Reviewerly/exHarmony huggingface dataset:
Impact of organizational culture on employees’ commitment: A study in the Sri Lankan state banking sector

The goal of this study is to investigate the impact of organizational culture on employee commitment in the Sri Lankan banking sector. As the antecedents that denote the organizational culture; organizational control, employee engagement and teamwork, innovativeness, and organizational values are selected while employee commitment is selected as the dependent variable. In a competitive environment that belongs to the service-providing nature of a bank, the employee’s role is significantly contributing to the organization’s performance level. Moreover, enhancing employee commitment has become a significant requirement; especially during the current unstable atmosphere which has been raised due to past strategic attempts. The responses to the questionnaire survey of a group of 314 respondents were analyzed using reliability and validity tests, correlation analysis, and multiple regression analysis of the SPSS software. The findings indicate that organizational control has an adverse impact on employee commitment, whereas the other three antecedents have positive impacts on the dependent variable. These findings highlight the significance of reducing excessive control, promoting innovation, reinforcing shared values, and fostering employee involvement and teamwork to enhance commitment levels. The study offers valuable insights for organizations in the state banking sector in Sri Lanka, guiding in cultivating a supportive organizational culture that encourages employee commitment.
Multiparameter estimation of quantum metrology in a Heisenberg system with Dzyaloshinskii–Moriya interaction

In this work, we study the quantum Fisher information on the Heisenberg model in presence of the Dzyaloshinskii–Moriya interaction, basing on the multi-parametric estimation strategy in quantum metrology. First, we employ the density matrix vectorization method with a tool of the quantum Fisher information matrix through quantum Cramer–Rao bound. Second, we show that the multi-parameter quantum Cramer–Rao bound for simultaneous and individual estimation of the temperature and Dzyaloshinskii–Moriya interaction strongly relies on the concepts of the quantum Fisher information matrix. We confirm also the simultaneous strategy that provides a high precision than the individual.
AI-Based Cancer Detection Model for Contrast-Enhanced Mammography

Background: The recent development of deep neural network models for the analysis of breast images has been a breakthrough in computer-aided diagnostics (CAD). Contrast-enhanced mammography (CEM) is a recent mammography modality providing anatomical and functional imaging of the breast. Despite the clinical benefits it could bring, only a few research studies have been conducted around deep-learning (DL) based CAD for CEM, especially because the access to large databases is still limited. This study presents the development and evaluation of a CEM-CAD for enhancing lesion detection and breast classification. Materials &amp; Methods: A deep learning enhanced cancer detection model based on a YOLO architecture has been optimized and trained on a large CEM dataset of 1673 patients (7443 images) with biopsy-proven lesions from various hospitals and acquisition systems. The evaluation was conducted using metrics derived from the free receiver operating characteristic (FROC) for the lesion detection and the receiver operating characteristic (ROC) to evaluate the overall breast classification performance. The performances were evaluated for different types of image input and for each patient background parenchymal enhancement (BPE) level. Results: The optimized model achieved an area under the curve (AUROC) of 0.964 for breast classification. Using both low-energy and recombined image as inputs for the DL model shows greater performance than using only the recombined image. For the lesion detection, the model was able to detect 90% of all cancers with a false positive (non-cancer) rate of 0.128 per image. This study demonstrates a high impact of BPE on classification and detection performance. Conclusion: The developed CEM CAD outperforms previously published papers and its performance is comparable to radiologist-reported classification and detection capability.
Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems

Expert assessments with pre-defined numerical or language terms can limit the scope of decision-making models. We propose that decision-making models can incorporate expert judgments expressed in natural language through sentiment analysis. To help make more informed choices, we present the Sentiment Analysis in Recommender Systems with Multi-person, Multi-criteria Decision Making (SAR-MCMD) method. This method compiles the opinions of several experts by analyzing their written reviews and, if applicable, their star ratings. The growth of online applications and the sheer amount of available information have made it difficult for users to decide which information or products to select from the Internet. Intelligent decision-support technologies, known as recommender systems, leverage users’ preferences to suggest what they might find interesting. Recommender systems are one of the many approaches to dealing with information overload issues. These systems have traditionally relied on single-grading algorithms to predict and communicate users’ opinions for observed items. To boost their predictive and recommendation abilities, multi-criteria recommender systems assign numerous ratings to various qualities of products. We created, manually annotated, and released the technique in a case study of restaurant selection using ‘TripAdvisor reviews’, ‘TMDB 5000 movies’, and an ‘Amazon dataset’. In various areas, cutting-edge deep learning approaches have led to breakthrough progress. Recently, researchers have begun to focus on applying these methods to recommendation systems, and different deep learning-based recommendation models have been suggested. Due to its proficiency with sparse data in large data systems and its ability to construct complex models that characterize user performance for the recommended procedure, deep learning is a formidable tool. In this article, we introduce a model for a multi-criteria recommender system that combines the best of both deep learning and multi-criteria decision-making. According to our findings, the suggested system may give customers very accurate suggestions with a sentiment analysis accuracy of 98%. Additionally, the metrics, accuracy, precision, recall, and F1 score are where the system truly shines, much above what has been achieved in the past.
SOFTWARE DEFINED RADIO FOR SPACECRAFTS COMMUNICATION LINKS

