tocauthor: Zhang_C* : Human-Computer Interaction Bibliography Search

BoomChaCha: A Rhythm-based, Physical Role-Playing Game that Facilitates Cooperation among Players Student Game Competition / Zhu, Fengyuan / Sun, Wangshu / Zhang, Carrie / Ricks, Rebecca Extended Abstracts of the ACM CHI'16 Conference on Human Factors in Computing Systems 2016-05-07 v.2 p.184-187

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Summary: We designed a new genre of gaming that is rhythm-based, cooperative, physical and involves role-playing. In the game, three players as a team combat monsters by waving physical weapons on certain beats of the background music. The game requires a collective effort, as each player plays a certain role that is responsible for attacking, healing or defending respectively. To defeat the monster, the three players need to wave the physical weapons according to the rhythm of a six-beat waltz, which encourages cooperation and promotes pro-social behaviors.

In the Eye of the Beholder: The Impact of Frame Rate on Human Eye Blink Late-Breaking Works: Interaction in Specific Domains / Tag, Benjamin / Shimizu, Junichi / Zhang, Chi / Kunze, Kai / Ohta, Naohisa / Sugiura, Kazunori Extended Abstracts of the ACM CHI'16 Conference on Human Factors in Computing Systems 2016-05-07 v.2 p.2321-2327

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Summary: We introduce a study investigating the impact of high frame rate videos on viewer's eye blink frequency. A series of videos with varying combinations of motion complexities and frame rates were shown to participants, while their eye blinks were counted with J!NS MEME (smart eye wear). Lower frame rates and lower motion complexity caused higher blink frequencies, which are markers for stress and emotional arousal.

CompuWoven: A Computer-Aided Fabrication Approach to Hand-Woven Craft Late-Breaking Works: Interaction in Specific Domains / Tao, Ye / Lu, Nannan / Zhang, Caowei / Wang, Guanyun / Yao, Cheng / Ying, Fangtian Extended Abstracts of the ACM CHI'16 Conference on Human Factors in Computing Systems 2016-05-07 v.2 p.2328-2333

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Summary: Weaving is a traditional technology for making everyday products by hand that involves interlacing planar pieces to endow tough and pliable properties, (e.g., bamboo and stiff paper) to create 3D shapes. The technique has been extensively applied throughout history by various means, and is favored due to its low cost, accessibility, and environmental friendliness. Traditional weaving technology, however, requires accumulated craft expertise and actual production through numerous iterations, which is generally very time-consuming -- this limits its design aesthetic and ubiquitous use. Inspired by these problems, we present a novel hand-woven fabrication approach called CompuWoven to customize and weave 3D objects from planar pieces. The key idea is to establish an approach for producing 3D objects that is similar to weaving tradition, but without the need for manual experience. In addition, CompuWoven eliminates the former need for supporting structures, reduces actual physical material waste and allows for more complicated and irregular designs.

Driver Classification Based on Driving Behaviors User Modelling / Zhang, Cheng / Patel, Mitesh / Buthpitiya, Senaka / Lyons, Kent / Harrison, Beverly / Abowd, Gregory D. Proceedings of the 2016 International Conference on Intelligent User Interfaces 2016-03-07 v.1 p.80-84

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Summary: In this paper we develop a model capable of classifying drivers from their driving behaviors sensed by only low level sensors. The sensing platform consists of data available from the diagnostic outlet (OBD) of the car and smartphone sensors. We develop a window based support vector machine model to classify drivers. We test our model with two datasets collected under both controlled and naturalistic conditions. Furthermore, we evaluate the model using each sensor source (car and phone) independently and combining both the sensors. The average classification accuracies attained with data collected from three different cars shared between couples in a naturalistic environment were 75.83%, 85.83% and 86.67% using only phone sensors, only cars sensors and combined car and phone sensors respectively.

