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Semantics-Preserved Distortion for Personal Privacy Protection in Information Management 33rd

Semantics-Preserved Distortion for Personal Privacy Protecti...

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33rd International Conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Li, Jiajia Yang, Lu Peng, Letian Zhang, Shitou Wang, Ping Li, Zuchao Zhao, Hai Wuhan Univ Sch Informat Management Wuhan Peoples R China NAAC Key Lab Archival Intelligent Dev & Serv Wuhan Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China Univ Calif San Diego Comp Sci & Engn San Diego CA 92103 USA Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9783031723438;9783031723445

In recent years, machine learning-particularly deep learning-has significantly impacted the field of information management. While several strategies have been proposed to restrict models from learning and memorizing sensitive information from raw texts, this paper suggests a more linguistically-grounded approach to distort texts while maintaining semantic integrity. To this end, we leverage Neighboring Distribution Divergence, a novel metric to assess the preservation of semantic meaning during distortion. Building on this metric, we present two distinct frameworks for semantic-preserving distortion: a generative approach and a substitutive approach. Our evaluations across various tasks, including named entity recognition, constituency parsing, and machine reading comprehension, affirm the plausibility and efficacy of our distortion technique in personal privacy protection. We also test our method against attribute attacks in three privacy-focused assignments within the NLP domain, and the findings underscore the simplicity and efficacy of our data-based improvement approach over structural improvement approaches. Moreover, we explore privacy protection in a specific medical information management scenario, showing our method effectively limits sensitive data memorization, underscoring its practicality.

关键词： Semantic-Preserved Distortion Personal Privacy Protection Neighboring Distribution Divergence

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A Preliminary Study to Examining Per-class Performance Bias via Robustness Distributions 1st

A Preliminary Study to Examining Per-class Performance Bias ...

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1st International Symposium on AI Verification (SAIV)

作者： Bosman, Annelot W. Muenz, Anna L. Hoos, Holger H. van Rijn, Jan N. Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands Rhein Westfal TH Aachen Chair AI Methodol Aachen Germany Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9783031651113;9783031651120

As neural networks are increasingly used in sensitive real-world applications, mitigating bias of classifiers is of crucial importance. One often-used approach to controlling quality in classification tasks is to ensure that predictive performance is balanced between different classes;however, it has been shown in previous work that even if class performance is balanced, instances of some classes are easier to perturb in such a way that they are misclassified, which indicates that per-class performance bias exists. In this preliminary study, we found that even when class performance is balanced, class robustness can vary strongly when assessing the robustness of a given neural network classifier in a more nuanced fashion. For this purpose, we use robustness distributions, i.e., empirical probability distributions of some robustness metric, such as the critical epsilon value, over a set of instances. We observed that the robustness of the same class over the same data can significantly differ from each other for different neural networks;this means that even when a neural network appears to be unbiased, it might be easier to perturb instances of a given class so that they are misclassified. Furthermore, we explored the robustness distributions when we have a predefined target class, i.e., a specific class into which an instance is misclassified after perturbation. Our empirical results indicate that in most cases, there are significant differences in robustness distributions for different classes. While our empirical results reported here are for MNIST classifiers, we are currently performing experiments using the German Traffic Sign Recognition Benchmark. Furthermore, we are running experiments with retrained networks for fairness, to see whether this has a significant effect on the per-class robustness distributions. Lastly, we aim to create a robust class fairness metric based on our findings.

关键词： neural network verification adversarial robustness fairness distribution analysis

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Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning 28th

Inferring Metabolic States from Single Cell Transcriptomic D...

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28th Annual International Conference on Research in Computational Molecular Biology (RECOMB)

作者： Steach, Holly Viswanath, Siddharth He, Yixuan Zhang, Xitong Ivanova, Natalia Hirn, Matthew Perlmutter, Michael Krishnaswamy, Smita Yale Univ Dept Genet New Haven CT 06520 USA Yale Univ Dept Comp Sci New Haven CT 06520 USA Univ Oxford Dept Stat Oxford England Michigan State Univ Dept Computat Math Sci & Engn E Lansing MI 48824 USA Univ Georgia Dept Biochem & Mol Biol Athens GA 30602 USA Boise State Univ Dept Math Boise ID 83725 USA Boise State Univ Program Comp Boise ID 83725 USA Yale Univ Wu Tsai Inst New Haven CT 06520 USA Yale Univ Program Computat Biol & Bioinformat New Haven CT 06520 USA

ISBN: (纸本)9781071639887;9781071639894

The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.

关键词： metabolomics single-cell omics transcriptomics

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Explainable Hybrid Semi-parametric Model for Prediction of Power Generated by Wind Turbines 24th

Explainable Hybrid Semi-parametric Model for Prediction of P...

