Reviewerly/exHarmony huggingface dataset:
Group-Based Bi-Directional Recurrent Wavelet Neural Network for Efficient Video Super-Resolution (Vsr)

Video super-resolution (VSR) aims to estimate a high-resolution (HR) frame from low-resolution (LR) frames. From temporal point of view, the recurrent neural network (RNN)-based approach is suitable for sequential data because it can use accumulated temporal information. However, since previous methods only tend to capture slow and symmetrical motion with low frame rate, there are still limitations to restore the missing details for dynamic motion. In terms of spatial information, most of the previous methods treat different types of the spatial features identically. It leads to lack of obtaining meaningful information and enhancing the fine details. In this paper, we propose a group-based bi-directional recurrent wavelet neural network (GBR-WNN) to exploit the sequential data and spatiotemporal information effectively for VSR. The proposed group-based bi-directional RNN (GBR) framework is built on the well-structured process with the group of pictures (GOP). In a GOP, we resolve the LR frames from border frames to center target frame. Because super-resolved features in a GOP are cumulative, neighboring features are improved progressively. In addition, since this structure can utilize different frame rate to select the previous and future neighboring features, we can also deal with asymmetrical motion. We propose a temporal wavelet attention (TWA) module, in which attention is adopted for both spatial and temporal features. We incorporate the attention mechanism and discrete wavelet transform (DWT) into the proposed TWA to obtain elaborated spatio-temporal features simultaneously. Experimental results demonstrate that the proposed method achieves superior performance compared with state-of-the-art methods in both of quantitative and qualitative evaluations.
Joint Content and Radio Access for the Internet of Things: A Smart-Contract-Based Trusted Framework

Access control plays a fundamental role in securing the Internet of Things (IoT) systems. Recently, blockchain-enabled secure and trusted access control has attracted remarkable attention since it alleviates the centralization concern in traditional frameworks. However, existing designs only deal with access request validation problems whereas overlooks content delivery issues. Considering that radio resources are increasingly stretched with the rapid growth of IoT devices in the 5G era, this may give rise to unacceptably high latencies of legal requests in peak hours. This article addresses this limitation by presenting JCRA, a Joint Content and Radio Access control framework for IoT applications. JCRA deploys access point contracts (APCs) and device contracts (DCs) to manage radio access points (APs) and IoT devices, respectively. The atom access contract (AAC) is responsible for conducting access control according to access policies in DCs as well as radio resource states in APCs. To enable flexible radio resource management that often involves solving optimization models in AAC, we introduce a task offloading scheme, which recruits specialized executor nodes to complete the computation-heavy part off-chain. A Proof-of-Solution-Quality rule is proposed for executor solution validation which ensures the off-chain calculation is trusted. We implement a prototype of JCRA to demonstrate its feasibility. The costs and performance of JCRA are also evaluated in detail.
Polarimetric dehazing utilizing Vis-NIR spectral image fusion

Polarimetric dehazing methods have good ability in restoring images contaminated by haze. But noise and color distortion come along with the dehazing process, and those methods lack the ability to recover targets of long-distance. Our previous study has shown that the penetration ability of light is related with wavelength, light of long wavelength, especially in the infrared band, receives less attenuation from haze. Based on that fact, we decompose the dehazing process into 16 different spectral bands to achieve the optimized dehazing performance. An infrared band is added to enhance the dehazing performance in short-wavelength channels, thus increases the range of visibility and corrects the color distortion. The dehazed image is reconstructed through image fusion of the dehazed bands. Experimental analyses reveal enhanced details and reduced noise of dehazed images. Particularly, our method works well in retaining the color information of haze contaminated images. The amount of blueshift can be reduced by 71% at least.
Web Usage Mining—Process, Tools and Practices

Web usage mining is the use of data mining techniques to find patterns in web data to better understand and service the demands of Web-based applications. Data is growing at an exponential pace on the internet, and the number of users is growing at an exponential rate as well. In this case, mining the relevant data available on the web is vital, since this massive volume of data may give essential insights into online user behavior. Web mining is a technique for extracting valuable information from server log files. Weblogs are mined for patterns that include relevant information, this type of web mining is called web usage mining. This article aims to provide an overview of the process, as well as the tools used to access server log files and filter out unwanted information from vast amounts of data from blogs, as well as the applications of online usage mining and tools. Preprocessing, pattern discovery, and pattern analysis are the three processes of web usage mining. Web usage mining has sparked a surge of interest from both the academic and practice areas, owing to its wide range of applications. There is also an up-to-date survey of the existing work. Each of these phases is described in depth in this work.
M-DA: A Multifeature Text Data-Augmentation Model for Improving Accuracy of Chinese Sentiment Analysis

