Reviewerly/exHarmony huggingface dataset:
Augmented Reality Interface for Complex Anatomy Learning in the Central Nervous System: A Systematic Review

The medical system is facing the transformations with augmentation in the use of medical information systems, electronic records, smart, wearable devices, and handheld. The central nervous system function is to control the activities of the mind and the human body. Modern speedy development in medical and computational growth in the field of the central nervous system enables practitioners and researchers to extract and visualize insight from these systems. The function of augmented reality is to incorporate virtual and real objects, interactively running in a real-time and real environment. The role of augmented reality in the central nervous system becomes a thought-provoking task. Gesture interaction approach-based augmented reality in the central nervous system has enormous impending for reducing the care cost, quality refining of care, and waste and error reducing. To make this process smooth, it would be effective to present a comprehensive study report of the available state-of-the-art-work for enabling doctors and practitioners to easily use it in the decision making process. This comprehensive study will finally summarise the outputs of the published materials associate to gesture interaction-based augmented reality approach in the central nervous system. This research uses the protocol of systematic literature which systematically collects, analyses, and derives facts from the collected papers. The data collected range from the published materials for 10 years. 78 papers were selected and included papers based on the predefined inclusion, exclusion, and quality criteria. The study supports to identify the studies related to augmented reality in the nervous system, application of augmented reality in the nervous system, technique of augmented reality in the nervous system, and the gesture interaction approaches in the nervous system. The derivations from the studies show that there is certain amount of rise-up in yearly wise articles, and numerous studies exist, related to augmented reality and gestures interaction approaches to different systems of the human body, specifically to the nervous system. This research organises and summarises the existing associated work, which is in the form of published materials, and are related to augmented reality. This research will help the practitioners and researchers to sight most of the existing studies subjected to augmented reality-based gestures interaction approaches for the nervous system and then can eventually be followed as support in future for complex anatomy learning.
Towards Classical Hardness of Module-LWE: The Linear Rank Case

We prove that the module learning with errors ( $$\mathrm {M\text {-}LWE}$$ ) problem with arbitrary polynomial-sized modulus p is classically at least as hard as standard worst-case lattice problems, as long as the module rank d is not smaller than the number field degree n. Previous publications only showed the hardness under quantum reductions. We achieve this result in an analogous manner as in the case of the learning with errors ( $$\mathrm {LWE}$$ ) problem. First, we show the classical hardness of $$\mathrm {M\text {-}LWE}$$ with an exponential-sized modulus. In a second step, we prove the hardness of $$\mathrm {M\text {-}LWE}$$ using a binary secret. And finally, we provide a modulus reduction technique. The complete result applies to the class of power-of-two cyclotomic fields. However, several tools hold for more general classes of number fields and may be of independent interest.
A Formal Approach for Detecting Vulnerabilities to Transient Execution Attacks in Out-of-Order Processors

Transient execution attacks, such as Spectre and Meltdown, create a new and serious attack surface in modern processors. In spite of all countermeasures taken during recent years, the cycles of alarm and patch are ongoing and call for a better formal understanding of the threat and possible preventions.This paper introduces a formal definition of security with respect to transient execution attacks, formulated as a HW property. We present a formal method for security verification by HW property checking based on extending Unique Program Execution Checking (UPEC) to out-of-order processors. UPEC can be used to systematically detect all vulnerabilities to transient execution attacks, including vulnerabilities unknown so far. The feasibility of our approach is demonstrated at the example of the BOOM processor, which is a design with more than 650,000 state bits. In BOOM our approach detects a new, so far unknown vulnerability, called Spectre-STC, indicating that also single-threaded processors can be vulnerable to contention-based Spectre attacks.
System for monitoring and adjusting the learning process of primary schoolchildren based on the EEG data analysis

Introduction: Monitoring the learning process usually involves an analysis of the higher mental functions of the student: imagination, memory, thinking, attention, etc. Currently, there are wide opportunities for objective monitoring and evaluation of these characteristics based on the achievements of modern neuroscience. Analysis of the personal characteristics of the student allows to perform personalization of educational process. In this context, the approaches based on the electroencephalography analysis are particularly promising. Currently, one of the main barriers to the widespread introduction of EEG based systems in the school educational process is the lack of ready-made solutions to such systems for use in the learning process of schoolchildren, as well as scenarios for their application. Purpose: Development and initial testing of the system for monitoring and adjusting the learning process, as well as strategies for its use for primary school students. Methods: EEG analysis, wavelet analysis of recorded multichannel EEG data, analysis of changes in the energies of the main rhythms of the neural activity of the brain, the feedbackprovided by the system. Results: The concept and diagram of the developed system for monitoring and adjusting the learning process and the strategy for its use for elementary school students are described in detail. An increase in the efficiency of learning process with developed system was demonstrated as a consequence of feedback based on the assessment of changes in the energies of the characteristic rhythms of the student’s brain activity. Practical relevance: The use of EEG systems for an objective analysis of the personal characteristics of students and the personalization of the learning process through the implementation of feedback based on the data obtained will increase the quality of the educational process and the efficiency of learning new material.
Quality assessment of DIBR-synthesized views: An overview

