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Cleo: Smart Glasses to Monitor Consumption of Alcohol and Cigarettes

SN computer science 2023年第1期4卷 53页

作者： Sethuraman, Sibi Chakkaravarthy Kompally, Pranav Mohanty, Saraju P. Raju, Bksp Kumar Cruz, Meenalosini Vimal School of Computer Science and Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR) VIT-AP University Amaravati India Department of Computer Science and Engineering University of North Texas Denton 76207 TX United States Department of Information Technology Allen E. Paulson College of Engineering and Computing Georgia Southern University Statesboro GA United States

It is estimated that over 60% of people around the globe consume alcohol and cigars daily. Many people use them beyond the permitted limit, which causes lung cancer, liver and kidney failure. If there is a system that could monitor their intake level, it will alert them in case of excess consumption, which could help them control the intake. To help the users monitor their consumption, we introduce Cleo Eyeglasses in this paper. Cleo is a wearable spectacle device with a mounted camera and single onboard computer that performs custom trained object recognition to identify alcoholic beverages and cigarettes. Upon recognition, a log is automatically maintained in the corresponding mobile application. If the intake exceeds the user threshold, then our system generates an alert to both the user and concerned medical personnel. Cigarette/alcohol consumption can immediately affect the body without noticeable symptoms. Cleo addresses this with keen monitoring of vital body parameters and generates an alert when abnormalities are detected. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Alcohol monitoring CNN computer vision Deep learning Smart glasses Smoke monitoring

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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

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Biophysics Reports 2023年第2期9卷 57-66页

作者： Peng Chen Zhenlei Li Zhaolin Hong Haoran Zheng Rong Zeng School of Computer Science and Technology University of Science and Technology of China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Department of Systems Biology University of Science and Technology of China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences School of Life Science and Technology ShanghaiTech University

Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasetsbased semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles:(1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning;(2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality;(3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas(TCGA) and the Gene Expression Comprehensive Database(GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.

关键词： Deep learning Tumor type classification Cancer-specific biomarkers MSSL

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Roopkotha: A Companion Robot for Enhancing Interactive Storytelling with Natural Interaction 3

Roopkotha: A Companion Robot for Enhancing Interactive Story...

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3rd International Conference on Image Processing and Robotics, ICIPRoB 2024

作者： Mehzabin, Kazi Mayesha Islam, Md. Zahidul Nur, Md. Ashaduzzaman Shidujaman, Mohammad Samani, Hooman Mi, Haipeng Independent University Bangladesh RIoT Center and Department of Computer Science and Engineering Dhaka Bangladesh Creative Computing Institute University of the Arts London London United Kingdom Tsinghua University Media and Interaction Lab Academy of Arts and Design Beijing China

ISBN: (纸本)9798350374766

Roopkotha is a storytelling robot that seamlessly combines traditional storytelling methods and technology, creating a captivating robot storyteller. We are creating a special prototype in the world of robots that tells stories in a way that is easy to understand and enjoy. In this era of technological advancement, Roopkotha combines voice recognition with Bangla Language processing, emotion recognition, human behavior detection. Roopkotha aims to revolutionize the way stories are told and engage with users on a deep emotional level. Furthermore, Roopkotha is equipped with advanced facial expressions and emotion recognition technology. The emotion recognition feature helps the robot to have a profound connection with the users. © 2024 IEEE.

关键词： Emotion Recognition

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MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficient Crowdsourcing for Video Analytics 40

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficien...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Chen, Yu Zhang, Sheng Zhou, Ziying Wang, Xiaokun Liang, Yu Chen, Ning Yan, Yuting Xiao, Mingjun Wu, Jie Qian, Zhuzhong Xu, Harry Nanjing University State Key Lab. for Novel Software Technology China School of Systems Science Beijing Normal University China School of Computer and Electronic Information School of Artificial Intelligence Nanjing Normal University China School of Computer Science and Technology University of Science and Technology of China China Temple University Center for Networked Computing United States Samueli School of Engineering University of California Computer Science Department Los Angeles United States

ISBN: (纸本)9798350317152

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy;meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches. © 2024 IEEE.

