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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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Enhanced DDoS Detection Using Advanced Machine Learning and Ensemble Techniques in Software Defined Networking

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computers, Materials & Continua 2024年第11期81卷 3003-3031页

作者： Hira Akhtar Butt Khoula Said Al Harthy Mumtaz Ali Shah Mudassar Hussain Rashid Amin Mujeeb Ur Rehman Department of Computer Science University of Management and TechnologySialkot51040Pakistan Department of Computer Science and Creative Technologies Global College of Engineering and TechnologyMuscat2546Sultanate of Oman Department of Computer Science University of WahWah Cantt47040Pakistan Department of Computer Science and IT University of ChakwalChakwal48800Pakistan

Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern *** detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)*** Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent *** this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN *** model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant *** adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack *** proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble *** proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in *** provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving *** comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing *** results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.

关键词： Table 1(continued)OSI layer Possible DDoS attack Data link MAC Address Flooding Physical Cable disconnection JaMMING physical impersonation

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An Efficient Two-State Feature Attention-Based GRU for Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第15期49卷 95-112页

作者： Mohammed, Dhurgham Ali Patel, Kalyani A. Faculty of Computer Science Gujarat University Ahmedabad India Department of Computer Science Faculty of Education for Girl University of Kufa Iraq K.S. School of Business Management and Information Technology Gujarat University India

Text classification has become crucial for mechanically sorting documents into specific categories. The goal of classification is to assign a predefined group or class to an instance based on its characteristics. To attain precise text categorization, a feature selection scheme is employed to categorize significant features and eliminate irrelevant, undesirable, and noisy ones, thereby reducing the dimensionality of the feature space. Many advanced deep learning algorithms have been developed to handle text classification drawbacks. Recurrent neural networks (RNNs) are broadly employed in text classification tasks. In this paper, we referred to a novel Two-state GRU based on a Feature Attention strategy, known as Two-State Feature Attention GRU (TS-FA-GRU). The proposed framework identifies and categorizes word polarity through consecutive mechanisms and word-feature capture. Furthermore, the developed study incorporates a pre-feature attention TS-FA-GRU to capture essential features at an early stage, followed by a post-feature attention GRU that mimics the decoder’s function to refine the extracted features. To enhance computational performance, the reset gate in the ordinary GRU is replaced with an update gate, which helps to reduce redundancy and complexity. The effectiveness of the developed model was tested on five benchmark text datasets and compared with five well-established traditional text classification methods. The proposed TS-FA-GRU model demonstrated superior performance over several traditional approaches regarding convergence rate and accuracy. Experimental outcomes revealed that the TS-FA-GRU model achieved excellent text classification accuracies of 93.86%, 92.69%, 94.73%, 92.46%, and 88.23 on the 20NG, R21578, AG News, IMDB, and Amazon review dataset respectively. Moreover, the results indicated that the proposed model effectively minimized the loss function and captured long-term dependencies, leading to exceptional outcomes when compared to the

关键词： Recurrent neural networks

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Effects of AI-generated drawing on students’ learning achievement and creativity in an ancient poetry course

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educational Technology and Society 2025年第2期28卷 295-309页

作者： Chu, Hui-Chun Hsu, Chia-Ying Wang, Chun-Chieh Department of Computer Science and Information on Management Soochow University Taiwan Department of Education National Pingtung University Taiwan

In ancient poetry courses, students are expected to learn literary skills and the ability for interpreting ancient poems, from which they can gain insight into the historical context and appreciate the beauty of the poems. However, due to the gaps between ancient and modern living contexts, students often encounter difficulties in comprehending ancient texts. Scholars have suggested that visualizing learning scenarios could enhance learners’ comprehension and knowledge retention, implying that engaging students in learning by illustrating the context of ancient poems could encourage them to explore the contexts in that historical background and comprehend the contents. Therefore, a generative Artificial Intelligence (GenAI) drawing mode is proposed in the present study. In this learning mode, students describe the characters, events, and scenes in the poems in words, and GenAI creates visual representations to immerse students in the contexts of the ancient poems. An experiment was conducted with two classes of students from a high school. One class was the experimental group consisting of 31 students, who used GenAI drawing mode to assist their learning of ancient poems. Another class was the control group consisting of 29 students, who used the conventional drawing mode. The experimental results showed that the experimental group had better learning achievement than the control group. Moreover, for the students in the experimental group, the higher creative ones showed better self-efficacy than the lower creative ones. Additionally, the drawings by the experimental group had rich contents with more details, including emotions, contexts, objects, and expressions. © (2024), (International Forum of educational Technology and Society). All Rights Reserved.

