Reviewerly/exHarmony huggingface dataset:
Dinamika Kepribadian Tokoh Utama dalam Novel Hyouka Karya Yonezawa Honobu: Sebuah Kajian Psikologi Sastra dan Implementasinya sebagai Bahan Ajar di Sekolah

The purpose of this research is: (1) describing the structure of Hyouka's novel by Yonezawa Honobu, (2) describing the personality dynamics of the main character in Yonezawa's novel Hyouka by Analysis of the literary psychology approach, (3) describing the implementation of the personality dynamics of the main character of Yonezawa Honobu's novel in high School. This type of research is descriptive qualitative and the research strategy is embedded case study. Data in the form of words, phrases, sentences, paragraphs that contain the dynamics of the main character's personality in the novel Hyouka by Yonezawa Honobu. The data source in this study is the novel Hyouka by Yonezawa Honobu published by Haru Media in 2017, 244 pages thick. The data collection technique used is literature study. The data analysis technique used is the semiotic reading method, which is heuristic and hermeneutic reading. Data validation techniques by triangulating data sources. The results of this study are: (1) The structure of the novel Hyouka is shown with the theme of friendship in solving mystery and self-searching, the advanced Groove is2depicted in three-dimensional (physiological, psychological, and sociological) and has a backdrop (the town of Kamiyama, a fictional town based on a reference from the city of Takayama in Gifu Prefecture), (2) The dynamics of personality Oreki Hotaro encompassing instincts, anxieties, and ego defense Mechanisms, (3) The research can be implemented on the 3.11 and KD 4.11 XI class and in accordance with the criteria of learning which includes three aspects: linguistic, psychological, and cultural background.
LibGuides: Open Educational Resources: Digital Assets

A guide to Open Educational Resources, Creative Commons licenses, and UW-Platteville digital assets.. Other digital assets available at UW-Platteville.
Circle Tracking Control of Car-like Robots by Path-generating Regulator Using Vector field

Extensive labor is required for environmental survey. In order to support it, we have developed an autonomous vehicle and the control method called Path-generating Regulator (PGR). PGR makes the heading of the vehicle converge to the predefined orientation angle in the field. In this paper, we consider the following characteristics and deviations of the Lyapunov Vector Field(LVF),the Supercritical Hopf Bifurcation Field(SHBF),and the Tangent Vector Field(TVF) from simulation and experimental results.
Product Review Ranking in e-Commerce using Urgency Level Classification Approach

Review ranking is useful to give users a better experience. Review ranking studies commonly use upvote value, which does not represent urgency, and it causes problems in prediction. In contrast, manual labeling as wide as the upvote value range provides a high bias and inconsistency. The proposed solution is to use a classification approach to rank the review where the labels are ordinal urgency class. The experiment involved shallow learning models (Logistic Regression, Naïve Bayesian, Support Vector Machine, and Random Forest), and deep learning models (LSTM and CNN). In constructing a classification model, the problem is broken down into several binary classifications that predict tendencies of urgency depending on the separation of classes. The result shows that deep learning models outperform other models in classification dan ranking evaluation. In addition, the review data used tend to contain vocabulary of certain product domains, so further research is needed on data with more diverse vocabulary.
Autonomous mobile robots using imitation learning incorporating image classification model

Imitation learning has been widely used for autonomous mobile robots. Imitation learning can be performed with deep neural network. Deep neural networks have feature extraction layers that grasp the features of the input and identification layers that identify a category of the input. This feature extraction layers can be reused to other tasks by a method called fine tuning. Fine tuning is expected to reduce the required training data. Fine tuning is used for various tasks. However, it has not been studied which neural network architectures and pre-training tasks are suitable for autonomous mobile robots. In this paper, we compare widely used neural network architectures pre-trained on both object recognition and image reconstruction tasks. We conduced experiments to compare the performance on mobile robot tasks among learner policies with several architectures and pre-training methods in a simulation environment.
Domain Adaptation for Autonomous Moving Control by Imitation Learning

Imitation learning is one of the techniques for autonomous control in robotics. This technique enables robot to autonomous control by human demonstrations. However, in a different environment from demonstrations, test of autonomous control based on learning result is often fail because domain that distribution of dataset between demonstration and test is difference. In domain adaptation, this approach transfers knowledge of an environment with sufficient domain to an environment with insufficient domain and it enables autonomous control even in an environment with a small amount of demonstration. We verify the domain adaptation to autonomous moving control by imitation learning and clarify the problem.
Proposal of a drone equipped with an iris mechanism robot hand

