Reviewerly/exHarmony huggingface dataset:
Conceptualizing and achieving industrial system transition for a dematerialized and decarbonized world

The concept of industrial system transition introduced in the IPCC special report on Global Warming of 1.5 °C remains poorly conceptualized. In this paper, we deepen the conceptualization of the industrial system transition to decarbonization, dematerialization, and sustainable industrial production. Aided by fuzzy cognitive maps that use perception-based data from stakeholders to model complex and difficult-to-model systems, we chart the pathways for industrial system transition. The industrial system transition entails interactions between dematerialization and decarbonization goals while enabling governance and systemic corporate strategies. The respondents of the fuzzy cognitive maps-based surveys comprised practitioners from companies, authors, and the policymaking community. Fuzzy cognitive map-based simulations reveal that resorting to technical measures of dematerialization and decarbonization is insufficient to accomplish industrial system transition. The efficient industrial system transition to dematerialization and decarbonization requires the combined measures of (i) dematerialization and decarbonization, (ii) governance, policies, and regulations (effective governance including transnational governance, technology push, market-pull, technology transfer and financial flows, carbon price and carbon market; and (iii) enabling corporate strategies (regenerative and conscious capitalism, a new conception of transparency, and collaborative and constructive lobbying). Large companies are mostly transnational entities, necessitating the adoption of effective transnational governance strategies for achieving the objectives of dematerialization and decarbonization. Several transnational governance networks have partnered under the public–private co-governance mechanism in the decarbonization space dominated by mainly larger players. The advent of polycentric governance provides new opportunities for trans-local governance where large numbers of small and medium enterprises can participate in the advancement of at least decarbonization objectives; however, such networks require support from national governments. Besides implications for governance, policy and regulations, the findings of this research could also have implications for corporate behavior in terms of promoting conscious and transparent organizational culture.
Video Memorability Prediction Via Late Fusion Of Deep Multi-Modal Features

Video memorability is a cornerstone in social media platform analysis, as a highly memorable video is more likely to be noticed and shared. This paper proposes a new framework to fuse multi-modal information to predict the likelihood of remembering a video. The proposed framework relies on late fusion of text, visual and motion features. Specifically, two neural networks extract features from the captions describing the video’ s content; two ResNet models extract visual features from specific frames, and two 3DResNet models, combined with Fisher Vectors, extract features from the video’ s motion information. The extracted features are used to compute several memorability scores via Bayesian Ridge regression, which are then fused based on a greedy search of the optimal fusion parameters. Experiments demonstrate the superiority of the proposed framework on the MediaEval2019 dataset, outperforming the state-of-the-art.
The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review

Abstract Given the prevailing state of cybersecurity, it is reasonable to understand why cybersecurity experts are seriously considering artificial intelligence as a potential field that can aid improvements in conventional cybersecurity techniques. Various progressions in the field of technology have helped to mitigate some of the issues relating to cybersecurity. These advancements can be manifested by Big Data, Blockchain technology, Behavioral Analytics, to name but a few. The paper overviews the effects of applications of these technologies in cybersecurity. The central purpose of the paper is to review the application of AI techniques in analyzing, detecting, and fighting various cyberattacks. The effects of the implementation of conditionally classified “distributed” AI methods and conveniently classified “compact” AI methods on different cyber threats have been reviewed. Furthermore, the future scope and challenges of using such techniques in cybersecurity, are discussed. Finally, conclusions have been drawn in terms of evaluating the employment of different AI advancements in improving cybersecurity.
A new predictive method supporting streaming data with hybrid recurring concept drifts in process industry

In the process industry, streaming data is characterized of high dimensionality, non-stationarity, and nonlinearity. Furthermore, hybrid recurring concept drifts occur due to repeated implementation of multiple control strategies, resulting in lower accuracies of time series prediction (TSP). This paper proposes an Online Autoregression with Deep Long-Short-Term Memory (OAR-DLSTM) method for the hybrid recurring concept drifts of process industry streaming data. According to the classification of recurring concepts, the prediction task is assigned to multiple independent prediction sub-models, in which the Deep Long Short Term Memory (DLSTM) method is introduced to acquire temporal correlation of nonlinear streaming data. Online Autoregression (OAR) is adopted to ensure TSP with more accurate results to address the overlap and transfer between different recurring concepts. The proposed method is applied to two data sets from a vertical roller mill system of a steel company in Jiangsu, China. Compared with the benchmark methods, including statistical approaches, variants of LSTM methods, and online learning methods, the predicted results of the proposed method are more accurate, and the effectiveness of the method is verified.
Effective forecasting of stock market price by using extreme learning machine optimized by PSO-based group oriented crow search algorithm

