Miracle-dz/UniFAR-FTUs huggingface dataset:
A soft dynamic service specific trust management and authentication scheme for mobile adhoc networks

Authentication and trust management in an adhoc network is a challenging task. Cryptography and other hard approaches do not provide a quantitative measurement of trust. Also, trust assignment to a new node having no history of any previous transaction had not been concrete. Human society authenticates people dynamically. This paper proposes a new strategy involving two parameters alpha and beta, which authenticates a node in a mobile adhoc network drawing inspiration from the way authentication, is done in a human society. The alpha parameter gives the quantitative measurement of seeking trust, and the beta parameter gives the quantitative measurement of assigning trust. The dynamism of trust change in a cluster of nodes is also explained by introducing time credit point, service credit point and discredit point. The strategy proposes to use a downloadable active security component from the trust authentication authority, rather than any passive certificate or encryption based authentication. This paper also says how to choose next trust authority in case the primary authority fails
Memory analysis for H.264/AVC scalable extension decoder

— In this paper, a systematic analysis for memory usage in H.264/AVC scalable extension (SVC) decoder is presented. This paper analyzes the memory requirements with three different decoding flows, macroblock, row and frame based, to find out a best method which can achieve optimal trade-off between internal memory usage and external memory access. The analysis shows that the SVC decoding needs 88% to 110% extra memory bandwidth compared to single layer H.264 decoding due to inter-layer prediction disregarding of decoding flow. However, extra internal memory storage by inter-layer prediction varies a lot according to the flow. This analysis could provide as a foundation to design a SVC decoder for further step.
Identify the Cracks; That's Where the Light Slips In: The Narratives of Latina/o Bilingual Middle-Class Youth.

In this qualitative study, I examine the intersections of learner identity, power, and language through the experiences and insights of Latina/o 2nd-generation middleclass children who occupy a unique positionality between the discourses surrounding bilingual education. Through narrative inquiry, emerging bilingual middle-class students actualize nonbinary thinking, able to depict identity as an inherently multifaceted process of construction. Their ways of knowing and experiences as language learners ultimately shape an outsider-within space, rupturing traditional binaries within bilingual education, namely EO/EL (English only versus English learner) and class binaries. They also proffer queer and cyber identities as additional salient variables that plow into language identity. In the end, these learners frame the contradiction and nuance of language learner identity, not as one of struggle, but as one of differential agency, the ability to move in and out of contradictory identities as both strong and advantageous tactics.
Gender in information technology: review of a mentoring initiative

There is, generally, a lack of women in the science, engineering and information technology research fields at all career levels. This is a worldwide phenomenon that seems related to social and cultural stereotypes. The Gender and Science ITC-irst group has investigated those actions that could be proposed to support the female researchers inside ITC-irst in spreading out effective role models and in improving their scientific skills. A first cycle of scientific lectures was organized in the winter 2002-2003. At the same time, a series of mentoring workshops was proposed with the main objective of increasing the experience of ITC-irst female researchers and to grow the self-esteem for all participants. Given the positive results of the first initiatives, the Gender and Science ITC-irst group organized a second series of scientific lectures and mentoring in winter 2003-2004. All the activities were organized whilst paying particular attention to gender issues. This paper describes the second series of activities, and their evaluation.
Scalable architecture of tone classification function for tonal speech recognizer

Tone classification function is used for improving recognition accuracy in tonal speech recognizer (TONE-SPEC). Although average magnitude difference function (AMDF) is generally used to find pitch period of fundamental frequency, there are many frame-repeated processes. This paper proposes scalable architecture of tone classification function for tonal speech recognizer. In the proposed architecture, the number of frames is reduced using vowel-AMDF (V-AMDF). Moreover, there is no frame iteration because the architecture converts series computation of conventional tone classification function into parallel. The parallel computation is designed to be able to reduce or extend the number of frame. Our architecture is set and evaluated with 10 Thai words selected from TV remote control commands and the words having the same phoneme but different tones. The experimental results show that the time consuming of general AMDF and series V-AMDF are improved 85.2% and 72.7%, respectively.
Multi-sensor devices for UAV in both simulation platform and realistic tests

The use of simulation platforms for UAV (Unmanned Aerial Vehicle) can both increase the efficiency and safety for testing the control algorithms. The main contribution of this article is the development of a multi-sensor UAV simulation platform and the implementation of a multi-sensor device for realistic tests. The multi-sensor consists of one IMU (Inertial Measurement Unit), a monocular camera and one customized ultrasonic sensor. Experimental results show that these sensors help to improve the robustness and practicability of vision based UAV control algorithms both in simulation platform and realistic test.
Understanding software evolution using a combination of software visualization and software metrics

