Reviewerly/exHarmony huggingface dataset:
Credit Card Fraud Detection using Local Outlier Factor and Isolation Forest

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
A Systematic Evaluation of Profiling Through Focused Feature Selection

Profiled side-channel attacks consist of several steps one needs to take.An important, but sometimes ignored, step is a selection of the points of interest (features) within side-channel measurement traces.A large majority of the related works start the analyses with an assumption that the features are preselected.Contrary to this assumption, here, we concentrate on the feature selection step.We investigate how advanced feature selection techniques stemming from the machine learning domain can be used to improve the attack efficiency.To this end, we provide a systematic evaluation of the methods of interest.The experiments are performed on several real-world data sets containing software and hardware implementations of AES, including the random delay countermeasure.Our results show that wrapper and hybrid feature selection methods perform extremely well over a wide range of test scenarios and a number of features selected.We emphasize L1 regularization (wrapper approach) and linear support vector machine (SVM) with recursive feature elimination used after chi-square filter (Hybrid approach) that performs well in both accuracy and guessing entropy.Finally, we show that the use of appropriate feature selection techniques is more important for an attack on the high-noise data sets, including those with countermeasures, than on the low-noise ones.
FACCT: FAst, Compact, and Constant-Time Discrete Gaussian Sampler over Integers

The discrete Gaussian sampler is one of the fundamental tools in implementing lattice-based cryptosystems. However, a naive discrete Gaussian sampling implementation suffers from side-channel vulnerabilities, and the existing countermeasures usually introduce significant overhead in either the running speed or the memory consumption. In this paper, we propose a fast, compact, and constant-time implementation of the binary sampling algorithm, originally introduced in the BLISS signature scheme. Our implementation adapts the Rényi divergence and the transcendental function polynomial approximation techniques. The efficiency of our scheme is independent of the standard deviation, and we show evidence that our implementations are either faster or more compact than several existing constant-time samplers. In addition, we show the performance of our implementation techniques applied to and integrated with two existing signature schemes: qTesla and Falcon. On the other hand, the convolution theorems are typically adapted to sample from larger standard deviations, by combining samples with much smaller standard deviations. As an additional contribution, we show better parameters for the convolution theorems.
The state of the art of two‐dimensional digital image correlation computational method

After remarkable work over the last three decades, digital image correlation (DIC) computational technique has made incredible progress under the unremitting efforts of numerous researchers, and it has already grown into one of the most powerful and nondestructive surface deformations measuring methods used in various areas of experimental mechanics. DIC is done by tracking the regional variation of gray‐scale features between two succession images recorded before and after deformation. The objective of this paper is to report on the development of 2D‐DIC. In this paper, we first describe the basic principle of the DIC algorithm. The second part mainly focuses on some important progress and interesting problems of the algorithm in the term of computational efficiency and measurement accuracy. Moreover, the major error sources that may exist in practical DIC measurement are discussed in detail. Finally, some typical application domains of DIC method are listed. This paper intends to help preliminary researchers who are of interest in 2D‐DIC algorithms to improve their understanding of this measurement technique.
About problems of decoding and searching for the maximum upper zero of discrete monotone functions

Abstract This article explores the algorithms for solving individual classes of discrete extremal problems to find the exact optimum. When obtaining algorithms, the procedures for decoding and finding the maximum upper zero of discrete monotonic functions are used. Formulations of the tasks of decoding and searching for the maximum upper zero of discrete monotonic functions are formulated. To solve the problems of decoding and searching for the maximum upper zero of individual classes of 3-digit monotonic functions, estimates are given for the complexity of deciphering monotonic functions in a class.
Inference of unexploded ordnance (UXO) by probabilistic inversion of magnetic data

