Reviewerly/exHarmony huggingface dataset:
Reduction of Traffic on Roads Using Big Data Applications

Mapreduce implementation simplifies big amount of data implementation on complex and large datasets by using parallelization of map tasks and reduce tasks. Several contributions and methodologies are made to eradicate the functionality and usage of Mapreduce tasks, the data generated in the shuffle phase should be ignored, which contributes as an important feature in functionality development. Shuffle phase uses hash aggregator to merge information into clusters which is useless in managing traffic data creating a bottleneck. Improvisation of the execution of system traffic in the shuffle stage is fundamental to enhance the execution of the tasks. Main objective of slacking off the structured dataset is developed using utilisation tasks and total. The implemented course of action is known to limit and sort out traffic cost in Mapreduce. The hash aggregators are implemented in various tasks, where every aggregator can diminish overall data traffic from different tasks. This dispersed estimation is supposed to maintain the extensive data organizing problem for huge data implemented applications. Besides, an integrated computation is proposed to change the data and assemble successfully in a proper orientation. In this paper, a study of traffic cost for a Map reduce tasks by determining new intermediate data division schema is carried out. We consider data lifting and other applications that have all around attributes from past data that is collected bit-by-bit using Map reduce. For example, a count that has all the means of taking after a word checking at the key signal may wrap up having all around various attributes, for instance, the numbers and transitional results, in that limit, we recognize the startling execution. Grabbing the data from various applications, we review the detailed report for bolstering data focused and implement the new data applications on server ranch scale systems. The promising results may include improving a business control structure with a medley of connections and function that support in a Map reduce structure. Finally, the outcome exhibits that our manifesto can tremendously diminish traffic congestion beneath any conditions.
Spherical LSB Data Hiding in 360° Videos Using Morphological Operations

Steganography has been widely used to embed secret data in cover multimedia carriers including digital image, audio, and video files such that the confidential information being communicated is hidden. Immersive multimedia, e.g., 360° videos, have recently received increased attention as promising applications for the Internet and 5G mobile networks. In this paper, we focus on using steganography for hiding secret data in an immersive multimedia carrier. Specifically, a spherical least significant bit (LSB) data hiding approach is proposed to embed secret videos in 360° cover videos along with morphological operations. Due to viewers are paying less attention to the North and South Poles compared to the Equator region of 360° videos, LSB data hiding is performed around the poles of the equirectangular projection of the sphere to the plane. Data hiding capacity is increased by using morphological operations in the remaining mid-region around the Equator which broadens the edge area for embedding secret data. The proposed spherical LSB data hiding technique may be used to hide secret videos or other confidential data in 360° cover videos. It can also be used to increase available bandwidth for communication purposes as long as embedded data has no significant impact on the quality as perceived by the end-user. A performance assessment of the proposed approach is conducted with respect to data hiding capacity and objective perceptual video quality. Numerical results are provided for a wide range of scenarios illustrating the trade-offs between capacity and quality.
EANRS: An Emotional Arabic News Recommender System

In “News field”, the user emotion plays significant roles in the decision making Process. The main objective of this paper is offering news to users according to their preferences, and to making positive news items that can have positive impact on users' mind and soul. Implementation of an emotional Arabic news recommender system (E-ANRS) application to display news articles for our users and get users' feedbacks including two categories as like/dislike via using android platform. We introduce our model as a solution for two problems as follows: the first problem is design a model which suggests positive Arabic news to excel the violence and vehemence people are shown in everyday life, a resource for recommending Arabic news with extract of emotion is unprecedented. The second problem, we introduce an (E-ANRS) as a solution to problem of cold-start by using IBM Bluemix server for first time, which provides two services (language translator and tone analyzer). We have two ways to measure accuracy of emotion for our model by using EEG and SAM techniques. The obtained result of EEG of our model is 90%. We compared performance of our proposed model with other studies and the results proved that our model offers a better and perfect recommendation process and emotion extracted performance greatly improved with the use of news texts in Arabic languages.
A Survey on Efficient Storage and Retrieval System for the Implementation of Data Deduplication in Cloud