关键词： Profitability

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BuddyBot: AI Powered Chatbot for Enhancing English Language Learning

BuddyBot: AI Powered Chatbot for Enhancing English Language ...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Dhivvya J P Sai Bhargav Karnati Department of Computer Science and Engineering Amrita School of Computing Center for Wireless Networks and Applications Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Today, many students are having a hard time learning to speak the English language and the problem is even worse for those in rural areas, where both teachers and resources are rare. We are trying to break this disparity and provide better resources for those in need utilizing Artificial Intelligence and Natural Language Processing (NLP) to contribute to the SDG4 Quality and Inclusive Education. In this paper, we present the idea of how we developed Chatbot named BuddyBot Utilizing OpenAI Generative Pre-Trained Transformer 2 (GPT-2) and Google Text-to-Text Transfer Transformer (FLAN-T5) models. This system incorporates diverse datasets for fine-tuning and specialized tasks in English language learning. BuddyBot follows a five-phase architecture - User interface, Pre-processing, Natural Language Processing (NLP) stack, language generation engine and adaptive learning personalization. The E-Learn web-based interface offers interactive courses with BuddyBot assistance, emphasizing adaptive learning personalization. Dataset preprocessing includes tokenization, sentiment analysis, and specialized formatting. The NLP stack integrates GPT-2 and FLAN-T5 models into the framework, maintaining dialogue structure and personalization. Validation tests reveal model effectiveness, with reduced training loss (0.778 to 0.414 by epoch 8) and increased accuracy (0.810 training, 0.792 validation). Overall, BuddyBot's robust methodology and validation results showcase its efficacy in providing personalized English language learning experiences.

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Integration of FDOSM and FWZIC under Homogeneous Fermatean Fuzzy Environment: A Prioritization of COVID-19 Patients for Mesenchymal Stem Cell Transfusion

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International Journal of Information Technology and Decision Making 2024年第4期23卷 1559-1599页

作者： Alsattar, H.A. Qahtan, Sara Mohammed, R.T. Zaidan, A.A. Albahri, O.S. Kou, Gang Alamoodi, A.H. Albahri, A.S. Zaidan, B.B. Al-Samarraay, Mohammed S. Malik, R.Q. Jasim, Ali Najm Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim35900 Malaysia Department of Business Administration College of Administrative Science The University of Mashreq Baghdad10021 Iraq Department of Computer Center Middle Technical University's Baghdad Iraq Sulaymaniyah Iraq Faculty of Engineering and IT The British University in Dubia United Arab Emirates Computer Techniques Engineering Department Mazaya University College Nassiriya Thi-Qar Iraq School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Foundation of Alshuhda Baghdad Iraq

Mesenchymal stem cell (MSC) transfusion has shown promising results in treating COVID-19 cases despite the limited availability of these MSCs. The task of prioritizing COVID-19 patients for MSC transfusion based on multiple criteria is considered a multi-Attribute decision-Analysis (MADA) problem. Although literature reviews have assessed the prioritization of COVID-19 patients for MSCs, issues arising from imprecise, unclear and ambiguous information remain unresolved. Compared with the existing MADA methods, the robustness of the fuzzy decision by opinion score method (FDOSM) and fuzzy-weighted zero inconsistency (FWZIC) is proven. This study adopts and integrates FDOSM and FWZIC in a homogeneous Fermatean fuzzy environment for criterion weighting followed by the prioritization of the most eligible COVID-19 patients for MSC transfusion. The research methodology had two phases. The decision matrices of three COVID-19 emergency levels (moderate, severe, and critical) were adopted based on an augmented dataset of 60 patients and discussed in the first phase. The second phase was divided into two subsections. The first section developed Fermatean FWZIC (F-FWZIC) to weigh criteria across each emergency level of COVID-19 patients. These weights were fed to the second section on adopting Fermatean FDOSM (F-FDOSM) for the purpose of prioritizing COVID-19 patients who are the most eligible to receive MSCs. Three methods were used in evaluating the proposed works, and the results included systematic ranking, sensitivity analysis, and benchmarking checklist. © 2024 World Scientific Publishing Company.

