Reviewerly/exHarmony huggingface dataset:
Temporal-Variation Skeleton Point Correction Algorithm for Improved Accuracy of Human Action Recognition

The technique of human action recognition can be applied in a number of fields, such as medical rehabilitation, posture in the domain of sports, and emotion perception in counselling. Deep learning action recognition models that consider the continuous changes in the human skeleton can efficiently identify action states, such as rehabilitation processes, posture, and changes in emotion. However, the keypoints of the human skeleton are susceptible to occlusion due to body movement, resulting in a lower confidence level in their positions, which in turn affects the training and execution of human action recognition models. This paper proposes a keypoint correction algorithm based on continuous-time images of temporal variation that can predict and correct the skeleton keypoints with low confidence, based on the previous and subsequent multiple images. The proposed temporal-variation skeleton keypoint correction algorithm can provide accurate feature association training for the locations of the skeleton keypoints. The proposed method is shown to improve the accuracy of human action recognition by at least 30% over three alternative models in the literature: STV-GCN, GCN-NAS, and 2S-AGCN. The proposed algorithm is also improved by at least 30% compared with the above-mentioned methods in the literature, based on an action recognition model with a Gaussian filter.
Deep Learning for Chinese Language Sentiment Extraction and Analysis

In recent years, vocabulary emotion processing has become immensely popular and the requirements for language emotion analysis mining and processing have become significantly abundant. The sentiment extraction and analysis work has always been very challenging; especially, the Chinese word segmentation operation is difficult to deal with effectively, the multiple combinations of implicit and explicit words make the task of sentiment analysis mining more difficult, and, in particular, the efficiency of machine analysis of language sentiment is feeble. We use some expressions and sentiment vocabulary dictionaries combined with hybrid structures and use information synergy methods to get in touch with sentiment analysis methods. We use the relevant sentiment to evaluate the explicit or implicit emotional association of the emotional connection of the vocabulary and add the unique emotional word matrix to analyze the related clustering results of the emotional words to continuously optimize and upgrade the performance, so that our sentiment analysis results are systematic in terms of efficiency and significantly improved.
Estimating Solar and Wind Power Production Using Computer Vision Deep Learning Techniques on Weather Maps

Accurate renewable energy production forecasting has become a priority as the share of intermittent energy sources on the grid increases. Recent work has shown that convolutional deep learning models can successfully be applied to forecast weather maps. Building on this capability, a ResNet‐inspired model that estimates solar and wind power production based on weather maps is proposed. By capturing both spatial and temporal correlations using convolutional neural networks with stacked input frames, the model is designed to capture the complex dynamics governing these energy sources. A dataset that focuses on the state of California is constructed and made available as a secondary contribution of the work. It is demonstrated that the novel model outperforms traditional deep learning techniques: it predicts an accurate power production profile that is smooth and includes high‐frequency details.
Underwater image restoration with Haar wavelet transform and ensemble of triple correction algorithms using Bootstrap aggregation and random forests

Abstract This paper presents both a new strategy for traditional underwater image restoration using Haar wavelet transform as well as a new learned model that generates an ensemble of triple correction algorithm labels based on histogram quadrants’ cumulative distribution feature instead of generating pixel intensities. The Haar wavelet transform is our tentative dynamic stretching plan that is applied on the input image and its contrast stretched image to generate the degraded wavelet coefficients which are blended using Gaussian pyramid of the saliency weights to restore the original image. The ensemble of triple corrections exerts three color correction algorithms sequentially on the degraded image for restoration. The ensemble of algorithms entails the superposition effect of the red channel mean shifting, global RGB adaptation, global luminance adaptation, global saturation adaptation, luminance stretching, saturation stretching, contrast stretching, adaptive Gamma correction for red spectrum, even to odd middle intensity transference using look-up table, green to red spectrum transference using histogram equalization, local brightening, Dark Channel Prior, fusion restoration, and our Haar wavelet transform restoration. The source is available at https://github.com/vahidr213/Underwater-Image-Restoration-And-Enhancement-Collection .
Older Adults’ Interaction With Intelligent Virtual Assistants: The Role of Information Modality and Feedback

Intelligent virtual assistants (IVAs) can help older adults with information queries. Examining older adults’ preferences for IVAs’ information presentation can help improve user experience and older adults’ acceptance of IVAs. This study investigated the effects of information modality and feedback on older adults’ social presence, attitudinal outcomes (i.e., perceived enjoyment and satisfaction), and acceptance (i.e., perceived ease of use, perceived usefulness, and behavioral intention to use). A total of 102 subjects were recruited to participate in two experiments. Results show that the visual-auditory bimodality is superior to single visual modality and single auditory modality for older adults based on the perceptions of social presence, attitudinal outcomes, and acceptance. Older adults perceived greater social presence, perceived enjoyment, satisfaction, and acceptance with text feedback than without in IVAs. Social-oriented voice feedback can improve older adults’ perceptions of social presence, enjoyment, satisfaction, and acceptance than task-oriented voice feedback. This study provides practical implications in the design of IVAs’ information presentation targeted at older adults.
State-of-the-Art in Open-Domain Conversational AI: A Survey

