Reviewerly/exHarmony huggingface dataset:
Adaptive methods of generating complex light arrays

Structured light arrays of various shapes have been a cornerstone in optical science, driven by the complexities of precise and adaptable generation. This study introduces an approach using a spatial light modulator (SLM) as a generator for these arrays. By projecting a holographic mask onto the SLM, it functions simultaneously as an optical convolution device, focusing mechanism, and structured light beam mask. Our approach offers unmatched versatility, allowing for the experimental fabrication of traditional beam arrays like azimuthal Laguerre-Gaussian (LG), Bessel-Gaussian (BG), and Hermite-Gauss (HG) in the far-field. Notably, it has enabled a method of generating Ince-Gauss (IG) and LG radial mode beam arrays using a convolution solution. Our system provides exceptional control over array periodicity and intensity distribution, bypassing the Talbot self-imaging phenomenon seen in traditional setups. We provide an in-depth theoretical discussion, supported by empirical evidence, of our far-field results. This method has vast potential for applications in optical communication, data processing, and multi-particle manipulation. It paves the way for rapid generation of structured light with high spatial frequencies and complex shapes, promising transformative advances in these domains.
Machine learning classification of diagnostic accuracy in pathologists interpreting breast biopsies

This study explores the feasibility of using machine learning to predict accurate versus inaccurate diagnoses made by pathologists based on their spatiotemporal viewing behavior when evaluating digital breast biopsy images.The study gathered data from 140 pathologists of varying experience levels who each reviewed a set of 14 digital whole slide images of breast biopsy tissue. Pathologists' viewing behavior, including zooming and panning actions, was recorded during image evaluation. A total of 30 features were extracted from the viewing behavior data, and 4 machine learning algorithms were used to build classifiers for predicting diagnostic accuracy.The Random Forest classifier demonstrated the best overall performance, achieving a test accuracy of 0.81 and area under the receiver-operator characteristic curve of 0.86. Features related to attention distribution and focus on critical regions of interest were found to be important predictors of diagnostic accuracy. Further including case-level and pathologist-level information incrementally improved classifier performance.Results suggest that pathologists' viewing behavior during digital image evaluation can be leveraged to predict diagnostic accuracy, affording automated feedback and decision support systems based on viewing behavior to aid in training and, ultimately, clinical practice. They also carry implications for basic research examining the interplay between perception, thought, and action in diagnostic decision-making.The classifiers developed herein have potential applications in training and clinical settings to provide timely feedback and support to pathologists during diagnostic decision-making. Further research could explore the generalizability of these findings to other medical domains and varied levels of expertise.
A Lightweight Multi-Scale Quadratic Separation Convolution Module for CNN Image-Classification Tasks

Currently, most convolutional networks use standard convolution for feature extraction to pursue accuracy. However, there is potential room for improvement in terms of the number of network parameters and model speed. Therefore, this paper proposes a lightweight multi-scale quadratic separable convolution module (Mqscm). First, the module uses a multi-branch topology to maintain the sparsity of the network architecture. Second, channel separation and spatial separation methods are used to separate the convolution kernels, reduce information redundancy within the network, and improve the utilization of hardware computing resources. In the end, the module uses a variety of convolution kernels to obtain information on different scales to ensure the performance of the network. The performance comparison on three image-classification datasets shows that, compared with standard convolution, the Mqscm module reduces computational effort by approximately 44.5% and the model training speed improves by a range of 14.93% to 35.41%, maintaining performance levels comparable to those of deep convolution. In addition, compared with ResNet-50, the pure convolution network MqscmNet reduces the parameters by about 59.5%, saves the training time by about 29.7%, and improves the accuracy by 0.59%. Experimental results show that the Mqscm module reduces the memory burden of the model, improves efficiency, and has good performance.
Sub-Diffraction Photon Trapping: The Possible Optical Energy Eigenstates within a Tiny Circular Aperture with a Finite Depth

