Reviewerly/exHarmony huggingface dataset:
Automated Hyperparameter Tuning for Adaptive Cloud Workload Prediction

Efficient workload prediction is essential for enabling timely resource provisioning in cloud computing environments. However, achieving accurate predictions, ensuring adaptability to changing conditions, and minimizing computation overhead pose significant challenges for workload prediction models. Furthermore, the continuous streaming nature of workload metrics requires careful consideration when applying machine learning and data mining algorithms, as manual hyperparameter optimization can be time-consuming and suboptimal. We propose an automated parameter tuning and adaptation approach for workload prediction models and concept drift detection algorithms utilized in predicting future workload. Our method leverages a pre-built knowledge-base based on historical data statistical features, enabling automatic adjustment of model weights and concept drift detection parameters. Additionally, model adaptation is facilitated through a transfer learning approach. We evaluate the effectiveness of our automated approach by comparing it with static approaches using synthetic and real-world datasets. By automating the parameter tuning process and integrating concept drift detection, in our experiments the proposed method enhances the accuracy and efficiency of workload prediction models by 50%.
NSDF-Services: Integrating Networking, Storage, and Computing Services into a Testbed for Democratization of Data Delivery

The lack of a readily accessible, tightly integrated data fabric connecting high-speed networking, storage, and computing services remains a critical barrier to the democratization of scientific discovery. To address this challenge, we are building National Science Data Fabric (NSDF), a holistic ecosystem to facilitate domain scientists in their daily research. NSDF comprises networking, storage, and computing services, as well as outreach initiatives. In this paper, we present a testbed integrating three services (i.e., networking, storage, and computing). We evaluate their performance. Specifically, we study the networking services and their throughput and latency with a focus on academic cloud providers; the storage services and their performance with a focus on data movement using file system mappers for both academic and commercial clouds; and computing orchestration services focusing on commercial cloud providers. We discuss NSDF's potential to increase scalability and usability as it decreases time-to-discovery across scientific domains.
EntropicFL: Efficient Federated Learning via Data Entropy and Model Divergence

Federated Learning (FL) is a strategy for training distributed learning models. This approach gives rise to significant challenges including the non-independent and identically distributed (non-IID.) characteristics inherent to the training data, which can wield influence over the comprehensive accuracy of the global model. Moreover, the collaborative involvement of multiple clients in training protocols frequently engenders increased communication overheads and resource management overheads. In this research, we present an innovative strategy to address the inherent challenges of federated learning, including the communication overhead, data heterogeneity, and privacy preservation concerns. Our proposed approach centers on the concept of adaptive client selection, comprising a two-step process: firstly, the identification of a subset of clients possessing pertinent and representative data for participation in model training, and secondly, the determination of whether to transmit the local updates from these selected clients to the central aggregation server. Our methodology leverages the metrics of data entropy and model divergence to guide this client selection process. By applying this approach, we effectively mitigate communication overhead without compromising the accuracy achieved in the federated learning process.
SDKV: A Smart and Distributed Key-Value Store for the Edge-Cloud Continuum

Many time-critical and data-intensive distributed applications for the computing continuum depend on low-latency, scalable, and highly available distributed key value storages. In this paper, we introduce SDKV, a scalable -Smart and Distributed Key-Value- store for the Edge-Cloud continuum to automatically place data in close proximity to clients resulting in low response times. The clients of SDKV can influence data availability and access latency by specifying the number of replicas and the desired level of data consistency (strong or eventual) on a per key-value pair basis, which favors the support of a wide range of applications. Results reveal that for different workloads and client access behaviors, SDKV outperforms existing distributed data storages and their data placement algorithms by 12--69% for both consistency models. Moreover, the proposed placement algorithm of SDKV provides fast decision times and scales linearly with the number of keys.
Identifying Network Congestion Using Knowledge Graphs and Link Prediction

In this work, we introduce a dynamic, context-aware system for managing communication networks using knowledge graphs. We utilize graph embeddings to create vector representations of a network's properties, while preserving each node's topological data. Our model employs link prediction techniques to proactively identify potential network congestion events. This methodology has been applied in a simulated communication network environment. The results demonstrate its promising ability to enhance network performance and reliability by predicting and mitigating congestion before it disrupts service delivery. By leveraging this enriched representation, our model identifies events that could disturb efficient network function, hence enabling more efficient and reliable delivery of digital services. This approach significantly contributes to the proactive and predictive management of digital communication networks, establishing a new way of enhancing network performance and reliability.
Cloud Workload Categorization Using Various Data Preprocessing and Clustering Techniques

