Reviewerly/exHarmony huggingface dataset:
Latin Hypercube Sampling Approach to Improve K-Nearest Neighbors Performance on Imbalanced Data

Imbalanced class is a common issue encountered in real-world datasets. Oversampling is a technique used to tackle imbalanced classes, with the Synthetic Minority Oversampling Technique (SMOTE) being the most popular method. However, SMOTE has limitations, including diversity problems, inconsistent synthesized data, and the generation of outliers or noise. To address these challenges, one proposed solution is to apply oversampling using the Latin Hypercube Sampling (LHS) approach. The LHS approach involves selecting the majority class based on the imbalanced ratio, synthesizing data using LHS, and then selecting the synthesized data. The wine quality and glass identification datasets were used in this study. The LHS-based data synthesis has been proven to enhance the performance of the k-Nearest Neighbors (k-NN) algorithm. The experimental results indicate an average improvement in performance metrics such as accuracy, precision, recall, and F1-Score, with values of 0.1656, 0.35805, 0.3728, and 0.36885, respectively.
HMPFL: Hypernetwork Model based Personalized Federated Learning for Industrial Anomaly Detection

Federated learning allows for collaborative training of distributed deep learning networks, tackling the issue of data silos while ensuring data privacy. However, in the distributed setting of industrial equipment, the collected data is dynamic, non-independent, and identically distributed(i.e., Non-IID), which negatively impacts the performance of federated learning. While the personalized solution can address the Non-IID problem, it falls short in effectively handling the dynamic environment of industrial equipment, which poses a significant challenge to model stability. Model stability enables the model to quickly transfer and share knowledge when dealing with dynamic data. To maintain model stability and generate customized models, we propose a personalized federated learning approach that leverages hypernetwork and model-oriented updates, named HMPFL, which aims to generate stable personalized models for Non-IID dynamic environments. Finally, we evaluate the effectiveness of our framework in addressing industrial equipment anomaly detection tasks using three real industrial air conditioning datasets from different factories. The results demonstrate that our method, surpasses existing personalized federated learning approaches, achieving an average accuracy improvement of 7.00%.
Shapley-value-based Contribution Evaluation in Federated Learning: A Survey

As a novel distributed and collaborative learning paradigm, federated learning is widely used in many scenarios with success to train better models via sharing multiple clients’ private data without leaking privacy. In this respect, evaluating clients’ contribution plays a key role in designing incentive mechanisms to motivate higher-quality data owners and also screen malicious clients, and thus is a vital component in maintaining the overall effectiveness and efficiency for federated learning. As a fair evaluation method originated from game theory, Shapley value has been intensively studied and used in evaluating contribution from clients in federated learning, and there is still no survey work on this topic. This motivates our work. In this paper, we presented a comprehensive survey for the current research progresses on Shapley-value-based contribution evaluation for federated learning, and also proposed several potential future research directions. To our knowledge, this is the first survey effort on Shapley-value-based contribution evaluation for federated learning, and we expect that our work can offer useful guidance and reference for future researchers.
A Hierarchical Entanglement Routing Protocol in Quantum Networks

We propose a hierarchical entanglement routing protocol over the nodes of a quantum network. The objectives are to develop realistic timing parameters of entanglement operations, design the protocol procedure steps, and conduct simulations and performance evaluation. The work has applications to link layer protocols for quantum networks and the future quantum internet.
Simulating dimensionally reduced 4D Yang-Mills theory on a quantum computer

We compute the low-lying spectrum of 4D SU(2) Yang-Mills in a finite volume using quantum simulations. In contrast to small-volume lattice truncations of the Hilbert space, we employ toroidal dimensional reduction to the ``femtouniverse'' matrix quantum mechanics model. In this limit the theory is equivalent to the quantum mechanics of three interacting particles moving inside a 3-ball with certain boundary conditions. We use the variational quantum eigensolver and quantum subspace expansion techniques to compute the string tension to glueball mass ratio near the small/large-volume transition point, finding qualitatively good agreement with large volume Euclidean lattice simulations.
A global progressive image secret sharing scheme under multi-group joint management

