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arXiv 2022年

作者： Souza, Rodrigo E.C. de Souza Santos, Ronnie E. Capretz, Luiz Fernando de Sousa, Marlon A.S. de Magalhães, Cleyton V.C. Agile Testing Program CESAR School Pernambuco Recife Brazil Faculty of Computer Science Dalhousie University HalifaxNS Canada Dep. of Electrical & Computer Engineering Western University LondonON Canada

Context. Recently, a family of studies highlighted the unpopularity of software testing careers among undergraduate students in software engineering and computer science courses. The original study and its replications explored the perception of students in universities in four countries (Canada, China, India, and Malaysia), and indicated that most students do not consider a career in software testing as an option after graduation. This scenario represents a problem for the software industry since the lack of skilled testing professionals might decrease the quality of software products and increase the number of unsuccessful projects. Goal. The present study aims to replicate, in Brazil, the studies conducted in the other four countries to establish comparisons and support the development of strategies to improve the visibility and importance of software testing among undergraduate students across the globe. Method. We followed the same protocol in the original study to collect data using a questionnaire and analyzed the answers using descriptive statistics and qualitative data analysis. Results. Our findings indicate similarities among the results obtained in Brazil in comparison to those obtained from other countries. We observed that students are not motivated to follow a testing career in the software industry based on a belief that testing activities lack challenges and opportunities for continuous learning. Conclusions. In summary, students seem to be interested in learning more about software testing. However, the lack of discussions about the theme in software development courses, as well as the limited offer of courses focused on software quality at the university level reduce the visibility of this area, which causes a decrease in the interest in this career. © 2022, CC BY.

关键词： Software testing

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Analyzing the Behavioral Aspects of Implementing Green IT in an Academic Environment

Analyzing the Behavioral Aspects of Implementing Green IT in...

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International Conference on Information Management and Technology (ICIMTech)

作者： Dedy Syamsuar Fero Triando Maria Ulfa Darius Antoni Adele Mailangkay Deden Witarsyah Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Postgradute Department Universitas Bina Darma Palembang Indonesia Information System Study Program Computer Science Faculty Universitas Bina Darma Palembang Indonesia Faculty of Computer Science Universitas Indo Global Mandiri Palembang Indonesia School of Industrial Engineering Telkom University Bandung Indonesia

Environmental awareness has recently emerged as one of the most crucial topics. As a result, various groups advocate for these technologies and research ways to promote their usage in various contexts. This study examines the factors influencing the intention and use of green technology among academics. This study integrates Price Value (PV) and Consideration of future consequence (CFC) to Theory Planned Behavior (TPB) as a theoretical basis. Two hundred five valid replies were gathered and processed through statistical analysis. The results of this study partly support the developed hypotheses. Four hypotheses developed from TPB have presented significant relationships. However, PV and CFC were not. The findings indicate that individuals in this study did not consider CFC or PV of green IT products as critical factors in their decision-making process. Findings also suggest that for implementation success, competent parties must consider the campaign to increase individual awareness and provide financial support regarding environmental policy.

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Application of Pose Estimation on Webcam for Fall Risk Assessment

Application of Pose Estimation on Webcam for Fall Risk Asses...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Surapong Uttama Patsakorn Chumpoo Worasak Rueangsirarak Center of Excellence in Artificial Intelligence and Emerging Technologies School of Information Technology Mae Fah Luang University Chiang Rai Thailand Computer Science Program School of Information Technology Mae Fah Luang University Chiang Rai Thailand Computer and Communication Engineering for Capacity Building School of Information Technology Mae Fah Luang University Chiang Rai Thailand

Fall risk assessment is an effective and simple measure to evaluate the balance of people especially the elderly who are likely to have balance disorder. Lose of body balance increases a fall risk which could lead to severe damage to aging persons. In this paper, we propose to apply pose estimation technique based on PoseNet method to detect body joints from webcam's video for two types of fall risk assessment: Five Times Sit to Stand Test (FTSTS) and 30-Second Chair Stand Test (30CST). The experiments were performed with 17 volunteers concurrently with the measures of a healthcare expert. The results revealed that our proposed technique corresponds well with the measure of the expert evaluating by a Pearson correlation coefficient which equals to 0.903 and 0.980 for FTSTS and 30CST respectively.