In this paper, we propose a drone equipped with an iris mechanism robot hand. By using this robot hand as the end effector of the drone, it is able to reduce the change in the center of gravity when grabbing the target. Since it can be driven by a single actuator, it is possible to suppress an increase in weight. The system for operating the proposed drone will also be configured in indoor environments. high-precision position control by GPS used outdoors becomes severe. In this study, we assume the operation in the indoor environment, and estimate the position of the drone in the indoor environment by the motion capture technology, and aim at the position control.
Subject Matter Expert Input

Presentation at the webinar FAIR data: OCLS-LIBER Small Group Discussion 01.10.2020. This is the second of a series of small group discussions for the OCLC-LIBER Open Science Discussion Series. https://www.oclc.org/research/events/oclc-liber-open-science-series/100120-oclc-liber-fair-data.html.
Invariant Shape Descriptors for Handwritten Strokes

In this work, we present a novel approach to extract rotation, translation and scale invariant shape descriptors for handwritten strokes. Invariant descriptions of shapes are useful for shape matching and retrieval, where shapes can be randomly rotated, translated and/or scaled. Using a minimal set of segment types and novel corner-point descriptors, we describe an algorithm to extract invariant descriptions for any planar outlines. The suitability of the proposed descriptors in shape matching is shown through a number of experiments.
Development of Right Turn Method at Crossroads Based on Road Area Detection for Wheelchair Robot

This paper presents the development of a right turn method at crossroads based on road area detection by using deep learning for automatic running wheelchair robot. Road images captured by a monocular camera and used as an input image. The road and non-road areas of the images are detected by using a deep learning. The wheelchair robot turns to the right at a crossroad by changing its control methods based on presences of non-road areas in front and on left. Also, the performance of this method is confirmed by constructing a wheelchair robot system to perform control applying this method.
Topometric Navigation Considering Object Behavior in Human Living Environment

These days, the number of people who need mobile manipulator-type human support robots has been increasing. Although in conventional navigation, these robots move only in free areas where no object is placed, there are many objects in human living environments, making it difficult for them to move efficiently. Therefore, they are required to go to the goal while a movable object to clear the passage, by adding the movability of objects to the environment maps. In this study, we propose topological navigation that uses a topological map based on sensing information and representing a path that robots can pass, and a metric map based on geometrical information of objects and representing the presence of objects. In the proposed navigation method, robots can determine whether an object on the passage and can select the most efficient path that minimizes the move cost. We confirmed the validation of the proposed method by experiments on real robots.
Model Predictive Control in Latent Space

In recent years, dynamical systems handled by machine learning become remarkably larger and more complex, but they require intractable costs due to high-dimensional raw observation space. Variational autoencoder (VAE) is one of the promising approaches to the problems since it has a capability to learn dynamical systems in latent state space hidden in data, which can reconstruct system characteristics. Until now, however, VAE has been applied only to prediction and/or anomaly detection. This study, therefore, applies VAE to control application task in the latent space. Specifically, iterative Linear Quadratic Regulator (iLQR), which is one of the model predictive control methods, is integrated to VAE. In a benchmark problem, our method achieved the desired motions even using the latent space dynamics.
Cloud Computing: Empowering the Education and Global Learning

Over the past decades, the education has been never refurbished to be delivered on a platform like cloud. An application like a cloud, creates an illusion of a live classroom with the help of technologies like virtualization which creates an instance of a real component like a server, operating system, data, storage etc. Education industry has taken a leap towards advancement by adopting certain technologies in order to improve the quality of teaching and to enhance the ease of understanding of the concepts. Teaching now days is not only confined to the chalk and boards rather it has new trends like 3-D animations to make the things more crystal clear. Hence, empowers the students with the real-world scenarios and career ready skills. Technology has paved the way for this disruptive change. Traditionally, it was really hard to believe that teaching will ever be able to cross the geographical barriers. But the technological advancements it is now possible to reach to each and every student and a teacher. Moreover, technology have removed all the constraints in learning by providing ease of access and enhanced the learning more. But with the increased reach ability and ease of access to any resource, comes the problem of resource provisioning. One of the advancements that drive development for this need is distributed computing. Distributed computing is a mind-boggling innovation that makes ready for progressions with the assistance of virtualization.
Safe Imitation Learning based on Anomaly Detection with VAE