One of the challenging riddles that is set by light is: do photons have wavefunctions like other elementary particles do? Wave–particle duality has been a prevailing fact since the beginning of quantum theory thought; in electromagnetism, light is already a kind of undulation, so what about the waves of probability then? Well, Quantum Field Theory (QFT) has a rigorous explanation and supports the idea when they are considered as fields of particles via second quantization; they do have wavefunctions of probability, and it does not have anything to do with the regular oscillations. They can be related to the energy and momentum signatures of harmonic oscillations, resembling an imitation of the behavior of a classical harmonic oscillator, which then has a wavefunction to solve the corresponding time-independent Schrödinger equation. For the last half century, electrical engineering has owned the best out of these implications of Quantum Electrodynamics (QED) and QFT by engineering better semiconductor techniques with finely miniaturized transistors and composite devices for digital electronics and optoelectronics fields. More importantly, these engineering applications have also greatly evolved into combined fields like quantum computing that have introduced a completely new and extraordinary world to electronics applications. The study takes advantage of the power of QFT to mathematically reveal the bosonic modes (Laguerre–Gaussian) that appear in a sub-diffraction cylindrical aperture. In this way, this may lead to the construction of the techniques and characteristics of room-temperature photonic quantum gates which can isolate photon modes under a diffraction limit.
Description and Analysis of Data Security Based on Differential Privacy in Enterprise Power Systems

The pursuit of environmental sustainability, energy conservation, and emissions reduction has become a global focal point. Electricity is the primary source of energy in our daily lives. Through the analysis of smart power systems, we can efficiently and sustainably harness electrical energy. However, electric power system data inherently contain a wealth of sensitive user information. Therefore, our primary concern is protecting these sensitive user data while performing precise and effective analysis. To address this issue, we have innovatively proposed three granular information models based on differential privacy. In consideration of the characteristics of enterprise electricity consumption data and the imperative need for privacy protection, we implement a reasonable modeling process through data processing, information granulation expression, and the optimization analysis of information granularity. Our datasets encompass enterprise electricity consumption data and related attributes from Hitachi Building Technology (Guangzhou) Co., Ltd’s cloud computing center. Simultaneously, we have conducted experiments using publicly available datasets to underscore the model’s versatility. Our experimental results affirm that granular computation can improve the utility of differential privacy in safeguarding data privacy.
The material geographies of Bitfury in Georgia: Integrating cryptoasset firms into global financial networks

Cryptocurrencies, such as Bitcoin, have garnered significant attention in scholarship and beyond. Geographical work on cryptocurrencies has focussed on how their energy demand interacts with local communities and economies. Less is said about the organization of cryptoasset firms and their associated demands. This paper illuminates the complex geographies of one such firm, Bitfury Group, to investigate the global and national forms and structures such companies take and the factors encouraging them to concentrate operations in certain areas. To investigate the latter, we adopt the case study of Bitfury’s operations in Georgia, a South-Caucasian country where its presence is significant. We adapt Haberly et al.’s analytical framework to explore Bitfury’s geographical dimensions. We highlight how cheap electricity, regulatory and taxation regimes, personal encounters and personalities, and the materialities of hardware and energy-saving technology define these geographies and illuminate how Bitfury actively curates advantageous regulatory spaces. We encourage future work exploring Blockchain and Bitcoin technologies to understand the companies involved as simultaneously material and virtual, and as centrepieces in global networks interweaving production and finance.
Augmented Grad-CAM++: Super-Resolution Saliency Maps for Visual Interpretation of Deep Neural Network

In recent years, deep neural networks have shown superior performance in various fields, but interpretability has always been the Achilles’ heel of deep neural networks. The existing visual interpretation methods for deep neural networks still suffer from inaccurate and insufficient target localization and low-resolution saliency maps. To address the above issues, this paper presents a saliency map generation method based on image geometry augmentation and super-resolution called augmented high-order gradient weighting class activation mapping (augmented grad-CAM++). Unlike previous approaches that rely on a single input image to generate saliency maps, this method first introduces the image geometry augmentation technique to create a set of augmented images for the input image and generate activation mappings separately. Secondly, the augmented activation mappings are combined to form the final saliency map. Finally, a super-resolution technique is introduced to add pixel points to reconstruct the saliency map pixels to improve the resolution of the saliency map. The proposed method is applied to analyze standard image data and industrial surface defect images. The results indicate that, in experiments conducted on standard image data, the proposed method achieved a 3.1% improvement in the accuracy of capturing target objects compared to traditional methods. Furthermore, the resolution of saliency maps was three times higher than that of traditional methods. In the application of industrial surface defect detection, the proposed method demonstrated an 11.6% enhancement in the accuracy of capturing target objects, concurrently reducing the false positive rate. The presented approach enables more accurate and comprehensive capture of target objects with higher resolution, thereby enhancing the visual interpretability of deep neural networks. This improvement contributes to the greater interpretability of deep learning models in industrial applications, offering substantial performance gains for the practical deployment of deep learning networks in the industrial domain.
Cycle‐Consistent Generative Adversarial Network and Crypto Hash Signature Token‐based Block chain Technology for Data Aggregation with Secured Routing in Wireless Sensor Networks

