Reviewerly/exHarmony huggingface dataset:
Assessing the optical configuration of a structured light scanner in metrological use

Abstract Structured light scanners for three-dimensional surface acquisition (SL scanners) are increasingly used for dimensional metrology. The optical configuration of SL scanners (focal length and baseline distance) influences the triangulation process, on which the scanners’ measurement principle relies. So far, only a limited number of studies has investigated the optical configuration’s influence on the accuracy of a SL scanner. To close this gap, this work presents a design of experiment in which the optical configuration of a SL scanner is systematically varied and its influence on the accuracy evaluated. Further, tactile reference measurements allow to separate random from systematical errors, while a special test specimen is used in two different configurations to ensure general applicability of the findings. Thus, this work provides support when designing a SL scanner by highlighting which optical configuration maximizes accuracy.
Mammogram Image Grade Gauging of Denoising Filters &amp; Enhancement Methods

Breast cancer is the most treacherous tumour among women and its early detection enhances the chances of survival of the patient. Screening mammography improves a physician's ability to detect even small tumours which cannot be felt physically by the patient. Mammographic image noises influence the diagnostic images which can affect the diagnostic process. Hence it is indispensable to filter out the noises by preserving important features of the image. This paper investigates and identifies the most appropriate denoising filter and enhancement technique among mean, median, adaptive median, gaussian, wiener, contrast stretching, histogram equalization and Contrast Limited Adaptive Histogram Equalization (CLAHE). The matrices used to analyse the performance is Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM). From experimental results and analysis, it is proved that adaptive median filter and histogram equalization techniques are efficacious in removing noise and thereby enhancing the calibre of the image.
Detection of Multipartite Entanglement Based on Heisenberg-Weyl Representation of Density Matrices

We study entanglement and genuine entanglement of tripartite and four-partite quantum states by using Heisenberg-Weyl (HW) representation of density matrices. Based on the correlation tensors in HW representation, we present criteria to detect entanglement and genuine tripartite and four-partite entanglement. Detailed examples show that our method can detect more entangled states than previous criteria.
Observability Informed Partial-Update Schmidt Kalman Filter

The partial-update filter concept is a recent development that generalizes the Schmidt Kalman filter and extends the range of nonlinearities and uncertainties that a Kalman filter can tolerate. Similar to the Schmidt filter, the intention of the partial-update filter is to ameliorate the negative impact that certain states have within the filter, often due to their poor observability. In contrast with the Schmidt filter, the partial-update filter can update the problematic states at any time step. In practice, the partial-update technique can apply a full (nominal), partial, or no update (Schmidt) to states, depending on user-selected percentages (or weights) that indicate how much of the nominal Kalman update is applied. To date, the update percentages are selected via trial and error, and any change in the system configuration requires re-tuning. Furthermore, because the update percentages are fixed, the partial-update is agnostic to situations where a full update, or even a Schmidt-like filter can be more suitable. To address these drawbacks, this paper proposes two observability informed approaches for online weight selection that do not require manual tuning. The proposed techniques are targeted for systems where the states to be partially updated are only the problematic states. Numerical simulation results demonstrate that the proposed approaches produce estimates comparable to those of a manually fine-tuned fixed partial-update, and that they leverage occasions where local observability increases to produce more accurate estimates.
HighlightMe: Detecting Highlights from Human-Centric Videos

We present a domain- and user-preference-agnostic approach to detect highlightable excerpts from human-centric videos. Our method works on the graph-based representation of multiple observable human-centric modalities in the videos, such as poses and faces. We use an autoencoder network equipped with spatial-temporal graph convolutions to detect human activities and interactions based on these modalities. We train our network to map the activity- and interaction-based latent structural representations of the different modalities to per-frame highlight scores based on the representativeness of the frames. We use these scores to compute which frames to highlight and stitch contiguous frames to produce the excerpts. We train our network on the large-scale AVA-Kinetics action dataset and evaluate it on four benchmark video highlight datasets: DSH, TVSum, PHD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and SumMe. We observe a 4–12% improvement in the mean average precision of matching the human-annotated highlights over state-of-the-art methods in these datasets, without requiring any user-provided preferences or dataset-specific fine-tuning.
Gaussian Process As a Benchmark for Optimal Sensor Placement Strategy

