Reviewerly/exHarmony huggingface dataset:
Research on Internet of Things security architecture based on fog computing

The traditional data processing of the Internet of Things is concentrated in cloud computing, and its huge number of devices and massive real-time data transmission are extremely stressful on network bandwidth and cloud computing data centers. Fog computing is the infrastructure that can use processing power anywhere in the cloud. Virtual computing extends the power of cloud computing to the edge of the network, enabling any computing device to host and process software services, analyzing and storing data closer to where data are generated. The architecture of the fog computing brings enormous processing power. Since its processing power is often located near the required equipment, the distance of data transmission is reduced and the delay is reduced. This article explores how to use the fog computing layer between the cloud data center and the end node layer to store and process large amounts of local data in a timely manner, speeding decision making and enabling Internet of Things manufacturers and software developers to limit their ability to send data. They reduced cloud computing costs and built a reasonable security architecture.
A radar signal sorting algorithm based on PRI

Based on the PRI transform method, this article has found a radar signal sorting algorithm which aims at sorting those received radar pulse signals and recognizing their classes. Firstly, this method will screen those received radar pulse signals through the PRI transform method. And then, for the different kinds of radar pulse signals, it will choose different methods for sorting. When it comes to the problem of poor recognition of PRI staggered radar signals by traditional radar signal sorting method, the author uses a combined sorting based on the PRI transform method and the SDIF algorithm to identify the PRI staggered radar signals by setting the judging conditions. Finally, the algorithm has been simulated, of which the result show that the algorithm can successfully sort out different PRI radar signals.
Deep Metric Learning for Cross-Domain Fashion Instance Retrieval

The goal of this paper is to find an effective method to retrieve an image with a fashion instance from one domain based on a similar fashion instance image from a different domain.Where existing works focus on retrieving relevant shop images based on a consumer instance, we introduce the reverse task and treat both tasks equally in our training setup.We use several deep metric learning techniques to get baseline scores for these tasks on the DeepFashion2 dataset and we show how ensemble methods can be used to boost the performance.
Deep Learning-Based Decision Region for MIMO Detection

In this work, a deep learning-based symbol detection method is developed for multi-user multiple-input multiple-output (MIMO) systems. We demonstrate that the linear threshold-based detection methods, which were designed for AWGN channels, are suboptimal in the context of MIMO fading channels. Furthermore, we propose a MIMO detection framework which replaces the linear thresholds with decision boundaries trained with neural network (NN) classifiers. The symbol error rate (SER) performance of the proposed detection model is compared against conventional methods under state-of-the-art system parameters. Here, we report to up to a 2 dB gain in SER performance using the proposed NN classifiers, allowing for exploiting higher-order modulation schemes, or transmitting with reduced power. The underlying gain in performance may be further enhanced from improvements to the NN architecture and hyper-parameter optimization.
The www-sites of federal libraries: The current state and prospects for search optimization

The www-sites represent libraries virtually on the Internet and perform many of the functions of these institutions. Currently, library site building is developing intensively. The researchers focus on the organization and content allocation, design concept, services marketing, assessment of information resources quality. One of the most important tasks to be solved by these social institutions is user acquisition. However, the issues of search engine optimization of www-sites are studied insufficiently. The author presents the findings of the study of federal libraries’ www-sites. The comparative analysis of www-sites was carried out by the criteria of www-site quality index, number of external links to the resource, total social activity, as well as the adaptability for mobile devices. The PR-CY.ru service was used as an analysis tool. According to the study, the one third of the federal libraries’ www-sites can be characterized, from the user's point of view, as high-quality information resources. The author also analyzes the information channels through which users get to the federal libraries www-sites. For 80% of www-sites under examination, the largest number of users find them with search engines. Only 40% of the federal libraries’ sites are adapted for mobile devices, which affects the quality of information resources and makes it difficult for users to access information. The author emphasizes the need for optimizing the www-sites of federal libraries, as well as for monitoring their information resources using web-analytics.
Irony Sentence Detection Techniques Using Fuzzy Historical Classifier

Abstract The purpose of this study is to presents a new approach to the extraction of the meaning of sentences that are irony, that is by way of classifying someone based on their utterances in the past. The history of one’s utterances influences the assessment of a sentence having an irony tendency or not, for example when someone often speaks negatively, suddenly gives positive opinion on a topic while other people give negative opinions on the topic. The fuzzy logic method needs to be used to assess the historical tendency of one’s utterances when the values of positive and negative sentiments are almost balanced so that the value of the majority of sentiments is unclear. The results show that the greater the level of difference in sentiment between a topic and the higher the level of the historical tendency of a person’s utterance, the higher the value of the potential irony of the utterance.
Hybrid algorithms of the person identification by face image

