Exploring How Rivals and Complementors Affect Evolutionary Rate of B2C Apps: An Empirical Study

The hyper competition among rivals and enveloping threats from complementors are crucial external sources that influence app update strategies of B2C platforms. However, prior app-related literature largely focuses on factors affecting app performance, with scant attention on external drivers of the continuous app evolution, that is app updates. Besides, the results of app updates on market performance are mixed in extant literature. Therefore, this study is motivated to explore how competitive pressures from rivals and enveloping threats from complementors affect evolutionary rate of B2C apps and its subsequent effects on market performance. Our empirical study demonstrates that quick evolution of rival and complementor apps increases evolutionary rate of B2C apps. In contrast, a greater number of better performed rival and complementor apps decreases the evolutionary rate. Furthermore, we unveiled an inverted U-shaped relationship between evolutionary rate of B2C apps and market performance. The theoretical implications are also discussed

Towards automated eyewitness descriptions: describing the face, body and clothing for recognition

A fusion approach to person recognition is presented here outlining the automated recognition of targets from human descriptions of face, body and clothing. Three novel results are highlighted. First, the present work stresses the value of comparative descriptions (he is taller than…) over categorical descriptions (he is tall). Second, it stresses the primacy of the face over body and clothing cues for recognition. Third, the present work unequivocally demonstrates the benefit gained through the combination of cues: recognition from face, body and clothing taken together far outstrips recognition from any of the cues in isolation. Moreover, recognition from body and clothing taken together nearly equals the recognition possible from the face alone. These results are discussed with reference to the intelligent fusion of information within police investigations. However, they also signal a potential new era in which automated descriptions could be provided without the need for human witnesses at all

Soft biometric fusion for subject recognition at a distance

Biometric recognition is an advanced technology that employs physical features (such as fingerprint, iris and face capture) and behavioural features (such as gait, signature and voice) to identify people. Biometric features are reliable and valid ways to describe the unique properties of individuals, but there are often rigorous requirements on the position and characteristics of devices used for data acquisition. Since biometric features can be difficult to capture at a distance, soft biometric features, such as height, weight, skin colour and gender, have received much attention. Although the uniqueness of soft biometric features is not as intuitively obvious as traditional biometric features, numerous experiments have demonstrated that the desired recognition accuracy can be achieved by using different soft biometric features. This thesis will propose state-of-the-art multimodal biometric fusion techniques to improve recognition performance of soft biometrics.The first contribution of this thesis is to estimate fusion performance based on three types of soft biometrics - face, body and clothing. Feature level and score level fusion strategies will be employed to measure and analyse the influence of fusion on soft biometric recognition.The second key contribution of this research is that the analysis of the influence of distance on soft biometric traits and an exploration of the potency of recognition using fusion at varying distances have been performed. A new soft biometric database, containing images of the human face, body and clothing taken at three different distances, was created and used to obtain face, body and clothing attributes. First, this new database was constructed to explore the suitability of each modality at a distance: intuitively, the face is suitable for near field identification, and the body becomes optimal when the subject is further away. The new dataset is used to explore the potential of face, body and clothing for human recognition using fusion. In this section, some novel fusion techniques on different levels (feature, score and rank level) are proposed to improve soft biometric recognition performance.A Supervised Generalised Canonical Correlation (SG-CCA) methodology is proposed to fuse the soft biometric features. The proposed SG-CCA is numerically validated to be the best fusion method compared with other multi-modal fusion methods. An SVM-weighted Likelihood Ratio Test (SVM-LRT) method is proposed for score level fusion. The experimental results demonstrate that SVM-LRT-based fusion significantly outperforms the single-mode recognition. A novel joint density distribution-based rank-score fusion is also proposed to combine rank and score information. Analysis using the new soft biometric database demonstrates that recognition performance is significantly improved by using the new methods over single modalities at different distances

Advances in Thermoelectric Composites Consisting of Conductive Polymers and Fillers with Different Architectures

Stretchable wireless power is in increasingly high demand in fields such as smart devices, flexible robots, and electronic skins. Thermoelectric devices are able to convert heat into electricity due to the Seebeck effect, making them promising candidates for wearable electronics. Therefore, high-performance conductive polymer-based composites are urgently required for flexible wearable thermoelectric devices for the utilization of low-grade thermal energy. In this review, mechanisms and optimization strategies for polymer-based thermoelectric composites containing fillers of different architectures will be introduced, and recent advances in the development of such thermoelectric composites containing 0- to 3-dimensional filler components will be presented and outlooked

