Employees’ workplace cyberloafing: based on the perspective of guanxi

Cyberloafing is the biggest time waster in organization, 69 percent of respondents admitted waste time on non-work related activities each day. This number might be higher in China for a larger population of cyber citizens. Previous Studies have investigated the antecedents from various perspectives, such as organization justice, deterrence and work stressor. No one addressed cyberloafing from the perspective of guanxi, even though the strength of guanxi directly determines the appropriate behavior of employees , and employees are grounded by such behavioral norms. To fill this gap, we proposed a research model from the perspective of guanxi theory to understand employees’ cyberloafing behavior

Biased Stochastic Gradient Descent for Conditional Stochastic Optimization

Conditional Stochastic Optimization (CSO) covers a variety of applications ranging from meta-learning and causal inference to invariant learning. However, constructing unbiased gradient estimates in CSO is challenging due to the composition structure. As an alternative, we propose a biased stochastic gradient descent (BSGD) algorithm and study the bias-variance tradeoff under different structural assumptions. We establish the sample complexities of BSGD for strongly convex, convex, and weakly convex objectives, under smooth and non-smooth conditions. We also provide matching lower bounds of BSGD for convex CSO objectives. Extensive numerical experiments are conducted to illustrate the performance of BSGD on robust logistic regression, model-agnostic meta-learning (MAML), and instrumental variable regression (IV)

FunCodec: A Fundamental, Reproducible and Integrable Open-source Toolkit for Neural Speech Codec

This paper presents FunCodec, a fundamental neural speech codec toolkit, which is an extension of the open-source speech processing toolkit FunASR. FunCodec provides reproducible training recipes and inference scripts for the latest neural speech codec models, such as SoundStream and Encodec. Thanks to the unified design with FunASR, FunCodec can be easily integrated into downstream tasks, such as speech recognition. Along with FunCodec, pre-trained models are also provided, which can be used for academic or generalized purposes. Based on the toolkit, we further propose the frequency-domain codec models, FreqCodec, which can achieve comparable speech quality with much lower computation and parameter complexity. Experimental results show that, under the same compression ratio, FunCodec can achieve better reconstruction quality compared with other toolkits and released models. We also demonstrate that the pre-trained models are suitable for downstream tasks, including automatic speech recognition and personalized text-to-speech synthesis. This toolkit is publicly available at https://github.com/alibaba-damo-academy/FunCodec.Comment: 5 pages, 3 figures, submitted to ICASSP 202

Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification

Gait-based person re-identification (Re-ID) is valuable for safety-critical applications, and using only 3D skeleton data to extract discriminative gait features for person Re-ID is an emerging open topic. Existing methods either adopt hand-crafted features or learn gait features by traditional supervised learning paradigms. Unlike previous methods, we for the first time propose a generic gait encoding approach that can utilize unlabeled skeleton data to learn gait representations in a self-supervised manner. Specifically, we first propose to introduce self-supervision by learning to reconstruct input skeleton sequences in reverse order, which facilitates learning richer high-level semantics and better gait representations. Second, inspired by the fact that motion's continuity endows temporally adjacent skeletons with higher correlations ("locality"), we propose a locality-aware attention mechanism that encourages learning larger attention weights for temporally adjacent skeletons when reconstructing current skeleton, so as to learn locality when encoding gait. Finally, we propose Attention-based Gait Encodings (AGEs), which are built using context vectors learned by locality-aware attention, as final gait representations. AGEs are directly utilized to realize effective person Re-ID. Our approach typically improves existing skeleton-based methods by 10-20% Rank-1 accuracy, and it achieves comparable or even superior performance to multi-modal methods with extra RGB or depth information. Our codes are available at https://github.com/Kali-Hac/SGE-LA.Comment: Accepted at IJCAI 2020 Main Track. Sole copyright holder is IJCAI. Codes are available at https://github.com/Kali-Hac/SGE-L

Novel Nonphosphorylated Peptides with Conserved Sequences Selectively Bind to Grb7 SH2 Domain with Affinity Comparable to Its Phosphorylated Ligand

The Grb7 (growth factor receptor-bound 7) protein, a member of the Grb7 protein family, is found to be highly expressed in such metastatic tumors as breast cancer, esophageal cancer, liver cancer, etc. The src-homology 2 (SH2) domain in the C-terminus is reported to be mainly involved in Grb7 signaling pathways. Using the random peptide library, we identified a series of Grb7 SH2 domain-binding nonphosphorylated peptides in the yeast two-hybrid system. These peptides have a conserved GIPT/K/N sequence at the N-terminus and G/WD/IP at the C-terminus, and the region between the N-and C-terminus contains fifteen amino acids enriched with serines, threonines and prolines. The association between the nonphosphorylated peptides and the Grb7 SH2 domain occurred in vitro and ex vivo. When competing for binding to the Grb7 SH2 domain in a complex, one synthesized nonphosphorylated ligand, containing the twenty-two amino acid-motif sequence, showed at least comparable affinity to the phosphorylated ligand of ErbB3 in vitro, and its overexpression inhibited the proliferation of SK-BR-3 cells. Such nonphosphorylated peptides may be useful for rational design of drugs targeted against cancers that express high levels of Grb7 protein

Towards Intelligent Decision Making in Emotion-aware Applications

In this paper, we propose an intelligent emotion-aware system (IES), which aims to provide a systematic approach that can make use of the online technology to improve the intelligence of different emotion-aware mobile applications. IES is constructed to provide multi-dimensional online social community data collection and processing approaches for decision making, so as to recommend intelligent services for emotion-aware mobile applications. Furthermore, we present a flow of intelligent decision making process designed on IES, and highlight the implementation and orchestration of several key technologies and schemes applied in this system for different emotion-aware mobile applications in run-time. We demonstrate the feasibility of the proposed IES by presenting a novel emotion-aware mobile application - iSmile, and evaluate the system performance based on this application