For realizing efficient CAE process, dimension-driven deformation of the FEM mesh models is required. In this paper, we propose a dimension-driven deformation of tetrahedral mesh models of mechanical parts. At first, in order to extract dimensions of the mesh models, a new surface segmentation method of tetrahedral mesh models is proposed. In our segmentation method, planar, cylindrical, conical, spherical, and torus surfaces are sequentially extracted by region-growing based on principal directions, normal vectors, and surface fittings. Secondly, a dimension-driven mesh deformation method in which vertices of the mesh models are moved using a space embedding method and the surface information obtained from the segmentation results is proposed. Our mesh deformation method can change parameters of form features of the mesh models such as radius of the fillet, angle of the chamfer, and so on. Finally, Phased Optimal Delaunay Triangulation (ODT) smoothing which improves element shape qualities from the boundary to the inside of the mesh model is extended. The extended Phased ODT smoothing can improve the surface mesh of the conical, spherical, and torus surface using local 2D parameter spaces.

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An effective method for virtualized network function forwarding graph (VNF-FG) placing based on graph attention network (GAT) and reinforcement learning (RL) is proposed for complex services and dynamic network conditions. We formulate a VNF-FG placing optimization problem, and designed a GAT-based RL agent to recognize graph structure and obtain placing policy. Experiments prove the method effectiveness.

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Feature visualization is of great significances in volume visualization, and feature extraction has been becoming extremely popular in feature visualization. While precise definition of features is usually absent which makes the extraction difficult. This paper employs probability density function (PDF) as statistical property, and proposes a statistical property guided approach to extract features for volume data. Basing on feature matching, it combines simple liner iterative cluster (SLIC) with Gaussian mixture model (GMM), and could do extraction without accurate feature definition. Further, GMM is paired with a normality test to reduce time cost and storage requirement. We demonstrate its applicability and superiority by successfully applying it on homogeneous and non-homogeneous features.

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In recent years, as the number of visually impaired people has increased, various walking assistance systems have been developed. One of the most important functions is to create a route to the destination and guide the user. However, visually impaired people are not always able to turn right or left or correct their path as instructed because they have difficulty in understanding their surroundings. Therefore, in this study, we considered it important to give instructions to the user at the appropriate timing and collected three types of data: the set route, his/her own trajectory, and the timing of giving instructions, with the aim of ensuring that the visually impaired person can walk on the proposed route. Using these data and a recurrent neural network (RNN), we developed a system that provides automatic voice guidance at the appropriate timing. The results of the verification showed that the system provided good straight-ahead guidance, but there is still room for improvement in the right/left turn guidance.

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In this paper, we propose an estimation of reader's emotion towards news articles using Twitter, and apply this method to the access log analysis of a news website. The proposed estimation method is divided into 3 steps: 1) The system collects tweets about news articles from Twitter. 2) The system tags each tweet with an emotion. 3) The system estimates the reader's emotion towards the news articles using tweets obtained in the second step. The system can tag an article with one or more of 11 emotions (anger, dislike, excitement, fear, like, joy, relief, sadness, shame, surprise, nothing). The experimental result shows the effectiveness of this method using Twitter for estimating the reader's emotion. For access log analysis based on emotion, we collected the access log of a self-produced news website. The system tags each collected news article with emotions using the proposed method. We analyzed the access log by using Hidden Markov model (HMM). As the result of access log analysis, we obtained some interesting findings such as that the users tend to read the article which causes excitement or surprise at the start.

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In this paper, a domain integral method to evaluate the J-integral along with a singular patch method for three-dimensional linear elastic fracture mechanics analyses using Isogeometric analysis (IGA) are presented. The analyses are carried out using a singular patch method that can realize the square-root singularity in the stress distribution in the vicinity of the crack front. The sizes of IGA elements adjacent to the crack front are generally much larger than those of finite elements in the crack analysis. The proposed domain integral method enables a pointwise evaluation of the J-integral at a point on the crack front curve. To achieve it, integral domain with a very small thickness is set at the crack front. To examine the accuracy, the proposed methods are applied to the problems of semi-elliptical crack problems with high aspect ratios. Stress intensity factors are computed by the J-integral method. The proposed methods are found to evaluate the stress intensity factors accurately.

