Reviewerly/exHarmony huggingface dataset:
TINJAUAN YURIDIS TERHADAP TINDAK PIDANA PERMAINAN JUDI JACKPOT (Studi Kasus Putusan Nomor 45/Pid.B/2017/PN.MDN)

Indonesia is a legal state where all aspects of people's lives in nation have norms that must be obeyed. These norms aim to prevent criminal acts. Criminal act is an act that is prohibited by the rule of law, and the prohibition is accompanied by threats and sanctions in the form of criminal violations of such prohibitions, such as gambling which is an act of betting deliberately carried out to benefit from a match, game or event that is uncertain about the result. For example, jackpot gambling games that use machine technology are played by inserting coins in the form of chips of money that have been exchanged for a certain value of money and inserted into the machine and then pulled by the player, then waiting for the combination image to come out simultaneously on the jackpot machine and if the image is the same, then it is considered to win a different cash prize depending on the interest or image that the player gets. This game is a chancy game that can be said as gambling because of bets between players. Keyword: Criminal Act, Gambling, Jackpot Gampling
Adaptation and Pedagogy at the Collective Level: Recommendations for Adaptive Instructional Systems

Adaptive training and intelligent tutoring functions provide mechanisms for balancing coaching and challenge during a practice scenario to maximize performance outcomes. Extending these practices into team and collective domain environments requires an understanding of the pedagogical requirements an Adaptive Instructional System (AIS) must account for in design. This includes identifying what pedagogical functions a system must support and how those functions apply within foundations of knowledge/skill acquisition and team development. In this paper, we present a high-level taxonomy of feedback and adaptation types for application in a team-based AIS. The taxonomy is designed to serve as a foundation of pedagogical activities to build policies around based on real-time monitoring and demographic information.
PEDAGOGICAL SYSTEM OF PROFESSIONAL TRAINING THE ARTILLERY AND MISSILE OFFICERS

The aim of the study is to develop a pedagogical system of professional training of intending artillery and missile officers in educational framework of a military institution of higher education. The training of intending artillery and missile officers is defined as the process of forming cadets’ readiness for professional activities, that should be substantiated by the principles of the systems approach. The main stages of pedagogical modeling are revealed which were used during projecting a pedagogical system of training the intending artillery and missile officers: the first stage is connected with studying the content of the modeled pedagogical phenomenon or object, revealing the patterns of its functioning and development, determining the crucial components; the second stage consists in determining the tasks of modeling; the third stage includes the creation of the model and pedagogical system; the fourth stage performs validation of the pedagogical system. Analyzing the main stages of pedagogical modeling, the pedagogical system of training the intending artillery and missile officers is considered as a perfect construction that represents a goal-oriented pedagogical interrelation in the subjects of the educational process at a military institution of higher education, with the help of which it (system) can be studied, reconstructed, as well as controlling its functioning and development.The complement of the pedagogical system is a structural-functional model which combines the following: structural aspect (it reflects the components and connections in the educational process that are formed for cadets’ educational and professional activities) and functional aspect (it allows to foresee the behavior of the pedagogical system for training the intending artillery and missile officers under the real conditions of the educational process). The structural-functional model of the pedagogical system of training the intending artillery and missile officers is the architectonics of the interrelated target unit, theoretical and methodological unit, motivational and stimulating unit, resource-supporting unit, instrumental and methodical unit, action-semantic unit, procedural and diagnostic-effective unit.To determine real opportunities of the pedagogical system of professional training the intending artillery and missile officers, the following complex of methods is applied: theoretical and basic scientific methods (for justifying the initial theoretical positions), specific scientific methods: structural (for revealing and characterizing the peculiarities of cadets’ training) and case studies method (for identifying real and promising opportunities).
Multi-Adapter RGBT Tracking

The task of RGBT tracking aims to take the complementary advantages from visible spectrum and thermal infrared data to achieve robust visual tracking, and receives more and more attention in recent years. Existing works focus on modality-specific information integration by introducing modality weights to achieve adaptive fusion or learning robust feature representations of different modalities. Although these methods could effectively deploy the modality-specific properties, they ignore the potential values of modality-shared cues as well as instance-aware information, which are crucial for effective fusion of different modalities in RGBT tracking. In this paper, we propose a novel Multi-Adapter convolutional Network (MANet) to jointly perform modality-shared, modality-specific and instance-aware feature learning in an end-to-end trained deep framework for RGBT tracking. We design three kinds of adapters within our network. In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object. Moreover, to reduce computational complexity for real-time demand of visual tracking, we design a parallel structure of generic adapter and modality adapter. Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against other state-of-the-art RGB and RGBT tracking algorithms.
A Fractional Lower-order Bi-spectrum Estimation Method Based on Autoregressive Model

