Reviewerly/exHarmony huggingface dataset:
Computationally Efficient Approach to Implementation of the Chinese Remainder Theorem Algorithm in Minimally Redundant Residue Number System

Abstract In this paper, we deal with the critical problem of performing non-modular operations in the Residue Number System (RNS). The Chinese Remainder Theorem (CRT) is widely used in many modern computer applications. Throughout the article, an efficient approach for implementing the CRT algorithm is described. The structure of the rank of an RNS number, a principal positional characteristic of the residue code, is investigated. It is shown that the rank of a number can be represented by a sum of an inexact rank and a two-valued correction to it. We propose a new variant of minimally redundant RNS, which provides low computational complexity for the rank calculation, and its effectiveness analyzed concerning conventional non-redundant RNS. Owing to the extension of the residue code, by adding the excess residue modulo 2, the complexity of the rank calculation goes down from $O\left (k^{2}\right )$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>O</mml:mi> <mml:mfenced> <mml:mrow> <mml:msup> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:mfenced> </mml:math> to $O\left (k\right )$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>O</mml:mi> <mml:mfenced> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> </mml:mfenced> </mml:math> with respect to required modular addition operations and lookup tables, where k equals the number of non-redundant RNS moduli.
Towards single atom computing via high harmonic generation

The development of alternative platforms for computing has been a longstanding goal for physics, and represents a particularly pressing concern as conventional transistors approach the limit of miniaturization. A potential alternative paradigm is that of reservoir computing, which leverages unknown, but highly nonlinear transformations of input-data to perform computations. This has the advantage that many physical systems exhibit precisely the type of nonlinear input-output relationships necessary for them to function as reservoirs. Consequently, the quantum effects which obstruct the further development of silicon electronics become an advantage for a reservoir computer. Here we demonstrate that even the most basic constituents of matter–atoms–can act as a reservoir for computing where all input-output processing is optical, thanks to the phenomenon of High Harmonic Generation. A prototype single-atom computer for classification problems is proposed, where a classification model is mapped to an all-optical setup, with linear filters chosen to correspond to the trained model’s parameters. We numerically demonstrate that this ‘all-optical’ computer can successfully perform classification tasks, and does so with an accuracy that is strongly dependent on dynamical nonlinearities. This may pave the way for the development of petahertz information processing platforms.
Lightweight Cryptography for Resource Constraint Devices: Challenges and Recommendation

In the recent few years, data communication through the Internet of Things (IoT) network is increased exponentially. However, the data is prone to several attacks on the network. The most popular attacks are eavesdropping, replay, man-in-the-middle attack, etc. To prevent these attacks, cryptography algorithms are used. The devices are deployed in the IoT network are resource constraint, limited memory, and low battery life. Therefore, the National Institute of Standard and Technology (NIST) recommended the lightweight cryptography algorithm to provide security in these devices. In this paper, we have reviewed the recent lightweight cryptography algorithms. Next, it defines the open research challenges and recommendations based on the literature review.
Vision-Based Autonomous Driving for Smart City: A Case for End-to-End Learning Utilizing Temporal Information

End-to-End learning models trained with conditional imitation learning (CIL) have demonstrated their capabilities in autonomous driving. In this work, we explore the use of temporal information with a recurrent network to improve driving performance especially in dynamic environments. We propose the TCIL (Temporal CIL) model that combines an efficient, pre-trained, deep convolutional neural network to better capture image features with a long short-term memory network to better explore temporal information. Experimental results in the CARLA benchmark indicate that the proposed model achieves performance gain in most tasks. Comparing with other CIL-based models in the most challenging task, navigation in dynamic environments, it achieves a 96% success rate while other CIL-based models had 82–92% in training conditions; it is also competitively by achieving 88% while other CIL-based models were at 42–90% in the new town and new weather conditions. We believe that this work contributes significantly towards safe, efficient, clean autonomous driving for future smart cities.
Using computational tools to support journalists’ creativity

This paper presents work surrounding INJECT, a newsroom innovation offering digital tools to support journalists. Research showing increasing time and resource pressure on journalists has led to concerns about the demise of investigative reporting and the ability of today’s journalists to interrogate information adequately. Some digital innovations (e.g. tools facilitating robot journalism) have been viewed with suspicion by newsrooms. This paper reports on a research project that seeks to create an innovative tool to support the creative capabilities of time and resource poor journalists. The INJECT project used the advanced information discovery capabilities of digitisation to help journalists find new angles on stories and this paper analyses the extent to which such initiatives might harness digital innovation to benefit both the quality and range of reporting and thereby enhance creativity. It examines the potential of an information processing model of creativity derived from the INJECT tool to assist and support journalists, exploring the theoretical impact as well as the practical implications reported from the newsroom.
One-Shot Image Segmentation with U-Net