&lt;abstract&gt; &lt;p&gt;Diverging from traditional secret sharing schemes, group secret sharing schemes enable the recovery of secret information through collaborative efforts among groups. Existing schemes seldom consider the issue of the secrecy level of image information between different groups. Therefore, we propose a global progressive image secret sharing scheme under multi-group joint management. For inter-group relations, multiple groups with different priority levels are constructed using the approach of bit-polar decomposition. In this arrangement, higher-level groups obtain clearer secret image information. For intra-group relations, a participant-weighted secret sharing scheme is constructed based on Chinese Remainder Theorem and Birkhoff interpolation, in which the participants' secret sub-shares are reusable. During the recovery process, the sub-images can be recovered within the intragroup with the corresponding level. Groups collaborate through lightweight overlay operations to obtain different layers of secret images, achieving a global progressive effect. Analysis results show that the scheme is both secure and practical for group secret sharing.&lt;/p&gt; &lt;/abstract&gt;
Exploration of Shallow Structural Characteristics in the Xinyi Segment of the Anqiu-Juxian Fault of Tan-Lu Fault Zone

Abstract The Anqiu-Juxian fault is the most active branch of the Tan-Lu fault zone. As most of the Xinyi segment of the fault is buried, there is little research on such important issues as its shallow structure and spatial distribution. In this paper, the shallow structural characteristics of the Xinyi segment of the Anqiu-Juxian fault are comprehensively studied by the shallow seismic exploration. The results of this study show that: (1) The Anqiu-Juxian fault runs through the entire Xinyi area, with its strike being approximately NNE5°∽15°. (2) The Xinyi segment of the fault follows a single branching–double branching–single branching pattern from north to south. In the single branch section, the fault is characterized by strike-slip thrust, while in the double-branch section, the east and west branches of the Anqiu-Juxian fault are both strike-slip normal faults. The results of this study, for the first time, comprehensively provided seismological evidence of the shallow structure of the Xinyi section of the Anqiu-Juxian fault and basic data for the planning and construction of Xinyi urban.…
Fully Decentralized Horizontal Autoscaling for Burst of Load in Fog Computing

With the increasing number of Web of Things devices, the network and processing delays in the cloud have also increased. As a solution, fog computing has emerged, placing computational resources closer to the user to lower the communication overhead and congestion in the cloud. In fog computing systems, microservices are deployed as containers, which require an orchestration tool like Kubernetes to support service discovery, placement, and recovery. A key challenge in the orchestration of microservices is automatically scaling the microservices in case of an unpredictable burst of load. In cloud computing, a centralized autoscaler can monitor the deployed microservice instances and make scaling actions based on the monitored metric values. However, monitoring an increasing number of microservices in fog computing can cause excessive network overhead and thereby delay the time to scaling action. We propose DESA, a fully DEcentralized Self-adaptive Autoscaler through which microservice instances make their own scaling decisions, cloning or terminating themselves through self-monitoring. We evaluate DESA in a simulated fog computing environment with different numbers of fog nodes. Furthermore, we conduct a case study with the 1998 World Cup website access log, examining DESA’s performance in a realistic scenario. The results show that DESA successfully reduces the scaling reaction time in large-scale fog computing systems compared to the centralized approach. Moreover, DESA resulted in a similar maximum number of instances and lower average CPU utilization during bursts of load.
Implementation of The Resident’s Dues Applications (SIUMAS) Using Waterfall Method in RT X Cinere Village

Payment of dues is one of the obligations that must be fulfilled by every resident in a neighborhood association area. These dues are used for common purposes such as cleanliness and security. The collection and expenditure of dues are usually handled by the management committee. However, there are still difficulties in managing residents' dues using conventional methods. As a result, dues cards are often lost and the dues collection are not transparent. Therefore, the author developed The Resident’s Dues Application (SIUMAS) as a solution used to manage residents' dues. The purpose of developing this application is to simplify the management tasks of the committee in handling the collection and expenditure of dues, as well as to facilitate residents in tracking the utilization of the dues. This application is built using the PHP programming language and utilizes the MySQL database. The development of this application adopts an object-based application design method. The results of the SIUMAS application testing using Black Box Testing shown that all functionalities of the SIUMAS application are functioning well and meet the expectations.
Architecture Design of 'Sangnoksu Library' in Bono-dong, Ansan

Libraries in modern society are expanding from the role of simply accumulating and providing information to become the center of overall local cultural activities as well as functions as education and research facilities. Therefore, the “public” library is considered a major public facility in the local community during construction, and is a complex cultural facility that provides various cultures and services to local residents, and should be able to play a role in supporting local residents' community activities based on local specialized programs. This study examines the design implementation process of the Sangnoksu Library located in Bono-dong, Ansan-si, and is meaningful in studying the planning direction of the library that plays an active role as a public facility in the local community. In order to respond to tangible and intangible needs, the library was designed with an emphasis on open access, connection with the surrounding area, and securing flexibility for complex programs.
A Study on Design Convergence Education Model for Enhancing Employability in Design Majors: Focusing on case analysis in the field of communication design major

