Reviewerly/exHarmony huggingface dataset:
테러방지법의 개정방향에 관한 소고 - 제20대 국회의 주요 개정안을 중심으로

At a time when new forms of terrorism, called “New Terrorism”, had been constantly occurring all over the world, Korea also enacted the ｢Act on Anti-Terrorism for the protection of citizens and public security｣(Act No.14071) as a countermeasure against it in March 2016 after a fierce controversy. However, the controversy over the law continued after the legislation, and a petition was filed with the Constitutional Court for unconstitutionality of some articles before the law was fully enforced. Even an amendment bill on the abolition of the law has been proposed and is pending in the 20th National Assembly. In the meantime, discussions in academia have been focused on improvement plan for securing measures to enhance effectiveness or efficiency of anti-terrorism. However, it is also necessary to keep in mind the concerns that due to the state power operated to prevent terrorism, large and small restrictions may be imposed on the domain of fundamental rights. In this regard, I have tried to find ways to strengthen the fundamental rights of the anti-terrorism system, in the aspect of the principle of the rule of law which is one of the core principles of constitutionalism, especially the amendments to the anti-terrorism law pending in the 20th National Assembly. In conclusion, since it is considered that the system of human rights protection which is the major guarantee of the fundamental rights in the current anti-terrorism system, has structural limitations, we will look for ways to strengthen the control of the National Assembly over the anti-terrorism system in the short-term approach and we will also propose a structural reform of the anti-terrorism system itself in the long-term approach.
Design of Visual Adaptive Control System for Mobile Robot Based on Sliding Mode Speed Tracking

Aiming at the stabilization problem of mobile robot system in a complex environment, the 2D trifocal tensor visual system is used for the real-time estimation of robot position, which can enhance the robustness of robot visual servo system. On this basis, an adaptive control method is used to realize the motion control of mobile robot. The design of the sliding mode speed tracking controller makes the actual speed of mobile robot tend to the expected speed of the adaptive controller, to avoid the shake problem when the speed of mobile robot changes. The simulation results show that the proposed visual adaptive control system has high stability and robustness, and the sliding mode speed tracker is effective.
Maximization Of The Capacity Of Flood Control Dam For Raw Water Reserve In Sampang City

The soil in Sampang Regency has low water absorption capacity. This is due to the topsoil is dominated by clay soil, while the subsoil isweathered from limestone. As a result, rainwater tends to be surface runoff and difficult to absorb into the ground, thus the flood discharge is high. Thiscondition makes Sampang Regency experience limited groundwater resources, so that raw water needs are more dependent on the river water (floodwater). Samiran river, with its dam, is one of the major sources of raw water for the fulfillment of irrigation water, and for drinking water as well. Thetopography around the dam location makes the Samiran river potential for flood water reservoir. This study aimed to analyze the water quality inSamiran dam, as well as the volume of flood water that can be used as raw water. In general, the results of this study show that the water quality ofSamiran dam was in accordance with the standards for raw water. In addition, the potential for flood water that can be used as raw water was 50 l/sec,to support water needs for 344 days.
Median-of-k Jumplists and Dangling-Min BSTs.

We extend randomized jumplists introduced by Bronnimann et al. (STACS 2003) to choose jump-pointer targets as median of a small sample for better search costs, and present randomized algorithms with expected $O(\log n)$ time complexity that maintain the probability distribution of jump pointers upon insertions and deletions. We analyze the expected costs to search, insert and delete a random element, and we show that omitting jump pointers in small sublists hardly affects search costs, but significantly reduces the memory consumption. We use a bijection between jumplists and BSTs, a variant of (fringe-balanced) binary search trees for the analysis. Despite their similarities, some standard analysis techniques for search trees fail for dangling-min trees (and hence for jumplists).
Recomera: Aplikasi Berbasis Pengenalan Wajah Dan Augmented Reality Yang Terhubung Dengan Profil Media Sosial Pada Smart Glasses Untuk Mengenali Lawan Bicara

Abstract-- Since the development of social networking at this time everyone can easily get other people's information. Evenas technology develops, applications that are connected to social networks also develop. Therefore, an idea based on facerecognition and augmented reality application that is connected with social media profiles on smart glasses is identified torecognize the other person. This research was implemented on smart glasses to capture the user's face. Every movement ofthe face to the left, right, up and down will also be recognized by the camera until it matches the user's face match. Whena face is recognized, the program will search for the same face data. The presence of Augmented Reality works whenmaking faces as markers to display social media information from face recognition.Keywords : Social networking, Facial recognition, Augmented reality, Camera, Information
Degree gaps for multipliers and the dynamical André–Oort conjecture

