Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Recognition and Implementation of Bitcoin based Central Bank Digital Currency through Embedded System

The Central bank digital currency plays an important role in the national economic development of a country. The central bank digital currency is adopted with the increase in the development of digital currency and blockchain technology. The management of digital data to adopt transparency and accountability is obtained through the blockchain technology. The blockchain technology is the fundamental attribute for internet of things that performs on the embedded system. The ledger of transaction is visible to both the members involved in the banking system through the blockchain technology. The privacy with transparency in the transaction is obtained through the peer to peer protocol. To increase the data storage with flexibility, accuracy in transactions with transparency, evolving privacy and transparency, the digital currency in commercial bank using blockchain technology is implemented.
Crowd Counting Study Based on Low Light Image Enhancement

In order to improve the uniformity of data brightness, we propose a crowd counting architecture based on low-light image enhancement in this research. Mainly, a module with curve estimation is added to the architecture, the RGB and T images in the RGBT-CC dataset are enhanced at the light level. The effective channel attention mechanism and exposure control loss are added to suppress low-light/dark areas while reducing background noise. Based on the BL as the backbone, a lot of experiments are conducted. The results show that the data enhancement at the light level of the dataset can improve the illumination uniformity and reduce crowd counting error, proving that the model has higher accuracy and robustness.
Toward Certified Robustness Against Real-World Distribution Shifts

We consider the problem of certifying the robustness of deep neural networks against real-world distribution shifts. To do so, we bridge the gap between hand-crafted specifications and realistic deployment settings by considering a neural-symbolic verification framework in which generative models are trained to learn perturbations from data and specifications are defined with respect to the output of these learned models. A pervasive challenge arising from this setting is that although S-shaped activations (e.g., sigmoid, tanh) are common in the last layer of deep generative models, existing verifiers cannot tightly approximate S-shaped activations. To address this challenge, we propose a general meta-algorithm for handling S-shaped activations which leverages classical notions of counter-example-guided abstraction refinement. The key idea is to “lazily” refine the abstraction of S-shaped functions to exclude spurious counter-examples found in the previous abstraction, thus guaranteeing progress in the verification process while keeping the state-space small. For networks with sigmoid activations, we show that our technique outperforms state-of-the-art verifiers on certifying robustness against both canonical adversarial perturbations and numerous real-world distribution shifts. Furthermore, experiments on the MNIST and CIFAR-10 datasets show that distribution-shift-aware algorithms have significantly higher certified robustness against distribution shifts.
Classification of Machine Learning and Power Requirements for HAI (Human Activity Identification)

Human Activity Recognition(HAR) is used in many applications, such as surveillance, anti-terrorists, anti-crime securities, medical, life logging, and assistance. Besides its positive effect on well-being, the recognition of human activity has many applications. One of the key research topics in the fields of computer vision and machine learning is the human capacity for activity recognition. Pose estimation and categorization algorithms, which are now available for use on pictures or video input, it is now possible to gather and store information on several elements of human mobility in a free environment. A hierarchical structure is inherent in human activities, which can be categorized into three levels based on the nature of the action. A typical example of a movement happens when one walks, talks, stands, or sits indoors, which are everyday indoor activities. In addition, they may be more focused on activities such as those performed in kitchens or factories. However, A major challenge for activity recognition is the diversity of methods used by individuals. As a technology assistive for eldercare and healthcare, it is expected to be used mainly with the Internet of Things (IoT). The paper has demonstrated that it follows a hierarchy of analytical approaches toward the issue in a clearly defined form, and the paper showed numerous strategies that have been a part of various other studies in the field. Even though a notable amount of progress has been observed in this critical area, there is still space for further improvements in the subject topic, particularly when it comes to applying cutting-edge categorization algorithms to a variety of problems. In order to deal with these challenges, Pose estimation and classification algorithms have been used to evaluate data collection and discover human activities accurately. This study also examines the activities performed by the user during Video/Image input.
Research on underwater seafood target detection algorithm based on improved YOLOv5

