Reviewerly/exHarmony huggingface dataset:
ProtoViewer: Visual Interpretation and Diagnostics of Deep Neural Networks with Factorized Prototypes

In recent years deep neural networks (DNNs) are increasingly used in a variety of application domains for their state-of-the-art performance in many challenging machine learning tasks. However their lack of interpretability could cause trustability and fairness issues and also makes model diagnostics a difficult task. In this paper we present a novel visual analytics framework to interpret and diagnose DNNs. Our approach utilizes ProtoFac to factorize the latent representations in DNNs into weighted combinations of prototypes, which are exemplar cases (e.g., representative image patches) from the original data. The visual interface uses the factorized prototypes to summarize and explain the model behaviour as well as support comparisons across subsets of data such that the users can form a hypothesis about the model's failure on certain subsets. The method is model-agnostic and provides global explanation of the model behaviour. Furthermore, the system selects prototypes and weights that faithfully represents the model under analysis by mimicking its latent representation and predictions. Example usage scenarios on two DNN architectures and two datasets illustrates the effectiveness and general applicability of the proposed approach.
Investigating Grammar Learning Strategies Employed by Freshmen at University of Darussalam Gontor

This study explored types of grammar learning strategies (GLS) used by freshmen of University Darussalam Gontor and their ways in applying GSL in grammar class. This research employed survey research design by gathering data from questionnaire adapted from Bayou (2015) which was modified from Oxford (1990) Language Learning Strategies Classification and open-ended interview. As many as 35 of 60 students (58.3%) who consisted of males and females completed the questionnaire voluntarily. The data were analyzed in simple numerical statistics for the RQ1 and described in details for the RQ2. The result of the study revealed that compensation strategies were the most preferred strategy used by students as many as 75%. It meant that the students were more conscious in using the strategy of grammar knowledge. These results of percentage illustrated that the students employed direct strategies more than indirect strategies. Further, the ways students employed GLS in grammar classroom were described in details.
DEVELOPMENT OF COMPUTER-BASED PHOTOSHOP LEARNING MEDIA USING COMPUTER BASED INTERACTION METHOD

Advances in communication and information technology need to be continuously adapted to various disciplines, including education. Nowadays, Photoshop is one of the most widely used editing software by designers to produce quality images. Factors that influence efforts to improve users' Photoshop learning abilities include teacher competence, learning facilities, learning environment, user learning motivation, and learning models used. To support Photoshop learning, it is necessary to design an attractive learning media. Computer-Based Instruction is one of the techniques commonly used to create a learning application, CBI is usually used for making interesting designs that apply human-computer interaction and how learning materials are made. And it can help users understand and further increase user enthusiasm and motivation to learn Photoshop. Macromedia Flash is a program used to design a learning application that describes the learning storyline. Photoshop CS 3 tutorial is designed using Macromedia Flash 8 which can be easily understood by users in the learning process
Dependency Structure Discovery from Interventions

Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data. However, there are theoretical limitations on the identifiability of underlying structures obtained from observational data alone. Interventional data provides much richer information about the underlying data-generating process. However, the extension and application of methods designed for observational data to include interventions is not straightforward and remains an open problem. In this paper we provide a general framework based on continuous optimization and neural networks to create models for the combination of observational and interventional data. The proposed method is applicable even in the challenging and realistic case that the identity of the intervened upon variable is unknown. We examine the proposed method in the setting of graph recovery both de novo and from a partially-known edge set. We establish strong benchmark results on several structure learning tasks, including structure recovery of both synthetic graphs as well as standard graphs from the Bayesian Network Repository.
Hybrid Query Execution on Linked Data With Complete Results

The link traversal strategies to query Linked Data over WWW can retrieve up-to-date results using a recursive URI lookup process in real-time. The downside of this approach comes with the query patterns having subject unbound (i.e. ?S rdf:type:Class). Such queries fail to start up the traversal process as the RDF pages are subject-centric in nature. Thus, zero-knowledge link traversal leads to the empty query results for these queries. In this paper, the authors analyze a large corpus of real-world SPARQL query logs and identify the Most Frequent Predicates (MFPs) occurring in these queries. The knowledge of these MFPs helps in finding and indexing a limited number of triples from the original data set. Additionally, the authors propose a Hybrid Query Execution (HQE) approach to execute the queries over this index for initial data source selection followed by link traversal process to fetch complete results. The evaluation of HQE on the latest real data benchmarks reveals that it retrieves at least five times more results than the existing approaches.
News topic classification as a first step towards diverse news recommendation

