Reviewerly/exHarmony huggingface dataset:
Network Security Concepts, Dangers, and Defense Best Practical

In today's highly interconnected world, network security has become a critical aspect of protecting organizations from cyber-attacks.The increasing sophistication of attackers and their ability to exploit software and firmware vulnerabilities pose significant dangers to the security of networks.However, many organizations often neglect the essential steps required to secure their networks, leading to an increased risk of security breaches.In this research article, we aim to address this issue by investigating network security concepts, potential dangers, and practical defense strategies.We begin by exploring the different types of cyber-attacks and their sources, highlighting the various ways attackers exploit network vulnerabilities.We also examine the reasons why organizations often overlook network security and the consequences of not prioritizing it.To better understand the complexity of network security, we categorize the different security concerns using the CIA (confidentiality, integrity, and availability) triangle.This approach allows us to identify the various areas of vulnerability and their potential impact on network security.Next, we focus on the most crucial basic concepts and steps involved in various network security operations.We outline the best practices and practical approaches organizations can take to improve their network security, including implementing security policies and procedures, using encryption and authentication methods, and conducting regular security assessments.By highlighting the importance of network security and providing practical guidance on how organizations can defend against cyber-attacks, we hope to raise awareness and help prevent security breaches.
Performance Evaluation of Classification Algorithm for Movie Review Sentiment Analysis

The majority of the current research on sentiment analysis, which covers topics like political reviews, movie reviews, and product reviews, has developed quickly. The classification and clustering stage of sentiment analysis research involves a number of subjects. Some of them cover text classification comparison research and algorithm performance optimization. An intricate issue in sentiment analysis research is dealing with unstructured or semi-structured data. The sentiment analysis procedure and improving the efficacy of the classifier’s algorithm are both hampered by unstructured data. In order to manage unstructured data successfully and provide accurate and relevant information, unique strategies are required. The proposed classification model performance evaluation using Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Decision Tree is specifically covered in this paper. According to the study’s findings, SVM has an accuracy rate of 96% and Naive Bayes is 86%. While the decision tree’s gain accuracy is 78 percent and the kNN classification model’s gain accuracy is 78 percent, respectively. The test results demonstrate that SVM is superior to other classification models in terms of accuracy performance.
Diagnosis of Mammographic Images for Breast Cancer Detection using FF-CSO Algorithm

Many diseases in the human being are can be detected through different imaging technology. The mammography is one of the imaging technique through which breast cancer cells can be identified. The mammographic breast image is preprocessed for eradicating the pectoral muscle in the breast cancer identification that contains a mammogram for encircling the process of detection. The cancer tissues having higher pixel intensities can be detected easier than the remaining breast region. It is difficult to classify the breast tumor tissues into malignant and benign ones. The mammographic breast image is generally pre-processed to eliminate pectoral muscle for the optimal diagnosis. Further, the active contour based segmentation is done to separate out the cancer region from entire mammographic image acquired by special technique. Segmenting accurate region of cancer will help in classification so segmentation stage has its own importance. Two different optimization algorithms are merged to improve optimization accuracy. The algorithms considered are Firefly and chicken swarm optimization (FF-CSO) is used optimize feature set and optimize error function of DBN. Once segmentation is performed on the image, the next process is to extract features from that segmented region to collect useful information for distinguishing the cases. In this we extracted LBP features from the segmented images. The deep learning architectures is for classification.
Accurate Shot-Noise-Limited Calibration of a Time-Domain Balanced Homodyne Detector for Continuous-Variable Quantum Key Distribution

The time-domain balanced homodyne detector (TBHD) is critical component in continuous-variable quantum key distribution (CVQKD). The shot-noise-limited attribute of a TBHD depends on the whole frequency spectrum of the detector, in contrast to a frequency-domain balanced homodyne detector that focuses only on part of the frequency spectrum. We propose a method for accurate calibration of the shot-noise-limited attribute of the TBHD by overlapping the gain lines of the TBHD and an integral photodetector which is realized by blocking one photodiode of the TBHD. The overlapping of two lines indicates that the detector is indeed shot noise limited, which is crucial to the security of CVQKD. Experimentally, we use this method to obtain a calibration error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm 1$</tex-math></inline-formula> ‰. Theoretical analysis showed that the CVQKD security is very sensitive to calibration errors of shot noise, and proper countermeasure was proposed to eliminate this issue. The presented methods are expected to contribute to CVQKD security and TBHD standardization.
Price Prediction for Bitcoin: Does Periodicity Matter?

Purpose: A major challenge traders, speculators and investors are grappling with is how to accurately forecast Bitcoin price in the cryptocurrency market. This study is aimed to uncover the best model for the forecasts of Bitcoin price as well as to verify the price series that offers the best predictions performance under different periodicity of datasets. Design/methodology/approach: The study adopts three different data periods to verify whether frequency matters in forecasting Bitcoin price. The Bitcoin price, from 01/01/15 to 11/01/2021, is trained and validated on selected forecast models, including the Naïve, Linear, Exponential Smoothing Model, ARIMA, Neural Network, STL and Holt-Winters filters. Five forecast accuracy measures (RSME, MAE, MPE, MAPE and MASE) are applied to confirm the best performing model. The Diebold‐Mariano test is used to compare the forecasts based on the daily price with those based on the weekly and monthly. Findings: Based on the accuracy measures, the results indicate that the Naïve model provides more accurate performance for the daily series, while the linear model outperforms others for the weekly and monthly series. Using the Diebold‐Mariano statistics, there is evidence that forecasting Bitcoin price is not sensitive to the data periodicity. Research limitations/implications: The study has a major limitation, which is the shared sentiment to apply actual Bitcoin price series, and not the returns or log transformation for the forecast models. Notably, actual data may sometimes be loud, hence increasing the possibility of over predictions. Originality/value: In forecasting, different approaches have been used, this paper compares outputs of both statistical and machine learning methods in order to arrive at the best option for the Bitcoin price forecasts. Hence, we investigate whether the machine learning tools offer better forecasts in terms of lower error and higher model’s accuracy relative to the traditional models.
Business Operation Using Identification of Product in Context of Developing Countries Emphasizing Nepal

Purpose: With the emergence of Online Purchasing, Product Identification is an essential model that allows the seller to add a product to a decentralized platform such as Blockchain and allows buyers to purchase the product from the decentralized platform. Fraud products, counterfeiting, and duplication are the current marketplace's major problems. This aims to develop a system for verifying product identification with their information, ownership, and validity detail. Design/Methodology/Approach: The proposed system applies Extreme Programming (XP) to reduce the risk caused by the fixed-time project using new technology and thus the final project could be delivered in time. Solidity and metamask being new technologies were unstable and to adopt the changes, the agile development model was the best through ABI and the bytecode are deployed into the Ethereum Blockchain. Findings/Result: This system maintains the buyers, sellers, and product details in a decentralized blockchain platform. This research details the entire product development process from planning, analysis, design, implementation, and testing for systematic online purchasing. Verifying the product ownership and its information to get the original product is the major difficulty in this space, but this research systematically solves some of those problems. This signifies an improvement in the current centralized way of purchasing goods online, where the information remains as it is entered by the seller while listing the product in Nepal and developing countries context. Originality/Value: The study has produced a decentralized, reliable, secure, and third-party independent marketplace for buying and selling products for fraud free market. Paper Type: Research paper
Scouting Firefly Algorithm and its Performance on Global Optimization Problems

For effective optimization, metaheuristics should maintain the proper balance between exploration and exploitation.However, the standard firefly algorithm (FA) posted some limitations in its exploration process that can eventually lead to premature convergence, affecting its performance and adding uncertainty to the optimization results.To address these constraints, this study introduces an additional novel search mechanism for the standard FA inspired by the behavior of the scout bee in the artificial bee colony (ABC) algorithm, termed the "Scouting FA".Specifically, fireflies stuck in the local optima will take directed extra random walks to escape toward the region of the optimum solution, thus improving convergence accuracy.Empirical findings on the five standard benchmark functions have validated the effects of this modification and revealed that Scouting FA is superior to its original version.
Nullifying the Prevalent Threats in IoT Based Applications and Smart Cities Using Blockchain Technology

Smart cities provide the best available service by ensuring the optimal usage of available resources and deploying various infrastructures to fulfill such services. By deploying blockchain in smart cities and Internet of Things (IoT), the quality of life would further improve while ensuring security, privacy, and other aspects. The utilization of integrated technology brings a risk aspect to the smart city and IoT applications. If risks are not well known and recognized, they can lead to harm related to security and privacy. As smart cities proliferate, so do concerns about data security, authentication, device-level vulnerabilities, piracy, and sustainability. This paper discusses dimension identification, smart city evaluation techniques, and accessible technologies. Despite having multiple benefits, there are many failings in the deployment of blockchain technology. Further, it discussed the blockchain applications in different dimensions of smart cities. This article proposes a security framework that leverages blockchain technology in electronic devices to enable secure and reliable communications in smart cities. Furthermore, we discussed potential future research directions which can be used for incorporating information related to security into smart cities.
An ensemble method for estimating the number of clusters in a big data set using multiple random samples

