Reviewerly/exHarmony huggingface dataset:
PENGABDIAN KEPADA MASYARAKAT SISTEM INFORMASI KEGIATAN TERPADU (SIKAT) REMAJA SINODE GMIM

Abstract&#x0D; The GMIM Synod Youth Services Commission is part of the ministry of the Evangelical Christian Church in Minahasa in the Province of North Sulawesi. In its service under the auspices of the Evangelical Christian Church in Minahasa (GMIM) the Youth Services Commission specializes in providing services to teenagers aged 12-16 years according to the administration of the GMIM church. With the current technology, the GMIM Synod Youth Services Commission has developed a sistem to assist the implementation of each activity or program implemented in the form of an Integrated Activity Information Sistem (SIKAT). The development of this Information Sistem has not been widely known by adolescents and youth coaches so that in practice there are still congregations or coaches who have not used this sistem. Therefore, the development of this information sistem needs to be followed by training on how to use an integrated activity information sistem (SIKAT), so that in the implementation of the GMIM Synod Youth activities all can be recorded properly through the sistem that has been built. Activity Output: Integrated Information Sistem Application for GMIM Synod Teens.
Towards a General Theory of Infinite-Width Limits of Neural Classifiers

Obtaining theoretical guarantees for neural networks training appears to be a hard problem in a general case. Recent research has been focused on studying this problem in the limit of infinite width and two different theories have been developed: a mean-field (MF) and a constant kernel (NTK) limit theories. We propose a general framework that provides a link between these seemingly distinct theories. Our framework out of the box gives rise to a discrete-time MF limit which was not previously explored in the literature. We prove a convergence theorem for it and show that it provides a more reasonable approximation for finite-width nets compared to the NTK limit if learning rates are not very small. Also, our framework suggests a limit model that coincides neither with the MF limit nor with the NTK one. We show that for networks with more than two hidden layers RMSProp training has a non-trivial discrete-time MF limit but GD training does not have one. Overall, our framework demonstrates that both MF and NTK limits have considerable limitations in approximating finite-sized neural nets, indicating the need for designing more accurate infinite-width approximations for them.
Interpretable Multimodal Learning for Intelligent Regulation in Online Payment Systems

With the explosive growth of transaction activities in online payment systems, effective and real-time regulation becomes a critical problem for payment service providers. Thanks to the rapid development of artificial intelligence (AI), AI-enable regulation emerges as a promising solution. One main challenge of the AI-enabled regulation is how to utilize multimedia information, i.e., multimodal signals, in Financial Technology (FinTech). Inspired by the attention mechanism in nature language processing, we propose a novel cross-modal and intra-modal attention network (CIAN) to investigate the relation between the text and transaction. More specifically, we integrate the text and transaction information to enhance the text-trade joint-embedding learning, which clusters positive pairs and push negative pairs away from each other. Another challenge of intelligent regulation is the interpretability of complicated machine learning models. To sustain the requirements of financial regulation, we design a CIAN-Explainer to interpret how the attention mechanism interacts the original features, which is formulated as a low-rank matrix approximation problem. With the real datasets from the largest online payment system, WeChat Pay of Tencent, we conduct experiments to validate the practical application value of CIAN, where our method outperforms the state-of-the-art methods.
Aircraft collision prediction based on binomial distribution

Introduction/purpose: Based on the binomial distribution of the probability density function, a new probabilistic model for aircraft position predicting is presented in this paper.Methods: The proposed algorithm is composed of three different blocks: Data Association, Tracking/Hybrid State Estimation and Calculation of Probability of Conflict.The information about aircraft current positions and flight plans is used to derive an algorithm for detecting possible conflicts between aircraft.The situations where aircraft may come closer than a certain distance to one another are predicted with high probability.The position estimate and indeterminacy refer to target association when two tracks fall in a validation region by using the Probabilistic Data Association Filter.Results: An efficient collision detection algorithm is designed and tested for a lot of multiple target tracking.Conclusion: The simulation results of aircraft conflict prevention in two trajectory scenarios verify the efficiency of the proposed algorithm.
Developing Higher School Students’ Engineering Thinking in the Process of Foreign Language Teaching

The article aims to describe the basic trends of developing higher school students’ engineering thinking in the process of foreign language teaching. The author justifies the relevance of systemic engineering thinking in the modern world, argues for the efficiency of the synergetic approach as a methodological basis of engineering thinking development. The basic characteristics of engineering thinking are presented; its development levels are identified. The level characteristics of engineering thinking are shown. Scientific novelty of the study lies in the fact that the author identifies and describes methodological techniques used to improve systemic engineering thinking in the process of foreign language teaching. The findings indicate that the proposed methodology promotes development of systematicity as an essential quality of future specialists’ engineering thinking.
Single Model Ensemble using Pseudo-Tags and Distinct Vectors

