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13th Innovations in Theoretical Computer Science Conference, ITCS 2022

作者： Chia, Nai-Hui Chou, Chi-Ning Zhang, Jiayu Zhang, Ruizhe Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States School of Engineering and Applied Sciences Harvard University BostonMA United States Department of Computer Science Boston University MA United States California Institute and Technology PasadenaCA United States Department of Computer Science The University of Texas AustinTX United States

ISBN: (纸本)9783959772174

In this work, we initiate the study of the Minimum Circuit Size Problem (MCSP) in the quantum setting. MCSP is a problem to compute the circuit complexity of Boolean functions. It is a fascinating problem in complexity theory - its hardness is mysterious, and a better understanding of its hardness can have surprising implications to many fields in computer science. We first define and investigate the basic complexity-theoretic properties of minimum quantum circuit size problems for three natural objects: Boolean functions, unitaries, and quantum states. We show that these problems are not trivially in NP but in QCMA (or have QCMA protocols). Next, we explore the relations between the three quantum MCSPs and their variants. We discover that some reductions that are not known for classical MCSP exist for quantum MCSPs for unitaries and states, e.g., search-to-decision reductions and self-reductions. Finally, we systematically generalize results known for classical MCSP to the quantum setting (including quantum cryptography, quantum learning theory, quantum circuit lower bounds, and quantum fine-grained complexity) and also find new connections to tomography and quantum gravity. Due to the fundamental differences between classical and quantum circuits, most of our results require extra care and reveal properties and phenomena unique to the quantum setting. Our findings could be of interest for future studies, and we post several open problems for further exploration along this direction. © Nai-Hui Chia, Chi-Ning Chou, Jiayu Zhang, and Ruizhe Zhang;licensed under Creative Commons License CC-BY 4.0

关键词： Boolean functions

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A More Practical Public-Key Authenticated Encryption with Keyword Search Scheme

SSRN

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SSRN 2022年

作者： Huang, Qiong Huang, Peisen Li, Hongbo Huang, Jianye Lin, Hongyuan College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China School of Computing and Information Technology University of Wollongong NSW2500 Australia

In order to solve the problem of effiffifficiently searching over encrypted fifiles, Boneh et al. fifirst proposed the notion of Public-key Encryption with Keyword Search (PEKS) in 2004. However, most PAEKS schemes suffffer from inside keyword guessing attacks (IKGA). The notion of Public-key Authenticated Encryption with Keyword Search (PAEKS) was proposed by Huang et al. in 2017. to solve this problem effiffifficiently, which requires the data sender not only encrypting a keyword, but also authenticating it. In the era of big data, an increasing number of encrypted fifiles are stored on cloud servers. Therefore, existing schemes gradually suffffer from the problems of huge storage overhead and slow retrieval speed. To resolve these issues, we propose a concrete construction of PAEKS which can easily check for duplicate keywords without additional algorithms. Therefore, it can reduce storage space by storing only one ciphertext of multiple identical keywords. We combine our scheme with inverted index for improving retrieval speed. When matching the keywords in the inverted index, we can get all the associated encrypted fifiles immediately. Finally, we compare our schemes with previous PAEKS schemes. © 2022, The Authors. All rights reserved.

关键词： Search engines

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Anti-Obfuscation Techniques: Recent Analysis of Malware Detection 21

Anti-Obfuscation Techniques: Recent Analysis of Malware Dete...

