Reviewerly/exHarmony huggingface dataset:
Extracting Emotion Quotient of Viral Information Over Twitter

In social media platforms, a viral information or trending term draws attention, as it asserts potential user content towards topic/terms and sentiment flux. In real-time sentiment analysis, this viral information deliver potential insights, as encompass sentiment and co-located ranges of emotions be useful for the analysis and decision support. A traditional sentiment analysis tool generates the level of predefined sentiments over social media content for the defined duration and lacks in the extraction of emotional impact created by the same. In these settings, it is a multifaceted task to estimate precisely the emotional quotient viral information creates. The proposed novel algorithm aims, to (i) extract the sentiment and co-located emotions quotient of viral information and (ii) utilities for comprehensive comparison on co-occurring viral informations, and sentiment analysis over Twitter text data. The generated emotion quotients and micro-sentiment reveals several valuable insight of a viral topic and assists in decision support. A use-case analysis over real-time extracted data asserts significant insights, as generated sentiments and emotional effects reveals co-relations caused by viral/trending information. The algorithm delivers an efficient, robust, and adaptable solution for the sentiment analysis also.
Investigation of biases in convolutional neural networks for semantic segmentation using performance sensitivity analysis

The application of deep neural networks for segmentation in medical imaging has gained substantial interest in recent years. In many cases, this variant of machine learning has been shown to outperform other conventional segmentation approaches. However, little is known about its general applicability. Especially the robustness against image modifications (e.g., intensity variations, contrast variations, spatial alignment) has hardly been investigated. Data augmentation is often used to compensate for sensitivity to such changes, although its effectiveness has not yet been studied. Therefore, the goal of this study was to systematically investigate the sensitivity to variations in input data with respect to segmentation of medical images using deep learning. This approach was tested with two publicly available segmentation frameworks (DeepMedic and TractSeg). In the case of DeepMedic, the performance was tested using ground truth data, while in the case of TractSeg, the STAPLE technique was employed. In both cases, sensitivity analysis revealed significant dependence of the segmentation performance on input variations. The effects of different data augmentation strategies were also shown, making this type of analysis a useful tool for selecting the right parameters for augmentation. The proposed analysis should be applied to any deep learning image segmentation approach, unless the assessment of sensitivity to input variations can be directly derived from the network.
Quantum phase estimation based algorithms for machine learning

Quantum computing is certainly one of the greatest advances in the computer science field. Thanks to the parallelism and entanglement properties, it has proved to offer several advantages compared to the classical algorithms especially in the great reduction of the processing time. Quantum phase estimation (QPE) is one of the most important algorithms for quantum computing. It is known as the eigenvalue finding module for unitary operators. The Fourier transform is the key to this procedure. It has been researched and used to solve many problems such as the order finding problem, and the factoring problem. It was also applied for quantum sampling algorithms and the calculation of the eigenvalues of unitary matrices. In this paper, we study three important quantum algorithms for machine learning that use the QPE algorithm as a subroutine: the quantum principal components analysis (PCA) for data visualization, the Harrow-Hassidim-Lloyd (HHL) algorithm for solving linear systems, and the quantum singular value thresholding (SVT) for matrix completion in recommender systems. We also discuss the advantages and limits of such algorithms compared to their classical versions. Then we discuss potential ways of amelioration of such algorithms, and end with a proposed approach for further improvement.
Algorithms to Synthesis for Adaptive Sub-Optimal Control of Dynamic Objects Based on Regular Methods

Regular algorithms for the synthesis of adaptive suboptimal control of dynamic objects based on regular methods are presented. Moreover, the statistical properties of the variational method for identifying linear dynamical systems are described. To determine the variational estimate of the parameters on the system, the conditions of the minimum “distance” between the set of measurements and the set of trajectories of the model are used. The possibilities of the variational method are analyzed to solve the problem of parametric identification when the length of observation interval is limited. To add, the algorithms presented allow to regularize the considered problem of adaptive suboptimal control of dynamic objects based on regular methods.
Knowledge-Based Medicine Recommendation Using Domain Specific Ontology

