Reviewerly/exHarmony huggingface dataset:
TEACHERS’ BELIEFS AND PRACTICES OF USING YOUTUBE IN EFL CONTEXT IN MUHAMMADIYAH SENIOR HIGH SCHOOLS OF SURABAYA

Purpose of Study: Considering Muhammadiyah’s big contribution to education in Indonesia by establishing thousands of schools, it is thus important to understand the competence and the beliefs of teachers who teach in Muhammadiyah schools. This quantitative study investigated Muhammadiyah teachers’ beliefs and practices of using YouTube in EFL context in all Muhammadiyah Senior High Schools of Surabaya. Employing a survey design, this research aims to describe Muhammadiyah Senior High Schools of Surabaya English teachers’ beliefs and practices of using YouTube in the EFL context. 22 English teachers participated as the subjects of the research.&#x0D; To obtain the data, the instrument used was a questionnaire which is copied and distributed to the targeted subjects. Interview and documentation were done to validate the results of the questionnaire. Results reveal that English teachers in Muhammadiyah Senior High Schools of Surabaya hold positive beliefs about using YouTube in the EFL context. And, despite the only view of them used it often, all the teachers had already used YouTube in their practices of teaching English. Overall, teachers’ beliefs about using YouTube in EFL context were in line with their practices.&#x0D; Methodology: This research applied the quantitative technique and the design employed was survey design. Survey designs are procedures in quantitative research in which investigators administer a survey to a sample or to the entire population of people to describe the attitudes, opinions, behaviors, or characteristics of the population. Creswell continues, “Surveys help identify important beliefs and attitudes of individuals.”&#x0D; Results: The results obtained from the participants were analyzed using an SPSS 17 programmer. Each participant was given a participation alphabet and the data were analyzed using descriptive analysis method. Then, the frequencies and percentages for each statement were presented in tables (see appendixes)&#x0D; Implications/Applications: English teachers in Muhammadiyah Senior High Schools of Surabaya hold positive beliefs about using YouTube in the EFL context.
An Optimized Level Set Method Based on QPSO and Fuzzy Clustering

A new fuzzy level set method (FLSM) based on the global search capability of quantum particle swarm optimization (QPSO) is proposed to improve the stability and precision of image segmentation, and reduce the sensitivity of initialization. The new combination of QPSO-FLSM algorithm iteratively optimizes initial contours using the QPSO method and fuzzy c-means clustering, and then utilizes level set method (LSM) to segment images. The new algorithm exploits the global search capability of QPSO to obtain a stable cluster center and a pre-segmentation contour closer to the region of interest during the iteration. In the implementation of the new method in segmenting liver tumors, brain tissues, and lightning images, the fitness function of the objective function of QPSO-FLSM algorithm is optimized by 10% in comparison to the original FLSM algorithm. The achieved initial contours from the QPSO-FLSM algorithm are also more stable than that from the FLSM. The QPSO-FLSM resulted in improved final image segmentation.
AUTO-CDD: automatic cleaning dirty data using machine learning techniques

Cleaning the dirty data has become very critical significance for many years, especially in medical sectors. This is the reason behind widening research in this sector. To initiate the research, a comparison between currently used functions of handling missing values and Auto-CDD is presented. The developed system will guarantee to overcome processing unwanted outcomes in data Analytical process; second, it will improve overall data processing. Our motivation is to create an intelligent tool that will automatically predict the missing data. Starting with feature selection using Random Forest Gini Index values. Then by using three Machine Learning Paradigm trained model was developed and evaluated by two datasets from UCI (i.e. Diabetics and Student Performance). Evaluated outcomes of accuracy proved Random Forest Classifier and Logistic Regression gives constant accuracy at around 90%. Finally, it concludes that this process will help to get clean data for further analytical process.
Leakage is prohibited: memory protection extensions protected address space randomization

Code reuse attacks pose a severe threat to modern applications. These attacks reuse existing code segments of vulnerable applications as attack payloads and hijack the control flow of a victim application. With high code entropy and a relatively low performance overhead, Address Space Layout Randomization (ASLR) has become the most widely explored defense against code reuse attacks. However, a single memory disclosure vulnerability is able to compromise this defense. In this paper, we present Memory Protection Extensions (MPX)- assisted Address Space Layout Randomization (M-ASLR), a novel code-space randomization scheme. M-ASLR uses several characteristics of Intel MPX to restrict code pointers in memory. We have developed a fully functioning prototype of M-ALSR, and our evaluation results show that M-ASLR: (1) offers no interference with normal operation; (2) protects against buffer overflow attacks, code reuse attacks, and other sophisticated modern attacks; and (3) adds a very low performance overhead (3.3%) to C/C++ applications.
Hybrid A∗ Based Motion Planning for Autonomous Vehicles in Unstructured Environment

