Firstly, I will present the study of a static analysis problem on schema-level k-secrecy, which is a metric for the security against inference attacks on XML databases. Also, the discussion of the decidability and computational complexity of this problem. Secondly, I will present the proposal of the query-based l-diversity, which is a new privacy notion obtained by combining k-secrecy and l-diversity in the relational database setting, and two approaches to the problem of verifying the query-based l-diversity. I will also show the effectiveness and scalability of two approaches based on experimental results. Lastly, I will end this presentation with the conclusion and future work.
For English, I conduct experiments in error correction targeting all types errors using statistical machine translation technique and I analyze the strength and weakness of grammatical error correction using statistical machine translation. I also propose the grammatical error correction method using discriminative reranking with POS/syntactic features. I show the effectiveness of reranking for grammatical error correction.
本論文の1章では，事故防止の観点から自動車の略史について述べ， 運転者状態を得ることの重要性を示す．次に，運転者状態推定の研究を概観し， 本研究の意義を明らかにする． 2章では，画像合成により作成した運転者の顔画像を学習データとして 顔検出器を生成することで従来よりも光環境に対し堅牢な顔検出技術を提案する． 3章では，運転者の顔検出結果を用い，運転者ごとの較正を自動化した 顔向きと目の向き，目閉じの検出方法を提案し， これらを用いた不注意の検出と，居眠り運転の検出の可能性について述べる． 4章では，3章で得た顔の特徴に加えて，副次行動と，運転操作を用いることで， 運転者の覚醒低下を従来よりも早期に推定する方法について述べる． 5章では，3章と4章で推定した運転者状態に地図情報を組み合わせ， 車両運転イベントの自動抽出と記録と運転者への提示方法と提案し， 運転者状態推定技術が運転者の安全運転評価に有用なことを示す． 最後に6章で，本論文を総括し今後の課題と展望を述べる．
In the presentation, I will introduce two novel network anomaly detection methods: (1) S-transform-based and (2) Renyi divergence-based methods. Both methods do not require the pre-defined signatures of targets and are able to detect unknown and new malicious and disruptive traffic with high accuracy and low false positive rates.
(1) Briefly, the S-transform-based method uses S-transform to convert a traffic signal (e.g., packet rate) to a time-frequency domain. The method then detects unusual time-frequency behavior caused by anomalies in the time-frequency domain. In the presentation, I will explain the method in detail and show the performance evaluation results obtained from testing the method with simulated and real-world backbone traffic from different datasets. Furthermore, I will show the comparison results between the proposed S-transform-based method and a popular Wavelet transform-based anomaly detection method in terms of accuracy and false positive rates. At the end of this part, I will show how to improve the detection performance of the S-transform-based method using sketch technique. The performance evaluation of the improved method with real-world backbone traffic is also presented in the presentation.
(2) For the Renyi divergence-based method, in summary, the method observes the port pair distribution of traffic and detects anomalies based on the R\'enyi divergence of the port pair distributions. The port pair distribution is a new statistical feature firstly proposed by us. In this presentation, I will present the details of the feature and method. In addition, I will show the performance comparison results between the proposed feature and four widely-used traffic features, namely the distributions of source IP, destination IP, source port, and destination port. I will also show the results obtained from comparing the performance between the proposed method and a Kullback-Leibler (KL) divergence-based anomaly detection method.
Lastly, I will discuss the limitations of both proposed methods and present the recommendations for future work.
Research on developing assistive technology exists, such as phones for the elderly with UIs that require prior knowledge and use experience. Latest research has introduced Ambient Assisted Living (AAL) concepts for users homes, for example using guidance projected in the environment. However, only a few empirical studies have attempted to define what type of projection-based UIs would be intuitive for the elderly and system design processes that would help in developing such AAL have not been researched thoroughly.
This work presents three design iterations and their empirical evaluations of which a body of knowledge is produced for designing and developing AALs with projected AR UIs. The first iteration has a sentence building UI implemented for a wearable Projector-Camera (ProCam) system, which had limits in technical suitability for the elderly. The second iteration changed the use metaphor to a simple icon-based menu, and produces a requirements guideline for UIs in AAL. In the final iteration the wearable was replaced with a fixed ProCam, allowing the elderly to make menu selections effectively and supports sequential tasks with visual guides such as taking medicine. Suitability of the new UI was compared between computer literate and elderly users, of whom many have memory and motor skill limitations. The comparison showed that the two groups are similar, but the elderly need a slower and direct UI. Assistance for the sequential tasks was found feasible. This work produced a set of
UI-related and technical factors that AAL designers have to take into account when developing suitable projector-based AR systems for the elderly with memory problems.
In addition, suggestions on how to conduct UI testing sessions with this user group can reduce amount of work and improve correctness of iterative development processes.