Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network-based Identification of Communication Room Indicator Status in Complex Environment

Recognizing and detecting the status indicators of communication room equipment is crucial to improving the security and stability of communication networks. Deep learning-based recognition methods have demonstrated higher accuracy and real-time performance for image recognition, with YOLOv5s being a small network-scale model that achieves high precision and real-time performance. It is particularly suitable for detecting indicator lights in computer room environments. However, target recognition faces significant challenges and limitations in complex production environments, such as physical phenomena like light reflection and refraction, which can make feature extraction and fusion difficult. In this paper, we address these challenges by enhancing the YOLOv5s network with a SimAM attention mechanism module. This improves the network's attention to critical information and enhances its feature fusion capability. Our goal is to improve the accuracy and recognition precision of the model in complex communication room environments. We conducted experiments on a self-built dataset based on the actual production environment in the server room. The results show that the improved YOLOv5s network achieved 94.7% recognition accuracy and 95.0% mAP-50, which is 1.6 and 1.3 percentage points higher than the baseline network YOLOv5s, respectively. The improved model showed better performance in the actual production environment of the complex communication server room.
A Multi-task Learning Based Interest Fusion Model for Recommendation

In recommender systems, click-through rate (CTR) prediction is an important task. Previous approaches model users’ preference based on their historical behaviors, but users’ interests change over time, so it’s necessary to consider both long-term and short-term needs of users. In this paper, we propose a long-short term preference fusion model. Besides, multi-task learning has been widely used in many fields, such as computer vision and natural language processing. We further propose a multi-task learning framework to improve the accuracy of click-through rate prediction by learning match task. And a new method is used to update the negative sample queue in the match task instead of the negative sampling process. In the experiments on public dataset, our model significantly outperforms competitive methods.
Reliable and Accurate Fault Detection with GPGPUs and LLVM

As the scale and complexity of cloud systems are increasing, system faults are becoming unavoidable. Therefore, they should be detected as reliably and accurately as possible. Black-box monitoring can reliably monitor a target system from a remote host, but it is often coarse-grained and cannot identify the root causes of system faults. In contrast, white-box monitoring can accurately obtain fault information inside a target system, but it is largely affected by system faults. This paper proposes GPUSentinel for more reliable white-box monitoring using general-purpose GPUs. GPUSentinel runs fault detectors in an isolated GPU, which is not easily affected by faults of a target system. For accurate detection, fault detectors in a GPU analyze main memory and directly monitor the state of the operating system. To easily develop such fault detectors, GPUSentinel provides a development environment with LLVM. We have implemented GPUSentinel and seven fault detectors and then confirmed that GPUSentinel could detect various system faults and identify the root causes.
Solving the Discretization-based Feature Construction Problem using Bi-level Evolutionary Optimization

Feature construction represents a crucial data preprocessing technique in machine learning applications because it ensures the creation of new informative features from the original ones. This fact leads to the improvement of the classification performance and the reduction of the problem dimensionality. Since many feature construction methods require discrete data, it is important to perform discretization in order to transform the constructed features given in continuous values into their corresponding discrete versions. To deal with this situation, the aim of this paper is to jointly perform feature construction and feature discretization in a synchronous manner in order to benefit from the advantages of each process. Thus, we propose here to model the discretization-based feature construction task as a bi-level optimization problem in which the constructed features are evaluated based on their optimized sequence of cut-points. The resulting algorithm is termed Discretization-Based Feature Construction (Bi-DFC) where the proposed model is solved using an improved version of an existing co-evolutionary algorithm, named I-CEMBA that ensures the variation of concatenation trees. Bi-DFC performs the selection of original attributes at the upper level and ensures the creation and the evaluation of constructed features at the upper level based on their optimal corresponding sequence of cut-points. The obtained experimental results on ten high-dimensional datasets illustrate the ability of Bi-DFC in outperforming relevant state-of-the-art approaches in terms of classification results.
A Flexible Approach for Solving Quality Issues by Means of Collecting Runtime Data

This paper presents an approach to solve specific quality issues in an industrial project. Some use cases are discussed that tackle quality and refactoring challenges. The solution consists of adding a few new reusable components to the existing code base of the project to achieve a flexible and configurable collection of runtime data. This runtime data is subsequently used to identify quality issues whereupon refactoring can take place. The paper demonstrates in detail the benefits of using aspect-orientation, particularly the AspectJ language, and reports on experiences.
Data Driven Machine Learning Model for Condition Monitoring and Anomaly Detection in Power Grids

