Reviewerly/exHarmony huggingface dataset:
사회복지전공생들의 현장실습경험이 진로자기효능감에 미치는 영향 연구

This study examines on college student's career path which becomes important issue everyday and reviewing career self-efficacy as for attending a vocational school students who are completed a field trip and seeking a job. Primarily it evaluate the impact of career decision-making by selecting the independent variables affecting the career self-efficacy in career decision-making type of each variable into career self-efficacy, career stress, relationships career decision-making type with Supervisor. the career self-efficacy carried out between the purpose of the present study investigated whether the effect of regulation. The results of this study were as follows. First, the study observes the rational and the intuitive type of career decision. the career decision dependent types and each type will have an impact on career self-efficacy 'research hypothesis that supported. Second, the 'career stress will have the effect of regulating the type and intuitively rational career decision. the career-dependent type and the type of career decision-making self-efficacy between' hypothesis that supported. Third, the between the supervisor will have the effect of regulating the type and intuitively rational career decision. the career decision making and career self-efficacy between the types of research hypothesis that was supported. Fourth, the relationship between the supervisor will have the effect of regulating the type-dependent careers and career decision-making self-efficacy between 'did not support that hypothesis.
Hierarchical ship detection method for space‐borne SAR image

Ship detection from space-borne SAR image is a challenging task. The main challenges in this task include the disturbance of sea clutter, island, coastline, and variability in ship sizes. Here, the authors propose a novel hierarchical ship detection framework to overcome the above challenges. Initially, the edge-enhanced maximally stable extremal regions (E-MSER) is employed as ship candidates. Then, through some simple shape analysis, they can eliminate the obvious false alarms which are not conformed to the shape feature of ship. Finally, candidates are represented by an effective multiple features learning framework for discriminating true ship targets. Experimental results on numerous space-borne SAR images demonstrate their proposed scheme that outperforms other traditional methods with higher detection accuracy and lower false alarm.
Identification of stereo rig alignment error based on vertical disparity ma

Poor quality of 3D video content can lead to headache, blurry vision and overall exhaustive experience for the viewer.To ensure quality and comfortable 3D experience for the end consumer, common production errors must be detected and corrected.Vertical disparity is one of these distortions and is caused by improper stereo-camera setup.This paper aims at identifying the possible rotational and placement errors that cause the vertical disparity.An estimation of these errors is necessary to produce good quality 3D content.According to my knowledge, there exists no method to identify rig alignment errors without the knowledge of camera setup parameters and this work is the first step in that direction.Feature point detection has proven to be an interesting approach to find vertical disparity present in the given stereo image pair.In this work feature extraction techniques such as SIFT, SURF and Harris features are efficiently used to compute reliable and robust vertical disparity patterns.This paper classifies vertical disparity patterns according to rig errors.If the vertical disparity values are too small or ambiguous to be identified by pattern analysis, this paper uses graphical analysis that highlights the relationship between the total vertical disparity and the contribution of each possible error to the total.Experimental results show that the proposed approach identifies the reason behind the presence of vertical disparity of a stereo image pair.
H.761 Support of a New concept Element and a New "recognition" Node-Event to Enable Deep Learning-based Analyses for Media-Nodes

Machine Learning field, methods based on Deep Learning (e.g. CNN, RNN) becomes the state-of-the-art in several problems of the multimedia domain, especially in audio-visual tasks. Typically, the training of Deep Learning Methods is done in a supervised manner, and it is trained on datasets containing thousands/millions of media examples and several related concepts/classes. During training, the Deep Learning Methods learn a hierarchy of filters that are applied to input data to classify/recognize the media content. In computer vision scenario, for example, given image pixels, the series of layers of the network can learn to extract visual features from it, the shallow layers can extract lower-level features (e.g. edges, corner, contours), while the deeper combine these features to produce higher-level features (e.g. textures, part of objects). These representative features can be clustered into groups, each one representing a specific concept. H.761 NCL currently lacks support for Deep Learning Methods inside their application specification. Because those languages still focus on presentations tasks such as capture, streaming, and presentation. They do not consider programmers to describe the semantic understanding of the used media and handle recognition of such under-standing. In this proposal, we aim at extending NCL to provide such support. More precisely, our proposal able NCL application support: (1) describe learning-based on structured multimedia datasets; (2) recognize content semantics of the media elements in presentation time. To achieve such goals, we propose, an extension that includes: (a) the new "knowledge" element describe concepts based on multimedia datasets; (b) "area" anchor with an associated "recognition" event that describes when a concept occurrences in multimedia content.
Visualize medical information using VISTouch technology

