University of Macau

About: University of Macau is a education organization based out in Macao, Macau, China. It is known for research contribution in the topics: Computer science & Population. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.

Cultural meaning of perceived control: A meta-analysis of locus of control and psychological symptoms across 18 cultural regions.

Abstract: Integrating more than 40 years of studies on locus of control (LOC), this meta-analysis investigated whether (a) the magnitude of the relationship between LOC and psychological symptoms differed among cultures with distinct individualist orientations and (b) depression and anxiety symptoms yielded different patterns of cultural findings with LOC. We included studies that examined global self-ratings of LOC and at least 1 of the criterion variables in nonclinical samples (age range: 18-80 years). Data were analyzed on the basis of 152 independent samples, representing the testing of 33,224 adults across 18 cultural regions. Results revealed moderately strong relationships for external LOC with depression symptoms (k = 123, N = 28,490, r = .30, 95% confidence interval [CI] [.27, .32]) and anxiety symptoms (k = 65, N = 13,208, r = .30, 95% CI [.27, .33]). Individualism explained 20% of unique variance only in the external LOC-anxiety relationship: The link between external LOC and anxiety symptoms was weaker for collectivist societies (k = 8, N = 2,297, r = .20, 95% CI [.13, .28]) compared with individualist societies (k = 54, N = 9,887, r = .32, 95% CI [.29, .34]). Such cultural differences were attributed to the reduced emphasis on agentic goals in more collectivist societies. It is noteworthy that external LOC does not carry the same negative connotations across cultures, and members of collectivist societies may be more ready to endorse such items. Culture has been examined at the country level, and the findings may not be applicable to any particular person in a cultural region. Implications for integrating cultural meaning of perceived control into formulation of theories, research design, and intervention programs are discussed.

We Can Hear You with Wi-Fi!

Abstract: Recent literature advances Wi-Fi signals to “see” people's motions and locations. This paper asks the following question: Can Wi-Fi “hear” our talks? We present WiHear, which enables Wi-Fi signals to “hear” our talks without deploying any devices. To achieve this, WiHear needs to detect and analyze fine-grained radio reflections from mouth movements. WiHear solves this micro-movement detection problem by introducing Mouth Motion Profile that leverages partial multipath effects and wavelet packet transformation. Since Wi-Fi signals do not require line-of-sight, WiHear can “hear” people talks within the radio range. Further, WiHear can simultaneously “hear” multiple people's talks leveraging MIMO technology. We implement WiHear on both USRP N210 platform and commercial Wi-Fi infrastructure. Results show that within our pre-defined vocabulary, WiHear can achieve detection accuracy of 91 percent on average for single individual speaking no more than six words and up to 74 percent for no more than three people talking simultaneously. Moreover, the detection accuracy can be further improved by deploying multiple receivers from different angles.

Authentication and Access Control in the Internet of Things

Abstract: Due to the inherent vulnerabilities of the Internet, security and privacy issues should be considered and addressed before the Internet of Things is widely deployed. This paper mainly analyzes existing authentication and access control methods, and then, it designs a feasible one for the Internet of Things.

Learning Deep Transformer Models for Machine Translation

Abstract: Transformer is the state-of-the-art model in recent machine translation evaluations. Two strands of research are promising to improve models of this kind: the first uses wide networks (a.k.a. Transformer-Big) and has been the de facto standard for the development of the Transformer system, and the other uses deeper language representation but faces the difficulty arising from learning deep networks. Here, we continue the line of research on the latter. We claim that a truly deep Transformer model can surpass the Transformer-Big counterpart by 1) proper use of layer normalization and 2) a novel way of passing the combination of previous layers to the next. On WMT'16 English- German, NIST OpenMT'12 Chinese-English and larger WMT'18 Chinese-English tasks, our deep system (30/25-layer encoder) outperforms the shallow Transformer-Big/Base baseline (6-layer encoder) by 0.4-2.4 BLEU points. As another bonus, the deep model is 1.6X smaller in size and 3X faster in training than Transformer-Big.