Reviewerly/exHarmony huggingface dataset:
Interface Automata for Choreographies

Formal choreographic models yield a suitable frameworks to specify, analyse, and develop message-passing applications. In this application domain, orchestration has so far been preferred to choreography because the former offers a simpler approach to tame the complexity of developing distributed applications. On the one hand, orchestration yields a toolbox of handy and relatively simple solutions to practitioners. However, orchestration shows scalability limitations that choreographies seem able to tackle. Another key advantage of choreographies is the possibility of guaranteeing behavioural properties of applications “by construction”. Correct components can be (semi)automatically derived from correct global specifications. In this context it is crucial to verify the correctness of global specifications. In this thesis, we adopt the global choreographies of Guanciale and Tuosto to specify global views while we abstract components as special automata. More precisely, we generalise the class of interface automata of de Alfaro and Henzinger to group interface automata and equip them with two internal products, instrumental to the verification of “sound” global specifications. In particular, these products allow us to characterise erroneous states that potentially spoil interactions. A main result of our approach is that the absence of those erroneous state is equivalent to well-formedness of global choreographies, that is a sufficient property guaranteeing well-behaviour of local components projected from the global specification.
PENERAPAN PEMBELAJARAN KOOPERATIF TIPE NUMBER HEAD TOGETHER UNTUK MENINGKATKAN PRESTASI BELAJAR PKN

Classroom action research conducted in SD No. 2 Blahkiuh in class IV Semester II SD No. 2 Blahkiuh Academic Year 2015/2016 aims to determine the improvement of learning achievement in PKN subjects by applying the Cooperative learning model Type Number Head Together. The data of this study were collected by giving the achievement test. In analyzing the data obtained used descriptive analysis method. The data generated from this research consist of preliminary data, cycle data I and Cycle II data. From the preliminary data, the average new class reached 69.28 and the learning completeness only reached 25.64%. This data is far below expectations considering that the PKN PKM in this school is 76. In the first cycle there has been an increase, namely the average class reaches 74.74 and the percentage of learning completeness reaches 53.85%. In the second cycle the acquisition of the average class had reached 82.79 and the percentage of mastery learning had reached 97.44%. The data in Cycle II is in line with expectations due to the use of constructivist learning models. The conclusions obtained are the Cooperative Type Number Head Together learning model can improve students' PKN learning achievement.
PMH63 USE OF ARTIFICIAL INTELLIGENCE METHODS TO BETTER DEFINE TREATMENT LINES IN SCHIZOPHRENIA

Large longitudinal administrative databases are an important source of information on treatment pathways, but clinical information is often incomplete or clouded by incidental events (e.g., loss of follow-up, lack of compliance, misreporting). Therefore the analysis of treatment pathways requires assumptions. The objective of this study is to assess the ability of Artificial Intelligence (AI) methods to classify prescriptions into treatment lines and help analysts defining rules for the analysis of treatment pathways, based on a sample of data from patients with schizophrenia. The study was conducted using the Japan Medical Data Center (JMDC) claims database. Patients aged ≥15 years with ≥2 claims associated with schizophrenia diagnosis and a first antipsychotic prescription recorded between January 2009 and March 2013 were included. Prescriptions were first classified into treatment lines based on assumptions including number of days allowed between consecutive refills and minimum number of overlap days to consider combinations (“standard approach”). Secondly, the dataset was split into training (80%) and test (20%) datasets. AI algorithms were developed on the training dataset to predict treatment lines defined previously on the test dataset. When available, the 20 previous and following prescriptions were used as inputs. AI implemented algorithms were Neural Networks, Decision trees, Random Forest and Extreme Gradient Boosting. 3142 patients met inclusion criteria and were eligible to be included in this study. Measures of algorithm accuracy were 33% for Neural Networks, 68% for Decision trees, 71% for Random Forest and 77% for Extreme Gradient Boosting. On 100 prescriptions with discrepancy between standard approach and Extreme Gradient Boosting method, 48% were deemed misclassified with the standard approach. The Extreme Gradient Boosting algorithm was the best performing method and helped to detect outlier patients. These results support the use of AI methods to check and refine assumptions for defining treatment pathways.
A Generative Face Completion Method Based on Associative Memory

Associative memory is an important brain function human being owns, which could realize storage and cognition of information. Recurrent neural networks possess the function of associative memory, and they can realize image storage and generation basically. Based on stacked recurrent neural networks, a generative face completion method is proposed to utilize their associative memory function to realize face completion. According to different masked regions, the stacked recurrent neural network can memorize the relationship among row pixels from four directions of images, and associate lost pixels of the masked region in face images. In our method, a parallel multi-streams recurrent architecture with context constraints consider both global and local context information to associate the memorized images efficiently. Moreover, we propose an image hybrid strategy to optimize face images, which merges the associated pixels generated from different angles. Experiments on the Wild Face data set (CelebA) reveal that our generative face completion method can get the state-of-the-art results on the face completion problem.
TOCO: A Framework for Compressing Neural Network Models Based on Tolerance Analysis

