Reviewerly/exHarmony huggingface dataset:
Fully Homomorphic Symmetric Key Encryption-Based Access Control over Outsourced Cloud Data Using Smith Normal Form

Cloud computing is an innovative technology to support outsourcing of computing resources on-demand. But it is lack of security, confidentiality, and privacy. This paper attempts to propose a solution to the above-said problems by a secure, confidential and Fully Homomorphic Encryption technique with the desired level of access control. The present scheme is based on symmetric keys and Smith Normal Form assures the homomorphic property. All the operations are simple matrix multiplications and consequently, the present scheme sustains the owners of the cloud to secure their private data from its uncertain clients.
Maritime Weather Predictor Design Based Neural Network and ANFIS to an Increase in Accuracy in the Java Sea

Sea transportation is a mainstay transportation in Indonesia, it is because Indonesia consists of thousands of islands, so that, to connect between islands, sea transportation is needed.Waves are very closely related to the sea, waves that are negative that is waves that can endanger shipping.One of the factors causing sea accidents is natural disasters, namely high waves.To minimize accidents due to high waves, wave predictions can be made in the hours to come using the neural network algorithm.Neural network was chosen because of its advantages in processing system input-output data even though the system is nonlinear.The advantage is that the neural network is chosen as a wave height predictor algorithm.ANFIS is an algorithm for the development of a combination of neural networks and fuzzy artificial intelligence.The ability of ANFIS to predict wave heights is no less good with neural networks, it is because ANFIS is a combination of neural network and fuzzy.It is hoped that by doing this research it can compare which algorithm is better in predicting wave heights.
Development of a Web-Based Education System for Deep Reinforcement Learning-Based Autonomous Mobile Robot Navigation in Real World

The technology that combined deep reinforcement learning and robotics is increasing interests in recent years. Although several online tools for studying this technology can be found, it is difficult for beginners to develop actual robot systems for autonomous navigation in the real world. In this study, we developed a web-based educational system that is able to help users to study mobile robot navigation based on deep reinforcement learning and develop actual robot systems. The proposed web system provides the following functions: setting the parameters of reinforcement learning for autonomous robot navigation, running learning scripts and monitoring status of the learning. The first experiment that a user develops an actual robot system was performed. In the experiment, the user tuned parameters on the web page started the training and obtained action policy models. The experimental results indicate the proposed system can be applied to develop an actual autonomous navigation system. Also, the user could decide better parameters through the trial and error process using the proposed system.
Bank Card Number Identification Program Based on Template Matching

The serial number of bank card is a number consists of 19 numbers. As the serial number of each bank card is unique, so by the bank card serial number we can be sure of the bank card and get the information of the bank card. The automatic recognition of bank card serial number has important theoretical significance and practical value to realize effective management of the bank card and bank card services. This paper designed a bank card serial number automatic recognition program, this program can basically complete the automatic recognition of bank card serial number. The program is based on image processing of the image, the image processing of the two value image dilation and erosion using X/Y transform, the direction of projection sequence number character position; the sequence number and character segmentation are normalized by projection method; finally, using template matching method to recognize character of bank card serial number. The experimental results show that, the program has a high recognition rate and accuracy, and has certain stability.
On Distributed Information Composition in Big Data Systems

Modern big data computing systems exemplified by Hadoop employ parallel processing based on distributed storage. The results produced by parallel tasks such as computing modules in scientific workflows or reducers in the MapReduce framework are typically stored in a distributed file system across multiple data nodes. However, most existing systems do not provide a mechanism to compose such distributed information, as required by many big data applications. We construct analytical cost models and formulate a Distributed Information Composition problem in Big Data Systems, referred to as DIC-BDS, to aggregate multiple datasets stored as data blocks in Hadoop Distributed File System (HDFS) using a composition operator of specific complexity to produce one final output. We rigorously prove that DIC-BDS is NP-complete, and propose two heuristic algorithms: Fixed-window Distributed Composition Scheme (FDCS) and Dynamic-window Distributed Composition Scheme with Delay (DDCS-D). We conduct extensive experiments in Google clouds with various composition operators of commonly considered degrees of complexity including O(n), O(n log n), and O(n^2). Experimental results illustrate the performance superiority of the proposed solutions over existing methods. Specifically, FDCS outperforms all other algorithms in comparison with a composition operator of complexity O(n) or O(n log n), while DDCS-D achieves the minimum total composition time with a composition operator of complexity O(n^2). These algorithms provide an additional level of data processing for efficient information aggregation in existing workflow and big data systems.
PurgeMEM: Towards Building A Memory Safe Cloud

