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Purpose Social signal processing under affective computing aims at recognizing and extracting useful human social interaction patterns. Fight is a common social interaction in real life. A fight detection system finds wide applications. This paper aims to detect fights in a natural and low-cost manner. Design/methodology/approach Research works on fight detection are often based on visual features, demanding substantive computation and good video quality. In this paper, the authors propose an approach to detect fight events through motion analysis. Most existing works evaluated their algorithms on public data sets manifesting simulated fights, where the fights are acted out by actors. To evaluate real fights, the authors collected videos involving real fights to form a data set. Based on the two types of data sets, the authors evaluated the performance of their motion signal analysis algorithm, which was then compared with the state-of-the-art approach based on MoSIFT descriptors with Bag-of-Words mechanism, and basic motion signal analysis with Bag-of-Words. Findings The experimental results indicate that the proposed approach accurately detects fights in real scenarios and performs better than the MoSIFT approach. Originality/value By collecting and annotating real surveillance videos containing real fight events and augmenting with well-known data sets, the authors proposed, implemented and evaluated a low computation approach, comparing it with the state-of-the-art approach. The authors uncovered some fundamental differences between real and simulated fights and initiated a new study in discriminating real against simulated fight events, with very good performance.

This paper describes a strategy to build community capacity to address the challenge of SDG#7 (Affordable and Clean Energy) through an international student service-learning programme. The Hong Kong Polytechnic University partnered with an NGO in Rwanda in a four-year collaborative effort to electrify a semi-rural village in Rwanda with solar power. For sustainability, we developed a three-tier capacity building model that combined efforts from university students (who provide the technical expertise), strengths and expertise of a partnering local NGO (who had previously developed a community support infrastructure in the form of strong self-help groups), and community commitment and support (who provide the manpower initially and subsequently acquired the technical capacity).

Affective computing has recently become an important area in human-computer interaction research. Techniques have been developed to enable computers to understand human affects or emotions, in order to predict human intention more precisely and provide better service to user to enhance user experience. In this paper, we investigate into the detection of human attention level as a useful from of human affect, which could be influential in intelligent e-learning applications. We adopt ubiquitous hardware available in most computer systems, namely, webcam and mouse. Information from multiple input modalities is fused together for effective human attention detection. We invite human subjects to carry out experiments in reading articles when being imposed upon different kinds of distraction to induce them into different levels of attention. Machine-learning techniques are applied to identify useful features to recognize human attention level by building up user-independent models. Our result indicate performance improvement with multimodal inputs from webcam and mouse over that of a single device. We believe that our work has revealed and intersting affective computing direction with potential application in e-learning.

This paper presents Personalized Affect Detection with Minimal Annotation (PADMA), a user-dependent approach for identifying affective states from spontaneous facial expressions without the need for expert annotation. The conventional approach relies on the use of key frames in recorded affect sequences and requires an expert observer to identify and annotate the frames. It is susceptible to user variability and accommodating individual differences is difficult. The alternative is a user-dependent approach, but it would be prohibitively expensive to collect and annotate data for each user. PADMA uses a novel Association-based Multiple Instance Learning (AMIL) method, which learns a personal facial affect model through expression frequency analysis, and does not need expert input or frame-based annotation. PADMA involves a training/calibration phase in which the user watches short video segments and reports the affect that best describes his/her overall feeling throughout the segment. The most indicative facial gestures are identified and extracted from the facial response video, and the association between gesture and affect labels is determined by the distribution of the gesture over all reported affects. Hence both the geometric deformation and distribution of key facial gestures are specially adapted for each user. We show results that demonstrate the feasibility, effectiveness and extensibility of our approach.