When developing an algorithm that uses news diversity as a key driver for personalized news recommendation it is crucial to focus on means to cluster news articles in a fine-grained manner, ideally by leveraging the content of the text. In this paper we investigate semantic classification of news articles in an unfiltered news stream. We first present an analysis of the EventDNA corpus: a collection of Dutch-language news articles annotated with event data according to apredefined typology. We found that the types assigned as features of events do not allow for such a semantic classification and investigate the IPTC News Media Topics standard as an alternative. By mapping event types with manually-assigned IPTC topics, we observe that a more diversified picture emerges, which leads us to conclude that the IPTC classification is a useful proxy. Based on a historical data sample of Dutch news articles covering the year 2018, we then perform a series of machine learning experiments in order to automatically predict the top two levels of the IPTC taxonomy. Various multi-label classification models are built with BERTje using a bottom-up and top-down approach. The results reveal that the top-down approach yields the best results, with an overall macro F-1 score of 86.4% and a Jaccard accuracy of 89.2% for the level-one topics and one of 83.7% and 87.5% for the level-two predictions.
Rewording the world: narrative and nature after poststructuralism

© 2015 Sense Publishers. All rights reserved. In this essay I address the question, ˜What am I to do? as an outdoor and environmental educator with some responsibilities for facilitating learnersâ€™ experiences of the outdoors, by exploring some of the stories in which my colleagues and I find ourselves a part.
Chimney Filter Model Wet Scrubber to Reduce Air Pollutant Emissions on the Incinerator

In general, inpatient health care facilities produce infectious and non-infectious waste 0.3 mᶟ / day. Non-infectious waste that is burned in an incinerator without a chimney filter, can cause particles, CO, SO2, NOx (air pollutants) and cause environmental pollution. This study aims to make a chimney filter design with a Scrubber model on an incinerator at the Public Health Center, Maospati District, Magetan Regency to reduce the amount of air pollutants emitted. This type of research is experimental research. This research designed a particle trapping device, gas by spraying water into the scrubber. The independent variable of this research was the variation of the water flow sprayed in the scrubber (3.2 liters / minute, 4 liters / minute, 5.6 liters / minute). The dependent variables of this study were particles, SO2, NOx, CO. Data collection using a digital gas detector method in the form of a UV spectrophotometer. Data were analyzed descriptively, in the form of frequency distribution, and percentage, presentation of data in a table based on air emission quality standards from thermal waste processing. The results illustrate that the use of a chimney scrubber filter with water spraying 3.2 liters / minute, 4 liters / minute, 5.6 liters / minute can reduce air pollutants, emission of SO2, CO to below the air quality standard. In addition, this tool can also reduce NOx gas and particles, but not yet below the quality standard. The conclusion from the results of this study is particulate emission air pollutants, gas SO2, CO, NOx. the incinerator can be lowered by modifying variations by spraying water 3.2 liters / minute, 4 liters / minute, 5.6 liters / minute on the chimney scrubber filter on the incinerator.&#x0D; &#x0D; Keywords: incinerator; scrubber; water discharge variations; particle; gas
PROTOTYPING OF THE INFORMATION SYSTEM OF AUTOMATIC MONITORING OF STUDENTS' MOTIVATION

The article provides solutions on the basis of which the motivation and mood of employees can be monitored using IT and topicality of developing a similar information system for motivating students is substantiated.&#x0D; Based on the tasks of the subject area, the requirements for the developed information system are formulated.&#x0D; To determine the motivation associated with the psychological model, Vladimir Yudin’s model used in the pedagogic process was chosen as the base one. The system should calculate the indicators of the student's qualities and, on the basis of the transition rules, determine the likelihood of belonging to the psychological type, and also give recommendations on the formation of motivation. Monitoring student motivation will help&#x0D; determine the list of activities in the university that contribute to the development of competences for each type of student. The article presents the developed user interfaces of the system to demonstrate its capabilities.
Abnormal behavior recognition based on edge feature and 3D convolutional neural network

With the rapid development of artificial intelligence and 5G technology, the development process of smart city has been greatly accelerated. An important part of the development of smart city is to improve the ability of video monitoring equipment to analyze the content of the video. This paper is to identify the specific abnormal behaviors in the video, including climbing, fighting and falling. The identification of these abnormal behaviors is helpful to find out the danger in time and intervene to ensure personal safety and social stability. Therefore, this paper proposes a deep learning method based on edge extraction. In the first step, the video is decomposed into several frames, and the canny edge detection algorithm is used to extract the edge of the images. In the second step, the extracted edge information is used as the input of the 3D convolutional neural network, and the 3D convolutional neural network model is trained iteratively to recognize the abnormal behaviors. Through a large number of experiments, the effectiveness of the proposed algorithm is verified.
The Current State of MALL in Japanese Universities

It has been almost 20 years since Thornton and Houser (2002) investigated the potential of using cellphones in Japanese university language classrooms. Since then, thousands of research articles have demonstrated the practical, theoretical, and pedagogical foundations of mobile-assisted language learning (MALL) in Japanese educational contexts. This chapter is a review of recent literature in the field of MALL, specifically focusing on the use of iPods, iPads, and smartphones in language learning. The research from the previous seven years reviewed herein includes studies about the use of video creation, social media, content instruction, and student perceptions of MALL. The intent of this chapter is to provide an overview of current MALL practices in use in Japanese educational contexts with ideas for research-based, practical application and to provide suggestions for continued research in this field.
Modeling the C(o)urse of Privacy-critical Location-based Services – Exposing Dark Side Archetypes of Location Tracking

With the ubiquitous use of mobile devices, locationbased services (LBS) have rapidly pervaded daily life.By providing context-and location-specific information, LBS enable a myriad of opportunities for individuals and organizations.However, the manifold advantages come along with a radical increase in location privacy concerns and non-transparent data flows between the various actors involved.While research often focuses on protecting the dyadic relation between the user and LBS provider, the entirety of dark sides constituting privacy violations remains hidden.In this paper, we follow the paradigm of architectural thinking to shed light on the diverse dark sides emerging in today's LBS.By drawing on a multiple case study and developing a notation for architectural maps that help understand LBS from a socio-technical and privacy-oriented perspective, we reveal six dark side archetypes of LBS.
Regularities of modernization and improvement of educational services quality

The paper reveals the main trends of the modern educational process, according to which the regularities of modernization of education and improvement of educational services quality are updated. The process of modernization is defined as a consistent change in educational practices under the influence of social, economic and cultural factors. The analysis of changes in the education system in the context of digitalization and informatization of society made it possible to build an educational paradigm, which demonstrates that the quality of educational services is proportional to the goals of education, the needs of society as well as to the education conditions and the possibilities of science in a specific period of time. This paradigm allows us to trace the causal relationship between the social and cultural context and changes in the education system as a form of students and teachers adaptive reaction. Thus, the regularities of education modernization are understood as pedagogical phenomena that are actualized at different stages of the educational process development with renewed power and thereby determine some periodicity in the educational process and didactic tradition. Building the educational process according to the gnoseological and psychological regularities of education modernization seems to be a constructive pedagogical decision that will allow educational organizations to stay competitive on the market of educational services.
The Influence of Scientific and Technological Innovation in Jiangxi Province's Universities on Regional Economic Growth

In an era led by high technology, the development of regional economy is inseparable from innovation, and scientific and technological innovation of universities makes a significant contribution to the development of science and technology in my country. Local colleges and universities can provide intellectual and technical guarantees for the development of the regional economy, and at the same time, strong regional financial capabilities can also meet the needs of colleges and universities' innovative projects, and provide a strong backing guarantee for the quality of schooling. These two complement each other. This article will take the local colleges and universities in Jiangxi Province as the starting point to explore how the technological innovation of colleges and universities can better fit with the regional economic development goals, so that the results of their own growth and development can be implemented in the regional economic development.
Invariant Causal Representation Learning

Due to spurious correlations, machine learning systems often fail to generalize to environments whose distributions differ from the ones used at training time. Prior work addressing this, either explicitly or implicitly, attempted to find a data representation that has an invariant causal relationship with the outcome. This is done by leveraging a diverse set of training environments to reduce the effect of spurious features, on top of which an invariant classifier is then built. However, these methods have generalization guarantees only when both data representation and classifiers come from a linear model class. As an alternative, we propose Invariant Causal Representation Learning (ICRL), a learning paradigm that enables out-of-distribution generalization in the nonlinear setting (i.e., nonlinear representations and nonlinear classifiers). It builds upon a practical and general assumption: data representations factorize when conditioning on the outcome and the environment. Based on this, we show identifiability up to a permutation and pointwise transformation. We also prove that all direct causes of the outcome can be fully discovered, which further enables us to obtain generalization guarantees in the nonlinear setting. Extensive experiments on both synthetic and real-world datasets show that our approach significantly outperforms a variety of baseline methods.
Science Fiction, BioEthics, and the Law: A Case-study of Machines and Me* as a Legal Pedagogical Tool

As technology advances, the law, as usual, is far behind. The area of AI and robotics is but one example. Here, untutored commentators and philosophers are pre-occupied with the person-hood status of these “synthetic sapiens”- all the while missing deep-seated issues. Enter the world of science fiction - which has the unique ability to place and present future dilemmas in a human context, allowing us to “see” issues that currently are eluding us and perhaps deal with them proactively. This essay presents the science fiction of Machines and Me by Ian McEwan as a hypothetical for teaching contemporary legal issues conjured by AI technology, issues which have yet to be imagined by contemporary legal theorists, let alone addressed. These include threshold concepts such as “what is a civil wrong” – a foundational principle of tort law. The clash between ethics, law, and morality as a determinant of punishment, vengeance/retribution, deterrence and the restorative justice functions of law is also encompassed within the narrative. This conceptualization of the conflicting objectives of machine deep learning and human morality, as portrayed in this sci-fi novel, presents fodder for discussion as a cautionary tale, a warning to prevent us from being sucked into an AI Future-world, unawares.
Perancangan Sistem Informasi Studio Foto Pada Click Five Studio Depok

Digital photography technology is developing very fast for the world of photography in Indonesia. According to these developments, studio space is needed that can accommodate all the needs of customers who vary with their needs. Click Five Studio Depok is a company engaged in the field of photography, but in its service is still using web based that requires something connectivity in the system and managing the data is still manual which uses facilities that make services inefficient. By using the NetBeans application, a photo studio information system was created to assist the process of ordering services at Click Five Studio Depok so that the system becomes computerized, more competent and can compete in the business world that can emerge in the world.
Residual Energy-Based Models for Text

Current large-scale auto-regressive language models display impressive fluency and can generate convincing text. In this work we start by asking the question: Can the generations of these models be reliably distinguished from real text by statistical discriminators? We find experimentally that the answer is affirmative when we have access to the training data for the model, and guardedly affirmative even if we do not. This suggests that the auto-regressive models can be improved by incorporating the (globally normalized) discriminators into the generative process. We give a formalism for this using the Energy-Based Model framework, and show that it indeed improves the results of the generative models, measured both in terms of perplexity and in terms of human evaluation.
VABD: Visual Analysis of Spatio-temporal Trajectory Based on Urban Bus Data

Spatio-temporal trajectory data cover a wide area, have high real-time performance, which can not only reflect the timing change of road traffic but also reflect the travel rules of residents. However, due to the lack of effective methods, it is difficult for analysts to conduct in-depth analysis and extract valuable information. To help users to understand the situation of urban traffic congestion, explore the social behavior pattern, this paper proposed and implemented a novel interactive visual analysis system, Visual Analyzer for Bus Data (VABD). Visual analysis of spatio-temporal trajectory data is introduced into “intelligent transportation” to provide new ideas and data support for solving urban congestion problems. A case study based on urban bus data of Mianyang proves the effectiveness of the system.
Social Groups Detection in Crowd by Using Automatic Fuzzy Clustering with PSO

Detecting social groups is one of the most important and complex problems which has been concerned recently. This process and relation between members in the groups are necessary for human-like robots shortly. Moving in a group means to be a subsystem in the group. In other words, a group containing two or more persons can be considered to be in the same direction of movement with the same speed of movement. All datasets contain some information about trajectories and labels of the members. The aim is to detect social groups containing two or more persons or detecting the individual motion of a person. For detecting social groups in the proposed method, automatic fuzzy clustering with Particle Swarm Optimization (PSO) is used. The automatic fuzzy clustering with the PSO introduced in the proposed method does not need to know the number of groups. At first, the locations of all people in frequent frames are detected and the average of locations is given to automatic fuzzy clustering with the PSO. The proposed method provides reliable results in valid datasets. The proposed method is compared with a method that provides better results while needs training data for the training step, but the proposed method does not require training at all. This characteristic of the proposed method increases the ability of its implementation for robots. The indexing results show that the proposed method can automatically find social groups without accessing the number of groups and requiring training data at all.