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41st International Conference on Machine Learning, ICML 2024

作者： Li, Baohong Li, Haoxuan Xiong, Ruoxuan Wu, Anpeng Wu, Fei Kuang, Kun College of Computer Science and Technology Zhejiang University Hangzhou China Center for Data Science Peking University Beijing China Department of Quantitative Theory & Methods Emory University Atlanta United States

One of the significant challenges in treatment effect estimation is collider bias, a specific form of sample selection bias induced by the common causes of both the treatment and outcome. Identifying treatment effects under collider bias requires well-defined shadow variables in observational data, which are assumed to be related to the outcome and independent of the sample selection mechanism, conditional on the other observed variables. However, finding a valid shadow variable is not an easy task in real-world scenarios and requires domain-specific knowledge from experts. Therefore, in this paper, we propose a novel method that can automatically learn shadow-variable representations from observational data without prior knowledge. To ensure the learned representations satisfy the assumptions of the shadow variable, we introduce a tester to perform hypothesis testing in the representation learning process. We iteratively generate representations and test whether they satisfy the shadow-variable assumptions until they pass the test. With the help of the learned shadow-variable representations, we propose a novel treatment effect estimator to address collider bias. Experiments show that the proposed methods outperform existing treatment effect estimation methods under collider bias and prove their potential application value. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Adaptive Compression-Encryption Scheme for Medical Image Based on Improved Compressive Sensing and Deoxyribonucleic Acid Coding-Compression

Adaptive Compression-Encryption Scheme for Medical Image Bas...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xue, Xianglian Jin, Haiyan Zhou, Changjun Xi'an University of Technology School of Computer Science and Engineering Xi'an China Shaanxi University of Chinese Medicine Section of Computer Teaching and Research Xianyang China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Zhejiang Normal University School of Computer Science and Technology Jinhua China

ISBN: (纸本)9798350386226

Medical images often occupy large storage space and contain patient privacy or sensitive information, which makes them difficult and unsafe to be transmitted through the network. This paper proposed an adaptive compression-encryption scheme based on improved compressive sensing (CS) and deoxyribonucleic acid (DNA) coding-compression, which helps to solve the above problem. In the proposed scheme, first the original medical image was compressed to a floating point CS matrix using the discrete wavelet transform and the partial Hadamard matrix. The CS matrix was then normalized and rounded for matrix quantification. The result of quantification was fed to DNA fixed encoding and DNA run length coding for encryption and secondary compression. Finally the compressed-encrypted image was obtained after DNA dynamic encoding-decoding and regroups operation. The proposed scheme was tested against 9 images and proved to be effective in reducing the quantization errors and enhancing the compression performance. For instance, when the benchmark compression ratio (CR) is 0.5, the CR can be reduced by 5%(CR = 0.4453) ∼ 23%(CR = 0.2636), with the corresponding peak signal-to-noise ratio values consistently surpassing the benchmark. Furthermore, the execution of DNA compression and DNA dynamic encoding-decoding provided double guarantee for the algorithm's security. © 2024 IEEE.

关键词： Encoding (symbols)

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teaching schoolchildren to program in python environment in online communities 1

Teaching schoolchildren to program in python environment in ...

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1st International Conference on Technology Enhanced Learning in Higher Education, TELE 2021

作者： Kasyanov, Sergey Komissarova, Svetlana Volgograd State Socio-Pedagogical University The Department of Computer Science and Methods of Teaching Computer Science Volgograd Russia

The article deals with the issues of teaching the schoolchildren of the online communities of computer programming in Python environment with the use of the systems of Learning Management Systems and the services of t... 详细信息

ISBN: (纸本)9781665443968

关键词： Python

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THE INCLUSION-EXCLUSION PRINCIPLE AND RECURRENCES FOR PARTITION NUMBERS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期87卷 97-106页

作者： Ballantine, Cristina Merca, Mircea Department of Mathematics and Computer Science College of The Holy Cross WorcesterMA01610 United States Department of Mathematical Methods and Models Fundamental Sciences Applied in Engineering Research Center National University of Science and Technology Politehnica Bucharest BucharestRO-060042 Romania Academy of Romanian Scientists BucharestRO-050044 Romania