Cloud computing is a methodology that gives users a possibility to keep their files, access and retrieve them from any units which are related to internet. Virtualization is accompanied with cloud computing to promote the smartness and extra efficient use of sources within a corporation with the aid of simplifying upkeep and speeding up the configuration of resources. Cloud computing has transformed the software aid for large systems from a single server to provider oriented model. While storing a massive quantity of archives in cloud server repetition of equal file takes region and storage potential is reduced. Since companies make investments by lot of money for storing the facts an environment friendly method is wanted for managing the duplication of big data. Deduplication is a mechanism used in cloud storages to sidestep repetition of same data that increases storage capacity. However cipher text based deduplication is crucial for identifying and removing duplicates when their data or keys are stored in encrypted form. This survey paper focuses on a number deduplication mechanisms and compares the techniques which are used to pick out and eliminate duplicated documents in cloud storage and by results of the survey we have planned to develop an algorithm to compare and identify duplicate data in cloud server by improving the modes of chunk and hash creations.
Any #JesuisIraq planned?*

Abstract The stem #jesuis followed by a toponym (e.g. #jesuisParis) has proved to be very productive in the gathering of affective publics ( Papacharissi 2015 ) around causes of mourning, after terrorist attacks and other disasters. However, not all attacks have given rise to such massive affective use of #jesuis hashtags. Our goal is to examine how Twitter users claim similar displays of affect for these “other” places. We analyze 297 tweets in which the Twitter user utters a condolence speech act while simultaneously contesting the unbalanced affective reactions expressed concerning some places, e.g. “Any #JesuisIraq planned?”. We observe the geographical granularity of the referred place, the structural complexity of the tweet and, if present, the underlying motives for unbalanced reactions suggested by the Twitter users. By doing so, we show how Twitter is used to claim attention for places that are deemed underrepresented, thus confirming the importance of Twitter for expressing solidarity.
High Speed Autonomous Navigation of Unmanned Aerial Vehicles using novel Road Identification, Following &amp; Tracking (RIFT) Algorithm

Autonomous road navigation in unmanned aerial vehicles flying at very low altitudes paves the way for a multitude of applications such as security surveillance, monitoring of traffic or pollution, package delivery, ground- vehicle tracking etc. Most research in the field of autonomous road vehicles is focused on lane tracking and other marker- dependent techniques. In these lines, a novel, computationally efficient method for (front-view) road identification and tracking based on monocular vision is proposed which is designed to work on roads independent of markings. Road identification is implemented using a combination of spectral component analysis, edge energy-based filtering and morphological processing. Road identification and tracking works at 30 frames/sec for a frame size of 120 x 160 pixels, which is profiled on an Odroid XU4 mini-computer. Results indicating the performance of the proposed method as assessed on Cityscapes and KITTI datasets are presented.
An ontology‐based system for discovering landslide‐induced emergencies in electrical grid

Summary Early warning systems (EWS) for electrical grid infrastructure have played a significant role in the efficient management of electricity supply in natural hazard prone areas. Modern EWS rely on scientific methods to analyze a variety of Earth Observation and ancillary data provided by multiple and heterogeneous data sources for the monitoring of electrical grid infrastructure. Furthermore, through cooperation, EWS for natural hazards contribute to monitoring by reporting hazard events that are associated with a particular electrical grid network. Additionally, sophisticated domain knowledge of natural hazards and electrical grid is also required to enable dynamic and timely decision‐making about the management of electrical grid infrastructure in serious hazards. In this paper, we propose a data integration and analytics system that enables an interaction between natural hazard EWS and electrical grid EWS to contribute to electrical grid network monitoring and support decision‐making for electrical grid infrastructure management. We prototype the system using landslides as an example natural hazard for the grid infrastructure monitoring. Essentially, the system consists of background knowledge about landslides as well as information about data sources to facilitate the process of data integration and analysis. Using the knowledge modeled, the prototype system can report the occurrence of landslides and suggest potential data sources for the electrical grid network monitoring.
A G2G Similarity Guided Pedestrian Re-identification Algorithm

Abstract Pedestrian re-identification aims to settle the matching problem for a given target pedestrian under the multi-camera with non-overlapping visual field, so as to achieve the goal of pedestrian retrieval. In this paper, the similarity between the gallery images (G2G similarity) will be used to guide and refine the similarity between query images and gallery images (P2G similarity). It is also introduced into the training process and playing a supervisory role. To fully utilize details of the images, the image features are horizontally overlapped into groups, and the similarities between each group of the query images and the gallery images are calculated respectively. By learning the weights of grouping features in the training process, the importance of key parts can be automatically perceived. The results on the Market-1501 and CUHK03 datasets prove the effectiveness of proposed algorithm.
Data Mining and Machine Learning for Software Engineering

