Reviewerly/exHarmony huggingface dataset:
Teaching Materials Robot that Seamlessly Supports from Individual Learning to Collaborative Learning

We have been developing a programming teaching-aid robot with intuitive motion instruction set using an embedded microcomputer and using it for introduction of programming in universities and national institute of technology. In school classrooms, students not only create programs individually but also make joint movements of robots. In this research, we made it possible to easily combine robots individually programmed by converting robot into IoT. This system seamlessly supports individual learning and cooperative learning.
THE IMPLEMENTATION OF SEMANTIC MAPPING TECHNIQUE IN THE TEACHING OF READING COMPREHENSION

This study aimed to describe the students’ involvement to the implementation of semantic mapping in reading activities and to describe the students’ responses to the implementation of semantic mapping technique in reading activities. This research used a qualitative case study conducted at MTs Maarif Sukorejo - Pasuruan. It involved one class of the eighth grade which consists of 32 students. The data were collected through observation, document interpretation, and interview. The data from the three instruments were analyzed qualitatively. The results of observation and documentation showed that the implementation of semantic mapping could actively involve the students’ reading activities dealing with pre-, whilst-, and post-reading activities at VIII A Grade of MTs. Maarif Sukorejo – Pasuruan. In addition, the results of interview indicated that all students had positive responses to reading activities and the implementation of semantic mapping in the learning of reading comprehension. Dealing with the results of documentation mainly in the field notes, most of students were interested in the implementation of semantic mapping technique such as: vocabulary brainstorming activities, reading aloud, finding detailed information, finding main idea, making a summary and answering question through semantic mapping.
Towards long-distance quantum communication using trapped ions and frequency qubits

Quantum communication requires long-lived memories and robust communication qubits. Long-lived memories and high fidelity two qubit gates are demonstrated using atomic qubits. Frequency qubits are shown to be robust against noises present in long distance communications. In this project we are working towards using trapped <sup>171</sup>Yb+ ions as memory qubits and frequency encoded photonic qubits as communication qubits to realise long distance quantum communication protocols.
CRITICAL HEAT FLUX PREDICTION USING DEEP LEARNING NEURAL NETWORKS

The exact prediction of critical heat flux (CHF) is extremely important for the safety of nuclear reactors. This paper proposes a CHF prediction method based on deep learning neural network (DLNN). DLNN is an innovative method which combines the deep learning technique with the conventional artificial neural networks (ANNs). It reinforces advantage of the deep learning and ANNs while eliminating the shortcomings. The CHF dataset used in this paper are collected from the open published literature. The CHF dataset are divided into the training dataset and testing dataset to avoid the overfitting problem. The reliability of DLNN is validated by analyzing the effect of main parameters on CHF. The prediction results of DLNN are compared with those of BPNN in previous studies. The results show that DLNN gives a satisfactory accuracy and fits the experimental data better than a backpropagation neural network (BPNN).
Generating Collective Behavior of Robotic Swarm based on Deep Reinforcement Learning and Analysis of Controller

Swarm robotics is the field of study that aims to generate a collective behavior of a large number of robots. In this paper, we applied deep reinforcement learning to design a controller for a robotic swarm. Deep reinforcement learning is a combination of reinforcement learning with deep neural networks that contain convolution layers. With the help of convolution layers, robots are able to learn from the camera image as the input of a deep neural network. When using deep reinforcement learning, however, it is difficult to understand the mechanism of improving performance. This paper analyses the convolution layers of the controller to understand the learning mechanism of a robotic swarm.
PERFORMA JURNALIS TELEVISI PADA IMPLEMENTASI JURNALISME BENCANA DI MAKASSAR

The study examines the performance of TVOne Journalists Makassar Bureau in the case of floods in Makassar City in January 2019. Through the live report program, this research wants to see the process of flood coverage and the obstacles they face when flood coverage. This research alsowants to see the concept of disaster journalism implemented by TVOne biro Makassar journalists. This research uses descriptive qualitative research. The application of disaster journalism goes according to ethical standards and Standard Operating Procedures (SOPs) set by TVOne Makassar Makassar in cases of flood coverage. When covering floods, all crews, including journalists, reporters, program directors, and program producers worked in teams to carry out flood coverage in several districts in South Sulawesi, starting from installing SNG (Satellite News Gathering), installing cameras, andcollecting information or disaster data. Constraints occur when the live report reporting process takes place in this South Sulawesi flood disaster, the access road to the location of flood coverage and landslides was cut off and the network at that time experienced, while the location of the coverage wason a hill located in Jeneponto. As a result, the tvOne crew had to go back and forth from the coverage location several times to SNG, to send the results of the landslide coverage within the specified time period quickly, in order to catch up to the program deadline.
Path Planning of a Mobile Robot in Dynamic Environment