We survey SoTA open-domain conversational AI models with the objective of presenting the prevailing challenges that still exist to spur future research. In addition, we provide statistics on the gender of conversational AI in order to guide the ethics discussion surrounding the issue. Open-domain conversational AI models are known to have several challenges, including bland, repetitive responses and performance degradation when prompted with figurative language, among others. First, we provide some background by discussing some topics of interest in conversational AI. We then discuss the method applied to the two investigations carried out that make up this study. The first investigation involves a search for recent SoTA open-domain conversational AI models, while the second involves the search for 100 conversational AI to assess their gender. Results of the survey show that progress has been made with recent SoTA conversational AI, but there are still persistent challenges that need to be solved, and the female gender is more common than the male for conversational AI. One main takeaway is that hybrid models of conversational AI offer more advantages than any single architecture. The key contributions of this survey are (1) the identification of prevailing challenges in SoTA open-domain conversational AI, (2) the rarely held discussion on open-domain conversational AI for low-resource languages, and (3) the discussion about the ethics surrounding the gender of conversational AI.
Order selection for regression-based hidden Markov model

Hidden Markov models (HMMs) describe the relationship between two stochastic processes: an observed process and an unobservable finite-state transition process. Owing to their modeling dynamic heterogeneity, HMMs are widely used to analyze heterogeneous longitudinal data. Traditional HMMs frequently assume that the number of hidden states (i.e., the order of HMM) is a constant and should be specified prior to analysis. This assumption is unrealistic and restrictive in many applications. In this study, we consider regression-based hidden Markov model (RHMM) while allowing the number of hidden states to be unknown and determined by the data. We propose a novel likelihood-based double penalized method, along with an efficient expectation-conditional maximization with iterative thresholding-based descent (ECM–ITD) algorithm, to perform order selection in the context of RHMM. An extended Group-Sort-Fuse procedure is proposed to rank the regression coefficients and impose penalties on the discrepancy of adjacent coefficients. The order selection consistency and convergence of the ECM–ITD algorithm are established under mild conditions. Simulation studies are conducted to evaluate the empirical performance of the proposed method. An application of the proposed methodology to a real-life study on Alzheimer's disease is presented.
Camera-LiDAR Cross-Modality Fusion Water Segmentation for Unmanned Surface Vehicles

Water segmentation is essential for the autonomous driving system of unmanned surface vehicles (USVs), which provides reliable navigation for making safety decisions. However, existing methods have only used monocular images as input, which often suffer from the changes in illumination and weather. Compared with monocular images, LiDAR point clouds can be collected independently of ambient light and provide sufficient 3D information but lack the color and texture that images own. Thus, in this paper, we propose a novel camera-LiDAR cross-modality fusion water segmentation method, which combines the data characteristics of the 2D image and 3D LiDAR point cloud in water segmentation for the first time. Specifically, the 3D point clouds are first supplemented with 2D color and texture information from the images and then distinguished into water surface points and non-water points by the early 3D cross-modality segmentation module. Subsequently, the 3D segmentation results and features are fed into the late 2D cross-modality segmentation module to perform 2D water segmentation. Finally, the 2D and 3D water segmentation results are fused for the refinement by an uncertainty-aware cross-modality fusion module. We further collect, annotate and present a novel Cross-modality Water Segmentation (CMWS) dataset to validate our proposed method. To the best of our knowledge, this is the first water segmentation dataset for USVs in inland waterways consisting of images and corresponding point clouds. Extensive experiments on the CMWS dataset demonstrate that our proposed method can significantly improve image-only-based methods, achieving improvements in accuracy and MaxF of approximately 2% for all the image-only-based methods.
Data Integrity Auditing Scheme for Preserving Security in Cloud based Big Data

Cloud computing has become an integral part of medical big data. The cloud has the capability to store the large data volumes has attracted more attention. The integrity and privacy of patient data are some of the issues that cloud-based medical big data should be addressed. This research work introduces data integrity auditing scheme for cloud-based medical big data. This will help minimize the risk of unauthorized access to the data. Multiple copies of the data are stored to ensure that it can be recovered quickly in case of damage. This scheme can also be used to enable doctors to easily track the changes in patients' conditions through a data block. The simulation results proved the effectiveness of the proposed scheme.
EduCrypto: Transforming Education using Blockchain