Human-centered computing is rapidly becoming a major research direction as new developments in sensor technology make it increasingly feasible to obtain signals from human beings. At the same time, the pervasiveness of computing devices is also encouraging more research in human---computer interaction, especially in the direction of personalized and adaptive user interfaces. Among the various research issues, affective computing, or the ability of computers to understand and react according to what a user "feels," has been gaining in importance. In order to recognize the human affect (feeling), computers rely on the analysis of signal inputs captured by a multitude of means. This paper proposes the use of human physiological signals as a new form of modality in determining human affects, in a non-intrusive manner. The principle of non-invasiveness is very important, since it imposes no extra burden on the user, which improves user accessibility and encourages user adoption. This goal is realized via the physiological mouse, as a first step toward the support of affective computing. The conventional mouse is converted with a small optical component for capturing user photoplethysmographic (PPG) signal. With the PPG signal, it is possible to compute and derive human physiological signals. A prototype of the physiological mouse was built and raw PPG readings measured. The accuracy of the approach was evaluated through empirical studies to determine human physiological signals from the mouse PPG data. Finally, pilot experiments to correlate human physiological signals with various modes of human---computer interaction, namely gaming and video watching, were conducted. The trend in physiological signals could be used as feedback to the computer system which in turn adapts to the needs or the mood of the user, for instance change the volume and the light intensity when watching a video or playing a game based on current user emotion. The authors argue that this research will provide a new dimension for multimodal affective computing research, and the pilot study has already shed some light toward this research goal.

The trend towards a globalized and knowledge-based society is one of the development hallmarks of the twenty-first century, and there have been numerous education reforms in response to this trend. Engineering education is no exception. Traditionally, engineering education places heavy emphasis on technical aspects, such as mathematical and scientific fundamentals. This heavy focus on the theory and technical aspects often hinders other professions from understanding the world of engineering. As a result, the general public often views engineering as a field that is isolated and difficult to comprehend, which also limits the development of engineering (Morris et al. in Eur J Eng Educ 32(2):135–142, 2007). However, it has become increasingly difficult to perform professionally in this evolving global environment within traditional curricular (National Academy of Engineering in The engineer of 2020: Visions of engineering in the new century. National Academies Press, Washington, DC, 2004). The National Academy of Engineering forecasts that, in 2020, engineers not only need to be technically competent, but also be trained in other ‘soft skills’, such as civic responsibility, intercultural competence, leadership, communication and management, for them to be able to contribute to society (Galloway in Civ Eng Mag Arch 77(11):46–104, 2007). In this chapter, we present an engineering service-learning subject that was designed to incorporate major and non-major students. This multidisciplinary subject, which incorporates an international service-learning project, aims to provide a holistic learning experience that raises the problem-solving skills and general knowledge for all students, in particular, to teach students about the impact of engineering on society, and human and societal factors in engineering. Course design, challenges that we encountered, impacts on major and non-major students, and the limitations of this multidisciplinary approach will be discussed.

This paper presents an investigation into user intention prediction in two common web-based tasks: crowdsourcing annotation and web search, based on human-mouse interaction information. User experience is gaining importance within the research area of human-centered computing, and is particularly useful for complex, multi-step tasks. To enhance user experience, the computer should be intelligent enough to be able to predict the user intention. For instance, an intelligent agent might be able to anticipate when the user is about to press a button, and helpfully enlarge or highlight it in advance. In this paper, we propose two prediction models on user intention: a classical model that considers only historical mouse activity sequence, and a multimodal model that utilizes mouse interaction signals as well as features extracted from mouse trajectory and clicking events. We evaluate our models and find that they achieve reasonable accuracy. Our preliminary results indicate that we can dynamically learn a multimodal model that can effectively predict a user’s next activity from historical activity sequence and mouse interaction signals.

The use of Information and Communication Technology (ICT) in K-12 education has been gaining attention in recent years. With using appropriate technology, not only can it allow people to have equal opportunity in education, but also improve the quality of teaching and learning. However, this is difficult in developing countries due to the lack of resources, infrastructure, support from the local community, skills, culture and policy. In 2014, according to World DataBank indicators, over half of the population in East Asia & Pacific region were not able to access the Internet. In Cambodia, this figure drops to less than one-tenth. This paper investigates complementing education in developing countries through appropriate technology and a sustainable model that maximizes short-term efforts from foreign teams. We present a case study in Cambodia whereby a zero-carbon community learning centre was designed and installed for a rural community. The first phase deployment involved a recycled twenty-foot container, furnished with solar panels, computer devices, Internet hotspot, books and toys. A sustainable model was developed through engaging local community leaders and empowering resident university students. Follow-up evaluations suggest that community ownership and buy-in are critical for success. We present design principles, implementation methods, evaluations from the experience, feedback from stakeholders, and lessons learnt.