Abstract Image segmentation is an important task in the field of computer vision research. Many applications need accurate and efficient segmentation mechanisms. However, most existing methods need a large support set and have struggled with dealing with new classes in solving computer vision problems. In order to deal with the problem, this paper modified the traditional model to adapt it to the task of few-shot segmentation. Especially, the part of encoders was changed into a Siamese neural network for support branch and query branch. Then, the decoded query image is compared with different levels of features from the encoded support image. Experimental results show that this proposed model achieved the best performance compared with several baseline methods, and the improvement is more than 8%.
Training for multi-resolution inference using reusable quantization terms

Low-resolution uniform quantization (e.g., 4-bit bitwidth) for both Deep Neural Network (DNN) weights and data has emerged as an important technique for efficient inference. Departing from conventional quantization, we describe a novel training approach to support inference at multiple resolutions by reusing a single set of quantization terms (the same set of nonzero bits in values). The proposed approach streamlines the training and supports dynamic selection of resolution levels during inference. We evaluate the method on a diverse range of applications including multiple CNNs on ImageNet, an LSTM on Wikitext-2, and YOLO-v5 on COCO. We show that models resulting from our multi-resolution training can support up to 10 resolutions with only a moderate performance reduction (e.g., ≤ 1%) compared to training them individually. Lastly, using an FPGA, we compare our multi-resolution multiplier-accumulator (mMAC) against other conventional MAC designs and evaluate the inference performance. We show that the mMAC design broadens the choices in trading off cost, efficiency, and latency across a range of computational budgets.
Shortest Bounded-Curvature Paths Via Circumferential Envelope of a Circle

The paper characterizes the shortest bounded-curvature paths for a Dubins vehicle between two configurations with specified location and heading angle via the boundary of an intermediate circle. Only two distinct cases can arise in such engagements, first, when the shortest path is tangent to the circle at only one point, and second, when a segment of the shortest path overlaps a part of the circular boundary. Control command for both the cases are proposed, and some geometric properties for the first case are established by using necessary conditions for state inequality constraints and Pontryagin’s maximum principle. Numerical examples are presented to illustrate the geometric properties of the shortest bounded-curvature paths. These geometric properties give insight about concatenation of different segments of the shortest path and allow us to state that the candidate shortest paths belong to a finite set.
Overview of Generative Adversarial Networks

Abstract The study of generative adversarial networks (GAN) provides a new approach and framework for computer vision and makes an outstanding choice for cross-domain image translation problems. In this work, the basic framework structure of GAN as well as the evolution of GAN are summarized. It is known that the unsupervised domain adaptation algorithms attempt to map representations between the two domains, or learn to extract features that are domain–invariant. An approach that learns in an unsupervised manner can be a transformation in the pixel space from one domain to the other. Finally, some existing problems of GAN and its potential future are also summarized and discussed.
Developing a medical artificial intelligence course for high school students

Artificial intelligence (AI) training courses often require prerequisites such as calculus or statistics. It is hence challenging to design and develop an introductory AI course for students of secondary education. This research intends to develop a medical AI course, provide high school students with an overview of deep learning applications in medical image analysis, and inspire them to pursue careers in the field of medical AI. We designed a 20-hour course, including lectures and two hands-on projects based on medical image classification. The proposed courses provided medical AI disciplines and built up their knowledge from basic to advanced levels. During the ten-day online courses, all the students were fully engaged and gave us positive feedback. The students endeavored to complete the experimental study in training, testing, and hypothesis of medical images application in the course. Their performance exceeded all expectations, for they did further analysis by tuning different hyperparameters. We designed a course evaluation form, which suggested that the students found it essential and expected to interact with the instructors. The results indicate that combining lectures with hands-on sessions would lead to evidently better achievement in terms of high school students’ medical AI knowledge and positive attitudes while addressing real-world problems in the projects. Through this innovative education model, high school students regained their enthusiasm and were encouraged to cultivate their medical AI skills through self-learning while finishing the project. We conclude that this course could be successfully applied to interdisciplinary education in high school.
An I/O Separation Model for Formal Verification of Kernel Implementations

