Privacy Aware Parallel Computation of Skyline Sets Queries from Distributed Databases

A skyline query finds objects that are not dominated by another object from a given set of objects. Skyline queries help us to filter unnecessary information efficiently and provide us clues for various decision making tasks. However, we cannot use skyline queries in privacy aware environment, since we have to hide individual's records values even though there is no ID information. Therefore, we considered skyline sets queries. The skyline set query returns skyline sets from all possible sets, each of which is composed of some objects in a database. With the growth of network infrastructure data are stored in distributed databases. In this paper, we expand the idea to compute skyline sets queries in parallel fashion from distributed databases without disclosing individual records to others. The proposed method utilizes an agent-based parallel computing framework that can efficiently compute skyline sets queries and can solve the privacy problems of skyline queries in distributed environment. The computation of skyline sets is performed simultaneously in all databases which increases parallelism and reduces the computation time

決定木・回帰木のための多変量判別ルール発見アルゴリズム

広島大学(Hiroshima University)博士(工学)Engineeringdoctora

Algorithms for finding attribute value group for binary segmentation of categorical databases

科研費報告書収録論文(課題番号:13680387・基盤研究(C)(2)・H13～H15/研究代表者:徳山, 豪/パラメトリック最適化を用いた幾何学データ処理の研究

The Diffusion of Lithium in Lithium-Zinc Alloy

In a new type of secondary battery with a Li-Zn alloy as one electrode, chlorine as the other and a molten salt solution of LiCl-KCl, polarizations during charge and discharge at the Li-Zn alloy electrode may occur due to the slow diffusion of Li companying K in the alloy. This was examined by the use of chronoamperometric and chronopotentiometric methods. With a cathodic charge, the apparent diffusion coefficient of Li companying K was about 3.5×lO⁻⁵ cm²/sec at 500°C, whereas that of Li alone was about 6.0×l0⁻⁵ cm²/sec. Underan anodic discharge, the apparent diffusion coefficient was extremely large (the order of 10⁻⁴ cm²/sec), and this may be caused by the existence of convection at the alloy-electrolyte interface. The optimum charging and discharging current densities of the battery were estimated to be 0.2 and 0.3 A/cm² respectively

Studies on the Lithium Alloys-Chlorine Secondary Battery

A new type of lithium alloys-chlorine secondary battery, by using molten salt of LiCl and KCl as an electrolyte, was constructed as an automobile and a stand-by battery. In this kind of battery, the electromotive force is somewhat less than that of a conventional lithium-chlorine battery ; but the cell structure is much simpler and the operating temperature much lower. Also, the self discharge rate is much lower. The output coulombic capacity, output power and output energy were calculated to be 55 Ahr, 260 W and 150 Whr respectively, when zinc of 1 kg was used as the substratum metal. From these data it may be concluded that this type of secondary battery is very promising for automotive and stand-by uses

Mining Optimized Association Rules for Numeric Attributes

AbstractGiven a huge database, we address the problem of finding association rules for numeric attributes, such as(Balance∈I)⇒(CardLoan=yes),which implies that bank customers whose balances fall in a rangeIare likely to use card loan with a probability greater thanp. The above rule is interesting only if the rangeIhas some special feature with respect to the interrelation betweenBalanceandCardLoan. It is required that the number of customers whose balances are contained inI(called thesupportofI) is sufficient and also that the probabilitypof the conditionCardLoan=yesbeing met (called theconfidence ratio) be much higher than the average probability of the condition over all the data. Our goal is to realize a system that finds such appropriate ranges automatically. We mainly focus on computing twooptimized ranges: one that maximizes the support on the condition that the confidence ratio is at least a given threshold value, and another that maximizes the confidence ratio on the condition that the support is at least a given threshold number. Using techniques from computational geometry, we present novel algorithms that compute the optimized ranges in linear time if the data are sorted. Since sorting data with respect to each numeric attribute is expensive in the case of huge databases that occupy much more space than the main memory, we instead apply randomized bucketing as the preprocessing method and thus obtain an efficient rule-finding system. Tests show that our implementation is fast not only in theory but also in practice. The efficiency of our algorithm enables us to compute optimized rules for all combinations of hundreds of numeric and Boolean attributes in a reasonable time

A Secure Medical Record Sharing Scheme Based on Blockchain and Two-fold Encryption

Usually, a medical record (MR) contains the patients disease-oriented sensitive information. In addition, the MR needs to be shared among different bodies, e.g., diagnostic centres, hospitals, physicians, etc. Hence, retaining the privacy and integrity of MR is crucial. A blockchain based secure MR sharing system can manage these aspects properly. This paper proposes a blockchain based electronic (e-) MR sharing scheme that (i) considers the medical image and the text as the input, (ii) enriches the data privacy through a two-fold encryption mechanism consisting of an asymmetric cryptosystem and the dynamic DNA encoding, (iii) assures data integrity by storing the encrypted e-MR in the distinct block designated for each user in the blockchain, and (iv) eventually, enables authorized entities to regain the e-MR through decryption. Preliminary evaluations, analyses, comparisons with state-of-the-art works, etc., imply the efficacy of the proposed scheme.Comment: 6 pages, 3 tables, 8 figures, ICCIT 202

A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training

The successful application of large pre-trained models such as BERT in natural language processing has attracted more attention from researchers. Since the BERT typically acts as an end-to-end black box, classification systems based on it usually have difficulty in interpretation and low robustness. This paper proposes a visual interpretation-based self-improving classification model with a combination of virtual adversarial training (VAT) and BERT models to address the above problems. Specifically, a fine-tuned BERT model is used as a classifier to classify the sentiment of the text. Then, the predicted sentiment classification labels are used as part of the input of another BERT for spam classification via a semi-supervised training manner using VAT. Additionally, visualization techniques, including visualizing the importance of words and normalizing the attention head matrix, are employed to analyze the relevance of each component to classification accuracy. Moreover, brand-new features will be found in the visual analysis, and classification performance will be improved. Experimental results on Twitter's tweet dataset demonstrate the effectiveness of the proposed model on the classification task. Furthermore, the ablation study results illustrate the effect of different components of the proposed model on the classification results

Ripple Knowledge Graph Convolutional Networks For Recommendation Systems

Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model's interpretability and accuracy. This paper introduces an end-to-end deep learning model, named RKGCN, which dynamically analyses each user's preferences and makes a recommendation of suitable items. It combines knowledge graphs on both the item side and user side to enrich their representations to maximize the utilization of the abundant information in knowledge graphs. RKGCN is able to offer more personalized and relevant recommendations in three different scenarios. The experimental results show the superior effectiveness of our model over 5 baseline models on three real-world datasets including movies, books, and music