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IEEE International Conference on Cluster Computing

作者： Yihao Sun Sidharth Kumar Thomas Gilray Kristopher Micinski Dept. Electrical Engineering and Computer Science Syracuse University Syracuse USA Dept. Computer Science University of Illinois at Chicago Chicago USA Dept. Computer Science University of Alabama at Birmingham Birmingham USA Dept. of Electrical Engineering and Computer Science Syracuse University Syracuse USA

Recursive aggregation has been of considerable interest due to its unifying a wide range of deductive-analytic workloads, including social-media mining and graph analytics. For example, Single-Source Shortest Paths (SSSP), Connected Components (CC), and PageRank may all be expressed via recursive aggregates. Implementing recursive aggregation has posed a serious algorithmic challenge, with state-of-the-art work identifying sufficient conditions (e.g., pre-mappability) under which implementations may push aggregation within recursion, avoiding the serious materialization overhead inherent to traditional reachability-based methods (e.g., Datalog).State-of-the-art implementations of engines supporting recursive aggregates focus on large unified machines, due to the challenges posed by mixing semi-naïve evaluation with distribution. In this work, we present an approach to implementing recursive aggregates on high-performance clusters which avoids the communication overhead inhibiting current-generation distributed systems to scale recursive aggregates to extremely high process counts. Our approach leverages the observation that aggregators form functional dependencies, allowing us to implement recursive aggregates via a high-parallel local aggregation to ensure maximal throughput. Additionally, we present a dynamic join planning mechanism, which customizes join order per-iteration based on dynamic relation sizes. We implemented our approach in PARALAGG, a library which allows the declarative implementation of queries which utilize recursive aggregates and executes them using our MPI-based runtime. We evaluate PARALAGG on a large unified node and leadership-class supercomputers, demonstrating scalability up to 16,384 processes.

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A Innovative Network Security Regulations Dependent on Improved Support Vector Machine from the Outlook of Modern Cities

A Innovative Network Security Regulations Dependent on Impro...

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International Conference on computer and Communications Technologies (ICCCT)

作者： Vasanthan P S. Srinivasan Dept. of Information Technology Sri Sairam Engineering College Dept. of Computer Science and Engineering R.M.D Engineering College

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

The concerns about the security created by the PC have gotten more advanced and complicated. Interruption detection is a pragmatic subject in the area of PC security whose essential target is to identify uncommon assault or attacks and to guarantee the safety of inside frameworks. This paper likewise suggests a semi-class interruption recognition strategy that joins various classifiers to mastermind exemptions and regular activities in a PC framework. In the consideration preference tree learning-iterative dichotomy 3, the maltreatment recognition method is developed and is gathered by using the cumulative knowledge based on the peculiarity detection system performed by one class- uphold vector machine. As of late, individuals have paid more thoughtfulness regarding ID/interruption avoidance framework, which is firmly identified with the insurance and use of framework the executives. A couple of AI principles including neural framework, genetic programming, and progressed uphold vector machines, Bayesian framework, multivariate adaptable backslide splines, feathery deduction systems and other analogical frameworks has been scrutinized for the layout of interruption identification framework. In this article, we suggest a combination strategy dependent on DTL-ID3 and OC-SVM assess the presentation of the extended procedure by utilizing a particular dataset and a hybrid technique to upgrade the precision of IDS/IPS when stood out from a solitary help vector machine.

关键词： Surveys Support vector machines Urban areas Vectors Data models Regulation Safety Registers Splines (mathematics) Pragmatics

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Deep Neural Networks in Neuroimaging: A U-Net Model for Precise Brain Tumor Detection

Deep Neural Networks in Neuroimaging: A U-Net Model for Prec...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Satya Prakash Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

This work holds the promise of being a considerable breakthrough in medical imaging diagnosis by studying advanced U-Net topologies for accurate brain tumour segmentation from magnetic resonance imaging (MRI) data. In this research, we present a more sophisticated pipeline that covers data preprocessing, robust model validation, and architecture tuning of the deep learning techniques. Performing accurately at 94.7% accuracy, the model showed a decent precision and recall rates with extremely promising results against traditional segmentation approaches as well as current deep learning frameworks. These results show how U-Net models could improve the diagnoses accuracy and guide personalized treatment tactics. Our research demonstrates the transformative ability of deep learning in medical diagnostics and opens a pathway for additional strides in individualized therapy, albeit diffuse tumour border segmentation barrier and MRI image heterogeneity limitations.

关键词： Deep learning Image segmentation Accuracy Magnetic resonance imaging Brain modeling Robustness Topology Telecommunication computing Planning Tuning

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OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sensing Framework

OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sen...

