Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity. Nevertheless, there exist multiple relations between items in real-world scenarios. Distinct from the collaborative similarity that implies co-interact patterns from the user perspective, these relations reveal fine-grained knowledge on items from different perspectives of meta-data, functionality, etc. However, how to incorporate multiple item relations is less explored in recommendation research. In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system. We find that both the relation type and the relation value are crucial in inferring user preference. To this end, we develop a two-level hierarchical attention mechanism to model user preference. The first-level attention discriminates which types of relations are more important, and the second-level attention considers the specific relation values to estimate the contribution of a historical item in recommending the target item. To make the item embeddings be reflective of the relational structure between items, we further formulate a task to preserve the item relations, and jointly train it with the recommendation task of preference modeling. Empirical results on two real datasets demonstrate the strong performance of RCF. Furthermore, we also conduct qualitative analyses to show the benefits of explanations brought by the modeling of multiple item relations

A METHOD TO OBTAIN 3D KINEMATICS DATA OF WHOLE HIGH JUMP MOVEMENT

The purpose of this study was to introduce how to use 3D image analysis with Pan/Tilt/Zoom cameras to obtain the 3D kinematical data in the event of high jump. The attempts by a chinese elite female highjumper were filmed with two cameras. In addition to control frame, the 3D coordinates of additional control points were measured by a theodolite and transformed into the same reference system. Then these parameters were used in the Motion Analysis System and in the software to level the reference system. Finally, the 3D kinematical data of whole high jump movement could be obtained for further analyzing techniques. This method can be used to analyze other sport events

THE DEVELOPMENT OF A REAL-TIME FEEDBACK SYSTEM IN WEIGHTLIFTING

The purpose of this study was to develop a real-time feedback system (RTFS), which can provide weightlifters some useful information, such as the heights of the bar, video clip and so on, immediately after finishing their attempts under training conditions. A Kinect was used to capture the depth data and RGB video, the methods of the pattern recognition and algorithm were established, and the software was developed to identify the barbell and calculate the 3-D data of barbell COM (Centre of Mass). An experiment was carried out to compare the data from RTFS and that from 3D analysis based on video to check the reliability of RTFS. The results showed that the data of barbell COM obtained by RTFS can describe the movement of barbell sufficiently. This new system can help weightlifters to diagnose their skills and improve their training effectively

Participation and Environmental Factors of Children with Physical Disabilities in Taiwan

Participation is a critical health and education outcome of children and can be optimized by environmental supports. Children with physical disabilities often experience participation restriction and environmental barriers. Research is limited in describing participation in everyday activities of children with physical disabilities and identifying environmental barriers faced by those children in Taiwan. This chapter presents data of 94 children with physical disabilities aged 2–6 years and their families in Taiwan. Children with physical disabilities were primarily children with cerebral palsy (36%) and developmental (motor) delay (34%). Parents completed the Chinese version of Assessment of Preschool Children’s Participation (APCP-C) and the Chinese version of the Child and Adolescent Scale of Environment (CASE-C) by structured interview to assess pattern of participation and impact of environment factors to their children’s daily life. Participation of children with physical disabilities differed on the basis of level of severity, but not age and sex. Parents reported increased impacts of problems with the quality and availability of family and community resources than problems with assistance/attitude supports and physical design and access. The findings provide a profile of children’s pattern of participation and environmental barriers that impact participation in Taiwan

A COMPARISON OF EMG AND KINEMATIC ANALYSIS BETWEEN GROUND AND TREADMILL RUNING FOR CHINESE ELITE SPRINTER-PU FAN FANG

Ms. Pu Fan-fang, a Chinese National championship, has been training on simulated treadmill for 4 years to improve her ability of velocity endurance. The purpose of the present study was to compare the changes of her movement structures in ground and treadmill running. EMG and' kinematical analysis were used in the test. The kinematical data results show that significant differences were noted between the two conditions for the take off angle, minimum knee angle of swing leg, the minimum angle between thigh and horizontal line, soar high and soar time. The EMG result revealed that the obvious differences of EMG distribution of eight muscles existed in the two conditions. According to the testing results, it should be considered that more using treadmill training could influence her movement structure although it is a good method to improve velocity endurance

Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts

Semantic communication (SemCom) holds promise for reducing network resource consumption while achieving the communications goal. However, the computational overheads in jointly training semantic encoders and decoders-and the subsequent deployment in network devices-are overlooked. Recent advances in Generative artificial intelligence (GAI) offer a potential solution. The robust learning abilities of GAI models indicate that semantic decoders can reconstruct source messages using a limited amount of semantic information, e.g., prompts, without joint training with the semantic encoder. A notable challenge, however, is the instability introduced by GAI's diverse generation ability. This instability, evident in outputs like text-generated images, limits the direct application of GAI in scenarios demanding accurate message recovery, such as face image transmission. To solve the above problems, this paper proposes a GAI-aided SemCom system with multi-model prompts for accurate content decoding. Moreover, in response to security concerns, we introduce the application of covert communications aided by a friendly jammer. The system jointly optimizes the diffusion step, jamming, and transmitting power with the aid of the generative diffusion models, enabling successful and secure transmission of the source messages