Model ensemble techniques often increase task performance in neural networks; however, they require increased time, memory, and management effort.In this study, we propose a novel method that replicates the effects of a model ensemble with a single model.Our approach creates K-virtual models within a single parameter space using K-distinct pseudotags and K-distinct vectors.Experiments on text classification and sequence labeling tasks on several datasets demonstrate that our method emulates or outperforms a traditional model ensemble with 1/K-times fewer parameters.
Extrapolatable Relational Reasoning With Comparators in Low-Dimensional Manifolds

While modern deep neural architectures generalise well when test data is sampled from the same distribution as training data, they fail badly for cases when the test data distribution differs from the training distribution even along a few dimensions. This lack of out-of-distribution generalisation is increasingly manifested when the tasks become more abstract and complex, such as in relational reasoning. In this paper we propose a neuroscience-inspired inductive-biased module that can be readily amalgamated with current neural network architectures to improve out-of-distribution (o.o.d) generalisation performance on relational reasoning tasks. This module learns to project high-dimensional object representations to low-dimensional manifolds for more efficient and generalisable relational comparisons. We show that neural nets with this inductive bias achieve considerably better o.o.d generalisation performance for a range of relational reasoning tasks. We finally analyse the proposed inductive bias module to understand the importance of lower dimension projection, and propose an augmentation to the algorithmic alignment theory to better measure algorithmic alignment with generalisation.
The role of technology in governance: The example of Privacy Enhancing Technologies

Abstract The collection of data, its analysis, and the publication of insights from data promise a range of benefits, but can carry risks for individuals and organizations. This paper sets out considerations regarding the potential role for technologies in governance of data use, and some key limitations. The paper examines the potential of Privacy Enhancing Technologies (PETs) to support organizations and institutions that handle data in governing data use, and considers their role based on their current state of development and the trajectory of technological development. This involves consideration both of how these technologies can potentially enable governments and others to unlock the value of data, and also recognition of both contingent and in principle limitations on the role of PETs in ensuring well-governed use of data.
Specification a model for study of Internet reliability

Gross mode, social reliability emerges after an agenda is established with issues focused on political corruption that, far from directing a debate and public consensus, generate negative emotions that result in social despair, a suitable setting for the propaganda of the rectory the authoritarian state. The objective of the present work is to establish a model for the study of the phenomenon in question. A documentary study was carried out with a non-probabilistic selection of sources indexed to national repositories, considering the year of publication in the period 2000 to 2019 , as well as the inclusion of concepts such as "reliability", " millennials ", "Internet users " There are lines of research regarding the extension of the model and its contrast in electoral processes.
A survey on deep hashing for image retrieval

Hashing has been widely used in approximate nearest search for large-scale database retrieval for its computation and storage efficiency. Deep hashing, which devises convolutional neural network architecture to exploit and extract the semantic information or feature of images, has received increasing attention recently. In this survey, several deep supervised hashing methods for image retrieval are evaluated and I conclude three main different directions for deep supervised hashing methods. Several comments are made at the end. Moreover, to break through the bottleneck of the existing hashing methods, I propose a Shadow Recurrent Hashing(SRH) method as a try. Specifically, I devise a CNN architecture to extract the semantic features of images and design a loss function to encourage similar images projected close. To this end, I propose a concept: shadow of the CNN output. During optimization process, the CNN output and its shadow are guiding each other so as to achieve the optimal solution as much as possible. Several experiments on dataset CIFAR-10 show the satisfying performance of SRH.
Adaptive Dialog Policy Learning with Hindsight and User Modeling.

Reinforcement learning methods have been used to compute dialog policies from language-based interaction experiences. Efficiency is of particular importance in dialog policy learning, because of the considerable cost of interacting with people, and the very poor user experience from low-quality conversations. Aiming at improving the efficiency of dialog policy learning, we develop algorithm LHUA (Learning with Hindsight, User modeling, and Adaptation) that, for the first time, enables dialog agents to adaptively learn with hindsight from both simulated and real users. Simulation and hindsight provide the dialog agent with more experience and more (positive) reinforcements respectively. Experimental results suggest that, in success rate and policy quality, LHUA outperforms competitive baselines from the literature, including its no-simulation, no-adaptation, and no-hindsight counterparts.
Emora STDM: A Versatile Framework for Innovative Dialogue System Development.