Abstract We demonstrate how recent work of Favre and Gauthier, together with a modification of a result of the author, shows that a family of polynomials with infinitely many post-critically finite specializations cannot have any periodic cycles with multiplier of very low degree, except those that vanish, generalizing results of Baker and DeMarco, and Favre and Gauthier.
GENERATING STUDENT'S LANGUAGE LEARNING PASSION THROUGH PROBLEM-BASED LEARNING

This article discusses the problem of how to cultivate student enthusiasm through problem-based learning. The discussion in the article focuses more on learning Indonesian. Language learning is essentially learning communication skills, both oral and written communication. This learning will be more interesting and can aro use students' enthusiasm for learning if it is done through a problemsolving process. Students are faced with daily problems and are encouraged to find answers to these problems. Therefore, problembased learning is the right choice of strategy to support student learning motivation. Problem-based learning steps make students search and discover their learning experiences creatively. This strategy is suitable to be used as an alternative learning strategy that can arouse student enthusiasm so that students can achieve meaningful learning experiences.
An Industrial Control Safety Situation Assessment Based on Standard Deviation

With the rapid development of information technology, and the integration of informationization and industrialization, industrial control systems access the Internet in various ways, which changes the original characteristics of these systems, including closure and specialization. However, many information security issues also appear in industrial control systems. In view of the status of industrial control safety, the multi-attribution evaluation based security situation assessment on industry control has been proposed in this paper, and this approach has also been applied to the evaluation and prediction on the network security situation of China's networked power system, in order to find the potential threat information. Meanwhile, facing with the threat, some technical methods are put forward to give early warnings effectively.
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax

The Gumbel-Softmax is a continuous distribution over the simplex that is often used as a relaxation of discrete distributions. Because it can be readily interpreted and easily reparameterized, it enjoys widespread use. We propose a modular and more flexible family of reparameterizable distributions where Gaussian noise is transformed into a one-hot approximation through an invertible function. This invertible function is composed of a modified softmax and can incorporate diverse transformations that serve different specific purposes. For example, the stick-breaking procedure allows us to extend the reparameterization trick to distributions with countably infinite support, thus enabling the use of our distribution along nonparametric models, or normalizing flows let us increase the flexibility of the distribution. Our construction enjoys theoretical advantages over the Gumbel-Softmax, such as closed form KL, and significantly outperforms it in a variety of experiments. Our code is available at this https URL.
Algoritma K-Means Untuk Clustering Rute Perjalanan Wisata Pada Agen Tour &amp; Travel

Government support for the development of tourism has an impact on the growth of business opportunities for travel agents. Along with the advancement of the domestic travel sector, tour &amp; travel agent business forms have sprung up that influence business competition between travel agents. The problem with tour &amp; travel agents is the lack of information about tourist routes that are most in-demand by customers. To solve this problem the method used to classify the most desirable travel routes using the method of data mining is clustering with the K-Means algorithm. Based on the results of the study found three groups of travel routes, namely the most desirable travel routes by 20%, the trips that are in demand by 30% and less desirable trips by 50%.
Digital means in the fight against COVID-19. Can a second wave be prevented? [Dutch]

Since the beginning of the Covid-19 epidemic, various digital tools have been developed, such as apps that can help fight SARS-CoV-2 With an app, GGDs can trace the contacts of people who have tested positive for SARS-CoV-2 more quickly and thus limit the further spread of the virus Can a second wave be prevented with this?
METODE PRESENTASI UNTUK MENINGKATKAN AKTIVITAS BELAJAR TEKNOLOGI SEDERHANA PADA SISWA KELAS B TAMAN KANAK-KANAK KARTIKA II-31 BANDAR LAMPUNG

The research objective is to increase the activity and learning achievement of Simple Technology by applying the Presentation Method to students of the Class BTK Kartika II-31 Bandar Lampung in 2019. The research method uses Classroom Action Research (Self Reflective Inquiry) which is carried out in three research cycles through the stages of planning, implementation. , observation and reflection. The implementation of 3 cycles and 3 meetings every cycle from January to April 2019. The results of the Cycle I study of learning outcomes averaged 63 with the category Enough, Complete 63%. In cycle II, the average learning result is 74 with Good category, Completed 83%. In Cycle III, the average learning outcome was 86 with the very good category, 96% complete. Learning activity in Cycle I was 63% active, Cycle II was 83% active and Cycle III was active 96%. Observer observations about teacher performance in learning average in good category. Thus, the application of the presentation method can increase the activity and achievement of simple technology learning in class B students at TK Kartika II-31 Bandar Lampung.
A Review on Applicability of Deep Learning for Remote Sensing Applications

