Reviewerly/exHarmony huggingface dataset:
A Generative Adversarial Network-based Attack for Audio-based Condition Monitoring Systems

Over the last years, several machine learning techniques have been proposed for the condition monitoring of physical assets based on audio. As a result, adversaries have been trying to circumvent the reliability of deployed systems, typically through the generation of maliciously altered audio samples that are subsequently introduced as input by the model. However, altering the input in production settings is not always feasible, on the contrary, samples are often collected through a microphone, significantly increasing the attack execution effort. In this paper, we propose a realistic generative adversarial network attack for an audio-based condition monitoring system. We first train a generator and a discriminator with a joint objective of generating audio samples corresponding to the difference between the two classes, e.g., normal and faulty. Additionally, we test our approach by overlapping our generated audio on the samples collected by the microphone. Our main goal is the proposal of a GAN-based attack capable of generating audio samples that when overlaid with the original microphone-captured audio may induce misclassification given a target class. Experiments performed through our captured audio dataset from normal and broken unmanned aerial vehicle propellers show that the proposed attack achieved a mean success rate of 40%, decreasing the F-measure concerning random noise by 13.3%, 20%, and 37.8% for ResNet-18, AlexNet, and DenseNet-169 models, respectively.
Two-Tier Anomaly Detection for an Internet of Things Network

Machine Learning (ML) has proven itself to be one of the most powerful tools to detect and prevent cyber-attacks. In this study, ML is used for anomaly detection in an Internet of Things (IoT) network. This paper proposes a Two-Tier Hybrid Learning Model for Clustering and Classification (TT-HLMCC) of unlabeled loT data. The proposed method uses two algorithms for clustering: K-means and DBSCAN (Density Based Spatial Clustering for Applications with Noise). Random Forest is used for the final classification stage on the newly labeled data. The TT-HLMCC model (Proposed Model) is compared to the performance metrics of three other algorithms. While the TT-HLMCC model matches and slightly beats the performance of [3], our computational time is significantly lower.
Towards Incorporating a Possibility of Zero-day Attacks Into Security Risk Metrics: Work in Progress

This paper reports on work in progress on incorporating a possibility of zero-day attacks into security risk metrics. System security is modelled by Attack Graph (AG), where attack paths may include a combination of known and zero-day exploits. While set of feasible zero-day exploits and composition of each attack path are known, only estimates of likelihoods of known exploits are available. We propose addressing uncertain likelihoods of zero-day exploits within framework of robust risk metrics. Assuming some base likelihoods of zero-day exploits, robust risk metrics assume worst-case Probabilistic or Bayesian AG scenario allowing for a controlled deviation of actual likelihoods of zero-day exploits from their base values. The corresponding worst-case scenario is defined with respect to the system losses due to a zero-day attack. These robust risk metrics interpolate between the corresponding probabilistic or Bayesian AG model on the one hand and purely antagonistic game-theoretic model on the other hand. Popular k-zero day security metric is a particular case of the proposed metric.
User Authentication by Fusion of Mouse Dynamics and Widget Interactions: Two Experiments with PayPal and Facebook

Utilization of Internet in everyday life has made us vulnerable in terms of security and privacy of our data and systems. For example, large-scale data breaches have occurred at Yahoo and Equifax because of lacking of robust and secure data protection within systems. Therefore, it is imperative to find solutions to further boost data security and protect privacy of our systems. To this end, we propose to authenticate users by utilizing score-level fusions based on mouse dynamics (e.g., mouse movement on a screen) and widget interactions (e.g., when clicking or hovering over different icons on a screen) on two novel datasets. In this study, we focus on two common applications, PayPal (a money transaction website) and Facebook (a social media platform). Though we fuse the same modalities for both applications, the purpose of investigating PayPal is to demonstrate how we can authenticate users when the users interact with the app for only a short period of time, while the purpose of investigating Facebook is to authenticate users based on social media browsing activities. We have a total of 10 users for PayPal with an average of 12 minutes of data per user and a total of 15 users for Facebook with an average of 2 hours of data per user. By fusing a single mouse trajectory with the associated widget interactions that occur during the trajectory, our mean EERs (Equal Error Rates) with a score-level fusion of mouse dynamics and widget interactions are 7.64% (SVM-rbf) and 3.25% (GBM), for PayPal, and 5.49% (SVM-rbf) and 2.54% (GBM), for Facebook. To further improve the performance of our fusion, we combine decision scores from multiple consecutive trajectories, which yields a 0% mean EER after 11 decision scores across all the users for both PayPal and Facebook.
Level of Detail-based 3D Space Point Cloud Streaming and its Evaluation

