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A Comparison of Data FAIRness Evaluation Tools 9

A Comparison of Data FAIRness Evaluation Tools

引用

9th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2022

作者： Slamkov, Dejan Stojanov, Venko Koteska, Bojana Mishev, Anastas Ss. Cyril and Methodius University Faculty of Computer Science and Engineering Skopje Macedonia

FAIR data principles represent a set of community-agreed guiding principles and practices for all researchers involved in the eScience ecosystem. the FAIR data principles were created to improve the reuse of data by making it findable, accessible, interoperable and reusable. the goal of these principles is to ensure that the inputs and outputs from computational analysis can be easily found and understood by data consumers, both humans and machines. Since the introduction of FAIR Data Principles in 2016, the interest in these principles has been constantly increasing and several research groups have started developing tools for evaluation of data FAIRness. In this paper, we aim to analyze the available online tools and checklists for data FAIRness evaluation and to provide tool comparison based on multiple features. Taking into account this analysis and tools advantages and disadvantages, we provide recommendations about the tools usage. A FAIRness practical evaluation is also conducted on seven data sets from different data repositories using the analysed tools. Findings show that there are no commonly accepted requirements evaluation of data FAIRness. the conclusions of this study could be used for further improvement of the FAIRness criteria design and making FAIR feasible in daily practice. © 2022 Copyright for this paper by its authors.

关键词： Reusability

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An Automatic Depression Recognition Method from Spontaneous Pronunciation Using Machine Learning 22

An Automatic Depression Recognition Method from Spontaneous ...

引用

proceedings of the 2022 9th International conference on Biomedical and Bioinformatics engineering

作者： Minghao Du Wenquan Zhang Tao Wang Shuang Liu Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin University China

ISBN: (纸本)9781450397223

the rapidly growing number of depressed people increases the burden of clinical diagnosis. Due to the abnormal speech signal of depressed patients, automatic audio-based depression recognition has the potential to become a complementary method for diagnosing. However, recognition performance varies largely with different speech acquisition tasks and classifiers, making results not comparable, and the performance requires further improvement before clinical application. this work extracted high-level statistical acoustic features (prosodic, voice-quality, and spectral features) of 23 depressed patients and 29 healthy subjects under spontaneous pronunciation tasks (interview and picture description) and mechanical pronunciation tasks (story reading and word reading), then applied principal component analysis (PCA) to reduce features dimensions, finally employed multilayer perceptron (MLP) to establish the classification model and compared with traditional classifiers (logistic regression, support vector machine, decision tree, and naive Bayes). the results showed that spontaneous pronunciation induced more significantly discriminative acoustic features and achieved better recognition performance accordingly. And the PCA retained 90% useful information with 50% features. Furthermore, MLP achieved the best performance with the accuracy 0.875 and average F1 score 0.855 under the picture description task. this study provides support for task design and classifier building for audio-based depression recognition, which could assist in mass screening for depression.

关键词： Audio Machine learning Depression recognition Speech task

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An IoT-based Smart Wearable System for Remote Health Monitoring 9

An IoT-based Smart Wearable System for Remote Health Monitor...

引用

9th International Japan-Africa conference on Electronics, Communications, and Computations, JAC-ECC 2021

作者： Abo-Zahhad, Mohammed Sayed, Mohammed S. Abd El-Malek, Ahmed H. Fawaz, Abdelrahman Zakaria, Ahmed A. Wahdan, Ahmed E. Elsayed, Moaz Taghian, Aya Egypt-Japan University of Science and Technology Department of Electronics and Communications Engineering New Borg El-Arab City Alexandria21934 Egypt Department of Electrical and Electronics Engineering Assiut University Assiut71515 Egypt

ISBN: (纸本)9781665482929

Internet of things (IoT) has reshaped our lives by being part of a wide range of fields. thus, using IoT in healthcare became highly demanded. this could be done through monitoring some of the vital health signs that provide information about the state of health. these vital signs include blood pressure, blood oxygen saturation, body temperature, and pulse rate. Besides, recognizing physical movements of the body leads to the detection of various hazards, such as falls and concussions. Being directed towards daily health monitoring, the design requirements for such device are portability, lightweight and ease of use. We can break down the process of constructing the system into four main stages: data acquisition, pre-processing the sensor readings using an embedded microprocessor, feature extraction and implementation in further recognition algorithms using a shared cloud platform, and data visualization of all vital signs in addition to callouts in case of emergency. © 2021 IEEE.

