Reviewerly/exHarmony huggingface dataset:
EFCANet: Exposure Fusion Cross-Attention Network for Low-Light Image Enhancement

Image capture devices capture poor-quality images under low-light conditions, and the resulting images have dark areas due to insufficient exposure. Traditional Multiple Exposure Fusion (MEF) methods fuse images with different exposure levels from a global perspective, which often leads to secondary exposure in well-exposed areas of the original image. At the same time, the image sequences with different exposure levels are not sufficient, and the MEF method is limited by the training data and benchmark labels. To address the above problems, this paper proposes an exposure fusion cross-attention network based low-light image enhancement (EFCANet). EFCANet is characterized by recovering normal light images from a single exposure-corrected image. First, the Exposure Image Generator (EIG) is used to estimate the single exposure-corrected image corresponding to the original input image. Then, the color space of the exposure-corrected image and the original input image are converted from RGB to YCbCr, aiming to maintain the balance of brightness and color. Finally, a Cross-Attention Fusion Module (CAFM) is used to fuse the images on the YCbCr color space to achieve image enhancement. We use a single CAFM as a recursive unit, and EFCANet progressively uses four recursive units. The intermediate enhancement results generated by the first recursive unit and the exposure-corrected image of the original input image in YCbCr color space are used as inputs for the second recursive unit. We conducted comparison experiments with 14 state-of-the-art methods on eight publicly available datasets. The experimental results demonstrate that the image quality of EFCANet enhancement is better than other methods.
Designing Dispatching Rules via Novel Genetic Programming with Feature Selection in Dynamic Job-Shop Scheduling

Genetic Programming (GP) has been widely employed to create dispatching rules intelligently for production scheduling. The success of GP depends on a suitable terminal set of selected features. Specifically, techniques that consider feature selection in GP to enhance rule understandability for dynamic job shop scheduling (DJSS) have been successful. However, existing feature selection algorithms in GP focus more emphasis on obtaining more compact rules with fewer features than on improving effectiveness. This paper is an attempt at combining a novel GP method, GP via dynamic diversity management, with feature selection to design effective and interpretable dispatching rules for DJSS. The idea of the novel GP method is to achieve a progressive transition from exploration to exploitation by relating the level of population diversity to the stopping criteria and elapsed duration. We hypothesize that diverse and promising individuals obtained from the novel GP method can guide the feature selection to design competitive rules. The proposed approach is compared with three GP-based algorithms and 20 benchmark rules in the different job shop conditions and scheduling objectives. Experiments show that the proposed approach greatly outperforms the compared methods in generating more interpretable and effective rules for the three objective functions. Overall, the average improvement over the best-evolved rules by the other three GP-based algorithms is 13.28%, 12.57%, and 15.62% in the mean tardiness (MT), mean flow time (MFT), and mean weighted tardiness (MWT) objective, respectively.
Embarrassingly Parallel Independent Training of Multi-Layer Perceptrons with Heterogeneous Architectures

In this paper we propose a procedure to enable the training of several independent Multilayer Perceptron Neural Networks with a different number of neurons and activation functions in parallel (ParallelMLPs) by exploring the principle of locality and parallelization capabilities of modern CPUs and GPUs. The core idea of this technique is to represent several sub-networks as a single large network and use a Modified Matrix Multiplication that replaces an ordinal matrix multiplication with two simple matrix operations that allow separate and independent paths for gradient flowing. We have assessed our algorithm in simulated datasets varying the number of samples, features and batches using 10,000 different models as well as in the MNIST dataset. We achieved a training speedup from 1 to 4 orders of magnitude if compared to the sequential approach. The code is available online.
Self-Supervised Node Classification with Strategy and Actively Selected Labeled Set

To alleviate the impact of insufficient labels in less-labeled classification problems, self-supervised learning improves the performance of graph neural networks (GNNs) by focusing on the information of unlabeled nodes. However, none of the existing self-supervised pretext tasks perform optimally on different datasets, and the choice of hyperparameters is also included when combining self-supervised and supervised tasks. To select the best-performing self-supervised pretext task for each dataset and optimize the hyperparameters with no expert experience needed, we propose a novel auto graph self-supervised learning framework and enhance this framework with a one-shot active learning method. Experimental results on three real world citation datasets show that training GNNs with automatically optimized pretext tasks can achieve or even surpass the classification accuracy obtained with manually designed pretext tasks. On this basis, compared with using randomly selected labeled nodes, using actively selected labeled nodes can further improve the classification performance of GNNs. Both the active selection and the automatic optimization contribute to semi-supervised node classification.
Study of Plans and Strategies for Cloud Computing Transformation

