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arXiv 2021年

作者： Oruche, Roland Egede, Lisa Baker, Tracy O'Donncha, Fearghal Department of Electrical Engineering & Computer Science University of Missouri Columbia United States Human Computer Interaction Institute Carnegie Mellon University United States The Nature Conservancy New YorkNY United States IBM Research Europe IE Switzerland

Effective water resource management requires information on water availability - both in terms of quality and quantity - spatially and temporally. In this paper, we study the methodology behind Transfer Learning (TL) through fine-tuning and parameter transferring for better generalization performance of streamflow prediction in data-sparse regions. We propose a standard recurrent neural network in the form of Long Short-Term Memory (LSTM) to fit on a sufficiently large source domain dataset and repurpose the learned weights to a significantly smaller, yet similar target domain datasets. We present a methodology to implement transfer learning approaches for spatiotemporal applications by separating the spatial and temporal components of the model and training the model to generalize based on categorical datasets representing spatial variability. The framework is developed on a rich benchmark dataset from the US and evaluated on a smaller dataset collected by The Nature Conservancy in Kenya. The LSTM model exhibits generalization performance through our TL technique. Results from this current experiment demonstrate the effective predictive skill of forecasting streamflow responses when knowledge transferring and static descriptors are used to improve hydrologic model generalization in data-sparse regions. © 2021, CC BY.

关键词： Long short-term memory

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Energy Efficiency Optimization for Full-Duplex D2D Communications Underlaying Distributed Antenna Systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Liu, Zhan Liao, Zhiyuan Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. Hunan University of Humanities Science and Technology School of Information Loudi417000 China Nanchang University School of Information Engineering Nanchang330031 China Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang330031 China South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

In this paper, we investigate the total system energy efficiency (EE) of full-duplex (FD) device-to-device (D2D) communications underlaying distributed antenna systems (DAS), where remote access units (RAUs), D2D users (DUs), and cellular users (CUs) are all capable of FD operation. Specifically, we jointly optimize subcarrier assignment and power allocation under the quality of service (QoS) requirements of CUs and DUs and the maximum power constraints of RAUs, CUs, and DUs. In addition, we propose a novel spectrum sharing strategy that allows each subcarrier to be assigned to multiple CUs and/or multiple D2D pairs (DPs) for flexibility. To solve the formulated non-convex optimization problem, we first employ fractional programming to transform the objective function in the optimization problem from the fractional form into the equivalent subtractive form. Then, an efficient iterative resource allocation algorithm is proposed, which needs to solve an inner problem in each iteration. After relaxing the variables and introducing penalty factors, the non-convex inner problem is transformed into a convex problem through the successive convex approximation (SCA) method and solved by iterative algorithm. Simulation results demonstrate that the proposed algorithm can considerably improve the system EE compared to other benchmark schemes. Furthermore, the proposed spectrum sharing strategy is superior to the existing sharing strategies. © 2017 IEEE.

关键词： Convex optimization

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Are deeper reflectors better goal-setters? AI-empowered analytics of reflective writing in pharmaceutical education

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computers and Education: Artificial Intelligence 2023年 5卷

作者： Li, Yuheng Raković, Mladen Dai, Wei Lin, Jionghao Khosravi, Hassan Galbraith, Kirsten Lyons, Kayley Gašević, Dragan Chen, Guanliang Centre for Learning Analytics Monash University Clayton 3168 VIC Australia Human-Computer Interaction Institute School of Computer Science Carnegie Mellon University Pittsburgh PA United States Institute for Teaching and Learning Innovation University of Queensland Brisband 4072 QLD Australia Experiential Development and Graduate Education Faculty of Pharmacy and Pharmaceutical Sciences Monash University Parkville 3052 VIC Australia Centre for Digital Transformation of Health University of Melbourne Parkville 3010 VIC Australia

Reflection and goal-setting are interrelated processes in well-established educational theories to promote in-depth self-reflection and self-regulated learning. Prior studies have considered reflection to be an important antecedent for meaningful goal-setting. Yet, there lacks empirical evidence to shed light on how students' abilities to reflect inform their abilities to set goals. Hence, in the present study, we aimed to quantify the connection between students' retrospective reflection and their subsequent goal-setting, and derive more in-depth insights to benefit educators in their teaching to promote deeper reflection, more specific goal-setting and better self-regulation. To this end, we utilised two fine-grained coding schemes, adapted from well-established reflection and goal-setting theories, respectively, as well as pertinent prior studies, to annotate the reflective and goal-setting elements within 600 student responses in pharmacy curricula. We visualised such elements as a network graph to study students' joint behavioural patterns in reflecting and setting goals. Then, we statistically analysed the correlation between students' reflective levels and the goal specificities using a Mann Whitney U test. We found that (1) descriptive reflection and goals that included content and actions with additional details more commonly presented jointly;(2) students who reflected deeply tended to set more specific goals. These findings are further summarised and discussed to guide educators to adopt reflective and goal-setting practices when designing teaching activities. Moreover, driven by these findings, we emphasised the significance of aiding instructors to provide timely assessment to students' written reflections so as to further ameliorate students' reflective abilities. Therefore, we attempted to automate such assessments using five traditional machine learning algorithms and one deep learning approach based on Bidirectional Encoder Representation of Transfo