Cloud computing has become the industry standard for rapid application deployment, scalable server support, and mobile and distributed services. It provides seamless scalability and access to infinite resource theoretically. Unfortunately, the co-tenancy of cloud computing makes Cloud Services vulnerable to privacy issues. In the past, researchers have shown that it is possible to compromise important information by exploiting the co-tenancy nature of clouds. In this paper, we first identify a new method which can be used to collect residual application data from the main memory of a virtual machine hosted in the cloud even the application is closed by the user. We show that using traditional forensic tools in all three major cloud providers (Amazon AWS, Google Cloud, and Microsoft Azure), one can gather information about a closed application from the main memory if the machine is compromised. To resolve this vulnerability, we propose PurgeMEM, a framework which provides a service for deleting residual memory left by a closed application. PurgeMEM continuously monitors processes launched by user applications. When a process is about to finish, PurgeMEM is notified and then it cleans up the physical memory location by writing known hex values and thereby preserving the privacy of the user data. We developed a PurgeMEM prototype for the Linux environment and evaluated it with four small applications. For all the applications, our prototype is able to sanitize and delete memory residuals successfully with a negligible performance overhead (2.97%).
Lightning Location System Database Construction And Application in AnHui Province

The lightning location system in Anhui province are introduced. Under the environment of SQL Server 2005, lightning location system database is established, unifying the lightning location data storage form and promoting the application of lightning location data and its secondary development. In combination with Doppler radar products, lightning nowcasting warning system has been developed. Strengthened The live monitoring, nowcasting forecast of lightning and early warning products published through web benefit the public a lot and supply the reference for forecasters in the process of early warning signal production.
An Improved Differential Evolution Algorithm with Opposition-Based Learning for Clustering Problems

Differential Evolution (DE) is a popular efficient population-based stochastic optimization technique for solving real-world optimization problems in various domains. In knowledge discovery and data mining, optimization-based pattern recognition has become an important field, and optimization approaches have been exploited to enhance the efficiency and accuracy of classification, clustering and association rule mining. Like other population-based approaches, the performance of DE relies on the positions of initial population which may lead to the situation of stagnation and premature convergence. This paper describes a differential evolution algorithm for solving clustering problems, in which opposition-based learning (OBL) is utilized to create high-quality solutions for initial population, and enhance the performance of clustering. The experimental test has been carried out on some UCI standard datasets that are mostly used for optimization-based clustering. According to the results, the proposed algorithm is more efficient and robust than classical DE based clustering.
Decomposition of Covariance Matrix Using Cascade of Trees

We are looking at the statistical model approximation for jointly Gaussian random vectors. To do so, we are using a cascade of linear transformations that go beyond tree approximations. Here, we propose an algorithm which incorporates the Cholesky factorization method to compute the decomposition matrix and thus can approximate a simple graphical model using a cascade of the Cholesky factorization of the tree approximation transformations. The Cholesky decomposition keeps the sparsity pattern of the inverse decomposition and thus reduces computations for the tree structure linear transformation at each cascade stage of the algorithm. This is a different perspective on the approximation model, and algorithms such as Gaussian belief propagation can be used on this overall graph. We conclude with some simulation results.
A comparative study of three scoring systems on palpable breast aspirates at a tertiary health-care center: A cross-sectional study

Background: The cytological grading system in fine-needle aspiration of breast lumps helps the surgeons to decide the method of management and assess the survival rate. However, till date, no single cytological grading system for breast aspirates has been adapted in routine practice. Hence, a comparative study of three different grading systems was done. Materials and Methods: This is a retrospective study conducted from January 2016 to June 2018. The stained aspirated smears of breast lump were assessed for cytomorphologic features and graded as per the Modified Masood's Scoring Index (MMSI), National Cancer Institute (NCI) Cytological Grading System, and Robinson's cytological grading and compared with histological diagnosis (four categories) and modified Scarff–Bloom–Richardson's scoring system (carcinoma cases). Results: The MMSI system had a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of 80%, 100%, 100%, 92.86%, and 94.44%, respectively. The NCI system had a sensitivity, specificity, PPV, NPV, and diagnostic accuracy of 60%, 97.44%, 90.0%, 86.36%, and 87.04%, respectively. The Robinson's cytological grading system had a sensitivity, specificity, PPV, NPV, and diagnostic accuracy of 100%, 84.62%, 71.43%, 100%, and 88.89%, respectively. Conclusion: Robinson's scoring system is the system with maximum sensitivity and is most effective in subcategorizing only malignant breast lesions. MMSI is the system with maximum specificity, PPV, and diagnostic accuracy, and can be considered as the standard grading system for both benign and malignant breast lesions on cytology.
Load Balancing in Cloud Computing using Dynamic Load Management Algorithm

