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computers in human Behavior: Artificial humans 2025年 4卷

作者： He Zhang Chuhao Wu Jingyi Xie Yao Lyu Jie Cai John M. Carroll College of Information Sciences and Technology The Pennsylvania State University Westgate Building University Park PA 16803 USA The School of Information University of Michigan MI 48109 USA The Pervasive Human Computer Interaction Laboratory Department of Computer Science and Technology Tsinghua University Future Internet Technology Research Center (FIT) Haidian District Beijing 100084 China

AI tools, particularly large-scale language model (LLM) based applications such as ChatGPT, have the potential to mitigate qualitative research workload. In this study, we conducted semi-structured interviews with 17 participants and held a co-design session with 13 qualitative researchers to develop a framework for designing prompts specifically crafted to support junior researchers and stakeholders interested in leveraging AI for qualitative research. Our findings indicate that improving transparency, providing guidance on prompts, and strengthening users' understanding of LLMs' capabilities significantly enhance their ability to interact with ChatGPT. By comparing researchers' attitudes toward LLM-supported qualitative analysis before and after the co-design process, we reveal that the shift from an initially negative to a positive perception is driven by increased familiarity with the LLM's capabilities and the implementation of prompt engineering techniques that enhance response transparency and, in turn, foster greater trust. This research not only highlights the importance of well-designed prompts in LLM applications but also offers reflections for qualitative researchers on the perception of AI's role. Finally, we emphasize the potential ethical risks and the impact of constructing AI ethical expectations by researchers, particularly those who are novices, on future research and AI development.

关键词： ChatGPT Qualitative analysis Prompt design Large language models AI-Assisted research Ethical considerations

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A Simulation Study of Transcranial Magnetoacoustic Stimulation of the Basal Ganglia Thalamic Neural Network to Improve Parkinson's Disease Status

SSRN

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SSRN 2023年

作者： Zhang, Yanqiu Zhang, Hao Xu, Tianya Liu, Jiahe Mu, Jiayang Chen, Rongjie Yang, Jiumin Wang, Peiguo Jian, Xiqi School of Biomedical Engineering and Technology Tianjin Medical University Tianjin300070 China Academy of Medical Engineering and Translational Medicine Tianjin International Joint Research Centre for Neural Engineering Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin300072 China Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin300392 China Neurology Department Tianjin Huanhu Hospital Tianjin300350 China Department of Oncology Radiotherapy Cancer Institute Hospital of Tianjin Medical University Tianjin300202 China

Background: Parkinson's disease (PD) is a common neurodegenerative disease. Transcranial magnetoacoustic stimulation (TMAS) is a new therapy that combines a transcranial focused acoustic pressure field with a magnetic field to excite or inhibit neurons in targeted area, which suppresses the abnormally elevated beta band amplitude in PD states, with high spatial resolution and non-invasively. Objective: To study the effective stimulation parameters of TMAS mononuclear and multinuclear stimulation for the treatment of PD with reduced beta band energy, improved abnormal synchronization, and no thermal damage. Methods: The TMAS model is constructed based on the volunteer's computed tomography, 128 arrays of phase-controlled transducers, and permanent magnets. A basal ganglia-thalamic (BG-Th) neural network model of the PD state was constructed on the basis of the Izhikevich model and the acoustic model. An ultrasound stimulation neuron model is constructed based on the Hodgkin-Huxley model. Numerical simulations of transcranial focused acoustic pressure field, temperature field and induced electric field at single and dual targets were performed using the locations of STN, GPi, and GPe in the human brain as the main stimulation target areas. And the acoustic and electric parameters at the focus were extracted to stimulate mononuclear and multinuclear in the BG-Th neural network. Results: When the stimulating effect of ultrasound is ignored, TMAS-STN suppresses the beta band amplitude of each nucleus, whereas TMAS-GPi affects only some nuclei. TMAS-STN&GPi can reduce the beta band amplitude. TMAS-STN&GPi&GPe is more effective. When considering the stimulatory effect of ultrasound, the stimulatory effect of lower sound pressures is negligible, but higher sound pressures may promote or inhibit the stimulatory effect of induced currents. Conclusions: TMAS stimulation of multinuclear with appropriate ultrasound intensity is effective in PD and may be clinically useful. © 202

