Trust Transfer and the Intention to Use App-enabled Carpooling Service

In China, with the rapid dissemination of mobile communications technology along with congested traffic and increasingly expensive transportation costs, consumers are turning to smartphone-enabled, ride-sharing services. Sharing economy requires trust in strangers. Based on trust transfer theory and a dyadic conceptualization of trust from cognitive to affective, the purpose of this study is to examine trust building through the use of Didi, a third-party, ride-sharing platform that mediates exchanges among strangers

Fully Learnable Front-End for Multi-Channel Acoustic Modeling using Semi-Supervised Learning

In this work, we investigated the teacher-student training paradigm to train a fully learnable multi-channel acoustic model for far-field automatic speech recognition (ASR). Using a large offline teacher model trained on beamformed audio, we trained a simpler multi-channel student acoustic model used in the speech recognition system. For the student, both multi-channel feature extraction layers and the higher classification layers were jointly trained using the logits from the teacher model. In our experiments, compared to a baseline model trained on about 600 hours of transcribed data, a relative word-error rate (WER) reduction of about 27.3% was achieved when using an additional 1800 hours of untranscribed data. We also investigated the benefit of pre-training the multi-channel front end to output the beamformed log-mel filter bank energies (LFBE) using L2 loss. We find that pre-training improves the word error rate by 10.7% when compared to a multi-channel model directly initialized with a beamformer and mel-filter bank coefficients for the front end. Finally, combining pre-training and teacher-student training produces a WER reduction of 31% compared to our baseline.Comment: To appear in ICASSP 202

Linking Managerial Capital with Explorative Strategy and Growth in China

Purpose –This study aims to consider the effect of managerial capital (psychological, intellectual and social) on business strategy and growth. Per upper echelon theory, managerial capital enables high-level managers to drive firm performance in uniquely personal ways. The authors test the effects of managerial capital on a manager’s dominant regulatory focus (promotion and prevention balance) and whether having an explorative strategy mediates the relationship between dominant regulatory focus and the percentage of business unit growth expected from new lines of business. Design/methodology/approach – Survey data from a sample of 211 Chinese executives were used to assess measurement and test hypotheses by means of structural equation modeling. Findings – Results indicate that the direction of business strategy is influenced by the balance between promotion and prevention focus, which is shaped by managers’ risk propensity, product-market familiarity and bonding tie diversity. Explorative strategy, in turn, mediates the relationship between dominant regulatory focus and expectations of innovative growth. Originality/value – Examining the effects of managerial capital on innovative firm strategy reveals the role of psychosocial traits of decision-makers

Formal Kinematic Analysis of a General 6R Manipulator Using the Screw Theory

Kinematic analysis is a significant method when planning the trajectory of robotic manipulators. The main idea behind kinematic analysis is to study the motion of the robot based on the geometrical relationship of the robotic links and their joints, such as the Denavit-Hartenberg parameters. Given the continuous nature of kinematic analysis and the shortcoming of the traditional verification methods, we propose to use high-order-logic theorem proving for conducting formal kinematic analysis. Based on the screw theory in HOL4, which is newly developed by our research institute, we utilize the geometrical theory of HOL4 to develop formal reasoning support for the kinematic analysis of a robotic manipulator. To illustrate the usefulness of our fundamental formalization, we present the formal kinematic analysis of a general 6R manipulator

Turn-taking and Backchannel Prediction with Acoustic and Large Language Model Fusion

We propose an approach for continuous prediction of turn-taking and backchanneling locations in spoken dialogue by fusing a neural acoustic model with a large language model (LLM). Experiments on the Switchboard human-human conversation dataset demonstrate that our approach consistently outperforms the baseline models with single modality. We also develop a novel multi-task instruction fine-tuning strategy to further benefit from LLM-encoded knowledge for understanding the tasks and conversational contexts, leading to additional improvements. Our approach demonstrates the potential of combined LLMs and acoustic models for a more natural and conversational interaction between humans and speech-enabled AI agents.Comment: To appear in IEEE ICASSP 202