Reviewerly/exHarmony huggingface dataset:
G-Transformer for Document-level Machine Translation

Document-level MT models are still far from satisfactory. Existing work extend translation unit from single sentence to multiple sentences. However, study shows that when we further enlarge the translation unit to a whole document, supervised training of Transformer can fail. In this paper, we find such failure is not caused by overfitting, but by sticking around local minima during training. Our analysis shows that the increased complexity of target-to-source attention is a reason for the failure. As a solution, we propose G-Transformer, introducing locality assumption as an inductive bias into Transformer, reducing the hypothesis space of the attention from target to source. Experiments show that G-Transformer converges faster and more stably than Transformer, achieving new state-of-the-art BLEU scores for both non-pretraining and pre-training settings on three benchmark datasets.
Photovoltaic Solar Array Mapping using Supervised Fully Convolutional Neural Networks

This study explores a supervised deep learning fully convolutional segmentation model for photovoltaic solar array mapping from aerial imagery. The deep learning imaging techniques present a fast and an inexpensive way for detecting distributed photovoltaic arrays installed on ground and building rooftops. The identification of correct photovoltaic array shapes and sizes is a necessary requirement for the estimation of energy from photovoltaic arrays within an area or city. This study proposes a modified and efficient UNet deep learning segmentation model by using depthwise-separable convolution for automated photovoltaic array detection from orthorectified RGB imagery with a resolution of less or equal to 0.3m. The result shows our model has better segmentation accuracy than various state of the art models and other previous studies on solar panel detection and is efficient in terms of parameters and complexity.
Pengaruh Motivasi Belajar terhadap Prestasi Belajar PAI di MIN 3 Bantul

This research aims to find out the level of motivation of students and the influence of motivation on PAI learning achievement in schools on MIN 3 Bantul students. This research is a quantitative study using product moment correlation statistics technique. The analytical technique used is an associative hypothesis. The variables studied include learning motivations that are divided into sub variables namely personal needs, school assignments, as well as the environment and people around. While dependent variables (bound variables) are learning achievements that include: academic achievement, non-academic achievement, social achievement and discipline-related achievements. The results showed that learning motivation affects PAI students' learning achievement of MIN 3 Bantul by 51.4%. This means that learning motivation is one of the determining factors of PAI's learning achievements. While 48.6% of PAI students' learning achievement of MIN 3 Bantul students is influenced by internal factors such as IQ, perseverance; and external factors such as family issues, friendships etc.). This indicates that motivation is both a driver and a direction in learning activities.
Multiple simultaneous solution representations in a population based evolutionary algorithm

The representation used for solutions in optimization can have a significant impact on the performance of the optimization method. Traditional population based evolutionary methods have homogeneous populations where all solutions use the same representation. If different representations are to be considered, different runs are required to investigate the relative performance. In this paper, we illustrate the use of a population based evolutionary method, Fresa, inspired by the propagation of Strawberry plants, which allows for multiple representations to co-exist in the population. Fresa is implemented in the Julia language. Julia provides dynamic typing and multiple dispatch. In multiple dispatch, the function invoked is determined, dynamically at run time, by the types of the arguments passed to it. This enables a generic implementation of key steps in the plant propagation algorithm which allows for a heterogeneous population. The search procedure then leads to a competition between representations automatically. A simple case study from the design of operating conditions for a batch reactor system is used to illustrate heterogeneous population based search.
Self-orthogonal codes over a non-unital ring and combinatorial matrices

There is a local ring $E$ of order $4,$ without identity for the multiplication, defined by generators and relations as $E=\langle a,b \mid 2a=2b=0,\, a^2=a,\, b^2=b,\,ab=a,\, ba=b\rangle.$ We study a special construction of self-orthogonal codes over $E,$ based on combinatorial matrices related to two-class association schemes, Strongly Regular Graphs (SRG), and Doubly Regular Tournaments (DRT). We construct quasi self-dual codes over $E,$ and Type IV codes, that is, quasi self-dual codes whose all codewords have even Hamming weight. All these codes can be represented as formally self-dual additive codes over $\F_4.$ The classical invariant theory bound for the weight enumerators of this class of codesimproves the known bound on the minimum distance of Type IV codes over $E.$
ATRAS: Adversarially Trained Robust Architecture Search