关键词： Ultrasonics

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Building human Values into Recommender Systems: An Interdisciplinary Synthesis

arXiv

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arXiv 2022年

作者： Stray, Jonathan Halevy, Alon Assar, Parisa Hadfield-Menell, Dylan Boutilier, Craig Ashar, Amar Beattie, Lex Ekstrand, Michael Leibowicz, Claire Sehat, Connie Moon Johansen, Sara Kerlin, Lianne Vickrey, David Singh, Spandana Vrijenhoek, Sanne Zhang, Amy Andrus, McKane Helberger, Natali Proutskova, Polina Mitra, Tanushree Vasan, Nina Center for Human-Compatible Artificial Intelligence UC Berkeley United States Meta AI United States Department of Electrical Engineering and Computer Science MIT United States Google Research United States Spotify Inc. Computer Science Department Boise State University United States Partnership on AI Hacks/Hackers Brainstorm The Stanford Lab for Mental Health Innovation Department of Psychiatry and Behavioral Sciences Stanford University United States BBC United Kingdom Meta United States New America's Open Technology Institute United States Faculty of Law Information Law University of Amsterdam Netherlands Allen School of Computer Science & Engineering University of Washington United States Institute for Information Law University of Amsterdam Netherlands Information and Computer Science University of Washington United States

Recommender systems are the algorithms which select, filter, and personalize content across many of the world's largest platforms and apps. As such, their positive and negative effects on individuals and on societies have been extensively theorized and studied. Our overarching question is how to ensure that recommender systems enact the values of the individuals and societies that they serve. Addressing this question in a principled fashion requires technical knowledge of recommender design and operation, and also critically depends on insights from diverse fields including social science, ethics, economics, psychology, policy and law. This paper is a multidisciplinary effort to synthesize theory and practice from different perspectives, with the goal of providing a shared language, articulating current design approaches, and identifying open problems. It is not a comprehensive survey of this large space, but a set of highlights identified by our diverse author cohort. We collect a set of values that seem most relevant to recommender systems operating across different domains, then examine them from the perspectives of current industry practice, measurement, product design, and policy approaches. Important open problems include multi-stakeholder processes for defining values and resolving trade-offs, better values-driven measurements, recommender controls that people use, non-behavioral algorithmic feedback, optimization for long-term outcomes, causal inference of recommender effects, academic-industry research collaborations, and interdisciplinary policy-making. Copyright © 2022, The Authors. All rights reserved.

关键词： Recommender systems

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Unlocking the Emotional World of Visual Media: An Overview of the science, Research, and Impact of Understanding Emotion

arXiv

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arXiv 2023年

作者： Wang, James Z. Zhao, Sicheng Wu, Chenyan Adams, Reginald B. Newman, Michelle G. Shafir, Tal Tsachor, Rachelle The Data Science and Artificial Intelligence Area the Human-Computer Interaction Area the College of Information Sciences and Technology The Pennsylvania State University University ParkPA16802 United States The Department of Psychology The Pennsylvania State University University ParkPA16802 United States BNRist Tsinghua University Beijing100084 China The Emily Sagol Creative Arts Therapies Research Center University of Haifa 3498838 Israel The School of Theatre and Music University of Illinois ChicagoIL60607 United States

The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible. Whereas recent advancements in deep learning have transformed the field of computer vision, automated understanding of evoked or expressed emotions in visual media remains in its infancy. This foundering stems from the absence of a universally accepted definition of "emotion," coupled with the inherently subjective nature of emotions and their intricate nuances. In this paper, we provide a comprehensive, multidisciplinary overview of the field of emotion analysis in visual media, drawing on insights from psychology, engineering, and the arts. We begin by exploring the psychological foundations of emotion and the computational principles that underpin the understanding of emotions from images and videos. We then review the latest research and systems within the field, accentuating the most promising approaches. We also discuss the current technological challenges and limitations of emotion analysis, underscoring the necessity for continued investigation and innovation. We contend that this represents a "Holy Grail" research problem in computing and delineate pivotal directions for future inquiry. Finally, we examine the ethical ramifications of emotion-understanding technologies and contemplate their potential societal impacts. Overall, this article endeavors to equip readers with a deeper understanding of the domain of emotion analysis in visual media and to inspire further research and development in this captivating and rapidly evolving field. Copyright © 2023, The Authors. All rights reserved.

关键词： Intelligent robots

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Bipartite flat-graph network for nested named entity recognition 58

Bipartite flat-graph network for nested named entity recogni...

