Reviewerly/exHarmony huggingface dataset:
Detection and Recognition of Vehicle’s Headlights Types for Surveillance Using Deep Neural Networks

Computer vision is been successfully used in the detection and recognition of any object for surveillance in the open and close environment. However, the detection and recognition of any vehicle headlights from image is not an easy task especially in nighttime. Different vehicles have different type of headlights such as Halogen Lamps, Light Emitting Diodes (LEDs), and High-intensity discharge (HID). To identify each vehicle headlights types is very important to control the traffic rules violations. This research work refers to the recent research in intelligent headlight control system (IHC). The main aim to carry out this research work is to mature the automated traffic control systems. This work focus to mature the nighttime traffic control system. It is difficult to monitor each vehicle on road especially in nighttime, so this proposed system is able to monitor each vehicle and identify the vehicles which are violating the traffic laws. Various problems exist in the recognition and detection of headlights, such as erroneous detection of street lights, reflection of water in rain, sign lights and the reflection plate. Some other techniques are also used for this kind of problems; one of them involves the infrared camera to predict the headlight nature. Infrared cameras are not only costly but also need some technical recourse to operate in right manners. This becomes difficult for traffic control authorities to operate them. The proposed system only uses simple images or videos which can be captured through any simple camera by any non technical resource. This work proposes a vehicle headlights type detection and identification method for surveillance using deep learning model such as single shot multibox detector (SSD) mobilenet in real-time video or images data. To detect and predict the nature of headlight of vehicle, we have to create the versatile type of vehicles headlights data and refine it. After preparing and refining the data, we use SSD model, through which we detect and identify the headlight type of vehicle, then we classify those outcomes into three classes (HID, HELOGEN, LED). The more clear data means the more accurate prediction. That’s why in this system we first focus on refining the data and then process the data on SSD model. By adopting this method we results become better, accurate and become faster as compared to other techniques and methods. This method can easily be used for detecting and identifying vehicle headlights from both image and video data.
Breaking the Hegemony of the Triangle Method in Clique Detection

We consider the fundamental problem of detecting/counting copies of a fixed pattern graph in a host graph. The recent progress on this problem has not included complete pattern graphs, i.e., cliques (and their complements, i.e., edge-free pattern graphs, in the induced setting). The fastest algorithms for the aforementioned patterns are based on a straightforward reduction to triangle detection/counting. We provide an alternative method of detection/counting copies of fixed size cliques based on a multi-dimensional matrix product. It is at least as time efficient as the triangle method in cases of K 4 and K 5 . The complexity of the multi-dimensional matrix product is of interest in its own rights. We provide also another alternative method for detection/counting K r copies, again time efficient for r ∈ {4, 5}.
Formal Verification of Quantum Programs: Theory, Tools, and Challenges

Over the past 27 years, quantum computing has seen a huge rise in interest from both academia and industry. At the current rate, quantum computers are growing in size rapidly backed up by the increase of research in the field. Significant efforts are being made to improve the reliability of quantum hardware and to develop suitable software to program quantum computers. In contrast, the verification of quantum programs has received relatively less attention. Verifying programs is especially important in the quantum setting due to how difficult it is to program complex algorithms correctly on resource-constrained and error-prone quantum hardware. Research into creating verification frameworks for quantum programs has seen recent development, with a variety of tools implemented using a collection of theoretical ideas. This survey aims to be a short introduction into the area of formal verification of quantum programs, bringing together theory and tools developed to date. Further, this survey examines some of the challenges that the field may face in the future, namely the development of complex quantum algorithms.
An Efficient Structural Analysis of SAS and its Application to White-Box Cryptography

Structural analysis is the study of finding component functions for a given function. In this paper, we proceed with structural analysis of structures consisting of the S (nonlinear Substitution) layer and the A (Affine or linear) layer. Our main interest is the S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> AS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> structure with different substitution layers and large input/output sizes. The purpose of our structural analysis is to find the functionally equivalent oracle F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> and its component functions for a given encryption oracle F(= S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ∘ A ∘ S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ). As a result, we can construct the decryption oracle F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*−1</sup> explicitly and break the one-wayness of the building blocks used in a White-box implementation. Our attack consists of two steps: S layer recovery using multiset properties and A layer recovery using differential properties. We present the attack algorithm for each step and estimate the time complexity. Finally, we discuss the applicability of S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> AS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> structural analysis in a White-box Cryptography environment.
Database of hourly and daily sums of total radiation at Russian antarctic stations: analysis of changes in total radiation for the entire period of observations in Antarctica

