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Partially-hiding functional encryption for degree-2 polynomials with fine-grained access control

Frontiers of Computer Science 2024年第3期18卷 231-233页

作者： Haifeng QIAN Cheng LIN Qiaohan CHU Jie CHEN Shanghai Key Laboratory of Trustworthy Computing Software Engineering InstituteEast China Normal UniversityShanghai 200062China

1 Introduction Boneh et al.[1]first systematically introduce the concept of Functional Encryption(FE)which overcomes all-or-nothing limitation of traditional public key ***,FE hides a potential problem:even if the secret key is obtained legitimately,once it is released,it can be used *** leads to that the secret key may leak information *** deal with this problem and make FE more practical,Abdalla et al.[2]recently constructed inner-product FE(IPFE)with fine-grained access control achieving strong security guarantees under standard assumptions.

关键词： grain Encryption encryption

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Open-Book Neural Algorithmic Reasoning 38

Open-Book Neural Algorithmic Reasoning

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Hefei Peng, Chao Xu, Chenyang Yang, Zhengfeng Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai China

Neural algorithmic reasoning is an emerging area of machine learning that focuses on building neural networks capable of solving complex algorithmic tasks. Recent advancements predominantly follow the standard supervised learning paradigm - feeding an individual problem instance into the network each time and training it to approximate the execution steps of a classical algorithm. We challenge this mode and propose a novel open-book learning framework. In this framework, whether during training or testing, the network can access and utilize all instances in the training dataset when reasoning for a given instance. Empirical evaluation is conducted on the challenging CLRS Algorithmic Reasoning Benchmark, which consists of 30 diverse algorithmic tasks. Our open-book learning framework exhibits a significant enhancement in neural reasoning capabilities. Further, we notice that there is recent literature suggesting that multi-task training on CLRS can improve the reasoning accuracy of certain tasks, implying intrinsic connections between different algorithmic tasks. We delve into this direction via the open-book framework. When the network reasons for a specific task, we enable it to aggregate information from training instances of other tasks in an attention-based manner. We show that this open-book attention mechanism offers insights into the inherent relationships among various tasks in the benchmark and provides a robust tool for interpretable multi-task training. © 2024 Neural information processing systems foundation. All rights reserved.

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Fuzzy Information Entropy and Region Biased Matrix Factorization for Web Service QoS Prediction 36

Fuzzy Information Entropy and Region Biased Matrix Factoriza...

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36th International Conference on software engineering and Knowledge engineering, SEKE 2024

作者： Tang, Guoxing Du, Yugen Chen, Xia Luo, Yingwei Ma, Benchi Software Engineering Institute East China Normal University Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China

ISBN: (纸本)1891706594

Nowadays, there are many similar services available on the internet, making Quality of Service (QoS) a key concern for users. Since collecting QoS values for all services through user invocations is impractical, predicting QoS values is a more feasible approach. Matrix factorization is considered an effective prediction method. However, most existing matrix factorization algorithms focus on capturing global similarities between users and services, overlooking the local similarities between users and their similar neighbors, as well as the non-interactive effects between users and services. This paper proposes a matrix factorization approach based on user information entropy and region bias, which utilizes a similarity measurement method based on fuzzy information entropy to identify similar neighbors of users. Simultaneously, it integrates the region bias between each user and service linearly into matrix factorization to capture the non-interactive features between users and services. This method demonstrates improved predictive performance in more realistic and complex network environments. Additionally, numerous experiments are conducted on real-world QoS datasets. The experimental results show that the proposed method outperforms some of the state-of-the-art methods in the field at matrix densities ranging from 5% to 20%. © 2024 Knowledge Systems institute Graduate School. All rights reserved.

