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9th International Conference on computing and Informatics, ICOCI 2023

作者： Mohamad Mohsin, Mohamad Farhan Umana, Muhammad Khalifa Hassan, Mohamad Ghozali Sharif, Kamal Imran Mohd Ismail, Mohd Azril Salleh, Khazainani Zahari, Suhaili Mohd Sarmani, Mimi Adilla Gordon, Neil School of Computing Universiti Utara Malaysia Kedah Sintok06010 Malaysia School of Technology Management and Logistics Universiti Utara Malaysia Kedah Sintok06010 Malaysia National Climate Center Malaysian Meteorological Department Selangor Darul Ehsan Petaling Jaya46667 Malaysia Computer Science University of Hull Hull United Kingdom

ISBN: (纸本)9789819995912

Rice production is critical to food security, and accurate yield predictions are required for planning and decision-making. However, precisely predicting rice yields using machine learning models can be difficult due to the complicated interactions of various factors, such as how climate affects rice production. This study sought to solve this rice production is critical to food security, and accurate yield predictions are required for planning and decision-making. However, accurately predicting rice yields using machine learning models can be difficult due to the complicated interactions of various factors, such as how climate affects rice production. This study aims to address this issue by investigating how climate data affect Malaysian rice yield prediction models. The study used a linear regression model trained on rice production data and compared its performance with models incorporating climate data. Both datasets covered the period from 2010 to 2021 in Malaysia. The study found that including climate data significantly improved the prediction accuracy, with an approximately 77% improvement in MAE and 69% in RMSE. The results suggest that incorporating climate data into yield prediction models is essential for accurate and reliable predictions. These findings have important implications for stakeholders in the agricultural industry who can use accurate yield predictions to make informed decisions. However, the study’s limitations include using a single predictive model and data from a single country, suggesting the need for future studies to explore other machine learning algorithms and expand the scope of the research to other regions. Overall, this study contributes to the growing body of literature on the impact of climate data on yield prediction models and highlights the importance of considering climate data in agricultural decision-making. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision making

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Machine Learning and Network Traffic to Distinguish Between Malware and Benign Applications 1st

Machine Learning and Network Traffic to Distinguish Between ...

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1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Abualigah, Laith Abualigah, Sayel Almahmoud, Mothanna Forestiero, Agostino Sachdeva, Gagan Hanandeh, Essam S. Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Technology Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang George Town Malaysia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Department of Computer Information Systems Jordan University of Science and Technology Ar-Ramtha Jordan Performance Computing and Networking National Research Council of Italy Rende87036 Italy First Abu Dhabi Bank Abu Dhabi United Arab Emirates Department of Computer Information System Zarqa University Zarqa Jordan

ISBN: (纸本)9783031314681

Virus detection software is widely used for servers, systems, and devices that seek to maintain security and reliability. Although these programs provide an excellent safety level, the traditional defense methods fail to detect new Malware. The more advanced approach relies on predicting malicious behavior with dynamic analysis of the process executed. This paper presents a new method for detecting malware using machine learning algorithms applied to data obtained from the Cuckoo sandbox. The Cuckoo sandbox isolates the file being analyzed, providing detailed dynamic analysis reports. The machine learning algorithms were compared and the most important features were identified. The results were obtained using six popular classifiers, including SVM, Random Forest, and LightGBM, and the XGBOOST algorithm had the highest accuracy, at an average of 97%. However, the research on machine learning-based malware analysis is limited in terms of computational complexity and detection accuracy. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Software reliability

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Please Be Nice: A Deep Learning Based Approach to Content Moderation of Internet Memes

Please Be Nice: A Deep Learning Based Approach to Content Mo...