The Depth-Image-Based-Rendering (DIBR) is one of the main fundamental technique to generate new views in 3D video applications, such as Multi-View Videos (MVV), Free-Viewpoint Videos (FVV) and Virtual Reality (VR). However, the quality assessment of DIBR-synthesized views is quite different from the traditional 2D images/videos. In recent years, several efforts have been made towards this topic, but there is a lack of detailed survey in the literature. In this paper, we provide a comprehensive survey on various current approaches for DIBR-synthesized views. The current accessible datasets of DIBR-synthesized views are firstly reviewed, followed by a summary analysis of the representative state-of-the-art objective metrics. Then, the performances of different objective metrics are evaluated and discussed on all available datasets. Finally, we discuss the potential challenges and suggest possible directions for future research.
Particle Swarm Optimization Algorithm With Self-Organizing Mapping for Nash Equilibrium Strategy in Application of Multiobjective Optimization

In this article, the Nash equilibrium strategy is used to solve the multiobjective optimization problems (MOPs) with the aid of an integrated algorithm combining the particle swarm optimization (PSO) algorithm and the self-organizing mapping (SOM) neural network. The Nash equilibrium strategy addresses the MOPs by comparing decision variables one by one under different objectives. The randomness of the PSO algorithm gives full play to the advantages of parallel computing and improves the rate of comparison calculation. In order to avoid falling into local optimal solutions and increase the diversity of particles, a nonlinear recursive function is introduced to adjust the inertia weight, which is called the adaptive particle swarm optimization (APSO). In addition, the neighborhood relations of current particles are constructed by SOM, and the leading particles are selected from the neighborhood to guide the local and global search, so as to achieve convergence. Compared with several advanced algorithms based on the eight multiobjective standard test functions with different Pareto solution sets and Pareto front characteristics in examples, the proposed algorithm has a better performance.
Color-Selective Schottky Barrier Modulation for Optoelectric Logic

The limitation on signal processes implementable using conventional semiconductor circuits based on electric signals necessitates a revolutionary change in device structures such that they can exploit photons or light. Herein, we introduce optoelectric logic circuits that convert optical signals with different wavelengths corresponding to different colors into binary electric signals. Such circuits are assembled using unit devices in which the electric current through the semiconductor channel is effectively gated by lights of different colors. Color-selective optical modulation of the device is cleverly achieved using graphene decorated with different organic dyes as the electrode of a Schottky diode structure. The drastic change in the electrode work function under illumination induces a change in the height of the Schottky barrier formed at the electrode/semiconductor junction and consequent modulation of the electric current; we term the developed device a photonic barristor. We construct logic circuits using an array of photonic barristors and demonstrate that they execute the functions of conventional NAND and NOR gates from optical input signals.
Discovering Symbolic Models from Deep Learning with Inductive Biases

We develop a general approach to distill symbolic representations of a learned deep model by introducing strong inductive biases. We focus on Graph Neural Networks (GNNs). The technique works as follows: we first encourage sparse latent representations when we train a GNN in a supervised setting, then we apply symbolic regression to components of the learned model to extract explicit physical relations. We find the correct known equations, including force laws and Hamiltonians, can be extracted from the neural network. We then apply our method to a non-trivial cosmology example-a detailed dark matter simulation-and discover a new analytic formula which can predict the concentration of dark matter from the mass distribution of nearby cosmic structures. The symbolic expressions extracted from the GNN using our technique also generalized to out-of-distribution data better than the GNN itself. Our approach offers alternative directions for interpreting neural networks and discovering novel physical principles from the representations they learn.
Latency of Waveform Data Delivery from the Southern California Seismic Network during the 2019 Ridgecrest Earthquake Sequence and Its Effect on ShakeAlert