Our research motivation is to make ease and support decision-making process in medical field by providing recommendations to medical practitioners, while carrying out any clinical diagnosis. There are many ongoing related research works which provide recommendation in medical field, our work stands out over others by providing the necessary information’s as recommendations, while carrying out clinical diagnosis. Thus, proposed system was developed in such a way that it acts as a second opinion to rectify their doubts during the entire process of clinical diagnosis. Existing works do not provide necessary information when combined to form meaningful knowledge representations. We try to sort out these problems by creating a conceptual structure which combines disease symptom and medicine domain. It also tries to create and represent knowledge on therapeutic domain by combining Allopathy medicines and Homeopathy medicines. To represent with end users, we developed an interface model namely knowledge-based clinical assistance (KBCA) to provide a working prototype to derive the recommendation.
Linguistic Dependencies and Statistical Dependence

Are pairs of words that tend to occur together also likely to stand in a linguistic dependency? This empirical question is motivated by a long history of literature in cognitive science, psycholinguistics, and NLP. In this work we contribute an extensive analysis of the relationship between linguistic dependencies and statistical dependence between words. Improving on previous work, we introduce the use of large pretrained language models to compute contextualized estimates of the pointwise mutual information between words (CPMI). For multiple models and languages, we extract dependency trees which maximize CPMI, and compare to gold standard linguistic dependencies. Overall, we find that CPMI dependencies achieve an unlabelled undirected attachment score of at most $\approx 0.5$. While far above chance, and consistently above a non-contextualized PMI baseline, this score is generally comparable to a simple baseline formed by connecting adjacent words. We analyze which kinds of linguistic dependencies are best captured in CPMI dependencies, and also find marked differences between the estimates of the large pretrained language models, illustrating how their different training schemes affect the type of dependencies they capture.
A Theoretical Framework for Managing Suspense in Games

Suspense is one of the most important emotions for the enjoyment of games. Many techniques have been developed to generate and manage suspense in games, but a theoretical framework for this body of work is missing. In this paper, we present a framework that identifies the main components for suspense management in games. This framework includes both the cognitive model and computational model of suspense. For the computational model of suspense, we identify the affective loop between Player Suspense Model, Suspense Manager, and game content. This framework can help game designers better understand the various options and tools for creating and managing suspense and provides a road map for developing new techniques.
Reversible Digital Image Watermarking scheme using Histogram Shifting Method

Basic requirement of reversible Image watermarking is large embedding capacity and better perceptual quality with minimum distortion. In this paper, a reversible image watermarking scheme using histogram shifting method is proposed. For embedding secret message data, peak point is selected and shifted to its nearest neighbor. The proposed technique is used to reconstruct the cover image and extract the embedded message bits accurately. Various test images are evaluated using proposed watermarking scheme and results show good performance in terms of embedding capacity, Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE). Experimental results are also compared with existing technique and proposed method performs better.
Radar Signal Sorting Using Combined Residual and Recurrent Neural Network (CRRNN)

Due to the density of the crowded electromagnetic environment nowadays and the complexity of modern radar signals, the performance of pulse repetition interval (PRI)-based sorting systems experience more deterioration than ever before. Such systems are considered unreliable when working in crowded circumstances, moreover, they require a long pulse stream and high signal-to-noise (SNR) ratio, which makes obtaining acceptable sorting accuracy a difficult task. In this paper, a new machine learning architecture, Combined Residual and Recurrent Neural Network (CRRNN), is proposed, where recurrent neural network (RNN) and residual neural network (ResNet) are incorporated to create an architecture which can be used to overcome the above-mentioned shortcomings of conventional sorting methods achieving more accuracy and stability. Separate ResNet and RNN models are investigated as well for comparison. Simulations are performed after discussion of the structure and the principle of work of each network architecture. Statistical results showing the high and reliable performance of the proposed method in different conditions are presented and discussed.
Hybrid Models Based on LSTM and CNN Architecture with Bayesian Optimization for ShortTerm Photovoltaic Power Forecasting

The precision and reliability of photovoltaic (PV) power forecasting play a crucial role in commercial PV plants. However, the stochastic and intermittent nature of solar radiation makes prediction difficult. Inspired by this, 4 different deep learning-based hybrid models are proposed to predict short-term PV power generation using long short term memory (LSTM) neural network and convolutional neural network (CNN) based on Bayesian Optimization (BO) in this paper. In addition, this paper explores feature selection using two benchmark models on different feature sets, and finally selects 5 features for prediction. The performances of direct forecasting results for both 1-hour ahead and 24-hour ahead of the above various models are compared on one year of hourly data from a real PV plant in Shandong, China. It is shown that using Bi-directional LSTM (BiLSTM) and CNN-BiLSTM models are more suitable for 1-hour ahead prediction, LSTM-CNN and CNN-BiLSTM models are more suitable for 24-hour ahead prediction. The case study shows that the model with Bayesian optimized optimal weights can reduce the error rate by up to 32.80% compared to the benchmark model and demonstrates the good prediction performance of the proposed approach on commercial PV plants.
Outlier Detection Method based on Improved K-means Clustering Algorithm

