Reviewerly/exHarmony huggingface dataset:
Enhance Safety and Security System for Children in School Campus by using Wearable Sensors

Child security in the school campus is most important in building a good society. In and around the world the children are abused and killed also in sometimes by the people those who are not in good attitude in the school campus. To track and resolve such issues an enhanced security feature system is required. Hence in this paper an enhanced version of security system for children is proposed by using `Wearable Sensors'. In this proposed method two wearable sensors nodes such as `Staff Node' and `Student Node' are paired by using `Bluetooth' communication technology and Smart Watch technology is also used to communicate the Security Center or Processing Node for tracking them about their location and whether the two nodes are moved away from the classroom. If the child node is not moving for a long period then it may be notified by the center and they will inform the security officers near to the place. This proposed method may satisfy the need of school management about the staff movements with students and the behavior of students to avoid unexpected issues.
Conditioned Variational Autoencoder for Top-N Item Recommendation

State-of-the-art recommender systems (RSs) generally try to improve the overall recommendation quality. However, users usually tend to explicitly filter the item set based on available categories, e.g., smartphone brands, movie genres. For this reason, an RS that can make this step automatically is likely to increase the user's experience. This paper proposes a Conditioned Variational Autoencoder (C-VAE) for constrained top-N item recommendation where the recommended items must satisfy a given condition. The proposed model architecture is similar to a standard VAE in which a condition vector is fed into the encoder. The constrained ranking is learned during training thanks to a new reconstruction loss that takes the input condition into account. We show that our model generalizes the state-of-the-art Mult-VAE collaborative filtering model. Experimental results underline the potential of C-VAE in providing accurate recommendations under constraints. Finally, the performed analyses suggest that C-VAE can be used in other recommendation scenarios, such as context-aware recommendation.
Estimation of functional regression model via functional dimension reduction

The functional linear regression model corresponding to a scalar response and a functional predictor is becoming increasingly common. Since the predictor is infinite dimensional, some form of dimension reduction is essential. There are two popular dimension reduction methods such as expanding the functional predictor or regression parameter function on the functional principal component basis or on a fixed bases (such as B-spline, Wavelet). In the present paper, we estimate the functional linear model by using the functional sufficient dimension reduction (FSDR) basis. Compared to the existing methods, the proposed method is appealing because the FSDR basis is related to both the functional predictor and the response variable, whereas the functional principal component basis is only related to the functional predictor and the fixed basis (B-spline, Wavelet) is independent from both the functional predictor and the response variable. Our techniques involve methods for giving a new expansion for the predictor, for giving a specific expression for the regression parameter function, for estimating the FSDR space by a new method and some asymptotical properties about the regression parameter function and the prediction for the test samples. Numerical studies, including both simulation studies and applications on real-life data, are presented to demonstrate the accuracy of the proposed method.
Performance Analysis of Malicious URL Detection by using RNN and LSTM

In the cutting edge age, all data is effectively open through sites and because of this reason individuals depend totally on online assets. On the in opposition to its focal points, protection and security in online media are the primary concern overall as a result of the ascent in phishing assaults propelled on the web. The quantity of phishing sites expands each month focusing on in excess of 450 brands, according to the reports distributed by against phishing working groups. Generally boycotts are utilized to distinguish the URL assaults. In any case, with the exponential increment in the quantity of phishing sites, this strategy has its own restrictions and it additionally neglects to identify recently created phishing URLs which can be unraveled utilizing AI or profound learning strategies. Here we present a near report between established AI procedure calculated relapse utilizing bigram, profound learning strategies like convolution neural network and Recurrent Neural Network long present moment memory as models used to identify noxious Uniform Resource Locators. On correlation Recurrent neural network and long short term memory gave the best exactness of about 98% for the grouping of phishing Uniform Resources.
Enhancing Privacy and Improving Security in Scalable Blockchain

Bitcoin is a decentralized blockchain-based cryptocurrency that has taken the world by storm. Since its introduction in 2009, it has grown tremendously in terms of popularity and market cap. The idea of having a decentralized public ledger while maintaining anonymity and security attracted the attention of developers and customers alike. Special nodes in the bitcoin network, called miners, are responsible for making the network secure by using a concept called proof-of-work. A certain degree of anonymity is also maintained as no personally identifiable information of a person, like name, address, etc., is linked to the bitcoin wallet. In terms of bitcoin, a user is anonymous if different interactions of the user cannot be linked to each other or the user. Recent research shows that bitcoin is not as anonymous as it appears to be. The inherently public nature of blockchain technology makes it difficult to achieve privacy. The purpose of this paper is to review how varying degrees of user privacy is maintained in bitcoin cryptocurrency. This paper is divided into two main segments. The first segment explores privacy-enhancing techniques adopted in bitcoin. The second segment critically analyzes these techniques.
A Radiolarian Classifier using Convolutional Neural Networks

