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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Nitin Naik Paul Jenkins Jonathan Gillett Haralambos Mouratidis Kshirasagar Naik Jingping Song School of Computing University of Portsmouth United Kingdom Faculty of Science and Technology Bournemouth University United Kingdom Centre for Secure Intelligent and Usable Systems University of Brighton United Kingdom Department of Electrical and Computer Engineering University of Waterloo Canada Software College Northeastern University China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Ransomware attacks are a prevalent cybersecurity threat to every user and enterprise today. This is attributed to their polymorphic behaviour and dispersion of inexhaustible versions due to the same ransomware family or threat actor. A certain ransomware family or threat actor repeatedly utilises nearly the same style or codebase to create a vast number of ransomware versions. Therefore, it is essential for users and enterprises to keep well-informed about this threat landscape and adopt proactive prevention strategies to minimise its spread and affects. This requires a technique to detect ransomware samples to determine the similarity and link with the known ransomware family or threat actor. Therefore, this paper presents a detection method for ransomware by employing a combination of a similarity preserving hashing method called fuzzy hashing and a clustering method. This detection method is applied on the collected WannaCry/WannaCryptor ransomware samples utilising a range of fuzzy hashing and clustering methods. The clustering results of various clustering methods are evaluated through the use of the internal evaluation indexes to determine the accuracy and consistency of their clustering results, thus the effective combination of fuzzy hashing and clustering method as applied to the particular ransomware corpus. The proposed detection method is a static analysis method, which requires fewer computational overheads and performs rapid comparative analysis with respect to other static analysis methods.

关键词： Ransomware Clustering methods Indexes Cryptography Engines Syntactics

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Regularizing Deep Convolutional Neural Networks with a Structured Decorrelation Constraint

Regularizing Deep Convolutional Neural Networks with a Struc...

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IEEE International Conference on Data Mining (ICDM)

作者： Wei Xiong Bo Du Lefei Zhang Ruimin Hu Dacheng Tao Computer School of Wuhan University National Engineering Research Center for Multimedia Software Wuhan China Collaborative Innovation Center of Geospatial Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong China National Engineering Research Center for Multimedia Software Computer School of Wuhan University China Centre for Quantum Computation & Intelligent Systems University of Technology Sydney NSW Australia

ISBN: (纸本)9781509054749

Deep convolutional networks have achieved successful performance in data mining field. However, training large networks still remains a challenge, as the training data may be insufficient and the model can easily get overfitted. Hence the training process is usually combined with a model regularization. Typical regularizers include weight decay, Dropout, etc. In this paper, we propose a novel regularizer, named Structured Decorrelation Constraint (SDC), which is applied to the activations of the hidden layers to prevent overfitting and achieve better generalization. SDC impels the network to learn structured representations by grouping the hidden units and encouraging the units within the same group to have strong connections during the training procedure. Meanwhile, it forces the units in different groups to learn non-redundant representations by minimizing the cross-covariance between them. Compared with Dropout, SDC reduces the co-adaptions between the hidden units in an explicit way. Besides, we propose a novel approach called Reg-Conv that can help SDC to regularize the complex convolutional layers. Experiments on extensive datasets show that SDC significantly reduces overfitting and yields very meaningful improvements on classification performance (on CIFAR-10 6.22% accuracy promotion and on CIFAR-100 9.63% promotion).

关键词： Neurons Correlation Decorrelation Training Data models Electronic mail Biological neural networks

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Artificial Intelligence Powered MOOCs: A Brief Survey

Artificial Intelligence Powered MOOCs: A Brief Survey

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IEEE International Conference on Agents (ICA)

作者： Simon Fauvel Han Yu Chunyan Miao Lizhen Cui Hengjie Song Liang Zhang Xiaoming Li Cyril Leung Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Software Engineering Shandong University Jinan China School of Software Engineering South China University of Technology Guangzhou China Institute of Network Computing and Information Systems Peking University Beijing China Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada

Massive Open Online Courses (MOOCs) have gained tremendous popularity in the last few years. Thanks to MOOCs, millions of learners from all over the world have taken thousands of high-quality courses for free. Artificial intelligence (AI) has played an important role in making MOOCs what they are today. By exploiting the vast amount of data generated by learners engaging in MOOCs, AI techniques have been proposed to improve our understanding of MOOC participants and enable MOOC practitioners to deliver better courses. These approaches have also greatly improved student experience and learning outcomes through constructing intelligent and personalized learning trajectories. In this paper, we first review the state-of-the-art AI research making an impact on MOOCs education, emphasizing on works which aim to enhance our understanding of student learning behaviours, improve student engagement, and improve learning outcomes. We then offer an overview of important future research to carry out in sub-fields of AI to enable MOOCs to reach their full potential.