关键词： Internet of things

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Combining Deductive and Statistical Explanations in the FRANK Query Answering System 12

Combining Deductive and Statistical Explanations in the FRAN...

引用

12th IEEE International conference on Big knowledge, ICBK 2021

作者： Bundy, Alan Nuamah, Kwabena University of Edinburgh School of Informatics EdinburghEH8 9AB United Kingdom

ISBN: (纸本)9781665438582

Both symbolic and sub-symbolic AI have their limi-tations, but their combination can be more than the sum of their parts. For instance, statistical machine learning has been hugely successful at classification and decision-making tasks, but not so good at deliberative systematic reasoning nor at explanation. We argue that by combining symbolic and sub-symbolic reasoning into hybrid systems, the whole will be more than the sum of its parts. To illustrate the potential of hybrid AI system, we describe the FRANK query answering system. FRANK infers new knowledge from the diverse and immense knowledge sources on the Web, using a combination of both deductive and statistical reasoning. this enables it to make predictions. For instance, to answer the question 'Which country in Europe will have the highest GDP growth rate by 2032?', it (i) decomposes Europe into its constituent countries, (ii) then for each country uses regression over their previous GDP growth rates to extrapolate each to 2032 and (iii) then returns the country which is predicted to then have the maximum value. the decompositions are explained deductively and the regressions by a prediction model that can be rendered graphically. this explanation of FRANK's reasoning merges deduction and statistics. In this paper, we highlight recent work on FRANK that focus on leveraging hybrid AI to tackle question answering with emphasis on explainability of the inference process and its inferred answers. We aim for whole system reasoning;that is, we are automating the choices of knowledge sources and the planning that constructs the inference process from the facts found in these knowledge sources. We intend that these 'engineering' choices are also explained to the user. © 2021 IEEE.

关键词： Decision making

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Summer Coding Camp: Curriculum, Experiences, and Evaluation

Summer Coding Camp: Curriculum, Experiences, and Evaluation

引用

2021 ASEE Virtual Annual conference, ASEE 2021

作者： LePendu, Paea Cheung, Cecilia Salloum, Mariam Sheffler, Pamela Downey, Kelly Department of Computer Science and Engineering University of California Riverside United States

Many education-related organizations in the U.S., from the National Science Foundation down to local districts, have been pushing to introduce computer science concepts into K-12. Nevertheless, many students complete high school never having the chance to learn CS. We have created a summer coding camp for high-school students (including 8th graders entering 9th grade) and designed a multi-year study to assess its effectiveness as an informal learning environment, based on theories of human motivation such as Self-Determination theory. the camp is a 1-week immersion experience, 9am to 5pm with food and activities, that introduces basic programming via MIT APP Inventor. Lecture material and in-class exercises draw upon meaningful applications, many appealing to "social good." One unique aspect is the inclusion of professional and career development activities that engage students and broaden perspectives on CS and its applications. For example, the camp includes a college information session, alumni talks, off-site visits to nearby companies, and research talks and demos by faculty. Using a pre-and-post survey design, the current study examines the effects of the camp on student self-efficacy and interest in computing, as well as general school engagement and motivation. Results confirm that participation in the summer camp increased students' self-efficacy and interest in computing, enhanced engagement in school on topics in general, and strengthened intrinsic motivation for completing schoolwork. the effects were similar for boys and girls. © American Society for engineering Education, 2021

关键词： Students

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Prompt-tuned Code Language Model as a Neural knowledge Base for Type Inference in Statically-Typed Partial Code 22

Prompt-tuned Code Language Model as a Neural Knowledge Base ...