Coping with huge amounts of data is one of the major problems in the context of software evolution. Current approaches reduce this complexity by filtering out irrelevant infor- mation. In this paper we propose an approach based on a combination of software visualization and software metrics, as software visualization is apt for complexity reduction and metrics in- troduce the possibility to qualify evolution. We discuss a simple and effective way to visualize the evolution of software systems which helps to recover the evolution of object oriented soft- ware systems. In addition we define a vocabulary that qualifies some specific situations that occurs when considering system evolution. RESUME. Analyser un tres grand volume de donnees est un des problemes majeurs lors de la comprehension de l'evolution de logiciels. Les approches existantes reduisent cette complexite en filtrant les informations non pertinentes. Dans cet article nous proposons une approche ba- see sur la combinaison de metriques et de visualisation, la visualisation permettant une re- duction d'information et les metriques permettant une qualification de l'evolution. Ainsi nous presentons une matrice d'evolution : une visualisation simple et efficace qui aide a comprendre l'evolution des applications orientees objets. En plus, nous definissons un vocabulaire permet- tant de qualifier les situations caracteristiques rencontrees.
Fixed order ℋ∞ controller for quarter car active suspension system

This paper presents an LMI based fixed-order controller design for quarter car active suspension system in order to ensure ℋ∞ performance and the stability in the predefined D-Region. The simulation results and dynamic response of the real system show the effectiveness of the proposed method over improvements on ride comfort. The proposed low-order controller is compared with full-order one in simulation and application phases. The advantages of using low-order controller contrary to high-order one are discussed.
Remembering and Emphasizing the Distinction between Prevention andTreatment

As such, what is actually being demonstrated by many studies is not true ‘prevention’ but rather treatment effects. According to the definition laid out by Horowitz and Garber [2], a true prevention study should not be focused on reducing symptomatology. Rather, a true ‘prevention’ study should be geared toward slowing or stopping the development of pathology – with the comparative results being identifiable in a control group that demonstrates a worsening of symptoms. When a so-called “prevention” study shows that a control group exhibits improvement on its own, this is not a true “prevention” study because the data indicates that, even with no intervention, individuals will (as a whole) improve. To remain adherent to the notion of ‘prevention,’ an intervention must show that an intervention group either progresses slower or does not progress at all toward an endpoint of disorder status; otherwise, if the only thing being demonstrated is that a so-called prevention group demonstrates better improvements in symptomatology than a control group, this would be an intervention study.
Accelerating Path Tracing by Eye Path Reprojection

Recently, path tracing has gained interest for real-time global illumination since it allows to simulate an unbiased result of the rendering equation. However, path tracing is still too slow for real-time applications and shows noise when displayed at interactive frame rates. The radiance of a pixel is computed by tracing a path, starting from the eye and connecting each point on the path with the light source. While conventional path tracing uses the information of a path for a single pixel only, we demonstrate how to distribute the intermediate results along the path to other pixels in the image. We show that this reprojection of an eye path can be implemented efficiently on graphics hardware with only a small overhead. This results in an overall improvement of the whole image since the number of paths per pixel increases. The method is especially useful for many indirections, which is often circumvented to save computation time. Furthermore, instead of improving the quality of the rendering, our method is able to increase the rendering speed by reusing the reprojected paths instead of tracing new paths while maintaining the same quality. Our solution remains the correct solution of the rendering equation up to a user-defined discretization threshold. This is the author’s version of the paper. The ultimate version has been published in the GRVR 2011 conference pro-
Exploring intercultural competence through an intercultural extracurricular activity in Taiwan

Abstract This study used interviews to explore how 10 Taiwanese college students' intercultural competence was developed after participating in an intercultural extracurricular activity designed with a purpose to facilitate foreign students’ study in Taiwan and to promote meaningful home-foreign student contacts. In-depth information and narratives from the interviews were collected as evidence of intercultural competence development. The results showed all the interviewees appreciated the experience and felt it to have been of value to them in terms of building international friendships, heightening their intercultural awareness and the benefits of intercultural competence, and having more appreciation and better understanding of Taiwanese culture. Students also realized that a lack of understanding of Taiwanese culture could exacerbate communication difficulties. Students thus became aware of the importance of describing Taiwanese cultural practices in English and realized that more language practice was necessary for smoother communication. Suggestions for both extracurricular activities and language programs improvement in the future were also provided in the conclusion.
Pattern Formation and Dynamics in Nonequilibrium Systems