关键词： Videos Artificial intelligence Tools Navigation Computational modeling Trajectory Education

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Compilation of algorithm-specific graph states for quantum circuits

arXiv

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arXiv 2022年

作者： Vijayan, Madhav Krishnan Paler, Alexandru Gavriel, Jason Myers, Casey R. Rohde, Peter P. Devitt, Simon J. Centre for Quantum Software and Information University of Technology Sydney SydneyNSW2007 Australia Aalto University Espoo02150 Finland Centre for Quantum Computation and Communication Technology Australia School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne MelbourneVIC3010 Australia Silicon Quantum Computing Pty Ltd. UNSW Sydney Level 2 Newton Building KensingtonNSW2052 Australia

We present a quantum circuit compiler that prepares an algorithm-specific graph state from quantum circuits described in high level languages, such as Cirq and Q#. The computation can then be implemented using a series of non-Pauli measurements on this graph state. By compiling the graph state directly instead of starting with a standard lattice cluster state and preparing it over the course of the computation, we are able to better understand the resource costs involved and eliminate wasteful Pauli measurements on the actual quantum device. Access to this algorithm-specific graph state also allows for optimisation over locally equivalent graph states to implement the same quantum circuit. The compiler presented here finds ready application in measurement based quantum computing, NISQ devices and logical level compilation for fault tolereant implementations. Copyright © 2022, The Authors. All rights reserved.

关键词： High level languages

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A Direct Proof of Improved Biased Random Walk with Gastric Cancer Dataset

A Direct Proof of Improved Biased Random Walk with Gastric C...

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International Conference on Applied Mathematics & Computer Science

作者： Choon Sen Seah Shahreen Kasim Mohd Farhan Md Fudzee Mohd Saberi Mohamad Rd Rohmat Saedudin Deden Witarsyah Rohayanti Hassan Mohd Arfian Ismail Rodziah Atan Soft Computing and Data Mining Centre Faculty of Computer Sciences and Information Technology Universiti Tun Hussein Onn Malaysia Faculty of Creative Technology and Heritage Universiti Malaysia Kelantan School of Industrial Engineering Telkom University Laboratory of Biodiversity and Bioinformatics Universiti Teknologi Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Department of Software Engineering & Information System Faculty of Computer Science and Information Technology University Putra Malaysia (UPM)

ISBN: (数字)9781728132587

ISBN: (纸本)9781728132594

The relationship between the weight of a single genes and the connection between the genes had been studied. Chemistry and physical sciences have proved the attraction between molecules. Molecules are attracted to each other by bond. This paper is presented to disclose the relationship between weight and connectivity of nodes with biased random walk. An equation of biased random walk which named as significant directed random walk is formed to enhance the connectivity of nodes in directed graph via weigh. To be completely biased to the random walk, references data is implement as directed graph. Weight of genes will be used as one of the parameter in the formula. While the adjacency matrix is further enhanced by Warshall's algorithm to increases the accessibility of nodes via vector. The evolution of random walk is disclosed in this paper as well. Significant directed random walk will be used to prove the importance of weight in this paper. Comparison of the result between biased random walk is presented to prove the enhancement of random walk.

关键词： Biased random walk Cancer classification Gene expression dataset Random walk cancer classification Biased Ganglia one-parameter Mentoring Physical science

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DRAFT DESCRIPTION OF PASCAL

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software-PRACTICE & EXPERIENCE 1979年第5期9卷 381-424页

作者： ADDYMAN, AM BREWER, R BURNETTHALL, DG DEMORGAN, RM FINDLAY, W JACKSON, MI JOSLIN, DA REES, MJ WATT, DA WELSH, J WICHMANN, BA Department of Computer Science The University Oxford Road Manchester M13 9PL Software Department GEC Computers Ltd. Elstree Way Borehamwood Hertfordshire Department of Computer Science University of York Heslington York Y01 5DD European Software Engineering Digital Equipment Company Ltd. Fountain House The Butts Centre Reading RG1 7QN Computing Science Department University of Glasgow Glasgow G12 8QQ International Data Engineering Centre ITT Business Systems and Communications Group Latimer House Potters Bar Herts EN6 5DN Hull College of Higher Education Inglemire Avenue Hull HU6 7LJ Computer Studies Group The University Southampton S09 5NH Department of Computer Science Queen's University Belfast BT7 1NN Department of Industry National Physical Laboratory Teddington Middlesex TW1 0LW

This paper provides a description of the programming language Pascal. It has been published to enable those without easy access to the official BSI ‘draft for comment’ to comment on the description.

关键词： Pascal Standards

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Denoising Auto-Encoders toward Robust Unsupervised Feature Representation

Denoising Auto-Encoders toward Robust Unsupervised Feature R...