SUMMARY Magnetic modelling of unexploded ordnance (UXO) is a well-documented method used to interpret magnetic anomalies occurring in UXO excavation surveys. By treating UXO as a ferrous spheroidal object, the induced dipole moment can be estimated by approximation of UXO characteristics such as shape, size and orientation. Inversion of magnetic data with respect to UXO requires one to solve the equation for the induced dipole moment, while also determining the location and orientation of the object. This is a highly nonlinear, non-unique problem, where many solutions often are present, which make it difficult for standard inversion methods, such as linearized approaches and maximum likelihood estimators, in assessing uncertainties and correlations in estimated model parameters that often result in an incomplete solution. In this study, we treat the problem concerning magnetic UXO inversion by a probabilistic approach using Markov chain Monte Carlo (McMC) sampling. To deal with the potential multimodality, a combination of two well-known McMC sampling methods is employed in a single-chain approach: the extended Metropolis algorithm is used for efficient local sampling, and the Gibbs sampler is used to help exploring the possible multimodal density of the posterior. By adding a Gibbs step we significantly increase the efficiency of the extended Metropolis, making it viable to use as a single-chain sampler for this problem. We refer to the algorithm as the Gibbs-within-Metropolis algorithm. We test and compare the proposed algorithm to a multichain McMC parallel tempering setup using the extended Metropolis algorithm. We then present synthetic cases and a real data case, where the algorithm is used. We demonstrate how the probabilistic approach allows full inference of the parameters describing a UXO while also including the possible presence of remanent magnetization.
Instantaneous Ghost Detection Identification in Automotive Scenarios

This paper addresses one of the most serious drawbacks of automotive radar sensors: The frequent occurrence of ghost detections. As such detections may cause undesired behavior, they need to be partially or completely eliminated. We present an algorithm, which uses a machine-learning-based classifier to distinguish between real and ghost detections. In contrast to other papers, this approach addresses all causes of ghost detections and not only simple multipath scenarios. Real world experiments - also in challenging situations - show success rates of 91%.
Image alignment based perceptual image hash for content authentication

Perceptual image hash is an emerging technology that is closely related to many applications such as image content authentication, image forging detection, image similarity detection, and image retrieval. In this work, we propose an image alignment based perceptual image hash method, and a hash-based image forging detection and tampering localization method. In the proposed method, we introduce an image alignment process to provide a framework for image hash method to tolerate a wide range of geometric distortions. The image hash is generated by utilizing hybrid perceptual features that are extracted from global and local Zernike moments combining with DCT-based statistical features of the image. The proposed method can detect various image forging and compromised image regions. Furthermore, it has broad-spectrum robustness, including tolerating content-preserving manipulations and geometric distortion-resilient. Compared with state-of-the-art schemes, the proposed method provides satisfactory comprehensive performances in content-based image forging detection and tampering localization.
When Neural Architecture Search Meets Hardware Implementation: from Hardware Awareness to Co-Design

Neural Architecture Search (NAS), that automatically identifies the best network architecture, is a promising technique to respond to the ever-growing demand for application-specific Artificial Intelligence (AI). On the other hand, a large number of research efforts have been put on implementing and optimizing AI applications on the hardware. Out of all leading computation platforms, Field Programmable Gate Arrays (FPGAs) stand out due to its flexibility and versatility over ASICs and its efficiency over CPUs and GPUs. To identify the best neural architecture and hardware implementation pair, a number of research works are emerging to involve the awareness of hardware efficiency in the NAS process, which is called "hardware-aware NAS". Unlike the conventional NAS with a mono-criteria of accuracy, hardware-aware NAS is a multi-objective optimization problem, which aims to identify the best network and hardware pair to maximize accuracy with guaranteed hardware efficiency. Most recently, the co-design of neural architecture and hardware has been put forward to further push forward the Pareto frontier between accuracy and efficiency trade-off. This paper will review and discuss the current progress in the neural architecture search and the implementation on hardware.
Face Recognition Method for Online Exams

In the development of this technology, biometric systems are highly developed for use in various applications. Biometric systems are usually used to identify and analyze the characteristics of the human body such as fingerprints, retina, sound patterns, facial patterns and other body structures that can be used for system authentication. As well as facial recognition technology more and more used and developed for various applications including security systems, attendance systems or other things. As well as attendance system that is a recurring transaction because it is associated with controlling the presence of a person in activity. in the field of education, the attendance system is very important because the presence of students is part of a good assessment for teaching and learning. This research is to develop a prototype of face-based online exam application using the Eigenface method to detect student attendance.
Real-Time Hyperspectral Image Compression Onto Embedded GPUs