Neural network compression methods have enabled deploying large models on emerging edge devices with little cost, by adapting already-trained models to the constraints of these devices. The rapid development of AI-capable edge devices with limited computation and storage requires streamlined methodologies that can efficiently satisfy the constraints of different devices. In contrast, existing methods often rely on heuristic and manual adjustments to maintain accuracy, support only coarse compression policies, or target specific device constraints that limit their applicability. We address these limitations by proposing the TOlerance-based COmpression (TOCO) framework. TOCO uses an in-depth analysis of the model, to maintain the accuracy, in an active learning system. The results of the analysis are tolerances that can be used to perform compression in a fine-grained manner. Finally, by decoupling compression from the tolerance analysis, TOCO allows flexibility to changes in the hardware.
Serbia: journalists physically and verbally attacked by football fans

The International and European Federations of Journalists (IFJ and EFJ) condemned physical attacks & threats against TV journalists by football fans ...
A Paraconsistent ASP-like Language with Tractable Model Generation

Answer Set Programming (ASP) is nowadays a dominant rule-based knowledge representation tool. Though existing ASP variants enjoy efficient implementations, generating an answer set remains intractable. The goal of this research is to define a new \asp-like rule language, 4SP, with tractable model generation. The language combines ideas of ASP and a paraconsistent rule language 4QL. Though 4SP shares the syntax of \asp and for each program all its answer sets are among 4SP models, the new language differs from ASP in its logical foundations, the intended methodology of its use and complexity of computing models. As we show in the paper, 4QL can be seen as a paraconsistent counterpart of ASP programs stratified with respect to default negation. Although model generation of well-supported models for 4QL programs is tractable, dropping stratification makes both 4QL and ASP intractable. To retain tractability while allowing non-stratified programs, in 4SP we introduce trial expressions interlacing programs with hypotheses as to the truth values of default negations. This allows us to develop a~model generation algorithm with deterministic polynomial time complexity. We also show relationships among 4SP, ASP and 4QL.
EXPERIENCE OF THE TEACHING-LEARNING PROCESS FOR AN INTRODUCTION TO THERMAL REGULATIONS FOR BUILDINGS IN INDUSTRIAL ENGINEERING SUBJECTS

EXPERIENCE OF THE TEACHING-LEARNING PROCESS FOR AN INTRODUCTION TO THERMAL REGULATIONS FOR BUILDINGS IN INDUSTRIAL ENGINEERING SUBJECTS
New Method of Metal Magnetic Memory Signal Measuring and Denoising

The metal magnetic memory (MMM) technique can be effective in determining the initial damage of materials and structures in service and is partly applied in engineering.However, the real signals measured in engineering practice usually contain interference of the background magnetic field and measurement noise.For the influence of the background magnetic field, we designed a measuring probe constituted by two magnetic sensors that were arranged at different heights in the same vertical direction.Through the channel compensated method, we extracted principal features of the self-magnetic leakage field (SMLF) signal.As for the influence of measurement noise, we built the structure elements combined with the SMLF signal characteristic and investigated multi-scale morphological filtering to reduce the noise.The simulation and experiment results show that the proposed methods can not only suppress the background magnetic field and many kinds of noise but also protect the SMLF signal detail effectively.
Prediction of the Dissemination of Health News on Microblogging Sites Based on Ample Feature Selection and Support Vector Machine

As a social networking service, microblogging sites provide an open platform facilitating the sharing and discussion of valuable news.This paper identifies the influencing factors of the dissemination of health news posts on Weibo, the leading microblogging site in China.The effects of these factors were tested with 863 news posts, all about public health issues.The content features, author features and a social feature of each post were evaluated, and collected into a set of ample, diverse features that characterize widely disseminated posts.In addition, the support vector machine (SVM) was adopted to differentiate between widely disseminated posts and normal posts, and compared with several classification methods through an experiment on microblog posts of health news.The results show that the SVM greatly outperformed the contrastive methods in predicting the dissemination trends of such news.The research results inform crisis managers about the public reaction towards specific news on public health issues, shedding important new light on news dissemination.
Evaluation of Placer Deposit Within Ghana a Case Study of the Kwabeng Placer Gold Deposits North Western Flank of Atewa Range, Eastern Region, Ghana

