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2nd International Conference on Computing and Information Technology, ICCIT 2022

作者： Alotaibi, Saud S. Rashid, Muhammad Azam, Farooque Abbas, Waseem Rasheed, Yawar Sinky, Mohammed H Anwar, Muhammad Waseem Umm Al-Qura University Department of Information Systems Makkah Saudi Arabia Umm Al-Qura University Computer Engineering Department Makkah Saudi Arabia H-12 Department of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665436052

Evolving concepts of smart cities include smart traffic lights and smart homes. These devices keep on observing the activities of users and share it with the cloud for further processing and decisions accordingly. This makes these systems complex ones. Therefore, a model-driven framework is needed which can integrate smart traffic lights with smart homes for the minimization of inherent development complexity. This article proposes a model driven framework, which integrates the smart traffic Fog nodes and smart homes. The basic objective is to share the mutual data related to traffic using cloud computing technology. To achieve this, a meta model is proposed. Subsequently, a Sirius based graphical modeling tool is developed which may be used to model the complex scenarios of smart lights and smart homes. A model to text transformation code is also written to automatically generate the target java code from the developed model. The validity of the proposed model has been demonstrated through a real world case study. In future, the proposed framework can be extended by incorporating the concepts of security and integrity of data. © 2022 IEEE.

关键词： Fog

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FEAT: Towards Fast Environment-Adaptive Task Offloading and Power Allocation in MEC 42

FEAT: Towards Fast Environment-Adaptive Task Offloading and ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Ren, Tao Hu, Zheyuan He, Hang Niu, Jianwei Liu, Xuefeng Institute of Software Chinese Academy of Sciences Laboratory for Internet Software Technologies Beijing China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Mobile edge computing (MEC) has been proposed to provide mobile devices with both satisfactory computing resources and latency. Key issues in MEC include task offloading and power allocation (TOPA), for which deep reinforcement learning (DRL) is becoming a popular methodology. However, most DRL-based TOPA approaches are typically developed in a certain environment, lacking the adaptability to unseen environments. Motivated by this, this paper proposes a Fast Environment-Adaptive TOPA (FEAT) approach that could adapt to unseen environments with little fine-tuning. Specifically, we first split MEC states into the internal state and environmental state. Then, based on these two types of states, we develop two main components of FEAT - a group of internal state-dependent TOPA meta-policies and an environmental state-embedded steerer. Meta-policies learn TOPA skills within the internal state space (allowing reusing meta-policies in different environments), while the steerer learns to choose appropriate meta-policies according to embedded environmental states. When encountering an unseen environment with the same internal state space, FEAT only needs to fine-tune the steerer using the newly embedded environmental state with few internal state explorations. Extensive experimental results on simulation and testbed demonstrate that FEAT outperforms the state-of-the-art by more than 16.4% in terms of fine-tuning speeds. © 2023 IEEE.

关键词： Mobile edge computing

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Smart Diagnostic System for Early Detection and Prediction of Polycystic Ovary Syndrome

Smart Diagnostic System for Early Detection and Prediction o...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Harish, K.P. Dhivyanchali, M.N. Devi, K.Nirmala Krishnamoorthy, N. Dhana Sree, R. Dharanidharan, R. Kongu Engineering College Department of Computer Science & Engineering Erode India Swamy Vivekanandha Medical College Hospital and Research Institute Elayampalayam Tamilnadu Tiruchengode637 205 India Vellore Institute of Technology Vellore Campus School of Information Technology and Engineering Department of Software Systems and Engineering Tamilnadu 632014 India

