Effect of Auricular Acupressure on Peri- and Early Postmenopausal Women with Anxiety: A Double-Blinded, Randomized, and Controlled Pilot Study

We tested effects of auricular acupressure on peri- and early postmenopausal women with anxiety (PPWA). Fifty PPWA were randomly assigned to the auricular acupressure group (AG) or the sham group (SG). After 3 meals and before sleep every day for 4 weeks, the AG received auricular acupressure on the bilateral ear shenmen and subcortex points for 3 min per point on alternating ears. The SG received sham auricular acupressure. The Alprazolam was reduced from 0.5 mg/day at baseline to 0.3 mg/day 4 weeks after auricular acupressure (4 W) in the AG (P < .05) whereas maintained at 0.5 mg/day in the SG (P > .05). The Zolpidem was reduced from 3.0 mg/day at baseline to 1.5 mg/day at 4 W (P < .05) whereas was reduced from 2.4 mg/day to 1.9 mg/day at 4 W in the SG (P > .05), thus, significant tapering medication, suggesting auricular acupressure is helpful to PPWA

Constrained K-means and Genetic Algorithm-based Approaches for Optimal Placement of Wireless Structural Health Monitoring Sensors

Optimal placement of wireless structural health monitoring (SHM) sensors has to consider modal identification accuracy and power efficiency. In this study, two-tier wireless sensor network (WSN)-based SHM systems with clusters of sensors are investigated to overcome this difficulty. Each cluster contains a number of sensor nodes and a cluster head (CH). The lower tier is composed of sensors communicating with their associated CHs, and the upper tier is composed of the network of CHs. The first step is the optimal placement of sensors in the lower tier via the effective independence method by considering the modal identification accuracy. The second step is the optimal placement of CHs in the upper tier by considering power efficiency. The sensors in the lower tier are partitioned into clusters before determining the optimal locations of CHs in the upper tier. Two approaches, a constrained K-means clustering approach and a genetic algorithm (GA)-based clustering approach, are proposed in this study to cluster sensors in the lower tier by considering two constraints: (1) the maximum data transmission distance of each sensor; (2) the maximum number of sensors in each cluster. Given that each CH can only manage a limited number of sensors, these constraints should be considered in practice to avoid overload of CHs. The CHs in the upper tier are located at the centers of the clusters determined after clustering sensors in the lower tier. The two proposed approaches aim to construct a balanced size of clusters by minimizing the number of clusters (or CHs) and the total sum of the squared distance between each sensor and its associated CH under the two constraints. Accordingly, the energy consumption in each cluster is decreased and balanced, and the network lifetime is extended. A numerical example is studied to demonstrate the feasibility of using the two proposed clustering approaches for sensor clustering in WSN-based SHM systems. In this example, the performances of the two proposed clustering approaches and the K-means clustering method are also compared. The two proposed clustering approaches outperform the K-means clustering method in terms of constructing balanced size of clusters for a small number of clusters. Doi: 10.28991/CEJ-2022-08-12-01 Full Text: PD

Privacy Perils of Open Data and Data Sharing: A Case Study of Taiwan\u27s Open Data Policy and Practices

Governments and private sector players have hopped on the open data train in the past few years. Both the governments and civil society in Taiwan are exploring the opportunities provided by the data stored in public and private sectors. While they have been enjoying the benefits of the sharing and flowing of data among various databases, the government and some players in the private sectors have also posed tremendous privacy challenges by inappropriately gathering and processing personal data. The amended Personal Data Protection Act was originally enacted as a regulatory mechanism to protect personal data and create economic benefits via enhancing the uses of public and private sector data. In reality, the Act has instead resulted in harm to Taiwan’s data privacy situation in this big data era. This article begins with an overview of the Taiwan’s open data policy history and its current practices. Next, the article analyzes cases in which the data sharing practices between different sectors have given rise to privacy controversies, with a particular focus on 2020, when Taiwan used data surveillance in response to the COVID-19 pandemic. Finally, this article flags problems related to an open data system, including the protection of sensitive data, de-identification, the right to consent and opt-out, and the ambiguity of “public interest,” and concludes by proposing a feasible architecture for the implementation of a more sensible open data system with privacy-enhancing characteristics

Knowledge-Rich Approach to Automatic Grammatical Information Acquisition: Enriching Chinese Sketch Engine with a Lexical Grammar

PACLIC 20 / Wuhan, China / 1-3 November, 200

Knowledge-Rich Approach to Automatic Grammatical Information Acquisition: Enriching Chinese Sketch Engine with a Lexical Grammar

PACLIC 20 / Wuhan, China / 1-3 November, 200

An Investigation of Telecom Mobile Data Billing Plans

In the recent years, mobile operators have provided many billing alternatives such as limited and unlimited billing plans, and shared and non-shared data plans for the users with different needs. A non-shared data plan is designed for a single user with a limited monthly data allowance. On the other hand, the monthly data allowance of a shared data plan is shared by a group of users with multiple devices. The mobile operators often conduct the primary price study to compare their billing plans, which shows the relationship between the prices of the billing plans against the fixed amounts of data usage. Although the primary price study can easily and quickly draw the conclusions, it only provides rough billing plan suggestions. In reality, the amounts of data usage are not fixed, and therefore should be measured from commercial mobile networks to reflect the user behaviors on data usage. This paper proposes an analytical approach by using the measured data of Chunghwa Telecom Co., Ltd. (CHT), the largest telecommunications company in Taiwan, to derive the expected payments of various billing plans. The results of the analytical model are more accurate than those of the primary price study, and therefore provide better suggestions for billing plan selection. Other mobile operators can easily use our model to analyze the billing alternatives with their measured data

Floating Point Arithmetic Protocols for Constructing Secure Data Analysis Application

AbstractA large variety of data mining and machine learning techniques are applied to a wide range of applications today. There- fore, there is a real need to develop technologies that allows data analysis while preserving the confidentiality of the data. Secure multi-party computation (SMC) protocols allows participants to cooperate on various computations while retaining the privacy of their own input data, which is an ideal solution to this issue. Although there is a number of frameworks developed in SMC to meet this challenge, but they are either tailored to perform only on specific tasks or provide very limited precision. In this paper, we have developed protocols for floating point arithmetic based on secure scalar product protocols, which is re- quired in many real world applications. Our protocols follow most of the IEEE-754 standard, supporting the four fundamental arithmetic operations, namely addition, subtraction, multiplication, and division. We will demonstrate the practicality of these protocols through performing various statistical calculations that is widely used in most data analysis tasks. Our experiments show the performance of our framework is both practical and promising