In this paper, we explore the effect of architecture completeness on adversarial robustness. We train models with different architectures on CIFAR-10 and MNIST dataset. For each model, we vary different number of layers and different number of nodes in the layer. For every architecture candidate, we use Fast Gradient Sign Method (FGSM) to generate untargeted adversarial attacks and use adversarial training to defend against those attacks. For each architecture candidate, we report pre-attack, post-attack and post-defense accuracy for the model as well as the architecture parameters and the impact of completeness to the model accuracies.
TARGET STUDENT MOTIVATION AS ONE OF THE MAIN COMPONENTS OF LEARNING EFFICIENCY

The effectiveness of the student learning is an urgent problem today, since with the transition to credit technology, the proportion of students' independent work, responsibility for the quality of work organization and self-control has increased. At this stage, it is very important to support students, help them to cope with possible problems in learning. Therefore, it is important for the teacher to understand the student's motivation to learn, to support and develop it. In this article, the authors consider ways to solve this problem, conclude that motivation for successful learning is a complex system that consists of the motives, goals, tasks of the persons participating in learning. The article describes the learning process in the form of a mathematical formula, where one of the components is the target motivation of students.
Implementasi program aplikasi rekapitulasi pembayaran SPP pada SMK Prima Tiara Makassar

This community service program aims to design and implement the SPP payment application at SMK Prima Tiara Makassar. The problem arises when the treasurer collects data on students who have paid and who have not paid tuition, this causes a lot of time to be wasted. In addition, the payment record book will become damaged so that the reporting of monthly payments will be late. So that the existence of a payment application helps in the payment process for SPP SMK Prima Tiara Makassar.
EXPERIMENTAL VALIDATION OF THE DEVELOPMENT LEVEL OF A PROFESSIONAL-PEDAGOGICAL COMPETENCE OF PRIMARY SCHOOL TEACHERS

The paper presents the stating stage of the pedagogical experiment in which the levels of the development of a professional-pedagogical competence of primary school teachers have been studied. We used the following research methods: observation, conversations and testing. Diagnostic blocks of a professional-pedagogical competence of primary school teachers were identified: theoretical-methodological and psychological-pedagogical training. The total number of the participants of the pedagogical experiment was 1960 people, including 960 people in an experimental group and 952 people in a control group, besides 48 teachers-experts from pedagogical universities were involved. Statistical results of the interviewing were summed up taking into account the number of questions, the highest possible number of points received for the answers to the questions, and the total number of points according to the level of a block development that was evaluated. We state that as far as the level of a theoretical-methodological training is concerned (knowledge and skills of theoretical principles of a professional-pedagogical activity; knowledge and skills of the use of the methods of a professional pedagogical activity; knowledge of the history of the development and a current state of the achievements in a professional-pedagogical activity), big amount of primary school teachers corresponded to a sufficient level. This can be explained with teaching the subjects of humane and fundamental training at pedagogical universities. The results of the experiment have shown that the process of the development of a professional-pedagogical competence of primary school teachers requires the improvement, it becomes possible with providing the readiness of an individual for distance education; the organizational-technical support of distance education; the teaching-methodological support of distance education; the organization of the process of distance education on an individual basis; a continuous psychological-pedagogical support of distance education; a system improvement of the qualification of distance education organizers.&#x0D; Keywords: professional-pedagogical competence, experimental group, control group, primary school teachers, university teachers, pedagogical experiment, distance education, diagnostics.
LibGuides: Music: Guitar for New and Experienced Players: Experienced players

Instructional books, CDs, and DVDs for Guitar in many styles, as well as songbooks and sheet music.
Nonparametric Hamiltonian Monte Carlo

Probabilistic programming uses programs to express generative models whose posterior probability is then computed by built-in inference engines. A challenging goal is to develop general purpose inference algorithms that work out-of-the-box for arbitrary programs in a universal probabilistic programming language (PPL). The densities defined by such programs, which may use stochastic branching and recursion, are (in general) nonparametric, in the sense that they correspond to models on an infinite-dimensional parameter space. However standard inference algorithms, such as the Hamiltonian Monte Carlo (HMC) algorithm, target distributions with a fixed number of parameters. This paper introduces the Nonparametric Hamiltonian Monte Carlo (NP-HMC) algorithm which generalises HMC to nonparametric models. Inputs to NP-HMC are a new class of measurable functions called "tree representable", which serve as a language-independent representation of the density functions of probabilistic programs in a universal PPL. We provide a correctness proof of NP-HMC, and empirically demonstrate significant performance improvements over existing approaches on several nonparametric examples.
THE PECULIARITIES OF NARRATIVE MANNER “STREAM OF CONSCIOUSNESS” OF FAULKNER'S NOVEL "THE SOUND AND THE FURY"