A neural network based on a word or character embedding is a mainstream model framework in text sentiment analysis and has achieved good results. However, there is a lack of learning about POS-Tagging and Sequence-Tagging. In this research, we propose a multifeature text data-augmentation model (M-DA) with a multiple-input single-output network structure to overcome this problem of Chinese text sentiment analysis. First, this paper sequentially obtains various sequences of Chinese text, including word sequence, pos sequence, char sequence, char_pos sequence, and char_4tag sequence, we use char_pos and the char_4tag to construct a new sequence (4tag_pos) and then use 4tag_pos to mark the characters to obtain the reconstructed characters sequence (char_4tag_pos), so as to achieve the purpose of text enhancement. Then, the Word2Vec method is used to train the initial reconstruction of the character embedding. Finally, the BiLSTM network is used to capture the long-term dependence between the sequences, and the dropout technology and attention are used to improve the accuracy. In the course of the experiment, we also realized that it is better to use the original sequence and the sequence after text enhancement technology as the input of the BiLSTM network. Therefore, our proposed model also discusses the concatenate or dot method to fuse multiple sequences as the final embedding. Multigroup comparison experiments are conducted on the data set, and the results show that the proposed M-DA model is superior to the traditional deep learning technology in terms of accuracy, recall rate, f-measure, and accuracy, and the relative time cost is small.
A Vision Transformer Model for Violence Detection from Real-Time Videos

Based on the rising incidences of crime and violence, it has become a matter of general importance that technology may be developed to automatically detect the presence of violence in the surveillance footage. For law enforcement, the detection of violent incidents can play an important role in urban safety. The efficiency of event detectors is usually measured in terms of detection speed, precision, and generality over many types of video inputs in a distinct format. However, various recent studies in this area have either focused on the correctness of the model, its response speed, or both, but have missed on considering the multiple data sources for effective model analysis. The main objective of the work is to propose a real-time violence event identifier based on state-of-the-art deep learning methods. Primarily the proposed model is based on the ViT (Vision Transformer) architecture, while other models like ConvLSTM and VGG16 with LSTM were also explored in this work. Two benchmark datasets viz. the RLVS (Real Life Violence Situations) and the Hockey fight datasets were used in this work for robust training and test analysis of the proposed model. For evaluating the model’s performance various metrics including accuracy, f1-score, precision, and recall were evaluated. The results show that the Vision Transformer-based model outperformed all the explored models with an overall accuracy of 98% and 97% on the RLVS dataset and the Hockey datasets respectively, which are also significantly higher than the recent solutions proposed on these datasets.
SNDProb: A probabilistic approach for streaming novelty detection

A probabilistic framework for streaming novelty detection is proposed and illustrated with a mixture of Gaussian distributions that models the set of classes. Instances are predicted based on the probability of belonging to each of the classes. Those for which the model cannot provide confident predictions are introduced into a fixed-sized buffer. When the buffer is full, an Expectation Maximization (EM) algorithm is run to search for new emerging classes in the buffer, and update the current model. The EM algorithm has to deal with an scenario where both probability distributions and instances are available. To overcome this issue, the probability distributions (classes) are weighted. The weights are inferred using a meta-regression model which has been pretrained and supplied with the proposed algorithm. Experiments have been run using synthetic datasets to have a close control over the class arrival strategies, the shape, and the overlapping degree between classes. It is shown that when the assumptions of the probabilistic model are fulfilled, the proposed method outperforms literature non-parametric approaches. Furthermore it obtains competitive results in the case of non-Gaussian classes. The experiments reveal, for the first time, the high sensitivity of the novelty detection algorithms to the class arrival strategies.
Crypto currency portfolio allocation Using Machine Learning