Blockchain is a trending technology that is gaining popularity for its widespread application in various sectors, including education. Academic certificates are an important measure of an individual’s abilities. They are especially important for higher education and employment. However, advanced technologies have enabled the creation of fake documents. The paper counters this problem by proposing a blockchain-IPFS based solution. The proposed system is an Ethereum blockchain and IPFS based system for storing, retrieving, and authenticating educational documents as well as streamlining the entire process of examination and result generation. The proposed system comprises three main modules: Admission; Examination and Results; Third-Party Module. The authenticity of any document is guaranteed since it is shared within a secured environment. Existing research on similar solutions suffers from poor testing, lack of evaluation, and inefficient storage models. The proposed system overcomes these drawbacks. The proposed system was also evaluated for uploading and fetching latency. The average results show that the system has a mean execution time of 10176 milliseconds for uploading a 1.45MB document and a mean execution time of 110.7 milliseconds for fetching 6 documents each of size 214 kB. Thus, the system is found to be efficient in addition to being safe and secure.
Toxic Speech Classification using Machine Learning Algorithms

In today's era of online social media platforms, there has been a massive surge in the propagation of toxic content speech. They provide many betterments. However, persons with considerable differences in their viewpoints have contributed to an increase in lethality of people in internet posts and debates.. With the outbreak of the pandemic, corporations, educational institutions, students, and the general public have all increased their usage in web sites. For a long time, the growing popularity of internet platforms like Twitter and Facebook has been a major cause of anxiety. These platforms not only allow for improved communication, but they also allow the users to express their thoughts, which are quickly shared with the rest of the world. Furthermore, given the diversity of these platforms' users' histories, beliefs, race, and customs, many of them choose to use disparaging, abusive, and antagonistic language while interacting with those who do not share their background. This online toxicity has been increasing exponentially by advancements provided by these social media platforms in this emerging world under the cloud of anonymity. Unlike manually, this problem can be solved using Machine Learning. Phrases like “Obscene”,”Toxic”, “Severe Toxic”,”Threat”, “Insult”,”Identity Hate” are used mutually and hence have been incorporated under “Toxic” speech content. As a result, it is vital to recognise and eliminate toxic speech from internet - based social media networks naturally. The numerous varieties of Machine Learning approaches, such as traditional Machine Learning, ensemble approach are explored in this paper. We use a corpus collected from online platform twitter to do binary and multi-class classification and investigate two techniques.: (a) a method which consists in extracting of word embeddings and then generating the model; (b)Improving the existing models- RF, DT, VC, LR, KNN. Any other sort of social media comment can be analyzed using the proposed methods. By this, we developed a model that can classify given comments into different categories of toxicity with greater precision, recall, and accuracy score.
Speckle Reduction in Ultrasound Images of the Common Carotid Artery Based on Integer and Fractional-Order Total Variation

Designing a technique with higher speckle noise suppressing capability, better edge preserving performance, and lower time complexity is a research objective for the common carotid artery (CCA) ultrasound despeckling. Total variation based techniques have been widely used in the image denoising and have good performance in preserving the edges in the images. However, the total variation based filters can produce the staircase artifacts. To address this issue, second-order total variation based techniques have been proposed for the image denoising. However, the previous study has been proved that the fractional differential model has better performance in reducing the speckles in ultrasound despeckling compared with the second-order model. Thus, to improve the performance of ultrasound despeckling and edge preserving, a novel despeckling model based on integer and fractional-order total variation (IFOTV) is proposed for CCA ultrasound images. Moreover, the minimization problems in our despeckling model are solved by the alternating direction method of multiplier (ADMM). In results with synthetic images, the edge preservation index (EPI) values of proposed method are 0.9524, 0.8797, and 0.7351 as well as 0.9137, 0.8253, and 0.6847 under three different levels of noise, which are the highest among four advanced methods. In results with simulated CCA ultrasound images, the speckle suppression and mean preservation indices of proposed method are 0.5596, 0.6571, and 0.8106 under three different levels of noise, which are the best among four advanced methods. In results with clinical images, the average absolute error of intima-media thickness measurements of proposed method is 0.0660 ± 0.0679 (mean ± std in mm), which is the lowest among four advanced methods. In conclusion, the IFOTV method has improved performance in suppressing the speckle noise and preserving the edge, and is thus a potential alternative to the current filters for the CCA ultrasound despeckling.
Convergence analysis and improvement of progressive Gaussian approximation filters