Abstract The occurrence of the 4–6 July 2019 Mw 6.4 and Mw 7.1 Ridgecrest earthquake sequence provided the first full-scale test of the network and telemetry readiness of the Southern California Seismic Network (SCSN), to support the ShakeAlert earthquake early warning (EEW) system in California. ShakeAlert is a U.S. Geological Survey (USGS)-led collaboration to detect earthquakes and, when possible, to alert the public before the arrival of the strongest shaking. The SCSN performed well in its regional monitoring role for both the 4 July Mw 6.4 and the 6 July Mw 7.1 earthquakes. In the EEW role, it provided timely delivery of 5 s of P-wave data to ShakeAlert, which issued its first alert 6.9 s after origin time. Data delivery at peak data volumes for many stations exhibited some latency, and, as a consequence, some data arrived too late for analysis by one of the EEW algorithms. We find that the average link bandwidth for each station was sufficient, because all waveform data were delivered automatically to the archive, but link capacity for many stations was insufficient for peak demand. We describe the performance of the data telemetry for the sequence, including cellular, radio, hybrid, and backhaul systems. Cellular-based telemetry systems maintained low latency throughout strong shaking and after, but some stations, even at great distances, experienced subsequent brief increases in latency. Performance of radio links depended mostly on the signal strength of the link, with short-distance direct shots to high-bandwidth backhaul systems showing no latency impact, whereas stations on some long distance or marginal quality links suffered latencies of tens or hundreds of seconds. Improvements are being implemented to move telemetry links onto USGS and partner high-bandwidth microwave systems, and to reduce dependency on less robust long-distance radio shots.
On Segmentation of Pectoral Muscle in Digital Mammograms by Means of Deep Learning

Computer-aided diagnosis (CAD) has long become an integral part of radiological management of breast disease, facilitating a number of important clinical applications, including quantitative assessment of breast density and early detection of malignancies based on X-ray mammography.Common to such applications is the need to automatically discriminate between breast tissue and adjacent anatomy, with the latter being predominantly represented by pectoralis major (or pectoral muscle).Especially in the case of mammograms acquired in the mediolateral oblique (MLO) view, the muscle is easily confusable with some elements of breast anatomy due to their morphological and photometric similarity.As a result, the problem of automatic detection and segmentation of pectoral muscle in MLO mammograms remains a challenging task, innovative approaches to which are still required and constantly searched for.To address this problem, the present paper introduces a two-step segmentation strategy based on a combined use of data-driven prediction (deep learning) and graph-based image processing.In particular, the proposed method employs a convolutional neural network (CNN) which is designed to predict the location of breast-pectoral boundary at different levels of spatial resolution.Subsequently, the predictions are used by the second stage of the algorithm, in which the desired boundary is recovered as a solution to the shortest path problem on a specially designed graph.The proposed algorithm has been tested on three different datasets (i.e., MIAS, CBIS-DDSm, and InBreast) using a range of quantitative metrics.The results of comparative analysis show considerable improvement over state-of-the-art, while offering the possibility of model-free and fully automatic processing.The average values of dice similarity coefficient (DSC) and accuracy (ACC) on the mentioned three datasets are 97.22 ± 1.96% and 99.64 ±.27%, respectively.
Survey on software defect prediction techniques

ABSTRACTRecent advancements in technology have emerged the requirements of hardware and software applications. Along with this technical growth, software industries also have faced drastic growth in the demand of software for several applications. For any software industry, developing good quality software and maintaining its eminence for user end is considered as most important task for software industrial growth. In order to achieve this, software engineering plays an important role for software industries. Software applications are developed with the help of computer programming where codes are written for desired task. Generally, these codes contain some faulty instances which may lead to the buggy software development cause due to software defects. In the field of software engineering, software defect prediction is considered as most important task which can be used for maintaining the quality of software. Defect prediction results provide the list of defect-prone source code artefacts so that quality assurance team scan effectively allocate limited resources for validating software products by putting more effort on the defect-prone source code. As the size of software projects becomes larger, defect prediction techniques will play an important role to support developers as well as to speed up time to market with more reliable software products. One of the most exhaustive and pricey part of embedded software development is consider as the process of finding and fixing the defects. Due to complex infrastructure, magnitude, cost and time limitations, monitoring and fulfilling the quality is a big challenge, especially in automotive embedded systems. However, meeting the superior product quality and reliability is mandatory. Hence, higher importance is given to V&V (Verification & Validation). Software testing is an integral part of software V&V, which is focused on promising accurate functionality and long-term reliability of software systems. Simultaneously, software testing requires much effort, cost, infrastructure and expertise as the development. The costs and efforts elevate in safety critical software systems. Therefore, it is essential to have a good testing strategy for any industry with high software development costs. In this work, we are planning to develop an efficient approach for software defect prediction by using soft computing based machine learning techniques which helps to predict optimize the features and efficiently learn the features.
Functional Encryption for Quadratic Functions from k-Lin, Revisited

