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IEEE Conference on Wireless Communications and Networking

作者： Wenzheng Wang Renchao Xie Qinqin Tang Tao Huang Tianjiao Chen Zehui Xiong State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China China Mobile Research Institute Pillar of Information Systems Technology and Design Singapore University of Technology and Design

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the rise of the computing power network (CPN), which integrate edge computing, cloud computing, and network infrastructure, replicated computing services are increasingly distributed to meet user demands for location-independent, reliable, and low latency services. Service anycast forwarding coordinates distributed service instances by binding them to a unified identifier and dynamically routing requests to the optimal instance. However, challenges such as varying user demand distribution, network complexity, and service instance heterogeneity complicate balanced service forwarding. To address these, we propose an SDN-based service anycast forwarding mechanism for CPN (SA-CPN). In the data plane, a cyclic forwarding queue efficiently maps weighted strategies and selects instances for each service request, improving policy performance. In the control plane, an optimal transport model balances network and computation latency based on service instance capabilities. We further design an optimal transport-based service anycast forwarding algorithm (OTSAF) using Sinkhorn iterations. Our implementation of SA-CPN in a real system shows that OTSAF consistently outperforms four baseline methods across various performance metrics.

关键词： Computational modeling software algorithms Distribution networks Routing software Complexity theory Reliability software defined networking Low latency communication Edge computing

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Two-Dimensional Multi-Deep CNNs for Accurate and Robust software Defect Detection: A Performance-Driven Approach

Two-Dimensional Multi-Deep CNNs for Accurate and Robust Soft...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Vinod Veeramachaneni Pradeep Kumar Mallick Subhashree Rout Piyush Kumar Pareek Department of Information Technology Colorado Technical University USA School of Computer Engineering KIIT Deemed to be University Department of Computer Science and Engineering Nalanda Institute of Technology Bhubaneswar Odisha Nitte Meenakshi Institute of Technology Yelahanka Bengaluru Karnataka India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

software systems are growing in size and complexity. These properties make software defect prevention challenging. Thus, automatically predicting software module defects can help engineers conserve resources. Several ways have been presented to discover and fix these issues cheaply. However, these solutions may need a performance bump. This paper introduces the Multi-Deep Convolutional Neural Network to improve binary and multi-class software classification. Instead of one-dimensional CNNs, the proposed model captures n-gram characteristics inside and between datasets using a two-dimensional multi-scale convolutional technique. Advanced crow search optimisation algorithm (ACSOA) is used to optimally select model parameters. Besides Apache Active MQ, the study employed Eclipse data sets to evaluate the recommended deep learning method. F1-score, recall, accuracy, and precision were utilized to validate the model. The results show that the suggested strategy finds software vulnerabilities better and more reliably than current models.

关键词： Accuracy Computational modeling Prevention and mitigation software algorithms Optimization methods Computer architecture Syntactics software systems software reliability Convolutional neural networks

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Improving software Quality Assurance Using a Cross-Context-Attentive Cosine Convolutional Model

Improving Software Quality Assurance Using a Cross-Context-A...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Deena Sunil Devika Dabke Surendra Kumar Saini Ravendra Ratan Singh Jandail MWP Maduranga Sharada Nandan Raw Department of Mathematics Indira Gandhi National Tribal University Amarkantak Madhya Pradesh Department of Mathematics Central University of Karnataka Gulbarga Karnataka Department of Mathematics Indira Gandhi National Tribal University Amarkantak MadhyaPradesh Department of Computer Science South Asian University New Delhi New Delhi Department of Electrical and Electronic Engineering University of Sri JayewardenepuraNugegoda Department of Mathematics National Institute of Technology Raipur Raipur Chhattisgarh

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The software quality determines system performance and reliability. As software systems are becoming increasingly complex, this is an important determinant. Detection of defects at the early stages of the development lifecycle improves software reliability and reduces costs. Traditional software Defect Prediction (SDP) models suffer from class imbalance, complexity of datasets, and inefficient use of contextual information, which affect the accuracy of their predictions. Therefore, the development of reliable software systems still requires a more robust and efficient defect prediction model. In this manuscript, Improving software Quality Assurance Using a Cross-Context-Attentive Cosine Convolutional Model (Sox-CNN-XA) is proposed. This study uses the NASA-MDP dataset containing historical records of software defects and various software metrics. Data pre-processing included data cleaning, normalization, and splitting, which was done in order to enhance the quality of the data and make it ready for training and validation purposes. A new framework for software Defect Prediction (SDP) has been proposed by using the integration of Cosine Convolutional Neural Networks (Cos-CNN) and Cross-Contextual Attention (XCA), which captures spatial and contextual features of data effectively. The hyperparameters in this model have been fine-tuned with Sardine Optimization Algorithm (SOA) in order to get improved performances over diverse datasets. Experimental results show that the proposed framework achieves an accuracy of 99.9%, a true positive rate of 99.9%, and a precision of 99.8% while maintaining a false positive rate of 7.2%. The results clearly prove that the model is performing better than traditional methods. The conclusion shows that the framework can increase the reliability of defect prediction, thus enabling software practitioners to deliver high-quality and maintainable software within tight deadlines and budgets

