Reviewerly/exHarmony huggingface dataset:
The Power of Entanglement

This book offers an accessible and engaging introduction to quantum cryptography, assuming no prior knowledge in quantum computing. Essential background theory and mathematical techniques are introduced and applied in the analysis and design of quantum cryptographic protocols. The title explores several important applications such as quantum key distribution, quantum money, and delegated quantum computation, while also serving as a self-contained introduction to the field of quantum computing. With frequent illustrations and simple examples relevant to quantum cryptography, this title focuses on building intuition and challenges readers to understand the basis of cryptographic security. Frequent worked examples and mid-chapter exercises allow readers to extend their understanding, and in-text quizzes, end-of-chapter homework problems, and recommended further reading reinforce and broaden understanding. Online resources available to instructors include interactive computational problems in Julia, videos, lecture slides, and a fully worked solutions manual.
Entering the Cognitive Revolution: Neuroscience and Cognitive Psychology

The Cognitive Revolution was an intellectual movement in the 1950s and 1960s focused on using the scientific method to understand human cognition, otherwise known as the process of learning and memory. This revolution in the field of psychology was happening at the same time that neuroscientists like Hubel and Wiesel were exploring how neurons in the brain are organized to enable behaviors such as visual perception. It was in this environment that both Peter and Janellen developed further as scientists. The connections between their respective fields provided fodder for discussion and debate throughout their careers. Peter focused on cellular events and biological processes in developmental neuroscience, while Janellen probed the mechanisms of human verbal and mathematical learning and memory.
Infrared Image Recovery Algorithm Based on MSR and CLAHE Algorithm Fusion

In this paper, an infrared image restoration algorithm based on the combination of Retinex algorithm and CLAHE algorithm is proposed to solve the problem of local detail loss and target information blurring caused by uneven illumination of infrared image. Firstly, the multi-scale Retinex algorithm is used to divide the infrared image into luminance image and reflection image. Then, the luminance image is globally compared and stretched; For the reflected image after denoising, the CLAHE algorithm is used to enhance its contrast. Finally, the processed luminance image and the reflected image are weighted and fused. The simulation results show that the obtained infrared image has uniform illumination, clear details, and greatly improved contrast, sharpness and information entropy.
Research on Pedestrian Detection Based on Jetson Xavier NX Platform and YOLOv4

With the rapid development of deep learning, object detection technology has been widely used in various fields. Pedestrian detection, as an important part of object detection, has also received attention and development. However, the development also brings a series of challenges. The rapid increase in information requires higher processing capabilities, and lightweight and real-time performance have become urgent issues while intelligent video surveillance tends to become routine. Therefore, obtaining accurate and real-time information flow while accurately locating pedestrian flow information, and quickly and accurately processing these information flows have become important issues. In response to the problems that may arise during pedestrian detection, this design uses the YOLOv4 algorithm in the software aspect, modifies it on the basis of the original algorithm, so that it can better achieve pedestrian detection while reducing the performance requirements on hardware, and ultimately achieve a high accuracy rate in real-time pedestrian detection. The NVIDIA Jetson Xavier NX platform is used in the hardware aspect. The powerful performance of Jetson Xavier NX can greatly meet the hardware performance requirements during pedestrian detection. In order to adapt to engineering requirements, the Jetson Xavier NX board is redesigned, and through comparative experiments, the help of the redesigned board for the experiment is verified. Finally, based on the modified YOLOv4 algorithm, the Jetson Xavier NX platform equipped with the redesigned board is used to achieve the design requirements for pedestrian detection through a series of experiments, and the research purpose of this article is achieved.
Reversible Data Hiding Based on Bidirectional Histogram Modification for 3D Mesh Models

This paper proposes a reversible data hiding algorithm for 3D mesh models. The steep histogram is dynamically constructed to avoid introducing additional model distortion due to excessive movement of prediction errors. A bidirectional histogram modification mechanism is used to maintain considerable embedding capacity which is no need to repeatedly search and record the available peak/zero bin. Experimental results demonstrate that the proposed algorithm achieves high visual quality for low-capacity embedding and is suitable for high-fidelity reversible data hiding.
Deep Learning-Driven Extraction of Superluminescent Diodes Parameters

We present a deep learning-based method for the automatic extraction of physical parameters from optical spectra and power values of a chirped, tapered, dual-section quantum dot superluminescent diode. The neural network is able to estimate a set of parameters that are capable of reproducing the behavior of the target device with high accuracy.
Identity Authentication of Automotive Ethernet Based on Signal Separation