Effectively managing cloud resources can be challenging due to the inter-dependencies of various cloud-hosted services. Workload categorization identifies and groups workloads with similar characteristics. Data center managers can make informed decisions on resource allocation, workload scheduling, and infrastructure maintenance, leading to better performance and reduced costs. However, since cloud workloads can be interpreted differently due to their characteristics, several well-founded categories can be concealed within the various data perspectives. This paper proposes a workload categorization approach to automate the categorization process of the scheduling workloads, utilizing different clustering and data preprocessing methods, evaluated using a cloud workload trace derived from Microsoft Azure. Our research highlights the importance of using advanced data preprocessing techniques and integrating them seamlessly into clustering methods to ensure precise workload segmentation.
Distributed Edge Inference: an Experimental Study on Multiview Detection

Computing is evolving rapidly to cater to the increasing demand for sophisticated services, and Cloud computing lays a solid foundation for flexible on-demand provisioning. However, as the size of applications grows, the centralised client-server approach used by Cloud computing increasingly limits the applications' scalability. To achieve ultra-scalability, cloud/edge/fog computing converges into the compute continuum, completely decentralising the infrastructure to encompass universal, pervasive resources. The compute continuum makes devising applications benefitting from this complex environment a challenging research problem. We put the opportunities the compute continuum offers to the test through a real-world multi-view detection model (MvDet) implemented with the FastFL C/C++ high-performance edge inference framework. Computational performance is discussed considering many experimental scenarios, encompassing different edge computational capabilities and network bandwidths. We obtain up to 1.92x speedup in inference time over a centralised solution using the same devices.
CCSF: Clustered Client Selection Framework for Federated Learning in non-IID Data

Federated Learning (FL) is a distributed approach where numerous devices train a shared global model for Machine Learning (ML) tasks. At every training round, the client devices must share their new local gradients with the central server to update the global model. Hence, FL requires high communication costs in terms of bandwidth and the number of messages exchanged between FL clients and the central server, leading to many issues, such as communication bottlenecks and scaling in the network. Consequently, having all devices participating in every training round is not practical. Moreover, the devices' local datasets are usually not Independent and Identically Distributed (IID), posing additional challenges for training the global model. In this sense, we introduce a Clustered Client Selection Framework (CCSF) to decrease the overall communication costs for training an ML model in the FL environment. CCSF clusters the client devices and employs a biased client selection strategy with two main objectives: (i) reducing the number of devices training at every round; and (ii) the number of rounds required to reach convergence. Our experimental evaluations, conducted on two well-known datasets, MNIST and MotionSense, show that CCSF is highly efficient, where the clients' local datasets can be grouped into homogeneous clusters. In MNIST, CCSF reaches an accuracy score above 60% in less than 50 rounds compared to FedAvg at 50% after 100 rounds. The performance gap is wider in the MotionSense data. CCSF reaches an accuracy score of 70% in a little more than 20 training rounds compared to FedAvg below 30% of accuracy in the first 100 FL rounds.
Machine Learning for Predictive Resource Scaling of Microservices on Kubernetes Platforms

Resource scaling is the process of adjusting the amount of resources allocated to a system or a service according to the changing demand. For microservices, resource scaling can be done at different levels, such as the container, the pod, or the cluster. However, the current approaches for resource scaling are not good enough because they rely on reactive or rule-based methods that do not account for the dynamic and complex nature of microservices. These methods often lead to over-provisioning or under-provisioning of resources, both affecting the quality of service and the cost efficiency. To address these issues, this work focuses on testing multiple machine learning approaches to optimize the pod dimensioning problem for Kubernetes platforms through predicting resource requirements for an upscaled number of users. The proposed approach aims to address the limitations of the standard Horizontal Pod Autoscaler (HPA), which often results in resource wastage or suboptimal performance. The results were promising and demonstrated high precision and performance of multiple ML models to accurately forecast future resource needs.
Problems of educational work with convicts in the context of their re-socialization

