Affective interaction between players of video games can elicit rich and varying patterns of emotions. In multiplayer activities that take place in a common space (such as sports and board games), players are generally aware of the emotions of their teammates or opponents as they can directly observe their behavioral patterns, facial expressions, head pose, body stance, and so on. Players of online video games, however, are not generally aware of the other players’ emotions given the limited channels of direct interaction among them (e.g., via emojis or chat boxes). It also turns out that the impact of real-time emotion-awareness on play is still unexplored in the space of online digital games. Motivated by this lack of empirical knowledge on the role of the affect of others to one’s gameplay performance, in this article, we investigate the degrees to which the expression of manifested emotions of an opponent can affect the emotions of the player and consequently his gameplay behavior. In this initial study, we test our hypothesis on a two-player adversarial car racing game. We perform a comprehensive user study to evaluate the emotions, behaviors, and attitudes of players in emotion aware versus emotion agnostic game versions. Our findings suggest that expressing the emotional state of the opponent through an emoji in real-time affects the emotional state and behavior of players that can consequently affect their playing experience.

People with neurological illnesses, such as MS, may benefit from effective training approaches that increase their potential to perform daily tasks. This paper focuses on the concept of adaptive training games for gait rehabilitation in people with multiple sclerosis (PwMS) using individualized exergames. These exergames were influenced by four separate routine physiotherapy exercises. A projection-mapping technique is used to view the planned games on the floor to indicate a safe space for play and to ensure realistic exercise training for PwMS. This is also aided by dynamic difficulty adjustment algorithms, which enable the games to be more adaptable. We anticipate that incorporating adaptability will enable them to provide appropriate challenges, provide an individualized training plan for PwMS, reduce boredom, and minimize therapist involvement. The following phase will be a thorough user analysis.

Although many privacy preserving frequent itemset mining protocols have been proposed to preserve the privacy of participants, most of them are vulnerable against collusion. Usually, these protocols are designed for semi-honest model; where in this model, it is assumed that the participants do not deviate from the protocol. However, in real world, participants may collude with each other in order to falsify the protocol or to obtain the secret values of other parties. In this paper, we analyse the vulnerability of previous privacy preserving frequent itemset mining protocols from privacy point of view, and then, we proposes a new protocol (CFM), which preserves the privacy of participants, even in collusion state. CFM is designed for mining frequent itemsets from homogenous (horizontally partitioned) data, which not only preserves the privacy of participants in collusion states, but also shows better performance in comparison with previous works. In order to achieve this goal, CFM employs a new secret sharing and secret summation scheme, which distributes secret values among participants. Privacy preserving level of CFM is evaluated based on the disclosure of sensitive information.

As a novel interaction and presentation technology, Augmented Reality (AR) has been widely used in education, tourism, entertainment, psychology, and many other applications. In the game industry, this revolutionary technology has introduced a new form of interaction between players and the gaming environment. In this article, we aim to find if exploiting AR in computer games can enhance the sense of immersion, and consequently, satisfying the players. To this end, a mobile game called Ladybug is implemented using three different techniques including the regular virtual reality (VR) camera, single AR camera, and stereoscopic AR camera. The results of a comprehensive user study on these techniques showed that the single AR camera and stereoscopic AR camera can result in more sense of immersion in players in comparison to the VR camera. However, the hypothesis that the stereoscopic AR results in more sense of presence in comparison to the single AR camera is rejected. On the other hand, our findings show that the AR stereoscopic camera can lead to a better emotional involvement and enjoyment in comparison to the single AR camera and VR camera.

Effective real-time feedback is crucial to enhance motor learning in physical and cognitive rehabilitation. Although monitor-based (MB) platforms are widely used to provide visual feedback for users, the discrepancy between where the feedback is presented and where the actual movement occurs can negatively affect the use of feedback. In this paper, we propose a novel visual feedback system using projection-based augmented reality (AR) to provide a better understanding of the relationship between body perception and movement kinematics. A set of experiments were performed to assess and compare the ability of unimpaired and impaired participants to detect real-time feedback and make modifications to gait using this feedback in MB and AR platforms. The results showed a significant improvement in participants' ability to better adapt the changes in projection-based AR than MB. This improvement represents the efficacy of this system in enabling users to better synchronize their foot-eye coordination.

