Reviewerly/exHarmony huggingface dataset:
A novel community-based trust aware recommender systems for big data cloud service networks

Internet of Things (IoT) has enhanced the appearance of the information and communications technology (ICT) infrastructure of smart sustainable cities that emerge as a growth of the urban development. The potential growth of environment sustainabilities is coming into the existence of the insightful visions of ICT that include big data analytics as a computing prototype. It may draw a penetrative path to mitigate the environmental effects that utilize the natural resource to manage the intelligent infrastructure and services. Moreover, the sustainable development process plays a crucial role to catalyze IoT and big-data application that connects the economic growth with the quality of smart cities. The IoT-enabled big-data applications choose a suitable thematic analysis to direct the use of recommendation systems. It supports users in the irresistible task of examination through large quantities of data in order to select appropriate information or items. Traditional recommender systems rely on similar neighbors irrespective of their preferences/choices when computing the prediction and assuming the users to be autonomous and indistinguishable to disregard the social events between the social users which are highly not reliable. Based on this intuition in this framework, the architecture of community-based trust aware recommender systems (CB-TARS) is proposed. Users’ preferences are expressed by incorporating trusted neighbors within the community of the target user are merged in order to find similar preferences. In addition, the worth of merged ratings is measured by the confidence considering the number of ratings inside the community and the percentage of clashes between negative and positive views. Further, rating confidence is incorporated into the computation of user similarity. The prediction for an unrated item is computed by aggregating the ratings of similar users within the community. Experimental results on real-world data set validate that the proposed method surpasses other complements in terms of accuracy.
The Use of Word2vec Model in Sentiment Analysis

Sentiment analysis is an area that gains wide interest from research because of its importance and advantages in various fields. Different approaches and techniques are used to classify the sentiment of texts. Word embedding is one of the effective methods that represent aspects of word meaning and help to improve sentiment classification accuracy. Word2vec is well-known and widely used in learning word embedding that includes two models: Skip-Gram (SG) model and Continuous Bag-of-Words model (CBOW). Some of the studies use one of these models and other studies use both of them. In this survey, we highlight the latest studies on using the Word2vec model for sentiment analysis and its role in improving sentiment classification accuracy.
DHOT-Repository and Classification of Offensive Tweets in the Hindi Language

While social media gives people an online platform for expressing their views, knowledge, experiences and emotions, a major problem occurs when social media interactions becomes a platform for abusive remarks, comments and conversations. Apart from slurs being offensive in conversations, slurs vary in usage to express contempt, difference of opinions, and in some cases humor. Abusive language can potentially be used to offend someone, to promote racism, sexism, etc. Hindi is the third most popular language in the world, based on the number of speakers globally. It is spoken by millions of Indians from different regional influences and linguistic preferences it has become very rich in it’s diversity and usage. While "Hinglish" (Hindi written in Roman script instead of the native Devanagari) is extensively used online, native Hindi speakers who write in Devanagari are on a steady rise. Despite this, little research has been done on the use of Hindi as an online language. This paper presents a model to distinguish and then classify offensive text from non-offensive using a fast Text-based model The model was able to classify text from a Devanagari Hindi Offensive Tweets (DHOT) data corpus. A grid-search method was applied to tune hyperparameters during fast Text model runs, and provided interesting insights on the model accuracy and precision. Our fast Text model achieved 92.2% accuracy employing desktop class machine for the processing. To our knowledge, this is the first attempt to establish a state of the art classification of offensive text in Hindi using fast Text models.
Intra- and Inter-Action Understanding via Temporal Action Parsing

Current methods for action recognition primarily rely on deep convolutional networks to derive feature embeddings of visual and motion features. While these methods have demonstrated remarkable performance on standard benchmarks, we are still in need of a better understanding as to how the videos, in particular their internal structures, relate to high-level semantics, which may lead to benefits in multiple aspects, e.g. interpretable predictions and even new methods that can take the recognition performances to a next level. Towards this goal, we construct TAPOS, a new dataset developed on sport videos with manual annotations of sub-actions, and conduct a study on temporal action parsing on top. Our study shows that a sport activity usually consists of multiple sub-actions and that the awareness of such temporal structures is beneficial to action recognition. We also investigate a number of temporal parsing methods, and thereon devise an improved method that is capable of mining sub-actions from training data without knowing the labels of them. On the constructed TAPOS, the proposed method is shown to reveal intra-action information, i.e. how action instances are made of sub-actions, and inter-action information, i.e. one specific sub-action may commonly appear in various actions.
Investigating Loss Functions for Extreme Super-Resolution

