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arXiv 2023年

作者： Jiang, Lu Wang, Yibin Wang, Jianan Wang, Pengyang Yin, Minghao School of Computer Science and Information Technology Northeast Normal University China Department of Computer and Information Science University of Macau China Key Laboratory of Applied Statistics of MOE Northeast Normal University Changchun China

In this paper, we study the problem of MOOC quality evaluation which is essential for improving the course materials, promoting students' learning efficiency, and benefiting user services. While achieving promising performances, current works still suffer from the complicated interactions and relationships of entities in MOOC platforms. To tackle the challenges, we formulate the problem as a course representation learning task-based and develop an Information-aware Graph Representation Learning(IaGRL) for multi-view MOOC quality evaluation. Specifically, We first build a MOOC Heterogeneous Network (HIN) to represent the interactions and relationships among entities in MOOC platforms. And then we decompose the MOOC HIN into multiple single-relation graphs based on meta-paths to depict the multi-view semantics of courses. The course representation learning can be further converted to a multi-view graph representation task. Different from traditional graph representation learning, the learned course representations are expected to match the following three types of validity: (1) the agreement on expressiveness between the raw course portfolio and the learned course representations;(2) the consistency between the representations in each view and the unified representations;(3) the alignment between the course and MOOC platform representations. Therefore, we propose to exploit mutual information for preserving the validity of course representations. We conduct extensive experiments over real-world MOOC datasets to demonstrate the effectiveness of our proposed method. © 2023, CC BY.

关键词： Heterogeneous networks

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Detecting Violations of Differential Privacy for Quantum Algorithms

arXiv

引用

arXiv 2023年

作者： Guan, Ji Fang, Wang Huang, Mingyu Ying, Mingsheng State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

Quantum algorithms for solving a wide range of practical problems have been proposed in the last ten years, such as data search and analysis, product recommendation, and credit scoring. The concern about privacy and other ethical issues in quantum computing naturally rises up. In this paper, we define a formal framework for detecting violations of differential privacy for quantum algorithms. A detection algorithm is developed to verify whether a (noisy) quantum algorithm is differentially private and automatically generates bugging information when the violation of differential privacy is reported. The information consists of a pair of quantum states that violate the privacy, to illustrate the cause of the violation. Our algorithm is equipped with Tensor Networks, a highly efficient data structure, and executed both on TensorFlow Quantum and TorchQuantum which are the quantum extensions of famous machine learning platforms - TensorFlow and PyTorch, respectively. The effectiveness and efficiency of our algorithm are confirmed by the experimental results of almost all types of quantum algorithms already implemented on realistic quantum computers, including quantum supremacy algorithms (beyond the capability of classical algorithms), quantum machine learning models, quantum approximate optimization algorithms, and variational quantum eigensolvers with up to 21 quantum bits. © 2023, CC BY.

关键词： Quantum noise

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Tenacity prediction of polyester materials based on an ensemble soft measurement model

Tenacity prediction of polyester materials based on an ensem...

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2024 China Automation Congress, CAC 2024

作者： Duan, Jiaxu Wang, Jun Tao, Youji Ma, Zhiping Wu, Xin Hu, Zhou State Key Laboratory of Environmental Adaptability for Industrial Products China National Electric Apparatus Research Institute Co. Ltd Guangzhou China Kinte Materials Science and Technology Co. Ltd Guangzhou China Department of Computer Science Hunan University of Technology and Business Changsha China School of Electronic Information and Electrical Engineering Yangtze University Jingzhou China

ISBN: (纸本)9798350368604

In the development process of polyester powder coating materials, it has great guiding significance for the development of new materials as the polyester material properties can be predicted through the given monomer structure and formula. In order to meet the requirements of rapid development of polyester materials, this paper establishes a prediction method based on an ensemble soft measurement model (ESM) for one key property-tenacity in prompting the acceleration of development cycle of polyester products. The processed data employed to train and test the model were collected from a local material company, and it covers over 100 kinds of polymerized resins. Firstly, the molecular expressions of monomers are converted to SMILES codes and further translated to ECFP fingerprints. Then, the principal component analysis (PCA) method is adopted to extract the elements with higher importance in each fingerprint. Finally, the ensemble model that consists of 4 machine learning models was established to make the multi-classification decision in tenacity prediction. The effectiveness of the prediction model is evaluated by comparing the predicted and the practical tensile test results. The results demonstrate that this approach achieves better performance in the material property prediction as well as generalization capability. © 2024 IEEE.