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2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

作者： MacRayo, Genevive Casino, Wilfredo Dalangin, Jerecho Gabriel Gahoy, Jervin Christian Reyes, Aaron Vitto, Christian Abisado, Mideth Lor Huyo-A, Shekinah Avelino Sampedro, Gabriel Technological University of the Philippines Department of Computer Science Manila Philippines National University College of Computing and Information Technologies Manila Philippines Philippine Coding Camp Research and Development Center Manila Philippines University of the Philippines - Open University Faculty of Information and Communication Studies Laguna Philippines

ISBN: (纸本)9798350320213

The internet has grown throughout time from a means for information distribution to a multipurpose tool that allows people to communicate, meet, and interact with one another. In socializing online, sending images with cultural and relatable context, otherwise known as memes, has become a common practice. Memes are sent everywhere, from forums and posts to even private messages. They are famous for converging thoughts, current events, or symbols creatively and colloquially. It combines text with a visual graphic and conveys many emotions, including hatred, which can harm other netizens. As a tool for communication, it can be used to convey humor or even as a tool for cyberbullying through hateful speech. Since memes are sent in image form, filtering out hateful speech in the form of memes can be difficult. To address this rising issue, researchers explore the use of deep learning approaches to detect hate speech on multimodal memes as a systematic approach for eliminating hate speech. The model performed well in this investigation, with an overall accuracy of 62.5%. When identifying pictures, it also has a high proportion of precision (71.37%). This brings the suggested model's ability to detect hate speech in memes to a close. © 2023 IEEE.

关键词： deep learning meme multimodal detection

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Portability and Scalability of OpenMP Offloading on State-of-the-art Accelerators

arXiv

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arXiv 2023年

作者： Fridman, Yehonatan Tamir, Guy Oren, Gal Department of Computer Science Ben-Gurion University of the Negev Israel Department of Physics Nuclear Research Center Negev Israel Scientific Computing Center Nuclear Research Center Negev Israel Intel Corporation United States Department of Computer Science Technion - Israel Institute of Technology

Over the last decade, most of the increase in computing power has been gained by advances in accelerated many-core architectures, mainly in the form of GPGPUs. While accelerators achieve phenomenal performances in various computing tasks, their utilization requires code adaptations and transformations. Thus, OpenMP, the most common standard for multi-threading in scientific computing applications, introduced offloading capabilities between host (CPUs) and accelerators since v4.0, with increasing support in the successive v4.5, v5.0, v5.1, and the latest v5.2 versions. Recently, two state-of-the-art GPUs - the Intel Ponte Vecchio Max 1100 and the NVIDIA A100 GPUs - were released to the market, with the oneAPI and NVHPC compilers for offloading, correspondingly. In this work, we present early performance results of OpenMP offloading capabilities to these devices while specifically analyzing the portability of advanced directives (using SOLLVE's OMPVV test suite) and the scalability of the hardware in representative scientific mini-app (the LULESH benchmark). Our results show that the coverage for version 4.5 is nearly complete in both latest NVHPC and oneAPI tools. However, we observed a lack of support in versions 5.0, 5.1, and 5.2, which is particularly noticeable when using NVHPC. From the performance perspective, we found that the PVC1100 and A100 are relatively comparable on the LULESH benchmark. While the A100 is slightly better due to faster memory bandwidth, the PVC1100 reaches the next problem size (4003) scalably due to the larger memory size. The results are available at: https://***/Scientific-computing-Lab-NRCN/Accel-OpenMP-Portability-Scalability. © 2023, CC BY.

关键词： Scalability

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Design and Implementation for Nonblocking Execution in GraphBLAS: Tradeoffs and Performance

Design and Implementation for Nonblocking Execution in Graph...

