Reviewerly/exHarmony huggingface dataset:
CROKAGE: Effective Solution Recommendation for Programming Tasks by Leveraging Crowd Knowledge

Developers often search for relevant code examples on the web for their programming tasks. Unfortunately, they face three major problems. First, they frequently need to read and analyse multiple results from the search engines to obtain a satisfactory solution. Second, the search is impaired due to a lexical gap between the query (task description) and the information in the solution (e.g., code example). Third, the retrieved solution may not be comprehensible, i.e., the code segment might miss a succinct explanation. To address these three problems, we propose CROKAGE (Crowd Knowledge Answer Generator), a tool that takes the description of a programming task (the query) as input and delivers a comprehensible solution for the task. Our solutions contain not only relevant code examples but also their succinct explanations written by human developers. We evaluate and compare our approach against ten baselines, including the state-of-art. We show that CROKAGE outperforms the ten baselines in suggesting relevant solutions for 902 programming tasks (i.e., queries) of three popular programming languages: Java, Python and PHP. Furthermore, we use 24 programming tasks (queries) to evaluate our solutions with 29 developers and confirm that CROKAGE outperforms the state-of-art tool in terms of relevance of the suggested code examples, benefit of the code explanations and the overall solution quality (code + explanation).
Vehicle Detection from Images using Deep Fully Convolutional Networks

Vehicle detection provides the facilitation of traffic planning and management. It also helps in finding suspected and stolen vehicles. Although it has many applications, it is a very complex problem due to variations in vehicle type and size. As the amount of vehicle types are very high the models find it hard to classify the correct type of the vehicle. In this paper, we are proposing a vehicle detection model based on YoloV3 convolutional neural network architecture with custom backbone. Our proposed backbone in the Yolov3 architecture helps classify the different types of vehicles accurately. This makes the classification of the images at pixel level and predicts the regression based ROI bounding box for the classified vehicles in the images. The model contains features extracted at different kernel sizes to find the features at multiple scales which will then be concatenated. Experiments were performed on the Kitti vehicle detection dataset have shown the superior performance of our proposed model.
KOMPUTERISASI AKUNTANSI PEMBELIAN KERTAS SECARA KREDIT BERBASIS WEB PADA CV CIPTA GRAFIKA

CV Cipta Grafika is a company engaged in printing, for the raw material needs for printing this company buys in cash or credit. In this study, the researcher will discuss the process of purchasing printing materials on credit, because in this process the existing applications have not provided maximum results, namely, there are often errors in the presentation of data or information. The researcher will create a web-based credit purchase and debt recording system with MySQL as the database. The output of this system is the payment of obligations to the supplier which will be due and the supplier's debt recording report can be monitored. It is hoped that this purchasing and recording system will make the payment process easier, avoid mistakes, and can streamline time.
Calibration and Auto-Refinement for Light Field Cameras

The ability to create an accurate three-dimensional reconstruction of a captured scene draws attention to the prin-ciples of light fields. This paper presents an approach for light field camera calibration and rectification, basedon pairwise pattern-based parameters extraction. It is followed by a correspondence-based algorithm for cameraparameters refinement from arbitrary scenes using the triangulation filter and nonlinear optimization. The effec-tiveness of our approach is validated on both real and synthetic data
Dynamic Scenarios Transformation in Software System Design in Software Architectural Pattern through MVC

In the real word many software performance prediction methodology are available such that the design decisions are clearly understandable by the stakeholders.To improve the existing software performance Palladio component model methodology is used by little improving algorithm of the problem context which in turn reflected in the system design.The software architectural patterns are the best tool in measuring the quality.There are different types of architectural patterns like distributed systems, interactive systems etc.There are two types of communicatingstructuresknown as Model View Controller (MVC) then Presentation Abstraction Controller (PAC).
Cast Away: A New Way to Read Value of Objects in Context of a Movie

This paper is the exploration of an assignment given in an industrial design undergraduate course called “Culture and Communication in Design”. The course aims to give an insight about how objects are analyzed and evaluated by tools provided by semiotics, products semantics and critiques of commodity culture. In this assignment, the movie “Cast Away” was used in order to make an analysis about the meanings and meaning shifts of artefacts that are placed in different contexts presented in scenes. The assumption is “reading” movies with a concentration on the artefacts placed in the scenes gives students a different perspective to see, understand and discuss some terms about the changing cultural significance and value systems of design. Cast Away, as an original interpretation of the story of Robinson Crusoe, enriched by the objects carried together with the main actor to an unknown island, provides a very rich ground to see and interpret the cultural changes and dynamics between objects, individuals and society. The significance of the exercise, or such exercises in the design education, lies in the motivation of training students not only as mere practitioners of design as creators of commodities but also as intellectuals that can reflect on their own profession in a critical lens.
Investigative advising: a job for Bayes

