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 The amended Personal Data Protection Act was originally enacted as a regulatory mechanism to protect personal data and create economic benefits via enhancing the uses of public and private sector data.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

Position Bias Estimation with Item Embedding for Sparse Dataset

Estimating position bias is a well-known challenge in Learning to Rank (L2R). Click data in e-commerce applications, such as targeted advertisements and search engines, provides implicit but abundant feedback to improve personalized rankings. However, click data inherently includes various biases like position bias. Based on the position-based click model, Result Randomization and Regression Expectation-Maximization algorithm (REM) have been proposed to estimate position bias, but they require various paired observations of (item, position). In real-world scenarios of advertising, marketers frequently display advertisements in a fixed pre-determined order, which creates difficulties in estimation due to the limited availability of various pairs in the training data, resulting in a sparse dataset. We propose a variant of the REM that utilizes item embeddings to alleviate the sparsity of (item, position). Using a public dataset and internal carousel advertisement click dataset, we empirically show that item embedding with Latent Semantic Indexing (LSI) and Variational Auto-Encoder (VAE) improves the accuracy of position bias estimation and the estimated position bias enhances Learning to Rank performance. We also show that LSI is more effective as an embedding creation method for position bias estimation

Biomechanical Characteristics and EMG Activities of Weighted Countermovement Jump

The purpose of this study was to investigate the biomechanical characteristics and EMG activities during a weighted countermovement jump (WCMJ) with 0%, 25% and 50% of body weight. Eight male college students participated this study. An AMTI force platform, Penny&Giles goniometer and Biovision EMG system were used synchronously to record the related parameters while subjects performed WCMJs. The results indicate that by increasing load, the eccentric mean force, the maximum force and concentric impulse increases significantly. With the load increase, the EMG activities of soleus and gastrocnemius did not changed significantly, while the eccentric mean EMG amplitude of rectus femoris got greater. This reveals that WCMJ has a marked influence on the lower extremity, especially on the rectus femoris

THE REGULATION OF LEG STIFFNESS AND EMG ACTIVITIES ON PERSON WITH VISUAL IMPAIRED DURING STEP-DOWN WALKING

The purpose of present study was to evaluate leg muscular regulation and neuromuscular activation by investigating the stiffness and EMG amplitude of normal vision students and visually impaired students. 10 normal vision (age: 24.3±20 years; height: 171.5±4.6cm; mass: 65.9±8.0kg) and 10 visually impaired students (age: 23.2±2.4 years; height: 163.4±9.6cm; mass: 62.8±15.0kg) were served as subjects. AMTI force platform (1200 Hz), Peak Performance motion analysis system (60Hz) and Biovision EMG system were used synchronously to record the ground reaction force, the kinematic parameters and EMG signals of lower extremity during the subjects stepped down from height 20, 30 and 40cm. The results revealed that the regulation of neuromuscular system of the impaired is less efficient compared to the normal one because of lower muscle stiffness and EMG activity

単純リグレットバンディットアルゴリズムを利用したモンテカルロ木探索

学位の種別: 課程博士審査委員会委員 : （主査）東京大学准教授 鶴岡 慶雅, 東京大学教授 相田 仁, 東京大学准教授 佐藤 周行, 東京大学教授 田浦 健次朗, 東京大学准教授 長谷川 禎彦University of Tokyo(東京大学

Survival and breakthrough: A case study of evolutionary change in a state-owned enterprise

This study examines the processes undertaken by a state-owned enterprise to overcome limitations and successfully reverse its decline through moderate, gradual and small-scale evolutionary change. This paper utilizes the "social process research model" to analyze and record organizational changes at Jiangnan Resort, and further observes strategic countermeasures employed to address crises and successful management of organizational change. Results from our study suggest that organizational evolutionary change strategies should include institutionalization strategies, development of marketing channels, festival event marketing, placement marketing, product packaging strategies, niche market segmentation, service quality enhancement, and manpower flexibility strategies. Key factors in change management include Total Quality Management (TQM), Management by Objective (MBO), organizational members understanding of urgency and need of change, gaining support through education and communication, employee empowerment and participation, and institutionalizing change. The resulting information can serve as a reference for future qualitative research and development of strategic concepts for organizations planning to adopt evolutionary change into their companies

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

Sample-Specific Debiasing for Better Image-Text Models

Self-supervised representation learning on image-text data facilitates crucial medical applications, such as image classification, visual grounding, and cross-modal retrieval. One common approach involves contrasting semantically similar (positive) and dissimilar (negative) pairs of data points. Drawing negative samples uniformly from the training data set introduces false negatives, i.e., samples that are treated as dissimilar but belong to the same class. In healthcare data, the underlying class distribution is nonuniform, implying that false negatives occur at a highly variable rate. To improve the quality of learned representations, we develop a novel approach that corrects for false negatives. Our method can be viewed as a variant of debiased constrastive learning that uses estimated sample-specific class probabilities. We provide theoretical analysis of the objective function and demonstrate the proposed approach on both image and paired image-text data sets. Our experiments demonstrate empirical advantages of sample-specific debiasing