Reviewerly/exHarmony huggingface dataset:
A New Categories Identification Method based on Reliability Test in Radar Signal Recognition System

In the field of radar electronic reconnaissance, radar signal recognition is a key technology. In the actual task, part of the signals to be recognized may come from new types of emitters, which can not be identified directly by the existing recognition system. In order to get the ability to recognize new categories, it is necessary to analyze the unrecognized samples for incremental learning. In this paper, a new categories identification method based on reliability test is proposed. Firstly, an existing clustering method is used to label the unrecognized samples, and then the reliability test criteria are designed, including quantity criterion, distance criterion and frequency criterion, to screen the clustered sample points. The proposed method provides better data support for incremental learning in radar signal recognition. Simulation results show the effectiveness of the proposed method.
Monocular Pseudo-LiDAR 3D Object Detection Method Based on Confidence and Feature optimization

3D object detection based on the monocular Pseudo-LiDAR is cost-effective compared with multi-sensor solutions in autonomous driving, and made great progress in recent. However, these methods still suffer from the following disadvantages, i.e., (1) the depth estimation error of the background points in the Pseudo-LiDAR is larger than that of the object points, which leads to the degradation of the object detection performance. (2) the existing point cloud detection network is difficult to effectively capture the local features and global correlation of Pseudo-LiDAR, which leads to the weak feature representation ability of the point cloud. To this end, we propose a 3D object detection method of Pseudo-LiDAR based on Confidence and Feature optimization. In our method, we first propose a hierarchical structure of the point cloud confidence optimization method, which carries out resampling to filter out the background points according to the confidence distribution, so as to improve the significance of the Pseudo-LiDAR object point cloud. Then, we design a hierarchical feature extraction module, which uses DGCNN to obtain the local features of the keypoints and the point transformer structure to capture the global relevance of the keypoints, so as to improve the feature representation ability of the point cloud. On the popular KITTI benchmark, our approach achieves better performance than the mainstream method.
Encryption Algorithm of Automatic Single Chip Computer and Embedded System Based on Big Data

With the rapid development of network technology and communication technology, modern society is rapidly transforming towards an information society based on big data. The single-chip microcomputer and embedded system have received widespread attention from all walks of life since they came out. Now it has fully penetrated into people's daily work and life. In this regard, it is of great significance to study the encryption algorithms of automated single-chip microcomputers and embedded systems based on big data. The purpose of this article is to study the encryption algorithm of the automatic single-chip microcomputer and embedded system based on big data. This article adopts research methods such as literature data method, mathematical statistics method, and logical analysis method to study the encryption algorithm of automatic single-chip computer and embedded system based on big data. It mainly optimizes and adjusts the existing data encryption algorithm and strengthens the information. Through the normal test and abnormal test of the data encryption system, it can be seen that the system can encrypt and store data, which can be said to realize transparent encryption and decryption in the process of reading and writing. Studies have shown that although the system's built-in function takes 5-6 seconds to open a file, the time it takes for the function to open a file is between 45-48 seconds, which takes a little longer. When reading the specified data from the specified file, the correct data can be read only by providing a consistent key. It can be seen that the system is equivalent to providing data security measures in two aspects: identity authentication and data encryption. This design scheme can effectively protect the data security of the system.
Video Quality Enhancement using Generative Adversarial Networks-based Super-Resolution and Noise Removal

Video quality enhancement is a challenging task as it not only involves super-resolution but also there is underlying noise in most of the real-world videos recorded almost a decade ago. Existing literature focuses on image super-resolution-based methods which fail to deliver satisfactory results in real-world scenario due to lack of high-resolution and low-resolution pairs. We propose a method based on image translation methodology coupled with super-resolution architecture. This does not require high-resolution, low-resolution pair, and learns the underlying noise automatically, Furthermore, it can learn the style of a High-Definition video and apply it on a low-resolution video. We present qualitative results that show excellent performance on unseen dataset.
Patterns of Subject Metadata Change in <scp>MARC</scp> 21 Bibliographic Records for Video Recordings

Abstract Study reported in this paper analyzed over twenty thousand machine‐readable library metadata records in MARC 21 bibliographic format that are based on Resource Description and Access (RDA) standard. The focus of this analysis is on change in subject metadata—data elements designed for representing the aboutness of information objects—over a 6‐year period between 2014 and 2020. The analyzed dataset is the entire population of the records representing English‐language video recordings in DVD format in WorldCat database as of one year after official transition to RDA data content standard in library metadata creation. Analysis of metadata representing audiovisual materials is needed as audiovisual metadata practices tend to differ from those for other materials due to high occurrence of unique resources. The study includes quantitative and qualitative analyses into the change in the application of data elements (fields and subfields) over time and categorizes the observed change.
NodeMCU Based Social Distancing Alarm Cap