The underwater environment of the sea bottom is complex, and it is easy to be affected by light, underwater impurities and shooting angle. Because of the dense and mutual occlusion of underwater targets, the collected images are not clear, fuzzy and easy to be resolved, which affects the accuracy of target detection and recognition. Aiming at the above problems, this paper proposes an improved YOLOV5 method for underwater seafood target detection. Firstly, the C3 module of high-level features is fused with swin transformer to create the C3STR module, and the improved C3STR module can enlarge the receptive field of the network, obtain the global information better and increase the ability of feature extraction, the DC3 module is constructed by adding convolution to the low-level feature C3 module, which improves the model detection ability with a little more computation. Finally, the network feature fusion module is improved, which integrates the deep-level information with the shallow-level information, the combination of semantic information and position information can improve the precision of model recognition and enhance the ability of target detection. The experimental results show that the detection accuracy of the improved YOLOv5 algorithm is increased to 88.3%, which is 2% higher than that of the original YOLOv5 algorithm.
Side-Channel-Secure Quantum Key Distribution with Imperfect Vacuum Sources

The side-channel-secure (SCS) quantum key distribution (QKD), SCS QKD has attracted much attention due to its higher security compared to the measurement-device-independent protocols. It is not only measurement-device-independent secure, but also secure against source-state side channels provided that Eve has no access to Alice's or Bob's setups inside their labs. In the original protocol for SCS QKD, the side channels and the intensities of the coherent sources are allowed to be different in different time windows, but the vacuum sources of Alice and Bob have to be perfect, which is impossible in practice due to the finite extinction ratio of the intensity modulators. In this paper, we present a method that does not need the perfect vacuum source requested by the original protocol and assures the result as secure as the original protocol. With the upper bounds of the amplitudes of the nonvacuum part of the sources, the secure key rate of our SCS QKD protocol here with imperfect vacuum and unstable sources can be calculated. The numerical results show that, the secure distance of the SCS QKD protocol exceeds 150 km, provided that the intensity of the imperfect vacuum source is less than ${10}^{\ensuremath{-}8}$, which can be achieved in the experiment by a two-stage intensity modulator, where we also show that the active odd-parity pairing method and standard pairing method using two-way classical communication can be applied to the SCS QKD protocol to improve the key rate. Given the side-channel security based on the existing technological level, this work makes it possible to realize side-channel-secure QKD with real devices.
Minimal port-based teleportation

Abstract There are two types of port-based teleportation (PBT) protocols: deterministic—when the state always arrives to the receiver but is imperfectly transmitted and probabilistic—when the state reaches the receiver intact with high probability. We introduce the minimal set of requirements that define a feasible PBT protocol and construct a simple PBT protocol that satisfies these requirements: it teleports an unknown state of a qubit with success probability <?CDATA $p_{\textrm {succ}} = 1-\frac{N+2}{2^{N+1}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>p</mml:mi> <mml:mrow> <mml:mrow> <mml:mtext>succ</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> <mml:mo>−</mml:mo> <mml:mfrac> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>+</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:mfrac> </mml:math> and fidelity <?CDATA $1-O(\frac{1}{N})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>1</mml:mn> <mml:mo>−</mml:mo> <mml:mi>O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mi>N</mml:mi> </mml:mfrac> <mml:mo stretchy="false">)</mml:mo> </mml:math> with the resource state consisting of N maximally entangled states. This protocol is not reducible from either the deterministic or probabilistic PBT protocol. We define the corresponding efficient superdense coding protocols which transmit more classical bits with fewer maximally entangled states. Furthermore, we introduce rigorous methods for comparing and converting between different PBT protocols.
Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI

Deep learning (DL) models for segmenting various anatomical structures have achieved great success via a static DL model that is trained in a single source domain. Yet, the static DL model is likely to perform poorly in a continually evolving environment, requiring appropriate model updates. In an incremental learning setting, we would expect that well-trained static models are updated, following continually evolving target domain data-e.g., additional lesions or structures of interest-collected from different sites, without catastrophic forgetting. This, however, poses challenges, due to distribution shifts, additional structures not seen during the initial model training, and the absence of training data in a source domain. To address these challenges, in this work, we seek to progressively evolve an "off-the-shelf" trained segmentation model to diverse datasets with additional anatomical categories in a unified manner. Specifically, we first propose a divergence-aware dual-flow module with balanced rigidity and plasticity branches to decouple old and new tasks, which is guided by continuous batch renormalization. Then, a complementary pseudo-label training scheme with self-entropy regularized momentum MixUp decay is developed for adaptive network optimization. We evaluated our framework on a brain tumor segmentation task with continually changing target domains-i.e., new MRI scanners/modalities with incremental structures. Our framework was able to well retain the discriminability of previously learned structures, hence enabling the realistic life-long segmentation model extension along with the widespread accumulation of big medical data.
Building color palettes in your data visualization style guides