Developing an efficient quality control method for streaming a 3D space expressed by point clouds is mandatory. Based on this motivation, we develop a Level of Detail (LOD)-based quality optimization method for 3D space point cloud streaming. The proposed method adopts a 3D tile-based method and considers a distance between a user's viewpoint and a 3D tile (i.e., LOD) to maximize the user's perceptual quality. In the experiment, we capture an actual 3D space of our laboratory room using a LiDAR camera and conduct performance evaluations on the Unity platform. The results conclude that the proposed method achieves the highest perceptual quality.
Estimation of physical activities of people in offices from time-series point-cloud data

This paper proposes an edge computing system that enables estimating physical activities of people in offices from time-series point-cloud data, obtained by using a light-detection-and-ranging (LIDAR) sensor network. The paper presents that the proposed system successfully constructs the model for estimating the number of typed characters from time-series point-cloud data, through an experiment using real LIDAR sensors.
Version++ Protocol Demonstration for Cryptocurrency Blockchain Handshaking with Software Assurance

Cryptocurrency software implements the cryptocurrency operations. We design a software assurance scheme for cryptocurrency and advance the cryptocurrency handshaking protocol. More specifically, we focus on Bitcoin for implementation and integration and advance its Version-message based hand-shaking and thus call our scheme Version++, The Version++ protocol provides software assurance, which is distinguishable from the previous research because it is permissionless, distributed, and lightweight to fit its cryptocurrency application. Utilizing Merkle Tree for the verification efficiency, we implement and test Version++ on Bitcoin software and conduct experiments in an active Bitcoin node prototype connected to the Bitcoin Mainnet. This paper for the conference demonstration supplements our technical paper at CCNC 2023 for synergy but highlights the prototyping and demonstration components of our research.
Optimized CNN Auto-Generator Using GA With Stopping Criterion: Design and a Use Case

The goal of this paper is to use the genetic algorithm with adapted stopping criterion to reduce the computational cost of optimizing hyperparameters and network architecture in convolutional neural networks (CNNs) and to apply it on baseband modulation classification. Hyperparameter optimization is the maximization of a neural network's performance using a set of input network parameters and network architecture. With much research going into neural networks (e.g., CNNs), most research uses manually chosen hyperparameters instead of choosing optimal hyperparameters. Much work has gone into optimizing hyperparameters as they greatly effect the accuracy of CNNs and enable automated machine learning. Choosing the optimal hyperparameters can be done by applying the genetic algorithm to search for them. Each search operation requires a full training using a set of hyperparameters which is computationally expensive. Research has shown advantages to applying a stopping criterion to improve the neural network accuracy while reducing the computational cost. A stopping criterion is a training constraint to stop learning after a certain goal is achieved. Using the genetic algorithm with adapted stopping criterion, a fast CNN auto-generator has been designed, which can simultaneously generate a network of layers and their parameters from a pool of many potential networks that might not have been considered. Such an optimized CNN auto-generator has been applied on automatic modulation classification (AMC) through exploring the dataset DeepSig RADIOML 2016.10A. Compared to the literature, our design can achieve greater accuracy and lower run time. It also provides human designed CNN levels of accuracy on the RADIOML dataset while starting from no network but generating an entirely custom network for the dataset using a small subset in the CNN generation process.
Encryption-based Security in Wearable Devices

Wearable devices have become common accessories used in tracking fitness data or augmenting daily smartphone usage. However, not all manufacturers of inexpensive wearable devices have done sufficient work to protect the privacy of their users. Some devices have shown vulnerabilities that allow attackers to obtain data stored on a locked device. In this paper, we examine using ASCON, a set of lightweight cryptographic algorithms, to enhance security of wearable devices by encrypting locally stored data.
On-the-Fly Edge Transcoding for Interactive VR

The recent rise of the Cloud Virtual Reality (VR) paradigm, in which VR frames are streamed from a remote server to the user's Head-Mounted Display (HMD), poses some interesting challenges from a networking perspective. VR flows are high-throughput and have strict latency requirements. In this paper, we analyze on-the-fly edge transcoding, which follows the opposite philosophy from the more common slicing approach: instead of adapting resource allocation to the content, we compress the frames to fit into the allocated bandwidth, guaranteeing limited latency. Our results show that this strategy is effective in maintaining low latency, but the picture quality is highly dependent on the computing power of the Base Station (BS).
RADTEC: Re-authentication of IoT Devices with Machine Learning

