Reviewerly/exHarmony huggingface dataset:
Hate speech or free speech: an ethical dilemma?

Freedom of expression is the cornerstone and the warrant of democracy, but like all other rights and freedoms we enjoy, also the right to free speech has its limits. Hate speech is one of the most resilient manifestations of cyberviolence, and is not to be equalled with free speech. This is partly related to our perception of the freedom of expression that has substantially changed through time; from the past focus on being able to freely express your opinion, to question, dissent and challenge the government, to today’s need to limit these rights in order to protect the targets of hate speech. The article first looks at how hate speech is defined at the level of the EU and at the problems connected with its regulation. It continues with an overview of relevant literature and research on the topic, and concludes by analyzing and interpreting the results of an online survey conducted among Slovenian university students in February 2022. Our goal is to understand the reasons behind the rise of hate speech and reactions to it; from its acceptability and underestimation to indifference and ignorance.
Investigations on speaker adaptation using a continuous vocoder within recurrent neural network based text-to-speech synthesis

Abstract This paper presents an investigation of speaker adaptation using a continuous vocoder for parametric text-to-speech (TTS) synthesis. In purposes that demand low computational complexity, conventional vocoder-based statistical parametric speech synthesis can be preferable. While capable of remarkable naturalness, recent neural vocoders nonetheless fall short of the criteria for real-time synthesis. We investigate our former continuous vocoder, in which the excitation is characterized employing two one-dimensional parameters: Maximum Voiced Frequency and continuous fundamental frequency (F0). We show that an average voice can be trained for deep neural network-based TTS utilizing data from nine English speakers. We did speaker adaptation experiments for each target speaker with 400 utterances (approximately 14 minutes). We showed an apparent enhancement in the quality and naturalness of synthesized speech compared to our previous work by utilizing the recurrent neural network topologies. According to the objective studies (Mel-Cepstral Distortion and F0 correlation), the quality of speaker adaptation using Continuous Vocoder-based DNN-TTS is slightly better than the WORLD Vocoder-based baseline. The subjective MUSHRA-like test results also showed that our speaker adaptation technique is almost as natural as the WORLD vocoder using Gated Recurrent Unit and Long Short Term Memory networks. The proposed vocoder, being capable of real-time synthesis, can be used for applications which need fast synthesis speed.
An effective blockchain‐based smart contract system for securing electronic medical data in smart healthcare application

Summary In today's world, data management plays a key role in smart healthcare applications. However, data availability, trustworthiness, confidentiality, and security are the major issues faced by current healthcare data management systems. The modern healthcare systems manage Electronic Medical Records (EMR) using a centralized manner, which increases the single point of failure in the event of a natural catastrophe. In this paper, a new robust Blockchain‐based Medical Cloud (BC‐MedCl) framework has been proposed to provide secure EMR sharing between patient and doctor. Primarily, Internet of Things (IoT) devices will gather the health‐related data of the patient periodically. The proposed framework then stores the encrypted EMRs in cloud storage while their correlating hash values are placed into the blockchain. Finally, a decentralized selective smart contract‐based access control mechanism is developed to enhance the security of the proposed system. The prototype file‐sharing performance of the proposed architecture has been evaluated using the Ethereum platform. The performance results manifest that the proposed blockchain framework is more effective to handle EMR in the real‐time healthcare system with a superior accuracy ratio of 98.7% and a lesser latency ratio of 25% as compared with the existing systems.
Comprehensive guidelines for emotion annotation

Emotions are psychological traits which are associated with an individuals' thoughts, feelings, behavioral responses, and experiences of pleasure and displeasure. The ability to recognise a conversational partner's emotional state from their speech (and respond accordingly) is a longstanding requirement of a fully capable intelligent virtual agent. However, despite the fact that current approaches to emotion recognition primarily depend upon supervised machine learning models, there are no comprehensive guidelines for emotion label annotation of the corpora used to train such models. We present comprehensive guidelines for consistent and effective annotation of text corpora with emotion labels. In particular, our proposal directly addresses the requirements of multi-label emotion recognition, and we demonstrate how an implementation of our proposed guidelines led to substantially (30%) higher agreement score among human annotators.
Fighting against terrorism: A real-time CCTV autonomous weapons detection based on improved YOLO v4