A joint density based rank-score fusion for soft biometric recognition at a distance

In order to improve recognition performance, fusion has become a key technique in the recent years. Compared with single-mode biometrics, the recognition rate of multi-modal biometric systems is improved and the final decision is more confident. This paper introduces a novel joint density distribution based rank-score fusion strategy that combines rank and score information. Recognition at a distance has only recently been of interest in soft biometrics. We create a new soft biometric database containing the human face, body and clothing attributes at three different distances to investigate the influence by distance on soft biometric fusion. A comparative study about our method and other state of the art rank level and score level fusion methods are also conducted in this paper. The experiments are performed using a soft biometric database we created. The results demonstrate the recognition performance is significantly improved by our proposed method

Supervised generalized canonical correlation analysis of soft biometric fusion for recognition at a distance

In order to improve biometric system performance,information fusion becomes a key technique in multi-modalbiometric systems. Multi-modal biometric fusion isconventionally divided into four levels: sensor level, featurelevel, score level and decision level. In this paper, we proposea supervised generalized canonical correlation (sg-CCA)method to fuse soft biometric features. The experiments wereperformed using a soft biometric database which contains thehuman face, body and clothing traits at three different distances.This paper describes the database and analyses the recognitionperformance. Furthermore, it explores the potency of face,body and clothing for human recognition using sg-CCA fusioncompared with other linear dimensionality reduction fusionmethods. The results demonstrate the superiority of softbiometric fusion using sg-CCA method for human recognition.<br/

Soft biometric fusion for subject recognition at a distance

There is societal need for techniques to identify subjects at a distance and when conventional biometrics are obscured, for example in fighting crime. Soft biometrics have this capability and include a subject's height, weight, skin colour and gender. Although the distinctiveness of soft biometric features is intuitively less than that of traditional biometric features, numerous experiments have demonstrated that the desired recognition accuracy can be achieved by using multiple soft biometric features. This paper will propose state-of-the-art multimodal biometric fusion techniques to improve recognition performance of soft biometrics. The key contribution of this paper is the analysis of the influence of distance on soft biometric traits and an exploration of the potency of recognition using fusion at varying distances. A new soft biometric database, containing images of the human face, body and clothing taken at three different distances, was created and used to obtain face, body and clothing attributes. This new database was constructed to explore the suitability of each modality at a distance: intuitively, the face is suitable for near field identification, and the body becomes the optimal choice when the subject is further away. The new dataset is used to explore the potential of face, body and clothing for human recognition using fusion. We present a novel fusion technique at score and rank level that improves identification performance. A novel joint density distribution-based rank-score fusion is also proposed to combine rank and score information. Analysis using the new soft biometric database demonstrates that recognition performance is significantly improved by using the new methods over single modalities at different distances

Fusion analysis of soft biometrics for recognition at a distance

There are many new soft biometric approaches thoughfew are used for identification, especially at a distance. Wecreate a new soft biometric database containing the humanface, body and clothing attributes at three differentdistances. One aim of this paper is to explore the potency offace, body and clothing for human recognition using fusion.Another aim is to explore the suitability for each modalityat a distance: intuitively face is suited to near fieldidentification and body when the subject is further away.The new database and the soft biometric features used areclarified and the influence of distance on soft biometrictraits is investigated. Results show the individualadvantages and disadvantages of face, body and clothingtraits and demonstrate the superiority of fusion method inhuman recognition compared with single-modality.<br/

Application of a Gas-Kinetic BGK Scheme in Thermal Protection System Analysis for Hypersonic Vehicles

One major problem in the development of hypersonic vehicles is severe aerodynamic heating; thus, the implementation of a thermal protection system is required. A numerical investigation on the reduction of aerodynamic heating using different thermal protection systems is conducted using a novel gas-kinetic BGK scheme. This method adopts a different solution strategy from the conventional computational fluid dynamics technique, and has shown a lot of benefits in the simulation of hypersonic flows. To be specific, it is established based on solving the Boltzmann equation, and the obtained gas distribution function is used to reconstruct the macroscopic solution of the flow field. Within the finite volume framework, the present BGK scheme is specially designed for the evaluation of numerical fluxes across the cell interface. Two typical thermal protection systems are investigated by using spikes and opposing jets, separately. Both their effectiveness and mechanisms to protect the body surface from heating are analyzed. The predicted distributions of pressure and heat flux, and the unique flow characteristics brought by spikes of different shapes or opposing jets of different total pressure ratios all verify the reliability and accuracy of the BGK scheme in the thermal protection system analysis