Optimal sensor placement is an important problem to look at. This problem becomes all the more relevant nowadays due to advancements in infrastructure monitoring robotic technologies including underground sensing. While there are multiple ways to solve optimal sensor placement problems, one of the most generic methods available is Bayesian Optimization and its variants. In this paper, we present a simple benchmark-like formulation for exploiting Gaussian Process uncertainty for sensor placement to measure a scalar field.
Time pressure in human cybersecurity behavior: Theoretical framework and countermeasures

Cybersecurity is a growing concern for private individuals and professional entities. Reports have shown that the majority of cybersecurity incidents occur because users fail to behave securely. Research on human cybersecurity (HCS) behavior suggests that time pressure is one of the important driving factors behind non-secure HCS behavior. However, there is limited conceptual work to guide researchers and practitioners in this regard. Against this backdrop, we investigate how the impact of time pressure on HCS behavior can be conceptualized within an integrative framework and which countermeasures can be used to reduce its negative impact. Altogether, we conducted 35 interviews with cybersecurity experts, non-security professionals, and private users. The results of our study shed light on the theoretical pathways through which time pressure can affect different types of security behaviors and identify a range of operational, human, technical, and physical countermeasures with important implications for research and practice.
Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos

Segmenting objects in videos is a fundamental computer vision task. The current deep learning based paradigm offers a powerful, but data-hungry solution. However, current datasets are limited by the cost and human effort of annotating object masks in videos. This effectively limits the performance and generalization capabilities of existing video segmentation methods. To address this issue, we explore weaker form of bounding box annotations.We introduce a method for generating segmentation masks from per-frame bounding box annotations in videos. To this end, we propose a spatio-temporal aggregation module that effectively mines consistencies in the object and background appearance across multiple frames. We use our predicted accurate masks to train video object segmentation (VOS) networks for the tracking domain, where only manual bounding box annotations are available. The additional data provides substantially better generalization performance, leading to state-of-the-art results on standard tracking benchmarks. The code and models are available at https://github.com/visionml/pytracking.
Application of Wavelet Denoising and Time- Frequency Domain Feature Extraction on Data Processing of Modulated Signals

Signal modulation is an essential part of the communication system. Researches on reducing the noise interference in the modulated signal and improving the signal-to- noise ratio can help recognize the signal. In this paper, it is proposed to apply the wavelet denoising and time-frequency domain feature extraction to the modulated signals. Combine the characteristics of the modulated signal and the theory of wavelet denoising and time-frequency domain feature extraction, remove the noise interference in the signal, extract the time-frequency domain feature, and input the processed data into the decision tree model for classification and recognition and evaluate the signal processing effect. In addition, the accuracy of decision trees before and after data processing under different signal-to- noise ratios is further studied. Experimental results show that wavelet denoising and time-frequency domain feature extraction obviously affect modulated signal processing and are generally applicable under different signal-to-noise ratios.
Artificial Intelligence Market Disruption

The introduction of Artificial Intelligence (AI) in our contemporary society imposes historically unique challenges for humankind. The emerging autonomy of AI holds unique potentials of eternal life of robots, AI and algorithms alongside unprecedented economic superiority, data storage and computational advantages. Yet to this day, it remains unclear what impact AI taking over the workforce will have on economic growth. The contemporary trend of slowbalisation is described, as the slowing down of conventional globalization of goods, services and Foreign Direct Investments (FDI) flows; yet at the same time, we still see human migration and air travel as well as data transfer continuing to rise. These market trends of conventional globalization slowing and rising AI-related industries are proposed as first market disruption in the wake of the large-scale entrance of AI into our contemporary economy. Growth in the artificial age is then proposed to be measured based on two AI entrance proxies of Global Connectivity Index and The State of the Mobile Internet Connectivity 2018 Index, which is found to be highly significantly positively correlated with the total inflow of migrants and FDI inflow â€“ serving as evidence that the still globalizing rising industries in the age of slowbalisation are connected to AI. Both indices are positively correlated with GDP output in cross-sectional studies over the world.
Financial institutions, poverty and severity of poverty in Sub-Saharan Africa

The study assesses how financial institution dynamics have affected poverty and the severity of poverty in 42 sub-Saharan African countries for the period 1980-2019. In order to increase for policy relevance of the study, three financial development indicators are used, namely: financial institutions depth, financial institutions access and financial institutions efficiency. The adopted empirical strategy is a quantile regressions approach which enables the study to assess how financial institutions dynamics affect poverty and the severity of poverty throughout the conditional distribution of poverty and severity of poverty. The findings show various tendencies, inter alia: (i) financial institutions depth (efficiency) consistently decreases the severity of poverty (poverty headcount) and (ii) financial institutions access consistently decreases both poverty and the severity of poverty and the decreasing effect increases with increasing levels of poverty in the top quantiles and throughout the conditional distribution of the severity of poverty. Policy implications are discussed with respect of SDG1 on poverty reduction.
Finding structure in time