关键词： Tensile testing

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Optical Fiber Tip Learns to Identify Liquids 27

Optical Fiber Tip Learns to Identify Liquids

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27th International Conference on Optical Fiber Sensors, OFS 2022

作者： Zhu, Chen Naku, Wassana Nambisan, Anand K. Gerald, Rex E. Huang, Jie Research Center for Optical Fiber Sensing Zhejiang Laboratory Hangzhou311100 China Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO65409 United States

We propose and demonstrate an extremely simple fiber-optic tip sensor system to identify liquids through droplet evaporation events on the fiber tip, analyzed using machine learning techniques. © 2022 The Author(s).

ISBN: (纸本)9781957171142

关键词： Optical fibers

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Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment

arXiv

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arXiv 2024年

作者： Zhang, Congzhi Zhang, Linhai Wu, Jialong He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King’s College London United Kingdom The Alan Turing Institute United Kingdom

Despite the notable advancements of existing prompting methods, such as In-Context Learning and Chain-of-Thought for Large Language Models (LLMs), they still face challenges related to various biases. Traditional debiasing methods primarily focus on the model training stage, including approaches based on data augmentation and reweighting, yet they struggle with the complex biases inherent in LLMs. To address such limitations, the causal relationship behind the prompting methods is uncovered using a structural causal model, and a novel causal prompting method based on front-door adjustment is proposed to effectively mitigate LLMs biases. In specific, causal intervention is achieved by designing the prompts without accessing the parameters and logits of LLMs. The chain-of-thought generated by LLM is employed as the mediator variable and the causal effect between input prompts and output answers is calculated through front-door adjustment to mitigate model biases. Moreover, to accurately represent the chain-of-thoughts and estimate the causal effects, contrastive learning is used to fine-tune the encoder of chain-of-thought by aligning its space with that of the LLM. Experimental results show that the proposed causal prompting approach achieves excellent performance across seven natural language processing datasets on both open-source and closed-source LLMs. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Software Reliability Prediction Using Ensemble Learning on Selected Features in Imbalanced and Balanced Datasets: A Review

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computer Systems science & Engineering 2024年第6期48卷 1513-1536页

作者： Suneel Kumar Rath Madhusmita Sahu Shom Prasad Das Junali Jasmine Jena Chitralekha Jena Baseem Khan Ahmed Ali Pitshou Bokoro Department of Computer Science and Engineering C.V.Raman Global UniversityBhubaneswar752054India Department of Computer Science and Engineering Birla Global UniversityBhubaneswar751029India School of Computer Engineering KIIT(Deemed to be)UniversityBhubaneswar751024India School of Electrical Engineering KIIT(Deemed to be)UniversityBhubaneswar751024India Department of Electrical and Computer Engineering Hawassa UniversityHawassaP.O.Box 05Ethiopia Center for Renewable Energy and Microgrids Huanjiang LaboratoryZhejiang UniversityShaoxing311816China Department of Electrical and Electronic Engineering Technology University of JohannesburgJohannesburg2028South Africa