Image based Static Facial Expression Recognition with Multiple Deep Network Learning Grand Challenge 2: Emotion Recognition in the Wild Challenge 2015 / Yu, Zhiding / Zhang, Cha Proceedings of the 2015 International Conference on Multimodal Interaction 2015-11-09 p.435-442

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Summary: We report our image based static facial expression recognition method for the Emotion Recognition in the Wild Challenge (EmotiW) 2015. We focus on the sub-challenge of the SFEW 2.0 dataset, where one seeks to automatically classify a set of static images into 7 basic emotions. The proposed method contains a face detection module based on the ensemble of three state-of-the-art face detectors, followed by a classification module with the ensemble of multiple deep convolutional neural networks (CNN). Each CNN model is initialized randomly and pre-trained on a larger dataset provided by the Facial Expression Recognition (FER) Challenge 2013. The pre-trained models are then fine-tuned on the training set of SFEW 2.0. To combine multiple CNN models, we present two schemes for learning the ensemble weights of the network responses: by minimizing the log likelihood loss, and by minimizing the hinge loss. Our proposed method generates state-of-the-art result on the FER dataset. It also achieves 55.96% and 61.29% respectively on the validation and test set of SFEW 2.0, surpassing the challenge baseline of 35.96% and 39.13% with significant gains.

A Unsupervised Person Re-identification Method Using Model Based Representation and Ranking Videos/Demos 1: / Liang, Chao / Huang, Binyue / Hu, Ruimin / Zhang, Chunjie / Jing, Xiaoyuan / Xiao, Jing Proceedings of the 2015 ACM International Conference on Multimedia 2015-10-26 p.771-774

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Summary: As a core technique supporting the multi-camera tracking task, person re-identification attracts increasing research interests in both academic and industrial communities. Its aim is to match individuals across a group of spatially non-overlapping surveillance cameras, which are usually interfered by various imaging conditions and object motions. Current methods mainly focus on robust feature representation and accurate distance measure, where intensive computations and expensive training samples prohibit their practical applications. To address the above problems, this paper proposes a new unsupervised person re-identification method featured by its competitive accuracy and high efficiency. Both merits stem from model based person image representation and ranking, with which, merely 4-dimension pixel-level features can achieve over 20% matching rate at Rank 1 on the challenging VIPeR dataset.

Multi-cue Augmented Face Clustering Poster Session 2 / Zhou, Chengju / Zhang, Changqing / Fu, Huazhu / Wang, Rui / Cao, Xiaochun Proceedings of the 2015 ACM International Conference on Multimedia 2015-10-26 p.1095-1098

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Summary: Face clustering is an important but challenging task since facial images always have huge variation due to change in facial expressions, head poses and partial occlusions, etc. Moreover, face clustering is actually an unsupervised problem which makes it more difficult to reach an accurate result. Fortunately, there are some cues that can be used to improve clustering performance. In this paper, two types of cues are employed. The first one is pairwise constraints: must-link and cannot-link constraints, which can be extracted from the temporal and spatial knowledge of data. The other is that each face is associated with a series of attributes (i.e, gender) which can contribute discrimination among faces. To take advantage of the above cues, we propose a new algorithm, Multi-cue Augmented Face Clustering (McAFC), which effectively incorporates the cues via graph-guided sparse subspace clustering technique. Specially, facial images from the same individual are encouraged to be connected while faces from different persons are restrained to be connected. Experiments on three face datasets from real-world videos show the improvements of our algorithm over the state-of-the-art methods.

Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time Session 5A: Trips and Trajectories / Zhang, Chenyi / Liang, Hongwei / Wang, Ke / Sun, Jianling Proceedings of the 2015 ACM Conference on Information and Knowledge Management 2015-10-19 p.911-920

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Summary: As location-based social network (LBSN) services become increasingly popular, trip recommendation that recommends a sequence of points of interest (POIs) to visit for a user emerges as one of many important applications of LBSNs. Personalized trip recommendation tailors to users' specific tastes by learning from past check-in behaviors of users and their peers. Finding the optimal trip that maximizes user's experiences for a given time budget constraint is an NP hard problem and previous solutions do not consider two practical and important constraints. One constraint is POI availability where a POI may be only available during a certain time window. Another constraint is uncertain traveling time where the traveling time between two POIs is uncertain. This work presents efficient solutions to personalized trip recommendation by incorporating these constraints to prune the search space. We evaluated the efficiency and effectiveness of our solutions on real life LBSN data sets.