Given the significant changes in the climate on the planet as a whole, databases and archives of data on the main climate-forming characteristics of the atmosphere, collected over long periods of time in various regions of the globe and, in particular, in the polar regions, acquire a special role. Total solar radiation is one of the most important parameters affecting the energy balance of the Earth-atmosphere system. We have created a database (DB) of hourly and daily sums of total radiation (Q) at the Russian Antarctic stations, designed to study the radiation regime of the Antarctic, from the beginning of actinometric observations to 2019. The information presented in the database was collected at five Antarctic stations — Bellingshausen, Vostok Mirny, Novolazarevskaya and Progress. The database has undergone a state registration procedure and is registered under No. 2020621401. The article gives a description of the structure of the DB and presents detailed information for each station. To provide an example of how database information can be used, characteristics of the total radiation in different parts of the Antarctic continent are obtained. Thus, it is found that the average monthly amounts of Q in the continental part of Antarctica on the high plateau (Vostok station) in conditions of minimal cloudiness and high transparency during the Antarctic summer are maximum and average 1240 MJ/m2. At the same time, at the tip of the Antarctic Peninsula (Bellingshausen station) during the same period, the average monthly amounts of Q due to the almost constantly present cloud cover do not exceed 570 MJ/m 2 . In the coastal areas at the three remaining stations, the average monthly amounts of total radiation range from 908 MJ/m 2 (Progress) to 950 MJ/m 2 (Mirny). Estimates of variability characteristics of daily, monthly, and annual sums of total radiation at all the five stations for the entire observation period up to 2019 were also obtained. The absence of statistically significant long-term trends in the annual and monthly sums of total radiation at all the stations under consideration was noted. The results of their analysis indicate that there are no significant changes in the inflow of total solar radiation to the Antarctic surface over more than sixty years of actinometric observations.
Tiba$$\eta $$sim: Information Access for Low-Resource Environments

In Africa, and other places around the world, there are numerous people who do not have access to information from the World Wide Web or other digital sources. This is not an issue of infrastructure only, but also of cultural and social factors, including low literacy. Most rural communities in the Northern Region of Ghana fall in this category, where information in written form and/or English is not accessible due to the inability of majority of community members to read and write. This paper presents Tiba\(\eta \)sim (originally “RadioNet”); a case-study of an appropriate ICT4D methodology in the development of an information delivery system hosted in low-resource areas, with empirical data from context analysis from the rural communities and other stakeholders. The paper also presents an evaluation of the system and the methodology, by way of User Evaluation and System Monitoring. The paper also shows how contextual issues are catered for through the methodology used. Tiba\(\eta \)sim focuses on available technologies and appropriate information formats by providing a system that relies on GSM and FM Radio, in the local language(s) of the community. Tiba\(\eta \)sim was deployed in 5 rural communities, reaching a total of almost 1000 people, providing them primarily farming-related information.
Optimization and Enhancement of Space Object Identification Algorithms and Implementation Using Deep Learning Network

Satellites are continuously launched by various countries for some expeditions. Satellites, after completing their work become nonfunctional and disintegrate in space, as debris continues to revolve in the space orbit. Now, these may collide with each other or with the fully functional satellite. This causes the formation of more debris. Hence, there is a huge possibility that these debris may harm the satellite. This may cost billions and the failure of new projects. The proposed work uses CNN model of the deep learning algorithm and its various applications to classify space objects as debris using light curves. This current work studies the different deep learning approaches and compares the result of their accuracies to suggest the best approach.
Identifying the Origin of Finger Vein Samples Using Texture Descriptors

Identifying the origin of a sample image in biometric systems can be beneficial for data authentication in case of attacks against the system and initiating sensor-specific processing pipelines in sensor-heterogeneous environments. Motivated by shortcomings of the photo response non-uniformity (PRNU) based method in the biometric context, we employ eight texture classification approaches, including frequency-, spatial-, and wavelet-based methods to detect finger vein samples images’ origin. Besides, We use eight publicly available finger vein datasets and applying all eight novel classical texture descriptors and SVM classification in the suggested pipeline. A novel wavelet-based approach termed WMV demonstrated an excellent result for raw finger vein samples and the more challenging region of interest data among mentioned employed methods to identify sensor model. The observed results establish texture descriptors as effective competitors to PRNU in finger vein sensor model identification.
Using Conv-LSTM to Refine Features for Lightweight Image Super-Resolution Network