Redundancy,correlation,feature irrelevance,and missing samples are just a few problems that make it difficult to analyze software defect ***,it might be challenging to maintain an even distribution of data relating to both defective and non-defective *** latter software class’s data are predominately present in the dataset in the majority of experimental *** objective of this review study is to demonstrate the effectiveness of combining ensemble learning and feature selection in improving the performance of defect *** the successful feature selection approach,a novel variant of the ensemble learning technique is analyzed to address the challenges of feature redundancy and data imbalance,providing robustness in the classification *** overcome these problems and lessen their impact on the fault classification performance,authors carefully integrate effective feature selection with ensemble learning *** selection demonstrates that a significant area under the receiver operating curve(ROC)can be attributed to only a small subset of *** Greedy forward selection(GFS)technique outperformed Pearson’s correlation method when evaluating feature selection techniques on the *** learners,such as random forests(RF)and the proposed average probability ensemble(APE),demonstrate greater resistance to the impact of weak features when compared to weighted support vector machines(W-SVMs)and extreme learning machines(ELM).Furthermore,in the case of the NASA and Java datasets,the enhanced average probability ensemble model,which incorporates the Greedy forward selection technique with the average probability ensemble model,achieved remarkably high accuracy for the area under the *** approached a value of 1.0,indicating exceptional *** review emphasizes the importance of meticulously selecting attributes in a software dataset to accurately classify damaged *** addition,t

关键词： Ensemble classifier hybrid classifier software reliability prediction

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Utilizing Lexicon-enhanced Approach to Sensitive Information Identification 27

Utilizing Lexicon-enhanced Approach to Sensitive Information...

引用

27th International Conference on Automation and Computing, ICAC 2022

作者： Cai, Lihua Zhou, Yujue Ding, Yulong Jiang, Jie Yang, Shuang-Hua Institute of Risk Analysis Prediction & Management Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen Key Laboratory of Future Industrial Internet Safety and Security Southern University of Science and Technology Shenzhen China Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Shenzhen China Department of Computer Science University of Warwick CoventryCV4 7AL United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Large-scale sensitive information leakage incidents have occurred frequently, causing huge impacts and losses to individuals, enterprises, and society. Most sensitive information exists in unstructured data, making it challenging for people to identify when it is leaked, an important cause of information leakage. Therefore, sensitive information identification from unstructured data has received extensive attention. In addition, the smallest unit of Chinese is a character, so its lexical boundary is flexible, which makes it very difficult to identify sensitive information in Chinese. It is worth mentioning that there are no publicly available datasets in this field of sensitive information identification due to the sensitivity. To address the above challenges, we first create the SPIDC (Sensitive Personal Information Dataset in Chinese) and release it as a public resource for related research. Second, we apply the existing sensitive information identification methods on the English datasets to the Chinese datasets. In addition, to solve the problem of uncertainty and ambiguity of Chinese vocabulary boundary, we apply three lexicon-enhanced technologies from NER (Named Entity Recognition) to the Chinese sensitive information identification for the first time. Experimental results on the SPIDC show that the lexicon-enhanced approach has better performance than other methods. © 2022 IEEE.

关键词： Sensitive data

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MEDA Biochip based Single- Target Fluidic Mixture Preparation with Minimum Wastage

MEDA Biochip based Single- Target Fluidic Mixture Preparatio...

引用

Euromicro Symposium on Digital System Design

作者： Debraj Kundu Sudip Roy Department of Computer Science and Engineering CoDA Laboratory IIT Roorkee India

ISBN: (数字)9781665474047

ISBN: (纸本)9781665474054

Sample preparation is an inherent procedure of many biochemical applications, and digital microfluidic biochips (DMBs) proved to be very effective in performing such a procedure. In a single mixing step, conventional DMBs can mix two droplets in 1:1 ratio only. Due to this limitation, DMBs suffer from heavy fluid wastage and large number of mixing steps. However, the next generation DMBs, i.e., micro-electrode-dot-array (MEDA) biochips can realize multiple mixing ratios and are able to overcome a lot of those limitations. In this paper, we present a heuristic-based sample preparation algorithm, specifically a mixing algorithm called Division by Factor Method for Mixing that exploits the mixing models of MEDA biochips. We propose another mixing algorithm for MEDA biochips called Single Target Waste Minimization (STWM), which minimizes the wastage of fluids and determines an optimized mixing graph. Simulation results confirm that the proposed STWM method outperforms the state-of-the-art method in terms of minimizing the number of waste fluids, reducing the total reagent usage, and minimizing the number of mixing operations.

