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IEEE International Conference on data Mining (ICDM)

作者： Kai-Huang Lai Wu-Dong Xi Xing-Xing Xing Wei Wan Chang-Dong Wang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Contribution during internship at NetEase Games UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

In the evolving landscape of sequential recommendation systems, the application of Large Language Models (LLMs) is increasingly prominent. However, current attempts typically utilize general-purpose LLMs, which present a mismatch in capability and a large semantic gap relative to the specialized needs of recommendation tasks. To tackle these issues, we introduce RecCoder, an innovative model that reformulates sequential recommendation as a code completion task. This approach leverages the superior reasoning capability of code LLMs as a backbone, aligning well with the requirements of recommendation systems. To bridge the semantic gap, RecCoder creates extra tokens for each item and employs item content to initialize token embeddings. Furthermore, we have developed a suite of Semantic Adaptation Fine-tuning tasks, tailored to enhance the model's acquisition of both content and collaborative semantic information, thus aligning the model's intrinsic capabilities with the unique demands of recommendation tasks. Through extensive testing on three public datasets, RecCoder has shown remarkable improvements over existing models in terms of recommendation accuracy and efficiency. This success highlights the substantial yet previously underexplored potential of code LLMs in improving recommendation accuracy and efficiency, suggesting a promising new direction for future research in this area. The implementation code is accessible at https://***/AllminerLab/Code-for-RecCoder-master.

关键词： Adaptation models Codes Accuracy Large language models Semantics Cognition data models data mining Recommender systems Testing

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Contrastive learning for Adapting Language Model to Sequential Recommendation

Contrastive Learning for Adapting Language Model to Sequenti...

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IEEE International Conference on data Mining (ICDM)

作者： Fei-Yao Liang Wu-Dong Xi Xing-Xing Xing Wei Wan Chang-Dong Wang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Contribution during internship at NetEase Games UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

With the explosive growth of information, recommendation systems have emerged to alleviate the problem of information overload. In order to improve the performance of recommendation systems, many existing methods introduce Large Language Models to extract textual information from description text. However, Large Language Models are trained on large-scale generic textual data and may face a semantic gap for downstream recommendation tasks. To address the above issues, we propose Contrastive learning for Adapting Language Model to Sequential Recommendation (CLA-Rec). In CLA-Rec, we first extract text embeddings from description text using Large Language Models and align the text embeddings learned by Large Language Models with the collaborative information through contrastive learning to obtain high-quality item representations. Through semantic alignment, we bridge the semantic gap between Large Language Models and the recommendation task. To map textual information and collaborative information into user representations, we utilize a Transformer model to learn user representations and capture user preferences by combining the semantically aligned item representations. Extensive experiments on three public datasets demonstrate that our method outperforms state-of-the-art approaches on multiple evaluation metrics, illustrating the effectiveness of the CLA-Rec model in adapting Large Language Models to recommendation tasks.

关键词： Measurement Adaptation models Large language models Semantics Collaboration Contrastive learning Transformers data models data mining Recommender systems

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Alchemical insights into approximately quadratic energies of iso-electronic atoms

arXiv

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arXiv 2024年

作者： Krug, Simon León Anatole von Lilienfeld, O. Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Chemical Physics Theory Group Department of Chemistry University of Toronto St. George Campus TorontoON Canada Department of Materials Science and Engineering University of Toronto St. George Campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Department of Physics University of Toronto St. George Campus TorontoON Canada Acceleration Consortium University of Toronto TorontoON Canada

Accurate quantum mechanics based predictions of property trends are so important for materials design and discovery that even inexpensive approximate methods are valuable. We use the Alchemical Integral Transform (AIT) to study multi-electron atoms, and to gain a better understanding of the approximately quadratic behavior of energy differences between iso-electronic atoms in their nuclear charges. Based on this, we arrive at the following simple analytical estimate of energy differences between any two iso-electronic atoms, ∆E ≈ −(1 + 2γ √Ne − 1) ∆Z Z¯. Here, γ ≈ 0.3766 ± 0.0020 Ha corresponds to an empirical constant, and Ne, ∆Z, and Z¯ respectively to electron number, and nuclear charge difference and average. We compare the formula’s predictive accuracy using experimental numbers and non-relativistic, numerical results obtained via DFT (pbe0) for the entire periodic table up to Radon. A detailed discussion of the atomic Helium-series is included. © 2024, CC BY.

