Reviewerly/exHarmony huggingface dataset:
Autoregressive-Elephant Herding Optimization based Generative Adversarial Network for copy-move forgery detection with Interval type-2 fuzzy clustering

A copy move forgery is considered as the general form of digital image forgery, where a portion of the image is copied as well as pasted in some other position in the same image. To act upon forgery is simple; however detecting the forgery is more complex due to the copied portions’ features similar to other parts of images. Therefore, an effectual forgery object detection approach is exploited by exploiting the adopted Autoregressive Elephant Herding Optimization based Generative Adversarial Network (A-EHO based GAN). The proposed A-EHO approach is derived by incorporating Conditional Autoregressive Value at Risk by Regression Quantiles (CAViaR) with Elephant Herding Optimization (EHO). First, features like Local optimal oriented pattern (LOOP) and Convolutional Neural Network (CNN) features are extracted for each foreground object. Then, the features are employed to the GAN for the computation of the forgery score. Here, the RideNN classifier detects the forgery image based on the feature vector and the forgery score. As a result, the adopted approach achieved higher performance regarding detection rate, ROC, TNR, as well as TPR with the values of 96.687%, 98.35%, 97.809%, and 96.971%, respectively.
Sonic Technologies of a Queer Breakup

Over the past decade, the growth of voice assistants has presented new challenges within domestic life. Prior research has shown that such technologies affect users unevenly or fail to account for some relationships and domesticities entirely. Our work investigates the under-examined topic of queerness at home. Drawing on the experience of queer breakup, we describe a design inquiry and a first-person research approach exploring two concurrent relationships in separate households both using Alexa. We explore issues of temporality, glitch, and shared accountability. We also ask critical questions with audio experiments, including: How do voice assistants differentiate between queer voices? How should we converse with voice assistants about queerness? And are voice assistants “queer enough”? We contribute a discussion of difference, inclusion, and queer cultures of adversarial use to highlight the limitations of both everyday and professional language for describing and analyzing the particulars of queerness and interaction design.
Will–Skill–Tool Components as Key Factors for Digital Media Implementation in Education: Austrian Teachers’ Experiences with Digital Forms of Instruction during the COVID-19 Pandemic

Although comprehensive digitalization (e.g., the provision of skills and resources) had already been placed on Austria’s education policy agenda prior to the emergence of COVID-19, there is evidence that educators had some difficulty ensuring digital learning opportunities for their students when schools closed in early 2020. Against this backdrop, the present study, which drew on qualitative data from the large-scale INCL-LEA (Inclusive Home Learning) study, aimed to determine whether secondary school teachers (n = 17) from Viennese schools met the prerequisites for successfully implementing digital instruction, formulated in the Will–Skill–Tool model developed by Christensen and Kzenek (2008). Findings reveal that teachers primarily associated their sufficient digital skills with three factors: (1) basic interest and competence, (2) recently attended training, and/or (3) a positive attitude toward changing teaching practices. Interestingly, some educators recognized that digitization offers great potential for implementing individualized teaching approaches. However, the findings point to the didactic necessity of digital socialization in terms of social communication and inclusion when establishing emergency digital education.
H-net: Unsupervised domain adaptation person re-identification network based on hierarchy

Due to the high cost of manual labeling for supervised person re-identification (re-ID), unsupervised domain adaptation (UDA) person re-ID has been attracting the attention of many scholars. In this research, target domain datasets and source domain datasets are two indispensable datasets, and although there are many different pictures of the same person in the target domain, these pictures are precious to the network in different degrees. However, the existing UDA person re-ID algorithms does not treat different samples in the target domain differently, they just treat positive samples as indistinguishable samples. Not only that, although the triplet loss has been re-identified by unsupervised person re-ID, the noise of the hardest sample hasn't been carried out well. In this paper, a novel and robust network model named unsupervised domain adaptation hierarchical person re-identification network (H-Net) is proposed, which not only effectively reduces the impact of inaccurate identification of the hardest sample but also treats different positive samples differently by hierarchical feature collection. Numerous experimental results on Market-1501 and DukeMTMC-reID demonstrate that the proposed H-Net outperforms the existing methods and can significantly improve the accuracy of person re-ID.
Throughput-oriented associated transaction assignment in sharding blockchains for IoT social data storage

