Reviewerly/exHarmony huggingface dataset:
BinCC: Scalable Function Similarity Detection in Multiple Cross-Architectural Binaries

With the undeniable increase in popularity of open source software, also the availability and reuse of source code have increased. While the detection of code clones helps tracking reuse and evolution while dealing with source code, little prior work exists that can be used in binary code. This is complicated by the increased difficulty posed by the compilation transformations. In this paper, we present a CFG refinement useful to find function-level clones in a fast and scalable way by comparing the high-level structure of multiple disassembled binaries altogether. We are capable of determining if functions belonging to other programs have been copied or reused, even when the processor architecture is different. Specifically, our algorithm consists in the extraction of the various functions flows and the reconstruction of a higher level structure, leveraging architectural differences and allowing efficient comparison in linear time with structural hashing. We implemented our idea in a tool called BinCC, and analyzed 24 million functions spanning different architectures and optimization levels. Results show that our approach can achieve precision between 91% and 99% within the same architecture and 75% in detecting clones among different architectures, and can also detect the presence of specific library functions inside an executable. Our approach can reach comparable precision of current state-of-the-art learning approaches while being three order of magnitude faster.
THE USE OF VISUAL ART TO FACILITATE STUDENTS IN LEARNING ENGLISH SPEAKING AT SMK NEGERI 2 MAKASSAR

This thesis aimed to find out does the use of Visual Art media improve the students to speaking accuracy and make the students in easier at in speaking fluency at SMK Negeri 2 Makassar. The researcher applied pre-experimental method with one gruoup pre-test and post-test design. To collect the data, the researcher used pre-test, treatment and post-test. The result of this research showed that the result of t-test value of level significant (p) = 0.05 with degree of freedom in accuracy (pronunciation) (df) + 29. In fluency (smoothness) (df) + 29. Indicated t-table value is 1.6991 and t-table accuracu (pronunciation) is 16.784, in fluency (smoothness) 14.262. Based on the result data analysis, it is concluded that there is difference significant mean score of the test that have given by researcher using Visual Art media in teaching speaking.
Pengembangan Media Pembelajaran Akuntansi Berbasis Android “LAKEUN” Pada Materi Laporan Keuangan Perusahaan Dagang Kelas XI Akuntansi SMKN 6 Surabaya

The development of science and technology has an impact on teaching and learning activities. Educators are required to make teaching and learning activities more varied so that students can play an active role during teaching and learning activities. Learning media can be an alternative for educators to create student participation during teaching and learning activities. Based on observations made by researchers, it was found that the media that had been used by accounting teachers at SMKN 6 Surabaya, especially in the accounting department, still depended on power points and lacked use of developed technology. This study has a goal to create an android-based accounting learning media for students of class XI Accounting at SMKN 6 Surabaya and see how feasible Android-based accounting learning media is as a creative medium in the accounting practicum learning process for service, trade, and manufacturing companies. In developing learning media using the 4D model by Thiagarajan which consists of the definition stage (Define), the design stage (Design), the development stage (Develop), and the dissemination stage (Disseminate). Data collection techniques were in the form of validation questionnaires and student response questionnaires related to the developed media. The results of the evaluation carried out by material experts and media experts, Lakeun's learning media can be said to be feasible, as well as student responses regarding the learning media made also obtain very understood criteria.
ANALISIS PERBANDINGAN KLASIFIKASI CITRA MYCROBACTERIUM TUBERCULOSIS

Tuberculosis (TB) is an infectious disease that remains a challenging health problem worldwide. This disease is caused by rod-shaped bacteria called Mycobacterium tuberculosis. These bacteria usually affect the lungs but can also spread to other parts of the body such as the eyes, bones and blood vessels. It was reported that around. 4.74 million new TB cases were identified and around eight hundred thousand people died from TB, during 2015 in Southeast Asia. In this study, color image segmentation techniques were carried out using classification methods by comparing several methods including Logistic Regression, Linear Discriminant Analysis, K- Neighbors Classifier, Decision Tree Classifier, Random Forest Classifier, Gaussian NaiveBayes, and Support Vector Machine. In the final stage it is known that the validation test on the Random Forest Classifier produces the highest value of 86.04%.
PENGARUH GAYA MANAJEMEN DAN STRUKTUR ORGANISASI TERHADAP KUALITAS SISTEM INFORMASI AKUNTANSI