Automotive Ethernet is a new type of in-vehicle network that connects the electronic control unit in the vehicle. When the gateway changes, the recognition accuracy of the traditional identity authentication method based on physical fingerprint for the illegally accessed electronic components in the vehicle will be seriously reduced. In this paper, a method of identity authentication for Automotive Ethernet equipment based on signal separation is proposed. The mixed signal is separated by time convolution neural network, the frequency spectrum fingerprint of the separated signal is extracted, and the XGBoost ((eXtreme Gradient Boosting) model is used for classification. In addition, principal component analysis can improve the accuracy of identity authentication by removing the interference of unimportant features. The experimental results show that this method has higher recognition accuracy regardless of whether the gateway is changed.
A Novel Privacy-Enhanced Fine-Grained Access Control Scheme

Access control technology ensures that information resources are not illegally used and accessed by restricting the ability and scope of users access to object resources. While, most access control models only focus on the optimization of access control policy, less attention is paid to the privacy of object resources. That is, authorized users have unlimited access to acquired object data. And it greatly increases the risk of privacy data being leaked. Therefore, our paper proposes a novel privacy-enhanced fine-grained access control scheme. The scheme introduces Differential Privacy (DP) into the role to resource allocation step of the Role-Based Access Control (RBAC) model. And the scheme realizes the privacy protection of the object data set and the fine-grained permissions control by assigning privacy budgets to different levels of roles. It enables users with different roles to obtain authorization object data sets with different accuracy. Finally, simulation results and performance analysis show that the scheme has high applicability of access control policy, strong privacy and high accuracy of object data set.
SAR Ship Detection Based on Convnext and New Feature Pyramid Network

In the field of SAR ship detection, numerous detecting methods with different backbones and feature enhancement algorithms have been proposed. However, existing backbones cannot generate high-quality multiscale feature maps, and existing feature enhancement algorithms cannot fully enrich semantic feature information. The above problems lead to poor performance on SAR ship detection, especially on small target detection. In this paper, a SAR ship detection method based on Convnext and New Feature Pyramid Network is proposed. First, Convnext is used as the backbone to extract high-quality multiscale feature maps. Second, new Feature Pyramid Network (NFPN) is designed to perform feature enhancement on each layer of feature maps. Experiments on SAR ship detection dataset (SSDD) have shown that our method has the best performance than other methods.
Fuzzy Transfer Learning Algorithm with Knowledge-Task Matching

Fuzzy transfer learning has the potential to enhance the performance of a task in the target scene by leveraging knowledge from the source scene. However, the existence of scene discrepancies between the two scenes poses two major problems for fuzzy transfer learning: 1) the mismatch between the distribution of source knowledge and the target task, and 2) the mismatch between the learning ability of the target task and the source knowledge. To address these challenges, we propose a fuzzy transfer learning algorithm called Knowledge-Task Matching Fuzzy Transfer Learning (KTM-FTL), which aims to adaptively match the source knowledge and the target task. Firstly, we develop a knowledge matching mechanism that balances matching accuracy and diversity to reconstruct the knowledge from the source scene. This process enables the source knowledge to align more accurately with the requirements of the target scene's task. Secondly, we design a task matching mechanism that allows for learning the source knowledge at different levels of granularity. This approach enables the learning ability of the target Feedforward Neural Network (FNN) to match the complexity of the source knowledge, thereby improving its generalization performance. Thirdly, we conduct theoretical analysis of the proposed KTM-FTL, including an examination of its computational complexity and error bound. These analyses provide valuable insights for the successful application of KTM-FTL. Finally, we evaluate the performance of the proposed KTM-FTL on several benchmark problems as well as real-world problems. The experimental results demonstrate the significant improvements achieved by KTM-FTL when compared to state-of-the-art algorithms.
Domain adaptation semantic segmentation for self-driving cars

As an extension of the traditional deep learning network, semantic segmentation aims to generate a class label for each pixel in the input image, thus grouping up together pixels that possess the same semantic label. It is always one of the key topics in computer vision domain, and it is also a vital concept in building self-driving automobile systems. One problem often met in real-life semantic segmentation problem is that researchers often can&apos;t provide a large enough dataset containing all diversely different scenes around the world, causing the model to underfit. In this article we show you how we use VGG16 deep learning model [7] to build a Fully Convolutional Network [5] as to achieve pixel-wise semantic prediction, and how we addressed aforementioned limitation by training the model using video game scenes from GTA5. Since there are style-level discrepancies between the game scenes and the real life scenes, we utilize Adversarial Domain Adaptation [2] to overcome problems caused by such domain shifts and to make the model perform better when working in real life scenes.
Low-Complexity Parallel Min-Sum Medium-Density Parity-Check Decoder for McEliece Cryptosystem

