Reviewerly/exHarmony huggingface dataset:
Multilevel Feature Extraction Using Wavelet Attention for Deep Joint Demosaicking and Denoising

Demosaicking and denoising are critical for deciding on digital camera performance.However, conventional joint demosaicking and denoising methods use traditional hand-crafted filters for preprocessing and image restoration.Therefore, it is susceptible to noise and can produce many artifacts for images with numerous edges.This paper proposes an end-to-end multi-level wavelet attention convolutional neural network (CNN) that improves image restoration performance by reducing false color artifacts, over-smoothing, and blurring during demosaicing and denoising.In detail, this paper proposes a CNN-based preprocessing method that learns the mosaic image's inter-color correlation, improving the edge's color restoration performance.Furthermore, this paper presents a multi-level feature extraction convolutional block using the Haar wavelet-based discrete wavelet transform (DWT) to remove noise from images and restore colors better at the same time.Therefore, it preserves the information of input image and feature maps, learns the correlation between global and local features, improves image restoration performance, and suppresses phenomena such as over-smoothing that tend to occur in DWT-based denoising.The proposed method is an end-to-end network structure with CNN-based preprocessing methods.In experimental results, the proposed method improves PSNR by 3.67 dB and 0.35 dB on average compared with the traditional methods and the CNN-based methods, respectively, for the dataset with many high-frequency components.
Some reflections on atmosphere and memory from the perspective of enactive cognition and neurophenomenology

In order to fully grasp the possibilities of Stimmung and its implementation nowadays, creating life-enhancing atmospheres responsive to human action, embodied emotional memories, and place in the fullest sense (as both natural and cultural context), a proper understanding of consciousness and perception beyond Cartesian misunderstandings is crucial. To this aim, insights drawn from neurophenomenology and enactive cognitive science prove indispensable. This paper discusses some of these insights.
Isotropization of Complex-Valued Reservoir Computing on 2-D Dynamical System

Reservoir computing (RC), which achieves high-speed learning through a reservoir layer with recurrent non-linear mapping to a high dimensional space, has been used for prediction and estimation of sequential data. To deal with complex amplitude data such as synthetic aperture radar (SAR) data, RC has been extended to the complex domain, complex-valued reservoir computing (CVRC). In this paper, we analyze 2-D dynamical systems and examined the isotropic dynamics of CVRC. We show that CVRC preserves the periodicity of the input signal and has a high robustness to initial values compared to non-isotropic real-valued RC (RVRC).
Mortality Risk Evaluation: A Proposal for Intensive Care Units Patients Exploring Machine Learning Methods

The high availability of clinical data and a heterogeneous and complex patient population makes Intensive Care Units (ICUs) environments opportune for developing a system that analyzes large amounts of raw data, which human specialists can neglect. Quantifying a patient’s condition to support the definition and adjustment of clinical treatment and predict future outcomes is a significant research problem in intensive care. This work’s main objective is to conceive an approach to predicting ICUs mortality risk. Therefore, the designed approach is a binary classification task that aims to predict whether patients will die or survive during their ICU stay. A cohort of 17,734 patients was used from the MIMIC-III database, considering 10 input predictor variables and 8 Machine Learning methods. Sensitivity, specificity, F1 score, AUC, and ROC curve are used to compare different models of mortality risk prediction in a 48-h window of data acquisition. The best performance was achieved by the Gradient Boosting Machine (GBM) method, which obtained 0,843 (±0,015) of AUC and 0,503 (±0,048) for the F1 score. The approach conceived enables the generation of robust models capable of detecting hidden patterns and having greater power of discrimination in classifications. The results are promising and, in some cases, superior to those obtained by other proposals identified in the literature review.
Feature Extraction Mechanism for Each Layer of Deep Echo State Network

