Reviewerly/exHarmony huggingface dataset:
Artificial narrow intelligence

This chapter introduces artificial narrow intelligence (ANI) and describes its main uses as well as its limitations. It compares ANI to general artificial intelligence (GAI) that is the subject of Chapter 7. A classification of ANI applications is included organized by type of application. A short treatment of NAI APIs is also included. The author also includes a short description of some of the main applications of mind mapping into ANI. A more detailed description of the application of mind mapping is included in Chapter 8 where several detailed examples are given. This chapter is thought for readers who are practitioners of mind mapping and who are not necessarily experts in AI.
LMIBE: Lattice-Based Matchmaking Identity-Based Encryption for Internet of Things

Under the usage of new technologies, Internet of Things (IoT) develops rapidly and provides a great convenience for our lives. It is critical for ensuring security to IoT systems as the tremendous growth of IoT applications. Although many cryptography tools (such as identity-based encryption) have been given to provide appropriate security in IoT covering various application fields such as smart home, how to guarantee data confidentiality, provide reasonable data source identification, and resist quantum attacks simultaneously has been a challenging problem. To address this problem, we propose a matchmaking encryption scheme named lattice-based matchmaking identity-based encryption (LMIBE) which can provide bilateral access control for both sender and receiver in IoT systems, and resist quantum attacks. Moreover, we give a formal definition and a security definition for our scheme. Security proof shows that our scheme is secure under the proposed security definition. Finally, by comparing the performance of our scheme with existing works, our proposed scheme has a broad application prospect in IoT environment.
Privacy-Preserving Face Recognition With Multi-Edge Assistance for Intelligent Security Systems

Face recognition is one of the key technologies in intelligent security systems. Data privacy and identification efficiency have always been concerns about face recognition. Existing privacy-preserving protocols only focus on the training phase of face recognition. Since intelligent security systems mainly complete the calculation of large-scale face data in the identification phase, existing privacy-preserving protocols cannot be well applied to intelligent security systems. In this article, we propose the first privacy-preserving face recognition protocol for the calculations in the identification phase for intelligent security systems. We introduce the Householder matrix to blind user data including model data and face data, which enables the proposed protocol to support privacy-preserving face recognition on semi-trusted edge servers. Utilizing edge computing, fast response for large-scale face recognition can be achieved. The user can offload heavy calculations of matrix multiplication and Euclidean distances to edge servers simultaneously. The proposed protocol supports parallel computing based on multiple edge servers and thus enhances the efficiency of face recognition in intelligent security systems. Moreover, the recognition accuracy in the proposed protocol is the same as that in the original PCA-based face recognition algorithm. The security analysis demonstrates that the protocol protects the privacy of user data. The numerical analysis and simulation experiments are carried out to show the efficiency and feasibility of the proposed protocol.
Blockchain-Based IoT Model and Experimental Platform Design in the Defence Supply Chain

This article aims to design, implement, and test a blockchain-based Internet of Things (IoT) platform model for the defence industry that enables the various supply chain (SC) actors to manage transactions and information in a transparent, secure, and traceable manner, while also fostering multilateral collaboration in the supply network. The platform was designed using a private and authorized model in the SC of MBDA Italy, part of MBDA—European leader in missile industry. Blockchain technology can facilitate the decentralization and automation of SC activities. Throughput, latency, infrastructure, interoperability, onboarding, and maintenance costs, data storage costs, waste of resources, block size restrictions, and the definition of smart contracts are the primary challenges and benefits that have influenced the design of a private permissioned blockchain. Managerial implications on the necessity to integrate the supply blockchain platform into other systems (e.g., finance, insurance, and social domains) are explored to provide recommendations for the development of a more integrated digital industrial ecosystem.
Pervasive Micro Information Flow Tracking

Detection of advanced security attacks that exploit zero-day vulnerabilities or application-specific logic loopholes has been challenging due to the lack of attack signatures or substantial deviations in the overall system behavior. One has to zoom in to the affected code regions and look for local anomalies distinguishable from the benign workload to detect such attacks. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pervasive micro information flow tracking</i> ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PerMIT</small> ) that realizes variable-level online dynamic information flow tracking (DIFT) as a means to detect the attacks. The system uses hardware virtualization extension to monitor access to taint source variables and performs asynchronous code emulation to infer the local information flow. We demonstrate that the pervasive micro information flow can sufficiently capture the attacks and incurs only a small overhead. Given the program source code, the system can further enrich the semantics of micro information flow by embedding the variable names. We have integrated the system with machine learning algorithms to demonstrate the effectiveness of anomaly detection for zero-day attacks with pervasive micro information flow.
Classification of Manifold Learning Based Flight Fingerprints of UAVs in Air Traffic