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International Conference on Distributed Computing Systems Workshop

作者： Cheng-Wei Ching Chirag Gupta Zi Huang Liting Hu Dept. of Computer Science and Engineering University of California Santa Cruz USA Dept. of Computer Science Virginia Tech USA

Compressed data aggregation (CDA) over wireless sensor networks (WSNs) is task-specific and subject to environmental changes. However, the existing compressed data aggregation (CDA) frameworks (e.g., compressed sensing-based data aggregation, deep learning(DL)-based data aggregation) do not possess the flexibility and adaptivity required to handle distinct sensing tasks and environmental changes. Additionally, they do not consider the performance of follow-up IoT data-driven deep learning (DL)-based applications. To address these shortcomings, we propose OrcoDCS, an IoT-Edge orchestrated online deep compressed sensing framework that offers high flexibility and adaptability to distinct IoT device groups and their sensing tasks, as well as high performance for follow-up applications. The novelty of our work is the design and deployment of IoT-Edge orchestrated online training framework over WSNs by leveraging an specially-designed asymmetric autoencoder, which can largely reduce the encoding overhead and improve the reconstruction performance and robustness. We show analytically and empirically that OrcoDCS outperforms the state-of-the-art DCDA on training time, significantly improves flexibility and adaptability when distinct reconstruction tasks are given, and achieves higher performance for follow-up applications.

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Leveraging compiler optimization for code clone detection 33

Leveraging compiler optimization for code clone detection

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33rd International Conference on Software engineering and Knowledge engineering, SEKE 2021

作者： Singh, Shirish Singhal, Harshit Mishra, Bharavi Dept. of Computer Science Columbia University United States Dept. of Computer Science and Engineering The LNMIIT India

ISBN: (纸本)1891706527

Finding similar code in software systems can guide several software engineering tasks such as code maintenance, program understanding, and code reuse. Similar code detection has been actively studied in the past. In the paper, we propose a novel approach that leverages compiler optimizations to transform semantically similar code and detect similar programs. The key observation of our work is that the compiler optimizations can be used to smooth out source code level idiosyncrasies introduced by the developers, thus making the optimized programs, for the same task, similar in structure. The similarity in structure can then be used to classify the programs. We conducted experiments on the Google CodeJam dataset to demonstrate the effectiveness of our approach. The experimental results show that our technique can achieve up to 85% accuracy on the program classification task, which is an improvement of more than 25% over the source code level classification. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： computer software reusability

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Metal Enhanced Fluorescence Microarray Biochip using Glancing Angle Deposited Ag nanorods 13

Metal Enhanced Fluorescence Microarray Biochip using Glancin...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Badshah, Mohsin Ali Lee, Seong-Min Jin, Chengjun Byeon, Seongyoung Hussain, Tasadduq Ali, Muzahir Kim, Seok-Min University of California Dept. of Chemical and Biomolecular Engineering Irvine United States Chung-Ang University Dept. Computer Science and Engineering Seoul Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Glancing angle deposition (GLAD) is a physical vapor deposition process in which the substrate is placed to have a large incidence angle (>75°, angle between incoming flux and substrate normal). The GLAD process can be considered a promising method to fabricate metallic nanostructure on a large area at a low cost. GLAD Ag nanorods were fabricated and their fluorescence enhancement performances were examined. A practical antibody chip was realized using a 500 nm thick vertical Ag nanorods substrate. To overcome the limitations of GLAD Ag nanorod MEF substrate, Ag nanorods on micro post array substrate were also investigated. © 2022 IEEE.

关键词： Biochips

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Movie Genre Prediction Using AI-Generated Data and Natural Language Processing

Movie Genre Prediction Using AI-Generated Data and Natural L...

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International Conference on computer and Information Technology (ICCIT)

作者： Sifat Tanvir Ayesha Parpia Abontee Md. Abu Rayhan Md. Fuadur Rahman Tasnim Sultana Ajwa Ahmed Dept. of Computer Science and Engineering Brac University Dhaka Bangladesh Dept. of Computer Science Brac University Dhaka Bangladesh

Every movie belongs to one or more specific genre. In this work, a dataset was prepared consisting of AI-generated movie titles and plots of 10 different genres. Text vectorization was used to convert every single word of the titles and plots into vectors. These vectors were used to predict which genre each movie belongs to. For training, a sequential neural network model was built and used, and an accuracy of almost 80% was achieved.