关键词： Atoms

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Transformer-based Annotation Bias-aware Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Liao, Zehui Xie, Yutong Hu, Shishuai Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Australian Institute for Machine Learning The University of Adelaide AdelaideSA Australia Ningbo Institute of Northwestern Polytechnical University Ningbo315048 China Research and Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

Manual medical image segmentation is subjective and suffers from annotator-related bias, which can be mimicked or amplified by deep learning methods. Recently, researchers have suggested that such bias is the combination of the annotator preference and stochastic error, which are modeled by convolution blocks located after decoder and pixel-wise independent Gaussian distribution, respectively. It is unlikely that convolution blocks can effectively model the varying degrees of preference at the full resolution level. Additionally, the independent pixel-wise Gaussian distribution disregards pixel correlations, leading to a discontinuous boundary. This paper proposes a Transformer-based Annotation Bias-aware (TAB) medical image segmentation model, which tackles the annotator-related bias via modeling annotator preference and stochastic errors. TAB employs the Transformer with learnable queries to extract the different preference-focused features. This enables TAB to produce segmentation with various preferences simultaneously using a single segmentation head. Moreover, TAB takes the multivariant normal distribution assumption that models pixel correlations, and learns the annotation distribution to disentangle the stochastic error. We evaluated our TAB on an OD/OC segmentation benchmark annotated by six annotators. Our results suggest that TAB outperforms existing medical image segmentation models which take into account the annotator-related bias. © 2023, CC BY-NC-ND.

关键词： Gaussian distribution

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Parkinson’s Disease Detection using Hand Drawn Spirals and Waves

Parkinson’s Disease Detection using Hand Drawn Spirals and ...

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Recent Advances in Science and engineering Technology (ICRASET), International Conference on

作者： Shashidhara H S Saish K Kalbag Tanya Mathias Srinivasa K G Niranjanamurthy M Department of Informaition Science and Engineering M S Ramaiah Institute of Techology and Engineering Affiliated to Visvesvaraya Technological University Belgaum Bangalore India Department of Data Science and Artififial Intelligece International Institute of Information Technology Naya Raipur Chhattisgarh India Department of Artificial Intelligence and Machine Learning B M S Institute of Technology and Management Affiliated to Visvesvaraya Technological University Belgaum Bangalore India

Parkinson’s disease, a neurodegenerative disorder, affects millions of people worldwide. Early detection is critical for effective treatment and management. Early detection of Parkinson’s disease is challenging due to the lack of accessible and non-invasive screening methods. Current diagnostic techniques often require specialized equipment and invasive procedures, limiting their feasibility for widespread screening. There is a need for cost-effective and accessible approaches that can accurately detect early stages of Parkinson’s disease. The model requires a high degree of precision in distinguishing between these patterns, which is a significant challenge to overcome. In this research work, we proposed a novel approach for detecting early stages of Parkinson’s disease by analyzing hand-drawn spirals and waves in photographs.