Blockchain is a viable solution to provide data integrity for the enormous volume of 5G IoT social data, while we need to break through the throughput bottleneck of blockchain. Sharding is a promising technology to solve the problem of low throughput in blockchains. However, cross-shard communication hinders the effective improvement of blockchain throughput. Therefore, it is critical to reasonably allocate transactions to different shards to improve blockchain throughput. Existing research on blockchain sharding mainly focuses on shards formation, configuration, and consensus, while ignoring the negative impact of cross-shard communication on blockchain throughput. Aiming to maximize the throughput of transaction processing, we study how to allocate blockchain transactions to shards in this paper. We propose an Associated Transaction assignment algorithm based on Closest Fit (ATCF). ATCF classifies associated transactions into transaction groups which are then assigned to different shards in the non-ascending order of transaction group sizes periodically. Within each epoch, ATCF tries to select a shard that can handle all the transactions for each transaction group. If there are multiple such shards, ATCF selects the shard with the remaining processing capacity closest to the number of transactions in the transaction group. When no such shard exists, ATCF chooses the shard with the largest remaining processing capacity for the transaction group. The transaction groups that cannot be completely processed within the current epoch will be allocated in the subsequent epochs. We prove that ATCF is a 2-approximation algorithm for the associated transaction assignment problem. Simulation results show that ATCF can effectively improve the blockchain throughput and reduce the number of cross-shard transactions.
KFSENet: A Key Frame-Based Skeleton Feature Estimation and Action Recognition Network for Improved Robot Vision with Face and Emotion Recognition

In this paper, we propose an integrated approach to robot vision: a key frame-based skeleton feature estimation and action recognition network (KFSENet) that incorporates action recognition with face and emotion recognition to enable social robots to engage in more personal interactions. Instead of extracting the human skeleton features from the entire video, we propose a key frame-based approach for their extraction using pose estimation models. We select the key frames using the gradient of a proposed total motion metric that is computed using dense optical flow. We use the extracted human skeleton features from the selected key frames to train a deep neural network (i.e., the double-feature double-motion network (DDNet)) for action recognition. The proposed KFSENet utilizes a simpler model to learn and differentiate between the different action classes, is computationally simpler and yields better action recognition performance when compared with existing methods. The use of key frames allows the proposed method to eliminate unnecessary and redundant information, which improves its classification accuracy and decreases its computational cost. The proposed method is tested on both publicly available standard benchmark datasets and self-collected datasets. The performance of the proposed method is compared to existing state-of-the-art methods. Our results indicate that the proposed method yields better performance compared with existing methods. Moreover, our proposed framework integrates face and emotion recognition to enable social robots to engage in more personal interaction with humans.
An analysis of self-other representations in the incelosphere: Between online misogyny and self-contempt

The present study intends to contribute to the analysis of digital discursive practices of hate speech expressed throughout the so-called ‘Manosphere’, a group of online communities in which men express their considerations about masculinity. Through qualitative and quantitative analysis, it investigates how one of the main Manosphere groups, the Incels, creates its in-group/out-group discourse through its representations of women and of themselves. Driven by Critical Discourse Studies and studies on the ideological function of metaphors, the first part of the analysis reveals a conflation of apparently sarcastic metaphors, dark humour, and misogyny used to talk about women, while the second section of the study focuses on the peculiar self-representations of the men who participate in the forum, which breach Van Dijk’s ‘us vs them’ identity square pattern: rather than emphasising the positive traits of the in-group, incels describe themselves through self-derogative nominations and predications that give way to a spiral of self-pitying and self-contempt, which might be used to create fraternal bonds within the community.
PV solar power forecasting based on hybrid MFFNN-ALO

Clean energy sources such as photovoltaic (PV) panels are widely employed. However, their performance is affected by the surroundings. A hybrid optimization technique that comprised an ant lion optimizer (ALO) and artificial neural network (ANN) is presented in this study, to forecast the PV cell temperature and output power. The optimizer’s major purpose was to create and improve an ANN approach that was based on training and forecasting. The ALO was used as MVO and GA to obtain the optimal hidden layers neurons number, weights, and biases, of the proposed ANNs. The accuracy of the multilayer feed forward neural networks (MFFNN) was evaluated using the data from the MFFNN-MVO, MFFNN-GA and MFFNN-ALO models. The panel output power and temperature were regulated by three variables: solar irradiation, ambient temperature, and wind speed. The Saudi Arabia, Shaqra City PV station with 4kW output power is the source of the two years testing and training. For the MFFNN-GA, MFFNN-MVO, and MFFNN-ALO models, the NRMSE for DC power predicting compared to 2019 observed data was 2.781E-3, 7.11E-4, and 6.08 E-04, respectively.
Forensic comparison of fired cartridge cases: Feature-extraction methods for feature-based calculation of likelihood ratios

