Reviewerly/exHarmony huggingface dataset:
Joint Feature Selection and Time Optimal Path Parametrization for High Speed Vision-Aided Navigation

We study a problem in vision-aided navigation in which an autonomous agent has to traverse a specified path in minimal time while ensuring extraction of a steady stream of visual percepts with low latency. Vision-aided robots extract motion estimates from the sequence of images of their on-board cameras by registering the change in bearing to landmarks in their environment. The computational burden of the latter procedure grows with the range of apparent motion undertaken by the projections of the landmarks, incurring a lag in pose estimates that should be minimized while navigating at high speeds. This paper addresses the problem of selecting a desired number of landmarks in the environment, together with the time parametrization of the path, to allow the agent execute it in minimal time while both (i) ensuring the computational burden of extracting motion estimates stays below a set threshold and (ii) respecting the actuation constraints of the agent. We provide two efficient approximation algorithms for addressing the aforementioned problem. Also, we show how it can be reduced to a mixed integer linear program for which there exist well-developed optimization packages. Ultimately, we illustrate the performance of our algorithms in experiments using a quadrotor.
A Unified Framework for Distance-Aware Domain Adaptation

Unsupervised domain adaptation has achieved significant results by leveraging knowledge from a source domain to learn a related but unlabeled target domain. Previous methods are insufficient to model domain discrepancy and class discrepancy, which may lead to misalignment and poor adaptation performance. To address this problem, in this paper, we propose a unified framework, called distance-aware domain adaptation, which is fully aware of both cross-domain distance and class-discriminative distance. In addition, second-order statistics distance and manifold alignment are also exploited to extract more information from data. In this manner, the generalization error of the target domain in classification problems can be reduced substantially. To validate the proposed method, we conducted experiments on five public datasets and an ablation study. The results demonstrate the good performance of our proposed method.
PENINGKATAN KETERAMPILAN BERBICARA DALAM MENANGGAPI PEMBACAAN CERPEN MELALUI MODEL PEMBELAJARAN KEPALA BERNOMOR STRUKTUR PADA PESERTA DIDIK DI MTsN 4 JOMBANG

Student learning outcomes are strongly influenced by the quality of learning carried out in madrasah. Onething that determines the quality of learning is the use of learning methods and models that are appropriateor in accordance with the material being taught. The results of learning speaking skills material in responseto short story reading at MTsN 4 Jombang are still very low. For this reason, the researcher wants to makechanges so that learning outcomes can be increased, namely by applying the structure numbered headlearning model. This research is a Classroom Action Research which is carried out in 2 cycles with each cycleincluding planning, implementing actions, observing activities, and reflecting. The subjects of this studywere students of class IX A MTsN 4 Jombang even semester 2018/2019 with a total of 34 students. Thedata analysis used in this research is descriptive qualitative analysis. Data collection techniques were carriedout through student activity observation sheets, achievement tests for students’ learning competencies,interviews, and questionnaires. The results of this study are [1] the group discussion method with thenumbered head model can actively involve all students in discussion activities, is effective enough to fosterdiscipline, interest, cooperation, activeness, and responsibility of students and is proven to be effective inovercoming students’ difficulties. in expressing opinions and giving responses in short story reading; [2]the group discussion method with the numbered head model can improve Indonesian learning outcomes inspeaking skills, especially in response to short story reading. This is evidenced by the increase in Indonesianlearning achievement, the material ability to respond to short story reading from the initial conditions withan average value of 70 with 20 % completeness to the final condition in the second cycle, an average valueof 85 with a completeness of 85.70 % in class IX A MTsN students. 4 Jombang Even semester of the 2018/2019 academic year.
Curvature Aware Motion Planning with Closed-Loop Rapidly-exploring Random Trees: Towards a generic motion planning algorithm

Last decades the autonomous driving research field has shown exponential growth. The social benefits, which include increased safety, mobility and productivity, are the main factor that drive this growth. One of the most difficult problems that vehicle engineers must solve to develop autonomous vehicles is the motion planning problem. They must solve the motion planning problem for environments ranging from unstructured to structured, such as parallel parking up to high-speed highway driving. Current literature presents many implementations that solve either the structured or unstructured planning environment or a small range of environments. Yet, the generic implementation of a single motion planning method, that can plan in the full range of environments, is still an open question. The aim of this thesis is to address the identified gap in the literature, by realizing a real-time implementation of a single motion planning method, that shows human-like and safe driving behavior, and can deal with any environment it encounters. In this thesis, a method is proposed that solves the planning problem by enhancing the Closed-Loop Rapidly-exploring Random Tree (CL-RRT) algorithm for planning on curved structured roads. The planner is aware of the road curvature and deforms the motion plan, so it follows the shape of the road. Extensive simulations have demonstrated that the proposed method can improve the path quality on curved highway roads when compared with the standard RRT and CL-RRT. Although the method can plan in any environment it encounters, it demonstrated limitations in its capability of dealing with complex dynamic environments.
PERENCANAAN ULANG SALURAN DRAINASE DAN PENAMBAHAN BANGUNAN RAMAH LINGKUNGAN PADA KAWASAN PEMUKIMAN KECAMATAN XX – KABUPATEN XX