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Affective Computing Has Changed: The Foundation Model Disruption

arXiv

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arXiv 2024年

作者： Schuller, Björn Mallol-Ragolta, Adria Almansa, Alejandro Peña Tsangko, Iosif Amin, Mostafa M. Semertzidou, Anastasia Christ, Lukas Amiriparian, Shahin MRI Technical University of Munich Germany MDSI - Munich Data Science Institute Germany MCML - Munich Center for Machine Learning Germany GLAM - Group on Language Audio & Music Imperial College London United Kingdom EIHW - Embedded Intelligence for Health Care & Wellbeing University of Augsburg Germany School of Engineering Universidad Autonoma de Madrid Spain AI R&D Team SyncPilot GmbH Germany

The dawn of Foundation Models has on the one hand revolutionised a wide range of research problems, and, on the other hand, democratised the access and use of AI-based tools by the general public. We even observe an incursion of these models into disciplines related to human psychology, such as the Affective Computing domain, suggesting their affective, emerging capabilities. In this work, we aim to raise awareness of the power of Foundation Models in the field of Affective Computing by synthetically generating and analysing multimodal affective data, focusing on vision, linguistics, and speech (acoustics). We also discuss some fundamental problems, such as ethical issues and regulatory aspects, related to the use of Foundation Models in this research area. © 2024, CC BY-NC-SA.

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learning nuclear cross sections across the chart of nuclides with graph neural networks

arXiv

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arXiv 2024年

作者： Mitra, Sinjini Choi, Hongjun Liu, Shusen Glatt, Ruben Wendt, Kyle Schunck, Nicolas Geometric Media Lab Arizona State University 1151 S Forest Ave. TempeAZ85281 United States Machine Intelligence Group Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94551 United States Machine Learning Group Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA94551 United States Nuclear Data and Theory Group Nuclear and Chemical Sciences Division Lawrence Livermore National Laboratory LivermoreCA94551 United States

In this work, we explore the use of deep learning techniques to learn how nuclear cross-sections change as we add or remove protons and neutrons. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime that are the most important in nuclear science and engineering. We use variational autoencoders (VAEs) and implicit neural representations (INRs) to build a learned feature representation space of nuclear cross sections and reduce the dimensionality of the problem. We then train graph neural networks (GNNs) on the resulting latent space to leverage the topological information encoded in the chart of isotopes and to capture the variations of cross sections with the number of particles in nuclei. We find that hypernetworks based on INRs significantly outperforms VAEs in encoding nuclear cross-sections. This superiority is attributed to INR’s ability to model complex, varying frequency details, which enables lower prediction errors when combined with GNNs. We also observe that GNN optimization is much more successful when performed in the latent space, whether using INRs or VAEs. However VAEs’ continuous representation also allows for direct GNN training in the original input space. We leverage these representational learning techniques and successfully predict cross sections for a 17 × 17 block of nuclei with high accuracy and precision. Using clustering algorithms, we map groups of latent vectors into regions of the nuclear chart and show that VAEs and INRs can discover some of the neutron magic numbers. These findings suggest that deep-learning models based on representation encoding of cross-sections combined with graph neural networks hold significant potential in augmenting nuclear theory models, e.g., providing reliable estimates of covariances of cross sections, including cross-material covariances. © 2024, CC BY.

关键词： Neutrons

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Deep learning for Uneven data in Industrial IoT Using a Distributed Bias-Aware Adversarial Network

Deep Learning for Uneven Data in Industrial IoT Using a Dist...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Raj Kumar Gupta Naveena N B. Srinivasa Rao Rajasree RS Swagata Sarkar Kallakunta Ravi Kumar Physics Department Sardar Vallabhbhai Patel College Veer Kunwar Singh University Bhabua Ara Bihar Computer Technology-UG Kongu Engineering College Perundurai Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering & Technology Hyderabad Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore Department of Artificial intelligence and Data Science Sri Sairam Engineering College Sai Leo Nagar Chennai 44 Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram AP

