Reviewerly/exHarmony huggingface dataset:
A novel conditional Butterfly Network Topology based chaotic image encryption

Abstract Nowadays, multimedia information, particularly images are shared via the internet and the security of this shared images is a major consideration due to lack in internet security. In this paper, a novel Conditional Butterfly Network Topology (CBNT) based bit-wise confusion, simple pixel-level based confusion and diffusion, based on simple XOR operations is suggested to securely transfer images over an insecure channel like social networks. First, an adaptive image content based initial vector generation is recommended to realize good plain image sensitivity with the aim of withstanding chosen/known plain text attacks. This will change the keys for every plain image in the encryption process. From these initial vectors, Henon map is iterated to acquire the key values to be utilized over the confusion and diffusion processes. To confuse the bits totally in random manner, conditional structure based Butterfly Network Topology (BNT) is utilized. Pixel-based permutation is performed, to disrupt the neighboring correlation of each pixel. Finally, a simple XOR based diffusion is implemented to diffuse the intensity of each pixel. Different security measures are encountered to investigate the amount of security of the suggested system using simulations. From simulations, Number of Pixel Change Rate (NPCR) and Unified Average Changed Intensity (UACI) values of 99.64% and 33.46% and an entropy value of 7.9995 are achieved. Moreover, the proposed model has very less computation time with high level of security.
Learning to Design Constellation for AWGN Channel Using Auto-Encoders

This paper proposes a novel constellation design in AWGN channel through learning based auto-encoder (AE). Additionally, this paper illustrates the reason why learning based constellation has better performance than the classical squareshaped QAM design by analyzing the Euclidean distance distribution and the bound of symbol error rate between learning designed symbols and other constellations. Moreover, the performance of learning based constellation will be compared to constellation based on convex optimization design. To solve the bit mapping problem of the learning based constellation, Q-ary LDPC encoding is applied to these specifically designed QAM modulation systems, where the soft decoding of Q-ary LDPC codes can be carried out with the symbol-level soft outputs of demodulation.
Techniques of Deep Learning for Image Recognition

Image recognition is the reference of machine vision, is the ability of software to identify objects, peoples, places, actions and writing in images. Deep learning is the core field of machine learning and the most prominent algorithm of deep learning is convolutional neural network (CNN). Convolutional neural networks with more convolution hidden layers have more complex structure and more powerful feature extraction abilities than existing machine learning techniques. Nowadays, CNN has got tremendous success in image recognition. In this paper review the different methods of deep learning which has been utilized in the area for the recognition of image such as localisation and classification.
Distributed Computing for Smart Meter Data Management for Electrical Utility Applications

With the advent of Internet-of-Things (IoT) devices, including smart meters and sensors in the smart grid, there has been immense research interest in big data management, analytics, and parallel processing of data. However, complex hardware and software parameters configurations and in-depth understanding of the data processing design are essential for efficient utilization of big data analytics platforms. In this work, we analyze the parallelization of load prediction by utilizing spark regression python library to assess the performance with workloads of up to 8 nodes. The results of different configurations have been studied and analyzed against the performance of Apache Spark. It was found that a trade-off between the number of nodes and cores is necessary to perform efficient parallel computing. Multiple sets of combinations of nodes and cores are considered in this paper to evaluate the performance. The work also signifies the importance of high-performance computing capability for smart meters big data management. The obtained results indicate that the computational time is not only dependent on the data size but also on the number of compute nodes and the number of cores assigned to run the job.
Temporal U-Nets for Video Summarization with Scene and Action Recognition

While videos contain long-term temporal information with diverse contents, existing approaches to video understanding usually focus on a short trimmed video clip with a specific content such as a particular action or object. For comprehensive understanding of untrimmed videos, we address an integrated video task of video summarization with scene and action recognition. We propose a novel convolutional neural network architecture for handling untrimmed videos with multiple contents. The proposed architecture is an encoder-decoder structure where the encoder captures long-term temporal dynamics from an entire video and the decoder predicts detailed temporal information of multiple contents of the video. Two-stream processing is adopted for obtaining feature representations, one for focusing on the spatial information and the other for the temporal information. We evaluate the proposed method on the benchmark of the Challenge on Comprehensive Video Understanding in the Wild (CoVieW 2019), and the experimental results demonstrate that our method achieves outstanding performance.
Interval-valued fuzzy reasoning method based on similarity measure