Outlier detection is one of the important methods of data mining and analysis. The previous K-means outlier detection methods are easy to be misled by outliers in the clustering process and cannot detect outliers well. To solve this problem, we propose a method of outlier detection based on an improved K-means clustering algorithm. This method adaptively detects edge points (including outliers and edge points of other clusters) through the local threshold obtained from the distance distribution of the data points in the cluster to the centroid. The edge points are ignored when the centroid is updated to avoid the situation that the edge points mislead the centroid update. After completing the clustering, the farthest point in each cluster is put into the outlier cluster iteratively. When the average distance of the data in the cluster reaches stability or reaches the maximum number of iterations, the iteration ends. This method retains the ease of use of traditional K-means which accelerates the speed of clustering iteration and finds outliers accurately in data sets. Experimental results show that the algorithm can effectively detect outliers and have good results on multiple data sets.
Properly calculating estat phtest in the presence of stratified hazards

The Schoenfeld residual-based test for proportional-hazards violations in the Cox duration model is predicated on a key assumption pertaining to homoskedasticity in the residuals. This assumption is likely to be violated in the presence of stratified hazards, which is noted in estat phtest’s help file. We provide a wrapper command, stratph, that implements a straightforward modification to the residual-based diagnostic that corrects for the potential assumption violation. We use the stratified hazards example from the stcox help file to demonstrate the stratph command’s workings.
Face recognition based on scale invariant feature transform and fuzzy reasoning

To alleviate the limitations that existing face recognition methods, this paper describes a new face recognition method based on scale invariant feature transform (SIFT) and fuzzy reasoning. Experimental results demonstrate that the proposed method yields a better performance in terms of the correct classification percentages compared with the recent face recognition algorithms.
Weather Forecast Using Sliding Window Algorithm Based on Hadoop and MapReduce

Weather has a huge impact on every day human life like planning outdoor events, agriculture, aviation, etc. It is very important to apprehend it in every way possible and be able to predict it as accurately as possible. In recent years, we have accumulated huge amounts of data, which make it presumptive to extract as much information as possible. In this paper, we propose an approach based on Hadoop and MapReduce technologies using Sliding Window Algorithm to forecast the weather in the region of Beni Mellal-Khenifra.
Detection of Wrong Direction Vehicles on Two-Way Traffic

In 2018, around 22,000 people were killed in road accidents in Thailand and more than 80% of road accidents involved motorcycles. There are several reasons and one of them is motorcycles driving in the wrong direction. Our paper proposes a system for road surveillance to detect and track moving vehicle's direction on two-way traffic roads from CCTV viewpoint using deep learning where the roadside noise is filtered out. The system can determine whether the vehicles drive in the correct direction in their lane. In this case, we will focus on motorcycles. To set up the system, there are two main steps: draw the area of interest and validate the correct direction. In the direction validation process, we detect and track vehicles by using YOLOv4-tiny and Deep SORT tracking algorithm. The system returns a satisfactory and accurate result for detecting direction without noise, such as moving objects on the sidewalk or moving vehicles in an unwanted area.
Hierarchical MultiClass AdaBoost

One of the most challenging machine learning problems is a particular case of classification in which classes are hierarchically structured and data instances can be assigned multiple labels residing in a path of the hierarchy. In this paper, we propose hierarchy–aware multiclass AdaBoost, allowing for the first time weak classifiers in an ensemble learning setting to be trained for hierarchical multiclass classification while incorporating a hierarchy–aware loss function directly into the training process. Experimental results on numerous real–world datasets show that, despite its simplicity, the proposed algorithm outperforms all baselines, arising as the state of the art in hierarchical multiclass classification.
Cybercrime in a time of coronavirus

An alert issued jointly by the US Department of Homeland Security's Cyber security and Infrastructure Security Agency (CISA) and the UK's National Cyber Security Centre (NCSC) warns that so-called advanced persistent threat (APT) groups are exploiting the Covid-19 pandemic to ramp up operations targeted against organisations involved in responses to the emergency at both national and international levels.
Design of Quadrotor Intelligent Rescue UAV Based on UAV Vision