Background: Bayesian approaches to police decision support offer an improvement upon more commonly used statistical approaches. Common approaches to case decision support often involve using frequencies from cases similar to the case under consideration to come to an isolated likelihood that a given suspect either a) committed the crime or b) has a given characteristic or set of characteristics. The Bayesian approach, in contrast, offers formally contextualized estimates and utilizes the formal logic desired by investigators. Findings: Bayes’ theorem incorporates the isolated likelihood as one element of a three-part equation, the other parts being 1) what was known generally about the variables in the case prior to the case occurring (the scientific-theoretical priors) and 2) the relevant base rate information that contextualizes the evidence obtained (the event context). These elements are precisely the domain of decision support specialists (investigative advisers), and the Bayesian paradigm is uniquely apt for combining them into contextualized estimates for decision support. Conclusions: By formally combining the relevant knowledge, context, and likelihood, Bayes’ theorem can improve the logic, accuracy, and relevance of decision support statements.
View Synthesis from Silhouette Using Deep Convolutional Neural Network

Multiv iew video could be the basis to support various applications, such as Three-Dimensional video (3DV), Virtual Reality (VR), and Free Viewpoint Video (FVV).Mult iview data is intrinsically redundant, in fact, the semantic contents of different views are almost similar, and this is especially true for small baseline views.Obviously, wide baseline v iews might significantly differ in their contents, where some objects might be comp letely absent in some views.However, the current approaches of representing mu ltiv iew data, even if they explo it inter-view correlation, require large bandwidth for transmission.This bandwidth is almost linear with the number of transmitted views.Thus, in this paper we propose to address this problem by representing lateral v iews solely using their edges, wh ile dropping their texture content.The texture content is synthesized, at the receiver side, by a convolutional neural network (CNN) explo iting the edges and the information in the central view.The edges of the lateral v iews represent the location of the "objects" in their corresponding views, whereas, we assume that their texture in other views does not change significantly, consequently there is no need to represent them in the lateral v iews.In this work, in addit ion to the proposed paradigm of representing mult iview data, we also propose a training framework fo r the CNN network.Experimental results demonstrate the effectiveness of the proposed framewo rk and demonstrate that the network is able to synthesis accurate and reliable lateral v iews starting from their edges .
Mobile Learning: Detrimental or Beneficial?

Mobile Learning also known as mLearning, is a new generation of learning where content which users wants to learn is accessible and available on mobile devices like smart phone and tablets. With the evolution of “world in the pocket” learning becomes much easier and one can start learning on the go. This paper illustrates how mLearning could be a better new way to learn and to interact with the learning content provided and how mLearning is proving to be a better alternative to traditional learning. Learning through traditional methods is not helping students to learn and gain new knowledge wherever and whenever they want and also it’s not that effective. It is also researched that not all mLearning applications are helpful for the students or learners to learn whatever they want, only apps with interactive and user-friendly user interface were found helpful. That’s why mobile learning applications are made with user-friendly user interface. Some practical strategies and methods of implementation of mLearning approach despite of its limitations and challenges were recommended in this paper as well.
AWARENESS AND APPRECIATION OF ADVERTISING LANGUAGE

This research focuses on advertising language in advertisement slogans of various products written in English. The grand theory used is Stylistics, which is the study of the style of language or the study of the use of distinctive linguistic expressions and how they affect the target audience. To limit the discussion, this research more specifically covered the use of rhetorical figures and doublespeak in advertising language. These language features basically have the same functions, namely the aesthetic and persuasive functions, which aim to attract the audiences attention. This research, using an explanatory research method, was done to find out peoples awareness and appreciation of the outstanding use of advertising language by distributing questionnaires with Likert scale to the students and alumni of the English Department of a private university in Indonesia. Considering that in an advertisement there is also the visual aspect besides the textual aspect, the research was also done to find out whether or not there is a strong correlation between peoples awareness and appreciation of the visual and textual aspects.Keywords: advertising language, awareness, appreciation
Question Expansion Technique On The Different Translations Of The Holy Quran By Using WordNet And Islamic Synonyms