关键词： Matrix factorization

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Boundary-Aware Periodicity-based Sparsification Strategy for Ultra-Long Time Series Forecasting 24

Boundary-Aware Periodicity-based Sparsification Strategy for...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Bao, Yiying Zhou, Hao Peng, Chao Xu, Chenyang Shi, Shuo Cai, Kecheng East China Normal University Shanghai China Software Engineering Institute Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China

ISBN: (纸本)9798400706868

In various domains such as transportation, resource management, and weather forecasting, there is an urgent need for methods that can provide predictions over a sufficiently long time horizon to encompass the period required for decision-making and implementation. Compared to traditional time series forecasting, ultra-long time series forecasting requires enhancing the model's ability to infer long time series, while maintaining inference costs within an acceptable range. To address this challenge, we propose the Boundary-Aware Periodicity-based sparsification strategy for Ultra-Long time series forecasting (BAP-UL).This method effectively captures periodic features in time series and reorganizes inputs and outputs into shorter sub-sequences for improved prediction accuracy. In the paper, we investigate several commonly used benchmark datasets and demonstrate that the proposed method can yield comparable performance across them. © 2024 ACM.

关键词： Weather forecasting

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RAHN: A Reputation Based Hourglass Network for Web Service QoS Prediction 36

RAHN: A Reputation Based Hourglass Network for Web Service Q...

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36th International Conference on software engineering and Knowledge engineering, SEKE 2024

作者： Chen, Xia Du, Yugen Tang, Guoxing Luo, Yingwei Ma, Benchi Software Engineering Institute East China Normal University Shanghai200062 China Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China

ISBN: (纸本)1891706594

As the homogenization of Web services becomes more and more common, the difficulty of service recommendation is gradually increasing. How to predict Quality of Service (QoS) more efficiently and accurately becomes an important challenge for service recommendation. Considering the excellent role of reputation and deep learning (DL) techniques in the field of QoS prediction, we propose a reputation and DL based QoS prediction network, RAHN, which contains the Reputation Calculation Module (RCM), the Latent Feature Extraction Module (LFEM), and the QoS Prediction Hourglass Network (QPHN). RCM obtains the user reputation and the service reputation by using a clustering algorithm and a Logit model. LFEM extracts latent features from known information to form an initial latent feature vector. QPHN aggregates latent feature vectors with different scales by using Attention Mechanism, and can be stacked multiple times to obtain the final latent feature vector for prediction. We evaluate RAHN on a real QoS dataset. The experimental results show that the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of RAHN are smaller than the six baseline methods. © 2024 Knowledge Systems institute Graduate School. All rights reserved.

关键词： Web services

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Think Before You Act: A Two-Stage Framework for Mitigating Gender Bias Towards Vision-Language Tasks

Think Before You Act: A Two-Stage Framework for Mitigating G...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Zhang, Yunqi Li, Songda Deng, Chunyuan Wang, Luyi Zhao, Hui Software Engineering Institute East China Normal University China Georgia Institute of Technology United States Shanghai Key Laboratory of Trustworthy Computing Shanghai China

ISBN: (纸本)9798891761148

Gender bias in vision-language models (VLMs) can reinforce harmful stereotypes and discrimination. In this paper, we focus on mitigating gender bias towards vision-language tasks. We identify object hallucination as the essence of gender bias in VLMs. Existing VLMs tend to focus on salient or familiar attributes in images but ignore contextualized nuances. Moreover, most VLMs rely on the co-occurrence between specific objects and gender attributes to infer the ignored features, ultimately resulting in gender bias. We propose GAMA, a task-agnostic generation framework to mitigate gender bias. GAMA consists of two stages: narrative generation and answer inference. During narrative generation, GAMA yields all-sided but gender-obfuscated narratives, which prevents premature concentration on localized image features, especially gender attributes. During answer inference, GAMA integrates the image, generated narrative, and a task-specific question prompt to infer answers for different vision-language tasks. This approach allows the model to rethink gender attributes and answers. We conduct extensive experiments on GAMA, demonstrating its debiasing and generalization ability. © 2024 Association for Computational Linguistics.