Objective: Unprecedented social and economic health patterns change or when a new normal arises, people prefer to seek safety and wealth preservation. Crypto is the buzzword nowadays where people worldwide are investing their money in a digital payment transaction and have ticked all the boxes when it comes to cases such as security, seemingly fast transactions, and has quickly emerged as one the world's fastest-growing financial markets. Bitcoin, the first cryptocurrency launched in 2009 showcased the proof of work of a decentralised system and what blockchain is capable of doing in a digital ecosystem. Scope: Our paper aims at suggesting currencies to future investors based on the sentiments on popular social media and predicting the overall growth of any coin to analyze its growth prospects. Method: In this paper, we have attempted a meticulous approach to predict the price movements in cryptocurrencies considering its fluctuating factors. Since no unique model can predict the market changes effectively, we focussed on creating a SETP (Sentiment Evaluation for Trend Prediction) model, which analyzes the sentiment surrounding the crypto market and the apprehensions that engulf an investor while investing. The statistical data analysis of the top 5 coins in the market has been undertaken effectively using IBM Watson services. Further enhancements have been made using the jupyter notebook to predict the market highs of the top trending coins. The sentimental evaluation has been done by extracting Twitter trends and hashtags thereby judging the sentiments of the microblogging site and paving a way to predict the exact emotion of investors all over the globe. Findings: Finally, we believe that this research has immense capability to predict the market speculation using the various technologies that are still in the rudimentary phase and has a lot of potential in invoking the interest of researchers and providing useful data/cognizance for future investors.
White Blood Cell Classification Using Convolutional Neural Network

 A CNN based model with low number of trainable parameters for classification of white blood cell types has been provided. The proposed model's performance assessed using several optimizers such as RMSprop, Adam, and Adagrad. We have compared four pre-trained models such as MobileNetV2, DenseNet121, InceptionV3 and ResNet50 with our proposed model.Unlike other studies, this paper provides a CNN based model with low number of trainable parameters for classification of white blood cell types.This model is made up of five convolution blocks, which are responsible for extracting the features, flatten layer, fully connected layers and a final FC layer with SoftMax activation function.Conv2D is used in the first convolution block, which is the input block, and SeparableConv2D (Separable convolutions) are used in the following convolution blocks.The proposed CNN model is shown in the below figure.Figure.Proposed CNN model.
Stealthy 3D Poisoning Attack on Video Recognition Models

Deep Neural Networks (DNNs) have been proven to be vulnerable to poisoning attacks that poison the training data with a trigger pattern and thus manipulate the trained model to misclassify data instances. In this article, we study the poisoning attacks on video recognition models. We reveal the major limitations of the state-of-the-art poisoning attacks on <i>stealthiness</i> and <i>attack effectiveness</i> : (i) the frame-by-frame poisoning trigger may cause temporal inconsistency among the video frames which can be leveraged to easily detect the attack; (ii) the feature collision-based method for crafting poisoned videos could lack both generalization and transferability. To address these limitations, we propose a novel stealthy and efficient poisoning attack framework which has the following advantages: (i) we design a 3D poisoning trigger as natural-like textures, which can maintain temporal consistency and human-imperceptibility; (ii) we formulate an ensemble attack oracle as the optimization objective to craft poisoned videos, which could construct convex polytope-like adversarial subspaces in the feature space and thus gain more generalization; (iii) our poisoning attack can be readily extended to the black-box setting with good transferability. We have experimentally validated the effectiveness of our attack (e.g., up to <inline-formula><tex-math notation="LaTeX">$95\%$</tex-math></inline-formula> success rates with only less than <inline-formula><tex-math notation="LaTeX">$\sim 0.5\%$</tex-math></inline-formula> poisoned dataset).
Multi-Teacher Knowledge Distillation for Incremental Implicitly-Refined Classification

Incremental learning methods can learn new classes continually by distilling knowledge from the last model (as a teacher model) to the current model (as a student model) in the sequentially learning process. However, these methods cannot work for Incremental Implicitly-Refined Classification (IIRC), an incremental learning extension where the incoming classes could have two granularity levels, a superclass label and a subclass label. This is because the previously learned superclass knowledge may be occupied by the sub-class knowledge learned sequentially. To solve this problem, we propose a novel Multi-Teacher Knowledge Distillation (MTKD) strategy. To preserve the subclass knowledge, we use the last model as a general teacher to distill the previous knowledge for the student model. To preserve the superclass knowledge, we use the initial model as a superclass teacher to distill the superclass knowledge as the initial model contains abundant superclass knowledge. However, distilling knowledge from two teacher models could result in the student model making some redundant predictions. We further propose a post-processing mechanism, called as Top-k prediction restriction to reduce the redundant predictions. Our experimental results on IIRC-ImageNet120 and IIRC-CIFAR100 show that the proposed method can achieve better classification accuracy compared with existing state-of-the-art methods.
Optimization of Hopfield Neural Network for Improved Pattern Recall and Storage Using Lyapunov Energy Function and Hamming Distance