In the paper an option of scientific information communication link, based on SDR technology, for “Chibis–AI” spacecraft is considered. The program package GNU Radio 3.10 has been used. To satisfy technical requirements for data rate new additional program blocks for GNU Radio 3.10 have developed. Engineering model of scientific data communication channels have been produced. Correspondence of the measured communication channels specifications for technical requirements has been show.
Pengaruh Daya Tarik Iklan Dan Brand Ambassador Song Joong Ki Terhadap Brand Image Scarlett Whitening

The purpose of this research is to verify the impacts of advertising appeal and Song Joong Ki as brand ambassador for Scarlett Whitening’s brand image. The method that used in this research is quantitative, populated by 519 UKSW FTI faculty’s female college student. The sample selection is using Purposive Sampling technique comprising of 100 samples as respondents that are eligible to be used in this research. The sampling technique used was is spreading the questionnaire and used multiple regression as an analysis technique. Research results found that : (1) The advertising appeal gives positive effect to Scarlett Whitening’s brand image with score t-count in the amount of 3.146 &gt; score t-table 1.984 and the level of significance is 0.002. (2) Song Joong Ki as brand ambassador gives positive effect to Scarlett Whitening’s brand image with score t-count in the amount of 5,781&gt; score t-table 1.984 and the level of significance is 0.000. (3) Advertising appeal and Song Joong Ki brand ambassador simultaneously gives positive effects to Scarlett Whitening’s brand image with F count score in the amount of 27.472 &gt; F table score 2.70 and the level of significance 0.000. (4) The amount of influence from advertising appeal and Song Joong Ki brand ambassador to Scarlett Whitening’s brand image (R2) is 36.2%.
Big data analytics capabilities and supply chain sustainability: Evidence from the hospitality industry

Big data analytics capabilities have piqued the curiosity of academics and practitioners in recent years. However, there has been little research conducted on the effects of these capabilities on supply chain sustainability, particularly in emerging economies. To address this gap, this article attempted to investigate the impact of big data analytics capabilities on the supply chain sustainability of Jordan's hospitality industry using quantitative data derived from 512 managers in senior and middle levels of hotels listed in Jordan Hotels Association (JHA). Structural Equation Modelling (SEM) was conducted for hypothesis evaluation. The research findings proved that the dimensions of big data analytics capabilities, which were infrastructure flexibility, management capabilities, and personnel capabilities, had a significant positive role in enhancing supply chain sustainability. Therefore, the research provided a series of recommendations for managers in these hotels, the most important of which was allocating significant investments in modern data-collecting technologies to record key changes across the supply chain, including manufacturing, transportation, and distribution.
MooseNet: A Trainable Metric for Synthesized Speech with a PLDA Module

We present MooseNet, a trainable speech metric that predicts the listeners' Mean Opinion Score (MOS).We propose a novel approach where the Probabilistic Linear Discriminative Analysis (PLDA) generative model is used on top of an embedding obtained from a self-supervised learning (SSL) neural network (NN) model.We show that PLDA works well with a non-finetuned SSL model when trained only on 136 utterances (ca.one minute training time) and that PLDA consistently improves various neural MOS prediction models, even stateof-the-art models with task-specific fine-tuning.Our ablation study shows PLDA training superiority over SSL model finetuning in a low-resource scenario.We also improve SSL model fine-tuning using a convenient optimizer choice and additional contrastive and multi-task training objectives.The fine-tuned MooseNet NN with the PLDA module achieves the best results, surpassing the SSL baseline on the VoiceMOS Challenge data.
Antecedents of understanding the investors’ acceptance of artificial intelligence: Perceptions from Jordanian context

This study examined the perception of investors toward the factors of Artificial Intelligence (AI) that impact behavioral intention of AI adoption. TAM was used with the addition of three factors of subjective norms, trust in service and service provider, and training. Jordanian stock market investors with basic investment knowledge were selected as the study participants using a convenience sampling method. The analyzed data were obtained from 610 responses. Results demonstrated the significant impact of trust in service and subjective norms on AI. Attitudes, perceived ease of use and perceived usefulness showed statistical significance toward intention to use AI. Results also showed significant impact of training on perceived ease of use.
Adequate legal rules in settling metaverse disputes: Hybrid legal framework for metaverse dispute resolution (HLFMDR)

