Reviewerly/exHarmony huggingface dataset:
Interactive Learning Media Based on Android for Computer Programming Course

Abstract Computer Programming is one of the basic compulsory subjects for the electrical engineering field. This subject needs continuous practice so that it can be mastered well. This study aims to develop an interactive learning media based on Android so that students can practice anywhere. The interactive learning media for computer programming course takes in explanation of operator c++, input-output statement, program structure, data type, condition, repetition, string, and array, complemented with evaluation facility using the cosine similarity method so that the automated essay scoring feature is available. Interactive features provided in this learning media are the auto complete syntax and function in the code editor. For the evaluation used the black box testing method to test the functionally of the android application and the distribution of questionnaire to determine the response of students. The responses of student have proven that the level of practically of this interactive learning media is 87.5%.
Augmented Reality-Marker Detection Measurement on Heroes of Surabaya Mobile Games

Abstract — Different arrangement in selling freemium programming application is the In-App Purchase (IAP) technique, where clients can download and enjoy the application for free; however, there are premium highlights that must be paid. We use Augmented Reality (AR) innovation into the IAP framework in the buying system of the game application, Heroes of Surabaya (HOS). The application allows user keeps run and play on the Android mobile system, but it will require AR marker cards to enable the premium feature on it. This investigation plans to test the effectiveness of AR marker cards that proposed as a substitute for computerized cash on the purchasing system of the game application. From the experimental results, the distance of the marker and the light factor are the main factors for the success-rate of the detection of the AR marker.
Development of mobile web-based learning model

Abstract This study aims at developing mobile web-based learning model for elementary school students in Jakarta in Indonesia. The research method was Research and Development (R&amp;D). This research product is mobile web using 8b with URL: https://webpanduan.8b.io. This mobile web containing some pages consisting of home, background, legal basis, objectives, and the guidance of character values integration. The result of expert validation achieved 3,88 from scores ranging 1 to 4 showing that this mobile web was already valid specifically for easiness of the terms used, application efficiency, navigation system, simplicity, clarity of navigation, consistency of navigation menu placement, attractiveness, compatibility support, and loading time. Revision based on expert validation was done to give a more detail explanation about the concrete ways in character values integration, photograph background, slideshow with pause, and relevant pictures displayed. Consequently, it can be concluded that this mobile web-based learning model has already been valid as a model in character values integration for elementary school students in Jakarta in Indonesia.
Optimal design of an Autonomous Emergency Braking (AEB) system for a passenger vehicle

Abstract This paper models the performance of a passenger vehicle equipped with Autonomous Emergency Braking (AEB) using a feedback control system in MATLAB Simulink. Two types of AEB control systems are simulated and compared; proportional-integral (PI) control and fuzzy logic control (FLC). Performance of both control systems simulated are compared using AEB City collision scenario defined in Euro New Car Assessment Protocol (NCAP). Results show that the proposed PI control system is able to meet Euro NCAP AEB City set requirements.
Designing and Building Secure Electronic Medical Record Application by Applying AES-256 and RSA Digital Signature

Abstract Medical records consist of health data that must be kept confidential and is regulated in Indonesian law. Until now the implementation of electronic medical records in Indonesia has not been clearly regulated in a law, while Regulation of the Minister of Health of the Republic of Indonesia state that medical records must be made in writing, complete, and clearly or electronically. In view of this, another approach than the regulation is needed to be able to meet the confidentiality of the data in the regulation, in electronic form. In this study, an electronic medical record application was designed and built that can meet the requirements of the legislation. The application encrypts patient’s health data stored on medical records using the AES-256 algorithm for data confidentiality. The use of AES-256 has been standardized based on the Federal Information Processing Standards Publication 197 and RSA Digital Signature digital signature because it has been standardized based on Federal Information Processing Standards Publication 186-4 and NIST Special Publication 800-89. The proposed application have been made have passed security testing using 6 testcase based on the reference paper.
Fast pedestrian detection method based on BING