Abstract In this paper, the problem of constructing hybrid algorithms for identifying a person (IAP) by the image is considered, which is the main one in the development of biometric systems based on them. To solve this problem, we propose a model of algorithms, consisting of the following stages: selection of the area of the face in the image; searching pupils on the face image (FI); determining the location of the mouth and nose; forming a set of geometric features; allocation of subsets of strongly coupled features; the definition of representative features in each subset of strongly related features; interim assessment for the FI class for each set of representative features; estimate for the FI class for all intermediate estimates. A distinctive feature of the proposed hybrid algorithm (HA) is the integration of the results of various methods for determining anthropometric points at the level of calculating the final score for each class of facial images. The main purpose of this paper is to develop an algorithm for integrating various methods of identifying features when IAP on a FI.
Lightweight Protection of User Identity Privacy Based on Zero-knowledge Proof

A number of solutions have been proposed to tackle the user privacy-preserving issue. Most of existing schemes, however, focus on methodology and techniques from the perspective of data processing. In this paper, we propose a lightweight privacy-preserving scheme for user identity from the perspective of data user and applied cryptography. The basic idea is to break the association relationships between User identity and his behaviors and ensure that User can access data or services as usual while the real identity will not be revealed. To this end, an interactive zero-knowledge proof protocol of identity is executed between CSP and User. Besides, a trusted third-party is introduced to manage user information, help CSP to validate User identity and establish secure channel between CSP and User via random shared key. After passing identity validation, User can log into cloud platform as usual without changing existing business process using random temporary account and password generated by CSP and sent to User by the secure channel which can further obscure the association relationships between identity and behaviors. Formal security analysis and theoretic and experimental evaluations are conducted, showing that the proposal is efficient and practical.
Mapping and Grouping of Farm Land with Graham Scan Algorithm on Convex Hull Method

Information on farm land ownership is important for Indonesia, where some of the population is farmers. The issue of unclear land ownership has caused farmer groups to experience difficulties in organizing activities to increase agricultural production. The land mapping information system can be a solution for farmer groups to see details of land ownership and information on farm land area within their organizing area. In this research a mapping system of farm land was developed and specifically this paper discusses the application of the Graham Scan algorithm to the Convex Hull method for grouping farm land. Based on the results of the system trials before and after using the convex hull, it is known that the grouping of 100 points is faster by an average of 2.44s. Memory usage is also quite good, after using Convex Hull it becomes more efficient 200KB. All results are obtained by comparing the application of the Convex Hull method and without the application of the method.
KMO: Kernel Memory Observer to Identify Memory Corruption by Secret Inspection Mechanism

Kernel vulnerability attacks may allow attackers to execute arbitrary program code and achieve privilege escalation through credential overwriting, thereby avoiding security features. Major Linux protection methods include Kernel Address Space Layout Randomization, Control Flow Integrity, and Kernel Page Table Isolation. All of these mitigate kernel vulnerability affects and actual attacks. In addition, the No eXecute bit, Supervisor Mode Access Prevention, and Supervisor Mode Execution Prevention are CPU features for managing access permission and data execution in virtual memory. Although combinations of these methods can reduce the attack availability of kernel vulnerability based on the interaction between the user and kernel modes, kernel virtual memory corruption is still possible (e.g., the eBPF vulnerability executes the attack code only in the kernel mode). To monitor kernel virtual memory, we present the Kernel Memory Observer (KMO), which has a secret inspection mechanism and offers an alternative design for virtual memory. It allows the detection of illegal data manipulation/writing in the kernel virtual memory. KMO identifies the kernel virtual memory corruption, monitors system call arguments, and enables unmapping from the direct mapping area. An evaluation of our method indicates that it can detect the actual kernel vulnerabilities leading to kernel virtual memory corruption. In addition, the results show that the overhead is 0.038 $$\upmu $$ s to 2.505 $$\upmu $$ s in terms of system call latency, and the application benchmark is 371.0 $$\upmu $$ s to 1,990.0 $$\upmu $$ s for 100,000 HTTP accesses.
Temporal Interpolation of Dynamic Digital Humans using Convolutional Neural Networks