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24th International Conference on Computational science (ICCS)

作者： Gijon, Alfonso Eiraudo, Simone Manjavacas, Antonio Bottaccioli, Lorenzo Lanzini, Andrea Molina-Solana, Miguel Gomez-Romero, Juan Univ Granada Dept Comp Sci & AI Granada Spain Imperial Coll London Dept Comp London England Politecn Torino Energy Ctr Lab Turin Italy

ISBN: (纸本)9783031637742;9783031637759

The ever-growing sector of wind energy underscores the importance of optimizing turbine operations and ensuring their maintenance with early fault detection mechanisms. Existing empirical and physics-based models provide approximate predictions of the generated power as a function of the wind speed, but face limitations in capturing the non-linear and complex relationships between input variables and output power. Data-driven methods present new avenues for enhancing wind turbine modeling using large datasets, thereby improving accuracy and efficiency. In this study, we use a hybrid semi-parametric model to leverage the strengths of two distinct approaches in a dataset with four turbines of a wind farm. Our model comprises a physics-inspired submodel, which offers a reliable approximation of the power, combined with a non-parametric submodel to predict the residual component. This non-parametric submodel is fed with a broader set of variables, aiming to capture phenomena not addressed by the physics-based part. For explainability purposes, the influence of input features on the output of the residual submodel is analyzed using SHAP values. The proposed hybrid model finally yields a 35-40% accuracy improvement in the prediction of power generation with respect to the physics-based model. At the same time, the explainability analysis, along with the physics grounding from the parametric submodel, ensure deep understanding of the analyzed problem. In the end, this investigation paves the way for assessing the impact, and thus the potential optimization, of several unmodeled independent variables on the power generated by wind turbines.

关键词： Hybrid semi-parametric models Explainable AI Wind Turbines

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Two Heads Are Better Than One: Teaching MLPs with Multiple Graph Neural Networks via Knowledge Distillation 29th

Two Heads Are Better Than One: Teaching MLPs with Multiple G...

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29th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Yang, Bo-Wei Chang, Ming-Yi Lui, Chia-Hsun Shen, Chih-Ya Natl Tsing Hua Univ Dept Comp Sci Hsinchu Taiwan Fu Jen Catholic Univ Dept Sociol Taipei Taiwan

ISBN: (纸本)9789819755615;9789819755622

Knowledge Distillation (KD) has been successfully applied to compress and accelerate Graph Neural Networks (GNNs) in recent years. Recently, KD has been adeptly applied to boost Multi-Layer Perceptrons (MLPs), enabling them to parallel GNNs in performance. This is notable as MLPs depend solely on node features and require extensive knowledge from multiple teachers for enhancement. However, since existing multi-teacher KD methods often rely on a graph's topological data, they are typically limited to GNN-based student models, thus excluding MLPs. In this paper, we introduce an innovative method that amalgamates various GNNs into a super teacher, which is then distilled into an MLP student. Moreover, our experiments suggest the promising number of selected teachers configurations to boost the performance of the student model. Extensive experiments on five benchmark datasets show that our proposed approaches outperform the state-of-the-art methods and achieve even higher accuracy than the teacher models. Also, the inference time of our approach is 30x-60x faster than GNNs and other KD methods.

关键词： Graph neural networks Knowledge distillation Knowledge amalgamation

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Depression Detection Using Deep Learning and Natural Language Processing Techniques: A Comparative Study 26th

Depression Detection Using Deep Learning and Natural Languag...

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26th Iberoamerican Congress on Pattern Recognition (CIARP)

作者： Mesquita, Francisco Mauricio, Jose Marques, Goncalo Polytech Coimbra ISEC Rua Pedro Nunes P-3030199 Coimbra Portugal Polytech Coimbra ESTGOH Rua Gen Santos Costa P-3400124 Oliveira Dos Hospital Portugal

ISBN: (纸本)9783031490170;9783031490187

Depression is a frequently underestimated illness that significantly impacts a substantial number of individuals worldwide, making it a significant mental disorder. The world today lives fully connected, where more than half of the world's population uses social networks in their daily lives. If we interpret and understand the feelings associated with a social media post, we can detect potential depression cases before they reach a major state associated with consequences for the patient. This paper proposes the use of natural language processing (NLP) techniques to classify the sentiment associated with a post made on the Twitter social network. This sentiment can be non-depressive, neutral, or depressive. The authors collected and validated the data, and performed pre-processing and feature generation using TF-IDF and Word2Vec techniques. Various DL and ML models were evaluated on these features. The Extra Trees classifier combined with the TF-IDF technique emerged as the most successful combination for classifying potential depression sentiment in tweets, achieving an accuracy of 84.83%.

