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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Ayesha Naseer Adil Masood Siddiqui Department of Computer Software Engineering National University of Sciences and Technology Islamabad Pakistan Department of Electrical Engineering National University of Sciences and Technology Islamabad Pakistan

ISBN: (纸本)9781665464789

The ongoing automation of business operations is putting enterprises at risk of cyber attacks more than ever before. Incident response teams are employed by the enterprises for the identification, management, and elimination of cybersecurity attacks along with for the recovery of business operations timely and effectively. In this paper, we argue that to effectively react to the cybersecurity attacks enterprises should build agility in their incident response method and big data analytics performs an effective role in developing agility in incident response. Grounded on twenty-one in depth expert interviews, we develop a framework that explains the salient features and effect of big data analytics in the incident response method at three stages, i.e., manual analysis, basic analysis, and advanced analysis. The agile properties of flexibility, innovation and swiftness are instilled in the incident response method by practicing big data analytics at higher stages of analysis. The results informed that the key features of big data analytics can be firstly utilize to estimate the existing analytical capability and secondly as an assisting tool to enhance incident response method capability.

关键词： Technological innovation Automation Manuals Big Data Interviews Cyberattack Business

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UNDERSTANDING USERS’ UX NEEDS THROUGH THE LENSES OF PSYCHOLOGICAL NEEDS THEORIES: USERS’ EXPECTATIONS AND FELT EXPERIENCES 16

UNDERSTANDING USERS’ UX NEEDS THROUGH THE LENSES OF PSYCHOL...

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16th International Conference on Interfaces and Human computer Interaction, IHCI 2022, and 15th International Conference on Game and Entertainment Technologies 2022, GET 2022 - Held at the 16th Multi Conference on computer Science and Information Systems, MCCSIS 2022

作者： Mkpojiogu, Emmanuel O.C. Okeke-Uzodike, Obianuju E. Omopariola, Victor School of Computing Universiti Utara Malaysia UUM Sintok06010 Malaysia Department of Software Engineering Veritas University Abuja Nigeria Department of Applied Management Durban University of Technology KwaZulu-Natal Pietermaritzburg South Africa Department of Computer and Information Technology Veritas University Abuja Nigeria

ISBN: (纸本)9789898704405

The purpose of this study was to conduct a literature review in order to gather material that would serve to underpin and support a link between UX requirements and psychological theories. In the paper, relevant sources from extant literature were accessed and synthesized. The outcome revealed that psychological theories support user experience needs. UX needs as psychological needs are fully supported by psychological theories. In the light of this, the search for ways to meet these needs should be done using psychological needs fulfillment strategies. Further studies will investigate ways of meeting these user experience needs and how these needs can be modeled for UX design and evaluation. © 2022 International Conference on Interfaces and Human computer Interaction

关键词： Human computer interaction

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Investigating Principle Component Analysis Impact on the Performance of Machine Learning Classifiers: A Health Informatics Application

Investigating Principle Component Analysis Impact on the Per...

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Information technology (ACIT)

作者： Ahmed BaniMustafa Sattam Almatarneh Olla Bulkrock Raed Alazaidah Hossam Almahasneh Ghassan Samara Computer Information Science Dept. Higher Colleges of Technology Abu Dhabi UAE Data Science & Artificial Intelligence Dept. Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah KSA Software Engineering Dept. Princess Sumaya University for Technology Amman Jordan Computer Science Dept. Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Principal Components Analysis (PCA) is a popular dimensionality reduction and feature selection technique. It works by swiping non-informative features while retaining those that are still potentially useful. However, regardless of its wide applications in statistical analysis, the influence of PCA on the performance of classification algorithms has been rarely studied, particularly in the domain of medical image classification. In this paper, we present a thorough analysis of the influence of applying PCA on the classification of medical images. The dataset used in this study consists of thousands of images that represent four types of blood cells. These images are used to train three machine-learning classifier models after transforming images into numerical vectors using a CNN pre-trained embedding algorithm, where each image is encoded using one thousand numerical vectors. The study involves building three classification models using (1) K-nearest Neighbor (KNN), (2) Logistic Regression (LR), and (3) Random Forests (RF). Each model was built twice, once after the application of PCA to reduce the dimensionality of the embedded features and once with the original embedded features. The performance of each model is evaluated using Classification accuracy, F1, AUC, Recall, precision, and specificity metrics. The recorded performance is then compared to drawing a decisive conclusion.