Radiolarians are zooplankton that drift around the oceans and settle to the bottom of the ocean after their death. Because of their diversity, abundance, and their existence since the Precambrian period, they became an important fossil for agedating, correlation, and studying deep-sea sediments. Existing traditional methods of identifying radiolarians are based largely on the study of skeletons and cross-referencing with known species, and were proven very difficult. In this study, we propose a method on classifying radiolarians skeletons with the use of Convolutional Neural Networks. We have covered both cases of radiolarians from different genera classes and from different species within a genus class, to cover both the breadth and depth of this organism's diversity.
Dimensional Reduction on Cross Project Defect Prediction

Abstract The complexity of the software can increase the possibility of defects. Defective software can cause high losses. The software containing defects can cause large losses. Most software developers don’t document their work properly so that making it difficult to analyse software development history data. The cross-project software defect prediction used several datasets from different projects and combining for training and testing. The dataset with high dimension can cause bias, contain irrelevance data, and require large resources to process it. In this study, several dimensional reduction algorithm and Decision Tree as classifier. Based on the analysis using ANOVA, all models that implement dimensional reduction can significantly improve the performance of the Decision Tree model.
Blockchain-Based Open Network in Technology Intermediation

Blockchain technology is developing using in reliable applications which can be designed to achieve decentralization and trustless. Based on the open network innovation theory, this paper proposes a technical intermediary management idea based on blockchain technology to improve the efficiency of technology intermediaries, providing accurate, reliable information and cutting cost for the market. This study demonstrates the advantage of blockchain to technology intermediaries. First, on a specific level, it can provide openness, transparency, decentralization and anonymity services. Second, the current industrial innovation elements are analyzed. blockchain improve the efficiency of technology intermediary, prevent risks and to make up for the shortcomings of traditional intermediaries. It has revolutionized the traditional technology intermediary. As this happens, it can revolutionize traditional technology intermediaries.
Co-occurrence of Edges and Valleys with Support Vector Machine for Content Based Image retrieval

Over the decade, image retrieval attracted many researchers owing to the enormous increase in digital images and digital image repositories. In this paper, co-occurrences of BDIP (block difference of inverse probabilities) is employed as a shape feature which extracts edges with valleys more effectively. After computing the BDIPs, GLCM has applied over the BDIPs of the image along with four directions and it results in four co-occurrence matrices of BDIPs and is combined for the proposed image retrieval. SVM is employed in the classification phase to significantly increase the accuracy and decrease the time cost of the proposed system. The proposed system is tested on Holiday, Corel 10k and Caltech 101 datasets and results evident that proposed system obtained better efficiency then state-of-the-art methods.
An information theory perspective on the balanced minimum evolution problem

We show that the Balanced Minimum Evolution Problem (BMEP) is a cross-entropy minimization problem. This new perspective both extends the previous interpretations of the BMEP length function described in the literature and enables the identification of an efficiently computable family of lower bounds on the value of the optimal solution to the problem.
The classification of the image segmentation algorithms

Purpose The purpose of this paper is the image segmentation algorithms (ISA) classification analysis, providing for advanced research and design of new computer vision algorithms. Design/methodology/approach For the development of the required algorithms a three-stage flowchart is suggested. An algorithm of quasi-optimal segmentation is discussed as a possible implementation of the suggested flowchart. A new attribute is introduced reflecting the specific hierarchical algorithm group, which the proposed algorithm belongs to. The introduced attribute refines the overall classification scheme and the requirements for the algorithms under development. Findings Optimal approximation generation is a computationally intensive task. The computational complexity can be reduced using a hierarchical data framework and a set of auxiliary algorithms, contributing to overall quality improvement. Because hierarchical solutions usually are distinctively suboptimal, further optimization to them was applied. A new classification attribute, proposed in this paper allows to discover previously hidden «blank spots», having decomposed the two-tier ISA classification scheme. The new classification attribute allows to aggregate algorithms, yielding multiple partitions at output and assign them to a dedicated group. Originality/value The originality of the paper consists in development of a high-level ISA classification, as well in introduction of a new classification attribute, pertinent to iterative algorithm groups and to hierarchically structured data presentation algorithms.
Traffic Sign Recognition Using Neural Networks Useful for Autonomous Vehicles