The estimation of Mineral Resources/Reserves (MR) together with the quantification of the distribution of metallurgical attributes provide strong evaluation of the large alluvials gold deposits within Kwabeng and its surrounding , which occurs along the north western flank of the famous Atewa Range.This reduces the risk arising from uncertainties within the ore body as far as geological and metallurgical information are concern and also improve decision making process during the production stage of the mining project.Hand dug Pits data formed the basis for the estimation of the MR as well as the determination of the spatial distribution of the metallurgical parameters of the Kwabeng deposits.A typical grid density of 200m x 50m was used for the pitting.Detailed logging of pit samples together with processing provided first hand information concerning the metallurgical behavior of the gold bearing gravels and subsequent distribution of these parameters.The estimation of the MR have been based on an initial estimation of the gold grade and gravel section using the inverse distance weighting (IDW) technique and subsequent estimation of the resources using the volume and mean grade of the ore.A total volume of 19328000m3 gold bearing gravels were estimated at average grade of 0.46g/m3 and fine ounces of 261,728 oz based on the gold fines of 920 for the alluvial gold within the area.The gold within the study is usually coarse in nature with majority of the coarse gold occurring at the upstream of the drainages in the area.Clayey materials also represent large portion of the gold bearing gravels in almost all the sampled areas.
FROM THE HISTORY OF THE DEPARTMENT OF SCIENTIFIC AND TECHNICAL ARCHIVES OF MGIAI – IAI RGGU: PERSONALITIES (1969 – EARLY 1990S)

The aim of the work is to study the history of the creation and activity of the Department of scientific and technical archives of MGIAI – IAI between 1969 and early 1990 through the prism of the study of the biographies of the first generation scholars. The article discusses the conditions for the formation of social demand for the development of educational and scientific direction connected with scientific and technical documents and archives. The study was based on the personnel documents from the archives ofthe Russian State University for the Humanities (prof. A.A. Kuzin, prof. N.G. Filippov, prof. K.B. Gel’man-Vinogradov, assoc. prof. P.S. Preobrazhenskaya). In the professional biographies of the scholars, the general and individual creative aspects of their activity have been revealed; their influence on the formation of the scientific school of scientific and technical documents and archives has been traced. The work is built in the context of the historiographical approach to the archival administration of scientific and technical documents. The main components of this educational and scientific knowledge have been highlighted. The contribution of the Department’s staff to its creation has been determined, particularly, the interdisciplinary character, the combination of archival and historical-technical approaches, the study of the institutional forms of scientific and technical activities in Russia, the construction of the methodological base of documentary and special archival science, the identification of historical and cultural properties of scientific and technical documents. The process of special archival knowledge formation reflected in educational-methodical and research works of the teachers of the Department has been traced. Conclusions are drawn about the dependence of the development of the scientific school of archival science of scientific and technical documents on the professional, creative and personal qualities of the team of teachers of the Department of scientific and technical archives, as well as about the completion  of the period of development of the Department of scientific and technical archives within the frames of the traditional domestic archival science in the early 1990s.
Impact of Displacement on Civil and Political Rights of the Internally Displaced Persons in Dolakha District of Nepal

The explanatory paper discusses the impact of displacement on the civil and political rights of the internally displaced persons (IDPs) in Dolakha District of Nepal with special reference to the displacement caused by the 2015 Nepal Earthquake. The discussion has focused on studying the answer for whether the disaster-induced IDPs enjoyed the civil and political rights particularly during the Federal, Provincial and Local elections in Nepal. The findings are based on the primary data collected from the respondents, IDPs and the people’s representatives, who are the key aspect of the real time empirical familiarity, and of course supported by secondary data. The main issue in the paper is whether the IDPs could enjoy the civil and political rights during the tri-phases of election in 2017 in Dolakha District of Nepal, and the claim is that some of the civil and political rights of the IDPs were unconstructively impacted because of their status of being displaced.
Design of Type 2- Interval Fuzzy PID Controller for CSTR

This paper proposes Type 2-Interval Fuzzy Proportional-Integral-Derivative (T2IFPID) controller for a non-linear system.Type 2-Interval fuzzy logic controller (T2IFLC) is self-possessed in such a way that it is an autonomous process.To decipher the influence, the impression of uncertainty on the controller execution to two different types of curves are outlined i.e. aggressive control curve and smoother control curve.Popov-Lyapunov approach is used to define the stability of the framework.
PERPUSTAKAAN DATA : SEBUAH PENGAMAT TERHADAP UNIVERSITY OF TORONTO MAP AND DATA LIBRARY