ISBN: (纸本)9798350348217

PCOS are the serious condition which affects female ovaries during their reproductive age of 15 to 45. This disease affects 5 to 10% of reproductive-age females. Although it is difficult to fully resolve this issue,the PCOS affected women can be mitigated through proper exercise, by taking proper nutritious diet and maintaining the healthy BMI. Until they take a pregnancy test, the majority of women are unaware of the disease. The clinical dataset has the 541 instances and 45 attributes of unbalanced classes of 0 and 1 (no and yes) which has 364 instances of 0 (no) class and 177 instances of 1 (yes) class. Preprocessing is done for the unbalanced dataset by filling the null values and changing the datatype of all attributes to numeric datatypes. The unbalanced dataset is balanced by the balancing techniques of SMOTE and Random Over Sampling. Comparing the both balanced techniques through the accuracy the random oversampling gives the *** supervised learning algorithms are Decision tree, KNN,Random Forest,AdaBoost, Logistic regression,Gradient boosting, cat boosting, XGBoosting, Linear SVM, Radial SVM, Linear discriminant analysis and Quadratic discriminant analysis are *** supervised learning algorithms are trained and tested by splitting the dataset to 70% for training and 30% for testing. The ensemble stacking techniques are used by implementing the all models at the cross validation of *** xgboost gives the accuracy of 96% for the balanced dataset. © 2023 IEEE.

关键词： Adaptive boosting

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Fast Resource Allocation for Virtual Network Functions Chain Placement

Fast Resource Allocation for Virtual Network Functions Chain...

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2022 International Telecommunications Conference, ITC-Egypt 2022

作者： Mohamed, Ramy Leivadeas, Aris Lambadaris, Ioannis Moris, Todd Djukic, Petar Carleton University Department of Systems and Computer Engineering OttawaON Canada École de technologie supérieure Department of Software and IT Engineering MontrealQC Canada Ciena Corporation OttawaON Canada

ISBN: (数字)9781665488082

ISBN: (纸本)9781665488082

Network slicing enables the creation and management of a network that meets and surpasses the evolving requirements of a diverse set of new services. Within 5G network slicing, we can describe a network service as a Service Function Chain (SFC), i.e., a graph of interconnected Virtual Network Functions (VNFs) residing in a particular network slice. Thus, the optimal resource allocation for these SFCs is critical for the network operator. Nevertheless, this problem stays partially unresolved because it needs managing resources spread across various Edge and Cloud sites in distinct geographical areas. Moreover, the complexity of the problem dramatically grows when considering practical constraints like end-to-end delay, VNF affinity, processing delay, and traffic requirements between VNFs. This paper addresses this problem and proposes practical solutions for the Virtual Network Functions Chain Placement Problem (VNF-CPP) established on Integer Linear Programming (ILP) and heuristic algorithms. Furthermore, we provide theoretical analysis and experiments to demonstrate the efficiency of the proposed placement algorithms. © 2022 IEEE.

关键词： 5G mobile communication systems

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Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing 42

Enabling Communication-Efficient Federated Learning via Dist...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Guan, Yixuan Liu, Xuefeng Ren, Tao Niu, Jianwei Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Chinese Academy of Sciences Laboratory for Internet Software Technologies Institute of Software Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Federated learning (FL) trains a shared global model by periodically aggregating gradients from local devices. Communication overhead becomes a principal bottleneck in FL since participating devices usually suffer from limited bandwidth and unreliable connections in uplink transmission. To address this problem, the gradient compression methods based on compressed sensing (CS) theory have been put forward recently. However, most existing CS-based works compress gradients independently, ignoring the gradient correlations between participants or adjacent communication rounds, which constrains the achievement of higher compression rates. In view of the above observation, we propose a novel gradient compression scheme named FedDCS, guided by distributed compressed sensing (DCS) theory. Following the design philosophy of separate encoding and joint decoding in DCS, FedDCS compresses gradients for participants in each round separately while reconstructing them at the central server jointly via fully exploiting correlated gradients from the previous round, which are known as side information (SI). Benefiting from this design, reconstruction performance is significantly improved with fewer decoding errors also iterations under the identical compression rate, and the total uploading bits to achieve model convergence are considerably reduced. Theoretical analysis and extensive experiments conducted on MNIST and Fashion-MNIST both verify the effectiveness of our approach. © 2023 IEEE.

