Reviewerly/exHarmony huggingface dataset:
Enhancing Underwater Image Quality Assessment with Influential Perceptual Features

In the multifaceted field of oceanic engineering, the quality of underwater images is paramount for a range of applications, from marine biology to robotic exploration. This paper presents a novel approach in underwater image quality assessment (UIQA) that addresses the current limitations by effectively combining low-level image properties with high-level semantic features. Traditional UIQA methods predominantly focus on either low-level attributes such as brightness and contrast or high-level semantic content, but rarely both, which leads to a gap in achieving a comprehensive assessment of image quality. Our proposed methodology bridges this gap by integrating these two critical aspects of underwater imaging. We employ the least-angle regression technique for balanced feature selection, particularly in high-level semantics, to ensure that the extensive feature dimensions of high-level content do not overshadow the fundamental low-level properties. The experimental results of our method demonstrate a remarkable improvement over existing UIQA techniques, establishing a new benchmark in both accuracy and reliability for underwater image assessment.
Evaluating Domain Randomization in Deep Reinforcement Learning Locomotion Tasks

Domain randomization in the context of Reinforcement learning (RL) involves training RL agents with randomized environmental properties or parameters to improve the generalization capabilities of the resulting agents. Although domain randomization has been favorably studied in the literature, it has been studied in terms of varying the operational characters of the associated systems or physical dynamics rather than their environmental characteristics. This is counter-intuitive as it is unrealistic to alter the mechanical dynamics of a system in operation. Furthermore, most works were based on cherry-picked environments within different classes of RL tasks. Therefore, in this work, we investigated domain randomization by varying only the properties or parameters of the environment rather than varying the mechanical dynamics of the featured systems. Furthermore, the analysis conducted was based on all six RL locomotion tasks. In terms of training the RL agents, we employed two proven RL algorithms (SAC and TD3) and evaluated the generalization capabilities of the resulting agents on several train–test scenarios that involve both in-distribution and out-distribution evaluations as well as scenarios applicable in the real world. The results demonstrate that, although domain randomization favors generalization, some tasks only require randomization from low-dimensional distributions while others require randomization from high-dimensional randomization. Hence the question of what level of randomization is optimal for any given task becomes very important.
INNT: Restricting Activation Distance to Enhance Consistency of Visual Interpretation in Neighborhood Noise Training

In this paper, we propose an end-to-end interpretable neighborhood noise training framework (INNT) to address the issue of inconsistent interpretations between clean and noisy samples in noise training. Noise training conventionally involves incorporating noisy samples into the training set, followed by generalization training. However, visual interpretations suggest that models may be learning the noise distribution rather than the desired robust target features. To mitigate this problem, we reformulate the noise training objective to minimize the visual interpretation consistency of images in the sample neighborhood. We design a noise activation distance constraint regularization term to enforce the similarity of high-level feature maps between clean and noisy samples. Additionally, we enhance the structure of noise training by iteratively resampling noise to more accurately depict the sample neighborhood. Furthermore, neighborhood noise is introduced to achieve more intuitive sample neighborhood sampling. Finally, we conducted qualitative and quantitative tests on different CNN architectures and public datasets. The results indicate that INNT leads to a more consistent decision rationale and balances the accuracy between noisy and clean samples.
Trap camera prototype with remote monitoring system through LoRa technology

Camera traps are widely used in the observation of wild animals in their natural habitat. Since these cameras must be installed seeking not to alter the habitat, their installation and maintenance are usually crucial, and their manipulation must be minimal. One of the risks of its use is the lack of knowledge of the battery status and the memory availability of images. Considering that the work environment usually lacks commercial wireless communication networks and that the equipment works with batteries, this work presents the development of a camera trap with remote wireless sending of the battery and memory status. The design considers different strategies for low energy consumption in a Raspberry board and the use of LoRa technology to send data.
Development of a cell counting system based on machine learning

Abstract Endothelial cells of the aorta are an excellent model system for studying inflammatory responses, angiogenesis, atherosclerosis, blood clotting, vascular contraction, and vasodilation. Cell counting is necessary when culturing human aortic endothelial cells (HAECs) in order to determine cell concentration and quantity, as well as to assess cell viability and proliferation. Here, a machine learning-based image recognition system was developed to create a cell counter. The system’s hardware includes a digital microscope, Raspberry Pi, and operating screen. Image capture, image processing, machine learning, and computer recognition are utilized in the processing techniques. The processing steps consist of training and testing stages. In the training stage, five HAECs images were selected as training samples while the remaining HAECs images were used as testing samples. Positive and negative samples were labeled using LabelImg and used to generate training images for the classifier program. The classifier program was trained using the built-in Adaboost and LBP models in Opencv to create a HAECs classifier. The system achieved a recognition rate of 95% for HAECs and 98% for colon cells in practical tests, demonstrating that this technology can be used as a tool for cell counting and can replace expensive and potentially inaccurate commercial cell counting software, making cell counting a more practical technique.
Transient‐based radio frequency fingerprinting with adaptive ensemble of transforms and convolutional neural network