The echo state network (ESN) is an efficient machine learning model that is the most typical type of reservoir-computing framework. Recently, research has been conducted on deep echo state networks (deepESN). The deepESN consists of an input layer, multiple reservoir layers, and an output layer, which achieve a very high memory capacity (MC). Furthermore, it has been suggested that deepESN can represent various temporal scales using layer hierarchization. However, the exact role of each layer in the feature extraction has not yet been revealed. Therefore, deepESN parameter adjustments must be conducted using empirical measurements or grid searches based on a trial-and-error method. To establish a design framework for deepESN, revealing the deepESN parameters related to feature extraction is crucial. To analyze the dynamics of neural networks, we applied multiscale entropy (MSE) analysis to a physiological neural network model and found that complex topological features and multiple neural module structures produce complex temporal-scale dependencies. Therefore, we hypothesized that the feature extraction function of each layer in the deepESN could be revealed using MSE analysis. To validate this hypothesis, we analyzed the output of each layer using MSE analysis and MC task. As a result, in this study, under small inner layer connections, a high memory capacity was achieved by temporal scale-specific feature extraction in each layer compared to larger inter-layer connections. In conclusion, this study revealed the feature extraction function in deepESN and provided the parameter setting method, especially regarding interlayer connection as a part of the design framework of deepESN.
Legal and technical trade-offs in the content moderation of terrorist live-streaming

Abstract Moderating terrorist live-streaming presents legal and technical trade-offs. The immediacy of live-streamed content requires a timely assessment of the content’s compliance with community guidelines and an even more expeditious action of restriction or removal in case of portrayal of illegal content. Social media companies have heavily relied on technology to moderate content. The most frequently used tools to screen and filter content and ensure compliance with community standards and regulations include hashing technology, video-fingerprinting, natural language processing and metadata analysis. Terrorist content online presents nuances that make the task of removing it particularly challenging especially when companies must rely on machine learning content moderation. We identify three trade-offs in regulating content moderation of live-streamed content: technological neutrality v specificity, explainability v adversarial machine learning, and accuracy v time-efficiency. For each trade-off, we provide an analysis of the considerations lawmakers and regulators must take into account when balancing competing interests.
Voltage-State Estimation in Non-Gaussian Noisy Environments

A key component in monitoring and controlling of power systems is state estimation (SE). The state-of-the art SE technologies today operate on the basis of slow varying dynamics of the current network and make simplifying linearity assumptions. However, due to the non-linear/non-Gaussian nature of power grid, there is need for more accurate and real-time algorithms. In this paper, we introduce a new SE method, the Belief Condensation Filter (BCF), that aims to achieve these measures by approximating the true distribution of the state variables, rather than a linearized version as done for instance in Kalman filtering. Through simulations we show that in the presence of non-linearities and non-Gaussian noise, our general SE framework improves accuracy over best known Kalman-like filters, such as Unscented Kalman Filter (UKF) and asymptotically exact estimators, such as the particle filter (PF). We provide two sets of experimental results. Our analysis demonstrates the advantage of using BCF over UKF in (maximum entropy) non-Gaussian noise scenarios and over PF in terms of complexity and computation time.
Online Extremism, AI, and (Human) Content Moderation

This paper has three main goals: (1) to clarify the role of artificial intelligence (AI)—along with algorithms more broadly—in online radicalization that results in “real world violence,” (2) to argue that technological solutions (like better AI) are inadequate proposals for this problem given both technical and social reasons, and (3) to demonstrate that platform companies’ (e.g., Meta, Google) statements of preference for technological solutions functions as a type of propaganda that serves to erase the work of the thousands of human content moderators and to conceal the harms they experience. I argue that the proper assessment of these important, related issues must be free of the obfuscation that the “better AI” proposal generates. For this reason, I describe the AI-centric solutions favoured by major platform companies as a type of obfuscating and dehumanizing propaganda.
Adversarial Promotion for Video based Recommender Systems

Short-form video content is fast to consume, easy to digest, and for most creators, inexpensive to make. Content Creators on Video Content platforms have a vested interest in having their videos appear as high as possible in recommendations that users are shown. This paper demonstrates how content creators can manipulate video content to adversarially promote their ranking in a recommendation model that uses action classification labels as an input feature. We focus on the context of these videos in terms of action classification to extract context about these videos to then rank and adversarially promote. Our attack successfully boosted the predicted like probability in 78 percent of generated lists for our model trained with non-perturbed inputs. However, after adversarial training, our model trained with perturbed inputs was 20 percent less effective in boosting the rank of targeted videos.
Solar Power Monitoring and Forecasting System using Artificial Intelligence