关键词： Ancient poetry GenAI drawing Learning by drawing Situated learning

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Multi-layered access control based auto tuning relational key implications in enterprise-level multi-tenancy

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Multimedia Tools and Applications 2025年第17期84卷 17805-17836页

作者： Henge, Santosh Kumar Rajakumar, R. Prasanna, P. Parivazhagan, A. Hu, Yu-Chen Chen, Wu-Lin Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation KL University AP Vaddeswaram India Centre for Automation School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Veltech Rangarajan Dr Sagunthala R &D Institute of Science and Technology Chennai India Department of CSE Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Computer Science Xitun District Tunghai University No. 1727 Sec. 4 Boulevard Taichung City407224 Taiwan Department of Computer Science and Information Management Providence University Taichung City Taiwan

This article discusses the importance of cloud-based multi-tenancy in private–public-private secure cloud environments, which is achieved through the isolation of end-user data and resources into tenants to ensure data confidentiality and integrity. Maintaining secure access control is a complex and critical task in multi-tenant cloud environments, and researchers have proposed innovative methods for implementing security, privacy, and authenticity. However, several challenges remain. The proposed research addresses these challenges by presenting a layered approach to multi-tenancy in an enterprise-level cloud environment. The study has three main objectives: (1) to describe and analyze the complexities of enterprise-based multi-tenancy and proposed technical solutions in a tabular format;(2) to propose multi-tenant test cases across application, software, infrastructure, and network as a service environment;and (3) to present a collaborative, relational optimal key-based multi-tenancy access control model. The proposed access control model involves integrating various secure layers, such as intranet, extranet, intra-extranet, and using feasible relational optimal keys to provide secure multi-tenant access control among various tenants of single and multiple enterprises. This model provides secure data transmission by integrating various multi-layer data filters and secure blades. The layered multi-tenancy approach offers many benefits and technical feasibilities to avoid security and privacy issues in enterprise-based multi-tenant environments. The process achieved a 93% secure mapping rate to authenticate all IN and OUT data access transactions among the various multi-tenancy of similar and distinct enterprises. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Virtualization

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Toward actionable testing of deep learning models

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science China(Information sciences) 2023年第7期66卷 300-302页

作者： Yingfei XIONG Yongqiang TIAN Yepang LIU Shing-Chi CHEUNG Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of Education School of Computer Science Peking University Department of Computer Science and Engineering The Hong Kong University of Science and Technology School of Computer Science University of Waterloo Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering Southern University of Science and Technology

Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of the deep learning models, i.e., neural architectures with parameters trained over a dataset, is crucial to our daily living and economy.

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LegoDroid: flexible Android app decomposition and instant installation

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science China(Information sciences) 2023年第4期66卷 155-173页

作者： Yi LIU Yun MA Xusheng XIAO Tao XIE Xuanzhe LIU School of Computer Science Peking University Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of Education Institute for Artificial Intelligence Peking University Department of Computer and Data Sciences Case Western Reserve University

Current mobile applications(apps) have become increasingly complicated with increasing features that are represented on the graphical user interface associated with application programming interfaces(APIs) to access backend functionality and data. Meanwhile, apps suffer from the “software bloat” in volume. Some app features may be redundant, with respect to those features(from other apps) that the users already desirably and frequently use. However, the current app release model forces users to download and install a full-size installation package rather than optionally choosing only their desired features. Large-size apps can not only increase the local resource consumption, such as CPU, memory, and energy, but also inevitably compromise the user experience, such as the slow load time in the app. In this article, we first conduct an empirical study to characterize the app feature usage when users interact with Android apps,and surprisingly find that users access only a very small subset of app features. Based on these findings,we design a new approach named Lego Droid, which automatically decomposes an Android app for flexible loading and installation, while preserving the expected functionality with a fast and instant app load. With such a method, a slimmer bundle will be downloaded and host the target APIs inside the original app to satisfy users' requirements. We implement a system for Lego Droid and evaluate it with 1000 real-world Android apps. Compared to the original full-size apps, apps optimized by Lego Droid can substantially improve the load time by reducing the base bundle and feature bundles by 13.06% and 10.93%, respectively,along with the app-package installation size by 44.17%. In addition, we also demonstrate that Lego Droid is quite practical with evolving versions, as it can produce substantial reusable code to alleviate the developers' efforts when releasing new app versions.

