On the basis of the lack of measurement tools and the research gap regarding social entrepreneurship, three studies were conducted to develop a new measure of social enterprise (SE) performance that is empirically valid and easy to administer. The purpose of this measure was to examine the relationship between entrepreneurs’ personality traits and their perceived SE performance. The results indicated that SE performance can be assessed using four dimensions: personal issues, social aspects, business elements, and service programmes. Extraversion positively influenced service programmes, and openness negatively affected service programmes. Neuroticism and conscientiousness positively predicted personal issues and service programmes, and agreeableness positively predicted all dimensions of perceived SE performance. The results also demonstrated the curvilinear relationship of the U-shaped curve between neuroticism and personal issues and the social aspects of SE performance. Furthermore, the results showed the curvilinear relationship of the inverted U-shaped curve between agreeableness and the four dimensions of SE performance

In binaural audio systems, for an optimal virtual acoustic space a set of head-related transfer functions (HRTFs) should be used that closely matches the listener’s ones. This study aims to select the most appropriate HRTF dataset from a large database for users without the need for extensive listening tests. Currently, there is no way to reliably reduce the number of datasets to a smaller, more manageable number without risking discarding potentially good matches. A neural network that estimates the appropriateness of HRTF datasets based on input vectors of anthropometric measurements is proposed. The shapes and sizes of listeners’ heads and pinnas were measured using digital photography; the measured anthropometric parameters form the feature vectors used by the neural network. A graphical user interface (GUI) was developed for participants to listen to music transformed using different HRTFs and to evaluate the fitness of each HRTF dataset. The listening scores recorded were the target outputs used to train the neural networks. The aim was to learn a mapping between anthropometric parameters and listener’s perception scores. Experimental validations were performed on 30 subjects. It is demonstrated that the proposed system produces a much more reliable HRTF selection than previously used methods.

Head-pose estimation plays an important role in computer vision. The head-pose estimation aims to determine the orientation of a human head by representing the yaw, pitch, and roll angles. Implementations can be achieved by different techniques depending on the type of input and training data. This article presents a simple three-dimensional (3-D) face model for estimating head poses. The personalized 3-D face model is constructed by 2-D face photographs. A frontal face photograph determines the plane coordinates of facial features. By knowing the yaw angles in the other averted face photograph, the depth coordinates can be determined. The yaw angle of the averted face is evaluated by the canthus positions. Once the 3-D face model is constructed, we can find the matching angles for a target head pose in a query 2-D photograph. The personalized 3-D face model rotates itself about the x -, y -, and z -axes and then projects its facial features onto plane coordinates. If the rotation angles are correct, the disparities between the 2-D facial features and those in the query face photograph are supposed to be at their minimum. The personalized 3-D face model is validated with the University of South Florida human-identification database. The performance of the proposed head-pose estimation is evaluated on the Biwi Kinect head-pose database and Pointing’04 head-pose image database. The results show that the proposed method outperforms state-of-the-art technologies on both benchmark databases.

Poverty alleviation has always been fundamental for China to achieve the goal of creating a moderately prosperous society. This study conducted a content-based spatiotemporal analysis of media coverage, regression analysis of panel data, and text mining to examine how China’s Targeted Poverty Alleviation (TPA) Strategy is characterised by online mainstream media platform. A total of 10,857 articles related to TPA in 31 specific provinces of mainland China were collected and analysed by Natural Language Processing (NLP) analysis. The results of this study indicated that spatiotemporal distribution of TPA coverage was consistent with the typical logic of the Chinese government in policy implementation based on spatial and social marginalisation. Media attention on TPA is influenced by economic, environmental, and community sustainability indicators, reflecting the sustainability trade-offs in TPA-related media coverage. The keywords embedded in media coverage indicated that agricultural product promotion in extremely impoverished areas and the experiences of economically developed agricultural areas were essential for poverty eradication. Keywords emphasise top-down administrative-led poverty governance for extremely impoverished areas and local autonomy for relatively impoverished areas. This study provides perspectives for antipoverty governance and media empowerment in the postpoverty era in China.

Image processing-based augmented reality (AR) is widely used in several fields. However, audio content is also crucial in certain cases, for example, focusing on appreciating artwork in a museum, rather than a virtually synthesized image. In this study, an attempt was made to provide ubiquitous learning (u-learning) services by using audio AR for smart cities where people do not need smartphones, tablets, or wearable devices for learning. The concept of “unmanned stores” was introduced in the proposed learning environments. A 360° camera was mounted on the ceiling of an exhibition room. An algorithm was used to detect moving objects and locate a visitor, from whose position an information-processing platform searched the showpiece nearest to the visitor and selected the corresponding audio guide. For multiple visitors, infrared (IR) sensors were used to count the visitors inside the room. A loudspeaker array was built to emit directional sounds for eliminating interference between multiple sound sources. Along with the proposed algorithm for increasing the accuracy while sensing multiple movements, the conventional sound field synthesis method was modified to improve the listening experience and learning quality. Image-based sensor technology, IR detection, and an audio signal-processing platform were integrated into a smart environment in which people can conduct u-learning without personal devices. The method can provide immersive audio tours with virtual narrators and peer-helpers in schools, museums, and art galleries. Qualitative and quantitative analyses indicated that effective hands-free and eyes-free learning was achieved.

With the advent of modern techniques, there has been an increase in mobile devices with powerful functions. This study aims to utilize application software instead of hardware equipment to alter the sound of electric musical instruments by developing the functions of audio effect units on a mobile device. The built-in software audio effects include dynamic effects, delay effects, mixing, and equalization. Multichannel audio signal processing is proposed to enhance sound externalization. The listening tests indicate that the sound effects together with a quadraphonic system produce superior special effects and spatial audio than conventional effect units. Additionally, a denoiser is proposed for noise reduction, which is a function especially helpful for singers performing on busy streets. The customization in the module is achieved by a personal voice preprocessing system. A dual-filter function was utilized for the denoiser to adjust to different environments. The objective performance measurements demonstrate that the proposed denoiser outperforms the state-of-the-art methods. A hardware audio interface serving as the connection between a mobile device and an electric instrument, such as an electric guitar, electric piano, or electric bass, was also built, providing impedance matching and voltage balance. The proposed interface transfers the electric instrument signal into a 3.5-mm jack in a smartphone or tablet. The developed audio interface is light and low-noise and can operate without being connected to an external power supply, thereby making it suitable for street musicians. The experiments validate the feasibility of using the proposed circuit for real-time audio signal conversion.