Reviewerly/exHarmony huggingface dataset:
Teaching Writing by Using Evernote Application

Abstract In writing, students may have numerous strategies in organizing their ideas. The technology invention can be utilized as a means in enhancing student’s writing project. One of them is Evernote application as one of the most popular software for keeping and organizing information. This article is aimed at 1) revealing the application of teaching writing by using Evernote as graphic organizer; 2) finding the strengths of using Evernote; and 3) finding the challenges of using Evernote. This article uses library research as a method to gather the information. From the result of study, writing in Evernote is as simple as using a word processor. We need to have a Google account first to sign up to this application. When we have already signed up, we can create “new note.” Once we have finished, give it a title to make it easy to find later and enter texts. The strengths are that Evernote can create different forms including words, audio, and images. Another study also shows that the group of students given graphic organizer can show more rational thought than students without graphic organizer. It also has more interesting display by adding various colors. The challenges for this application are that it requires constant internet connection. It also should be provided by sufficient hardware. Therefore, it is suggested that the institution could provide sufficient means to meet this need. It is because despite of its challenges, this application is beneficial for students and teachers for becoming technology literate in learning process.
Data Fusion for Industry 4.0: General Concepts and Applications

The 4th Industrial Revolution represents a new industrial era through the combination of Cyber-Physical Systems, Internet of Things, and the Internet of Services. Data are the new raw material of the 21st century, and it is necessary to turn these data into meaningful information to provide a more flexible, reliable, and efficient operation. To overcome challenges related to acquisition and analysis of a large amount of data, the data fusion strategy has gained focus as a data preprocessing phase to support the fast-growing data-intensive applications. This article presents a systematic mapping of general concepts and applications of data fusion in the context of Industry 4.0 assisting the research community in future studies as well as practitioners and students, providing support for the use of data fusion strategy.
Real-World Driving Cycle: Case Study Muscat Performance Analysis

This precise study addresses the performance and potential of using Electric Vehicles (EVs) in Oman. It starts with identifying the representative driving cycle where 40 driving tests are performed to obtain the representative driver profile. The tests are done with a vehicle equipped with measurement tools to log important values. The driving cycle and driver profile are used as input data for a vehicle simulation model, which consists of detailed sub-models for the driver, driving cycle, vehicle components and its dynamics. The model is used to evaluate the performance in terms of energy consumption for Internal Combustion Engine (ICE) vehicles and EVs. Using of EVs seems a promising option to replace the existing ICE vehicles that use petrol. A great saving potential in driving cost can be earned when using EVs, as a direct reflection of the reduction in energy consumption. The measurements and the simulation results encourage further investigation of exploiting electric vehicles with renewable energy.
A Method of Resource Isolation and Security Enhancement of Container Cloud Based on Multiple Security Domains

Container cloud adopts the container virtualization technology with weak isolation. In order to ensure the security of container cloud, the problem of isolation of computing, network and storage resources of different tenants in container cloud environment should be solved first. This paper presents a multi-security domain-based container cloud security enhancement method, which provides a complete resource isolation solution for container cloud.
Cascade multi-scale object detection on high-resolution images

Precise object detection is one of important task in computer vision. Recent achievements in convolutional neural networks open possibilities to detect objects with precision close to humans. However, current neural networks struggles to detect objects with large difference in scale. In this report proposed approach to process high-resolution images by same neural network multiple times with decreasing resolution in cascade way to enhance scale range of network pre-trained on typical dataset. Combined result of object detection of all passes processed by non-maximal suppression algorithm. Proposed approach demonstrated on Yolo3 network trained on COCO dataset. Scale range of network and upper size limit for detected object are estimated, scale technique for cascade decreasing resolution proposed.
Face Detection Method in Surveillance Systems Using Haar Feature and Deep Neural Network

Face detection is one of the most widespread applications in the modern world society. Tremendous strides have been made to improve the performance of face detection, however, accuracy and efficiency of face localization in the wild remains an open challenge. This paper presents an efficient method for face detection in a surveillance system. The proposed method combines traditional computer vision method and modern technique. The method has been experimented under several datasets to evaluate the accuracy and efficiency of face localization. The result achieves 96.66% and 26.85 frame per second.
Simulation and experimental study on chip module warpage by digital image correlation