The power system complexity and associated stability problems are greatly linked to the increasing penetration of unconventional energy sources and loads, such as renewable energies. The application of renewable for climate change, sustainability, and Net Zero come at the cost of deteriorated power quality, faults, instability, and disturbances in the power system. It gives rise to various problems such as equipment malfunctioning, power factor problems, transformer heating, inertia, voltage sags/swells, transmission lines overloading, etc. This requires and adjudicates the need for efficient monitoring and identification of faults and anomalies happening in the power system so as to accordingly mitigate these in a timely manner. The fault data however is not readily available and requires on-site inspection and accumulation. This paper thus aims at developing a synthetic database for various abnormal power system conditions captured from a well-known Kundr’s two-area system. These include symmetrical and asymmetrical faults, frequency, and phase variations, as well as voltage amplitude disturbances (sag/swell). The synthetic database is then combined with artificial intelligence techniques to enable fault detection and identification featuring low linear complexity and small memory requirements. The paper includes a benchmark study for three unsupervised anomaly detection algorithms, evaluating their performance in terms of both Area under the ROC Curve (AUC) and the execution time. The results show that iForest and iNNE provide competitive results in detecting anomalies of all fault types, with iNNE providing significantly better execution time performance.
Image Retrieval Technology and System Implementation of Handicraft Based on Genetic Algorithm

Aiming at the traditional image retrieval methods, the image retrieval method based on genetic algorithm and rough set is used to get the simplest visual feature subset of the image. As a machine learning algorithm, genetic algorithm has many advantages compared with other machine learning algorithms because of its inspiration from biological evolution. In order to make the image retrieval algorithm based on multi-features have better retrieval effect, this paper proposes an adaptive image retrieval algorithm based on genetic algorithm. Focusing on the adaptive image retrieval model based on genetic algorithm, based on the research of traditional image retrieval algorithm based on color moment, this paper puts forward a block weighted color moment algorithm to express the color features of images. In order to change the traditional way of finding the best weight by giving the weight manually, and make the algorithm more intelligent, this paper puts forward a genetic usurpation method to get the best solution of the weight and assign the weight automatically. An image retrieval system with human-computer interaction characteristics and easy operation is designed and implemented. This system can verify the performance of the proposed and improved algorithm and the traditional algorithm. Through statistical analysis of experimental data, the feasibility of this algorithm is proved.
Intent-based Management for the Distributed Computing Continuum

Managing digital and connected systems has become increasingly challenging in the past decade due to their scale and complexity. A new perspective is required to manage these systems, considering the infrastructure and components from edge to cloud, i.e., in the distributed computing continuum. Serverless computing offers improved scalability and cost efficiency, but balancing and coordinating serverless systems remain complex. Intent-based systems, popular in networking, can provide a solution by translating stakeholder inputs into actions that meet Service Level Objectives (SLOs). Their application in the computing continuum can be highly beneficial, but it has yet to be deeply explored. To bridge this gap, we propose a methodology for deploying an intent-based system for the computing continuum. We implement an architectural framework leveraging the serverless paradigm. Furthermore, we focus on defining and implementing the main components for translating the management requirements into actions executed by serverless functions inspired by a three-layer model. Through a Proof of Concept (PoC) deployed in Amazon's AWS cloud and detailed simulations, we showcase how such an approach can resolve conflicts in a complex system, i.e., balancing efficiency and availability. Our work aims to contribute to effectively managing the computing continuum and highlight the potential of intent-based systems in this domain. The experiments' results show our framework's ability to make appropriate scaling decisions, fulfilling both objectives.
Instrumentation for quantum correlation analysis of polarized Stokes-anti-Stokes photon pairs

This paper presents an optical spectroscopy instrumentation system engineered to measure time correlation of single photons. We explore the phenomenon of correlated Stokes-anti-Stokes (SaS) Raman scattering, analyzing the quantum nature of the phenomenon. An experimental apparatus was implemented to measure the scattering in diamond, confirming its quantum character, with the purpose of using the SaS photon pair in the codification and transmission of information.
Adaptive Stochastic Inventory Control Algorithm for Supply Chain Based on Deep Learning Supported By Internet of Things

