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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Yuvaraj, S. Thinakaran, K. Srinivas, S. Porandla Sivakumar, S. Ishwarya, M.V. Thirumaraiselvan, P. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Chennai Tamil Nadu Kattankulathur India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Department of Computer Science Tamil Nadu Perambalur India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Predicting the severity of hand injuries in emergency care is the focus of this study, which suggests using cloud-based Bayesian networks. It is crucial to have accurate severity prediction in order to treat emergency hand injuries promptly since they are prevalent. By using the scalability and flexibility of cloud computing, this method constructs a Bayesian severity assessment network with patient data, injury characteristics, and historical medical records. The model's training data comes from a massive database of hand injury cases with different results. Emergency medical professionals may use real-time parameter modifications to strengthen and adapt the Bayesian network. Through the use of the cloud, healthcare providers may simply exchange data and reach consensus. It demonstrates that, compared to other well-established methods of evaluating hand injury severity, the model performs better in making predictions. The suggested method improves severity forecasts and advances cloud-based emergency care. The method for emergency hand injury care uses Bayesian networks in the cloud to improve scalability and accessibility. The findings show that the data-driven and efficient hand injury severity prediction method might transform emergency care by improving patient outcomes. © 2024 IEEE.

关键词： Bayesian networks

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What Makes Good In-Context Demonstrations for Code Intelligence Tasks with LLMs? 23

What Makes Good In-Context Demonstrations for Code Intellige...

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Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering

作者： Shuzheng Gao Xin-Cheng Wen Cuiyun Gao Wenxuan Wang Hongyu Zhang Michael R. Lyu School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong China School of Big Data and Software Engineering Chongqing University China

ISBN: (纸本)9798350329964

Pre-trained models of source code have gained widespread popularity in many code intelligence tasks. Recently, with the scaling of the model and corpus size, large language models have shown the ability of in-context learning (ICL). ICL employs task instructions and a few examples as demonstrations, and then inputs the demonstrations to the language models for making predictions. This new learning paradigm is training-free and has shown impressive performance in various natural language processing and code intelligence tasks. However, the performance of ICL heavily relies on the quality of demonstrations, e.g., the selected examples. It is important to systematically investigate how to construct a good demonstration for code-related tasks. In this paper, we empirically explore the impact of three key factors on the performance of ICL in code intelligence tasks: the selection, order, and number of demonstration examples. We conduct extensive experiments on three code intelligence tasks including code summarization, bug fixing, and program synthesis. Our experimental results demonstrate that all the above three factors dramatically impact the performance of ICL in code intelligence tasks. Additionally, we summarize our findings and provide takeaway suggestions on how to construct effective demonstrations, taking into account these three perspectives. We also show that a carefully-designed demonstration based on our findings can lead to substantial improvements over widely-used demonstration construction methods, e.g., improving BLEU-4, EM, and EM by at least 9.90%, 175.96%, and 50.81% on code summarization, bug fixing, and program synthesis, respectively.

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Predictive Analysis of COVID-19 data using Machine Learning

Predictive Analysis of COVID-19 Data using Machine Learning

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Cross Strait Quad-Regional Radio science and Wireless Technology Conference (CSRWTC)

作者： Bin Gong Yizhao Zhu Mingchao Li Wei Lv Faculty of Data Science City University of Macau Macau China School of Business Shenzhen Institute of Technology Shenzhen China Alibaba Cloud College of Big Data Application Zhuhai College of Science and Technology Zhuhai China

The COVID-19 pandemic has had far-reaching health consequences, including mental health issues and long-term physical symptoms. Mental health problems such as anxiety and depression have surged due to isolation and uncertainty. Concerns are rising regarding the pandemic's lasting impact on mental well-being. Furthermore, COVID-19 patients may experience persistent symptoms like fatigue and muscle weakness, affecting their long-term health. Leveraging machine learning (ML) techniques, and analyzing post-pandemic health impacts can reveal patterns and research insights. ML can utilize extensive data from electronic health records and public health databases to identify trends and potential future health issues. Additionally, ML models can monitor medical records and public health data, aiding in the swift detection and response to disease outbreaks by public health officials.