Preface 1. Introduction 2. Linear instability: basics 3. Linear instability: application to reacting and diffusing chemicals 4. Nonlinear states 5. Models 6. One-dimensional amplitude equation 7. Amplitude equations for two-dimensional patterns 8. Defects and fronts 9. Patterns far from threshold 10. Oscillatory patterns 11. Excitable media 12. Numerical methods Appendixes References Index.
A ROBUST SURFACE MACTHING TECHNIQUE FOR DEM INTEGRATION IN THE CONTEXT OF COASTAL GEOHAZARD MONITORING

With the increasing availability of a diverse range of datasets from sources such as airborne and terrestrial laser scanning, InSAR, and high resolution satellite remote sensing, there are improved opportunities for dataset integration and the synergistic benefits which this can offer. However, accurate registration is a fundamental pre-requisite for data fusion, and an issue which is often overlooked. This paper presents a strategy for improving the effectiveness of coastal geohazard monitoring through the integration of airborne and terrestrial laser scanning datasets. This approach is based upon a robust least squares surface matching technique, which enables the reliable reconciliation of disparate datasets, overcoming disparities between the input surfaces. The development of the matching algorithm, which incorporates a robust M-estimator function, is detailed. Application of this approach to a test site located on the east coast of England highlights the effectiveness of the robust matching algorithm for data integration, improving the quality of the resultant surface models, and facilitating subsequent analysis of change over a four month period. Robust surface matching is a flexible technique, which shows significant potential for a range of data fusion tasks, particularly where there is also a requirement for reliable change detection.
Policies Hierarchies for Distributed Systems Management

Distributed system management, involves monitoring the activity of a system, making management decisions and performing control actions to modify the behavior of the system. Most of the research on management has concentrated on management mechanisms related to network management or operating systems. However, in order to automate the management of very large distributed systems, it is necessary to be able to represent and manipulate management policy within the system. These objectives are typically set out in the form of general policies which require detailed interpretation by the system managers. The paper explores the refinement of general high-level policies into a number of more specific policies to form a policy hierarchy in which each policy in the hierarchy represents, to its maker, his plans to meet his objectives and, to its subject, the objectives which he must plan to meet. Management action policies are introduced, and the distinction between imperatival and authority policies is made. The relationship of hierarchies of imperatival policies to responsibility, and to authority policies, is discussed. An outline approach to the provision of automated support for the analysis of policy hierarchies is provided, by means of a more formal definition of policy hierarchy refinement relationships in Prolog. >
Recognition of Multi-Pose Head Gestures in Human Conversations

We address the problem of recognizing multi-pose head nodding and shaking gestures in human conversations. Existing methods mainly recognize head gestures in restricted environments like human robot interaction, where face poses are near frontal and head motions are not natural. However, in human conversations, faces of subjects might be in arbitrary poses while head gestures are often subtle. Since the face pose change and head gesture movement are of different scale, we propose to track the face of varied poses with a mixed-state particle filter and detect the subtle head movement by a Kanade-Lucas-Tomasi tracker. The motion patterns in both horizontal and vertical directions are detected and then head gestures are analyzed by a Finite State Machine. Experiments on natural human conversations demonstrated the effectiveness of our method.
Representational Blending in Human Conditional Learning: Implications for Associative Theory

In two experiments, participants were presented with pictures of different foods (A, B, C, D, X,) and learned which combinations resulted in an allergic reaction in a fictitious patient, MrX. In Problem 1, when Aor B (but not Cor D) was combined with food Xan allergic reaction occurred, and when C or D(but not A or B) was combined with Y an allergic reaction occurred. In Experiment 1, participants also received Problem 2 in which A, B, C, and Dinteracted with foods V and Weither in the same way as X and Y, respectively, or in a different way. Participants performed more proficiently in the former than in the latter condition. In Experiment 2, after training on Problem 1, participants judged whether or not novel combinations of foods (e.g., AB, CD, AD, CB) would cause an allergic reaction in MrX. They were no more likely to indicate that ABor CD would cause an allergic reaction than AD or CB, but made their judgements more rapidly and with greater confidence on AB and CDtrials than on ADand CB trials. These results (1) indicate that shared representations come to be addressed by the components of similar compounds (e.g., AX and BX) that have predicted the same outcome (an allergic reaction), and (2) are inconsistent with standard, associative theories of learning, but (3) are consistent with findings from nonhuman animals and with a connectionist interpretation of these findings.
The Relationship Between Problematic Internet Use, Alexithymia, Dissociative Experiences And Self-Esteem In University Students

