Reviewerly/exHarmony huggingface dataset:
A Proposal Video Encryption Using Light Stream Algorithm

Video encrypting is one technique to protect digital videos, it used to avoid unwanted interference and viewing of the transmitted videos. In this paper, a new selective video cryptography algorithm is suggested using light stream algorithm. As it known video size is large in size and it consume time in the encryption process, ChaCha a light encryption algorithm has been used to reduce the encryption time, encryption is done by Xoring frames of video with the key generated from ChaCha algorithm, it produced an acceptable results from robustness point view, but still encryption process consumed time, thus to speed up this process, feature detection operator (FAST) is used to encrypt key points result from FAST operator, in addition key points from this is increased to optimized between speed and robustness of proposed algorithm. In evaluation process, some of measuring quality factors MSE, PSNR, Correlation, NPCR, UACI and entropy are specified for evaluating and comparing between two suggested encryption algorithms which gave good result in encryption process (ChaCha and ChaCha with FAST Enhancement). Experimental results have discovered that the current projected has less encrypting time and better encrypting influence
Comparative Analysis of Machine Learning-Based Approaches for Anomaly Detection in Vehicular Data

The rapid growth of demand for transportation, both for people and goods, as well as the massive accumulation of population in urban centers has augmented the need for the development of smart transport systems. One of the needs that have arisen is to efficiently monitor and evaluate driving behavior, so as to increase safety, provide alarms, and avoid accidents. Capitalizing on the evolution of Information and Communication Technologies (ICT), the development of intelligent vehicles and platforms in this domain is getting more feasible than ever. Nowadays, vehicles, as well as highways, are equipped with sensors that collect a variety of data, such as speed, acceleration, fuel consumption, direction, and more. The methodology presented in this paper combines both advanced machine learning algorithms and open-source based tools to correlate different data flows originating from vehicles. Particularly, the data gathered from different vehicles are processed and analyzed with the utilization of machine learning techniques in order to detect abnormalities in driving behavior. Results from different suitable techniques are presented and compared, using an extensive real-world dataset containing field measurements. The results feature the application of both supervised univariate anomaly detection and unsupervised multivariate anomaly detection methods in the same dataset.
Fast and Robust Cascade Model for Multiple Degradation Single Image Super-Resolution

Single Image Super-Resolution (SISR) is one of the low-level computer vision problems that has received increased attention in the last few years. Current approaches are primarily based on harnessing the power of deep learning models and optimization techniques to reverse the degradation model. Owing to its hardness, isotropic blurring or Gaussians with small anisotropic deformations have been mainly considered. Here, we widen this scenario by including large non-Gaussian blurs that arise in real camera movements. Our approach leverages the degradation model and proposes a new formulation of the Convolutional Neural Network (CNN) cascade model, where each network sub-module is constrained to solve a specific degradation: deblurring or upsampling. A new densely connected CNN-architecture is proposed where the output of each sub-module is restricted using some external knowledge to focus it on its specific task. As far we know, this use of domain-knowledge to module-level is a novelty in SISR. To fit the finest model, a final sub-module takes care of the residual errors propagated by the previous sub-modules. We check our model with three state-of-the-art (SOTA) datasets in SISR and compare the results with the SOTA models. The results show that our model is the only one able to manage our wider set of deformations. Furthermore, our model overcomes all current SOTA methods for a standard set of deformations. In terms of computational load, our model also improves on the two closest competitors in terms of efficiency. Although the approach is non-blind and requires an estimation of the blur kernel, it shows robustness to blur kernel estimation errors, making it a good alternative to blind models.
Blind image deblurring via enhanced sparse prior

We present an effective blind image deblurring method based on the reweighted L1 norm prior. The prior is motivated by that the traditional L1 norm highly depends on the pixel itself, that is, the larger the pixel value, the greater the penalty. However, the blur reduces the high-frequency components of the clear image, and minimizing the high-frequency part will result in a blur solution or delta function kernel. To overcome this limitation, we employ the reweighted L1 norm and it eliminates this dependence within wisely weighting. The image prior compensates for the degeneration of high intensities and greatly stabilizes the intermediate image estimation process. However, the prior proposed introduces a challenging optimization problem. We develop an efficient optimization scheme to obtain a reliable intermediate image for estimating the blur kernel. Extensive experiments on different kinds of challenging blurry images demonstrate the superiority of our proposed method over the state-of-the-art blind deblurring methods. Moreover, our blind deblurring algorithm is effective in various scenarios, such as natural, text, and low-light images.
Survey of Tools and Techniques for Sentiment Analysis of Social Networking Data