The inclusion-exclusion principle together with Legendre type theorems for number of distinct restricted partitions weighted by the parity of their length are used to give several recurrence relations for restricted partition numbers as well as for overpartition numbers. In the case of overpartitions, POD partitions and 3-color partitions, we give combinatorial proofs for the Legendre type theorems and arrive to combinatorial proofs for the recurrence relations. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Combinatorial mathematics

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ControlVideo: conditional control for one-shot text-driven video editing and beyond

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science China(Information sciences) 2025年第3期68卷 150-162页

作者： Min ZHAO Rongzhen WANG Fan BAO Chongxuan LI Jun ZHU Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML CenterTsinghua Laboratory of Brain and Intelligence Lab Tsinghua University ShengShu Technology Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Pazhou Laboratory (Huangpu)

This paper presents ControlVideo for text-driven video editing — generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions(such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r.t. the source content, and temporal consistency, all while aligning with the text. By incorporating low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing(e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous studies achieved. The code is available at https://***/thu-ml/controlvideo.

关键词： diffusion models controllable generation text-driven editing video editing long video editing

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Enhancing Timely Graduations: An Explainable AI Approach to Predict Academic Risks in South African Students

Enhancing Timely Graduations: An Explainable AI Approach to ...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Bokgoshi, Lenny Sixhaxa, Khanyisile Jadhav, Ashwini Nyamane, Seipati Ajoodha, Ritesh School of Computer Science and Applied Mathematics University of Witwatersrand Johannesburg South Africa University of Witwatersrand Science Teaching and Learning Unit Faculty of Science Johannesburg South Africa Sol Plaatje University Department of Computer Science and Information Technology Kimberley South Africa

ISBN: (纸本)9798350327816

With rising dropout rates and extended degree completion times in South African institutions, there's a pressing need to better understand and address the hurdles faced by students during their academic journey. This research harnesses the power of explainable AI to predict and classify students based on their likelihood of not completing their degrees on time. Utilizing synthetic data generated via a Bayesian network, we used predictive models that categorize students into four distinct risk profiles. This clarity in prediction not only illuminates the underlying causes of academic delays but also empowers faculty, advisors, and student support services with actionable insights. The goal of this research is to facilitate timely interventions, tailored support, and seamless transitions for students transferring between universities, ensuring more students can realize their academic aspirations within expected time frames. © 2023 IEEE.

关键词： academic performance at-risk students classification models Machine learning student attrition

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A Large-Scale Group Decision-Making Approach Employing Large Language Models to Detect Assertive Groups 23

A Large-Scale Group Decision-Making Approach Employing Large...

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23rd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： Carbó-García, Santiago Trillo, José Ramón Tapia, Juan Miguel Pérez, Ignacio Javier Herrera-Viedma, Enrique Cabrerizo, Francisco Javier Andalusian Research Institute in Data Science and Computational Intelligence Department of Computer Science and Artificial Intelligence University of Granada Granada Spain Department of Quantitative Methods for Business and Economics University of Granada Granada Spain

ISBN: (纸本)9781643685380

Large-Scale Group Decision-Making, propelled by the advent of Large Language Models and the imperative for assertiveness in decision-making processes, emerges as a pivotal area of research. This paper navigates through the landscape of Large-Scale Group Decision-Making, delineating its significance across diverse domains, including social networks and e-democracy. Amidst its nascent status, Large-Scale Group Decision-Making encounters formidable challenges, particularly in information management and fostering consensus among a multitude of experts. This paper aims to illuminate the goals and hurdles facing Large-Scale Group Decision-Making approaches through an exhaustive review of contemporary literature and methodologies. By confronting these challenges head-on, Large-Scale Group Decision-Making holds the potential to redefine decision-making paradigms, bolster assertiveness, and elevate collective problem-solving capabilities in an increasingly interconnected world. © 2024 IOS Press. All rights reserved.

关键词： Information management

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Breast Cancer Classification Based on KNNI Imputation of Missing Data 5

Breast Cancer Classification Based on KNNI Imputation of Mis...

