The effects of source cues on online news perception

Among various interface cues, expertise, identity, and bandwagon cues have been consistently found to have significant effects on media users’ perceptions of online news content. To examine the effects of these three types of heuristic cues in the context of online news consumption, the current study involved a 2 (expertise cue: low vs. high) × 2 (identity cue: in-group vs. out-group) × 2 (bandwagon cue: low vs. high) online experiment. A total of 121 undergraduate students participated in the study. Significant two-way interaction effects between the expertise and bandwagon cues on perceived credibility suggested the positive combined effect of these two cues. Moreover, significant three-way interaction effects among expertise, identity and bandwagon cues indicated that the interaction effects between expertise and bandwagon cues tend to work as a function of the identity cue. While confirming the importance of the identity cue in users’ perceptions of online news, three-way interaction effects confirmed the co-occurrence of heuristic and systematic processing. The interaction effects also suggested that people process news systematically when the recommenders are out-group members, whereas they process news heuristically when the recommenders are in-group members. Theoretical as well as practical implications have also been discussed in this article.     

Speech emotion recognition: Features and classification models

To solve the speaker independent emotion recognition problem, a three-level speech emotion recognition model is proposed to classify six speech emotions, including sadness, anger, surprise, fear, happiness and disgust from coarse to fine. For each level, appropriate features are selected from 288 candidates by using Fisher rate which is also regarded as input parameter for Support Vector Machine (SVM). In order to evaluate the proposed system, principal component analysis (PCA) for dimension reduction and artificial neural network (ANN) for classification are adopted to design four comparative experiments, including Fisher + SVM, PCA + SVM, Fisher + ANN, PCA + ANN. The experimental results proved that Fisher is better than PCA for dimension reduction, and SVM is more expansible than ANN for speaker independent speech emotion recognition. The average recognition rates for each level are 86.5%, 68.5% and 50.2% respectively.     

Effects of visual and linguistic anthropomorphic cues on social perception,self-awareness,and information disclosure in a health website

Acknowledging the lack of studies examining both visual and linguistic anthropomorphic cues and the underlying mechanisms of their effects, we investigated how the different modalities of anthropomorphic cues in a health website influenced information disclosure. In a 2 (visual cues: human vs. non-human image) × 2 (linguistic cues: conversational vs. impersonal language) × 2 (question type: less vs. more sensitive questions) between-subjects experiment (N = 254), participants registered with a mock-up health website. We assessed a behavioral outcome of not disclosing personal information and psychological outcomes of social perception and self-awareness as potential mediators. Results revealed distinctive effects of the two modalities of the anthropomorphic cues. Anthropomorphic images, on one hand, increased public and private self-awareness, and public self-awareness in turn led to less information disclosure. Anthropomorphic language, on the other hand, heightened social perception and promoted information disclosure, but social perception did not predict the disclosure. These results indicate unique underlying mechanisms of the effects of anthropomorphism: priming effect of visual cues, and communicative effects of linguistic cues.     

A traffic cellular automata model based on road network grids and its spatial and temporal resolution’s influences on simulation

Microscopic Traffic Simulation Model based on Cellular Automata (CA) has become more and more popular since it was firstly introduced by Creamer and Ludwig in 1986. Cellular automata are simpler to implement on computers, provide a simple physical picture of the system and can be easily modified to deal with different aspects of traffic. However, in a traditional traffic CA model, the spatial resolution of CA and temporal resolution of simulation are low. Take TRANSIMS for example. The size of cellular automata is 7.5 m and the time step equals 1 s. In such a case, if a vehicle drives at a speed of 4 cells per s, the speed difference between 95 km/h (3.5 1 7.5 m/s) and 121 km/h (4.4999 1 7.5 m/s) will not be distinguished by simulation models. And the temporal resolution of 1 s makes the system hard to model different drivers’ reaction time, which plays a very important role in vehicular movement models. In this paper, a microscopic traffic cellular automata model based on road network grids is proposed to overcome the low spatial and temporal resolutions of traditional traffic CA models. In our model, spatial resolution can be changed by setting different grid size for lanes and intersections before or during simulation and temporal resolution can be defined according to simulation needs to model different drivers’ reaction time, whereas the vehicular movement models are still traditional CA models. By doing so, the low spatial and temporal resolution of CA model can be overcome and the advantages of using CA to simulate traffic are preserved. The paper also presents analyses of the influences on simulation of different 1D lane grid size, 2D intersection grid size and different combinations of temporal resolution and mean drivers’ reaction time. The analysis results prove the existence of spatial and temporal resolution thresholds in traffic CA models. They also reveal that the size of grids, the combinations of different temporal resolutions and mean drivers’ reaction time do pose influences on the speed of vehicles and lane/intersection occupancy, but do not affect the volume of traffic greatly.     