The importance of cloud computing is increasing as the latest technologies such as big data and artificial intelligence, which are core technologies of the 4th industrial revolution develop and cloud computing technology spreads to utilize big data and artificial intelligence.Cloud consumers can expect cost savings in system configuration, operation, and maintenance by introducing cloud computing, as well as reducing initial construction costs, scalability, and function automation that can use resources as needed.In this paper introduces strategy establishment and considerations for cloud computing transition, presents a systematic, realistic transition plan and strategy for a successful cloud computing transition.It also analyzes the expected effects of cloud transition.The cloud computing transformation plan should analyze the company's business strategy and internal and external status to establish a strategy according to the cloud transition.The cloud computing transformation strategy should implement a phased transition for service stability rather than an overall transition.Cloud computing transformation can expect various effects such as system elasticity, agility, scalability, reliability, and cost reduction.
Avatars for Clinical Assessment

Avatars are an important feature of digital environments. Existing both in social networks and webchats (usually as static images) and in single-player and online video games (as dynamic characters, often humanoid), avatars are meant to represent users' action and communication within digital environments. Research has shown that, when they are customized by users, avatars are not created “randomly,” rather they maintain some kind of relationship with users' actual self-representation and identity. However, more recent studies showed that users may have multiple digital representations: the same person could create multiple avatars depending on which facet of the self is primed by an experimental manipulation, or on which aims they have to pursue in the given virtual environments (e.g., to seduce, to play, to work). With this background, this contribution explores the possibility to use customized avatars within psychological assessment, as adjunctive assessment tools useful to get information on patients' self-representation(s) and communicative intentions.
Revisiting Musings on Co-Designing Identity-Aware Realities in Virtual Learning

Previously, musings on co-designing identity-aware realities in the virtual space focused on the individual self during a learning experience. While preserving the individual self of the learner for a comprehensive outlook, the chapter will expand on the exploratory journey looking into the identity of the collective self, the shared experiences of those co-creating the moment, and the potential for a community of practice to emerge while learning in virtual environments. In the spirit of the contemplative goal of the chapter, design considerations in the creation and facilitation of learning experiences inclusive of the community's self are postulated.
Design of Hash Algorithm for Blockchain Security

Blockchain is the invention that permits digitally generated information to be allocated without being copied. Blockchain technology is the heart of the new internet (i.e., virtual currency). Emerging clever settlement structures over decentralized cryptocurrencies permit jointly suspicious events to transact competently without relying on third parties (i.e., the reason to provide wide security to blockchain technology). Cryptography has so many algorithms to provide security such as MD5, AES, RSA, SHA family, etc. Hash functions are extremely useful and appear in almost all information security applications, so hashing techniques are more secure among them. The authors are designing a new approach (i.e., SHA-512) in a local blockchain application. SHA-512 is a very secure algorithm that uses 64-bit words and operates on 1024-bit blocks. They are proving that SHA-512 is more collision-resistant than its predecessor with a few mathematical models.
Intelligent Devices, Device Management, and Device Security for Cloud Platforms

The phrase “intelligent device” refers to a package that contains either a full measurement system or a component inside a measurement system that includes a digital processor. The processing of measurement sensor output to adjust for flaws inherent in the measurement process results in significant gains in measurement accuracy. Intelligent device management is a technique used in corporate software applications to monitor and manage distant equipment, systems, and goods through the Internet. Device security in cloud platforms will be made using proactive attack surface management and improved protection against ransomware and other sophisticated threats safeguarding data and devices. Cloud security is a set of processes and technologies that are meant to handle both external and internal risks to enterprise security. This chapter explains about intelligent devices, the need for security and management of intelligent devices in cloud platforms, and finally ends with challenges associated with this.
Cryptocurrency Brings New Meaning to Purchase Power and Bridges the Wealth Gap

Launched in 2009, Bitcoin is considered the first cryptocurrency. This chapter takes a strategic approach to investigating Bitcoin usage and how it has brought new meaning to purchase power around the world. The chapter begins with background information on Bitcoin, followed by a description of the current Bitcoin user, followed by an explanation of how Bitcoin has brought new meaning to purchase power around the world as well during the coronavirus pandemic. Additionally, the chapter provides supporting evidence for Bitcoin's ability to bridge the wealth gap.
Distributed Deep Learning for IoT