Data libraries have been widely known in Europe and America. The fact that these libraries have been optimized for upporting research makes it significant for Indonesia to initiate the development data libraries. However, Indonesian language literature on this specific type of library is still very limited. Although some of data library aspects have been considerably implemented, the author has not found any data library exist in Indonesia. Therefore, this paper is aimed at defining a representative data library by looking at the University of Toronto Map and Digital Library, as one of data libraries, and observing its facilities. The author will attempt to make a basic observation and description in regard. It is expected that the results will be useful as a reference for developing data libraries in Indonesia.
Pengembangan Aplikasi Perhitungan Internet Protocol(IP) Address Berbasis Java Untuk Siswa SMK

Siti Cahaya Rezeki. A710150029. PENGEMBANGAN APLIKASIPERHITUNGAN INTERNET PROTOCOL (IP) ADDRESS BERBASIS JAVAUNTUK SISWA SMK. Skripsi. Pendidikan Teknik Informatika. FakultasKeguruan dan Ilmu PendidikanLearning media e-learning using Netbeans 8 software makes the concept ofelectronic media learning media that can be accessed anywhere and anytime. Theproblems faced at SMK Batik 1 Surakarta are the lack of interest in learning andthe difficulty of students in calculating internet protocol (ip) addresses by thesubnetting method. Students feel this internet protocol address (ip) addressmaterial is too difficult so students are less interested in learning because theteacher delivering this material still uses lecture methods that are considered lesseffective in teaching and learning so students need learning media that can helpstudents be more active and add students' enthusiasm for understanding internetaddress (IP) addressing material. The development of e-learning learning mediauses java-based Netbeans 8 software which is more effective and efficient tocreate an IP address calculation application to make it easier for students tocalculate IP addresses. This study uses a research and development (R & D)method with the approach of analysis, design, development, implementation, andevaluation (ADDIE) which is expected to develop media that are easy for studentsto use. There is an average result of the percentage of products from mediaexperts 90% categorized as very feasible and material experts 90% categorized asvery feasible. So based on the average results of media experts and learningmedia material experts using Java-based netbeans 8 software is categorized asvery feasible. The average results of a student questionnaire 81,062% categorizedas very feasible.
EvoMan: Game-playing Competition

This paper describes a competition proposal for evolving Intelligent Agents for the game-playing framework called EvoMan. The framework is based on the boss fights of the game called Mega Man II developed by Capcom. For this particular competition, the main goal is to beat all of the eight bosses using a generalist strategy. In other words, the competitors should train the agent to beat a set of the bosses and then the agent will be evaluated by its performance against all eight bosses. At the end of this paper, the competitors are provided with baseline results so that they can have an intuition on how good their results are.
PAM: An Efficient Hybrid Dimension Reduction Algorithm for High-Dimensional Bayesian Network

In recent years, machine learning has been gradually widely applied to the big data in medical field, such as prediction and prevention of disorders. Bayesian Network has been playing an important role in machine learning and has been widely applied to the medical diagnosis field for its advantages in reasoning under uncertainty. But as the rise of the number of interest variables, the Bayesian Network structure search space is growing super-exponentially. Aiming at improving the efficiency of finding the optimize structure from the large search space of high-dimensional network, in this paper we propose a method, PAM, which is applied to Bayesian Network learning to constrain the search space of high-dimensional network. Several Experiments are performed in order to confirm our hypothesis.
Bipartite Conditional Random Fields for Panoptic Segmentation

We tackle the panoptic segmentation problem with a conditional random field (CRF) model. Panoptic segmentation involves assigning a semantic label and an instance label to each pixel of a given image. At each pixel, the semantic label and the instance label should be compatible. Furthermore, a good panoptic segmentation should have a number of other desirable properties such as the spatial and color consistency of the labeling (similar looking neighboring pixels should have the same semantic label and the instance label). To tackle this problem, we propose a CRF model, named Bipartite CRF or BCRF, with two types of random variables for semantic and instance labels. In this formulation, various energies are defined within and across the two types of random variables to encourage a consistent panoptic segmentation. We propose a mean-field-based efficient inference algorithm for solving the CRF and empirically show its convergence properties. This algorithm is fully differentiable, and therefore, BCRF inference can be included as a trainable module in a deep network. In the experimental evaluation, we quantitatively and qualitatively show that the BCRF yields superior panoptic segmentation results in practice.
Using of JPEG image as a cover media for text hiding

This paper, will overview the use of data hiding techniques in JPEG digital image file format. In particular, will describe how one can use Steganography to hide information in a JPEG digital image. This research will review of hiding data by using Least Significant Bit (LSB) as a method, and hiding a text (document) in a JPEG image as a media cover. This paper was implemented by using matlab to complete hiding data. Data hiding is a technique that is used to embed secret information into a cover media. However, the transmitted images may be compressed or not. When it is transmitted, an errors transmitting may occur. If such errors occurred, the receiver cannot extract the information correctly from the cover_image. Digital media have been massively produced, easily manipulated, and swiftly transmitted to almost anywhere in the world at anytime.