This demo paper presents Emora STDM (State Transition Dialogue Manager), a dialogue system development framework that provides novel workflows for rapid prototyping of chat-based dialogue managers as well as collaborative development of complex interactions. Our framework caters to a wide range of expertise levels by supporting interoperability between two popular approaches, state machine and information state, to dialogue management. Our Natural Language Expression package allows seamless integration of pattern matching, custom NLP modules, and database querying, that makes the workflows much more efficient. As a user study, we adopt this framework to an interdisciplinary undergraduate course where students with both technical and non-technical backgrounds are able to develop creative dialogue managers in a short period of time.
Chat-Based Approach Applied to Automatic Livestream Highlight Generation

Highlight generation and subsequentDrankou, Pavel video production processes are expensive when it is done by humans.Zykov, Sergey Viktorovich The paper shows how the process can be automated. It defines the highlight generation problem, suggests and discusses five different approaches for solving this problem. Statistics-based approach is discussed separately in deep detail along with algorithm implementation elements. Web-based highlight generation and video production service requirements and architecture are identified and discussed, respectively.
A Study on the research trend of Korean language education for occupational purpose - Focusing on the language network analysis -

The purpose of this study is to analyze research trends of Korean language education for occupational purpose. For this research, the Research Information Sharing Service(RISS) collected paper related to Korean language education for occupational purpose, and collected a total 261 papers, including 209 academic papers and 52 academic journals. Based on the collected papers, the number of papers was analyzed by paper count course(year, time), network analysis(word cloud, frequency, centrality, word pairs, visualization), and topic modeling, respectively. Based on this, we identified the discussion points and future research trends of occupational Korean language education research. Research began in 2001 and has been focused on 'textbooks', 'development', 'plan' and 'analysis'. Looking at the word pairs, "development-textbooks" also appeared the most. According to the analysis of the topic modeling, the development and analysis of textbooks(60.5%), curriculum and measures(30.7%), and education status(8.8%) showed a ratio of about 6:3:1. As a result of the above analysis of research trends, I think that first, learners need necessary textbooks, second, development of textbooks for various occupational groups, and finally, the goal of improving learners' Korean language skills.
Self-Supervised Learning Aided Class-Incremental Lifelong Learning

Lifelong or continual learning remains to be a challenge for artificial neural network, as it is required to be both stable for preservation of old knowledge and plastic for acquisition of new knowledge. It is common to see previous experience get overwritten, which leads to the well-known issue of catastrophic forgetting, especially in the scenario of class-incremental learning (Class-IL). Recently, many lifelong learning methods have been proposed to avoid catastrophic forgetting. However, models which learn without replay of the input data, would encounter another problem which has been ignored, and we refer to it as prior information loss (PIL). In training procedure of Class-IL, as the model has no knowledge about following tasks, it would only extract features necessary for tasks learned so far, whose information is insufficient for joint classification. In this paper, our empirical results on several image datasets show that PIL limits the performance of current state-of-the-art method for Class-IL, the orthogonal weights modification (OWM) algorithm. Furthermore, we propose to combine self-supervised learning, which can provide effective representations without requiring labels, with Class-IL to partly get around this problem. Experiments show superiority of proposed method to OWM, as well as other strong baselines.
IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR UNTUK PENENTUAN KELULUSAN MAHASISWA TEPAT WAKTU

The selection was an attempt College to get qualified prospective students. Test data for new students able to describe the quality of academic and connect to graduate on time. Recognizing the academic quality of students is required in the implementation of the lecture to obtain optimal results. Real conditions today, timely graduation has not achieved optimally, need to be improved to reach the limits of reasonableness. Data that has no need to do a classification based on academic quality, in order to obtain predictions timely graduation. Therefore, proposed an effort to resolve the problem by applying the K-Nearest Neighbor algorithm to re-clustering the test result data for new students. The procedure is to determine the amount of data clusters, determining the center point of the cluster, calculate the distance of the object with the centroid, classifying objects. If the new data group calculation results together with the results of calculation of new data group then finished its calculations. The data will be used in clustering is the result of the entrance exam for new students 3 years old, and has been declared STMIK Amik Riau. This study aims to predict the graduation on time or not. Results of research on testing the value of k, maximum accuracy is obtained when k = 5, reaching 99.25%. Accuracy will decline if the k value the greater the more inaccurate results. The data will be used in clustering is the result of the entrance exam for new students 3 years old, and has been declared STMIK Amik Riau. This study aims to predict the graduation on time or not. Results of research on testing the value of k, maximum accuracy is obtained when k = 5, reaching 99.25%. Accuracy will decline if the k value the greater the more inaccurate results. The data will be used in clustering is the result of the entrance exam for new students 3 years old, and has been declared STMIK Amik Riau. This study aims to predict the graduation on time or not. Results of research on testing the value of k, maximum accuracy is obtained when k = 5, reaching 99.25%. Accuracy will decline if the k value the greater the more inaccurate results.&#x0D;
Minimal Systems of Temporal Logic