Social media has rapidly expanded over a period of time and generated a huge repository of content.Sentiment analysis of this data has a vast scope in decision support and attracted many researchers to explore various possibilities for technique enhancement and accuracy improvement.Twitter is one of the social media platforms that are widely explored in the area of sentiment analysis.This paper presents a systematic survey related to Social Networking Sites Sentiment Analysis and mainly focus on Twitter sentiment analysis.The paper explores and identifies the techniques and tools used in a well-structured approach to find out the research gaps and identify future scope in this area of research.The techniques evolved over time to improve the efficiency of classification.Total 55 research papers are included in this survey.The result reflects that Twitter is the most explored social networking site for opinion mining.Naïve Bayes and SVM machine learning algorithms are implemented in maximum researches.As the latest advancements, Stack based ensemble, fuzzy based and neural network based classifiers are also implemented to enhance the efficiency of classification.WEKA, R Studio, Python are mostly used tools by research scholars for implementation.The overall evolution of the research goes through various changes in terms of technologies, tools, social media platforms and data corpus targeted.
Handling inconsistency in partially preordered ontologies: the Elect method

Abstract We focus on the problem of handling inconsistency in lightweight ontologies. We assume that the terminological knowledge base (TBox) is specified in DL-Lite and that the set of assertional facts (ABox) is partially preordered and may be inconsistent with respect to the TBox. One of the main contributions of this paper is the provision of an efficient and safe method, called Elect, to restore the consistency of the ABox with respect to the TBox. In the case where the assertional base is flat (i.e. no priorities are associated with the ABox) or totally preordered, we show that our method collapses with the well-known intersection ABox repair semantics and the non-defeated semantics, respectively. The semantic justification of the Elect method is obtained by first viewing a partially preordered ABox as a family of totally preordered ABoxes and then applying non-defeated inference to each of the totally preordered ABoxes. We introduce the notion of elected assertions which allows us to provide an equivalent characterization of the Elect method without explicitly generating all the totally preordered ABoxes. We show that computing the set of elected assertions is done in polynomial time with respect to the size of the ABox. The second part of the paper discusses how to go beyond the Elect method. In particular, we discuss to what extent the Elect method can be generalized to description logics that are more expressive than DL-Lite.
Corporate Diversification and Financial Performance of Listed Firms in Kenya: Does Firm Size Matter?

Purpose: The study tested the hypothesis about the relationship between corporate diversification and financial performance. Moreover, moderating effect of firm size on the relationship between corporate diversification and financial performance of listed firms at Nairobi securities exchange (NSE) in Kenya was tested. Methodology/Approach/Design: The study was informed by market power and resource-based view (RBV) theories. To test the hypotheses, secondary panel data were collected from 35 listed firms at NSE from 2003 to 2017. Results: From panel regression analysis output, there was a significant positive (β = 2.225, p value = .000 &lt; .05) relationship between corporate diversification and financial performance. Furthermore, firm size had a negative and significant (β = -.155, p value = .031&lt;.05) moderating effect in the relationship between corporate diversification and financial performance. Practical Implications: The study thus concluded that firm size had a buffering effect in the link between corporate diversification and the financial performance of listed firms in Kenya. The findings of the study could be relevant to policymakers in drafting policies that affect diversification strategies of firms. For further research, the study recommended an increase of scope, other measurement approaches, analysis of corporate diversification from different perspectives other than product, and controlling for board characteristics. Originality/Value: The study while controlling the age of the firm tested the moderation effect of firm size in the relationship between corporate diversification and financial performance.
Improvement of K-Means Algorithm for Accelerated Big Data Clustering

With the rapid development of the computer level, especially in recent years, “Internet +,” cloud platforms, etc. have been used in various industries, and various types of data have grown in large quantities. Behind these large amounts of data often contain very rich information, relying on traditional data retrieval and analysis methods, and data management models can no longer meet our needs for data acquisition and management. Therefore, data mining technology has become one of the solutions to how to quickly obtain useful information in today's society. Effectively processing large-scale data clustering is one of the important research directions in data mining. The k-means algorithm is the simplest and most basic method in processing large-scale data clustering. The k-means algorithm has the advantages of simple operation, fast speed, and good scalability in processing large data, but it also often exposes fatal defects in data processing. In view of some defects exposed by the traditional k-means algorithm, this paper mainly improves and analyzes from two aspects.
Understanding Consumer Product Sentiments through Supervised Models on Cloud: Pre and Post COVID