In this paper, we propose a lightweight network that uses conv-LSTM for feature fusion (LFN) to improve image super-resolution performance and save the number of parameters. The network extracts features of different levels from the input image through a deep extraction block (DB) composed of two hourglass blocks (HB). HB progressively compresses and expands the channel of the input feature map to achieve compact feature aggregation and amplification, thereby making the information in the network more compact. The information at different levels in the network is regarded as a sequence, and Conv-LSTM is used to repeatedly fuse and extract more effective information from this sequence to obtain an effective expression. Experimental results show that our network can achieve a good balance between performance, number of parameters and amount of calculation.
Website Evaluation Using Opinion Mining

In this modern era everyone is aware of comment analysis. For example, if a person wants to buy a product, the first thing will do is to see the review of that particular product, based on review they choosing the product. By this way the reviews made a huge impact on the products and on websites. There are number of websites increasing in the internet, which provides various facilities like shopping, trading, online betting etc. Some of them are good and some are questionable like whether the website is beneficial or not. Many of them are facing the issues when choosing a website, in order to overcome some of these fraud websites, we proposed a system that evaluates the websites based on the reviews or feedback provided by the customer. For this we collected the twitter dataset because on twitter platform various number of emotions are expressed on the products, user interface, online transactions etc. as well as we used different kinds of classifiers for classifying the text such are Naive-Bayes, Random forest, Decision tree, Support Vector Machine, K Nearest Neighbor's, Logistic Regression. In this paper we utilize the Logistic Regression classifier is used for classification of the text and for generating the sentiment of the word, which achieves the better accuracy (99.50%), recall and F1 score among the other classifiers used in this paper and naïve-bayes has achieved the best high precision rather among the classifiers used. This paper emphasizes that the critiques expressed in the form of reviews are used to analyze the behavior is positive, negative or neutral and the system is used to rank satisfactory classifiers for sentiments.
QR Based Paperless Out-Patient Health and Consultation Records Sharing System

Clinics and hospitals always have this exhausting task of handling patients’ health records. Especially in this time of Covid-19 pandemic, number of outpatients visiting hospitals surge and it is also paramount to maintain the outpatient records to retrieve patient’s medical history at times of emergency. However, there has been no standard, easy to use, cost-effective and secure applications available for the maintenance of the out-patient records that contain their medical history, medication details and diet suggestions of the outpatients who walk in and out of clinics that are present across the country. Also, there is no system of cross-sharing patients’ records among the hospitals securely. It is noteworthy that these health records are of value at critical times. The proposed system employs a lightweight, holistic approach to maintain outpatient records and facilitate sharing of the same among clinics at times of need. The proposed system aims at recording vital information (Audio) such as Doctor’s advice, medications and dietary suggestions during consulting and converts the same to text and subsequently to QR Code. The QR codes along with medical tests data, if any are stored chronologically. The application also facilitates sharing of this systematically stored data among clinics, as and when required. In this paper, implementation of the above said system is presented with techniques used for voice capturing, speech to text conversion, bilingual translation and QR code generation. Notable benefits of the proposed application, especially during pandemic times include paperless medical consultation and prescription related records and a framework for sharing the same across clinics as needed.
A hemispheric surface improvement method based on parametric method

In recent years, multi-mode ultra-wide Angle imaging sensor. Because of the wide field of view, it can synthesize the complementary information of all kinds of images. The multi-mode fusion image has more abundant scene information, and more and more attention has been paid to it. Ultra-wide Angle images usually have image distortion. It brings difficulties to image registration and fusion. In this paper, lens distortion classification and lens imaging principle of ultra-wide Angle image distortion are analyzed. An improved hemispheric algorithm is proposed to correct the barrel distortion of ultra-wide Angle images. Finally, the experimental results of the three distortion correction algorithms are compared and evaluated. Our method reduces the correction error and improves the correction effect.
On Charles Stein’s contributions to (in)admissibility