Remote sensing data are being utilized to understand the features of earth surface. Due to the extensive spatial and temporal coverage of satellites the information received from them have many advantages. However, the data received from satellite’s optical sensors are susceptible of errors because of the presence of clouds. Further, the data may be of low quality due to technical glitches and get a miss for some unavoidable human errors. Attempts were made in the literature to bridge the missing data. They may not, however, perform well with degraded observations of quality and are restricted by inadequate temporal data. This paper, presents the analysis and comparison of these methods, followed by Deep Learning technology and its applicability to remote sensing applications.
Applied Aspects of Blockchain Technology in Travel and Tourism Industry

Blockchain technology is in the early stage of its development, but there’s consensus about its potential. The technology is at an inflection point, with momentum shifting from “blockchain tourism” and exploration to the building of practical business applications. Tourism industry and blockchain have the possibility to become a powerful combination as the technology can bring safety and transparency of transactions. This article aims to examine the features of blockchain as an innovative technology and to present its more important advantages and applied aspects in the field of tourism. There are a growing number of emerging disruptors across travel industry, challenging traditional business models with the use of blockchain. Given some of the blockchain technology’s characteristics and possibilities, this article is pointed towards important applications within the tourism.
Calling and media bots

As a final chapter on bots, I could not miss the opportunity to discuss calling and media bots in Teams with you. In my opinion, they are the ones that have the most future potential. Chatbots are indeed cool and have their use cases, but bots where we can have actual live discussions are just on another level. But we aren’t there yet. The technology is not advanced enough for most use cases, which means that, except for some cool demos, it’s way too difficult (meaning expensive) to build these. Some parts are even still in preview. There are so many steps to take and things to take into consideration that projects like these are the opposite of what we have seen with the QnA Bot. But still, very cool stuff to play with so let’s dive in.
Integrating Social Media into a Pan-European Flood Awareness System: A multilingual Approach

This paper describes a prototype system that integrates social media analysis into the European Flood Awareness System (EFAS). This integration allows the collection of social media data to be automatically triggered by flood risk warnings determined by a hydro-meteorological model. Then, we adopt a multi-lingual approach to find flood-related messages by employing two state-of-the-art methodologies: language-agnostic word embeddings and language-aligned word embeddings. Both approaches can be used to bootstrap a classifier of social media messages for a new language with little or no labeled data. Finally, we describe a method for selecting relevant and representative messages and displaying them back in the interface of EFAS.
Covid-19 implications on right to healthcare in Nigeria and China

The quest by states to contain the spread of the Covid-19 has led to the adoption of extraordinary measures to deal with the extraordinary situation. Some of those measures have the tendency to impact on the citizens’ right to healthcare as protected by the domestic laws of such states and by the relevant international instruments. The paper examines the impact of the responses by the governments in Nigeria and China to the Covid-19 pandemic to ascertain the extent to which the right to healthcare of citizens are respected, and concludes that the governments in the respective countries failed to fulfil their obligations to their citizens.
Multi-Hypothesis Motion Planning under Uncertainty Using Local Optimization

Motion planning is defined as the problem of computing a feasible trajectory for an agent to follow. It is a well-studied problem with applications in fields such as robotics, control theory and ar ...
Allen's Interval Algebra Makes the Difference.

Allen’s Interval Algebra constitutes a framework for reasoning about temporal information in a qualitative manner. In particular, it uses intervals, i.e., pairs of endpoints, on the timeline to represent entities corresponding to actions, events, or tasks, and binary relations such as precedes and overlaps to encode the possible configurations between those entities. Allen’s calculus has found its way in many academic and industrial applications that involve, most commonly, planning and scheduling, temporal databases, and healthcare. In this paper, we present a novel encoding of Interval Algebra using answer-set programming (ASP) extended by difference constraints, i.e., the fragment abbreviated as ASP(DL), and demonstrate its performance via a preliminary experimental evaluation. Although our ASP encoding is presented in the case of Allen’s calculus for the sake of clarity, we suggest that analogous encodings can be devised for other point-based calculi, too.
Per un indice condiviso di possessori e provenienze in SBN: una prospettiva concreta

Following the analysis of strengths and weaknesses of online national and international catalogues and provenance databases, this article introduces a project by ICCU (The Central Institute for the Union Catalogue of Italian Libraries and for Bibliographic Information) that is currently undergoing a feasibility study. This project aims to create a shared index of book owners for Italy’s National Library Service (SBN).
Train-by-Reconnect: Decoupling Locations of Weights from Their Values

What makes untrained deep neural networks (DNNs) different from the trained performant ones? By zooming into the weights in well-trained DNNs, we found it is the location of weights that hold most of the information encoded by the training. Motivated by this observation, we hypothesize that weights in stochastic gradient-based method trained DNNs can be separated into two dimensions: the locations of weights and their exact values. To assess our hypothesis, we propose a novel method named Lookahead Permutation (LaPerm) to train DNNs by reconnecting the weights. We empirically demonstrate the versatility of LaPerm while producing extensive evidence to support our hypothesis: when the initial weights are random and dense, our method demonstrates speed and performance similar to or better than that of regular optimizers, e.g., Adam; when the initial weights are random and sparse (many zeros), our method changes the way neurons connect and reach accuracy comparable to that of a well-trained fully initialized network; when the initial weights share a single value, our method finds weight agnostic neural network with far better-than-chance accuracy.
Neural Networks with Small Weights and Depth-Separation Barriers.

