Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis Technique and Neutrosophic Set Theory for Mining and Ranking Big Data From Online Reviews

Recently, a huge amount of online consumer reviews (OCRs) is being generated through social media, web contents, and microblogs.This scale of big data cannot be handled by traditional methods.Sentiment analysis (SA) or opinion mining is emerging as a powerful and efficient tool in big data analytics and improving decision making.This research paper introduces a novel method that integrates neutrosophic set (NS) theory into the SA technique and multi-attribute decision making (MADM) to rank the different products based on numerous online reviews.The method consists of two parts: Determining sentiment scores of the online reviews based on the SA technique and ranking alternative products via NS theory.In the first part, the online reviews of the alternative products concerning multiple features are crawled and pre-processed.A neutral lexicon consists of 228 neutral words and phrases is compiled and the Valence Aware Dictionary and sEntiment Reasoner (VADER) for sentiment reasoning is adapted to handle the neutral data.The compiled neutral lexicon, as well as the adapted VADER, are utilized to build a novel adaptation called Neutro-VADER.The Neutro-VADER assigns positive, neutral, and negative sentiment scores to each review concerning the product feature.In this stage, the novel idea is to point out the positive, neutral, and negative sentiment scores as the truth, indeterminacy, and falsity memberships degrees of the neutrosophic number.The overall performance of each alternative concerning each feature based on a neutrosophic number is measured.In the second part, the ranking of alternatives is being evaluated through the simplified neutrosophic number weighted averaging (SNNWA) operator and cosine similarity measure methods.A case study with real datasets (Twitter datasets) is provided to illustrate the application of the proposed method.The results show good performance in handling the neutral data on the SA stage as well as the ranking stage.In the SA stage, findings show that the Neutro-VADER in the proposed method can deal successfully with all types of uncertainties including indeterminacy comparable with the traditional VADER in the other methods.In the ranking stage, the results show a great similarity and consistency while using other ranking methods such as PROMETHEE II, TOPSIS, and TODIM methods.
An image matching optimization algorithm based on pixel shift clustering RANSAC

This paper focuses on improving the accuracy of image matching by eliminating the residual mismatches in the matching results of standard RANSAC. Based on pixel shift clustering and RANSAC algorithms, a matching optimization algorithm called pixel shift clustering RANSAC, PSC-RANSAC in short, is proposed in this paper. Firstly, the pixel shift model of space point from two perspectives are established by parallax principle and camera projection model. Then, based on the established pixel shift model, density peaks clustering (DPC) algorithm is used to select the mismatches out to enhance the accuracy of image matching. Meanwhile the comparisons among PSC-RANSAC, standard RANSAC, progressive sample consensus and graph-cut RANSAC show that PSC-RANSAC can more effectively and robustly eliminate the residual mismatches in initial matching results. The proposed method provides an effective tool for optimization on image matching.
TSingNet: Scale-aware and context-rich feature learning for traffic sign detection and recognition in the wild

Traffic sign detection and recognition in the wild is a challenging task. Existing techniques are often incapable of detecting small or occluded traffic signs because of the scale variation and context loss, which causes semantic gaps between multiple scales. We propose a new traffic sign detection network (TSingNet), which learns scale-aware and context-rich features to effectively detect and recognize small and occluded traffic signs in the wild. Specifically, TSingNet first constructs an attention-driven bilateral feature pyramid network, which draws on both bottom-up and top-down subnets to dually circulate low-, mid-, and high-level foreground semantics in scale self-attention learning. This is to learn scale-aware foreground features and thus narrow down the semantic gaps between multiple scales. An adaptive receptive field fusion block with variable dilation rates is then introduced to exploit context-rich representation and suppress the influence of occlusion at each scale. TSingNet is end-to-end trainable by joint minimization of the scale-aware loss and multi-branch fusion losses, this adds a few parameters but significantly improves the detection performance. In extensive experiments with three challenging traffic sign datasets (TT100K, STSD and DFG), TSingNet outperformed state-of-the-art methods for traffic sign detection and recognition in the wild.
Real Time Object Detection with Audio Feedback using Yolo vs. Yolo_v3