In this study, we applied a novel system “VISTouch” to visualize medical imaging information. We enhanced the system to enable multiple devices to be used in combination dynamically and spatially. When a mobile device (parent) is placed horizontally, it displays an image that can be viewed from above. When another mobile device (child) is placed on this display, relative positions between them are dynamically acquired. The child device can then displays images viewed from its contact position, direction, and angle in real time. By using this VISTouch system, we achieved the visualization of medical images from arbitrary cross sections and arbitrary viewpoints in real time. It was also possible to indirectly view different cross sections as well as a general overview. Moreover, we attempted to visualize information from various angles on the actual scale of the human body using the installation artwork “CUBISTA,” which was built using VISTouch Air technology. From these implementations, we confirmed the potential of VISTouch technology to visualize medical imaging information.
Simulation of Grasping Attitude Control of Robotic Arm Based on Synovial Error Feedback

In order to improve the attitude stabilisation of robotic arms during grasping, a method of grasping attitude control of the robotic arm based on synovial error feedback is proposed. Based on it, the attitude parameter information of the robotic arm is measured and fused by using multi-sensor fusion information acquisition method to construct a constraint parameter model and control objective function for grasping attitude control of the robotic arm. The experimental results show that the proposed method has high control precision in the attitude control of the robot arm, and can accurately track the position information of the object to be captured in real time, realise zero error capture, and the parameter tracking performance is good, which improves the grip on the robot arm. Taking the control quality of the attitude, the overall has an application advantage.
Spoke Bracket with Single Arm

Spoke Bracket with Single Arm. The arm is 2-3/8 in outside diameter. Item #: PTB100 - LightMart.com
Informatic Foundations of Training Theory

The Training Theory or General Didactics has historically evolved in the pedagogy asserted in the modern and postmodern era, as a sub-element of the General Theory of Education (which integrates the Foundations of Pedagogy and Education Theory, approached in a narrow sense, referring only to "the sides of education").In the early modern age, its foundations were ethical and psychological.The ethical foundations aimed at developing the goals of "education theory", predominantly related to intellectual education.The psychological foundations aimed at developing the methodology of education that includes the forms of organizing training.In modern and advanced modern (postmodern, contemporary), the general didactics, asserted under the formula of the "theory and methodology of the training", is founded philosophical (epistemological), psychological, sociological and political, anthropological and cultural.The multitude of perspectives calls for a special analysis model of computer-based training activities.We consider: a) the valorisation of the fundamental concepts of informatics (Data, Information, Algorithms, Graphs, Database, Networks, Feedback) in defining and analyzing the object of study, normativeness and methodology of research-specific to the training theory education / general didactics; b) their use in the validation of the pedagogical language involved and perfected in a general ideal-model (the curriculum model of the training activity), but also in the applied models (see the curriculum model of the lesson, managerial approached).On their basis we can consolidate the specific pedagogical language of the training theory, structured at the level of a "disciplinary matrix" or a "strong epistemic nucleus".
Shoot motion analysis in basketball formation practice