Abstract Radio Frequency Fingerprinting (RFF) has been recently investigated by the research community to enhance wireless security. This letter proposes an RFF approach based only on the complex ramp‐up transient portion of the transmitted frame. The well known issues in literature with the use of the transients for RFF is that they have very short duration, they are strongly non‐stationary and their analysis is sensitive to noise. Then, this letter proposes the application of an ensemble of transforms to the original complex time domain representation in combination with Convolution Neural Network (CNN) to address these issues. To avoid the computing demanding calculation of all the considered transforms, a pre‐processing step based on feature extraction and filter feature selection is implemented to select the most discriminating transforms to reduce the input data to the CNN. The approach is applied to two public RFF data sets, where the complex transient ramp‐up have been extracted. The result shows that the proposed approach is able to obtain a classification accuracy higher than the baseline based on the original signal or selected transforms across various values of Signal to Noise Ratio (SNR).
A verifiable and efficient cross-chain calculation model for charging pile reputation

To solve the current situation of low vehicle-to-pile ratio, charging pile(CP) operators incorporate private CPs into the shared charging system. However, the introduction of private CP has brought about the problem of poor service quality. Reputation is a common service evaluation scheme, in which the third-party reputation scheme has the issue of single point of failure; although the blockchain-based reputation scheme solves the single point of failure issue, it also brings the challenges of storage and query efficiency. It is a feasible solution to classify and store information on multiple chains, and at this time, reputation needs to be calculated in a cross-chain mode. Crosschain reputation calculation faces the problems of correctness verification, integrity verification and efficiency. Therefore, this paper proposes a verifiable and efficient cross-chain calculation model for CP reputation. Specially, in this model, we propose a verifiable cross-chain contract calculation scheme that adopts polynomial commitment to solve the problems of polynomial damage and tampering that may be encountered in the crosschain process of outsourced polynomials, so as to ensure the integrity and correctness of polynomial calculations. In addition, the miner selection and incentive mechanism algorithm in this scheme ensures the correctness of extracted information when the outsourced polynomial is calculated on the blockchain. The security analysis and experimental results demonstrate that this scheme is feasible in practice.
FedQMIX: Communication-efficient federated learning via multi-agent reinforcement learning

Since the data samples on client devices are usually non-independent and non-identically distributed (non-IID), this will challenge the convergence of federated learning (FL) and reduce communication efficiency. This paper proposes FedQMIX, a node selection algorithm based on multi-agent reinforcement learning(MARL), to address these challenges. Firstly, we observe a connection between model weights and data distribution, and a clustering algorithm can group clients with similar data distribution into the same cluster. Secondly, we propose a QMIX-based mechanism that learns to select devices from clustering results in each communication round to maximize the reward, penalizing the use of more communication rounds and thereby improving the communication efficiency of FL. Finally, experiments show that FedQMIX can reduce the number of communication rounds by 11% and 30% on the MNIST and CIFAR-10 datasets, respectively, compared to the baseline algorithm(Favor).
Open Journal System: Assistance and training in submitting scientific journals to be well-indexed in Google Scholar

The technical issue with current human resources is that they still require writing instruction. When Google has published and indexed the manuscript, another problem is incorrectly registering the title, author's name, journal name, pages, and other identification information. Modern technology influences the methods and applications of each open journal system (OJS). Understanding how to submit a manuscript to OJS from the outset is important. The service seeks to improve Google Scholar's indexing of scientific journal submissions to OJS by enhancing their comprehension. The method for implementing the service comprises three stages: preparation, implementation, and evaluation. Based on the activity results, it is evident that participants are enthusiastic and motivated to participate in the training because the knowledge garnered from this training will assist them in completing assignments to publish journals, works, final reports, dissertations, and PKM.
Zero-Knowledge Proofs based delegation authentication for Industrial Internet of Things in certificateless proxy signatures