In this paper, warpage experiment was carried out on electronic module in heating process by the digital image correlation. As a widespread used measurement in recent years, digital image correlation technology was used in the electronic packaging for measuring warpage and its strain. DIC includes two key steps: camera calibration and image matching. The camera calibration involves a pinhole model approach. It employs feature-matching-based initial guess, multiple subsets, iterative optimization algorithm, and reliability-guided computation path to achieve fast and accurate image matching. Image matching process was calculated by software Vic-3D. Simulation have been conducted to verify the experimental results by Abaqus. By comparing the results of experiment and simulation, the measurement results for chip's warpage are correct.
Unsupervised Pronunciation Fluency Scoring by infoGan

Pronunciation fluency scoring (PFS) is a primary task in computer-aided second language (L2) learning. Most of existing PFS algorithms are based on supervised learning, where human-labeled scores are used to train the scoring model. However, the human labeling is rather costly and tends to be biased. In order to tackle this problem, we propose an unsupervised learning approach, where an infoGan model is constructed to infer latent speech codes, and then these codes are used to build a classifier that distinguishes native and foreign speech. We found that this native-foreign classifier can generate good utterance-based fluency scores.
Adversarial Machine Learning for Network Security

With the rapid growth of machine learning applications in communication networks, it is essential to understand the security issues associated with machine learning. In this paper, we choose a flow-based Deep Neural Network (DNN) classifier as a target and study various attacks on this target classifier. The target classifier detects malicious HTTP traffic (i.e., bots, C&C, etc.). We first launch an exploratory attack under a black box assumption against the target DNN classifier. We start from a simple case that the attacker can collect the same set of features used in the target classifier and then consider the case that the attacker can only collect a set of features based on its judgement. We also design the attacks with conditional Generative Adversarial Network (cGAN) to reduce the requirement on the amount of collected data. We show that the attacker can build its own classifier to predict the target classifier's classification results with about 93% accuracy. Once the exploratory attack is successful, we can perform further attacks, e.g., evasion attack and causative attack. We show that these attacks are very effective. Evasion attack can identify samples to double error probability of the target classifier while under causative attack, the new classifier makes classification errors on more than 60% of samples.
Digitalization of the thesaurus approach in education

The article describes what the thesaurus approach in education is like, its origins are justified, and it also suggests the implementation of certain areas of digitalization of the thesaurus approach. The thesaurus approach is illustrated by two tasks: 1) the identification of individual stylistic features of authors’ texts; 2) assessment of learning outcomes (semi-automatic assessment of texts generated by students during the educational process). The first task is illustrated in more details, in it the Kullback— Leibler information measures are used, familiarity with which, in our opinion, will be useful to informatics teachers and readers of the journal. In addition, the calculations made in the first task can be reproduced even by school students in the framework of, for example, project activity. In the second task, the less sensitive Spearman statistical method was used. The thesaurus approach allows using information methods to consider a number of other tasks related to the pedagogical process. In particular, the main direction of work on the thesaurus approach is seen in the search and testing of algorithmic prescriptions for the quantitative/numerical analysis of texts related to education or arising in the educational process. The aforementioned texts include the corpus of educational and teaching aids, texts of individual academic subjects, disciplines, topics, essays, written works, final qualifying works of students, essays made by school students, etc.
Conditionally-Minimax Nonlinear Filtering for Continuous-Discrete Stochastic Observation Systems: Comparative Study in Target Tracking

The paper presents an algorithm of the Conditionally-Minimax Nonlinear Filtering (CMNF) of the stochastic differential system state given indirect noisy discrete-time observations. The filter has a two-step "prediction-correction" form with fixed basic prediction and correction terms, which are jointly processed in the optimal way. The obtained estimates are unbiased and the theoretical covariance matrix of CMNF errors, which may be calculated a priori, meets the real a posteriori values. The key feature of the CMNF algorithm is a possibility to choose the preliminary observation transformation, basic prediction and correction functions for a particular case of the observation system, which allows to improve the estimation accuracy significantly. All the features of CMNF are demonstrated by the maneuvering target tracking model in comparison with the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) estimates.
Verifiable Arithmetic Computations Using Additively Homomorphic Tags

It is possible to represent significant amount of arithmetic functions using only addition and subtraction operations, looping and conditional branching constructions. Combining this representation with an additively homomorphic cryptosystem allows building a simple yet efficient non-interactive scheme for verifying integrity of delegated arithmetic computations within a stateless model.
Speaker-discriminative Embedding Learning via Affinity Matrix for Short Utterance Speaker Verification