An autonomous mobile robot in a dynamic environment needs to avoid static obstacles and also needs to perform path planning while considering dynamic obstacles. In this paper, a path planning method is proposed for avoiding encounters with moving objects by incorporating an abundance map that represents the possibility of existence of previously observed moving objects. The proposed method can generate a path that avoids areas with high human traffic, which reduces the probability of obstacle encounters and path re-planning. Results of the evaluation experiment indicate that compared to conventional method, the proposed method can generate a shorter path in a dynamic environment having high human traffic.
A Fundamental Study of Local Analysis Model for Deforming Biomaterial with a Lump

This research aims at the development of fast calculation and accuracy of numerical analysis for biomaterial deformation of maxillofacial palpation using local area analysis method. The target of this paper is the non-uniform stiffness biomaterial deformation like as cervical lymph node cancer. In particular, this paper mainly studies the effect of the size of local hemisphere analytical region on the deformation and stress distribution. We use the commercial FEM software ANSYS16.1 to study it. The local hemisphere analytical model is consisted of fat material (Young’s modulus, E=1.0×103Pa) with 35, 40, 45 and 50mm in diameters, which the sphere lump 10mm in diameters (Young’s modulus, E=1.0×104Pa) exists at the center of the model. The nonlinear deformation analysis under pressing 300Pa on the circular surface 10mm in diameters at the center. As the results, it concludes that the appropriate local hemisphere size is from 40mm to 45mm in diameters.
[Preprint] ObjectMap: Detecting Insecure Object Deserialization

In recent years there is a surge of serialization-based vulnerabilities in web applications which have led to serious incidents, exposing private data of millions of individuals. Although there have been some efforts in addressing this problem, there is still no unified solution that is able to detect implementation-agnostic vulnerabilities. We aim to fill this gap by proposing ObjectMap, an extendable tool for the detection of deserialization and object injection vulnerabilities in Java and PHP based web applications. Furthermore, we also introduce the first deserialization test environment which can be used to test deserialization vulnerability detection tools and for educational purposes. Both of these tools are easily extendable and the first to implement this combination of features to the best of our knowledge and they bring together a synthesis of cross-complementing functionalities that are able to ignite further research in the field and help in the development of more feature-rich solutions.
Fundamental Investigation on distinguishing a non-target person and a target person at person tracking

When tracking a person using a laser range finder (LRF), our previous method could not separate multiple persons when a non-target person overlapped the target person. The proposed method detects the boundary of two or more persons by checking whether the detected object is convex or not. Then, if concave points are detected, that part is determined as the boundary of two persons. From the experimental result, we confirmed that the proposed method robustly separated the target person even when a non-target person walked by the target person.
ENGLISH KEREN REK (EKR) WEB BASE GAME APPLICATION MODEL TO ENRICH X GRADE STUDENTS’ ENGLISH GRAMMAR ACHIEVEMENT

This research was aimed at establishing, describing the implementation, and finding out the influence of the English Keren Rek (EKR) Web Base Game Application Model. This research applied Qualitative and Quantitative approches with Research and Development Design by using adapted Sugiyono’s Model. This research consisted of three phases, which were first: foundational research to identify potencies and problems; second: design and development research; and third: experimental research. The valid final model contained of opening, home display, games display, learning materials, and evaluation. The validation score of overall model components was 77.75% which meant very propper. The tests validity and reliability values were (0.999) by alpa Corn-batch and (0.999) by using split half technique. The one-group pre and post test experimental research showed that before using the model, the average students’ achievement was (51) and after using the model it reached (85.05) by which the students gained (34.05) which meant 60% increase of the mean score. Having the value of t-test with tcount -35.556 &lt; ttabel 2.03011 by Significant value of 0.05, this showed that the model positively influenced the students’ grammar achievement. The implementation turned out to be interesting and motivating.
Landing and path planning using three dimentional terrain for a unmanned ground vehicle transported by an unmanned aerial vehicle