In a distributed inventory system based on a coordination center composed of several warehouses, the coordination center plays the role of joint inventory management. Each customer sends an order to the coordination center, and the coordination center designates the corresponding warehouse to supply goods according to each customer’s location, delivery date, demand T and the inventory situation of each warehouse. In addition, the supply chain inventory model for a class of perishable goods also has requirements for customer demand satisfaction time, customer demand satisfaction rate and total system cost. In the face of these problems, the coordination of supply chain system can be effectively improved from the perspective of inventory supply, and the average customer demand satisfaction rate can be improved. This paper studies the control strategy and optimization method of multi-level inventory in coordinated supply chain, and mainly summarizes the existing domestic and foreign research from three aspects: supply chain structure, model objective function and solution algorithm, and supply chain inventory control strategy. In supply chain, as the basic and core link, the original management mode of inventory management has limitations, and it is no longer suitable for the new competition mode. Therefore, in the Internet of Things environment. In the corresponding supply chain, the management mode of inventory management, which is the basic and core link, has limitations and does not match the current market competition mode. Therefore, it is necessary to discuss the basic reform of inventory management in the supply chain based on the Internet of Things environment.
Research on data security risk of intelligent and connected vehicles

With increased connectivity and the application of intelligent technologies, intelligent and connected vehicles are evolving rapidly, which offers new opportunities for vehicle data security risks. However, there are currently insufficient studies to comprehensively map the security risks throughout the life cycle of intelligent and connected vehicle data. The object of this paper is to identify the main data security risks at different data life cycle phases in the field of intelligent and connected vehicles, and the data security problems those risks may bring. The following are some of the techniques used to protect the security of data against risks. The test verification is implemented by using functional reproduction and data packet capture analysis. The results indicate that there are vehicle data security risks to personal information, including location and biometric information. This paper is useful for intelligent and connected vehicle data processors in their targeted application of technical and managerial measures to mitigate data security risks in the whole data life cycle.
Real-Time Vehicle Passenger Detection Through Deep Learning

This study investigates the application of Convolutional Neural Networks (CNNs) in object detection and tracking, with specific focus on determining the number of passengers inside moving vehicles as recorded roadside video cameras. The objective is to develop an accurate and efficient method for counting the number of individuals in vehicles. We focus on the one-stage CNN-based model, You Only Look Once (YOLO) applied to two distinct data sets: static images and video streams. The performance of the model under different situations reveals that image quality factors such as lighting conditions, weather conditions and viewing angles play an important part in the task of identifying passengers inside vehicles. The model achieves a mean average precision of over 90% when including weather factors such as fog, rain, snow and spatter.
Application of Symmetric Key Algorithm in Data Security Design of Smart Grid

There are a large number of distributed transaction management in the smart grid. If the traditional centralized control method is adopted, it will cause problems such as low security level, privacy leakage and high maintenance cost. Protecting the privacy and security of data is the most important link in network transmission. The input of data parties will not interfere with each other, which protects the privacy of users. The research purpose of this paper is the application of symmetric key algorithm in smart grid data security design. In the experiment, according to the symmetric key security evaluation index, using the run-length test and frequency test methods, the data security design of the symmetric key algorithm in the smart grid is analyzed, thereby proving the security of the symmetric key algorithm.
Research on Privacy Protection of Dynamic Dataset Republication Based on Local Modularity

With the emergence and development of a large number of data applications such as data mining and data publishing. How to protect people’s privacy and prevent the leakage of sensitive information has become an urgent problem. For example, demographic data of a certain unit, illness records of hospital patients, etc., are of great research value. Because these data often contain some private information of individuals, if the private information is leaked after the data is released, it will infringe on personal privacy. Anonymization technology can protect privacy information and ensure the authenticity of data released to the outside world, which is suitable for applications in many fields and has become a hot spot in the research field of privacy protection technology in recent years. However, most of the existing anonymous publishing technologies are based on static data sets, that is, assuming that the data sets are published “once” without any update. In other words, most privacy protection anonymous algorithms do not support the republication of data sets after insertion, deletion and modification. In reality, data sets are changing all the time. If the original static data set method is directly applied to the republishing process of dynamic data sets, it will lead to the leakage of a large amount of privacy information. Therefore, the republication of dynamic data sets faces more challenges.
Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment

Resource-intensive computations are a major factor that limits the effectiveness of automated machine learning solutions. In the paper, we propose a modular approach that can be used to increase the quality of evolutionary optimization for modelling pipelines with a graph-based structure. It consists of several stages - parallelization, caching, and evaluation. Heterogeneous and remote resources can be involved in the evaluation stage. The conducted experiments confirm the correctness and effectiveness of the proposed approach. The implemented algorithms are available as a part of the open-source framework FEDOT.
A Comparison of Distributed Tracing Tools in Serverless Applications