William Faulkner (1897-1962) is an American writer, a Nobel laureate (1949), whose work has the power to influence the fate not only of English, but also of world literature, comparable with the works of Shakespeare, Byron and Joyce. Paradoxically, the American writer became a figure of global scale, which had distinguished much in European literature. Faulkner is a native of Mississippi, the center of the most acute contradiction of the South where he lived all his life. Not only Faulkner`s biography, but all his creativity is tightly connected with the special mental structure of this region of the United States, with subliminal (southern) social and psychological problems going deep into history.
APLIKASI SISTEM PENDUKUNG KEPUTUSAN SELEKSI CALON BINTARA PK (PRAJURIT KARIR) KOREM 0207 SIMALUNGUN SUMATERA UTARA DENGAN METODE TOPSIS

The process of accepting Bintara PK (Career Warrior) in Korem 0207 SIMALUNGUN NORTH SUMATERA has 4 stages of selection, namely administration, health, a physical and mental ideology so that the selection process requires the value of each test to produce a value or ranking from highest to lowest. Around 276 people (Ajenrem 022 East Coast) prospective Bintara who applied in 2019 in the Simalungun area, while only 53 people would be accepted. Where to get Bintara PK (Career Warrior) who graduated at the regional level requires ranking from the highest value to the lowest value. To get the results of the participants' commemoration following the criteria set by the agency, the TOPSIS method is needed. In this study using the waterfall software development method, for designing applications using Unified Modeling Language (UML) and testing applications using Blackbox testing, in addition to designing databases using MySQL and in terms of implementing applications using the desktop. From the results of this study, it can be concluded that this application can help the agency in recommending and giving consideration to prospective participants who will continue the selection to the central level.
Analisis Manajemen Kurikulum Dalam Pelaksanaan Mutu Pendidikan Pada Program Studi Sastra Inggris di Fakultas Sastra Universitas Islam Sumatera Utara

This research aims to obtain and determine the concept of English Literature at The Faculty Literature Islamic University of North Sumatera. This study uses a qualitative approach, naturalistic and triangulation techniques. The data collection techniques are using observation, interviews and documentation. The purpose of this research is to know and understand the concepts of the English Literature study program curriculum at the Faculty of Letters, Islamic University of North Sumatra. The results of the study showed that in the planning section it was seen from the readiness of the lecturer in providing a special time to make lectures by achieving the number of semester credits. In addition lecturers have the ability to predict the readiness of students in completing courses per semester. The planning of making English literature curriculum is in line with the output that will be produced by English literature study programs. In implementing the curriculum that is realized in the lecture process, the lecturers will adjust the face-to-face hours to what will be achieved by students at the end of the lecture, making Semester learning plans, giving their own space to lecturers in providing appropriate media selection courses to help students quickly understand material precisely. In addition, lecturers are able to realize the total number of Semester Credit System by adjusting the number of student attendance. Meanwhile the Evaluation section is also to give freedom to lecturers. To work more wisely and in applying and implementing policies during the lecture process.
Automatic Classification and Entity Relation Detection in Hungarian Spinal MRI Reports

A great number of radiologic reports are created each year which incorporate the expertise of radiologists. This knowledge could be exploited via machine understanding. This could provide valuable statistics and visualization of the reports, and as training data, and it could also contribute to later automatic reporting applications. In our current work, we present our first steps toward the machine understanding of clinical reports of the spinal region, written in the Hungarian language. Our system provides an automatic classification and connection detection for various entities in the text. Our classification is achieved via bi-directional long short-term memory and conditional random fields producing 0.87-0.95 F1-score values, while the extraction of connection relies on linguistic analysis and predefined rules. The extracted information is displayed in an easily comprehensible, well-formed tree-structure.
Dont Just Divide; Polarize and Conquer!

In data containing heterogeneous subpopulations, classification performance benefits from incorporating the knowledge of cluster structure in the classifier. Previous methods for such combined clustering and classification are either 1) classifier-specific and not generic, or 2) independently perform clustering and classifier training, which may not form clusters that can potentially benefit classifier performance. The question of how to perform clustering to improve the performance of classifiers trained on the clusters has received scant attention in previous literature, despite its importance in several real-world applications. In this paper, we design a simple and efficient classification algorithm called Clustering Aware Classification (CAC), to find clusters that are well suited for being used as training datasets by classifiers for each underlying subpopulation. Our experiments on synthetic and real benchmark datasets demonstrate the efficacy of CAC over previous methods for combined clustering and classification.
Badminton Trajectory Tracking Based on D-H Kinematics Model