In this paper, we propose a multiconnection-based Hopfield neural network (MC-HNN) based on the hamming distance and Lyapunov energy function to address the limited storage and inadequate recalling capability problems of Hopfield Neural Network (HNN). This study uses the Lyapunov energy function and Hamming Distance to recall correct stored patterns corresponding to noisy test patterns during the convergence phase. The proposed method also extends the traditional HNN storage capacity by storing the individual patterns in the form of etalon arrays through the unique connections among neurons. Hence, the storage capacity now depends on the number of connections and is independent of the total number of neurons in the network. The proposed method achieved the average recall success rate of 100% for bit map images with a noise level of 0, 2, 4, 6 bits, which is a better recall success rate than traditional and genetic algorithm-based HNN methods, respectively. The proposed method also shows quite encouraging results on hand-written images compared with some latest state of art methods.
Credit Card Fraud Detection Using Machine Learning Algorithms

Wrenching out of information from imbalanced information has been accepting expanding intrigued all over the world. Credit card based online installments has developed heightening, compelling the money related organizations to execute and ceaselessly progress their extortion location framework. In any case, credit card extortion dataset is intensely imbalanced and diverse sorts of misclassification mistakes may have distinctive costs and it is important to control them, to a certain degree, to compromise those mistakes. Classification procedures are the promising arrangements to identify the fraudulent and non-fraud exchanges. Classification procedures don't perform well when it comes to highly imbalanced dataset as the dataset is biased with the majority classes the accuracy score will come 99% but it will be of no use. So, with the use of different classifiers based on Random Forest, SVM, Decision Tree, logistics regression and oversampling by using SMOTE the performance metrics can be compared between balance and imbalance dataset.
Review of Serial and Parallel Min-Cut/Max-Flow Algorithms for Computer Vision

Minimum cut/maximum flow (min-cut/max-flow) algorithms solve a variety of problems in computer vision and thus significant effort has been put into developing fast min-cut/max-flow algorithms. As a result, it is difficult to choose an ideal algorithm for a given problem. Furthermore, parallel algorithms have not been thoroughly compared. In this paper, we evaluate the state-of-the-art serial and parallel min-cut/max-flow algorithms on the largest set of computer vision problems yet. We focus on generic algorithms, i.e., for unstructured graphs, but also compare with the specialized GridCut implementation. When applicable, GridCut performs best. Otherwise, the two pseudoflow algorithms, Hochbaum pseudoflow and excesses incremental breadth first search, achieves the overall best performance. The most memory efficient implementation tested is the Boykov-Kolmogorov algorithm. Amongst generic parallel algorithms, we find the bottom-up merging approach by Liu and Sun to be best, but no method is dominant. Of the generic parallel methods, only the parallel preflow push-relabel algorithm is able to efficiently scale with many processors across problem sizes, and no generic parallel method consistently outperforms serial algorithms. Finally, we provide and evaluate strategies for algorithm selection to obtain good expected performance. We make our dataset and implementations publicly available for further research.
Feature Selection for Identification of Fake Profiles on Facebook

Technology is advancing at a breakneck speed these days. Online Social Network (OSN), which has become a part of everyone's life in terms of making new friends and keeping track of friends and their interests. Social networking sites make social life better, but there are many problems when using these Social Media sites, especially Facebook. Problems i.e., privacy, offline, hacking are mainly done through fake profiles. Researchers found that 20–40% of profiles on social networking sites such as Facebook are fake profiles. So, the problem is to build an accurate model to detect if a Facebook profile is a fake profile based on the user's social activity using machine learning techniques. As it is an automatic detection technique, machine can make it easier for the sites to manage the huge number of profiles, which cannot be done manually. There are many previous works on the identification of fake profiles. So this paper proposes the minimal set of generic features to identify the fake profiles on Facebook and the study determines that minimized set of main features are significant in the detection of the fake accounts on Facebook.
Digital Transformation and Health Systems Performance in Global Settings During COVID-19

This chapter aims at providing a view of the recent digital transformations features and applications that have taken place amid the COVID-19 pandemic. The government-coordinated efforts worldwide have focused mainly on the pandemic containment and mitigation. The scale of coordination and data management required for effective implementation of the various containment and mitigation strategies across many countries has relied on adopting digital health applications, integrating them into health policy and healthcare services. This was observed in pandemic planning, surveillance, testing, contact tracing, quarantine, and healthcare services. Evaluating the implementation of these technologies, practices, and health delivery services worldwide, as well as factors supporting or hampering their use are highlighted in this chapter.