Reviewerly/exHarmony huggingface dataset:
The effect of employee motivation and employee engagement on job performance mediated by job satisfaction

Authors:
raw_author_name: Nur Khusniyah Indrawati
raw_affiliation_string: University of Brawijaya
raw_author_name: Desi Tri Kurniawati
raw_affiliation_string: University of Brawijaya

Abstract: This study aims to investigate the effect of employee motivation and employee engagement mediated by job satisfaction on job performance. This study employs explanatory research with a quantitative approach. The population in this study consisted of all employees of retail company X. The sampling technique used was saturated sampling, and the number of research samples was 150. Data was collected through surveys and documentation, and the instrument was tested using Likert scales. The data was analysed using Structural Equation Model Partial Least Squares (SEM-PLS) modelling. The results of this study indicate that employee motivation can increase job performance, while employee engagement cannot increase job performance. Furthermore, the findings revealed that employee motivation can increase job satisfaction while employee engagement cannot. Job satisfaction can increase job performance. Job satisfaction can mediate the effect of employee motivation on job performance, but it cannot mediate the effect of employee engagement on job performance. According to the findings of this study, Company X should be able to provide motivational stimulus to employees by increasing salaries and work incentives, as well as increasing training and developing employee skills, so that workers can work effectively and efficiently.
Improving the prediction of social media engagement in universities by utilizing feature selection in machine learning

Authors:
raw_author_name: Dino Keco
raw_affiliation_string: Faculty of Engineering, Natural and Medical Sciences, Department of Information Technology, International Burch University, Francuske revolucije bb., 71000 Sarajevo, Bosnia and Herzegovina
raw_author_name: Mersid Poturak
raw_affiliation_string: Faculty of Economics and Social Sciences, Department of Management, International Burch University, Francuske revolucije bb., 71000 Sarajevo, Bosnia and Herzegovina

Abstract: This study aims to examine the importance of feature selection in machine learning, specifically in predicting user engagement with social media post photographs on university Facebook pages. The paper uses a thorough analysis to demonstrate the crucial significance of choosing suitable features and their corresponding algorithms. The research intends to demonstrate how this strategic approach affects the accuracy of prediction findings in social media interaction. The research presents a compelling case study involving 24 leading universities from Australia, the United Kingdom, and the United States. The results underscore the efficacy of the method, stressing that the meticulous selection of characteristics and the use of appropriate algorithms are crucial elements for attaining the best results in social media forecasts. Implications: The study's results have important consequences, particularly within the changing environment of machine learning and its use in social media. Feature selection and algorithm choice are vital for optimizing social media initiatives for institutions.
FSKT‐GE: Feature maps similarity knowledge transfer for low‐resolution gaze estimation

Authors:
raw_author_name: Weiguo Pan
raw_affiliation_string: Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China; Institute for Brain and Cognitive Sciences, College of Robotics Beijing Union University Beijing China
raw_author_name: Songyin Dai
raw_affiliation_string: Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China; Institute for Brain and Cognitive Sciences, College of Robotics Beijing Union University Beijing China
raw_author_name: Bingxin Xu
raw_affiliation_string: Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China; Institute for Brain and Cognitive Sciences, College of Robotics Beijing Union University Beijing China
raw_author_name: Cheng Xu
raw_affiliation_string: Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China; Institute for Brain and Cognitive Sciences, College of Robotics Beijing Union University Beijing China
raw_author_name: Hongzhe Liu
raw_affiliation_string: Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China; Institute for Brain and Cognitive Sciences, College of Robotics Beijing Union University Beijing China
raw_author_name: Xuewei Li
raw_affiliation_string: Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China; Institute for Brain and Cognitive Sciences, College of Robotics Beijing Union University Beijing China