As the number of UAVs (Unmanned Aerial Vehicles) and the market size have been expanding rapidly in recent years, projects such as NextGen and SESAR aim to include UAVs in air traffic. Therefore, different perspectives on understanding flight patterns can contribute to more effective management of future air traffic. Analysis of flight data offers an important insight into the operations of a UAV. In this study, it is aimed to extract a flight fingerprint using different machine learning techniques by means of a public dataset and the data obtained from our experimental flights. To get the individual flight pattern, multidimensional UAV sensor data has been reduced using manifold learning methods. By comparison, the most proper manifold method that allows highest classification accuracy (CA) has been investigated. Their performances are compared using both different manifold types and different classification methods. Then, the obtained manifold is used as flight fingerprints and validated by classification techniques. Various unsupervised manifold learning techniques such as t-Distributed Stochastic Neighbor Embedding (t-SNE), Locally Linear Embedding (LLE), Isometric Feature Mapping (ISOMAP) were tried for dimension reduction. For flight fingerprint classification, supervised machine learning techniques such as k-Nearest Neighbors (k-NN), Adaboost, Neural Network, Bayes, etc., were tested. It has been observed that the highest classification accuracy is achieved with the t-SNE manifold and k-NN classification pair. The extracted fingerprint can find many application areas such as performance tests in production lines, air traffic control, risk analysis, anomaly detection, observing pilot performance, drone efficiency over time.
ONASP: A web application for groundwater data visualization in Panama

Abstract Groundwater data in Panama is scattered and held by different institutions, which complicates water management decision-making. Integrating different data sources into a central, web-accessible location that displays near-real-time and accurate groundwater data will facilitate informed decision-making. In this paper, we present a web application to visualize the location and hydrographs associated with monitoring wells and rain gages from the La Villa Watershed, located in south-central Panama. The app was built using the R programming language and interactive elements from the shiny framework. It represents a first step towards organizing, centralizing, and making groundwater data in Panama web-accessible, to enable informed, evidence-based water management decisions.
Training Logistic Regression Model by Hybridized Multi-verse Optimizer for Spam Email Classification

Spam emails pose a significant threat to end users, annoying them and wasting their time. To counter this problem, numerous spam detection systems have been proposed recently, where the most of the solutions have grounds in the machine learning algorithms, due to their efficiency in classification tasks. Unfortunately, existing spam detection solutions typically face low detection rate and generally have troubles in dealing with high-dimensional data. To address this problem, this paper suggests a hybrid spam detection approach by combining the logistic regression classifying model with the hybridized multi-verse optimizer swarm intelligence metaheuristics. The proposed approach was validated on a public benchmark dataset (CSDMC2010) and compared to other cutting-edge techniques. The obtained results indicate that the suggested hybrid approach outperforms other spam detection solutions included in the comparative analysis, by achieving the highest classification accuracy.
CollabGraph: A Graph-Based Collaborative Search Summary Visualization

Currently, the search history in search engines is presented in a list view of some combination of enumerated results by title, url or search query. However, this classical list view is not ideal in collaborative search environments as it does not always assist users in understanding collaborators' search history results and the project's status. We present CollabGraph, a system for graph-based summary visualisation in collaborative search learning environments. Our system differentiates from existing solutions by visualising the summary of the collaboration results in a graph and having its core Personal Knowledge Graphs (PKGs) for each user. Our research questions concentrate around the CollabGraph's usefulness, preference, and enhancement of participation of student's and teacher's feedback compared to the list view of search history results. We evaluate our approach with an online questionnaire in 6 different project-based Search as Learning (SaL) scenarios. The evaluation of users' experience indicates that the CollabGraph is useful, highly likeable and could benefit users' participation and teacher's feedback by providing more precise insights into the project status. Our approach helps users better perceive about everyone's work, and it is a highly preferable feature alongside the list view. Also, the results demonstrate that graph summary visualisations, such as the CollabGraph, are more suitable for closed-end scenarios and collaborative projects with many participants.
A Reference Signal-Aided Deep Learning Approach for Overlapped Signals Automatic Modulation Classification

