Reinforcement Learning (RL) and Imitation Learning (IL) have made great progress in robotic control in recent years. However, these methods show obvious deterioration for new tasks that need to be completed through ne...
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Informing people of health threats is crucial as they may lack situation awareness (SA) of risky situations when they do not have personal experiences or lessons learned from dangerous encounters. In this work, we exp...
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This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this...
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Background: Online mental health communities (OMHCs) are an effective and accessible channel to give and receive social support for individuals with mental and emotional issues. However, a key challenge on these platf...
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Background: Online mental health communities (OMHCs) are an effective and accessible channel to give and receive social support for individuals with mental and emotional issues. However, a key challenge on these platforms is finding suitable partners to interact with given that mechanisms to match users are currently underdeveloped or highly naive. Objective: In this study, we collaborated with one of the world’s largest OMHCs;our contribution is to show the application of agent-based modeling for the design of online community matching algorithms. We developed an agent-based simulation framework and showcased how it can uncover trade-offs in different matching algorithms between people seeking support and volunteer counselors. Methods: We used a comprehensive data set spanning January 2020 to April 2022 to create a simulation framework based on agent-based modeling that replicates the current matching mechanisms of our research site. After validating the accuracy of this simulated replication, we used this simulation framework as a “sandbox” to test different matching algorithms based on the deferred acceptance algorithm. We compared trade-offs among these different matching algorithms based on various metrics of interest, such as chat ratings and matching success rates. Results: Our study suggests that various tensions emerge through different algorithmic choices for these communities. For example, our simulation uncovered that increased waiting time for support seekers was an inherent consequence on these sites when intelligent matching was used to find more suitable matches. Our simulation also verified some intuitive effects, such as that the greatest number of support seeker–counselor matches occurred using a “first come, first served” protocol, whereas relatively fewer matches occurred using a “last come, first served” protocol. We also discuss practical findings regarding matching for vulnerable versus overall populations. Results by demographic group reveal
Metaphorical expressions are widely used in daily communication between humans to improve the understanding of both complex and abstract concepts. However, it is unclear whether the usage of metaphorical language is h...
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ISBN:
(数字)9798331530631
ISBN:
(纸本)9798331530648
Metaphorical expressions are widely used in daily communication between humans to improve the understanding of both complex and abstract concepts. However, it is unclear whether the usage of metaphorical language is helpful for enhancing the conversational engagement between humans and AI. In this work, we leverage a state-of-the-art computational metaphor processing tool to gain insights from human and ChatGPT conversations. Our quantitative analysis finds that although metaphors may enhance the quality of human-AI interactions, they do not directly lead to higher levels of conversational engagement, which is measured by the duration of the conversation. However, regression analysis shows a notable relationship between user and ChatGPT metaphor usage, suggesting that ChatGPT is adept at reflecting the linguistic style of users, especially in terms of metaphorical language. Additional topic modeling and concept mapping analyses further explored the patterns of metaphorical language across various engagement levels and topics in user and ChatGPT messages.
LGBTQ+ people have received increased attention in HCI research, paralleling a greater emphasis on social justice in recent years. However, there has not been a systematic review of how LGBTQ+ people are researched or...
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ISBN:
(纸本)9798400703300
LGBTQ+ people have received increased attention in HCI research, paralleling a greater emphasis on social justice in recent years. However, there has not been a systematic review of how LGBTQ+ people are researched or discussed in HCI. In this work, we review all research mentioning LGBTQ+ people across the HCI venues of CHI, CSCW, DIS, and TOCHI. Since 2014, we find a linear growth in the number of papers substantially about LGBTQ+ people and an exponential increase in the number of mentions. Research about LGBTQ+ people tends to center experiences of being politicized, outside the norm, stigmatized, or highly vulnerable. LGBTQ+ people are typically mentioned as a marginalized group or an area of future research. We identify gaps and opportunities for (1) research about and (2) the discussion of LGBTQ+ in HCI and provide a dataset to facilitate future Queer HCI research.