Abstract: Abstract The limited of texture details information in low‐resolution facial or eye images presents a challenge for gaze estimation. To address this, FSKT‐GE (feature maps similarity knowledge transfer for low‐resolution gaze estimation) is proposed, a gaze estimation framework consisting of both a high resolution (HR) network and low resolution (LR) network with the identical structure. Rather than mere feature imitation, this issue is addressed by assessing the cosine similarity of feature layers, emphasizing the distribution similarity between the HR and LR networks. This enables the LR network to acquire richer knowledge. This framework utilizes a combination loss function, incorporating cosine similarity measurement, soft loss based on probability distribution difference and gaze direction output, along with a hard loss from the LR network output layer. This approach on low‐resolution datasets derived from Gaze360 and RT‐Gene datasets is validated, demonstrating excellent performance in low‐resolution gaze estimation. Evaluations on low‐resolution images obtained through 2×, 4×, and 8× down‐sampling are conducted on two datasets. On the Gaze360 dataset, the lowest mean angular errors of 10.97°, 11.22°, and 13.61° were achieved, while on the RT‐Gene dataset, the lowest mean angular errors of 6.73°, 6.83°, and 7.75° were obtained.
Mobile robot path planning based on bi-population particle swarm optimization with random perturbation strategy

Authors:
raw_author_name: Jae-Hoon Kim
raw_affiliation_string: Department of Computer Engineering and Interdisciplinary Major of Maritime AI Convergence, Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-Gu, Busan 49112, South Korea

Abstract: Path planning for mobile robots poses a challenging optimization problem, requiring the discovery of a near-optimal path within diverse constraints. Conventional particle swarm optimization (PSO) algorithms encounter limitations in solving constrained problems, vulnerability to local optima, and premature convergence. To address these challenges, this paper proposes a bi-population PSO algorithm with a random perturbation strategy (BPPSO), which divides particles into two subpopulations. The first subpopulation enhances global search capabilities by considering the quality of particles and the optimal solution of a randomly selected particle when updating velocities. The second subpopulation strengthens local search using a linear cognitive coefficient adjustment strategy. Moreover, a counter tracks iteration without improvement in the global best position. Upon reaching a predefined threshold, random perturbation is added to the positions of all particles in both subpopulations, increasing diversity and enhancing the ability to escape local optima. The performance of BPPSO was experimentally validated across three benchmark functions and four environment models. The results have demonstrated that the proposed BPPSO outperforms existing PSO algorithms and other established path planning algorithms in terms of path quality and running time, highlighting the feasibility of BPPSO in resolving the challenge of mobile robot path planning.
Challenges of Requirements Engineering in Agile Projects: A Systematic Review

Authors:
raw_author_name: Oscar Benito Pacheco
raw_author_name: Hugo Vega Huerta
raw_author_name: Ronald Melgarejo Solis
raw_author_name: Javier Cabrera Díaz

Abstract: The practice of Requirements Engineering (RE) within Agile development contexts markedly differs from that within traditional development process. As the RE discipline in Agile Software Development continues to evolve, this research highlights and assesses various challenges to refine its impact on software product development. This study describes inductive and empirical research on Agile RE challenges and practices based on an analysis of data collected in the binding academic and scientific literature. This research is also based on the guidelines of a Systematic Review of the Literature (SRL) proposed by Kitchenham. Our results indicate eight type of challenges that we grouped into four themes: suitability delimitations, on software product development projects, the formation of the development team, and the efficient participation of the client during the process. Finally, this paper contributes detailed insights into RE challenges specific to Agile project development, evaluates their resolution extent, and outlines existing research gaps in RE practices
WaterPairs: a paired dataset for underwater image enhancement and underwater object detection

Authors:
raw_author_name: Long Chen
raw_affiliation_string: Faculty of Engineering, Imperial College London, Electrical Engineering, South Kensington Campus, London, SW7 2AZ, UK
raw_author_name: Yunzhou Xie
raw_affiliation_string: Faculty of Natural Sciences, Imperial College London, Faculty Building, South Kensington Campus, London, SW7 2AZ, UK
raw_author_name: Sen Wang
raw_affiliation_string: Faculty of Engineering, Imperial College London, Electrical Engineering, South Kensington Campus, London, SW7 2AZ, UK