Transportation is a vastly developed research field that tries to optimize humans' safety in everyday life. Recently, autonomous vehicles are being thoroughly developed by a lot of companies such as Uber[1] and Tesla[2], which will improve safety on the roads. These vehicles are using artificial intelligence to correctly interact with their environment. First, we developed a Java program called “ARIBAN” which provides algorithms for recognition and classification of traffic signs with Multi-Layer Perceptron Neural Networks (MLPNN). Second, we use back propagation algorithm in a supervised manner to establish the network. Third, the neural networks are trained and used with a large amount of traffic signs. Finally, the post-processing combines the results to make a recognition decision. Eventually, we have tested our trained network with more than 62 types of traffic signs. Experimental results have demonstrated the effectiveness of the proposed system. Furthermore, the proposed system was deployed within an architecture for autonomous driving.
Secure Block-level Data Deduplication approach for Cloud Data Centers

The ongoing growth in information and technology sector has increased storage requirement in cloud data centers with unprecedented pace. Global storage reached 2.8 trillion GB as per EMC Digital Universe study 2012 [1] and will reach 5247GB per user by 2020. Data redundancy is one of the root factors in storage scarcity because clients upload data without knowing the content available on the server. Ponemon Institute detected 18% redundant data in "National Survey on Data Centers Outages" [15]. To resolve this issue, the concept of data deduplication is used, where each file has a unique hash identifier that changes with the content of the file. If a client tries to save duplicate of an existing file, he/she receives a pointer for retrieving the existing file. In this way, data deduplication helps in storage reduction and identifying redundant copies of the same files stored at data centers. Therefore, many popular cloud storage vendors like Amazon, Google Dropbox, IBM Cloud, Microsoft Azure, Spider Oak, Waula and Mozy adopted data deduplication. In this study, we have made a comparison of commonly used File-level deduplication with our proposed Block-level deduplication for cloud data centers. We implemented the two deduplication approaches on a local dataset and demonstrated that the proposed Block-level deduplication approach shows 5% better results as compared to the File-level deduplication approach. Furthermore, we expect that the performance will further be improved by considering a large dataset with more users working in similar domain.
Performance Analysis of Inception-v2 and Yolov3-Based Human Activity Recognition in Videos

Recently, many deep learning solutions have been proposed for human activity recognition (HAR) in videos. However, the HAR accuracies obtained using models is not adequate. In this work, we proposed two HAR architectures using Faster RCNN Inception-v2 and YOLOv3 as object detection models. In particular, we considered the human activities like walking, jogging and running as activities to be recognized by our proposed architectures. We used the pretrained Faster RCNN Inception-v2 and YOLOv3 object detection models. We then analyzed the performance of proposed architectures using benchmarked UCF-ARG dataset of videos. The experimental results show that Yolov3-based HAR architecture outperforms Inception-v2 in all scenarios.
Image inpainting with contextual attention and partial convolution

Image inpainting is a skill developed to recover the damage part of an images. Many researches states that application wise there are various plausible techniques implemented for an image inpainting. By using artificial intelligence here image inpainting is executed. Image inpainting utilizes the contextual attention to fill pixel wise missing region of an image, missing region filling is done by deeply understanding of available known context. Partial convolution plays an important role to reconstruct an image for matching with the realistic images with structures and textures. The work starts with applying various masks on the image and then reconstruction of an image form by applying partial convolution layer. The main difference of this technique cleared from the traditional methods of image inpainting that it is not only reconstruct the image with new content but also specifies and compress structures available in surrounding region whereas other techniques only focuses and borrow textures from the remaining image. This model is trained with two dataset where fully feedforward partial convolution network is experimented. This gives better qualitative image reconstruction because it comprises both the contextual attention and partial convolution method.
Fault Detection of Electric Vehicle Charging Piles Based on Extreme Learning Machine Algorithm

With electric cars, large-scale development, in order to make the electric vehicles charging more convenient and efficient, public charging piles began to be used on a large scale. However, traditional fault detection methods are still used in charging piles, which makes the detection efficiency low. This paper proposes an error detection procedure of charging pile founded on ELM method. Different from the traditional charging pile fault detection model, this method constructs data for common features of the charging pile and establishes a classification prediction frame work that relies on the Extreme Learning Machine (ELM) algorithm. Experimental results evinces that the frame works accuracy is 83%, with a high efficiency, strong practicability, and is easy to popularize.
Inference with Nearly-Linear uncertainty models