Defragging Subgraph Features for Graph Classification Short Papers: Databases / Wang, Haishuai / Zhang, Peng / Tsang, Ivor / Chen, Ling / Zhang, Chengqi Proceedings of the 2015 ACM Conference on Information and Knowledge Management 2015-10-19 p.1687-1690

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Summary: Graph classification is an important tool for analysing structured and semi-structured data, where subgraphs are commonly used as the feature representation. However, the number and size of subgraph features crucially depend on the threshold parameters of frequent subgraph mining algorithms. Any improper setting of the parameters will generate many trivial short-pattern subgraph fragments which dominate the feature space, distort graph classifiers and bury interesting long-pattern subgraphs. In this paper, we propose a new Subgraph Join Feature Selection (SJFS) algorithm. The SJFS algorithm, by forcing graph classifiers to join short-pattern subgraph fragments, can defrag trivial subgraph features and deliver long-pattern interesting subgraphs. Experimental results on both synthetic and real-world social network graph data demonstrate the performance of the proposed method.

Build Emotion Lexicon from Microblogs by Combining Effects of Seed Words and Emoticons in a Heterogeneous Graph Session 9 / Song, Kaisong / Feng, Shi / Gao, Wei / Wang, Daling / Chen, Ling / Zhang, Chengqi Proceedings of the 2015 ACM Conference on Hypertext and Social Media 2015-09-01 p.283-292

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Summary: As an indispensable resource for emotion analysis, emotion lexicons have attracted increasing attention in recent years. Most existing methods focus on capturing the single emotional effect of words rather than the emotion distributions which are helpful to model multiple complex emotions in a subjective text. Meanwhile, automatic lexicon building methods are overly dependent on seed words but neglect the effect of emoticons which are natural graphical labels of fine-grained emotion. In this paper, we propose a novel emotion lexicon building framework that leverages both seed words and emoticons simultaneously to capture emotion distributions of candidate words more accurately. Our method overcomes the weakness of existing methods by combining the effects of both seed words and emoticons in a unified three-layer heterogeneous graph, in which a multi-label random walk (MLRW) algorithm is performed to strengthen the emotion distribution estimation. Experimental results on real-world data reveal that our constructed emotion lexicon achieves promising results for emotion classification compared to the state-of-the-art lexicons.

Usage Diversity, Task Interdependence and Group Innovation Industry, Academia, Innovation and Market / Luo, Yumei / Zhang, Cheng / Xu, Yunjie HCIB 2015: 2nd International Conference on HCI in Business 2015-08-02 p.717-726

Keywords: Individual innovative use; Group innovative use; Innovativeness diversity; Task interdependence

Link to Digital Content at Springer

Summary: Investments on information stems (IS) are costly. After the initial adoption of Information Systems, the value of IS to an organization depends on employees' innovative use of various features of IS in the infusion stage. Innovative use of IS, a key activity of technology infusion, depends not only on individual effort, but also on group effort of teams. Grounded on the research of individual-collective process, this paper seeks to build a situational contingency model of how individual innovative use of IS affects group innovative use.

Exploiting Collective Hidden Structures in Webpage Titles for Open Domain Entity Extraction Technical Papers 2 / Song, Wei / Zhao, Shiqi / Zhang, Chao / Wu, Hua / Wang, Haifeng / Liu, Lizhen / Wang, Hanshi Proceedings of the 2015 International Conference on the World Wide Web 2015-05-18 v.1 p.1014-1024

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Summary: We present a novel method for open domain named entity extraction by exploiting the collective hidden structures in webpage titles. Our method uncovers the hidden textual structures shared by sets of webpage titles based on generalized URL patterns and a multiple sequence alignment technique. The highlights of our method include: 1) The boundaries of entities can be identified automatically in a collective way without any manually designed pattern, seed or class name. 2) The connections between entities are also discovered naturally based on the hidden structures, which makes it easy to incorporate distant or weak supervision. The experiments show that our method can harvest large scale of open domain entities with high precision. A large ratio of the extracted entities are long-tailed and complex and cover diverse topics. Given the extracted entities and their connections, we further show the effectiveness of our method in a weakly supervised setting. Our method can produce better domain specific entities in both precision and recall compared with the state-of-the-art approaches.