Systems that adapt to changes in human needs automatically are useful, built upon advancements in human-computer interaction research. In this paper, we investigate the problem of how well the eye movement of a user when reading an article can predict the level of reading comprehension, which could be exploited in intelligent adaptive e-learning systems. We characterize the eye movement pattern in the form of eye gaze signal. We invite human subjects in reading articles of different difficulty levels being induced to different comprehension levels. Machine-learning techniques are applied to identify useful features to recognize when readers are experiencing difficulties in understanding their reading material. Finally, a detection model that can identify different levels of user comprehension is built. We achieve a performance improvement of over 30% above the baseline, translating over 50% reduction in detection error.

We present PACE, a Personalized, Automatically Calibrating Eye-tracking system that identifies and collects data unobtrusively from user interaction events on standard computing systems without the need for specialized equipment. PACE relies on eye/facial analysis of webcam data based on a set of robust geometric gaze features and a two-layer data validation mechanism to identify good training samples from daily interaction data. The design of the system is founded on an in-depth investigation of the relationship between gaze patterns and interaction cues, and takes into consideration user preferences and habits. The result is an adaptive, data-driven approach that continuously recalibrates, adapts and improves with additional use. Quantitative evaluation on 31 subjects across different interaction behaviors shows that training instances identified by the PACE data collection have higher gaze point-interaction cue consistency than those identified by conventional approaches. An in-situ study using real-life tasks on a diverse set of interactive applications demonstrates that the PACE gaze estimation achieves an average error of 2.56o, which is comparable to state-of-the-art, but without the need for explicit training or calibration. This demonstrates the effectiveness of both the gaze estimation method and the corresponding data collection mechanism.

Affective computing in human-computer interaction research enables computers to understand human affects or emotions to provide better service. In this paper, we investigate the detection of human attention useful in intelligent e-learning applications. Our principle is to use only ubiquitous hardware available in most computer systems, namely, webcam and mouse. Information from multiple modalities is fused together for effective human attention detection. We invite human subjects to carry out experiments in reading articles being subjected to different kinds of distraction to induce different attention levels. Machine-learning techniques are applied to identify useful features to recognize human attention level. Our results indicate improved performance with multimodal inputs, suggesting an interesting affective computing direction.

In this study, a real-time collaborative 3D assembling system (Co-assembler) has been described that allows casual customers to interactively and virtually configure the 3D products over the Internet. It provides a framework for a web-based 3D assembly system that uses a customizable product model to significantly simplify the configuration and the assembly process, and a customer-driven recommendation helper to assist the customers make their design decisions. Two assembly-specific data formats - Assembly ML and Parametric Product ML are developed for archival and data transfer. The system aims to capture the customers’ needs and convert them into technical specifications, indirectly helping to promote e-commerce for manufacturing enterprises.

Standard for the Exchange of Product Model Data (STEP) provides a systematic approach for user communities to model and exchange data on specific types of products and manufacturing technologies. With the increasing popularity of the Extensible Mark-up Language (XML) on the Internet, mapping STEP data into XML seems a logical way to make STEP data more accessible through the Internet. On the other hand, Internet-based information systems generally consist of data sources and clients that access the data sources; and mediators have been proposed as a middle layer that provides added value by converting the basic data into information required by the clients. To work effectively, mediators require machine-readable data models and standardized interfaces, which can be developed based on XML. In this paper we discuss the ways of automated conversion from STEP into XML, and the exchange of STEP data through the XML-based mediator architecture.

The Internet and the World-Wide Web created a new infrastructure and new possibilities for concurrent engineering. In order to support real-time (synchronous) collaborative solid shape design, however, some major issues still have to be resolved. These involved deployment of solid modeling software on the Web, shared access to common solid models, communication and control of the design actions among designers, and efficient representation of the many different but related design versions of the product. This paper discusses the development of a real-time collaborative solid modeling system that addresses these issues. The prototype system has been implemented in a client-server environment in Java, using the Java Remote Method Invocation package for networking support.

This paper discusses the synchronous viewing and editing of object-oriented models based on the Unified Modeling Lan guage (UML) by multiple users on the internet. Each user works on a copy of the model, which is continuously synchronized with the master copy on a central server. A group of users can work together in the common single writer-multiple reader mode, controlled by a token that can be passed from user to user. Alternatively, different users can lock different parts of the model for exclusive editing while the changes are visible to all other users. One can improve on basic granular locking by considering the semantics of the graphical nota tion. A semantics-based locking mechanism has been designed and implemented for UML class diagrams in order to allow users to col laborate more effectively: A prototype collaborative UML (CUML) editing system has been implemented in Java to validate the design.