In studying the expressiveness of neural networks, an important question is whether there are functions which can only be approximated by sufficiently deep networks, assuming their size is bounded. However, for constant depths, existing results are limited to depths $2$ and $3$, and achieving results for higher depths has been an important open question. In this paper, we focus on feedforward ReLU networks, and prove fundamental barriers to proving such results beyond depth $4$, by reduction to open problems and natural-proof barriers in circuit complexity. To show this, we study a seemingly unrelated problem of independent interest: Namely, whether there are polynomially-bounded functions which require super-polynomial weights in order to approximate with constant-depth neural networks. We provide a negative and constructive answer to that question, by showing that if a function can be approximated by a polynomially-sized, constant depth $k$ network with arbitrarily large weights, it can also be approximated by a polynomially-sized, depth $3k+3$ network, whose weights are polynomially bounded.
UofL Libraries: Lateral Reading: Handout

Practice “lateral reading,” a strategy used by professional fact-checkers to investigate the reliability of online sources.
User satisfaction Assessment Framework for ASYCUDA Customs Information System in Jordan

The objective of this research is to propose a framework for assessing user satisfaction of ASYCUDA customs information system in Jordan from the employees' perspective. This research applies a quantitative research method, using questionnaire as the research instrument. A sample of 257 employees who used ASYCUDA was chosen using simple random technique. Data were analysed using descriptive statistics, correlation, factor analysis and multiple regression analysis. The findings of this research have demonstrated that all constructs were significantly correlated and predicted perceived usefulness. This study will support Jordan customs in ensuring that all customs employees have the knowledge, skills, and support in order to work in a higher efficiency atmosphere, which creates motivation for work and production. The study implicates that the developed model can enhance the employees performance and the effectiveness of ASYCUDA information system.
PENGARUH METODE PEMBELAJARAN BUZZ GROUP TERHADAP HASIL BELAJAR SISWA

In learning Indonesian, methods and approaches have been used to improve results. One of them is to form students into discussion groups that are very beneficial for students. But the reality is not many teachers do it. The learning methods undertaken by teachers tend to be limited to the delivery of lectures, giving examples of questions, exercises and ending with assignments to be done at home. Many teachers still use the conventional approach so that the learning process only takes one direction where the teacher explains and students hear or take notes, so that there is often a tendency to minimize student involvement. The aim of this research is to find out the influence of buzz group learning methods on student learning outcomes in Indonesian VII Class subjects in MTs NW Pepao, Central Lombok District, 2018/2019 Academic Year. The formulation of the problem in this study is whether there is an influence of the buzz group learning method on student learning outcomes in Indonesian VII Class subjects in MTs NW Pepao, Central Lombok District 2018/2019 Academic Year. The learning method used in the sampling technique in this study is using random rampling techniques. Data collection techniques in this study used the test method as the main method while the documentation, as a complementary method. Data analysis techniques using statistical analysis with the t-test formula. The analysis results obtained a t-value of 4.882 and a table value at the Significance level of 5% with d.b (N-1) = 30-1 = 29 greater than the value of t in the table (4.882> 2.045), meaning that Ho is rejected and Ha is accepted.
Retrieving Multi-Entity Associations

Word embeddings have gained significant attention as learnable representations of semantic relations between words, and have been shown to improve upon the results of traditional word representations. However, little effort has been devoted to using embeddings for the retrieval of entity associations beyond pairwise relations. In this paper, we use popular embedding methods to train vector representations of an entity-annotated news corpus, and evaluate their performance for the task of predicting entity participation in news events versus a traditional word cooccurrence network as a baseline. To support queries for events with multiple participating entities, we test a number of combination modes for the embedding vectors. While we find that even the best combination modes for word embeddings do not quite reach the performance of the full cooccurrence network, especially for rare entities, we observe that different embedding methods model different types of relations, thereby indicating the potential for ensemble methods.
Research Guides: Troubleshooting Electronic Resources: A guide for Patrons: Authentication Loop Problem

Guide for trouble shooting directions for known electronic resources issues. directions for Authentication Loop Problem when trying to access full text