关键词： COVID-19

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation 40

Logical Relation Modeling and Mining in Hyperbolic Space for...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Tan, Yanchao Lv, Hang Zhou, Zihao Guo, Wenzhong Xiong, Bo Liu, Weiming Chen, Chaochao Wang, Shiping Yang, Carl College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Emory University Department of Computer Science Atlanta United States

ISBN: (纸本)9798350317152

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable. © 2024 IEEE.

关键词： Recommender systems

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Adaptive energy-preserving algorithms for guiding center system

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Plasma science and Technology 2023年第4期25卷 11-22页

作者：朱贝贝刘健张嘉炜祝爱卿唐贻发 Department of Applied Mathematics School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083Peopleʼs Republic of China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026Peopleʼs Republic of China Advanced algorithm Joint Lab Shandong Computer Science CenterQilu University of TechnologyJinan 250014Peopleʼs Republic of China The State Key Laboratory of Scientific and Engineering Computing The Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190Peopleʼs Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049Peopleʼs Republic of China

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

关键词： guiding center system energy-preserving methods adaptive energy conservation

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Forebrain excitatory neuron-specific loss of Brpf1 attenuates excitatory synaptic transmission and impairs spatial and fear memory

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Neural Regeneration Research 2024年第5期19卷 1133-1141页

作者： Baicheng Zhao Hang Zhang Ying Liu Gaoyu Zu Yuxiao Zhang Jiayi Hu Shuai Liu Linya You Department of Human Anatomy&Histoembryology School of Basic Medical SciencesFudan UniversityShanghaiChina Shanghai Key Laboratory of Brain Functional Genomics(Ministry of Education) Affiliated Mental Health Center(ECNU)School of Psychology and Cognitive ScienceEast China Normal UniversityShanghaiChina Shanghai Changning Mental Health Center ShanghaiChina NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai ShanghaiChina Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai ShanghaiChina

Bromodomain and plant homeodomain(PHD)finger containing protein 1(Brpf1)is an activator and scaffold protein of a multiunit complex that includes other components involving lysine acetyltransferase(KAT)6A/6B/***1,KAT6A,and KAT6B mutations were identified as the causal genes of neurodevelopmental disorders leading to intellectual *** previous work revealed strong and specific expression of Brpf1 in both the postnatal and adult forebrain,especially the hippocampus,which has essential roles in learning and ***,we hypothesized that Brpf1 plays critical roles in the function of forebrain excitatory neurons,and that its deficiency leads to learning and memory *** test this,we knocked out Brpf1 in forebrain excitatory neurons using *** found that Brpf1 deficiency reduced the frequency of miniature excitatory postsynaptic currents and downregulated the expression of genes Pcdhgb1,Slc16a7,Robo3,and Rho,which are related to neural development,synapse function,and memory,thereby damaging spatial and fear memory in *** findings help explain the mechanisms of intellectual impairment in patients with BRPF1 mutation.

关键词： behavioral test Brpf1 CAMKIIa-Cre intellectual disability miniature excitatory postsynaptic current mRNA-Seq

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Comparisons Are All You Need for Optimizing Smooth Functions

arXiv

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arXiv 2024年

作者： Zhang, Chenyi Li, Tongyang Computer Science Department Stanford University United States Center on Frontiers of Computing Studies Peking University China School of Computer Science Peking University China

When optimizing machine learning models, there are various scenarios where gradient computations are challenging or even infeasible. Furthermore, in reinforcement learning (RL), preference-based RL that only compares between options has wide applications, including reinforcement learning with human feedback in large language models. In this paper, we systematically study optimization of a smooth function f: Rn → R only assuming an oracle that compares function values at two points and tells which is larger. When f is convex, we give two algorithms using Õ(n/ǫ) and Õ(n2) comparison queries to find an ǫ-optimal solution, respectively. When f is nonconvex, our algorithm uses Õ(n/ǫ2) comparison queries to find an ǫ-approximate stationary point. All these results match the best-known zeroth-order algorithms with function evaluation queries in n dependence, thus suggest that comparisons are all you need for optimizing smooth functions using derivative-free methods. In addition, we also give an algorithm for escaping saddle points and reaching an ǫ-second order stationary point of a nonconvex f, using Õ(n1.5/ǫ2.5) comparison queries. © 2024, CC BY.

关键词： Reinforcement learning

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