Stock index price forecasting is the influential indicator for investors and financial investigators by which decision making capability to achieve maximum benefit with minimum risk can be improved. So, a robust engine with capability to administer useful information is desired to achieve the success. The forecasting effectiveness of stock market is improved in this paper by integrating a modified crow search algorithm (CSA) and extreme learning machine (ELM). The effectiveness of proposed modified CSA entitled as Particle Swarm Optimization (PSO)-based Group oriented CSA (PGCSA) to outperform other existing algorithms is observed by solving 12 benchmark problems. PGCSA algorithm is used to achieve relevant weights and biases of ELM to improve the effectiveness of conventional ELM. The impact of hybrid PGCSA ELM model to predict next day closing price of seven different stock indices is observed by using performance measures, technical indicators and hypothesis test (paired t-test). The seven stock indices are considered by incorporating data during COVID-19 outbreak. This model is tested by comparing with existing techniques proposed in published works. The simulation results provide that PGCSA ELM model can be considered as a suitable tool to predict next day closing price.
The alt-right digital migration: A heterogeneous engineering approach to social media platform branding

In recent years, social media platforms such as Twitter have removed users that espouse alt-right narratives of White nationalism and xenophobia from their platforms. This mass removal has caused alt-right users to migrate in droves to alternative social media sites, such as Gab. This migration reflects the “platform branding” of these social media platforms which dictates users’ choices of where to migrate based on the affordances and culture of a given site. Using heterogeneous engineering, this article analyzes the contextual history, language, and technological affordances of Twitter, Reddit, and Gab. The article finds that Twitter’s focus on politics and civil conversation is unhospitable to alt-right content. Reddit’s somewhat neutral positioning and decentralized moderation system make alt-right content possible but unpopular. Finally, Gab provides a haven to alt-right beliefs, constructing its platform around “free speech” and alt-right extremism. Platforms embody holistic brand images through contextual, linguistic, and technological features.
Probing model signal-awareness via prediction-preserving input minimization

This work explores the signal awareness of AI models for source code understanding. Using a software vulnerability detection use case, we evaluate the models' ability to capture the correct vulnerability signals to produce their predictions. Our prediction-preserving input minimization (P2IM) approach systematically reduces the original source code to a minimal snippet which a model needs to maintain its prediction. The model's reliance on incorrect signals is then uncovered when the vulnerability in the original code is missing in the minimal snippet, both of which the model however predicts as being vulnerable. We measure the signal awareness of models using a new metric we propose -- Signal-aware Recall (SAR). We apply P2IM on three different neural network architectures across multiple datasets. The results show a sharp drop in the model's Recall from the high 90s to sub-60s with the new metric, highlighting that the models are presumably picking up a lot of noise or dataset nuances while learning their vulnerability detection logic. Although the drop in model performance may be perceived as an adversarial attack, but this isn't P2IM's objective. The idea is rather to uncover the signal-awareness of a black-box model in a data-driven manner via controlled queries. SAR's purpose is to measure the impact of task-agnostic model training, and not to suggest a shortcoming in the Recall metric. The expectation, in fact, is for SAR to match Recall in the ideal scenario where the model truly captures task-specific signals.
Diagnosis of Leukemia Disease Based on Enhanced Virtual Neural Network

White Blood Cell (WBC) cancer or leukemia is one of the serious cancers that threaten the existence of human beings.In spite of its prevalence and serious consequences, it is mostly diagnosed through manual practices.The risks of inappropriate, sub-standard and wrong or biased diagnosis are high in manual methods.So, there is a need exists for automatic diagnosis and classification method that can replace the manual process.Leukemia is mainly classified into acute and chronic types.The current research work proposed a computer-based application to classify the disease.In the feature extraction stage, we use excellent physical properties to improve the diagnostic system's accuracy, based on Enhanced Color Co-Occurrence Matrix.The study is aimed at identification and classification of chronic lymphocytic leukemia using microscopic images of WBCs based on Enhanced Virtual Neural Network (EVNN) classification.The proposed method achieved optimum accuracy in detection and classification of leukemia from WBC images.Thus, the study results establish the superiority of the proposed method in automated diagnosis of leukemia.The values achieved by the proposed method in terms of sensitivity, specificity, accuracy, and error rate were 97.8%, 89.9%, 76.6%, and 2.2%, respectively.Furthermore, the system could predict the disease in prior through images, and the probabilities of disease detection are also highly optimistic.
Nonlinear Behavior of Dendritic Polymer Networks for Reservoir Computing