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21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： Gorment, Nor Zakiah Selamat, Ali Krejcar, Ondrej Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia College of Computing and Informatics Universiti Tenaga Nasional Jalan IKRAMUNITEN Selangor Kajang43000 Malaysia School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor Skudai81310 Malaysia Universiti Teknologi Malaysia Johor Bahru Skudai81310 Malaysia Center for Basic and Applied Research Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove50003 Czech Republic

ISBN: (纸本)9781643683164

One of the challenging issues in detecting the malware is that modern stealthy malware prefers to stay hidden during their attacks on our devices and be obfuscated. They can evade antivirus scanners or other malware analysis tools and might attempt to thwart modern detection, including altering the file attributes or performing the action under the pretense of authorized services. Therefore, it's crucial to understand and analyze how malware implements obfuscation techniques to curb these concerns. This paper is dedicated to presenting an analysis of anti-obfuscation techniques for malware detection. Furthermore, an empirical analysis of the performance evaluation of malware detection using machine learning algorithms and the obfuscation techniques was conducted to address the associated issues that might help researchers plan and generate an efficient algorithm for malware detection. © 2022 The authors and IOS Press. All rights reserved.

关键词： Learning algorithms

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Industry Recommendation for Undergraduate Internship using Decision Tree

Industry Recommendation for Undergraduate Internship using D...

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computing (ICOCO), IEEE International Conference on

作者： Intan Norsyafiqa Kamalbahrin Haslizatul Mohamed Hanum Nur Atiqah Sia Abdullah Noor Latiffah Adam Nor Ashikin Mohamad Kamal Zainab Abu Bakar College of Computing Informatics and Media Universiti Teknologi MARA Shah Alam Selangor Malaysia School of Science and Technology Asia e University Selangor Malaysia

ISBN: (纸本)9781665489973

The process of matching student profiles to industry profiles is critical to ensuring that students are placed in industries that are a good fit for their program. Therefore, to solve this problem, a system is presented that will give suggestions on suitable industrial types and internship placement from companies in the suggested industry for undergraduate students. This project maps student profiles from seven computer science programs and seven industrial types. There are 284 sample profiles collected from undergraduate students of Universiti Teknologi MARA. The profiles are gathered from previous records of placement for internship training. A decision tree model is constructed based on the sample profiles. The student’s Cumulative Grade Point Average (CGPA) and registered program are used as the main feature of industry recommendation. As a result, a web-based system for mapping students’ profiles to industries’ profiles has been developed. The application stores students’ and industries’ profiles and recommends suitable industries for each student’s profile.

关键词： Industries Training Computer science Computational modeling Companies Decision trees

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Using Business Intelligence Dashboard for Sustainable Urban Water Security Monitoring

Using Business Intelligence Dashboard for Sustainable Urban ...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Zainuddin, Zada Yahya, Farashazillah Lin, Wee Bui Fazli, Bashirah Mohd Zulkifli, Harlisa Abdullah, Mohammad Fikry Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Universiti Malaysia Sarawak Faculty of Computer Science and Information Technology Sarawak Malaysia National Water Research Institute of Malaysia Water Quality and Environment Research Centre Selangor Malaysia National Water Research Institute of Malaysia Information Management Division Selangor Malaysia Leeds University Business School University of Leeds Leeds United Kingdom

ISBN: (纸本)9798350331790

This paper presents the findings from the experts on the effective factor of monitoring dashboards for urban water security. The urban water security dashboard monitors the water quality, availability, and other relevant indicators. Although monitoring dashboards exist, there are potential improvements in the context of urban water security. The study integrates the technological factors in the domain area. An expert review is conducted to get confirmation from professionals in the chosen sector about the effective factors for the dashboards from the IT perspective. This methodology uses qualitative via face-to-face and online sessions. Thematic analysis and content validity index (CVI) have been used to evaluate the experts' review. The findings show security and knowledge discovery is the main factor emphasised. The experts deemed visual design the least priority when creating a business intelligence dashboard. Experts also recommend embedding Artificial Intelligence (AI) with the data analysis for dashboards. The discussion identified challenges and future work for this research. The discussion identified three potential areas of future research in water security and informatics: i) investigating other dimensions of effective dashboards, ii) conducting additional research on the environmental dashboard, and iii) investigating dashboards' real-world application in urban water security. © 2023 IEEE.