Real-time hyperspectral imaging on-board compression represents a critical processing step in many remote sensing applications where the acquired hyperspectral data need to be efficiently stored and/or transferred. However, the complexity of the compression algorithms as well as the volume of data to be compressed and the limited computational resources of the hardware devices available on-board turn the real-time compression into a very challenging task. This paper presents a low-power-consumption solution for real-time lossy compression of hyperspectral images. The lossy compression algorithm for hyperspectral image system (HyperLCA) compressor has been implemented onto two NVIDIA Jetson developer kits. These NVIDIA boards include low-power embedded graphic processing units, which allow parallel programing for speeding up the compression process at a reasonable low power consumption. The experiments carried out in this paper are oriented to the necessities imposed by a specific smart farming application although all drawn conclusions are extrapolable to other fields in which remotely sensed hyperspectral images are to be compressed in real time. The obtained results verify both the good performance of the HyperLCA compressor for the targeted application and the achievement of a real-time performance by using the developed implementations. Additionally, several comparisons and conclusions have been drawn from the experiments in relation to the different strategies employed for accelerating the compression process.
$$L^*$$-Based Learning of Markov Decision Processes

Automata learning techniques automatically generate system models from test observations. These techniques usually fall into two categories: passive and active. Passive learning uses a predetermined data set, e.g., system logs. In contrast, active learning actively queries the system under learning, which is considered more efficient. An influential active learning technique is Angluin's $$L^*$$ algorithm for regular languages which inspired several generalisations from DFAs to other automata-based modelling formalisms. In this work, we study $$L^*$$-based learning of deterministic Markov decision processes, first assuming an ideal setting with perfect information. Then, we relax this assumption and present a novel learning algorithm that collects information by sampling system traces via testing. Experiments with the implementation of our sampling-based algorithm suggest that it achieves better accuracy than state-of-the-art passive learning techniques with the same amount of test data. Unlike existing learning algorithms with predefined states, our algorithm learns the complete model structure including the states.
Short-Term Bitcoin Price Fluctuation Prediction Using Social Media and Web Search Data

In recent years, the social network has been widely used among the public to share their views and for communication as well. Further, sentiments of the text used in emails, blogs, and social media posts affect human decision making and behavior. Bitcoin being a decentralized and peer-to-peer cryptocurrency has attracted a large number of users on the web search and social media. The goal of this paper is to correlation among Bitcoin price and Twitter and Google search patterns. Linear regression, polynomial regression, Recurrent Neural Network, and Long Short Term Memory based analysis concludes that there is a relevant degree of correlation of Google Trends and Tweet volume data with the price of Bitcoin, and with no significant relation with the sentiments of tweets.
Development Second and Third Phase of the Selective Frame Processing Method

In work the solution of a operational and confidential data transmission problem with the necessary quality level of decoding in the conditions of continuous increase in volume of video content containing data on critical infrastructure objects is proposed. The mechanisms allowing to define availability of planimetric information at stages of color-difference and spectral transformation are developed. Evaluation of the work of the developed method is given.
An Efficient and Robust Unsupervised Anomaly Detection Method Using Ensemble Random Projection in Surveillance Videos

Video anomaly detection is widely applied in modern society, which is achieved by sensors such as surveillance cameras. This paper learns anomalies by exploiting videos under the fully unsupervised setting. To avoid massive computation caused by back-prorogation in existing methods, we propose a novel efficient three-stage unsupervised anomaly detection method. In the first stage, we adopt random projection instead of autoencoder or its variants in previous works. Then we formulate the optimization goal as a least-square regression problem which has a closed-form solution, leading to less computational cost. The discriminative reconstruction losses of normal and abnormal events encourage us to roughly estimate normality that can be further sifted in the second stage with one-class support vector machine. In the third stage, to eliminate the instability caused by random parameter initializations, ensemble technology is performed to combine multiple anomaly detectors' scores. To the best of our knowledge, it is the first time that unsupervised ensemble technology is introduced to video anomaly detection research. As demonstrated by the experimental results on several video anomaly detection benchmark datasets, our algorithm robustly surpasses the recent unsupervised methods and performs even better than some supervised approaches. In addition, we achieve comparable performance contrast with the state-of-the-art unsupervised method with much less running time, indicating the effectiveness, efficiency, and robustness of our proposed approach.
Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos

We propose an end-to-end learning framework for segmenting generic objects in both images and videos. Given a novel image or video, our approach produces a pixel-level mask for all "object-like" regions-even for object categories never seen during training. We formulate the task as a structured prediction problem of assigning an object/background label to each pixel, implemented using a deep fully convolutional network. When applied to a video, our model further incorporates a motion stream, and the network learns to combine both appearance and motion and attempts to extract all prominent objects whether they are moving or not. Beyond the core model, a second contribution of our approach is how it leverages varying strengths of training annotations. Pixel-level annotations are quite difficult to obtain, yet crucial for training a deep network approach for segmentation. Thus we propose ways to exploit weakly labeled data for learning dense foreground segmentation. For images, we show the value in mixing object category examples with image-level labels together with relatively few images with boundary-level annotations. For video, we show how to bootstrap weakly annotated videos together with the network trained for image segmentation. Through experiments on multiple challenging image and video segmentation benchmarks, our method offers consistently strong results and improves the state-of-the-art for fully automatic segmentation of generic (unseen) objects. In addition, we demonstrate how our approach benefits image retrieval and image retargeting, both of which flourish when given our high-quality foreground maps. Code, models, and videos are at: http://vision.cs.utexas.edu/projects/pixelobjectness/.
The Need for New Antiphishing Measures Against Spear-Phishing Attacks

In this study, we provide extensive analysis of the (unique) characteristics of phishing and spear-phishing attacks, argue that spear-phishing attacks cannot be well captured by current countermeasures, identify ways forward, and analyze an advanced spear-phishing campaign targeting white-collar workers in 32 countries.
Bots, Elections, and Social Media: A Brief Overview

Bots, software-controlled accounts that operate on social media, have been used to manipulate and deceive. We studied the characteristics and activity of bots around major political events, including elections in various countries. In this chapter, we summarize our findings of bot operations in the context of the 2016 and 2018 US Presidential and Midterm elections and the 2017 French Presidential election.
Event-Triggered Discrete-Time Cubature Kalman Filter for Nonlinear Dynamical Systems With Packet Dropout

We propose a design framework for discrete-time event-triggered cubature Kalman filter for nonlinear dynamic systems over a wireless network with packet dropout. The design ensures that the prediction error covariance is bounded when packet delivery rate has a lower bound. We then show that the estimation error can be guaranteed to be bounded by properly tuning the threshold of the event-triggered mechanism. An example is given to illustrate the filter's performance.
IDENTIFICATION AND CLASSIFICATION OF CANCER CELLS USING CAPSULE NETWORK WITH PATHOLOGICAL IMAGES

Cancer is a deadly disease that is costing the lives of many people. Over 9.6 million death is reported in 2018 due to cancer. We propose an ideal methodology to identify and classify cancer cells using pathological images with the help of capsule network. Capsule network’s capability to learn patterns based on previous iterations can be exploited for this purpose. This can help in identification of cancer at early stages and work at the root cause of the disease and walk towards completely shutting down the disease. Image processing is done along with fuzzification and further, it is handled with capsule network classifier and analysed.
Systematic Literature Review of Blockchain Applications: Smart Contracts

Blockchain technology has received extensive attention recently, but still a large of technical challenges such as scalability and security. This paper helps to find where recent studies have been focused on and offers a broad perspective relating blockchain applications and smart contracts, their main problems and corresponding solutions and will help to specify gaps and future research. The study extracted 292 articles from top Digital Libraries such as IEEE, ACM, Science Direct and Springer. After a detailed review process only 28 publications were considered based on defined inclusion and exclusion criteria.
Deep Cascaded Attention Network for Multi-task Brain Tumor Segmentation

Multi-modal magnetic resonance images (MRIs) play an important role in the diagnosis and treatment of brain tumors. Due to heterogeneous diversities, it’s of great challenge to segment gliomas into hierarchical regions. Decomposing the multi-class segmentation task into sequential subtasks with cascaded models has proved its effectiveness, but leads to model redundancy, training complexity, and task isolation. In this paper, we propose a simple yet efficient 3D deep cascaded attention network (DCAN) for brain tumor segmentation. Specifically, we settle multi-tasks into corresponding branches with a shared feature extractor to reduce model complexity. Second, instead of explicitly extracting spatial evolutionary relationships of sub-regions using several consecutive models, a cascaded attention mechanism is introduced to implicitly involve potential subregions correlations as guidance. Moreover, we present a feature bridge module (FBM) to narrow feature fusion gaps. Thus, DCAN is able to capture the hierarchical correlations of overlapping regions and simultaneously tackle multi-tasks in a single model. The comprehensive experimental comparisons on the BRATS 2018 dataset show DCAN achieves top performance with dice scores of 81.71%, 91.18% and 86.19% for the enhancing tumor, whole tumor and tumor core, respectively.