Commodity I/O hardware often fails to separate I/O transfers of isolated OS and applications code. Even when using the best I/O hardware, commodity systems sometimes trade off separation assurance for increased performance. Remarkably, device firmware need not be malicious. Instead, any malicious driver, even if isolated in its own execution domain, can manipulate its device to breach I/O separation. To prevent such vulnerabilities with high assurance, a formal I/O separation model and its use in automatic generation of secure I/O kernel code is necessary.This paper presents a formal I/O separation model, which defines a separation policy based on authorization of I/O transfers and is hardware agnostic. The model, its refinement, and instantiation in the Wimpy kernel design, are formally specified and verified in Dafny. We then specify the kernel implementation and automatically generate verified-correct assembly code that enforces the I/O separation policies. Our formal modeling enables the discovery of heretofore unknown design and implementation vulnerabilities of the original Wimpy kernel. Finally, we outline how the model can be applied to other I/O kernels and conclude with the key lessons learned.
Automated Assistance for Breast Cancer Identification on Mammograms using Computer Vision Algorithms

one of the greatest health problems in the world is breast cancer. If these breast cancer abnormalities are identified early, there is a maximum chance of recovery. We can go for this early prediction. It is one of the most effective detection and screening strategies and is widely used. The basic goal of CAD systems is to support physicians in the process of diagnosis. CAD systems, however, are very expensive. Our emphasis is on developing a CAD system that is low-cost and effective. To categorize breast cancer as either benign or malignant, a computer-aided detection approach is suggested. The standard mammogram image corpus, Digital Database used for Screening Mammography, images are used for enhancement, segmented and GLCM, intensity and histogram methods are used to extract features. The work is carried out by effective multilayer perceptron classifier (MLP) and support vector machine (SVM). Compare the performance of the classifiers. The proposed approach achieved 96 % accuracy and 8% improvement in accuracy compared to previous approaches with same dataset [4].
Underwater Moving Object Detection Using GMG

The development in ocean exploration and observation make the demand for moving object detection in underwater increasingly urgent. The moving object detection in underwater with dynamic or moving background is the challenging and difficult task for the researchers. In dynamic environment, foreground object as well as background of the scene both are in motion condition, thus it is very difficult to detect foreground moving object in underwater medium. In proposed work, background is categorized into three types i.e. static, moderate dynamic and high dynamic backgrounds. The GMG algorithm invented by Andrew B. Godbehere, Akihiro Matsukawa, Ken Goldberg is implemented to detect moving object and compare the results for the three background categories. The sensitivity and accuracy parameter values are considered for the comparison of the results.
Research on Obstacle Avoidance Control Method of Multi-UAV Based on Model Predictive Control

Aiming at the problem of multi-UAV obstacle avoidance control, a method of multi-UAV obstacle avoidance path planning based on model predictive control is proposed. Firstly, based on the UAV kinematics model and the analysis of the spatial geometric relationship between multiple UAV aircraft and obstacles, the obstacle avoidance rule based on Angle change was designed, and the optimal control model of multi-UAV collaborative obstacle avoidance was established. The nonlinear model predictive control method was used to solve the collaborative obstacle avoidance problem, and the specified UAV behavior pattern was used to shear the search space to improve the search efficiency of the method. Simulation results show that the proposed obstacle avoidance control method can not only solve the static obstacle avoidance control of the UAV, but also solve the problem of collision prevention between multiple UAV aircraft, which can effectively improve the working safety of the multi-UAV system.
Kumaraswamy Distribution Based Bi-histogram Equalization for Enhancement of Microscopic Images

Among all image enhancement techniques, histogram equalization is the most used technique. However, preserving brightness is the main issue, and it creates a weird look by destroying its originality. This paper proposes a new method that has command on the brightness issue of histogram equalization to enhance the quality of microscopic images. The method splits the histogram of each color channel into two sub-histograms based on their mean as the threshold and supplanting their cumulative distribution with Kumaraswamy distribution. The proposed method is tested with color microscopic images of cancer-affected lymph nodes gathered from Biological Image Repository IICBU, and objective and subjective assessments confirm that the proposed approach performs more efficiently compared to other state-of-the-art methods.
Towards Automated GDPR Compliance Checking

