Reviewerly/exHarmony huggingface dataset:
Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration

Authors:
raw_author_name: Ming-Yen Lin
raw_affiliation_string: Department of Information Engineering and Computer Science, Feng Chia University, Taichung 402, Taiwan
raw_author_name: Sue-Chen Hsueh
raw_affiliation_string: Department of Information Management, Chaoyang University of Technology, Taichung 413, Taiwan

Abstract: This study introduces session-aware recommendation models, leveraging GRU (Gated Recurrent Unit) and attention mechanisms for advanced latent interaction data integration. A primary advancement is enhancing latent context, a critical factor for boosting recommendation accuracy. We address the existing models’ rigidity by dynamically blending short-term (most recent) and long-term (historical) preferences, moving beyond static period definitions. Our approaches, pre-combination (LCII-Pre) and post-combination (LCII-Post), with fixed (Fix) and flexible learning (LP) weight configurations, are thoroughly evaluated. We conducted extensive experiments to assess our models’ performance on public datasets such as Amazon and MovieLens 1M. Notably, on the MovieLens 1M dataset, LCII-PreFix achieved a 1.85% and 2.54% higher Recall@20 than II-RNN and BERT4Rec+st+TSA, respectively. On the Steam dataset, LCII-PostLP outperformed these models by 18.66% and 5.5%. Furthermore, on the Amazon dataset, LCII showed a 2.59% and 1.89% improvement in Recall@20 over II-RNN and CAII. These results affirm the significant enhancement our models bring to session-aware recommendation systems, showcasing their potential for both academic and practical applications in the field.
Unveiling Cryptocurrency Impact on Financial Markets and Traditional Banking Systems: Lessons for Sustainable Blockchain and Interdisciplinary Collaborations

Authors:
raw_author_name: Umar Kayani
raw_affiliation_string: College of Business, Al Ain University, Abu Dhabi 112612, United Arab Emirates
raw_author_name: Fakhrul Hasan
raw_affiliation_string: Newcastle Business School, Northumbria University, Newcastle NE7 7XA, UK

Abstract: The advent of cryptocurrencies and blockchain technology has sparked a revolutionary shift in the financial sector. This study sets out on a wide-ranging investigation to understand the nuanced dynamics, repercussions, and potential future paths of this shifting environment in the UK and USA. The primary goals of the research are to examine how cryptocurrencies affect financial markets and conventional banking systems; to examine how blockchain technology might be used in the financial sector; to assess policy and regulatory considerations; and to predict and plan for the future. This research digs into how cryptocurrencies have revolutionized the banking and finance sectors. Analysis of adoption rates, market volatility, and integration methods sheds light on the changing position of cryptocurrencies in investment portfolios, reconfiguration of asset classes, and coping mechanisms of conventional financial institutions. When looking at the financial sector as a whole, the transformational potential of blockchain technology becomes clear. The advent of DeFi, smart contracts, and asset tokenization offers new prospects to improve financial transactions, increase transparency, and broaden participation in the investment market. The research analyzes cryptocurrencies and blockchain technology from a policy and regulatory perspective. The delicate balancing act between stimulating innovation and guaranteeing consumer protection, market integrity, and financial stability is highlighted by a comparison of the regulatory methods adopted in the United Kingdom and United States, as well as proposals from international organizations. The research identifies potential future paths for these technologies and their implications. Opportunities and challenges that will influence the future of finance emerge, with a focus on central bank digital currencies (CBDCs), sustainable blockchain solutions, and interdisciplinary collaborations. As this deep dive comes to a close, the transformational power of cryptocurrencies and blockchain technology is highlighted. It sheds light on the forces that are altering the structures of the world’s financial markets, conventional banking structures, and regulatory frameworks. The findings and critical assessment stress the need for well-considered choices, ethical innovation, and interdisciplinary cooperation in order to succeed in an ever-changing environment. To further democratize access, improve transparency, and reshape the economic fabric of our planet, the future of finance resides at the confluence of tradition and innovation, where cryptocurrencies and blockchain technology exist.
Multi-Channel Graph Convolutional Networks for Graphs with Inconsistent Structures and Features

Authors:
raw_author_name: Xinglong Chang
raw_affiliation_string: Qijia Youdao Network Technology (Beijing) Co., Ltd., Beijing 100012, China; School of New Media and Communication, Tianjin University, Tianjin 300350, China
raw_author_name: Jianrong Wang
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin 300350, China; School of New Media and Communication, Tianjin University, Tianjin 300350, China
raw_author_name: Rui Wang
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
raw_author_name: Tao Wang
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
raw_author_name: Yingkui Wang
raw_affiliation_string: Department of Computer Science and Technology, Tianjin Renai College, Tianjin 301636, China
raw_author_name: Weihao Li
raw_affiliation_string: Data61-CSIRO, Black Mountain Laboratories, Canberra, ACT 2601, Australia