In recent years, deep learning has demonstrated tremendous potential in the real world. Object detection is a critical real-world task for deep learning. You Only Look Once (YOLO) object detection model recognizes interesting regions in images with impressive accuracy and real-time performance. The objective of this paper is to apply object detection to the field of security and counter-terrorism. Individuals are protected from violence by recognizing and locating the guns on closed-circuit television (CCTV). This paper presents a real-time detection approach for CCTV autonomous weapons based on YOLO v4. For the characteristics of CCTV scenarios, we propose the YOLO v4 backbone with Spatial Cross Stage Partial-ResNet (SCSP-ResNet). Meanwhile, the receptive field enhancement module is shown to capture fine semantic features of high-dimensional small objects. The Fusion-PANet (F-PaNet) module has been used to fuse multi-scale information to improve the model's perceptive power on the region of interest. Furthermore, we merge synthetic and real-world datasets to comprehensively investigate the effects of synthetic datasets on detectors. The experimental results reveal that our suggested detection model improves mAP (mean Accuracy Precision) and inference time by 7.37% and 4.2%, respectively. The model's parameter is reduced by 0.349 BFLOP/s(billion floating point operations per second). The proposed detector outperforms the baseline model in terms of accuracy, real-time, and robustness.
Concept Drift in Japanese COVID-19 Infection Data

In this study, we analyze concept drifts in the daily infection data of COVID-19 in Japan. A lockdown, the spread of vaccines, and the emergence of new variants of COVID-19 have had a significant impact on the number of daily infections. These changes, also known as concept drifts, make the prediction of COVID-19 infection rates difficult. Because the prediction of infection trends is crucial to protect people from the disease, this study aims to generate accurate predictions by handling concept drifts in the trend data. The key concept behind this method is a brute-force tuning of the training period. Although prior studies tended to require pre-tuned parameters to locate the drift points, this can be avoided through brute-force tuning. Experimental results show significant improvements in prediction accuracy. Furthermore, the extracted points where concept drifts occur appear to correspond to new COVID-19 variants and other important state changes.
Covid-19 Protesters and the Far Right on Telegram: Co-Conspirators or Accidental Bedfellows?

The COVID-19 pandemic led to the creation of a new protest movement, positioned against government lockdowns, mandatory vaccines, and related measures. Efforts to control misinformation by digital platforms resulted in take downs of key accounts and posts. This led some of these protest groups to migrate to platforms with less stringent content moderation policies, such as Telegram. Telegram has also been one of the destinations of the far right, whose deplatforming from mainstream platforms began a few years ago. Given the co-existence of these two movements on Telegram, the article examines their connections. Empirically, the article focused on Irish Telegram groups and channels, identifying relevant protest movements and collecting their posts. Using computational social science methods, we examine whether far-right terms and discourses are present and how this varies across different clusters of Telegram Covid-19 protest groups. In addition, we examine which actors are posting far-right content and what kind of roles they play in the network of Telegram groups. The findings indicate the presence of far-right discourses among the COVID-19 groups. However, the existence of these groups was not solely driven by the extreme right, and the incidence of far-right discourses was not equal across all COVID-19 protest groups. We interpret these findings under the prism of the mediation opportunity structure: while the far right appears to have taken advantage of the network opportunity structure afforded by deplatforming and the migration to Telegram, it did not succeed in diffusing its ideas widely among the COVID-19 protest groups in the Irish Telegram.
Transformer-based models for ICD-10 coding of death certificates with Portuguese text

Natural Language Processing (NLP) can offer important tools for unlocking relevant information from clinical narratives. Although Transformer-based models can achieve remarkable results in several different NLP tasks, these models have been less used in clinical NLP, and particularly in low resource languages, of which Portuguese is one example. It is still not entirely clear whether pre-trained Transformer models are useful for clinical tasks, without further architecture engineering or particular training strategies. In this work, we propose a BERT model to assign ICD-10 codes for causes of death, by analyzing free-text descriptions in death certificates, together with the associated autopsy reports and clinical bulletins, from the Portuguese Ministry of Health. We used a novel pre-training procedure that incorporates in-domain knowledge, and also a fine-tuning method to address the class imbalance issue. Experimental results show that, in this particular clinical task that requires the processing of relatively short documents, Transformer-based models can achieve very strong results, significantly outperforming tailored approaches based on recurrent neural networks.
Weakly supervised learning and interpretability for endometrial whole slide image diagnosis