Summary A fundamental component of Wireless Sensor Networks (WSN) is routing, because it is responsible for data transmission to the base stations (BS). Routing attacks are attacks that have the ability to interfere with the operation of WSN. A reliable routing system is needed for guaranteeing routing security including WSN effectiveness. Many studies have been conducted to improve trust among routing nodes, cryptographic algorithms and centralized routing decisions. Nevertheless, most of the routing techniques are not practical because it is challenging to detect suspicious actions of routing nodes. Generally, there is lack of reliable technique for preventing malicious node attacks. Therefore in this manuscript, Cycle‐Consistent Generative Adversarial Network (CCGAN) optimized with Ebola Optimization Search Algorithm (EOSA) and Crypto Hash Signature (CHS) Token‐based Block chain (BC) Technology for Data Aggregation with Secured Routing in Wireless Sensor Networks (SR‐CCGAN‐EOSA‐BDA‐WSN) is proposed for data aggregation with secured optimum routing in WSN. The proposed methodology uses a Proof of Authority (PoA) method in the block chain network to authenticate the process of node transmission. A Cycle‐Consistent Generative Adversarial Network optimized with the Ebola optimization algorithm methodology (CCGAN‐EOSA) is used to select the validation group required for proofing and select the proper next hop as a forwarding node proficient of securely and easily transmitting messages. The performance metrics, like delay, average latency with energy consumption, block chain token transactions throughput is analyzed. The performance of SR‐CCGAN‐EOSA‐BDAWSN proposed method provides 76.26%, 65.57%, and 42.9% lesser delay under 30% spiteful routing environment; 73.06% are compared with existing methods.
Decentralised collaborative job reassignments in additive manufacturing

Cloud Manufacturing (CMfg) is a promising approach that leverages the sharing economy to reduce costs and enhance supply chain flexibility. Particularly, when utilised alongside Additive Manufacturing (AM), CMfg is considered a key enabler for collaborative production (CP) systems. However, there is still a lack of planning models that reduce entry barriers for CP. Therefore, we propose a decentralised CP planning framework for AM. In our approach, machines autonomously select jobs from an existing production plan to forward them to other suppliers that can produce these parts more efficiently. A CMfg platform facilitates job forwarding and creates promising part bundles and manufacturing machines autonomously places bids on the packages via a combinatorial 2nd price reverse auction. Costs of the reallocated bundles are shared throughout a Shapley value-based approach without the need to disclose critical information. We benchmark our proposed framework against a centralised planning approach and find that it achieves comparable effectiveness as the benchmark solution. We also show that this mechanism promotes individual rationality and that agents particularly benefit when participating in both offering and acquiring production jobs through the auction.
Singing in Dark Times: Improvisational Singing with Children Amidst Ecological Crisis

Through this research-creation project -- which is represented by a process-driven ten-minute video -- the author asks what ways of knowing emerge when children and adults, more-than-human, and inhuman engage in improvised singing together in an urban park? This project recognizes our current "dark times" within ecological collapse and operates from a space that hopes to build relationality with sonic ecologies through listening-and-singing experiences, while centering the voices of children and other singers within the ecologies we sing in-and-with.
Optimally-Fair Exchange of Secrets via Delay Encryption and Commutative Blinding

Abstract We propose a new fair exchange protocol that takes advantage of delay encryption and commutative encryption to achieve optimal partial fairness among all protocols involving one-way messages. Our protocol consists of 3 setup messages and $$2N+1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mi>N</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> exchange messages and it is fair against covert adversaries with probability $$1- \frac{1}{2N}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>-</mml:mo> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mrow> <mml:mn>2</mml:mn> <mml:mi>N</mml:mi> </mml:mrow> </mml:mfrac> </mml:mrow> </mml:math> . We prove that this is optimal up to shortening the setup phase which is notably more efficient than existing protocols.
Technology adoption theories towards environmentally sustainable pharma 4.0: a rational selection approach