The article identifies and analyzes the problems of educational work with convicts in the context of their re-socialization. The author comes to the conclusion that the current penal enforcement legislation does not fully reflect the role and content of educational work with convicts. It is proposed to provide an approximate list of educational measures, to identify the subjects responsible for conducting educational work, as well as the forms of their interaction with convicts in order to correct and re-socialize them. In order to effectively organize educational work, it is necessary to achieve an approximately equal ratio of the number of employees and convicts in a correctional institution. It is recommended to develop and test the technology of organizing the work of the school for the preparation of convicts for release with a focus on taking into account in its work the resources of both state structures and non-profit organizations, as well as the convicts themselves and their immediate environment.
The problem of the concept and effectiveness of socially useful communication of the convict

At the present stage of the development of public relations in the field of execution of punishments related to isolation from society, it is difficult to overestimate the importance of the problems of maintaining socially useful ties by convicts serving sentences in the form of imprisonment. The positive impact exerted on convicts serving sentences in correctional institutions is an effective means of achieving the goals facing punishment in the form of imprisonment. However, due to insufficient legal support for the regulation of public relations in the field of execution of punishment in the form of deprivation of liberty, in the field of maintaining socially useful ties by convicts, this tool does not fully realize its potential in achieving the goals facing punishment in the form of deprivation of liberty. The purpose of the study is to formulate a theoretical justification for the need to introduce amendments to the penal enforcement legislation that will increase the effectiveness of the socially beneficial influence exerted on convicts by persons with whom convicts maintain ties; to create a system of criteria and indicators of the effectiveness of maintaining socially useful ties of convicts serving a sentence of imprisonment. The scientific novelty of the work consists in the disclosure of the concept of «socially useful connection of a convict», the construction of a model for measuring the effectiveness of maintaining socially useful connections by convicts serving a sentence of imprisonment. The main part reveals the problems of maintaining socially useful connections by convicts serving a sentence of imprisonment. Attention is focused on the need to introduce a model for calculating the effectiveness of maintaining socially useful relationships by convicts. In conclusion, proposals have been put forward to amend the current penal enforcement legislation. A model is proposed for calculating the effectiveness of maintaining socially useful relationships by convicts serving a sentence of imprisonment.
GCF-FedCE : An Approach to Evaluating Contributions within Federated Learning based on the Growth Curve Function

Federated Learning (FL), as a new machine learning paradigm, is applied in many directions. In federated learning, samples are preserved locally without transmitting or exchanging and the intermediate variables have to be exchanged under the privacy protection technology. In the real world, due to the value generated by data capitalization and the cost of communication training, data owners do not actively contribute their own data as expected, so a reasonable incentive mechanism is needed to motivate data owners to contribute high-quality models trained based on their local data. The core of the incentive mechanism for federated learning lies in two aspects: the evaluation of the contributions of each participant, and the income distribution plan. In this article, we focus on t the contribution evaluation of each participant. GCF-FedCE, a method for evaluating the contribution of participants in federated learning based on the growth curve function, is proposed. The simulations shows that GCF-FedCE can accurately and real-time evaluate he contributions in each round and the overall contributions of each participant. And it is easy to catch the malicious or free-rider participant. In addition, GCF-FedCE is of adaptability for data distributions and number of participants, which makes the evaluation fairer and more accurate.
Healing and decompression APP based on deep learning framework

In recent years, with the rapid growth of the economy and the improvement of people's material living standards, "work pressure" and "mental internal attrition" have become the issues that people focus on. Traditional life does not have an effective way to solve the social problems arising from the new era, combined with the rapid development of today's Internet technology, so this study intends to design a simple and can analyze the user's psychological emotions of the calculation model to solve the problem of excessive pressure in people's hearts, provide a simple and effective way to relieve stress.
Analysis and Visualization of Vehicle Off-road Path Planning

