A Process to Facilitate Automated Automotive Cybersecurity Testing

Modern vehicles become increasingly digitalized with advanced informationtechnology-based solutions like advanced driving assistance systems and vehicle-to-x communications . This security also has to be validated andverified . In order to keep pace with the need for more thorough, quicker andcomparable testing, today’s generally manual testing processes have to bestructured and optimized .…

E cheating Prevention Measures Detection of Cheating at Online Examinations Using Deep Learning Approach A Case Study

This study addresses the current issues in online assessments, which are particularly relevant during the Covid-19 pandemic . The intelligence agent monitors the behaviour of the students and hasthe ability to prevent and detect any malicious practices . It can be used toassign randomised multiple-choice questions in a course examination and beintegrated with online learning programs .…

Privacy Preserving Techniques Applied to CPNI Data Analysis and Recommendations

Consumer ProprietaryNetwork Information (CPNI) can offer extremely valuable information to different sectors, including policymakers . Traditional de-anonymization measures, suchas pseudonymization and standard de-identification, have been shown to be insufficient to protect privacy . As an example, researchers have shown that with only four datapoints of approximate place and time information of a user, 95% of users could be re-identified in a dataset of 1.5 million mobile phone users .…

VConstruct Filling Gaps in Chl a Data Using a Variational Autoencoder

Remote sensing of Chlorphyll-a is vital in monitoring climate change . Measurements give us an idea of algae concentrations in the ocean, which lets us monitor ocean health . We propose a machine learning approach to reconstruction of the data using a Variational Autoencoder (VAE) Our accuracy results to date are competitive with but slightly less accurate than DINEOF .…

D Net Siamese based Network with Mutual Attention for Volume Alignment

Alignment of contrast and non-contrast-enhanced imaging is essential for thequantification of changes in several biomedical applications . Existing deep learning-based methods for alignment require a commontemplate or are limited in rotation range . We present a novel network, D-net, to estimate arbitrary rotation and translation between 3D CTscans that additionally does not require a prior standard template .…

Lightweight Convolutional Neural Network with Gaussian based Grasping Representation for Robotic Grasping Detection

Current object detectors are difficult to strike a balance between high accuracy and fast inference speed . The proposed network is a lightweight generative architecture for grasping detection in one stage . The network is an order of magnitudesmaller than other excellent algorithms while achieving better performance with an accuracy of 98.9%$ and 95.6%$ on the Cornell and Jacquard datasets,respectively.…

Proba V ref Repurposing the Proba V challenge for reference aware super resolution

The PROBA-V Super-Resolution challenge distributes real low-resolution imageseries and corresponding high-resolution targets to advance research onMulti-Image Super Resolution (MISR) for satellite images . We argue that in doing so, the challenge ranks theproposed methods not only by their MISR performance, but mainly by the heuristics used to guess which image in the series is the most similar to thehigh-resolution target .…

Spectral Leakage and Rethinking the Kernel Size in CNNs

Convolutional layers in CNNs implement linear filters which decompose the input into different frequency bands . We show that the small size of CNN kernels make them susceptible to spectral leakage . To address this issue, we propose theuse of larger kernel sizes along with the Hamming window function to alleviateleakage in CNN architectures .…

Unsupervised Anomaly Detection and Localisation with Multi scale Interpolated Gaussian Descriptors

Current unsupervised anomaly detection and localisation systems are commonlyformulated as one-class classifiers that depend on an effective estimation of the distribution of normal images and robust criteria to identify anomalies . However, the current systems tendsto be unstable for classes that are under-represented in the training set, and the anomaly identification criteria commonly explored in thefield does not work well for multi-scale structural and non-structuralanomalies .…

Weakly supervised Video Anomaly Detection with Contrastive Learning of Long and Short range Temporal Features

Multiple-scale TemporalNetwork trained with top-K Contrastive Multiple Instance Learning (MTN-KMIL) The code is available athttps://://://github.com/tianyu0207/MIL . The method outperforms several state-of-the-art methods by a large margin on three benchmark data sets (ShanghaiTech, UCF-Crimeand XD-Violence) The method is a novelsynthesis of a pyramid of dilated convolutions and a self-attention mechanism, with the former capturing the multi-scale short-range temporal dependencies between snippets and the latter capturing long-range time-relationship of snippets .…

Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms A Comparative Study

Medical imaging has been employed to support medical diagnosis and treatment . It may also provide crucial information to surgeons to facilitate optimalsurgical preplanning and perioperative management . This paper addresses to propose the most efficient DL architectures for Liversegmentation by adapting and comparing state-of-the-art DL frameworks, studiedin different disciplines .…

Adversarial Text to Image Synthesis A Review

With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area . However, the field still faces several challenges that require further research efforts such as enabling the generation of high-resolution images with multiple objects .…

Weakly Supervised Thoracic Disease Localization via Disease Masks

Using disease masks, we propose a spatial attention method using disease masks thatdescribe the areas where diseases mainly occur . We then apply the spatialattention to find the precise disease area by highlighting the highestprobability of disease occurrence . We show that the proposed method results in superior localization performances compared to state-of-the-art methods .…

A Two stage Framework for Compound Figure Separation

Scientific literature contains large volumes of complex, unstructured figuresthat are compound in nature . Separation of these compound figures is critical for informationretrieval from these figures . In this paper, we propose a new strategy forcompound figure separation, which decomposes the compound figures intoconstituent subfigures .…

Spatio temporal Data Augmentation for Visual Surveillance

Visual surveillance aims to stably detect a foreground object using acontinuous image acquired from a fixed camera . Recent deep learning methods based on supervised learning show superior performance compared to classicalbackground subtraction algorithms . However, there is still a room for improvement in static foreground, dynamic background, hard shadow, illumination changes, camouflage, etc.…

Weakly Supervised Learning for Facial Behavior Analysis A Review

In recent years, there has been a shift in facial behavior analysis from the laboratory-controlled conditions to the challenging in-the-wild conditions due to superior performance of deep learning based approaches for many realworld applications . Labeling process of huge training data demands lot of human support with strong domainexpertise for facial expressions or action units, which is difficult to obtainin real-time environments .…

A new approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms

In stable coronary artery disease, reduction in mortality and/ormyocardial infarction with revascularization over medical therapy has not beenreliably achieved . We aim to develop an automatic algorithm by deep learning to extractcoronary arteries from ICAs . After segmentation, an arterial stenosis detection algorithm was developed to extract vascular centerlines and calculate arterial diameter to evaluate stenotic level .…

Creating a Virtuous Cycle in Performance Testing at MongoDB

Performance testing is part of the development process at MongoDB, and integrated into our continuous integration system . We believe that we have created and exploited a virtuous cycle:performance test improvements drive impact, which drives more use . Overall, MongoDB is gettingfaster and we avoid shipping major performance regressions to our customers because of this infrastructure .…

Novel Dynamic Load Balancing Algorithm for Cloud Based Big Data Analytics

The proposedload balancer reduces the execution response time in big data applicationsperformed on clouds . Scheduling, in general, is an NP-hard problem . We recommend two mathematicaloptimization models to perform dynamic resource allocation to virtual machines and task scheduling . We evaluate the performance of proposed algorithms in terms of response time, turnaround time, throughput metrics, andrequest distribution with some of the existing algorithms that show significantimprovements .…

Computational Workflows for Designing Input Devices

The typical user-centered design workflow requires the developersand users to go through many iterations of design, implementation, andanalysis . I envision a sample-efficient multi-objective optimization algorithm cleverly selects design instances, which are instantly deployed onphysical simulators . A meta-reinforcement learning user model then simulatesthe user behaviors when using the design instance upon the simulators.…

Democratizing information visualization A study to map the value of graphic design to easier knowledge transfer of scientific research

Visual representations are becoming important in science communication and education . This study investigates the perception of STEM researchers without any specific visual design background . Early findings show that visual representations can positively support scientists toshare research outcomes in a more compelling, visually clear, and impactfulmanner, reaching a wider audience across different disciplines .…

Estimating the Total Volume of Queries to a Search Engine

We study the problem of estimating the total number of searches (volume) ofqueries in a specific domain, which were submitted to a search engine in agiven time period . Our statistical model assumes that the distribution ofsearches follows a Zipf’s law, and that the observed sample volumes are biasedaccordingly to three possible scenarios .…

On the Performance of Image Recovery in Massive MIMO Communications

Massive MIMO (Multiple Input Multiple Output) has demonstrated as a potential candidate for 5G-and-beyond wireless networks . Alow-pass filter is exploited to enhance efficiency of the remaining noiseand artifacts reduction in the recovered image. Numerical results demonstratethe necessity of a post-filtering process in enhancing the quality of imagerecovery.…