This study aims to determine how much influence the management style and structure organization on the quality of accounting information system in the Islamic sharia BJB and BJB in Bandung. The research approach used in this study is descriptive and verification analysis using primary data. The sampling technique used was with purposive sampling. The Statistical analysis used in this study is a classic assumption test, validity test, reliability test, and multiple linear regression analysis. Number of population and sample of this study 35 respondents, sampling using purposive samping method with sources obtained through the result of filling out the questionnaire and processed with the help of SPSS software Version 25.0. This results showed that the influence of management style and organization structure affect the quality of accounting information system both partially and simultaneously. Partially management style influences the quality of accounting information system and organization structure influences the quality of accounting information system. Simultaneously management style and organization structure significant affect the quality of accounting information system.
Lexical and Syntactic Features for Reader Rating Prediction

Finding a correlation between the structural features of the text and its reception has recently become a challenging task of computational linguistics. However, the correlation between the reader’s reception of a literary work and its linguistic features suffers from the ambiguity of many textual parameters, which can be considered in calculations. Concerning Russian-language literature, such a process is complicated by the lack of representative databases of reader reviews and by rather noticeable discrepancies in the methods of text data analysis. In this paper, I propose to investigate the possibility of predicting the rating of a text only by its lexical and syntactic features. To design an experiment, four steps were taken: First, a corpus of Russian novels of different genres was built. Next, the literary rating was scraped from bookstore LitRes via a devised parser. Due to the small size of the corpus, the obtained results were manually cleaned to avoid ambiguity of text ratings. Most of data preprocessing was the selection of linguistic features to be considered. 23 different parameters were extracted after designing a proper software to mine those features. The final part o f the work was focused on checking whether the lexical and syntactic parameters correlate with the texts rating and setting a proper predictive model. Random Forest, Cat Boost, Logistic, Linear Regression, and K-Nearest Neighbors algorithms were compared. Since the coefficient of determination for the regression approach had a poor value, it was decided to move on to the classification problem, which brought more significant r esults. The obtained results confirmed the existence of a correlation between the structure of texts and their ratings and shed a light on new prospects in the research of the features of the text and its perception.
Klasifikasi Kondisi Gizi Bayi Bawah Lima Tahun Pada Posyandu Melati Dengan Menggunakan Algoritma Decision Tree

One of the health problems in Cirebon is about nutritional status. This happens because the increase and decrease in the number of children under five who experience nutritional status problems each year is uncertain. Toddlers are a group of people who are vulnerable to nutrition. The incidence of malnutrition if not addressed will cause a bad impact for toddlers. The impacts include death and chronic infection. Early detection of undernourished children (malnutrition and malnutrition) can be done with an examination of weight for age (W/U) to monitor the child's weight, the parameters used to calculate the nutritional status of toddlers include age, weight and height/length. To classify the nutritional status of children under five, a knowledge or scientific study is needed that can classify data based on the data from measurements and weighing. This study uses 8 criteria, namely Name, Address, Mother's Name, Gender, Age, Weight, Height, Status Classification. The accuracy results obtained are 98.86% with details, namely the Prediction Results of Malnutrition and it turns out that the True Malnutrition is 13 data. Poor Nutrition Prediction Results and turns out to be True Normal by 1 Data. Normal Prediction Results and it turns out to be True Malnutrition is 1 Data. Normal Prediction Results and turns out to be True Normal of 161 data. The results of the classification of infant levels based on age, infants aged 0 months to 10 months had normal nutrition, while infants aged 10 months to 19.5 months were prone to malnutrition for infants, and those aged more than 19.5 months had poor nutrition. normal.
Sistem Informasi Izin Online Berbasis Web Menggunakan Framework Codeigniter

So far, the absence of work permit that is still valid until now is consodered inefficient, when an employee want to apply for leave of absence from work, the employee must first come to the office, then ask or approval from his superiors and the application letter is approved and then printed as evidence. The purpose of this study is to make easier for employee to apply for leave absence from work online and to help manage employee data and data for an absent work permit automatically enter the Development Sector. The system development model in this study is the Waterfall Model. Software testing is carried out using User Acceptance Testing (UAT). Data collection techniques used in this study were observation, interviews and documentation. The application used to support sending notifications is the Whatsapp API. This system is built using sublime text 3 editor and uses the PHP as programming language and MySQL as database management. The final result of this research is an online validation system or submitting a permit for not going to work.
Symmetrical Robust Canonical Correlation Analysis for Image Classification

Canonical correlation analysis (CCA) is a useful technique for multivariate data analysis, which can find correlations between two sets of multidimensional data. CCA projects two sets of data into a low-dimensional space in which the correlations between them are maximized. However, CCA is sensitive to noise or outliers in the collected data of real-world applications, which will degrade its performance. To overcome this disadvantage, we propose symmetrical robust canonical correlation analysis (SRCCA) for image classification. By using low-rank learning, the noise is removed, and CCA is used to encode correlations between images and their symmetry samples. To verify effectiveness, four public image databases were tested. The result was that SRCCA was more robust than CCA and had good performance for image classification.
Actor-Focused Interactive Visualization for AI Planning