When a volcano erupts, it is too dangerous for humans to observe the volcano from close to it. Therefore, a UAV-UGV robotic observation system was developed by the authors’ research group. In this system, UGV (unmanned ground vehicle) is transported to a landing point by UAV (unmanned aerial vehicle), and the UGV moves to observation points by remote operation. The effectiveness of the system was confirmed in actual field environments. However, during such remote operations, the UGV may turn over or get stuck because a suitable landing place and path for the UGV have not planned. To solve the problem, in this research, we propose a landing point planning and path planning for UGV using three-dimensional terrains to prevent UGV’s turning over or getting stuck during its mission. As a result of landing point planning with real environment data, the landing point was flat enough and no vegetation. Also, as the result of path planning, the path was in the travelable area, and the distance to the obstacle was enough for safe traversal. Therefore, by the proposed method, safe landing and moving of UGV is realized without overturning or getting stuck.
Improvement of Similarity Coefficients Based on Item Rating and Item Genre

Item-based collaborative filtering recommendation system has been widely used in many fields, which generates recommendations based on similarity between items. However, the conventional similarity calculation may produce inaccurate results because of data sparsity. To alleviate this problem, this paper proposes a new method of similarity calculation based on item rating and genre. Firstly, similarity calculation based on item rating are proposed, which reduces similarity between items with fewer co-rating users. Genre information is an inherent attribute of an item which could not be changed by user behavior. It reflects the common characteristics among items, then item similarity based on the item's dependency on genre are calculated. Finally, a trade-off between rating and genre similarity are proposed to calaulate the similarity between items. Experimental results show that the proposed method can alleviate the issue of inaccurate similarity caused by sparse data and improve the recommendation quality.
Person detection from a part of the body using a 3D LIDAR

In recent years, development of mobile service robots has been actively conducted. It is necessary for such a robot to recognize persons reliably. This paper describes a person detection using 3D LIDAR for person tracking with a small robot. Since our LIDAR has a vertical viewing angle of 30 degrees and is put at a low viewpoint, the whole body of a person is not observable if the distance to the person is less than 1.5 m. By adding data of partially observed persons for training, we improved the person detection rate. We evaluated the precision and the recall for the discriminator.
A Study on Shadow Painting Automatic Generation Algorithm in Shadow Generating Robot DeSCaRo

We have developed the Deformable Shadow Casting Robot (DeSCaRo) that articulated robot with 39 degrees of freedom. The purpose of this robot is to perform the shadow art. The shadow art is one of the fields of art attracting attention from people in recent years. Thus, DeSCaRo gives us a shadow art and a surprise by that. However, there is one problem. It is difficult to make shadow art, because extensive calculations and precise calculations are necessary. Therefore, in this study, we developed an algorithm to automatically generate the shadow arts.
Client-Side Revisitation Support with Contextual and Dynamic Suggestions for the Speed Dial's Thumbnail

Recurrent behavior is common in web navigation and users find many problems in re-accessing the useful resources and relevant content. A variety of approaches are being introduced to support in re-finding through various features. Among all, back and forward buttons are specific to the current session, while history and browser search is generic to all the links visited throughout. Contextual tabs need a keyword to suggest options of possible pages. Furthermore, implicit bookmarks need less human intervention and empower revisitation through intelligent automatic techniques. Popular implicit bookmarks in browsers are New-Tab speed dials. These thumbnails are few in number, less attractive, usually not ranked as per user interests. They don't provide true support for the fluctuating activity of a user at different times of the day. This paper represents some issues and changeless in existing methods. Moreover, this paper proposed a novel model for making these New-Tab speed dials thumbnails more useful to use. We design a new ranking formula for the thumbnails including temporal and visual context to the layout. Results show that the proposed model improves user satisfaction, relevance to the resources for revisitation.
Basic study on automatization of coating work using a brush mounted on a robot arm

This paper discusses coating work using a brush mounted on a robot arm to automatize the work by human beings. When position and orientation errors of an object to be coated occur, the robot has to control not only position but also contact force appropriately. By using Open Dynamics Engine (ODE) and OpenGL, we constructed a simulator that generates coating motion automatically.
Study on smooth work with robots using reinforcement learning

Modern robots are required to interact with humans and changing environments in the real-world as well as to perform different works in the factory. However, the environment changes unpredictably according to the time and actions taken by humans and robots, and humans make different decisions in various situations. To solve these problems with more adaptation and flexibility, this research focuses on reinforcement learning that learns appropriate behavior by trial and error with the environment. In addition, we hypothesize that robots could adapt to various personality of humans. We could express various strategies by changing parameters in reinforcement learning as functions related to the human personality, where the neuroparameters, actions and rewards in the learning strategy can be adapted and modified. We show that during even a simple cognitive learning task experiment, human personality affects the result, and human improved their learning result in a human-robot collaborative learning task.
A RULE-BASED APPROACH FOR PLANNING COUNTER-ACTIONS WITH MULTILEVEL FLOW MODELING