The article discusses minimal temporal logic systems built on the basis of classical logic as well as intuitionistic logic. The constructions of these systems are discussed as well as their basic properties. The K t system was discussed as the minimal temporal logic system built based on classical logic, while the IK t system and its modification were discussed as the minimal temporal logic system built based on intuitionistic logic.
Covid-19: NHS Test and Trace releases first figures, with experts calling for improvements

Three months after the UK stopped contact tracing, a new NHS Test and Trace service launched last week. Can teething problems be ironed out, and is it fit for purpose? <b>Elisabeth Mahase</b> finds out
Pengembangan Bahan Ajar Panca Indera Berbasis Multiple Intelligences Pada Siswa Kelas I Sekolah Dasar

The main purpose of this research is to develop the teaching materials of five senses based on multiple intelligences which are valid, and to know the level of effectiveness of the materials teaching the five senses, a multiple intelligences to the observation of students ' activities and the study results test. This research uses research methods Research &amp; Development (R&amp;D). To develop the product, researchers use ADDIE's model of development consisting of five phases: 1. Analysis, 2. Design, 3. Development, 4. Implementation, and 5. Evaluation. The teaching materials are developed in the form of a multiple intelligences-based five senses module which is the subject of a trial is 20 students. The technique of data collection in this study is the students ' intelligences test, validation sheet, observation sheets, student activities, and student study results tests. The data analysis technique used by researchers to test the valid is analyzed quantitatively and to test its effectiveness will be in quantitative analysis. For the analysis quantitatively used descriptive statistics
From UAV Lidar and Magnetic Data to Geological Mapping

Summary CVA Airborne, an alliance between the INFOGEO start-up, specialized in the acquisition and processing of data from drones and the CA group, expert in integrated studies in geosciences, combines drones and miniaturized sensors and develops a methodology for use to produce very high resolution topographic (lidar) and geophysics (magnetics and radiometrics) survey for areas less than 100 km2. Integrated with field observations and ground geophysic measurements such as gravimetry or seismic, they provide the nature and geometry of the sub-surface structures (oil and gas, mining and energy storage). In the summer of 219, a project covering the entire value chain of exploration (acquisition, processing, analysis and interpretation of data) was carried with a survey of 110 ha in the south of France. The peculiarity of this project was to work in a very rugged topographic context, with vertical differences in the order of 250 meters. The project combined magnetic measurements, allowing to characterize the variability of the magnetic properties of the subsurface, under the vegetal and alluvial conver, and topographic measurements by lidar in order to extract the main topographic lineaments and magnetic anomalies from the survey area.
Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image.

In this paper, we propose a deep convolutional recurrent neural network that predicts action sequences for task and motion planning (TAMP) from an initial scene image. Typical TAMP problems are formalized by combining reasoning on a symbolic, discrete level (e.g. first-order logic) with continuous motion planning such as nonlinear trajectory optimization. Due to the great combinatorial complexity of possible discrete action sequences, a large number of optimization/motion planning problems have to be solved to find a solution, which limits the scalability of these approaches. To circumvent this combinatorial complexity, we develop a neural network which, based on an initial image of the scene, directly predicts promising discrete action sequences such that ideally only one motion planning problem has to be solved to find a solution to the overall TAMP problem. A key aspect is that our method generalizes to scenes with many and varying number of objects, although being trained on only two objects at a time. This is possible by encoding the objects of the scene in images as input to the neural network, instead of a fixed feature vector. Results show runtime improvements of several magnitudes. Video: this https URL
Totalitarismo y lenguaje: el campo de concentración como espacio multilingüe y la figura del intérprete concentracionario

This paper examines the relationships between absolute power and language. Nazi concentration camps embody the space where the maximum expression of totalitarianism was put into practice. By becoming forcedly multilingual societies from the onset of the war, the German language grew to be, more than ever, an instrument of oppression at the service of the system: deformed and distorted, it practically evolved into a violent and rudimentary protolanguage. Being able to acquire and use this new form of speech was directly related to the prisoners’ prospects of survival. In addition, a lingua franca that incorporated features of several languages ​​spoken by the deportees naturally emerged; it was used in the communication among inmates and revealed the social structures of the camp. In this intercultural space, concentrarionary interpreters arose as the communicative link between persecutors and persecuted. The study of their behavior seems especially interesting for researchers concerned about enlightening the mechanisms through which interlinguistic mediation develops in spaces of maximum pressure.