Pedestrian detection is one of hot topics in the field of computer vision and pattern recognition, which is of great value to video surveillance, intelligent traffic and human-computer interaction, etc. and how to improve detection rate and speed is the key. Most traditional pedestrian detection methods are based on the pyramid sliding window scanning method, and for images in which the majority of the region does not contain a body, the detection is inefficient. This study presents a body window sampling algorithm based on binarised normed gradients, which can quickly and effectively extract the window of the image that most likely contains human body to be identified, thus greatly improving the detection speed and obtaining a lower false alarm rate. The authors employ the histogram of oriented gradient feature and the linear support vector machine to train the classifier. For the same false test case in comparison with the pyramid scanning method, when the authors used 2,000 sampling windows for detection, they observed a detection rate increase of 11%, the detection speed increased 19.6 times. For 5,000 sampling windows, the detection rate increased by 20% and the detection speed increased 7.8 times.
Sharp bounds for the variance of linear statistics on random permutations

We are concerned with the variance of a completely additive function defined on the symmetric group endowed with the Ewens probability. Overcoming specific dependence of the summands, we obtain the upper and lower bounds including optimal constants. We also derive a decomposition of such a function into a sum with uncorrelated summands. The results can be reformulated for the linear statistics defined on vectors distributed according to the Ewens sampling formula.
An Innovative Approach for Aerial Video Surveillance Using Video Content Analysis and Indexing

There is growing interest in the use of video cameras to conduct aerial surveillance. Compared to conventional framing cameras, videos can monitor ongoing action within a scene, and adjust the camera automatically to track the action. Video documents dynamic events that are not available from aerial photos. This enables the feedback and control of dynamic event-based actions and delivers timely and important knowledge and interpretation that is not accessible elsewhere. Video recordings can be used to map and geo-locate dynamic objects or events in real-time. In this paper, we propose an innovative approach for aerial video surveillance using video content analysis and indexing, to monitor the violation of activities during the emergency lockdown period. Instead of continuously monitoring the video content, this method detects the free movement of persons and vehicles along with the location details from the geotagged videos sent by drone video cameras.
Predictive Modeling of Rater Behavior: Implications for Quality Assurance in Essay Scoring

We evaluate the feasibility of developing predictive models of rater behavior, that is, rater-specific models for predicting the scores produced by a rater under operational conditions. In the present study, the dependent variable is the score assigned to essays by a rater, and the predictors are linguistic attributes of the essays used by the e-rater® engine. Specifically, for each rater, the linear regression of rater scores on the linguistic attributes is obtained based on data from two consecutive time periods. The regression from each period was cross validated against data from the other period. Raters were characterized in terms of their level of predictability and the importance of the predictors. Results suggest that rater models capture stable individual differences among raters. To evaluate the feasibility of using rater models as a quality control mechanism, we evaluated the relationship between rater predictability and inter-rater agreement and performance on pre-scored essays. Finally, we conducted a simulation whereby raters are simulated to score exclusively as a function of essay length at different points during the scoring day. We concluded that predictive rater models merit further investigation as a means of quality controlling human scoring.
Comments Scraping Application For Review Youtube Content

Abstract YouTube is one of social media that allows people to upload, view, and share videos through website. YouTube’s mission statement is to give everyone a voice and show them the world. Through YouTube, people can express their voices or show their creativity effortlessly. YouTube provides service for users to give opinions through comment section. The comments of users can be used to decide either the video has good ratings or not. Web Scraping can be used to take the comments of users from comment section. Web Scraping is a method to take some information from website by extracting data through tags in html. By inspecting code the YouTube website, the information of comments is contained in ‘ytd-comment-thread-renderer’. Information of comments is divided into reviewer and content of comments. Reviewer’s information is contained in id ‘author-text’ and the content is in ‘content-text’.
MODELLING EXPERT SYSTEM FOR LUNG DISEASE

Abstract One of the vital organs in humans is the lungs. Lungs infected with the disease and not or late diagnosed can cause fatal conditions that have the potential to cause death. The rapid development of artificial intelligence, especially the expert system, is one of the solutions for the early diagnosis of respiratory diseases, especially in the lungs. By applying the forward chaining method that analyzes the facts given by the user to determine the diagnosis results, the expert system for lung disease that was built focused on six lung diseases that are common in Indonesia.
Power Analysis of Symmetric Block Cipher Kuznyechik