In the recent scenario, Cloud Computing has become a catchword in the Information Technology and is a next phase in the development of internet ,it provides a vast amount of storage services and computing to the users via internet. In cloud virtual environments Load Balancing plays a crucial role. As Cloud Computing is increasing rapidly there are many users all over the world are important for optimal results, so Load Balancing become an essential research area. This paper is a detailed discussion on the existing Load balancing methods and it also compares with various parameters. This paper result is based on Existing Round robin and throttled scheduling.
A Hybrid Framework for Interconnecting Various Software Engineering Process Models and Techniques

In the software development lifecycle, a software process model provides a standardized format for planning, organizing, and executing a software project. Dozens of different models exist and are used in the software industries, but many of them has only some minor variations on a small number of basic software engineering models. Nevertheless, in real life scenario software developer does not implement most of process models that we have seen theoretically exists because there are many modes available and it is very difficult to analysis which models should be implemented in which stage of the software development process. Hence, this study presents a hybrid framework that describes three categories of process models based on the project characteristics such as small, medium and large. And provides an analysis of finding which model supports which types of project and what steps need to be followed. The contribution of this study lays on the software engineer who will be assisted with the hybrid framework to take informed decisions about the model selection based on making query about the project characteristics such as project requirements, risk factors involved in the project, availability of the customers, budget and expenses incur in the project, etc.
Rain-streaks Detection and Removal In Single Image Using Curvelet Transform

This study implements a new way to address the issue of rain streaks detection and elimination from a single picture based on the transform of Curvelet. This approach depends on a decomposing of the rainy image into different scales and sub-bands frequencies by using the curvelet transform. Features have been extracted from each sub-band frequency and the neural network will classify these features into “rain” or “non-rain” signatures. The reconstructed image is obtained without the sub-bands that have the rain signature. The findings from the experiments indicate that the proposed approach improves the visualizing quality as well as PSNR and outperforms previous rain removal algorithms.
Word2Vec and Evolutionary Computing Driven Hybrid Deep Learning-Based Sentiment Analysis

In this study, we propose three novel hybrid deep learning architectures for sentiment classification. We hybridized convolution neural network (CNN) with two evolutionary neural networks, viz. fuzzy logic-driven self-tuned particle swarm optimization-trained neural network (fstPSONN) and differential evolution-trained neural network (DENN) and probabilistic neural network (PNN) for sentiment classification. These deep networks were preceded by Word2Vec that yielded embeddings obtained by Google. In these hybrid deep learning architectures CNN-fstPSONN, CNN-DENN and CNN-PNN, CNN was primarily responsible to accomplish the task of feature extraction and latter were responsible for classification. All the three architecture were developed with three variants of CNN, i.e. CNN-random, CNN-static and CNN-non-static based on the embedding training of embedding layer. We demonstrated the effectiveness of our hybrids on the IMDB movie review dataset. Out all the models, CNN-PNN and CNN-fstPSONN gave a better accuracy of 81.9, 81.5 and 78.0% for the non-static, static and random variants of CNN, respectively. Furthermore, we validate our results by performing t-test analysis of the mean obtained from 10-fold cross-validation, and it turned out that all the three variants are statistically the same. Consequently, we recommend CNN-PNN as preferred architecture owing to its least complexity.
Detection Algorithm of DoS High Camouflage Attack Feature Based on S_Kohonen Neural Network

Because the traditional detection algorithm has the problems of low detection accuracy, high false alarm rate, time-consuming and so on, it cannot achieve high efficiency detection. This paper proposed a method based on S_Kohonen neural network to detect the high camouflage attack feature of DoS. The two-dimensional grid S-Kohonen neural network structure was constructed, and the high camouflage attack characteristics were analyzed; The feature data was fetched and preprocessed to obtain the attack event feature, and the framework of multi-level tree structure for attack feature detection was constructed, and the feature detection steps were designed. The experimental results show that this method has the advantages of high accuracy, low false alarm rate, short time consumption and high efficiency detection of DoS high camouflage attack features.
On Decentralized Self-localization and Tracking Under Measurement Origin Uncertainty