Software engineering is one of the most utilizable research areas for data mining. Developers have attempted to improve software quality by mining and analyzing software data. In any phase of software development life cycle (SDLC), while huge amount of data is produced, some design, security, or software problems may occur. In the early phases of software development, analyzing software data helps to handle these problems and lead to more accurate and timely delivery of software projects. Various data mining and machine learning studies have been conducted to deal with software engineering tasks such as defect prediction, effort estimation, etc. This study shows the open issues and presents related solutions and recommendations in software engineering, applying data mining and machine learning techniques.
Data Analysis for Robotics and Programming Project Evaluation Involving Female Students Participation

Educational robotics is an example of building an innovative and effective educational model in its proposal. The model in question consists in the creation of learning environments that bring together assembly materials composed of diverse, interactive and computer controllable parts through programming. It is possible to control the functioning of assembled prototypes. This interaction creates interest and direct involvement by students, and can integrate with other knowledge areas, developing activities in the interdisciplinary context, in which students experience in a practical way and directly apply the contents viewed theoretically in the classroom. However this area is mostly dominated by male students. Thus, the objective of this paper is to present a study case discussion of a robotics and programming project whose objective is to increase girl project participation and from there, to encourage these students to pursue careers in science, technology and engineering. The data were treated through data mining for further analysis, and the results are promising regarding the interaction of these student girls with the project and with the robotics and programming areas applied to education and competition.
On the Theory of a Nonlinear Dynamic Circuit Filtering

Stochastic Differential Equations (SDEs) describe physical systems to account for random forcing terms in the evolution of the state trajectory. The noisy sampling mixer, a component of digital wireless communications, can be regarded as a potential case from the dynamical systems’ viewpoint. The universality of the noisy sampling mixer is attributed to the fact that it adopts the structure of a nonlinear SDE and its linearized version becomes a time-varying bilinear SDE. This paper develops a mathematical theory for the nonlinear noisy sampling mixer from the filtering viewpoint. Since the filtering of stochastic systems hinges on the structure of dynamical systems and observation equation set up, we consider three ‘filtering models’. The first model, accounts for a nonlinear SDE coupled with a nonlinear observation equation. In the second model, we consider a bilinear SDE with a linear observation equation to achieve the nonlinear sampling filtering. Note that the bilinear SDE coupled with the linear observation is a consequence of the Carleman linearization to the nonlinear SDE and the nonlinear observation equation. In the third model, we consider a Stratonovich SDE coupled with a nonlinear observation equation. The filtering equation of this paper can be further utilized to guide the design process of the noisy sampling mixer.
Simplistic Deep Learning for Japanese Handwritten Digit Recognition

We show a simplistic approach using simple Convolutional Neural Network (CNN) to classify Japanese handwritten digit dataset Kuzushiji-MNIST. We use combinations of loss functions and optimisers on an empirically sound model of Convolutional Neural Netwok (CNN) to come up with a new State-of-the-art accuracy for all Simple CNN approaches on the Kuzushiji-MNIST dataset with accuracy of 96.13 %.
Advertising video automatic visual effects processing for a novel mobile application

Today, advertising videos are in a heavy demand on e-commerce platform; however, for most small and micro enterprises, producing an advertising video which could attain a satisfied advertising effect easily at a low cost is a huge challenge due to the lack of the professional knowledge. With the advent of the 5G era, the programmatic advertising video production will be pushed to a new enthusiasm. In the future, the production of advertising video through mobile devices will be a development trend. This paper explores the mobile advertising video generation system, and proposes an automatic video visual effect processing method for a novel mobile application. This method combines intelligent video recognition technology and visual dynamic effect processing, aiming to assist users to generate compelling product advertising videos.
An Intelligent Surveillance System Based on Lightweight Object Detection Network L-SSD

This paper proposes an intelligent surveillance system(ISS) based on Lightweight Single Shot Multibox Detector(L-SSD). L-SSD simplifies the backbone of SSD, dramatically reducing the cost of network computation by 28x fewer parameters than SSD. Furthermore, L-SSD adopts a novel method which can perform cross-layer feature fusion without increasing of computational resources. Firstly we implement L-SSD network on GPU and gets the performance of 6.5mAP at 65 fps for the VOC 2007 detection task. Then when L-SSD is actually implemented on NCS, its real-time frame rate can reach 7–8 fps.
A new way to distribute secret encryption keys

this article describes a new method for distributing secret encryption keys, in which processes use algebra with a parameter, the secrecy of which provides increased durability of the key.
A new activity classification method K-SVM using Smartphone data

The aim of the Human activity recognition (HAR) is to understand and predict the physical behavior of the human body whether in movement or in a stagnated position. In this paper, we try to improve the classification rate of common physical activities. We design a new classification method K-SVM using the combination between the Weighted K Nearest Neighbour (WKNN) and the Support Vector Machines (SVM). On the other part, the exploitation of the different positions of the sensors on the body is a very interesting research track. We studied the impact of each body part sensor position on the human activity recognition accuracy rate, and then tried to improve the classification rate by using the K-SVM method.
Signature Recognition and Verification Using Zonewise Statistical Features