In recent years, there has been an increased interest in point cloud representation for visualizing digital humans in cross reality. However, due to their voluminous size, point clouds require high bandwidth to be transmitted. In this paper, we propose a temporal interpolation architecture capable of increasing the temporal resolution of dynamic digital humans, represented using point clouds. With this technique, bandwidth savings can be achieved by transmitting dynamic point clouds in a lower temporal resolution, and recreating a higher temporal resolution on the receiving side. Our interpolation architecture works by first downsampling the point clouds to a lower spatial resolution, then estimating scene flow using a newly designed neural network architecture, and finally upsampling the result back to the original spatial resolution. To improve the smoothness of the results, we additionally apply a novel technique called neighbour snapping. To be able to train and test our newly designed network, we created a synthetic point cloud data set of animated human bodies. Results from the evaluation of our architecture through a small-scale user study show the benefits of our method with respect to the state of the art in scene flow estimation for point clouds. Moreover, correlation between our user study and existing objective quality metrics confirm the need for new metrics to accurately predict the visual quality of point cloud contents.
Objective Perceptual Video Quality Prediction Using Spatial and Temporal Information Differences

In this paper, objective perceptual video quality models are proposed that use spatial and temporal perceptual information differences for predicting video quality as perceived by human observers. Spatial perceptual information characterizes the complexity and temporal perceptual information quantifies the motion contained in a video. As such, differences in the spatial and temporal perceptual information of a reference video (original) and test video (processed) may be used to predict quality of videos that have undergone encoding, transmission, or other processing. In particular, several video quality prediction functions are derived using curve fitting along with training and validation on data from a publicly available annotated database. The obtained functions provide predicted mean opinion scores as a measure of perceptual quality subject to spatial and temporal perceptual information differences. The analysis of the video quality prediction performance of the proposed models shows that differences in spatial and temporal perceptual information can be used for objective video quality prediction.
Advantage E-Commerce Technology in Ornamental Plant Business

Abstract The purpose of this research is to find out the effectiveness of using e-commerce in ornamental plant business, and what benefits can be made for ornamental plant farmers. This research used a descriptive method for a complete review of the situation that occurred at the time of the study, the results of this study to find out how much results can be obtained if using e-commerce in the world of ornamental plant business, and what benefits can be made by ornamental plant farmers, this research is conducted by discussing how orders, sales and payments transactions are made using e-commerce, the results of this research that e-commerce can improve sales efficiency for ornamental plant farmers, of course e-commerce technology can affect the level of ornamental plant sales
Thermo Field Dynamics on a Quantum Computer

In this project we develop a quantum algorithm to realize finite temperature simulation on a quantum computer. As quantum computers use real-time evolution we did not use the imaginary time methods popular on classical algorithms. Instead, we implemented a real-time therom field dynamics formalism, which has the added benefit of being able to compute quantities that are both time-and temperature-dependent. To implement thermo field dynamics we apply a unitary transformation [1] to discrete quantum mechanical operators to make new Hamiltoni-ans with encoded temperature dependence. The method works normally for fermions, which have a finite representation, but needs some modification to work with bosons. These Hamilto-nians are then processed into a Pauli matrix representation in order to be used as input for IBM's Qiskit package [2]. We then use IBM's quantum simulator to calculate an approximation to the Hamiltonaian's ground state energy via the variational quantum eigensolver (VQE) algorithm [3]. This approximation is then compared to a classically calculated value for the exact energy. The thermo field dynamics quantum algorithm has general applications to material science, high-energy physics and nuclear physics, particularly in those situations involving realtime evolution at high temperature.
Batik Image Retrieval Using Maximum Run Length LBP and Sine-Cosine Optimizer

This paper investigates the effectiveness of Maximum Run Length (MRL) from Local Binary Pattern (LBP) for content-based Batik image retrieval system. The MRL of state ‘1’ derived from LBP code has been proven to yield a promising result for image classification task. The features generated from MRL of state ‘1’ have lower dimensionality compared to that of the classical LBP feature even though they are from an identical binary string. The additional color features are added for achieving a high image retrieval accuracy. To further increase the accuracy, the Sine-Cosine Algorithm (SCA) and its variants are further applied in the system to obtain the Optimum Similarity Weights (OSW). As documented in Experimental Section, the fusion of MRL and color features along with OSW significantly improves the retrieval accuracy with low feature dimensionality.
Node Overlap Removal Algorithms: A Comparative Study

Many algorithms have been designed to remove node overlapping, and many quality criteria and associated metrics have been proposed to evaluate those algorithms. Unfortunately, a complete comparison of the algorithms based on some metrics that evaluate the quality has never been provided and it is thus difficult for a visualization designer to select the algorithm that best suits his needs. In this paper, we review 21 metrics available in the literature, classify them according to the quality criteria they try to capture, and select a representative one for each class. Based on the selected metrics, we compare 8 node overlap removal algorithms. Our experiment involves 854 synthetic and real-world graphs.
Side Channel Analysis Using EM Radiation of RSA Algorithm Implemented on Raspberry Pi