关键词： BERT Deep learning Goal-setting Machine learning Reflective writing Self-regulated learning

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Trajectory prediction model for crossing-based target selection

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Virtual Reality & Intelligent Hardware 2019年第3期1卷 330-340页

作者： Hao ZHANG Jin HUANG Feng TIAN Guozhong DAI Hongan WANG Beijing Key Lab of Human-Computer Interaction Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Lab of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Background Crossing-based target selection motion may attain less error rates and higher interactive speed in some *** of the research in target selection fields are focused on the analysis of the interaction ***,as trajectories play a much more important role in crossing-based target selection compared to the other interactive techniques,an ideal model for trajectories can help computer designers make predictions about interaction results during the process of target selection rather than at the end of the whole *** In this paper,a trajectory prediction model for crossing based target selection tasks is proposed by taking the reference of a dynamic model *** Simulation results demonstrate that our model performed well with regard to the prediction of trajectories,endpoints and hitting time for target-selection motion,and the average error of trajectories,endpoints and hitting time values were found to be 17.28%,2.73mm and 11.50%,respectively.

关键词： Target selection Crossing-based selection Trajectory prediction

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Reconsidering Accountability in the Present and Future of Work

Reconsidering Accountability in the Present and Future of Wo...

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Companion Publication of the 2022 Conference on computer Supported Cooperative Work and Social Computing

作者： Sarah Elizabeth Fox Daniel Marcus Greene Karen Gregory Mohammad Hossein Jarrahi Vera Khovanskaya Human-Computer Interaction Institute Carnegie Mellon University United States College of Information Studies University of Maryland United States School of Social and Political Science University of Edinburgh Scotland School of Information and Library Science University of North Carolina at Chapel Hill United States Feminist Labor Lab University of California San Diego United States

ISBN: (纸本)9781450391900

There is a growing effort within CSCW and related fields to understand the effects of artificial intelligence, algorithmic management, and other contemporary and near-future transformations of digitally-mediated work. This panel builds on this effort by asking three guiding questions: how should academics more fully account for the consequences of new transformations in the workplace, how should we engage with workers who are already accounting for the ways that these systems impact their labor conditions and experience of work, and how should we be made accountable to workers for the design interventions we propose or deploy. The panelists will discuss topics including the impact of AI technology on frontline and essential workers, the transformative effect of algorithmic management on organizational practices within and beyond platform work, and how demands and efforts to attain algorithmic transparency by platform/gig-workers both reveal power imbalances within the workplaces and point to avenues for worker advocacy. We will also draw on scholarship that analyzes the shifting labor conditions of CSCW to situate contemporary concerns within the field's disciplinary history. We aim to generate conversation about the ways that these forms of accountability have already been unfolding, identify shared themes and concerns across parallel topics, and identify future directions for researchers moving forward in this space.

关键词：

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Material manifestations of dislocation and (Re)connection 17

Material manifestations of dislocation and (Re)connection

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17th European Conference on computer Supported Cooperative Work, ECSCW 2019

作者： Smit, Dorothé Krischkowsky, Alina Beuthel, Janne Mascha Maurer, Bernhard Fuchsberger, Verena Murer, Martin Tscheligi, Manfred Devendorf, Laura Zaman, Bieke Nouwen, Marije Aal, Konstantin Center for Human-Computer Interaction University of Salzburg Austria ATLAS Institute Dept. of Information Science University of Colorado United States KU Leuven Belgium Institute for Information Systems and New Media University of Siegen Germany

This workshop focuses on the material qualities of dislocation. The process of humans becoming separated from each other is likely to have diverse consequences;from shifting frequency, modes, or routines of communication and collaboration, to completely alternate means of connection. In this workshop, we aim to discuss a broad range of material manifestations and implications of (researching and designing for) dislocation. While engaging with material qualities of dislocation, we will reflect on the state of the art, discuss research gaps and potentials, and explore hands-on how design can create opportunities for (re)connection in response to dislocation through the creation of tangible interfaces. © 2019 held by Authors.

关键词： Interface states

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Challenging common interpretability assumptions in feature attribution explanations

arXiv

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arXiv 2020年

作者： Dinu, Jonathan Bigham, Jeffrey Zico Kolter, J. Human-Computer Interaction Institute Carnegie Mellon University United States School of Computer Science Carnegie Mellon University United States

As machine learning and algorithmic decision making systems are increasingly being leveraged in high-stakes human-in-the-loop settings, there is a pressing need to understand the rationale of their predictions. Researchers have responded to this need with explainable AI (XAI), but often proclaim interpretability axiomatically without evaluation. When these systems are evaluated, they are often tested through offline simulations with proxy metrics of interpretability (such as model complexity). We empirically evaluate the veracity of three common interpretability assumptions through a large scale human-subjects experiment with a simple "placebo explanation" control. We find that feature attribution explanations provide marginal utility in our task for a human decision maker and in certain cases result in worse decisions due to cognitive and contextual confounders. This result challenges the assumed universal benefit of applying these methods and we hope this work will underscore the importance of human evaluation in XAI research. Supplemental materials—including anonymized data from the experiment, code to replicate the study, an interactive demo of the experiment, and the models used in the analysis—can be found at: https://***/challenging-xai. © 2020, CC0.