We have developed a video analysis method of shoot motion in basketball formation of practice. Our method can estimate accurately the position and movement of players from practice videos, and judge whether the player's shoot has succeeded. And by combining these, it is possible to estimate the shoot timing of players. Finally, it is possible to calculate the shoot probability by each positions. We evaluated the proposed method and also verified its usefulness. By this method, basketball formation practice and shoot practice can be performed efficiently at the same time.
Novel Ultrasound Texture Based Similarity Metric Using Autoregressive Modelling

Designing an ultrasound (US) specific similarity metric is essential in integrating advanced techniques like image segmentation and registration to US based interventional procedures. Applying conventional similarity metrics to ultrasound images is hampered by intrinsic noise patterns in an US image. In this work, we propose a texture based similarity metric (TexSimAR) using Autoregressive (AR) modelling. The key idea is to treat an US image as data resulting from a dynamical process which can be parametrically modelled. Using this approach it is possible to compute a parametric spectrum of individual US images and subsequently use it to estimate a similarity value between them. For evaluation, we used thyroid US images and similarity values were calculated between thyroid and non-thyroid regions. A cost function was designed to compare TexSimAR with other conventional similarity metrics. TexSimAR clearly distinguished between thyroid and non-thyroid regions outperfoming the conventional similarity metrics.
A Comparative Study on the Legislation about Anonymization or De-identification for Personal Information in Big Data Environment

제4차 산업혁명 시대에 신기술 및 신산업 육성을 위해서는 빅데이터 활용이 필수적인 동시에 개인정보의 보호는 함께 달성해야 하는 중요한 과제이다. 익명화 또는 비식별화 조치는 빅데이터의 자유로운 활용을 위한 기반을 제공하는 동시에 개인정보 보호를 강화하는 도구가 될 수 있다. 이는 개인정보의 개념 체계의 정립과 밀접한 관련성이 있는 것으로서, 제4차 산업혁명 시대의 출발을 좌우하는 중요한 논의 사항이다. 빅데이터는 수집 당시에 해당 정보가 개인을 식별할 수 없는 정보이거나 이미 익명화 또는 비식별화된 정보일지라도 다양한 정보와의 결합을 통해 그 상관관계를 밝히고 추론함으로써 개인정보를 생성할 가능성이 매우 높다. 따라서 아무리 뛰어난 익명화 또는 비식별화 기술을 가지고 있어도 특정 개인을 재식별하여 그 정보를 이용함으로써 정보주체의 개인정보 및 사생활을 침해할 위험성이 있다. 이러한 재식별 위험을 감소시키기 위해서는 구체적 기준을 정하여 재식별 위험성 판단을 해야 한다. 또한 정보보유 기간이 장기화될수록 개인 식별위험성이 증대되기 때문에 개인정보보호법상 개인정보의 종류에 따라 보유기간을 명시하는 것이 필요하다. 그리고 가이드라인상에 머물러 있던 비식별 조치와 관련한 일반적 내용을 법률에 규정함으로써 법적 근거를 마련하고 비식별 조치의 법적 구속력을 확보해야 한다.In the era of the fourth industrial revolution, the use of big data is essential for fostering new technologies and new industries, while the protection of personal information is an important task that must be achieved together. Anonymization or de-identification measures provide a basis for the free use of big data, while at the same time enhancing the protection of personal information. This is closely related to the establishment of the conceptual framework of personal information, and it is an important discussion that determines the start of the era of the Fourth Industrial Revolution. Anonymized information or de-identified information can be identified through the combination with different kinds of information in big data environment. So, there is a chance that they might infringe people’s personal information and privacy. We need to judge the danger of re-identification with specific standards. As the danger of re-identification tends to increase due to the long retention period for personal information, we need to stipulate the retention period in Personal Information Protection Act according to the types of personal information. In addition, de-identification measures should be legally binding by stipulating general provisions about de-identification.
Applying Machine Learning Techniques in Older People Activity Recognition usingWearable and Mobile Devices

