Reviewerly/exHarmony huggingface dataset:
Users’ Reaction Time for Improvement of Security and Access Control in Web Services

This paper concerns the case of the development of a technology for increasing the efficiency of access control based on the user behavior monitoring built into a software system’s user interface. It is proposed to use the time of user reactions as individual indicators of psychological and psychophysical state. This paper presents the results and interpretation of user reactions collected during a mass web survey of students of the Russian Federation. The total number of users was equal to 22,357. To reveal the patterns in user reactions, both quantitative and qualitative approaches were applied. The analysis of the data demonstrated that the user could be characterized by their psychomotor reactions, collected during the answering of a set of questions. Those reactions reflected the personal skills of the interface interaction, the speed of reading, and the speed of answering. Thus, those observations can be used as a supplement to personal verification in information systems. The collection of the reaction times did not load the data volumes significantly nor transmit confidential information.
Validation of an Alternative Neural Decision Tree

A modified version of Neural Decision Tree (NDT) which was initially proposed by Biau is presented in this paper with significantly extended statistical consistency theoretical results. Numerical experiments are performed to investigate several key features of this alternative NDT. Our experimental results demonstrate the attractiveness of NDT over the traditional artificial neural networks by adopting a strategically selected network structure with appropriate initial weights. Moreover, our results indicate that in all cases we have tested, NDT provides higher classification accuracy than a traditional neural network with a comparable degree of freedom. Empirical evidence also suggests that NDT is more stable and robust with respect to hyper-parameters commonly involved in the training of neural networks.
Improved Real-Time Approach to Static Hand Gesture Recognition

A real-time hand detection and tracking system is developed in this work in which the gestures are recognized by counting the number of fingers. More than fifteen kinds of hand gestures are recognized in this system and these gestures are then mapped with the keyboard keys. For background Subtraction we used Grayscale color space followed by. contour of hand, which is developed using Canny's Algorithm to which we applied Chan's algorithm to obtain the convex hull. From the defect points of Convex Hull, we obtained the center point of the hand. Using this, we counted the fingers and thereby recognizing the gestures. We performed several trials to validate the feasibility and applicability of the proposed system, and confirmed the accuracy of 93.20%.
Quantitative review of articles about comics &amp; education in Ibero-America

This paper reviews articles in educational journals about comics and education in Ibero-American journals. Time-lapse analysis covers the years 2010 - 2019. Scientific journals of the educational field included in the virtual repository Dialnet are taken as reference. The data search is confined to the field of Psychology and Education. The terms used for the search have been'comic', in journals with full text included in Latindex. The aim is to explore state of the art, paying attention to four issues: years, journals, countries and themes. The results show a higher quantity of articles published in Spain; these articles being the best positioned in the categories CIRC and CARHUS + 2014. Contributions from Spain, Mexico, Colombia, Brazil and Chile have been analyzed in all the languages found (Spanish, Portuguese and Galician). Most of the production is in the years 2010 and 2017. About the journals, the dispersion is remarkable, finding only five journals that contribute more than one. On the developed topics, they stand out: language, social and natural knowledge and other learnings. To conclude, this review can serve as a basis for deepening this issue within the Ibero-American environment and make comparisons between countries.
Automatic Segmentation and Classification of Thyroid Nodules in Ultrasound Images with Convolutional Neural Networks

Ultrasound image plays an important role in the diagnosis of thyroid disease. Accurate segmentation and classification of thyroid nodules are challenging due to their heterogeneous appearance. In this paper, we propose an efficient cascaded segmentation framework and a dual-attention ResNet-based classification network to automatically achieve the accurate segmentation and classification of thyroid nodules, respectively. We evaluate our methods on the training dataset TN-SCUI 2020 Challenge. The 5-fold cross validation results demonstrate that the proposed methods achieve average IoU of 81.43% in segmentation task, and average F1 score of 83.22% in classification task. Finally, our method ranks the first place of segmentation task on the test set through the final online verification. The source code of the proposed methods is available at https://github.com/WAMAWAMA/TNSCUI2020-Seg-Rank1st .
Analysis and Implementation of Optimization Techniques for Facial Recognition

Amidst the wide spectrum of recognition methods proposed, there is still the challenge of these algorithms not yielding optimal accuracy against illumination, pose, and facial expression. In recent years, considerable attention has been on the use of swarm intelligence methods to help resolve some of these persistent issues. In this study, the principal component analysis (PCA) method with the inherent property of dimensionality reduction was adopted for feature selection. The resultant features were optimized using the particle swarm optimization (PSO) algorithm. For the purpose of performance comparison, the resultant features were also optimized with the genetic algorithm (GA) and the artificial bee colony (ABC). The optimized features were used for the recognition using Euclidean distance (EUD), K-nearest neighbor (KNN), and the support vector machine (SVM) as classifiers. Experimental results of these hybrid models on the ORL dataset reveal an accuracy of 99.25% for PSO and KNN, followed by ABC with 93.72% and GA with 87.50%. On the central, an experimentation of the PSO, GA, and ABC on the YaleB dataset results in 100% accuracy demonstrating their efficiencies over the state-of-the art methods.
A Cybersecurity Moonshot