The use of Internet of Things (IoT) devices is higher than ever and is growing rapidly. Many IoT devices are manufactured by home appliance manufacturers where security and privacy is not the foremost concern. There does not exist a strict authentication method that verifies the identity of the device. This allows any rogue IoT device to authenticate and spoof various IoT device activities using compromised credentials. This paper addresses the issue by introducing a novel method for re- and continuous authentication utilizing a device-type classification as a new identity paradigm. We present RADTEC: a protocol for authenticating a device in a network by leveraging machine learning to classify the type of an IoT device attempting to connect to the network with an accuracy of over 95% in less than 0.65 milliseconds. We investigate multiple machine learning classifiers to infer the types of IoT devices and use them to develop a stricter and more efficient method for authentication.
Blockchain framework for managing machine-learning models for 3D object detection

Smart monitoring plays an important role in securing people's safety in smart cities. In smart monitoring, machine-learning (ML) models are used for detecting objects in the environment, such as pedestrians and vehicles. Such ML models need to be securely managed against malicious attacks, such as tampering. This paper proposes a blockchain framework for securely managing ML models. It demonstrates the performance of a prototype of the proposed framework.
Robust Method for Breast Cancer Classification Based on Feature Selection Using RGWO Algorithm

Breast cancer is a leading cause of mortality in women all over the world. According to the worldwide cancer statistics, early detection and treatment are keys components for improving the recovery rate of breast cancer and lowering the death rate. Machine learning solutions have been proved to be particularly very successful in exploring the origins of such severe diseases, which requires processing vast amounts of data. In the present study, robust grey wolf optimisation-Random Forest (RGWO-RF) approach was proposed. Our proposed approach based on two steps feature selection process and classification. Modified Grey Wolf Optimizer is used to locate and determine the most significant features. Then, utilizing the prior optimum selections of features, by using Random Forest (RF) classifier to classify breast cancer disease. The reason for using RF it's robustness and highest accuracy. We apply the proposed approach on Wisconsin Diagnostic Breast Cancer (WDBC) database. The experimental result improve that the hybridation between RGWO for feature selection and RF classifier increase the accuracy rate of classification and demonstrating it's robustness in identifying the breast cancer.
Multi-Objective Optimization Analysis of School Financial Security Management System Based on Genetic Algorithm

Colleges and universities are an indispensable part of talent training and construction. With the continuous development of information technology, there are many drawbacks based on traditional manual management. It is necessary to study and design the school financial security. In order to improve the financial security of the school, it is necessary to conduct in-depth research on its management system. Genetic algorithm(GA) can play a role of multi-objective optimization in the system. This paper is mainly based on GA, and based on the security of college financial information, combined with the actual situation of the school to analyze and summarize. The school financial security management system proposed in this paper is tested through systematic experiments. The experimental results show that the pressure test indicators of the system designed in this paper basically meet the requirements, the response time is lower than the expected time, and the response ability is excellent.
Automatic Identification Algorithm for Semantic Relations of Chinese Natural Language Entities

Semantic parsing, in a sense, is a formal language process that translates natural language into computer recognizable language. Therefore, semantic parsing can be treated as a special machine translation problem. Modern Chinese semantic research oriented to natural language processing, on the one hand, can deepen our understanding of the nature and characteristics of Chinese, on the other hand, can provide valuable semantic resources for computer processing of Chinese. This research strives to make breakthroughs in both theory and practice. Based on the principle of serving natural language processing, it constructs a Chinese sentence semantic knowledge system, and on this basis, it conducts theoretical research and practical exploration on the construction of Chinese sentence semantic knowledge base, which can not only provide a useful knowledge system for Chinese information processing, but also provide a reference for the construction of large-scale Chinese sentence semantic knowledge base in the next stage.
Computer Aided Pathological Observation and Etiological Diagnosis of Pullorum

Through the statistical analysis of the health status of the chickens raised by the farmers, it was found that the incidence of chicken death caused by pullorum in recent years was increasing year by year, and the incidence of the disease also showed an upward trend. On account of this, this paper first analyses the computer-aided observation of chicken pullorum pathology, then studies the computer-aided pathology of chicken pullorum, and finally gives the computer-aided etiological diagnosis of chicken pullorum and specific preventive measures.
Cultural Publicity Website Based on Apriori Improved Algorithm