Data exchange is the process of generating an instance of a target schema from an instance of a source schema such that source data is reflected in the target. The prevailing approach for data exchange is based on schema mappings, which are high level expressions that describe relationships between database schemas [1]. However, schema-mapping based data exchange techniques suffer from two problems: (1) entity fragmentation, in which information about a single entity is spread across several tuples in the target schema, and (2) ambiguity in generalization, in which incorrect mappings result from using different methods to represent entity type generalization in source and target schemas. In this paper, we propose the Scalable Entity Preserving Data Exchange (SEDEX) method, which combines schema-level and datalevel information to address these problems. We also provide extensive evaluation to demonstrate the benefits and scalability of the approach.

Virtual try-on applications make it possible for buyers to watch themselves wearing different garments without physically trying on them. The prevailing approach for virtual try-on has been based on virtual fitting rooms, in which several cameras are used to identify the skeleton and posture of a user in order to render a garment on the user's image. Although this approach has been implemented successfully using different techniques, the privacy of users can be compromised as some users might be reluctant to stand in front of cameras in a fitting room. This paper proposes an alternative approach that allows a customer to watch a three-dimensional (3D) model of her/him wearing garments on a personal mobile device using augmented reality (AR). Among 3D human models that are automatically generated, a model selection technique is proposed that makes it possible to find the right size model representing the anthropometric features of the user. This approach is accompanied by body customization and face generation modules to generate a realistic representation. Several quantitative experiments as well as user studies were performed to evaluate the accuracy, efficiency, usefulness, and privacy of the proposed technique.

Data exchange is the process of generating an instance of a target schema from an instance of a source schema such that source data is reflected in the target. Generally, data exchnge is performed using schema mapping, representing high level relations between source and target schemas. In this paper, we argue that data exchange solely based on schema level information limits the ability to express semantics in data exchange. We show such schema level mappings not only may result in entity fragmentation, they are unable to resolve some ambiguous data exchange scenarios. To address this problem, we propose Scalable Entity Preserving Data Exchange (SEDEX), a hybrid method based on data and schema mapping that employs similarities between relation trees of source and target relations to find the best relations that can host source instances. Our experiments show SEDEX outperforms other methods in terms of quality and scalability of data exchange.

Privacy preserving data mining (PPDM) algorithms attempt to reduce the injuries to privacy caused by malicious parties during the rule mining process. Usually, these algorithms are designed for the semi-honest model, where participants do not deviate from the protocol. However, in the real-world, malicious parties may attempt to obtain the secret values of other parties by probing attacks or collusion. In this study, the authors study how to preserve the privacy of participants in a collusion-free model of the frequent itemset mining process, where the protocol protects against probing attacks and collusion. The mining of frequent itemsets is the main step of association rule mining algorithms, and, in this study, the authors propose two privacy-preserving frequent itemset mining algorithms for both two-party and multi-party states in a collusion-free model for vertically partitioned (heterogeneous) data; in addition, a privacy measuring technique is proposed, which quantifies privacy based on the amount of disclosed sensitive information.

Purpose In traditional commerce, an auction is known as a mechanism of determining the value of a commodity that does not have a fixed price. Auctions are exciting and an increasing number of transactions are performed through e‐auctions. But most current auctions cannot address all the important security requirements. Usually, auction systems force bidders and sellers to trust the auctioneer and, on the other hand, do not provide anonymity for bidders and sellers. This paper aims to solve these problems by presenting an efficient anonymous secure auction schema (ASAS) without a fully trustworthy auctioneer. Design/methodology/approach The paper analyzes security properties and the complexity of previous works in auction security and then proposes a new ASAS that is more secure and efficient than previous works. Finally, security properties and the complexity of the new schema and previous works are compared with one another. Findings The proposed auction protocol does not force bidders and sellers to trust the auctioneer. In addition, it provides anonymity for both of them. Owing to these newly added features and high degree of security of ASAS, it is suggested that its use in high‐value auctions should require tighter security. Originality/value The paper proposes a new schema for electronic auctions that is secure and efficient and, in addition, does not force bidders and sellers to trust the auctioneer.