关键词： Training Accuracy software algorithms software quality Predictive models software systems Prediction algorithms software reliability Convolutional neural networks Optimization

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algorithms, HARDWARE, AND software FOR A DIGITAL SIGNAL PROCESSOR MICROCOMPUTER-BASED SPEECH PROCESSOR IN A MULTIELECTRODE COCHLEAR IMPLANT SYSTEM

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 1989年第6期36卷 573-584页

作者： MORRIS, LR BARSZCZEWSKI, P BELL NO RES SCI STAFF MONTREAL QUEBEC CANADA

The authors discuss software and hardware developed to create a powerful, inexpensive, compact digital signal processing system that in real time extracts a low-bit-rate linear predictive coding (LPC) speech system model. The model parameters derived include accurate spectral envelope, format, pitch, and amplitude information. The system is based on the Texas Instruments TMS320 family, and the most compact realization requires only three chips (TMS320E17, A/D-D/A, op-amp), consuming a total of less than 0.5 W. The processor is part of a programmable cochlear implant system under development by a multi-university Canadian team, but also the speech features extracted can serve as input to vibrotactile and visual aids to the hearing handicapped.< >

关键词： software algorithms Signal processing algorithms Hardware Digital signal processors Speech processing Cochlear implants Linear predictive coding Power system modeling Digital signal processing chips Real time systems

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Methods and software Tools for Converting Road Marking Line Segmentation Mask to Object Representation for Highly Automated Vehicles

Methods and Software Tools for Converting Road Marking Line ...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Danil Dolgov Alexander Dolgov Imil Khairullin Faculty of Information Systems and Technologies Ulyanovsk State Technical University Ulyanovsk Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This paper describes the development of an algorithm for converting segmentation masks derived from neural networks into ordered sets of point coordinates representing road markings for highly automated vehicles (HAVs). Several methods are considered, including the nearest neighbor method, polynomial approximation, Hough transform, and parallel contouring. Performance tests and complexity analyses are performed for each method. The parallel contour method with dynamic search for opposite points was the most effective, demonstrating linear complexity and high accuracy in approximating road marking contours. This solution ensures efficient processing of large real-time datasets, critical for HAV navigation. The study integrates the algorithm into a unified system, combining segmentation with mask-to-line conversion, enhancing autonomous driving precision.

关键词： Industries Accuracy Navigation Roads Neural networks software algorithms Transforms Approximation algorithms Polynomials Real-time systems

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Dynamic Load Balancing on Switches of software Defined Network Managed by OpenDayLight Controller

Dynamic Load Balancing on Switches of Software Defined Netwo...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Manasa Kulkarni Bhargavi Goswami Muhammad Furqan Joy Paulose CHRIST (Deemed to be University) Bangalore India Queensland University of Technology Brisbane Australia

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

In recent times, the world is becoming a global village where connectivity is a new norm irrespective of geographical location. Within corporate networks, huge setbacks are faced due to a lack of efficient resource management. Load balancing is inevitable to cater to a reliable, faster, and congestion-free communication experience for exponentially increasing online enterprises. Dynamic network resource management for high performance and low data transmission latency in a network is necessary. The major issue faced by the traditional network is that it relies on static hardware switches. software-Defined Network approaches paved a way to overcome the limitations of traditional networks. This research proposes the Dynamic Load Balancing Algorithm for software-Defined Networks to utilize network resources optimally. The major function of the proposed algorithm is to determine alternative paths and further distribute the incoming and outgoing network flows to achieve optimum network resource utilization with faster traffic flow completion. The experiment is performed with the OpenDayLight Controller on the Mininet simulator, which emulates the network with the novel scheme. The results prove that the proposed solution has accomplished the benchmarks of optimum throughput, reduced redundancy, and reduced flow completion time.

