Reviewerly/exHarmony huggingface dataset:
Four-Stage Space-Time Hybrid Model For Distributed Photovoltaic Power Forecasting

The precision and reliability of distributed photovoltaic (PV) power forecasting play a crucial role in commercial PV plants for power integration and operation. However, the stochastic and intermittent nature of solar radiation affects forecasting accuracy. What's more, the diverse location and various meteorological information have brought challenges to real-time distributed forecasting. Therefore, it is more difficult for forecasting distributed power than centralized power. To solve this problem, we propose a four-stage space-time hybrid method for power forecasting of distributed PV plants. Firstly, Global and Local Feature Fusion Network (GLFFNet) is proposed to forecast the power output of centralized PV plants. Secondly, the weather clustering type is obtained to get ready for studying the relationship between centralized and distributed PV plants. Then, uncovered coefficients are figured out to show the distribution features of PV power both in the time and space domain. Finally, the correlation models are established based on Copula analysis. According to the above steps, the centralized forecasting results are transformed to obtain the distributed forecasting model. The case study shows that the proposed GLFFNet is more suitable for 1-hour ahead forecasting. Compared to 9 different neural models, MAPE reductions of GLFFNet model (time steps of 1 hour and 15 minutes) are 12.17%–23.23% and 3.18%–18.22%, respectively. Thus, MAPE of the case distributed PV power forecasting models are 0.62% (Type A), 0.96% (Type B) and 3.21% (Type C), respectively.
A Study of Adaptive Fractional-Order Total Variational Medical Image Denoising

Following the traditional total variational denoising model in removing medical image noise with blurred image texture details, among other problems, an adaptive medical image fractional-order total variational denoising model with an improved sparrow search algorithm is proposed in this study. This algorithm combines the characteristics of fractional-order differential operators and total variational models. The model preserves the weak texture region of the image improvement based on the unique amplitude-frequency characteristics of the fractional-order differential operator. The order of the fractional-order differential operator is adaptively determined by the improved sparrow search algorithm using both the sine search strategy and the diversity variation processing strategy, which can greatly improve the denoising ability of the fractional-order differential operator. The experimental results reveal that the model not only achieves the adaptivity of fractional-order total variable differential order, but also can effectively remove noise, preserve the texture structure of the image to the maximum extent, and improve the peak signal-to-noise ratio of the image; it also displays favorable prospects for applications in medical image denoising.
Potential Influences of the Darknet on Illicit Drug Diffusion

Darknet-hosted drug markets ('cryptomarkets') are an established model of illicit drug distribution which makes use of specialised online hosting and payment systems to link buyers and sellers remotely. Cryptomarkets appear to professionalise, gentrify and integrate drug markets. Therefore, they can be hypothesised to have effects on drug availability by allowing purchases by people who use drugs (PWUD) outside of face-to-face networks that have typified drug distribution. They may attract new buyers and may change use patterns by offering a greater range of higher-potency drugs. This paper examines the research on cryptomarkets' potential impacts on drug availability.1. Cryptomarkets tend to address established PWUD who mainly already have access to existing distribution systems. Their greatest impact may be on what is available and the quantities available, and not the overall ease of access.2. Cryptomarkets may provide new data sources which can inform our understanding of drug markets.3. Cryptomarkets may define PWUD as consumers and contribute to reshaping their identities around principles of self-directed, informed consumption.4. In terms of size, cryptomarkets are currently smaller than other modes of digital drug distribution such as through social media and messaging apps and should be seen as a specialist subset of that genre.5. Users of cryptomarkets often integrate drug-purchase and consumption repertoires across multiple sites, online and offline, and cryptomarkets can be one element.The cryptomarkets are of interest partly because they alter the practical calculus around drug diffusion and partly because they contribute to the formation of digitally enabled drug use which emphasises a consumer relationship between buyer and seller.
Microscopic image analysis in breast cancer detection using ensemble deep learning architectures integrated with web of things

Breast cancer is a leading cause of death among women. Death rate from breast cancer is reduced when it is detected early. Deep learning (DL) methods have become a viable alternative for diagnosis, overcoming the limitations of traditional classification methods. Because biosensors and deep learning are required to identify tumors based on microscopic pictures, automation is needed. The goal of machine learning is to make it easier for computers to learn independently. This research proposed novel technique in breast cancer detection utilizing ensemble DL techniques in classification with features extraction. Input image has been collected based on web of things (WoT) in which the pre-historic data and collected microscopic images have been taken as input dataset. The input image has been processed for noise removal using Gaussian filtering and segmented using active contour convolutional neural networks. Then the classification was carried out using convoluted transfer learning integrated with regional attention mechanism. Compared to existing methods in the domain, the simulation results show that the suggested localization-based cancer classification method is superior. It has reported average classification accuracy of 96%, detection accuracy of 92%, Mean Average Precision (mAP) of 82%, sensitivity of 92%, specificity of 91%, root mean square error (RMSE) of 70% on various breast cancer microscopic image datasets.
Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models