Solar photovoltaic (PV) systems have been improving over the years resulting in an increased installation at the residential sites. This paper proposes an effective solar power monitoring and forecasting system to help residential users to monitor their solar PV systems more efficiently. Most of the current solar monitoring systems are either too expensive, or not smart enough to predict the future profile of the power generation. A ready app called ‘Geo-Solar Power Monitoring and Forecasting App’ for local monitoring and a ThingSpeak graphical user interface for remote monitoring has been developed. Compared to the existing apps, this app is enhanced to self-train when a new data set is received. Feed Forward Back Propagation Neural Network (FF-BP-NN) has higher accuracy of prediction than Nonlinear-Autoregressive neural network with external exogenous input (NARX).
VR/AR/MR in the Electricity Industry: Concepts, Techniques, and Applications

With the development of immersive techniques, Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) have been widely used in various fields, especially in the industrial and manufacturing fields. As one of the key techniques, a set of studies have focused on the design and adoption of VR/AR/MR. However, few studies explore the potential applications of VR/AR/MR in the electricity industry. To this end, we give a survey from the three aspects: the concepts, key techniques, and application cases, to investigate the immersive techniques of VR/AR/MR in the electricity industry. An electric power system is a complex giant system that consists of generation, distribution, transmission, and consumption systems. VR/AR/MR techniques play an important role in these components. This study discusses how VR/AR/MR techniques are used in the electricity industry for production, operation, and management. We also summarize the potential issues and opportunities of VR/AR/MR techniques in the electricity industry. We hope that this study is the first step to inspire researchers or electricity workers on future work and development implementation of VR/AR/MR techniques.
A particle swarm optimization algorithm with novelty search for combustion systems with ultra-low emissions and minimum fuel consumption

The particle swarm optimization algorithm is primarily inspired by the natural behaviour of swarms and achieves important results in different applications. However, it is not exempt from stagnation in local optima and has a tendency to prematurely converge to them. Novelty Search is a concept that appeared recently in different fields of computational intelligence. It aims at exploring non-visited areas of the search space through solutions that bring novelty to already discovered solutions. The novelty of this work can be divided into two steps: on one side, this article proposes a variant of the particle swarm optimization algorithm which uses Novelty Search concepts to improve the algorithm’s performance. Our proposal is first checked and compared using the CEC 2005 benchmark suite and then, we apply it to solve a real-world optimization problem: the design of a combustion system targeting the reduction of pollutant emissions and fuel consumption. The combustion chamber design phase usually is a complex and time-consuming process even with advanced supercomputers, since it depends on several input variables which are highly non-linear and with crossed interaction. Then, the second contribution of this work is to develop a methodology that couples a computational fluid dynamics (CFD) simulation tool with the new optimization algorithm for minimizing the specific fuel consumption of a compression-ignited engine, while constraining the NOx and soot emissions. A 3D-CFD model of the combustion system was built to predict and analyse the performance of the combustion system and hence, select the parameters with a higher impact on the system. The method reduces the computational time and includes tools for the automatic preparation of the input parameters and geometry of the system. The input parameters correspond to geometrical variables that control the bowl shape, the number of holes in the injector, the injection pressure, the swirl number and the exhaust gas recirculation rate. Results show how the simulation tool and the new PSO with Novelty Search algorithm allow us to obtain a new combustion system that minimizes the fuel consumption by 3%, simultaneously reducing NOx and soot emissions.
AI will alter US digital ad industry

Significance This segment of the advertising industry faces formidable challenges, from growing privacy-related impediments to targeted advertising to the emergence of ad networks run by retailers and the rapidly increasing use of artificial intelligence (AI) in advertising (AdAI), posing an existential threat to advertising agencies. Impacts Brands will create their own generative AI models, trained on their data, and rely less on generative AI (GenAI). Honing a strong brand identity will be increasingly important to companies when GenAIs can readily mimic any brand. Gen Alphas (born 2010-24) have a more ambiguous attitude to GenAI than their elders, fearing it will replace their creativity. Consumer acceptance of GenAI-embedded ads is an unknown at this early stage.
Calibration of stereo-digital image correlation for large field of view measurement based on photogrammetry