The signature is defined as special kind of handwriting that includes special characters and flourishes. Handwritten signatures are most accepted individual attribute for individuality authentication of the person. This provides novel method for the signature recognition and verification by using zone based statistical features. It contains mainly two phases. During the first phase, the knowledge base is constructed by training samples using the zone wise statistical features. During second stage i.e., testing phase, the processed image is obtained having zoning wise statistical features and signature is recognized using neural network classifiers. An accuracy rate of 97.5% is achieved by testing 200 samples. MATLAB is used for designing this signature recognition and verification system and is robust and avoids noise, blur and change in size, lightening conditions and other possible degradation.
Dominant Feature Descriptors with Self Organising Map for Image Retrieval

In this research work, we have presented an efficient system for image retrieval using the distribution of dominant rotation local binary pattern and dominant edgels. The proposed combination is robust to rotation invariant and moderately robust to the illumination invariant. We performed classification using self-organizing map (SOM), which performs well by identifying the intrinsic features in the underlying data. We adopted Canberra distance to measure the likeness of images. The experiments on benchmark dataset depict that proposed feature descriptors work well with SOM and Canberra distance and provides higher precision against rotation and lightning variations.
A Survey on Linguistic Interpretation of Facial Expressions and Technologies

In recent years, many studies have been conducted for analysis of facial expressions. The importance of facial expressions is particularly evident in sign languages because they help in social communication between deaf and dumb among themselves as well as their interaction with the world. These facial expressions are called Grammatical Facial Expressions (GFEs). In this paper, an overview of several methods used for face tracking based on tracking a set of landmark points projected on the face and also a review of classification techniques that used in order to recognize grammatical expressions. This paper also mentions available datasets that used to facilitate linguistic and computational research on signed languages and the gestural components of spoken languages.
Edge-Assisted Learning for Real-Time UAV Imagery via Predictive Offloading

Real-time decision making with unmanned aerial vehicles (UAVs) imagery is desired in many applications. Deep learning (DL) is a promising enabler for such applications thanks to its recent advancements. However, direct execution of DL models on UAVs, especially small and micro ones, would not only introduce severe delay but also significantly shorten the flight time of UAVs due to the high energy consumption. Realtime transmission of UAV images to ground edge devices for deep analysis can mitigate the computational complexity but may introduce severe interference to ground devices, in addition unpredictable delays due to the dynamic network conditions. To minimize real-time image transmission, this paper designs a new offloading prediction algorithm which first estimates nearfuture need for DL of each UAV and transmit images only when necessary. Holistic resource allocation is made at the edge based on the offloading likelihood analysis of multiple UAVs as well as available resources. Experimental results on real UAV video clips show that our design can save 92% of the communication costs with less than 4% false positive rate.
Digital Signal Processing Based On Spline Functions

The development of the modern world's information and communication technologies and their implementation in various industries are important, are essential to the creation of parallel algorithms for solving problems of the recovery and digital signal processing, on the basis of the treatment processes a multi-core architecture and the search for optimal solutions. The current stages of development of the structure of machines, complexes and systems installed at facilities that are part of the structure of devices specialized for conducting scientific research or located on rolling stock are characterized by on-line analysis of complex processes and fields and increased demands on the speed of processing large amounts of data in real time. Particular attention is paid to the development of methods for solving problems of image and signal processing by using modern methods, algorithms and structural tools, architecture of computing and software. In that work the result of the study of methods of approximation of functions and functional dependencies obtained in the course of experiments for the selected class of signals improved method of spline functions on the basis of analytical analysis, parallel algorithms for digital signal processing have been developed, efficient algorithms have been developed for digital processing of vibrio signals in multi-core processors based on cubic and bi cubic basis splines.
Look Ahead Distributed Planning For Application Management In Cloud

In this paper, we propose and implement a distributed autonomic manager to maintain service level agreements (SLA) for each application' scenario. The proposed autonomic manager seeks to support SLAs by configuring bandwidth ratios for each application scenario using overlay network before provisioning more computing resources. The most important aspect of the proposed autonomic manager is scalability which allows us to deal with geographically distributed cloud-based applications and large volume of computation. This can be useful in look ahead optimization and when using complex models, such as machine learning. Through experiments on Amazon AWS cloud, we demonstrate the elasticity of the autonomic manager.