Abstract: Abstract Due to its importance in marine engineering and aquatic robotics, underwater image enhancement works as a preprocessing step to improve the performance of high-level vision tasks such as underwater object detection and recognition. Although several studies exhibit that underwater image enhancement algorithms can boost the detection accuracy of detectors, no work has focused on studying the relationship between these two tasks. This is mainly because current underwater datasets lack either bounding box annotations or high-quality reference images, based on which detection accuracy or image quality assessment metrics are calculated. To examine how underwater image enhancement methods affect underwater object detection tasks, we provide a large-scale underwater object detection dataset with both bounding box annotations and high-quality reference images, namely, the WaterPairs dataset. The WaterPairs dataset offers a platform for researchers to comprehensively study the influence of underwater image enhancement algorithms on underwater object detection tasks. We will release our dataset at https://github.com/IanDragon/WaterPairs once this paper is accepted.
Multimodal abstractive summarization using bidirectional encoder representations from transformer with attention mechanism

Authors:
raw_author_name: Dakshata Argade
raw_affiliation_string: Terna Engineering College, Nerul, Navi Mumbai, 400706, India
raw_author_name: Vaishali Khairnar
raw_affiliation_string: Terna Engineering College, Nerul, Navi Mumbai, 400706, India
raw_author_name: Deepali Vora
raw_affiliation_string: Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, 412115, India
raw_author_name: Shruti Patil
raw_affiliation_string: Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology Pune Campus, Symbiosis International (Deemed University) (SIU), Lavale, Pune, 412115, India
raw_author_name: Ketan Kotecha
raw_affiliation_string: Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology Pune Campus, Symbiosis International (Deemed University) (SIU), Lavale, Pune, 412115, India
raw_author_name: Sultan Alfarhood
raw_affiliation_string: Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O.Box 51178, Riyadh, 11543, Saudi Arabia

Abstract: In recent decades, abstractive text summarization using multimodal input has attracted many researchers due to the capability of gathering information from various sources to create a concise summary. However, the existing methodologies based on multimodal summarization provide only a summary for the short videos and poor results for the lengthy videos. To address the aforementioned issues, this research presented the Multimodal Abstractive Summarization using Bidirectional Encoder Representations from Transformers (MAS-BERT) with an attention mechanism. The purpose of the video summarization is to increase the speed of searching for a large collection of videos so that the users can quickly decide whether the video is relevant or not by reading the summary. Initially, the data is obtained from the publicly available How2 dataset and is encoded using the Bidirectional Gated Recurrent Unit (Bi-GRU) encoder and the Long Short Term Memory (LSTM) encoder. The textual data which is embedded in the embedding layer is encoded using a bidirectional GRU encoder and the features with audio and video data are encoded with LSTM encoder. After this, BERT based attention mechanism is used to combine the modalities and finally, the BI-GRU based decoder is used for summarizing the multimodalities. The results obtained through the experiments that show the proposed MAS-BERT has achieved a better result of 60.2 for Rouge-1 whereas, the existing Decoder-only Multimodal Transformer (D-MmT) and the Factorized Multimodal Transformer based Decoder Only Language model (FLORAL) has achieved 49.58 and 56.89 respectively. Our work facilitates users by providing better contextual information and user experience and would help video-sharing platforms for customer retention by allowing users to search for relevant videos by looking at its summary.
Redox conditions of Datangpo-type manganese ores constrained by statistical analysis of pyrite framboids and iron isotopes

Authors:
raw_author_name: Hongxi He
raw_affiliation_string: State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Science, Guiyang 550081, China
raw_author_name: Haiying Yang
raw_affiliation_string: School of Earth Sciences, Yunnan University, Kunming 650500, China
raw_author_name: Chuang Yang
raw_affiliation_string: State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Science, Guiyang 550081, China