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International Joint Conference on Neural Networks

作者： Wei Xiong Bo Du Lefei Zhang Liangpei Zhang Dacheng Tao State Key Laboratory of Software Engineering School of Computer Science Wuhan University China Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China Centre for Quantum Computation & Intelligent Systems University of Technology Sydney NSW 2007 Australia

ISBN: (纸本)9781509006212

Deep networks like the convolutional neural network and its variants usually learn hierarchical features from labeled images, which is very expensive to obtain. How can we find an unsupervised way to effectively extract deep and abstract features from images without annotations? Even from large qualities of images with noise? In this paper, we propose a robust deep neural network, named as stacked convolutional denoising auto-encoders (SCDAE), which can map raw images to hierarchical representations in an unsupervised manner. Our network is elaborately designed to fit for the visual recognition tasks. It is established by stacking the denoising auto-encoders. Unlike the prior works, in the training phase, the auto-encoders are trained patch-wisely so that the latent features can be applied to powerful regularizers for better representation;in the inference phase, the denoising auto-encoders are stacked convolutionally, hence the generated feature maps in the higher layers can preserve the coherent structures within the features in the lower layers. To achieve better performance, we apply whitening to each layer to sphere the input features. Our network is evaluated on the challenging image datasets MNIST, CIFAR-10 and STL-10 and demonstrates superior performance to the state-of-the-art unsupervised networks.

关键词： Noise reduction Convolutional codes Training Feature extraction Kernel Optimized production technology Robustness

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare 7

Artificial Intelligence-based Service Chains Scheduling for ...

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7th International Conference on Contemporary computing and Informatics, IC3I 2024

作者： Soni, Mukesh Rusho, Maher Ali Byeon, Haewon Alghamdi, Azzah Hannan, Shaikh Abdul Dave, Parth Rameshchandra Dr. D. Y. Patil School of Science & Technology Tathawade Dr. D. Y. Patil Vidyapeeth Marashtra Pune India University of Colorado Department of Lockheed Martin Engineering Management BoulderCO80309 United States Inje University Department of Ai and Software & Medical Big Data Research Centre Gimhae50834 Korea Republic of Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia AlBaha University Faculty of Computing and Information AlBaha Saudi Arabia L. D. College of Engineering Department of Computer Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350350067

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare. © 2024 IEEE.

关键词： Electronic health record

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Book review: software inspection: an industry best practice. By David A. Wheeler, Bill Brykczynski and Reginald N. Meeson, Jr. Published by IEEE Computer Society Press, Los Alamitos, California, U.S.A., 1996. ISBN: 0 8186 7340 0, 293 pages. Price: U.S.A. $30.00 members/$38.00 non-members. Including disk, soft cover.

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software Testing, Verification and Reliability 1998年第2期7卷

作者： Robin Whitty Centre for Systems & Software Engineering School of Computing Information Systems & Mathematics South Bank University London SE1 0AA UK.

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A motion-based compression algorithm for resource-constrained video camera traps

arXiv

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arXiv 2024年

作者： Ratnayake, Malika Nisal Gallon, Lex Toosi, Adel N. Dorin, Alan Agents and Decision-Making Group Dept. of Data Science and AI Faculty of Information Technology Monash University ClaytonVIC3800 Australia Monash e-Research Centre Monash University ClaytonVIC3800 Australia School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne ParkvilleVIC3052 Australia Dept. of Software Systems and Cybersecurity Faculty of Information Technology Monash University ClaytonVIC3800 Australia

Field-captured video facilitates detailed studies of spatio-temporal aspects of animal locomotion, decision-making and environmental interactions including predator-prey relationships and habitat utilisation. But even though data capture is cheap with mass-produced hardware, storage, processing and transmission overheads provide a hurdle to acquisition of high resolution video from field-situated edge computing devices. Efficient compression algorithms are therefore essential if monitoring is to be conducted on single-board computers in situations where such hurdles must be overcome. Animal motion tracking in the field has unique characteristics that necessitate the use of novel video compression techniques, which may be underexplored or unsuitable in other contexts. In this article, we therefore introduce a new motion analysis-based video compression algorithm specifically designed for camera traps. We implemented and tested this algorithm using a case study of insect-pollinator motion tracking on three popular edge computing platforms. The algorithm identifies and stores only image regions depicting motion relevant to pollination monitoring, reducing overall data size by an average of 87% across diverse test datasets. Our experiments demonstrate the algorithm’s capability to preserve critical information for insect behaviour analysis through both manual observation and automatic analysis of the compressed footage. The method presented in this paper enhances the applicability of low-powered computer vision edge devices to remote, in situ animal motion monitoring, and improves the efficiency of playback during behavioural analyses. Our new software, EcoMotionZip, is available Open Access. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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