The McEliece cryptosystem based on medium-density parity-check (MDPC) codes remains a candidate in the fourth round submission of post-quantum cryptography standard. The low-density parity-check (LDPC) decoders used in digital communications have been extensively studied. However, the MDPC codes for the McEliece cryptosystem have much higher column weight and different structure in their parity-check matrices. As a result, simplification techniques for LDPC decoders are not applicable to MDPC decoders. Besides, existing MDPC decoder designs have been focusing on the simplest bit-flipping algorithm, whose performance is inferior compared to that of the Min-sum algorithm. This paper first optimizes the scaled Min-sum algorithm for codes with high column weight to improve the performance with simple scalar multiplications. The overall decoder architecture is re-designed to take into account the sparsity of the parity-check matrix and nontrivial min-sum check node processing. Besides, a flexible message storage scheme is proposed to reduce the worst-case decoding latency of the randomly constructed codes utilized in the McEliece cryptosystem. Then a 2-stage scaling scheme is developed to reduce the long critical path caused by the high column weight and a group size re-balancing scheme is introduced to mitigate the precision loss caused by the 2-stage scaling in parallel decoders. For an example MDPC decoder, the proposed optimized 2-stage scaled Min-sum algorithm leads to orders of magnitude error-correcting performance improvement and 16% higher clock frequency with negligible silicon area overhead compared to unoptimized Min-sum decoders.
Continuous-Variable Quantum Secret Sharing in Fast-Fluctuating Channels

Recently, several continuous-variable quantum secret sharing (CV-QSS) protocols were proposed, while most of them are limited to the fiber channel systems with a relatively stable transmissivity. However, by means of complex channels, the transmissivity fluctuates dramatically in time with a probability distribution, which will lead to a fast-fluctuating attack. Therefore, the security analysis of CV-QSS in fiber channels may not apply to CV-QSS in complex channels. In this paper, we study the CV-QSS protocol in the absence of uniform fast-fluctuating channels whose transmissivity changes with respect to a uniform probability distribution. We give a lower bound of secret key rate to provide security analysis against the fast-fluctuating attack for the CV-QSS protocol. In particular, the realistic highly asymmetric beam splitter (HABS) in CV-QSS protocol is investigated in detail here for the first time, and numerical simulation shows that the security bound is overestimated when the HABS is treated as the perfect device.
Private Information Retrieval and Its Extensions: An Introduction, Open Problems, Future Directions

Private information retrieval (PIR) is a privacysetting that allows a user to download a required message from a set of messages stored in a system of databases without revealing the index of the required message to the databases. PIR was introduced under computational privacy guarantees, and is recently re-formulated to provide information-theoretic guarantees, resulting in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">information theoretic privacy</i> . Subsequently, many important variants of the basic PIR problem have been studied focusing on fundamental performance limits as well as achievable schemes. More recently, a variety of conceptual extensions of PIR have been introduced, such as, private set intersection (PSI), private set union (PSU), and private read-update-write (PRUW). Some of these extensions are mainly intended to solve the privacy issues that arise in distributed learning applications due to the extensive dependency of machine learning on users' private data. In this article, we first provide an introduction to basic PIR with examples, followed by a brief description of its immediate variants. We then provide a detailed discussion on the conceptual extensions of PIR, along with potential research directions.
An Interpretable Fuzzy Graph Learning for Label Propagation Assisting Data Classification

Graph-based Semi-Supervised Learning (GSSL) has become an indispensable tool for data classification recently, owing to its innate capability of efficient data structuring and representation. However, their reliance on predefined graphs constrains the efficacy of Label Propagation (LP) and interpretability in predictions, especially in high-dimensional feature spaces with limited information. Addressing these challenges, this paper employs a Fuzzy Graph-based Label Propagation (FGLP) model, which is inherently interpretable in exploring the similarities of the normalized histogram envelope-based scaled features assisting data categorization. FGLP initiates the structuring of an undirected fuzzy-weighted graph using the novel fuzzy distance matrix by exploiting the local data affinity with reduced influence of outliers. The learned information is then optimized using two distinct SoftMax-constrained objective functions coupled with cross-entropy and lasso regularization to construct the similarity and projection matrices in tandem assisting LP and Feature Selection in scarcely labeled high-dimensional feature space. Performance validation on heterogeneous datasets showcases FGLP's superiority, achieving over 88% accuracy with just 10% labeled data, surpassing prior methods by an average enhancement of 18.29%.
Theoretical Backbone of Library and Information Science: A Quest

