Reviewerly/exHarmony huggingface dataset:
Transferable Adversarial Attacks against Automatic Modulation Classifier in Wireless Communications

Deep neural network-based automatic modulation recognition (AMR) technology has become an increasingly important area due to the advantages of self-extraction of features and high identification accuracy. Based on the view of security threats to machine learning classifiers, we investigate the influence of adversarial samples on the AMR model in this paper. The traditional method is based on label gradient attack without taking advantage of the feature-level transferability, resulting in the attack effect that is not perfect. So, we exploit the feature-level transferability property that could be met to fulfill realistic imperceptibility and transfer needs. In this paper, firstly, we proposed an AMR scheme with high recognition accuracy as our attack model. Secondly, we proposed a transferable attack method based on a feature gradient-based, which increases perturbation to clean signal based on features space. Finally, we introduce a new attack strategy, in which we select two original and one adversarial target signal sample as the input of triplet loss to achieve higher attack strength and high transferability. Meanwhile, this paper proposes indicators of signal characteristics to test the effectiveness of our proposed attack method. Based on experimental results, our proposed feature gradient-based adversarial attack method outperforms the currently labeled gradient attack methods regarding attack effectiveness and transferability.
Analysis of Ancient Literary Works Based on Intelligent Image Text Recognition

The analysis of ancient literary works in the era of digital intelligence needs to keep pace with the times. In order to improve the analysis effect of ancient literary works, this paper combines the intelligent image text recognition algorithm to extract the features of ancient literary works and proposes an intelligent algorithm that can be used for the analysis of ancient literary works. Moreover, this paper combines the analysis needs of literary works to improve the algorithm. In order to verify the role of the intelligent image text recognition algorithm proposed in this paper in the analysis of ancient literary works, this paper scans a large number of pictures of ancient literary works in the library by scanning to construct the experimental database of this paper. Finally, this paper combines experimental research to verify the algorithm proposed in this paper. From the experimental results, it can be seen that the method proposed in this paper has a certain effect, and it can be used as a reference for the digital processing and digital preservation of subsequent literary works, and it can also be used as a reference for the management of digital libraries.
Evaluation of the Students’ Learning Status in the Foreign Language Classroom Based on Machine Vision

In order to improve the effectiveness of the evaluation of student’s learning status in foreign language classrooms, this paper applies machine vision to classroom teaching. Through an in-depth analysis of the relative motion relationship between the end marker points of classroom feature recognition and the center point of the machine vision system window, this paper first proposes an autonomous tracking motion algorithm of the machine vision system window based on the preset field of view parameters. Moreover, this paper realizes the motion function of the window to track the marker points autonomously, completes the simulation analysis through two sets of planned trajectories and two sets of master hands to collect the actual trajectories, and verifies the correctness and feasibility of the algorithm. The research study shows that the algorithm based on machine vision proposed in this paper can effectively judge the real-time state of students in the foreign language classroom.
An Application of Inverse Reinforcement Learning to Estimate Interference in Drone Swarms

Despite the increasing applications, demands, and capabilities of drones, in practice they have only limited autonomy for accomplishing complex missions, resulting in slow and vulnerable operations and difficulty adapting to dynamic environments. To lessen these weaknesses, we present a computational framework for deducing the original intent of drone swarms by monitoring their movements. We focus on interference, a phenomenon that is not initially anticipated by drones but results in complicated operations due to its significant impact on performance and its challenging nature. We infer interference from predictability by first applying various machine learning methods, including deep learning, and then computing entropy to compare against interference. Our computational framework begins by building a set of computational models called double transition models from the drone movements and revealing reward distributions using inverse reinforcement learning. These reward distributions are then used to compute the entropy and interference across a variety of drone scenarios specified by combining multiple combat strategies and command styles. Our analysis confirmed that drone scenarios experienced more interference, higher performance, and higher entropy as they became more heterogeneous. However, the direction of interference (positive vs. negative) was more dependent on combinations of combat strategies and command styles than homogeneity.
Reconfiguring the Smartphone to Support Intentional Use

This paper explores how the smartphone's form could be designed to help users establish more intentional usage patterns. A research through design approach that was based in design fiction principles resulted in three design proposals and scenarios, the Planning Phone, the Parasite Phone, and the Placeful Phone. These were evaluated in a focus group and analysed to develop a deeper understanding of how design researchers and developers can use ideas of authorship, barriers and norms to support intentional smartphone use.
A new version of the Langelier-Ludwig square diagram under a compositional perspective