Text-independent short utterance speaker verification (TI-SUSV) task remains more challenging compared to the full-length utterance SV task due to inaccurately estimated feature statistics or insufficient distinguishable speaker embeddings. It is noted that recently developed end-to-end SV systems (E2E-SV) achieve the state-of-the-art on several datasets, which directly learn a mapping from speech features to the compact fixed length speaker embeddings. In this study, following the E2E-SV pipeline, we strive to further improve the accuracy of TI-SUSV task. Our research is based on two intuitive ideas: better speech feature representation for SUs and better training loss function to obtain more discriminative embeddings. Specifically, a bidirectional gated recurrent unit network with residual connection (Res-BGRU) is firstly designed to improve feature representation capability. Secondly, a novel affinity loss is proposed where the mini-batch data has been manipulated to obtain more supervision information. In details, a speaker identity affinity matrix formed by one-hot speaker identity vectors is taken as the supervisor of the speaker embedding affinity matrix to obtain better inter-speaker separability and intra-speaker compactness. Experimental results on the Voxceleb1 dataset show that our system outperforms a conventional i-vector and x-vector system on TI-SUSV.
Analysis of Model based Shadow Detection and Removal in Color Images

Shadow detection and removal efficiency in color images has become most important requirement while processing aerial images. The successive thresholding scheme (STS) is presented in this paper. Scheme increases shadow detection accuracy. In this paper, we have modified ratio map to obtain accurate gap between shadow and non-shadow pixels. The idea is derived from original Tsai's method. The global thresholding scheme is used to classify the pixels into shadow and non-shadow class. The coarse map obtained for candidate shadow pixels is then processed using connected component and candidate shadow region pixels are grouped. The iterative method is used for detecting true shadow pixels. Experimental results show that, the shadow detection correctness of our suggested model based algorithm is analogous to Tsai's algorithm. Dataset of images is tested using both approaches to calculate precision (P), recall (R) and Fscore (F). The results show outstanding performance for our method.
A New Inversionless Berlekamp-Massey Algorithm with Efficient Architecture

This paper presents a new inversionless Berlekamp-Massey (BM) algorithm as well as its efficient architecture. Starting with a lesser-known version of BM algorithm, we develop a serial of inversionless variants by successively applying algorithmic transformations. The final algorithm has a very compact description and a highly regular structure, which can be naturally mapped to a systolic architecture. Compared with the state-of-the-art architecture RiBM, the proposed one possesses a different cell structure and has slightly lower hardware requirements. More importantly, it enables us to establish a new architectural equivalence between the BM algorithm and the Euclidean algorithm.
Improved Approach to Extract Knowledge from Unstructured Data Using Applied Natural Language Processing Techniques

Extraction of meaningful knowledge from a unstructured data is a complex task. In the literature, efforts have been made using text mining approaches. These approaches employ rich amount of resources in mining the textual datasets. In this paper, we focus on optimization of mining algorithm of text data to generate the automatic text summarization. In this approach, we extract text summaries from the text data carpus using natural language processing techniques. We propose a mining approach in semantic parsing and generate the automatic text summaries. We conduct the experiments on the real-world dataset and show the proposed approach is useful than the existing approaches.
Double Layers Quantum Key Distribution with Ability to Against PNS Attacks

This paper presents a modified quantum key distribution (QKD) protocol with its advantage ability to against the photon number splitting (PNS) strategy. As a result of the proposed protocol with the specified parameters of k = 1024 bits, y = 10 bits, and the round repeating of n = 10, the eavesdropper can achieve the correct information with a probability of only 1.97×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-31</sup> . Moreover, the proposed protocol allows each the photon pulse can consist of a few of photons, which is valuable to the maximize photon number of μ=1 and resulting in a significant higher useful bit rate. From the simulation results, the obtained useful bits rates for each the photon pulse of 800 nm, 1300 nm, and 1550 nm, are approximately of 242.2 kbit/s, 7.4 Mbit/s, and 119.8 kBit/s, can be achieved at the distances of 20 km, 60 km, and 120 km, respectively. The QKD system parameters for each the considered wavelength are specified as: the photon pulses rate of 1 GHz and 10 GHz, the photon number of μ=0.1 and 1.0, The attenuations of 2.0, 0.35, and 0.25 dB/km, the detector efficiencies of 50%, 20%, and 10%, with the dark count probabilities of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-7</sup> , 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> , and 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> , respectively. The potential of using such the proposed protocol with a double layers QKD system is performed and discussed.
A Variable Relationship Excavating Based Optimization Algorithm for Solving 0-1 Knapsack Problems