This study primarily aims to identify unique theories and specific uses of theories in the library and information science (LIS) domain. It provides a comprehensive list of the theories used in LIS journal articles indexed by Scopus (an abstract and citation database) from 1970–2021. It expands on the most common theories and highlights the areas and purposes for which used theories in the LIS domain. Our goal is to demonstrate the usages and applications of various borrowed theories from complementary disciplines in the LIS domain. A systematical methodology is applied, following a few open-source AI-based software packages (such as ASReview, and OpenRefine), to analyse the theories against different parameters, keeping in mind the drawbacks of the previous studies. The study's findings show that the LIS domain's theoretical foundations are understudied. Researchers mainly borrowed theories from social sciences such as sociology, psychology, and management studies to solidify their domain. The paper provides a clear road map for the theoretical development of LIS research. And the resulting outputs may help policymakers, academicians, and researchers, irrespective of disciplines in general and information science in particular, understand the foundations and theoretical and methodological trends of theories that may lead to a better understanding of the theories before their selection and applications.
Comportamento de Consumo, COVID-19 e Gestão do Conhecimento

The post-Covid-19 pandemic scenario, with evident changes in consumer behavior, indicates the need for further studies. Especially for organizations, it is necessary to understand how these transformations can interfere in the way of managing information and impact decision-making through knowledge management of this new reality. This article explores research trends and patterns on consumer behavior in the existing literature from the beginning of the Covid-19 pandemic to July 2022, creating a perspective on the study of consumer behavior, through a bibliometric analysis of articles found in the Scopus database. The result of the bibliometric survey diagnosed that studies on consumer behavior and the Covid-19 pandemic are mainly characterized by themes centered on e-commerce, food consumption, food waste, social distancing and sustainability. The analysis of the articles indicated a growth trend of e-commerce and a greater awareness with regard to food consumption and waste, as well as suggesting an increase in concern about food safety and waste generation. It is expected to contribute to new research related to the highlighted themes. &nbsp
Traceable Attribute-Based Encryption With Equality Test for Cloud Enabled E-Health System

The emerging Internet of Things (IoTs) and cloud technologies spark dramatic growth in efficiency and productivity for the conventional e-health sector. However, the extensive applications of the communication network also expose the sensitive medical data to the unprecedented cyber threats. To protect the data privacy in IoTs-based e-health cloud environments, we propose an adaptively secure data sharing scheme with traceability and equality test (T-ABEET). The T-ABEET not only allows flexible access control to the massive data but also provides the functionality of traitor tracing to identity the users who leak their decryption keys. Meanwhile, through carrying out the equality test, the target ciphertext can be retrieved efficiently without revealing anything about the plaintext. Particularly, distinct from previous traceable ABE works, the tracing cost in our T-ABEET scheme keeps constant even with the increasing number of users. Also, by introducing the multi-authority mechanism, our T-ABEET can avoid the inherent key escrow problem of ABE. Furthermore, our T-ABEET is demonstrated adaptively secure under subgroup decision assumption. Finally, performance comparison reveals that our T-ABEET has superior practicality, efficiency, and security in cloud-enabled e-health systems.
Estimation of Optimal Number of Topics Detection and their Assessment Using Hybrid Topic Models for Visualization Enhancement

For more than a decade, topic models have been extensively utilized to create document clusters, but there have been issues with selecting the right number of subjects. The fundamental issue is the absence of a reliable indicator of the topic quality obtained during topic model creation. In estimating the number of topics non-parametric methodology and quality coherence and perplexity methods were applied in previous work. To complement clustering and select the most relevant topics, the parametric technique with Visual Assessment Tendency (VAT) has been employed in this study. In this work, the detection of optimal number of topics on different types of extracted datasets is done by using hybrid topic models. Some of key challenges such as selecting optimal topics to improve clustering accuracy and visualization, and usage of appropriate metrics in analysis have been implemented and the results on different types of datasets are recorded. Results are represented in the form of tables, graphs, and VAT images. Comparative study and assessment were carried out and the results are represented.
Perancangan Basis Data Kawasan Suci Danau Tamblingan dengan Menerapkan Model Data Relasional

The indigenous community of Tamblingan highly reveres the forest as the true source of life (Merta Jati). Within the forest area, there are temples and shrines that are interconnected. Information about these sacred temples and shrines is currently limited to a few individuals who possess specific manuscripts, making it not easily accessible. A database is a crucial component of information technology implementation, as it facilitates faster information retrieval, enhances data security, and reduces data storage redundancy. The Database Lifecycle (DBLC) is a method that outlines the life cycle of a database. In this study, the design of a database for the sacred area of Lake Tamblingan is carried out, starting from conceptual database design to physical database design using a relational model. The design is created using the Power Designer tool. Data collection is conducted through literature review and interviews, while data processing is performed through descriptive analysis. This research aims to produce a database design for the sacred area of Lake Tamblingan that can be utilized for further studies, such as developing an information system for the sacred area of Lake Tamblingan.