The Holy Quran is the most significant religious text, which is followedby thebelievers of the Islamic religion. The translations of the Quran are the interpretation of its meaning in different languages, to assist those who are not familiar with the Arabic language.Most of the Quranic search systems that support theneeds of who are non-native speakers of the Arabic language of the contents of the Holy Quran are basedon keywords matching. Since there are several known English translations of the Holy Quran and each translation has its terminology based on the style of the translator. In addition to that, there are Islamic terms might be not used in the English language, such as "Hajj" or "Saum,"where these terms might vary from one translation to another. Therefore, these search systems do not support the needs of the user, and often there isno matching between the terms of user's questionandtheterms of the Quran.Consequently, this research has used the synonymsofWordNet to expand the user's question,and to cover all the possibilities of theterms that might be usedin the Quran translation, but there are many of Islamic terms are not available in the WordNet database. The WordNet has been enhanced with a set of Islamic synonyms that have been used in the well-known Islamic translations of the English language, which it was created by the researcher, to increase the likelihood of providing a sure matching between the question's words and the contents of the Quran translation. According to the results of this study, the evaluation of this research proved that the method of question expansion by using the both of the WordNet and the Islamic synonyms showed better accuracy and efficiency than the traditional keywords matching systems, or the system that uses only the WordNet synonyms in the question expansion method in terms of matching all user's questions words with documents and retrieve the correct answers to these questions.
Scheduling to Maximize the Data Transfer Rate for Big Data Applications in Cloud System

In cloud platform, parallel computing is precisely one of the methods to handle various computational tasks which need to perform fast on a large dataset. In a system each job was run by the respective processors. Jobs may need to be accompanying through nodes and it will share resources. So scheduling is important to share the resources and path diversity is very much of important in order to get the data within least retrieval time. The existing scheduling algorithms should not efficiently find the optimum solution. In this paper we make a survey to provide the better transfer scheduling algorithm for transfer the data within stipulated time, to maximize the data transfer rate and to choose cost effective paths
Impact of Knowledge Sharing on the Relationship between Technological Factors and Supply Chain Performance

This study is an attempt to resolve the issue the mediating role of knowledge sharing on supply chain performance (SCP), by considering different technological factors, in the context of SMEs in Jordan. The study specifically examined the relationship between technological factors and SCP, with the mediating role of knowledge sharing among the SMEs. The study adopted a self-administered questionnaire as a data collection technique, with which data were gathered from 371 supply chain members of various SMEs in the Jordanian industries. Data was then exposed to analysis using AMOS and SEM. The obtained findings showed support for the formulated hypothesis, in that the findings indicated the mediating role of knowledge sharing on the technological factors-SCP relationship at the significance level of 1%. The findings also showed that IT infrastructure and social media usage (technological factors) had no direct significant relationship with SCP but the technological factors had a significant positive relationship with knowledge sharing at the significance level of 1%.
EduBlocerts: Securing Institutional Data using Block-chain Technology

Educational Institutions are storing their data and their transactions in the physical media such as registers, books, records and in digital media like hard disks and in cloud storage. These type of storage media are insecure and are prone to data tampering. Even when they are protected with a password, they can be altered if the user’s password has been hacked, which means the traditional methods of record keeping are insecure. Blockchain has a decentralized architecture which store and secure transactions transparently and there will be no central authority over those occurring transactions. So, a model is implemented which uses the core technology of the Block-chain to store and secure the institutional data which will be in the form of transactions. These transactions can be easily viewed by anyone in the connected network. Even if the data is tampered it can be detected easily. This model has the advantages of Block-chain minus the investments and risks associated with it.
A New Domain Specific Scripting Language for Automated Machine Learning Pipeline

This paper concerns on two difficulties faced by non-experts’ users in the utilization of machine learning; design of the model and the programming task for implementation. From the varieties of the machine learning algorithms, selecting the best model with the best configurations is a critical and complex design issue. In light of this situation, automated machine learning pipeline is highly beneficial. Research has proved that Genetic Programming is highly useful to find the best pipeline of an automated machine learning model. However, in respond to the implementation difficulty, there exists a limited software tool that support easy implementation for automated machine learning based on Genetic Programming. This paper presents the specifications of a domain specific scripting language for the easy development of an automated machine learning with the underlying Genetic Programming. The scripting language has a very minimal characters of codes, hence easier to understand and more concise than the Python programming language.
Solar Panel Efficiency Calculation using Incremental Conductance Algorithm