关键词： Digital microfluidic biochips Fluids Digital systems Heuristic algorithms Biological system modeling Simulation Biochips

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Electron Transportation Engineering for Record High External Quantum Efficiency of 16.8% for Quantum Rod Light-Emitting Diodes

Electron Transportation Engineering for Record High External...

引用

SID International Symposium Digest of Technical Papers, 2023

作者： Mallem, Kumar Prodanov, Maksym F. Liao, Zebing Kang, Chengbin Vashchenko, Valeri V. Srivastava, Abhishek K. State Key Laboratory on Advanced Displays and Optoelectronics Technologies and Centre for Display Research Department of Electronics and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong IAS Center for Quantum Technologies The Hong Kong University of Science and Technology Hong Kong

The semiconductor quantum rods (QRs) offer unique benefits that differ from the spherical QDs, such as linearly polarized light and higher light outcoupling efficiency, which can double the external quantum efficiency of the light-emitting diodes (LEDs). Despite having many potential advantages, the QRs are not well explored as QDs in electroluminescent LEDs. In this work, we synthesized CdSe/CdS QRs and verified them in QR-based LEDs. The charge injection into the QRLEDs is precisely controlled to get the maximum charge balance in the device to improve the device's efficiency. We achieved an EQE of 16.8%, which is the highest EQE reported for QRLEDs. The turn-on voltage, peak luminance, and current efficiency (CE) of the QRLEDs are 2.2 V, 10000 cd/m^2, and 11.6%, respectively. The electron injection does not influence the device turn-on voltage but severely impacts the QRLEDs brightness, EQE and CE. The spin coated QRs film has small voids on the surface, which needs to be optimized further to improve the QRLEDs efficiency. Overall, these findings have insightful information on QRLEDs fabrication, which can open up a new opportunity for device applications. © 2023, John Wiley and Sons Inc. All rights reserved.

关键词： II-VI semiconductors

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TriSAM: Tri-Plane SAM for zero-shot cortical blood vessel segmentation in VEM images

arXiv

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arXiv 2024年

作者： Wan, Jia Li, Wanhua Adhinarta, Jason Ken Banerjee, Atmadeep Sjostedt, Evelina Wu, Jingpeng Lichtman, Jeff Pfister, Hanspeter Wei, Donglai The School of Computer Science and Technology Harbin Institute of Technology Shenzhen China The School of Engineering and Applied Sciences Harvard University United States The Department of Computer Science Boston College United States The Department of Molecular and Cellular Biology Harvard University United States The Lin Gang Laboratory China

While imaging techniques at macro and mesoscales have garnered substantial attention and resources, microscale Volume Electron Microscopy (vEM) imaging, capable of revealing intricate vascular details, has lacked the necessary benchmarking infrastructure. In this paper, we address a significant gap in this field of neuroimaging by introducing the first-in-class public benchmark, BvEM, designed specifically for cortical blood vessel segmentation in vEM images. Our BvEM benchmark is based on vEM image volumes from three mammals: adult mouse, macaque, and human. We standardized the resolution, addressed imaging variations, and meticulously annotated blood vessels through semi-automatic, manual, and quality control processes, ensuring high-quality 3D segmentation. Furthermore, we developed a zero-shot cortical blood vessel segmentation method named TriSAM, which leverages the powerful segmentation model SAM for 3D segmentation. To extend SAM from 2D to 3D volume segmentation, TriSAM employs a multi-seed tracking framework, leveraging the reliability of certain image planes for tracking while using others to identify potential turning points. This approach effectively achieves long-term 3D blood vessel segmentation without model training or fine-tuning. Experimental results show that TriSAM achieved superior performances on the BvEM benchmark across three species. Our dataset, code, and model are available online at https://***/bvem. © 2024, CC BY-NC-ND.

关键词： Image segmentation

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