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5th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2023

作者： Wang, Yong Zhu, Rong Center for Experimental Teaching and Equipment Management Qufu Normal University Rizhao China School of Computer Science Qufu Normal University Rizhao China

ISBN: (纸本)9781643684840

Some disease datasets have different degrees of missing, which will lead to the problem of low classification accuracy. To improve the effectiveness of breast cancer disease detection and diagnosis, a classification prediction method combining KNNI and XGBoost was proposed and applied to the classification and analysis of breast cancer data. First, the KNNI method is used to impute the missing data in the breast cancer patient dataset;Then, the original dataset is equalized by the SMOTE oversampling method;Finally, XGBoost is used to extract features that are strongly related to breast cancer malignancies as the input of the model, optimize the XGBoost model by grid search algorithm, find the optimal model parameters, and classify and diagnose the breast cancer dataset. The experimental results show that KNNI can effectively recover the lost data, improve the data quality, and improve the subsequent classification accuracy. Applying imputation methods to flexibly apply missing data to machine learning methods holds great promise. © 2024 The Authors.

关键词： Diseases

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Hyper Elliptic Curve Based Certificateless Signcryption Scheme for Secure IIoT Communications

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computers, Materials & Continua 2022年第5期71卷 2515-2532页

作者： Usman Ali Mohd Yamani Idna Idris Jaroslav Frnda Mohamad Nizam Bin Ayub Roobaea Alroobaea Fahad Almansour Nura Modi Shagari Insaf Ullah Ihsan Ali Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science University of SwatSaidu Sharif19130Pakistan Center for Research in Mobile Cloud Computing University of MalayaKuala Lumpur50603Malaysia Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026 ZilinaSlovakia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Sciences and Arts in RassQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science Hamdard Institute of Engineering and TechnologyIslamabad44000Pakistan

Industrial internet of things (IIoT) is the usage of internet of things(IoT) devices and applications for the purpose of sensing, processing andcommunicating real-time events in the industrial system to reduce the unnecessary operational cost and enhance manufacturing and other industrial-relatedprocesses to attain more profits. However, such IoT based smart industriesneed internet connectivity and interoperability which makes them susceptibleto numerous cyber-attacks due to the scarcity of computational resourcesof IoT devices and communication over insecure wireless channels. Therefore, this necessitates the design of an efficient security mechanism for IIoTenvironment. In this paper, we propose a hyperelliptic curve cryptography(HECC) based IIoT Certificateless Signcryption (IIoT-CS) scheme, with theaim of improving security while lowering computational and communicationoverhead in IIoT environment. HECC with 80-bit smaller key and parameterssizes offers similar security as elliptic curve cryptography (ECC) with 160-bitlong key and parameters sizes. We assessed the IIoT-CS scheme security byapplying formal and informal security evaluation techniques. We used Realor Random (RoR) model and the widely used automated validation of internet security protocols and applications (AVISPA) simulation tool for formalsecurity analysis and proved that the IIoT-CS scheme provides resistance tovarious attacks. Our proposed IIoT-CS scheme is relatively less expensivecompared to the current state-of-the-art in terms of computational cost andcommunication overhead. Furthermore, the IIoT-CS scheme is 31.25% and 51.31% more efficient in computational cost and communication overhead,respectively, compared to the most recent protocol.

关键词： IoT security authentication protocols hyperelliptic curve cryptography certificateless public key cryptography

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On the Issue of Developing Financial Literacy of Future Digital Consultants in the Context of the Digitalisation of Society

On the Issue of Developing Financial Literacy of Future Digi...

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Technology Enhanced Learning in Higher Education (TELE), International Conference on

作者： Sergey Kasyanov Katerina Fadeeva The Department of Computer Science and Methods of Teaching Computer Science Volgograd State Socio-Pedagogical University Volgograd Russia The Department of Economics and Entrepreneurship Volgograd State Technical University Volgograd Russia

This study delves into the unique aspects of the formation of digital financial literacy among future digital consultants., particularly focusing on the modules of the training program and the structure of the module ... 详细信息

ISBN: (数字)9798350353532

ISBN: (纸本)9798350353549

关键词： Training Sociology Focusing Organizations Security Statistics Investment

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