Reviewerly/exHarmony huggingface dataset:
EbolApp – A digital tool for smart contact tracing in infectious diseases (HCID) outbreaks

Background: Outbreaks due to infectious diseases like Ebola virus disease require quick and proper epidemiological responses to implement effective countermeasures. Traditional epidemiological tools are often not easy to apply. PC-based digital tools can be cumbersome in field work. As smart phone adoption is very high in many parts of the world, smartphone-based tools could be an alternative. An app was developed for this purpose and tested in a proof-of-principle study. Methods and materials: EbolApp is an Android-based application that assigns identifiers to smartphones after installation and stores GSM/GPS tracking data on the mobile. With activated Bluetooth function any further device within a defined distance is recorded. If a user contracts a disease, ID-based user profiles are synchronized on a server by an alarm function, which informs any detected contact person and recommends a doctor's visit. Server data will be completely deleted afterwards. Individual movement profiles can be read out by authorized persons. A proof-of-principle analysis regarding compatibility and functionality was carried out by health care workers from a clinic for infectious diseases and tropical medicine. Results: On a total of 16 smartphones with Android version 6, 7 and 8, the app (Version 0.9) was installed without any problems. The complete app functionality was given for 13/16 smartphones (81.3%), no tracking was recorded in two, in one the app crashed while reading profiles. Battery consumption with continuously activated Bluetooth was significantly increased in 50% of users. Thirteen users were able to use the secure alarm function receiving corresponding contact warnings. Graphic mapping and temporal assignment of the motion profiles confirmed the correctness of the alarms. Conclusion: EbolApp is promising digital tool for contact tracing in infectious diseases. It allows fast track detection of contacts and can provide information as well as instructions to those which are at risk. This also may allow timely use of post-expositional vaccinations or medical therapies. The prerequisite is the adequate distribution of appropriate telephones within risk populations and compliance with corresponding data protection laws. In the updated Version 2.0, compatibility of the app has been improved again. A field trial in the Republic of Liberia is on the way.
Research on Optimization of Object Detection Technology Based on Convolutional Neural Network

Aiming at the optimization problem of object detection technology, an instance segmentation model algorithm based on the improved Cascade Mask R-CNN is proposed. Firstly, information fusion is performed on the mask branches of each cascade stage, so that the mask information of the current stage is jointly determined by the segmentation results of the previous stage and the current stage. Secondly, GA-RPN is used to replace the original RPN to generate high-confidence and low-density proposal regions. At the beginning of the cascade, the IoU threshold is increased for training, thereby improving the performance of the network model. Finally, the model of this paper is implemented, and compared with the official implementation on the public data set, which verifies the efficiency of improved algorithm.
Visual Tracking of Small Unmanned Aerial Vehicles Based on Object Proposal Voting

In this paper, we propose a novel visual tracking method for unmanned aerial vehicles (UAVs) in aerial scenery. To track the UAVs robustly, we present a new object proposal method that can accurately determine the object regions that are likely to exist. The proposed object proposal method is robust to small objects and severe background clutter. For this, we vote on candidate areas of the object and increase or decrease the weight of the area accordingly. Thus, the method can accurately propose the object areas that can be used to track small-sized UAVs with the assumption that their motion is smooth over time. Experimental results verify that UAVs are accurately tracked even when they are very small and the background is complex. The proposed method qualitatively and quantitatively delivers state-of-the-art performance in comparison with conventional object proposal-based methods.
Multimodal Feature Fusion Recognition of Modulated Signals Based on Image and Waveform Domain

Communication signal modulation type recognition has a wide range of applications in electronic reconnaissance equipment such as electronic support, electronic intelligence and radar threat warning systems. The common modulation feature recognition algorithms usually only focus on one feature, ignoring the complementarity between different features. Considering the importance of feature fusion, this paper proposes a feature fusion method based on deep learning model. Extracting the image domain features and I/Q waveform domain features of the signal through suitable deep learning models, then combine the extracted features and use Kernel Principal Component Analysis (KPCA) to reduce the dimensionality of the joint features, finally obtain the classification recognition result in the classifier. Simulation experiments show that the signal recognition method based on feature fusion can have a higher recognition rate at low SNR than when only single features are considered, which can reach 93.15% at -2 dB.
A Chaos Theory Approach to Understand Neural Network Optimization