We present simple and improved constructions of public-key functional encryption (FE) schemes for quadratic functions. Our main results are: Both constructions achieve selective, simulation-based security against unbounded collusions, and rely on the (bilateral) k-linear assumption in prime-order bilinear groups. At the core of these constructions is a new reduction from FE for quadratic functions to FE for linear functions.
ERICA

We present ERICA, a digital personal trainer for users performing free weights exercises, with two key differentiators: (a) First, unlike prior approaches that either require multiple on-body wearables or specialized infrastructural sensing, ERICA uses a single in-ear "earable" device (piggybacking on a form factor routinely used by millions of gym-goers) and a simple inertial sensor mounted on each weight equipment; (b) Second, unlike prior work that focuses primarily on quantifying a workout, ERICA additionally identifies a variety of fine-grained exercising mistakes and delivers real-time, in-situ corrective instructions. To achieve this, we (a) design a robust approach for user-equipment association that can handle multiple (even 15) concurrently exercising users; (b) develop a suite of statistical models to detect several commonplace repetition-level mistakes; and (c) experimentally study the efficacy of multiple in-situ corrective feedback strategies. Via an end-to-end evaluation of ERICA with 33 participants naturally performing 3 dumbbell-based exercises, we show that (a) ERICA identifies over 94% of mistakes during the first 5 repetitions of a set, (b) the resulting feedback is viewed favorably by 78% of users, and (c) the feedback is effective, reducing mistakes by 10+% during subsequent repetitions.
Innovative microfossil (radiolarian) analysis using a system for automated image collection and AI-based classification of species

Abstract Microfossils are a powerful tool in earth sciences, and they have been widely used for the determination of geological age and in paleoenvironmental studies. However, the identification of fossil species requires considerable time and labor by experts with extensive knowledge and experience. In this study, we successfully automated the acquisition of microfossil data using an artificial intelligence system that employs a computer-controlled microscope and deep learning methods. The system was used to calculate changes in the relative abundance (%) of Cycladophora davisiana , a siliceous microfossil species (Radiolaria) that is widely used as a stratigraphic tool in studies on Pleistocene sediments in the Southern Ocean. The estimates obtained using this system were consistent with the results obtained by a human expert (&lt; ± 3.2%). In terms of efficiency, the developed system was capable of performing the classification tasks approximately three times faster than a human expert performing the same task.
Texture Hallucination for Large-Factor Painting Super-Resolution

We aim to super-resolve digital paintings, synthesizing realistic details from high-resolution reference painting materials for very large scaling factors (e.g., 8 $$\times $$ , 16 $$\times $$ ). However, previous single image super-resolution (SISR) methods would either lose textural details or introduce unpleasing artifacts. On the other hand, reference-based SR (Ref-SR) methods can transfer textures to some extent, but is still impractical to handle very large factors and keep fidelity with original input. To solve these problems, we propose an efficient high-resolution hallucination network for very large scaling factors with efficient network structure and feature transferring. To transfer more detailed textures, we design a wavelet texture loss, which helps to enhance more high-frequency components. At the same time, to reduce the smoothing effect brought by the image reconstruction loss, we further relax the reconstruction constraint with a degradation loss which ensures the consistency between downscaled super-resolution results and low-resolution inputs. We also collected a high-resolution (e.g., 4K resolution) painting dataset PaintHD by considering both physical size and image resolution. We demonstrate the effectiveness of our method with extensive experiments on PaintHD by comparing with SISR and Ref-SR state-of-the-art methods.
TICO-19: the Translation Initiative for COvid-19

The COVID-19 pandemic is the worst pandemic to strike the world in over a century.Crucial to stemming the tide of the SARS-CoV-2 virus is communicating to vulnerable populations the means by which they can protect themselves.To this end, the collaborators forming the Translation Initiative for COvid-19 (TICO-19) 1 have made test and development data available to AI and MT researchers in 35 different languages in order to foster the development of tools and resources for improving access to information about COVID-19 in these languages.In addition to 9 highresourced, "pivot" languages, the team is targeting 26 lesser resourced languages, in particular languages of Africa, South Asia and South-East Asia, whose populations may be the most vulnerable to the spread of the virus.The same data is translated into all of the languages represented, meaning that testing or development can be done for any pairing of languages in the set.Further, the team is converting the test and development data into translation memories (TMXs) that can be used by localizers from and to any of the languages. 2