In this paper, a new similarity measure of interval-valued fuzzy sets is introduced. Meanwhile, interval-valued fuzzy reasoning method based on similarity measure for Fuzzy Modus Ponens and Fuzzy Modus Tollens are proposed, and the solutions of interval-valued fuzzy reasoning method based on similarity measure are given. Moreover, the robustness of interval-valued fuzzy reasoning method based on similarity measure is discussed. Finally, general interval-valued fuzzy reasoning method based on similarity measure is proposed.
Augmented Reality: A New Way to Build Knowledge. Bibliometric Analysis and Apps Testing

Augmented Reality (AR) combines real and digital images, and is fast becoming a new immersive source of data for building knowledge. This research analyzes its real potential, through a bibliometric analysis of recent research and a descriptive analysis of the characteristics of 100 AR apps following testing. The results reveal several emerging lines, a range of user profiles and the most suitable areas for these technologies to be applied; this study also points to new emerging immersive methodologies, and the opposing perspectives of this phenomenon. The empirical descriptive testing of these apps also reveals the limitations of these technologies in the structured construction of knowledge.
Airborne gamma-ray spectrometric data interpretation on Wadi Queih and Wadi Safaga area, Central Eastern Desert, Egypt

Wadi Queih and Wadi Safaga lie in the central part of the Eastern Desert of Egypt. The airborne gamma-ray spectrometric data of the study area have been interpreted qualitatively and quantitatively and correlated with the surface lithologic units to define the anomalous uranium zones and to reveal if any association is present between the radiometric anomalies and the structural trends.The radiometric data have been treated statistically. The results revealed that the area has a wide range of radioactivity ranging from 0.1 to 23.6 Ur for the total-count (TC), 0.1 to 3.3 % for potassium (K), 0.01 to 19.1 ppm for equivalent uranium (eU), and 0.29 to 18.9 ppm for equivalent thorium (eTh). The calculated CV values for all the rock units in the study area are less than 100% for three radio-elements (K, eU, eTh), except for potassium in Thebes Formation. The younger granites and Duwi Formation have the highest radiometric values. The lowest values exist over ophiolitic metagabbro, basic metavolcanics, metasediments, serpentinites, and Umm Gheig Formation. The generated composite radio-elements and composite image maps define the locations of the high anomalous radiometric and eU zones as bright white areas. The most radioactive anomalies have NW-SE and NE-SW trends.
Aeroengines Remaining Useful Life Prediction Based on Improved C-Loss ELM

A correntropy induced loss (C-loss) function is a loss function developed based on entropy theory. Due to the non-convexity of the C-loss function, ELM based on C-loss has been proven to have better regression effects and robustness. Inspired by generalized quantile learning, we find that there is a further improvement in the C-loss function. The main work of this paper is to propose a new improved C-loss function, which lead to a novel algorithm ICELM, and the half-optimized algorithm is used to solve the ICELM. Experiments on several benchmark data sets prove the superiority of the ICELM. Compared with CELM, ICELM has more efficient regression performance and robustness, and is obviously more suitable for complex aeroengines remaining useful life prediction field which affected by noise and outliers. Experiments on a benchmark C-MAPSS dataset also validate the theory.
RGB-D Object Tracking with Occlusion Detection

Tracking objects in RGB-D images is a challenging task in computer vision, especially under occlusion. In this paper, we proposed an object tracking method based on the 3D point cloud. Firstly, we convert RGB-D images to point clouds. Secondly, features of point clouds are extracted by PointNet and finally integrated into the 3D object tracking algorithm for template matching across frames. A strategy of occlusion detection and target retrieval is applied to handle target missing under occlusion. For example, when the number of point clouds is decreasing abruptly, the occlusion may take place. Then a YOLOv3 detector is used to re-find this target. Our network is insensitive to appearance variation of object and robust to object tracking. The experimental results show that the proposed method achieves comparable results to state of the art on the Princeton RGB-D Tracking Benchmark.
A Direct Least-Squares Solution to Multi-View Absolute and Relative Pose from 2D-3D Perspective Line Pairs

We propose a new algorithm for estimating the absolute and relative pose of a multi-view camera system. We derive a direct least squares solver using Grobner basis which works both for the minimal case (set of 3 line pairs for each camera) and the general case using all inlier 2D-3D line pairs for a multi-view camera system. The algorithm has been validated on a large synthetic dataset as well as real data. Experimental results confirm the stable and real-time performance under realistic outlier ratio and noise on the line parameters. Comparative tests show that our method compares favorably to the latest state of the art algorithms.
Software Defect-Prone Classification using Machine Learning: A Virtual Classification Study between LibSVM &amp; LibLinear