Calibration of a stereo-digital image correlation (stereo-DIC) system is essential for three-dimensional (3D) shape and deformation measurement. Although the traditional planar calibration method is flexible in most application scenarios, it still has difficulties in large field of view (FOV) calibration due to the limited size of calibration board. In this paper, a stereo-vision calibration method is proposed for large FOV measurement based on the close-range photogrammetry. Specifically, a certain number of coded targets are first arranged in the area to be measured. Then, the left and right cameras are used to capture images at the calibration and measurement positions. With these images, accurate camera intrinsic parameters can be obtained by close-range photogrammetry methods through encoded targets. The extrinsic parameters can be obtained by the 3D coordinates of these corresponding points and further optimized using corresponding points obtained from both coded targets and speckle patterns. Laboratory experiments with field-of-view ranges of 25 cm by 25 cm and 2.8 m by 2.8 m demonstrate that this method can obtain precise calibration parameters and has good deformation measurement accuracy. Compared to traditional planar calibration methods, this method does not require high-quality or large-sized calibration objects, and all parameters can be calibrated on-site. A large shaking table experiment demonstrated the potential of this method in the field of engineering measurements.
An Examination of the Virtual Event Experience of Cyclists Competing on Zwift

The Virtual Sport Experience Design (VSX) framework, which was adapted from Funk’s (2017) Sport Experience Design (SX) framework, was used to examine the event experience of cyclists who competed on the Mixed Reality platform, Zwift. Using a mixed methods approach, the findings suggest that Zwift is a viable substitute for real-life cycling events in that it provided users with realistic and serious competition in a social environment. High levels of technological embodiment, the quality of the in-game graphics and the gamification elements added to the experience by creating a sense of presence and control. However, it was the social dimension and the ability to communicate and create a sense of ‘communitas’ with other riders that provided the most compelling argument that Mixed Reality can provide a digital alternative to real-life events.
Distilling Vision-Language Pre-Training to Collaborate with Weakly-Supervised Temporal Action Localization

Weakly-supervised temporal action localization (WTAL) learns to detect and classify action instances with only category labels. Most methods widely adopt the off-the-shelf Classification-Based Pre-training (CBP) to generate video features for action localization. However, the different optimization objectives between classification and localization, make temporally localized results suffer from the serious incomplete issue. To tackle this issue without additional annotations, this paper considers to distill free action knowledge from Vision-Language Pre-training (VLP), as we surprisingly observe that the localization results of vanilla VLP have an over-complete issue, which is just complementary to the CBP results. To fuse such complementarity, we propose a novel distillation-collaboration framework with two branches acting as CBP and VLP respectively. The framework is optimized through a dual-branch alternate training strategy. Specifically, during the B step, we distill the confident background pseudo-labels from the CBP branch; while during the F step, the confident foreground pseudo-labels are distilled from the VLP branch. As a result, the dualbranch complementarity is effectively fused to promote one strong alliance. Extensive experiments and ablation studies on THUMOS14 and ActivityNet1.2 reveal that our method significantly outperforms state-of-the-art methods.
Ambient assisted living systems for falls monitoring at home

ABSTRACTIntroduction Monitoring systems at home are critical in the event of a fall, and can range from standalone fall detection devices to activity recognition devices that aim to identify behaviors in which the user may be at risk of falling, or to detect falls in real-time and alert emergency personnel.Areas covered This review analyzes the current literature concerning the different devices available for home fall detection.Expert opinion Included studies highlight how fall detection at home is an important challenge both from a clinical-assistance point of view and from a technical-bioengineering point of view. There are wearable, non-wearable and hybrid systems that aim to detect falls that occur in the patient’s home. In the near future, a greater probability of predicting falls is expected thanks to an improvement in technologies together with the prediction ability of machine learning algorithms. Fall prevention must involve the clinician with a person-centered approach, low cost and minimally invasive technologies able to evaluate the movement of patients and machine learning algorithms able to make an accurate prediction of the fall event.KEYWORDS: Fall preventionneurorehabilitationdisabilitytelemedicinetelerehabilitationwearable deviceshome devices Article highlights Falls are a difficult problem to mitigate with enormous direct and indirect repercussions on national health systems.The relationship between frailty and fall is bidirectional, and leads to a rapid clinical/functional deterioration of the person.Identifying people potentially at risk of falling is one of the major challenges for the near future.Currently available systems for home monitoring are mainly focused on identifying a fall but not on preventing it.Among the most important areas to improve is the fall risk detection at home.Fall prevention at home should involve low-cost and minimally invasive technologies able to evaluate patients’ movement, and include machine learning algorithms able to make an accurate prediction of the fall event.Clinicians must increase their knowledge of the functional skills of a person at home, with a medical approach that is more person-centered based than pathology-based, in line with the WHO – ICF.Declaration of interestThe authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.Reviewers disclosurePeer reviewers on this manuscript have no relevant financial relationships or otherwise to disclose.Additional informationFundingThis paper was not funded.