Autonomous vehicles require a collision-free and comfortable motion trajectory at every time instant. It is a common method to generate a feasible path to the target state and append an optimization-based method for path postprocessing. In this paper a Hybrid A* based motion planning algorithm is presented for autonomous vehicles under unstructured circumstances. Firstly, the Hybrid A* algorithm is improved with a better heuristic function and a better search policy to realize a less-time consuming graph search in consideration of vehicle's motion model. Then, nonlinear optimization algorithm is applied to optimize the generated path further and realizes high quality of security and smoothness of the discrete trajectory. Finally, Catmull-Rom interpolation is combined to make waypoints continuous and easy-to-control. Simulation results concerning different tasks are described to demonstrate the validity of the proposed algorithm.
Text Detection In Indonesian Identity Card Based On Maximally Stable Extremal Regions

Most of Indonesian organizations either it is government or non government sometime required their member to provide their identity card (E-KTP) as legal document collection in their database. This collection of image usually being used as manual verification method. These document images acquired by each person with their own device, there are variations of angles they are used to acquire the image. This situation created problems in text recognition by OCR softwares especially in text detection part, orientation and noise will affect their accuracy. These cases making the text detection more complex and cannot be solved by simple vertical projection profile of black pixels. This research proposed a method to improve text detection in identity document by fixing the orientation first, then using MSER regions to form text region. We fix the orientation using the line that made by Progressive Probabilistic Hough Transform. Then we used MSER to obtain all candidate regions and Horizontal RLSA acts as connector between those candidate. The orientation fixing strategy reach average of margin error 0.377o (in 360o system) and the text detection method reach 84.49% accuracy in best condition.
Trust Factor Based Key Distribution Protocol in Hybrid Cloud Environment

In the Hybrid cloud deployment model, security is essential to restrict access while using resources such as virtual machine, platform, and application. Many protocols were developed to provide security via the cryptography technique, but these protocols rarely considered the trust factor which is an essential factor for cloud security. The existing Elliptic Curve Cryptography and Diffie Hellman key distribution mechanism failed to stress the trust factor, and further, they have provided not only higher complexity but also lower security and reliability. The proposed method comprised two stages: first stage, Group Creation using the trust factor and develop key distribution security protocol. It performs the communication process among the virtual machine communication nodes. Creating several groups based on the cluster and trust factors methods. The second stage, the ECC (Elliptic Curve Cryptography) based distribution security protocol is developed. The proposed Trust Factor Based Key Distribution Protocol reduced error rate, improve the key computation time and maximize resource utilization.
User-specified random sampling of quantum channels and its applications

Random samples of quantum channels have many applications in quantum information processing tasks. Due to the Choi-Jamio\l{}kowski isomorphism, there is a well-known correspondence between channels and states, and one can imagine adapting state sampling methods to sample quantum channels. Here, we discuss such an adaptation, using the Hamiltonian Monte Carlo method, a well-known classical method capable of producing high-quality samples from arbitrary, user-specified distributions. Its implementation requires an exact parametrization of the space of quantum channels, with no superfluous parameters and no constraints. We construct such a parametrization, and demonstrate its use in three common channel sampling applications.
From proof-of-concept to exploitable

Exploitability assessment of vulnerabilities is important for both defenders and attackers. The ultimate way to assess the exploitability is crafting a working exploit. However, it usually takes tremendous hours and significant manual efforts. To address this issue, automated techniques can be adopted. Existing solutions usually explore in depth the crashing paths, i.e., paths taken by proof-of-concept (PoC) inputs triggering vulnerabilities, and assess exploitability by finding exploitable states along the paths. However, exploitable states do not always exist in crashing paths. Moreover, existing solutions heavily rely on symbolic execution and are not scalable in path exploration and exploit generation. In this paper, we propose a novel solution to generate exploit for userspace programs or facilitate the process of crafting a kernel UAF exploit. Technically, we utilize oriented fuzzing to explore diverging paths from vulnerability point. For userspace programs, we adopt a control-flow stitching solution to stitch crashing paths and diverging paths together to generate exploit. For kernel UAF, we leverage a lightweight symbolic execution to identify, analyze and evaluate the system calls valuable and useful for exploiting vulnerabilities. We have developed a prototype system and evaluated it on a set of 19 CTF (capture the flag) programs and 15 realworld Linux kernel UAF vulnerabilities. Experiment results showed it could generate exploit for most of the userspace test set, and it could also facilitate security mitigation bypassing and exploitability evaluation for kernel test set.
An Evaluation of Chinese Human-Computer Dialogue Technology