Cybersecurity needs radical rethinking to change its current landscape. This article charts a vision for a cybersecurity moonshot based on radical but feasible technologies that can prevent the largest classes of vulnerabilities in modern systems.
Probabilistic assessment of water quality in terms of oxidability

Abstract There has been analyzed the nature of the distribution of the oxidability of water. It is revealed that the nature of the distribution of indicator values during the year depends largely on the seasonal factor, and therefore the analysis of the distribution of oxidability is proposed to be studied separately for each month. A variation series is constructed and empirical distribution functions of oxidability distribution is derived. It is established that the law of water oxidability distribution differs from the normal and lognormal distributions, but it is described with sufficient accuracy by the gamma distribution or by a cubic polynomial function (being the simplest). The hypothesis about the distribution law is confirmed by the Kolmogorov–Smirnov test. The water oxidability distribution function allows to determine the probability of exceeding the specified values of the indicator and quantitatively assess the risks of exceeding them, which can become the basis for developing solutions for managing water quality and increasing the efficiency of the water treatment process.
Time-Aligned Edge Plots for Dynamic Graph Visualization

We present time-aligned edge plots: time- and edge-scalable representations of dynamic graphs. Vertices are mapped to two vertical parallel axes. The left axis depicts the source vertices, whereas the right one depicts the destination vertices. The time axis is horizontally embedded in-between the two axes, resulting in a two-dimensional graph layout. Edges are added by drawing straight lines connecting the corresponding source and destination vertices through time, while the pixels along the lines are used to encode the time-varying information. In this way, the depiction of edges at the individual timepoints is reduced to only a few pixels, resulting in a less cluttered representation of dynamic graphs, while the alignment of edges over time reveals the temporal patterns in the data and preserves the users' mental map. We evaluate our approach by comparing it theoretically and empirically against the state-of-the-art using dynamic graphs of varying complexities.
An Approach to Image Denoising Using Autoencoders and Spatial Filters for Gaussian Noise

At a prime age in data science where information extracted from a large variety of data sources drives a lot of research, the need for having clean data is important. Accumulation and extraction of pure and specific data from such sources is an important stage in this area. It is important to consider noise from the data sources in order to pre-process the data. The work focuses specifically on handling Gaussian noise in acquired images that are used in specific domains like image classification. The work proposes a denoising pipeline involving spatial filters and autoencoder networks to remove Gaussian noise in image data to achieve high performance in computer vision tasks. Image data that is abundantly available can be put to good use by the proposed denoising pipeline which enhances machine interpretation of such data. The work provides an empirical analysis on the results obtained and discusses important trends observed.
Digital mammographic interpretation by UK radiographer mammographers: A JAFROC analysis of observer performance

Introduction Radiologists utilise mammography test sets to bench mark their performance against recognised standards. Using a validated test set, this study compares the performance of radiographer readers against previous test results for radiologists. Methods Under similar test conditions radiographer readers were given an established test set of 60 mammograms and tasked to identify breast cancer, they were measured against their ability to identify, locate and give a confidence level for cancer being present on a standard set of mammographic images. The results were then compared to previously published results for radiologists for similar or the same test sets. Results The 10 radiographer readers demonstrated similar results to radiologists and for lesion sensitivity were the highest scoring group. The study group score a sensitivity of 83; a specificity of 69.3 and lesion sensitivity of 74.8 with ROC and JAFROC scores of 0.86 and 0.74 respectively. Conclusion Under test conditions radiographers are able to identify and accurately locate breast cancer in a range of complex mammographic backgrounds. Implications for practice The study was performed under experimental conditions with results comparable to breast radiologists under similar conditions, translation of these findings into clinical practice will help address access and capacity issues in the timely identification and diagnosis of breast cancer.
Evofficient: Reproducing a Cartesian Genetic Programming Method