The design industry is rapidly evolving with new technologies and knowledge. IT developments have created an adaptable job market. Artificial intelligence, like Chat-GPT, is changing labor structures and production methods. Universities must enhance employability skills for design majors promptly. In this study, we explored design trends, addressed educational changes, and developed a design convergence curriculum. To boost employability, we modeled employability in connection with learning ability, enabling education consumers to plan their courses, careers, and employment. This study is significant for conceptualizing and presenting a design convergence education model.
Challenges for teaching Administration and Accounting in the context of Emergency Remote Teaching (ERT): overcoming barriers and advancing in learning

This study examines the primary challenges encountered by professors in the Business Administration and Accounting courses at educational institutions in the metropolitan region of Belém during the implementation and maintenance of Emergency Remote Teaching (ERT) amid the pandemic. Through interviews, it was found that professors invest more time in class preparation without always receiving satisfactory compensation. They also acknowledge a decline in student learning, influenced by students' socioeconomic status, aligning with existing literature. Furthermore, practical, and calculation-related subjects were identified as the most challenging in the Emergency Remote Teaching (ERT) model. To address remaining gaps, further research is needed to understand the limitations, challenges, and possibilities of the hybrid teaching model, which aims to replace remote teaching.
Report of the 8th Workshop on Empirical RequirementsEngineering (EmpiRE 2023)

The Eighth International Workshop on Empirical Requirements Engineering (EmpiRE 2023), co-located with the 31st IEEE International Requirements Engineering conference (RE 2023), was held on September 5, 2023 in Hannover, Germany. This report presents the workshop structure, the keynote speech, the themes of the presented papers, and the panel discussion.
Mathematical modeling of mechatronic shuttles as automation objects for multilevel systems of intra-warehouse logistics

The subject of the study is the mathematical modeling of mechatronic shuttles of multilevel intra-warehouse logistics systems. The purpose of the article is to ensure energy efficiency and high productivity of multilevel warehouse logistics systems by developing mathematical models of mechatronic electromechanical drives as part of automated autonomous shuttle systems (Pallet Shuttle). To achieve this goal, it is necessary to solve the following tasks: to build a mathematical model of the processes occurring in the transportation shuttle as an electromechanical and mechatronic system; to build a mathematical model of the transportation shuttle as an automation object and to obtain a transfer function of the autonomous shuttle system for automatic control of its speed. Conclusions. The paper develops generalized approaches to the construction of mathematical models of electric drives as automation objects. The developed approach assumes that the linearized mathematical model of an electric drive as an automation object is represented as a linear differential equation with constant coefficients that relate the control and the controlled parameter. The construction of such a differential equation is proposed to be carried out by generalizing the properties inherent in electromechanical systems, and it is assumed that these properties are represented by Lagrange equations of the 2nd kind using electromechanical analogies. As a result of the research, it was shown that the use of this approach leads to a definite equation of the mathematical model of the mechatronic shuttle as an automation object. It is shown that the linearized mathematical model of the shuttle as an automation object should be determined by a differential equation of at least the second order. The research results prove the possibility of improving automation systems in design by taking into account the properties inherent in shuttles as electromechanical and mechatronic systems in more detail. It is shown that the use of this approach leads to a definite equation of the mathematical model of the mechatronic shuttle as an automation object, but it is generally almost impossible to obtain such an equation explicitly. To illustrate the proposed approach, we consider the modeling of a typical mechatronic electric drive of machines used in automated storages and present the results of modeling processes in the electric drive. It is shown that the linearized mathematical model of a mechatronic shuttle as an automation object should be determined by a differential equation of at least the second order. The research results show the possibility of improving automation systems during design due to a more detailed consideration of the properties inherent in shuttles as mechatronic electromechanical systems.
Detection of quantum steering for the generalized Werner states via artificial neural networks

