Buildings make a city’s landscape and are home to its people. The demand for smart buildings and housing is growing by the need for cities to make their buildings more efficient, green and livable. This emergent intelligence is underpinned by the use of Information and Communications Technology (ICT) linked by Pervasive Sensing and real-time data analytics. In a typical growth of smart buildings, Smart Campuses are going to be amazing community hubs which will be more sustainable, efficient and supportive of its inhabitants. In this regard, huge amount of useful and real-time generated data are being analyzed to help people and machines infer instant decisions in relation to energy efficiency. However, because of different terminologies used by different players, structural, representational and semantic heterogeneity constrain the interoperability between applications and misleads to adaptive and context-aware control behavior. In this paper, the focus is to alleviate the current problem by designing a semantic framework that represents the smart campus data and activities in an ontological model. Also, the framework is deepened by an Artificial Intelligent (AI) method using Weighted Case Based Reasoning (WCBR) for enabling context awareness. An illustration will be the elaboration of an adaptive and autonomous control of HVAC (Heating Ventilation and Air Conditioning) system, in this example the WCBR is discussed and case representation, case adaptation, and similarity computation are sketched in detail.

In this challenge paper, we consider the importance of preferences in smart homes and assistive environments and discuss the potential application of models and algorithms developed within the computational preferences community. We suggest the value of future research collaborations.

Communication involves transferring information from one agent to another. An intelligent agent, either human or machine, is often able to choose to hide information in order to protect their interests. The notion of information hiding is closely linked to secrecy and dishonesty, but it also plays an important role in domains such as software engineering. In this paper, we consider the ethics of information hiding, particularly with respect to intelligent agents. In other words, we are concerned with situations that involve a human and an intelligent agent with access to different information. Is the intelligent agent justified in preventing a human user from accessing the information that they possess? This is trivially true in the case where access control systems exist. However, we are concerned with the situation where an intelligent agent is able to using a reasoning system to decide not to share information with all humans. On the other hand, we are also concerned with situations where humans hide information from machines. Are we ever under a moral obligation to share information with a computional agent? We argue that questions of this form are increasingly important now, as people are increasingly willing to divulge private information to machines with a great capacity to reason with that information and share it with others.

Understanding trust, influence and reputation and constructing computational models of these notions are two essential scientific challenges in computer science as well as social sciences. Although scientists in both disciplines have independently conducted research on these topics over the last couple of decades, there is a huge gap between two literatures. This paper therefore illustrates an interdisciplinary work-in-progress on trust, influence and reputation modeling based on human reasoning. Using a survey-based data collection approach, we would like to understand how humans gain/lose trust in their daily life interactions and how behavior/attitudes of humans can be influenced or shaped in various social encounters. The data will be then transformed into mathematical models to be used in technological or software systems.

With the rapid development of mobile devices, global position system (GPS) and Web 2.0 technologies, location-based social networks (LBSNs) have attracted millions of users to share rich information, such as experiences and tips. Point-of-Interest (POI) recommender system plays an important role in LBSNs since it can help users explore attractive locations as well as help social network service providers design location-aware advertisements for Point-of-Interest. In this paper, we present a brief survey over the task of Point-of-Interest recommendation in LBSNs and discuss some research directions for Point-of-Interest recommendation. We first describe the unique characteristics of Point-of-Interest recommendation, which distinguish Point-of-Interest recommendation approaches from traditional recommendation approaches. Then, according to what type of additional information are integrated with check-in data by POI recommendation algorithms, we classify POI recommendation algorithms into four categories: pure check-in data based POI recommendation approaches, geographical influence enhanced POI recommendation approaches, social influence enhanced POI recommendation approaches and temporal influence enhanced POI recommendation approaches. Finally, we discuss future research directions for Point-of-Interest recommendation.

Western societies are facing demographic challenges due the rapid aging of their population. In this context, economic and social issues are emerging, such as an increasing number of elderly in need of home cares and a shortage of caregivers. Smart home technology has imposed itself as a potential avenue of solution to these important issues. Its goal is to provide adapted assistance to a semi-autonomous resident in the form of hints, suggestions, reminders, and to take preventive actions, for instance turning off the oven, in the case of an emergency. The main scientific challenge related to this kind of assistance concerns the problem of recognizing, in real time, of the on-going activities of the resident in order to provide punctual guidance for the completion of everyday tasks. In the literature, the majority of the proposed solutions for activity recognition exploit a complex and expensive network of intrusive sensors (i.e. infrared, radio-identification, electromagnetic, pressure, cameras, etc.). A recent and innovative way of performing activity recognition is based on the monitoring of electrical household appliances by analyzing the electrical signals solely at the main panel. This approach is less intrusive and required only one sensor. In this paper, we present new advancements in that field, which take the form of an efficient method for recognizing electrical appliances within smart home based on the analysis of the features of the load signatures (active and reactive power, FFT) and on the use of the C4.5 algorithm to extract decision trees. This method has been implemented and tested in real smart home infrastructure showing that it is economical, simple and efficient.

We propose to introduce social media opinion mining research into the field of computational sustainability. Opinion mining from social media can be a faster and less expensive alternative to traditional survey and polling, on which many sustainability research are based. We describe a framework for such analysis, examine the challenges in our proposed framework and current status of research on those challenges. We also propose some possible research directions for tackling these challenges.

Auction mechanisms are very well known methods to allocate tasks when several agents are involved. Particularly, multi-attribute auctions are a special mechanism that allows the consideration of task attributes other than prices, such as delivery time or energy consumptions. Incentive compatible mechanisms encourage agents to reveal the attributes which agents estimate truthful, however, these mechanisms by themselves cannot know if such estimations are reliable or not due to uncertainty. Under such circumstances, trust could complement incentive compatibility reducing the risk of losses by the auctioneer. The use of trust in auctions is a well-studied problem; however, most of the works in the literature focus on how to model trust rather on how trust is used in the mechanism. Thus, this paper proposes an easy and systematic way to include a multi-faceted model of trust into multi-attribute auctions. Conversely to other previous works where trust is only used in the winner determination problem, the presented approach uses trust both in deciding the winner of the auction and in the payment to the corresponding bidder. According to the results obtained from the experimentation, the use of trust following the methodology presented in this paper highly reduces the number of winner bids from unreliable bidders and, therefore, the number of tasks executed in worse conditions than the agreed. Complementary, this paper proposes a new trust adaptation method which consists of increasing or decreasing the trust value (depending on whether the task is executed properly or not) according to a simple mathematical function with asymptotes on 0 and 1. This model does not present the rigidity problem present in other models of the literature when it comes to agents that have inconstant performances.

How can knowing the location of my friends be used to more accurately predict my location? This paper explores socially-aware location prediction under a particularly challenging setting where the underlying interactions and social network are unknown and must be inferred over continuous spatiotemporal data. Our method samples inferred network topology using a linear regression model to predict future individual locations. We present an in-depth empirical study comparing different network models and network sampling regimes under a bootstrapped sampling baseline. Furthermore, our qualitative analysis demonstrates the value of social information in population mobility modeling under our application’s challenges.

Under the auspices of “being green,” we have given our printed word over to a cyber-medium that cannot be touched or felt or folded. Our information is as volatile as the authority protecting our storage devices. Eliminating a book or changing its text can be done by literally pressing a button -- without a fire or an erasure marking, without smoke, without evidence. Our data is ephemeral along with the web in which we weave it. This paper considers the current ease of censorship, and that the non-permanence of data and links can wreak havoc on our societal infra-structure if the wrong entities (human or machine) with the wrong motives have the control to determine its fate.