Object recognition is one of the challenging application of computer vision, which has been widely applied in many areas for e.g. autonomous cars, Robotics, Security tracking, Guiding Visually Impaired Peoples etc. With the rapid development of deep learning many algorithms were improving the relationship between video analysis and image understanding. All these algorithms work differently with their network architecture but with the same aim of detecting multiple objects within complex image. Absence of vision impairment restraint the movement of the person in an unfamiliar place and hence it is very essential to take help from our technologies and trained them to guide blind peoples whenever they need. This paper proposes a system that will detect every possible day to day multiple objects on the other hand promt a voice to alert person about the near as well as farthest objects around them. In this paper system is developed using two different algorithms i.e. Yolo and Yolo_v3 and tested under same criterias to measure the accuracy and performance. In Yolo Tensor flow _SSD _Mobile Net model and in Yolo_v3 Dark net model is used. To get the audio Feedback gTTS (Google Text to Speech), python library used to convert statements into audio speech. To play the audio pygame python module is used. Testing of both the algorithms is done on MS-COCO Dataset consist of more than 200 K images. Both the algorithms are analysed using webcam in various situations to measure accuracy of the algorithm in every possibility.
ViNet: Pushing the limits of Visual Modality for Audio-Visual Saliency Prediction

We propose the ViNet architecture for audio-visual saliency prediction. ViNet is a fully convolutional encoder-decoder architecture. The encoder uses visual features from a network trained for action recognition, and the decoder infers a saliency map via trilinear interpolation and 3D convolutions, combining features from multiple hierarchies. The overall architecture of ViNet is conceptually simple; it is causal and runs in real-time (60 fps). ViNet does not use audio as input and still outperforms the state-of-the-art audio-visual saliency prediction models on nine different datasets (three visual-only and six audio-visual datasets). ViNet also surpasses human performance on the CC, SIM and AUC metrics for the AVE dataset, and to our knowledge, it is the first model to do so. We also explore a variation of ViNet architecture by augmenting audio features into the decoder. To our surprise, upon sufficient training, the network becomes agnostic to the input audio and provides the same output irrespective of the input. Interestingly, we also observe similar behaviour in the previous state-of-the-art models [1] for audio-visual saliency prediction. Our findings contrast with previous works on deep learning-based audio-visual saliency prediction, suggesting a clear avenue for future explorations incorporating audio in a more effective manner. The code and pre-trained models are available at https://github.com/samyak0210/ViNet.
FixationNet: Forecasting Eye Fixations in Task-Oriented Virtual Environments

Human visual attention in immersive virtual reality (VR) is key for many important applications, such as content design, gaze-contingent rendering, or gaze-based interaction. However, prior works typically focused on free-viewing conditions that have limited relevance for practical applications. We first collect eye tracking data of 27 participants performing a visual search task in four immersive VR environments. Based on this dataset, we provide a comprehensive analysis of the collected data and reveal correlations between users' eye fixations and other factors, i.e. users' historical gaze positions, task-related objects, saliency information of the VR content, and users' head rotation velocities. Based on this analysis, we propose FixationNet - a novel learning-based model to forecast users' eye fixations in the near future in VR. We evaluate the performance of our model for free-viewing and task-oriented settings and show that it outperforms the state of the art by a large margin of 19.8% (from a mean error of 2.93° to 2.35°) in free-viewing and of 15.1% (from 2.05° to 1.74°) in task-oriented situations. As such, our work provides new insights into task-oriented attention in virtual environments and guides future work on this important topic in VR research.
Fast and effective Keypoint-based image copy-move forgery detection using complex-valued moment invariants

Copy-move forgery is one of the most common image tampering schemes, with the potential use for misleading the opinion of the general public. Keypoint-based detection methods exhibit remarkable performance in terms of computational cost and robustness. However, these methods are difficult to effectively deal with the cases when 1) forgery only involves small or smooth regions, 2) multiple clones are conducted or 3) duplicated regions undergo geometric transformations or signal corruptions. To overcome such limitations, we propose a fast and accurate copy-move forgery detection algorithm, based on complex-valued invariant features. First, dense and uniform keypoints are extracted from the whole image, even in small and smooth regions. Then, these keypoints are represented by robust and discriminative moment invariants, where a novel fast algorithm is designed especially for the computation of dense keypoint features. Next, an effective magnitude-phase hierarchical matching strategy is proposed for fast matching a massive number of keypoints while maintaining the accuracy. Finally, a reliable post-processing algorithm is developed, which can simultaneously reduce false negative rate and false positive rate. Extensive experimental results demonstrate the superior performance of our proposed scheme compared with existing state-of-the-art algorithms, with average pixel-level F-measure of 94.54% and average CPU-time of 36.25 s on four publicly available datasets.
An Overall Configuration Planning Method of Continuum Hyper-Redundant Manipulators Based on Improved Artificial Potential Field Method