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GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features

arXiv

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arXiv 2022年

作者： Song, Siyang Song, Yuxin Luo, Cheng Song, Zhiyuan Kuzucu, Selim Jia, Xi Guo, Zhijiang Xie, Weicheng Shen, Linlin Gunes, Hatice The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FT United Kingdom Baidu Inc Beijing100193 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Department of Computer Engineering Middle East Technical University Ankara Turkey School of Computer Science University of Birmingham Birmingham United Kingdom

Graph is powerful for representing various types of real-world data. The topology (edges’ presence) and edges’ features of a graph decides the message passing mechanism among vertices within the graph. While most existing approaches only manually define a single-value edge to describe the connectivity or strength of association between a pair of vertices, task-specific and crucial relationship cues may be disregarded by such manually defined topology and single-value edge features. In this paper, we propose the first general graph representation learning framework (called GRATIS) which can generate a strong graph representation with a task-specific topology and task-specific multi-dimensional edge features from any arbitrary input (i.e., graph or non-graph data). To learn each edge’s presence and multi-dimensional feature, our framework takes both of the corresponding vertices pair and their global contextual information into consideration, enabling the generated graph representation to have a globally optimal message passing mechanism for different down-stream tasks. The principled investigation results achieved for various graph analysis tasks (e.g., graph classification, vertex classification, and link prediction) on 11 graph and non-graph datasets show that our GRATIS can not only largely enhance pre-defined graphs but also learns a strong graph representation for non-graph data, with clear performance improvements on all tasks. In particular, the learned topology and multi-dimensional edge features provide complementary task-related cues for graph analysis tasks. Our framework is effective, robust and flexible, and is a plug-and-play module that can be combined with different backbones and Graph Neural Networks (GNNs) to generate a task-specific graph representation from various graph and non-graph data. Our code is made publicly available at https://***/SSYSteve/Learning-Graph-Representation-with-Task-specific-Topology-and-Multi-dimensional-Edge-Feature

关键词： Graph neural networks

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Selective Multi-Scale Learning for Object Detection

arXiv

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arXiv 2022年

作者： Chen, Junliang Lu, Weizeng Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence of Robotics of Society Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

Pyramidal networks are standard methods for multi-scale object detection. Current researches on feature pyramid networks usually adopt layer connections to collect features from certain levels of the feature hierarchy, and do not consider the significant differences among them. We propose a better architecture of feature pyramid networks, named selective multi-scale learning (SMSL), to address this issue. SMSL is efficient and general, which can be integrated in both single-stage and two-stage detectors to boost detection performance, with nearly no extra inference cost. RetinaNet combined with SMSL obtains 1.8% improvement in AP (from 39.1% to 40.9%) on COCO dataset. When integrated with SMSL, two-stage detectors can get around 1.0% improvement in AP. © 2022, CC BY.

关键词： Object detection

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Observation of the symmetry-protected signature of 3-body interactions

arXiv

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arXiv 2024年

作者： Zhukas, Liudmila A. Wang, Qingfeng Katz, Or Monroe, Christopher Marvian, Iman Duke Quantum Center Departments of Electrical and Computer Engineering and Physics Duke University DurhamNC27708 United States Chemical Physics Program Institute for Physical Science and Technology University of Maryland College ParkMD United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States

Identifying and characterizing multi-body interactions in quantum processes remains a significant challenge. This is partly because 2-body interactions can produce an arbitrary time evolution, a fundamental fact often called the universality of 2-local gates in the context of quantum computing. However, when an unknown Hamiltonian respects a U(1) symmetry such as charge or particle number conservation, N-body interactions exhibit a distinct symmetry-protected signature known as the N-body phase, which fewer-body interactions cannot mimic. We develop and demonstrate an efficient technique for the detection of 3-body interactions despite the presence of unknown 2-body interactions. This technique, which takes advantage of GHZ states for phase estimation, requires probing the unitary evolution and measuring its determinant in a small subspace that scales linearly with the system size, making it an efficient approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

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Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview

arXiv

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arXiv 2024年

作者： Hu, Yingbai Abu-Dakka, Fares J. Chen, Fei Luo, Xiao Li, Zheng Knoll, Alois Ding, Weiping Multi-Scale Medical Robotics Centre Ltd. The Chinese University of Hong Kong Hong Kong School of Computation Information and Technology Technical University of Munich Munich85748 Germany Electronic and Computer Science Department Faculty of Engineering Mondragon Unibertsitatea Arrasate20500 Spain Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Department of Surgery The Chinese University of Hong Kong Hong Kong School of Information Science and Technology Nantong University Nantong226019 China

Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds significant promise for capturing expert motor skills through efficient imitation, facilitating adept navigation of complex scenarios. A persistent challenge in IL lies in extending generalization from historical demonstrations, enabling the acquisition of new skills without re-teaching. Dynamical system-based IL (DSIL) emerges as a significant subset of IL methodologies, offering the ability to learn trajectories via movement primitives and policy learning based on experiential abstraction. This paper emphasizes the fusion of theoretical paradigms, integrating control theory principles inherent in dynamical systems into IL. This integration notably enhances robustness, adaptability, and convergence in the face of novel scenarios. This survey aims to present a comprehensive overview of DSIL methods, spanning from classical approaches to recent advanced approaches. We categorize DSIL into autonomous dynamical systems and non-autonomous dynamical systems, surveying traditional IL methods with low-dimensional input and advanced deep IL methods with high-dimensional input. Additionally, we present and analyze three main stability methods for IL: Lyapunov stability, contraction theory, and diffeomorphism mapping. Our exploration also extends to popular policy improvement methods for DSIL, encompassing reinforcement learning, deep reinforcement learning, and evolutionary strategies. The primary objective is to expedite readers' comprehension of dynamical systems' foundational aspects and capabilities, helping identify practical scenarios and charting potential future directions. By offering insights into the strengths and limitations of dynamical system methods, we aim to foster a deeper understanding among readers. Furthermore, we outline potential extensions and enhancements within the realm of dynamical systems, outlining avenues for further exploration. © 2024, CC BY.