The input stream was passed through the network one final time, with no learning taking place. There are many human behaviors which unfold over time. Indeed, it seems that the solution to the problem of time may interact with other problems for connectionist architectures, including the problem of symbolic representation and how connectionist representations encode structure. The networks are able to learn interesting internal representations which incorporate task demands with memory demands; indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context-dependent, while also expressing generalizations across classes of items. The chapter explores some of the problems that arise when time is represented externally as a spatial dimension. Syntactic structure, selective restrictions, subcategorization, and discourse considerations are among the many factors which join together to fix the order in which words occur.
Autaptic Circuits of Integrated Laser Neurons

The presence of autapses in neural networks enables complex temporal dynamics and information storage. We experimentally demonstrated feedback dynamics in an integrated laser neuron, which provides a proof-of-principle demonstration of cascadability and stable recurrent memory.
Wait or Reset Gas Price?: A Machine Learning-based Prediction Model for Ethereum Transactions' Waiting Time

The gas mechanism on the Ethereum blockchain attempts to set charges for every smart contract operation in order to prevent infinite control of computational resources by the executing transactions. To satisfy this requirement, users need to specify in their transactions how much gas fees (in terms of gas limit and gas price) they would like to pay for. In essence, these gas fees are paid to the miners in return for their computational services. Naturally, miners tend to maximize their profits by considering the transactions with higher gas fees, and as a result, the transactions with lower gas fees remain in the waiting pool for a long time. This paper proposes a machine learning-based approach to predict whether a transaction with offered gas fees is likely to be included in the blockchain within the expected time or not. Such prior prediction of transactions' waiting time definitely assists users to reset their gas fees accordingly. The proposed model is evaluated on nearly one million real transactions from Ethereum mainnet, and the experimental results demonstrate a better performance than the existing one in the literature, with an achievement of 90.18% accuracy and 0.897 F1-score when the model is trained with Random Forest on the dataset balanced with SMOTETomek.
A blind watermarking system based on deep learning model

In recent years, as an important means of digital image copyright protection, image invisible watermarking technology has attracted more and more attention. Based on the watermarking system built by deep learning model, this paper proposes an optimization algorithm which can extract watermark and improve robustness. The watermark is embedded and extracted by the encoder and decoder of the model respectively. In order to make the model robust to various image attacks, two noise layers are added to the model. In order to further improve the robustness and concealment of watermark, adversarial training is used. In order to improve the accuracy of model joint training, a two-step training strategy is adopted. The first step is to train the best pre training model of encoder and decoder. The second step is to train different decoders according to different image attack means, so as to improve the robustness of the whole watermarking system. Experimental results show that, compared with other watermarking algorithms based on deep learning model, the proposed method is superior to other algorithms in the concealment and robustness.
A Lightweight Certificate-Based Aggregate Signature Scheme Providing Key Insulation

Recently, with the advancement of Information and Communications Technology (ICT), Internet of Things (IoT) has been connected to the cloud and used in industrial sectors, medical environments, and smart grids.However, if data is transmitted in plain text when collecting data in an IoTcloud environment, it can be exposed to various security threats such as replay attacks and data forgery.Thus, digital signatures are required.Data integrity is ensured when a user (or a device) transmits data using a signature.In addition, the concept of data aggregation is important to efficiently collect data transmitted from multiple users (or a devices) in an industrial IoT environment.However, signatures based on pairing during aggregation compromise efficiency as the number of signatories increases.Aggregate signature methods (e.g., identity-based and certificateless cryptography) have been studied.Both methods pose key escrow and key distribution problems.In order to solve these problems, the use of aggregate signatures in certificate-based cryptography is being studied, and studies to satisfy the prevention of forgery of signatures and other security problems are being conducted.In this paper, we propose a new lightweight signature scheme that uses a certificate-based aggregate signature and can generate and verify signed messages from IoT devices in an IoT-cloud environment.In this proposed method, by providing key insulation, security threats that occur when keys are exposed due to physical attacks such as side channels can be solved.This can be applied to create an environment in which data is collected safely and efficiently in IoT-cloud is environments.
Key factors for the success of connectivism in the elearning modality in the context of Covid-19