The term “metaverse” refers to a virtual reality setting where users may engage in sustained and immersive interactions with other users and digital information. The metaverse offers new potential for entertainment, education, commerce, sociability, and creativity; therefore, it is anticipated to play a significant role in the future of the digital economy. However, the metaverse presents additional difficulties in resolving conflicts that can develop between its users, producers, and providers. Intellectual property rights, privacy, contract enforcement, fraud, harassment, and cybercrime are some of the concerns that may be raised in these conflicts. The existing legal system for settling these conflicts is disjointed and insufficient since it does not consider the metaverse’s unique qualities and complexity. This study investigates the present legal framework to provide fair and effective conflict resolution in the metaverse. It then establishes tenable fundamentals within the context of scientific and legal foundations. and propose a theoretical model named Hybrid Legal Framework for Metaverse Dispute Resolution (HLFMDR).
THE ASSESSMENT AND IMPACT OF 360-DEGREE LEADERSHIP PERFORMANCE APPRAISAL AT UNIVERSITY LEVEL

This research focuses on examining effects associated with implementing a comprehensive leadership performance evaluation system in a university environment. The main objective is to examine the relationships between utilizing 360-degree appraisal approach to assess leadership performance and its impact on faculty members and students in the university. These impacts encompass various aspects, such as improvements of leadership skills, shaping of organizational culture, and enhancement of employee satisfaction and overall performance. Through extensive survey conducted among university's teaching staff, valuable insights were gathered about potential advantages &amp; challenges of introducing 360-degree leadership performance evaluation system within the university's unique context. The study primarily employed quantitative methods to gather and analyze the data, involving 522 teaching staff participants from both public and private universities of Lahore. The results provide significant information in extracting the desired information, reaching the conclusion and offering recommendations to employees, stakeholders, institutions and the future researchers.
Cross-domain policy adaptation with dynamics alignment

The implementation of robotic reinforcement learning is hampered by problems such as an unspecified reward function and high training costs. Many previous works have used cross-domain policy transfer to obtain the policy of the problem domain. However, these researches require paired and aligned dynamics trajectories or other interactions with the environment. We propose a cross-domain dynamics alignment framework for the problem domain policy acquisition that can transfer the policy trained in the source domain to the problem domain. Our framework aims to learn dynamics alignment across two domains that differ in agents’ physical parameters (armature, rotation range, or torso mass) or agents’ morphologies (limbs). Most importantly, we learn dynamics alignment between two domains using unpaired and unaligned dynamics trajectories. For these two scenarios, we propose a cross-physics-domain policy adaptation algorithm (CPD) and a cross-morphology-domain policy adaptation algorithm (CMD) based on our cross-domain dynamics alignment framework. In order to improve the performance of policy in the source domain so that a better policy can be transferred to the problem domain, we propose the Boltzmann TD3 (BTD3) algorithm. We conduct diverse experiments on agent continuous control domains to demonstrate the performance of our approaches. Experimental results show that our approaches can obtain better policies and higher rewards for the agents in the problem domains even when the dataset of the problem domain is small.
Federated Semantic Learning Driven by Information Bottleneck for Task-Oriented Communications

In this letter, we present a novel federated semantic learning (FedSem) framework to collaboratively train the semantic-channel encoders of multiple devices with the coordination of a base station-based semantic-channel decoder, which is more practical for task-oriented communications rather than performing the vanilla distributed learning merely at the user’s side. Particularly, one of the most potential tasks, i.e., constructing semantic knowledge graph (SKG), is considered due to its powerful semantic structured representation and cross-modal comprehension capacity. In this case, the proposed FedSem can make full use of the distributed data and computing resources while protecting user privacy to directly parse images into SKGs. Furthermore, the information bottleneck theory is leveraged to drive the loss design by formalizing a rate-distortion tradeoff, which eliminates the redundancies of semantic features while maintaining the task-relevant information. Compared to the baselines, simulation results demonstrate that the proposed FedSem achieves better rate-distortion performance and convergence under different number of users and channel conditions.
Features optimization selection in hidden layers of deep learning based on graph clustering

Abstract As it is widely known, big data can comprehensively describe the inherent laws governing various phenomena. However, the effective and efficient analysis of available data has become a major challenge in the fields of artificial intelligence, machine learning, data mining, and others. Deep learning, with its powerful learning ability and effective data-processing methods, has been extensively researched and applied in numerous academic domains. Nevertheless, the data obtained during the deep learning process often exhibits feature homogenization, resulting in highly redundant features in the hidden layers, which, in turn, affects the learning process. Therefore, this paper proposes an algorithm based on graph clustering to optimize the features of hidden layer units, with the aim of eliminating redundancy and improving learner generation.
3D mouse pose from single-view video and a new dataset