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Sparsing and Smoothing for the seq2seq Models

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 464-472页

作者： Zhao, Shuai Liang, Zhuoqian Wen, Jinming Chen, Jie College of Information Science and Technology Jinan University Guangzhou511400 China Pazhou Lab Guangzhou510330 China Shanghai Key Laboratory of Trustworthy Computing School of Software Engineering East China Normal University Shanghai200062 China

Current neural language models are trained to minimize cross-entropy and use softmax to compute the locally normalized probabilities over the target. While this setup provides solid results in several natural language processing (NLP) tasks, one unsatisfying aspect is its dense output. This density is wasteful, making models hard to interpret and assigning probability mass to many implausible outputs. To overcome this problem, we propose T-softmax, a simple but effective method to draw considerably sparse probability out of neural language models than softmax. Our method avoids dense output by truncating the unreliable tail of the probability distribution to improve the model's performance. In addition, we generalize logits with temperature, a critical regularization technique, from the softmax to T-softmax. To show our approach as a drop-in replacement for softmax, we evaluate them on three NLP tasks: summary generation, question answer, and math word problem. Experimental results show that our proposed model significantly improves performance without sacrificing speed;notably, in all experiments, our method outperforms the softmax. © 2020 IEEE.

关键词： Probability distributions

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Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word Problem 30

Benchmarking Hallucination in Large Language Models based on...

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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： Sun, Yuhong Yin, Zhangyue Guo, Qipeng Wu, Jiawen Qiu, Xipeng Zhao, Hui Software Engineering Institute East China Normal University China School of Computer Science Fudan University China Shanghai AI Laboratory China Shanghai Key Laboratory of Trustworthy Computing Shanghai China

ISBN: (纸本)9782493814104

Large language models (LLMs) are highly effective in various natural language processing (NLP) tasks. However, they are susceptible to producing unreliable conjectures in ambiguous contexts called hallucination. This paper presents a new method for evaluating LLM hallucination in Question Answering (QA) based on the unanswerable math word problem (MWP). To support this approach, we innovatively develop a dataset called Unanswerable Math Word Problem (UMWP) which comprises 5200 questions across five categories. We developed an evaluation methodology combining text similarity and mathematical expression detection to determine whether LLM considers the question unanswerable. The results of extensive experiments conducted on 31 LLMs, including GPT-3, InstructGPT, LLaMA, and Claude, demonstrate that in-context learning and reinforcement learning with human feedback (RLHF) training significantly enhance the model's ability to avoid hallucination. We show that utilizing MWP is a reliable and effective approach to assess hallucination. Our code and data are available at https://***/Yuki-Asuuna/UMWP. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Reinforcement learning

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Multi-View Dynamic Kernelized Evidential Clustering

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

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Web Service QoS Prediction Based on Reputation and Location Aware Matrix Factorization 19

Web Service QoS Prediction Based on Reputation and Location ...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Chen, Fan Du, Yugen Zhong, Wenhao Wang, Hanting East China Normal University Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute Shanghai China

ISBN: (纸本)9798350346558

With the development of cloud computing and Internet technologies, the number of Web services has increased dramatically. It is increasingly difficult for users to locate applicable services among a large number of functionally equivalent candidates. Considering the high cost of time and resources, users cannot invoke all Web services to obtain the desired quality of service (QoS). Therefore, the problem of QoS prediction of Web services has attracted much attention in recent years. Although QoS is often used as a measure of Web service performance, the value of QoS may vary significantly between users depending on their network and geographical location. Furthermore, most traditional approaches perform QoS prediction directly based on historical QoS values provided by users. However, these historical QoS data may contain unreliable values from unreliable users, resulting in significantly lower prediction accuracy. To overcome the above limitations, we propose a reputation and location aware matrix factorization (RLMF) approach for QoS prediction of Web services in this paper. First, we cluster the users and calculate their reputation based on the clustering information through Dirichlet distribution. Then, we integrate the user's reputation and location information into the matrix factorization model to obtain more accurate prediction results. Additionally, we use Cauchy loss to measure the difference between the observed and predicted QoS values, which makes our approach robust to even outliers. We conducted experiments on a largescale dataset of 1,974,675 QoS values to evaluate our approach. The experimental results show that our approach performs better than state-of-the-art baseline approaches. © 2022 IEEE.

关键词： Forecasting

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