关键词： Water quality

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Weighted Density for The Win: Accurate Subspace Density Clustering

Weighted Density for The Win: Accurate Subspace Density Clus...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Maixuan Peng Yuyang Wu Yang Lu Mengke Li Yiqun Zhang Yiu-Ming Cheung School of Computer Science and Technology Guangdong University of Technology Guangzhou China Faculty of Science Wilfrid Laurier University Brantford Canada Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

k-clustering typically struggles with the detection of irregular-distributed clusters due to the natural bias, while density clustering usually cannot well-adapt to different datasets and clustering tasks as it is not an oriented optimization process. This paper, therefore, proposes to perform density clustering in dynamically learned subspaces. To exploit the irregular-distributed clusters obtained by density clustering for the subspace determination, we design a new strategy to appropriately evaluate the importance of attributes. It turns out that the proposed Weighted Density-based Subspace Clustering (WDSC) algorithm inherits the unbiased merits of density clustering, and also upgrades the unlearning density clustering to be learnable under the subspace learning paradigm of k-clustering. A comprehensive evaluation including significance tests, ablation studies, qualitative comparisons, etc., shows the superiority of WDSC.

关键词： Accuracy Heuristic algorithms Signal processing algorithms Clustering algorithms Signal processing Acoustics Speech processing Optimization

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Evaluation of Machine Learning Techniques for Prediction of Breast Cancer 21

Evaluation of Machine Learning Techniques for Prediction of ...

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21st IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2025

作者： Yi Hoong, Jared Tan Ajibade, Samuel-Soma M. Jasser, Muhammed Basheer Abdul Majeed, Anwar P.P. Alebiosu, David Olayemi Issa, Bayan Adediran, Anthonia Oluwatosin Chen Kwan, Charis Shwu Sunway University No.5 Jalan Universiti School of Engineering and Technology Department of Data Science and Artificial Intelligence Selangor Darul Ehsan 47500 Malaysia Sunway University No.5 Jalan Universiti Research Centre for Nanomaterials and Energy Technology (RCNMET) Selangor Darul Ehsan 47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syrian Arab Republic Universiti Malaya Faculty of Built Environment Department of Real Estate Kuala Lumpur Malaysia Sunway University No.5 Jalan Universiti School of Engineering and Technology Department of Smart Computing and Cyber Resilience Selangor Darul Ehsan 47500 Malaysia

ISBN: (纸本)9798331522193

The prediction of breast cancer prognosis has been a significant challenge in medical research due to the complex nature of cancer progression and the variability in patient-specific factors. Machine learning techniques have emerged as powerful tools for enhancing prediction accuracy in cancer diagnosis and prognosis. This study evaluates the performance of three widely used machine learning classifiers - Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) - in predicting breast cancer outcomes using the Wisconsin Breast Cancer Database. A robust framework was designed to preprocess the dataset, scale features, and assess the classifiers using key evaluation metrics, including accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). The results reveal that all classifiers demonstrated high predictive accuracy, with SVM achieving the highest accuracy of 97.08%, followed by RF and ANN with 96.67%. While RF recorded superior precision (95.40%), SVM outperformed others in recall (97.70%) and F1-score (96.05%), showcasing its ability to balance prediction precision and sensitivity. These findings underscore the potential of machine learning to augment traditional cancer prognosis methods, providing healthcare professionals with data-driven insights for personalized treatment strategies. This research highlights the critical role of machine learning in transforming cancer care and lays the groundwork for future studies focused on hybrid models and real-world implementation. © 2025 IEEE.