Charles Stein made fundamental contributions to admissibility and inadmissibility in estimation and testing. This paper surveys some of the more important ones. Particular attention will be paid to his monumentally important, and at the time, incredibly surprising discovery of the inadmissibility of the usual estimator of the mean in three and higher dimensions. His result on admissibility of Pitman's estimator of a mean in one and two dimensions, and his results on estimation of a mean matrix and a covariance matrix are also discussed. His work on testing is briefly covered.
Implementation of an intelligent target detection system for edge node

With the rapid development of the artificial intelligence industry, the application of object detection technology in real life is becoming increasingly widespread, such as intelligent monitoring, autonomous driving, and augmented reality. In this paper, object detection system is deployed on edge devices which is low complexity and low-cost device such as the Raspberry Pi. Implement Mobilenet-SSD based on the deep learning framework and deploy on edge devices, such as image acquisition, object detection and result display. The results show that the object detection technology can also be achieved on devices with scarce computing resources such as the Raspberry Pi and satisfies actual business requirements.
An Introduction to Domain Adaptive Object Detection from Synthesis to Reality

In recent years, deep learning based object detection has achieved great progress. These methods typically assume that large amount of labeled training data is available, meanwhile, training and test data are independent and identically distributed. However, the two assumptions are not always hold in practice. In many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data. Using computer graphics technology to generate a large number of labeled data provides a solution to this problem. Unfortunately, direct transfer across domains from synthesis to reality often performs poorly due to the presence of domain shift. Domain adaptive object detection are concerned with accounting for these types of challenges. In this paper, we present an introduction to these fields. Firstly, we briefly introduce the object detection and domain adaptation. Secondly, the synthetic object detection datasets and related software tools are summarized. Thirdly, we present a categorization of approaches, divided into discrepancy-based methods, adversarial discriminative methods, reconstruction-based methods and others. Finally, we also discuss some potential deficiencies of current methods and several open problems which can be explored in future work.
Scene Text Transfer for Cross-Language

Scene text transfer for cross-language aims to erase the original scene text and generate another language text image into the original scene text image with the same style, including the style of fonts, colors, size, and background texture. Scene text transfer for cross-language is a challenging problem as the complicated background scene and a huge difference between languages, which demanding high-quality performance for both text transfer and text erasing. In this work, we propose a scene text transfer framework for cross-language which consists of three steps: regional text extraction, style transfer, and scene text combination. The regional text extraction is designed to crop the text region of a natural scene image and transform it to be a rectangle text image. In the second step, a style transfer network is proposed to retain the style of text image and transfer the text content. In the step of the scene text combination, our model combines the rendered text image with the original scene image to produce the final result. In the optimization part, we introduce a novel background consistent loss to improve the performance of background generation. Experiments demonstrate that our framework generates scene text images of higher quality than previous methods.
A Study Report on Introducing Sanskrit Language Based on the Technologies Related to Current Trends

In the ancient period, on words, Sanskrit devises some features that remain most useful in dissimilar areas. The Sanskrit stands a natural language presence, the finest selection to be transformed into the computing platform for robotic controller and artificial intelligence knowledge. The study focuses on Sanskrit between the pool of numerous human languages classifying it as one of the best suitable ones for computing methods. The computing has two aspects like logic and amp and language. The technical language employed by Indian Shastra disciplines is a derivation of Sanskrit leaving all disqualifications of natural language are taken qualities of an artificial language. Hence, it is a natural human being and as good as artificial for a machine. Logically, it has prevalent faith in natural languages that are incompatible for the diffusion of various concepts that artificial languages might extract with the finest accuracy and calculated consistency. In this, studying of Sanskrit is an honor for the Indo-Aryan language and it is fantastic to see a machine program with natural language and communication in Sanskrit.
Analysis and Segmentation Algorithm of Dongba Pictograph Document

Dongba character is a kind of pictograph to picto-graph, phonetic character transition form. It has the charac-teristics of large differences in personalization of writing and has more variants. Because it does not have a standard, open data set, it has brought a huge obstacle to the study of Dong-ba characters. The popularization and sharing of electronic documents provides a more convenient and faster way to sample Dongba characters. Therefore, based on the projection segmentation algorithm, combined with the structure of the Dongba hieroglyphic document, we set the segmentation conditions that meet the structure of the Dongba character document, and realize the structure analysis and segmentation of the document.