The GDPR is one of many legal texts which can greatly benefit from the support of automated reasoning. Since its introduction, efforts were made to formalize it in order to support various automated operations. Nevertheless, widespread and efficient automated reasoning over the GDPR has not yet been achieved. In this paper, a tool called the NAI suite is being used in order to annotate article 13 of the GDPR. The annotation results in a fully formalized version of the article, which deals with the transparency requirements of data collection and processing. Automated compliance checking is then being demonstrated via several simple queries. By using the public API of the NAI suite, arbitrary tools can use this procedure to support trust management and GDPR compliance functions.
WITHDRAWN: A semi automated assessment and marking approach of decision tree diagram

In the present scenario every educational institution having large number of students and it has improved the need for automated testing program tools. With such many of students in need of teaching and assessment, there is a need for teachers to consider assessment models with new technologies, in addition to those, the method of accepting the assessment is another factor. Currently, computer-assisted test programs are widely used to mark multiple choice questions. Abort from MCQs, sine there is a need to create tools that can be tested student’s knowledge in a general way such as testing the drawing solutions for the assessments. The primary objective of this study is to investigate a new experimental approach that provides the use of computers system to support the proposed method of tree construction method. In this way, research seeks to reduce the burden of marking on drawings related assessments and improve the quality of feedback provided to students on their answers. A case study was conducted to identify the needs of a new automated testing tool on drawing related assessments. Research is being done to investigate and show that it is possible to mark a tree drawing using new techniques. In addition, the process of the new method is developed based on the outcome of the case study. The proposed method of automated automation was based on a new marking method, which was tested by marking a few student responses to tree drawings and then analyzing using mathematical numerical tests. Assessment evaluation process included the use of Likert questionnaires, yielding positive results, with most participants conducting research to make the new process and preferring the use of a new method over the traditional marking technique.
Identification of Spoken Language using Machine Learning Approach

Identification of spoken language is the way to detect the specific language which is spoken by an anonymous speaker. We will also find out several techniques of machine learning for detecting spoken language. Our major task is to identify parameters and features from spoken language that can be used to separate languages. To extract feature from audio file we will use Mel Frequency Cepstral Coefficient (MFCC). So far, many methods have been used for language identification (LTD). Of all the techniques, the accuracy of machine learning is the best. That's why we also used machine learning in our project for lid. Our system will train with 30,000 data. This project aims to classify Spanish, German & English languages. Main goal of this project is to find out best algorithm for detecting specific language. We get the best accuracy from random forest algorithm.
Fast Mumford-Shah Two-Phase Image Segmentation Using Proximal Splitting Scheme

The Mumford-Shah model is extensively used in image segmentation. Its energy functional causes the content of the segments to remain homogeneous and the segment boundaries to become short. However, the problem is that optimization of the functional can be very slow. To attack this problem, we propose a reduced two-phase Mumford-Shah model to segment images having one prominent object. First, initial segmentation is obtained by the k-means clustering technique, further minimizing the Mumford-Shah functional by the Douglas-Rachford algorithm. Evaluation of segmentations with various error metrics shows that 70 percent of the segmentations keep the error values below 50%. Compared to the level set method to solve the Chan-Vese model, our algorithm is significantly faster. At the same time, it gives almost the same or better segmentation results. When compared to the recent k-means variant, it also gives much better segmentation with convex boundaries. The proposed algorithm balances well between time and quality of the segmentation. A crucial step in the design of machine vision systems is the extraction of discriminant features from the images, which is based on low-level segmentation which can be obtained by our approach.
Multi-exposure virtual photometer: A tool for evaluating the illumination robustness of feature detectors

Feature detection is a basic issue in computer vision, and the illumination robustness of the detector is an important evaluation indicator. However, no indicators that can directly and quantitatively evaluate the robustness of illumination have been found in the known evaluation methods. In this paper, we propose a novel evaluation method that can quantify the evaluation results. The proposed method constructs a multi-exposure virtual photometer, and finds the mapping relationship between feature points and photometric exposure based on the photometer. Further, experiments prove that the mapping relationship can be fitted by Gaussian function. Then, we designed a novel evaluation index based on the mapping relationship between features and photometric exposure. Extensive quantitative evaluation shows that our method can effectively reflect the illumination robustness of feature detectors. In particular, the quantitative display is more intuitive and facilitates the comparison of different detection methods.