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Molecule opacity study on low-lying states of CS

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Chinese Physics B 2022年第10期31卷 362-370页

作者： Rui Li Jiqun Sang Xiaohe Lin Jianjun Li Guiying Liang Yong Wu Department of Physics College of ScienceQiqihar UniversityQiqihar 161006China National Key Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China Faculty of Foundation Space Engineering UniversityBeijing 101416China School of Data Science and Artificial Intelligence Jilin Engineering Normal UniversityChangchun 130052China HEDPS Center for Applied Physics and TechnologyPeking UniversityBeijing 100084China

CS molecule, which plays a key role in atmospheric and astrophysical circumstances, has drawn great attention for long time. Owing to its large state density, the detailed information of the electronic structure of CS is still lacking. In this work, the high-level MRCI+Q method is used to compute the potential energy curves, dipole moments and transition dipole moments of singlet and triplet states correlated with the lowest dissociation limit of CS, based on which high accurate vibration-rotation levels and spectroscopic constants of bound states are evaluated. The opacity of CS relevant to atmospheric circumstance is computed at a pressure of 100 atms for different temperatures. With the increase of temperature,band systems from different transitions mingle with each other, and band boundaries become blurred, which are originated from the increased population on vibrational excited states and electronic excited states at high temperature.

关键词： CS transition dipole moment opacity excited state

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Cloud-Based AI Solutions for Early Wound Infection Detection and Treatment Recommendations 4

Cloud-Based AI Solutions for Early Wound Infection Detection...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Rajasekaran, Mothiram Ranganathan, Chitra Sabapathy Manikandan, G. Bhuvaneswari, G. Ganeshbabu, T.R. Rajmohan, M. Pine Candle Way Saint AugustineFL United States Mphasis Corporation ChandlerAZ United States Rmk Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Wound infections are a major problem for healthcare systems worldwide. Infections cause patients to remain in the hospital longer., which increases healthcare expenses and negatively impacts their health outcomes. For patient care to be successful, it is crucial to identify these illnesses quickly and treat them appropriately. Recent developments in cloud computing and artificial intelligence (AI) have encouraged optimism for the potential of these technologies to improve wound infection diagnosis and treatment. Within healthcare systems, this research outlines a new method for early wound infection diagnosis and treatment recommendations generation using cloud-based AI algorithms. The proposed method employs machine learning to appropriately detect early indications of infection by analyzing a variety of patient data inputs, such as wound features, vital signs, laboratory findings, and medical history. Also, healthcare practitioners may benefit from real-time monitoring and decision assistance due to cloud-based architecture's easy interaction with existing electronic health record (HER) systems. The system is designed with a dynamic feedback loop that can be updated with fresh data inputs to make it more accurate and flexible. The proposed cloud-based AI system has been thoroughly tested and proven reliable in improving the early diagnosis and treatment of wound infections. This has led to better patient outcomes and more efficient use of healthcare resources. Key challenges include data privacy issues, the integration of diverse technologies, the reliability of precise forecasts, and maintaining system resiliency in changing environments. © 2024 IEEE.

关键词： Electronic health record

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Exploring a New Grammatico-functional Type of Measure as Part of a Language Learning Expert System 18

Exploring a New Grammatico-functional Type of Measure as Par...

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18th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2023

作者： Mallart, Cyriel Simpkin, Andrew Venant, Rémi Ballier, Nicolas Stearns, Bernardo Li, Jen Yu Gaillat, Thomas LIDILE Université Rennes 2 France School of Mathematics Statistics and Applied Mathematics University of Galway Ireland LIUM Université du Mans France CLILLAC-ARP Université Paris Cité France Insight Data Science Institute University of Galway Ireland

ISBN: (纸本)9781959429807

This paper explores the use of L2-specific grammatical microsystems as elements of the domain knowledge of an Intelligent Computer-assisted Language Learning (ICALL) system. We report on the design of new grammatico-functional measures and their association with proficiency. We illustrate the approach with the design of the IT, THIS, THAT proform microsystem. The measures rely on the paradigmatic relations between words of the same linguistic functions. They are operationalised with one frequency-based and two probabilistic methods, i.e., the relative proportions of the forms and their likelihood of occurrence. Ordinal regression models show that the measures are significant in terms of association with CEFR levels, paving the way for their introduction in a specific proform microsystem expert model. © 2023 Association for Computational Linguistics.

