The advance of the Internet in the past decade has radically changed the way people communicate and collaborate with each other. Physical distance is no more a barrier in online social networks, but cultural differences (at the individual, community, as well as societal levels) still govern human-human interactions and must be considered and leveraged in the online world. The rapid deployment of high-speed Internet allows humans to interact using a rich set of multimedia data such as texts, pictures, and videos. This position paper proposes to define a new research area called ‘connected multimedia’, which is the study of a collection of research issues of the super-area social media that receive little attention in the literature. By connected multimedia, we mean the study of the social and technical interactions among users, multimedia data, and devices across cultures and explicitly exploiting the cultural differences. We justify why it is necessary to bring attention to this new research area and what benefits of this new research area may bring to the broader scientific research community and the humanity.

Health trend prediction has become an effective way to ensure the safe operation of highly reliable systems, and online prediction is always necessary in many real applications. To simultaneously obtain better or acceptable online prediction accuracy and shorter computing time, we propose a new adaptive online method based on least squares support vector regression (LS-SVR). This method adopts two approaches. One approach is that we delete certain support vectors by judging the linear correlation among the samples to increase the sparseness of the prediction model. This approach can control the loss of useful information in sample data, improve the generalization capability of the prediction model, and reduce the prediction time. The other approach is that we reduce the number of traditional LS-SVR parameters and establish a modified simple prediction model. This approach can reduce the calculation time in the process of adaptive online training. Simulation and a certain electric system application indicate preliminarily that the proposed method is an effective prediction approach for its good prediction accuracy and low computing time.

It is extremely time-consuming to restart a long-running simulation from the beginning when a failure occurs. Checkpointing is a viable solution that enables simulations to be resumed from the point of failure. We study three models to determine the optimal checkpoint interval between contiguous checkpoints so that the total execution time is minimized and we demonstrate that optimal checkpointing can facilitate self-optimizing. This study greatly advances our knowledge of and practice in optimizing long-running scientific simulations.

Principal component analysis (PCA) is fundamental in many pattern recognition applications. Much research has been performed to minimize the reconstruction error in L1-norm based reconstruction error minimization (L1-PCA-REM) since conventional L2-norm based PCA (L2-PCA) is sensitive to outliers. Recently, the variance maximization formulation of PCA with L1-norm (L1-PCA-VM) has been proposed, where new greedy and non-greedy solutions are developed. Armed with the gradient ascent perspective for optimization, we show that the L1-PCA-VM formulation is problematic in learning principal components and that only a greedy solution can achieve robustness motivation, which are verified by experiments on synthetic and real-world datasets.

Online learners are individuals, and their learning abilities, knowledge, and learning performance differ substantially and are ever changing. These individual characteristics pose considerable challenges to online learning courses. In this paper, we propose an online course generation and evolution approach based on genetic algorithms to provide personalized learning. The courses generated consider not only the difficulty level of a concept and the time spent by an individual learner on the concept, but also the changing learning performance of the individual learner during the learning process. We present a layered topological sort algorithm, which converges towards an optimal solution while considering multiple objectives. Our general approach makes use of the stochastic convergence of genetic algorithms. Experimental results show that the proposed algorithm is superior to the free browsing learning mode typically enabled by online learning environments because of the precise selection of learning content relevant to the individual learner, which results in good learning performance.

We analyze the effect of using a vehicular ad-hoc network (VANET) on accident avoidance. As shown in our analysis, a higher frequency of safety packets can prevent accidents, even for high speed vehicles and dense roads. To overcome connectivity problems in blind crossing situations, a genetic algorithm (GA) based method is presented for VANET infrastructure planning. The proposed approach tries to remove coverage sight holes in low sight distance cases in a traveling path in the road. In such places, drivers might not have enough sight for proper action and also environmental obstacles prevent direct communication between vehicles. Furthermore, curved roads affect mobility. Simulation results show that the density of vehicles is increased right before a curve and is decreased after that. Therefore, in this kind of road, a high frequency of packet generation may not act well in accident avoidance. The method proposed in this paper tries to cover such places considering the lowest safety distances according to traffic theory. For this, the road must be covered directly by infrastructure. Therefore, the problem is to find the best number and also positions of road side units. Using GA, the algorithm minimizes the summation of total uncovered and overlapped points in the roads which are covered by more than one antenna. Simulation on a real road map confirmed the capabilities of the proposed approach.

An optimization method is proposed for designing an LED freeform lens which produces a uniform circular pattern with high energy efficiency. This method is composed of three main aspects: design of the initial guess, parameterization of the freeform surface, and construction of the merit function. The initial guess is created by solving an ordinary differential equation numerically. An approach of selecting optimization points is introduced for parameterization of the freeform surface. The merit function is constructed by use of the irradiance uniformity and the efficiency of the lens. Design examples are given, and the results show that the irradiance distribution is well controlled with a maximum uniformity (the relative standard deviation of irradiance, RSD) of 0.0122 and a maximum efficiency of 93.88%. This optimization method can be generalized to design freeform lenses with different lighting patterns or without rotational symmetry.

A low drift current reference based on PMOS temperature correction technology is proposed. To achieve the minimum temperature coefficient (TC), the PMOS cascode current mirror is designed as a cross structure. By exchanging the bias for two layers of the self-biased PMOS cascode structure, the upper PMOS, which is used to adjust the TC together with the resistor of the self-biased PMOS cascode structure, is forced to work in the linear region. As the proposed current reference is the on-chip current reference of a high voltage LED driver with high accuracy, it was designed using a CSMC 1 μm 40 V BCD process. Simulation shows that the TC of the reference current was only 23.8×10^?6/°C over the temperature range of ?40–120 °C under the typical condition.

A literal circuit with a three-track-output structure is presented based on resonant tunneling diodes (RTDs). It can be transformed conveniently into a single-track-output structure according to the definition and properties of the literal operation. A ternary resonant tunneling JK flip-flop is created based on the RTD literal circuit and the module-3 operation, and the JK flip-flop also has two optional types of output structure. The design of the ternary RTD JK flip-flop is verified by simulation. The RTD literal circuit is the key design component for achieving various types of multi-valued logic (MVL) flip-flops. It can be converted into ternary D and JK flip-flops, and the ternary JK flip-flop can also be converted simply and conveniently into ternary D and ternary T flip-flops when the input signals satisfy certain logical relationships. All these types of flip-flops can be realized using the traditional Karnaugh maps combined with the literal and module-3 operations. This approach offers a novel design method for MVL resonant tunneling flip-flop circuits.