The Langelier-Ludwig square diagram is a commonly used diagnostic tool in groundwater chemistry. Suitable groupings of cations and anions are selected and plotted as percentages of milliequivalents with the sums of the selected cations and anions plotted on the y- and x-axes, respectively. It displays relative ratios rather than absolute concentration whereby each axis ranges from 0 to 50 meq%. However, the sample space in which data are represented in a Langelier-Ludwig square diagram is indeed given by the simplex. Incorrect conclusions may be drawn when the compositional nature of compositional data is not taken into account, i.e., a change in one value in one component changes all other values due to due to the constant sum constraint of the measured chemical elements. Correlations are thus influenced by the presence of negative bias in the covariance structure and linear or nonlinear patterns on the square diagram can be misinterpreted. A new version of the Langelier-Ludwig square diagram based on a well-chosen coordinate representation of cations and anions is proposed. The advantage of the revised diagram is that all the information is contained in the log-ratios describing the intricate relationship between chemical species in aqueous solutions. It is shown that the geochemical interpretation of this new diagram – based on the relative dominance of major ions and distance from the (robust) barycenter of the data – provides a better and unbiased understanding of water-environment interactions. To further aid interpretation, (robust) tolerance ellipses show the correlation structure in the new version of the Langelier-Ludwig square diagram, and clustering algorithms can be applied to divide the data into groups beforehand. A bunch of different plotting options and interactive representations complete the implementation in free open-source software. It is recommended to replace the classic Langelier-Ludwig diagram with the new version.
Analysis of Student Satisfaction on Virtual Learning Platforms During COVID-19

As a result of the global spread of COVID-19, e-Learning has recently experienced extraordinary growth. Many educational sectors have made the transition from traditional classroom learning to virtual learning via various online platforms. In this epidemic, virtual learning has enabled all schools and universities to continue to provide education. This rapidly growing alternative modality necessitates the provision of robust and high-quality education. It is also important to figure out whether online learning satisfies the needs of pupils. Even if learning has become easier, many people still confront difficulties, poor connectivity and e-platform. This study aims to identify the students' satisfaction by conducting a survey and analyzing it by means of data analysis and data visualization.
Classification of Sentiment Reviews for Indian Railways Using Machine Learning Methods

AI provides the concept of machine learning that helps to automate the decision-making process by analyzing data inputs. It trains machines by providing it sample data and thus makes the system intelligent that is helpful for real-world AI applications. Machine learning algorithms are applied to such social feedback data to excerpt useful information that confers a competitive edge to several enterprises. There are enough machine learning technologies in the existing literature on sentiment analysis. However, it still needs optimizations for a better decision making process for several enterprises. In this paper, we proposed a scheme for Indian Railways for determining sentiments from Facebook. This is a more specific scheme that clouts business intelligence over different classifiers, viz. SVM, NB, RF, and decision tree, K-NN. The proposed scheme is provided with various parameters like F-measure, recall, precision, logarithmic loss, and accuracy. The first section of this paper provides the preface of sentiment analysis, and the next section presents the related work and motivation for sentiment analysis then methodology adopted for better decision making through machine learning to bring out in depth knowledge for future marketing game plans; it then discussed the experimental results, and finally, the paper encapsulates the conclusion and future scope in the area of sentiment analysis.
Pattern mining algorithms for data streams using itemset

Knowledge discovery and data mining are fast growing fields of study that span a variety of disciplines, including distributed systems, databases, artificial intelligence, visualization, statistics, high-performance computing, and parallel computing. Raw data is collected by people in business, science, medicine, academia, and government, and there are various commercial programmes that process the data to provide general and specific purpose knowledge discovery. “Turn Data into Knowledge” is an important goal in knowledge discovery and data mining. Data mining is the process of examining data previously stored in databases in order to solve problems. The technique of detecting patterns in vast data repositories is known as data mining. In order to complete a data mining task, effective exploratory strategies are always required. Association rules, correlations, sequential patterns, classification, clustering, and other data mining techniques are only a few examples. Every technique has its own value, which is determined by the application area and challenges for which it is used. The objectives of this study, as stated in the synopsis, are met using association rule mining methodology. The original motivation for association rules mining was the problem of supermarket transaction data. The problem with supermarket transaction data is that it is used to investigate client purchasing habits. The frequency with which things have been purchased together is described by association rules. For instance, the association rule “cool drink=> chips (80%)" implies that 80% of customers who purchase cool drink also purchase chips. Such criteria can be beneficial in making judgments about store layout, product pricing, and marketing, among other things.
English learner corpora and research in Korea