Several simplified uncertainty models are derived from a given probability P0 of which they are a perturbation. Among these, we introduced in previous work Nearly-Linear (NL) models. They perform a linear affine transformation of P0 with barriers, obtaining a couple of conjugate lower/upper probabilities, and generalise several well known neighbourhood models. We classified NL models, partitioning them into three subfamilies, and established their basic consistency properties in [5]. In this paper we investigate how to extend NL models that avoid sure loss by means of their natural extension, a basic, although operationally not always simple, inferential procedure in Imprecise Probability Theory. We obtain formulae for computing directly the natural extension in a number of cases, supplying a risk measurement interpretation for one of them. The results in the paper also broaden our knowledge of NL models: we characterise when they avoid sure loss, express some of them as linear (or even convex) combinations of simpler models, and explore relationships with interval probabilities.
Prediction of Breast Cancer Recurrence Using Ensemble Machine Learning Classifiers

Breast Cancer is the most common type of cancer prevalent among female cancer patients, while it is also the second most dreaded disease, causing cancer deaths among women. This study proposes new criteria for the prediction of survival of breast cancer patients, based on the analysis performed using four ensemble machine learning techniques, which include, AdaBoost M1, Bagging, Voting, and Stacking. For this study, we have used a breast cancer dataset consisting of 23 attributes and containing 575 samples obtained from Mizoram State Cancer Institute of Aizawl, Mizoram, India. We have employed ensemble machine learning classifiers to predict the recurrence of breast cancer within a period of three years evaluated based on the comparison of their performance. We have used 10 fold cross-validation technique and ROC curve to arrive at the results. From the dataset, attributes are ranked according to their contribution towards the prediction.
Discovering software developer's coding expertise through deep learning

The field of software development is growing rapidly and prevailing in every walk of life. The role of software developers in such a challenging and complex activity is very much important. The allocation of right software developers (i.e. who possesses appropriate coding skills) to projects is one of the crucial factors for successful software development. The problem is that it is very difficult for a client, project manager, as well as for software development organisations to find out an appropriate developer and assign him/her to a particular project. To achieve this, there is a need for such a sound mechanism that could detect the level of software developer coding expertise. This study has formulated criteria for novice and expert developers and carried out such criteria to discover the level of coding expertise of software developers using three different models of deep learning. These models include long short-term memory (LSTM), convolution 1D and hybrid (a combination of LSTM and convolution 1D). The deep learning models have analysed software developers’ previously written source code collected from the GitHub repository. An experiment was conducted to evaluate the performance of models. The results showed that the LSTM model performed better in comparison to other models by achieving 96.25% accuracy.
An Improved Reversible Data Hiding Using Pixel Value Ordering and Context Pixel-Based Block Selection

In recent years, pixel value ordering (PVO) and prediction-error expansion (PEE) methods are combined to better exploit the pixel correlation in reversible data hiding (RDH) techniques. In these techniques, the cover image is split into non-overlapping blocks of equal size. The pixels of each block are sorted in ascending, according to their intensity values. The maximum and minimum pixels within each block are used to calculate prediction errors which are either expanded or shifted for embedding data. The data are embedded into smooth blocks which are identified using every two adjacent pixels of neighbor block. However, this block classification is not very effective since it considers only the neighbor block and ignores the pixels in current block. In this paper, to overcome this drawback and to improve the performance further, a novel reversible data hiding using context pixel-based block selection (CPBS) strategy is proposed. After the block division and sorting process, the complexity of each block is measured using the context pixel chosen from the current block and the connected pixels of neighbor block. Smooth blocks with low complexity are determined using a threshold value to achieve higher efficiency. When the desired capacity is achieved, the index orders of all pixels in the block are preserved and thus the reversibility is guaranteed. Experimental results verify that the proposed scheme achieves better capacity-distortion trade-off and thus outperforms with the existing PVO-based techniques.
An Inclusive Study of Several Machine Learning Based Non-functional Requirements Prediction Techniques

Requirement Engineering is a critical area in the arena of Software Engineering. Non Functional Requirements (NFRs) are very important as Functional Requirements but often ignored. Especially the automated correct prediction and prioritization of Non Functional Requirements (NFRs) is highly needed. Non Functional Requirements can be predicted with the help of machine learning models. The Machine Learning models have shown more promising output than Natural Language Processing. In this paper, the comprehensive review on the recently proposed NFRs prediction techniques has been presented. The output of the review showed that the different authors have used different Clustering and Classification algorithms. But Naïve Bayes algorithm has used by most of the researchers. The most of researchers have used Precision and Recall as their performance metric. Machine learning techniques like Ensembling and Parameter Tuning has neglected by researchers. The overall objective is to evaluate the numerous shortcomings in machine learning based NFRs prediction techniques and to draw suitable future directions.