The performance of image super-resolution (SR) has been greatly improved by using convolutional neural networks. Most of the previous SR methods have been studied up to ×4 upsampling, and few were studied for ×16 upsampling. The general approach for perceptual ×4 SR is using GAN with VGG based perceptual loss, however, we found that it creates inconsistent details for perceptual ×16 SR. To this end, we have investigated loss functions and we propose to use GAN with LPIPS [23] loss for perceptual extreme SR. In addition, we use U-net structure discriminator [14] together to consider both the global and local context of an input image. Experimental results show that our method outperforms the conventional perceptual loss, and we achieved second and first place in the LPIPS and PI measures respectively for NTIRE 2020 perceptual extreme SR challenge.
Learning Video Stabilization Using Optical Flow

We propose a novel neural network that infers the per-pixel warp fields for video stabilization from the optical flow fields of the input video. While previous learning based video stabilization methods attempt to implicitly learn frame motions from color videos, our method resorts to optical flow for motion analysis and directly learns the stabilization using the optical flow. We also propose a pipeline that uses optical flow principal components for motion inpainting and warp field smoothing, making our method robust to moving objects, occlusion and optical flow inaccuracy, which is challenging for other video stabilization methods. Our method achieves quantitatively and visually better results than the state-of-the-art optimization based and deep learning based video stabilization methods. Our method also gives a ~3x speed improvement compared to the optimization based methods.
StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching

Large-scale synthetic datasets are beneficial to stereo matching but usually introduce known domain bias. Although unsupervised image-to-image translation networks represented by CycleGAN show great potential in dealing with domain gap, it is non-trivial to generalize this method to stereo matching due to the problem of pixel distortion and stereo mismatch after translation. In this paper, we propose an end-to-end training framework with domain translation and stereo matching networks to tackle this challenge. First, joint optimization between domain translation and stereo matching networks in our end-to-end framework makes the former facilitate the latter one to the maximum extent. Second, this framework introduces two novel losses, i.e., bidirectional multi-scale feature re-projection loss and correlation consistency loss, to help translate all synthetic stereo images into realistic ones as well as maintain epipolar constraints. The effective combination of above two contributions leads to impressive stereo-consistent translation and disparity estimation accuracy. In addition, a mode seeking regularization term is added to endow the synthetic-to-real translation results with higher fine-grained diversity. Extensive experiments demonstrate the effectiveness of the proposed framework on bridging the synthetic-to-real domain gap on stereo matching.
Convolutional neural network–based person tracking using overhead views

In video surveillance, person tracking is considered as challenging task. Numerous computer vision, machine and deep learning–based techniques have been developed in recent years. Majority of these techniques are based on frontal view images/video sequences. The advancement of convolutional neural network reforms the way of object tracking. The network layers of convolutional neural network models trained on a number of images or video sequences improve speed and accuracy of object tracking. In this work, the generalization performance of existing pre-trained deep learning models have investigated for overhead view person detection and tracking, under different experimental conditions. The object tracking method Generic Object Tracking Using Regression Networks (GOTURN) which has been yielding outstanding tracking results in recent years is explored for person tracking using overhead views. This work mainly focused on overhead view person tracking using Faster region convolutional neural network (Faster-RCNN) in combination with GOTURN architecture. In this way, the person is first identified in overhead view video sequences and then tracked using a GOTURN tracking algorithm. Faster-RCNN detection model achieved the true detection rate ranging from 90% to 93% with a minimum false detection rate up to 0.5%. The GOTURN tracking algorithm achieved similar results with the success rate ranging from 90% to 94%. Finally, the discussion is made on output results along with future direction.
Self-Supervised Learning of Video-Induced Visual Invariances

We propose a general framework for self-supervised learning of transferable visual representations based on Video-Induced Visual Invariances (VIVI). We consider the implicit hierarchy present in the videos and make use of (i) frame-level invariances (e.g. stability to color and contrast perturbations), (ii) shot/clip-level invariances (e.g. robustness to changes in object orientation and lighting conditions), and (iii) video-level invariances (semantic relationships of scenes across shots/clips), to define a holistic self-supervised loss. Training models using different variants of the proposed framework on videos from the YouTube-8M (YT8M) data set, we obtain state-of-the-art self-supervised transfer learning results on the 19 diverse downstream tasks of the Visual Task Adaptation Benchmark (VTAB), using only 1000 labels per task. We then show how to co-train our models jointly with labeled images, outperforming an ImageNet-pretrained ResNet-50 by 0.8 points with 10× fewer labeled images, as well as the previous best supervised model by 3.7 points using the full ImageNet data set.
Progressive Adversarial Networks for Fine-Grained Domain Adaptation