We investigate the potential of the combined use of smartwatch accelerometer data and smartphone apps for online older adultsactivity recognition.We selected machine learning algorithms which resulted in a posteriori recognition accuracy of 98.92%. Our smartphone app, with the selected machine learning algorithms, carried out online recognition from data captured on the smartwatch. These results allow us, as future work, assess the accuracy of online recognition when the system is used by older adults.
Document recommendation based on interests of co-authors for brain science

Personalized knowledge recommendation is an effective measure to provide individual information services in the field of brain science. It is essential that a complete understanding of authors’ interests and accurate recommendation are carried out to achieve this goal. In this paper, a collaborative recommendation method based on co-authorship is proposed to make. In our approach, analysis of collaborators’ interests and the calculation of collaborative value are used for recommendations. Finally, the experiments using real documents associated with brain science are given and provide supports for collaborative document recommendation in the field of brain science.
WhatsApp as A Learning Media to Support Student’s Reading Comprehension Skill

Science and technology have developed quickly in the world.It affects all aspects in various fields of live, including learning activities.The development of technology is not followed by student's learning skills.Student's reading comprehension skill is poor.Internet-based technology is only used to play game by students, so that time becomes.Now, the learning process can be done anytime and through various media, one of which is WhatsApp.As a digital technology-based social media, WhatsApp can be used to support learning activities.The purpose of this study is to observe the role WhatsApp as a learning media to support student's reading comprehension skill.The type of data in this study is qualitative.The data sources of this study are document and informant.Purposive sampling is considered more effective in various realities.In this study, to take the data used questionnaires arranged in a closed manner, while for data validity used data triangulation technique.This study is different from other studies because it specifically observes about the use internet-based social media applications to support student's reading comprehension skill.In general, students who learn by using WhatsApp as a learning media, have high motivation and have better reading comprehension skill.
Deep Learning Search by Social Image Re-ranking

The quantity of pictures related with pitifully administered client gave tags has expanded drastically lately.Client gave tags are insufficient, abstract what's more, uproarious.In proposed framework, we centre on issue of social picture understanding, that is, tag task, picture recovery and tag refinement.Unique in relation to past work, framework propose a novel weakly supervised deep matrix factorization algorithm, in which reveals the dormant picture portrayals and tag portrayals installed in the inert subspace by cooperatively investigating the feebly directed tagging data, the visual structure, and the semantic structure.The semantic and visual structures are mutually fused to take in a semantic subspace without over-fitting the uproarious, deficient, or abstract tags.Additionally, to expel the loud or repetitive visual highlights, an inadequate model is forced on the change grid of the first layer in the profound design.Broad examinations on true social picture databases are led on the assignments of picture understanding: picture tag refinement, task, and recovery.Empowering results are accomplished, which shows the adequacy of the proposed strategy.
PEMODELAN ARUS TEROBOSAN PADA TRANSISTOR EFEK MEDAN TEROBOSAN BILAYER ARMCHAIR GRAPHENE NANORIBBON DENGAN METODE PENDEKATAN FUNGSI AIRY

Metal oxide semiconductor field effect transistors (MOSFETs) has an important role in electric circuits with its nanoscale sizes. But with its small size, MOSFETs have performance limitation to work in ultra-low data, leaky currents, short channel effects, and increased speed. This performance limitation makes the tunneling field effect transistor (TFET) an alternative to replace the MOSFET. In this study, a drain current on devices based on bilayer armchair graphene nanoribbon (BAGNR) was modeled using the Airy function approach. Schrodinger equation solved with the Airy function approach produces a transmittance value, from this transmittance the drain current value can be determined from the Landauer formula with the help of the Gauss Legendre Quadrature (GLQ) method. The results of the modeling show that the drain current value increases with increasing gate voltage (Vg), drain voltage (Vd) and width of BAGNR while the increase in oxide thickness and temperature makes the drain current decrease. The characteristics of the drain current against gate voltage (Vg) can determine the cut-off frequency of the device. The cut-off frequency increases with increasing drain voltage (Vd) while for increasing N index and oxide thickness the cut-off frequency decreases.
Automatic generation of 3D faces expressing different utterances by modeling individual variation of shape deformation from expressionless faces