Answer Quality Characteristics and Prediction on an Academic Q&A Site: A Case Study on ResearchGate WebQuality 2015 / Li, Lei / He, Daqing / Jeng, Wei / Goodwin, Spencer / Zhang, Chengzhi Companion Proceedings of the 2015 International Conference on the World Wide Web 2015-05-18 v.2 p.1453-1458

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Summary: Despite various studies on examining and predicting answer quality on generic social Q&A sites such as Yahoo! Answers, little is known about why answers on academic Q&A sites are voted on by scholars who follow the discussion threads to be high quality answers. Using 1021 answers obtained from the Q&A part of an academic social network site ResearchGate (RG), we firstly explored whether various web-captured features and human-coded features can be the critical factors that influence the peer-judged answer quality. Then using the identified critical features, we constructed three classification models to predict the peer-judged rating. Our results identify four main findings. Firstly, responders' authority, shorter response time and greater answer length are the critical features that positively associate with the peer-judged answer quality. Secondly, answers containing social elements are very likely to harm the peer-judged answer quality. Thirdly, an optimized SVM algorithm has an overwhelming advantage over other models in terms of accuracy. Finally, the prediction based on web-captured features had better performance when comparing to prediction on human-coded features. We hope that these interesting insights on ResearchGate's answer quality can help the further design of academic Q&A sites.

Beyond Eco-Feedback: Adding Online Manual and Automated Controls to Promote Workplace Sustainability Eco-Green: Encouraging Energy Conservation / Yun, Ray / Aziz, Azizan / Scupelli, Peter / Lasternas, Bertrand / Zhang, Chenlu / Loftness, Vivian Proceedings of the ACM CHI'15 Conference on Human Factors in Computing Systems 2015-04-18 v.1 p.1989-1992

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Summary: Whereas eco-feedback has been widely studied in HCI and environmental psychology, online manual control and automated control have been rarely studied with a focus on their long-term quantitative impact and usability. To address this, an intervention was tested with eighty office workers for twenty-seven weeks. Through the long-term field test, it was found that the addition of online controls in the feedback intervention led to more energy savings than feedback only and worked better for light and phone usage than computer and monitor usage. The addition of automated control led to the greatest savings but was less effective for efficient users than inefficient ones.

BeyondTouch: Extending the Input Language with Built-in Sensors on Commodity Smartphones Multimodal / Touch / Gesture / Zhang, Cheng / Guo, Anhong / Zhang, Dingtian / Southern, Caleb / Arriaga, Rosa / Abowd, Gregory Proceedings of the 2015 International Conference on Intelligent User Interfaces 2015-03-29 v.1 p.67-77

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Summary: While most smartphones today have a rich set of sensors that could be used to infer input (.e.g., accelerometer, gyroscope, microphone), the primary mode of interaction is still limited to the front-facing touchscreen and several physical buttons on the case. To investigate the potential opportunities for interactions supported by built-in sensors, we present the implementation and evaluation of BeyondTouch, a family of interactions to extend and enrich the input experience of a smartphone. Using only existing sensing capabilities on a commodity smartphone, we offer the user a wide variety of additional tapping and sliding inputs on the case of and the surface adjacent to the smartphone. We outline the implementation of these interaction techniques and demonstrate empirical evidence of their effectiveness and usability. We also discuss the practicality of BeyondTouch for a variety of application scenarios.