We propose an algorithm for simultaneous Cooperative Self-localization (CS) of a network of mobile agents and multi-target tracking (MTT) under complete data association uncertainty. Specifically, the associations between measurements and objects, i.e., agents and targets, are unknown. Existing CS-MTT algorithms do not assume origin uncertainty for both interagent and agent-target measurements. Due to the joint density being intractable, a message passing scheme is employed to approximately infer the marginals of agent and target states, where the number of targets is unknown and time-varying. Based on average consensus, we propose a distributed Gaussian implementation of the proposed method, which only requires communication between one-hop neighbors. Numerical experiments show the improved performance of the proposed CS-MTT algorithm as compared to the conventional approach of separate localization followed by tracking.
A Study on the Robustness of Pitch Range Estimation from Brief Speech Segments

Pitch range estimation from brief speech segments is important for many tasks like automatic speech recognition. To address this issue, previous studies have proposed to utilize deep-learning-based models to estimate pitch range with spectrum information as input [1-2]. They demonstrated it could still achieve reliable estimation results when speech segment is as brief as 300ms. In this work, we further investigate the robustness of this method. We take the following situation into account: 1) increasing the number of speakers for model training hugely; 2) second-language(L2) speech data; 3) the influence of monosyllabic utterances with different tones. We conducted experiments accordingly. Experimental results showed that: 1) We further improved the accuracy of pitch range estimation after increasing the speakers for model training. 2) The estimation accuracy on the L2 learners is similar to that on the native speakers. 3) Different tonal information has an influence on the LSTM-based model, but this influence is limited compared to the baseline method. These results may contribute to speech systems that demanding pitch features.
An Security Analysis of Ext Filesystem metadata

Until recently, various researches on Linux have been conducted, but the characteristics of the filesystem that can be changed as the Linux kernel version is diversified in terms of security have not been considered. Digital forensic investigations, which are not properly analyzed for major metadata changes by kernel version, can undermine investigative capabilities and lead to serious doubts about evidence. Since investigations can be conducted on a variety of Linux filesystems at the actual forensic investigation, it is necessary to analyze metadata of various filesystems by Linux distribution and kernel version. Therefore, this paper compares the difference of metadata changes that occur when deleting files for various kernel versions of Ext2 filesystems. Furthermore, we provide information about the kernel version and change time which has the change in metadata related to file recovery.
Effect of Preprocessing for Distributed Representations: Case Study of Japanese Radiology Reports

A radiology report is a medical document based on an examination image in a hospital. However, the preparation of this report is a burden on busy physicians. To support them, a retrieval system of past documents to prepare radiology reports is required. In recent years, distributed representation has been used in various NLP tasks and its usefulness has been demonstrated. However, there is not much research about Japanese medical documents that use distributed representations. In this study, we investigate preprocessing on a retrieval system with a distributed representation of the radiology report, as a first step. As a result, we confirmed that in word segmentation using Morphological analyzer and dictionaries, medical terms in radiology reports are not handled as long nouns, but are more effective as shorter nouns like subwords. We also confirmed that text segmentation by SentencePiece to obtain sentence distributed representation reflects more sentence characteristics. Furthermore, by removing some phrases from the radiology report based on frequency, we were able to reflect the characteristics of the document and avoid unnecessary high similarity between documents. It was confirmed that preprocessing was effective in this task.
A Survey on Medical Image Registration Using Deep Learning Techniques

Image registration is one of the most significant and useful approaches in diagnosing disease by providing complementary information from different medical images. Image registration is a process of overlaying two or more images into a single integrated image. This process is widely used in medical imaging analysis to overlay images obtained from different devices at different time. Traditional methods to geometrically align images are time-consuming, while deep learning techniques are less time-consuming. In recent years, deep learning is a growing technology and has gained many breakthroughs in various image processing problems such as classification, reconstruction, and registration. In particular, convolutional neural networks (CNNs) is one of the most powerful tools in computer vision task. Recently, deep learning techniques are being developed for medical image registration, and image fusion is clearly evidenced from high-quality research. The intention of this survey is to provide perspective about the recent development of registration techniques using machine learning and deep learning techniques.
CAD modelling of fire safe valve body by reverse engineering

Abstract The government of Indonesia has a regulation called local content to push the manufacturing industries in order to increase their competitiveness. In order to reach that goal, technology transfer has been done, but it is a long way to go and impractical. So, one way to go is by the understanding of the product that has to be made. Reverse engineering is a specific technique to use because of its practicality. As the name implied, reverse engineering is the inverse of engineering design because the starting point is the product itself instead of the idea. In this paper, the product used as a case is the body of the fire-safe valve. The final goal of the reverse of the body part of the valve is the idea behind it or called the design intent. However, it is going to be a long process, and as the first step that will be presented in this paper, the goal is to get its CAD model of the fire-safe valve body.