In the frontier research, UAV technology has developed rapidly, showing excellent application prospects in disaster relief and emergency treatment, etc. According to the international requirements of “accelerating the construction of early warning and monitoring system for scientific and technological safety around artificial intelligence, UAV and other technologies”. In this study, the dynamic modeling and control design of quadrotor UAV is carried out by PID control, and the HOG-SVM pedestrian detection model is realized by OpenCV and VS compiler, and a quadrotor rescue UAV based on SVM model is designed. Experiments show that the UAV performs well in pedestrian recognition in actual search.
A novel model for voice command fingerprinting using deep learning

Smart speakers are becoming increasingly popular and permeate many aspects of human life. To improve the security of smart speakers, voice commands transmitted over a network are encrypted; however, user privacy issues related to smart speakers continue to emerge. In fact, attackers are still able to infer the content of a user's specific voice commands from encrypted traffic through machine learning methods to obtain private information for advertising or to carry out malicious attacks. This traffic analysis attack is referred to as a voice command fingerprinting attack. In recent years, research on improving the accuracy of voice command fingerprinting attacks has become a hot topic and remains a challenging task. To improve the accuracy of voice command fingerprinting attacks, we design a new method in this paper. We use an adaptive and dilated residual network to process spatial features. In addition, we find that using temporal features helps improve fingerprinting attack accuracy, and therefore design an attention-based bidirectional gated recurrent unit. Then, we effectively combine the two models. Our method achieves an accuracy greater than 93.36% in a closed-world scenario, which exceeds those of other state-of-the-art methods (2020 WiSec Wang et al.). In a more realistic open-world setting, our model is still effective, obtaining a true-positive rate of 99.50% and a false-positive rate of 0.1% compared to Sirinam et al.'s rates of 90.66% and 0.1%, respectively. We also demonstrate that our model has good generalizability, as our model can also be applied to website fingerprinting and outperforms 2018 CCS Sirinam et al.
A Neurodynamic Model of Saliency Prediction in V1

Lateral connections in the primary visual cortex (V1) have long been hypothesized to be responsible for several visual processing mechanisms such as brightness induction, chromatic induction, visual discomfort, and bottom-up visual attention (also named saliency). Many computational models have been developed to independently predict these and other visual processes, but no computational model has been able to reproduce all of them simultaneously. In this work, we show that a biologically plausible computational model of lateral interactions of V1 is able to simultaneously predict saliency and all the aforementioned visual processes. Our model's architecture (NSWAM) is based on Penacchio's neurodynamic model of lateral connections of V1. It is defined as a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation, and scale. We tested NSWAM saliency predictions using images from several eye tracking data sets. We show that the accuracy of predictions obtained by our architecture, using shuffled metrics, is similar to other state-of-the-art computational methods, particularly with synthetic images (CAT2000-Pattern and SID4VAM) that mainly contain low-level features. Moreover, we outperform other biologically inspired saliency models that are specifically designed to exclusively reproduce saliency. We show that our biologically plausible model of lateral connections can simultaneously explain different visual processes present in V1 (without applying any type of training or optimization and keeping the same parameterization for all the visual processes). This can be useful for the definition of a unified architecture of the primary visual cortex.
Lightweight Alternatives for Hyper-parameter Tuning in Drifting Data Streams

Scenarios dealing with data streams often undergo changes in data distribution, which ultimately lead to a performance degradation of algorithms learning from such data flows (concept drift). This phenomenon calls for the adoption of adaptive learning strategies for algorithms to perform resiliently after a change occurs. A multiplicity of approaches have so far addressed this issue by assorted means, e.g. instances weighting, ensembling, instance selection, or parameter tuning, among others. This latter strategy is often neglected as it requires a hyper-parameter tuning process that stream learning scenarios cannot computationally afford in most practical settings. Processing times and memory space are usually severely constrained, thus making the tuning phase unfeasible. Consequently, the research community has largely opted for other adaptive strategies with lower computational demands. This work outlines a new perspective to alleviate the hyper-parameter tuning process in the context of concept drift adaptation. We propose two simple and lightweight mechanisms capable of discovering competitive configurations of learning algorithms used for data stream classification. We compare its performance to that of a modern hyper-parametric search method (Successive Halving) over extensive experiments with synthetic and real datasets. We conclude that our proposed methods perform competitively, while consuming less processing time and memory.