Designing Neural Network Architectures requires expert knowledge and extensive parameter searches. Neural Architecture Search (NAS) aims to change that by automating the design process. It is important that these approaches are reproducible so they can be used in real-life scenarios. In our work, we reproduce a genetic programming approach to designing convolutional neural networks called CGP-CNN. We show that this is difficult and requires many changes to the training scheme, reducing real-life applicability. We achieve a final accuracy of $$90.6\% \pm 0.005$$ , substantially lower than the reported $$93.7\% \pm 0.005$$ . This negates some of the benefits of using CGP-CNN for NAS. We establish a random search as a consensus baseline and show that it produces similar results to the evolutionary method of CGP-CNN. To assess the adaptability and generality of the presented algorithm, it is applied to CIFAR-100 and SVHN with a final accuracy of 63.1% and 95.6%, respectively. We extend the investigated NAS by two methods for predicting candidate fitnesses from partial learning curves. This improves CGP-CNN runtime efficiency by a factor of 1.69.
Digital Transformation in Legal Metrology: An Approach to a Distributed Architecture for Consolidating Metrological Services and Data

In this paper the digital transformation of sovereign processes are outlined. The driving force is to streamline and innovate metrological processes for measuring instruments under legal control. The main challenge is to preserve the same level of trust and security for digitally transformed procedures as for conventional paper-based procedures. In the digital domain, a new balance has to be negotiated between security and process requirements without burdening the digital procedures with additional security prerequisites. Considering the strict legal framework, a distributed software architecture approach is presented that offers privacy, security and resilience. The cornerstone of the digital transformation is the Administrative Shell, representing measuring instruments in the digital domain. It will hold all measuring instrument related documents throughout its life cycle for the first time and offers access to all stakeholders. For this reason, a new security concept with additional roles is introduced including the Metrological Administrator. It manages digital metrological processes. At the same time, the platform approach seamlessly integrates existing public and private infrastructures. Moreover, a service hub is created with interdependent services that support the digital transformation of paper-based processes, such as verification and software update. These main use cases are introduced, and their requirements and implementation approach are outlined. At the current state of research, all evaluated metrological processes will still depend on human interaction. Currently, it is not possible to fully automatize and streamline the process chain to its full extent. Nevertheless, the presented results are important steps towards this long term goal.
Validating SAREF in a Smart Home Environment

SAREF is an ontology created to enable interoperability between smart devices. While the IoT community has shown interest and understanding of SAREF as a means for interoperability, there is a lack in the literature of practical examples to implement SAREF in real applications. In order to validate the practical implementation of SAREF we perform two experiments. First we map IoT data available in a smart home into RDF using SAREF. In the second part of the paper an IoT environment is created by using the Knowledge Engine, a framework created to allow communication between smart devices, operating on Raspberry Pi’s emulating IoT devices, where the communication of the IoT devices is performed by sharing knowledge represented with SAREF. These experiments demonstrate that SAREF is an ontology that is successfully applicable in different situations, with data-mapping showing that SAREF is able to represent the information of different smart devices and by using the Knowledge Engine showing that SAREF can enable interoperability between smart devices.
How to Train Your HERON

In this letter we apply Deep Reinforcement Learning (Deep RL) and Domain Randomization to solve a navigation task in a natural environment relying solely on a 2D laser scanner. We train a model-based RL agent in simulation to follow lake and river shores and apply it on a real Unmanned Surface Vehicle in a zero-shot setup. We demonstrate that even though the agent has not been trained in the real world, it can fulfill its task successfully and adapt to changes in the robot's environment and dynamics. Finally, we show that the RL agent is more robust, faster, and more accurate than a state-aware Model-Predictive-Controller. Code, simulation environments, pre-trained models, and datasets are available at https://github.com/AntoineRichard/Heron-RL-ICRA.git.
A VMAF Directed Perceptual Rate Distortion Optimization for Video Coding

Video Multimethod Assessment Fusion (VMAF), as a state-of-the-art learning-based objective assessment metric, has been demonstrated to achieve a stronger correlation with human visual system than others. However, combining multiple metrics by Support Vector Machine (SVM) makes it unrealistic to be directly integrated into encoder due to its high complexity and non-differentiability. In this paper, to alleviate this problem, we propose a novel perceptual distortion metric directed by VMAF (D-VMAF) to optimize perceptual quality. The relationship between frame-level VMAF and block-level content is fitted by DVMAF with the combination of spatial and temporal factors. By the proposed perceptual distortion metrics, the Lagrangian multiplier in RDO is adjusted adaptively. Experimental results demonstrate that 3% coding performance in BD-VMAF can be achieved using our method compared to the original HM encoder.
Soft-CSRNet: Real-time Dilated Convolutional Neural Networks for Crowd Counting with Drones

