独居老人のための癒し型知的介護システム

金沢大学理工研究域電子情報通信学系本研究で明らかにしたことは、以下の機能の実現である。これらの機能を申請者がこれまで開発してきた独居老人介護システムの機能として追加する。1.犯罪/災害を判定するシステムに装備予定の推論システムのスピードを向上させた。2.マルチプルアライメントを応用して、独居老人の生活環境から、未知の機能や状態を予測するために、マルチプルアライメントの最適解探索手法を開発した。3.ペットロボットを用いた感情認識システムを開発した。4.独居老人の行動パターンを分類するために、環境の変化によって生じる複雑な形をした雪の結晶に対応ずけて、類似パターンを生成する手法を開発した。5.独居老人の行動パターンを遺伝的アルゴリズムを用いて予測するために、多様性の維持が必要となり、そのために改善集団と改悪集団による進化的停滞を回避する手法を開発した。6.癒し設備実現のための感情認識システムを開発した。7.独居老人の近い未来の行動予測のためのPSO(粒子群最適化)の改良を試みた。8.超音波センサを用いた浴槽内の異常検知システムを開発した。9.独居老人宅でのセンサエージェントを用いた空気汚染方向診断システムを開発した。10.温度センサと荷重センサを用いた就寝時健康モニタリングシステムを開発した。11.歩行特性に基づく老人歩行支援システムを開発した。12.感覚概念を導入したニオイセンサによる独居老人環境認識システムを開発した。13.マルチエージェントを用いた店舗内老人行動シミュレータを開発した。14.マルチエージェントとニオイセンサを用いて室内空気汚染方向の推定を可能にした。15.風速の変化に伴って植物の生体電位応答が変化することを実証した。16.ゲシュタルト心理学に基づく抽象図形の群化領域の認識方法を開発した。In this research, we executed the foundation of research of the Intelligent Care System of Healing Type for the Aged. The results are as follows.1. State prediction for bather using ultrasonic sensors2. Automatic Generation of MetaData for Semantic Web3. The Straight Walk Support System for Visually Impaired with Walk Characteristics4. New Evaluation for Risk Quantification Needed5. Discovery of Knowledge for Treatment Support of School Phobia6. The Study of an Individual Authentication using Pet Robot7. A Study on Bidding Agent with Price Expectatio for Internet Auction8. DSS for Strategic Arrangement of Goods Shelves in Supermarket9. GENDER DETERMINATION OF PEDESTRIAN USING AREA SCANNER10. A Study on Environment Recognition by Oder Sensor with SenseConcept11. A Study on Recognition of Environment using Sensor-agent with Human Sense12. A Speedup Algorithm for Repetition of Hypothetical Reasoning13. GENDER DETERMINATION OF PEDESTRIAN USING AREA SCANNER14. A Study on a Guardian Distinction System15. A Study on Web Page Search Support System Using Web Annotatio16. Research on Diagnosis Support System of School Phobia17. Prediction Model about Risk Degree for Companies18. Portfolio Selection Using Particle Swarm Optimization19. Identifying System for Cusromer\u27s Gender Using Infrared Area Scanners20. Information Hiding for Hardcopy by Using Cutting Processed Characters and Morse Code.研究課題/領域番号:17500106, 研究期間(年度):2005 – 2006出典：研究課題「独居老人のための癒し型知的介護システム」課題番号17500106（KAKEN：科学研究費助成事業データベース（国立情報学研究所）） （https://kaken.nii.ac.jp/ja/report/KAKENHI-PROJECT-17500106/175001062006kenkyu_seika_hokoku_gaiyo/）を加工して作

Parallel and Distributed Statistical-based Extraction of Relevant Multiwords from Large Corpora

The amount of information available through the Internet has been showing a significant growth in the last decade. The information can result from various sources such as scientific experiments resulting from particle acceleration, recording the flight data of a commercial aircraft, or sets of documents from a given domain such as medical articles, news headlines from a newspaper, or social networks contents. Due to the volume of data that must be analyzed, it is necessary to endow the search engines with new tools that allow the user to obtain the desired information in a timely and accurate manner. One approach is the annotation of documents with their relevant expressions. The extraction of relevant expressions from natural language text documents can be accomplished by the use of semantic, syntactic, or statistical techniques. Although the latter tend to be not so accurate, they have the advantage of being independent of the language. This investigation was performed in the context of LocalMaxs, which is a statistical method, thus language-independent, capable of extracting relevant expressions from natural language corpora. However, due to the large volume of data involved, the sequential implementations of the above techniques have severe limitations both in terms of execution time and memory space. In this thesis we propose a distributed architecture and strategies for parallel implementations of statistical-based extraction of relevant expressions from large corpora. A methodology was developed for modeling and evaluating those strategies based on empirical and theoretical approaches to estimate the statistical distribution of n-grams in natural language corpora. These approaches were applied to guide the design and evaluation of the behavior of LocalMaxs parallel and distributed implementations on cluster and cloud computing platforms. The implementation alternatives were compared regarding their precision and recall, and their performance metrics, namely, execution time, parallel speedup and sizeup. The performance results indicate almost linear speedup and sizeup for the range of large corpora sizes

