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International Conference on Electronic engineering and Informatics (EEI)

作者： Zuoming Zhang Man Shu Zixuan Wang Hanwen Wang Xin Wang College of Software Engineering Jilin University Changchun China College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun China

ISBN: (纸本)9781728140773

In augmented reality, smart devices need to sense their own position in real physical space and complex scene structure to achieve good virtual reality interaction and three-dimensional registration. This paper proposes a technical framework of visual SLAM, which uses VO and BA methods based on feature points to obtain pose, and applies it to the augmented reality system to realize pose estimation, registration, tracking, collision and other effects of intelligent equipment independent of artificial or natural landmarks.

关键词： augmented reality image registration pose estimation robot vision SLAM (robots) augmented reality pose estimation robot vision image registration supersonic low altitude missile Registration

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Digital Image Recovery Based on Lifting Wavelet Transform

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Journal of Physics: Conference Series 2021年第1期1962卷

作者： Taha Basheer Taha Dujan B. Taha Ruzelita Ngadiran Phaklen Ehkhan IT Department Faculty of Sciences TISHK International University Erbil Iraq. Software Engineering Department College of Computer Science and Mathematics Mosul University Iraq Centre of Excellence Advanced Computing (AdvComp) Faculty of Electronic Engineering Technology University Malaysia Perlis Perlis Malaysia

Recently, with the wide distribution of digital media, the need for authenticating digital images was increased. Therefore, many image tamper detection and recovery algorithms were introduced in literature to detect malicious modifications and retrieve the original images. The process of detection and recovery, however, used to have complex operation which requires long processing time. In this paper, a simplified image recovery algorithm is presented by using lifting wavelet transform. In the proposed method, the approximation band is hidden inside the bits of the original image and to be retrieved without relying on source image. For images with hidden data, the average PSNR and SSIM values were 31.22 and 0.977 respectively, and images were successfully retrieved after block attack.

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CNNLoc: Deep-Learning Based Indoor Localization with WiFi Fingerprinting

CNNLoc: Deep-Learning Based Indoor Localization with WiFi Fi...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Xudong Song Xiaochen Fan Xiangjian He Chaocan Xiang Qianwen Ye Xiang Huang Gengfa Fang Liming Luke Chen Jing Qin Zumin Wang College of Information Engineering Dalian University Dalian China School of Electrical and Data Engineering University of Technology Sydney Sydney Australia School of Software and Microelectronics Northwestern Polytechnical University China College of Computer Science Chongqing University Chongqing China School of Computer Science and Informatics De Montfort University United Kingdom

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

With the ubiquitous deployment of wireless systems and pervasive availability of smart devices, indoor localization is empowering numerous location-based services. With the established radio maps, WiFi fingerprinting has become one of the most practical approaches to localize mobile users. However, most fingerprint-based localization algorithms are computationintensive, with heavy dependence on both offline training phase and online localization phase. In this paper, we propose CNNLoc, a Convolutional Neural Network (CNN) based indoor localization system with WiFi fingerprints for multi-building and multifloor localization. Specifically, we devise a novel classification model by combining a Stacked Auto-Encoder (SAE) with a onedimensional CNN. The SAE is utilized to precisely extract key features from sparse Received Signal Strength (RSS) data while the CNN is trained to effectively achieve high success rates in the positioning phase. We evaluate the proposed system on the UJIIndoorLoc dataset and Tampere dataset with several stateof-the-art methods. The results show CNNLoc outperforms the existing solutions with 100% and 95% success rates on buildinglevel localization and floor-level localization, respectively.

关键词： Training Wireless fidelity Buildings Data models Databases Machine learning Systems architecture

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Opti-SW: An improved gene sequence alignment algorithm

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Ingenierie des Systemes d'Information 2018年第6期23卷 73-85页

作者： Li, Leixiao Gao, Jing Liu, Yanfeng College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080 China

This paper aims to improve the speed and complexity of Smith-Waterman (SW) algorithm. For this purpose, the SW algorithm was improved by reducing the complexity and task load of the computation of the scoring matrix without sacrificing the alignment accuracy. Then, the optimized algorithm, denoted as the Opti-SW, was verified through experiment. The results show that the Opti-SW boasts low time complexity, fast computing speed and light computing load. The research findings shed new light on the database search for gene sequences. © 2018 Lavoisier.