关键词： Depression Detection Sentiment Analysis Natural Language Processing (NLP) Twint Antidepressant

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Q-Learning-Augmented Grant-Free NOMA for URLLC 9th

Q-Learning-Augmented Grant-Free NOMA for URLLC

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9th International Conference on Ubiquitous Networking (UNet)

作者： Oueslati, Ibtissem Habachi, Oussama Cances, Jean Pierre Meghdadi, Vahid Sabir, Essaid Xlim 123 Av Albert Thomas F-87000 Limoges France UCA 49 Bd Francois Mitterrand F-63000 Clermont Ferrand France TELUQ Dept Sci & Technol Montreal PQ H2S 3L4 Canada

ISBN: (纸本)9783031624872;9783031624889

Grant-Free (GF) Non-Orthogonal Multiple Access (GFNOMA) has emerged as a promising technology for 5G networks requiring Ultra-Reliable Low Latency Communications (URLLC). However, the grant-free nature of these transmissions can introduce significant interference, thereby, negatively affecting URLLC system performance. To address this challenge, this paper introduces a novel, distributed GF-NOMA-based Q-learning framework that aims to minimize network latency based on a developed Mean Opinion Score (MOS) of packet age, while also maintaining high transmission success rates. Real-time feedback from the gNodeB (gNB) is employed to assist Machine-Type Devices (MTDs) in making adaptive decisions of joint power control and sub-carrier selection. Simulation results validate the effectiveness of our approach in minimizing delay and optimizing overall system performance.

关键词： NOMA URLLC Q-Learning Grant-Free Access Low Latency

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Towards Adaptive Multi-modal Augmentative and Alternative Communication for Children with CP 19th

Towards Adaptive Multi-modal Augmentative and Alternative Co...

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19th International Conference on computers Helping People with Special Needs (ICCHP)

作者： Zisman, Andrea Katz, Dmitri Bennasar, Mohamed Alrimawi, Faeq Price, Blaine Johnston, Anthony Open Univ Milton Keynes Bucks England Univ Limerik Limerick Ireland

ISBN: (纸本)9783031628481;9783031628498

Effective communication can pose significant challenges for nonverbal children with Cerebral Palsy (CP). Augmentative and Alternative Communication (AAC) systems help many but can fail to meet the needs of some users. This research proposes a hybrid adaptive approach, utilizing sensors and machine learning (ML) algorithms to create a personalized mobile communication system for those whose abilities are ill-suited to existing approaches. The system aims to tailor to individual abilities, reducing the need for users to adapt to system requirements. Online surveys gathered data on gestures, actions, and sounds used by non-verbal CP children, informing a classification system and functional requirements. The participants reported 28 communication messages with diverse means of expression. Representative examples and their classification highlight the intricacies of non-verbal communication. The proposed architecture emphasizes real-time classification, multiple sensors, and a feedback loop for continuous improvement, enhancing communication for non-verbal children with CP.

关键词： Cerebral Palsy Augmentative Alternative Communication (AAC) Adaptive Technology Machine Learning User-Centered Design Multi-modal

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Interactive Evolutionary Computation Improving Voice Impressions with Keeping Speaker Personality for Real-Time Speech 23rd

Interactive Evolutionary Computation Improving Voice Impress...

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23rd International Conference on computer Information Systems and Industrial Management (CISIM)

作者： Fukumoto, Makoto Fukushima, Yuta Miyamoto, Taichi Fukuoka Inst Technol Fukuoka 8110295 Japan Fukuoka Inst Technol Grad Sch Engn Fukuoka 8110295 Japan

ISBN: (纸本)9783031711145;9783031711152

Recently, we generally have meetings via the Internet. In this situation, we use background display to improve our impression of other members of the meetings. To improve the users' voice via the Internet, this study proposes an Interactive Evolutionary Computation (IEC) that adjusts the voice filter based on real-time pronunciations while keeping user's personality. The concrete system was constructed by employing a Genetic Algorithm and Koigoe, a software voice filter. The listening experiments were conducted to investigate the efficiencies of the proposed IEC from perspectives of increasing the fitness values and keeping the speaker's personality. The results showed that the proposed IEC has enough possibility to find a good parameter set of the voice filter;however, we need to improve its performance because the obtained best filter did not overcome the impression of the original voice without any filter. Furthermore, the proposed IEC could be considered to keep the user's personality based on the result of the evaluation experiment.

关键词： Interactive Evolutionary Computation Genetic Algorithm Voice Filter Impression

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On Bidirectional Deterministic Finite Automata 28th

On Bidirectional Deterministic Finite Automata

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28th International Conference on Implementation and Application of Automata (CIAA)

作者： Dieck, Simon Verwer, Sicco Delft Univ Technol Delft Netherlands

ISBN: (纸本)9783031711114;9783031711121

Bidirectional deterministic finite automata (biDFA) are a recent innovation with many potential applications. In this paper, we present novel theoretical results for bidirectional automata. We show that there exist regular languages, where minimal biDFA models are exponentially smaller than minimal DFA models. We show this for a language that has a structure common to software logs. This makes biDFA especially interesting when inferring models from such data. However, we also prove that the problem of biDFA minimization is NP-hard. As our key contribution, we provide a Myhill-Nerode style congruence-based characterization for the languages they can recognize. Since most algorithms for learning DFAs are based on such a congruence, this characterization is an important building block for obtaining learning algorithms.

关键词： DFA Regular languages Context-free languages Linear languages

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