We describe and validate a feature-based system for calculation of likelihood ratios from 3D digital images of fired cartridge cases. The system includes a database of 3D digital images of the bases of 10 cartridges fired per firearm from approximately 300 firearms of the same class (semi-automatic pistols that fire 9 mm diameter centre-fire Luger-type ammunition, and that have hemispherical firing pins and parallel breech-face marks). The images were captured using Evofinder®, an imaging system that is commonly used by operational forensic laboratories. A key component of the research reported is the comparison of different feature-extraction methods. Feature sets compared include those previously proposed in the literature, plus Zernike-moment based features. Comparisons are also made of using feature sets extracted from the firing-pin impression, from the breech-face region, and from the whole region of interest (firing-pin impression + breech-face region + flowback if present). Likelihood ratios are calculated using a statistical modelling pipeline that is standard in forensic voice comparison. Validation is conducted and results are assessed using validation procedures and validation metrics and graphics that are standard in forensic voice comparison.
Going into Depth: Learning from a Survey of Interactive Designs for Aquatic Recreation

Aquatic recreation encompasses a variety of water-based activities from which participants gain physical, mental, and social benefits. Although interactive technologies for supporting aquatic recreation activities have increased in recent years, the HCI community does not yet have a structured understanding of approaches to interaction design for aquatic recreation. To contribute towards such an understanding, we present the results of a systematic review of 48 papers on the design of interactive technology for aquatic recreation, drawn from the ACM, IEEE, and SPORTDiscus libraries. This review presents an aquatic recreation user experience framework that details problems and opportunities concerning water and HCI. Our framework brings us closer to understanding how technology can interact with users and the aquatic environment to enhance the existing recreational experiences that connect us to aquatic environments. We found that designers can elicit delight, enablement, challenge, and synergy in aquatic recreation experiences.
VisuaLizations As Intermediate Representations (VLAIR): An approach for applying deep learning-based computer vision to non-image-based data

Deep learning algorithms increasingly support automated systems in areas such as human activity recognition and purchase recommendation. We identify a current trend in which data is transformed first into abstract visualizations and then processed by a computer vision deep learning pipeline. We call this VisuaLization As Intermediate Representation (VLAIR) and believe that it can be instrumental to support accurate recognition in a number of fields while also enhancing humans’ ability to interpret deep learning models for debugging purposes or for personal use. In this paper we describe the potential advantages of this approach and explore various visualization mappings and deep learning architectures. We evaluate several VLAIR alternatives for a specific problem (human activity recognition in an apartment) and show that VLAIR attains classification accuracy above classical machine learning algorithms and several other non-image-based deep learning algorithms with several data representations.
Sentiment analysis using web scraping for live news data with machine learning algorithms

The web scraping approach extracts large portions of information in a short amount of time using its definition. Further, Web Scraping offers records retrieval, newsgathering, internet monitoring, aggressive advertising and marketing, and many more. The use of the internet for scraping makes getting access to a tremendous quantity of records online, smooth and simple. Like in manufacturing electronic and automobile industries product reviews and campaigning require customer feedback which can be easily gathered through web scraping of websites. It is quicker and less complicated than manually extracting records from websites. Web Scraping is turning into a great data access tool these days. Apart from web scraping, web crawling and data mining or web mining are also some of the areas or methods that permit the easy compilation and storage of information on the web. In this paper, some of the famous supervised machine learning algorithms like Naïve Bayes and Logistic Regression are implemented on the live news data to effectively check the impact of these algorithms on data. Further, the application of sentiment analysis is explored and analyzed by using machine learning algorithms. This naive approach helps in understanding the comments, blogs from famous news websites by dividing the opinions into various categories at great scale and depth. This paper aims to combine the supervised learning methods with web scraping techniques for deriving optimized results of news articles about accuracy, precision, and recall.
Autonomous CNN (AutoCNN): A data-driven approach to network architecture determination