Abstract Organic electrochemical devices are an emerging class of devices with synaptic properties that might allow for the implementation of next‐generation neuromorphic circuits for power‐efficient computing. Here, a brain‐inspired neural network approach, namely reservoir computing, which relies on a nonlinear transformation of a low‐dimensional input signal onto a high‐dimensional output space for information processing is utilized. The implementation of reservoir computing using dendritic networks of polymeric fibers is demonstrated and the nonlinear response of the polymer networks are analyzed and the sources of nonlinearity are identified. Furthermore, by adding a delayed feedback loop to the reservoir, it is proven that such a network can undergo a bifurcation into a chaotic state, proving sufficient complexity of the system for advanced classification tasks with time‐dependent data. Ultimately, a classification task is carried out and the accuracy is compared of the classification of different degrees of complexity of the system, showing an increase in accuracy from 60% for the base network to 80% when the delayed feedback loop is incorporated.
A Data-Independent Radio Frequency Fingerprint Extraction Scheme

Radio frequency fingerprint (RFF) has been utilized to mitigate spoofing attacks in open wireless environments, making use of the inherent characteristics of hardware. However, most existing RFF technologies are data-dependent, e.g., based on preambles or synchronization sequences. In this letter, we propose a novel data-independent RFF extraction scheme, called Least mean square-based Adaptive Filter and Stacking, abbreviated as LAFS, that is implemented on random data segments, like communication data. Intuitively, we extract converged tap coefficients as RFF by minimizing the divergence between the desired signal and the demodulated reference signal. To further improve the effect, we introduce a tap coefficient stacking (TSC) technique to stabilize the RFF. Our experiment on ZigBee devices shows that the proposed LAFS method successfully identifies transmitters with 98.9% accuracy at 10 dB by stacking 25 times.
Orthogonal Modality Disentanglement and Representation Alignment Network for NIR-VIS Face Recognition

Near-infrared and visual (NIR-VIS) face matching, as the most typical task in Heterogeneous Face Recognition (HFR), has attracted increasing attention in recent years. However, due to the large within-class discrepancies, including domain differences and residual discrepancies (i.e., lighting, expressions, occlusion, blurry, pose, etc), this is still a difficult task. Conventional NIR-VIS FR methods only focus on reducing the modality gap between cross-domain images, while neglecting to eliminate the residual variations. To better solve the above problems, this paper proposes a novel Orthogonal Modality Disentanglement and Representation Alignment (OMDRA) approach, which consists of three key components, including Modality-Invariant (MI) loss, Orthogonal Modality Disentanglement (OMD) and Deep Representation Alignment (DRA). Firstly, the MI loss is designed to learn modality-invariant and identity-discriminative representation, by increasing between-class separability and within-class compactness between NIR and VIS heterogeneous data. Secondly, the high-level Hybrid Facial Feature (HFF) layer of the backbone network is projected into two subspaces: the modality-related and identity-related subspaces. The OMD is designed to decouple modal information via an adversarial process, and we further impose Orthogonal Representation Decorrelation (ORD) to the OMD to decrease the correlation between identity representations and domain representations, as well as enhancing their representation capabilities. Finally, the DRA aims to eliminate the residual variations by performing a high-level representation alignment between non-neutral face and neutral face, which can effectively guides the network to learn discriminative and residual-invariant face representation. The joint scheme enables the disentanglement of modality variations, elimination of residual discrepancies, and the purification of identity information. Extensive experiments on challenging cross-domain databases indicate that our OMDRA method is superior to the state-of-the-art methods.
EBOD: An ensemble-based outlier detection algorithm for noisy datasets

Real-world datasets often comprise outliers (e.g., due to operational error, intrinsic variability of the measurements, recording mistakes, etc.) and, hence, require cleansing as a prerequisite to any meaningful machine learning analysis. However, data cleansing is often a laborious task that requires intuition or expert knowledge. In particular, selecting an outlier detection algorithm is challenging as this choice is dataset-specific and depends on the nature of the considered dataset. These difficulties have prevented the development of a “one-fits-all” approach for the cleansing of real-world, noisy datasets. Here, we present an unsupervised, ensemble-based outlier detection (EBOD) approach that considers the union of different outlier detection algorithms, wherein each of the selected detectors is only responsible for identifying a small number of outliers that are the most obvious from their respective standpoints. The use of an ensemble of weak detectors reduces the risk of bias during outlier detection as compared to using a single detector. The optimal combination of detectors is determined by forward–backward search. By taking the example of a noisy dataset of concrete strength measurements as well as a broad collection of benchmark datasets, we demonstrate that our EBOD method systematically outperforms all alternative detectors, when used individually or in combination. Based on this new outlier detection method, we explore how data cleansing affects the complexity, training, and accuracy of an artificial neural network.
Towards self-calibrated lens metrology by differentiable refractive deflectometry