The past decades have seen an extensive investigation of evolutionary algorithms. The recombination operators of most evolutionary algorithms are either single-point crossover or multi-point crossover for solving 0-1 combinatorial optimization problems. There is a little studies on excavating the variable relationship to improve the efficiency of the recombination operator. Hence, we propose an optimization algorithm based on excavating variable relationship for solving 0-1 combinational problems. The aim of the recombination operator is fully utilizing the inherent information from every slice component of all individuals. We compared the proposed algorithm with the classic evolutional algorithm with single-point crossover operation on several 0-1 knapsack problems, which is a classical combinatorial optimization problem. The simulation results show the convergence efficiency of the proposed algorithm.
Solution Separation Unscented Kalman Filter

State estimation of nonlinear stochastic dynamic systems includes the unscented Kalman filter. The paper focuses on the self-assessment ability of state estimation algorithms, which shows to be useful in the problems involving strong nonlinearity of the model. An algorithm with the self-assessment is capable of informing the user that the state estimate may not be credible. A new algorithm, called solution separation unscented Kalman filter (S2UKF) is proposed, which is inspired by the solution separation technique of the Global Navigation Satellite System (GNSS) receiver autonomous integrity monitoring. The algorithm compares a set of sub-solution estimates with a full-solution estimate and informs about non-credible estimates. This ability is demonstrated using a simulation example involving the bearings-only tracking problem.
Scalable distributed algorithms for multi-robot near-optimal motion planning

This paper investigates a class of motion planning problems where multiple unicycle robots desire to safely reach their respective goal regions with minimal traveling times. We present a distributed algorithm which integrates decoupled optimal feedback planning and distributed conflict resolution. Collision avoidance and finite-time arrival at the goal regions are formally guaranteed. Further, the computational complexity of the proposed algorithm is independent of the robot number. A set of simulations are conduct to verify the scalability and near-optimality of the proposed algorithm.
Data Backup in Geo-Distributed Data Center Networks Under Time and Budget Constraints

Region failure caused by disaster (e.g., earthquakes, hurricanes, etc.) can lead to the damages of geo-distributed data center networks (DCNs), which induce huge amounts of data loss. To implement data protection under disaster, data backup problem is considered in geo-distributed DCNs before DCNs are affected by an upcoming disaster and then an emergency data backup scheme is investigated, which takes the time constraint and budget differentiation into account. The former is used to ensure that data backup is carried out under the constraint of early warning time of disaster, while the latter meets the different budget constraints for data backup from different DCNs. To solve the data backup problem, an Integer Linear Program (ILP) is designed to obtain the optimal solution for backup under the time and budget constraints, such that the overall amount of backup data is maximized. The proposed ILP model is illustrated by extensive numerical results.
A Texture based Image Retrieval for Different Stages of Alzheimer’s Disease

In the last few years, using digital images have become significant across most of the sectors including healthcare and medical labs. To analyze and interpret the large collection of images having a complex disease pattern requires the knowledge of medical experts. So, the image retrieval technique plays an important role to assist the doctors to carefully examine an image of a new patient by comparing with most similar images existing in the database and also to take a correct decision during diagnosis. So, we carried out our studies by collecting the images of Magnetic Resonance Imaging (MRI) from the Open Access Series of Imaging Studies (OASIS) database. Later, we have categorized the collected MRI images into three different groups based on the size of a ventricular region of the brain and then employed second and higher order statistical methods to extract the textural features from each image. Thus, we obtain multiple textural features using Gray Level Co-occurrence Matrix (GLCM) and Law Texture Energy Measure. After obtaining the textural features, the top matched images are retrieved based on the similarity measure computed between the feature vector of a query image and the images present in the database. Finally, the retrieval performance is compared for the extracted texture features from GLCM, Laws Texture Energy Measure and a combination of these two methods. The combination of features from the above methods shows the better precision of 80% and 60 % in the retrieval of Group1 and Group3 images.
An Object-Oriented Software Complexity Metric for Cohesion

The quality of object oriented (OO) software is often measured with the help of OO metrics—A specific type of software metrics that particularly evaluates the features of OO programming such as modularity, understandability, readability, reusability and extensibility. OO metrics that are derived by the concepts of cohesion and coupling depict good measures of module design in such way the module that shares high quotient of data within the module is better than sharing data between them. Thus, the quantification of data/information sharing within the software module is still in the thrust area of research for the approval of module design. The adaptability of existing cohesion metrics is a major concern for the developer as the metric value does not define a clear boundary between low, medium and high cohesions. To overcome this issue, in this chapter, a novel cohesion metric called Cohesion in Method (CohM) is proposed. The metric clearly defines a boundary between the levels of cohesionsby taking the benefits of Jaccard similarity measure. Thus, helps to evaluate software for reusability and maintainability. The results show that the metric comparatively gives better results than the traditional cohesion metrics by clearly differentiating medium and high level cohesions in software module.