Solar energy is a vital renewable source of energy in India. To extract and utilize this renewable source of energy photovoltaic solar panels are used. To gather the maximum amount of energy a good efficiency solar panels are used. The maximum efficiency is achieved when the maximum irradiation from the sun is received by the earth. The irradiation and temperature are proportional with time, a solar tracker should be developed and positioned to extract the maximum energy with respect to time. This paper is aimed to find the maximum tracking point and the maximum efficiency using Incremental Conductance (IC) algorithm. The IC method is the simplest and the easiest way to find the efficiency of the panels with respect to the time throughout the day.
Transformasi Literasi Kewarganegaraan Mahasiswa Pendidikan Guru Sekolah Dasar dalam Pembelajaran Jarak Jauh

The research aims to identify and analyze the transformation of Citizenship literacy in Elementary School Teacher Education students, in the implementation of distance learning in the era of the Covid-19 pandemic. The research method in this research is descriptive qualitative. The results showed that the Citizenship literacy of Elementary School Teacher Education students in distance learning, is transformed in the form of an understanding of the rights and obligations as Indonesian citizens. Nationality Literacy of Primary School Teacher Education students is quite good, if it is analyzed from the depth of coverage regarding the rights and obligations as citizens. The results also show that the obstacle in the transformation of citizenship literacy is about the shift in learning patterns from distance or online.
Design of Hybrid Method for Automatic Sentiment Classification System

The Developing enthusiasm for the field of opinion mining and its applications in various regions of information and also, sociology has activated numerous researchers to investigate the field The chance to catch the opinion of the overall public about get-togethers, political developments, organization systems, advertising efforts, and item inclinations has raised expanding enthusiasm of both scientific community (as a result of the energizing open difficulties) and the business world (due to the wonderful advantages for promoting and money related market expectation). Today, sentiment analysis investigation has its applications in a few unique situations. There are a decent number of organizations, both huge and little scale, that focuses on opinions and sentiments as a major aspect of their central goal. This work introduces hybrid approach that includes lexicon based approach and machine learning approach for extracting aspects and sentiments
Noise Reduction Technique In Scanned Document Using Cuckoo Optimization Algorithm

While scanning, digitization and transmission, scanned documents can be contaminated with noise. Noise can be categorized by identifying its characteristics. Noise observed for similar pattern scanned document is the source for selecting appropriate noise removal techniques. Any image processing method can have few phases like (i) Pre-processing, (ii) Segmentation, (iii) Recognition and (iv) Post processing. This pre-processing stage is an essential stage, which primarily deals with noise removal. This paper involves the use of the Cuckoo Optimization Algorithm (COA) to remove noise in preprocessing. COA has shown its superior capabilities in fast convergence and better optimal global performance. It finds the most likely pixel value to restore noisy pixels.
Psycholinguistic conditions in vocabulary acquisition

Present article examines the psycholinguistic factors that affect ease of learning foreign language vocabulary acquisition. Demonstrate the orthographic and phonological patterns of vocabulary acquisition.
A hybrid fault tolerance framework for SaaS services based on hidden Markov model

With the booming of cloud computing, more and more applications adopt cloud services to implement their critical business. However, failures causing either service downtime or producing invalid results in such applications may range from a mere inconvenience to significant monetary penalties or even loss of human lives. In critical systems, making the cloud services highly dependable is one of the main challenges. Existing researches show that using fault injection for experimental assessment of fault tolerance architecture for cloud services is still an open problem because of the complexity and diversity of failures in cloud environment. Therefore, we propose a hybrid fault tolerance framework which utilises replication and design diversity techniques for SaaS service. In order to verify the effectiveness of the fault tolerance framework in various pragmatic failure scenarios, a mixed fault simulator based on urn and ball model in hidden Markov model is introduced. A series of experiments are carried out for evaluating the reliability of the SaaS service, including single service without replication, single service with retry or reboot, and a service with spatial replication. The results show that the mixed fault simulator is flexible for simulating various faults in cloud environment, and both temporal and spatial redundancy have better effect on the availability and reliability improvement of the SaaS service.
STUDENTS ORDER THINKING SKILL IN LEARNING ENGLISH

This research aims to know the students order thinking skill from their written answer in reading skills. By knowing their order thinking skill, it is used as basic for the teachers to design the learning process which fit to improve students higher order thinking. This research used qualitative design, content analysis, which is analyzed students answer document in reading skill. The data is the students answer in reading skill by using herringbone technique. The data shows that 18 students are in Low Order Thinking Skill (LOTS) in which in making several questions based on WH- Questions they only do the repetition and imitation from the information given, 11 students are in Middle Order Thinking Skill (MOTS) in which they are able to think in different domain, and only 1 student is in Higher Order Thinking Skill (HOTS) in which he can improve and create an idea from the information given.Key words: HOTS, Order Thinking, Reading