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The material behavior intrinsically possesses the aleatory uncertainty (i.e., the natural variability). Against uncertainty in a given data set of elastic material responses, this paper presents a data-driven approach to reliability-based truss topology optimization under the compliance constraint. We utilize the order statistics to guarantee the confidence level of the probability that the reliability on the compliance constraint is no smaller than the target reliability, and formulate the truss optimization problem in a bi-level optimization form. By using the duality of linear optimization, we recast this bi-level optimization problem as a single-level optimization problem, which can be solved with a standard nonlinear optimization approach. Numerical examples illustrate the validity, as well as the characteristic of optimal solutions, of the proposed method.

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    There has been extensive debate about the principle of compositionality among philosophers of language and formal semanticists, according to which the meaning of a complex expression is determined by the meanings of its parts and the way these parts are combined. Questions about this principle can be broadly divided into three categories: why we want compositionality, what the proper definition of compositionality should be, and how various semantic phenomena can be analyzed compositionally. In this paper, I will focus on the first question. After briefly surveying well-known arguments for compositionality, I will present two methodological arguments and examine their validity.

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In this research, we proposed a data cleansing method to support the management and utilization of access logs in the operation of websites. We focused on detecting and removing bots hidden in access logs. The method we propose discovers the hidden features of bots by extracting access logs using multiple methods. We propose a new method that combines the hidden features of bots with rule-based methods and traditional anomaly detection methods. The proposed method can automate the same degree of accuracy as manual bot discrimination. In addition, the proposed method improves the prediction accuracy of recommender systems.

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Deep neural networks (DNNs) have achieved significant success in the field of automatic speech recognition. One main advantage of DNNs is automatic feature extraction without human intervention. However, adaptation under limited available data remains a major challenge for DNN-based systems because of their enormous free parameters. In this paper, we propose a filterbank-incorporated DNN that incorporates a filterbank layer that presents the filter shape/center frequency and a DNN-based acoustic model. The filterbank layer and the following networks of the proposed model are trained jointly by exploiting the advantages of the hierarchical feature extraction, while most systems use pre-defined mel-scale filterbank features as input acoustic features to DNNs. Filters in the filterbank layer are parameterized to represent speaker characteristics while minimizing a number of parameters. The optimization of one type of parameters corresponds to the Vocal Tract Length Normalization (VTLN), and another type corresponds to feature-space Maximum Linear Likelihood Regression (fMLLR) and feature-space Discriminative Linear Regression (fDLR). Since the filterbank layer consists of just a few parameters, it is advantageous in adaptation under limited available data. In the experiment, filterbank-incorporated DNNs showed effectiveness in speaker/gender adaptations under limited adaptation data. Experimental results on CSJ task demonstrate that the adaptation of proposed model showed 5.8% word error reduction ratio with 10 utterances against the un-adapted model.

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本研究では，弾塑性材料の破壊挙動の現象論的理解に基づく新たな低減破壊エネルギーを導入したphase-field延性損傷モデルを提案する．提案モデルの自由エネルギーは弾性・塑性・損傷の3成分の和として構成され，支配方程式は連続体熱力学理論に基づくローカル・グローバルエネルギー最適化問題の停留条件として導出される．その際，塑性変形及び負の静水圧によって起こされる微小欠陥やそれらの結合を反映した低減破壊エネルギーを導入する．このおかげで，提案モデルは材料剛性および破壊エネルギーの低減を実現し，弾塑性材料の破壊挙動をシミュレート可能となる．2つの数値計算例を通して，典型的な延性損傷を再現し，提案モデルの性能を例証する．

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Transportation infrastructures, as both enablers and victims of social movements, often become major political arenas of cities. In this article, we unravel the understudied role that social movements play in the formation of grassroots transport initiatives. We illustrate this role by synthesizing how grassroots efforts have created, diversified, and networked social innovations in urban transport systems in the 2019 Anti-Extradition Law Amendment Bill Movement in Hong Kong. We document the timeline of the movement and related solidarity activities, which led to various grassroots transport initiatives. We identify three lines of research that appear to be decisively informative to transport literature: a) emergency experiences, community capacities and mobility resilience, b) grassroots engagement in transport disruption management and c) community-oriented governance for a participatory smart city. These issues require particular attention in future research to guide transport policy on grassroots initiatives toward sustainable transport systems.