引用

58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Luo, Ying Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781952148255

In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and a graph module for all the entities located in inner layers. Bidirectional LSTM (BiLSTM) and graph convolutional network (GCN) are adopted to jointly learn flat entities and their inner dependencies. Different from previous models, which only consider the unidirectional delivery of information from innermost layers to outer ones (or outside-to-inside), our model effectively captures the bidirectional interaction between them. We first use the entities recognized by the flat NER module to construct an entity graph, which is fed to the next graph module. The richer representation learned from graph module carries the dependencies of inner entities and can be exploited to improve outermost entity predictions. Experimental results on three standard nested NER datasets demonstrate that our BiFlaG outperforms previous state-of-the-art models. © 2020 Association for Computational Linguistics

关键词： Long short-term memory

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Enhancing Pathogen Identification in Cheese with High Background Microflora Using an Artificial Neural Network-Enabled Paper Chromogenic Array Sensor Approach

SSRN

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SSRN 2024年

作者： Jia, Zhen Lin, Zhuangsheng Luo, Yaguang Cardoso, Zachary Wang, Dayang Flock, Genevieve Thompson-Witrick, Katherine A. Yu, Hengyong Zhang, Boce Food Science and Human Nutrition Department University of Florida GainesvilleFL32611 United States Department of Biomedical & Nutritional Sciences University of Massachusetts LowellMA01854 United States Environmental Microbial and Food Safety Lab and Food Quality Lab U.S. Department of Agriculture Agricultural Research Service BeltsvilleMD20705 United States Department of Chemical Engineering University of Massachusetts LowellMA01854 United States Department of Electrical and Computer Engineering University of Massachusetts LowellMA01854 United States US Army Natick Soldier Research Development and Engineering Center NatickMA01760 United States

Biohazards, which may occur at all stages of the supply chain, pose significant threats to food safety and public health. Addressing these concerns and enhancing food safety necessitates a nondestructive pathogen surveillance approach capable of continuously and simultaneously detecting multiple pathogens. Detecting and differentiating low concentrations of pathogenic bacteria amid high background microflora levels in foods is challenging, requiring technology with high sensitivity and robust discriminatory capability. This study introduces an artificial neural network -driven paper chromogenic array sensor (ANN-PCA) technique developed for the nondestructive, continuous, and simultaneous detection of Salmonella Enteritidis (SE) and Escherichia coli O157:H7 (Ec) from a high background microflora in shredded cheddar cheese. This method enables accurate detection of SE and Ec in monoculture and cocktail culture, while distinguishing them from a high level of background microflora (~7.5 log CFU/g), with accuracies ranging from 72 ± 11% to 92 ± 3%. In addition, SE and Ec were successfully identified at concentrations as low as 1 log CFU/g within one day, with an accuracy of 72 ± 11%. This approach exhibits promising potential for integration into a digitalized, smart, and resilient nondestructive surveillance system for real-time pathogen detection in foods throughout the supply chain without enrichment, incubation, or other sample preparation steps. © 2024, The Authors. All rights reserved.

关键词： Health risks

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Do I enjoy my friends? Friendship and enjoyment during recess in autistic and non-autistic children

Research in Autism

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Research in Autism 2025年 124卷

作者： Jiayin Zhao Boya Li Yung-Ting Tsou Mitra Baratchi Els Blijd-Hoogewys Özgür Gülen Carolien Rieffe Unit of Developmental and Educational Psychology Institute of Psychology Leiden University Leiden the Netherlands Department of Computer Science Faculty of Science Vrije Universiteit Amsterdam Amsterdam the Netherlands Leiden Institute of Advanced Computer Science (LIACS) Leiden University Leiden the Netherlands Developmental Psychology Faculty of Behavioral and Social Sciences. University of Groningen Groningen the Netherlands Psychiatrie Noord Groningen the Netherlands Department of Psychology Faculty of Economics Administrative and Social Sciences Bahçeşehir University İstanbul Turkey Department of Human Media Interaction Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede the Netherlands Department of Psychology and Human Development Institute of Education University College London London UK

