Reviewerly/exHarmony huggingface dataset:
Supporting program comprehension by generating abstract code summary tree

Reading through code, finding relevant methods, classes and files takes a significant portion of software development time. Having good tool support for this code browsing activity can reduce human effort and increase overall developer productivity. To help with program comprehension activities, building an abstract code summary of a software system from its call graph is an active research area. A call graph is a visual representation of the caller-callee relationships between different methods of a software system. Call graphs can be difficult to comprehend for a large code-base. Previous work by Gharibi et al. on abstract code summarizing suggested using the Agglomerative Hierarchical Clustering (AHC) tree for understanding the codebase. Each node in the tree is associated with the top five method names. When we replicated the previous approach, we observed that the number of nodes in the AHC tree is burdensome for developers to explore. We also noticed only five method names for each node is not sufficient to comprehend an abstract node. We propose a technique to transform the AHC tree using cluster flattening for natural grouping and reduced nodes. We also generate a natural text summary for each abstract node derived from method comments. In order to evaluate our proposed approach, we collected developers' opinions about the abstract code summary tree based on their codebase. The evaluation results confirm that our approach can not only help developers get an overview of their codebases but also could assist them in doing specific software maintenance tasks.
Sentiment analysis of SOCO by Sociolla e-commerce application review on Google Play Store site using naïve Bayes classifier method

Beauty product store SOCO (Sociolla Connect) is a application that sells about beautyproducts and tools, skin care, body care, hair care and parfume. Beauty products store app available and easy to use on Google Play Store not only there is SOCO by Sociolla or two appsonly. the large number of beauty products store apps available causes users to be more selectivein determining what apps are better and suitable for use. This can influence users to select an appby looking at the reviews that have been presented on the app's download page. These reviews are often used as a benchmark in an app's assessment. This study aims to find out the sentimentanalysis of SOCO by Sociolla app reviews using naïve bayes classifier method. The result fromcalculation of classification have a accuracy value of 75.41% with a precission value of 72.32%and a recall value of 78.98%. Wordcloud sentiment positive class obtained a word that is often used to give reviews e-commerce application SOCO by Sociolla is a good word, application, product, shopping, Sociolla, and likes, while on negative class sentiment the word that is often used is the word application, update, exit, shopping, and open.
PAC-Net: Highlight Your Video via History Preference Modeling

Autonomous highlight detection is crucial for video editing and video browsing on social media platforms. General video highlight detection aims at extracting the most interesting segments from the entire video. However, interest is subjective among different users. A naive solution is to train a model for each user but it is not practical due to the huge training expense. In this work, we propose a Preference-Adaptive Classification (PAC-Net) framework, which can model users’ personalized preferences from their user history. Specifically, we design a Decision Boundary Customizer (DBC) module to dynamically generate the user-adaptive highlight classifier from the preference-related user history. In addition, we introduce Mini-History (Mi-Hi) mechanism to capture more fine-grained user-specific preferences. The final highlight prediction is jointly decided by the user’s multiple preferences. Extensive experiments demonstrate that PAC-Net achieves state-of-the-art performance on the public benchmark dataset, whilst using substantially smaller networks.
Disentanglement of Deep Features for Adversarial Face Detection

Existing adversarial face detectors are mostly developed against specific types of attacks, and limited by their generalizability especially in adversarial settings. In this paper, we propose a new detection strategy based on a dual-classifier driven deep-feature disentanglement model for detecting different types of adversarial faces. Experimental results over adversarial examples and face forgery attacks show that the proposed detection method is effective with better generalizability and more adversarially robust comparing with previous methods.
Dispersion of Carbon Monoxide Pollutant and The Effect of Health (Case Study on Frontage Road Surabaya by Gaussian Line Source Equation Model)

