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Information and Communication Technology (ICICT), International Conference on

作者： Francis Rudra D Cruze Md. Faruk Hosen Md. Suhag Ali Mohammad Sarwar Hossain Mollah Muhammad Shahin Uddin Monir Morshed Department of Computing and Information System Daffodil International University (DIU) Ashulia Dhaka Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology Univeristy Tangail Bangladesh Department of Software Engineering Daffodil International University (DIU) Ashulia Dhaka Bangladesh

ISBN: (数字)9798331508227

ISBN: (纸本)9798331508234

Sophisticated detection mechanisms are required to protect the integrity of information in the digital age, as the widespread dissemination of false news on social media platforms has become a critical challenge. To tackle the practical issue of spreading false information, there is an urgent need for reliable systems that can effectively identify and counteract the propagation of fake news. This work presents a novel method for identifying fake news by combining sophisticated encoding techniques and utilizing deep learning architectures. Our approach integrates TF-IDF, CountVectorizer (CV), and Word2Vec (W2V) techniques for encoding. These techniques are coupled sequentially to capture the subtle details in fake news content successfully. Through the utilization of attention-based Convolutional Neural Network (CNN) models, we improve the comprehension and categorization of intricate textual material. Our methodology prioritizes scalability to efficiently analyze big datasets while retaining high detection accuracy in many situations and languages. Our approach has been thoroughly tested and proven to be strong and applicable in a wide range of false news situations. We have attained a state-of-the-art level of precision, with an accuracy of 99.11 %, by employing an attention-based Convolutional Neural Network (CNN) coupled CountVectorizer and Word2Vec embeddings,

关键词： Deep learning Accuracy Social networking (online) Scalability Organizations Feature extraction Encoding Convolutional neural networks Reliability Fake news

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Efficient cube computing on an extended multidimensional model over uncertain data

Efficient cube computing on an extended multidimensional mod...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Chunyang Wei Hongyan Li Kai Lei Tengjiao Wang Key Laboratory of High Confidence Software Technologies Ministry of Education School of Electronics Engineering and Computer Science Peking University Beijing China The Shenzhen Key Laboratory for Cloud Computing Technology and Applications (SPCCTA) Peking University Shenzhen Graduate School Shenzhen China

Data uncertainty is an inherent property in various applications due to reasons such as measurement errors, incompleteness of data and so on. While On-Line Analytical Processing (OLAP) has been a powerful method for analyzing large data warehouse, OLAP over uncertain data has become a valuable and attractive issue because of the increasingly demand for handling uncertainty in multidimensional data. In this paper, we firstly describe our UStar-Schema model that extends the traditional OLAP model to support uncertain dimension attributes in fact table, uncertain measures in fact table and uncertainty in dimension table. Then we extend the processing model of the aggregate queries and cube computing on Ustar-Schema. Secondly, we design a novel index structure called PSI-Index on UStar-Schema to improve efficiency of OLAP quering and cube computing. Furthermore, an advanced index structure called HB-Index and an efficient algorithm are proposed to accelerate iceberg cube computing based on our model using pruning techniques to eliminate huge amounts of useless computations. Finally, extensive experiments are performed to examine the efficiency and effectiveness of our proposed techniques.

关键词： Indexes Aggregates Uncertainty Computational modeling Sensors Algorithm design and analysis

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Robust Bidirectional Generative Network For Generalized Zero-Shot Learning

Robust Bidirectional Generative Network For Generalized Zero...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yun Xing Sheng Huang Luwen Huangfu Feiyu Chen Yongxin Ge Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society School of Big Data and Software Engineering at Chongqing University Chongqing P.R.China Fowler College of Business at San Diego State University San Diego USA

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

In this work, we propose a novel generative approach named Robust Bidirectional Generative Network (RBGN) based on Conditional Generative Adversarial Network (CGAN) for Generalized Zero-shot Learning (GZSL). RBGN employs the adversarial attack to train a more rigorous discriminator, thus enhancing the generalizability and robustness of the feature generator under minimax strategy. Moreover, RBGN decodes the generated visual features back to their semantic representations to further improve the representational ability of generated visual features and alleviate the hubness problem. The experimental results of GZSL on four datasets, i.e. CUB, SUN, AWA1, AWA2, demonstrate that our model achieves competitive performance compared to state-of-the-art approaches and owns better generalizability to the unseen classes over conventional generative GZSL models. Further robustness analysis also validates the strong robustness of our model to the different types of semantic disturbance.