The problem of inundation in the study area coupled with the available drainage channels is not functioning optimally due to the presence of sediment and the overflow of the Marmoyo river that has been unable to accommodate the flow of water to be the cause of flooding in the area. In this case, it’s necessary to evaluate the existing drainage channel is required by hidrological and hydraulic analysis. Based on the calculation result rainfall design at the time of the 5-year return period is 77.06 mm/day the smallest plan channel dimensions are 0.6 m x 0.3 m and the largest dimensions are 1.60 m x 0.8 m dimensions of the infiltrasion well supporting building with a diameter of 1 m and a depth of 1.5 m. The budget plans is Rp. 9,364,975,000.00. Implementation time of 26 weeks.
Subfield algorithms for ideal- and module-SVP based on the decomposition group

Whilst lattice-based cryptosystems are believed to be resistant to quantum attack, they are often forced to pay for that security with inefficiencies in implementation. This problem is overcome by ring- and module-based schemes such as Ring-LWE or Module-
Opportunities for Australian Publishers in Indonesia

Presentation at the Independent Publishing Conference of the Small Press Network, 23 Nov 2018
특허 기반 블록체인 기술 동향 분석: 기술 재정의 프로세스를 바탕으로

Purpose Blockchain technology is becoming important in the fourth industrial revolution, but standardization of technologies is not keeping up with the pace. Therefore, this paper focused on defining what blochchain technology is, and analyzed technological trends using redefined concepts.Methods This research conducted a technical redefining process for blockchain technology, and suggested three types of blockchain technology: core, extended, and general. We used patent co-classification and network analysis for each technology groups.Results Blockchain technology has been evolved from network-based technology to the data privacy and payment-application, recently focusing on data applications. Machine learning technology is actively applied, and many different industries such as healthcare applications also turned out to be increasing with respect to the blockchain technology.Conclusion This study provide important clues in defining the blockchain technology, analyzing the technological trend, and identifying possible innovation chances in blockchain technologies.
Self-perception of digital competence in teachers: Variations after confinement

The year 2020 will be remembered as the year in which a human, health, social and economic tragedy was experienced as a result of the COVID-19 virus. Education faced a scenario not experienced before and like the rest of society it was doomed to severe confinement. The confinement was followed by a partial reincorporation of the students and in both scenarios digital technology played a fundamental role. The formative work was forced to adopt digital methodologies with the urgency that the situation required and the teaching staff had to adapt as they go. Bimodality, synchronous education, online classes are now common terms that accompany the complex teaching work. The study that we present analyzes the starting point of Spanish and European teachers regarding attitudes and training levels in ICT, as well as the possible variations after the outbreak of the pandemic. For this, the data collected in the Teaching And Learning International Study, TALIS 2018, regarding the use of Information and Communication Technologies, have been compared, together with the data provided by the Digital Education Action Plan 2021-2027 by through the Open Public Consultation. It can be concluded that although the teachers, before COVID-19 crisis, had high participation levels on ICT training, they did not value the need to be trained with the same recognition as once the pandemic began. Even so, we propose the need for a pedagogical approach in digital training that goes beyond technical learning by proposing didactic strategies that allow the equidistance between face-to-face and virtual training. © 2021 Univ Nacional de Educacion a Distancia (UNED). All rights reserved.
Advancing Inference in Supervised Learning Procedures via Permutation Tests and Importance Sampling, with Applications to Environmental Science

Random forests, since being proposed by Breiman (2001), have become popular supervised regression and classification techniques. Their popularity stems from being easy to implement - the default hyper-parameter settings are often not far from optimal and are often competitive with more involved supervised models. While random forests are complex, they are not completely impenetrable to theoretical analysis. In this thesis, we present several contributions to random forest methodology. First, we provide a motivating application of random forests to ornithological data, where we develop a novel hypothesis test for testing equality of distribution of random forest curves. Then, we refine an observation made during that application into a means of testing hypotheses about the validation error of random forests, allowing for computationally efficient tests that are analogous to the F-test for linear regression. Finally, we propose a means of accounting for a discrepancy in test and training distributions, motivated by the problem of forecasting power outages from hurricanes.
Perancangan Sistem Pencatatan Dan Laporan Keuangan Berbasis Website Pada Leebra Laundry