In order to realize accurate tracking and recognition of badminton movement track, a badminton movement track tracking model based on D-H kinematics model is proposed. Badminton trajectory detection is carried out by adopting video sensors and image vision imagers, an array distribution model of badminton trajectory tracking is established by combining D-H kinematics parameter fusion method, key feature quantities of badminton trajectory are extracted by badminton motion feature distributed detection method, and a badminton trajectory kinematics D-H kinematics data feature decomposition model is established according to high-dimensional matrix feature decomposition method. The badminton motion trajectory sensing information is fused in the neighboring points, and the badminton motion trajectory estimation model is established by combining feature reconstruction matching and fuzzy information weighted analysis methods. The badminton motion trajectory recognition is carried out on the sampled D-H kinematics big data, and the badminton motion trajectory sampling points are inserted. By identifying the key feature points of the badminton motion trajectory, the badminton motion trajectory tracking model is constructed. The simulation results show that the feature recognition ability of badminton trajectory tracking model constructed by this method is better, and the badminton trajectory tracking recognition ability is stronger, which improves the badminton trajectory estimation.
Algoritma Backtracking Pada Perjalanan Pulang Robot Pemadam Api

Kontes Robot Pemadam Api Indonesia or commonly in short KRPAI is a robot competition that has the main mission of putting out fires. During the competition there are several bonus points that must be taken. One bonus that must be taken is the robot able to return home after holding a fire. In this research the KRPAI Udayana University robot will use the backtracking algorithm to return home. The purpose of using backtracking alogritma is to shorten the distance back home to the robot. In KRPAI University robot Udayana use SRF-05 sensors to measure the distance of the robot to the wall and use 18 servo as the driving leg of the robot. the use of backtracking algorithm, the path that has been passed by the robot when looking for fire will be simplified. the results of the simplification of the fire search path will be used as the return route. Based on the test results, using the robot backtracking algorithm is able to minimize the distance traveled when returning home, the room entered is only 1 room, and there is no detecting dolls.
INFORMATIONAL METABOLISM OF THE IT SOCIETY

This article presents a study and analysis of the distribution of Socionics personality types in the IT community, covering different IT professionals from 6 countries. The integral psycho - informational metabolism of the IT society has been determined and proved by a socionic analysis of the statistical data, using different practices and tools. The conclusions drawn define the main peculiarities of the IT guild as well as the specific motivational, communication and value characteristics of the study group. The factors that differentiate IT people from the rest of the population are also analyzed. The illustrated methodology is applicable to studies of the behavioral characteristics of different communities, subcultures, social and minority groups, as well as various corporate units with integral informational metabolism. The author assumes that the reader is familiar with the fundamentals, terminology and concepts of Socionics as well as Jungian theory.
Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance Sacrifice

Pre-trained contextualized language models (PrLMs) have led to strong performance gains in downstream natural language understanding tasks. However, PrLMs can still be easily fooled by adversarial word substitution, which is one of the most challenging textual adversarial attack methods. Existing defence approaches suffer from notable performance loss and complexities. Thus, this paper presents a compact and performance-preserved framework, Anomaly Detection with Frequency-Aware Randomization (ADFAR). In detail, we design an auxiliary anomaly detection classifier and adopt a multi-task learning procedure, by which PrLMs are able to distinguish adversarial input samples. Then, in order to defend adversarial word substitution, a frequency-aware randomization process is applied to those recognized adversarial input samples. Empirical results show that ADFAR significantly outperforms those newly proposed defense methods over various tasks with much higher inference speed. Remarkably, ADFAR does not impair the overall performance of PrLMs. The code is available at https://github.com/LilyNLP/ADFAR
Augmented reality for dies alignment in machine setup

Forging is one of the material deformation processing techniques. While the operation cycle is short and efficient, the machine setup process generally long and results in time waste. One of the difficulties in operation is position alignment procedure of the equipment during the mold changing process. Due to the size and shape complexity of the machine and equipment, the operators are unable to clearly see the important parts - e.g. guided pins, holes, and sub-plate - which need to be delicately moved to adjust the alignment. Augmented Reality (AR) technology is used to solve this problem by rendering virtual objects and guides associated to those parts over the real equipment. As a result, the operators are able to monitor the position of those parts during the alignment process through the virtual objects. This AR system is developed based on ARcore SDK together with Unity on the Android platform. The experiment was conducted in real factory environment.
Belief Functions and their Applications

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