Friendships play a crucial role in children’s well-being and school experiences. This study aimed to gain a better understanding of how autistic and non-autistic children's friendships are related to their enjoyment of the school time spent with peers, i.e. at recess time (school break time). A multi-method approach, including self-reports, peer nominations, and objective measures based on sensor data was used. Forty-five autistic children and 45 non-autistic children from two special education schools participated, aged between 8 to 14 years. Outcomes showed that autistic and non-autistic children did not differ regarding the number of reciprocal and non-reciprocated friends. Yet, autistic children spent less time in contact with their reciprocal friends during recess at the schoolyard compared to their non-autistic peers. Also, while non-autistic pupils spent more time with reciprocal friends than with non-reciprocated ones, this difference was not found among autistic pupils. Notably, spending more time with non-reciprocated friends during recess was related to lower levels of enjoyment in both autistic and non-autistic children. Our findings suggest that autistic children may approach friendships with different priorities. Furthermore, this study underscores the need to consider broader factors beyond reciprocity when assessing children’s social experience at school.

关键词： Peer contact Social Functioning School environment RFID data Schoolyard

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An intelligent clustering scheme based on whale optimization algorithm in flying ad hoc networks

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Vehicular Communications 2024年 49卷

作者： Hosseinzadeh, Mehdi Tanveer, Jawad Alanazi, Faisal Aurangzeb, Khursheed Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Darwesh, Aso Lee, Sang-Woong Rahmani, Amir Masoud Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Computer Science and Engineering Sejong University Seoul 05006 South Korea Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Department of Computer Engineering College of Computer and Information Sciences King Saud University P. O. Box 51178 Riyadh 11543 Saudi Arabia Center of Research and Strategic Studies Lebanese French University Kurdistan Region Iraq Department of Information Technology University of Human Development Kurdistan Region Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan

Due to the progress of unmanned aerial vehicles (UAVs), this new technology is widely applied in military and civilian areas. Multi-UAV networks are often known as flying ad hoc networks (FANETs). Due to these applications, FANET must ensure communication stability and have high scalability. These goals are achieved by presenting clustering techniques in FANETs. However, the characteristics of these networks, like high-mobility nodes, limited energy, and dynamic topology, have created great challenges in two important processes of clustering protocols, namely cluster construction and the selection of cluster heads. In this paper, an intelligent clustering scheme based on the whale optimization algorithm called ICW is suggested in flying ad hoc networks. Firstly, each UAV specifies its hello interval based on the lifespan of adjacent links to guarantee the adaptability of ICW to FANET. Then, a centralized clustering process is done using a whale optimization algorithm (WOA) to find the best cluster centers on the network. To determine the membership of each UAV in a cluster, ICW employs a new criterion, i.e. closeness ratio, so that each UAV joins a cluster with the best closeness ratio. In addition, the evaluation of each whale is carried out based on a fitness function, consisting of three components, namely the number of isolated clusters, the ratio of inter-cluster distance to intra-cluster distance, and cluster size. Then, a cluster head is selected for each cluster based on a score value. This score is dependent on the weighted sum of four metrics, namely remaining energy, the average link lifespan between each UAV and its neighbors, neighbor degree, and the average distance between each UAV and its neighbors. In the last step, two routing processes, namely intra-cluster routing and inter-cluster routing, are introduced in FANET. Then, the evaluation and implementation of ICW is performed through the NS2 simulator. After completing the simulation process, ICW i

关键词： Artificial Intelligence (AI) Clustering Flying ad hoc networks (FANETs) Nature-inspired algorithms Unmanned aerial vehicles (UAVs)

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A Novel Superhydrophilic Hyperbranched Polysiloxane Grafted Membrane for Oil-in-Water Emulsion Separation

SSRN

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SSRN 2024年

作者： Sun, Qilong Yu, Ping Xu, Man Qiao, Yiyang Zhang, Yingying Lu, Yang Yu, Miao Dai, Xiaohui Human Resource Department Jilin Normal University 1301 Haifeng Street Siping136000 China School of Mathematics and Computer Science Jilin Normal University 1301 Haifeng Street Siping136000 China Jilin Prov Key Lab Numer Simulat Jilin Normal University 1301 Haifeng Street Siping136000 China Institute of Green Chemistry and Chemical Technology School of Chemistry and Chemical Engineering Jiangsu University Zhenjiang212013 China