As we grow more reliant on AI systems for an increasing variety of applications in our lives, the need to understand and interpret such systems also becomes more pronounced, be it for improvement, trust, or legal liability. AI Planning is one type of task that provides explanation challenges, particularly due to the increasing complexity in generated plans and convoluted causal chains that connect actions and determine overall plan structure. While there are many recent techniques to support plan explanation, visual aids for navigating this data are quite limited. Furthermore, there is often a barrier between techniques focused on abstract planning concepts and domain-related explanations. In this paper, we present a visual analytics tool to support plan summarization and interaction, focusing in robotics domains using an actor-based structure. We show how users can quickly grasp vital information about actions involved in a plan and how they relate to each other. Finally, we present a framework used to design our tool, highlighting how general PDDL elements can be converted into visual representations and further connecting concept to domain.
An empirical study on implicit constraints in smart contract static analysis

Smart contracts are usually financial-related, which makes them attractive attack targets. Many static analysis tools have been developed to facilitate the contract audit process, but not all of them take account of two special features of smart contracts: (1) The external variables, like time, are constrained by real-world factors; (2) The internal variables persist between executions. Since these features import implicit constraints into contracts, they significantly affect the performance of static tools, such as causing errors in reachability analysis and resulting in false positives. In this paper, we conduct a systematic study on implicit constraints from three aspects. First, we summarize the implicit constraints in smart contracts. Second, we evaluate the impact of such constraints on the state-of-the-art static tools. Third, we propose a lightweight but effective mitigation method named ConSym to deal with such constraints and integrate it into OSIRIS. The evaluation result shows that ConSym can filter out 96% of false positives and reduce false negatives by two-thirds.
THE EFFECT OF SELF REGULATED LEARNING ON LEARNING OUTCOMES THROUGH LEARNING MOTIVATION IN CLASS V IN THE LITUK CLUSTER, SOUTH ATAMBUA DISTRICT

The purpose of this study is to (1) determine the effect of self-regulated learning on learning outcomes and (2) determine the effect of self-regulated learning on learning outcomes through student learning motivation in class V. This research is an associative research with quantitative research type, using a questionnaire path analysis model that used include The Motivated Strategies for Learning Questionnaire (MSLQ) developed by Pintrich and Groot (1990) to measure self-regulated learning and learning motivation. The results of the analysis show that: (1) There is a significant influence between self-regulated learning on student learning outcomes, seen from the two aspects of self-regulated learning indicators related to the use of cognitive strategies and self-regulation. (2) and there is a significant effect between self-regulated learning on student learning outcomes through learning motivation, seen from the three aspects related to self-efficacy, intrinsic value, and text anxiety.
The Effect of Class Scheduling on The Students’ English Performance

The study focuses on the effect of the class scheduling toward the students’ English performance during the latest policy of new normal face to face learning. The research method used was case study research design. The researchers used an interview and questionnaire as the research instrument to gain the data. The subject of the research was X MIPA 1 to X MIPA 8 in SMA Negeri 1 Gedangan; in specific number were 15 students as the interviewee and 130 students who were willing to participate filling the survey. The data analysis technique was descriptive analysis with the percentage technique from the questionnaire analysis result to validate the outcome of the research. The results of this study showed that the school has prepared an effective schedule yet the indiscipline of teachers and students who are not able to implement the class scheduling in accordance with the provisions of the applicable rules. Therefore, students’ English performance was disrupted due to the implementation of inefficient time allocations. Thus, the school must optimize teacher performance in managing time and improve student performance so that the short lesson hours can be more effective and help students learn without any interference from time allocation problems, class scheduling, and other internal factors.
Chemical disease relation extraction through the combination of multiple mention levels: RelSCAN+

Chemical-induced disease (CID) relation extraction has been pivotal in the understanding of biological processes.A CID relation between a chemical and disease entity may be extracted either from a single sentence or from two or more adjacent sentences.We use 'intrasentence level' to refer to the mention of the desired entities in the same sentence and 'intersentence level' to refer to the mention of these entities in two or more adjacent sentences.This study proposes a three-phase architecture for extracting CID relations from biomedical literature by considering both sentence levels and additionally the combination of these two sentence levels which we describe as the 'joint level'.In phase 1, we construct relation instances at the intra-and intersentence levels which are subsequently combined to form the joint level.In phase 2, we extracted features specifically for an individual relation instance at the three levels.At each of these levels, we trained three classifier models that consist of the combination of two classifiers.We used the training dataset for training and later classified the CID relation instances using the test dataset.Phase 3 consists of two steps; in step 1, the classifier outputs from both the intra-and intersentence levels are combined and in step 2, the results from step 1 are combined with the results from the classifier trained at joint level using a prediction probability-based voting algorithm to determine the final result.Using the BioCreative V corpus for validation, we obtain results that outperform all the state-of-the-art systems for CID relation extraction on the standard chemical-disease relation corpus.
The Image Encryption Algorithm Based on Artificial Images and Complex Logistics Mapping