Behavioral cloning, which is one of the imitation learning methods, enables a robot to imitate an expert’s policy from the expert’s state and action demonstrations. In that case, the robot does not need to interact with environment, thereby preventing robot failure. However, in general, it is difficult to obtain expert action information. Although behavioral cloning from observation allows the robot to learn the policy without that, it requires a few interactions with the environment to infer expert action, which leaves the risk of robot failures. Detecting faced situations are safe or dangerous is an effective way to prevent such dangerous interactions. Suppose that the expert’s demonstrations only visited the safe states, this paper proposes a new outlier detector using variational autoencoder learned by the expert’s data. It can easily find unexperienced and dangerous scenes since all the data used for learning are mapped to limited space. The proposed method improved the policy performance in simulations with the limited number of robot failures.
Gait Teaching and Posture Control of a Miniature Biped Robot Considering Transition to a Slope

In this paper, we propose a control method to stabilize the movement from a flat ground to a slope by using a miniature bipedal robot with 10 degrees of freedom on the legs. First, a method for teaching movement like walking and standing on one leg. Next, we propose movement stabilization control. This control method stabilizes the posture when moving from a flat plane to a slope by using an inertial sensor built into the robot. Finally, experiments were performed to confirm the effectiveness of the proposed method.
Development of A Cold Feeling Presentation Unit Using Gap Flow of Air

We have developed a unit that presents a sense of coolness by the air flowing between the presentation unit and the body surface, which is generated when air is released from the presentation unit toward the body surface. We report the evaluation experiment of the perceived area by the developed cooling sensation presentation unit.
Crowdsourcing Data Collection and Automatic Annotation of Infant’s Behaviors

This paper describes a novel approach toward automatic annotation of infant’s behaviors following the collection of action descriptions in the crowdsourcing. Videos containing actions are publicly opened, and are manually annotated by internet users in the crowdsourcing platform. This results in a large training dataset of infant’s actions and their relavant descriptions. Relation between the actions and their descriptions are extracted by a probabilistic framework with two modules; semantic module and syntax module. The semantic module trains the association of the words from the action, and the syntax module trains the word sequence with grammatical consistency. This framework allows for automatic conversion of action observation to annotations. Our proposed approach was tested on actions performed by an infant and its validity was demonstrated.
Motion Planning with RGB image via Deep Imitation Learning

In this paper, we proposed learning-based motion planner for autonomous mobile robot which outputs discrete motion commands by referring from the onboard RGB image and target position without using map. It is difficult to get policy for navigation of mobile robots using only visual sensor. In recent years, research on visual navigation using deep neural networks has been conducted. However, these methods do not have a structure to store time-series information in a network. Therefore, this paper presents the DNN motion planner with LSTM trained by using expert trajectories. As a result, we show that our method can navigate agent in simulation environment without map.
Development of gripper for tableware handling

The purpose of this research is to develop a gripper with a surface structure that exerts a high frictional force on tableware’s even when their surface is wet with water, oil, detergent, etc. (wet state). Several textures for the finger surfaces were prepared by providing patterns of grooves and protrusions, to suppress the friction reduction even in the wet state, and then evaluated to see which pattern is appropriate for the finger surface.
Peculiarities of Professional Area of University Students in the Era of Digitalization of Education

In the digital era, the success of the professional development of future specialists in the process of obtaining higher education largely depends on their professional orientation (PN). Despite the significant interest in the subject of the study, the problem of identifying the factors that affect PN remains poorly understood.&#x0D; The purpose of the study is to determine the factors of the formation of PN of students enrolled in training and retraining programs, and the features of PN in the digital era.&#x0D; The article reveals the content and structure of PN of future specialists in the era of digitalization. According to the results of the empirical research, the relationship of students' PN with indicators of meaningful life orientations, motives for choosing a profession and training was revealed; the relationship between students' life-meaning orientations and indicators of motives for choosing a profession and training at the stage of digitalization of education. The factors influencing the personal condition and the factors of the formation of students' personal condition, who are trained according to the programs of training and retraining of specialists, have been determined.&#x0D; As a result of empirical research, it was concluded that negative influence on the formation of PN is created by factors caused by various life circumstances, lack of independence of decisions in choosing a profession, low reflection of life goals, future prospects, rigidity of volitional and personal qualities, internal conflict in the structure of personality relationships, low pleasure training and the like. At the same time, the level of PN is significantly higher among students who receive a second higher education and who understand its importance at the stage of digitalization of education.
Knowledge Base Completion for Constructing Problem-Oriented Medical Records