关键词： Compressed sensing

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Towards Connecting Discrete Mathematics and software engineering

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Tsinghua Science and Technology 2020年第3期25卷 325-335页

作者： Tun Li Wanwei Liu Juan Chen Xiaoguang Mao Xinjun Mao the School of Computer National University of Defense Technology and are also with Laboratory of Software Engineering for Complex Systems

To enhance training in software development,we argue that students of software engineering should be exposed to software development activities early in the *** entails meeting the challenge of engaging students in software development before they take the software engineering *** this paper,we propose a method to connect courses in the software engineering curriculum by setting comprehensive development projects to students in prerequisite courses for software *** the Discrete Mathematics(DM)course as an example,we describe the implementation of the proposed method and teaching practices using several practical and comprehensive projects derived from topics in discrete *** descriptions of the sample projects,their application,and training results are *** and lessons learned from applying these practices show that it is a promising way to connect courses in the software engineering curriculum.

关键词： Discrete Mathematics(DM) software engineering proof checker database management system symbolic execution engine

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A Review of Artificial Intelligence in Serious Game for Public Health 3

A Review of Artificial Intelligence in Serious Game for Publ...

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3rd International Conference on Computational Science and Information Management, ICoCSIM 2019

作者： Rafiq, Abdul Asmawaty Abdul Kadir, Tuty Normaziah Ihsan, Siti Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Pahang Kuantan Malaysia

Serious games used in public health for purposes including training, learning, prediction, coaching, diagnostic, rehabilitation and supporting. However, the serious game focuses mainly on user experience and graphical application, but less attention on applying artificial intelligence (AI). By enhancing artificial intelligence, it will have the capability to solve issues, especially in the field of public health. This article discussed a review of the use of artificial intelligence in a serious game for public health. The main idea of this paper is to gather all the related articles and create a trend analysis of the use of Artificial Intelligence (AI) in a serious game for public health. The related articles were applied to Artificial intelligence (AI) in the area of decision-making. The final section discussed the new trend and the future of Artificial intelligence using current AI techniques. © Published under licence by IOP Publishing Ltd.

关键词： Public health

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Sentiment Analysis of Covid-19 Twitter Data using Deep Learning Algorithm 2

Sentiment Analysis of Covid-19 Twitter Data using Deep Learn...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Valarmathi, B. Gupta, N. Srinivasa Karthick, V. Chellatamilan, T. Santhi, K. Chalicheemala, Dhanush Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Assistant Systems Engineer Tata Consultancy Services Chennai India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

A key method used in the study of Natural Language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying emotions within textual content, classifying them as positive, neutral, or negative sentiments. A variety of textual inputs can be used with this process, including entire documents, individual sentences, and more. Understanding the sentiments expressed by individuals is of paramount importance to organizations in today's digital age. Clients and customers now have the means to express their thoughts and emotions with greater ease and immediacy than ever before. This wealth of sentiment data can offer valuable insights that organizations can leverage to enhance their products, services, and overall customer experience. The present study focuses on the sentiment analysis of Twitter data related to the COVID-19 pandemic, employing the Long Short-Term Memory (LSTM) algorithm. To improve the accuracy of sentiment analysis, a pre-processing procedure is introduced, which involves the use of the Neat Text module in Python to clean the tweets. In this research endeavor, a dataset comprising 1,79,107 COVID-19-related tweets is subjected to sentiment analysis using the proposed pre-processing module and LSTM. The results demonstrate an impressive accuracy rate of 96% in accurately determining the sentiments conveyed in these COVID-19 tweets. In contrast, the existing algorithm, based on Artificial Neural Network (ANN), achieved a significantly lower accuracy rate of 76%. This research not only showcases the effectiveness of LSTM and pre-processing in sentiment analysis but also highlights the significance of sentiment analysis in gaining valuable insights from vast amounts of textual data, especially when important events like the COVID-19 pandemic are involved. © 2024 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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Xtractor: A Two-Step Tweet Extractor for Sentiment Analysis