Under the current situation, it provides people with a convenient, efficient, timely and easy-to-use information service platform. It is widely used and far-reaching to strengthen the construction of the portal website of the Publicity Department, and the security of the website is extremely important. In order to solve the shortcomings of the existing cultural publicity website design research, this paper discusses the website development environment and website user interest calculation based on the discussion of Apriori improved algorithm and cultural publicity website design demand analysis, and designs various functional modules of the cultural publicity website. This paper focuses on the research on the news information recommendation system of cultural publicity websites through the improved algorithm of Apriori. Finally, the analysis is carried out through specific experiments. The data shows that the AprioriTidStr algorithm improved by Apriori has the fastest execution time compared with the original algorithm. Among the 100 to 500 transaction items, the execution time is up to 96 seconds. Therefore, it is verified that the improved algorithm based on Apriori designed in this paper has high practical value in the design and application of cultural publicity websites.
Data Classification Algorithm Based on Association Rules from the Perspective of Data Mining

Association rules(AR) are a common data classification method. It can create more value by studying how to better mine user information and establish connections between these large number(LR) of reusable objects. For better studying the data classification algorithm, this paper studies from the perspective of data mining. This paper mainly discusses and studies some common data mining technologies, and designs a method to deal with related events based on learning rules to solve the problems in practical applications. The experimental data shows that when the number of concurrent users increases, the time of different algorithms also increases, but the time spent in data mining is less than 2 minutes. It shows that the data classification algorithm under the data mining can play a certain role.
Electronic Signature Handwriting Classification and Recognition System Based on Edge Computing

With the rapid development of pattern recognition technology, online handwriting identification has become more and more important, and the application scenarios are more abundant. There is a great demand for online handwriting identification in finance, self-study examination, justice and other fields. Online handwriting recognition is an important part of feature recognition research, which realizes identity verification through the signature handwriting of handwritten objects and the behavioral characteristics embodied in the writing process. Online handwritten signature authentication technology is a high combination of electronic signature and traditional handwritten signature. It solves the problem that traditional signatures are easy to be imitated, and has important practical significance. This paper mainly studies the electronic signature handwriting classification and recognition system based on edge computing. This paper proposes a secure distributed image classification scheme based on heterogeneous edge computing for the signature image classification process. PyQT 5, QT Designer, MySQL and other technologies are used to design and develop the computer handwriting verification system software, provide an interactive interface for relevant personnel to use, and conduct relevant tests to ensure the integrity of software functions and logical correctness.
Construction of Smart Library Service System Based on Internet of Things Data Technology

The construction of a smart library is an inevitable trend in the era with the emergence and rapid development of the Internet of Things, AioT, big data, cloud computing, mobile Internet, 5G, artificial intelligence, blockchain, and quantum information technology. Librarians, readers, space, literature resources, and reading promotion activities can all be connected to each other through the Internet of Things data technology after building a smart library, and monitor and evaluate the development process. This article not only introduces the face recognition system, book management system, book recommendation and purchase platform, book positioning system, book self-checkout system, reader service reservation system, reader activity management system and librarian attendance system in smart libraries, and also took the innovative and entrepreneurial talent training service carried out by the library of JiLin Agricultural Science and Technology University as an example, expounding how the smart library operates based on the Internet of Things data technology.
College English Bisection Based on Intelligent Algorithm

College English teaching is in the process of continuous reform. College English is an indispensable part of colleges and universities, so college English teaching can not be ignored, especially in English listening and speaking. The bisection classroom teaching style was proposed by Professor Zhangxuexin of Fudan University in 2013, and it offers new insight for scholars working on classroom teaching reform in colleges and institutions. This study applies the split classroom teaching model to college English listening and speaking teaching to encourage students to actively participate in classroom learning and improve art college students' English listening and speaking ability. The research on College English dichotomy classroom teaching based on intelligent algorithm is a new generation of intelligent algorithm, which can analyze data and find out the best teaching method. It can be used to improve students' learning ability in Colleges and universities. The research team collected data on more than 10 million college students from different countries, including China. Based on these massive data, it has developed an intelligent algorithm that can help teachers optimize their teaching skills and better use their time for each student.
Blockchain Technology Based Secure Data Sharing in Cloud Computing

Cloud computing has become an essential technology for delivering infrastructure and data service needs with lower cost, low effort and high scalability, hence it is increasingly implemented in all aspects of the IT industry. Despite the quick adoption of cloud computing, the problem of information security is still not fully resolved. Information security issues are still hampering the evolution of cloud computing to some extent and need to be addressed. Blockchain is one of the decentralized processes in a worldview that works with parallel and distributed ledger technology, the application process, and service-oriented design. However, the primary security issue is key leakage and third parties easily hack the secured information. To address this issues, in this novel presents Blockchain based Proof of staking with Elliptic Curve Encryption (BPECE) algorithm is used to secure data transmission in the cloud. This proposed method efficiently checks each node verification using Policy based Key Authentication (PBKA) algorithm. The proposed technique generates public key for each document. Thus the proposed method gives better security performance than previous methods.