This paper introduces a powerful image contrast Now the pandemic COVID-19 caused by corona virus is spreading fast throughout the world. According to World Health Organization (WHO) social distancing is proven to be the only solution to prevent spread of virus. Conventional methods of ensuring people to follow physical distancing are not effective. An automatic social distancing device is needed to assist and train individuals to stay at safe distance of at least 1 meter. This paper proposes project of a wearable device NodeMCU based social distancing alarm cap which uses NodeMCU with four ultrasonic sensors to detect the distance between two persons in all four directions and alerts by blowing buzzer if the person fails to maintain social distancing. The device performs social distancing detection accurately and assists movement of wearer in an area.
High-Dimensional Entanglement of Photonic Angular Qudits

We propose a method for generation of entangled photonic states in high dimensions, the so-called qudits, by exploiting quantum correlations of Orbital Angular Momentum (OAM) entangled photons, produced via Spontaneous Parametric Down Conversion. Diffraction masks containing N angular slits placed in the path of twin photons define a qudit space of dimension N 2 , spanned by the alternative pathways of OAM-entangled photons. We quantify the high-dimensional entanglement of path-entangled photons by the Concurrence, using an analytic expression valid for pure states. We report numerical results for the Concurrence as a function of the angular aperture size for the case of high-dimensional OAM entanglement and for the case of high-dimensional path entanglement, produced by N × M angular slits. Our results provide additional means for preparation and characterization of entangled quantum states in high-dimensions, a fundamental resource for quantum simulation and quantum information protocols.
Secure and dependable: Area-efficient masked and fault-tolerant architectures

Masking is a powerful instrument for protecting cryptographic devices against side-channel analysis. Multiple masking schemes were introduced providing provable security against attacks of arbitrary order even in the presence of glitches. When a device is a part of some safety-critical system, it needs to meet dependability requirements; therefore, it should be protected against spontaneously occurring faults. Existing commonly used fault-tolerance architectures involve high area overhead as so as the masking schemes do. In this paper, we propose architectures meeting dependability properties of simple modular-redundancy schemes and SCA resistance of masking schemes, but decreasing the area overhead utilizing the randomness involved in the masking schemes.We compare our Masked Duplex architecture with Triple Modular Redundancy. While using one less redundant module, our architecture saves around 20% of the area in comparison with TMR in the case of Threshold Implementation of PRESENT cipher, promising more savings for more complex cryptographic schemes.
Deep One-Class Crop Extraction Framework for Multi-Modal Remote Sensing Imagery

Large scale crop mapping is an important task in agricultural resource monitoring. To obtain a distribution map of crops, traditional methods usually require the well-designed manufacture features and the ground-truth labels of all land-cover types for training a multi-class classifier. However, the redundant labeling for each land-cover type is time-consuming and labor-intensive, and the feature design for different remote sensing data is complex and limited to human prior knowledge. In this paper, a deep one-class crop extraction framework is proposed to solve the problems mentioned above. Specifically, it uses the deep one-class crop extraction module to extract the feature automatically for any remote sensing imagery and the one-class crop extraction loss to address the lack of negative class in the deep one-class classification model. In addition, the proposed framework can be applied to multi-modal remote sensing data, i.e. hyperspectral, multispectral, and SAR images, which is verified in the experiments and the proposed framework can achieve the highest accuracy on each multi-modal data.
A Machine Learning Inspired Task Scheduling Process Improved Novel Algorithm (TspINA) for Reduction in Overall System Requirements to Perform Multi-functional Tasks by Boosting Efficiency

The ability to successfully execute task scheduling for any computing system is a performance measurement in any multi-processing environment, such as working of cloud or continuous scheduling and much more. Over the years, for many cloud deployments, cost-effective resource scheduling (CERS) algorithms and dynamic and integrated resource scheduling (DAIRS) algorithms were commonly used. But these algorithms have certain drawbacks in terms of retention and efficient task response time and setting threshold values along with causing lot of overhead. Through this paper, we try to put forward an algorithm based on machine learning (ML) paradigm, task scheduling process improved novel algorithm (TspINA). Our proposed algorithm explores the methods used by CERS and DAIRS algorithms and provides a reasonable enhancement by integrating machine learning features techniques for prior-learning and persistent adaptation to minimize the completion time for a defined series of objectives and tasks. Our suggested methodology would be further compared to the existing CERS and DAIRS algorithms by using generic datasets and the outcomes will be correlated with the amount of machine effort, delay and responsiveness.
Forecasting foreign exchange rate using a combination of linear regression and flower pollination algorithm

