Reviewerly/exHarmony huggingface dataset:
A User Feedback Centric Approach for Detecting and Mitigating God Class Code Smell Using Frequent Usage Patterns

Code smells are the fragments in the source code that indicates deeper problems in the underlying software design. These code smells can hinder software evolution and maintenance. Out of different code smell types, the God Class (GC) code smell is one of the many important code smells that directly affects the software evolution and maintenance. The GC is commonly defined as a much larger class in systems that either know too much or do too much as compared to other classes in the system. God Classes are generally accidentally created overtime during software evolution because of the incremental addition of functionalities to it. Generally, a GC indicates a bad design choice and it must be detected and mitigated in order to enhance the quality of the underlying software. However, sometimes the presence of a GC is also considered a good design choice, especially in compiler design, interpreter design and parser implementation. This makes the developer’s feedback important for the correct classification of a class as a GC or a normal class. Therefore, this paper proposes a new approach that detects and proposes refactoring opportunities for GC code smell. The proposed approach makes use of different code metrics in combination along with utilizing user feedback as an important aspect while correctly identifying the GC code smell. The proposed approach that considers combined use of code metrics, is based on two newly proposed code metrics in this paper. The first newly proposed metric is a new approach of measuring the connectivity of a given class with other classes in the system (also termed as coupling). The second newly proposed code metric is proposed to measure the extent to which a given classes make use of foreign member variables. Finally, the proposed approach is also empirically evaluated on two standard open-source commonly used software systems. The obtained result indicates that the proposed approach is capable of correctly identifying the GC code smell.
Implementation of AI

In March 2019, a shooter live-streamed on Facebook his brutal killing of 50 people in two mosques in New Zealand. It was viewed about 4,000 times and was not shut off until 29 minutes after the attack. The video was then uploaded to other platforms and was viewed millions of times.
No Mind Reading Necessary: Conducting Evidence-Based Electronic Resource Marketing and Outreach Using Marketing Research Assessment

In her presentation entitled “No Mind Reading Necessary: Conducting Evidence-Based Electronic Resource Marketing and Outreach” at the North Carolina Serials Conference April 2019, Kate Hill discussed marketing research strategies and outreach tools used to increase online users’ access to electronic resources and primary resources. In an effort to connect users to seldom-used resources, Jackson Library collaborated with university partners to develop surveys to identify the information-seeking behavior of its users and apply the gathered marketing research to strategically plan outreach activities.
Multi-task Transfer Learning for Timescale Graphical Event Models

Graphical Event Models (GEMs) can approximate any smooth multivariate temporal point processes and can be used for capturing the dynamics of events occurring in continuous time for applications with event logs like web logs or gene expression data. In this paper, we propose a multi-task transfer learning algorithm for Timescale GEMs (TGEMs): the aim is to learn the set of k models given k corresponding datasets from k distinct but related tasks. The goal of our algorithm is to find the set of models with the maximal posterior probability. The procedure encourages the learned structures to become similar and simultaneously modifies the structures in order to avoid local minima. Our algorithm is inspired from an universal consistent algorithm for TGEM learning that retrieves both qualitative and quantitative dependencies from event logs. We show on a toy example that our algorithm could help to learn related tasks even with limited data.
COMPUTATIONAL THINKING AS A PEDAGOGICAL TOOL FOR UKRAINIAN STUDENTS

The article focuses on the importance of developing computational thinking of Ukrainian students and the ways computational thinking can be integrated into the curricula of higher educational institutions.The incorporation of enhanced pedagogy into the educational curricula is particularly needed in countries such as Ukraine which is currently undergoing strategic modernization and reform.The author defines the concept of computational thinking as it is used in constructivism learning theory and social-constructivism theories.It is emphasized in the article that computational thinking reduces complex problems into smaller and more manageable problems, which make it easier to solve either using a computer or without technology.A wide range of researches conducted by the scientists all over the world are presented.The aim of the article is to analyse the implementation of computational thinking as a pedagogical tool for Ukrainian educational system.The special attention is paid to developing computational thinking of children at an early age.Pattern Recognition is considered to be one component of computational thinking and can be used to teach the process of searching for trends, similarities, differences, or regularities.The computational thinking instructions can be carried out in the area of the computer science or outside of computer science.Students who learn computer-programming skills as part of a have been shown to experience numerous benefits to their education because computational thinking is a problem-solving skill for all disciplines that can be taught through the integration into a particular content area or alternatively by teaching it as a stand-alone content area.The author recommends Ukrainian educators to consider the integration of unplugged CT activities into their lesson plans.Unplugged curricular activities are implemented without the use of computers and are typically considered as an important first step toward comprehensive CT integration.Unplugged experiences are claimed to serve as a foundational in learning CT because they typically require the least amount of technical knowledge.
Nonparametric Regression Estimation for Circular Data

Non-parametric regression with a circular response variable and a unidimensional linear regressor is a topic which was discussed in the literature. In this work, we extend the results to the case of multivariate linear explanatory variables. Nonparametric procedures to estimate the circular regression function are formulated. A simulation study is carried out to study the sample performance of the proposed estimators.
MINERALOGY AND GEOCHEMISTRY OF RECENT SEDIMENTS IN BASRAH, SOUTHERN IRAQ

The study of mineralogical and geochemical properties of recent sediments included collecting and analyzing thirty one surface samples from fifteen sites in Basrah city. The results of grain size analysis showed that the sand increases towards the west and southern part of Basrah, whilst clay fractions increase towards the north and eastern parts. Quartz, calcite, gypsum, feldspar, dolomite, and halite represent the light minerals in the study area sediments, while the clay minerals are represented by kaolinite, palygorskite, illite, chlorite, montmorillonite, and montmorillonite- chlorite. Calcium oxide and silica are the most abundant oxides in all analyzed sediments in the study area followed by Fe2O3, Al2O3, TiO2, MgO, Na2O, and K2O with high percentage of loss of ignition.
Complexity Metrics for Component Based Software — A Comparative Study

The Component Based Software Development (CBSD) approach is becoming the trend for software development and is based on developing the software from existing components instead of developing software from the scratch level.Measuring the software complexity is an important aspect during software development as it is an important determinant of software development effort, testing effort, cost, maintainability etc. Interactions/interfaces among components play an important role in contributing complexity to a component based software.In this paper a comparison between different complexity metrics developed by different authors is performed.These metrics are performed by taking different factors to calculate the complexity of the components based software, these factors are instance variables, instance methods, control flow and interface methods etc..The comparison is performed by taking some quality factors into consideration like maintainability, Integrity, complexity, testability, customizability etc.
Imitation Learning from Observation

Humans and other animals have a natural ability to learn skills from observation, often simply from seeing the effects of these skills: without direct knowledge of the underlying actions being taken. For example, after observing an actor doing a jumping jack, a child can copy it despite not knowing anything about what's going on inside the actor's brain and nervous system. The main focus of this thesis is extending this ability to artificial autonomous agents, an endeavor recently referred to as "imitation learning from observation." Imitation learning from observation is especially relevant today due to the accessibility of many online videos that can be used as demonstrations for robots. Meanwhile, advances in deep learning have enabled us to solve increasingly complex control tasks mapping visual input to motor commands. This thesis contributes algorithms that learn control policies from state-only demonstration trajectories. Two types of algorithms are considered. The first type begins by recovering the missing action information from demonstrations and then leverages existing imitation learning algorithms on the full state-action trajectories. Our preliminary work has shown that learning an inverse dynamics model of the agent in a self-supervised fashion and then inferring the actions performed by the demonstrator enables sufficient action recovery for this purpose. The second type of algorithm uses model-free end-to-end learning. Our preliminary results indicate that iteratively optimizing a policy based on the closeness of the imitator's and expert's state transitions leads to a policy that closely mimics the demonstrator's trajectories.
TUNTUTAN DAN TANTANGAN PENDIDIK DALAM TEKNOLOGI DI DUNIA PENDIDIKAN DI ERA 21

The demands and challenges of educators in technology in the world of education in the 21 st era are now the use of technology in the world of education where educators are required to literate information and technological progress. With this technology educators are required to be able to use technology optimally in the learning process. With the rapid technological advancements like now where educators feel being challenged to be able to master the process of transforming technology use in the learning process, educators must be willing to learn about the use of technology where learning in the 21 st era now applies hybrid learning systems. Besides having teaching skills in delivering face-to-face learning material, educators also have the skills and knowledge to develop digitalization-based learning resources or the internet or computers, educators must also be able to innovate learning methods or be able to apply the two learning methods in that learning. The combination of learning can also be applied to e-learning (online) integration. Educators must be able to meet the demands of knowledge with high competitiveness in the use of technology so that students are able to compete and exist in their time.
Reagent: Converting Ordinary Webpages into Interactive Software Agents

We introduce Reagent, a technology that can be used in conjunction with automated speech recognition to allow users to query and manipulate ordinary webpages via speech and pointing. Reagent can be used out-of-the-box with third-party websites, as it requires neither special instrumentation from website developers nor special domain knowledge to capture semantically-meaningful mouse interactions with structured elements such as tables and plots. When it is unable to infer mappings between domain vocabulary and visible webpage content on its own, Reagent proactively seeks help by engaging in a voice-based interaction with the user.
Database Docker persistence Framework based on Swarm and Ceph