Fully supervised learning for whole slide image–based diagnostic tasks in histopathology is problematic due to the requirement for costly and time-consuming manual annotation by experts. Weakly supervised learning that utilizes only slide-level labels during training is becoming more widespread as it relieves this burden, but has not yet been applied to endometrial whole slide images, in iSyntax format. In this work, we apply a weakly supervised learning algorithm to a real-world dataset of this type for the first time, with over 85% validation accuracy and over 87% test accuracy. We then employ interpretability methods including attention heatmapping, feature visualization, and a novel end-to-end saliency-mapping approach to identify distinct morphologies learned by the model and build an understanding of its behavior. These interpretability methods, alongside consultation with expert pathologists, allow us to make comparisons between machine-learned knowledge and consensus in the field. This work contributes to the state of the art by demonstrating a robust practical application of weakly supervised learning on a real-world digital pathology dataset and shows the importance of fine-grained interpretability to support understanding and evaluation of model performance in this high-stakes use case.
Deep Learning-Based Synthesized View Quality Enhancement with DIBR Distortion Mask Prediction Using Synthetic Images

Recently, deep learning-based image quality enhancement models have been proposed to improve the perceptual quality of distorted synthesized views impaired by compression and the Depth Image-Based Rendering (DIBR) process in a multi-view video system. However, due to the lack of Multi-view Video plus Depth (MVD) data, the training data for quality enhancement models is small, which limits the performance and progress of these models. Augmenting the training data to enhance the synthesized view quality enhancement (SVQE) models is a feasible solution. In this paper, a deep learning-based SVQE model using more synthetic synthesized view images (SVIs) is suggested. To simulate the irregular geometric displacement of DIBR distortion, a random irregular polygon-based SVI synthesis method is proposed based on existing massive RGB/RGBD data, and a synthetic synthesized view database is constructed, which includes synthetic SVIs and the DIBR distortion mask. Moreover, to further guide the SVQE models to focus more precisely on DIBR distortion, a DIBR distortion mask prediction network which could predict the position and variance of DIBR distortion is embedded into the SVQE models. The experimental results on public MVD sequences demonstrate that the PSNR performance of the existing SVQE models, e.g., DnCNN, NAFNet, and TSAN, pre-trained on NYU-based synthetic SVIs could be greatly promoted by 0.51-, 0.36-, and 0.26 dB on average, respectively, while the MPPSNRr performance could also be elevated by 0.86, 0.25, and 0.24 on average, respectively. In addition, by introducing the DIBR distortion mask prediction network, the SVI quality obtained by the DnCNN and NAFNet pre-trained on NYU-based synthetic SVIs could be further enhanced by 0.02- and 0.03 dB on average in terms of the PSNR and 0.004 and 0.121 on average in terms of the MPPSNRr.
An Analytic Hierarchy Process for Selection of Blockchain-Based Platform for Product Lifecycle Management

Blockchain technology has disrupted traditional business processes and hence gained significant attention and popularity in recent years. Consequently, a number of blockchain-based platforms are available today that offer vast applications across multiple sectors and industries. Implementing these blockchain-based platforms as an alternative to traditional product lifecycle management systems (PLMs) is one of the applications. However, before any platform is adopted, its nature, functionalities, and adaptability need to be clearly defined, evaluated, and verified. In this context, the proposed work explores the available blockchain-based platforms that can be used for the purpose of product lifecycle management. We then apply one of the multi-criteria decision-making techniques, i.e., the analytic hierarchy process (AHP), to select the best possible blockchain-based platform for PLM. As transaction speed, data privacy, and scalability are our prime concerns in PLM, we only considered the permissioned (private) blockchain platforms as available alternatives in the final selection process. Results achieved on the basis of considered criteria show that Hyperledger Fabric is the top-ranked among available alternatives to be used for PLM. Furthermore, as blockchain is a new technology, a clear comparison of the available platforms based on the performance-based metrics and key performance indicators is not completely matured and is still in the development stage. However, our proposed approach can be considered an attempt to create a procedure for evaluating blockchain-based platform implementation in any sector.
Modeling Under-Reporting in Cyber Incidents

Under-reporting in cyber incidents is a well-established problem. Due to reputational risk and the consequent financial impact, a large proportion of incidents are never disclosed to the public, especially if they do not involve a breach of protected data. Generally, the problem of under-reporting is solved through a proportion-based approach, where the level of under-reporting in a data set is determined by comparison to data that is fully reported. In this work, cyber insurance claims data is used as the complete data set. Unlike most other work, however, our goal is to quantify under-reporting with respect to multiple dimensions: company revenue, industry, and incident categorization. The research shows that there is a dramatic difference in under-reporting—a factor of 100—as a function of these variables. Overall, it is estimated that only approximately 3% of all cyber incidents are accounted for in databases of publicly reported events. The output of this work is an under-reporting model that can be used to correct incident frequencies derived from data sets of publicly reported incidents. This diminishes the “barrier to entry” in the development of cyber risk models, making it accessible to researchers who may not have the resources to acquire closely guarded cyber insurance claims data.
FastPicker: Adaptive independent two-stage video-to-video summarization for efficient action recognition