Traditional likelihood-based and handcrafted feature-based methods for overlapped signals automatic modulation classification (OS-AMC) suffer from the uncertainty of the overlapped numbers in practical application scenarios, while existing deep learning methods still require a complex training process. In this letter, a deep learning approach with a hybrid network combining ConvNeXt and atrous self-attention transformer is proposed to solve this problem. Specifically, a reference signal-aided training is introduced to generate the decision threshold of the proposed network automatically, which omits the searching process of the decision threshold and makes the training process more efficient. The simulation results indicate that the proposed method can achieve superior classification accuracy with a simpler training process and lower computational complexity and memory cost.
Regulated Subspace Projection Based Local Model Update Compression for Communication-Efficient Federated Learning

Despite high utility in distributed networks, federated learning entails enormous communication overhead due to the requirement of trained model exchange at every global iteration. When the communication resources are limited, as in wireless environments, learning performance can be severely degraded by the communication overhead. On this account, communication efficiency is one of the primary concerns in federated learning. In this paper, we put forth a communication-efficient federated learning system based on the projection of local model updates. Leveraging the correlation of consecutive local model updates, we devise a novel local model update compression scheme based on the projection onto the selected subspace. Furthermore, to avoid error propagation over global iterations and thus improve learning performance, we also develop novel criteria for deciding whether to compress the local model updates or not. The convergence of the proposed algorithm is also mathematically proved by deriving an upper bound on the mean square error of the global parameter. The merits of the proposed algorithm over the state-of-the-art benchmark schemes are verified by various simulations.
Guidelines to Develop Consumers Cyber Resilience Capabilities in the IoE Ecosystem

The IoE ecosystem presents ongoing cybersecurity challenges that need to be addressed by all actors to effectively defend against the dynamics of the relevant cyber threat landscape. The consumer IoT market constitutes a key element of the IoE ecosystem that must be protected from cyber criminals. To effectively defend against cybercriminals, consumers must adopt a more active role and become more resilient to attacks. This means that they need to be able to proactively anticipate attacks, defend and effectively respond to a security incident. To this end, it is essential to promote the development of basic technical skills to a level appropriate for consumers. This is an important aspect that should drive the design of specialized cybersecurity curricula in the context of the IoE ecosystem. This research work provides guidelines to curricula designers and trainers as to the thematic areas they should consider in the design and delivery of specialized cybersecurity curricula to build consumers’ cyber resilience competencies in the context of the IoE ecosystem. For each thematic area, the key topics to consider in the design of the curricula are specified, highlighting specific skills and knowledge that should be developed. The design of such curricula can contribute in upskilling consumers and improving the cyber resilience level across society.
Blockchain for transparent, privacy preserved, and secure health data management

Healthcare has become one of the most critical issues in human life. Most of society became elder, and they need to streamline monitoring and treatment to improve life quality and decrease potential risks. Using IoT technology for patient monitoring and health data gathering has increased dramatically. However, as we know, medical data that is usually formatted in electronic medical records contains sensitive data and should be protected from unauthorized access, tampering, and collusion. Using traditional methods like k-anonymity and l-diversity to preserve privacy does not apply to healthcare data because of intrinsic properties of Medicare data, such as volume, variety, and velocity. To share healthcare data between patients, doctors, and health keepers, healthcare systems are motivated to provide reliable, immutable, and confidential structures build on Blockchain. Using this technology, we can provide anonymity and transparency in the storage and sharing of healthcare data; at the same time, the accuracy of data remains intact.
Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy

Most of the existing k-medoid algorithms select the initial medoid randomly or use a specific formula based on the proximity matrix. This study proposes a block-based k-medoids partitioning method for clustering objects. To get the initial medoids, we search for an object representative from the block of the standard deviation and the sum of the variable values. We optimized the initial groups to update medoids, so this step can reduce the number of iterations to obtain partitioned data. The block-based k-medoids partitioning method applies to all types of data. To improve clustering accuracy, we operate pre-processing through data standardization. We conducted a series of experiments on eight real data sets and three artificial data to evaluate the proposed method's performance in terms of clustering accuracy. The experiment results show that the Block-based K-Medoids partitioning is more efficient in reducing the number of iterations. The clustering accuracy of the Block-KM for eight real datasets is also comparable to other methods. The data standardization is effective to increase clustering accuracy, especially for block k-medoids, k-means, simple and fast k-medoids, and the Ward method.
Research and Implementation of Automatic Navigation and Driving of Tracked Robot in Tunnel Based on SLAM