Abstract: Datangpo-type manganese ores which originated in the Cryogenian Datangpo Formation, are overlaid and underlaid by Nantuo diamicite and Tiesi'ao silistones, respectively. A thorough understanding of the manganese mineralization process is currently limited by the continued uncertainty regarding the metallogenic redox condition of the Mn-bearing rock series. This study employed statistical analysis of pyrite framboid sizes and iron isotope (δ56Fe) to determine the redox conditions and constrain the mineralization process of Datangpo-type manganese ores in South China, considering the Gaolou in Chongqing, Yanglizhang in Guizhou, and Minle and Zhenxing in Hunan as examples. The results showed that Tiesi'ao siltstones mainly developed relatively larger diameter (7.29–7.68 μm) framboidal pyrites, whereas mudstones in the Datangpo Formation developed relatively smaller diameter (2.63–5.56 μm) framboidal pyrites. Furthermore, manganese ores produce non-framboidal pyrites. In three profiles, framboidal pyrite concentrations were found to be negatively correlated with Mn contents. These characteristics suggested that siltstones, mudstones, and manganese ores were deposited in oxic-anoxic, anoxic-euxinic, and oxic-anoxic conditions, respectively. The δ56Fe of whole rocks (δ56FeWR) exhibited a range from −0.73 ‰ to +0.48 ‰ (average of −0.32 ‰), whereas pyrite (δ56FePy) varied from +0.03 ‰ to +0.83 ‰ (average of +0.36 ‰). Furthermore, δ56FePy in manganese ore (average = +0.27 ‰) were lighter than those in mudstone (average = +0.49 ‰). These results of this study demonstrated that oxidation deposition played a role in pyrite formation, with manganese ores exhibiting more oxic conditions compared to mudstones. The findings of this study suggested that idiomorphic pyrite formation was similar to that of manganese ore, in which Mn precipitated in the form of manganese (hydro)oxides under oxic seawater, and then converted into rhodochrosite under anoxic sulfidic diagenetic conditions, with the involvement of microbes. This research has the potential to enhance comprehension regarding redox conditions and the regulation of sedimentary manganese ores via redox transformation.
A survey on multi-agent reinforcement learning and its application

Authors:
raw_author_name: Zepeng Ning
raw_affiliation_string: School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore
raw_author_name: Lihua Xie
raw_affiliation_string: School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore

Abstract: Multi-agent reinforcement learning (MARL) has been a rapidly evolving field. This paper presents a comprehensive survey of MARL and its applications. We trace the historical evolution of MARL, highlight its progress, and discuss related survey works. Then, we review the existing works addressing inherent challenges and those focusing on diverse applications. Some representative stochastic games, MARL means, spatial forms of MARL, and task classification are revisited. We then conduct an in-depth exploration of a variety of challenges encountered in MARL applications. We also address critical operational aspects, such as hyperparameter tuning and computational complexity, which are pivotal in practical implementations of MARL. Afterward, we make a thorough overview of the applications of MARL to intelligent machines and devices, chemical engineering, biotechnology, healthcare, and societal issues, which highlights the extensive potential and relevance of MARL within both current and future technological contexts. Our survey also encompasses a detailed examination of benchmark environments used in MARL research, which are instrumental in evaluating MARL algorithms and demonstrate the adaptability of MARL to diverse application scenarios. In the end, we give our prospect for MARL and discuss their related techniques and potential future applications.
The Effect of Storytelling Learning Using Role Playing Model Assisted by Audiovisual Media on Students Written and Storytelling Skills of Students Grade V Elementary School in Pringsurat Sub-District Temanggung District

Authors:
raw_author_name: Tri Joko Raharjo
raw_affiliation_string: Department of Primary Education, Postgraduate School, Semarang State University, Semarang City,Central Java, Indonesia
raw_author_name: Muh Sholeh
raw_affiliation_string: Department of Primary Education, Postgraduate School, Semarang State University, Semarang City,Central Java, Indonesia