Air pollution was being a very important problem and danger for human life. This was related to diseases that arise due to motor vehicle emissions, especially carbon monoxide. Simulation of air dispersion models is the one way to study about air quality that is needed in this regard. This study aims to determine the distribution of carbon monoxide pollutants in Ahmad Yani's frontage and to anticipate the dangers of these pollutants to the health of the people living around the research location. This research discussed about the mathematical model of the dispersion of CO that emitted from cars that passed through the frontage road on the Ahmad Yani Street, Surabaya. The method used is direct observation in the field and numerical simulation using a mathematical model, Gaussian Line Source Equation Model (GLSEM). GLSEM had prepared based on the mechanism of transport of pollutants in dispersion, diffusion and advection. With GLSEM we calculated CO gas concentration values for certain heights downwind. We validated the model by comparing numerical results and measurements of CO concentration. We used the R2 test and we got an R2 close to one. We simulated GLSEM by used Fortran programming language and visualized it with Surfer. The results of the visualization in June showed that the pattern of CO gas dispersion was influenced by the direction and speed of the wind. The results obtained are that the distribution of CO pollutants in the Ahmad Yani frontage is horizontal/downwind. CO concentrations at night are higher than during the daytime. From the CO dispersion pattern, we had known that there were dangerous of air around the frontage for people health. We conclude that around the frontage road of the Ahmad Yani highway there is sufficient open air space so that the danger of CO pollutants being emitted can be minimized so that the health of the community, namely pedestrians, motorcycle drivers and the community around the location can be protected.
The Best Laid Schemes o’ Mice an’ Men

In this article, composer Stephen Baysted reports on the challenges of producing the music for two high-profile racing games during the COVID-19 pandemic.
An Accurate, Flexible and Private Trajectory-Based Contact Tracing System on Untrusted Servers

Infections by the Covid-19 coronavirus have proliferated since the end of 2019, and many privacy-protective contact tracing systems have been proposed to limit infections from spreading. However, the existing Bluetooth-based contact tracking systems lack accuracy and flexibility. In addition, it is desirable to have a contact tracing system that, in the future, can contribute to limiting the proliferation of new coronaviruses and as yet unknown viruses. In this study, we propose a method to extend a contact tracing system to be more flexible, accurate, and capable of dealing with unknown viruses by using trajectory data and infection factor information while protecting privacy. We also implemented the proposed extension method and measured its execution time and confirmed its practicality.
Embedding Model with Attention over Convolution Kernels and Dynamic Mapping Matrix for Link Prediction

Knowledge Graph Completion, especially its sub-task link prediction attracts the attention of the research community and industry because of its applicability as a premise for developing several potential applications. Knowledge graph embedding (KGE) shows promising results to solve this problem. This paper focuses on the neural networks-based approach for KGE, which can extract features from the graphs better than other groups of embedding methods. The ConvE model is the first work using 2D convolution over embeddings and stacking multiple nonlinear feature layers to model knowledge graphs. However, its computation is inefficient and does not preserve translation between entity and relation embedding. Therefore, dynamic convolution was designed to solve limited representation capability issues and show the promised performance. This work introduces a mixture model that incorporates attention into performing the convolutional operation on projection embeddings. The TransD idea is used to project entity embedding from entity space to relation space. Then, it is stacked with relation embedding to perform dynamic convolution over stacked embedding without reshaping, following the idea that comes from Conv-TransE. So the translational property between the entity and the relation is preserved, and their diversity is considered. We experimented on benchmark datasets and showed how our proposed model is better than baseline models in terms of MR, MRR, and Hits@K.
Frank-Wolfe-based Multi-task Learning for Historical Document Restoration

During the last few years, research in historical document restoration and understanding (HDRU) has gained increasing popularity. One major problem facing HDRU is the presence of degradation, which renders historical documents unreadable. Although promising results have been obtained, these methods lack the ability to generalize across different datasets. Also, multiple pre-processing and post-processing steps are used, which add more computational complexity and make inference unpractical in real-life settings. Deep Learning (DL) has been successfully used to solve various supervised learning problems in computer vision, where labeled datasets are readily available. However, in HDRU, large annotated historical document datasets are not available. In this paper, we propose an efficient multitask learning (MTL) approach that is based on jointly training self-supervised and supervised learning modules. In the self-supervised learning module, we define two tasks that can be trained with unlabeled data. The first task consists of denoising, and the second task is to learn handwritten characteristics (text orientation). In the supervised learning module, a small subset of labeled data is used to perform text extraction or binarization. All the tasks are formulated as a multi-objective Frank-Wolfe-based optimization problem. We show that convergence to a Pareto optimal solution of jointly training multiple tasks together improves the overall invariance and accuracy of the model. DIBCO 2010-2017 datasets were used for training and DIBCO 2018 for testing. We achieved state-of-the-art results with an F-Score measure of 91.21.
Efficiency Analysis of European Higher Education in Achieving Sustainable Development Goals