In minority class and noisy data situations, supervised learning performs more favorably for the majority class but cannot generalize testing data. Performance in the aforementioned use cases might be improved with the help of neural-based data augmentation approaches for generating new data and deep convolutional models for classification. GANs (generative adversarial networks) have recently demonstrated impressive advancements in picture generation. To address the restrictions imposed by the distribution bias problem between the produced data and the unique data, and to provide a more vigorous data augmentation, the presented distribution bias aware collaborative GAN (DGAN) model for unbalanced deep learning in industrial IoT. By including an auxiliary classifier in the foundational GAN model, a comprehensive data augmentation system may be built. In particular, a provisional source of energy with random labels is envisioned and trained combatively with the classification model to appropriately augment the amount of data specimens in minority classes, and a mass fraction system is newly envisioned between two distinct feature extraction, allowing the cooperative adversarial training among some of the power source, voltage divider, and classification algorithm. The next step is to develop an augmentation approach for smart anomaly detection in class imbalance, which, by adjusting for dispersion bias using the properly balanced data, might significantly improve classification precision. Experiment assessments using real-world unbalanced datasets show the superior recital of the suggested model in addressing the distribution biased issue for multi-class classification in class imbalance for commercial IoT applications, as compared to five baseline techniques.

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OADAT: Experimental and Synthetic Clinical Optoacoustic data for Standardized Image Processing

arXiv

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arXiv 2022年

作者： Ozdemir, Firat Lafci, Berkan Deán-Ben, Xosé Luís Razansky, Daniel Perez-Cruz, Fernando Swiss Data Science Center ETH Zurich and EPFL Zurich Switzerland Institute of Pharmacology and Toxicology Institute for Biomedical Engineering Faculty of Medicine University of Zurich Switzerland Institute for Biomedical Engineering Department of Information Technology and Electrical Engineering ETH Zurich Switzerland Institute for Machine Learning Department of Computer Science ETH Zurich Switzerland

Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by subsequent detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. This enabled the exploration of a number of attractive new applications both in clinical and laboratory settings. However, no standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings. This complicates an objective comparison between new and established data processing methods, often leading to qualitative results and arbitrary interpretations of the data. In this paper, we provide both experimental and synthetic OA raw signals and reconstructed image domain datasets rendered with different experimental parameters and tomographic acquisition geometries. We further provide trained neural networks to tackle three important challenges related to OA image processing, namely accurate reconstruction under limited view tomographic conditions, removal of spatial undersampling artifacts and anatomical segmentation for improved image reconstruction. Specifically, we define 44 experiments corresponding to the aforementioned challenges as benchmarks to be used as a reference for the development of more advanced processing methods. © 2022, CC BY-NC-SA.

关键词： Photoacoustic effect

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Evaluating the Impact of Special Characters on Password Security: a Comparative Analysis

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IEEE Network 2025年

作者： He, Daojing Liu, Zhiyong Chan, Sammy Zhou, Beibei Guizani, Mohsen Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Guizhou University State Key Laboratory of Public Big Data Guizhou China Research and Development Center Guangzhou Jingyuan Security Technology Co. Ltd Guangzhou China City University of Hong Kong Kowloon Department of Electrical Engineering Hong Kong Hong Kong Shanghai Urban Construction Vocational College School of Artificial Intelligence Application Shanghai China Machine Learning Department Abu Dhabi United Arab Emirates

Text-based password authentication remains essential for identity verification on internet platforms and blockchain systems. However, as computational power advances, security threats to user passwords have intensified, raising concerns about their effectiveness. While special characters (i.e., non-alphanumeric symbols, which include punctuation marks, mathematical symbols, and other non-letter/number characters like '.', '@', '#', '$', and similar) are often recommended to enhance password strength, the National Institute of Standards and Technology (NIST) prioritizes length over complexity, challenging their necessity. This study fills a critical gap in understanding user behavior regarding special character usage. By analyzing 15 datasets from Chinese, English, and German languages, we examined patterns of special character incorporation and their impact on password strength. Our findings indicate that users frequently employ predictable methods for including special characters, potentially compromising security, especially under stringent policies. We present three key contributions: (1) an analysis of special character usage patterns, (2) a quantitative assessment of strength differences using the PCFGv4.1 model, and (3) actionable recommendations for stakeholders to enhance password security practices. This research further advocates for aligning password policies with user behavior for more effective security. © 1986-2012 IEEE.

关键词： Authentication Protocol

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