The swarm cluster has two main limitations: 1) the data of database container will lost after the container goes down; 2) the lack of migration ability of database container across the hosts. To tackle these issues, we propose a novel persistence framework in both single database and database cluster. To be specific, we use ceph to provide migrable data volumes, and use two frameworks to migrate container from the perspective of container downtime recovery. By comparing the processing time of downtime database container, the experimental results demonstrate that our proposed method is able to shorten the recovery time of database container and improve the availability of database services.
RDA Austria - Removing Barriers in Data Sharing

Research Data Alliance Austria (RDA-AT) is a national RDA node dedicated to representing emerging research and data management communities throughout Austria. RDA-AT will operate as a formal participant of RDA Europe and RDA Global, linking Austrian data management initiatives and RDA Working and Interest Groups, providing assistance in adoption of RDA recommendations, and allowing Austrian stakeholders to benefit directly from RDA support mechanisms. These new connections will bring key Austrian issues to the global data management table, and global discussions back to Austria.&#x0D; This paper details the goals of the RDA-AT and describes its community and sustainability plans.
New Analog Processing Technique in Multichannel Neural Signal Recording with Reduce Data Rate and Reduce Power Consumption

Nowadays, the design of multichannel recording systems for neural signals used as an irreplaceable tool in the storage and analysis of neural signals for the diagnosis and treatment of various cardiovascular diseases.In order to increase the information received and thus reduce the risk of using these sorts of systems, designers try to use more electrodes or channels in this systems, but if the number of channels increases the new constraint is added to these systems which is stores a vast amount of data, and makes a wireless transport of information impossible.So it causes an increase in the number of channels in signal recording systems are severely restricted.The purpose of this study is to create a new structure for the analog processors to we do not only transfer the stored spikes completely to the system output but also reduce the amount of information which need to transmit.This new method consists of two separate compressive sampling blocks and spike detecting which is implanting together.Through this study, it was found that by using this method, we can increase the channel of neural signal recording system without any limitation, So The findings of this research significantly decrease the risk of using neural signal recording systems for biomedical applications.
The Influence of Geographical Factors on Extreme Rainfalls in Lampung Province

Many people in Indonesia calculate design rainfall before calculating the design flooddischarge. The design rainfall with a certain return period will eventually be convertedinto a design flood discharge by combining it with the characteristics of the watershed.However, the lack of a network of rainfall recording stations makes many areas that arenot hydrologically measured (ungauged basin), so it is quite difficult to know thecharacteristics of rain in the area concerned. This study aims to analyze thecharacteristics of design rainfall in Lampung Province. The focus of the analysis is toinvestigate whether geographical factors influence the design rainfall that occurs in theparticular area. The data used in this study is daily rainfall data from 15 rainfallrecording stations spread in Lampung Province. The method of frequency analysis usedin this study is the Gumbel method. The research shows that the geographical location ofan area does not have significant effect on extreme rainfall events. The effect of risingearth temperatures due to natural exploitation by humans tends to be stronger as a causeof extreme events such as extreme rainfall.Keywords: Influence, geographical, factors, extreme, rainfall.
A DIK-based Question-Answering Architecture with Multi-sources Data for Medical Self-Service (S)

Medical data is amplified in terms of speed and capacity in a very fast way, which creates obstacles for users to quickly access valid information.We present a DIK-based Question-Answering Architecture for Medical Self-Service.In addition, we propose a model based on the attention mechanism to extract high-quality medical entity concepts from the Chinese Electronic Medical Records (EMR).Then we modeled the medical data based on the DIK architecture (Data graph, Information graph, and Knowledge graph), construct a Question-Answering model (DIK-QA) for medical self-service that meets the needs of users to quickly and accurately find the medical information they need in massive medical data.Finally, we have realized this approach and applied it to real-world systems.The experimental results on our medical dataset show that the DIK-QA can effectively handle 4W (who/what/why/how) questions, which can help users find the information they need accurately.
Feature Evaluation for Automatic Bug Report Summarization (S)