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作者： Mastoras, Aristeidis Anagnostidis, Sotiris Yzelman, Albert-Jan N. Computing Systems Lab Huawei Zurich Research Center Thurgauerstrasse 40 Zörich8050 Switzerland Department of Computer Science ETH Zurich Universitätstrasse 6 Zörich8092 Switzerland

GraphBLASis a recent standard that allows the expression of graph algorithms in the language of linear algebra and enables automatic code parallelization and optimization. GraphBLAS operations are memory bound and may benefit from data locality optimizations enabled by nonblocking execution. However, nonblocking execution remains under-evaluated. In this article, we present a novel design and implementation that investigates nonblocking execution in GraphBLAS. Lazy evaluation enables runtime optimizations that improve data locality, and dynamic data dependence analysis identifies operations that may reuse data in cache. The nonblocking execution of an arbitrary number of operations results in dynamic parallelism, and the performance of the nonblocking execution depends on two parameters, which are automatically determined, at run-Time, based on a proposed analytic model. The evaluation confirms the importance of nonblocking execution for various matrices of three algorithms, by showing up to 4.11× speedup over blocking execution as a result of better cache utilization. The proposed analytic model makes the nonblocking execution reach up to 5.13× speedup over the blocking execution. The fully automatic performance is very close to that obtained by using the best manual configuration for both small and large matrices. Finally, the evaluation includes a comparison with other state-of-The-Art frameworks for numerical linear algebra programming that employ parallel execution and similar optimizations to those discussed in this work, and the presented nonblocking execution reaches up to 16.1× speedup over the state of the art. © 2022 Association for computing Machinery.

关键词： Matrix algebra

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An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation 14

An Adaptive Decision-Making Approach for Better Selection of...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Kaafarani, Rima Ismail, Leila Zahwe, Oussama ICCS-Lab Computer Science Department American University of Culture and Education Beirut1507 Lebanon Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds. © 2023 Elsevier B.V.. All rights reserved.

关键词： Smart contract

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A framework for associating structural variants with cell-specific transcription factors and histone modifications in defect phenotypes

A framework for associating structural variants with cell-sp...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Becker, Timothy James Bayarsaihan, Dashzeveg Shin, Dong-Guk University of Hartford Department of Computing Sciences West Hartford United States Reconstructive Sciences Uconn Health Center Farmington United States University of Connecticut Computer Science and Engineering Storrs United States

ISBN: (纸本)9781665468190

Structural Variations (SVs) naturally occur in healthy human populations and account for more genomic difference than Single Nucleotide Variations (SNVs). However, because of the low prevalence in gene coding regions and the ubiquity in the intergenic regions, determining SV association for defect phenotypes remains challenging. We developed a framework that classifies five categories of SVs using defect associated genes along with cell-specific histone modifications (HM) concurrently with transcription factor binding sites (TFBS). We completed a comprehensive structural variation analysis of 17 family trios consisting of healthy paternal and maternal genomes along with the non-syndromic Orofacial cleft (OFC) child genomes which are publicly available from the Kids First Data Resource Portal. For the integrative analysis, we used ChIP-seq data from Neural Crest and Mesenchymal Stem Cell types from ENCODE along with TFBS taken from JASPAR. We found that the OFC children had elevated regulatory SVs when classified with our framework, aligning with prior studies regarding the complex non-syndromic phenotype. This result supports the use of a defect-specific gene paralog lists integrated with HMs and TFBS to identify plausible SV regions connected to a defect phenotype. © 2022 IEEE.

关键词： Defects

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On tightness of Tsaknakis-Spirakis descent methods for approximate Nash equilibria

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Information and Computation 2023年第1期293卷

作者： Chen, Zhaohua Deng, Xiaotie Huang, Wenhan Li, Hanyu Li, Yuhao Center on Frontiers of Computing Studies Peking University Beijing100871 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Computer Science Department Columbia University New YorkNY10027 United States

This article explores the minimum approximation ratio for Nash equilibrium in bi-matrix games, focusing on the Tsaknakis and Spirakis (TS) methods. The previous SOTA, TS algorithm, achieved an approximation ratio of 0.3393, but efforts to improve the analysis of the TS algorithm have been unsuccessful. This work demonstrates that the bound of 0.3393 is tight for the TS algorithm and presents a theoretical worst-case analysis. A condition for identifying tight instances is provided, along with a generator. While most generated instances are unstable, indicating potential improvements, stable instances exist where perturbations cannot enhance the 0.3393 bound. Other approximate algorithms, such as regret-matching and fictitious play, achieve better ratios on these instances. The generated instances can serve as benchmarks for approximate Nash equilibrium algorithms. The article also mentions progress in the TS algorithm, achieving an approximation ratio of 1/3, which can be further studied using the presented techniques. © 2023 Elsevier Inc.