Fine-grained visual categorization has long been considered as an important problem, however, its real application is still restricted, since precisely annotating a large fine-grained image dataset is a laborious task and requires expert-level human knowledge. A solution to this problem is applying domain adaptation approaches to fine-grained scenarios, where the key idea is to discover the commonality between existing fine-grained image datasets and massive unlabeled data in the wild. The main technical bottleneck lies in that the large inter-domain variation will deteriorate the subtle boundaries of small inter-class variation during domain alignment. This paper presents the Progressive Adversarial Networks (PAN) to align fine-grained categories across domains with a curriculum-based adversarial learning framework. In particular, throughout the learning process, domain adaptation is carried out through all multi-grained features, progressively exploiting the label hierarchy from coarse to fine. The progressive learning is applied upon both category classification and domain alignment, boosting both the discriminability and the transferability of the fine-grained features. Our method is evaluated on three benchmarks, two of which are proposed by us, and it outperforms the state-of-the-art domain adaptation methods.
Beyond Short-Term Snippet: Video Relation Detection With Spatio-Temporal Global Context

Video visual relation detection (VidVRD) aims to describe all interacting objects in a video. Different from relationships in static images, videos contain an addition temporal channel. A majority of existing works divide a video into short segments, predict relationships in each segment, and merge them. Such methods cannot capture relations involving long motions. Predicting the same relationship across neighboring video segments is also inefficient. To address these issues, this work proposes a novel sliding-window scheme to simultaneously predict short-term and long-term relationships. We run windows with different kernel sizes on object tracklets to generate sub-tracklet proposals with different duration, while the computational load is similar to that in segment-based methods. To fully utilize spatial and temporal information in videos, we construct one spatial and one temporal graph and employ Graph Convloutional Network to generate contextual embedding for tracklet proposal compatibility evaluation. We only predict relationships on highly-compatible proposal pairs. Our method achieves state-of-the-art performance on both ImageNet-VidVRD and VidOR dataset across multiple tasks. Especially for ImageNet-VidVRD, we obtain an average of 3% (R@50 from 8.07% to 11.21%) improvement under all evaluation metrics.
Online Joint Multi-Metric Adaptation From Frequent Sharing-Subset Mining for Person Re-Identification

Person Re-IDentification (P-RID), as an instance-level recognition problem, still remains challenging in computer vision community. Many P-RID works aim to learn faithful and discriminative features/metrics from offline training data and directly use them for the unseen online testing data. However, their performance is largely limited due to the severe data shifting issue between training and testing data. Therefore, we propose an online joint multi-metric adaptation model to adapt the offline learned P-RID models for the online data by learning a series of metrics for all the sharing-subsets. Each sharing-subset is obtained from the proposed novel frequent sharing-subset mining module and contains a group of testing samples which share strong visual similarity relationships to each other. Unlike existing online P-RID methods, our model simultaneously takes both the sample-specific discriminant and the set-based visual similarity among testing samples into consideration so that the adapted multiple metrics can refine the discriminant of all the given testing samples jointly via a multi-kernel late fusion framework. Our proposed model is generally suitable to any offline learned P-RID baselines for online boosting, the performance improvement by our model is not only verified by extensive experiments on several widely-used P-RID benchmarks (CUHK03, Market1501, DukeMTMC-reID and MSMT17) and state-of-the-art P-RID baselines but also guaranteed by the provided in-depth theoretical analyses.
Particle Swarm Optimization With Probability Sequence for Global Optimization