Bug reports can be lengthy due to long descriptions and long conversation threads.Automatic summarization of the text in a bug report can reduce the time spent by software project members on understanding the content of a bug report.Our work further examines Rastkar et al.'s use of a logistic regression model to determine which sentences from the text of a bug report should be extracted for creating a summary.Using their publicly available bug report corpus, which contains manually annotated bug reports, we examined two aspects regarding the features used by the model.First, we examined how much of a reduction occurs in the precision and recall if some of the more complex features are not used.Second, we examined how the use of different feature combinations affects the precision and recall of the models.We found that the absence of some of the complex features resulted in a modest decrease in precision and recall, and confirmed that some features, such as sentence length, were the most significant features for bug report summarization.
Crosside: A Cross-Surface Collaboration by Sketching Design Space

This paper introduces, motivates, defines, and exemplifies CROSSIDE, a design space for representing capabilities of a software for collaborative sketching in a crosssurface setting, i.e., when stakeholders are interacting with and across multiple interaction surfaces, ranging from lowend devices such as smartwatches, mobile phones to high-end devices like wall displays.By determining the greatest common denominator in terms of system properties between forty-one references, the design space is structured according to seven dimensions: user configurations, surface configurations, input interaction techniques, work methods, tangibility, and device configurations.This design space is aimed at satisfying three virtues: descriptive (i.e., the ability to systematically describe any particular work in cross-surface interaction by sketching), comparative (i.e., the ability to consistently compare two or more works belonging to this area), and generative (i.e., the ability to generate new ideas by identifying potentially interesting, undercovered areas).A radar diagram graphically depicts the design space for these three virtues.
New class of 2D material could improve quantum computing

Recently Dzhafarov and Kon published the paper advertising the possibility to use the coupling technique of classical probability theory to model incompatible observables in quantum physics and quantum-like models of psychology. Here I present comments on this paper by stressing advantages and disadvantages.
A Novel Large-scale Chinese Encyclopedia Knowledge Parallel Refining Method Based on MapReduce

The open collaborative characteristics of online encyclopedia and the large number of ambiguity phenomena in the encyclopedia entry lead to inappropriate classification of plenty of Infobox knowledge triples of entries, which requires for refining and denoising of large-scale knowledge to improve the precision of Knowledge Base (KB). The enormous amount of triples in the KBs will cause excessive serial computing time expenditure by knowledge denoising and disambiguation processing. Existing knowledge refinement and disambiguation techniques have limitations in terms of scalability and time-efficient. There is still few typical research on the parallel processing of knowledge refinement in distributed environment. Therefore, this paper proposes a novel parallel algorithm for Chinese large-scale knowledge refinement based on MapReduce to further improve the overall system computing speed through parallel optimization for serial algorithm. Based on the original serial refining algorithm which can enhance the precision of encyclopedia-oriented KBs, results show that the novel parallel denoising algorithm proposed in this paper can further provide the system with good scalability and high speedup.
Mobile Application Identification Over HTTPS Traffic Based on Multi-view Features

The expanding volume of HTTPS traffic (both legitimate and malicious) creates even more challenges for mobile network security and management. In this work, we propose AIBMF(Application Identification Based on Multi-view Features), a fine-grained approach to classify HTTPS traffic by their application type. The key idea of AIBMF is to combine three kinds of features—payload convolution features, packet size sequence and packet content type sequence. Based on these different view features, a deep learning model (using CNN, embedding and RNN) is constructed for HTTPS traffic identification task. To evaluate the effectiveness of AIBMF, we run a comprehensive set of experiments on a real-world dataset (about 100,000+ flows), which shows that our approach achieves 96.06% accuracy and outperforms the state-of-the-art method (3.6% on F1 score).
PUGTIFs: Passively User-Generated Thermal Invariant Features

Feature detection is a vital aspect of computer vision applications, but adverse environments, distance and illumination can affect the quality and repeatability of features or even prevent their identification. Invariance to these constraints would make an ideal feature attribute. Here we propose the first exploitation of consistently occurring thermal signatures generated by a moving platform, a paradigm we define as passively user-generated thermal invariant features (PUGTIFs). In this particular instance, the PUGTIF concept is applied through the use of thermal footprints that are passively and continuously user generated by heat differences, so that features are no longer dependent on the changing scene structure (as in classical approaches) but now maintain a spatial coherency and remain invariant to changes in illumination. A framework suitable for any PUGTIF has been designed consisting of three methods: first, the known footprint size is used to solve for monocular localisation and thus scale ambiguity; second, the consistent spatial pattern allows us to determine heading orientation; and third, these principles are combined in our automated thermal footprint detector (ATFD) method to achieve segmentation/feature detection. We evaluated the detection of PUGTIFs in four laboratory environments (sand, grass, grass with foliage, and carpet) and compared ATFD to typical image segmentation methods. We found that ATFD is superior to other methods while also solving for scaled monocular camera localisation and providing user heading in multiple environments.