关键词： Training Logistic regression Cells (biology) Nearest neighbor methods Vectors Numerical models Classification algorithms Random forests Principal component analysis Biomedical imaging

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Cacomp: A Cloud-assisted Collaborative Deep Learning Compiler Framework for DNN Tasks on Edge

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IEEE Transactions on computers 2025年

作者： Lin, Weiwei Lin, Jinhui Zhang, Haotong Wu, Wentai Wu, Weizheng Li, Zhetao Li, Keqin South China University of Technology School of Computer Science and Engineering Guangzhou510641 China Pengcheng Laboratory Shenzhen518066 China South China University of Technology School of Software Engineering Guangzhou510641 China Jinan University Department of Computer Science College of Information Science and Technology Guangzhou510632 China Jinan University College of Information Science and Technology Guangzhou510632 China State University of New York Department of Computer Science New PaltzNY12561 United States

With the development of edge computing, DNN services have been widely deployed on edge devices. The deployment efficiency of deep learning models relies on the optimization of inference and scheduling policy. However, traditional optimization methods on edge devices still suffer from prohibitively long tuning time due to devices’ low computational power. Meanwhile, the widely used scheduling algorithm, the dominant resource fairness algorithm(DRF algorithm), struggles to maximize the efficiency of model execution on edge devices and inevitably increases average waiting time as it is not applicable in the real-time distributed computing environment. In this paper, we propose Cacomp, a distributed cloud-assisted deep learning compiler framework that features accelerating the optimization on edge devices with assistance from the cloud and a novel inference task scheduling algorithm. Our framework utilizes the tuning records from the cloud devices and proposes a two-step distillation strategy to obtain the best tuning record set for the edge device. For the scheduling process, we propose an RD-DRF algorithm to allocate inference tasks to edge devices based on dominant resource matching in real time. Extensive results show that our framework can achieve up to 2.19× improvement in the optimization time compared with other methods on edge devices. Our proposed scheduling algorithm significantly shortens the average waiting time of inference tasks by 30% and improves resource utilization by 20% on edge devices. © 1968-2012 IEEE.

关键词： Distributed cloud

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FGNN-Based Improved Resource Distribution Framework for V2X Wireless Networks

FGNN-Based Improved Resource Distribution Framework for V2X ...

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IEEE Conference on Vehicular technology (VTC)

作者： Syed Muhammad Waqas Yazhe Tang Fakhar Abbas Mehboob Hussain Yawar Abbas Bangash School of Computer Science and Technology Xi'an Jiaotong University Xi'an P.R. China Centre for Trusted Internet and Community National University of Singapore (NUS) Singapore School of Information Science and Technology Southwest Jiaotong University Chengdu China Department of Computer Software Engineering National University of Sciences and Technology (NUST) Pak.

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

Recently, deep learning has emerged as a promising approach for solving challenging resource distribution (RD) problems in vehicle-to-everything (V2X) wireless networks. How-ever, existing neural network architectures lack scalability, in-terpretability, and generalization. To address these limitations, in this study, we propose a new flexible graph neural network (FGNN)-based resource distribution framework for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) user selection and power management in V2X networks with several next- generation access points (APs) and a cluster of V2V and V2I communication users. In the proposed framework, we formu-lated an optimization problem with each V2V and V2I user with the least power constraint that adapts to V2X wireless network settings through training inactive users. Likewise, we consider the situation when every V2I user shares the band with a different group of V2V users. Moreover, we introduce a parameterization of the RD framework strategy employing a flexible graph neural network (FGNN) context derived from instantaneous channel conditions to learn the low-dimension features of every user/vehicle. To assess the execution of the framework, we conduct simulation experiments comparing it with baseline methods in terms of efficiency, sum rate, and fairness.

关键词： Training Vehicular and wireless technologies Wireless networks Scalability Power system management Vehicular ad hoc networks Graph neural networks

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Depth Perspective-aware Multiple Object Tracking

arXiv

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arXiv 2022年

作者： Quach, Kha Gia Le, Huu Nguyen, Pha Duong, Chi Nhan Bui, Tien Dai Luu, Khoa Computer Science and Software Engineering Department Concordia University MontrealQC Canada Department of Electrical Engineering Chalmers University of Technology Sweden Computer Science and Computer Engineering University of Arkansas United States

This paper aims to tackle Multiple Object Tracking (MOT), an important problem in computer vision but remains challenging due to many practical issues, especially occlusions. Indeed, we propose a new real-time Depth Perspective-aware Multiple Object Tracking (DP-MOT) approach to tackle the occlusion problem in MOT. A simple yet efficient Subject-Ordered Depth Estimation (SODE) is first proposed to automatically order the depth positions of detected subjects in a 2D scene in an unsupervised manner. Using the output from SODE, a new Active pseudo-3D Kalman filter, a simple but effective extension of Kalman filter with dynamic control variables, is then proposed to dynamically update the movement of objects. In addition, a new high-order association approach is presented in the data association step to incorporate first-order and second-order relationships between the detected objects. The proposed approach consistently achieves state-of-the-art performance compared to recent MOT methods on standard MOT benchmarks. Copyright © 2022, The Authors. All rights reserved.