The field of machine learning has been developing quickly, delivering an assortment of learning algorithms for various software applications. A definitive estimation of those algorithms is, all things considered, made a decision by their accomplishment in taking care of genuine issues. Machine Learning systems can be utilized to dissect data from alternate points of view and empower engineers to recover valuable data. Machine learning has been effectively applied to make forecasts in different datasets. Given the tremendous number of bug datasets accessible today, foreseeing the nearness of bugs also should be possible utilizing different machine learning procedures. The machine learning systems that can be utilized to identify bugs in software datasets by use of classification technique. Classification is a data mining and machine learning approach, helpful in software Defect-Prone model. It includes order of software modules into buggy, faulty or non-buggy, non-faulty that is signified by a lot of software intricacy measurements by using a classification model that is gotten from before improvement ventures data. we classifying effectiveness accuracy and efficiency software defect-prone model dependent on classification technique where we have utilized LibSVM and LibLinear classification. In our examination, we have seen that the during classification, LibSVM have increased the accuracy and efficiency special in train-set way. The TP-Rate and F-Measure positive accuracy is highly increased rather than other techniques. The area under curve is also enhanced using LibSVM in training datasets. But the correctly classified instances rate also increased in all classification. Bute with the use of % split, LibLinear an SVM are also good in few evaluation measures for their enhancement accuracy and efficiency.
Decentralized Access Control Infrastructure using Blockchain for Big Data

Big Data technology has demonstrated its effectiveness in several areas, and several projects confirm the existence of innovative opportunities. However, The security and privacy issues are gradually magnified in these environments. Traditional access control mechanisms cannot guarantee the user's anonymity and privacy. This article proposes a new access control infrastructure based on Blockchain technology for Big Data, to publish the policies, deployed in Smart contract, expressing the right to have access to a resource and provide the identification and authentication processes. In our infrastructure the policies are visible to the public, and any user can know the policy paired with a resource and who has the right to access, which compromises privacy. To deal with this problem, we encrypt the attributes in the access policy represented by the smart contract. We also encrypt the attributes in the access requester, and the verification is done solely on the basis of the comparison of encrypted attributes, whether they are identical or not. If so, an authorization token is generated to allow access.
Bearings-only Passive Target Tracking: Range Uncertainty Ellipse Zone

In passive underwater target tracking, the observer uses bearings-only measurements generated by radiation of the target. The measurements are always corrupted with noise, which creates an uncertainty zone around the target position. Unscented Kalman filter (UKF) is proved to be efficient nonlinear estimator to estimate the target motion parameters. It is of interest to know in many practical situations, regarding the convergence of the solution in terms of the reduction of the uncertainty zone within some specified limit. An effort is made to reduce and find the range uncertainty ellipse zone of the target using UKF covariance matrix in Monte Carlo simulation. Once the range uncertainty ellipse zone becomes less than a specified value, the solution is said to be converged.ABBREVIATIONS: RUEZ: Range Uncertainty Ellipse Zone; UKF: Unscented Kalman Filter; UT: Unscented Transformation; LHMA: Length of Half Major Axis
Intelligent Recruitment System

This paper reviews studies and developments in recruitment techniques interact with computer science. And novel approach proposed as Intelligent Recruitment System (IRS) which consist of resume classification and ranking with deep learning alongside with NLP techniques, Automatic question generation(AQG) system which measure technical proficiency of an applicant using the knowledge base of merged ontology designed using the web and local sources and soft skills measuring with question answering about different skill sets and matching the similarity of the predefined answers against the applicant given answer by using both the syntactical similarity measurement and semantic similarity measurements. The final output comes from a combination of the above input parameters. The purpose of this model is to propose a new framework based on the fuzzy inference system (FIS) and Mamdani's method. The decision-making mechanism of the IRS is based on the final total score of each parameter and maximum quota. If two or more candidates receive the same final total score, it is difficult to make a definitive decision. Therefore, a FIS approach is proposed to help decision makers choose the most qualified applicants and avoid unnecessary problems.
Domain Identification of Urdu News Text

In recent years, the amount of data available online has grown exponentially which makes automatic text classification of documents an ever-increasing important task. Many research papers conducted the study on text classification of English, Chinese, Arabic, and other languages. However, fewer studies are done on classification of Urdu documents. This paper addresses the issue of Urdu text classification. For this purpose, we build a high quality Urdu news dataset COUNT19 with labeled categories. In addition, we evaluate the performance of state-of-the-art machine learning algorithms with and without applying pre-processing steps of stopword removal and stemming. Unigram and bigram features with Term Frequency-Inverse Document Frequency (TF-IDF) are used for feature representation of text. Our analysis shows that Multi-Layered Perceptron (MLP) is the best performing classifier with 91.4% accuracy. Additionally, results show that stemming does not improve the performance of classifiers. However, stopword removal impacts the performance of classifiers negatively. This study can help in the selection of a classifier with best performing pre-processing techniques to build Urdu text classification system, which is an essential tool used for Information Retrieval (IR) and Natural Language Processing (NLP) tasks.
Interest-Oriented Video Summarization with Keyframe Extraction