Both electronic health records and personal health records are typically organized by data type, with medical problems, medications, procedures, and laboratory results chronologically sorted in separate areas of the chart. As a result, it can be difficult to find all of the relevant information for answering a clinical question about a given medical problem. A promising alternative is to instead organize by problems, with related medications, procedures, and other pertinent information all grouped together. A recent effort by Buchanan (2017) manually defined, through expert consensus, 11 medical problems and the relevant labs and medications for each. We show how to use machine learning on electronic health records to instead automatically construct these problem-based groupings of relevant medications, procedures, and laboratory tests. We formulate the learning task as one of knowledge base completion, and annotate a dataset that expands the set of problems from 11 to 32. We develop a model architecture that exploits both pre-trained concept embeddings and usage data relating the concepts contained in a longitudinal dataset from a large health system. We evaluate our algorithms' ability to suggest relevant medications, procedures, and lab tests, and find that the approach provides feasible suggestions even for problems that are hidden during training. The dataset, along with code to reproduce our results, is available at this https URL.
Predicting observation results from a new viewpoint using deep learning for mobile robot viewpoint planning

In this paper, we describe a viewpoint planning method by predicting observation results using deep neural networks. We use two networks for viewpoint planning. The first network predicts the size of a newly observed area, while the second one predicts an updated map after observation. We develop a look-ahead viewpoint planning using these results. We compare the proposed method with a geometrical method and a method which uses the first neural network to consider only the current state. The proposed method is the most balanced method among them.
Geostatistical appraisal of the Upper Precambrian gold deposits of the Cachoeiras de Minas region, Paraíba, Brazil

The variogram modeling and point kriging of the gold assays obtained from the two quartz veins of the Upper Precambrian formations of the folded belt of the state of Paraiba in northeastern Brazil were performed. The directional variograms along the quartz veins show a well defined structure. Improvement of the models was achieved when the outliers were excluded from the analysis. The quartz vein which is relatively highly deformed due to faulting and shearing has erratic assay trend and thereby gives an higher error variance than the less deformed vein.
Position and Posture Estimation of Primitive Shapes Using Deep Learning-Based Object Extraction and Point Cloud-Based Face Extraction

In this paper, for robot vision as home assistance, a pose estimation method of the generic objects is proposed. The proposed method approximates genetic objects as primitive shapes using a deep learning technique. Furthermore, the surfaces are extracted from normals and curvatures which are calculated by using depth data. Finally, the positions and postures are estimated using pose of surfaces.
Fast and Robust 3D Measurement by Light Transport Matrix Estimation

3D Measurements for metallic objects or semi-transparent objects are required; however, they are still a hard problem in the robot vision field. We have developed a novel 3D measurement method that utilizes LTM sparse estimation and epipolar geometry constraint, which can measure these complex lighting objects accurately. In this presentation, we will show our results from the perspectives of computation cost and robustness. Na¨ıve LTM estimation takes a very long time; thus, we have developed computation cost and the number of projection and capturing reduction methods. We also developed a robust LTM sparse estimation method for non-linear projector-camera system behavior.
Mechatronics Education using Exploration Rovers and Lunar simulated Environment

There are various terms, design of mechanical engineering, mechanism, electronics, programing etc. on basic of mechatronics education. This study reports about the mechatronics educational project associated with engineers in Saitama and university students. This project has important keywords, “Lunar Exploration”, “robots” and “relationship with society”, and is designed using a lunar simulated environment and exploration rovers. First of all, project’s participants discuss a strategy to travel the lunar simulated environment and start designing special wheels for rovers. The contest using the lunar simulated environment and rovers with proposed wheels improved by each group is carried out at the end of this project. This report mentions the state of the contest and comments from participants.
Strictly incoherent operations for one-qubit systems

Strictly incoherent operations (SIO) proposed in Winter and Yang (2016) [13] are promising to be a good candidate of free operations in the resource theory of quantum coherence, setting against the central role of local operations and classical communication in the resource theory of quantum entanglement. An important open problem for SIOs is to find minimal parametrization with operational efficiency. Such a description plays key role for axiomatic study of resource theory of quantum coherence. We are aimed to give a structural characterization of bistochastic SIOs in terms of Pauli operators and the Phase operator for one-qubit systems. Some applications of our results are also sketched in reconstructing quantum thermal averages via a quantum computer and in coherence manipulation.