Video datasets suffer from huge inter-frame redundancy, which prevents deep networks from learning effectively and increases computational costs. Therefore, several methods adopt random/uniform frame sampling or key-frame selection techniques. Unfortunately, most of the learnable frame selection methods are customized for specific models and lack generality, independence, and scalability. In this paper, we propose a novel two-stage video-to-video summarization method termed FastPicker, which can efficiently select the most discriminative and representative frames for better action recognition. Independently, the discriminative frames are selected in the first stage based on the inter-frame motion computation, whereas the representative frames are selected in the second stage using a novel Transformer-based model. Learnable frame embeddings are proposed to estimate each frame contribution to the final video classification certainty. Consequently, the frames with the largest contributions are the most representative. The proposed method is carefully evaluated by summarizing several action recognition datasets and using them to train various deep models with several backbones. The experimental results demonstrate a remarkable performance boost on Kinetics400, Something-Something-v2, ActivityNet-1.3, UCF-101, and HMDB51 datasets, e.g., FastPicker downsizes Kinetics400 by 78.7% of its size while improving the human activity recognition.
Using time-windowed solar radiation profiles to assess the daily uncertainty of solar thermal electricity production forecasts

This work is aimed at analysing the impact of real-time weather forecasting on the simulation of solar thermal electricity plants. A novel method is presented that allows generating random solar radiation profiles to assess the uncertainty of forecasting solar thermal electricity production. Rather than a weather prediction model, this work proposes a fast and simple method for the generation of synthetic solar radiation data that focuses on the dynamics relevant for electricity production, not requiring long historical data series or meteorological details. Variable window lengths are used to reproduce the growing uncertainty of forecasts with longer time horizons. In addition, a simplified model of solar thermal power plant is described and implemented in MATLAB®. Both the solar profile generator and the plant model are used to simulate massive cases for four representative days with eight forecasting hours based on electricity markets. The results show uncertainty values for day-ahead predictions from 15% to 60% in cloudy days and from 2% to 40% in sunny days, with decreasing ranges for intra-day forecasts.
Inferring the shape of data: a probabilistic framework for analysing experiments in the natural sciences

A critical step in data analysis for many different types of experiments is the identification of features with theoretically defined shapes in N-dimensional datasets; examples of this process include finding peaks in multi-dimensional molecular spectra or emitters in fluorescence microscopy images. Identifying such features involves determining if the overall shape of the data is consistent with an expected shape; however, it is generally unclear how to quantitatively make this determination. In practice, many analysis methods employ subjective, heuristic approaches, which complicates the validation of any ensuing results-especially as the amount and dimensionality of the data increase. Here, we present a probabilistic solution to this problem by using Bayes' rule to calculate the probability that the data have any one of several potential shapes. This probabilistic approach may be used to objectively compare how well different theories describe a dataset, identify changes between datasets and detect features within data using a corollary method called Bayesian Inference-based Template Search; several proof-of-principle examples are provided. Altogether, this mathematical framework serves as an automated 'engine' capable of computationally executing analysis decisions currently made by visual inspection across the sciences.
IIB–CPE: Inter and Intra Block Processing-Based Compressible Perceptual Encryption Method for Privacy-Preserving Deep Learning

Perceptual encryption (PE) of images protects visual information while retaining the intrinsic properties necessary to enable computation in the encryption domain. Block-based PE produces JPEG-compliant images with almost the same compression savings as that of the plain images. The methods represent an input color image as a pseudo grayscale image to benefit from a smaller block size. However, such representation degrades image quality and compression savings, and removes color information, which limits their applications. To solve these limitations, we proposed inter and intra block processing for compressible PE methods (IIB-CPE). The method represents an input as a color image and performs block-level inter processing and sub-block-level intra processing on it. The intra block processing results in an inside-out geometric transformation that disrupts the symmetry of an entire block thus achieves visual encryption of local details while preserving the global contents of an image. The intra block-level processing allows the use of a smaller block size, which improves encryption efficiency without compromising compression performance. Our analyses showed that IIB-CPE offers 15% bitrate savings with better image quality than the existing PE methods. In addition, we extended the scope of applications of the proposed IIB-CPE to the privacy-preserving deep learning (PPDL) domain.