While a lot of work is done on extracting sentiments and opinions in unstructured text, majority of it is focused on contextual sentiment mining and features that are more focused on sentiments. The team attempted to use contextual text analytics to identify product or service features that drives the sentiment of the user. This is done through application of cosine similarity and neural networks. Customers speak about product or service feature when it is important for the them. The second stage of the analysis is focused on supervised learning, that identifies key drivers of a product or service. It helps in deriving those elements which are subconsciously being evaluated by customers but not spoken. We also test the significant difference in views of people pre and post Covid in their reviews. We found that factors related to Covid have gone up by 30% but not statistically significant. Given the volume of data, the team has analyzed these on cloud to assess the cloud computing readiness for such analysis. Feedback around the post Covid topics helps us understand the issues that need to be addressed by restaurant industry.
Communists spoke differently: An analysis of Czechoslovak and Czech annual presidential speeches

Abstract Annual speeches of Czech and Czechoslovak presidents on the occasion of the end of the year are analyzed in this study. Several stylometric methods are used, namely, vocabulary richness expressed by the moving-average type–token ratio, an index of text activity, mean word length, mean verb distance, and cluster analysis of the most frequent words. The authors primarily focus on the differences between presidential addresses given in the democratic and in the communist era. Peculiarities of individual styles of particular presidents are also investigated.
Effective Utilization of Resources Through Optimal Allocation and Opportunistic Migration of Virtual Machines in Cloud Environment

Cloud computing has become more prominent, and it is used in large data centers. Distribution of well-organized resources (bandwidth, CPU, and memory) is the major problem in the data centers. The genetically enhanced shuffling frog leaping algorithm (GESFLA) framework is proposed to select the optimal virtual machines to schedule the tasks and allocate them in physical machines (PMs). The proposed GESFLA-based resource allocation technique is useful in minimizing the wastage of resource usage and also minimizes the power consumption of the data center. The proposed GESFL algorithm is compared with task-based particle swarm optimization (TBPSO) for efficiency. The experimental results show the excellence of GESFLA over TBPSO in terms of resource usage ratio, migration time, and total execution time. The proposed GESFLA framework reduces the energy consumption of data center up to 79%, migration time by 67%, and CPU utilization is improved by 9% for Planet Lab workload traces. For the random workload, the execution time is minimized by 71%, transfer time is reduced up to 99%, and the CPU consumption is improved by 17% when compared to TBPSO.
Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems

Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this work, a Lightweight Deformable Deep Learning Framework is implemented, in which the problem of disparity into LF images is treated. To this end, an angular alignment module and a soft activation function into the Convolutional Neural Network (CNN) are implemented. For performance assessment, the proposed solution is compared with recent state-of-the-art methods using different LF datasets, each one with specific characteristics. Experimental results demonstrated that the proposed solution achieved a better performance than the other methods. The image quality results obtained outperform state-of-the-art LF image reconstruction methods. Furthermore, our model presents a lower computational complexity, decreasing the execution time.
Vision-Based Mobile Robot Controllers: A Scientific Review

Today, there are different types of self-controlled robots. Some of them had critical effects on our lives like industrial and medical robots. Others are for military usages such as drones and the pets robots just for entertainment. The crucial differences between this kind of robot and the controlled ones are their ability to move on their own and make decisions based on their observations of the world around them. Mobile robots must have a data source used as an input dataset and processed to change their behavior; for instance, moving, stopping, rotating, or doing any required action based on the information gathered from the surrounding environment. Different types of sensors were used to feed robots controllers with data. Such data source could be ultrasonic sensor, laser sensor, torque sensor, or vision sensor. Robots integrated with cameras were becoming an essential field of study. They recently attracted significant attention from researchers it has been commonly used in many sectors of healthcare, manufacturing, and many other services. The robot needs a controller with a powerful mechanism of realization to deal with such incoming data. The world of mobile robot controllers is discussed in this paper, and the latest trends were reviewed. This review aims to provide a general understanding of robot controllers and navigation methods developed over the last few years.
First Quantization Estimation by a Robust Data Exploitation Strategy of DCT Coefficients