关键词： Approximation algorithms

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MRLATO: An Adaptive Task Offloading Mechanism Based on Meta Reinforcement Learning in Edge computing Environment

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Peiying Liu, Jiamin Guizani, Maher Wang, Jian Kumar, Neeraj Tan, Lizhuang Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao266580 China Ministry of Education Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China University of Texas Arlington Computer Science and Engineering Department TX United States College of Science Qingdao266580 China Thapar University Department of Computer Science and Engineering Patiala147004 India Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Traditional cloud computing models struggle to meet the requirements of latency-sensitive applications when processing large amounts of data. As a solution, Multi-access Edge computing (MEC) extends computing resources to the edge of the network to reduce processing delays and improve user experience. However, in dynamically changing edge computing environments, effective decision making on whether to offload tasks to edge servers remains a core challenge. For this purpose, we propose MRLATO, an adaptive task offloading mechanism based on Meta Reinforcement Learning (MRL), which exploits a large amount of a priori knowledge of different tasks to achieve fast adaptation. The task offloading process is modelled as multiple Markov Decision Processes (MDPs) and solved using a Sequence to Sequence (Seq2Seq) neural network integrating multi-head attention and recursive task sequencing. It is shown by the experimental results that the proposed method has the lowest latency in all experimental settings and the convergence efficiency is improved by 14.06% compared to the traditional Deep Reinforcement Learning (DRL) algorithm. This research fully demonstrates the significant benefits of the deep integration of MRL with the edge computing domain, providing new optimisation ideas for task offloading decisions. © 1967-2012 IEEE.

关键词： Computation offloading

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Resource provisioning in collaborative fog computing for multiple delay-sensitive users

Resource provisioning in collaborative fog computing for mul...

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作者： Lu, Shuaibing Wu, Jie Wang, Ning Duan, Yubin Liu, Haiming Zhang, Jiayue Fang, Juan Faculty of Information Technology Beijing University of Technology Beijing China Center for Networked Computing Temple University PhiladelphiaPA United States Department of Computer Science Rowan University GlassboroNJ United States School of Software Engineering Beijing Jiaotong University Beijing China

Fog computing is an emerging paradigm that supplies storage, computation, and networking resources between traditional cloud data centers and end devices. This article focuses on the resource provisioning problem in collaborative fog computing for multiple delay-sensitive users. Our goal is to implement a resource provisioning strategy for network operators to minimize the total monetary cost by considering the deadline and capacity constraints. Two scenarios are considered: unlimited-processor fog nodes (UPFN) and limited-processor fog nodes (LPFN). In either scenario, we prove that the resource provisioning problem is NP-hard. First, we consider the UPFN scenario that the processors of fog nodes are unlimited and users' requests can be ideally processed in parallel. Two algorithms are proposed which greedily delete fog nodes based on the local or global collaborative influences until there is no feasible provisioning to guarantee the deadline of users. Then we extend the resource provisioning problem to a more realistic and complicated scenario LPFN in which the scheduling delay cannot be ignored. Two types of tasks are considered. One is the arbitrarily divided tasks, and a near-optimal solution bounded by (Formula presented.) has been found. m is the number of fog nodes, and (Formula presented.) is the upper bound on the Lipschitz constant of the delay function. Another one is the application-driven tasks, and we propose a heuristic algorithm. Extensive experiments validate the efficiency of the proposed algorithms. © 2021 John Wiley & Sons, Ltd.

关键词： Fog

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