This study aims to design a recording system and financial reports at Leebra Laundry with functions according to company needs. Data collection methods used are through observation and interviews, both directly and indirectly with business owners to find out the problems that arise at the Leebra Laundry. In this study, Leebra Laundry has problems with the recording system and business financial reports. Leebra Laundry still uses a manual recording system in books. The recording is only limited to income and expenditure transactions. Therefore, the author wants to carry out the practical work at Leebra Laundry by designing a system of recording and financial reports based on a website system. The making of this system uses existing lateral framework-free templates and is tailored to the needs of Lebra Laundry. In the implementation of this practical work, the owner can use the accounting system developed by using the website to reduce errors in recording transactions and provide the financial information needed. The next practical work recommendation is to make the best use of a computerized system, especially in the field of accounting, in order to produce better financial reports than the existing ones and also to add other financial reports that the authors have not made.
THE VALUE COMPONENT OF THE LIFE STRATEGY OF THE FUTURE TEACHER

The article provides a theoretical analysis of the “value” concept, as well as its impact on the peculiarities of pedagogical activities. It is noted that values are deep human desires for the nature of interaction with the world, with other people and with themselves. It is established that values are a reflection of what a person wants to develop in his life, in the structure of his personality, what he wants to defend. It is the values that determine a certain direction of the life strategy. A survey of future teachers was conducted to understand the concepts of “goals” and “values”, their differences, the role in the life of personality and the delineation of their own values. The result of the survey showed a lack of clear knowledge about these concepts in 90% of respondents. A comprehensive training lesson was developed, which was conducted in three stages: acquaintance with a profound list of life values; an analysis of your own life values; selection of the most relevant areas of life in the current period of time; building of the congruent goals to improve the quality of life, based on an individual value system; outlining of a plan for achieving goals in the SMART system. As a result of the training session, we received a great amount of information on the peculiarities of the value orientations of future teachers. The elaboration of the priority values made it possible to single out the most popular of them: health, acceptance, kindness, love, support. The most selected areas of life that are the most relevant today are family relationships, relationships with friends, relationships with one's own body, spirituality, leisure, work and professional development. Students found it difficult to set goals in the SMART system, guided by their own values, which took a lot of time and personal consultation. As a result of work, students formed knowledge about the concept of values and their role in life of the personality, an awareness of respondents about their own value personality structure was increased, the skills of forming goals that are congruent with their own values were improved, which was a powerful motivating factor for meaningful, harmonious and productive life.
구조방정식모형을 이용한 국가직무능력표준(NCS)기반의 물류교육과정의 물류성과에 대한 효과 분석

This study analyzed 11 capacity units and 38 competency unit elements which have been developed for logistics management tasks in National Competency Standards (NCS) and suggested a logistics education models for operation of training courses for logistics related employees. In addition, the effect on the logistics performance of an enterprise was analyzed by using a structural equation according to the character of the curriculum when the NCS-based education course is operating. The result show that the effect on logistics performance of the company was different by each unit of capacity and the same units of capability have different effects on logistics performance depending on whether NCS-based educational operation are mediating.In addition, we found that there is the unnecessary structural curriculum in NCS structure and have a synergy effect when it is operated with another curriculum. Therefore, supporting system should be provided to flexible curriculum operations according to the character of the tasks in accordance with the needs of logistics enterprise.
고객 감성 분석을 위한 학습 기반 토크나이저 비교 연구

Purpose: The purpose of this study is to compare and analyze the tokenizer in natural language processing for customer satisfaction in sentiment analysis.Methods: In this study, a supervised learning-based tokenizer Mecab-Ko and an unsupervised learning-based tokenizer SentencePiece were used for comparison. Three algorithms: Naive Bayes, k-Nearest Neighbor, and Decision Tree were selected to compare the performance of each tokenizer. For performance comparison, three metrics: accuracy, precision, and recall were used in the study.Results: The results of this study are as follows; Through performance evaluation and verification, it was confirmed that SentencePiece shows better classification performance than Mecab-Ko. In order to confirm the robustness of the derived results, independent t-tests were conducted on the evaluation results for the two types of the tokenizer. As a result of the study, it was confirmed that the classification performance of the SentencePiece tokenizer was high in the k-Nearest Neighbor and Decision Tree algorithms. In addition, the Decision Tree showed slightly higher accuracy among the three classification algorithms.Conclusion: The SentencePiece tokenizer can be used to classify and interpret customer sentiment based on online reviews in Korean more accurately. In addition, it seems that it is possible to give a specific meaning to a short word or a jargon, which is often used by users when evaluating products but is not defined in advance.
SVR PARAMETERS OPTIMIZATION USING PSO AND WAPSO IN STOCK PRICE PREDICTIONS