Abstract Several currencies exist in the world. Each currency will have value. The currency exchange will go through a conversion process to adjust the amount. Each currency value can fluctuate based on the conditions of the currency area. The fluctuating changes in value provide profit opportunities. Maximizing profits can make forecasts so that the right decisions are made. One of the forecasts can use regression. Regression is capable of forecasting based on historical data. The regression in this study will be optimized using the Flower Pollination Algorithm (FPA). The use of the Flower Pollination Algorithm (FPA) aims to obtain appropriate parameters for regression to reduce forecast errors. The data in this study were obtained by utilizing extraction from the Meta trader application. This data will be the basis for the system learning stage and the testing phase. Obtaining a good hyperparameter can make the forecasting system closer to the actual value. Good system accuracy can be a trader’s supporting data in making transactions. Forecasting in this study used the parameter 5 window sizes, 20 population sizes, and 0.7 probability switch. This experiment resulted in MSE 0.0331 and RMSE 0.1756. This forecasting has sufficient results to support a trader’s decision. Further research is needed to improve accuracy and determine the direction of the forecast to improve this research.
Hyperspectral Image Classification by Fractional Discrete Cosine Transform Based Feature Extraction

Feature extraction (FE) can greatly improve classification performance, when dealing with hyperspectral imagery (HSI) which is notable by its high dimensionality. Unsupervised FE methods have proven to perform competitively in this arena. Preprocessing methods have been used to further improve their performance. One such an approach to improving FE for classification of HSI is investigated in this paper. This approach is based on exploiting the intermediate nature of spectral signatures transformed via fractional fourier transform (FrFT) as the result from the transform, which carry information from both the original reflectance spectral domain and its fourier transfer domain. The fractional discrete cosine transform (FrDCT) is also investigated for the same purposes. Pairing these methods involved with dimensionality reduction, with a well-known FE algorithm, i.e., principal component analysis (PCA), is explored. Results obtained with a real hyperspectral dataset are promising with preprocessing based on selecting an appropriate fractional transform order, low-pass (LP) filtering in the fractional domain to an appropriate degree, followed by the PCA.
Approaches Based on the Ising Model

In this chapter, we explore two approaches that link quantum machine learning to the Ising model. First, we will look at probabilistic models that emerge when we take a Boltzmann machine and add quantum dynamics to the underlying physical system. We then discuss proposals for quantum machine learning with quantum annealers, which are devices that solve optimisation problems with an Ising-type energy function.
Riesgos de seguridad en las pruebas de penetración de aplicaciones web : Security risks in web application penetration testing

This paper systematizes the main security risks that may be associated with penetration testing in web applications. Bibliographic sources and reports of a high scientific and technical level were consulted for the study. Thirty-one risks were identified and described, classified into two groups: those associated with direct damage to the confidentiality, integrity and availability of web application information and those related to the performance of a deficient penetration test and whose partial results also indirectly affect the security of web portals, the latter were divided into risks of scope and time, technological infrastructure and personnel. For the treatment of the described risks, a set of 14 basic recommendations is provided for the conformation of a mitigation strategy according to the existing test scenarios. It also focuses on how to apply automated vulnerability assessment tools to limit damage to web applications. The results achieved are highly relevant given the need for those involved in penetration testing processes to have a conceptual starting point that favors the treatment of risks and better contextualizes the decisions taken in order to solve the security vulnerabilities found through this type of security assessment.
FPX-G: First Person Exploration for Graph

Data exploration is a fundamental user task in the information seeking process. In data exploration, users have ambiguous information needs, and they traverse across the data for gathering information. In this paper, a novel data exploration system, called FPX-G, is proposed that uses virtual reality (VR) technology. VR-based data exploration (or immersive analytics) is a recent trend in data analytics, and the existing work approaches involve aggregated information in an interactive and 3D manner. However, exploration for individual pieces of data scarcely has been approached. Traditional data exploration is done on 2D displays, therefore space is limited, and there is no depth. FPX-G fully utilizes 3D space to make individual piece of data visible in the user’s line of sight. In this paper, the data structure in FPX-G is designed as a graph, and the data exploration process is modeled as graph traversal. To utilize the capability of VR, FPX-G provides a first person view-based interface from which users can look at individual pieces of data and can walk through the data (like walking in a library). In addition to the walking mechanism, to deal with limited physical space in a room, FPX-G introduces eye-tracking technology for traversing data through a graph. A simulation-based evaluation reveals that FPX-G provides a significantly efficient interface for exploring data compared with the traditional 2D interface.
Real-Time Puddle Detection Using Convolutional Neural Networks with Unmanned Aerial Vehicles