Abstract: The Role Playing learning approach aims to make learning more meaningful for students. The Role Playing learning approach, combined with multimedia resources, stands out as a chosen and viable strategy for overcoming emerging challenges. In this study, experimental research with a quantitative approach was applied to assess the impact of using storytelling learning models paired with various media on storytelling skills and writing stories in a fifth-grade elementary school class. Based on the assessment process that has been carried out, the results in this study are an assessment of typical differences in comprehension tests between experimental and control groups supporting the acceptance of H1 versus H0. With a bidirectional significance probability value of 0.005, H1 is accepted, and H0 is rejected because 0.000 is less than 0.05. Therefore, this study ensures that the Role Playing learning model has a significant influence on students' storytelling skills. Similarly, the evaluation of typical differences in comprehension tests between the experimental group and the control group also favored the acceptance of H1 over H0. With a bidirectional significance probability value of 0.005, H1 is accepted, and H0 is rejected because 0.000 is less than 0.05. In summary, role playing has a significant influence on students' storytelling skills. Assessment of typical differences in comprehension tests between the experimental group and the control group led to acceptance of H1 versus H0. With a bidirectional significance probability value of 0.005, H1 is accepted, and H0 is rejected because 0.000 is less than 0.05. In conclusion, Role Playing significantly affects students' ability to tell stories and write stories. Keywords: role playing, storytelling skills, story writing skills, audio-visual media.
Experimental Integration of Quantum Key Distribution and Post‐Quantum Cryptography in a Hybrid Quantum‐Safe Cryptosystem

Authors:
raw_author_name: Lydia Garms
raw_affiliation_string: Information Security Group Royal Holloway University of London  Egham TW20 0EX UK
raw_author_name: Taofiq K. Paraïso
raw_affiliation_string: Toshiba Europe Ltd. Cambridge Research Laboratory  Cambridge CB4 0GZ UK
raw_author_name: Neil Hanley
raw_affiliation_string: Centre for Secure Information Technologies (CSIT) Queen's University Belfast  Belfast BT3 9DT UK
raw_author_name: Ayesha Khalid
raw_affiliation_string: Centre for Secure Information Technologies (CSIT) Queen's University Belfast  Belfast BT3 9DT UK
raw_author_name: Ciara Rafferty
raw_affiliation_string: Centre for Secure Information Technologies (CSIT) Queen's University Belfast  Belfast BT3 9DT UK
raw_author_name: Andrew J. Shields
raw_affiliation_string: Toshiba Europe Ltd. Cambridge Research Laboratory  Cambridge CB4 0GZ UK
raw_author_name: Carlos Cid
raw_affiliation_string: Information Security Group Royal Holloway University of London  Egham TW20 0EX UK; Okinawa Institute of Science and Technology Graduate University  Okinawa 904‐0495 Japan; Simula UiB  Bergen 5006 Norway
raw_author_name: Maire O'Neill
raw_affiliation_string: Centre for Secure Information Technologies (CSIT) Queen's University Belfast  Belfast BT3 9DT UK

Abstract: Abstract Quantum key distribution (QKD) and post‐quantum cryptography (PQC) are the two counter measures against cryptographic attacks via quantum computing. While QKD offers information theoretic security but limited authentication scalability, PQC facilitates scalable authentication in high density networks but is not information theoretic secure. Therefore, an ideal quantum‐safe framework should efficiently leverage the complementarity of both techniques. However, despite growing efforts in integrating both, current realizations have focused on channel authentication, and a complete cryptosystem addressing both hybrid authentication and hybrid key exchange is yet to be demonstrated. Here, an authenticated hybrid key exchange protocol is introduced that incorporates PQC and QKD in a modular and information‐theoretic secure architecture. The quantum‐safe protocol is inherently resilient to catastrophic cryptographic failures and provides both forward and post‐compromise security. As proof‐of‐concept implementation, the cryptosystem on a QKD hardware prototype is integrated, with the QKD processing, PQC key exchange and secret state masking via physical unclonable functions (PUFs) all running on a single field programmable gate array (FPGA). This work paves the way for the deployment of versatile and modular quantum‐safe networks that exploit the complementarity of PQC and QKD.