This paper studies the convergence problem of progressive Gaussian approximation filters (PGAFs). The convergence analysis of the PGAF is presented based on the Lyapunov method. It is proved that the estimation error is bounded when the convergence conditions hold. Moreover, based on the convergence analysis, the filtering performance of the PGAF is improved by adjusting the strategy of the progressive measurement update. Finally, a simulation experiment of mobile target tracking is designed to verify the effectiveness of the analysis results by comparing the improved PGAF with some existing methods.
Night-Time Human Detection From UAV

The Unmanned Aerial Vehicle is an effective vehicle for rescue, search, and surveillance missions. A thermal camera improves the UAV system to operate these missions in the nighttime. Real-time human detection is an algorithm to increase performance and improve to be fully autonomous in rescue missions. Many studies have led to the integration of realtime human detection from thermal aerial images, but the task remains difficult from various human features from multi capture angle and UAV altitude. This paper proposes an experimental process for implementing real-time human detection from UAVs in the nighttime. We choose the YOLOv3 model for real-time human detection. Then, we create a custom thermal aerial human dataset that multi-capturing angle and altitude. The dataset is captured in the same condition of UAVs operation. We prepare and preprocess the dataset before sending it to the model training process. Finally, we evaluate a trained model for mean-Average Precision. The accuracy of prediction is evaluated with a test set and real-time detection performance. The results demonstrate that the model can detect a human in real-time with a thermal image from a UAV view and the accuracy of detection is mAP of 64.8% in the operating range of the UAV.
A Clustering Ensemble Framework with Integration of Data Characteristics and Structure Information: A Graph Neural Networks Approach

Clustering ensemble is a research hotspot of data mining that aggregates several base clustering results to generate a single output clustering with improved robustness and stability. However, the validity of the ensemble result is usually affected by unreliability in the generation and integration of base clusterings. In order to address this issue, we develop a clustering ensemble framework viewed from graph neural networks that generates an ensemble result by integrating data characteristics and structure information. In this framework, we extract structure information from base clustering results of the data set by using a coupling affinity measure After that, we combine structure information with data characteristics by using a graph neural network (GNN) to learn their joint embeddings in latent space. Then, we employ a Gaussian mixture model (GMM) to predict the final cluster assignment in the latent space. Finally, we construct the GNN and GMM as a unified optimization model to integrate the objectives of graph embedding and consensus clustering. Our framework can not only elegantly combine information in feature space and structure space, but can also achieve suitable representations for final cluster partitioning. Thus, it can produce an outstanding result. Experimental results on six synthetic benchmark data sets and six real world data sets show that the proposed framework yields a better performance compared to 12 reference algorithms that are developed based on either clustering ensemble architecture or a deep clustering strategy.
Competitive Swarm Improved Invasive Weed Optimization-Based Secret Sharing Scheme for Visual Cryptography

Visual cryptography is a cryptographic method that encrypts visual facts and the information decrypted appears as an original visual image. Here, the confidential data is shared by dividing the visual details into multiple shares such that the encryption and the decryption are performed for reconstructing the original image back by stacking the splitted shares. However, the data sharing process lacks adequate security protection. To overcome the issues, this paper presents a robust visual cryptography-based data sharing scheme, named Competitive Swarm Improved Invasive Weed Optimization (CSIIWO) for sharing multiple data. Initially, the grayscale input image is shared between several parties, such that the shared image is encrypted and decrypted for retrieving the original image back. However, the cryptographic key generation is done using the devised CSIIWO algorithm such that the cryptographic keys are generated for the data sharing process to maintain confidentiality among multiple parties. Moreover, the devised method achieved the maximum Peak signal-to-noise ratio (PSNR) of 40.749 dB, minimum conditional privacy loss of 0.508, and maximum entropy of 7.987, respectively using Lena image. Furthermore, the proposed scheme can protect the multiple data sharing information and has numerous advantages compared with the previous mechanisms.
Formally verified lifting of C-compiled x86-64 binaries

Lifting binaries to a higher-level representation is an essential step for decompilation, binary verification, patching and security analysis. In this paper, we present the first approach to provably overapproximative x86-64 binary lifting. A stripped binary is verified for certain sanity properties such as return address integrity and calling convention adherence. Establishing these properties allows the binary to be lifted to a representation that contains an overapproximation of all possible execution paths of the binary. The lifted representation contains disassembled instructions, reconstructed control flow, invariants and proof obligations that are sufficient to prove the sanity properties as well as correctness of the lifted representation. We apply this approach to Linux Foundation and Intel's Xen Hypervisor covering about 400K instructions. This demonstrates our approach is the first approach to provably overapproximative binary lifting scalable to commercial off-the-shelf systems. The lifted representation is exportable to the Isabelle/HOL theorem prover, allowing formal verification of its correctness. If our technique succeeds and the proofs obligations are proven true, then – under the generated assumptions – the lifted representation is correct.