Smartphones have become an all-in-one device due to their constant connectivity and ability to provide a wide range of functions. However, they can expose users to various forms of malware, posing significant threats ...
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ISBN:
(数字)9798350351187
ISBN:
(纸本)9798350351194
Smartphones have become an all-in-one device due to their constant connectivity and ability to provide a wide range of functions. However, they can expose users to various forms of malware, posing significant threats to their privacy, security, and financial well-being. In this context, the detection and classification of Android malware have emerged as critical research areas in cybersecurity. Our study develops six distinct models/classifiers for detecting and classifying Android malware, leveraging the following machine-learning algorithms: MLP, logistic regression, random forest, SVM, XGBoost, and AdaBoost. Through a systematic evaluation process, we assess the efficacy of each model, highlighting their respective strengths and weaknesses. These findings not only contribute to the existing body of knowledge but also pave the way for future research and innovation in the field of Android security. Furthermore, we investigate the impact of data preprocessing and feature selection strategies on model performance and generalization capabilities. Our experimental results reveal that Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers outperformed others in classifying Android malware, showcasing performance of around $93 \%$ and $92 \%$, respectively across accuracy, precision, recall, and f1-score. With an AUC of 0.93 for RF and 0.92 for XGBoost, these models can clearly distinguish between malware and benign samples with minimum misclassifications. Our findings shed light on the effectiveness of machine learning algorithms in combating Android malware and offer valuable insights into the most suitable models. Ultimately, this research study advances the understanding of Android malware detection and classification, providing a foundation for developing robust security solutions in the mobile computing landscape.
作者:
Guo, KuoLi, YifanChen, HaoShen, Hong-BinYang, YangShanghai Jiao Tong University
Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University
Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University
School of Computer Science Computational Biology Department PittsburghPA15213 United States
Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bio...
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The widespread adoption of 3D printers exacerbates existing environmental challenges as these machines increase energy consumption, waste output, and the use of plastics. Material choice for 3D printing is tightly con...
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ISBN:
(纸本)9781450398930
The widespread adoption of 3D printers exacerbates existing environmental challenges as these machines increase energy consumption, waste output, and the use of plastics. Material choice for 3D printing is tightly connected to these challenges, and as such researchers and designers are exploring sustainable alternatives. Building on these efforts, this work explores using spent coffee grounds as a sustainable material for prototyping with 3D printing. This material, in addition to being compostable and recyclable, can be easily made and printed at home. We describe the material in detail, including the process of making it from readily available ingredients, its material characteristics and its printing parameters. We then explore how it can support sustainable prototyping practices as well as HCI applications. In reflecting on our design process, we discuss challenges and opportunities for the HCI community to support sustainable prototyping and personal fabrication. We conclude with a set of design considerations for others to weigh when exploring sustainable materials for 3D printing and prototyping.
In the era of social distancing, distance learning represents a crucial educational challenge. Several 2D information technologies have been provided, yet these share multiple limitations and have negative social, edu...
In the era of social distancing, distance learning represents a crucial educational challenge. Several 2D information technologies have been provided, yet these share multiple limitations and have negative social, educational, and psychological implications for learners. Metaverse promises to revolutionize education as we know it: this is a persistent, virtual, three-dimensional environment that is supposed to address most of the limitations of 2D information technologies. Nonetheless, there are still software engineering challenges to face to enable such a metaverse, especially when turning to software security and privacy. In this paper, we aim at performing the first steps toward an improved understanding of the security perspective of educational metaverse, by analyzing how blockchain can be employed within educational environments and how applications may be designed. Our ultimate goal is to provide insights into how blockchain can be further tailored in the context of educational metaverse. We conduct a systematic literature review, which targets 20 primary studies. The key findings of the study showcase the use of blockchain in 3 educational tasks, other than describing the blockchain design approaches, which protocol they commonly use and the associated limitations. We conclude by developing a conceptualization of a blockchain-based educational metaverse.
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