关键词： Artificial Intelligence Artificial Neural Network Breast cancer Machine learning Prognosis prediction Support Vector Machine

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Beyond Raw Performance: Neural Machine Translation Based Multi-lingual Classification of Tweets for Automated Disease Surveillance

Research Square

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Research Square 2021年

作者： Magumba, Mark Abraham Nabende, Peter Makerere University College of Computing and Information Sciences School of Computing and Informatics Technology Department of Information Systems Uganda

Twitter and social media as a whole have great potential as a source of disease surveillance data however the general messiness of tweets presents several challenges for standard information extraction methods. Most deployed systems employ approaches that rely on simple keyword matching and do not distinguish between relevant and irrelevant keyword mentions making them susceptible to false positives as a result of the fact that keyword volume can be influenced by several social phenomena that may be unrelated to disease occurrence. Furthermore, most solutions are intended for a single language and those meant for multilingual scenarios do not incorporate semantic context. In this paper we experimentally examine a translation based approach that allows for incorporation of semantic context in multi-lingual disease surveillance in the social web. © 2021, CC BY.

关键词： Data mining

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Survey on AI-Enabled Resource Management for 6G Heterogeneous Networks: Recent Research, Challenges, and Future Trends

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Computers, Materials and Continua 2025年第3期83卷 3585-3622页

作者： Hayder Faeq Alhashimi Mhd Nour Hindia Kaharudin Dimyati Effariza Binti Hanafi Feras Zen Alden Faizan Qamar Quang Ngoc Nguyen Centre of Advanced Communication Research and Innovation (ACRI) Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur 50603 Malaysia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi Mara Shah Alam 40450 Malaysia Centre for Cyber Security Faculty of Information Science and Technology (FTSM) Universiti Kebangsaan Malaysia (UKM) Bangi 43600 Malaysia Faculty of Science and Engineering Waseda University Tokyo 169-8555 Japan Faculty of Telecommunications Posts and Telecommunications Institute of Technology Hanoi 11518 Vietnam

The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks. Artificial Intelligence (AI) advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models, deep learning models, and hybrid models. Furthermore, intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods, which in turn improves the performance of 6G networks. Hence, 6G networks rely substantially on AI methods to manage resources. This paper comprehensively surveys the recent work of AI methods-based resource management for 6G networks. Firstly, the AI methods are categorized into Deep Learning (DL), Federated Learning (FL), Reinforcement Learning (RL), and Evolutionary Learning (EL). Then, we analyze the AI approaches according to optimization issues such as user association, channel allocation, power allocation, and mode selection. Thereafter, we provide appropriate solutions to the most significant problems with the existing approaches of AI-based resource management. Finally, various open issues and potential trends related to AI-based resource management applications are presented. In summary, this survey enables researchers to understand these advancements thoroughly and quickly identify remaining challenges that need further investigation.

关键词： Artificial intelligence (AI) resource management deep learning (DL) federated learning (FL) reinforcement learning (RL) evolutionary learning (EL)

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CGS-Mask: Making Time Series Predictions Intuitive for All

arXiv

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arXiv 2023年

作者： Lu, Feng Li, Wei Sun, Yifei Song, Cheng Ren, Yufei Zomaya, Albert Y. School of Computer Science and Technology Huazhong University of Science and Technology China The Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

Artificial intelligence (AI) has immense potential in time series prediction, but most explainable tools have limited capabilities in providing a systematic understanding of important features over time. These tools typically rely on evaluating a single time point, overlook the time ordering of inputs, and neglect the time-sensitive nature of time series applications. These factors make it difficult for users, particularly those without domain knowledge, to comprehend AI model decisions and obtain meaningful explanations. We propose CGS-Mask, a post-hoc and model-agnostic cellular genetic strip mask-based saliency approach to address these challenges. CGS-Mask uses consecutive time steps as a cohesive entity to evaluate the impact of features on the final prediction, providing binary and sustained feature importance scores over time. Our algorithm optimizes the mask population iteratively to obtain the optimal mask in a reasonable time. We evaluated CGS-Mask on synthetic and real-world datasets, and it outperformed state-of-the-art methods in elucidating the importance of features over time. According to our pilot user study via a questionnaire survey, CGS-Mask is the most effective approach in presenting easily understandable time series prediction results, enabling users to comprehend the decision-making process of AI models with ease. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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