Inferring Meal Eating Activities in Real World Settings from Ambient Sounds: A Feasibility Study Affect / Health / Thomaz, Edison / Zhang, Cheng / Essa, Irfan / Abowd, Gregory D. Proceedings of the 2015 International Conference on Intelligent User Interfaces 2015-03-29 v.1 p.427-431

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Summary: Dietary self-monitoring has been shown to be an effective method for weight-loss, but it remains an onerous task despite recent advances in food journaling systems. Semi-automated food journaling can reduce the effort of logging, but often requires that eating activities be detected automatically. In this work we describe results from a feasibility study conducted in-the-wild where eating activities were inferred from ambient sounds captured with a wrist-mounted device; twenty participants wore the device during one day for an average of 5 hours while performing normal everyday activities. Our system was able to identify meal eating with an F-score of 79.8% in a person-dependent evaluation, and with 86.6% accuracy in a person-independent evaluation. Our approach is intended to be practical, leveraging off-the-shelf devices with audio sensing capabilities in contrast to systems for automated dietary assessment based on specialized sensors.

Perception-Guided Multimodal Feature Fusion for Photo Aesthetics Assessment Multimedia HCI and QoE / Zhang, Luming / Gao, Yue / Zhang, Chao / Zhang, Hanwang / Tian, Qi / Zimmermann, Roger Proceedings of the 2014 ACM International Conference on Multimedia 2014-11-03 p.237-246

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Summary: Photo aesthetic quality evaluation is a challenging task in multimedia and computer vision fields. Conventional approaches suffer from the following three drawbacks: 1) the deemphasized role of semantic content that is many times more important than low-level visual features in photo aesthetics; 2) the difficulty to optimally fuse low-level and high-level visual cues in photo aesthetics evaluation; and 3) the absence of a sequential viewing path in the existing models, as humans perceive visually salient regions sequentially when viewing a photo.
To solve these problems, we propose a new aesthetic descriptor that mimics humans sequentially perceiving visually/semantically salient regions in a photo. In particular, a weakly supervised learning paradigm is developed to project the local aesthetic descriptors (graphlets in this work) into a low-dimensional semantic space. Thereafter, each graphlet can be described by multiple types of visual features, both at low-level and in high-level. Since humans usually perceive only a few salient regions in a photo, a sparsity-constrained graphlet ranking algorithm is proposed that seamlessly integrates both the low-level and the high-level visual cues. Top-ranked graphlets are those visually/semantically prominent graphlets in a photo. They are sequentially linked into a path that simulates the process of humans actively viewing. Finally, we learn a probabilistic aesthetic measure based on such actively viewing paths (AVPs) from the training photos that are marked as aesthetically pleasing by multiple users. Experimental results show that: 1) the AVPs are 87.65% consistent with real human gaze shifting paths, as verified by the eye-tracking data; and 2) our photo aesthetic measure outperforms many of its competitors.

Immersive 3D Communication Tutorials / Wu, Wanmin / Zhang, Cha Proceedings of the 2014 ACM International Conference on Multimedia 2014-11-03 p.1229-1230

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Summary: The last few decades have witnessed tremendous advances in telecommunication, with the invention of technologies such as radio, telephone, voice-over-IP, and video conferencing. While all these communication tools are useful and valuable, the ultimate goal of telecommunication is to enable fully immersive remote interaction in a way that simulates or even surpasses the face-to-face experience. Immersive 3D communication technologies are developed aiming at that goal. The objective of this tutorial is to present an overview of the recent advances in immersive 3D communication. Topics include the basics of human 3D perception, new systems and algorithms in real-time 3D scene capture and reconstruction, 3D data compression and dissemination, 3D displays, etc. We intend to provide insights into the latest immersive 3D communication technologies, and highlight some open research challenges for the future.