Continuum hyper-redundant manipulators (CHRMs) have been widely applied in aerospace, medical or other fields to complete tasks in narrow and multi-obstacles environments with its unique structural advantages. Due to the redundancy, the inverse kinematics of CHRMs is rather complex and the trajectory planning has always been the focal and challenging point in the research of CHRMs. To tackle this issue, the present study proposes an overall configuration planning method of CHRMs based on the improved artificial potential field (APF) method. The method avoids the complicated inverse kinematics and vastly reduces the computational complexity. First, a virtual guiding pipeline (VGP) with no obstacles inside is constructed based on a known target configuration and the minimum safe distance. The movement of the CHRM is restricted to the pipe by the APF method. Then, we propose a method to construct a guided potential field in the VGP to solve the local minimum problem of the traditional artificial potential field method. Further, to address the issue of contradictory forces in the overall configuration planning, the maximum-work planning strategy is proposed. Finally, simulations are conducted and the results demonstrate the high performance of the method in terms of planning efficiency, environmental adaptability.
A Hybrid Supervised Machine Learning Classifier System for Breast Cancer Prognosis Using Feature Selection and Data Imbalance Handling Approaches

Nowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification models for the prognosis of breast cancer are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Search, and Greedy Stepwise has been used to identify the important features. On these selected features popular machine learning classifiers Support Vector Machine, J48 (C4.5 Decision Tree Algorithm), Multilayer-Perceptron (a feed-forward ANN) were used in the system. The methodology of the proposed system is structured into five stages which include (1) Data Pre-processing; (2) Data imbalance handling; (3) Feature Selection; (4) Machine Learning Classifiers; (5) classifier’s performance evaluation. The dataset under this research experimentation is referred from the UCI Machine Learning Repository, named Breast Cancer Wisconsin (Diagnostic) Data Set. This article indicated that the J48 decision tree classifier is the appropriate machine learning-based classifier for optimum breast cancer prognosis. Support Vector Machine with Particle Swarm Optimization algorithm for feature selection achieves the accuracy of 98.24%, MCC = 0.961, Sensitivity = 99.11%, Specificity = 96.54%, and Kappa statistics of 0.9606. It is also observed that the J48 Decision Tree classifier with the Genetic Search algorithm for feature selection achieves the accuracy of 98.83%, MCC = 0.974, Sensitivity = 98.95%, Specificity = 98.58%, and Kappa statistics of 0.9735. Furthermore, Multilayer Perceptron ANN classifier with Genetic Search algorithm for feature selection achieves the accuracy of 98.59%, MCC = 0.968, Sensitivity = 98.6%, Specificity = 98.57%, and Kappa statistics of 0.9682.
Using recurrent neural networks for continuous authentication through gait analysis

This letter presents a novel framework for continuous user authentication of mobile devices based on gait analysis, exploiting inertial sensors and Recurrent Neural Network for deep-learning based classification. The proposed framework handles all the continuous authentication stages, starting from data collection to data preprocessing, classification, and policy enforcement. The letter will emphasize the data analysis aspects, discussing the methodologies used to improve the quality of classification, including data augmentation and a sliding window interval approach for improved training. Furthermore, will be discussed the enforcement, which is based on the Usage Control paradigm for continuous policy enforcement. A set of real experiments will demonstrate the effectiveness and efficiency of the proposed framework.
EfficientPose: Efficient human pose estimation with neural architecture search

Abstract Human pose estimation from image and video is a key task in many multimedia applications. Previous methods achieve great performance but rarely take efficiency into consideration, which makes it difficult to implement the networks on lightweight devices. Nowadays, real-time multimedia applications call for more efficient models for better interaction. Moreover, most deep neural networks for pose estimation directly reuse networks designed for image classification as the backbone, which are not optimized for the pose estimation task. In this paper, we propose an efficient framework for human pose estimation with two parts, an efficient backbone and an efficient head. By implementing a differentiable neural architecture search method, we customize the backbone network design for pose estimation, and reduce computational cost with negligible accuracy degradation. For the efficient head, we slim the transposed convolutions and propose a spatial information correction module to promote the performance of the final prediction. In experiments, we evaluate our networks on the MPII and COCO datasets. Our smallest model requires only 0.65 GFLOPs with 88.1% PCKh@0.5 on MPII and our large model needs only 2 GFLOPs while its accuracy is competitive with the state-of-the-art large model, HRNet, which takes 9.5 GFLOPs.
EASBF: An efficient authentication scheme over blockchain for fog computing-enabled internet of vehicles