Abstract Objectives Data visualization style guides are standards for formatting and designing representations of information, like charts, graphs, tables, and diagrams. To assist researchers communicate their visual content in better and more effective ways, this article accomplishes two tasks. First, we take a detailed look at a data visualization style guide and its components—what it is and what it should include. Second, we create a detailed template for the color section of a data visualization style guide. Target Audience Creating a data visualization style guide as described here should help researchers across multiple disciplines create better and more consistent charts, graphs, and diagrams. Such style guides are useful for individuals and organizations in their efforts to be more efficient and consistent in their data communication products. Scope Data visualization style guides often include explaining the what (eg, types of charts), the why (eg, reasons for using specific colors), and the how (eg, tools or templates) of creating consistent and effective visuals that can also fit within an individual’s or organization’s larger design system. We use a variety of tools to create, test, and implement a data visualization color palette. We provide sample color palettes and provide step-by-step instructions on how to import those palettes into six popular data visualization tools: Microsoft Excel and PowerPoint, Tableau, PowerBI, and Adobe Illustrator and InDesign.
Using a Self-Growing Neural Network Approach to CCS Monitoring

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Metaverse is the Next Normal and Digital Future: A Systematic Review

The Digital Future of the World is on the verge of being revolutionized. The Technology, wisdom, and internet thinking of Metaverse will extend the impacts on all aspects of life including education, economy, politics, life, and culture. This study is an overview of the metaverse applications in the near future. The outcomes of this review are aimed to understand the usage and importance of metaverse in various fields by using augmented and virtual realities. Finally, we propose the application of Metaverse in education, economy, politics, and entertainment because of its huge future implications.
Data

A large language model (LLM) processes huge amounts of data for its generative AI systems. They are on the scale of petabytes. Consider that a petabyte is 1000 terabytes. This would hold about 500 billion pages of standard text. No doubt, the generative models for images and videos are much larger.
Training needs analysis: the impact of the quality of teaching on student learning, staff satisfaction, and institute business performance

This training needs analysis (TNA) study investigated and determined the influence of teaching quality on student learning, staff satisfaction, and institute business performance. Various data collection methodologies were adopted based on Tobey and McGoldrick (2016) BPLL structure which was then subjected to respective data analysis tools and yielded training and non-training issues and training recommendations. It was observed that the deployment of competent academic staff and assurance of high teaching quality was required to better understand the diverse international students as a means to increase student recruitment rate and boost the business performance. The TNA findings also revealed that it was critical to ensure the motivation and attitude of the academic staff towards reskilling and upskilling in areas that would improve their work performance. Accordingly, four training recommendations with respective learning outcomes and overviews, and two non-training recommendations were proposed to heighten academic staff's global awareness and motivation at work.
The Use of Machine Learning to Predict Late Arrivals at the Adult Outpatient Department

Introduction: Patient unpunctuality leads to delays in the delivery of care and increased waiting times, resulting in crowdedness.Late arrivals for adult outpatient appointments are a challenge for healthcare, contributing to negative effects on the efficiency of health services as well as wasted time, budget, and resources.This study aims to identify factors and characteristics associated with tardy arrivals at adult outpatient appointments using machine learning and artificial intelligence.The goal is to create a predictive model using machine learning models capable of predicting adult patients arriving late to their appointments.This would support effective and accurate decision-making in scheduling systems, leading to better utilization and optimization of healthcare resources.Methods: A retrospective cohort review of adult outpatient appointments between January 1, 2019, and December 31, 2019, was undertaken at a tertiary hospital in Riyadh.Four machine learning models were used to identify the best prediction model that could predict late-arriving patients based on multiple factors.Results: A total of 1,089,943 appointments for 342,974 patients were conducted.There were 128,121 visits (11.7%) categorized as late arrivals.The best prediction model was Random Forest, with a high accuracy of 94.88%, a recall of 99.72%, and a precision of 90.92%.The other models showed different results, such as XGBoost with an accuracy of 68.13%, Logistic Regression with an accuracy of 56.23%, and GBoosting with an accuracy of 68.24%.Conclusion: This paper aims to identify the factors associated with late-arriving patients and improve resource utilization and care delivery.Despite the overall good performance of the machine learning models developed in this study, not all variables and factors included contribute significantly to the algorithms' performance.Considering additional variables could improve machine learning performance outcomes, further enhancing the practical application of the predictive model in healthcare settings.
Regulating crypto assets