The human-computer dialogue has recently attracted extensive attention from both academia and industry as an important branch in the field of artificial intelligence (AI). However, there are few studies on the evaluation of large-scale Chinese human-computer dialogue systems. In this paper, we introduce the Second Evaluation of Chinese Human-Computer Dialogue Technology, which focuses on the identification of a user's intents and intelligent processing of intent words. The Evaluation consists of user intent classification (Task 1) and online testing of task-oriented dialogues (Task 2), the data sets of which are provided by iFLYTEK Corporation. The evaluation tasks and data sets are introduced in detail, and meanwhile, the evaluation results and the existing problems in the evaluation are discussed.
Dynamic attribute-based privacy-preserving genomic susceptibility testing

Developments in the field of genomic studies have resulted in the current high availability of genomic data which, in turn, raises significant privacy concerns. As DNA information is unique and correlated among family members, it cannot be regarded just as a matter of individual privacy concern. Due to the need for privacy-enhancing methods to protect these sensitive pieces of information, cryptographic solutions are deployed and enabled scientists to work on encrypted genomic data. In this paper, we develop an attribute-based privacy-preserving susceptibility testing method in which genomic data of patients is outsourced to an untrustworthy platform. We determine the challenges for the computations required to process the outsourced data and access control simultaneously within patient-doctor interactions. We obtain a non-interactive scheme regarding the contribution of the patient which improves the safety of the user data. Moreover, we exceed the computation performance of the susceptibility testing over the encrypted genomic data while we manage attributes and embedded access policies. Also, we guarantee to protect the privacy of individuals in our proposed scheme.
Penerapan Algoritma Pencarian Binary Search dan QuickSort pada Aplikasi Kamus Bahasa Palembang Berbasis Web

Dictionaries can be used as a means to find meaning and meaning in a word. Palembang language is one of the regional languages in the province of South Sumatra. In their daily lives besides using Indonesian, the Palembang people also use the Palembang language as a means of communication with each other. This study aims to create a web-based Palembang language dictionary application so that it can be accessed using internet media. This dictionary application was created to help people who are not native to the city who live in the city of Palembang. This dictionary application provides a simple user interface that makes it easy for users to use it. In the development of this dictionary application is made using the RAD method. The search algorithm used in word search in a database is using the binary search method. The binary search method has the advantage of linear search. In practice binary search searches by first dividing into two data in a database that has been sorted first. This application can do an effective word search and the information displayed in this application in addition to the meaning and meaning of the word also provides an example of how to use the searched word in Palembang sentences.
Expressive facial style transfer for personalized memes mimic

Meme, usually represented by an image of exaggerated expressive face captioned with short text, are increasingly produced and used online to express people’s strong or subtle emotions. Meanwhile, meme mimic apps continuously appear, such as the meme filming feature in WeChat App that allow users to imitate meme expressions. Motivated by such scenarios, we focus on transferring exaggerated or unique expressions which is rarely noticed by previous works. We present a technique—“expressive style transfer”—which allows users to faithfully imitate popular memes’ unique expression styles both geometrically and textually. To conduct distortion-free transferring of exaggerated geometry, we propose a novel accurate feature curve-based face reconstruction algorithm for 3D-aware image warping. Furthermore, we propose an identity preserving blending model, based on a deep neural network, to enhance facial expressive textural details. We demonstrate the effectiveness of our method on a collection of Internet memes.
Web Crawler for Social Network User Data Prediction Using Soft Computing Methods

This paper addresses how elementary data from a public user profile in Instagram can be scraped and loaded into a database without any consent.Furthermore, discusses how soft computing methods such neural networks can be used to determine the popularity of a user's post.Conclusively, raises questions about user's privacy and how tools like this can be used for better or for worse.
Analisis Kinerja Jaringan Internet dengan Metode Class Based Queueing di Universitas Dhyana Pura

Dhyana Pura University (Undhira) is one of the higher education institutions that utilize the advancement of information technology to achieve optimal results. The observation shows that bandwidth utilization is not well managed. On this basis, this study aims to analyze Quality of Service (QoS) with parameters throughput, delay, packet loss, jitter and bandwidth by Class Based Queuing (CBQ) methods as a queue method that regulates usage network bandwidth allocated to each user. This study also measured the level of employee satisfaction using the Customer Satisfaction Index (CSI) method. The results of this study indicate that the CBQ method is a method of bandwidth management in reducing bandwidth in crisis clients, so as to increase employee satisfaction in using internet services in the process of service for students.
Feature-based Sentiment Analysis for Slang Arabic Text