Can biological quantum networks solve NP-hard problems?

There is a widespread view that the human brain is so complex that it cannot be efficiently simulated by universal Turing machines. During the last decades the question has therefore been raised whether we need to consider quantum effects to explain the imagined cognitive power of a conscious mind. This paper presents a personal view of several fields of philosophy and computational neurobiology in an attempt to suggest a realistic picture of how the brain might work as a basis for perception, consciousness and cognition. The purpose is to be able to identify and evaluate instances where quantum effects might play a significant role in cognitive processes. Not surprisingly, the conclusion is that quantum-enhanced cognition and intelligence are very unlikely to be found in biological brains. Quantum effects may certainly influence the functionality of various components and signalling pathways at the molecular level in the brain network, like ion ports, synapses, sensors, and enzymes. This might evidently influence the functionality of some nodes and perhaps even the overall intelligence of the brain network, but hardly give it any dramatically enhanced functionality. So, the conclusion is that biological quantum networks can only approximately solve small instances of NP-hard problems. On the other hand, artificial intelligence and machine learning implemented in complex dynamical systems based on genuine quantum networks can certainly be expected to show enhanced performance and quantum advantage compared with classical networks. Nevertheless, even quantum networks can only be expected to efficiently solve NP-hard problems approximately. In the end it is a question of precision - Nature is approximate.Comment: 38 page

Action Sequencing Using Visual Permutations

Humans can easily reason about the sequence of high level actions needed to complete tasks, but it is particularly difficult to instil this ability in robots trained from relatively few examples. This work considers the task of neural action sequencing conditioned on a single reference visual state. This task is extremely challenging as it is not only subject to the significant combinatorial complexity that arises from large action sets, but also requires a model that can perform some form of symbol grounding, mapping high dimensional input data to actions, while reasoning about action relationships. This paper takes a permutation perspective and argues that action sequencing benefits from the ability to reason about both permutations and ordering concepts. Empirical analysis shows that neural models trained with latent permutations outperform standard neural architectures in constrained action sequencing tasks. Results also show that action sequencing using visual permutations is an effective mechanism to initialise and speed up traditional planning techniques and successfully scales to far greater action set sizes than models considered previously.Comment: This paper has been accepted for publication at IEEE RA-

Optimal Deterministic Massively Parallel Connectivity on Forests

We show fast deterministic algorithms for fundamental problems on forests in the challenging low-space regime of the well-known Massive Parallel Computation (MPC) model. A recent breakthrough result by Coy and Czumaj [STOC'22] shows that, in this setting, it is possible to deterministically identify connected components on graphs in $O(\log D + \log\log n)$ rounds, where $D$ is the diameter of the graph and $n$ the number of nodes. The authors left open a major question: is it possible to get rid of the additive $\log\log n$ factor and deterministically identify connected components in a runtime that is completely independent of $n$? We answer the above question in the affirmative in the case of forests. We give an algorithm that identifies connected components in $O(\log D)$ deterministic rounds. The total memory required is $O(n+m)$ words, where $m$ is the number of edges in the input graph, which is optimal as it is only enough to store the input graph. We complement our upper bound results by showing that $\Omega(\log D)$ time is necessary even for component-unstable algorithms, conditioned on the widely believed 1 vs. 2 cycles conjecture. Our techniques also yield a deterministic forest-rooting algorithm with the same runtime and memory bounds. Furthermore, we consider Locally Checkable Labeling problems (LCLs), whose solution can be verified by checking the $O(1)$-radius neighborhood of each node. We show that any LCL problem on forests can be solved in $O(\log D)$ rounds with a canonical deterministic algorithm, improving over the $O(\log n)$ runtime of Brandt, Latypov and Uitto [DISC'21]. We also show that there is no algorithm that solves all LCL problems on trees asymptotically faster.Comment: ACM-SIAM Symposium on Discrete Algorithms (SODA) 202