关键词： performance software bloat instant installation mobile applications program analysis

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Retrainable Hand Gesture Recognition Model Driven by Edge AI 11

Retrainable Hand Gesture Recognition Model Driven by Edge AI

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11th International Conference on Dependable Systems and Their Applications, DSA 2024

作者： Hsu, Tse-Chuan Chang, Dong-Meau Soochow University Department of Computer Science & Information Management Taipei Taiwan Lingnan Normal University School of Computer Science and Intelligence Education China

ISBN: (纸本)9798331532390

Based on computer vision and machine learning, accurate hand behavior and gesture recognition are crucial, especially for missing or incomplete hand models. Finger joint point prediction technology is of great value for applications such as virtual reality and human posture recognition. However, hand joint point prediction models, such as Google's MediaPipe, perform well in full-finger models but are ineffective in missing-finger situations. Missing fingers are familiar in clinical medicine, rehabilitation facilities, and unique scenarios such as hand injuries or congenital hand deformities. Accurate prediction of hand joints is critical for hand rehabilitation, prosthetic design, and human motion analysis and inspires the development of related technologies. Since traditional prediction models rely on complete hand data, and missing fingers can make the hand structure irregular, the complexity of prediction increases. This study proposes an improved prediction model focusing on hand joint point prediction in the case of missing fingers, hoping to improve prediction accuracy and provide valuable reference for future applications and research. Among the research results, we successfully integrated the new training image model with MediaPipe, significantly improving missing fingers' prediction accuracy. © 2024 IEEE.

关键词： Prediction models

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XGBoost-Liver:An Intelligent Integrated Features Approach for Classifying Liver Diseases Using Ensemble XGBoost Training Model

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computers, Materials & Continua 2025年第4期83卷 1459-1474页

作者： Sumaiya Noor Salman A.AlQahtani Salman Khan Business and Management Sciences Department Purdue UniversityWest LafayetteIN 47907USA New Emerging Technologies and 5G Network and Beyond Research Chair Department of Computer EngineeringCollege of Computer and Information SciencesKing Saud UniversityRiyadh11564Saudi Arabia Department of Computer Science Abdul Wali Khan University MardanMardan23200KPKPakistan

The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion,metabolism,detoxification,and *** diseases result from factors such as viral infections,obesity,alcohol consumption,injuries,or genetic *** significant health risks and demand timely diagnosis and treatment to enhance survival ***,diagnosing liver diseases relied heavily on clinical expertise,often leading to subjective,challenging,and time-intensive ***,early detection is essential for effective intervention,and advancements in machine learning(ML)have demonstrated remarkable success in predicting various conditions,including Chronic Obstructive Pulmonary Disease(COPD),hypertension,and *** study proposed a novel XGBoost-liver predictor by integrating distinct feature methodologies,including Ranking and Statistical Projection-based strategies to detect early signs of liver *** Fisher score method is applied to perform global interpretation analysis,helping to select optimal features by assessing their contributions to the overall *** performance of the proposed model has been extensively evaluated through k-fold cross-validation ***,the performance of the proposed model is evaluated using individual and hybrid ***,the XGBoost-Liver model performance is compared to that of commonly used classifier ***,its performance is compared with the existing state-of-the-art computational *** experimental results show that the proposed model performed better than the existing predictors,reaching an average accuracy rate of 92.07%.This paper demonstrates the potential of machine learning to improve liver disease prediction,enhance diagnostic accuracy,and enable timely medical interventions for better patient outcomes.

关键词： Machine learning deep neural network SHAP(SHapley Additive exPlanation) liver disease classifica-tion SMOTE(synthetic minority over-sampling technique)

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Various optimized machine learning techniques to predict agricultural commodity prices

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Neural Computing and Applications 2024年第19期36卷 11439-11459页

作者： Sari, Murat Duran, Serbay Kutlu, Huseyin Guloglu, Bulent Atik, Zehra Department of Mathematical Engineering Faculty of Science and Letters Istanbul Technical University Istanbul34469 Turkey Department of Mathematics and Science Education Faculty of Education Adiyaman University Adiyaman Turkey Department of Computer Technologies Besni Vocational School Adiyaman University Adiyaman Turkey Economics Department Faculty of Management Istanbul Technical University Istanbul34367 Turkey

Recent increases in global food demand have made this research and, therefore, the prediction of agricultural commodity prices, almost imperative. The aim of this paper is to build efficient artificial intelligence methods to effectively forecast commodity prices in light of these global events. Using three separate, well-structured models, the commodity prices of eleven major agricultural commodities that have recently caused crises around the world have been predicted. In achieving its objective, this paper proposes a novel forecasting model for agricultural commodity prices using the extreme learning machine technique optimized with the genetic algorithm. In predicting the eleven commodities, the proposed model, the extreme learning machine with the genetic algorithm, outperforms the model formed by the combination of long short-term memory with the genetic algorithm and the autoregressive integrated moving average model. Despite the fluctuations and changes in agricultural commodity prices in 2022, the extreme learning machine with the genetic algorithm model described in this study successfully predicts both qualitative and quantitative behavior in such a large number of commodities and over such a long period of time for the first time. It is expected that these predictions will provide benefits for the effective management, direction and, if necessary, restructuring of agricultural policies by providing food requirements that adapt to the dynamic structure of the countries. © The Author(s) 2024.

关键词： Forecasting

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