State-of-the-art (SOTA) deep learning mammogram classifiers, trained with weakly-labelled images, often rely on global models that produce predictions with limited interpretability, which is a key barrier to their successful translation into clinical practice. On the other hand, prototype-based models improve interpretability by associating predictions with training image prototypes, but they are less accurate than global models and their prototypes tend to have poor diversity. We address these two issues with the proposal of BRAIxProtoPNet++, which adds interpretability to a global model by ensembling it with a prototype-based model. BRAIxProtoPNet++ distills the knowledge of the global model when training the prototype-based model with the goal of increasing the classification accuracy of the ensemble. Moreover, we propose an approach to increase prototype diversity by guaranteeing that all prototypes are associated with different training images. Experiments on weakly-labelled private and public datasets show that BRAIxProtoPNet++ has higher classification accuracy than SOTA global and prototype-based models. Using lesion localisation to assess model interpretability, we show BRAIxProtoPNet++ is more effective than other prototype-based models and post-hoc explanation of global models. Finally, we show that the diversity of the prototypes learned by BRAIxProtoPNet++ is superior to SOTA prototype-based approaches.
GANTouch: An Attack-Resilient Framework for Touch-Based Continuous Authentication System

Previous studies have shown that commonly studied (vanilla) implementations of touch-based continuous authentication systems (V-TCAS) are susceptible to active adversarial attempts. This study presents a novel Generative Adversarial Network assisted TCAS (G-TCAS) framework and compares it to the V-TCAS under three active adversarial environments viz. Zero-effort, Population, and Random-vector. The Zero-effort environment was implemented in two variations viz. Zero-effort (same-dataset) and Zero-effort (cross-dataset). The first involved a Zero-effort attack from the same dataset, while the second used three different datasets. G-TCAS showed more resilience than V-TCAS under the Population and Random-vector, the more damaging adversarial scenarios than the Zero-effort. On average, the increase in the false accept rates (FARs) for V-TCAS was much higher (27.5% and 21.5%) than for G-TCAS (14% and 12.5%) for Population and Random-vector attacks, respectively. Moreover, we performed a fairness analysis of TCAS for different genders and found TCAS to be fair across genders. The findings suggest that we should evaluate TCAS under active adversarial environments and affirm the usefulness of GANs in the TCAS pipeline.
Efficient Biomedical Instance Segmentation via Knowledge Distillation

Biomedical instance segmentation is vulnerable to complicated instance morphology, resulting in over-merge and over-segmentation. Recent advanced methods apply convolutional neural networks to predict pixel embeddings to overcome this problem. However, these methods suffer from heavy computational burdens and massive storage. In this paper, we present the first knowledge distillation method tailored for biomedical instance segmentation to transfer the knowledge from a cumbersome teacher network to a lightweight student one. Different from existing distillation methods on other tasks, we consider three kinds of essential knowledge of the instance segmentation task, i.e., instance-level features, instance relationships in the feature space and pixel-level instance boundaries. Specifically, we devise two distillation schemes: (i) instance graph distillation that transfers the knowledge of instance-level features and instance relationships by the instance graphs built from embeddings of the teacher-student pair, respectively, and (ii) pixel affinity distillation that converts pixel embeddings into pixel affinities and explicitly transfers the structured knowledge of instance boundaries encoded in affinities. Experimental results on a 3D electron microscopy dataset (CREMI) and a 2D plant phenotype dataset (CVPPP) demonstrate that the student models trained through our distillation method use fewer than 1% parameters and less than 10% inference time while achieving promising performance compared with corresponding teacher models. Code is available at https://github.com/liuxy1103/BISKD .
Collaborative granular sieving: A deterministic multievolutionary algorithm for multimodal optimization problems