To address the requirements of summarising large video files, we introduce a novel approach incorporating the interest of the user to advance the current approaches to develop a well-defined single summary for a single video. Our approach has the capability of generating multiple summaries for a single video catering to different user interests, thus advancing the current state of the art. The proposed method takes the video to be summarized as well as the user interested feature(s) which we define as Object of Interest, as inputs. Inputs are processed through two pipelines. The first pipeline takes the video frames and divide the video into video shots based on the colour features of the frames. Then a generalized keyframe based summary is generated by extracting static and dynamic features of the video from the defined shots. The second pipeline compares each frame with the Object of Interest and selects the set of frames, which have features similar to the Object of Interest. In order to generate the summary of the video, the proposed solution takes the intersection operation of both pipeline-outputs. Evaluation of the system is conducted against benchmark datasets to demonstrate the effectiveness and validity of the proposed approach.
Sensor Combinations in Heterogeneous Multi-sensor Fusion for Maritime Target Tracking

Safe navigation for autonomous surface vehicles requires a robust and reliable tracking system that maintains and estimates position and velocity of other vessels. This paper demonstrates a measurement level sensor fusion system for tracking in a maritime environment using lidar, radar, electrooptical and infrared cameras. The backbone of the system is a multi-sensor version of the Joint Integrated Probabilistic Data Association (JIPDA) with both existence and visibility probabilities. Using reference targets equipped with GPS receivers, the performance of different sensors and sensor combinations are evaluated for autonomous surface vehicles (ASVs), Several interesting observations are made, among them that passive sensors can help resolve merged measurements issues in radar tracking, and that the choice between radar and lidar may boil down to a trade-off between fast track initiation and large numbers of false tracks.
On some determinants involving Jacobi symbols

In this paper we study some conjectures on determinants with Jacobi symbol entries posed by Z.-W. Sun. For any positive integer n≡3(mod4), we show that(6,1)n=[6,1]n=(3,2)n=[3,2]n=0 and(4,2)n=(8,8)n=(3,3)n=(21,112)n=0 as conjectured by Sun, where(c,d)n=|(i2+cij+dj2n)|1≤i,j≤n−1 and[c,d]n=|(i2+cij+dj2n)|0≤i,j≤n−1 with (⋅n) the Jacobi symbol. We also prove that (10,9)p=0 for any prime p≡5(mod12), and [5,5]p=0 for any prime p≡13,17(mod20), which were also conjectured by Sun. Our proofs involve character sums over finite fields.
Modeling functional requirements using tacit knowledge: a design science research methodology informed approach

Abstract The research in this paper adds to the discussion linked to the challenge of capturing and modeling tacit knowledge throughout software development projects. The issue emerged when modeling functional requirements during a project for a client. However, using the design science research methodology at a particular point in the project helped to create an artifact, a functional requirements modeling technique, that resolved the issue with tacit knowledge. Accordingly, this paper includes research based upon the stages of the design science research methodology to design and test the artifact in an observable situation, empirically grounding the research undertaken. An integral component of the design science research methodology, the knowledge base, assimilated structuration and semiotic theories so that other researchers can test the validity of the artifact created. First, structuration theory helped to identify how tacit knowledge is communicated and can be understood when modeling functional requirements for new software. Second, structuration theory prescribed the application of semiotics which facilitated the development of the artifact. Additionally, following the stages of the design science research methodology and associated tasks allows the research to be reproduced in other software development contexts. As a positive outcome, using the functional requirements modeling technique created, specifically for obtaining tacit knowledge on the software development project, indicates that using such knowledge increases the likelihood of deploying software successfully.
Duals of non-weakly regular bent functions are not weakly regular and generalization to plateaued functions

It is known that the dual of a weakly regular bent function is again weakly regular. On the other hand, the dual of a non-weakly regular bent function may not even be a bent function. In 2013, Çesmelioğlu, Meidl and Pott pointed out that the existence of a non-weakly regular bent function having weakly regular bent dual is an open problem. In this paper, we prove that for an odd prime p and n∈Z+, if f:Fpn→Fp is a non-weakly regular bent function such that its dual f⁎ is bent, then f⁎⁎(−x)=f(x), and f⁎ is non-weakly regular, which solves the open problem. We also generalize our results to plateaued functions.