Regulation is one way of influencing the evolution and use of crypto assets. However, many crypto-asset activities and markets are not compliant with applicable regulations or have remained unregulated (FSB, 2022). One reason may be the technological novelties of crypto assets. Many activities escape existing regulation because decentralization and the limited transparency of the crypto universe creates significant data gaps. Definitional issues, such as difficulties in establishing whether a stablecoin issuer is a narrow bank or a money market mutual fund, or whether Ether is a commodity or a security, also hamper effective regulation.
Selecting Key Features of Online Behaviour on South African Informative Websites Prior to Unsupervised Machine Learning

The main aim of the study was to explore the feature selection process of online web data prior to unsupervised machine learning models. At the time of writing, no such literature could be found reporting the use of feature selection in this context. Feature selection was determined by inspecting the variability and association between features. The variability of numeric features were quantified using the variance, mean absolute difference and dispersion ratio metrics whilst the coefficient of unalikeability was employed for categorical features. To quantify association, correlation matrices were used for numeric features, chi-squared independence tests between categorical features and box-and-whisker plots between mixed features. The main findings showed the variance, mean absolute difference, dispersion ratio and coefficient of unalikeability metrics have successfully highlighted features with very low variability within the observed data. Whilst the correlation matrix, chi-squared test for independence and box-and-whisker plots highlighted possible redundancy, natural relationships and insightful relationships between the features thereby suggesting features to be considered for omission prior to unsupervised modelling. The proposed methods and findings can be applied to various other applications of feature selection and exploration.
Detecting the Unseen: Understanding the Mechanisms and Working Principles of Earthquake Sensors

The application of movement-detection sensors is crucial for understanding surface movement and tectonic activities. The development of modern sensors has been instrumental in earthquake monitoring, prediction, early warning, emergency commanding and communication, search and rescue, and life detection. There are numerous sensors currently being utilized in earthquake engineering and science. It is essential to review their mechanisms and working principles thoroughly. Hence, we have attempted to review the development and application of these sensors by classifying them based on the timeline of earthquakes, the physical or chemical mechanisms of sensors, and the location of sensor platforms. In this study, we analyzed available sensor platforms that have been widely used in recent years, with satellites and UAVs being among the most used. The findings of our study will be useful for future earthquake response and relief efforts, as well as research aimed at reducing earthquake disaster risks.
Survey on Deep Fuzzy Systems in Regression Applications: A View on Interpretability

Abstract Deep learning (DL) has captured the attention of the community with an increasing number of recent papers in regression applications, including surveys and reviews. Despite the efficiency and good accuracy in systems with high-dimensional data, many DL methodologies have complex structures that are not readily transparent to human users. Accessing the interpretability of these models is an essential factor for addressing problems in sensitive areas such as cyber-security systems, medical, financial surveillance, and industrial processes. Fuzzy logic systems (FLS) are inherently interpretable models capable of using nonlinear representations for complex systems through linguistic terms with membership degrees mimicking human thought. This paper aims to investigate the state-of-the-art of existing deep fuzzy systems (DFS) for regression, i.e. , methods that combine DL and FLS with the aim of achieving good accuracy and good interpretability. Within the concept of explainable artificial intelligence (XAI), it is essential to contemplate interpretability in the development of intelligent models and not only seek to promote explanations after learning (post hoc methods), which is currently well established in the literature. Therefore, this work presents DFS for regression applications as the leading point of discussion of this topic that is not sufficiently explored in the literature and thus deserves a comprehensive survey.
Optimizing DNNs Model Partitioning for Enhanced Performance on Edge Devices

Deep Neural Networks (DNNs) have proven effective in various applications due to their dominant performance.However, integrating DNNs into edge devices remains challenging due to the large size of the DNN model, which requires efficient model parallelization and workload partitioning.Previous attempts to address these challenges have focused on data and model parallelism but have fallen short in terms of finding the optimal DNN model partitions for efficient distribution, considering available resources.This paper presents a pipelined DNN model parallelism framework that improves the performance of DNNs on edge devices.The framework optimizes DNN model training by determining the optimal number of partitions based on available edge resources.This is achieved through a combination of data and model parallelism techniques, which efficiently distribute the workload across multiple processors to reduce training time.The framework also includes a task controller to manage computing resources effectively.The experimental results demonstrate the effectiveness of the proposed approach, showing a significant reduction in the model training time compared to a baseline model AlexNet.