Evolutionary algorithms (EAs) that integrate niching techniques are among the most effective methods for multimodal optimization problems. However, most algorithmic contributions are based on empirical performance observations rather than rigorous mathematical convergence support; this makes most existing methods parameter sensitive. Inspired by a recently proposed deterministic global optimization method, granular sieving (GrS), an extended global optimization method named collaborative GrS (Co-GrS) and a novel deterministic multi-EA design framework are proposed in this paper. The innovations are threefold. (1) Existing EAs are stochastic methods, and this paper introduces the principle of deterministic global optimization into EA for the first time in the literature. (2) A deterministic multi-EA framework is designed and implemented in the paper; from the perspective of population evolution, an easy-to-operate survival-of-the-fittest strategy based on mathematical principles is established in Co-GrS. (3) Unlike existing stochastic EAs, where the reproducibility of optimal solutions is achieved in a statistical sense, Co-GrS does not involve random parameters, and it automatically runs the algorithm only once with pre-set fixed parameters to find all optimal solutions. The experimental results demonstrate the effectiveness and competitiveness of our method compared to 16 state-of-the-art multimodal algorithms on the CEC'2013 benchmark suite.
A Novel Neighborhood‐Weighted Sampling Method for Imbalanced Datasets

The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However, they usually ignore the positional relationship between a sample and the heterogeneous samples in its neighborhood when calculating sample weight. This paper proposes a novel neighborhood-weighted based sampling method named NWBBagging to improve the Bagging algorithm's performance on imbalanced datasets. It considers the positional relationship between the center sample and the heterogeneous samples in its neighborhood when identifying critical samples. And a parameter reduction method is proposed and combined into the ensemble learning framework, which reduces the parameters and increases the classifier's diversity. We compare NWBBagging with some state-of-the-art ensemble learning algorithms on 34 imbalanced datasets, and the result shows that NWBBagging achieves better performance.
Beyond Chronology, Using Bayesian Inference to Evaluate Hypotheses in Archaeology

ABSTRACT Archaeologists frequently use probability distributions and null hypothesis significance testing (NHST) to assess how well survey, excavation, or experimental data align with their hypotheses about the past. Bayesian inference is increasingly used as an alternative to NHST and, in archaeology, is most commonly applied to radiocarbon date estimation and chronology building. This article demonstrates that Bayesian statistics has broader applications. It begins by contrasting NHST and Bayesian statistical frameworks, before introducing and applying Bayes's theorem. In order to guide the reader through an elementary step-by-step Bayesian analysis, this article uses a fictional archaeological faunal assemblage from a single site. The fictional example is then expanded to demonstrate how Bayesian analyses can be applied to data with a range of properties, formally incorporating expert prior knowledge into the hypothesis evaluation process.
SMESwin Unet: Merging CNN and Transformer for Medical Image Segmentation

Vision transformer is the new favorite paradigm in medical image segmentation since last year, which surpassed the traditional CNN counterparts in quantitative metrics. The significant advantage of ViTs is to utilize the attention layers to model global relations between tokens. However, the increased representation capacity of ViTs comes with corresponding shortcomings: short of CNN's inductive biases (locality), translation invariance, and hierarchical structure of visual information. Consequently, well-trained ViTs require more data than CNNs. As high quality data in medical imaging area is always limited, we propose SMESwin UNet. Firstly, based on Channel-wise Cross fusion Transformer (CCT) we fuse multi-scale semantic features and attention maps by designing a compound structure with CNN and ViTs (named MCCT). Secondly, we introduce superpixel by dividing the pixel-level feature into district-level to avoid the interference of meaningless parts of the image. Finally, we used External Attention to consider the correlations among all data samples, which may further reduce the limitation of small datasets. According to our experiments, the proposed superpixel and MCCT-based Swin Unet (SMESwin Unet) achieves better performance than CNNs and other Transformer-based architectures on three medical image segmentation datasets (nucleus, cells, and glands).
From Virtual Reality to 360° Videos

This chapter aims to describe the multidimensional virtual reality tools applied to healthcare: in particular the comparison between virtual reality traditional tolls and the 360° videos. The VR traditional devices could differ in terms of specific graphics (2D/3D), display devices (head mounted display), and tracking/sensing tools. Although they are ecological tools, they have several problems such as cybersickness, high-cost software, and psychometric issues. Instead, the 360° videos can be described as an extension of virtual reality technology: they are immersive videos or spherical videos that give the opportunity to immerse the subject in authentic natural environments, being viewed via an ordinary web browser in that a user can pan around by clicking and dragging. The comparison between those two technologies stems from the question if 360° videos could solve and overcome the problems related to virtual reality and be an effective and more ecological alternative.
Factors Affecting Organisations’ Adoption Behaviour toward Blockchain-Based Distributed Identity Management: The Sustainability of Self-Sovereign Identity in Organisations