The increased number of Arab users on microblogging services who use Arabic language to write and read has triggered several researchers to study the posted data and discover the user's opinion and feelings to support decision making.In this paper, a sentiment analysis framework is presented for slang Arabic text.A new dataset with Jordanian dialect is presented.Numerous specific Arabic features are shown with their impact on slang Arabic Tweets.The new set of features consists of lexicon, writing style, grammatical and emotional features.Several experiments are conducted to test the performance of the proposed scheme.The new proposed scheme produces better results in comparison with others.The experiments show that the system performs well without translating the tweets to English or standard Arabic.
Pengaruh Edukasi Media Video dan Flipchart terhadap Motivasi dan Sikap Orangtua dalam Merawat Balita dengan Pneumonia

This study aims to determine the effect of providing education with video media and flip charts on the motivation and attitudes of parents in caring for toddlers with pneumonia. This type of research is a quasi-experimental design with a non randomized without control group pretest-posttestdesign. The results showed there were differences in the average score of motivation and attitudes of parents in caring for toddlers with pneumonia before and after providing education. In the video media group ρ motivation value (0.001) and attitude (0,000), in the media group flip chart with ρ motivation value (0.002) and attitude (0,000) and in the video media and flip chart combination group with ρ motivation value (0,000) and attitude (0,000) . Conclusion, there is a significant influence of the use of educational media in providing education to an increase in the average score of motivation and attitudes of parents in caring for children with pneumonia. The combination of education using video media and flip charts gives more effective results than other education groups.&#x0D; &#x0D; Keywords: Supporting Media, Motivation, Health Education, Attitudes
Simultaneous dimming and brightening under all and clear sky at Camagüey, Cuba (1981–2010)

The annual trends of the all sky (Eg↓ASky) and clear sky global horizontal irradiances (Eg↓CSky) for the climatologically standard period 1981–2010 at Camagüey, Cuba, are negative and positive respectively, showing the simultaneous occurrence of dimming and brightening phenomena. Positive cloud cover (CC) annual trend, is among the plausible causes of the Eg↓ASky dimming. The Eg↓CSky annual mean increasing trend could be explained in part by the decreasing broadband aerosol optical depth trend. For the 1981–2016 a period non-standard climatologically the absolute magnitudes of the trends decrease for both Eg↓ASky and Eg↓CSky annual means, with no change in their signs but becoming non-significant statistically. That feature resembles an attenuation process for both dimming and brightening. The series of Eg↓ and CC observations were subject of test for determining in homogeneities identifying change points. For each one of the Eg↓ASky and Eg↓CSky and CC series show no change point statistically significant. The consistency and stability of the Eg↓ASky annual means series were tested using measured and calculated Eg↓ annual means for conditions where the solar disk is empty of clouds. The magnitudes and signs of trends for the measured and calculated Eg↓ annual means are very similar and both are statistically significant at the same level, confirming the series of Eg↓ASky annual means are consistent and stable.
Privacy-preserving quantum multi-party computation based on circular structure

Secure multi-party computation (SMC) is a collaborative computation problem that protects privacies among a group of distrustful participants. In this paper, a privacy-preserving quantum multi-party computation scheme based on circular structure is presented. In the scheme, each participant implements a certain quantum operation (i.e., a specific function) on the quantum state, and hands the result state to the next participant. Until the first participant receives the quantum state from the last participant and measures the state to get the result of the data calculation, the circle ends. We also analyze the security and efficiency of the scheme, and demonstrate the possibility of utilizing the scheme to solve some statistical calculation problems.
Towards urban crowd visualization

Crowd simulation and visualization is an emergent research area that studies and reproduces this phenomenon on virtual environments.We present a system designed for simulation and visualization of pedestrians in urban environments, this system is focused on solving problems such as rendering, character animation, artificial intelligence to deal with steering behaviors and motion planning of each pedestrian.The present work incorporates steering pedestrian behaviors using real data collected from several sources such as GPS traces and surveillance systems, all these data allows virtual characters to adapt their navigation according to several environment factors such as other agents or obstacles.This kind of approach requires massive quantities of resources, such as data, memory and computation.We present a formulation that includes a multi-agent system that assigns individual characteristics, both physical and psychological to the virtual agents, which are based on data obtained from real pedestrians to better depict reality.The presented system is able to simulate crowds in complex urban environments; for that purpose the system was built in two stages, urban environment generation and pedestrian simulation, for the first stage we integrate the WRLD3D plug-in with real data collected from GPS traces, then we use a hybrid approach done by incorporating steering pedestrian behaviors with the goal of simulating the subtle variations present in real scenarios without needing large amounts of data for those low-level behaviors, such as pedestrian motion affected by other agents and static obstacles nearby.