MaC: A Probabilistic Framework for Query Answering with Machine-Crowd Collaboration DB Session 1: Query Processing / Zhang, Chen Jason / Chen, Lei / Tong, Yongxin Proceedings of the 2014 ACM Conference on Information and Knowledge Management 2014-11-03 p.11-20

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Summary: The popularity of crowdsourcing has recently brought about brand new opportunities for engaging human intelligence in the process of data analysis. Most existing works on crowdsourcing have developed sophisticated methods to utilize the crowd as a new kind of processor, a.k.a. Human Processor Units (HPU). In this paper, we propose a framework, called MaC, to combine the powers of both CPUs and HPUs. In order to build MaC, we need to tackle the following two challenges: (1) HIT Selection: Selecting the "right" HITs (Human Intelligent Tasks) can help reducing the uncertainty significantly and the results can converge quickly. Thus, we propose an entropy-based model to evaluate the informativeness of HITs. Furthermore, we find that selecting HITs has factorial complexity and the optimization function is non-linear, thus, we propose an efficient approximation algorithm with a bounded error. (2) Uncertainty Management: Crowdsourced answers can be inaccurate. To address this issue, we provide effective solutions in three common scenarios of crowdsourcing: (a) the answer and the confidence of each worker are available; (b) the confidence of each worker and the voting score for each HIT are available; (c) only the answer of each worker is available. To verify the effectiveness of the MaC framework, we built a hybrid Machine-Crowd system and tested it on three real-world applications -- data fusion, information extraction and pattern recognition. The experimental results verified the effectiveness and the applicability of our framework.

Exploring Features for Complicated Objects: Cross-View Feature Selection for Multi-Instance Learning KM Session 20: Entity and Feature Extraction / Wu, Jia / Hong, Zhibin / Pan, Shirui / Zhu, Xingquan / Cai, Zhihua / Zhang, Chengqi Proceedings of the 2014 ACM Conference on Information and Knowledge Management 2014-11-03 p.1699-1708

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Summary: In traditional multi-instance learning (MIL), instances are typically represented by using a single feature view. As MIL becoming popular in domain specific learning tasks, aggregating multiple feature views to represent multi-instance bags has recently shown promising results, mainly because multiple views provide extra information for MIL tasks. Nevertheless, multiple views also increase the risk of involving redundant views and irrelevant features for learning. In this paper, we formulate a new cross-view feature selection problem that aims to identify the most representative features across all feature views for MIL. To achieve the goal, we design a new optimization problem by integrating both multi-view representation and multi-instance bag constraints. The solution to the objective function will ensure that the identified top-m features are the most informative ones across all feature views. Experiments on two real-world applications demonstrate the performance of the cross-view feature selection for content-based image retrieval and social media content recommendation.

A Bootstrapping Based Refinement Framework for Mining Opinion Words and Targets KM Track Posters / Zhao, Qiyun / Wang, Hao / Lv, Pin / Zhang, Chen Proceedings of the 2014 ACM Conference on Information and Knowledge Management 2014-11-03 p.1995-1998

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Summary: This paper proposes a novel bootstrapping based framework jointed with automatic refinement to extract opinion words and targets. We employ a reasonable set of opinion seed words and pre-defined rules to start bootstrapping. We leverage statistical word co-occurrence and dependency patterns for propagation between opinion words and targets. A Sentiment Graph Model (SGM) is constructed to evaluate these opinion relations. Furthermore, we employ Automatic Rule Refinement (ARR) to refine the rules to extract false results. By using false results pruning and ARR process, we can efficiently alleviate the error propagation problem in traditional bootstrapping-based methods. Preliminary evaluation shows the effectiveness of our method.

Empirical study on continuance intentions towards E-Learning 2.0 systems / Wu, Bing / Zhang, Chenyan Behaviour and Information Technology 2014-10-03 v.33 n.10 p.1027-1038

Link to Article at Taylor & Francis

Summary: Although E-Learning 2.0 has played a significant role in training and development within the organisational environment, after an initial acceptance, its use is frequently discontinued. Prior studies offered insights into participation in E-Learning; however, there is limited research on continuance intention towards E-Learning 2.0 systems in organisational contexts. Furthermore, the most widely used research models, such as technology acceptance model (TAM), neglect the interactive social processes in E-Learning 2.0. Therefore, this study proposes a unified model integrating the TAM, the information system success model and social motivation theories to investigate continuance intentions towards E-Learning 2.0 in an organisational context. A sample of 284 participants from companies in China that have already implemented E-Learning 2.0 systems took part in this study. Structural equation modelling was conducted to test the research hypotheses. The results show that the unified model provides a more comprehensive understanding of the cognitive processes and behaviours related to this context: (1) perceived usefulness and attitude were critical to the continuance intention towards an E-Learning 2.0 system; (2) perceived usefulness was a significant mediator of the effects from perceived ease of use, information quality and social influence on continuance intention; (3) perceived ease of use, information quality and social influence were found to play important roles in predicting the continuance intention; (4) system quality played an important role in affecting the perceived ease of use; and (5) unexpectedly, social motivations had no significant effect on attitude.