Blockchain-based Distributed Identity Management (BDIDM) can enhance sustainable identification and authentication of users on organisations’ digital systems. But there is not a clear consensus on how organisations perceive the value proposition of such technology, nor what might affect their adoption behaviour toward it. This research explains how technological, organisational and environmental (TOE) factors affect organisations’ adoption behaviour toward BDIDM. This study aims to determine the most critical factors affecting the behaviour while assessing the effectiveness and appropriateness of the model involved, i.e., TOE-BDIDM. Online questionnaires are used to survey 111 information and cybersecurity practitioners within South African organisations. The analysis combines binary logistic regression modelling, Structural Equation Modelling of the measurement model, and chi-squared tests. The results suggest TOE factors positively or negatively affect adoption behaviour. The behaviour is significantly affected by technology characteristics, i.e., BDIDM’s disruptive nature, and is associated with Blockchain type. Indeed, the majority of participants intended to recommend BDIDM to their organisations yet paradoxically preferred private-permissioned blockchain the most, revealing resistance to decentralised and privacy-preserving BDIDM models like Self-Sovereign Identity (SSI). The latter might be utopian or unsustainable for organisations. TOE-BDIDM was found relatively appropriate and effective but arguably ‘incomplete’ for explaining the adoption of disruptive technologies like SSI in organisations. TOE should extend to TOEU by including the User factors.
An improved adaptive robust information filter for spacecraft relative navigation

This work concentrates on addressing the problem of time-varying measurement noise covariance and outliers (contaminated Gaussian distribution), which can significantly deteriorate the estimation accuracy of conventional Kalman filter . Adaptive filters or robust filters are only suitable for solving one of the above-mentioned problems. Meanwhile, the existing adaptive robust filters, performing unsatisfactorily, also can not meet the requirements of spacecraft relative navigation, which needs an accurate and reliable filter. This paper proposes an improved adaptive robust information filter, referred to as variational Bayesian (VB) adaptive dynamic-covariance-scaling (DCS)-based cubature information filter (VB-DCSCIF). The proposed filter, based on the cubature information filter framework, uses VB approximation to track measurement noise covariance with time-varying statistical characteristics, and relies on the improved DCS kernel function to suppress outliers in measurements. Therefore, VB-DCSCIF incorporates the advantages of VB approximation and the improved DCS kernel, exhibiting adaptivity and robustness. Spacecraft relative navigation simulations and Monte Carlo simulations demonstrate that VB-DCSCIF outperforms other filters for providing high-precision state estimation under time-varying measurement noise covariance and outliers.
Sign Language Certification Platform with Action Recognition using LSTM Neural Networks

The American Sign Language substantially facilitates communication among people that are either hard of hearing or mute. According to a few research studies, at least 70 million people all across the world communicate through sign language. However, there are only a few hundred thousand speakers, limiting the number of persons with whom they can interact comfortably. Alternative modes of communication, such as written communication, can be inconvenient, impersonal, and even cumbersome on a daily basis, and even more so in an emergency. [5]. We present an ASL learning platform that employs LSTM neural networks to recognize the user‧s gesture (action recognition) and deliver real-time feedback in order to overcome this barrier and enable dynamic communication. According to the experimental results, the accuracy achieved while training ASL words was 99.43%, while training ASL alphabets was 91.01% and while training ASL numbers was 98.80%.
Does Alexa Live Up to the Hype? Contrasting Expectations from Mass Media Narratives and Older Adults' Hands-on Experiences of Voice Interfaces

Voice user interfaces (VUIs) are advertised as easy to use and beneficial to older adults (OAs). Disparities between expectations and OAs' hands-on experiences with VUIs may discourage OAs' further use of VUIs and widen digital divides. To understand such disparities, we conducted two-week in-home field deployments of the Amazon Echo Dot with OAs. We interviewed participants before and after deployment on their perceptions of VUIs in relation to prevailing media-derived expectations about VUIs. Our analysis revealed mismatches between expectation and hands-on experiences with VUIs; namely, VUIs were found to be more primitive than expected, there were more limitations to VUIs than expected, more prerequisites were required to fully make use of VUIs, and the sources that VUIs drew from fell short in earning trust. Our findings contribute aspects to be considered to close the gap between expectations and experiences related to VUIs for older adults.
Articulate+ : An Always-Listening Natural Language Interface for Creating Data Visualizations

Natural Language Interfaces and Voice User Interfaces for expressing data visualizations face ambiguities, such as, speech disfluency, under-specification, and abbreviations. In this paper, we describe Articulate+, an Artificial Intelligence Agent that is always listening, built to disambiguate requests while also spontaneously presenting informative visualizations. We conducted a preliminary user study to gain insight into the issues involved in providing an always-listening interface for data visualization. Our early results suggest that by leveraging Articulate+'s always-listening interface, users are able to obtain their desired visualizations with fewer queries while also being able to benefit from spontaneous visualizations generated by the system.