With the proliferation of wearable devices and ubiquitous computing, context-aware music recommendation systems are evolving to deliver more personalized experiences. Traditional methods relying on explicit feedback, such as listening history and song ratings, struggle to adapt to users' dynamic contextual states, limiting their effectiveness. In this paper, we introduce Wear2Rec, a privacy-preserving, IoT-driven music recommendation system that leverages passive physiological, psychological, and environmental data from wearable devices to enhance personalization. At its core, Wear2Rec employs an innovative dual-expert-dual-task network architecture that separately extracts context and music features, minimizing cross-modal interference. Unlike conventional models, it simultaneously optimizes both music recommendation and mood improvement prediction, ensuring both relevant music suggestions and an emotionally supportive listening experience. Experimental results show its superiority, achieving 0.8411 AUC for music recommendation and 0.5928 MAE for mood prediction, outperforming traditional models by integrating emotional adaptation. Wear2Rec represents a significant step forward in human-centric, real-time recommendation systems, setting new standards for personalized music experiences in IoT-driven ubiquitous computing environments.

An important work of data analysis is to identify correlation structures and classify the data in unlabeled high-dimensional data, which usually requires iterative experiments on clustering parameters, attribute weights and instances. For a large dataset, the number of clusters may be huge, and it is a great challenge to explore in this huge space. People usually have a more comprehensive understanding of some data. For example, they think that data A is better than data B, but they do not know which attributes are important. Therefore, a powerful interactive analysis tool can help people greatly improve the effectiveness of exploratory clustering analysis. This paper provides a visual analysis method for sorting and classifying multivariate data. It can determine the weight of each attribute through user’s interaction, thus, generating sorting, and then complete classification according to sorting results. Through visual display, users can understand the characteristics of data as well as category characteristics intuitively and quickly, and it helps users improve sorting and classification results.

Online transaction log records the relevant information of the users, commodities and transactions, as well as changes over time, which can help analysts understand commodities’ sales. The existing visualization methods mainly analyze the purchase behavior from the perspective of users, while analyzing the sales trend of commodities can better help merchants to make business decisions. Based on the transaction log, this paper puts forward the visual analysis framework of commodity sales trend and the corresponding data processing algorithm. The concepts of volatility and dynamic performance of sales trend are proposed, through which the multi-dimensional sales data of time-oriented are displayed in two-dimensional space. The “Feature Ring” is designed to display the detailed sales information of the products. Based on the above methods, a visual analysis system is designed and implemented. The usability and validity of the visualization methods are verified by using JD online transaction data. The visualization methods enable manufacturers to formulate production plans and carry out product research and develop better.

In this paper, we propose to improve the verification performance of a contract-free palmprint recognition system by means of feature- level image registration and pixel-level fusion of multi-spectral palm images. Our method involves image acquisition via a dedicated device under contact-free and multi-spectral environment, preprocessing to locate region of interest (ROI) from each individual hand images, feature-level registration to align ROIs from different spectral images in one sequence and fusion to combine images from multiple spectra. The advantages of the proposed method include better hygiene and higher verification performance. Given a database composed of images from 330 hands, two out of four state of the art fusion strategies offer significant performance gain and the best equal error rate (EER) is 0.5%.

Fingerprint matching is one of the most important modules in automatic fingerprint identification systems (AFIS). Traditional methods treat this problem as point pattern matching, which is essentially an intractable problem. We propose an effective fingerprint matching algorithm based on error propagation. First, we find initial correspondence according to the ridge information associated with each minutia. Secondly, we construct a MatchedSet which is composed of initial correspondence and the surrounding matched pairs. Finally, the matching errors of the minutiae pairs in the MatchedSet are adopted to guide the subsequent matching process and the newly matched pairs are added into the MatchedSet. The operation is repeated until the elements in the MatchedSet do not change any more. In this procedure, the local deformation can be tracked, thus the algorithm provides robustness to non-linear deformation commonly seen in fingerprint images. The algorithm was tested on NIST-24 database, and the result is promising.