Serverless computing can favor the emergence of complex and error-prone applications. In order to gain observability in such applications, distributed tracing can be used. However, as serverless computing relies on the pay-per-use billing model, utilizing distributed tracing tools can have a noticeable impact on the resulting costs. Therefore, this paper investigates the impact of distributed tracing in serverless applications by exploring and comparing the efficiency characteristics of three selected distributed tracing tools - Zipkin, OpenTelemetry, and SkyWalking. In particular, the runtime, the memory usage, and the initialization duration were examined by benchmarking AWS Lambda function invocations. In the experiments, Zipkin imposed the lowest runtime overhead with an average of 10.73 %, while SkyWalking introduced the highest overhead with an average runtime overhead of 50.67 %. OpenTelemetry added 24.19 % additional runtime. Besides runtime overheads, significantly higher memory usage and initialization durations were detected for all tools. Therefore, the results suggest that distributed tracing can significantly impact the efficiency of serverless applications. Nevertheless, differences could be observed concerning tracing mechanisms and use cases. This helps developers to carefully select the most suitable tracing tool considering factors such as runtime overhead, memory usage, and initialization durations.
Cybersecurity Issues in Generative AI

Generative AI technology is being applied in various fields. However, the advancement of these technologies also raises cybersecurity issues. In fact, there are cases of cyber attack using Generative AI, and the number is increasing. Therefore, this paper analyzes the potential cybersecurity issues associated with Generative AI. First, we looked at the fields where Generative AI is used. Representatively, Generative AI is being used in text, image, video, audio, and code. Based on these five fields, cybersecurity issues that may occur in each field were analyzed. Finally, we discuss the obligations necessary for the future development and use of Generative AI.
PlanX: A Toolbox for Building and Integrating AI Planning Systems

We introduce the PlanX toolbox to support and promote building and integrating AI planning systems. The prototype toolbox is aimed at researchers and developers who need to build and use advanced AI planning systems to address planning problems. It is also well suited for composing AI planning systems that need to be integrated into larger software architectures. The toolbox offers an initial set of planning services realised using existing tools and our own implementations. The planning services are based on technologies that enable the resulting system to be easily set up and run on different platforms. We show on a use case that building and expanding an AI planning system with the PlanX toolbox is not only possible but also quick. PlanX is released as open-source to encourage its wider use and contributions to the code base.
A Data-driven Technique for Network Line Parameter Estimation Using Gaussian Processes

This paper proposes a unique data-driven physics-informed approach to network line parameter estimation, where the parameters of linear continuous-time domain equation governing line dynamics are learnt by modeling the line end voltage and current signals as Gaussian processes. The proposed method allows parameter estimation along with prediction of measurement signal and associated uncertainty in a single framework. The method is tested for parameter estimation on IEEE 14-bus and 9-bus network under steady-state operating condition.
Unveiling the Psychology of Human-Computer Interaction: Bridging the Gap for Future Advancements

This paper brings some light to the importance of psychology as a concept, its initial role in human-computer interaction, and exactly how necessary it is to maintain this aspect when spearheading the future of the field. The method used is to decipher what psychology gives to Human-Computer Interaction (HCI), including the primary components, the task, user, and computer. The results of this study discuss the framework of previous research, and how it stands today considering modern sub-theories. In the end, the verdict stands that psychology as one of the feature of HCI applies primarily to authors in the United States, and very much less when it comes to European researchers, who would generally take different approaches to their research. It is necessary to have a consistent conversation on the gravity of connecting the researchers of HCI and advancements with the full extent and realm of studies that exist. One of the biggest things that this paper brings to the field is some focus on the necessity that there is for in-depth analysis of psychology as a method of approaching HCI.
Reversible Network Covert Channel by Payload Modulation in Streams of Decimal Sensor Values

We investigate decimal number representations in large data streams. When decimal numbers are encoded in bits, not all possibilities are used. This opens possibilities to establish a covert channel, i.e., to inject secret data into the data stream and transport it hidden in the mass of data, which poses a security risk, as covert channels are mostly used for criminal purposes. We present a novel covert channel approach for streams of decimal data, a field which so far has been neglected. Moreover, we sketch an application scenario and analyze the covert channel's detectability, in particular how detectability and steganographic bandwidth can be traded against each other. The approach and proposals for detection are tested via simulation experiments.