Distributed deep learning is a type of machine learning that uses neural networks to learn and make predictions at scale. This is achieved by having many different computer systems that are connected via the internet. This allows for more parallel processing and faster results. In addition, when it comes to IoT, this type of technology can be used in conjunction with sensors and other devices to create more accurate predictions about the environment around us. Distributed deep learning can be used in many ways with the IoT because it can be applied to various aspects of IoT data processing, such as image recognition, speech recognition, natural language processing (NLP), or anomaly detection. The neural net is the most computationally intensive component of the system, and it requires a significant amount of energy. To make this system more cost-effective, there are two ways to lower the number of memory accesses: by reducing the size of images (so precision decreases), or by increasing network bandwidth so that there are fewer loop iterations required for each memory access.
Deep Learning-Based Intelligent Sensing in IoT

The heart of Industry 4.0 is established by a new technology called the internet of things (IoT). Through the internet, the IoT makes it possible for machines and gadgets to share signals. Using artificial intelligence (AI) approaches to manage and regulate the communications between various equipment based on intelligent decisions is made possible by the internet of things (IoT) technology. Data collection devices can be fundamentally altered to “lock in” to the best sensing data with regard to a user-defined cost function or design constraint by utilizing inverse design and machine learning techniques. By allowing low-cost and small sensor implementations developed through iterative analysis of data-driven sensing outcomes, a new generation of intelligence sensing systems reduces the data load while significantly enhancing sensing capabilities. Machine learning-enabled computational sensors can encourage the development of widely distributed applications that leverage the internet of things to build robust sensing networks that have an influence across a variety of industries.
Reflection on the Latent Role of Institutional Trust for the Adoption of Cryptocurrencies

Cryptocurrencies emerged with Bitcoin as a decentralized currency bringing freedom from institutions as well as uncertainty. Since then, lots of new cryptocurrencies have emerged. However, their diffusion among investors or among users as payment methods has been uneven. Several reasons could explain this discrepancy. This chapter will focus on the role of institutional base trust and present the potential role of four different institutions behind cryptocurrencies. To do so, it will describe four types of cryptocurrencies: Bitcoin as an independent cryptocurrency, JP Morgan Coin as a cryptocurrency backed by a reputable investment bank, Diem as a cryptocurrency managed by a famous brand, and Japanese-listed cryptocurrencies.
From Visual Culture in the Immersive Metaverse to Visual Cognition in Education

This chapter discusses visual culture in the immersive metaverse through the visual cognition lens. Visual cognition pertains to how we learn through visual means. As educators, we should be aware of how our students learn consciously and unconsciously through the visual sense so that we can help them navigate the immersive metaverse they encounter. Culture and visual culture are discussed. Visual perception, specifically schema and Gestalt, are explained. Learning in the immersive metaverse is as concrete as in the physical world; therefore, teaching students to decode images, perceive the metaverse, and think about images from multiple cultural backgrounds becomes an issue of special importance when education occurs in the visualized immersive metaverse.
Significance of Blockchain Technology in Banking

Blockchain, the technology that supports Bitcoin, has recently received a lot of attention. Blockchain acts as an unchangeable record that allows decentralised transactions to take place. Blockchain-based applications are getting popular in a variety of industries, particularly financial services, reputation management, the internet of things (IoT), and many others. Furthermore, there are still a number of issues with blockchain technology to address, including scalability and security concerns. This chapter provides an in-depth look of blockchain technology.
Engaging Students in a Computer Diversity Course Through Virtual Worlds

Virtual world technology allows for an immersive 3D experience with rich content and interactive potential for students. Through this richness and interactivity, educators have abundant creative power to design and facilitate meaningful learning experiences and collaboration opportunities. In this chapter, the authors discuss one such initiative using Second Life as an educational space for a community college course activity to enhance student engagement. A brief literature review of the educational use of virtual worlds will be presented, which underpin our pedagogical methodology for the project framework. Focusing on a specific community college course titled “Diversity in a Technological Society,” the course goals and project requirements will be discussed. The chapter concludes with a detailed description of the proposed methodology for the next phase, recommendations, and future work.
Stepping Out of the Classroom