In view of the low accuracy of traditional GPS positioning and the inability to locate accurately in the tunnel effectively, this paper studies and designs an intelligent crawler robot based on SLAM technology. The research scheme is based on the ROS robot operating system of raspberry pie as the software framework, based on the STM32 core circuit as the execution system, using IMU technology to locate the position of the crawler robot, using Lidar Scanning to input environmental information, accurately drawing a map based on Hector algorithm, calculating the driving route based on global path planning algorithm, and adjusting the path in real time based on local path planning algorithm to avoid obstacles, Finally, the tracked robot based on SLAM can drive automatically in the complex tunnel environment. Finally, the feasibility, reliability and stability of the design are proved by experimental tests, which provides some reference and help for the research of intelligent mobile robot in complex tunnel environment.
Steganography in TCP/IP Networks

Due to the large amount of data that is transmitted over computer networks, web traffic is an attractive environment for steganographic techniques. The question is, can data that is sent over the Internet be used as a medium, perhaps without the knowledge of the sender and recipient? In this work, methods of hiding and data transmission are considered, using some shortcomings of data transmission protocols of computer networks. The IP and TCP protocols, existing possibilities of embedding in the headers of data that are invisible and do not affect the transmission of packets are considered in detail. Steganographic transmission was simulated under conditions of a loaded network (packet loss), estimates of the possibility of reliable transmission of a steganographic message were obtained to detect the compromise of fields that appeared to be safe.
Extended Research on the Security of Visual Reasoning CAPTCHA

CAPTCHA is an effective mechanism for protecting computers from malicious bots. With the development of deep learning techniques, current mainstream text-based and traditional image-based CAPTCHAs have been proven to be insecure. Therefore, a major effort has been directed toward developing new CAPTCHAs by utilizing some other hard Artificial Intelligence (AI) problems. Recently, some commercial companies (Tencent, NetEase, Geetest, etc.) have begun deploying a new type of CAPTCHA based on visual reasoning to defend against bots. As a newly proposed CAPTCHA, it is therefore natural to ask a fundamental question: are visual reasoning CAPTCHAs as secure as their designers expect? This paper explores the security of visual reasoning CAPTCHAs. We proposed a modular attack and evaluated it on six different real-world visual reasoning CAPTCHAs, which achieved overall success rates ranging from 79.2% to 98.6%. The results show that visual reasoning CAPTCHAs are not as secure as anticipated; this latest effort to use novel, hard AI problems for CAPTCHAs has not yet succeeded. Then, we summarize some guidelines for designing better visual-based CAPTCHAs, and based on the lessons we learned from our attacks, we propose a new CAPTCHA based on commonsense knowledge (CsCAPTCHA) and show its security and usability experimentally.
Towards Multi-User, Secure, and Verifiable $k$NN Query in Cloud Database

With the boom in cloud computing, data outsourcing in location-based services is proliferating and has attracted increasing interest from research communities and commercial applications. Nevertheless, since the cloud server is probably both untrusted and malicious, concerns about data security and result integrity have become on the rise sharply. In addition, in the single-user situation assumed by most existing works, query users can capture query content from each other even though the queries are encrypted, which may incur the leakage of query privacy. Unfortunately, there exists little work that can commendably assure data security and result integrity in the multi-user setting. To this end, in this article, we study the problem of multi-user, secure, and verifiable <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> nearest neighbor query ( <b>MSV <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="cui-ieq3-3237879.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></inline-formula> NN</b> ). To support MSV <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN, we first propose a novel unified structure, called verifiable and secure index (VSI). Based on this, we devise a series of secure protocols to facilitate query processing and develop a compact verification strategy. Given an MSV <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN query, our proposed solution can not merely answer the query efficiently while can guarantee: 1) preserving <i>data privacy</i> , <i>query privacy</i> , <i>result privacy</i> , and <i>access patterns privacy</i> ; 2) authenticating the <i>correctness</i> and <i>completeness</i> of the results; 3) supporting <i>multi-user</i> with different keys. Finally, the formal security analysis and complexity analysis are theoretically proven and the performance and feasibility of our proposed approach are empirically evaluated and demonstrated.