Reviewerly/exHarmony huggingface dataset:
공원 동상의 영어 안내판 오류 분석 : 통시적 자료 분석을 중심으로

The aim of present research is to examine translation errors that occur with Korean to English translations on Korean statues in a public place located in Seoul. The authors collected data of the English information boards on the statues three times during the period of 2013-2019, which included August 2013, March 2015, and February 2019. The translation error patterns of these three different versions on English information boards were compared. Through careful investigations, the format errors, grammatical errors, and contextual errors were detected in the revised versions as well as in the original version. Furthermore, it was also found that there was little improvement with the revised versions of English information boards. As a result, this paper proposes that thorough preparations and planning should be implemented when information boards on statues are translated from Korean into English.
IMPLEMENTASI ALGORITMA C 4.5 UNTUK PENENTUAN PREDIKAT KINERJA PEGAWAI (STUDI KASUS : PT. VIRAMA KARYA PUSAT)

Determination of the predicate of employee performance at PT. Virama Karya Pusat is still done conventionally or manually. The system that runs only uses two criteria, namely work experience and levels of education, making it difficult for HRD to determine the predicate of employee performance. The method used in this research is C4.5 algorithm. The purpose of this study is to develop applications for determining employee performance using the web-based C4.5 algorithm at PT. Virama Karya Pusat to know the personnel development of employee performance, find out whether C4.5 can be used to determine the predicate of employee performance and to know the accuracy of the C4.5 algorithm to determine employee performance predicate data. The criteria used to determine the predicate of employee performance are work experience, education level, absenteeism, work goals, hard skills and soft skills. Testing the system using Black Box and testing the accuracy of the method using K-Fold Cross Validation. The accuracy of the C4.5 algorithm is 73.3%. The benefits obtained from determining employee performance can be used as recommendations for employees who need training and get bonuses that are consistent with employee performance results and the bureau chief can monitor employee performance regularly every period.
A HYPER-SURFACE-BASED MODELING AND CORRECTION OF BIAS FIELD IN MR IMAGES

Dealing with the different artifacts in medical images is necessary to perform several tasks, in particular segmentation and registration. We introduce in this paper a novel method for bias field correction in Magnetic Resonance Images (MRI). Using the segmentation results obtained by a modified Expectation Maximization clustering, the bias field is fitted as an hyper-surface in a 4D hyper-space. Then, it is corrected based on the fact that voxels belonging to the same tissue should have the same intensity in the whole image. So, after a quick and coarse unsupervised voxel labeling by clustering by parts is performed, the bias field is computed at the voxels that have been reliably labeled as belonging to one of the tissues of interest. For the less reliably labeled voxels the bias field isinterpolated using an hyper-surface, estimated by a 4D lagrangian interpolation. In order to evaluate the efficiency of the proposed method, we compare the MRI segmen- tation results with and without bias field correction, and we use also the coefficient of variations within the MRI volume.Segmentation results, and the coefficient of variations results were significantly enhanced after bias field correction by the proposed method. Furthermore, the proposed method is considered fast compared to other methods, that are often time consuming du to their iterative nature.
Response-Hiding Encrypted Ranges: Revisiting Security via Parametrized Leakage-Abuse Attacks.

Despite a growing body of work on leakage-abuse attacks for encrypted databases, attacks on practical response-hiding constructions are yet to appear. Response-hiding constructions are superior in that they nullify access-pattern based attacks by revealing only the search token and the result size of each query. Response-hiding schemes are vulnerable to existing volume attacks, which are, however, based on strong assumptions such as the uniform query assumption or the dense database assumption. More crucially, these attacks only apply to schemes that cannot be deployed in practice (ones with quadratic storage and increased leakage) while practical response-hiding schemes (Demertzis et al. [SIGMOD’16] and Faber et al. [ESORICS’15]) have linear storage and less leakage. Due to these shortcomings, the value of existing volume attacks on response-hiding schemes is unclear.In this work, we close the aforementioned gap by introducing a parametrized leakage-abuse attack that applies to practical response-hiding structured encryption schemes. The use of non-parametric estimation techniques makes our attack agnostic to both the data and the query distribution. At the very core of our technique lies the newly defined concept of a counting function with respect to a range scheme. We propose a two-phase framework to approximate the counting function for any range scheme. By simply switching one counting function for another, i.e., the so-called parameter of our modular attack, an adversary can attack different encrypted range schemes. We propose a constrained optimization formulation for the attack algorithm that is based on the counting functions. We demonstrate the effectiveness of our leakage-abuse attack on synthetic and real-world data under various scenarios.
THE USE OF JIGSAW TO IMPROVE THE STUDENTS READING COMPETENCE OF THE TENTH GRADE STUDENTS OF SMK DWIJENDRA DENPASAR IN THE ACADEMIC YEAR 2018/2019