关键词： Semantics Visualization Robustness Generators Training Decoding Gallium nitride

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Over-the-Air Federated Learning via Second-Order Optimization

arXiv

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arXiv 2022年

作者： Yang, Peng Jiang, Yuning Wang, Ting Zhou, Yong Shi, Yuanming Jones, Colin N. The Shanghai Key Lab. of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai200062 China The Automatic Control Laboratory EPFL Lausanne1015 Switzerland The School of Information Science and Technology ShanghaiTech University Shanghai201210 China

Federated learning (FL) is a promising learning paradigm that can tackle the increasingly prominent isolated data islands problem while keeping users' data locally with privacy and security guarantees. However, FL could result in task-oriented data traffic flows over wireless networks with limited radio resources. To design communication-efficient FL, most of the existing studies employ the first-order federated optimization approach that has a slow convergence rate. This however results in excessive communication rounds for local model updates between the edge devices and edge server. To address this issue, in this paper, we instead propose a novel over-the-air second-order federated optimization algorithm to simultaneously reduce the communication rounds and enable low-latency global model aggregation. This is achieved by exploiting the waveform superposition property of a multi-access channel to implement the distributed second-order optimization algorithm over wireless networks. The convergence behavior of the proposed algorithm is further characterized, which reveals a linear-quadratic convergence rate with an accumulative error term in each iteration. We thus propose a system optimization approach to minimize the accumulated error gap by joint device selection and beamforming design. Numerical results demonstrate the system and communication efficiency compared with the state-of-the-art approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Wireless networks

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Asymmetric Commutative Encryption Scheme Based Efficient Solution to the Millionaires' Problem

Asymmetric Commutative Encryption Scheme Based Efficient Sol...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Meng Liu Priyadarsi Nanda Xuyun Zhang Chi Yang Shui Yu Jianxin Li School of Mechanical Electrical and Information Engineering Shandong University Weihai China School of Electrical and Data Engineering University of Technology Sydney Australia Department of Electrical and Computer Engineering University of Auckland New Zealand School of Computing and Information Technology University of Wollongong Australia School of Information Technology Deakin University Burwood Australia School of Computer Science and Software Engineering University of Western Australia Australia

Secure multiparty computation (SMC) is an important scheme in cryptography and can be applied in various real-life problems. The first SMC problem is the millionaires' problem which involves two-party secure computation. Because the efficiency of public key encryption scheme appears less than symmetric encryption scheme, most existing solutions based on public key cryptography to this problem is inefficient. Thus, a solution based on the symmetric encryption scheme has been proposed. Although it is claimed that this approach can be efficient and practical, we discover that there exist several severe security flaws in this solution. In this paper, we analyze the vulnerability of existing solutions, and propose a new scheme based on the Decisional Diffie-Hellman hypothesis (DDH). Our solution also uses two special encodings (0-encoding and 1-encoding) generated by our modified encoding method to reduce the computation cost of modular multiplications. Extensive experiments are conducted to evaluate the efficiency of our solution, and the experimental results show that our solution can be much more efficient and be approximately 8000 times faster than the solution based on symmetric encryption scheme for a 32-bit input and short-term security. Moreover, our solution is also more efficient than the state-of-the-art solution.

关键词： Encryption Protocols Encoding Public key Australia

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Boundary-Guided Learning for Gene Expression Prediction in Spatial Transcriptomics

arXiv

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arXiv 2024年

作者： Qu, Mingcheng Wu, Yuncong Di, Donglin Su, Anyang Su, Tonghua Song, Yang Fan, Lei Faculty of Computing Harbin Institute of Technology Harbin China School of Astronautics Harbin Institute of Technology Harbin China Space AI Li Auto Beijing China College of Software Jilin University Changchun China Computer Science and Engineering UNSW Sydney Australia

Spatial transcriptomics (ST) has emerged as an advanced technology that provides spatial context to gene expression. Recently, deep learning-based methods have shown the capability to predict gene expression from WSI data using ST data. Existing approaches typically extract features from images and the neighboring regions using pretrained models, and then develop methods to fuse this information to generate the final output. However, these methods often fail to account for the cellular structure similarity, cellular density and the interactions within the microenvironment. In this paper, we propose a framework named BG-TRIPLEX, which leverages boundary information extracted from pathological images as guiding features to enhance gene expression prediction from WSIs. Specifically, our model consists of three branches: the spot, in-context and global branches. In the spot and in-context branches, boundary information, including edge and nuclei characteristics, is extracted using pretrained models. These boundary features guide the learning of cellular morphology and the characteristics of microenvironment through Multi-head Cross-Attention. Finally, these features are integrated with global features to predict the final output. Extensive experiments were conducted on three public ST datasets and the Visium dataset. The results demonstrate that our BG-TRIPLEX consistently outperforms existing methods in terms of Pearson Correlation Coefficient (PCC). This method highlights the crucial role of boundary features in understanding the complex interactions between WSI and gene expression, offering a promising direction for future research. Codes are available at: https://***/WcloudC0416/BG-TRIPLEX. Copyright © 2024, The Authors. All rights reserved.

关键词： Gene expression

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Comparative Analysis of Context Based Classification of Twitter

Comparative Analysis of Context Based Classification of Twit...

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International Conference on Advances in computing, Communication, & Automation (ICACCA) (Fall)

作者： Muhammad Salman Tiwana Farooq Javeed Muhammad Ikramullah Lali Hafsa Dar Muhammad Bilal Dept. of CS&IT University of Sargodha Sargodha Pakistan Dept. of Computer Science University of Gujrat Gujrat Pakistan Dept. of Software Engineering University of Gujrat Gujrat Pakistan School of Computing & IT Taylor’s University Subang Jaya Malaysia

ISBN: (纸本)9781538671689

Social media offers online data source where internet users' share their opinions, views and discuss any news or events that happen around the world. Twitter is a popular social media which effectively takes part in any mega event and is used before, during and after live events. Some tweets become trend based on their popularity. The trending tweets are classified by twitter into categories like: Sports (S), Entertainment (E), Politics (P), and Technology (T) which are used in our work. We have used context based meanings to further improve the trending tweet classification results. In this article, we have verified the twitter's automated classification results with manual classification by experts. Furthermore, we compared the classification results with SVM, Linear Regression, Logistic Regression and Naive Bayes implemented in Weka tool. We found that twitter's classifier works with almost 89% accuracy in our dataset. This work would contribute in improving misclassification of data of different categories on social media.

关键词： Twitter Sports Data mining Media Support vector machines

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On Secrecy Performance of MISO SWIPT Systems with TAS and Imperfect CSI

On Secrecy Performance of MISO SWIPT Systems with TAS and Im...

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作者： Pan, Gaofeng Lei, Hongjiang Deng, Yansha Fan, Lisheng Yang, Jing Chen, Yunfei Ding, Zhiguo Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing School of Electronic and Information Engineering Southwest University Chongqing400715 China School of Computing and Communications Lancaster University LancasterLA14YW United Kingdom Chongqing Key Laboratory of Mobile Communications Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Centre for Telecommunications King's College London LondonWC2R2LS United Kingdom School of Computer Science and Educational Software Guangzhou University Guangzhou510000 China Department of Electronic Engineering Shantou University Guangdong515063 China School of Information Engineering Yangzhou University Yangzhou225009 China School of Engineering University of Warwick CoventryCV47AL United Kingdom

In this paper, a multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system, including one base station (BS) equipped with multiple antennas, one desired single-antenna information receiver (IR), and N (N > 1) single-antenna energy-harvesting receivers (ERs) is considered. Assuming that the information signal to the desired IR may be eavesdropped by ERs if ERs are malicious, we investigate the secrecy performance of the target MISO SWIPT system when imperfect channel state information (CSI) is available and adopted for transmit antenna selection at the BS. Considering that each eavesdropping link experiences independent but not necessarily identically distributed Rayleigh fading, the closed-form expressions for the exact and the asymptotic secrecy outage probability, and the average secrecy capacity are derived and verified by simulations. Furthermore, the optimal power splitting factor is derived for each ER to realize the tradeoff between the energy harvesting and the information eavesdropping. Our results reveal the impact of the imperfect CSI on the secrecy performance of MISO SWIPT systems in the presence of multiple wiretap channels. © 2016 IEEE.

关键词： Energy harvesting

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MT-PART: Metamorphic-Testing-Based Adaptive Random Testing Through Partitioning

MT-PART: Metamorphic-Testing-Based Adaptive Random Testing T...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Zhihao Ying Dave Towey Tsong Yueh Chen Zhi Quan Zhou School of Computer Science University of Nottingham Ningbo China Zhejiang People's Republic of China Department of Computer Science and Software Engineering Swinburne University of Technology Hawthorn VIC Australia School of Computing and Information Technology University of Wollongong Wollongong NSW Australia

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Metamorphic Testing (MT) has been repeatedly proven effective in detecting software faults. MT detects faults by checking the Metamorphic Relations (MRs) among Source Test Cases (STCs) and Follow-up Test Cases (FTCs) and the corresponding outputs. Metamorphic Groups (MGs) denote the associated STCs and FTCs. The performance of MT relates strongly to the MRs and MGs. However, previous studies that on MG generation mainly focused on improving the effectiveness (i.e. fault-detection capability) of MT, but to some extent overlooked the efficiency. This paper proposes a new kind of MG generation algorithms called Metamorphic-Testing-based Adaptive Random Testing through Partitioning (MT-PART). These algorithms at-tempt to improve both the effectiveness and the efficiency of MT by dynamically partitioning the input domain and generating new STCs and FTCs that are uniformly distributed over their corresponding input domains. Through empirical experiments, we found that our algorithms are able to significantly outper-form other existing MG generation algorithms in terms of test efficiency, while maintaining good test effectiveness.

关键词： Heuristic algorithms software algorithms software Partitioning algorithms Testing

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Co-Production of a Cognitive Behavioral Therapy Digital Platform for Families of Individuals Impacted by Psychosis

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Journal of Technology in Behavioral Science 2024年第1期9卷 140-148页

作者： Cheng, Sunny Chieh Kopelovich, Sarah Si, Dong Divina, Myra Gao, Ningjun Serene Wang, Mia Yunqi Kim, Jamie Jaesook Li, Ziyi Blank, Jennifer Brian, Rachel Turkington, Douglas School of Nursing and Healthcare Leadership University of Washington Tacoma WA United States Department of Psychiatry and Behavioral Sciences University of Washington School of Medicine Seattle WA United States Division of Computing and Software Systems University of Washington Bothell WA United States Department of Human Centered Design & Engineering University of Washington Seattle WA United States Department of Economics University of Washington Seattle WA United States Department of Statistics University of Washington Seattle WA United States Newcastle University Newcastle Upon Tyne United Kingdom

Education and skills training have consistently been recognized as an unmet need for caregivers of individuals with psychosis, as traditional models of clinician-delivered family interventions remain inaccessible to most caregivers across low-, middle-, and high-income countries. We endeavored to expand access to psychosis education and skills training by co-developing a digital training platform with experts and families. We engaged in collaborative co-design sessions with caregivers who had received training in Psychosis REACH—an evidence-based psychosocial family intervention for psychosis—to elicit design principles, develop a functional prototype, and engage in usability testing of an online interactive training program. Seven participants reported six design principles for a prototype. These included simplicity, accessibility, skill acquisition, usability, sense of community, and hope. Guided by these principles, participants’ preferred features were (1) online modules with didactic content, (2) a training “coach” conversational agent to provide instruction and feedback to users, and (3) a conversational agent simulation of an individual experiencing psychosis with whom users can practice skills. Participants completed the usability testing. The levels of ease of use ranged from 4.2 to 4.8 on a 5-point scale with 5 being easiest to use and the level of usefulness ranged from 4 to 4.75 on a 5-point scale, with 5 being most useful. This study is the first to develop a digital platform for family caregivers to support interactive skills training for psychosis caregiving at home. Iterative user-centered design resulted in a functional alpha prototype ripe for testing. Future directions include testing the design requirements on more diverse users and evaluating the effects of the online platform on the caregiving experience. © 2024, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Caregiver Co-production Cognitive behavior therapy Digital platform Mental health Psychosis Usability User-centered design

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