This study discusses the optimization of Support Vector Regression (SVR) parameters to predict stock prices using Particle Swarm Optimization (PSO) algorithm and Weight Attribute Particle Swarm Optimization (WAPSO) algorithm to improve SVR accuracy and to determine the effect of the inertia weight attribute in the optimization. WAPSO is a Particle Swarm Optimization algorithm with the addition of an inertia weight attribute. The implementation of the SVR, SVR-PSO and SVR-WAPSO algorithms is carried out on three LQ45 stock data, namely TLKM, BBRI and ADRO. The results of the accuracy of the implementation of the SVR algorithm are 79.02493% (TLKM), 67.83047% (BBRI), 88.94952% (ADRO), the SVR-PSO algorithm is 98.64916% (TLKM), 98.32181% (BBRI), 97.90267% (ADRO) and the SVR-WAPSO algorithm is 98.64921% (TLKM), 98.32496% (BBRI), 97.89889% (ADRO). Based on statistical tests in 3 data, it is concluded that the weight attribute has an effect in increasing the mean fitness value so that WAPSO has a better mean fitness value. The difference in the increase in the mean fitness value at WAPSO and PSO on TLKM data is 0.1230, BBRI data is 0.2202, and ADRO 0.0597.
Informasi Geografis untuk Mengetahui Jalur Rawan Kecelakaan di Kabupaten Sidoarjo Menggunakan GPS Berbasis Android

nSidoarjo is a densely populated city, with a high population level so that the movement of mobilization of transportation facilities and infrastructure is also increasing. From that impact arises congestion problems and a relatively high level of traffic accidents from year to year. Traffic accidents according to RI Law Article 1 No. 22 of 2009 Article 1 is an incident on an unexpected and unintentional highway involving a vehicle with or without other road users resulting in human casualties and / or property losses. In the occurrence of an accident, it always contains elements of accidental and uninjured and will cause feelings of surprise, surprise and trauma for people who have experienced the accident. If an accident occurs intentionally and has been planned in advance, then this is not a traffic accident, but is classified as a criminal act of either persecution or premeditated murder. Based on the results of trials conducted on three Android devices, this application runs well on all types of Android devices with different OS versions. In terms of appearance, this application uses a layout with listview, so that the display will adjust to the resolution of the Android device. While in terms of stability, it is a bit heavy on some android devices due to the specifications.
ON THE AGE AND GEODYNAMIC FACTORS OF THE FORMATION OF GOLD MINERALIZATION AT THE MALINOVKA DEPOSIT

An interpretation of the first obtained Re-Os dating of pyrite and arsenopyrite of the Malinovsky gold ore deposit is given. A comparison of the obtained data and the known dates of the ore-bearing granitoids of the ore field made it possible to determine the age of mineralization at 100-90 Ma. This age corresponds to the time of completion of the Alb-Cenomanian transform margin of Asia continent geodynamic setting with significant left-shear kinematics, as indicated in the article by the structural features of the localization of ore bodies and magmatic bodies. The distribution of gold ore deposits in this time within the Sikhote-Alin orogenic belt and in the shear structures of the south of the Korean Peninsula are noticeably shown.
MuSeM: Detecting Incongruent News Headlines using Mutual Attentive Semantic Matching

Measuring the congruence between two texts has several useful applications, such as detecting the prevalent deceptive and misleading news headlines on the web. Many works have proposed machine learning based solutions such as text similarity between the headline and body text to detect the incongruence. Text similarity based methods fail to perform well due to different inherent challenges such as relative length mismatch between the news headline and its body content and non-overlapping vocabulary. On the other hand, more recent works that use headline guided attention to learn a headline derived contextual representation of the news body also result in convoluting overall representation due to the news body’s lengthiness. This paper proposes a method that uses inter-mutual attention-based semantic matching between the original and synthetically generated headlines, which utilizes the difference between all pairs of word embeddings of words involved. The paper also investigates two more variations of our method, which use concatenation and dot-products of word embeddings of the words of original and synthetic headlines. We observe that the proposed method outperforms prior arts significantly for two publicly available datasets.
SLURP: An Interactive SPARQL Query Planner

Triple Pattern Fragments (TPFs) allow for querying large RDF graphs with high availability by offering triple pattern-based access to the graphs. The limited expressivity of TPFs leads to higher client-side querying and communication costs with potentially many intermediate results that need to be transferred. Thus, the challenge of devising efficient query plans when evaluating SPARQL queries lies in minimizing these costs. Different heuristics and cost-based query planning approaches have been proposed to obtain such efficient query plans. However, we also require means to visualize, manually modify, and execute alternative query plans, to better understand the differences between existing planning approaches and their potential limitations. To this end, we propose Slurp ( https://people.aifb.kit.edu/zg2916/slurp/ ), an interactive SPARQL query planner that assists RDF data consumers to visualize, modify, and compare the performance of different query execution plans over TPFs.