LifeDelivery: recruiting participants to deliver users' daily goods! Posters / Zhao, Weidan / Du, Zhanwei / Yang, Yongjian / Zhang, Chijun / Liao, Wu Adjunct Proceedings of the 2014 International Joint Conference on Pervasive and Ubiquitous Computing 2014-09-13 v.2 p.199-202

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Summary: Crowdsourcing has emerged in recent years as an online, distributed problem-solving and production model. Based on crowdsourcing, kinds of systems are developed to deliver people's packages through crowd. These systems are applied in large area and based on vehicle-mounted movement mode. In contrast to prior work, we focus on system used in little communities with walking-based movement mode, which is capable of delivering people's daily goods (such as thermos, fruit, assignment, etc.) in campus. Due to the different demands and behavior features of students in the prior system, many students give others help just to make friends but not for money. Based on this difference, we presented LifeDelivery, a crowdsourcing service able to use the Friends Mechanism, which will regard strangers who offer help as friends, and the Cloud Perception Technology which can process, classify and percept all the requirement orders, to recruit participants to deliver the daily goods for users. And this will bring great convenience to users and help students, make friends and share life.

Interaction Design of a Semi-automatic Video Face Annotation System Cross-Cultural Issues in Interaction / Liu, Cailiang / Xiong, Tao / Zhang, Chenguang / Wang, Zhibing CCD 2014: 6th International Conference on Cross-Cultural Design 2014-06-22 p.201-210

Keywords: User Interfaces; Video Content Analysis; Video Advertisement; Face Track Annotation

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Summary: In this paper, we propose an intelligent system that allows people to annotate face tracks of video content. To reduce the workload, we adopt two strategies in the system: 1) visually similar face tracks could be grouped together to reduce human reaction time; 2) face models could be learned to automatically recognize celebrity identities, leaving annotator only simple judgement tasks. With more precise face models, the recognized results require much less time for confirmation. Altogether, these strategies significantly reduce the workload of human annotation/confirmation. Experiments on a very large video repository prove the efficiency and effectiveness of the proposed system.

The Design and Evaluation of Intelligent Energy Dashboard for Sustainability in the Workplace Design for Environment and Sustainability / Yun, Ray / Aziz, Azizan / Lasternas, Bertrand / Zhang, Chenlu / Loftness, Vivian / Scupelli, Peter / Mo, Yunjeong / Zhao, Jie / Wilberforce, Nana DUXU 2014: Third International Conference on Design, User Experience, and Usability, Part III: User Experience Design for Everyday Life Applications and Services 2014-06-22 v.3 p.605-615

Keywords: energy dashboard; sustainability; workplace; behavior change; ecofeedback; remote and automated control; plug load management; organization

Link to Digital Content at Springer

Summary: Office workers typically don't know how much energy they consume at work. Since the workers don't pay the energy bills, they tend to waste energy. To support energy conservation and motivate workers, the Intelligent Dashboard for Occupants (ID-O) was developed using multiple intervention strategies -- eco-feedback (self-monitoring, advice, and comparison), remote controls, and automated controls. The baseline data was collected for fourteen weeks from eighty office workers and ID-Os with different features were deployed for seven weeks. The results show that the group with all the features (eco-feedback, remote controls, automated controls) made the biggest energy savings at 35.4%, the group that had eco-feedback and the remote controls showed 20.2% energy savings, the feedback only group achieved 9% energy savings, and the last group (the control group) produced 3.6% energy savings. The automated control feature produced the biggest energy savings, and was most effective in energy management for lights and phones, but not for computers and monitors.