Oily wastewater, posing serious environmental and social issues, has aroused widespread concerns. Membrane separation is regarded as an excellent candidate to solve the problem of oily wastewater, although membrane pollution is inevitable in the process of separation. The construction of hydrophilic surface reported so far can effectively mitigate membrane pollution. Herein, we reported a novel hyperbranched polysiloxane grafted polyvinylidene fluoride membrane (HBPSi-g-PVDF), which was prepared through grafting amino-terminated hyperbranched polysiloxanes onto the PVDF membrane surface via an amidation reaction. The HBPSi-g-PVDF membrane possessed superhydrophilic/underwater superoleophobic properties and excellent resistance to oil droplet adhesion. Meanwhile, the resulting membrane presented favorable water permeance (16924 Lžm-2žh-1žbar-1) and permeance recovery ratio (99.0%). In addition, the HBPSi-g-PVDF membrane displayed satisfactory separation efficiency (>99.1%) for diverse oil-in-water emulsions having over 6000 Lžm-2žh-1žbar-1 permeance. Furthermore, HBPSi-g-PVDF membrane also showed excellent environmental durability and recyclability, maintaining stable performance in hash environments, highlighting its great future for the disposal of oily wastewater. © 2024, The Authors. All rights reserved.

关键词： Membranes

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Fully implantable and battery-free wireless optoelectronic system for modulable cancer therapy and real-time monitoring

引用

npj Flexible Electronics 2023年第1期7卷 119-130页

作者： Kiho Kim In Sik Min Tae Hee Kim Do Hyeon Kim Seungwon Hwang Kyowon Kang Kyubeen Kim Sangun Park Jongmin Lee Young Uk Cho Jung Woo Lee Woon-Hong Yeo Young Min Song Youngmee Jung Ki Jun Yu Functional Bio-integrated Electronics and Energy Management Lab School of Electrical and Electronic EngineeringYonsei University50 Yonsei-roSeodaemun-guSeoul 03722Republic of Korea Center for Biomaterials Biomedical Research InstituteKorea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea Department of Fusion Research and Collaboration Biomedical Research InstituteSeoul National University HospitalSeoul 03080Republic of Korea School of Electrical Engineering and Computer Science(EECS) Gwangju Institute of Science and Technology(GIST)Gwangju 61005Republic of Korea PA1 Foundry BusinessSamsung Electronics1 Samsungjeonja-roHwaseong-siGyeonggi-doRepublic of Korea R&D Center DNA Lab PharmaresearchSeongnam 13486Republic of Korea KU-KIST Graduate School of Converging Science and Technology Korea UniversitySeoul 136-705Republic of Korea School of Materials Science and Engineering Energy Materials for Soft Electronics LaboratoryPusan National UniversityBusan 46241Republic of Korea IEN Center for Human-Centric Interfaces and Engineering and George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of MedicineAtlantaGA 30332USA Institute for Robotics and Intelligent Machines Parker H.Petit Institute for Bioengineering and BiosciencesInstitute for MaterialsNeural Engineering CenterGeorgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Electronic Engineering YU-Korea Institute of Science and Technology(KIST)InstituteYonsei University50Yonsei-roSeodaemun-guSeoul 03722Republic of Korea

Photodynamic therapy(PDT)is attracting attention as a next-generation cancer treatment that can selectively destroy malignant tissues,exhibit fewer side effects,and lack pain during *** PDT systems have recently been developed to resolve the issues of bulky and expensive conventional PDT systems and to implement continuous and repetitive *** implantable PDT systems,however,are not able to perform multiple functions simultaneously,such as modulating light intensity,measuring,and transmitting tumor-related data,resulting in the complexity of cancer ***,we introduce a flexible and fully implantable wireless optoelectronic system capable of continuous and effective cancer treatment by fusing PDT and hyperthermia and enabling tumor size monitoring in *** system exploits micro inorganic light-emitting diodes(μ-LED)that emit light with a wavelength of 624 nm,designed not to affect surrounding normal tissues by utilizing a fully programmable light intensity ofμ-LED and precisely monitoring the tumor size by Si phototransistor during a long-term implantation(2–3 weeks).The superiority of simultaneous cancer treatment and tumor size monitoring capabilities of our system operated by wireless power and data transmissions with a cell phone was confirmed through in vitro experiments,ray-tracing simulation results,and a tumor xenograft mouse model in *** all-in-one single system for cancer treatment offers opportunities to not only enable effective treatment of tumors located deep in the tissue but also enable precise and continuous monitoring of tumor size in real-time.

关键词： optoelectronic enable battery

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