关键词： Decision making

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Theory-based habit modeling for enhancing behavior prediction

arXiv

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arXiv 2021年

作者： Zhang, Chao Vanschoren, Joaquin van Wissen, Arlette Lakens, Daniël de Ruyter, Boris IJsselsteijn, Wijnand A. Human-Technology Interaction Group Eindhoven University of Technology Eindhoven Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Brain Behavior and Cognition Philips Research Eindhoven Netherlands

Psychological theories of habit posit that when a strong habit is formed through behavioral repetition, it can trigger behavior automatically in the same environment. Given the reciprocal relationship between habit and behavior, changing lifestyle behaviors (e.g., toothbrushing) is largely a task of breaking old habits and creating new and healthy ones. Thus, representing users’ habit strengths can be very useful for behavior change support systems (BCSS), for example, to predict behavior or to decide when an intervention reaches its intended effect. However, habit strength is not directly observable and existing self-report measures are taxing for users. In this paper, built on recent computational models of habit formation, we propose a method to enable intelligent systems to compute habit strength based on observable behavior. The hypothesized advantage of using computed habit strength for behavior prediction was tested using data from two intervention studies, where we trained participants to brush their teeth twice a day for three weeks and monitored their behaviors using accelerometers. Through hierarchical cross-validation, we found that for the task of predicting future brushing behavior, computed habit strength clearly outperformed self-reported habit strength (in both studies) and was also superior to models based on past behavior frequency (in the larger second study). Our findings provide initial support for our theory-based approach of modeling user habits and encourages the use of habit computation to deliver personalized and adaptive interventions. © 2021, CC BY.

关键词： Intelligent systems

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Region and decision tree-based segmentations for multi-objects detection and classification in outdoor scenes 17

Region and decision tree-based segmentations for multi-objec...

引用

17th International Conference on Frontiers of Information Technology, FIT 2019

作者： Ahmed, Abrar Jalal, Ahmad Kim, Kibum Dept. of Computer Science Air University Islamabad Pakistan Dept. of Human-Computer Interaction Hanyang University Seoul Korea Republic of

ISBN: (纸本)9781728166254

Accurate segmentation and detection of all mixed and occluded objects in the complex indoor/outdoor scenes become a vital topic of computer vision. These above-mentioned scenarios are the significant part of important vision applications such as autonomous driving, traffic monitoring, security surveillance, humane body parts detection, objects tracking and scene recognition. It is still difficult to accurately detect all the objects in the image due to illumination changes, occlusion and different directions. Meanwhile, segmenting the image in parts helps to detect the multi objects accurately. In this paper, we designed a system having improved techniques for the accurate segmentation and detection of multi objects. Firstly, we have combined the results of two methods for accurate segmentation of multiple objects, (i) Decision trees for labeling every neighboring pixel and assigning a separate color to all object present in complex images and (ii) Region-based segmentation for significant detection of multiple regions and drawing boundaries of all objects. Secondly, detection is performed by searching the objects with previously assigned colors. Finally, we have performed labeling with class name to all objects present in the images. We have performed our experimental over two benchmarked datasets as Instance Saliency images and MSRC. Our experimental work has shown improved results with respect to other state of the art algorithms. © 2019 IEEE.

关键词： Decision trees

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Sensors Technologies for human Activity Analysis Based on SVM Optimized by PSO Algorithm

Sensors Technologies for Human Activity Analysis Based on SV...

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2019 International Conference on Applied and Engineering Mathematics, ICAEM 2019

作者： Batool, Mouazma Jalal, Ahmad Kim, Kibum Dept. of Computer Science Air University Islamabad Pakistan Dept. of Human-Computer Interaction Hanyang University Seoul Korea Republic of

ISBN: (纸本)9781728123530

The rapid growth of wearable sensors have increased the importance of human activity analysis in different areas of information technologies. Motion artifacts often degrade the performance of wearable sensors. Several wearable sensors have been used since the last decades in order to recognize physical activity detection. The wearable sensors could have numerous applications in medical and daily life routine activities like human gait analysis, health care, fitness, etc. In this paper, accelerometer and gyroscope sensors dataset has been used to propose an efficient model for physical activity detection. We designed a new feature extraction algorithm, Mel-frequency cepstral coefficient and statistical features to extract valuable features. Then, classification of different daily life activities is performed via Particle Swarm Optimization (PSO) together with SVM algorithm over bench mark motion-sense dataset. The results of our model shows that pre-classifier as PSO and SVM along with feature extraction module excel in term of accuracy and efficiency. Our experimental results have shown accuracy of 87.50% over motion-sense dataset. This model is recommended for the system associating in physical activity detection, especially in medical fitness field. © 2019 IEEE.

关键词： Wearable sensors

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