The growing deployment of Industrial Internet of Things (IIoTs) raises significant authentication concerns, especially in scenarios where resource-constrained devices need to delegate tasks with more powerful entities. This critical factor affects the information exchange during the delegation process between the Original User (OU) and the Proxy Signer (PS). The process of delegation communication entails the transfer of signing authority from a delegator to a PS. It enables the proxy signer to sign messages on behalf of delegator. As a result, complexity is increased significantly to enhance both the security and efficiency of communication overhead. To address these challenges in a transparent way, this article proposed and explores the utilization of Zero-Knowledge Proofs (ZKPs) as an additional authentication step in certificateless proxy signature mechanism. Whereas, for security hardness, Hyperelliptic Curve (HEC) is being amalgamated using ‘Schnorr signature’. Performance analysis depicts that the proposed approach exhibits not only minimized communication overhead but also provides optimal security as compared to existing approaches.
Edge Analytics Solution for Online Monitoring of Building Operations Data

Edge based analytics solutions are an essential part of the continued shift towards creating efficient and secure IoT systems to support growing needs across every industry. These analytics often utilize machine learning models to provide informed feedback on the data being generated. As these IoT and edge systems continue to grow, the amount of data being generated is also growing exponentially. Utilizing compute heavy analytics poses many difficulties, especially in harsh environments that restrict the amount and quality of resources available to these systems. To address this, some proposed analytics solutions allow for more efficient use of these restricted systems. This paper is an investigation and implementation of one of these solutions, the QUANTS monitoring algorithm, on building operations data. This data is from many types of sensors throughout a buildings HVAC system and provides a variety of different data types to allow the QUANTS algorithm’s capability with heterogenous data to be displayed. Alterations to the proposed algorithm made during this investigation were made to allow for a more decentralized method. This evaluation of the QUANTS algorithm demonstrates an efficient use of simple resources to support online monitoring and anomaly detection of heterogenous data.
Secure and Scalable Permissioned Blockchain using LDE-P2P Networks

This study presents a novel method that utilizes Linear Diophantine Equations (LDE) within permission-based blockchains. The technique offers a unique approach to improve access control and decrease data lookup complexity. The suggested approach introduces a novel blockchain architecture called Linear Diophantine Equation-based Hierarchical (LDEPTH), which aims to enhance permission management and improve operational efficiency. Through the integration of LDE (Least-Privilege, Default-Deny, and Economy) principles, this architectural framework establishes a hierarchical arrangement that optimizes the management of access rights, hence guaranteeing the secure exchange of data and facilitating efficient transaction processes. The integration of many components inside the permissioned blockchain ecosystem leads to a notable reduction in the complexity associated with data lookup processes. This study illuminates the potential of the innovative technique to significantly transform permission-based blockchains. It presents a framework that effectively tackles access control difficulties while simultaneously enhancing the overall performance of the network.
Self-Supervised Image Anomaly Detection and Localization with Synthetic Anomalies

In visual anomaly detection, anomalies are often rare and unpredictable. For this reason, we aim to build a detection framework that can detect unseen anomalies with only anomaly-free examples. In this paper, we introduce a Self-supervised Anomaly Detection approach with Synthetic Anomalies (SADSA), a two-stage framework for detecting and localizing anomalies in images with self-supervised learning. We create anomalies through the designed augmentation strategy. In the first stage, the model learns to distinguish normal data from synthetic anomalies. In the second stage, we extend the self-supervised task to downstream anomaly representation extraction and aggregate features from different semantic levels to improve the detection and localization performance. Without extra training samples and pre-trained models, SADSA achieves 96.4% detection AUROC and 96.1% localization AUROC on the MVTec AD benchmark. This is competitive against existing unsupervised methods. The results demonstrate the potential of SADSA for industrial applications.
Quantum Measurements

Abstract In this chapter we first review projective or von Neumann measurements from the point of view of the projection postulate and the Lüders rule, before turning to more general measurements modelled using positive operator-valued measures (POVMs). After examining their mathematical description, we discuss symmetric informationally complete (SIC) POVMs and Zauner’s conjecture, as well as the representation of POVMs as projective measurements on a larger Hilbert space, a result called Naimark dilation. We then a model for non-ideal projective measurements and analyse the properties of such measurements in relation to the properties unbiasedness, faithfulness, and non-invasiveness of ideal projective measurements. Finally, we consider the problem of distinguishing non-orthogonal quantum states and examine how the use of POVMs can be beneficial for this task.
Multipartite Entanglement