The pandemic because of Covid-19 brought with it the need to analyze educational practices in order to carry out transformation processes, which allow us to understand how to face the new educational scenarios. The objective of this research is to analyze the key factors that allow achieving significant learning in the e-learning modality. The study was conducted with 219 male high school students in the Arequipa region of Peru. The educational problem in different contexts because of the pandemic, is going through a series of adaptation, and evaluation processes, to minimize the negative impacts on learning achievements. For these reasons, it is important to analyze the factors that are involved in the current educational model, in such a way that new methodologies and strategies can be implemented that allow the provision of a comprehensive and quality education. The results of the study will allow to maintain a policy of constant evaluation of educational practices in the e-learning modality to determine the factors that favor connectivist education and specify the factors that are weakening the success of the system; this continuous evaluative exercise will make it possible to propose and determine practices and methodologies in order to maximize learning levels.
A Similarity Measure Across Ontologies for Web Services Discovery

Given the critical and difficult nature of discovering Web services in the development process of service oriented architectures, several studies have been proposed to solve this problem. There is a real need to work for matching semantic Web services which use different ontologies. In responding to this need, measuring semantic similarity between SWS may be reduced to the calculation of similarity between ontological concepts. This work is a contribution to achieve semantic interoperability for Web services in a multi-ontology environment, for which the authors present a generic framework for Web services discovery. Here their focus is on the semantic similarity measure-based core of their framework and the authors present a novel algorithm for concepts matching between different ontologies. Results of the experiments confirm the viability of the semantic similarity measure.
Mobile Learning in and out of the K-12 Classroom

Today there is widespread ownership of mobile technology, including mobile phones, tablets, Chromebooks, among school-aged youth. To wit, researchers report that nearly 70% of 8- to 18-year-olds own their own mobile device. In parallel with increased accessibility and usage, researchers in various fields of education have begun to explore how mobile technology can impact teaching and learning. In this chapter, the work of pioneering scholars in the area of emerging technology in K-12 education are discussed. Next, a description is provided of the current scientific knowledge on the ways in which mobile technologies are used by students in K-12 education. Then, important research is reviewed that highlights the added effects of mobile technology to support non-traditional learners. Lastly, recommendations for future lines of investigation and further reading are discussed.
Emotion Recognition from Speech Signals Using DCNN with Hybrid GA-GWO Algorithm

In recent days, from the speech signal the recognition of emotion is considered as an extensive advanced investigation subject because the speech signal is considered as the rapid and natural method to communicate with humans.Numerous examinations have been progressed related to this topic.This paper develops the emotions recognition from the speech signal in an accurate way, with the knowledge of numerous examined models.Therefore, to study the multimodal fusion of speech features, a Deep Convolutional Neural Network model is proposed.Moreover, the hybrid Genetic Algorithm (GA)-Grey Wolf Optimization (GWO) algorithm is presented that is the combination of both the GA and GWO technique features towards training the network.Finally, the developed recognition model is verified and compared with the existing techniques in correlation with diverse performance measures such as Accuracy, Sensitivity, Precision, Specificity, False Positive Rate (FPR), False Discovery Rate (FDR), False Negative Rate (FNR), F1Score, Negative Predictive Value (NPV), and Matthews correlation coefficient (MCC).
Blockchain

A blockchain is a distributed, time-stamped, append-only ledger of data connected with addresses, simultaneously kept on all the nodes within a decentralized network. Blockchain technologies vary depending on their openness, consensus algorithms, and use cases. Cryptoassets are one application for monetary and financial purposes, and they have expanded dramatically in number and market capitalization. In the form of Initial Coin Offerings and Token Sales, cryptoassets have had large impacts in start-up financing, competing with Venture Capital. Beyond cryptoassets, the range of use cases expanded dramatically through smart contracts, and blockchain technologies are currently used in managing identity, personal data, elections, supply chains, energy grids, property registries, and Internet of Things. Blockchain's novelty and design pose regulatory challenges about data protection, illicit uses, geographic delimitation of competent jurisdictions, and how to enforce judicial decision on ostensibly immutable ledgers. Blockchain technologies offer a fertile ground to explore issues of trust, power, identity, ownership, and value. Geographic research can unpack blockchain's potentialities and ambiguities for disintermediation and reintermediation, decentralization and control, co-production and capitalization. It can also look into the economic geographies of blockchain firms, the environmental and energetic footprint of distributed calculation.