Despite the complicated structure of modern deep neural network architectures, they are still optimized with algorithms based on Stochastic Gradient Descent (SGD). However, the reason behind the effectiveness of SGD is not well understood, making its study an active research area. In this paper, we formulate deep neural network optimization as a dynamical system and show that the rigorous theory developed to study chaotic systems can be useful to understand SGD and its variants. In particular, we first observe that the inverse of the instability timescale of SGD optimization, represented by the largest Lyapunov exponent, corresponds to the most negative eigenvalue of the Hessian of the loss. This observation enables the introduction of an efficient method to estimate the largest eigenvalue of the Hessian. Then, we empirically show that for a large range of learning rates, SGD traverses the loss landscape across regions with largest eigenvalue of the Hessian similar to the inverse of the learning rate. This explains why effective learning rates can be found to be within a large range of values and shows that SGD implicitly uses the largest eigenvalue of the Hessian while traversing the loss landscape. This sheds some light on the effectiveness of SGD over more sophisticated second-order methods. We also propose a quasi-Newton method that dynamically estimates an optimal learning rate for the optimization of deep learning models. We demonstrate that our observations and methods are robust across different architectures and loss functions on CIFAR-10 dataset.
Design of Point-to-Point Power Mutual Transaction Between Residents Based on the Technical Characteristics of Blockchain

In order to deepen the reform of electric power market, guide the electric power residents to bring flexible energy resources into the market, and carry out the Point-to-Point(P2P) electric power trade(PEPT)among regional residents. This paper proposes PEPT method based on blockchain and “two-round trade matching” (TRTM). Firstly, based on the blockchain technology framework, a consensus mechanism for Ripple consistency with low computing power requirements is proposed and a P2P operation architecture is designed; Secondly, the TRTM process of PEPT between residents is proposed based on the technical characteristics of blockchain. Purchasers and sellers first declare the energy transaction information for the next period, perform matching and authentication according to the pre-declared power transaction (first round, FRPT) matching mechanism, clear PEPT orders; When there is a deviation in the execution of the transaction, purchasers and sellers are matched according to the deviation power transaction (second round, SRPT) mechanism, and the transaction costs are cleared according to PEPT clearing mechanism; For the problem of power measurement and price between residents, key technology designs, such as the matching mechanism of PEPT, the design of prices with equilibrium prices (EP) and flexible prices (FP). And the clearing method of SRPT are proposed. Finally, this paper verifies the feasibility of the mechanism and model of PEPT between residents through case, and provides a reference for the design of a flexible and open power trading mechanism between distribution network residents.
Examining the Relationship Between the Readability and Comprehensibility of Practice Test Questions and Failure Rates on Learner's Permit Knowledge Tests

Purpose The readability and comprehensibility of Learner's Permit Knowledge Test practice questions and the relationship with test failure rates across states and the District of Columbia were examined. Method Failure rates were obtained from department representatives. Practice test questions were extracted from drivers' manuals and department websites and examined for readability using Flesch–Kincaid Grade Level and comprehensibility using Question Understanding Aid. The influence of readability and comprehensibility on test failure rates was explored. Results The average failure rate from reporting jurisdictions was 42.76%. In total, 11 out of 28 jurisdictions reported that test takers fail more than half the time, while 25 out of 28 reported that test takers fail at least a quarter of the time. While 33.09% of the variability in failure rates could be accounted for by syntactic complexity of the questions, 54.18% could be accounted for by the reading ease. Discussion With few exceptions, test failure rates are systematically high across the United States. The current findings suggest that these tests may be inappropriately biased against individuals with lower levels of literacy and language ability. Implications for test developers and clinicians are discussed.
Research on Semantic Feature Disambiguation Based on Improved Collaborative Filtering Algorithm

Abstract Artificial intelligence is developing rapidly, and the amount of information data is also increasing. Human-computer interaction technology can use computers to understand and process massive amounts of large data texts and accurately locate useful information. Moreover, it is very difficult to achieve smooth human-computer interaction. The main reason for the difficulty is that there are various ambiguities in natural language. Therefore, a noun phrase referential disambiguation model is established by improving the convolutional collaborative filtering algorithm in the paper, where the hand-crafted features are applied to clean up data to remove redundancy, and the remaining correct data is used as the input of the model. After experiments, the MACSSR algorithm and the CSSR algorithm are used for comparative testing under different M directs. It proves that the research content of this paper is superior to the comparative test method, and can extract semantic dependencies more quickly and effectively, which contributes to the intelligent semantic disambiguation technology.
Recognition and Reconstruction of Road Marking with Generative Adversarial Networks (GANs)

Road markings play an essential role in road safety and are one of the most important elements to guide autonomous vehicles and help the driver on the road. The recognition and detection of road markings has been very successful in recent years with the rapid development of deep learning technology. Although considerable work and progress has been made in this area, they often depend excessively on unrepresentative datasets and inappropriate conditions. In this article, to overcome these drawbacks, we propose a deep learning system for the extraction, classification, and completion of road markings which generates high-quality samples for data augmentation. For this, an in-depth learning network proposed to successfully recover a clean road marking of a fuzzy route using generative contradictory networks (GAN). The proposed data augmentation method, based on mutual information, can preserve and learn the semantic context from the actual dataset. We build and train a GAN model to increase the size of the training dataset, which makes it suitable for recognizing the target. Our system can generate clean samples from fuzzy samples and surpasses other methods, even with unconstrained road marking datasets.
Progressive Image Super-Resolution via Neural Differential Equation

We propose a new approach for the image super-resolution (SR) task that progressively restores a high-resolution (HR) image from an input low-resolution (LR) image on the basis of a neural ordinary differential equation. In particular, we newly formulate the SR problem as an initial value problem, where the initial value is the input LR image. Unlike conventional progressive SR methods that perform gradual updates using straightforward iterative mechanisms, our SR process is formulated in a concrete manner based on explicit modeling with a much clearer understanding. Our method can be easily implemented using conventional neural networks for image restoration. Moreover, the proposed method can super-resolve an image with arbitrary scale factors on continuous domain, and achieves superior SR performance over state-ofthe-art SR methods.
Walsh transforms of trace forms with three or more terms and some maximal Artin-Schreier curves

We introduce some results for the Walsh transforms of quadratic trace forms with three or more terms. We also generalize some results on Kasami--Welch functions. We use Walsh transforms to obtain the number of rational points on Artin--Schreier curves over $\mathbb{F}_{2^k}.$ Later we construct some maximal Artin--Schreier curves over $\mathbb{F}_{2^k}$ for an even integer $k$.
Novel Embedding Based Ranking Approach for Effective Product Marketing on E-Commerce Websites

Right now, characterize every one of those viewpoints as a component of item, and present a multi-dimensional feeling investigation approach for E-business audits. Specifically, we utilize an assessment dictionary growing system to evacuate the word uncertainty among various measurements, and propose a calculation for estimation investigation on E-business audits dependent on rules and a dimensional feeling vocabulary. Make word net lexicon: In this sort of archive, every single positive word are worked out independently and every negative word are worked out at one spot. Extraction of dataset: First dataset of openly accessible item audits were downloaded from the web and afterward the entry extraction structure recognizes significant areas of the content which is generally illustrative of the substance of the record. All the more explicitly, this progression includes distinguishing and extricating those particular item includes and the assessments on them.
Fast Multi-Output Relevance Vector Regression

This paper has applied the matrix Gaussian distribution of the likelihood function of the complete data set to reduce time complexity of multi-output relevance vector regression from O(VM3) to O(V3 +M3), where V and M are the number of output dimensions and basis functions respectively and V < M. Our experimental results demonstrate that the proposed method is more competitive and faster than the existing methods like Thayananthan et al. (2008). Its computational efficiency and accuracy can be attributed to the different model specifications of the likelihood of the data, as the existing method expresses the likelihood of the training data as the product of Gaussian distributions whereas the proposed method expresses it as the matrix Gaussian distribution.
Using Segmentation to Enhance Frame Prediction in a Multi-Scale Spatial-Temporal Feature Extraction Network

Designing a machine to predict future events is a challenging problem to even existing state-of-the-art approaches. It require great computation power either in adversarial training and in segmentation and optical flow. By combining conventional segmentation and the DNN we proposed in this paper, we have a simpler architecture which effectively and efficiently predicts both future frames and semantics more precise than the previous approaches. The input is a raw image sequence, and each frame of it is segmented for semantics, extracted for spatial features, analyzed for temporal features at different scales in a top down path; and then the prediction of frames and segmentation are synthesized in the bottom-up path. Results of our model show superiority of prediction to other state-of-the-art ones in (1) precision of frames, and (2) accuracy of segmentation masks.
Learning Optimal Forecast Aggregation in Partial Evidence Environments

We consider the forecast aggregation problem in repeated settings where the forecasts are of a binary state of nature. In each period multiple experts provide forecasts about the state. The goal of the aggregator is to aggregate those forecasts into a subjective accurate forecast. We assume that the experts are Bayesian and the aggregator is non-Bayesian and ignorant of the information structure (i.e., the distribution over the signals) under which the experts make their forecasts. The aggregator observes the experts’ forecasts only. At the end of each period, the realized state is observed by the aggregator. We focus on the question of whether the aggregator can learn to optimally aggregate the forecasts of the experts, where the optimal aggregation is the Bayesian aggregation that takes into account all the information in the system. We consider the class of partial evidence information structures, where each expert is exposed to a different subset of conditionally independent signals. Our main results are positive: we show that optimal aggregation can be learned in polynomial time in quite a wide range of instances in partial evidence environments. We provide an exact characterization of the instances where optimal learning is possible as well as those where it is impossible.
NreASAM: Towards an Ontology-Based Model for Authentication and Auto-Grading Online Submission of Psychomotor Assessments