The traditional fractional lower-order (FLO) spectrum estimation method cannot observe a sufficient volume of data, leading to a large variance of estimation results.To solve the problem, this paper puts forward a FLO bi-spectrum estimation method based on autoregressive (AR) model, and gives new definitions for FLO three order cumulant.The author discussed the determination of AR model parameters, and introduced how to implement the bi-spectrum estimation method based on AR model.Then, a series of tests were performed to verify the correctness of our method.The results show that our method outperformed the traditional approaches in suppressing FLO noise and identifying relevant information of signals.
A Robust Two-Sample Test for Time Series data

We develop a general framework for hypothesis testing with time series data. The problem is to distinguish between the mean functions of the underlying temporal processes of populations of times series, which are often irregularly sampled and measured with error. Such an observation pattern can result in substantial uncertainty about the underlying trajectory, quantifying it accurately is important to ensure robust tests. We propose a new test statistic that views each trajectory as a sample from a distribution on functions and considers the distributions themselves to encode the uncertainty between observations. We derive asymptotic null distributions and power functions for our test and put emphasis on computational considerations by giving an efficient kernel learning framework to prevent over-fitting in small samples and also showing how to scale our test to densely sampled time series. We conclude with performance evaluations on synthetic data and experiments on healthcare and climate change data.
형태 분석 말뭉치 구축을 위한 한국어 구어 분석

In this study, I discussed various problems that can occur when constructing a POS Tagged corpus of Korean spoken. The morphological analysis corpus is the most basic annotation corpus, while the study and construction work on the annotation of written corpus is done in various angles, but the research on the spoken corpus has hardly been done.First, I introduce the corpus and the morphological analysis tool, Utagger, which will be used in the analysis. Then, I have examined the construction process of the spoken corpus. Then, I summarized the characteristics of Korean spoken language, especially the contraction which is the most characteristic feature of colloquialism. And I discussed why information about the contraction phenomenon is important in the POS Tagged corpus of spoken language.In this paper, I tried to analyze the various sentences including the spoken variation by using the Utagger used in constructing the large corpus. However, due to the limitations of the tools developed around written language, about half of them did not show correct results. Especially, the contracted forms were rarely processed. Finally, I describe the differences in the distribution of parts of speech that are revealed through comparison with the written corpus for the analyzing of spoken corpus.In this paper, I deal only with morphological analysis, which is the most basic annotation, but I think it is also necessary to study how the distribution of homophonic words in written and spoken corpus differs for the constructing of a annotation corpus separated by lexical meaning.
Studi Komparasi Hasil Belajar Antara Mahasiswa Yang Berorientasi Untuk bekerja Dengan Yang Berminat Melanjutkan Studi Pascasarjana Pada Mahasiswa Pendidikan Akuntansi FKIP UMS Angkatan 2014 Universitas Muhammadiyah Surakarta

This study aims to determine whether there are differences in learning outcomes between students who are oriented to work with those who continue to pursue postgraduate studies in Accounting Education students Faculty of Teacher Training and Education UMS class of 2014. This thesis uses quantitative research through a comparative method. The total population of the study was 102 students with a sample size of 78 students using the help of the Krejcie table with an error rate of 5%. Sample selection uses the Probability Sampling technique of simple random sampling. Data was collected using the questionnaire method with Likert scale and documentation. Test requirements analysis using normality test and homogeneity test. The analytical method used is the t test or T-test. The results of t-test calculations at the level of significance obtained t value of 0.390, while t-table at the 5% significance level with Degree of freedom (76) is 2000. Due to tcount <t table (0.390 <2000) then H0 is accepted, which means there are no differences in learning outcomes between students who are oriented to work with students who are interested in continuing postgraduate studies in accounting education students Faculty of Teacher Training and Education UMS class of 2014.From the analysis above, it can be seen that the orientation differences between students who want to work with those who want to pursue postgraduate studies do not affect the activities of the two groups of students in achieving learning outcomes (GPA).
Analysis and result of classification algorithm on email classification