It is well known that the JPEG compression pipeline leaves residual traces in the compressed images that are useful for forensic investigations. Through the analysis of such insights the history of a digital image can be reconstructed by means of First Quantization Estimations (FQE), often employed for the camera model identification (CMI) task. In this paper, a novel FQE technique for JPEG double compressed images is proposed which employs a mixed approach based on Machine Learning and statistical analysis. The proposed method was designed to work in the aligned case (i.e., 8 ×8 JPEG grid is not misaligned among the various compressions) and demonstrated to be able to work effectively in different challenging scenarios (small input patches, custom quantization tables) without strong a-priori assumptions, surpassing state-of-the-art solutions. Finally, an in-depth analysis on the impact of image input sizes, dataset image resolutions, custom quantization tables and different Discrete Cosine Transform (DCT) implementations was carried out.
A Novel Resource Productivity Based on Granular Neural Network in Cloud Computing

In recent years, due to the growing demand for computational resources, particularly in cloud computing systems, the data centers’ energy consumption is continually increasing, which directly causes price rise and reductions of resources’ productivity. Although many energy-aware approaches attempt to minimize the consumption of energy, they cannot minimize the violation of service-level agreements at the same time. In this paper, we propose a method using a granular neural network, which is used to model data processing. This method identifies the physical hosts’ workloads before the overflow and can improve energy consumption while also reducing violation of service-level agreements. Unlike the other techniques that use a single criterion, namely, worked on the basis of the history of using the processor, we simultaneously use all the productivity rates criteria, that is, processor productivity rates, main memory, and bandwidth. Extensive real-world simulations using the CloudSim simulator show the high efficiency of the proposed algorithm.
Texture-Generic Deep Shape-From-Template

Shape-from-Template (SfT) solves the registration and 3D reconstruction of a deformable 3D object, represented by the template, from a single image.Recently, methods based on deep learning have been able to solve SfT for the wide-baseline case in real-time, clearly surpassing classical methods.However, the main limitation of current methods is the need for fine tuning of the neural models to a specific geometry and appearance represented by the template texture map.We propose the first texture-generic deep learning SfT method which adapts to new texture maps at run-time, without the need for texture specific fine tuning.We achieve this by dividing the problem into a segmentation step and a registration and reconstruction step, both solved with deep learning.We include the template texture map as one of the neural inputs in both steps, training our models to adapt to different ones.We show that our method obtains comparable or better results to previous deep learning models, which are texture specific.It works in challenging imaging conditions, including complex deformations, occlusions, motion blur and poor textures.Our implementation runs in real-time, with a low-cost GPU and CPU.
A New Approach for Eye-Blink to Speech Conversion by Dynamic Time Warping

Motor neuron patients such as paralysis, locking syndrome, and amyotrophic lateral sclerosis can see and hear what is happening in their environment, but cannot communicate with their environment. It is very important for these patients, who do not have any physical function other than eye movements, to be able to express their needs, feelings and thoughts. Therefore, to express the thoughts, needs and feelings of these patients, a system that converts eye-blink signals to speech was developed in this study. The main purpose of the designed system is high accuracy, low cost, high speed and independence from environmental factors. Undoubtedly, it is also very important that it causes as little discomfort to the patient as possible. Morse-coded signals generated by voluntary eye-blinks and the single-channel wireless NeuroSky MindWave Mobile device eliminates the need for cost-increasing equipment such as a camera or eye tracker and environmental factors such as light. With the use of Dynamic Time Warping (DTW), an algorithm which works at high speed and high accuracy at the time domain and does not require any training process has been implemented. In this way, the recorded speech was performed with a quite impressive accuracy.
AWSMOTE: An SVM-Based Adaptive Weighted SMOTE for Class-Imbalance Learning

In class-imbalance learning, Synthetic Minority Oversampling Technique (SMOTE) is a widely used technique to tackle class-imbalance problems from the data level, whereas SMOTE blindly selects neighboring minority class points when performing an interpolation among them and inevitably brings collinearity between the generated new points and the original ones. To combat these problems, we propose in this study an adaptive-weighting SMOTE method, termed as AWSMOTE. AWSMOTE applies two types of SVM-based weights into SMOTE. A kind of weight is used in variable space to combat the drawbacks of collinearity, while another weight is utilized in sample space to purposefully choose those support vectors from the minority class as the neighboring points in the interpolation. AWSMOTE is compared with SMOTE and its improved versions with six simulated datasets and 22 real-world datasets. The results demonstrate the effectiveness and advantages of the proposed approach.