Designing a Convolutional Neural Networks (CNN) is a complex task and requires expert knowledge to optimize the performance and network architecture. In this paper, a novel data-driven approach is proposed to determine the architecture of CNN models. The proposed Autonomous Convolutional Neural Networks (AutoCNNThe executable code and original numerical results can be downloaded from (https://tinyurl.com/AutoCNN)) algorithm introduces data driven strategies for addition of new convolutional layers, pruning of redundant filters and training cycle optimization. AutoCNN is evaluated using MNIST, MNIST-rot-back-image, Fashion MNIST and the ADHD200 datasets to measure the performance on small datasets with varied feature distributions. The results indicate that AutoCNN optimizes the CNN network architecture and helps maximise the classification performance. The data-driven network determination approach introduced in this paper was found to not only provides competitive performance similar to existing evolutionary computation based network determination algorithms in literature, but was found to be an effective optimization tool to improve the performance of existing CNN architectures. Further, the AutoCNN was found to highly immune to noise in the dataset and has proven to be effective method to transfer knowledge between related datasets. Therefore, the AutoCNN is a highly versatile CNN architecture determination tool that has a wide range of applications in the field of autonomous driving, medical image analysis, image enhancement, camera based security monitoring and image based fault detection.
Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation

We present a new encoder-decoder Vision Transformer architecture, Patcher, for medical image segmentation. Unlike standard Vision Transformers, it employs Patcher blocks that segment an image into large patches, each of which is further divided into small patches. Transformers are applied to the small patches within a large patch, which constrains the receptive field of each pixel. We intentionally make the large patches overlap to enhance intra-patch communication. The encoder employs a cascade of Patcher blocks with increasing receptive fields to extract features from local to global levels. This design allows Patcher to benefit from both the coarse-to-fine feature extraction common in CNNs and the superior spatial relationship modeling of Transformers. We also propose a new mixture-of-experts (MoE) based decoder, which treats the feature maps from the encoder as experts and selects a suitable set of expert features to predict the label for each pixel. The use of MoE enables better specializations of the expert features and reduces interference between them during inference. Extensive experiments demonstrate that Patcher outperforms state-of-the-art Transformer- and CNN-based approaches significantly on stroke lesion segmentation and polyp segmentation. Code for Patcher is released to facilitate related research. (Code: https://github.com/YanglanOu/patcher.git .).
CrowdLBM: A lightweight blockchain-based model for mobile crowdsensing in the Internet of Things

With the commercialization of fifth generation mobile networks (5G) and continuous innovation of the Internet of Things (IoT), crowdsensing, as a new emerging IoT application, has further deepened our knowledge, utilizing various smart objects distributed throughout the whole industrial system for data collection and processing. To avoid a single point of failure and ensure data integrity, blockchain technology was widely applied. However, due to the demand for computing power and resources, IoT devices with limited capabilities and resources are unsuitable for crowdsensing implementation in the blockchain network. Therefore, this paper proposes a lightweight blockchain-based crowdsensing model named CrowdLBM with a novel consensus mechanism based on global reputation to resolve the issues raised above. Furthermore, we propose a two-stage scheme and two types of smart contracts to support the crowdsensing process automatically without the intervention of any third party. Finally, we implement a blockchain prototype on Hyperledger Sawtooth, extensive experimental results show that the proposed CrowdLBM outperforms other crowdsensing systems in terms of both scalability and security.
Jupyter Notebook, JupyterLab – Integrated Environment for STEM Education

The article deals with the application of integrated environments Jupyter Notebook and JupyterLab in the learning process together with STEAM (Science, Technology, Engineering, Art, Mathematics) technology. It is shown that these environments support basic learning activities including formulating problem statements with formulas and images, performing scientific and engineering calculations, computational experiments, creating graphical user interfaces, publishing interactive e-textbooks, and interactive web-applications. The article describes how to conduct classes using Jupyter Notebook and JupyterLab.
Sentiment Analysis for IMDb Reviews Using Deep Learning Classifier

Due to the sheer volume of opinion-rich web resources, a lot of current researches are focusing on sentiment analysis. The goal is to create new models that are able to recognize and classify the opinions or sentiments expressed in an electronic text to evaluate and improve a given system. Many solutions have been proposed to deal with this problem, and most of them are based on Machine Learning techniques. Currently, Deep Learning is a rapidly growing field, it has proven its effectiveness to solve many complex problems due to its ability to learn and extract meaningful information from data. Therefore, many contributions attempt to adopt this approach in the sentiment analysis area such as that of sentiment classification. In this work, we address the problem of user sentiment analysis by proposing a new solution based on natural language processing and deep convolutional neural network. The proposed solution has been tested on the IMDB dataset containing 50,000 movie reviews. The obtained results were very convincing with an accuracy of 99% in the training phase and 89% in the testing phase.