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The classical application of rate and state dependent frictional constitutive laws has involved the instabilities developed between two sliding surfaces. In such a situation, the behaviour and evolution of asperities is the controlling mechanism of velocity weakening. However, most faults have a substantial thickness and it would appear that it is the bulk behaviour of the fault gouge, at whatever scale, that is important. The purpose of this paper is to explore how bulk frictional sliding behaviour may be described. We explore here the consequences of applying the rate and state framework initially developed to describe the frictional behaviour at the interface between two interacting sliding blocks, to frictional behaviour within a layer of gouge that has bulk elasticplastic constitutive behaviour. The approach taken here is to replace the relative sliding velocity in the classical formulation with the maximum shear strain rate, D, and the characteristic length with a characteristic shear strain, γ_c. This means that the frictional behaviour of the bulk material now evolves with shear strain rate, D, over a characteristic shear strain, γ_c. This approach still does not address the problem of reproducing natural recurrence times between instabilities, but perhaps places the problem in a new framework.

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Weblogs have greatly changed the communication ways of mankind. Affective analysis of blog posts is found valuable for many applications such as text-to-speech synthesis or computer-assisted recommendation. Traditional emotion recognition in text based on single-label classification can not satisfy higher requirements of affective computing. In this paper, the automatic identification of sentence emotion in weblogs is modeled as a multi-label text categorization task. Experiments are carried out on 12273 blog sentences from the Chinese emotion corpus Ren_CECps with 8-dimension emotion annotation. An ensemble algorithm RAKEL is used to recognize dominant emotions from the writer's perspective. Our emotion feature using detailed intensity representation for word emotions outperforms the other main features such as the word frequency feature and the traditional lexicon-based feature. In order to deal with relatively complex sentences, we integrate grammatical characteristics of punctuations, disjunctive connectives, modification relations and negation into features. It achieves 13.51% and 12.49% increases for Micro-averaged F₁ and Macro-averaged F₁ respectively compared to the traditional lexicon-based feature. Result shows that multiple-dimension emotion representation with grammatical features can efficiently classify sentence emotion in a multi-label problem.

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AI技術の発展・高度化により，社会インフラ維持管理分野へのAI応用に関する研究が活発に進められている．これらの研究の多くは，維持管理業務等で得られる画像の性質を考慮した学習理論の構築に注目しており，ひび割れ検出や変状の種類分類，劣化レベル推定等，様々なタスクにおいて，AIの有効性が示されている．一方，真に業務効率化を実現するAIを構築するためには，実務に必要な機能を備えたAIを構築する必要がある．さらに，構築したAIを高度化し，継続的に活用するためには，実務においてAI構築に適した画像を取得する必要がある．そこで，本稿では，維持管理業務等で得られる画像を対象に構築されてきた学習理論，実務に必要な機能を備えたAI構築に関する従来研究および画像の取得方法に関する著者らの考えについて紹介する．

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We developed and implemented an instructional method and teaching materials aimed at sprouting a programming mindset for 4th grade students who have not yet been exposed to programming education. The teaching material developed was a model of an automatic door. One of these models was distributed to each student, and the students were asked to think of a program to operate the automatic door through free trials. In a survey of children’s awareness before and after the educational practice, the mean values of all items tended to be higher in the survey. The effect size of the interest value was small to medium, and the effect size of the use value and thought intention was medium to large. These results indicate that the instructional methods and teaching materials developed in this study are effective for the sprouting of computational thinking.

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This study examines the legal framework for wiretapping in Indonesia, focusing on the inherent conflicts between law enforcement demands and individual privacy rights. The aim is to analyze existing laws, such as the CPC and ITE laws, in the context of international human rights norms, with a focus on the right to privacy. The study, which uses a normative legal investigation methodology, compares the interception laws of various other jurisdictions and examines primary and secondary legal publications in depth. The results show significant legal ambiguity and inconsistent enforcement of wiretapping laws, highlighting the lack of coherent regulatory frameworks. As current oversight procedures often lack accountability and transparency, law enforcement officers are more likely to abuse their power. The study provides comprehensive recommendations for legal reform to address these issues, arguing for strict judicial oversight, open reporting procedures and clearly defined limitations on wiretapping. According to the study, Indonesia's wiretapping laws must be in line with international human rights standards to prevent unjustified violations of people's right to privacy in the name of public safety. With a view to developing a fairer and more balanced interception framework in Indonesia, this study complements the ongoing debate on privacy rights and law enforcement in the legal context, highlighting the links between the problems identified and the solutions that work.

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Cognitive Diagnostic Models (CDMs) have been developed for a few decades to reveal students' knowledge status. The purpose of this paper is to provide a review of recent advances in CDMs, and to figure out the trend in its model development. In the paper, we first introduce the fundamental concept of CDMs, and then summarize broad range of modern CDMs by classifying them into compensatory, noncompensatory, and integrated models as well as other emerging approaches. Finally, we discuss the future orientation of CDM studies with focus on the empirical applications.

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