In this time, one of the most and fastest forms of communication is electronic mail or what we call e-mail. However, the increase of e-mail users has resulted in the dramatic increase of spam emails in the past few years. Spam is the use of electronic messaging systems to send bulk data. In this paper, e-mail data were classified as ham email and spam email using supervised learning algorithms. Three different classifiers such as Naive Bayesian (NB) classifier, K-nearest neighbor (KNN) classifier and Support Vector Machine (SVM) classifier were used. The experiment was performed by applying filtering on the classifiers. The result shows the difference between the classifier before and after applying filtering algorithm. To examine the performance of the selected classification methods or algorithms, namely Naive Bayes, SVM and KNN, true positive, false positive, precision, recall and F-measure were validated. There was a time difference using those classification algorithms. KNN and SMO algorithms are almost the best classifiers among the three before applying filtering algorithm. Sequential minimal optimization (SMO) is an algorithm used to solve quadratic programming (QP) problem that arises during the training of support vector machines (SVM) and after applying filtering algorithm. SMO algorithm is the best classifier algorithm.  For this experiment, the data mining tool called WEKA was used.Key words: WEKA, classifier, K-nearest neighbor (KNN), support vector machines (SVM), Naive Bayesian (NB), boosting.
Research on System Design of “Shared” Smart Kitchen in Youth Apartment in the Era of Internet

A challenge for kitchen designers, manufacturers and installers is to think in terms of kitchens that are more flexible and adaptable to people’s changing needs. This study develops this work by reviewing current problems and offering recommendations that others could follow without necessarily redesigning the whole kitchen. The participatory design method is adopted to study the “shared” smart kitchen system. Young people in the youth apartment in the era of Internet are studied for an investigation into the status of their lives, work and entertainment, by means of interviews and observations, so that their eating habits and social needs can be further understood. By finding out the balance point of young people between high-quality diet activities and fast-pace life style, a new social platform is designed based on kitchen activities, and a design scheme for the “shared” smart kitchen system is proposed for the youth apartment in the era of Internet.
Beyond the Clouds, Part 2: What Happens to the Files You Store in the Clouds When You Die?

This paper considers what happens to users’ rights in relation to the digital files they have stored in the cloud when they die. It is based in part on a survey of the terms and conditions of popular consumer-facing cloud services. A minority of cloud terms of service explicitly exclude heirs inheriting rights to access files stored in the cloud. Most terms do not address the point directly, but instead exclude the assignment of users’ contractual rights and copyright licenses. As a result, these rights and licenses do not constitute things in action. We argue that such rights will therefore not form part of the user’s estate and so not be transmitted to his or her heirs when the user dies. Unlike for physical assets, heirs therefore lack a clear legal claim to recover a deceased’s digital files from the cloud, many of which may instead be lost or deleted. This paper considers the extent to which this outcome could be challenged under consumer protection law and whether the law should be amended so as to grant heirs rights of access to digital assets. The paper focuses on English law, while drawing on examples from Canada, the United States, Germany, France, and the Netherlands.
NTT's Machine Translation Systems for WMT19 Robustness Task

This paper describes NTT's submission to the WMT19 robustness task. This task mainly focuses on translating noisy text (e.g., posts on Twitter), which presents different difficulties from typical translation tasks such as news. Our submission combined techniques including utilization of a synthetic corpus, domain adaptation, and a placeholder mechanism, which significantly improved over the previous baseline. Experimental results revealed the placeholder mechanism, which temporarily replaces the non-standard tokens including emojis and emoticons with special placeholder tokens during translation, improves translation accuracy even with noisy texts.
The different viewing experiences between special and visual effects

Computer graphics field is rapidly growing and is widely used in many industries, especially in cinematics. The visual effects, which is a part of the field, have become more affordable in comparis ...
Deep network as memory space: complexity, generalization, disentangled representation and interpretability

By bridging deep networks and physics, the programme of geometrization of deep networks was proposed as a framework for the interpretability of deep learning systems. Following this programme we can apply two key ideas of physics, the geometrization of physics and the least action principle, on deep networks and deliver a new picture of deep networks: deep networks as memory space of information, where the capacity, robustness and efficiency of the memory are closely related with the complexity, generalization and disentanglement of deep networks. The key components of this understanding include:(1) a Fisher metric based formulation of the network complexity; (2)the least action (complexity=action) principle on deep networks and (3)the geometry built on deep network configurations. We will show how this picture will bring us a new understanding of the interpretability of deep learning systems.
A Relational Static Semantics for Call Graph Construction