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Fairness-driven link scheduling approach for heterogeneous gateways for digital twin enabled industry 4.0

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International Journal of Intelligent Networks 2023年第1期4卷 162-170页

作者： Patil, Suvarna Kaur, Mandeep Rogulj, Katarina Dr. D Y Patil Institute of Engineering Management and Research Pune India Dept. of Computer Science Savitribai Phule Pune University Pune India Permtech Research Solutions Pune India Faculty of Civil Engineering Architecture and Geodesy University of Split Split Croatia

The advent of Industry 4.0 has brought with it the integration of digital twin technology, which has enabled businesses to develop a virtual replica of their physical assets. This technology allows businesses to optimize their operations and improve their overall efficiency. However, the successful implementation of digital twin technology in Industry 4.0 heavily relies on the effective utilization of gateways. A significant challenge in gateway utilization is the fair allocation of resources, particularly in heterogeneous environments where gateways have different capabilities. Digital Twin is helping Industry 4.0 vision by connecting the authorized people to the exact data and processes to protect the data/assets from unauthorized access. It is accomplished by connecting sensing devices using a unique addressing system and transmitting their combined data to the Internet of Things (IoT) cloud. Massive volumes of heterogeneous data have resulted from the rapid growth of IoT applications and services. As a result, evaluation of data which affects the Digital Twin enabled industry is studied in this article which focuses on data traffic generated from different Industry 4.0 applications and protection of data along with the industry assets is looked by Digital Twin technology. IoT gateways are currently used to connect the devices from various technologies to the Digital Twins. In such networks, sudden increase in demand of IoT gateways will increase with the increase in IoT devices and the operational cost will also be increased. In the proposed system, low-cost specific gateways are proposed to minimize cost and maximize network performance for protecting assets of smart city through Digital Twin technology. In order to accomplish effective resource allocation in a Digital Twin based infrastructure, data transmission fairness at every gateway is accomplished in an IIoT network by considering link scheduling issues. To address these issues and provide fairness in he

关键词： Internet of things

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COPAR: Coordinated Packet Rerouting Attacks in NoC 12

COPAR: Coordinated Packet Rerouting Attacks in NoC

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12th International Conference on Intelligent Systems and Embedded Design, ISED 2024

作者： Saritha, Sreehari Udayakumar Rajan, Manju Jose, John Govt. Engg. College Barton Hill Dept. of Information Technology Kerala India Indian Institute of Technology Guwahati Dept. of Computer Science & Engineering Assam India

ISBN: (纸本)9798331522612

In modern System-on-Chips (SoCs), the reliance on third - party intellectual property (IP) to meet demanding time-to-market and cost constraints introduces significant security vulnerabilities, particularly through the insertion of Hardware Trojans (HTs). These malicious circuits can severely compromise the performance and reliability of Tiled Chip Multicore Processors (TCMPs), which utilize Network-on-Chip (NoC) architectures for efficient inter-core communication. This paper presents a coordinated HT model that coordinates a packet rerouting attack within an 8×8 mesh NoC. By embedding HTs within the input units of multiple routers, the attack dynamically alters the destination addresses of packets, causing them to take unnecessary and indirect routes through the network. This malicious rerouting results in significant delays, leading to increased average packet latency, reduced instructions per cycle (IPC), and decreased overall throughput. Experimental results demonstrate the effect of this coordinated HT attack, increasing the packet latency by 2.33x, increasing the link utilization by 1.74x and decreasing the instructions per cycle by 45 %, degrading N oC performance. © 2024 IEEE.

关键词： DoS Hardware Trojan (HT) Network-on-Chip (NoC) Security System-on-Chip (SoC)

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FogMedX-Transform: A Transformer-based Task Interoperability Framework for Energy-Efficient Fog-Enabled IoMT 8

FogMedX-Transform: A Transformer-based Task Interoperability...

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8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Jayakarthik, R. Suneel, Sajja Thatipudi, Jayendra Gopal Jaganraja, V. Vohra, Minesh Gopinath, D. Dept of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS India Department of CSE(Data Science) Institute of Aeronautical Engineering Dundigal Telangana Hyderabad 500043 India Dept. of Computer Science University of North Texas United States Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India School of Mechanical Engineering Lovely Professional University Phagwara 144401 India Department of Computer Science Kristu Jayanti College (Autonomous) Karnataka Bengaluru India

ISBN: (纸本)9798331512248

The increasing speed of Internet of Medical Things (IoMT) development requires framework approaches that secure, efficient, and scalable for healthcare data management. The study presents FogMedX-Transform as a Transformer-based task interoperability framework purpose-built for energy-efficient fog-enabled IoMT systems. A customized Transformer design utilizing multi-head self-attention allows the framework to deliver timely critical healthcare data processing alongside optimized energy usage. This model demonstrated a 98.7% task interoperability success rate coupled with a 97.5% anomaly detection accuracy that exceeded results from conventional models based in fog environments and the cloud. The framework improved performance through latency reduction between 50% and 70% and energy consumption minimization reached between 25% to 40% thus proving its effectiveness in resource administration. The scalability tests proved steady system functionality when dealing with different device quantities and built-in security features blocked data breaches and had a false positive rate of 2.3% during testing. FogMedX-Transform demonstrates the capability for healthcare transformation through its union of fog computing and deep learning technology integration according to test results. © 2025 IEEE.

关键词： Anomaly Detection Deep Learning Healthcare Data Security Internet of Medical Things Latency Reduction Real-Time Processing

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