This research was to improve Grade X students’ reading competence by the using Jigsaw in SMK Dwijendra Denpasar. It was an action research study which consisted of two cycles. The researcher worked collaboratively with the English teacher, and the students. The subjects of the research were students of X grade AK1 of SMK Dwijendra Denpasar in the academic year of 2018/2019. The quantitative data were collected through the students’ reading competence of the pretest and post-test 1 and 2. The procedure of the research consisted of planning, observing, acting, and reflecting. The results of this research showed that the use Jigsaw was successful improve the students’ reading competence. Based on the quantitative data, the use of Jigsaw helped the students to understand narrative text and to be more familiar with English. It also helped them to enrich their vocabulary and build their knowledge. Furthermore, the use of Jigsaw could attract their attention during the class. Based on the quantitative data, the students’ mean scores for the reading competence improved from 60% in the pretest to in post-test 1 70% and posttest II 80%.
SURVIVAL STUDY ON FACE RECOGNITION BASED ON POSE, AGE AND OCCLUSION

Biometrics is employed in computer science for identification and access control process. A biometric system is termed as recognition scheme for individualdetection through determining the authenticity of behavioral attributeowned by user. Biometric authentication is the method of identifying the human with help of their features. Face is used as an essential biometric to identify the people. Face recognition (FR) has large considerationbecause of the development in biometric recognition. But, the face recognition performance was not improved with many challenging conditions like occlusion, pose variations, makeup and age gap. Many existing studies were reviewed in this paper to address the face recognition problems.
A Black-box Adversarial Attack Strategy with Adjustable Sparsity and Generalizability for Deep Image Classifiers

Constructing adversarial perturbations for deep neural networks is an important direction of research. Crafting image-dependent adversarial perturbations using white-box feedback has hitherto been the norm for such adversarial attacks. However, black-box attacks are much more practical for real-world applications. Universal perturbations applicable across multiple images are gaining popularity due to their innate generalizability. There have also been efforts to restrict the perturbations to a few pixels in the image. This helps to retain visual similarity with the original images making such attacks hard to detect. This paper marks an important step which combines all these directions of research. We propose the DEceit algorithm for constructing effective universal pixel-restricted perturbations using only black-box feedback from the target network. We conduct empirical investigations using the ImageNet validation set on the state-of-the-art deep neural classifiers by varying the number of pixels to be perturbed from a meagre 10 pixels to as high as all pixels in the image. We find that perturbing only about 10% of the pixels in an image using DEceit achieves a commendable and highly transferable Fooling Rate while retaining the visual quality. We further demonstrate that DEceit can be successfully applied to image dependent attacks as well. In both sets of experiments, we outperformed several state-of-the-art methods.
Implementasi Model Jigsaw Pada Pembelajaran Sistem Kelistrikan Kapal

Abstract. This study aims to determine the implementation of the Jigsaw cooperative learning model on the learning outcomes of the ship's electrical system in the second semester of the Barombong Shipping Polytechnic. The jigsaw cooperative learning model is a model of student learning in small groups of 5-6 people with heterogeneous groups, cooperating positively and each member is responsible for learning a particular problem from the material provided and conveying the material to other group members. This research is a Classroom Action Research consisting of two cycles with the stages of planning, implementation, observation and reflection. The data analysis technique used is descriptive qualitative analysis technique. The subjects of this study were 16 people in class 2 B majoring in ship engineering. Data collection techniques using tests, observation, documentation, questionnaires and field notes. The results showed that based on the learning outcomes obtained in the first cycle it can be seen that the total average score of 75.62 and 75% classical completeness and the second cycle the average score of 82 and 73% classical completeness. The implementation of the jigsaw cooperative learning model can improve learning outcomes because it provides opportunities for students to think critically on a problem and find a concept directly from the results of the analysis.
Power Failure Sensitivity Analysis via Grouped L 1/2 Sparsity Constrained Logistic Regression

To supply precise marketing and differentiated service for the electric power service department, it is very important to predict the customers with high sensitivity of electric power failure. To solve this problem, we propose a novel grouped L1/2 sparsity constrained logistic regression method for sensitivity assessment of electric power failure. Different from the L1 norm and k-support norm, the proposed grouped L1/2 sparsity constrained logistic regression method simultaneously imposes the inter-class information and tighter approximation to the nonconvex L0 sparsity to exploit multiple correlated attributions for prediction. Firstly, the attributes or factors for predicting the customer sensitivity of power failure are selected from customer sheets, such as customer information, electric consuming information, electrical bill, 95598 work sheet, power failure events, etc. Secondly, all these samples with attributes are clustered into several categories, and samples in the same category are assumed to be sharing similar properties. Then, L1/2 norm constrained logistic regression model is built to predict the customer's sensitivity of power failure. Alternating direction of multipliers (ADMM) algorithm is finally employed to solve the problem by splitting it into several sub-problems effectively. Experimental results on power electrical dataset with about one million customer data from a province validate that the proposed method has a good prediction accuracy.
Improving Speaking Skills through Online-Based Interactive Picture Media in Elementary Schools