Modeling and simulation of the open-end equity mutual fund market in Taiwan by using self-organizing map

This research applies artificial intelligence (AI) of unsupervised learning self-organizing map neural network (SOM-NN) to establish a model to select the superior funds. This research period is from year 2000 to 2010 and picks 100 domestic equity mutual funds as study object. This research used 30 days prior to the beginning of each month’s prior 30 days, 60 days, 90 days on fund’s net asset value and the Taiwan Weighted Stock Index (TAIEX) return as the fund’s relative performance evaluation indicators classified by month. Finally, based on the superior rate or the average return rate, this research select the superior funds and simulate investment transactions according to this model.The empirical results show that using the mutual fund’s net asset value and the TAIEX’s relative return as SOM-NN input variables not only finds out the superior fund but also has a good predictive ability. Applying this model to simulate investment transactions will be better than the random trading model and market. The experiments also found that the investment simulation of a three-month interval has the highest profitability. The model operation suggests that it is more suitable for short-term and medium-term investment. This research can assist investors in making the right investment decisions while facing rapid financial environment changes.     

Fuzzy model tuning using simulated annealing

This paper presents the use of simulated annealing metaheuristic for tuning Mamdani type fuzzy models. Structure of the Mamdani fuzzy model is learned from input–output data pairs using Wang and Mendel’s method and fuzzy c-means clustering algorithm. Then, parameters of the fuzzy system are tuned through simulated annealing. In this paper, we perform experiments to examine effects of (a) initial solution generated by Wang and Mendel’s method and fuzzy c-means clustering method, (b) membership function update procedure, (c) probability parameter for the calculation of the initial temperature, (d) temperature update coefficient used for cooling schedule, and (e) randomness level in the disturbance mechanism used in simulated annealing algorithm on the tuning of Mamdani type fuzzy models. Experiments are performed with Mackey–Glass chaotic time series. The results indicate that Wang and Mendel’s method provides better starting configuration for simulated annealing compared to fuzzy c-means clustering method, and for the membership function update parameter, MFChangeRate ∈  (0, 1], and the probability parameter for the calculation of the initial temperature, P₀ ∈  (0, 1), values close to zero produced better results.     

Mechanical design and characterization of a resonant magnetic field microsensor with linear response and high resolution

A resonant magnetic field microsensor based on Microelectromechanical Systems (MEMS) technology including a piezoresistive detection system has been designed, fabricated, and characterized. The mechanical design for the microsensor includes a symmetrical resonant structure integrated into a seesaw rectangular loop (700 μm × 450 μm) of 5 μm thick silicon beams. An analytical model for estimating the first resonant frequency and deflections of the resonant structure by means of Rayleigh and Macaulay's methods is developed. The microsensor exploits the Lorentz force and presents a linear response in the weak magnetic field range (40–2000 μT). It has a resonant frequency of 22.99 kHz, a sensitivity of 1.94 V T^?1, a quality factor of 96.6 at atmospheric pressure, and a resolution close to 43 nT for a frequency difference of 1 Hz. In addition, the microsensor has a compact structure, requires simple signal processing, has low power consumption (16 mW), as well as an uncomplicated fabrication process. This microsensor could be useful in applications such as the automotive sector, the telecommunications industry, in consumer electronic products, and in some medical applications.     