引用

proceedings of the 37th IEEE/ACM International conference on Automated software engineering

作者： Qing Huang Zhiqiang Yuan Zhenchang Xing Xiwei Xu Liming Zhu Qinghua Lu School of Computer Information Engineering Jiangxi Normal University China CSIRO's Data61 & Australian National University Australia CSIRO's Data61 Australia CSIRO's Data61 & School of CSE UNSW Australia

ISBN: (纸本)9781450394758

Partial code usually involves non-fully-qualified type names (non-FQNs) and undeclared receiving objects. Resolving the FQNs of these non-FQN types and undeclared receiving objects (referred to as type inference) is the prerequisite to effective search and reuse of partial code. Existing dictionary-lookup based methods build a symbolic knowledge base of API names and code contexts, which involve significant compilation overhead and are sensitive to unseen API names and code context variations. In this paper, we formulate type inference as a cloze-style fill-in-blank language task. Built on source code naturalness, our approach fine-tunes a code masked language model (MLM) as a neural knowledge base of code elements with a novel “pre-train, prompt and predict” paradigm from raw source code. Our approach is lightweight and has minimum requirements on code compilation. Unlike existing symbolic name and context matching for type inference, our prompt-tuned code MLM packs FQN syntax and usage in its parameters and supports fuzzy neural type inference. We systematically evaluate our approach on a large amount of source code from Github and Stack Overflow. Our results confirm the effectiveness of our approach design and the practicality for partial code type inference. As the first of its kind, our neural type inference method opens the door to many innovative ways of using partial code.

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Consensus Node Group Selection and Adjustment Algorithm based on Dual Random Selection Mechanism 23

Consensus Node Group Selection and Adjustment Algorithm Base...

引用

proceedings of the 2023 9th International conference on Communication and Information Processing

作者： Xiaojian Gu Tianyu Kang Anshun Zhou Li Guo Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education Beijing University of Posts and Telecommunications China School of Artificial Intelligence Beijing University of Posts and Telecommunications China and National Engineering Research Center for Mobile Internet Security Technology Beijing University of Posts and Telecommunications China China Unicom (Hainan) Innovation Research Institute China

ISBN: (纸本)9798400708909

the PBFT algorithm is a consensus algorithm that reduces communication complexity to a polynomial level, addressing the inefficiency of traditional Byzantine fault tolerance mechanisms. It is commonly adopted in the consensus layer of blockchain systems. However, as the number of system nodes increases, the algorithm faces the challenge of rapidly increasing communication complexity. One solution to this problem is to use deterministic algorithms to select smaller consensus groups. However, in an open network, this approach is susceptible to Distributed Denial of Service (DDoS) attacks against the main nodes, posing a risk of system unavailability. Another solution involves using Verifiable Random Functions (VRF) technology to select consensus group nodes. However, this approach often introduces additional time overhead. To combine the advantages of both solutions, this paper proposes an improved PBFT algorithm based on a Dual-Random-Selection mechanism (DRS-BFT). the algorithm uses an unbiased random selection function and a verifiable random function to select consensus group members and the primary node. In situations with a large number of nodes, the algorithm enhances consensus efficiency by selecting a consensus group, minimizing additional communication overhead, and ensuring system security. Security analysis demonstrates that the system, under the assumption of security requirements, can guarantee data integrity, consistency, and availability. It can also prevent targeted attacks against the primary node that could lead to system unavailability. To validate the system's functionality and performance, the paper designs and develops a prototype system and establishes an experimental environment. the experiments show that the algorithm dynamically adjusts the consensus group range through the Dual-Random-Selection mechanism to adapt to changes in network conditions. Additionally, compared to traditional PBFT, the algorithm exhibits superior performance.

