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An infrastructure software perspective toward computation offloading between executable specifications and foundation models

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science China(Information sciences) 2025年第4期68卷 380-382页

作者： Dezhi RAN Mengzhou WU Yuan CAO Assaf MARRON David HAREL Tao XIE Key Laboratory of High Confidence Software Technologies (PKU) Ministry of Education School of Computer SciencePeking University School of Electronics Engineering and Computer Science Peking University Department of Computer Science and Applied Mathematics Weizmann Institute of Science

Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of software engineering theories and methodologies [2]. Instead of replacing existing software modules implemented by symbolic logic, incorporating FMs' capabilities to build software systems requires entirely new modules that leverage the unique capabilities of ***, while FMs excel at handling uncertainty, recognizing patterns, and processing unstructured data, we need new engineering theories that support the paradigm shift from explicitly programming and maintaining user-defined symbolic logic to creating rich, expressive requirements that FMs can accurately perceive and implement.

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Unsupervised multiplex graph diffusion networks with multi-level canonical correlation analysis for multiplex graph representation learning

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science China(Information sciences) 2025年第3期68卷 66-79页

作者： Sichao FU Qinmu PENG Yange HE Baokun DU Bin ZOU Xiao-Yuan JING Xinge YOU School of Electronic Information and Communications Huazhong University of Science and Technology Platform Operation and Marketing Center JD Retail Faculty of Mathematics and Statistics Hubei Key Laboratory of Applied Mathematics Hubei University School of Computer Science Wuhan University

In recent years, unsupervised multiplex graph representation learning(UMGRL) has received increasing research interest, which aims to learn discriminative node features from the multiplex graphs supervised by data without the guidance of labels. Although these designed UMGRL methods have obtained great success in various graph-related tasks, most existing UMGRL models still have the following issues: highly depending on complex self-supervised strategies(i.e., data augmentation,pretext tasks, and negative pairs sampling), restricted receptive fields, and only aggregating low-frequency information between nodes. In this paper, we propose a simple unsupervised multiplex graph diffusion network(UMGDN) with the aid of multi-level canonical correlation analysis to solve the above issues. Specifically, we first decouple the feature transform and propagation processes of the graph convolution layer to further improve the generalization of the learnable parameters. And then, we propose adaptive diffusion propagation to capture long-range dependency relationships between nodes, not the local neighborhood interactions. Finally, a multi-level canonical correlation analysis loss on both the feature transform and propagation processes is proposed to maximize the correlation of the same node features from multiple graphs for guiding model optimization. Compared to the existing UMGRL models, our proposed UMGDN does not need to introduce any data augmentation, negative pairs sampling techniques, complex pretext tasks, and also adaptively aggregates the optimal frequency information between nodes to generate more robust node embeddings. Extensive experiments on four popular datasets and two graph-related tasks demonstrate the effectiveness of the proposed method.

关键词： unsupervised multiplex graph representation learning graph neural networks node classification node clustering

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DP-ARTS: Dynamic Prioritization for Adaptive Resource Allocation and Task Scheduling in Cloud Computing

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第3期52卷 793-807页

作者： BEN ALLA, Hicham BEN ALLA, Said EZZATI, Abdellah Computer Science Mathematics and Computer Science Department Science and Technical Faculty Hassan 1 University Settat Morocco Embedded Systems and Computer Science National School of Applied Sciences of Berrechid Hassan 1 University Settat Morocco

Efficient task scheduling and resource allocation are essential for optimizing performance in cloud computing environments. The presence of priority constraints necessitates advanced solutions capable of addressing the increased complexity in the task scheduling process and resource allocation management. In this paper, we propose a novel approach, Dynamic Prioritization and Adaptive Resource Allocation and Task Scheduling (DP-ARTS), to tackle these challenges by integrating dynamic task prioritization with adaptive resource allocation and scheduling strategies. Our approach introduces a scoring mechanism to prioritize tasks based on deadlines, resource requirements, and other critical characteristics. Furthermore, we utilize K-means clustering to group Virtual Machines (VMs) according to their capabilities, facilitating more efficient task-to-VM mapping. Through simulations conducted with real-world workload traces in the CloudSim toolkit, DP-ARTS demonstrates superior performance in reducing makespan and energy consumption, while also improving resource utilization and load balancing compared to existing solutions. © (2025), (International Association of Engineers). All rights reserved.