The study was carried out in order to enable systems with weak processing power and motion to detect objects using cloud services. In addition, the dataset is expanded by continuous labeling to create big data. In the study, it is aimed to detect objects using cloud-based deep learning methods with an unmanned aerial vehicle (UAV). In the study, training processes were carried out with Google Colaboratory, a cloud service provider. The training processes are a YOLO-based system, and a convolutional neural network was created by revising the parameters in line with the needs. The convolutional neural network model provides communication between neurons in the convolutional layers by bringing the image data to the desired pixel ranges. Unlabeled pictures are included in the training by being tagged. In this way, it is possible to continuously enlarge the data pool. Since the microcomputers used in UAVs are insufficient for these processes, a cloud-based training model has been created. As a result of the study, cloud-based deep learning models work as desired. It is possible to show the accuracy of the model with the low losses seen in the loss functions and the mAP value. Graphic cards with high processing power are needed to provide training. It is essential to use powerful graphics cards when working on image data. Cost reduced by using cloud services. The training was accelerated and high-rate object detections were made. YOLOv5x was used in the study. It is preferred because of its fast training and high frame rate. Recall 80% Precision 93% mAP 82.6% values were taken.
Shaped Policy Search for Evolutionary Strategies using Waypoints

In this paper, we try to improve exploration in Blackbox methods, particularly Evolution strategies (ES), when applied to Reinforcement Learning (RL) problems where intermediate waypoints/subgoals are available. Since Evolutionary strategies are highly parallelizable, instead of extracting just a scalar cumulative reward, we use the state-action pairs from the trajectories obtained during rollouts/evaluations, to learn the dynamics of the agent. The learnt dynamics are then used in the optimization procedure to speed-up training. Lastly, we show how our proposed approach is universally applicable by presenting results from experiments conducted on Carla driving and UR5 robotic arm simulators.
FairNet: A Measurement Framework for Traffic Discrimination Detection on the Internet

Network neutrality is related to the nondiscriminatory treatment of packets on the Internet. Any deliberate discrimination of traffic of one application while favoring others violates the principle of neutrality. Many countries have enforced laws against such discrimination. One requires tools to detect any net neutrality violations to enforce such laws. However, detecting such violations is challenging as it is hard to separate any degradation in quality due to natural network effects and selective degradation. Also, legitimate traffic management and deliberate discrimination methods can be technically the same, making it challenging to distinguish them further. We developed an end-to-end measurement framework named FairNet to detect discrimination of traffic. It compares the performance of similar services. Our focus is on HTTPS streaming services which constitute a predominant portion of the Internet traffic. The effect of confounding factors (congestion, traffic management policy, dynamic rate adaptation) is made similar on the test services to ensure a fair comparison. FairNet framework uses a replay server and user-client that exchanges correctly identifiable traffic streams over the Internet. The Server Name Indication (SNI) field in the TLS handshake, which goes in plain text, ensures that the traffic from the replay server appears to network middle-boxes as that coming from its actual server. We validated that appropriate SNIs result in the correct classification of services using a commercial traffic shaper. FairNet uses two novel algorithms based on application-level throughput and connection status to detect traffic discrimination. We also validated the methodologys effectiveness by collecting network logs through mobile apps over the live Internet and analyzing them.
Context-Aware and Data-Augmented Transformer for Interactive Argument Pair Identification

Interactive argument identification is an important research field in dialogical argumentation mining. This task aims to identify the argument pairs with the interactive relationship in the online forum. In this paper, we tackle the task as sentence pair matching. We build our model based on the pre-trained language model (LM) RoBERTa due to its strong ability in modeling semantic information. Based on the peculiarities of the argument texts, we combine the arguments and their corresponding contexts to better identify the interactive relationship of the argument pair. Besides, we adopt data augmentation and vote strategy based on cross-validation to further enhance the performance. Our system rank 5th on track2 of the NLPCC-2021 shared task on Argumentative Text Understanding for AI Debater.