Abstract: Graph convolutional networks (GCNs) have attracted increasing attention in various fields due to their significant capacity to process graph-structured data. Typically, the GCN model and its variants heavily rely on the transmission of node features across the graph structure, which implicitly assumes that the graph structure and node features are consistent, i.e., they carry related information. However, in many real-world networks, node features may unexpectedly mismatch with the structural information. Existing GCNs fail to generalize to inconsistent scenarios and are even outperformed by models that ignore the graph structure or node features. To address this problem, we investigate how to extract representations from both the graph structure and node features. Consequently, we propose the multi-channel graph convolutional network (MCGCN) for graphs with inconsistent structures and features. Specifically, the MCGCN encodes the graph structure and node features using two specific convolution channels to extract two separate specific representations. Additionally, two joint convolution channels are constructed to extract the common information shared by the graph structure and node features. Finally, an attention mechanism is utilized to adaptively learn the importance weights of these channels under the guidance of the node classification task. In this way, our model can handle both consistent and inconsistent scenarios. Extensive experiments on both synthetic and real-world datasets for node classification and recommendation tasks show that our methods, MCGCN-A and MCGCN-I, achieve the best performance on seven out of eight datasets and the second-best performance on the remaining dataset. For simpler graph structures or tasks where the overhead of multiple convolution channels is not justified, traditional single-channel GCN models might be more efficient.
Locality-Based Action-Poisoning Attack against the Continuous Control of an Autonomous Driving Model

Authors:
raw_author_name: Wonseok Yang
raw_affiliation_string: Department of Computer Science and Engineering, Graduate School of Soongsil University, Sadang-ro 50, Seoul 07027, Republic of Korea
raw_author_name: Daeseon Choi
raw_affiliation_string: Department of Computer Science and Engineering, Graduate School of Soongsil University, Sadang-ro 50, Seoul 07027, Republic of Korea

Abstract: Various studies have been conducted on Multi-Agent Reinforcement Learning (MARL) to control multiple agents to drive effectively and safely in a simulation, demonstrating the applicability of MARL in autonomous driving. However, several studies have indicated that MARL is vulnerable to poisoning attacks. This study proposes a ’locality-based action-poisoning attack’ against MARL-based continuous control systems. Each bird in a flock interacts with its neighbors to generate the collective behavior, which is implemented through rules in the Reynolds’ flocking algorithm, where each individual maintains an appropriate distance from its neighbors and moves in a similar direction. We use this concept to propose an action-poisoning attack, based on the hypothesis that if an agent is performing significantly different behaviors from neighboring agents, it can disturb the driving stability of the entirety of the agents. We demonstrate that when a MARL-based continuous control system is trained in an environment where a single target agent performs an action that violates Reynolds’ rules, the driving performance of all victim agents decreases, and the model can converge to a suboptimal policy. The proposed attack method can disrupt the training performance of the victim model by up to 97% compared to the original model in certain setting, when the attacker is allowed black-box access.
Leveraging Swarm Intelligence for Optimal Thermal Camera and Sensor Placement in Industrial Environments

Authors:
raw_author_name: Dawid Ewald
raw_affiliation_string: Faculty of Computer Science, Kazimierz Wielki Univesrity, 85-064 Bydgoszcz, Poland
raw_author_name: Piotr Prokopowicz
raw_affiliation_string: Faculty of Computer Science, Kazimierz Wielki Univesrity, 85-064 Bydgoszcz, Poland

Abstract: This research addresses the sensor placement optimization problem (SPOP) in the industrial sector, aiming to enhance operational efficiency and safety through the strategic deployment of sensors. The focus is on optimizing the locations of thermal cameras and motion sensors, with a dual objective of maximizing coverage and minimizing redundancy in the production hall. To solve this challenge, a method based on the application of the nature-inspired bat algorithm was employed. The study reveals noteworthy findings, emphasizing the proficiency of the bat algorithm (BA) in optimizing the placement of thermal cameras and motion sensors. Numeric outcomes demonstrate the algorithm’s effectiveness in maximizing machine coverage while minimizing sensor usage within a real-world industrial environment. These results underscore the versatility and reliability of the BA, establishing it as a valuable tool for addressing complex optimization tasks in industrial settings.
Novel Multi-Criteria Group Decision Making Method for Production Scheduling Based on Group AHP and Cloud Model Enhanced TOPSIS