While three-dimensional virtual learning environments have attracted plenty of research interest, mirror-world-like virtual campuses have been used mainly for virtual tours, promotions, or for simulation purposes. In this article, the authors investigate the use of geographically accurate mirror-world-like virtual campus models as an interactive learning environment. The initial prototype of the virtual campus covers about 2,300 m2 of a university campus and contains basic pedagogical, communicational, and content creation functionalities. A qualitative study with 14 participants explored their anticipated user experiences as well as their needs for the services and functionalities of the virtual campus. The findings suggest that a more profound link of reality and virtuality than just mirroring physical spaces in the virtual realm is needed. A hybrid reality approach is required to foster social community building and collaboration, 3D space design, and service integration. Finally, stepping out of the classroom introduces privacy issues that should be considered carefully.
Field of employment for new physics bachelors in the private sector

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Susan C. White; Field of employment for new physics bachelors in the private sector. The Physics Teacher 1 January 2023; 61 (1): 92. https://doi.org/10.1119/10.0016766 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search
Dynamic S-box with Reversible Gates for both Classical and Quantum Computer

Reversible logic gates are universally adopted to replace the classical gates in various circuits to avail the advantages of power reduction and speed maximization. The application of reversible gates includes various fields like Quantum computing, Nano technology, low power CMOS, optical computing etc. Reversible gates are the key components for the quantum circuits. Here reversible gates are used to design a dynamic light weight S-box. The S-box is the key circuit component for cryptographic algorithm. The combination of Controlled NOT(CNOT), Peres and Selim Al Mamun (SAM) gates are used to achieve the dynamic nature of the S-Box. The simulation analysis is made to evaluate the cryptographic properties such as Avalanche Criteria (AC), Strict Avalanche Criteria (SAC), Bit Independence Criteria (BIC) and Nonlinearity to measure the strength and reliability of the proposed S-box. The comparative performance investigation is done with the Rijndael S-box used in popular AES algorithm as it was widely accepted by NISTIR and still shows that the proposed S-Box outperform with respect to the existing S-box. Use of input dependant reversible logic gate combination enhances the performance of the S-box in both classical and quantum computer.
Analysis and Comparison of Image-Based UAV Detection and Identification

This paper presents Unmanned Aerial Vehicles (UAVs) detection and classification with the help of different image-based machine learning modalities. The field of UAVs attracted researchers in recent years in response to the exponential rise in the number of UAVs available in the market with applications ranging from entertainment to defense operations and the risk associated risk by the same. Presently, visual, radar, radio frequency, and acoustic sensing systems are the prevailing technologies in the field of detection and identification of UAVs. The general results of this study show that UAV machine learning-based classifications are propitious and that there are many successful individual contributions. In this research, UAVs were detected and classified using classification methods like Support Vector Machines (SVM), k-nearest neighbor (KNN), and Convolutional Neural Networks (CNN). The results demonstrated that CNN, SVM, and KNN had an accuracy of 91%, 87%, and 78%, respectively. The classifier CNN outperformed other classifiers under the same empirical circumstances.
Text to Face generation using Wasserstein stackGAN

Text-to-image generation is a difficult problem; with face datasets, the difficulty increases due to the finer details the models must learn. A model is proposed with the backbone as stack architecture with two stages. Stage-I generates low-resolution face images, which are then taken as input by Stage-II to generate high-resolution face images. Wasserstein loss has been used to stabilize training for both stages, and multiple text embeddings present for each real face dataset are provided while training in different epochs. In terms of FID score, the proposed model performs better than models like AttnGAN and DF-GAN and is comparable to TediGAN, which is the state-of-the-art architecture for text-to-face generation.
A Hybrid Resampling Approach to Handle Class Imbalance Problem and Missing Data

Class imbalance and missing data is a frequent issue in machine learning, particularly in classification issues. When working with real-life datasets to train or test classification methods, there is a possibility to run into class imbalance and incompleteness of data. Both scenarios can frequently be found in the same dataset. Class imbalance, as well as missing data, imposes limitations on the ability of classifiers to evaluate and predict the correct class. This work provides a hybrid combination of oversampling and undersampling strategies for dealing with class imbalance issues and data incompleteness. First, an oscillator built using a factor is presented that quantifies how much missing data belongs to the majority class compared to those on the minority class. The training dataset is then divided into several subsets, and the oscillator, whose value varies between 0 and 100, is used to determine whether to apply undersampling or multiple imputations based on oversampling to each subset. The effectiveness of the proposed method has been validated after experiments on benchmarked datasets.