The effect of media richness factors on representativeness for video skim

The study examines the effect of four important aspects of film skimming, including segmentation process, proportion of total skimmed length (TSL), multiple cues available, and genre/domain of the film. We design three experiments to explore their effects on representativeness for video skim. The results of Experiment 1 show that the skimmed video combined with 10% of total skimmed length and 5 or 10 s of skimmed segment (SS) is more efficient for representativeness. The results of Experiment 2 show that the skimmed video with mostly ending part and multiple cues can significantly improve representativeness. The results of Experiment 3 reveal that the representativeness of skimmed video with different types of movie is significantly different.In our experiments, the proportion of TSL is set to three levels, 5%, 10%, and 15%, while the size of SS is also set to three levels, 2.5, 5, and 10 s for the segmentation process. We observe that the skimmed video with the longer TSL and SS has the better representativeness of movie content, but the four combinations for 10% and 15% with 5 s and 10 s are insignificantly different. The finding is helpful for reducing the time cost of skimming video. Furthermore, we applied two important factors—personality focus of the medium and multiple cues, from media richness theory to our skimming method in order to raise the representativeness of video skim for different films. In the personality focus of the medium, we define a movie as having three parts—beginning, middle, and ending. In the multiple cues, the skimmed video with synchronized subtitle, audio, and video can assist our comprehension and reduce the uncertainty. We find that the skimmed video with mostly ending part and synchronized subtitle, audio, and video can raise the representativeness of movie content.     

Constructing a novel mortality prediction model with Bayes theorem and genetic algorithm

Intensive care is one of the most important components of the modern medical system. Healthcare professionals need to utilize intensive care resources effectively. Mortality prediction models help physicians decide which patients require intensive care the most and which do not. The Simplified Acute Physiology System 2nd version (SAPS II) is one of the most popular mortality scoring systems currently available. This study retrospectively collected data on 496 patients admitted to intensive care units from year 2000 to 2001. The average patient age was 59.96 ± 1.83 years old and 23.8% of patients died before discharge. We used these data as training data and constructed an exponential Bayesian mortality prediction model by combining BSM (Bayesian statistical model) and GA (genetic algorithm). The optimal weights and the parameters were determined with GA. Furthermore, we prospectively collected data on 142 patients for testing the new model. The average patient age for this group was 57.80 ± 3.33 years old and 21.8% patients died before discharge. The mortality prediction power of the new model was better than SAPS II (p < 0.001). The new model combining BSM and GA can manage both binary data and continuous data. The mortality rate is predicted to be high if the patient’s Glasgow coma score is less than 5.     

Credit rating by hybrid machine learning techniques

It is very important for financial institutions to develop credit rating systems to help them to decide whether to grant credit to consumers before issuing loans. In literature, statistical and machine learning techniques for credit rating have been extensively studied. Recent studies focusing on hybrid models by combining different machine learning techniques have shown promising results. However, there are various types of combination methods to develop hybrid models. It is unknown that which hybrid machine learning model can perform the best in credit rating. In this paper, four different types of hybrid models are compared by ‘Classification + Classification’, ‘Classification + Clustering’, ‘Clustering + Classification’, and ‘Clustering + Clustering’ techniques, respectively. A real world dataset from a bank in Taiwan is considered for the experiment. The experimental results show that the ‘Classification + Classification’ hybrid model based on the combination of logistic regression and neural networks can provide the highest prediction accuracy and maximize the profit.     

Do shy people feel less communication apprehension online? The effects of virtual reality on the relationship between personality characteristics and communication outcomes

This study examines the effect of 3D virtual worlds on an individual’s communication experience in comparison with Face-to-Face communications (FtFCs). Using 2 × 2 posttest only group design where subjects were asked to discuss over a given topic. This study investigated whether individuals’ different personality characteristics (i.e. shyness) influence communication experiences (i.e. communication apprehension). The data suggest that shy individuals felt less of communication apprehension during the discussion conducted online than FtFC. On the other hand, FtFC turned out to be more effective in influencing individuals’ intention to change behavior compared to virtual worlds. The results suggest that lack of visual/auditory cues in virtual reality is the major factor that influenced the outcome.     

Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff

This study investigated the effects of upstream stations’ flow records on the performance of artificial neural network (ANN) models for predicting daily watershed runoff. As a comparison, a multiple linear regression (MLR) analysis was also examined using various statistical indices. Five streamflow measuring stations on the Cahaba River, Alabama, were selected as case studies. Two different ANN models, multi layer feed forward neural network using Levenberg–Marquardt learning algorithm (LMFF) and radial basis function (RBF), were introduced in this paper. These models were then used to forecast one day ahead streamflows. The correlation analysis was applied for determining the architecture of each ANN model in terms of input variables. Several statistical criteria (RMSE, MAE and coefficient of correlation) were used to check the model accuracy in comparison with the observed data by means of K-fold cross validation method. Additionally, residual analysis was applied for the model results. The comparison results revealed that using upstream records could significantly increase the accuracy of ANN and MLR models in predicting daily stream flows (by around 30%). The comparison of the prediction accuracy of both ANN models (LMFF and RBF) and linear regression method indicated that the ANN approaches were more accurate than the MLR in predicting streamflow dynamics. The LMFF model was able to improve the average of root mean square error (RMSE_ave) and average of mean absolute percentage error (MAPE_ave) values of the multiple linear regression forecasts by about 18% and 21%, respectively. In spite of the fact that the RBF model acted better for predicting the highest range of flow rate (flood events, RMSE_ave/RBF = 26.8 m³/s vs. RMSE_ave/LMFF = 40.2 m³/s), in general, the results suggested that the LMFF method was somehow superior to the RBF method in predicting watershed runoff (RMSE/LMFF = 18.8 m³/s vs. RMSE/RBF = 19.2 m³/s). Eventually, statistical differences between measured and predicted medians were evaluated using Mann-Whitney test, and differences in variances were evaluated using the Levene's test.     

Sex role stereotyping is hard to kill: A field experiment measuring social responses to user characteristics and behavior in an online multiplayer first-person shooter game

Sex role stereotyping by players in first-person shooter games and other online gaming environments may encourage a social environment that marginalizes and alienates female players. Consistent with the social identity model of deindividuation effects (SIDE), the anonymity of online games may engender endorsement of group-consistent attitudes and amplification of social stereotyping, such as the adherence to gender norms predicted by expectations states theory. A 2 × 3 × 2 virtual field experiment (N = 520) in an online first-person shooter video game examined effects of a confederate players’ sex, communication style, and skill on players’ compliance with subsequent online friend requests. We found support for the hypothesis that, in general, women would gain more compliance with friend requests than men. We also found support for the hypothesis that women making positive utterances would gain more compliance with friend requests than women making negative utterances, whereas men making negative utterances would gain more compliance with friend requests than men making positive utterances. The hypothesis that player skill (i.e., game scores) would predict compliance with friend requests was not supported. Implications for male and female game players and computer-mediated communication in online gaming environments are discussed.     

Predicting guild commitment in World of Warcraft with the investment model of commitment

Many MMORPG offer players the possibility to become a member of a guild, a hierarchical organization of characters with common objectives. Guild membership can be beneficial to game progress, and offer opportunities for social interaction. In the current study we focus on the MMORPG World of Warcraft (WoW), with the main aim to examine whether guild commitment and players’ intention to remain in their guild can be predicted by players’ satisfaction, investments, and perceptions of alternatives to their guild. To this end, 165 WoW players completed an online questionnaire and answered questions related to their guild membership. They also completed the investment model scale which was reworded so all questions pertained to their guild and their fellow guild members. Results show that satisfaction level, quality of alternatives, and investment size significantly predict commitment level (p’s < .001), which in turn predicted likelihood of participants’ staying with their current guild (p < .001) and the number of guilds they had been a member of in the past (p < .001). Moreover, high levels of guild commitment were indicative of better mental health, whereas weekly hours of game play was negatively related to mental health. In the discussion, we conclude that interdependence theory and the investment model of commitment are applicable to online gaming environments, and we argue that commitment to one’s guild is one factor that could prevent the risks associated with online game play (i.e. problematic use).     