Authors:
raw_author_name: Zhimin Lv
raw_affiliation_string: Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China
raw_author_name: Yang Liu
raw_affiliation_string: Design and Research Institute Co., Ltd., University of Science and Technology Beijing, Beijing 100083, China
raw_author_name: Xiong Xiao
raw_affiliation_string: National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing, University of Science and Technology Beijing, Beijing 100083, China
raw_author_name: Dong Xu
raw_affiliation_string: National Engineering Research Center of Flat Rolling Equipment, University of Science and Technology Beijing, Beijing 100083, China

Abstract: Optimized production scheduling can greatly improve efficiency and reduce waste in the steel manufacturing industry. With the increasing demands on the economy, the environment, and society, more and more factors need to be considered in the production scheduling process. Currently, only a few methods are developed for the comprehensive evaluation and prioritization of scheduling schemes. This paper proposes a novel MCGDM (multi-criteria group decision making) method for the ranking and selection of production scheduling schemes. First, a novel indicator system involving both qualitative and quantitative indicators is put forward. Diverse statistical methods and evaluation functions are proposed for the evaluation of quantitative indicators. The evaluation method of qualitative indicators is proposed based on heterogeneous data, cloud model theory, and group decision-making techniques. Then, a novel Group AHP model is proposed to determine the weights of all evaluation indicators. Finally, a novel cloud-model-enhanced TOPSIS (technique for order of preference by similarity to ideal solution) method is proposed to rank alternative production scheduling schemes. A practical example is presented to show the implementation details and demonstrate the feasibility of our proposed method. The results and comparative analysis indicate that our hybrid MCGDM method is more reasonable, flexible, practical, and effective in evaluating and ranking production scheduling schemes in an uncertain environment.
Specific Emitter Identification Based on Attractor Feature Space of System under Blind Equalization

Authors:
raw_author_name: Hu Jin
raw_affiliation_string: College of Electronic Countermeasures, National University of Defense Technology, Hefei 230037, China
raw_author_name: Fei Teng
raw_affiliation_string: College of Electronic Countermeasures, National University of Defense Technology, Hefei 230037, China
raw_author_name: Caiyi Lou
raw_affiliation_string: 36th Research Institute of China Electronics Technology Group Corporation, Jiaxing 314033, China

Abstract: In the process of the individual identification of radiation sources, the effective extraction of fine features of target radiation sources can be regarded as being crucial for the subsequent individual identification. However, in the complex electromagnetic environment, the effective extraction of the radiation source features is still facing great challenges. To solve this problem, we propose an algorithm for constructing the attractor feature space of the radiation source system based on blind equalization to solve this problem. Firstly, we use blind equalization to process the target signal. Secondly, we use the phase-space reconstruction technique to construct the system attractor feature space of the target signal processed, and explore the adaptive relationship between feature-space-embedding dimensions, the delay time and the neural network, finding the optimal values. Finally, the feature space is used as the input of the neural network for the subsequent individual discrimination decision. Experimentally, it is proved that our proposed algorithm improves the individual recognition rate of the target radiation source under the complex electromagnetic environment to a certain extent, which is of practical application value.
Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

Authors:
raw_author_name: Andrey Gorshenin
raw_affiliation_string: Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 44-2 Vavilova St., 119333 Moscow, Russia

Abstract: The advancement of cloud computing technologies has positioned virtual machine (VM) migration as a critical area of research, essential for optimizing resource management, bolstering fault tolerance, and ensuring uninterrupted service delivery. This paper offers an exhaustive analysis of VM migration processes within cloud infrastructures, examining various migration types, server load assessment methods, VM selection strategies, ideal migration timing, and target server determination criteria. We introduce a queuing theory-based model to scrutinize VM migration dynamics between servers in a cloud environment. By reinterpreting resource-centric migration mechanisms into a task-processing paradigm, we accommodate the stochastic nature of resource demands, characterized by random task arrivals and variable processing times. The model is specifically tailored to scenarios with two servers and three VMs. Through numerical examples, we elucidate several performance metrics: task blocking probability, average tasks processed by VMs, and average tasks managed by servers. Additionally, we examine the influence of task arrival rates and average task duration on these performance measures.
Privacy-Preserving Deep Learning Framework Based on Restricted Boltzmann Machines and Instance Reduction Algorithms

Authors:
raw_author_name: Khalil El Hindi
raw_affiliation_string: Department of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia

Abstract: The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the potential to improve decision-making, adaptability, and efficiency in dynamic and distributed environments. However, it brings privacy, communication, and resource restrictions concerns that must be properly addressed for successful implementation in real-world CPS systems. Various privacy-preserving techniques have been proposed, but they often add complexity and decrease accuracy and utility. In this paper, we propose a privacy-preserving deep learning framework that combines Instance Reduction Techniques (IR) and the Restricted Boltzmann Machine (RBM) to preserve privacy while overcoming the limitations of other frameworks. The RBM encodes training data to retain relevant features, and IR selects the relevant encoded instances to send to the server for training. Privacy is preserved because only a small subset of the training data is sent to the server. Moreover, it is sent after encoding it using RBM. Experiments show that our framework preserves privacy with little loss of accuracy and a substantial reduction in training time. For example, using our framework, a CNN model for the MNIST dataset achieves 96% accuracy compared to 99% in a standard collaborative framework (with no privacy measures taken), with training time reduced from 133.259 s to 99.391 s. Our MLP model for MNIST achieves 97% accuracy compared to 98% in the standard collaborative framework, with training time reduced from 118.146 s to 87.873 s. Compared to other studies, our method is a simple approach that protects privacy, maintains the utility of deep learning models, and reduces training time and communication costs.
Data Augmentation for Regression Machine Learning Problems in High Dimensions

Authors:
raw_author_name: Francisco Chinesta
raw_affiliation_string: CNRS@CREATE Ltd., 1 Create Way, 08-01 CREATE Tower, Singapore 138602, Singapore; ESI Group, Parc Icade, Immeuble le Seville, 3bis, Saarinen, CEDEX, 94528 Rungis, France; PIMM, Arts et Métiers Institute of Technology, 151 Boulevard de l’Hopital, 75013 Paris, France

Abstract: Machine learning approaches are currently used to understand or model complex physical systems. In general, a substantial number of samples must be collected to create a model with reliable results. However, collecting numerous data is often relatively time-consuming or expensive. Moreover, the problems of industrial interest tend to be more and more complex, and depend on a high number of parameters. High-dimensional problems intrinsically involve the need for large amounts of data through the curse of dimensionality. That is why new approaches based on smart sampling techniques have been investigated to minimize the number of samples to be given to train the model, such as active learning methods. Here, we propose a technique based on a combination of the Fisher information matrix and sparse proper generalized decomposition that enables the definition of a new active learning informativeness criterion in high dimensions. We provide examples proving the performances of this technique on a theoretical 5D polynomial function and on an industrial crash simulation application. The results prove that the proposed strategy outperforms the usual ones.
Automated Mapping Methodology and Case Study of Healthcare User Activities and Spaces from a Sustainable Perspective

Authors:
raw_author_name: Jian Wang
raw_affiliation_string: School of Business, Xinjiang University, Urumqi 830091, China
raw_author_name: Xinjie Liu
raw_affiliation_string: School of Business, Xinjiang University, Urumqi 830091, China

Abstract: Operational inefficiencies in healthcare buildings and sustainability issues have become increasingly prominent. The root cause of this phenomenon is the irregular and incomplete structured data related to hospital user activities and spaces that are difficult to resolve using traditional manual methods. In order to solve this problem, an automated mapping approach is presented for hospital user activity and space. Using the Revit API technology and the C# language on the Visual Studio platform, a program for automated mapping is developed. It achieves the automatic mapping of hospital user activities and spaces, along with automatic calculation of utilization rates. An illustrative case study is conducted, focusing on the activity data and building space utilization of healthcare users at Shanghai Renji Hospital. The results indicate that (1) This method can realize the automatic combination of hospital user activities and hospital spaces, and hospital administrators can optimize the utilization rate of hospital spaces quickly and accurately. (2) This approach is highly adaptable, especially to changes in hospital user activities and hospital spatial data. (3) This method promotes the sustainable development of healthcare buildings and has great practical application value.
The findings of the marketing analysis academic library’s services

Authors:
raw_author_name: N. P. Krylova
raw_affiliation_string: Cherepovets State University

Abstract: The author accomplished the marketing analysis of academic library services as the case study of Cherepovets State University Library, a flagship university of Vologda Region. She characterizes the status of library services, strengths and weaknesses, opportunities and threats, challenges and expectations of modern library users. The novelty of the article lies in the use of marketing analysis tools and the results obtained. The practical value of library services for users is revealed. With four criteria, namely demographic, geographic, psychographic and behavioral, the target user of university library services is characterized in detail. The author applies the research methods of analysis, comparison, observation, Google survey, in-depth interview, and empathy mapping. Four levels of university library services aree analyzed based on the “service pyramid”. The quantitative findings of marketing research are provided. The author demonstrates that despite most students prefer electronic communications, they do not ignore the hybrid, online and offline, mode of interaction with the library. She also shows that modern academic library users are emotionally neutral towards the library services. Recommendations are offered for the further development of academic library services.