关键词： Genes

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Android Malware Family Classification and Characterization Using CFG and DFG

Android Malware Family Classification and Characterization U...

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Theoretical Aspects of software engineering (TASE)

作者： Zhiwu XU Kerong Ren Fu Song College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Information Science and Technology ShanghaiTech University Shanghai China

Android malware has become a serious threat for our daily life, and thus there is a pressing need to effectively mitigate or defend against them. Recently, many approaches and tools to analyze Android malware have been proposed to protect legitimate users from the threat. However, most approaches focus on malware detection, while only a few of them consider malware classification or malware characterization. In this paper, we propose an extension of CDGDroid to classifying and characterizing Android malware families automatically. We first perform static analysis used in CDGDroid to extract control-flow graphs and data-flow graphs on the instruction level. Then we encode the graphs into matrices, and use them to build the family classification models via deep learning. For family characterization, we extract the n-gram sequences from the graphs, which are filtered according to the weights of the classification model built for the target family. And then we construct a vector space model and select the top-k sequences as a characterization of the target family. We have conducted some experiments to evaluate our approach and have identified that the family classification model taking the horizontal combination of CFG and DFG as features offers the best performance in terms of accuracy among all the models. Compared with CDGDroid, Drebin and many antivirus tools gathered in VirusTotal, our family classification model gives a better performance. Finally, We have also conducted experiments on family characterization, and the experimental results have shown that our characterization can capture the malicious behaviors of the testing families.

关键词： Malware Static analysis Tools Task analysis Deep learning Testing software engineering

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Data Mining Techniques Used in Predicting Student Retention in Higher Education: A Survey

Data Mining Techniques Used in Predicting Student Retention ...

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International Conference on Electrical and Computing Technologies and Applications (ICECTA)

作者： Zaid Shuqfa Saad Harous College of Infrmation Technology United Arab Emirates University Al Ain City UAE Department of Computer Science and Software Engineering CIT UAEU Al Ain City UAE

Predicting student retention is a crucial task for all stakeholders in higher education. This paper surveyed the Educational Data Mining (EDM) literature to explore the most recent methods used in building predictive models to predict student's retention, and to foresee the future trends in different context of higher education. We review a diversified set of approaches, models, data sets, tools, techniques, and performance measures. The approaches vary as the educational context varies where opportunities and challenges are associated with each approach. We also present a discussion and a foresight of future directions.

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Multi-indicator bacterial foraging algorithm with kriging model for many-objective optimization 9th

Multi-indicator bacterial foraging algorithm with kriging mo...

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9th International Conference on Swarm Intelligence, ICSI 2018

作者： Wang, Rui Chen, Shengminjie Ma, Lianbo Cheng, Shi Shi, Yuhui College of Software Northeastern University Shenyang China Faculty of Science Kunming University of Science and Technology Kunming China School of Computer Science Shaanxi Normal University Xi’an China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9783319938141

In order to efficiently reduce computational expense as well as manage the diversity and convergence in many-objective optimization, this paper proposes a novel multi-indicator bacterial foraging algorithm with Kriging model (K-MBFA) to guide the search process toward the Pareto front. In the proposed algorithm, a set of preferential individuals for the improved Kriging model are appropriately selected according to the different indicators. Specifically, the stochastic ranking technique is adopted to avoid the search biases of different indicators, which would lead the population to converge to local region of the Pareto front. With several test instances from DTLZ sets with 3, 5, 8 and 10 objectives, K-MBFA is verified to be significantly superior to other compared algorithms in terms of inverted generational distance (IGD). © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Computational efficiency

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An Expected Patch Log Likelihood Denoising Method Based on Internal and External Image Similarity

An Expected Patch Log Likelihood Denoising Method Based on I...