Core and integral to the fourth industrial revolution, knowledge economy, and beyond is information and communication technology (ICT); more so, during and post the novel coronavirus pandemic. Yet, there exists a skills gap in ICT networking and networks engineering. Not only do students perceive ICT networking to be difficult to comprehend, lecturers and institutions grapple with the adequacy of ICT networking equipment. Real-life simulators, like the Cisco Packet Tracer, hold the promise of alternate teaching opportunities and evidenced-based environments for (higher-order) assessment. Research in the last decade on ontology for assessments have focused on taxonomy and multiple-choice questions and auto-generation and marking of assessments. This chapter extends the body of knowledge through its ontology-based model for enabling and auto-assessing performance-based and/or pseudo-psychomotor assessment. The auto-grading online submission system assists with authenticity and enables authentic and/or sustainable assessments.
Moments of a Wishart Matrix

The paper discusses the moments of Wishart matrices, in both the central and noncentral cases. The first part of the paper shows that the expectation map has certain homogeneity and equivariance properties which impose considerable structure on the moments, hitherto unrecognised. The second part of the paper explains how the moments may be computed efficiently. The two parts of the paper are completely independent, but the computations produce precisely the algebraic structure predicted in the first part, as well as reproducing all previously known formulae. A number of examples are given for the more manageable cases.
Generating Adversarial Examples in Limited Queries with Image Encoding and Noise Decoding

Deep neural network (DNN) has been widely used in many application scenarios, but it is easy to be affected by slight disturbance, which is referred to as adversarial examples, and produces wrong decisions endangering system security. It is difficult to obtain the structure and parameter information of the target network in the actual scene. In this paper, we propose an optimization-based method to generate adversarial examples. Inspired by the autoencoder and Conditional Generative Adversarial Net (CGAN), we train the encoder to encode the image into latent vector, of which the specific region (category field) is used to represent the category information of the image, and then train the decoder to generate adversarial examples of the specified category. We perform Natural Evolution Strategy in the low-dimensional latent space to further improve the attack. The result shows that our method can successfully attack the target network within only one or a few queries. We evaluate our method on Sign-Language-Digits (SLD) and Cifar-10 datasets. Compared with other methods, our approach can attack the target model with less queries, and maintain a high success rate.
Adapting the modern training system to the digital economy

The article presents the results of the assessment of the readiness of the modern education system of the Russian Federation to the global challenges of the digital economy. On the basis of the statistical and sociological research, the advantages and disadvantages of distance learning were revealed, directions for optimizing the process of training personnel in the conditions of digitalization were proposed.
Case Study: Match Fixing on the Dark Web

The process of betting on large sporting events is recognised as part of mainstream culture and as a result has been valued as a billion pound marketplace that has led to the thriving of betting companies across the globe. As with many processes which involve huge opportunities for revenue, betting on sporting events has been adopted by organised crime groups and transferred onto the Dark Web as a means of advertising and creating illegal betting companies. To meet the growing demands within this market, organised crime groups have become heavily reliant on match fixing as a means of establishing large audiences and to produce a sustainable income to allow the organisation to flourish. This case study presents an insight into how law enforcement agencies are tackling match fixing on the Dark Web as an expected but challenging phenomena. The case reflects on previous operations and how law enforcement aims to prevent and discourage betting on illegal sites.
It’s Personal: 4IR and the Future of Learning

The new technologies of the Fourth Industrial Revolution (4IR) are disrupting traditional models of work and learning. While the impact of digitalization on education was already a point of serious deliberation, the COVID-19 pandemic has expedited ongoing transitions. With 90% of the world’s student population having been impacted by national lockdowns—online learning has gone from being a luxury to a necessity, in a context where around 3.6 billion people are offline. As the impacts of the 4IR unfold alongside the current crisis, it is not enough for future policy pathways to prioritize educational attainment in the traditional sense; it is essential to reimagine education itself as well as its delivery entirely. Future policy narratives will need to evaluate the very process of learning and identify the ways in which technology can help reduce existing disparities and enhance digital access, literacy and fluency in a scalable manner. In this context, this chapter analyses the status quo of online learning in India and Germany. Drawing on the experiences of these two economies with distinct trajectories of digitalization, the chapter explores how new technologies intersect with traditional education and local sociocultural conditions. Further, the limitations and opportunities presented by dominant ed-tech models is critically analyzed against the ongoing COVID-19 pandemic.