In this study, we used a Kinect V2 RGB-Depth sensor to measure the three-dimensional shape of faces and calculated the information of the differences among several individuals. Based on Principle Component Analysis of the obtained information, we attempted to verify the possibility of generating various expressions from an inexpressive face of an arbitrary individual. Additionally, we attempted to improve the quality of generated utterance expressions.
DICTOGLOSS IN IMPROVING LISTENING COMPREHENSION

The objective of this classroom action research was to know whether or not listening comprehension of the Basic Listening class students of English Language Education Study Program, Faculty of Teacher Training, Mahasaraswati Denpasar University academic year 2016/2017 can be improved through dictogloss technique. The present research was carried out through dictogloss technique in two cycles; each cycle consisted of two sessions. The data were collected by administering the instruments to the subjects. The result of the post test in each cycle showed that there was significant improvement of the subjects’ comprehension in listening. The subjects also gave positive responses toward the implementation of dictogloss technique in teaching and learning process. To sum up, the present classroom action research showed that listening comprehension of the Basic Listening class students of English Language Education Study Program, Faculty of Teacher Training, Mahasaraswati Denpasar University academic year 2016/2017 can be improved through dictogloss technique.
Analisis Sentiment Tweets Berbahasa Sunda Menggunakan Naive Bayes Classifier dengan Seleksi Feature Chi Squared Statistic

At present the development of the use of social media in Indonesia is very rapid, in Indonesia there are a variety of regional languages, one of which is the Sundanese language, where some people especially those living in West Java use Sundanese language to express comments, opinions, suggestions, criticisms and others in social media. This information can be used as valuable data for individuals or organizations in decision making. The huge amount of data makes it impossible for humans to read and analyze it manually. Sentiment analysis is the process of classifying opinions, analyzing, understanding, evaluating, emotions and attitudes towards a particular entity such as individuals, organizations, products or services, topics, events, in order to obtain information. The purpose of this research is the Naїve Bayes Classifier (NBC) classification algorithm and Feature Chi Squared Statistics selection method can be used in Sundanese-language tweets sentiment analysis on Twitter social media into positive, negative and neutral categories. Chi Square Statistic feature test results can reduce irrelevant features in the Naïve Bayes Classifier classification process on Sundanese-language tweets with an accuracy of 78.48%.
Research on Assessment and Evaluation of Teacher in Police Colleges

The assessment of teacher is an important issue in colleges and university, especially for personnel management of university in China.The purpose of this study is to develop an assessment system of teacher for applied police undergraduate college.This paper first analyzed the problems existing in the teacher assessment, and then discussed the basic characteristics and components of the teacher assessment system by means of comprehensive assessment and dynamic analysis, finally established the teacher assessment index system.The research shows that the effectiveness of teacher assessment depends on the correct cognition of assessment content, the reasonable selection of assessment indicators, the credibility of assessment standards and the scientificity of assessment methods.
Collateral effects of the Kalman Filter on the Throughput of a Head-Tracker for Mobile Devices

We have developed an image-based head-tracker interface for mobile devices that uses the information of the front camera to detect and track the user's nose position and translate its movements into a pointing metaphor to the device.However, as already noted in the literature, the measurement errors of the motion tracking leads to a noticeable jittering of the perceived motion.To counterbalance this unpleasant and unwanted behavior, we have applied a Kalman filter to smooth the obtained positions.In this paper we focus on the effect that the use of a Kalman filter can have on the throughput of the interface.Throughput is the human performance measure proposed by the ISO 9241-411 for evaluating the efficiency and effectiveness of non-keyboard input devices.The softness and precision improvements that the Kalman filter infers in the tracking of the cursor are subjectively evident.However, its effects on the ISO's throughput have to be measured objectively to get an estimation of the benefits and drawbacks of applying a Kalman filter to a pointing device.