This research aims to evaluate the technical efficiency of higher education in EU member states in achieving the Sustainable Development Goals. Measuring the coefficient of technical efficiency provides more information on the extent to which inputs are used to pro­duce outputs. Data Envelopment Analysis is used to determine the coefficients of technical effi­ciency. We use three inputs and three outputs in the evaluation of technical efficiency. The inputs used in this research are the total number of students, the number of academic staff and public expenditure on higher education, while the outputs are the number of graduates, employment rates of recent graduates and the overall score of the EU member state in the Sustainable Devel­opment Goals. Assessing technical efficiency ratios will help us determine which state uses its re­sources most efficiently in the higher education sector. It will also highlight less efficient coun­tries and point out ways to improve their efficiency.
Attribute-Based Signatures of Fiat-Shamir Type in Bilinear Groups: Scheme and Performance

We propose an attribute-based signature (ABS) scheme that is in the algebraic setting of bilinear groups. As is usual for ABS schemes, our scheme is capable of treating monotone boolean formulas as signing policies as well as attaining attribute privacy. A feature of our scheme is that the scheme is obtained by applying the Fiat-Shamir transformation to a ∑-protocol. As a previous work, there is already a scheme of the Fiat-Shamir type that is in the algebraic setting of discrete logarithm but that does not rely on bilinear groups (a “pairing-free” ABS scheme by Herranz 2016) with an irregular constraint that the maximum number L of users should be fixed in its setup phase. Hence we compare the performance of the two schemes by proof-of-concept software-implementation. As a result, when L and the size N of the attribute universe are both small, the pairing-free scheme performs well, but as at least one of the two parameters grows, our proposed pairing-used scheme outperforms it.
A Predictive Framework for Detection of Corona Virus based on Transfer Learning Approach

Early identification of COVID -19 has a substantial influence on reducing COVID -19 transmission at a faster rate and is the need of the moment.AI diagnostics utilizing deep learning models trained on X-ray pictures of COVID-infected and uninfected persons is a viable new technique for early prediction and diagnosis of COVID-infected patients.This study presents a technique that can be used to automatically identify the corona virus from machine-made chest X-ray images in less than five minutes.For this we use a collection of chest X-ray images of pneumonia, COVID 19 disease, and healthy infected patients.Transfer Learning is used because it has the advantage of reducing training times for a neural network model.The result shows 99.49% accuracy in predicting Corona virus from an X-ray of a suspect patient using the VGG Transfer Learning framework.
Contrastive and Consistent Learning for Unsupervised Human Parsing

How to learn pixel-level representations of human parts without supervision is a challenging task. However, despite its significance, a few works explore this challenge. In this work, we propose a contrastive and consistent learning network ( $$C^{2}L$$ ) for unsupervised human parsing. $$C^{2}L$$ mainly consists of a part contrastive module and a pixel consistent module. We design a part contrastive module to distinguish the same semantic human parts from other ones by contrastive learning, which pulls the same semantic parts closer and pushes different semantic ones away. A pixel consistent module is proposed to obtain spatial correspondence in each view of images, which can select semantic-relevant image pixels and suppress semantic-irrelevant ones. To improve the pattern analysis ability, we perform a sparse operation on the feed-forward networks of the pixel consistent module. Extensive experiments on the popular human parsing benchmark show that our method achieves competitive performance.
New Objects on the Road? No Problem, We'll Learn Them Too

Object detection plays an essential role in providing localization, path planning, and decision making capabilities in autonomous navigation systems. However, existing object detection models are trained and tested on a fixed number of known classes. This setting makes the object detection model difficult to generalize well in real-world road scenarios while encountering an unknown object. We address this problem by introducing our framework that handles the issue of unknown object detection and updates the model when unknown object labels are available. Next, our solution includes three major components that address the inherent problems present in the road scene datasets. The novel components are a) Feature-Mix that improves the unknown object detection by widening the gap between known and unknown classes in latent feature space, b) Focal regression loss handling the problem of improving small object detection and intra-class scale variation, and c) Curriculum learning further enhances the detection of small objects. We use Indian Driving Dataset (IDD) and Berkeley Deep Drive (BDD) dataset for evaluation. Our solution provides state-of-the-art performance on open-world evaluation metrics. We hope this work will create new directions for open-world object detection for road scenes, making it more reliable and robust autonomous systems.
Determining the online learning challenges during COVID-19 pandemic at the University of Mataram using Principal Component Analysis