Deflectometry, as a non-contact, fully optical metrology method, is difficult to apply to refractive elements due to multi-surface entanglement and precise pose alignment. Here, we present a computational self-calibration approach to measure parametric lenses using dual-camera refractive deflectometry, achieved by an accurate, differentiable, and efficient ray tracing framework for modeling the metrology setup, based on which damped least squares is utilized to estimate unknown lens shape and pose parameters. We successfully demonstrate both synthetic and experimental results on singlet lens surface curvature and asphere-freeform metrology in a transmissive setting.
Efficient Face Recognition System for Operating in Unconstrained Environments

Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. Recently, several deep neural networks algorithms have been developed to achieve state-of-the-art performance on this task. The present work was conceived due to the need for an efficient and low-cost processing system, so a real-time facial recognition system was proposed using a combination of deep learning algorithms like FaceNet and some traditional classifiers like SVM, KNN, and RF using moderate hardware to operate in an unconstrained environment. Generally, a facial recognition system involves two main tasks: face detection and recognition. The proposed scheme uses the YOLO-Face method for the face detection task which is a high-speed real-time detector based on YOLOv3, while, for the recognition stage, a combination of FaceNet with a supervised learning algorithm, such as the support vector machine (SVM), is proposed for classification. Extensive experiments on unconstrained datasets demonstrate that YOLO-Face provides better performance when the face under an analysis presents partial occlusion and pose variations; besides that, it can detect small faces. The face detector was able to achieve an accuracy of over 89.6% using the Honda/UCSD dataset which runs at 26 FPS with darknet-53 to VGA-resolution images for classification tasks. The experimental results have demonstrated that the FaceNet+SVM model was able to achieve an accuracy of 99.7% using the LFW dataset. On the same dataset, FaceNet+KNN and FaceNet+RF achieve 99.5% and 85.1%, respectively; on the other hand, the FaceNet was able to achieve 99.6%. Finally, the proposed system provides a recognition accuracy of 99.1% and 49 ms runtime when both the face detection and classifications stages operate together.
XAI tools in the public sector: a case study on predicting combined sewer overflows

Artificial intelligence and deep learning are becoming increasingly prevalent in contemporary software solutions. Explainable artificial intelligence (XAI) tools attempt to address the black box nature of the deep learning models and make them more understandable to humans. In this work, we apply three state-of-the-art XAI tools in a real-world case study. Our study focuses on predicting combined sewer overflow events for a municipal wastewater treatment organization. Through a data driven inquiry, we collect both qualitative information via stakeholder interviews and quantitative measures. These help us assess the predictive accuracy of the XAI tools, as well as the simplicity, soundness, and insightfulness of the produced explanations. Our results not only show the varying degrees that the XAI tools meet the requirements, but also highlight that domain experts can draw new insights from complex explanations that may differ from their previous expectations.
A Semantic Conceptualization Using Tagged Bag-of-Concepts for Sentiment Analysis

Sentiment could be expressed implicitly or explicitly in the text. Hence, it is the main challenge for current sentiment analysis (SA) approaches to identify hidden sentiments, other common challenges include false classification of opinion words, ignoring context information, and bad handling of a short text that arise from the bad interpretation of the text and lack of enough data required for analysis tasks. In this study, a semantic conceptualization method using tagged bag-of-concepts for SA is proposed to detect the correct sentiment towards the actual target entity that considers all affective and conceptual information conveyed in the text with a special focus on the short text. Tagged bag-of-concepts (TBoC) is a novel approach to analyze and decompose text to uncover latent sentiments while preserving all relations and vital information to boost the accuracy of SA. This study answers questions: Does the information provided via TBoC enhance sentiment classification results on different analysis levels? Is building a structure of concepts increases the accuracy of overall sentiment towards specific opinion target? Does TBoC approach enhance SA results for short text messages? The proposed solution has been applied on two datasets from the restaurant domain, sentiment analysis is performed using the TBoCs structure on multiple levels including document, aspect, aspect-category, and topic levels. TBoC method with domain-specific sentiment lexicon showed exceptional performance and outperformed other state-of-the-art NB, SVM, and NN methods, especially for aspect-level SA. The use of TBoC within the semantic conceptualization model that leverages NLP tasks, Ontology, and semantic methods proved its high capabilities for concept extraction while preserving the information about the context, interrelations, and latent feelings.