The main purpose of this study is to determine the time-dependent learning difficulty of "solving problems that require making four operations with natural numbers" of the sixth grade students. The study, adopting the scanning model, consisted of a total of 140 students, including 69 female and 71 male students at the sixth grade. Data was collected using an assessment tool consisting of 12 open-ended questions. The findings show that the learning group consisting of 140 students was behind the value that is closest to the full learning level by a score of 0.011. While the female students reached the lower limit of 0.989 specified for the full learning level in a period of 2.55 course hours, the male students reached this limit in 2.87 course hours. The learning amount of 0.999, which is the closest value to the full learning level, was reached by the learning group in a period of 6.1 course hours, the female students in 5.65 course hours, and the male students in 6.71 course hours. In addition to this, the data obtained showed that learning difficulties belonging to the learning groups decreased as the space below the curve of time and learning amount decreased. As a result of the study, it was recommended that it is possible to determine the closest course hours for the full learning level for each of the gains included in all levels of education and all teaching programs.
Pipelines, Templates and Transformations: XML for Natural Language Generation

The paper discusses a number of ways in which XML can be used in natural language generation, including XML-based pipeline architectures, template-based generation with XSL templates, and tree-totree transformations. The ideas are based on practical experience in building an experimental XMLbased generation component for a spoken dialogue system. Prototype implementations using DOM, XSL and Translets are briefly compared.
Profile characteristics of fake Twitter accounts

In online social networks, the audience size commanded by an organization or an individual is a critical measure of that entity’s popularity and this measure has important economic and/or political implications. Such efforts to measure popularity of users or exploit knowledge about their audience are complicated by the presence of fake profiles on these networks. In this study, analysis of 62 million publicly available Twitter user profiles was conducted and a strategy to identify automatically generated fake profiles was established. Using a combination of a pattern-matching algorithm on screen-names and an analysis of update times, a reasonable number (∼0.1% of total users) of highly reliable fake user accounts were identified. Analysis of profile creation times and URLs of these fake accounts revealed their distinct behavior relative to a ground truth data set. The characteristics of friends and followers of users in the two data sets further revealed the very different nature of the two groups. The ratio of number of followers-to-friends for ground truth users was ∼1, consistent with past observations, while the fake profiles had a median ratio ∼30, indicating that the fake users we identified were primarily focused on gathering friends. An analysis of the temporal evolution of accounts over 2 years showed that the friends-to-followers ratio increased over time for fake profiles while they decreased for ground truth users. Our results, thus, suggest that a profile-based approach can be used for identifying a core set of fake online social network users in a time-efficient manner.
Visual simultaneous localization and mapping using stereo vision with human body elimination for service robotics

Generally speaking, vision based simultaneous localization and mapping (V-SLAM) systems have to avoid obstacles especially for the people moving around in the environment because it will cause temporary landmarks captured by camera, and thus will increase computational loading and estimative error in the V-SLAM system. However, in some situations, we cannot always evacuate people when the robot is collecting data. In this paper, we propose a V-SLAM system which consists of human body elimination and verifying that the human body filter can decrease estimative error to the system. We employ FAST machine learning approach to perform corner detection which is faster than most of the other feature detection methods with similar quality for detecting the location of landmarks. We al so implement “landmark buffer” to find robust landmarks in a frame. We also detect where the human body appears in a frame and eliminate the landmark features extracted from human body. Through landmark buffer and human body elimination, our approach derives accuracy estimation of the robot pose. This proposed approach has been successfully demonstrated through experimental verification and the results are summarized in the conclusion.
Optimizing Pixel Predictors Based on Self-Similarities for Reversible Data Hiding

This paper presents a clustering and optimizing pixel prediction method for reversible data hiding, which exploits self-similarities and group structural information of image patches. Pixel predictors plays an important role for current prediction-error expansion (PEE) based reversible data hiding schemes. Instead of using a fixed or a content-adaptive predictor for each pixel independently, we first employ pixel clustering according to the structural similarities of image patches, and then for all the pixels assigned to each cluster, an optimized pixel predictor is estimated from the group context. Experimental results demonstrate that the proposed method outperforms state-of-art counterparts such as the simple rhombus neighborhood, the median edge detector, and the gradient-adjusted predictor et al.