Particle Swarm Optimization (PSO) has been frequently employed to solve diversified optimization problems.Choosing initial placement for population plays an important role in meta-heuristic methods since they can significantly converge.In this study, probability distribution has been introduced to enhance the diversity of swarm and convergence speed.Population initialization method based on uniform distribution is normally used when there is no preceding knowledge available regarding the candidate solution.In this paper, a new approach to initialize population is proposed using probability sequence Weibull marked as (WI-PSO) that applies the probability distribution to generate numbers at random locations for swarm initialization.The proposed method (WI-PSO) is tested on sixteen well-known uni-modal and multi-modal benchmark test functions broadly adopted by the research community and its encouraging performance is investigated and compared with the Exponential distribution based PSO (E-PSO), Beta distribution based PSO (BT-PSO), Gamma distribution based PSO (GA-PSO) and Log-normal distribution based PSO (LN-PSO).Artificial Neural Networks (ANNs) have become the most powerful tool for classification of complex benchmark problems.We have experimented the proposed method (WI-PSO) for weight optimization of a feed-forward neural network to ensure its purity and have compared with conventional back-propagation algorithm (BPA), E-PSO, BT-PSO, GA-PSO and LN-PSO.Due to flexible behaviour in the degree of freedom, the experimental results infer the perfection and dominance of the Weibull based population initialization.The result exhibits the anticipation of influence exerted by the proposed technique on all sixteen objective functions and eight real-world benchmark data sets.
GP-NAS: Gaussian Process Based Neural Architecture Search

Neural architecture search (NAS) advances beyond the state-of-the-art in various computer vision tasks by automating the designs of deep neural networks. In this paper, we aim to address three important questions in NAS: (1) How to measure the correlation between architectures and their performances? (2) How to evaluate the correlation between different architectures? (3) How to learn these correlations with a small number of samples? To this end, we first model these correlations from a Bayesian perspective. Specifically, by introducing a novel Gaussian Process based NAS (GP-NAS) method, the correlations are modeled by the kernel function and mean function. The kernel function is also learnable to enable adaptive modeling for complex correlations in different search spaces. Furthermore, by incorporating a mutual information based sampling method, we can theoretically ensure the high-performance architecture with only a small set of samples. After addressing these problems, training GP-NAS once enables direct performance prediction of any architecture in different scenarios and may obtain efficient networks for different deployment platforms. Extensive experiments on both image classification and face recognition tasks verify the effectiveness of our algorithm.
Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm

Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a new transfer function, and the update process is designed to switch the position of the particles between 0 and 1 in the binary search space. Aiming at the existed BPSO algorithms which are easy to fall into the local optimum, a new Z-shaped probability transfer function is proposed to map the continuous search space to a binary space. By adopting nine typical benchmark functions, the proposed Z-probability transfer function and the V-shaped and S-shaped transfer functions are used to carry out the performance simulation experiments. The results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm.
Efficient 3D medical image segmentation algorithm over a secured multimedia network

Image segmentation has proved its importance and plays an important role in various domains such as health systems and satellite-oriented military applications. In this context, accuracy, image quality, and execution time deem to be the major issues to always consider. Although many techniques have been applied, and their experimental results have shown appealing achievements for 2D images in real-time environments, however, there is a lack of works about 3D image segmentation despite its importance in improving segmentation accuracy. Specifically, HMM was used in this domain. However, it suffers from the time complexity, which was updated using different accelerators. As it is important to have efficient 3D image segmentation, we propose in this paper a novel system for partitioning the 3D segmentation process across several distributed machines. The concepts behind distributed multimedia network segmentation were employed to accelerate the segmentation computational time of training Hidden Markov Model (HMMs). Furthermore, a secure transmission has been considered in this distributed environment and various bidirectional multimedia security algorithms have been applied. The contribution of this work lies in providing an efficient and secure algorithm for 3D image segmentation. Through a number of extensive experiments, it was proved that our proposed system is of comparable efficiency to the state of art methods in terms of segmentation accuracy, security and execution time.
Reinforcement Learning With Task Decomposition for Cooperative Multiagent Systems

In this article, we study cooperative multiagent systems (MASs) with multiple tasks by using reinforcement learning (RL)-based algorithms. The target for a single-agent RL system is represented by its scalar reward signals. However, for an MAS with multiple cooperative tasks, the holistic reward signal consists of multiple parts to represent the tasks, which makes the problem complicated. Existing multiagent RL algorithms search distributed policies with holistic reward signals directly, making it difficult to obtain an optimal policy for each task. This article provides efficient learning-based algorithms such that each agent can learn a joint optimal policy to accomplish these multiple tasks cooperatively with other agents. The main idea of the algorithms is to decompose the holistic reward signal for each agent into multiple parts according to the subtasks, and then the proposed algorithms learn multiple value functions with the decomposed reward signals and update the policy with the sum of distributed value functions. In addition, this article presents a theoretical analysis of the proposed approach. Finally, the simulation results for both discrete decision-making and continuous control problems have demonstrated the effectiveness of the proposed algorithms.