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Electricity Carbon Quota Trading Scheme based on Certificateless Signature and Blockchain

Computer Modeling in Engineering & sciences 2024年第2期138卷 1695-1712页

作者： Xiaodong Yang Runze Diao Tao Liu Haoqi Wen Caifen Wang College of Computer Science and Engineering Northwest Normal UniversityLanzhou730070China Institute of China TelecomWanwei China TelecomWanwei Information Technology Co.Ltd.Lanzhou730030China College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Key Laboratory of Cryptography and Data Analytics Northwest Normal UniversityLanzhou730070China

The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading *** signatures are a new cryptographic technology that can address traditional cryptography’s general essential certificate requirements and avoid the problem of crucial escrowbased on identity ***,most certificateless signatures still suffer fromvarious security *** present a secure and efficient certificateless signing scheme by examining the security of existing certificateless signature *** ensure the integrity and verifiability of electricity carbon quota trading,we propose an electricity carbon quota trading scheme based on a certificateless signature and *** scheme utilizes certificateless signatures to ensure the validity and nonrepudiation of transactions and adopts blockchain technology to achieve immutability and traceability in electricity carbon quota *** addition,validating electricity carbon quota transactions does not require time-consuming bilinear pairing *** results of the analysis indicate that our scheme meets existential unforgeability under adaptive selective message attacks,offers conditional identity privacy protection,resists replay attacks,and demonstrates high computing and communication performance.

关键词： Electricity carbon trading certificateless signature blockchain forgery attack carbon quota

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EGG-SynC: Exact GPU-parallelized Grid-based Clustering by Synchronization 26

EGG-SynC: Exact GPU-parallelized Grid-based Clustering by Sy...

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26th International Conference on Extending database Technology, EDBT 2023

作者： Jørgensen, Jakob Rødsgaard Assent, Ira Department of Computer Science Aarhus University Aarhus Denmark Department of Computer Science DIGIT Aarhus University Centre for Digitalisation Big Data and Data Analytics Aarhus University Aarhus Denmark

ISBN: (纸本)9783893180882

Clustering by synchronization (SynC) is a clustering method that is motivated by the natural phenomena of synchronization and is based on the Kuramoto model. The idea is to iteratively drag similar objects closer to each other until they have synchronized. SynC has been adapted to solve several well-known data mining tasks such as subspace clustering, hierarchical clustering, and streaming clustering. This shows that the SynC model is very versatile. Sadly, SynC has an O(T × n2 × d) complexity, which makes it impractical for larger datasets. E.g., Chen et al. [8] show runtimes of more than 10 hours for just n = 70, 000 data points, but improve this to just above one hour by using R-Trees in their method FSynC. Both are still impractical in real-life scenarios. Furthermore, SynC uses a termination criterion that brings no guarantees that the points have synchronized but instead just stops when most points are close to synchronizing. In this paper, our contributions are manifold. We propose a new termination criterion that guarantees that all points have synchronized. To achieve a much-needed reduction in runtime, we propose a strategy to summarize partitions of the data into a grid structure, a GPU-friendly grid structure to support this and neighborhood queries, and a GPU-parallelized algorithm for clustering by synchronization (EGG-SynC) that utilize these ideas. Furthermore, we provide an extensive evaluation against state-of-the-art showing 2 to 3 orders of magnitude speedup compared to SynC and FSynC. © 2023 ***. All rights reserved.

关键词： Synchronization

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Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets 12th

Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchm...

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12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2023

作者： Schäpermeier, Lennart Kerschke, Pascal Grimme, Christian Trautmann, Heike Big Data Analytics in Transportation TU Dresden & ScaDS.AI Dresden Germany Data Science: Statistics and Optimization University of Münster Münster Germany

ISBN: (纸本)9783031272493

The design and choice of benchmark suites are ongoing topics of discussion in the multi-objective optimization community. Some suites provide a good understanding of their Pareto sets and fronts, such as the well-known DTLZ and ZDT problems. However, they lack diversity in their landscape properties and do not provide a mechanism for creating multiple distinct problem instances. Other suites, like bi-objective BBOB, possess diverse and challenging landscape properties, but their optima are not well understood and can only be approximated empirically without any guarantees. This work proposes a methodology for creating complex continuous problem landscapes by concatenating single-objective functions from version 2 of the multiple peaks model (MPM2) generator. For the resulting problems, we can determine the distribution of optimal points with arbitrary precision w.r.t. a measure such as the dominated hypervolume. We show how the properties of the MPM2 generator influence the multi-objective problem landscapes and present an experimental proof-of-concept study demonstrating how our approach can drive well-founded benchmarking of MO algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Analysis on DeepFake dataset, Manipulation and Detection Techniques: A Review

Analysis on DeepFake Dataset, Manipulation and Detection Tec...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2022

作者： Rasool, Aale Katarya, Rahul Big Data Analytics and Web Intelligence Laboratory Department of Computer Science Delhi Technological University New Delhi India

ISBN: (纸本)9789819959730

The ability to spot false news is becoming increasingly important in today’s environment. Modern, rapid communication and easy access to all kinds of information were made possible by social networks and internet media. On the other hand, it opens up new avenues for malicious people to exploit vast amounts of publicly available data. Fake images and videos may now be predicted with greater accuracy because of advances in machine learning, particularly deep learning. Many studies have been conducted in recent years to better understand how deepfakes work, and several deep learning-based algorithms to detect deepfake videos or images have been developed. This study provides a comprehensive summary of the most recent manipulation techniques and datasets utilized in this subject. In addition, we reviewed the recent work of other academics on the subject and provided a roadmap for future study to aid scholars in combating the issue of deep fakes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Convolutional neural networks

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MOMENTUM BENEFITS NON-IID FEDERATED LEARNING SIMPLY AND PROVABLY 12

MOMENTUM BENEFITS NON-IID FEDERATED LEARNING SIMPLY AND PROV...

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12th International Conference on Learning Representations, ICLR 2024

作者： Cheng, Ziheng Huang, Xinmeng Wu, Pengfei Yuan, Kun Peking University China University of Pennsylvania United States National Engineering Labratory for Big Data Analytics and Applications AI for Science Institute Beijing China

Federated learning is a powerful paradigm for large-scale machine learning, but it faces significant challenges due to unreliable network connections, slow communication, and substantial data heterogeneity across clients. FEDAVG and SCAFFOLD are two prominent algorithms to address these challenges. In particular, FEDAVG employs multiple local updates before communicating with a central server, while SCAFFOLD maintains a control variable on each client to compensate for "client drift" in its local updates. Various methods have been proposed to enhance the convergence of these two algorithms, but they either make impractical adjustments to the algorithmic structure or rely on the assumption of bounded data heterogeneity. This paper explores the utilization of momentum to enhance the performance of FEDAVG and SCAFFOLD. When all clients participate in the training process, we demonstrate that incorporating momentum allows FEDAVG to converge without relying on the assumption of bounded data heterogeneity even using a constant local learning rate. This is novel and fairly surprising as existing analyses for FEDAVG require bounded data heterogeneity even with diminishing local learning rates. In partial client participation, we show that momentum enables SCAFFOLD to converge provably faster without imposing any additional assumptions. Furthermore, we use momentum to develop new variance-reduced extensions of FEDAVG and SCAFFOLD, which exhibit state-of-the-art convergence rates. Our experimental results support all theoretical findings. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Momentum

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Neural Machine Translation Models with Attention-Based Dropout Layer

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Computers, Materials & Continua 2023年第5期75卷 2981-3009页

作者： Huma Israr Safdar Abbas Khan Muhammad Ali Tahir Muhammad Khuram Shahzad Muneer Ahmad Jasni Mohamad Zain School of Electrical Engineering and Computer Science(SEECS) National University of Science and TechnologyIslamabadPakistan Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARA40450Shah AlamSelangorMalaysia

In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of *** model has obtained state-of-the-art performance for several language ***,there has been little work exploring useful architectures for Urdu-to-English machine *** conducted extensive Urdu-to-English translation experiments using Long short-term memory(LSTM)/Bidirectional recurrent neural networks(Bi-RNN)/Statistical recurrent unit(SRU)/Gated recurrent unit(GRU)/Convolutional neural network(CNN)and *** results show that Bi-RNN and LSTM with attention mechanism trained iteratively,with a scalable data set,make precise predictions on unseen *** trained models yielded competitive results by achieving 62.6%and 61%accuracy and 49.67 and 47.14 BLEU scores,*** a qualitative perspective,the translation of the test sets was examined manually,and it was observed that trained models tend to produce repetitive output more *** attention score produced by Bi-RNN and LSTM produced clear alignment,while GRU showed incorrect translation for words,poor alignment and lack of a clear ***,we considered refining the attention-based models by defining an additional attention-based dropout *** dropout fixes alignment errors and minimizes translation errors at the word *** empirical demonstration and comparison with their counterparts,we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation *** ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as *** empirically concluded that adding an attention-based dropout layer helps improve GRU,SRU,and Transformer translation and is considerably more efficient in translation quality and speed.

关键词： Natural language processing neural machine translation word embedding attention perplexity selective dropout regularization Urdu Persian Arabic BLEU

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Cooperation and Contract Design in Project Management with Outsourcing

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Journal of Systems science and Systems Engineering 2023年第1期32卷 34-70页

作者： Xiaoqiang Cai Nicholas G.Hall Siyi Wang Feng Zhang School of Data Science The Chinese University of Hong KongShenzhen518172China Shenzhen Research Institute of Big Data Shenzhen518172China Department of Operations&Business Analytics Fisher College of BusinessOhio State UniversityColumbusOhioUSA School of Science and Engineering The Chinese University of Hong KongShenzhen518172China Suning Bank Nanjing210019China

We study a project management problem where the prime contractor needs to outsource tasks to subcontractors with the required *** execution of the project requires proper coordination among the subcontractors,as well as contract design by the prime contractor to incentivize the *** modeling the subcontractors’coordination problem as a cooperative game,we develop a profit sharing scheme to facilitate the subcontractors’*** consider two contract designs for the prime contractor:a uniform contract across all subcontractors,and a nonuniform one that customizes incentives for each *** propose efficient algorithms to solve the implicit optimization problems for optimal contract *** experiments show that the pooling effect of subcontractors’cooperation mitigates the negative impact of poor estimates about the crashing cost and resource *** observe three unexpected results through the randomized computation experiments:(i)the subcontractors’profits may decrease if they provide false information;(ii)it is safer for the prime contractor to overestimate subcontractors’crashing costs than underestimate them;and(iii)uniform contracts deliver more project profit for the subcontractors in the coalitions.

关键词： Project management with outsourcing subcontracting cooperative game contract design

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A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction 30

A Semantic Mention Graph Augmented Model for Document-Level ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Jian Yang, Changlin Zhu, Haiping Lin, Qika Xu, Fangzhi Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics Xi'an China National University of Singapore Singapore

ISBN: (纸本)9782493814104

Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from an unstructured document. The advanced approaches on DEAE utilize prompt-based methods to guide pre-trained language models (PLMs) in extracting arguments from input documents. They mainly concentrate on establishing relations between triggers and entity mentions within documents, leaving two unresolved problems: a) independent modeling of entity mentions;b) document-prompt isolation. To this end, we propose a semantic mention Graph Augmented Model (GAM) to address these two problems in this paper. Firstly, GAM constructs a semantic mention graph that captures relations within and between documents and prompts, encompassing co-existence, co-reference and co-type relations. Furthermore, we introduce an ensembled graph transformer module to address mentions and their three semantic relations effectively. Later, the graph-augmented encoder-decoder module incorporates the relation-specific graph into the input embedding of PLMs and optimizes the encoder section with topology information, enhancing the relations comprehensively. Extensive experiments on the RAMS and WikiEvents datasets demonstrate the effectiveness of our approach, surpassing baseline methods and achieving a new state-of-the-art performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Extraction

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Machine Learning Techniques for Autism Spectrum Disorder: Current trends and future directions 4

Machine Learning Techniques for Autism Spectrum Disorder: Cu...

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4th International Conference on Innovative Trends in Information Technology, ICITIIT 2023

作者： Khan, Kainat Katarya, Rahul Delhi Technological University Big Data Analytics and Web Intelligence Laboratory Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781665494144

ASD or autism spectrum disorder is a critical neuro-developmental disorder that hinders an individual's capability of social communication and interaction. This disorder has acquired considerable attention and importance due to its ubiquity among individuals covering all the countries worldwide. Individuals with ASD struggles in daily life activities. Detection of autism with the help of medical tests is a tedious and very costly task. However, detection and care of ASD still remains unfamiliar due to inadequate awareness, knowledge among the society, limited number of diagnostic devices and limited verbal therapy services for ASD patients. This paper investigates and displays reviews of various machine learning approaches on extracting useful data associated with distinctive characteristics of ASD such as brain functioning, hyperactivitperactivity, language disability, etc. Current researches reveal that analysis of biological traits by employing machine learning techniques have helped in the progress of early detection of ASD. ABIDE dataset is very much explored for the research in ASD. Additionally, numerous studies for the advancement of tools are still in progression. The presented research work can remarkably aid future studies on machine learning for ASD. © 2023 IEEE.

关键词： Supervised learning

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Intelligent E-Commerce Chatbot for Personalized Product Recommendations Using Text Mining and Machine Learning 2

Intelligent E-Commerce Chatbot for Personalized Product Reco...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Minu, M.S. Adithya Kavya SreeRanjini Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering with Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331541217

Innovative technology solutions have been developed in response to the growing need for effective and customized client contact on e-commerce platforms. This work introduces an intelligent chatbot system that uses machine learning and text mining techniques to improve user satisfaction and answer customer concerns. Advanced Natural Language Processing (NLP) models have been trained on large datasets, allowing for precise user intent interpretation and pertinent product recommendations. A strong recommendation engine is included into the system architecture to aid in decision-making, ultimately enhancing the customer experience by providing individualized, real-time help. The study also highlights the significance of data privacy by using privacy-preserving techniques to safeguard user data. The system's scalability and performance benefits are highlighted through thorough comparisons with current models. By showcasing the useful uses of machine learning and text mining in enhancing customer contact and service delivery, this study makes a substantial contribution to the development of AI-driven solutions in e-commerce. © 2024 IEEE.

关键词： big data Chatbot Cost data Mining e-Commerce Hadoop Machine Learning Product Purchasing Recommendation

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