关键词： Kalman filters

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Disjoint Compatibility via graph classes

arXiv

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arXiv 2024年

作者： Aichholzer, Oswin Obmann, Julia Paták, Pavel Perz, Daniel Tkadlec, Josef Vogtenhuber, Birgit Graz University of Technology Institute of Software Technology Austria Czech Technical University in Prague Department of Applied Mathematics Czech Republic University of Perugia Department of Engineering Italy Charles University Prague Computer Science Institute Czech Republic

Two plane drawings of graphs on the same set of points are called disjoint compatible if their union is plane and they do not have an edge in common. Let S be a convex point set of 2n ≥ 10 points and let H be a family of plane drawings on S. Two plane perfect matchings M1 and M2 on S (which do not need to be disjoint nor compatible) are disjoint H-compatible if there exists a drawing in H which is disjoint compatible to both M1 and M2. In this work, we consider the graph which has all plane perfect matchings as vertices and where two vertices are connected by an edge if the matchings are disjoint H-compatible. We study the diameter of this graph when H is the family of all plane spanning trees, caterpillars or paths. We show that in the first two cases the graph is connected with constant and linear diameter, respectively, while in the third case it is disconnected. © 2024, CC BY-NC-ND.

关键词： Graphic methods

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EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classification

arXiv

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arXiv 2024年

作者： Yang, Suorong Shen, Furao Zhao, Jian State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Electronic Science and Engineering Nanjing University China

Data augmentation (DA) has been widely used to improve the generalization of deep neural networks. While existing DA methods have proven effective, they often rely on augmentation operations with random magnitudes to each sample. However, this approach can inadvertently introduce noise, induce distribution shifts, and increase the risk of overfitting. In this paper, we propose EntAugment, a tuning-free and adaptive DA framework. Unlike previous work, EntAugment dynamically assesses and adjusts the augmentation magnitudes for each sample during training, leveraging insights into both the inherent complexities of training samples and the evolving status of deep models. Specifically, in EntAugment, the magnitudes are determined by the information entropy derived from the probability distribution obtained by applying the softmax function to the model’s output. In addition, to further enhance the efficacy of EntAugment, we introduce a novel entropy regularization term, EntLoss, which complements the EntAugment approach. Theoretical analysis further demonstrates that EntLoss, compared to traditional cross-entropy loss, achieves closer alignment between the model distributions and underlying dataset distributions. Moreover, EntAugment and EntLoss can be utilized separately or jointly. We conduct extensive experiments across multiple image classification tasks and network architectures with thorough comparisons of existing DA methods. Importantly, the proposed methods outperform others without introducing any auxiliary models or noticeable extra computational costs, highlighting both effectiveness and efficiency. Code is available at https://***/Jackbrocp/EntAugment. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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PredART: Towards Automatic Oracle Prediction of Object Placements in Augmented Reality Testing 22

PredART: Towards Automatic Oracle Prediction of Object Place...

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Proceedings of the 37th IEEE/ACM International Conference on Automated software engineering

作者： Tahmid Rafi Xueling Zhang Xiaoyin Wang Department of Computer Science University of Texas at San Antonio USA Department of Software Engineering Rochester Institute of Technology USA Department of Computer Science University of Texas at San Antonio United States of America

ISBN: (纸本)9781450394758

While the emerging Augmented Reality (AR) technique allows a lot of new application opportunities, from education and communication to gaming, current augmented apps often have complaints about their usability and/or user experience due to placement errors of virtual objects. Therefore, identifying noticeable placement errors is an important goal in the testing of AR apps. However, placement errors can only be perceived by human beings and may need to be confirmed by multiple users, making automatic testing very challenging. In this paper, we propose PredART, a novel approach to predict human ratings of virtual object placements that can be used as test oracles in automated AR testing. PredART is based on automatic screenshot sampling, crowd sourcing, and a hybrid neural network for image regression. The evaluation on a test set of 480 screenshots shows that our approach can achieve an accuracy of 85.0% and a mean absolute error, mean squared error, and root mean squared error of 0.047, 0.008, and 0.091, respectively.

关键词： Virtual Objects Placement Error Augmented Reality

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METHODS FOR THE EVALUATION OF UX OVER TIME 16

METHODS FOR THE EVALUATION OF UX OVER TIME

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ISBN: (纸本)9789898704405

In this paper a literature review strategy was utilized to assess the methods of evaluating UX longitudinally. The review revealed that studies on user experience evaluation over time are rare. This rareness is attributed to the cost involved in carrying out such studies. This research nonetheless suggests that lack of sufficient information and adequate knowledge on methods used for the evaluation of UX over time may also be the reason for the scarcity of studies on long-term user experience evaluation. On this note, this paper reviewed previous works on long-term evaluation of UX to elicit the methods employed in conducting such evaluation to inform possible researchers on the existing methods. The outcome of the study indicated a number of methods for the evaluation of UX over time. © 2022 International Conference on Interfaces and Human computer Interaction

关键词： Human computer interaction

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