We present a method to infer the 3D pose of mice, including the limbs and feet, from monocular videos. Many human clinical conditions and their corresponding animal models result in abnormal motion, and accurately measuring 3D motion at scale offers insights into health. The 3D poses improve classification of health-related attributes over 2D representations. The inferred poses are accurate enough to estimate stride length even when the feet are mostly occluded. This method could be applied as part of a continuous monitoring system to non-invasively measure animal health, as demonstrated by its use in successfully classifying animals based on age and genotype. We introduce the Mouse Pose Analysis Dataset, the first large scale video dataset of lab mice in their home cage with ground truth keypoint and behavior labels. The dataset also contains high resolution mouse CT scans, which we use to build the shape models for 3D pose reconstruction.
Towards Granting of Legal Personality to Autonomous Robots in the UAE

The Dubai Digital Government launched its recent guidelines, which call for artificial intelligence systems to be subject to legal accountability. This study discusses the extent to which autonomous robots can be granted legal personality in UAE law and the consistency of this approach with the provisions of Islamic jurisprudence. This research paper answered two main questions: First, the extent to which these guidelines are considered the beginning of work on granting legal personality to AI systems in the UAE. Second, what form of legal personality can be given to autonomous robots in UAE law to be consistent with the provisions of Islamic jurisprudence as a primary source of legislation in the country? The research concluded the impossibility of considering autonomous robots as a "thing" and classifying them within the concept of "persons." It also concluded that it is possible to give them legal personality according to two legislative solutions: granting them partial or incomplete performance eligibility like minors.
AOBLMOA: A Hybrid Biomimetic Optimization Algorithm for Numerical Optimization and Engineering Design Problems

The Mayfly Optimization Algorithm (MOA), as a new biomimetic metaheuristic algorithm with superior algorithm framework and optimization methods, plays a remarkable role in solving optimization problems. However, there are still shortcomings of convergence speed and local optimization in this algorithm. This paper proposes a metaheuristic algorithm for continuous and constrained global optimization problems, which combines the MOA, the Aquila Optimizer (AO), and the opposition-based learning (OBL) strategy, called AOBLMOA, to overcome the shortcomings of the MOA. The proposed algorithm first fuses the high soar with vertical stoop method and the low flight with slow descent attack method in the AO into the position movement process of the male mayfly population in the MOA. Then, it incorporates the contour flight with short glide attack and the walk and grab prey methods in the AO into the positional movement of female mayfly populations in the MOA. Finally, it replaces the gene mutation behavior of offspring mayfly populations in the MOA with the OBL strategy. To verify the optimization ability of the new algorithm, we conduct three sets of experiments. In the first experiment, we apply AOBLMOA to 19 benchmark functions to test whether it is the optimal strategy among multiple combined strategies. In the second experiment, we test AOBLMOA by using 30 CEC2017 numerical optimization problems and compare it with state-of-the-art metaheuristic algorithms. In the third experiment, 10 CEC2020 real-world constrained optimization problems are used to demonstrate the applicability of AOBLMOA to engineering design problems. The experimental results show that the proposed AOBLMOA is effective and superior and is feasible in numerical optimization problems and engineering design problems.
Fine-grained Audible Video Description

We explore a new task for audio-visual-language modeling called fine-grained audible video description (FAVD). It aims to provide detailed textual descriptions for the given audible videos, including the appearance and spatial locations of each object, the actions of moving objects, and the sounds in videos. Existing visual-language modeling tasks often concentrate on visual cues in videos while undervaluing the language and audio modalities. On the other hand, FAVD requires not only audio-visual-language modeling skills but also paragraph-level language generation abilities. We construct the first fine-grained audible video description benchmark (FAVDBench) to facilitate this research. For each video clip, we first provide a one-sentence summary of the video, i.e., the caption, followed by 4–6 sentences describing the visual details and 1–2 audio-related descriptions at the end. The descriptions are provided in both English and Chinese. We create two new metrics for this task: an EntityScore to gauge the completeness of entities in the visual descriptions, and an AudioScore to assess the audio descriptions. As a preliminary approach to this task, we propose an audio-visual-language transformer that extends existing video captioning model with an additional audio branch. We combine the masked language modeling and auto-regressive language modeling losses to optimize our model so that it can produce paragraph-level descriptions. We illustrate the efficiency of our model in audio-visual-language modeling by evaluating it against the proposed benchmark using both conventional captioning metrics and our proposed metrics. We further put our benchmark to the test in video generation models, demonstrating that employing fine-grained video descriptions can create more intricate videos than using captions. Code and dataset are available at https://github.com/OpenNLPLab/FAVDBench. Our online benchmark is available at www.avlbench.opennlplab.cn.