Vehicle cross-country path planning, as a part of the functional analysis of battlefield environment, is an effective means to assist commanders in making battlefield maneuver plans, and it is also the main factor to accomplish coordinated operations efficiently. It is widely used in battlefield investigation, strategic material transportation, field rescue and other military operations. Its main goal is to study the influence of various factors on vehicle maneuver in complex off-road environment and plan appropriate traffic routes for vehicles. However, in the off-road environment, the types, attribute characteristics and distribution range of related factors have an impact on off-road traffic. Therefore, taking into account the geographical environment factors and vehicle performance factors, this paper uses the multi-factor superposition analysis method to build a vehicle off-road traffic area model and visually express it, so as to improve the efficiency of off-road path planning, meet the needs of vehicle off-road traffic path planning in the process of military maneuvering in the battlefield environment, and is of great significance for troops to quickly perform their tasks.
Deep Annotation of The Tang Dynasty Seven-Character Quatrain Corpus and Generation of Data Set for Poetry Composition Teaching System

In the era of AI, the poetry writing system represented by "Jiuge" provides learners with an intelligent platform for teaching poetry creation. Both the previous poetry generation method and the current mainstream deep learning method require large-scale and better quality training corpus as data support. In this study, we take the 304 representative seven-character quatrains of Quan Tang Shi as the corpus source, and complete the following labelling work in accordance with the hierarchy of "lexical meaning (word segmentation, part-of-speech tagging)-imagery-emotion". Firstly, the deep annotation of words is carried out at the levels of disambiguation, lexical annotation and sense class annotation to complete the description of the semantic hierarchy. Secondly, the labelling of imagery is carried out on the basis of word labelling, showing the relationship between words and imagery through the lexical composition within the imagery. Third, based on the multilayered nature of the semantics of imagery, the literal and non-literal meanings of imagery and the mapping relationship between the two are labelled. Finally, the emotion of the poem is labelled to provide new materials for teaching the appreciation and writing of the quatrains, and to provide refined corpus data for the automatic generation of the quatrains. In this study, 6,122 word examples, 2,615 word types, 5,882 sense class examples, 743 sense types, 427 imagery examples, 296 imagery types, and other in-depth annotated data were completed, which provide a high-quality data base for the AI poetry creation teaching system to carry out training and learning effectively.
Research on Intelligent Clustering Algorithms for Operations and Maintenance Data

Operation and maintenance data is a prerequisite for ensuring the stability, safety, and orderly production of a company. During the operation and maintenance process, a large number of text records will be generated to indicate the normal operation and maintenance work. However, sometimes the recording of operation and maintenance data is not standardized, and data organization and classification require a large amount of manpower and material resources. Based on this, artificial intelligence and machine learning algorithms are used to intelligently organize and classify company operation and maintenance data, in order to reduce labor costs and improve the efficiency of operation and maintenance work management. In response to this requirement, the article integrates text data preprocessing methods, data vector representation models, and similarity calculation methods, improves the K-Means algorithm, realizes intelligent clustering of operation and maintenance data, and supports high-quality management and recording of operation and maintenance documents.
A Hybrid Data-Knowledge-Driven Domain Ontology Construction Method for China's Financial Domain

The rapid increase of data in China's financial domain has brought difficulties in data organization and management, and the traditional flat organization of financial big data ignores the rich knowledge association in the data, and the study of knowledge association in the financial domain has received more and more attention from academia. Financial ontology can meet the demand of the financial industry for data quality and program rigor, and reflect the structure of the financial capital market and the associations between different entities. This study investigates and compares various ontology construction methods at home and abroad, and proposes a data-knowledge hybrid-driven approach to construct domain ontologies, which realizes the collaboration among people, knowledge and data with the core of "knowledge generation-data expansion-quality control". Under the guidance of this method, this study invited a research group of experts in the financial field to successfully construct the first financial ontology applicable to China's financial system by using financial data provided by several financial and economic information providers, such as Wind, CSMAR, CNRDS, etc., which provides exploration and guidance for the development of informatization in China's financial field, and on the basis of which a more comprehensive knowledge graph can be constructed in the future to further promote the construction of information resources in China's financial domain.
Innovation and Upgrading of Enterprise Human Resource Management in the Era of Artificial Intelligence

With the arrival of a new generation of artificial intelligence, enterprise development and human resource management have been significantly affected, and artificial intelligence has become an important asset and production factor for organizations. With the digital transformation of enterprises entering a new stage, people and artificial intelligence will form a new partnership. This article explores the new generation of artificial intelligence and its impact on organizations and human resource management, and provides inspiration for organizations to innovate and upgrade their human resource management.