Paper presents an overview of power analysis attack and experimental results of the first main stage of simple power analysis (SPA) and differential power analysis (DPA) attacks-a practical correlation of power consumption measurement charts to each of encryption operations for standard Kuznyechik cipher (GOST R 34.12-2015 (n = 128)). As part of experiments we construct lightweight hardware implementation of Kuznyechik cipher on Arduino electric board (namely Arduino Nano) and compile measurements of power consumption by LECROY WavePro 7100A oscilloscope (with a 1 GHz bandwidth and 10 GHz / 4 channel sampling frequency). Our experiments led to finding signature (fixed structure and power consumption value) for encryption operations of Kuznyechik cipher: additions with round key, nonlinear transformation and linear transformation.
Inter-Cell Interference Suppression for MIMO-OFDM Systems Based on Complex-Valued Neural Network

In theory, complex-valued neural network (CVNN) is more suitable for processing complex signals. In this paper, we propose an effective complex-valued neural network structure based on the traditional interference rejection combining (IRC) algorithm to suppress the inter-cell interference in MIMO-OFDM systems. The proposed CVNN has small size and supports fast convergence within several pilot symbols in resource blocks (RBs), which can meet the real-time requirements in practical application scenarios. Numerical results based on standardized channels and measured channels show that the proposed method outperforms the traditional interference suppression algorithms and requires comparable computational complexity.
Leveraging Prior Knowledge Asymmetries in the Design of Location Privacy-Preserving Mechanisms

The prevalence of mobile devices and Location-Based Services (LBS) necessitates the study of Location Privacy-Preserving Mechanisms (LPPM). However, LPPMs reduce the utility of LBSes due to the noise they add to users' locations. Here, we consider the remapping technique, which presumes the adversary has a perfect statistical model for the user location. We consider this assumption and show that under practical assumptions on the adversary's knowledge, the remapping technique leaks privacy not only about the true location data, but also about the statistical model. Finally, we introduce a novel method termed Randomized Remapping to provide a trade-off between leakage of the users' location and leakage of the users' model for a given utility.
Effects of Approximation in Computation on the Accuracy and Performance of Deep Neural Network Inference

Abstract Recently, deep learning is at the forefront of the state-of-the-art machine learning algorithms and has shown excellent results in a variety of applications such as medical field, consumer as well as autonomous vehicles. Convolutional Neural Network (CNN) – is the leading deep learning architecture that is mostly applied. However, huge dataset is needed to train with complex architecture to achieve precise learning. Inference can be performed when given a ready CNN model and its weight file to another user. Inference takes time with precise weights and huge dataset. To overcome this problem, and enhance the inference system, approximation computation will be applying in terms of weight for changed of decimal place. The smaller size of the dataset is used in the inference process to reduce the inference time. MobileNetV2 architecture is used with the new weight for inference. Also, open source libraries such as TensorFlow, Keras and python is used. GPU (NVIDIA GeForce GTX 1060 6GB 64 Bit) is used as training and inference platform. Inference time is shortened, and the accuracy of performance for new weights compare with the precise weight only has a small gap which still has a great performance for classification. This work has proved that with 4 decimal places is able to obtain the same accuracy for inference when compared to benchmark with 9 decimal places. Inference time for 4 decimal places is also less than benchmark time.
Distributed Frames: Pattern-Based Characterization of Functional Requirements for Distributed Systems

In the connected world, the complexity of software-based systems increases. Many of those systems consist of different subsystems which are connected with each other via a network. The decomposition into those subsystems requires a detailed analysis and documentation of their functional requirements. Documenting and managing such requirements in a consistent manner is a challenge for software engineers. The requirements for each subsystem cannot be considered in isolation, but it is necessary to state the relations between the functional requirements, too. In previous work, we proposed a method that allows systematically identifying and documenting functional requirements for distributed systems. The method is model-based and makes use of Jackson’s problem frames approach which defines patterns for reoccurring software development problems. We now extend his approach with patterns for problems specifically for distributed systems which we call Distributed Frames. Using a pattern description template, we provide different examples of such frames. To exemplify the application of those patterns, we show how they can be embedded into our requirements elicitation method.
Reinforcement Learning using Kalman Filters

In this investigation, we discuss a game of pursuit-evasion, or a hunter-prey problems using Q-learning framework. This has always been a popular research subject in the field of robotics where a hunter moves around in pursuit a prey. We involve Kalman filters to estimate the prey's status (location and velocity) and learn Q-values based on the estimated status. We evaluate our approach by convergence of Q-values and capturing steps.
Image Enhancement Applied to Water Holding Capacity Images