Abstract Clustering a big dataset without knowing the number of clusters presents a big challenge to many existing clustering algorithms. In this paper, we propose a Random Sample Partition-based Centers Ensemble (RSPCE) algorithm to identify the number of clusters in a big dataset. In this algorithm, a set of disjoint random samples is selected from the big dataset, and the I-niceDP algorithm is used to identify the number of clusters and initial centers in each sample. Subsequently, a cluster ball model is proposed to merge two clusters in the random samples that are likely sampled from the same cluster in the big dataset. Finally, based on the ball model, the RSPCE ensemble method is used to ensemble the results of all samples into the final result as a set of initial cluster centers in the big dataset. Intensive experiments were conducted on both synthetic and real datasets to validate the feasibility and effectiveness of the proposed RSPCE algorithm. The experimental results show that the ensemble result from multiple random samples is a reliable approximation of the actual number of clusters, and the RSPCE algorithm is scalable to big data.
Biometric authentication sensor with an encryption module for prevention of h/w hacking in digital custody services

Abstract In this paper, a digital personal safe system was proposed with Fast ID Online (FIDO) transaction authentication and multiple decentralized ID (DID) personal authentications based on a home gateway. This individual custody model of personal multiple authentications will be linked to the transaction authentication of digital assets stored in the storage space of the home gateway by the DID of smart devices and the FIDO server for a public platform that excludes digital asset (cryptocurrency [e.g., Bitcoin] or NFT asset) management based on individual private keys and user interaction (user interface and user experience [UI/UX]) at home. To strengthen the security of the proposed digital custody model, it was proposed to apply a local device to which a biometric sensor equipped with an encryption module that can fundamentally block the hacking of biometric information is applied. It is implemented with Verilog for an embedded fingerprint sensor block and decryption program implemented with C.
Lightweight Detection System with Global Attention Network (GloAN) for Rice Lodging

Rice lodging seriously affects rice quality and production. Traditional manual methods of detecting rice lodging are labour-intensive and can result in delayed action, leading to production loss. With the development of the Internet of Things (IoT), unmanned aerial vehicles (UAVs) provide imminent assistance for crop stress monitoring. In this paper, we proposed a novel lightweight detection system with UAVs for rice lodging. We leverage UAVs to acquire the distribution of rice growth, and then our proposed global attention network (GloAN) utilizes the acquisition to detect the lodging areas efficiently and accurately. Our methods aim to accelerate the processing of diagnosis and reduce production loss caused by lodging. The experimental results show that our GloAN can lead to a significant increase in accuracy with negligible computational costs. We further tested the generalization ability of our GloAN and the results show that the GloAN generalizes well in peers’ models (Xception, VGG, ResNet, and MobileNetV2) with knowledge distillation and obtains the optimal mean intersection over union (mIoU) of 92.85%. The experimental results show the flexibility of GloAN in rice lodging detection.
GMT: A deep learning approach to generalized multivariate translation for scientific data analysis and visualization

In scientific visualization, despite the significant advances of deep learning for data generation, researchers have not thoroughly investigated the issue of data translation. We present a new deep learning approach called generalized multivariate translation (GMT) for multivariate time-varying data analysis and visualization. Like V2V, GMT assumes a preprocessing step that selects suitable variables for translation. However, unlike V2V, which only handles one-to-one variable translation during training and inference, GMT enables one-to-many and many-to-many variable translation in the same framework. We leverage the recent StarGAN design from multi-domain image-to-image translation to achieve this generalization capability. We experiment with different loss functions and injection strategies to explore the best choices and leverage pre-training for performance improvement. We compare GMT with other state-of-the-art methods (i.e., Pix2Pix, V2V, StarGAN). The results demonstrate the overall advantage of GMT in translation quality and generalization ability.
A Hybrid Framework Based on Classifier Calibration for Imbalanced Aerial Scene Recognition

Aerial scene images are often imbalanced, where the most common classes as majorities and a few significant classes as minorities. We observe that the majority classes not only dominate the classification optimization but also generate deviations that affect the classifier weight matrices. In this work, we propose a hybrid framework based on classifier calibration, which mitigate the effect of the class imbalance problem in aerial scene recognition. In particular, the framework progressively incorporates feature representation and classifier learning branches, while building a memory bank of learned representations for approximating deviations derived from imbalanced data. We calibrate the classifier by excluding the deviations in the prediction of the testing stage. Extensive experiments are evaluated on class imbalanced aerial scene image datasets, which show the advantages of the proposed hybrid framework with classifier calibration outperforming state-of-the-art aerial scene recognition methods.
CrossNet: Cross-scene Background Subtraction Network via 3D Optical Flow