关键词： Expert systems

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A Rational Design of Metal–Organic Framework Nanozyme with High‑Performance Copper Active Centers for Alleviating Chemical Corneal Burns

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Nano-Micro Letters 2023年第8期15卷 49-71页

作者： Yonghua Tang Yi Han Jiachen Zhao Yufei Lv Chaoyu Fan Lan Zheng Zhisen Zhang Zuguo Liu Cheng Li Youhui Lin Department of Physics Research Institute for Biomimetics and Soft MatterFujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005People’s Republic of China Fujian Provincial Key Laboratory of Ophthalmology and Visual Science&Ocular Surface and Corneal Diseases Eye Institute&Affiliated Xiamen Eye CenterSchool of MedicineXiamen UniversityXiamen 361102People’s Republic of China National Institute for Data Science in Health and Medicine Xiamen UniversityXiamen 361102People’s Republic of China Postdoctoral Mobile Station of Basic Medical Sciences Hengyang Medical SchoolDepartment of OphthalmologyThe First Affiliated Hospital of University of South ChinaUniversity of South ChinaHengyangHunan 421001People’s Republic of China

Metal–organic frameworks(MOFs)have attracted significant research interest in biomimetic ***,the modulation of the activity of MOFs by precisely tuning the coordination of metal nodes is still a significant *** by metalloenzymes with well-defined coordination structures,a series of MOFs containing halogen-coordinated copper nodes(Cu-X MOFs,X=Cl,Br,I)are employed to elucidate their structure–activity ***,experimental and theoretical results strongly support that precisely tuning the coordination of halogen atoms directly regulates the enzyme-like activities of Cu-X MOFs by influencing the spatial configuration and electronic structure of the Cu active *** optimal Cu–Cl MOF exhibits excellent superoxide dismutase-like activity with a specific activity one order of magnitude higher than the reported Cu-based *** importantly,by performing enzyme-mimicking catalysis,the Cu–Cl MOF nanozyme can significantly scavenge reactive oxygen species and alleviate oxidative stress,thus effectively relieving ocular chemical ***,the antioxidant and antiapoptotic properties of Cu–Cl MOF are achieved by regulating the NRF2 and JNK or P38 MAPK *** work provides a novel way to refine MOF nanozymes by directly engineering the coordination microenvironment and,more significantly,demonstrating their potential therapeutic effect in ophthalmic disease.

关键词： Metal-organic frameworks nanozyme Superoxide dismutase Halogen Chemical ocular burn Corneal diseases

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A Scalable, Efficient, and Robust Dynamic Memory Management Library for HLS-based FPGAs

A Scalable, Efficient, and Robust Dynamic Memory Management ...

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Qinggang Wang Long Zheng Zhaozeng An Shuyi Xiong Runze Wang Yu Huang Pengcheng Yao Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Nowadays, high-level synthesis (HLS) has gained prominence for FPGA-based architecture prototyping, enhancing productivity significantly. Despite this advancement, HLS tools are impeded by a critical drawback: they lack support for dynamic memory management (DMM), leading to static mem-ory allocation and suboptimal use of memory resources. In response, numerous efforts have been made to develop DMM solutions compatible with HLS. However, our analysis indicates that existing solutions fail to concurrently meet the desired trifecta of scalability (efficient management of memory of any size), efficiency (minimal latency in memory (de-)allocation), and robustness (low allocation failure rates). This limitation hampers their applicability in real-world scenarios. In this paper, we introduce GraDMM, a “three-birds-one- stone” solution that comprehensively enhances the scalability, efficiency, and robustness of DMM. The key insight is to formulate memory (de-)allocation as graph analytics and lever-age sophisticated FPGA-based graph processing techniques. To achieve scalability, GraDMM specializes a simplified pipeline that significantly suppresses resource utilization expansion caused by managed memory scaling. This is crucial for managing arbitrarily sized memory on resource-limited FPGA platforms. For efficiency, GraDMM implements a data-centric concurrent traversal scheme and a shortcut-assisted fast traversal policy to accelerate (de-)allocation-guided graph traversal, reducing mem-ory (de-)allocation latency. To enhance robustness, GraDMM incorporates an adaptive memory defragmenter that defragments managed memory to minimize fragmentation-induced allocation failures. GraDMM is encapsulated as a library, providing high- level interfaces for users and ensuring synthesizability with Vi- vado HLS. Experimental results demonstrate that GraDMM out-performs three state-of-the-art HLS-compatible DMM solutions by significant margins: 56.71 %-85.59% in resource consumption sa