The COVID-19 pandemic has had an unfavorable impact on all aspects of life, including aspects of health, economy, tourism, social and education. Students can also use learning support applications such as study houses, teacher rooms, thinglink, netboard, oodlu, quizwhizzer and other applications. In fact, not all students will be successful in online learning. This research aims to improve speaking skills through the application of online-based interactive picture media to 5th graders at SD Negeri 2 Blitaran, Nganjuk Regency. This research is a classroom action research with the research design determined are planning, implementation, observation and reflection. There are two data obtained, namely process data and result data. Process data obtained from teacher activity data and student activities in applying online-based interactive image media. Result data obtained from student learning outcomes data in the form of students' speaking skill scores through the application of online-based interactive image media. The research subjects were 31 fifth grade students at SD Negeri 2 Blitaran, Nganjuk Regency, for the 2020/2021 academic year. The results showed that the teacher's activities continued to increase starting from the pre-action cycle II. In the pre-action, the average score of students was only 63.1 with a very poor category. After taking action in cycle 1 at meeting 1, the average score of students increased to 80%. The percentage of 80% indicates that there are still activities that have not been carried out at meeting 1. The action is continued in the second friendship, where the ability of students has reached 87%. With this percentage, improvements need to be made in cycle II. The teacher must review the steps in the lesson plans that have been prepared so that learning can run according to the plan. In Cycle II meeting 1, the percentage of success was 92.8%. The thing that must be maintained in cycle II is that the teacher carries out learning according to the steps that have been planned and provides explanations to students regarding important things that must be considered when retelling a reading. The action was continued in Cycle II meeting 2 where the student success achieved reached 95.7%. This happens because the teacher carries out learning in accordance with the planned RPP based on the results of the cycle reflection. Teachers should emphasize
Defining meaningful units. Challenges in sign segmentation and segment-meaning mapping

European Commission. Grant No. 101017255. ICT-57-2020 – An empowering, inclusive Next Generation Internet
The role and place of economic growth in the resistive economy of the Islamic Republic of Iran

The purpose of this study was to investigate the role and place of economic growth in the resistive economy of the Islamic Republic of Iran. For this purpose, descriptive-analytic research method was used. In the qualitative part of the information, interviews with experts and experts were selected using the method of theoretical sampling and snowball technique to theoretical saturation. For collecting quantitative information, a statistical society was comprised of experts, thinkers and experts. . Regarding the existence of different classes in the statistical society and the differences between the groups from the structural and functional point of view, a random-class random sampling method was used to determine the sample size of the data. The number of samples required was 78 questionnaires. For reliability of Cronbach's questionnaire, its value was 0.87. The findings of this study indicate that the economic growth damages including unfavorable business conditions, technical and technological retardation of production, lack of mobility and production competitiveness, lack of econometric knowledge structure, inefficiency of production factors, unfavorable market conditions and private sector And the government and the public sector, and the disadvantages of monetary and financial markets. Finally, dimensions, components and indicators of growth of the resistance economy were extracted.
Reverse Engineering-based Steganalys is of Crypto123 Tool for Automatic Detection and Extraction on Concealed Messages

Background/Objectives: Recently, steganography tools for concealing messages in images have been widely used, and internal mechanisms for hiding messages using steganography tools are mostly unknown to public.Methods/Statistical analysis: Therefore, we performed the reverse engineering analysis and analyzed the information hiding algorithm and operation mechanism applied to Crypto123 steganography tool. The key features of the Crypto123 software, which is rapidly increasing in use among the steganography tools, were analyzed. The IDA tool was used to reverse the Crypto123 tool. Performing complicated reverse analysis, we can find out in reverse the cryptosystem, encoding and steganographic algorithm used in Crypto123 software.Findings: By reversing the steganography tool executable, we disclosed the internal steganographic mechanism that was wrapped in the veil. In detail, we found that the hidden messages are transformed using Twofish encryption algorithm after Xxencoding process for concealing message. Then, if any of the 128 bytes at the end of the cover image file has a value of '0x1A', then the subsequent bytes on cover image is replaced with the encrypted messages. Therefore, based on the software reverse engineering of the steganography mechanism applied to the Crypto123 tool, we propose a method to efficiently detect and automatically extract messages hidden in stego data using the mechanisms found correctly.Improvements/Applications: Reverse engineering of Crypto123 software enables automatic detection of messages concealed on Internet. Therefore, it is possible to discriminate steganography tools used for covert communication. Keywords: Steganalysis, Reverse Engineering, Crypto123 Software, Hidden Message Detection, Information Hiding, Anti-Forensic Tools.
SSstory: 3D data storytelling based on SuperSQL and Unity