Response planning is one of the most important cognitive tasks when operators deal with anomalies in complex process systems such as nuclear power plants. Operators can face great challenges when no existing procedure can address the current plant situations, as referred to the knowledge-based decision making. It is crucial to develop a support system to aid operators in the decision making of planning. The support system is expected to be capable to identify not only essential resources but also executable solutions. Multilevel flow modeling (MFM) is a functional modeling methodology that can describe planning resources in multiple abstraction levels of systems’ intentional knowledge in perspective of functions and goals. In this paper, a rule-based approach for planning counter-actions particularly operating procedures with MFM is proposed. In order to implement the model-based planning procedure, new rules for identifying control actions and their conditions are defined in addition to the existing causal reasoning rules for MFM. Action solutions, i.e. operating procedures to counter the abnormal situation can be identified from a tree structure including all desired states and required actions that are inferred from a prescribed goal by using different categories of reasoning rules.
Development of a Reduction Gear Built-in Type Multi-Motor Power Unit Consist of different Characteristic Motors

We have developed many types of Multi-Motor Power Unit (MMPU) that drives an output shaft by using power of multiple motors. In this study, we suggest a new concept of MMPU that consists of different characteristic motors. Also we integrate a reduction gear into a suggested MMPU. The reduction gear is dual ratio reduction, 1st step of reduction gear consists of a star-type planetary gear, and 2nd step of reduction gear consists of mechanical paradox gear mechanism.We developped the reduction gear built-in type MMPU that have two types of motor and applied it for competitiive robot of reactor abolishment technology. In this report, we had suggested imporvement of reduction gear to increse maximum output toruqe of MMPU.
TRENDNERT: A Benchmark for Trend and Downtrend Detection in a Scientific Domain

Computational analysis and modeling of the evolution of trends is an important area of research in Natural Language Processing (NLP) because of its socio-economic impact. However, no large publicly available benchmark for trend detection currently exists, making a comparative evaluation of methods impossible. We remedy this situation by publishing the benchmark TRENDNERT, consisting of a set of gold trends and downtrends and document labels that is available as an unrestricted download, and a large underlying document collection that can also be obtained for free. We propose Mean Average Precision (MAP) as an evaluation measure for trend detection and apply this measure in an investigation of several baselines.
Tags Re-ranking Using Multi-level Features in Automatic Image Annotation

Automatic image annotation is a process in which computer systems automatically assign the textual tags related with visual content to a query image. In most cases, inappropriate tags generated by the users as well as the images without any tags among the challenges available in this field have a negative effect on the query's result. In this paper, a new method is presented for automatic image annotation with the aim at improving the obtained tags, as well as reducing the effect of unrelated tags. In the proposed method, first, the initial tags are determined by extracting the low-level features of the image and using neighbor voting method. Afterwards, each initial tag is assigned by a degree based on the neighbor image features of the query image. Finally, they will be ranked based on summing the degrees of each tag and the best tags will be selected by removing the unrelated tags. The experiments conducted on the proposed method using the NUS-WIDE dataset and the commonly used evaluation metrics demonstrate the effectiveness of the proposed system compared to the previous works.
The Contact Force Control using UAV based on Sliding Mode Control

Contact force control of a UAV allows for achieving aerial tasks that require contact motions with objects. Force control systems for a multi-rotor UAV whose tilt angle is not variable have been achieved. Although invariable tilt angle structure is simple, the UAVs can not change their thrust direction quickly. Hence, to improve performance, a multi-rotor UAV whose tilt angle is variable (tilt-rotor UAV) have been proposed. However, this research has not guaranteed its stability. Therefore, in this paper, force and position control system for tilt-rotor UAV is designed by sliding mode control, and evaluates its stability using Lyapunov stability analysis. Lastly an operation confirmation of this method is done by simulation.
Adaptive Control considering the dynamics of the UAV with a passive rotating spherical shell

Our research team is developing a UAV with a passive rotating spherical shell whose purpose of inspecting bridge. The UAV with a passive rotating spherical shell can avoid crashes caused by contact with some objects. This UAV have been successful in inspecting under some bridges. But for now, there is no optimal controller that can be applied to this UAV. This is because of the existence of the passive rotating spherical shell. So, the dynamics of this UAV is different from general UAVs. In this study, we propose a new dynamic model and controllers for a UAV with a passive rotating spherical shell. The new dynamic model is considering the moment of inertia of the spherical shell. Because the moment of inertia is unknown, the new controller is used adaptive control via backstepping to estimate it. We conducted an experiment using simulation and confirm that a controller could be used for actual usage.