关键词： K-means clustering

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Mathematical modelling of the dynamics of typhoid fever and two modes of treatment in a Health District in Cameroon

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Mathematical Biosciences and Engineering 2025年第2期22卷 477-510页

作者： Tsafack, Thierry Jimy Kum, Cletus Kwa Tassé, Arsène Jaurès Ouemba Tsanou, Berge Department of Mathematics and Computer Science Faculty of Science University of Dschang P.O. Box 67 Dschang Cameroon Department of Mathematics Higher Teacher Training College - Bambili The University of Bamenda P.O. Box 39 Bambili Cameroon School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa Department of Mathematics and Applied Mathematics University of Pretoria South Africa IRD Sorbonne University UMMISCO BondyF-93143 France

In this paper, we propose a novel mathematical model for indirectly transmitted typhoid fever disease that incorporates the use of modern and traditional medicines as modes of treatment. Theoretically, we provide two Lyapunov functions to prove the global asymptotic stability of the disease-free equilibrium (DFE) and the endemic equilibrium (EE) when the basic reproduction number (R0) is less than one and greater than one, respectively. The model is calibrated using the number of cumulative cases reported in the Penka-Michel health district in Cameroon. The parameter estimates thus obtained give a value of R0 = 1.2058 > 1,which indicates that the disease is endemic in the region. The forecast of the outbreak up to November 2026 suggests that the number of cases will be 21,270, which calls for urgent attention on this endemic disease. A sensitivity analysis with respect to the basic reproduction number is conducted, and the main parameters that impact the widespread of the disease are determined. The analysis highlights that the environmental transmission rate β and the decay rate µb of the bacteria in the environment are the most influential parameters for R0. This underscores the urgent need for potable water and adequate sanitation within this area to reduce the spread of the disease. Numerically, we illustrate the usefulness of recourse to any mode of treatment to lessen the number of infected cases and the necessity of switching from modern treatment to the traditional treatment, a useful adjuvant therapy. Conversely, we show that the relapse phenomenon increases the burden of the disease. Hence adopting a synergistic therapy approach will significantly mitigate typhoid disease cases and overcome the cycle of poverty within the afflicted communities. © 2025 the Author(s)

关键词： Medicinal chemistry

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TRLLD:Load Level Detection Algorithm Based on Threshold Recognition for Load Time Series

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computers, Materials & Continua 2025年第5期83卷 2619-2642页

作者： Qingqing Song Shaoliang Xia Zhen Wu Faculty of Applied Mathematics and Computer Science Belarusian State UniversityMinsk220030Belarus Higher School of Management and Business Belarus State Economic UniversityMinsk220070Belarus

Load time series analysis is critical for resource management and optimization decisions,especially automated analysis *** research has insufficiently interpreted the overall characteristics of samples,leading to significant differences in load level detection conclusions for samples with different characteristics(trend,seasonality,cyclicality).Achieving automated,feature-adaptive,and quantifiable analysis methods remains a *** paper proposes a Threshold Recognition-based Load Level Detection Algorithm(TRLLD),which effectively identifies different load level regions in samples of arbitrary size and distribution type based on sample *** utilizing distribution density uniformity,the algorithm classifies data points and ultimately obtains normalized load *** the feature recognition step,the algorithm employs the Density Uniformity Index Based on Differences(DUID),High Load Level Concentration(HLLC),and Low Load Level Concentration(LLLC)to assess sample characteristics,which are independent of specific load values,providing a standardized perspective on features,ensuring high efficiency and strong *** to traditional methods,the proposed approach demonstrates better adaptive and real-time analysis *** results indicate that it can effectively identify high load and low load regions in 16 groups of time series samples with different load characteristics,yielding highly interpretable *** correlation between the DUID and sample density distribution uniformity reaches 98.08%.When introducing 10% MAD intensity noise,the maximum relative error is 4.72%,showcasing high ***,it exhibits significant advantages in general and low sample scenarios.