关键词： Practical Byzantine Fault Tolerance (PBFT) Verifiable Random Function (VRF) blockchain consensus algorithm

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Self-directed Learning Compared to Traditional engineering Approach: Case Studies in Developing Machine Learning Capabilities to Solve Practical Problems

Self-directed Learning Compared to Traditional Engineering A...

引用

9th International conference on Interactive Collaborative and Blended Learning, ICBL 2020

作者： Hsiao, Yih-Chyuan Al-emara, Sohaib Gadhrri, Anoop Singh Singh, Ishwar Gao, Zhen McMaster University HamiltonONL8S 0A3 Canada

ISBN: (纸本)9783030672089

In this work, the authors present the study from personal experience to gain some insight into the different aspects of efficient self-directed learning. Two case studies regarding the self-directed learning approach and process are conducted and elaborated. the first is more focused on how learning objectives are achieved based on the implementation of detailed plans via concrete steps. the second one is more focused on the online learning of Reinforcement Learning. this work also provides insight from the perspective and teaching experience of AI/ML instructors. Overall, the proposed approach of self-directed learning is an individual initiative, which involves consultation with mentors/professors, formulation of learning goals, comprehension of knowledge, assessment of knowledge, monitoring of minds-on and hands-on training, identification of resources, planning of learning roadmaps, and evaluation of learning outcomes. the proposed approach is also applicable for self-directed learning of other engineering and computer science courses. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： engineering education

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knowledge-based Defect Prediction and Test Case Generation for Radar software Fault Prevention 7

Knowledge-based Defect Prediction and Test Case Generation f...

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7th International conference on Dependable Systems and their Applications, DSA 2020

作者： Li, Chengfeng Liu, Xi Wu, Hui Li, Haifeng Nanjing Research Institute of Electronics Technology Nanjing China Institute of Reliability Engineering Beihang University Beijing China

ISBN: (纸本)9780738124223

software is playing an increasingly important role in modern radar software. software faults often lead to serious system failure. A fault prevention approach is proposed for radar software. the proposed approach follows the quality assurance link of 'knowledge-prediction-control' and combines knowledge collection, defect prediction, test case recommendation, and automatic test case generation. Case studies show the proposed approach can effectively help fault detection on radar software. © 2020 IEEE.

关键词： Forecasting

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Towards Model Transformation from a CBM Model to CEP Rules to Support Predictive Maintenance 8th

Towards Model Transformation from a CBM Model to CEP Rules t...

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8th International conference on Model-Driven engineering and software Development (MODELSWARD)

作者： Sarazin, Alexandre Truptil, Sebastien Montarnal, Aurelie Lamothe, Jacques Commanay, Julien Sagaspe, Laurent IMT Mines Albi Ind Engn Ctr Albi France APSYS Digital & Software Dept Blagnac France

ISBN: (纸本)9789897584008

Over the past decades, the development of predictive maintenance strategies, like Prognostics and Health Management (PHM), have brought new opportunities to the maintenance domain. However, implementing such systems addresses several challenges. First, all information related to the system description and failure definition must be collected and processed. In this regard, using an expert system (ES) seems interesting. the second challenge, when monitoring complex systems, is to deal with the high volume and velocity of the input data. To reduce them, Complex Event Processing (CEP) can be used to identify relevant events, based on predefined rules. these rules can be extracted from the ES knowledge base using model transformation. this process consists in transforming some concepts from a source to a target model using transformation rules. In this paper, we propose to transform a part of the knowledge from a condition-based maintenance (CBM) model into CEP rules. After further explaining the motivations behind this work and defining the principles behind model-driven architecture and model transformation, the transformation from a CBM model to a "generic rules" model will be proposed. this model will then be transformed into an Event Processing Language (EPL) model. Examples will be given as illustrations for each transformation.

关键词： Maintenance knowledge Base Model Transformation

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