SuperSQL is an extended SQL language, which brings out a rich layout presentation of a relational database with a particular query. This paper proposes SSstory, a storytelling system in a 3D data space created by a relational database. SSstory uses SuperSQL and Unity to generate a data video and add cinematic directions to the data video. Without learning special authoring tooling, users can easily create data videos with a small quantity of code.
Episode 70: Scam Awareness and Avoidance with Officer David Causey

Podcast produced by the Osher Lifelong Learning Institute (OLLI) that features an interview with UNT Alumnus David Causey, Community Relations Officer for the UNT Police Department. Susan and David discuss common forms of fraud and identity theft, and preventative measures people can take to avoid having their identities stolen.
Why Probability isn’t Magic

Abstract “What data will show the truth?” is a fundamental question emerging early in any empirical investigation. From a statistical perspective, experimental design is the appropriate tool to address this question by ensuring control of the error rates of planned data analyses and of the ensuing decisions. From an epistemological standpoint, planned data analyses describe in mathematical and algorithmic terms a pre-specified mapping of observations into decisions. The value of exploratory data analyses is often less clear, resulting in confusion about what characteristics of design and analysis are necessary for decision making and what may be useful to inspire new questions. This point is addressed here by illustrating the Popper-Miller theorem in plain terms and using a graphical support. Popper and Miller proved that probability estimates cannot generate hypotheses on behalf of investigators. Consistently with Popper-Miller, we show that probability estimation can only reduce uncertainty about the truth of a merely possible hypothesis. This fact clearly identifies exploratory analysis as one of the tools supporting a dynamic process of hypothesis generation and refinement which cannot be purely analytic. A clear understanding of these facts will enable stakeholders, mathematical modellers and data analysts to better engage on a level playing field when designing experiments and when interpreting the results of planned and exploratory data analyses.
DevChar: An Extensive Dataset for Optical Character Recognition of Devanagari Characters

The advent of cameras has only accelerated the need to digitize content as it helps prevent data corruption by natural processes and enables faster transfer of the data across communities. Handwritten documents and ancient manuscripts form a large part of this data as they call for a need to be translated from the local languages they were written in. The first step into solving this problem is the recognition of handwritten text. Existing handwritten datasets for the Devanagari script can be used for the recognition of individual characters, but they fail to perform well when the text contains matras and conjuncts created by joining character modifiers. This also introduces a dependency between the model and the data source due to required pre-processing for extracting characters recognized by the model from the word itself. These datasets also lack variation in their penmanship which is essential to encompass diversity in the writing style. We present an extensive dataset that addresses these issues. Our dataset has around 4 million characters of varying handwriting styles, complex characters and matras. Training a simple CNN on our data, to detect characters with matras, gave accuracies exceeding 98%. We also show that using this dataset allows a separation of the input data format from the model design, thus allowing researchers to focus on the latter. This dataset is made publicly available at DevChar2020 .
Designing AI-based Conversational Agent for Diabetes Care in a Multilingual Context

Conversational agents (CAs) represent an emerging research field in health information systems, where there are great potentials in empowering patients with timely information and natural language interfaces. Nevertheless, there have been limited attempts in establishing prescriptive knowledge on designing CAs in the healthcare domain in general, and diabetes care specifically. In this paper, we conducted a Design Science Research project and proposed three design principles for designing health-related CAs that embark on artificial intelligence (AI) to address the limitations of existing solutions. Further, we instantiated the proposed design and developed AMANDA - an AI-based multilingual CA in diabetes care with state-of-theart technologies for natural-sounding localised accent. We employed mean opinion scores and system usability scale to evaluate AMANDA’s speech quality and usability, respectively. This paper provides practitioners with a blueprint for designing CAs in diabetes care with concrete design guidelines that can be extended into other healthcare domains.
Psychological Features of the Modern Intern

Based on the anonymous survey with a ten-year interval, we created a comparative description of the dynamics of psychological characteristics among interns, such as social behavior and the ability to remember, reproduce, systematize and use the obtained information in practice. Construction of the educational process in accordance with the needs of the curriculum should take into account its own tasks in the setting of progressive changes in the young people’s way of thinking.