关键词： Load time series load level detection threshold recognition density uniformity index outlier detection management systems engineering

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ZigzagNetVis: Suggesting temporal resolutions for graph visualization using zigzag persistence

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 3450-3458页

作者： Tinarrage, Raphaël Ponciano, Jean R. Linhares, Claudio D. G. Traina, Agma J. M. Poco, Jorge Fundação Getulio Vargas School of Applied Mathematics Rio de Janeiro Brazil University of São Paulo Institute of Mathematics and Computer Sciences São Carlos Brazil Linnaeus University Department of Computer Science and Media Technology Växjö Sweden

Temporal graphs are commonly used to represent complex systems and track the evolution of their constituents over time. Visualizing these graphs is crucial as it allows one to quickly identify anomalies, trends, patterns, and other properties that facilitate better decision-making. In this context, selecting an appropriate temporal resolution is essential for constructing and visually analyzing the layout. The choice of resolution is particularly important, especially when dealing with temporally sparse graphs. In such cases, changing the temporal resolution by grouping events (i.e., edges) from consecutive timestamps - a technique known as timeslicing - can aid in the analysis and reveal patterns that might not be discernible otherwise. However, selecting an appropriate temporal resolution is a challenging task. In this paper, we propose ZigzagNetVis, a methodology that suggests temporal resolutions potentially relevant for analyzing a given graph, i.e., resolutions that lead to substantial topological changes in the graph structure. ZigzagNetVis achieves this by leveraging zigzag persistent homology, a well-established technique from Topological Data Analysis (TDA). To improve visual graph analysis, ZigzagNetVis incorporates the colored barcode, a novel timeline-based visualization inspired by persistence barcodes commonly used in TDA. We also contribute with a web-based system prototype that implements suggestion methodology and visualization tools. Finally, we demonstrate the usefulness and effectiveness of ZigzagNetVis through a usage scenario, a user study with 27 participants, and a detailed quantitative evaluation. © 1995-2012 IEEE.

关键词： Image color analysis Vibrations Visualization Color Fluctuations Training Metaverse Sensitivity Gaze tracking Brightness

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Global Piecewise Analysis of HIV Model with Bi-Infectious Categories under Ordinary Derivative and Non-Singular Operator with Neural Network Approach

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computer Modeling in Engineering & sciences 2025年第1期142卷 609-633页

作者： Ghaliah Alhamzi Badr Saad TAlkahtani Ravi Shanker Dubey Mati ur Rahman Department of Mathematics and Statistics College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh13318Saudi Arabia Department of Mathematics College of ScienceKing Saud UniversityRiyadh11989Saudi Arabia Department of Mathematics Amity School of Applied SciencesAmity University RajasthanJaipur302002India School of Mathematical Sciences Jiangsu UniversityZhenjiang212013China Department of Computer Science and Mathematics Lebanese American UniversityBeirut13-5053Lebanon

This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary *** HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered *** total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,*** proposed model is tested separately for unique solutions and existence on bi *** numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons *** proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler *** cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said *** study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered *** obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.

关键词： HIV infection model qualitative scheme approximate solution piecewise global operator neural network

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Some Results on Ricci-Yamabe Soliton and Its Cosmological Models

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IAENG International Journal of applied mathematics 2025年第4期55卷 979-983页

作者： Pandey, Pankaj Yadav, Santosh Kumar Sharma, Kamakshi Chaturvedi, Braj Bhushan Department of Mathematics School of Chemical Engineering and Physical Sciences Lovely Professional University Punjab Phagwara144411 India Department of Mathematics School of Computer Science & Artificial Intelligence SR University Telangana Warangal506371 India Department of Pure & Applied Mathematics Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495009 India

This article aims to investigate the characteristics of Ricci-Yamabe Soliton (briefly: (RYS)n). We study the cosmological models on (RYS)4 under Lorentzian para Sasakian (LPS)4 spacetime. Parallel Ricci tensor, Poisson structure and gradient property also reveal on (RYS)n. Example of (RYS)4 constructed to verify the results. © (2025), (International Association of Engineers). All rights reserved.