This paper investigates an intriguing yet unsolved problem of cross-scene background subtraction for training only one deep model to process large-scale video streaming. We propose an end-to-end cross-scene background subtraction network via 3D optical flow, dubbed CrossNet. First, we design a new motion descriptor, hierarchical 3D optical flows (3D-HOP), to observe fine-grained motion. Then, we build a cross-modal dynamic feature filter (CmDFF) to enable the motion and appearance feature interaction. CrossNet exhibits better generalization since the proposed modules are encouraged to learn more discriminative semantic information between the foreground and the background. Furthermore, we design a loss function to balance the size diversity of foreground instances since small objects are usually missed due to training bias. Our whole background subtraction model is called Hierarchical Optical Flow Attention Model (HOFAM). Unlike most of the existing stochastic-process-based and CNN-based background subtraction models, HOFAM will avoid inaccurate online model updating, not heavily rely on scene-specific information, and well represent ambient motion in the open world. Experimental results on several well-known benchmarks demonstrate that it outperforms state-of-the-art by a large margin. The proposed framework can be flexibly integrated into arbitrary streaming media systems in a plug-and-play form. Codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/dongzhang89/HOFAM</uri> .
Pd,Hg-Rich Gold and Compounds of the Au-Pd-Hg System at the Itchayvayam Mafic-Ultramafic Complex (Kamchatka, Russia) and Other Localities

The unique minerals of the Au-Pd-Hg system in gold grains from heavy concentrates of the Itchayvayam placers and watercourses draining and ore samples of the Barany outcrop at the Itchayvayam mafic–ultramafic complex (Kamchatka, Russia) were investigated. Gold grains from watercourses draining and heavy concentrates of the Itchayvayam placers contain substitution structures formed by Pd,Hg-rich low-fineness gold (Au0.59–0.52Pd0.24–0.25Hg0.17–0.23, 580‰–660‰) and Pd,Hg-poor high-fineness gold (Au0.94–0.90Pd0.02–0.04Hg0.03, 910‰–960‰). Potarite (PdHg) without and with impurities (Au &lt; 7.9, Cu &lt; 3.5, Ag &lt; 1.2 wt.%), Ag-poor high-fineness gold (Au0.91Ag0.09, 950‰), Ag,Pd,Hg-bearing middle-fineness gold (Au0.75Ag0.08Pd0.09Hg0.08—Au0.88Ag0.09Pd0.02Hg0.01, 820‰–930‰), and Pd,Hg-rich low-fineness gold with minor contents Ag and Cd (Au0.51–0.55Pd0.25–0.22Hg0.21–0.16Ag0.03–0.06Cd0.01, fineness 580‰–630‰) were observed as individual microinclusions in the ore samples of the Barany outcrop. XRD and EBSD study results show that the Pd,Hg-rich low-fineness gold is isotypic to gold and has the same structure type, but different cell dimensions. According to data obtained for the Itchayvayam and some deposits and ore occurrences with Pd,Hg-bearing gold, the stable ternary phases and solid solutions of the following compositions in the Au-Pd-Hg system have been identified: Pd,Hg-poor gold (Au0.94–0.90Pd0.02–0.04Hg0.03), Pd,Hg-rich gold (Au0.59–0.52Pd0.24–0.25Hg0.17–0.23), Au-potarite (PdHg0.62Au0.38—Pd1.04Hg0.96—Au0.80Pd0.68Hg0.52), and Au,Hg-bearing palladium (Pd0.7Au0.3Hg0.1). The genesis of Pd,Hg-rich gold is insufficiently studied. We supposed that the meteoric waters or low-temperature hydrotherms rich in Pd and Hg could lead to the replacement Pd,Hg-poor gold by Pd,Hg-rich gold. High concentrations of Pd in Pd,Hg-bearing gold indicate a genetic relationship with mafic–ultramafic rocks.
Multimodal Learning of Audio-Visual Speech Recognition with Liquid State Machine

Audio-visual speech recognition is to solve the multimodal lip-reading task using audio and visual information, which is an important way to improve the performance of speech recognition in noisy conditions. Deep learning methods have achieved promising results in this regard. However, these methods have complex network architecture and are computationally intensive. Recently, Spiking Neural Networks (SNNs) have attracted attention due to their being event-driven and can enable low-power computing. SNNs can capture richer motion information and have been successful in work such as gesture recognition. But it has not been widely used in lipreading tasks. Liquid State Machines (LSMs) have been recognized in SNNs due to their low training costs and are well suited for spatiotemporal sequence problems of event streams. Multimodal lipreading based on Dynamic Vision Sensors (DVS) is also such a problem. Hence, we propose a soft fusion framework with LSM. The framework fuses visual and audio information to achieve the effect of higher reliability lip recognition. On the well-known public LRW dataset, our fusion network achieves a recognition accuracy of 86.8%. Compared with single modality recognition, the accuracy of the fusion method is improved by 5% to 6%. In addition, we add extra noise to the raw data, and the experimental results show that the fusion model outperforms the audio-only model significantly, proving the robustness of our model.