The concept of the Internet of Vehicles (IoV) has attracted the attention of many sectors such as the automotive industry, academia, engineering, etc., offering a wide variety of benefits, such as reduced congestion, safer driving, and less pollution. However, since the deployment of IoT based devices is in an open field, the IoV environments can be a vital issue in the absence of security protections. In this paper, we propose an efficient authentication scheme over blockchain, named EASBF, for secure fog computing-enabled internet of vehicles. Specifically, the proposed EASBF scheme consists of five phases: initialization, registration, mutual authentication, and exchanging keys, consensus, and certificate update. Based on the elliptic curve cryptography, one-way hash function, and blockchain technology, the EASBF scheme can achieve confidentiality, integrity, authenticity, privacy, anonymity, non-repudiation, and perfect forward secrecy. The EASBF scheme is robust against cyber attacks, including, DDoS attack, replay attack, man-in-the-middle attacks, identity theft attack, traffic analysis attack, masquerading attack, and session key disclosure attack. Both the AVISPA tool and the random oracle model are used in security analysis. Finally, we demonstrate the effectiveness of the EASBF scheme in terms of two performance metrics, namely, computation overhead, communication overhead, and storage overhead.
Fuzzy C-Means-based Isolation Forest

The problem of finding anomalies (outliers) in databases is one of the most important issues in modern data analysis. One of the reasons is the occurrence of this issue in almost every type of database, including numerical, categorical, time, mixed, or graphic data. There are currently many methods often dedicated to specific data analysis. Finally, this topic is extremely interesting per se, as a research problem that intrigues researchers. One of the classic methods of data analysis dedicated to finding the anomalies in the data is Isolation Forest. However, this method, with a few exceptions, has not been modified from the time of its first publication, and, in particular, it has not yet appeared in combination with the typical fuzzy methods used for grouping such as Fuzzy C-Means (FCM) clustering. In this study, we thoroughly analyze this approach, as well as several related ones. We examine the possibilities of this technique and analyze it in detail for characteristics of data (database size, number of attributes, records, their type, etc.). It is worth noting that FCM allows to obtain membership grades of elements forming Isolation Forest nodes to clusters on the basis of which these nodes are built. Hence, at the stage of calculating the anomaly scores, this information is effectively used, in particular to express how much a given element may belong to a group of similar elements, which can be inferred from the characteristics of the cluster in which it lies. In this study, we propose a set of methods enhancing the Isolation Forest on a basis of Fuzzy C-Means. The results of numerical experiments carried using 27 various datasets and reported in this paper lead us to the conclusion that FCM can play a pivotal role in an enhancement of Isolation Forest approach and raises up the values of particular measures of effectiveness of the anomaly detection methods.
Energy-aware workflow task scheduling in clouds with virtual machine consolidation using discrete water wave optimization

The scientific workflows are high-level complex applications that demand more computing power. The cloud data center (CDC) remains one of the essential models of economic infrastructure for workflow applications. These CDCs consume a lot of electric power while running workflow applications. Hence, efficient energy-aware scheduling techniques are required to perform the task to a virtual machine (VM) mapping. The existing researches overlooked to join the workflow scheduling and VM consolidation which addresses resource utilization and energy consumption effectively. In this article, we propose an energy-aware algorithm for workflow scheduling in cloud computing with VM consolidation called EASVMC. The proposed EASVMC approach is modeled to address the multi-objectives such as energy consumption, resource utilization, and VM migrations. The EASVMC algorithm runs in two phases task scheduling and VM consolidation (VMC). In the first phase, the task with maximum execution length is mapped to the virtual machine that will perform it with the minimum energy. The second phase contains VM consolidation is a prominent NP-hard problem. The VMC phase categorizes the physical hosts into the normal load, under-loaded and overloaded hosts based on CPU utilization. Double threshold values are used for this purpose. VMs from underloaded and overloaded hosts are migrated to normally loaded hosts. For the VMC phase, we used a nature-inspired meta-heuristic approach called the Water Wave Optimization (WWO) algorithm, which finds a suitable migration plan to reduce the energy consumption by increasing the overall resource utilization and switch off idle hosts after migrating its VMs to a suitable target host. The efficiency of our proposed method evaluated using the WorkflowSim simulation tool with five different real-world scientific workloads. The experimental results show that the EASVMC approach surpassed the similar works in stated objectives irrespective of diverse workloads.
Enhancing Few-Shot Image Classification with Unlabelled Examples

We develop a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance. Our approach combines a regularized Mahalanobis-distance-based soft k-means clustering procedure with a modified state of the art neural adaptive feature extractor to achieve improved test-time classification accuracy using unlabelled data. We evaluate our method on transductive few-shot learning tasks, in which the goal is to jointly predict labels for query (test) examples given a set of support (training) examples. We achieve state of the art performance on the Meta-Dataset, mini-ImageNet and tiered-ImageNet benchmarks. All trained models and code have been made publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .