Reviewerly/exHarmony huggingface dataset:
CaCL: Class-aware Codebook Learning for Weakly Supervised Segmentation on Diffuse Image Patterns

Weakly supervised learning has been rapidly advanced in biomedical image analysis to achieve pixel-wise labels (segmentation) from image-wise annotations (classification), as biomedical images naturally contain image-wise labels in many scenarios. The current weakly supervised learning algorithms from the computer vision community are largely designed for focal objects (e.g., dogs and cats). However, such algorithms are not optimized for diffuse patterns in biomedical imaging (e.g., stains and fluorescence in microscopy imaging). In this paper, we propose a novel class-aware codebook learning (CaCL) algorithm to perform weakly supervised learning for diffuse image patterns. Specifically, the CaCL algorithm is deployed to segment protein expressed brush border regions from histological images of human duodenum. Our contribution is three-fold: (1) we approach the weakly supervised segmentation from a novel codebook learning perspective; (2) the CaCL algorithm segments diffuse image patterns rather than focal objects; and (3) the proposed algorithm is implemented in a multi-task framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) via joint image reconstruction, classification, feature embedding, and segmentation. The experimental results show that our method achieved superior performance compared with baseline weakly supervised algorithms. The code is available at https://github.com/ddrrnn123/CaCL.
Reinforcement learning for autonomous agents exploring environments: An experimental framework and preliminary results

Department of Computer Science and Engineering (DISI) and the Alma Mater Research Institute for Human-Centered Artificial Intelligence (Alma AI) of the University of Bologna, Italy
ANALISIS PENJADWALAN MATA PELAJARAN MENGGUNAKAN ALGORITMA WELCH-POWELL

At the beginning of each semester, subjects scheduling is always carried out by the curriculum representatives and academic staff. There were several problems that must be avoided in subjects scheduling, these problems were the schedule of teachers who teach one subject at the same time are scheduled in different classes, teachers who teach more than one subject are scheduled in the same class at the same time, teachers who are lack of scheduled for teaching. In the subject of graph theory, there is a concept of graph coloring, one of which is vertex coloring. In vertex coloring, there is a Welch-Powell Algorithm application which produces a color for each vertex. In subject scheduling, it is assumed that the vertex is the subject and the teacher, while the edge is the class. In vertex coloring, graph vertices are colored so that there's no two neighboring vertices have the same color. The aim of this research was to arrange a lesson schedule so that problems do not occur such as clashes between teachers, subjects, and teaching hours. The method used in arranging this lesson schedule used the Welch-Powell Algorithm. The results obtained were using the Welch-Powell Algorithm to produce a lesson schedule every day where if there are two classes that have the same subject, they can meet the same day requirements but come in different hours and get a lesson schedule that has no clash between teachers, subjects, and teaching hours.
MASTER’S PROGRAMS IN THE FOREIGN LANGUAGE EDUCATION FIELD: MODELING QUASI-PROFESSIONAL EDUCATIONAL ENVIRONMENT

Statement of the problem. The article deals with the possibility of modeling quasi-professional educational environment in the field of foreign language education (master’s level) through the use of multilevel step-by-step integration of preparation for professional activity with PEP concept (Praxiserkundungsprojekte), designed to meet the didactic requirements of modern higher education, which prescribes the reproduction of quasi-professional activities within the framework of an educational organization. The need for educational, educational and game programs and training programs that prepare students for professional activity is in contradiction with the insufficient development of conditions for their use in practice. The purpose of the article is to present the authors’ recommendations on modeling quasi-professional educational environment for implementing master’s programs in the field of foreign language education of master’s level. The methodology consists in the analysis and synthesis of the research results on the quasi-professional educational environment of Russian and foreign scientists and the authors’ experience in the implementation of master’s programs. Research results. The authors’ recommendations for modeling a quasi-professional educational environment have been developed, including training technologies (didactic and role-playing games; quasi-professional situations; group work within the framework of the PEP project), tested in practice. Conclusion. Analyzing the results of approved recommendations, the authors come to the conclusion that modeling quasi-professional educational environment with PEP concept (Praxiserkundungsprojekte), during implementation of master’s programs in the field of foreign language education contributes to solving one of the main problems of modern education – a combination of theory and practice in the formation of future professionals. The authors’ recommendations can be applied in the course of master’s training, majoring in 44.04.01 “Pedagogical education” (full-time and part-time forms).
Adversarial Context Aware Network Embeddings for Textual Networks

Representation learning of textual networks poses a significant challenge as it involves capturing amalgamated information from two modalities: (i) underlying network structure, and (ii) node textual attributes. For this, most existing approaches learn embeddings of text and network structure by enforcing embeddings of connected nodes to be similar. Then for achieving a modality fusion they use the similarities between text embedding of a node with the structure embedding of its connected node and vice versa. This implies that these approaches require edge information for learning embeddings and they cannot learn embeddings of unseen nodes. In this paper we propose an approach that achieves both modality fusion and the capability to learn embeddings of unseen nodes. The main feature of our model is that it uses an adversarial mechanism between text embedding based discriminator, and structure embedding based generator to learn efficient representations. Then for learning embeddings of unseen nodes, we use the supervision provided by the text embedding based discriminator. In addition this, we propose a novel architecture for learning text embedding that can combine both mutual attention and topological attention mechanism, which give more flexible text embeddings. Through extensive experiments on real-world datasets, we demonstrate that our model makes substantial gains over several state-of-the-art benchmarks. In comparison with previous state-of-the-art, it gives up to 7% improvement in performance in predicting links among nodes seen in the training and up to 12% improvement in performance in predicting links involving nodes not seen in training. Further, in the node classification task, it gives up to 2% improvement in performance.
How Academia of SMVD University Pursue for Various Available Online Search Tools

A questionnaire-based survey was carried out for the current study in order to stimulate information from the respondents who comprises of 115 teachers and researchers of SMVDU. Krejcie and Morgan sampling table and stratified random sampling technique were adopted for calculating the sample size from the whole population. The process of analyzing the data was done by using Statistical Package for Social Science (SPSS), however in order to measure the level of significance of different revealed facts and difference among respondents of varied status, ‘Pearson’s’ and ‘Kruskal Wallis’ Chi-Square test (χ2)’ were used as per their need felt. The result of the study clearly reveals that among various available search engines, Google consistently ascertained to be exceedingly preferred search engines among teachers and researchers of SMVDU. Majority of academia of SMVDU prefer to use ‘Free Access’ web resources as compared to ‘Toll Access’ ones.
Thickness distribution of Boolean functions in 4 and 5 variables and a comparison with other cryptographic properties

This paper explores the distribution of algebraic thickness of Boolean functions (that is, the minimum number of terms in the ANF of the functions in the orbit of a Boolean function, through all affine transformations), in four and five variables, and the complete distribution is presented. Additionally, a complete analysis of some complexity properties (e.g., nonlinearity, balancedness, etc.) of all relevant orbits of Boolean functions is presented. Some properties of our notion of rigid function (which enabled us to reduce significantly the computation) are shown and some open questions are proposed, providing some further explanation of one of these questions.
Combining Code Embedding with Static Analysis for Function-Call Completion

Code completion is an important feature of integrated development environments (IDEs). It allows developers to produce code faster, especially novice ones who are not fully familiar with APIs and others code. Previous works on code completion have mainly exploited static type systems of programming languages or code history of the project under development or of other projects using common APIs. In this work, we present a novel approach for improving current function-calls completion tools by learning from independent code repositories, using well-known natural language processing models that can learn vector representation of source code (code embeddings). Our models are not trained on historical data of specific projects. Instead, our approach allows to learn high-level concepts and their relationships present among thousands of projects. As a consequence, the resulting system is able to provide general suggestions that are not specific to particular projects or APIs. Additionally, by taking into account the context of the call to complete, our approach suggests function calls relevant to that context. We evaluated our approach on a set of open-source projects unseen during the training. The results show that the use of the trained model along with a code suggestion plug-in based on static type analysis improves significantly the correctness of the completion suggestions.
Identification of Tuna and Skipjack Fish Texture Using GLCM With Naive Bayes Classification

Fish in Indonesian waters have various types, the famous ones are tuna and skipjack. The two types of fish look similar, because they come from the same family, namely scombridae. To find out and differentiate types of tuna and skipjack fish, it can be seen based on the texture image. The method that can be used in analyzing texture is the Gray Level Coocurent Matrix (GLCM) method. There are several methods of image classification, one of which is the Naive Bayes method. This study aims to identify types of tuna and skipjack based on texture analysis using the GLCM and Naive Bayes methods. Based on the results of testing data analysis on types of tuna and skipjack meat using GLCM with angles and , the distance of each pixel is 1, indicating the value of Energy, Entropy Contrast, Homogeneity, Correlation, Sum Average, and Sum of Variance are highly varies. As well as the Naive Bayes classification results obtained a probability of 0.58 or 58% categorized as tuna meat, while the remaining probability of 0.42 or 42% is categorized as skipjack.
Corpora Evaluation and System Bias Detection in Multi-document Summarization

Multi-document summarization (MDS) is the task of reflecting key points from any set of documents into a concise text paragraph. In the past, it has been used to aggregate news, tweets, product reviews, etc. from various sources. Owing to no standard definition of the task, we encounter a plethora of datasets with varying levels of overlap and conflict between participating documents. There is also no standard regarding what constitutes summary information in MDS. Adding to the challenge is the fact that new systems report results on a set of chosen datasets, which might not correlate with their performance on the other datasets. In this paper, we study this heterogeneous task with the help of a few widely used MDS corpora and a suite of state-of-the-art models. We make an attempt to quantify the quality of summarization corpus and prescribe a list of points to consider while proposing a new MDS corpus. Next, we analyze the reason behind the absence of an MDS system which achieves superior performance across all corpora. We then observe the extent to which system metrics are influenced, and bias is propagated due to corpus properties. The scripts to reproduce the experiments in this work are available at this https URL.
5W1H-based Expression for the Effective Sharing of Information in Digital Forensic Investigations

Digital forensic investigation is used in various areas related to digital devices including the cyber crime. This is an investigative process using many techniques, which have implemented as tools. The types of files covered by the digital forensic investigation are wide and varied, however, there is no way to express the results into a standardized format. The standardization are different by types of device, file system, or application. Different outputs make it time-consuming and difficult to share information and to implement integration. In addition, it could weaken cyber security. Thus, it is important to define normalization and to present data in the same format. In this paper, a 5W1H-based expression for information sharing for effective digital forensic investigation is proposed to analyze digital forensic information using six questions--what, who, where, when, why and how. Based on the 5W1H-based expression, digital information from different types of files is converted and represented in the same format of outputs. As the 5W1H is the basic writing principle, application of the 5W1H-based expression on the case studies shows that this expression enhances clarity and correctness for information sharing. Furthermore, in the case of security incidents, this expression has an advantage in being compatible with STIX.
The Efforts of Both Mentors and Mentees Matter

Women in Higher EducationVolume 29, Issue 11 p. 10-15 In Her Own Words The Efforts of Both Mentors and Mentees Matter Christine M. Estel, Christine M. EstelSearch for more papers by this author Christine M. Estel, Christine M. EstelSearch for more papers by this author First published: 27 October 2020 https://doi.org/10.1002/whe.20915Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume29, Issue11November 2020Pages 10-15 RelatedInformation
Sragen Islamic Boarding School (Penekanan pada Konsep Zero Energy Building)

Islamic Boarding School is an educational institution with the concept of Islamic boarding schools, where supporting facilities are provided such as dormitories, mosques because of places of worship and schools as places of teaching and learning. This Islamic boarding school is located in Sragen Regency, which accommodates junior high school education until the end. Sragen Islamic Boarding School can be a place for special school children in Sragen Regency itself and also to reduce dropout children and schools that manage general knowledge with Islamic knowledge through Al-Sunah and Al-Qur'an. In the pattern of education (curriculum) that puts forward the fields of science and technology, as learning for students who support the development of the environment and the development of increasingly sophisticated technology in the future. As is felt now with a variety of issues of global warming due to damage to the environment, massive exploitation of fossil fuels used for the needs of people's lives and carbon dioxide emissions that continue to increase. The application of the Zero Energy Building concept is to reduce energy use by utilizing surrounding energy such as solar energy and air. Building design that discusses technological innovations to create comfort for users without using excessive energy.
Cooperative Design of an Interactive Museum Guide

This paper deals with the problem of supporting museum visitors in searching for interesting artworks and finding their visiting routes compatible with personal preferences. The proposed application uses knowledge related to museum building structure and topology, and to substantive content of museum collections. Therefore the cooperation between the designer of a museum guide with the museum curator, who provides information about artworks and their assignment to exhibitions, collections and rooms, is indispensable. All this data is stored in a graph, where nodes represent building spaces, edges correspond to accessibility relations between these spaces, while node attributes describe both geometrical properties of spaces and semantic information related to art pieces and collections. The user can specify artworks, exhibitions, collections he wants to visit together with personal preferences concerning the visiting path (like using lifts). Then graph algorithms are used to find the most appropriate route for the user. Moreover, the user can define personalized collections and share them with other users of the application.
Data-free Knowledge Distillation for Segmentation using Data-Enriching GAN

Distilling knowledge from huge pre-trained networks to improve the performance of tiny networks has favored deep learning models to be used in many real-time and mobile applications. Several approaches that demonstrate success in this field have made use of the true training dataset to extract relevant knowledge. In absence of the True dataset, however, extracting knowledge from deep networks is still a challenge. Recent works on data-free knowledge distillation demonstrate such techniques on classification tasks. To this end, we explore the task of data-free knowledge distillation for segmentation tasks. First, we identify several challenges specific to segmentation. We make use of the DeGAN training framework to propose a novel loss function for enforcing diversity in a setting where a few classes are underrepresented. Further, we explore a new training framework for performing knowledge distillation in a data-free setting. We get an improvement of 6.93% in Mean IoU over previous approaches.
Improving English University Students’ Speaking Skill Through Effortless English Movie Technique

The aim of this article was to explain how to implement Effortless English Movie Technique in order to improve students’ English speaking skill. Effortless English Movie is a six-day implementation technique that has different instructions for each day. It was a library research since the data are taken by gathering information from books and journals. However, the researchers also did pre-liminary observation to see the students’ perception toward the learning technique at university level to get a better comprehension about the real situation in learning process. The researchers, then, adapted the theory of the Effortless English Movie by A.J. Hoge with the real situation, so it can be used by the teachers well into their classrooms. This technique leads learners to focus on listening to be familiar with English conversation or dialogue, and continue to the speaking section to practice their pronunciation with the movie that has been chosen by the teacher. By using repetition strategy to practice the speaking skill, this technique focuses on speaking like a native speaker with an amusing way. Therefore, Effortless English Movie Technique is an optional easy and enjoyable technique that helps students a lot in learning to communicate fluently and accurately. The researchers hope that this article is useful for the readers, students, teachers, and next researchers in giving information about improving speaking skill. The researchers also expect that this technique can be one of the most favorite techniques that teachers use in classroom for English lesson, especially speaking skill.
Implementasi Pembelajaran Sastra Berbantuan Media Digital di MI Najmul Huda Kemloko

The rapid development of literature in MI education (Islamic Elementary Education) became one of the strategies to face the era of the Industrial Revolution 4.0 and Society 5.0. One way to make literary learning affective in current conditions is the use of digital media. This study aims to determine the level of understanding of literary learning through the use of digital media at the 4th grade of MI Najmul Huda Kemloko. This research refers to a descriptive research method by using a qualitative approach. The data collecting technique is used in this qualitative research. The data analysis is inductive based on the facts finding which found in the field.. Data collecting techniques were made by interviewing, observing documenting. Data analysis techniques were carried out based on the miles and Huberman models which include data reduction, presentation, and conclusion. The results of the study are as follows: (1) digital media is used to develop the literary enthusiasm of students at MI Najmul Huda Kemloko, (2) digital media provides opportunities for students to publish literature.
Blockchain-enabled Identity Verification for Safe Ridesharing Leveraging Zero-Knowledge Proof

The on-demand mobility market, including ridesharing, is becoming increasingly important with e-hailing fares growing at a rate of approximately 130% per annum since 2013. By increasing utilization of existing vehicles and empty seats, ridesharing can provide many benefits including reduced traffic congestion and environmental impact from vehicle usage and production. However, the safety of riders and drivers has become of paramount concern and a method for privacy-preserving identity verification between untrusted parties is essential for protecting users. To this end, we propose a novel privacy-preserving identity verification system, extending zero-knowledge proof (ZKP) and blockchain for use in ridesharing applications. We design a permissioned blockchain network to perform the ZKP verification of a driver's identity, which also acts as an immutable ledger to store ride logs and ZKP records. For the ZKP module, we design a protocol to facilitate user verification without requiring the exchange of any private information. We prototype the proposed system on the Hyperledger Fabric platform, with the Hyperledger Ursa cryptography library, and conduct extensive experimentation. To measure the prototype's performance, we utilize the Hyperledger Caliper benchmark tool to perform extensive analysis and the results show that our system is suitable for use in real-world ridesharing applications.