关键词： Reinforcement learning

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Geometry Constrained Weakly Supervised Object Localization 16th

Geometry Constrained Weakly Supervised Object Localization

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16th European Conference on computer Vision, ECCV 2020

作者： Lu, Weizeng Jia, Xi Xie, Weicheng Shen, Linlin Zhou, Yicong Duan, Jinming Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science University of Birmingham Birmingham United Kingdom Department of Computer and Information Science University of Macau Zhuhai China

ISBN: (纸本)9783030585730

We propose a geometry constrained network, termed GC-Net, for weakly supervised object localization (WSOL). GC-Net consists of three modules: a detector, a generator and a classifier. The detector predicts the object location defined by a set of coefficients describing a geometric shape (i.e. ellipse or rectangle), which is geometrically constrained by the mask produced by the generator. The classifier takes the resulting masked images as input and performs two complementary classification tasks for the object and background. To make the mask more compact and more complete, we propose a novel multi-task loss function that takes into account area of the geometric shape, the categorical cross-entropy and the negative entropy. In contrast to previous approaches, GC-Net is trained end-to-end and predict object location without any post-processing (e.g. thresholding) that may require additional tuning. Extensive experiments on the CUB-200-2011 and ILSVRC2012 datasets show that GC-Net outperforms state-of-the-art methods by a large margin. Our source code is available at https://***/lwzeng/GC-Net. © 2020, Springer Nature Switzerland AG.

关键词： Geometry

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Fast Detection of Multi-Direction Remote Sensing Ship Object Based on Scale Space Pyramid

Fast Detection of Multi-Direction Remote Sensing Ship Object...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Ziying Song Li Wang Guoxin Zhang Caiyan Jia Jiangfeng Bi Haiyue Wei Yongchao Xia Chao Zhang Lijun Zhao School of Computer and Information Technology Beijing Jiaotong University Beijing China School of Vehicle and Mobility Tsinghua University Beijing China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang China

Ships in remote sensing images are usually arranged in arbitrary direction, small in size, and densely arranged. As a result, existing object detection algorithms cannot detect ships quickly and accurately. In order to solve the above problems, a lightweight object detection network for fast detection of ships is proposed. The network is composed of backbone network, four-scale fusion network and rotation branch. First, a lightweight network unit S-LeanNet is designed and used to build a low-computing and accurate backbone network. Then, a four-scale feature fusion module is designed to generate a four-scale feature pyramid, which contains more features such as ship shape and texture, and at the same time is conducive to the detection of small ships. Finally, a novel rotation branch module is designed, using balance L1 loss function and R-NMS for post-processing, to realize the precise positioning and regression of the rotating bounding box in one step. Experimental results show that the detection precision of our method in the DOT A remote sensing data set is compared with the latest SCRDet detection method, the precision is increased by 1.1%, and the operating speed is increased by 8 times, which can meet the fast detection requirements of ships.

关键词： Sea surface Shape Object detection US Department of Transportation Feature extraction Sensors Marine vehicles

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Probabilistic genotype-phenotype maps reveal mutational robustness of RNA folding, spin glasses, and quantum circuits

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Physical Review Research 2025年第1期7卷 013118-013118页

作者： Anna Sappington Vaibhav Mohanty Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Harvard-MIT Program in Health Sciences and Technology Harvard Medical School Boston Massachusetts 02115 USA and Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Harvard/MIT MD-PhD Program Harvard Medical School Boston Massachusetts 02115 USA and Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Chemistry and Chemical Biology Harvard University Cambridge Massachusetts 02138 USA

Recent studies of genotype-phenotype maps have reported universally enhanced phenotypic robustness to genotype mutations, a feature essential to evolution. Virtually all of these studies make a simplifying assumption that each genotype—represented as a sequence—maps deterministically to a single phenotype, such as a discrete structure. Here we introduce probabilistic genotype-phenotype (PrGP) maps, where each genotype maps to a vector of phenotype probabilities, as a more realistic and universal language for investigating robustness in a variety of physical, biological, and computational systems. We study three model systems to show that PrGP maps offer a generalized framework which can handle uncertainty emerging from various physical sources: (1) thermal fluctuation in RNA folding, (2) external field disorder in the spin-glass ground state search problem, and (3) superposition and entanglement in quantum circuits, which are realized experimentally on IBM quantum computers. In all three cases, we observe a biphasic robustness scaling which is enhanced relative to random expectation for more frequent phenotypes and approaches random expectation for less frequent phenotypes. We derive an analytical theory for the behavior of PrGP robustness, and we demonstrate that the theory is highly predictive of empirical robustness.

关键词： Quantum computers

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