The COVID-19 pandemic had an impact on the world of education and it leads to the cancellation of all educational activities. An online learning system was an educational system or concept that utilizes information technology in the teaching and learning process. The basic principles in the online learning process are clarity of messages, learning strategies, interactivity, growth of motivation and creativity, and the use of media for effective communication. The purpose of this study was to determine what factors are hindering students in online lectures by using Principal Component Analysis. The research was conducted using a survey method, namely by filling in forms for undergraduate students at the University of Mataram. The method used to analyze the data was a quantitative descriptive technique which was expressed in the distribution of scores and percentages. This form contains 15 observed variables, after factor analysis was carried out, and obtained 3 factors that most hamper online lectures. The dominant factor is Factor 1 that can explain 28.957% of the variation. The variables included in Factor 1 are the lack of concentration, material understanding, not direct discussion, unconcern (boredom), and lack of study companion variables. The results obtained can be used as a consideration to maximize online lectures during the COVID-19 pandemic.
IMPROVEMENT OF LABORATORY WORKSHOP DURING THE STUDY OF ORGANIC CHEMISTRY IN MODERN CONDITIONS

The purpose of the article is to analyze the features of conducting a small laboratory workshop as a means of studying the course of organic chemistry without the use of reagents included in the list of narcotic drugs, psychotropic substances andprecursors.&#x0D; During the writing of the article, such methods of scientific research as analysis, synthesis, comparison, generalization were used - to find out the state of solving the researched problem in the theory and methodology of teaching chemistry, to determine the theoretical foundations of the research, as well as a chemical experiment - to conduct experimental research and proving the effectiveness of theoretical assumptions.&#x0D; Based on the analysis of literary sources, the tasks, main functions and requirements for a chemical experiment in the process of learning organic chemistry are determined. It has been found that the problem of using a chemical experiment tostudy the basic principles of the structure of organic compounds, methods of their synthesis and properties requires further research in order to adapt the methodology of conducting experiments to the conditions under which the use of precursors andtoxic substances is limited. The greatest attention was focused on methodological aspects of reproduction and description of experiments on the oxidation of organic compounds, since most of such experiments had to be adapted to the conditions underwhich the use of potassium permanganate is prohibited.&#x0D; The results of the conducted research showed the possibility of adapting the methods of a small laboratory workshop in organic chemistry to the conditions under which the use of precursors and toxic substances is excluded. As a substitute forpotassium permanganate solution, potassium chromate and potassium dichromate solutions were used and a positive result was obtained. Developed recommendations can be used for research and conducting laboratory work in laboratories ofeducational institutions.
Evaluation of Bilingual Mathematics Learning Program at Al-Azhar Islamic Middle School, South Tangerang

This study evaluates the bilingual mathematics learning program at SMP Islam Al-Azhar 25, South Tangerang. The evaluation model used is CIPP (Context, Input, Process, Product). Data collection techniques using interviews, observation, and document analysis. The evaluation results show that the bilingual mathematics learning program at SMP Islam Al-Azhar 25 South Tangerang has been running well. The program aims to follow the school's "globally competitive" vision. Facilities and infrastructure are categorized as very good, teachers have met the academic qualifications of undergraduate Mathematics Education, students have met the qualifications with tested students through the stages of entering the bilingual class, and the readiness of teachers and students of class VII is categorized as good, and the readiness of teachers and students of class VIII categorized as very good. The mathematics learning process uses the Bilingual Immersion type of learning, with a translation and transcription approach. The average score for daily assignments and tests is above the KKM (Minimum Completeness Criteria). The evaluation component has met the criteria for success, but some need to be improved and developed, such as the availability of a language laboratory and the development of bilingual classes from two classes to all classes so that there are no social gaps.
An Exploratory Study of Documentation Strategies for Product Features in Popular GitHub Projects

[Background] In large open-source software projects, development knowledge is often fragmented across multiple artefacts and contributors such that individual stakeholders are generally unaware of the full breadth of the product features. However, users want to know what the software is capable of, while contributors need to know where to fix, update, and add features. [Objective] This work aims at understanding how feature knowledge is documented in GitHub projects and how it is linked (if at all) to the source code. [Method] We conducted an in-depth qualitative exploratory content analysis of 25 popular GitHub repositories that provided the documentation artefacts recommended by GitHub’s Community Standards indicator. We first extracted strategies used to document software features in textual artefacts and then strategies used to link the feature documentation with source code. [Results] We observed feature documentation in all studied projects in artefacts such as READMEs, wikis, and website resource files. However, the features were often described in an unstructured way. Additionally, tracing techniques to connect feature documentation and source code were rarely used. [Conclusions] Our results suggest a lacking (or a low-prioritised) feature documentation in open-source projects, little use of normalised structures, and a rare explicit referencing to source code. As a result, product feature traceability is likely to be very limited, and maintainability to suffer over time.