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Vader protocol based sentiment analysis technique using LSTM for Stock Trend prediction 14

Vader protocol based sentiment analysis technique using LSTM...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Ticku, Amrita Tripathy, Niva Mishra, Pankaj Kumar Sinha, Anurag Jadon, Shivangi Raj, Aditya Kulshrestha, Harsh Rai, Vipin Shamim, Rejuwan Bharti Vidyapeeth's College of Engineering India Driems Autonomous Engineering College Cuttack India Rungta College of Engg. & Technology R1 Department of Electronics and Telecommunication Engg. CG Bhilai India Ignou School of Computing and Information Science New Delhi India Galgotias University Dept. of Computer Science and Engineering Noida India Galgotias University Department of Computer Science and Engineering Noida India Maharishi University of Information Technology Dept. of Computer Science and Engineering with Data Science Noida India

ISBN: (纸本)9798350335095

Stock prediction has always been a difficult task, and numerous strategies for predicting stock market behavior have been offered. Researchers have recently begun to investigate the use of sentimental analysis and deep learning models such as Long Short-Term Memory (LSTM) to predict stock prices. We offer a stock prediction model that integrates sentimental analysis and LSTM in this research. The model considers the mood of stock market news items and use LSTM to forecast future stock prices. The results of our suggested model's evaluation on the S& P 500 dataset reveal that it outperforms traditional stock prediction approaches. © 2023 IEEE.

关键词： Forecasting

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Efficacy of Chinese Medicine Treatment Based on Syndrome Differentiation for Primary Insomnia:A Randomized Placebo Controlled Triple-Blinded Trial

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Chinese Journal of Integrative Medicine 2024年第10期30卷 867-876页

作者： XIONG Zhi-yi LU Ying HE Li-yun ZHANG Run-shun ZHOU Xue-zhong LI Peng LIU Yan-jiao ZHU Jian-gui YAN Shi-yan LIU Bao-yan International Acupuncture and Moxibustion Innovation Institute School of Acupuncture-Moxibustion and TuinaBeijing University of Chinese MedicineBeijing100029China Department of Biomedical Data Science Stanford University School of MedicineStanfordCA94305USA Institute of Basic Research in Clinical Medicine China Academy of Chinese Medical SciencesBeijing100700China Guang’anmen Hospital China Academy of Chinese Medical SciencesBeijing100053China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing100044China Beijing Xicheng District Community Healthcare Center Beijing100120China China Academy of Chinese Medical Sciences Beijing100700China

Objective:To assess efficacy of Chinese medicine(CM)on insomnia considering characteristics of treatment based on syndrome ***:A total of 116 participants aged 18 to 65 years with moderate and severe primary insomnia were randomized to the placebo(n=20)or the CM group(n=96)for a 4-week treatment and a 4-week *** CM clinicians independently prescribed treatments for each patient based on syndromes *** primary outcome was change in total sleep time(TST)from *** endpoints included sleep onset latency(SOL),wake time after sleep onset(WASO),sleep efficiency,Pittsburgh Sleep Quality Index(PSQI)and CM ***:The CM group had an average 0.6 h more(95%confidence interval(CI):0.3–0.9,P<0.001)TST and 34.1%(10.3%–58.0%,P=0.005)more patients beyond 0.5 h TST increment than that of the placebo *** was changed–3.3(–3.8 to–2.7)in the CM group,a–2.0(–3.2 to–0.8,P<0.001)difference from the placebo *** CM symptom score in the CM group decreased–2.0(–3.3 to–0.7,P=0.003)more than the placebo *** and WASO changes were not significantly different between *** analysis of prescriptions by these clinicians revealed blood deficiency and Liver stagnation as the most common *** for these clinicians displayed relative stability,while the herbs *** adverse events were mild and were not related to study ***:CM treatment based on syndrome differentiation can increase TST and improve sleep quality of primary *** is effective and safe for primary *** future studies,the long-term efficacy validation and the exploratory of eutherapeutic clinicians'fixed herb formulas should be addressed.(Registration ***01613183).

关键词： primary insomnia Chinese medicine syndrome differentiation randomized controlled trial

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