Water Holding Capacity (WHC) is one of the most important attributes related to the quality of pork meat. Normally, WHC is estimated by measuring the juice released after the compression of meat samples. However, all the evaluation process is still manually handled by experts, making the samples subject to many factors, such as variations in illumination during the acquisition process. In this sense, there is a great need to automate this process and digital image processing techniques can be very useful for this. In this paper, we approach the problem of image enhancement, which is an important and necessary stage to better identify the different regions of a WHC image. We developed an image enhancement approach that corrects both illumination and color variations present in the images. We compared our approach with other approaches found in literature in terms of visual aspect and through Measure of Enhancement (EME). Results indicated that our technique presents a superior performance in the problem of correcting illumination problems in WHC images.
Looking for your keys: The interaction of attention, memory, and eye movements in visual search

Visual search is an everyday task that requires the coordination of vision, attention, and memory. The challenge in studying a complex task like visual search is trying to determine the relative contributions of each of these components. Eye movements provide incredibly useful data for parsing complex tasks that rely on visual processing. In this chapter, I will review work on how eye movements have been used in visual search research, focusing on the effect of memory on visual search. Specifically, I will discuss why eye movements are useful for highlighting the role of memory in visual search and the consequences of visual search processing on memory. Eye movement data help isolate the processing components of visual search and permit a finer-grained analysis of the task. However, eye movements are not always a perfect predictor of the outcome of processing, pointing to interesting aspects of the effect of visual search.
Bug Priority Assessment in Cross-Project Context Using Entropy-Based Measure

Software users report bugs on the bug tracking system in a distributed environment with different levels of understanding and knowledge about the software. As a result of this, software bug repository data are increasing day by day with noise and uncertainty in it. The noise and uncertainty present in bug summary need to be handled, so that it should not affect the performance of learning strategies for different bug attributes and fix time predictions. Bug prioritization is a process of deciding the sequence of bugs to be fixed. Wrong bug prioritization results in unresolved important bugs with delayed release of the software, thus affecting the quality and evolution of the software. Bug priority prediction requires historical data for the training of the classifiers. However, such historical data is not always available in practice for all the software. In such circumstances, designing prediction models with training data from other projects is the solution. This process of bug priority prediction using training and testing bug data from two different projects is called cross-project bug priority prediction. We have used Shannon entropy to measure the uncertainty in bug summary in addition to bug severity and summary weight for bug priority prediction. In this paper, we have proposed different machine learning classifiers to predict the priority of a reported bug in cross-project context by handling the uncertainty. Results show performance improvement for proposed entropy-based cross-project bug priority prediction over existing summary-based cross-project bug priority prediction for a newly coming bug report.
A new undeniable signature scheme on general linear group over group ring

The concept of undeniable signature scheme was put forward by D. Chaum and H. van Antwerpen in 1989. Such signature scheme requires the signer’s co-operation at the verification step which defends the signer against the verification of his/her signature by an unwanted entity. In this paper, we propose a new undeniable signature scheme in a non-abelian group over group ring whose security relies on the difficulty of solving Factorization with Discrete Logarithm Problem (FDLP).
Emergency Shelter Design For Disaster Preparation

Abstract Indonesia is geographically a country located in an area that is often subject to natural disasters. As a result of natural disasters that damaged residents homes, many people lost their homes and needed temporary shelter to evacuate. However, shelters in practice are often inappropriate and have poor quality and are late or come in damaged condition. The purpose of this study is to develop an emergency shelter spatial design that functions for disaster emergencies. In this study the method used is a qualitative research method with a descriptive approach, this method aims to describe the conditions that currently apply, in order to understand more deeply the needs of disaster victims who are displaced in shelters that are currently being used in the field. Based on several theories used, conclusions will be drawn to become a tool in the analysis. The objects to be observed are several emergency shelter units that were used in the latest disaster, the disaster in Palu, Information and data will be obtained through interviews and field observations accompanied by documentation. This research shows that emergency shelters currently in the field do not yet have spatial functions that can fulfill activities and accommodate disaster victims’ activities well. The results of this study are three alternative plans that have been adapted to the needs of space and function in accordance with the activities and activities of disaster victims in Palu in order to create the expected comfort and privacy. Suggestions for layout and emergency shelter space requirements are manifested in the form of tables and floor plans.