controller. First, we evaluated the effects of its form factor on user behavior and performance. Although the size and shape of a rollable controller are expected to influence user behavior and performance, their effects have not been empirically explored in prior works. Next, we evaluated a rollable controller on bimanual 3D assembly tasks. A rollable controller may incur a high mental load as it requires users to use finger dexterity; therefore, the benefit of using such a device in each hand is not obvious. We found that a 5 cm-diameter ball-shaped controller was the most effective among the sizes and forms that we considered, and that a pair of in-hand rollable controllers showed significantly faster completion time than a pair of VR controllers for complex bimanual assembly tasks involving frequent rotations.
TeleStick: Video Recording and Playing System Optimized for Tactile Interaction using a Stick

TeleStick is a system that records and replicates tactile experience and visual information using common types of camera and video monitor environment. TeleStick uses a stick-type device with a tactile microphone attached to a camera so that it is consistently visible in the video, and records video and with tactile and audio information using stereo 2ch. The users can feel as if they were in the video as they watch it while holding a stick-type device with a speaker and a vibrator.