The problem of resolving virtual method and interface calls in object-oriented languages has been a long standing challenge to the program analysis community. The complexities are due to various reasons, such as increased levels of class inheritance and polymorphism in large programs. In this paper, we propose a new approach called type flow analysis that represent propagation of type information between program variables by a group of relations without the help of a heap abstraction. We prove that regarding the precision on reachability of class information to a variable, our method produces results equivalent to that one can derive from a points-to analysis. Moreover, in practice, our method consumes lower time and space usage, as supported by the experimental results.
A Greedy Approach to Hide Sensitive Frequent Itemsets with Reduced Side Effects

Frequent itemsets mining discovers associations present among items from a large database. However, due to privacy concerns some sensitive frequent itemsets have to be hidden from the database before delivering it to the data miner. In this paper, we propose a greedy approach which provides an optimal solution for hiding frequent itemsets that are considered sensitive. The hiding process maximizes the utility of the modified database by introducing least possible amount of side effects. The algorithm employs a weighing scheme which computes transaction weight that allows it to select at each stage of iteration candidate transactions, based on side effects measurement. We investigated the effectiveness of proposed algorithm by comparing it with other heuristic algorithm using parameters such as number of sensitive frequent itemsets, length of sensitive frequent itemsets and minimum support on a number of datasets which are publicly available through the Frequent Itemset Mining (FIMI) repository. The experiment results demonstrated that our approach protects more non-sensitive frequent itemsets from being over-hidden than those produced by heuristic approach.
Recognition of Marathi Numerals Using MFCC and DTW Features

Numeral recognition is one amongst the foremost important problems in pattern recognition. Its numerous uses like reading communication postal code, worker code, bank cheque method etc. To the simplest of our information, very less work has been wiped out Marathi language as compared with other Indian and non-Indian languages. It has mentioned a unique technique for recognition of isolated Marathi numerals. It introduces Marathi numerals and identification technique using MFCC and DTW as attributes. The accuracy of the pre-recorded samples is greater than that of online testing samples. We have got additionally seen that the accuracy of the speaker dependent samples is over that of the speaker independent samples. Another technique known as HMM is additionally discussed. By experimentation, it’s ascertained that identification exactness is higher for HMM than DTW, but the training method in DTW is extremely straightforward and quick, as compared to HMM. The time needed for recognition of numerals using HMM is additional as compared to DTW, because it should bear the various states, iterations and lots of additional mathematical modeling, thus DTW is most well-liked for the real-time applications.
PENGGUNAAN MODEL HIDDEN MARKOV DAN METODE NEURAL NETWORK SEBAGAI PENERAPAN TEKNOLOGI PENGENALAN WAJAH

The human face recognition system is one of the fields that is quite developed at this time, where applications can be applied in the field of security (security system) such as permission to access room, surveillance (surveillance), as well as the search for individual identities in the police database. The face recognition approach aims to detect faces in 2-dimensional images and sequential images of videos that have many methods such as local, global, and hybrid approaches. Hidden Model Markov (HMM) is another promising method that works well for images with different lighting variations, facial expressions, and orientations. HMM is a set of statistical models used to characterize signal properties. An artificial neural network-based approach is learned from image examples and relies on techniques from machine learning to find relevant facial image characteristics. The characteristics studied were in the form of discriminant functions (ie non-linear decision surfaces), then used for face recognition. In this study there will be an application to compare Hidden Markov Models and Neural Network Method as a Face Recognition Technology Algorithm Model.
An Approach to a New Network Security Architecture of Nuclear and Research Facilities

The advances in information technology (IT) systems and network security have become main necessary requirements for any business in current time and future. Any organization needs a full description of all the key elements and relationships for applying good IT systems and good network security. This is known as network security architecture. This architecture increases the capabilities of any organization in terms of its practical application. This work has a new approach to network security architecture by using the Zachman Framework capabilities. The network security architecture of nuclear and academic facilities academic centers is discussed to show how a conceptual model can be implemented in a real organization. This new approach makes any Local Area Network LAN more secure and more flexible than any conventional security procedures without affecting the performance of the LAN. Creating security architecture for nuclear and research facilities by the Zachman matrix (data, function, and network) provide the answers to what data assets the organization controls, how they are used, and where they are located. Nuclear facilities, as one of the major users of information and communication technology, present a good case study for applying the proposed architecture. Therefore the Zachman matrix provides the answers to what data assets the nuclear and research facilities controls by setting up the ideal use and ideal places for these data.