Abstract Quantum steering is an important nonlocal resource and has a wide range of applications in quantum information processing. Although a lot of steering criteria have been proposed, it is still very difficult to efficiently detect quantum steering in experiment. Here we employ machine learning techniques to tackle the problem of quantum steering detection in two-qubit system. The quantum steering and un-steering inequalities are combined together, so as to construct quantum steering classifiers for the generalized Werner states via artificial neural networks. More steerable and unsteerable quantum states can be identified by the classifiers proposed here than by the quantum steering inequality or un-steering inequality, which provides a new way to detect steering with only partial information of the given quantum states. We consider two types of artificial neural networks, one is the single-layer perceptron and the other is the multi-layer perceptron. The result shows that the multi-layer perceptron outperforms the single-layer perceptron in terms of accuracy. Compared with the existing quantum steering criteria, our methods do not require the whole information of the quantum state, and the steering of it is detected by using state-independent measurements, so it is easy to realize in experiment.
New Insights on the Generation of Rain Streaks: Generating-Removing United Unpaired Image Deraining Network

Most existing deraining methods use synthetic rainy images to train models. They focus on extracting the features to establish mapping models from rainy images to clean background images, while ignoring the domain gap between synthetic and real rainy images. Further more, the rain streaks generation is not considered as important as the rain removal. Hence, we propose a novel Generating-Removing United Unpaired image deraining Network(GRUUNet) to generate and remove rain streaks unitedly. It helps to reduce the difference between synthetic and real rain streaks to improve the performance of deraining. Specifically, (1)we adopt a dual-way transform strategy between real and synthetic rainy images. There are both rain streaks generation and removal network for real and synthetic rainy images respectively; (2)our model learns the prototypes of rainy degradation images by self-supervised sparse-addressing memory modules; (3)we align the domain gap between real and synthetic rainy images with shared rain streaks generation network. We empirically evaluate our GRUUNet on Cityscape and RainHQ, and the outstanding results prove the promising performance of our method on both real and synthetic rainy images.
Determinants of Ethereum Return: Insights from Bitcoin, Investor Sentiment, and Financial Markets

The study explores the spillover effect on Ethereum one of the leading cryptocurrencies stemming from key variables in the domains of cryptocurrencies, investor sentiment, and traditional financial markets. This paper is the first to analyze the influence of such dominant representatives from diverse, external fields on cryptocurrency. We select bitcoin, the Fear and Greed index, the Standard and Poors 500 index and the United States Dollar to Euro Exchange Rate as representatives to investigate the spillover effect on Ethereum. Utilizing linear regression models and vector autoregressive (VAR) models, we find strong correlations between Ethereums return and that of Bitcoins, along with investor sentiment. However, the influence of financial market variables on Ethereum are found to be virtually static and negligible. This research offers valuable insights to those seeking to forecast or manipulate crypto market movement through analyzing the complex interplay between these variables and Ethereum.
L$$^2$$DM: A Diffusion Model for Low-Light Image Enhancement

Low-light image enhancement is a challenging yet beneficial task in computer vision that aims to improve the quality of images captured under poor illumination conditions. It involves addressing difficulties such as color distortions and noise, which often degrade the visual fidelity of low-light images. Although tremendous CNN-based and ViT-based approaches have been proposed, the potential of diffusion models in this domain remains unexplored. This paper presents L $$^2$$ DM, a novel framework for low-light image enhancement using diffusion models. Since L $$^2$$ DM falls into the category of latent diffusion models, it can reduce computational requirements through denoising and the diffusion process in latent space. Conditioning inputs are essential for guiding the enhancement process, therefore, a new ViT-based network called ViTCondNet is introduced to efficiently incorporate conditioning low-light inputs into the image generation pipeline. Extensive experiments on benchmark LOL datasets demonstrate L $$^2$$ DM’s state-of-the-art performance compared to diffusion-based counterparts. The L $$^2$$ DM source code is available on GitHub for reproducibility and further research.
Local Outlier Reclassifier (LORec): a Method for Relocating Local Outliers Generated by K-means

The occurrence of local outliers produced by the K-means algorithm remains challenging since they affect the clustering performance due to misclassification. While current algorithms can identify the local outliers in the clusters, they do not relocate them to their correct clusters. Also, modifications of K-means addresses problems including time complexity, identifying the initial centroids and number of clusters, and preventing the occurrence of outliers in every cluster, but only a few focused on relocating the local outliers to their correct clusters. Hence, this paper introduces a Local Outlier Reclassifier (LORec) method capable of relocating local outliers generated by K-means. Results of the study using the three datasets show that the LORec integrated into K-means improved its clustering performance. The generation of false-positive instances is reduced by an average of 25.01%. Additionally, the integration yielded an average improvement of 8.50 % accuracy, 5.39% in the rand index, and 8.9% in f-measure. These results indicate that integrating LORec into K-means is an effective method for relocating local outliers to their correct clusters.