The interest in the exploitation of corpora in the study of Korean L2 learners’ use of English has risen dramatically over the past two decades, leading to the compilation of learner corpora and to numerous empirical investigations into Korean learners’ use of English. This paper will give an overview of the compilation and characteristics of English learner corpora in Korea and will also provide an analysis of the recent trends in learner corpus research. It was not until the mid-2000s that Korean academics started to compile English learner corpora, such as the snu Korean-speaking English Learner Corpus (skelc), the Yonsei English Learner Corpus (yelc), the Gachon Learner Corpus (glc), the Neungyule Interlanguage Corpus of Korean Learners of English (nickle), the efl Teacher Corpus (etc), the Korean English Learners’ Spoken Corpus (kelsc) and the ets Corpus of Non-native Written English (TOEFL11). There have also been a growing number of learner corpus-based studies that used the existing learner corpora as well as self-compiled corpus data. All the learner corpus-based research articles published in two Korean academic journals ( English Teaching and Korean Journal of Applied Linguistics) will be reviewed and analysed in terms of research topics and areas, data types, analysis methods and corpus compilation practices. Finally, this paper will suggest some future directions for learner corpus compilation and research in Korea.
DiagAnalyzer: User behavior analysis and visualization using Windows Diagnostics logs

Windows Diagnostics, which is used by default in Windows 10 and Windows 11, records basic device information as well as various detailed user activities of those who use Windows. Previously, there have been several preceding studies that attempted to apply diagnostics information to digital forensics analysis, but there have been no practical methods or publicly available tools to analyze data in relation to user behavior. Therefore, this paper analyzed how three representative activities (attaching and detaching USB storage devices, web browser activities, and wireless network activities) are recorded in Window Diagnostics. Furthermore, based on the analysis results, we developed DiagAnalyzer, which automatically analyzes the diagnostics event log and visualizes the user's behavior. Through the methodology and tool of this paper, the application of Windows Diagnostics deserves further attention as an important artifact in digital forensics investigation for Windows in the future.
Forensic investigation of Google Meet for memory and browser artifacts

Web applications have experienced a widespread adaptation owing to the agile Service Oriented Architecture (SOA) reflecting the ever-changing software needs of users. Google Meet is one of the top video conferencing applications, especially in the post-COVID19 era. Security and privacy concerns are therefore critical. This paper presents an extensive digital forensic analysis of Google Meet running on multiple browsers and software platforms including Google Chrome, Mozilla Firefox, and Microsoft Edge browsers in Windows 10 and Linux. Artifacts, traces of potential evidence, are extracted from different locations on a client's desktop, including the memory and browser. These include meeting records, communication records, email addresses, profile pictures, history, downloads, bookmarks, cache, cookies, etc. We explore how different Random Access Memory (RAM) sizes of client devices impact the persistence and format of extracted memory artifacts. A memory artifact extraction tool is developed to automate the extraction of artifacts identified via unstructured string analysis. Google Meet forensic artifacts are critical in that they are potential digital evidence in relevant criminal investigations. Additionally, they highlight that user data can be extracted despite implementing multiple privacy and security mechanisms.
Retrieving deleted records from Telegram

One of the most popular instant messaging applications and platforms is Telegram. In this paper, an investigation into digital forensics on Telegram is provided, deployed on contemporary android mobile phones. Due to its structure complexity, it is very challenging for forensic tools and software to properly decode its data during a forensic examination, and, especially, when it comes to deleted records retrieval. For the analysis, a realistic scenario was implemented by exchanging thousands of messages and media files among four active Telegram users, while trying to recover content from deleted text messages and exchanged picture files. Abundance of modern and up to date forensic software and tools were utilized to verify and crosscheck the results.
Clause Level Attention Network for Emotion Cause Pair Extraction

Abstract The Emotion-Cause Pair Extraction task aims to identify one or more pairs of emotion-causes in a document, and earlier work has focused on the implicit interaction between ECPE and two auxiliary tasks Emotion Extraction and Cause Extraction. However, existing models cannot capture the complex connection between emotion and cause. To address this problem, we propose an end-to-end model. The model utilizes properties of graph attention networks (GATs) to model the bidirectional dependencies between emotion and cause clauses, and experimental results on a benchmark emotion cause corpus verify the feasibility of our approach.
A SECI Method Based on Improved YOLOv4 for Traffic Sign Detection and Recognition