Purpose This study is centered on the identification of the most appropriate Technology Adoption (TA) model for investigating the adoption of Industry 4.0 technologies within pharmaceutical and related enterprises. The aim is to facilitate a smooth transition to advanced technologies while concurrently achieving environmental sustainability. Design/methodology/approach Selection of a suitable TA theory is carried out using a hybrid multi-criteria decision-making (MCDM) approach incorporating PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) and Fuzzy Measurement of alternatives and ranking according to Compromise solution (F-MARCOS) methods. A group of three experts is formulated for the ranking of criteria and alternatives based on those criteria. Findings The results indicate that out of all six TA models considered unified theory of acceptance and use of technology (UTAUT) model gets the highest utility function value, followed by the technical adoption model (TAM). Further, sensitivity analysis is conducted to confirm the validity of the MCDM model employed. Research limitations/implications Challenging times like COVID-19 pointed out the importance of technology in the pharmaceutical and healthcare sectors. TA studies in this area can help in the identification of critical factors that can assist pharmaceutical firms in their efforts to embrace emerging technologies, enhance their outputs and increase their efficiency. Originality/value The novelty of this research lies in the fact that the utilization of a TA theory prior to its implementation has not been witnessed in existing scholarly literature. The utilization of a TA theory, specifically within the pharmaceutical industry, can assist enterprises in directing their attention toward pertinent factors when contemplating the implementation of emerging technologies and achieving sustainable development.
An edge‐assisted federated contrastive learning method with local intrinsic dimensionality in noisy label environment

Abstract The advent of federated learning (FL) has presented a viable solution for distributed training in edge environment, while simultaneously ensuring the preservation of privacy. In real‐world scenarios, edge devices may be subject to label noise caused by environmental differences, automated weakly supervised annotation, malicious tampering, or even human error. However, the potential of the noisy samples have not been fully leveraged by prior studies on FL aimed at addressing label noise. Rather, they have primarily focused on conventional filtering or correction techniques to alleviate the impact of noisy labels. To tackle this challenge, a method, named DETECTION , is proposed in this article. It aims at effectively detecting noisy clients and mitigating the adverse impact of label noise while preserving data privacy. Specially, a confidence scoring mechanism based on local intrinsic dimensionality (LID) is investigated for distinguishing noisy clients from clean clients. Then, a loss function based on prototype contrastive learning is designed to optimize the local model. To address the varying levels of noise across clients, a LID weighted aggregation strategy (LA) is introduced. Experimental results on three datasets demonstrate the effectiveness of DETECTION in addressing the issue of label noise in FL while maintaining data privacy.
Reconfigurable Physical Reservoir Enabled by Polarization of Ferroelectric Polymer P(VDF–TrFE) and Interface Charge‐Trapping/Detrapping in Dual‐Gate IGZO Transistor

Abstract Neuromorphic computers promise to enhance computing efficiency by eliminating conventional von Neumann architecture bottlenecks. Bio‐inspired artificial neural networks, such as feedforward neural networks and reservoir computing (RC), face challenges due to the unique memristor requirements. In this study, a dual‐gate ferroelectric polymer P(VDF–TrFE)‐coupled thin film transistor (DG–TFT) with an IGZO channel is presented. It yields complementary short‐ and long‐term memory functionalities are derived from the charge‐trapping/detrapping process at the IGZO‐SiO 2 dielectric interface and ferroelectric polarization. These memory functionalities can be switched using different gated modes to meet the requirements of the reservoir and readout layers in RC. The bottom‐gated mode (BG‐mode) exhibits short‐term memory effects and nonlinear dynamics, whereas the top‐gated mode (TG‐mode) displays improved long‐term memory characteristics. To evaluate the long‐term memory properties, Python is used for pattern recognition. For the nonlinear dynamics and short‐term memory response of the BG‐mode, the DG–TFT is employed as a reservoir layer to handle various temporal tasks. Notably, the polarization level of the ferroelectric layer is coupled to improve the richness of the reservoir states, providing a reconfigurable RC system with an expanded capacity to effectively process and accommodate diverse signals. This holds potential for next‐generation hybrid intelligent applications.
A Comprehensive Study on the Use of Blockchain Technology in Healthcare

Blockchain technology has emerged as a potential solution to improve healthcare services by offering secure, transparent and efficient management of healthcare data. The paper presents an overview of the applications, benefits, challenges and recommendations for using Blockchain technology in healthcare. A systematic literature review has been conducted to identify relevant articles published in recent years, using the PRISMA framework to ensure methodological rigor. The review has revealed that Blockchain technology can be used in various healthcare applications, including data management, drug supply chain management, clinical trials, medical record keeping, and telemedicine. Blockchain technology offers several benefits in healthcare, such as secure and efficient data sharing, real-time access to patient data, improved patient outcomes, and lower costs. However, the adoption of Blockchain technology in healthcare also presents some challenges, such as regulatory barriers, interoperability issues, data privacy and security concerns, and technical limitations. To overcome these challenges, recommendations are provided, including developing a regulatory framework, addressing interoperability issues, implementing robust data privacy and security measures, and investing in Blockchain technology research and development.