Automation of creative thinking is very important as well as very difficult. Research shows that creative thinking is a very complicated dynamical system. We first prove that the variation of any feature of this system is able to reflect the dynamic information of the whole system. Based on this, we give a method of how to measure the reminded objects so that the measure sequence can be constructed which is proved to be chaotic. It follows that we present a creative reminding method according to chaos theory. This method is built on the prediction by a Lyapunov exponent calculated on the thinking history, which consists of the exponent calculation, exponent reminding mode, etc. We present a comprehensive reminding algorithm consisting of the usual and fantastic parts. When the relevance of the target to the given base is bigger than the given threshold the target is to be reminded, or else a simulated annealing-based algorithm is applied to make a decision. When the system fails continuously many times to produce reminders, the system will try to employ the exponent prediction to produce the fantastic reminder which often results in inspiration. The proposed method can be used to find the conditions leading to the inspiration so as to provide the means for the system to yield creative thinking. Now the proposed method has been integrated into the prototype system of creative design. The results of the experiment show its advantages in some aspects.

This paper exploits the impact of the asynchronous transfer mode (ATM) transport standard on the mutual synchronization of multiple media servers in a distributed multimedia system. On the basis of a bursty traffic model, we develop and analyze an effective media synchronization scheme. In our scheme the media terminal (MT), which receives continuous streams of data from different media servers, acts as the coordinator for mutual synchronization of the media servers. The correlation between the time skew of clocks in the media servers and the network transmission behavior is analyzed, with the bursty traffic of the ATM network taken into account. We quantify the correlation between the resynchronization time interval, the clock drift rate, the time skew of servers, and the network jitter. Our results suggest that in allocation of ATM network resources to virtual channels (VC), the transmission delay, cell loss probability and synchronization of clocks are equally important in distributed multimedia systems.>

This paper analyzes a load-balancing scheme, called All-Sharing Load Balancing (ASLB), which evenly distributes the load of the system among all nodes on a CSMA/CD local area network in a collision-free manner for each load-balancing activity. A novel analytical model is presented to evaluate performance of the ASLB scheme and its effect on the normal communication message transmission and vice versa. The evaluation also takes into account the tradeoff between system response time and message delay. Our analysis shows that ASLB can achieve good performance in both system response time and message transmission delay. Our analysis techniques are applicable to various distributed algorithms involved with interprocess coordination.

Presents a speaker adaptation scheme which transforms the prototype speaker's Hidden Markov word models to those of a new speaker. Transformations are applied to both the state transition matrix and the probability distribution functions of a hidden Markov word model. These transformations are optimized through maximizing the joint probability of a set of input pronunciations of the new speaker. Details of these parameter transformations and experimental verification are presented. The test uses a 210-word vocabulary with each having a four-state Hidden Markov word model. The test speaker consists of three males and two females with one male heavily accented. By having the system retrained up to a four-minute adaptation speech, a subset of the 210-word vocabulary, the performance shows an improvement of recognition accuracy from 22.5% to 92.1%.>

A large-scale distributed computing system architecture for the area-wide real-time traffic control system (ARTC) of J.L. Kim et al. (1992) is presented. In the ARTC system, signal controllers coordinate with each other through the communications network to achieve real-time adaptive traffic control. For reliable service, the ARTC system is designed to be fault-tolerant at the I/O component, processor, and network levels. A modular system architecture that enables the ARTC signal controllers to support fault-tolerant design strategies is proposed. A flexible cost-effective networking strategy is presented to support different level network redundancy. In order to estimate the expected service quality of the ARTC control system and to study the reliability effects of signal controller failure rate, the repair efficiency, and the degree of system redundancy, an analytical model is developed.>

The authors present an efficient software application of a distributed system for area-wide traffic control. This system, called area-wide real-time traffic control (ARTC), can provide orderly movement of traffic, shorten average vehicle delay, prevent congestion, and improve road utilization. The signal controllers in ARTC, which are microcomputers, are interconnected through a computer network. By exchanging traffic flow information among the signal controllers, ARTC provides a novel concept in traffic control by using the computer network. Simulation results of the ARTC prototype control algorithm are presented. The results show substantial improvement over an optimized fixer time control. The signal controllers and the computer network were designed to support real-time communication requirements and a sufficient level of fault tolerance.>

A protocol for dynamic load balancing in distributed systems on the CSMA/CD local area network is presented. Using the protocol, the workload is evenly distributed throughout the system when load-balancing activity is triggered, and effective load distribution is accomplished through transmission of load-balancing messages in a collision-free manner. Analytical and simulation results are presented to show the efficiency of the protocol.>