Abstract Traffic sign detection and recognition (TSDR) has become extraordinarily significant in autonomous driving. To trade-off between high accuracy and fast detection speed, a four-stage method, Selecting-Extracting-Cleaning and Identifying (SECI) based on improved algorithm of You Only Look Once version 4 (YOLOv4) is proposed, including selecting the anchor boxes of traffic signs by using k-means++ clustering algorithm, extracting traffic sign features by using subsampled layer, cleaning the redundant information by amending structure of the Spatial Pyramid Pooling (SPP) and identifying traffic signs by adding auxiliary branches and multi-scale feature fusion. Compared to the origin YOLOv4, the results show that, the SECI method improves the mean average precision (mAP) by 6.71%, the detection speed is improved by 37%, and the F1 score is improved by 13%.
Development and Optimization of Network Music Course Resources Based on Data Mining Technology under the Personalized Online Education Environment

The ever-evolving Internet technology has brought more information resources for music teaching than ever before. For music educators, figuring out how to create and use these tools more effectively has become a pressing concern. China will usher in a new age of innovation and integration between the Internet and different traditional businesses with the idea of "Internet Plus." MOOCs present significant challenges to traditional teachers and teaching methods as a typical practice of the transformation of education informatization to innovation, and they also drive global education toward a new process of informatization development. This paper adopts Internet thinking as the guiding ideology for the development of future music education concepts and, concurrently, conducts specific transformation research based on Internet technology from four aspects: music teaching content, music teaching methods, music education concepts, and the passing down of traditional music culture. The improved association rule algorithm is used to organise the students' access data, decide which high-frequency websites are accessible to the students, and realize the intuitive positioning of high-frequency access and learning issues. Assist the school's decision-making and educational levels in identifying the issues that students have with their learning, and then, improve the network teaching platform's material to complete the task of personalized network education.
Construction of College Chinese Blended Teaching Mode Based on Decision Tree Classification Model in New Media Context

Based on the context of new media and big data, this article uses the decision tree classification model to construct the college Chinese hybrid teaching mode. In order to verify the accuracy of ID3 algorithm prediction, the comparison of the ID3 algorithm, K-means algorithm, and support vector machine classification algorithm was made, and the experimental results show that the ID3 decision tree classification algorithm has better prediction and classification ability, for the construction of college Chinese hybrid teaching mode provides certain practical value and reference basis.
Industrial IoT Technologies and Protocols

With the advent of technology, the development of IoT system has increased over the years, especially in IIoT. This chapter focuses on the use of an IoT framework and its basic components for various purposes. This chapter describes the IoT protocols, the IoT architecture, its elements, and the different models to address industrial issues when developing Internet of Things (IoT) systems. The standard protocols and their component designs have been standardized to enable an IoT system, including various parameters such as frequency range, bandwidth, power, data transmission rate, and maximum radiated power are examined here. With the implemented designs, it becomes simple to understand and create a framework or optimize parameters with a proprietary one that enables the experimental problems to be reliable and scalable.
Energy-Efficient Resource Management of Virtual Machine in Cloud Infrastructure

Large organizations and business centers use cloud services as a computing technology for their business purposes. Nevertheless, the use of cloud computing has resulted in creation of huge data centers. The major issues that occur in data centers are managing the infrastructural resources, maintaining the cost of applications (tasks), security, and high usage of energy. It represents cloud computing provides resources based on the principle of virtualization and pay-as-you-go model. The resources such as storage, CPU, network, and memory that are available in virtual machine need to be monitored frequently. This resources management has become a wide area of research. The optimization algorithm called Genetically Enhanced Shuffling Frog Leaping Algorithm (GESFLA) is implemented for the VM allocation and execution of tasks. The idea behind the proposed work is to address some of the issues such as minimizing the power consumption, costs of the running application, and to optimize the resource usage. Cloudsim toolkit is used to find the efficiency of this proposed algorithm with a Genetic Algorithm and Particle Swarm Optimization (GAPSO). Experiments are conducted using PlanetLab workload and Google Cluster Datasets which is very huge data. The experimental results indicate GESFLA’s superiority over GAPSO in terms of resource usage ratio, time to migrate the VMs, and total energy consumption.The proposed algorithm increases the performance of data center by maximizing resource utilization by 16% and migration time by 17%. Also, energy consumption is reduced in comparison with the existing algorithm GAPSO by 6%.