The side-channel information (power consumption, electromagnetic radiation, etc.) leaked unintentionally from a cryptographic system during a cryptographic process can be used by attackers to reveal secret data using side-channel analysis. Some of the most common side-channel analysis are SPA (Simple Power Analysis), DPA (Differential Power Analysis), SEMA (Simple Electromagnetic Analysis) and DEMA (Differential Electromagnetic Analysis). Although it is relatively easy to analyze the side-channel information obtained from the ASIC or FPGA implementations, the analysis of a side channel information of cryptographic operations running in an operating system (OS) has different difficulties - for example, alignment of measurements and extracting crypto process signal through noises of other OS operations. In this study, side-channel attacks are performed against two different realizations of the RSA Algorithm by using electromagnetic radiation related with power consumption of a cryptographic operation implemented on Raspberry Pi platform. The first realization of the RSA Algorithm is implemented using unprotected binary algorithm and then private key bits of the RSA Algorithm are get individually by using SEMA attack. For the second realization, the RSA Algorithm is implemented by using a protected binary algorithm in order to resist against to SEMA attack. In presented work, the first bit of the private key has been found using 1800 measurements with the distance of mean test and correlation analysis.
Sarcasm Detection for Workplace Stress Management

Working stress is becoming very common. Handling working stress at the workplace is really going to be challenging. As a result, most of the time most of the time people start behaving in sarcastic ways through verbal communication, through different gestures, using emoticons, or writing reviews or comments that leads to increasing their anxiety sometimes promotes depression. It is difficult to identify sarcasm in written notes or communication. Feedback analysis is not a direct method since feedback or employer reviews are written in more formal language. This motivates the authors to work on the employee feedback system. The currently developed system helps to detect the sarcastic emotions by applying different methodologies on several types of statements. This will help corporations and other big organizations to identify reasons behind sarcastic behavior or increased anxiety. As a result, they can promote counseling programs, psychological treatment, or yoga-meditation camps.
Unmanned rescue vehicle navigation with fused DQN algorithm

When unmanned rescue vehicle (URV) performing rescue missions in the disaster area, URV will not be feasible to continue to use the built-in map for path planning, and even cause more serious consequences. Therefore, when a rescue vehicle works with a human being in a dynamic environment such as disaster recovery, it is necessary to quickly complete the task of adapting to the scene and learning to perform its duties. In this paper, the search and rescue robot first collects environmental information according to the camera sensor installed by itself, and then constructs the intelligent vehicle behavior decision model from the vehicle driving efficiency and optimal path. Secondly, the search and rescue robot estimates through the improved DQN network structure value function. And update the network parameters to get the corresponding Q value through the training network. Finally, the experimental results show that the algorithm can quickly generate a safe and smooth path that satisfies the kinematic constraints of the vehicle.
VPS

Polymorphism and inheritance make C++ suitable for writing complex software, but significantly increase the attack surface because the implementation relies on virtual function tables (vtables). These vtables contain function pointers that attackers can potentially hijack and in practice, vtable hijacking is one of the most important attack vector for C++ binaries.
Investigating Non-classical Correlations Between Decision Fused Multi-modal Documents

Correlation has been widely used to facilitate various information retrieval methods such as query expansion, relevance feedback, document clustering, and multi-modal fusion. Especially, correlation and independence are important issues when fusing different modalities that influence a multi-modal information retrieval process. The basic idea of correlation is that an observable can help predict or enhance another observable. In quantum mechanics, quantum correlation, called entanglement, is a sort of correlation between the observables measured in atomic-size particles when these particles are not necessarily collected in ensembles. In this paper, we examine a multimodal fusion scenario that might be similar to that encountered in physics by firstly measuring two observables (i.e., text-based relevance and image-based relevance) of a multi-modal document without counting on an ensemble of multi-modal documents already labeled in terms of these two variables. Then, we investigate the existence of non-classical correlations between pairs of multi-modal documents. Despite there are some basic differences between entanglement and classical correlation encountered in the macroscopic world, we investigate the existence of this kind of non-classical correlation through the Bell inequality violation. Here, we experimentally test several novel association methods in a small-scale experiment. However, in the current experiment we did not find any violation of the Bell inequality. Finally, we present a series of interesting discussions, which may provide theoretical and empirical insights and inspirations for future development of this direction.