关键词： Solitons

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Investigating the Impact of Language-Adaptive Fine-Tuning on Sentiment Analysis in Hausa Language Using AfriBERTa 1

Investigating the Impact of Language-Adaptive Fine-Tuning on...

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1st Workshop on Language Models for Low-Resource Languages, LoResLM 2025 - co-located at the 31st International Conference on Computational Linguistics, COLING 2025

作者： Sani, Sani Abdullahi Muhammad, Shamsuddeen Hassan Jarvis, Devon School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa Department of Computing Imperial College London United Kingdom

ISBN: (纸本)9798891762152

Sentiment analysis (SA) plays a vital role in Natural Language Processing (NLP) by identifying sentiments expressed in text. Although significant advances have been made in SA for widely spoken languages, low-resource languages such as Hausa face unique challenges, primarily due to a lack of digital resources. This study investigates the effectiveness of Language-Adaptive Fine-Tuning (LAFT) to improve SA performance in Hausa. We first curate a diverse, unlabeled corpus to expand the model’s linguistic capabilities, followed by applying LAFT to adapt AfriBERTa specifically to the nuances of the Hausa language. The adapted model is then fine-tuned on the labeled NaijaSenti sentiment dataset to evaluate its performance. Our findings demonstrate that LAFT gives modest improvements, which may be attributed to the use of formal Hausa text rather than informal social media data. Nevertheless, the pre-trained AfriBERTa model significantly outperformed models not specifically trained on Hausa, highlighting the importance of using pre-trained models in low-resource contexts. This research emphasizes the necessity for diverse data sources to advance NLP applications for low-resource African languages. We will publish the code and the data set to encourage further research and facilitate reproducibility in low-resource NLP. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Metrics of Success: Evaluating User Satisfaction in AI Chatbots 24

Metrics of Success: Evaluating User Satisfaction in AI Chatb...

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8th International Conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Møller, Cecilie Grace Ang, Ke En de Lourdes Bongiovanni, María Khalid, Md Saifuddin Wu, Jiayan Department of Digitalization Copenhagen Business School Fredriksberg Denmark Department of Applied Mathematics and Computer Science Danmarks Tekniske Universitet Lyngby Denmark

ISBN: (纸本)9798400718014

The rapid advancement of Artificial Intelligence (AI), particularly through Large Language Models (LLMs), has catalysed a technological revolution, leading to the widespread adoption of AI-driven chatbots across industries. OpenAI’s customisable generative pretrained transformer (GPT) offerings have popularised generative AI, enabling organisations of all sizes to implement chatbots for customer support. This development presents an opportunity for businesses to offer 24/7, cost-efficient customer service that can overcome the historical limitations of chatbots that lack a "human element." However, despite the proliferation of AI chatbots, there remains a crucial need to evaluate their effectiveness in meeting user needs and preferences for human-like interaction. Current service quality assessment tools, such as SERVQUAL and E-SERVQUAL, are unable to evaluate AI-specific capabilities like language intelligence and recognition. Existing research also lacks information on factors that affect user satisfaction and the continued use of AI chatbots. Based on a mixed-methods study, this paper proposes a new instrument for measuring user satisfaction with AI chatbots, specifically for customer support roles. Using the Stanford five-step Design Thinking Process, this study devised a customer support AI chatbot evaluation instrument through a literature review, Cheatstorming, and SCAMPER techniques, followed by testing in a Danish company. The research employs Prentice and Nguyen’s three-stage scale development process to ensure content, reliability, and construct validity, addressing gaps in current scholarship and advancing understanding of AI chatbot user satisfaction. © 2024 Copyright held by the owner/author(s).

关键词： Sales

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