A decision rules approach for improvement of airport service quality

The passenger’s perception of the airport’s level of service (LOS) may have a significant impact on promoting or discouraging future tourism and business activities. In this study, we take a look at this problem, but unlike in traditional statistical analysis, we apply a new method, the dominance-based rough set approach (DRSA), to an airport service survey. A set of “if … then … ” decision rules is used in the preference model. The passengers indicate their perception of airport LOS by rating a set of criteria/attributes. The proposed method provides practical information that should be of help to airport planners, designers, operators, and managers to develop LOS improvement strategies. The model was implemented using survey data from a large sample of customers from an international airport in Taiwan.     

A Generalized Sylvester Identity and Fraction-free Random Gaussian Elimination

Sylvester’s identity is a well-known identity that can be used to prove that certain Gaussian elimination algorithms are fraction free. In this paper we will generalize Sylvester’s identity and use it to prove that certain random Gaussian elimination algorithms are fraction free. This can be used to yield fraction free algorithms for solving Ax = b(x ≥  0) and for the simplex method in linear programming.     

Managing online wait: Designing effective waiting screens across cultures

We investigate how to effectively manage online wait using a waiting screen with progress cues in different cultures. A research model is developed based on uncertainty reduction model, resource allocation model, color psychology, and Hofstede’s cultural model. Two controlled lab experiments (n = 269 subjects each) were conducted in U.S. and Taiwan. We found that high hedonic valence and high time affordance were more effective to induce greater enjoyment and less uncertainty respectively, resulting in shorter perceived waiting time. We also found the significant background color effect and interaction effects between culture and progress cue design.     

Utilization of a composite hole transporting layer and novel homogeneous double emitting layers for performance improvement and low efficiency roll-off in organic light-emitting diodes

Blue organic light-emitting devices (OLEDs) combing a composite hole transporting layer (c-HTL) and novel homogeneous double emitting layers (DELs) have been fabricated. The c-HTL plays a significant role of rectification in balancing the carriers’ injection concentration which matches well with the DELs structure. The DELs is consisted of two homogeneous hosts, such as 2-methyl-9,10-di(2-naphthyl) anthracene (MADN) and 9,10-di(2-naphthyl) anthracene (ADN). The optimal device presents the maximal current efficiency of 15.9 cd/A at 4.9 mA/cm² and the minor efficiency roll-off of 13.4% under the driving voltage varying from 5 V to 10 V, respectively. Meanwhile, the device’s maximal current efficiency and the corresponding efficiency roll-off have been obviously improved by 55.9% and 63.9% compared with those of the conventional device. These results indicate that the homogeneous DELs not only greatly facilitate carriers’ injection into the emitting layer (EML), but also evenly modulate carriers’ distribution due to natural energy barrier of the interface. The transient photoluminescence decay of double hosts further illustrates that the DELs structure can increase the recombination ratio of electron–hole pairs and improve the exciton’s utilization. Additionally, the optimal device’ current density is reduced by 44.1% under the same luminance of 25,780 cd/m² compared with that of the conventional device.     

Exploitation of aeroelastic effects for drift reduction,in an all-polymer air flow sensor

This paper presents a vibration amplitude measurement method that greatly reduces the effects of baseline resistance drift in an all-polymer piezoresistive flow sensor or microtuft. The sensor fabrication is based on flexible printed circuit board (flex-PCB) technology to enable the potential for low-cost and scalable manufacture. Drift reduction is accomplished by discriminating the flow-induced vibration (‘flutter’) amplitude of the microtuft-based sensor as a function of flow velocity. Flutter peak-to-peak amplitude is measured using a microcontroller-based custom readout circuit. The fabricated sensor with the readout circuitry demonstrated a drift error of 2.8 mV/h, which corresponds to a flow-referenced drift error of 0.2 m/s of wind velocity per hour. The sensor has a sensitivity of 14.5 mV/(m/s) with less than 1% non-linearity over the velocity range of 5–16 m/s. The proposed vibration amplitude measurement method is also applied to a sensor array with a modified structure and a reduced dimension, which demonstrated a sensitivity of 13.2 mV/(m/s) with a flow-referenced drift error of 0.03 m/s of wind velocity per hour.