关键词： Heuristic algorithms software algorithms Redundancy Packet loss Load management Throughput Dynamic scheduling Control systems Reliability software defined networking

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software tool-set for automated quantum system identification and device bring up

Software tool-set for automated quantum system identificatio...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Anurag Saha Roy Kevin Pack Nicolas Wittler Shai Machnes Peter Grünberg Institute – Quantum Computing Analytics (PGI 12) Forschungszentrum Jülich Jülich Germany Theoretical Physics Saarland University Saarbrücken Germany Qruise GmbH Saarbrücken Germany

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

We present a software tool-set which combines the theoretical, optimal control view of quantum devices with the practical operation and characterization tasks required for quantum computing. In the same framework, we perform model-based simulations to create control schemes, calibrate these controls in a closed-loop with the device (or in this demo —by emulating the experimental process) and finally improve the system model through minimization of the mismatch between simulation and experiment, resulting in a digital twin of the device. The model based simulator is implemented using TensorFlow, for numeric efficiency, scalability and to make use of automatic differentiation, which enables gradient-based optimization for arbitrary models and control schemes. Optimizations are carried out with a collection of state-of-the-art algorithms originated in the field of machine learning. All of this comes with a user-friendly Qiskit interface, which allows end-users to easily simulate their quantum circuits on a high-fidelity differentiable physics simulator.

关键词： Quantum system Computational modeling Scalability software algorithms Process control software Numerical models Object recognition Quantum circuit Optimization

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Method and Process Mining Based software for ECG Analysis and Arrhythmia Detection

Method and Process Mining Based Software for ECG Analysis an...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Kseniya Demushkina Andrey Kuzmin Information and Computing Systems Penza state University Penza Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Currently, the use of neural network based solutions in medical decision support systems remains popular. In order to overcome the hidden decision-making process called “black box”, the authors propose to use the “white box” principle based on the process mining approach. The aim of the work is to develop and implement a process mining based method of electrocardiosignal analysis and arrhythmia detection. Pan-Tompkins algorithm for electrocardiosignal signal analysis and ProM 6.11 framework for software implementation were used. As a results a new intelligent process mining based electrocardiosignal analysis method is proposed. It consists of conversion of digital data of cardiac signal into event log, construction of Petri net and construction of diagnostic decision tree. The original plugin generates the event log in the .xes format using Pan-Tompkins algorithm. ProM 6.11 AlphaMiner plugin generates a Petri net that reflects the relationship of events in the log. The resulting event log allows building a diagnostic decision tree using ProM 6.11 discovery of the process data-flow plugin. The study demonstrates the applicability of the proposed method using normal and arrhythmic cardiac signal records from PhysioNet database. The proposed solution on the base of process mining technology and ProM 6.11 framework allows implementing software for transparent analysis of cardiac signals data for medical decision support systems. The next step is to conduct statistical assessments of the effectiveness of the proposed solution and to expand the number of informative parameters used for analysis.

关键词： Process mining Decision support systems Arrhythmia PROM software algorithms Signal processing algorithms Electrocardiography software Decision trees Medical diagnostic imaging

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Boosting software Quality: A Machine Learning Technique for Predicting Defects

Boosting Software Quality: A Machine Learning Technique for ...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Akilan E. Daniel Alwin Infant P Jainish G R Computer science & Engineering Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Computer Science and Engineering Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. This work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. The objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. These indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. These algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. This approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges.

关键词： Computational modeling software algorithms software quality Predictive models Prediction algorithms Data models software reliability Decision trees software measurement Embedded software

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Integrating AI into software Engineering: A Critical Review and Future Directions

Integrating AI into Software Engineering: A Critical Review ...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Aditya Pai H Sharath K R Kavita Babalad Guru Prasad M S Chandrappa S Pratik Kumar MIT School of Computing MIT Art Design and Technology University Pune India Department of Computer Science and Engineering Graphic Era (Deemed-to-be University) Dehradun India School of Computing and Information Technology REVA University Bangalore India School of Computer Science and Engineering JAIN (Deemed-to-be University) Bengaluru India Department of Computer Applications Graphic Era (Deemed-to-be University) Dehradun India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

The field of Artificial Intelligence (AI) has been witnessing a huge demand in the field of research, tools development, and applications of deployment. There are multiple software companies which are shifting their focus to developing AI systems, many are also deploying the AI paradigms to their current system. Correspondingly, the academic research community has also used AI paradigms to solve the traditional software engineering problems. AI has become very useful to the software engineering community. The paper also uses a survey of the most commonplace methods of using AI into the software Engineering, that covers the methods for SE phases such as Requirements, Design, Development, Testing, Release, and Maintenance. The paper also aims in answering to the sufficient intelligence in the SE lifecycle, this evaluates the overlapping of the SE and AI domains, and challenge the current conventional wisdom of the state-of-the-art.

关键词： software testing Surveys software maintenance Reviews software algorithms Prediction algorithms Requirements engineering Artificial intelligence software engineering software development management

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