In recent years, the measurement of crowd density in a real-time video sequence has been a significant field of study. The use of these methods to stop protest scrambling, and social distancing to protect from COVID-19 is a crucial task nowadays. In this article, we introduce a different model for estimating crowd density based on front and vertical drone video sequences. Our proposition consists of an optimized version of a widely used crowd counting model called “CSRNET”. The proposed “SOFT CSRNET” is composed of two parts: a CNN front-end and CNN back-end. The front-end is composed of VGG16 layers constructed in the same way as CSRNet. On the other hand, in the back-end we select five convolutional layers of different size in the aim to get better results in less time. The results demonstrate that our method outperforms CSRNET in terms of MAE, image par second (ips) and proof of efficiency for a real-time videos sequence of drones. Our results are validated, executing the proposed method on Visdrone2019-DET and Visdrone2020-DET datasets.
On a Partially Verifiable Multi-party Multi-argument Zero-knowledge Proof

The term “digital signature” refers to electronic information that is used to identify signatories and indicate that they have signed a document; such information is either attached to or logically combined with a specific electronic document. However, digital signatures entail privacy infringements because it is possible to verify such signatures only when all the data are disclosed to the verifier. Zero-knowledge proofs are considered to be capable of solving this privacy problem. In general, a zero-knowledge proof can be established even if the prover hides the information required in the verification process from the verifier. Zero-knowledge succinct non-interactive argument of knowledge (ZK-SNARK), a prevalent zero-knowledge proof, has been optimized to generate non-interactive and succinct proofs; nevertheless, the generation of proofs is excessively time consuming, making the application of ZK-SNARK impractical in most scenarios. In this paper, we show that existing cryptographic algorithms, such as a one-way hash function or digital signature, can be combined with a zero knowledge proof. Particularly, we propose the multi-argument zero-knowledge argument (MAZKA) algorithm, which can verify data by exposing only the part to be verified and also verify that the part of data has not been manipulated compared to the original. In addition, the proposed algorithm satisfies the adaptive proof of knowledge, perfect zero-knowledze and combinatorial succinctness conditions.
Egocentric viewpoint in mixed reality situated visualization: challenges and opportunities

Mixed reality (MR) is well suited for situated visualization (SV), a method to represent data in a context, with potential in many situations. However, MR-based visualizations are commonly constrained to the users' single egocentric viewpoint reducing their ability to explore all the available information. This article discusses the main limitations and challenges of this approach based on the analysis of existing literature and identifies opportunities, as well as relevant aspects that must be considered when devising new methods aimed at overcoming those limitations.
Graph Adversarial Attacks and Defense: An Empirical Study on Citation Graph

This paper details the methodologies and decisions making processes used while developing the attacking and defending models for the Graph Adversarial Attacks and Defense applied to a large citation graph. To handle the large graphs, our attack strategy is twofold: 1) randomly attack the structure first, 2) keep the structure unchanged, then continue the attack on the features using the gradient-based method. On the other hand, the defender is based on 1) filtering and normalizing the feature data, 2) applying the Graph Convolutional Network model, and 3) selecting the models with the highest accuracy and robustness based on our own attacking data. We applied these strategies in KDD Cup 2020 on Graph Adversarial Attacks and Defense dataset. The attacker can drop the accuracy of a surrogate 2-layer Graph Convolutional Network model from 60% to 30% on the test set. Our defending model has 68% accuracy on the validated data and has 89% of the target labels remained the same while adding fake nodes, generated by our attacking method, to the graph.
Application of Seq2Seq Models on Code Correction

We apply various seq2seq models on programming language correction tasks on Juliet Test Suite for C/C++ and Java of Software Assurance Reference Datasets and achieve 75% (for C/C++) and 56% (for Java) repair rates on these tasks. We introduce pyramid encoder in these seq2seq models, which significantly increases the computational efficiency and memory efficiency, while achieving similar repair rate to their nonpyramid counterparts. We successfully carry out error type classification task on ITC benchmark examples (with only 685 code instances) using transfer learning with models pretrained on Juliet Test Suite, pointing out a novel way of processing small programming language datasets.
Deep Learning Network

Artificial intelligence (AI) is defined as a machine that can do everything a human being can do and produce better results. Means AI enlightening that data can produce a solution for its own results. Inside the AI ellipsoidal, Machine learning (ML) has a wide variety of algorithms produce more accurate results. As a result of technology, improvement increasing amounts of data are available. But with ML and AI, it is very difficult to extract such high-level, abstract features from raw data, moreover hard to know what feature should be extracted. Finally, we now have deep learning; these algorithms are modeled based on how human brains process the data. Deep learning is a particular kind of machine learning that provides flexibility and great power, with its attempts to learn in multiple levels of representation with the operations of multiple layers. Deep learning brief overview, platforms, Models, Autoencoders, CNN, RNN, and Appliances are described appropriately. Deep learning will have many more successes in the near future because it requires very little engineering by hand.