Abstract This chapter takes a closer look at entanglement in multipartite systems. An examination of pure tripartite systems serves a starting point from which we discover the equivalence classes of Greenberger-Horne-Zeilinger (GHZ) states and W states. We then continue with the mixed-state case and discuss the notions of full separability, partition-separability and biseparability versus genuine tripartite entanglement, before stating the GHZ theorem in the formulation following Mermin. For systems of three or more parties we then formulate the definitions for k-separability and genuine multipartite entanglement (GME), as well as for k-producibility and entanglement depth, followed by prominent examples for GME states such as the generalized GHZ states and Dicke states. We give an overview of the problem of detecting GME using various linear and non-linear GME witnesses, lifted witnesses, PPT mixers, before discussing challenges for the characterization and quantification of multipartite entanglement. We close by analysing the phenomenon of entangled entanglement.
Quantum Entropy and Correlations in Quantum Information

Abstract Chapter 20 provides a discussion of entropies and entropic quantities in quantum information theory, briefly considering the generalizations of the Rényi entropy to the quantum regime, but mainly focusing on the von Neumann entropy and its properties such as concavity, the Araki-Lieb inequality, and subadditivity. We introduce the quantum relative entropy, quantum joint entropy, quantum conditional entropy, and quantum mutual information, and prove key properties and results for these quantities, including Klein’s inequality, joint convexity, additivity, and monotonicity under completely positive and trace-preserving maps of the relative entropy, as well as weak monotonicity and strong subadditivity of the von Neumann entropy. We then analyse the relation of (negative) conditional entropy and entanglement. In this context we discuss the conditional amplitude operator and the mutual amplitude operator, as well as conditional Rényi entropies and their role for entanglement detection.
Entanglement and Separability

Abstract This chapter gives a detailed overview of entanglement for pure and mixed states. We begin with the Schmidt-decomposition theorem, which allows us to associate the subsystem mixedness of pure state with entanglement. We discuss the purification of mixed quantum states, the convexity of the set of separable states, and entanglement as a form of correlation. As an example we examine the Bloch decomposition for two qubits. For the detection of entanglement the Peres-Horodecki criterion is introduced, before we analyse the relation between entanglement and non-locality via the CHSH-Operator Criterion and the family of Werner states. We further consider Tsirelson’s bound and hidden non-locality. We then discuss important mathematical tools for entanglement detection, including the positive-map theorem, the Størmer-Woronowicz theorem, and the reduction criterion. In this context we examine the family of isotropic states. Finally, the Weyl states provide an example for which we study the geometry of two-qubit entanglement
Multi-modal Medical Image Denoising using Wavelet Transform and Principal Component Analysis

Medical image denoising performs a crucial role in image processing since noise can considerably deteriorate image quality. In medical imaging, Gaussian and Salt & Pepper noise are commonly encountered during image acquisition, transfer, and storage. To enhance the quality of medical images, researchers are continuously investigating effective denoising techniques that can produce high-quality images, characterized by low Root Mean Square Error (RMSE) values and high Peak Signal-to-Noise Ratio (PSNR). Two powerful denoising methods, Wavelet Transform (WT) and Principal Component Analysis (PCA), have been widely utilized in denoising medical images. In this research, experiments are conducted on X-ray, MRI and CT images, by adding Gaussian and Salt & Pepper noise. The denoising performance of WT and PCA was compared based on their RMSE and PSNR results.
MIMIC III Text classification with the generalization of BERT transformer model synergized with XGBoost classifier

Natural Language Processing (NLP) is a field of machine learning in which machine learning models are trained to analyze and synthesize natural language and speech. Text classification is a popular application in NLP. Previous text classification algorithms that were implemented include the usage of the Naive Bayes Classifier and Recurrent Neural Networks (RNN). The introduction of Transformer based Neural Network architecture, which uses Attention heads to understand the text and its context, has shown state-of-the-art results in various NLP tasks. With the development of Artificial Intelligence (AI) in the medical field, many AI techniques and models have also developed, and the need for good performance is crucial in the medical field. One of the medical applications of text-based AI is classifying Electronic Health Records (EHRs). The data-set we have chosen is the Note events data-set from the MIMIC III (Medical Information Mart for Intensive Care) database. In this work, we are introducing an integrated framework that uses a BERT based transformer in synergy with an XGBoost classifier in its tail to perform text classification, which theoretically and practically gives more efficient results. This novel synergy of XGBoost with the BERT model resulted in a significant boost in test classification accuracy compared to using the BERT model alone. Specifically, the proposed hybrid model achieved an excellent improvement factor by boosting the classification performance of the training accuracy metric by a factor approaching 10 to 13% and the testing accuracy by a factor of 3%.