Due to the intuitiveness of image transmission information, the application of images can be ubiquitous, correspondingly, the security of image information has become more significant. As chaos is compatible with cryptography due to some of its characteristics such as sensitivity of initial values, randomness, etc., it is extensively used in image encryption. In this paper, based on artificial images and 2D lagging complex Logistics mapping (2D-LCLM), a novel image encryption algorithm was proposed. Firstly, the pseudorandom sequences generated by 2D-LCLM is used to construct an artificial image. Artificial images can replace the traditional iterative encryption mechanism. Secondly, adopt chaotic sequences to position the original and artificial images. Finally, DNA XOR manipulation is used to merge the two images. Experimental simulation of the standard test graph, through the analysis we can know the encryption algorithm based on artificial image has good encryption effect and security performance.
Where did Brazilians get their diabetes drugs during the COVID-19 pandemic? DIABETESvid results

Access to diabetes medications is critical to disease control. However, social distancing in the COVID-19 pandemic may have limited the provision of medicines and supplies. Thus, the objective of this study was to analyze the sources of obtaining diabetes drugs in the COVID-19 pandemic in Brazil. The data came from the DIABETESvid survey, and this analysis considered a convenience sample of 1608 participants who responded to the questionnaire published on social media of scientific societies related to diabetes. This is a cross-sectional study, conducted in the form of a web survey, from September 1 to October 19, 2020. The outcome analyzed was the sources for obtaining diabetes drugs categorized in pharmacies of the "Sistema Único de Saúde", pharmacies of the “Aqui tem Farmácia Popular” program, as well as private pharmacies and drugstores and other sources. Most participants self-reported a diagnosis of type 1 diabetes mellitus (49.8%), used insulin (61.9%), and had a private health plan (68.7%). Private pharmacies and drugstores and other sources were the main places where medicines for diabetes were supplied (47.5%). Participants residing in the Midwest region of the country had a lower frequency of supply in pharmacies in the “Aqui tem Farmácia Popular” program (0.57; 95%CI 0.36-0.92). In the studied sample, there was a higher frequency of supply of diabetes drugs in private pharmacies and drugstores and other sources with a reduction in frequency in public pharmacies during the COVID-19 pandemic in Brazil.
ENGLISH FOR TEENS : UPAYA MENINGKATKAN KESADARAN ANGGOTA KARANG TARUNA UNTUK MEMAHAMI DAN MEMPRAKTEKKAN BAHASA INGGRIS DALAM BERKOMUNIKASI

Understanding and using English in communicating has become a necessity for everyone who wants to win the increasingly fierce competition in work and business in the era of globalization. However, there are still many people who have not realized and prepared themselves to learn English. The ease of accessing English learning materials in the current digital era is not used to improve communication skills. English For Teens, a training to communicate in English, was held with the aim of building and raising awareness of the younger generation so that they are motivated to learn and practice English. This qualitative research focuses on the problems faced by the managers of Karang Taruna RW.02 Bambu Apus, East Jakarta, and provides the best solution in building and increasing the awareness of its members to learn and practice English. Low understanding of English causes difficulties in communicating and accessing various information that is important for personal and organizational progress. English For Teens also tries to provide learning materials that are simple, varied, but easy to understand so as to eliminate the impression that learning English is very difficult and boring. The results of the training showed that the participants easily understood the learning material provided and could do the exercises. However, the main problem is when doing speaking, especially in how to pronounce words (pronunciation), so the tutor has to give examples of correct pronunciation over and over again.
Research on the Augmented Reality (AR) Technology to Optimize Humanized Design of Carbon-neutral Urban Landscapes

This research involves the path, form and design method of augmented reality technology in the humanized design of carbon-neutral landscapes, with the purpose to explore how to use augmented reality technology to optimize the humanized design of carbon-neutral landscapes. During the research, it was found that: for the landscape design, to achieve carbon-neutrality, it is necessary to reduce the trial and error cost of design through the use of augmented reality technology; and for the humanized design of carbon-neutral landscapes, the use of virtuality and reality combination feature of computer augmented reality can better deliberate the simulation design, so as to achieve a real design humanization, and provide a new design idea for the realization of the carbon-neutral landscape design with technology and art collaborative innovation.