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2018 International Symposium in Sensing and Instrumentation in IoT Era, ISSI 2018

作者： He, Wen Zhao, Dan Zheng, Yuhui Xie, Jiacen Institute of System Engineering Chinese Academy of Military Sciences Beijing China College of Computer and Software Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9781538656389

Natural images always exhibit a certain nonlocal self-similarity property, which implies that the patch matrix formed by similar image patches is low-rank. In this paper, the self-similarity of images is combined with the EPLL (Expected patch log likelihood) method based on external similarity, and an EPLL denoising model based on internal and external image similarity is proposed. The experimental results show that compared with the original EPLL method, the proposed method not only has higher quantization index, but also has a good visual effect. © 2018 IEEE.

关键词： Image denoising

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Integrating the pressure-state-response model with the extension catastrophe progression for flood risk and resilience assessment

Environmental and Sustainability Indicators

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Environmental and Sustainability Indicators 2025年 27卷

作者： Shahab S. Band Sultan Noman Qasem Sajad Eslami Zulkefli Mansor Brij B. Gupta Hao-Ting Pai Meghdad Biyari Amir Mosavi Farzaneh Sajedi Hosseini Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia School of Business Stevens Institute of Technology Hoboken 07030 NJ USA Research Center for Software Technology and Management Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Bandar Baru Bangi Selangor Darul Ehsan Malaysia Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan School of Cybersecurity Korea University Seoul South Korea Symbiosis Centre for Information Technology Symbiosis International University Pune India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan Department of Information Management National Yunlin University of Science and Technology Yunlin Taiwan John von Neumann Faculty of Informatics Obuda University 1034 Budapest Hungary Ludovika University of Public Service Budapest Hungary Abylkas Saginov Karaganda Technical University Karaganda Kazakhstan Reclamation of Arid and Mountainous Regions Department Faculty of Natural Resources University of Tehran Karaj Iran

Despite the devastating effects of floods, the concept of resilience is still not fully considered in the assessment and management of flood risk. To study how resilience can lower the risk of floods and further enhance disaster response, this research aims to close this knowledge gap. With a focus on the Kashkan watershed in Iran, the study combines the extended catastrophe progression method with the pressure-state-response model. Three catastrophe models, namely the cusp, swallowtail, and butterfly, are applied. According to the findings, southern regions, i.e., Pol-Dokhtar city, have the highest risk of floods and the lowest resilience. Resilience and flood risk have a complementary relationship, according to the analysis, and resilience is a helpful metric for risk assessment. The results emphasize the necessity to incorporate resilience-focused pre-disruption planning and post-disaster recovery into flood risk management strategy. This work offers a foundation to incorporate resilience into future flood policies and strategies.

关键词： Flood Risk Resilience Pressure-state-response model Extension catastrophe progression method

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Relation graph network for 3D object detection in point clouds

arXiv

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arXiv 2019年

作者： Feng, Mingtao Gilani, Syed Zulqarnain Wang, Yaonan Zhang, Liang Mian, Ajmal College of Electrical and Information Engineering Hunan University Changsha410082 China School of Software Xidian University Xian710071 China Department of Computer Science and Software Engineering University of Western Australia Perth CrawleyWA6009 Australia School of Science Edith Cowan University Joondalup6027 Australia

—Convolutional Neural Networks (CNNs) have emerged as a powerful strategy for most object detection tasks on 2D images. However, their power has not been fully realised for detecting 3D objects in point clouds directly without converting them to regular grids. Existing state-of-art 3D object detection methods aim to recognize 3D objects individually without exploiting their relationships during learning or inference. In this paper, we first propose a strategy that associates the predictions of direction vectors and pseudo geometric centers together leading to a win-win solution for 3D bounding box candidates regression. Secondly, we propose point attention pooling to extract uniform appearance features for each 3D object proposal, benefiting from the learned direction features, semantic features and spatial coordinates of the object points. Finally, the appearance features are used together with the position features to build 3D object-object relationship graphs for all proposals to model their co-existence. We explore the effect of relation graphs on proposals’ appearance features enhancement under supervised and unsupervised settings. The proposed relation graph network consists of a 3D object proposal generation module and a 3D relation module, makes it an end-to-end trainable network for detecting 3D object in point clouds. Experiments on challenging benchmarks (SunRGB-D [1] and ScanNet [2] datasets) of 3D point clouds show that our algorithm can perform better than the existing state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Object recognition

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