Xtractor: A Two-Step Tweet Extractor for Sentiment Analysis

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Muhammad Bux Alvi Naeem Ahmed Mahoto Mukhtiar Ali Unar Muhammad Akram Shaikh Computer Systems Engineering The Islamia University of Bahawalpur Bahawalpur Pakistan Software Engineering Department Mehran UET Jamshoro Jamshoro Pakistan Computer Systems Engineering Department Mehran UET Jamshoro Jamshoro Pakistan PASTIC Pakistan Science Foundation Islamabad Pakistan

Twitter (now X) has been gaining popularity with each passing day since its inception in 2006. People have been using Twitter as an instant repository to collect data and gain insight into folks’ minds on trending issues. Although Twitter allows access to its data through streaming and rest APIs, extracting the required data is difficult. The data (tweet) returned by Twitter is in .json format, having at least 26 fields. Each field is bundled in the dictionary form data structure. tweet metadata such as tweet “likes” and “retweets” increase the volume and complexity of a tweet, making data extraction in a cleaner format more difficult. This work aims to develop an effective two-step tweet extractor (Xtractor: an online-offline keyword-based Twitter data extractor). In the first step, Xtractor collects publicly available topical tweets using keywords, hashtags, or their list on the fly, parses all the tweet fields, and filters them to retain potential field contents without additional payload. The second step of the Xtractor evicts partially matching tweets using the regular expression method to acquire the targeted domain-specific tweets. Using the proposed two-step Twitter data extraction method, datasets concerning the people of Pakistan have been collected, which can be leveraged to get insight for decision-making, specially in the context of sentiment analysis. We found this method efficient, capacitive, and productive.

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IC Layouts Patterns Topological Profiling Using Directional Geometrical Kernels 2

IC Layouts Patterns Topological Profiling Using Directional ...

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DTCO and Computational Patterning II 2023

作者： Hamed, Ahmed Hamed Fathi El-Sewefy, Omar Khalaf, Sara Hegazy, Hazem Dessouky, Mohamed Salem, Ashraf Ain Shams University Faculty of Engineering Electronics Engineering and Electrical Communications Department Cairo11835 Egypt Ain Shams University Faculty of Engineering Computer and Systems Engineering Department Cairo11835 Egypt Siemens EDA Calibre Semi-Manufacturing Solutions Siemens Digital Industries Software Cairo11535 Egypt Siemens EDA Calibre Design-to-Silicon Siemens Digital Industries Software Cairo11535 Egypt

ISBN: (纸本)9781510660977

As the semiconductor manufacturing technology node scales down in the deep submicron domain, hotspot detection becomes more challenging and geo-contextually dependent than ever before. The need to profile IC layout patterns based on geometrical commonalities becomes a significant demand either during IC layout design or manufacturing phases. Identified hotspots during the manufacturing phase are usually correlated to specific geometrical configurations sensitive to the lithography process or other manufacturing processes. Accordingly, identifying similar geometrical configurations is an important step toward locating potential hotspots. Once these hotspots are identified, their patterns can be provided to the router to avoid using these patterns and find other valid alternatives. Furthermore, in the IC design sign-off phase or Design Rules Check (DRC), layout profiling can identify patterns with high commonalities to these problematic patterns that potentially lead to a yield loss. In this paper, we introduce automated IC layout patterns topological profiling approach using Directional Geometrical Kernels (DGKs) to capture the context of patterns around a Point-Of-Interest (POI) in an IC layout, such as a hotspot. The DGKs pattern representation provides a direct one-to-one mapping with physical geometrical measurements centered by the POI and doesn't need further feature extraction models or maps used by other pixelized gridded image-based or density-based representations, which are both time and computational resources-consuming. The DGKs are decomposed into topological and dimensional components. This makes the mechanism of patterns topological profiling not in need of complex models and can be precisely fine controlled to produce adequate patterns profiling granularity that is not easily approached by other patterns profiling alternatives. © 2023 SPIE.

关键词： Integrated circuits

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