Reviewerly/exHarmony huggingface dataset:
Elastic Adaptively Parametric Compounded Units for Convolutional Neural Network

The activation function introduces nonlinearity into convolutional neural network, which greatly promotes the development of computer vision tasks. This paper proposes elastic adaptively parametric compounded units to improve the performance of convolutional neural networks for image recognition. The activation function takes the structural advantages of two mainstream functions as the function’s fundamental architecture. The SENet model is embedded in the proposed activation function to adaptively recalibrate the feature mapping weight in each channel, thereby enhancing the fitting capability of the activation function. In addition, the function has an elastic slope in the positive input region by simulating random noise to improve the generalization capability of neural networks. To prevent the generated noise from producing overly large variations during training, a special protection mechanism is adopted. In order to verify the effectiveness of the activation function, this paper uses CIFAR-10 and CIFAR-100 image datasets to conduct comparative experiments of the activation function under the exact same model. Experimental results show that the proposed activation function showed superior performance beyond other functions.
Using a forced aligner for prosody research

Abstract Forced alignment is a speech technique that can automatically align audio files with transcripts. With the help of forced alignment tools, annotating audio files and creating annotated speech databases have become much more accessible and efficient. Researchers have recently started to evaluate the benefits and accuracy of forced aligners in speech research and have provided insightful suggestions for improvement. However, current work has so far paid little attention to evaluating forced aligners in prosody research, which focuses on suprasegmental features. In this paper, we take ambiguous sentence-level audio input in Mandarin Chinese, which can be disambiguated prosodically, to evaluate the alignment accuracy of the Montreal Forced Aligner (MFA). With a satisfactory result for syllable-by-syllable alignment, we further explore the possibility and benefits of using the forced alignment tool to generate phrase-by-phrase alignment. This topic has barely been studied in previous research on forced alignment. Our paper demonstrates that the forced alignment tool can effectively generate accurate alignment at both syllable and phrase levels for tonal languages, such as Mandarin. We found that the average differences between human annotators and MFA were smaller than the gold standard, indicating a satisfactory level of performance by the tool. Moreover, the MFA-assisted annotation rate by human transcribers was at least 20 times faster than previously reported manual annotation efficiency, providing significant time and resource savings for prosody researchers. Our results also suggest that phrase-level alignment accuracy of MFA can be affected by the quality of the recording, calling prosody researchers’ attention to controlling the audio quality in the recording. The finding that de-stressed words/phrases pose challenges for MFA also provides a reference for improving forced aligners.
Ethnographic evidence for practice

Evidence-based practice is a growing paradigm for improving practical results by informing professional recommendations and decision-making with the best available evidence. In social sciences, such as information systems, qualitative research results provide a valuable source of evidence to inform practical recommendations and decision-making. Ethnography is especially powerful because it senses cultural values at play in organizations, societies, and communities. But many ethnographic studies can be too deeply involved, extensive, and time-consuming for practical use. There are some ethnographic methods that provide rapid results. This chapter introduces three such ethnographic methods for rapidly developing evidence for decision-making in practice: Agarian Ethnography, Design Ethnography, and Research-Ethnography. Agarian Ethnography can develop evidence from a small number of intensive interviews. Design Ethnography can develop evidence from comparatively short design engagements. Research-Ethnography can develop evidence from small bodies of published research findings that proceed from diverse epistemologies, research paradigms, and methodologies.
Emergence of Blockchain Technology in Various Smart IoMT-Enabled Healthcare Security Scenarios

The Internet of Medical Things (IoMT) and blockchain technology are two key technologies that have influenced different aspects of human life. Smart healthcare and medical diagnosis are one of them. These latest trends are adding value to the existing frameworks by providing enhanced security and better data management. In today’s world, the healthcare frameworks that people are utilizing are sufficient, but they are suffering with lots of difficulties and challenges. The advanced frameworks are able to handle the massive healthcare records and also provide great security to the data by proper monitoring and real-time tracking. Nowadays, monitoring and security of medical data are the major requirements of any healthcare framework. Lots of work for improving the important parameters of medical data have been done in this domain. In order to improve the major hurdles of medical frameworks, blockchain technology has proved one of the best options. This paper shows a brief introduction about the relevance of blockchain technology in IoMT-enabled smart healthcare frameworks. Further, the paper discusses an exhaustive literature review on the existing technology based on IoMT, their issues and research challenges. In later sections, different comparative studies based on various parameters like types of blockchain frameworks, various existing frameworks and then their corresponding solutions and advantages proposed by blockchain technology have been discussed.
Quantum Genetic Algorithm with Individuals in Multiple Registers

Genetic algorithms are heuristic optimization techniques inspired by Darwinian evolution, which are characterized by successfully finding robust solutions for optimization problems. While quantum resources have allowed for the acceleration of many computational tasks, it remains an open question whether they can enhance the efficacy of evolutionary algorithms, particularly genetic algorithms. Here, we propose a subroutine-based quantum genetic algorithm with individuals codified in independent registers. This distinctive codification allows our proposal to depict all the fundamental elements characterizing genetic algorithms, i.e. population-based search with selection of many individuals, crossover, and mutation. Our subroutine-based construction permits us to consider several variants of the algorithm. For instance, we firstly analyze the performance of two different quantum cloning machines, a key component of the crossover subroutine. Indeed, we study two paradigmatic examples, namely, the biomimetic cloning of quantum observables and the Bužek-Hillery universal quantum cloning machine. We observed a faster average convergence of the former, but better final populations of the latter. Additionally, we analyzed the effect of introducing a mutation subroutine, concluding a minor impact on the average performance. Furthermore, we introduce a quantum channel analysis to prove the exponential convergence of our algorithm and even predict its convergence-ratio.
Personalized event prediction for Electronic Health Records

Clinical event sequences consist of hundreds of clinical events that represent records of patient care in time. Developing accurate predictive models of such sequences is of a great importance for supporting a variety of models for interpreting/classifying the current patient condition, or predicting adverse clinical events and outcomes, all aimed to improve patient care. One important challenge of learning predictive models of clinical sequences is their patient-specific variability. Based on underlying clinical conditions, each patient's sequence may consist of different sets of clinical events (observations, lab results, medications, procedures). Hence, simple population-wide models learned from event sequences for many different patients may not accurately predict patient-specific dynamics of event sequences and their differences. To address the problem, we propose and investigate multiple new event sequence prediction models and methods that let us better adjust the prediction for individual patients and their specific conditions. The methods developed in this work pursue refinement of population-wide models to subpopulations, self-adaptation, and a meta-level model switching that is able to adaptively select the model with the best chance to support the immediate prediction. We analyze and test the performance of these models on clinical event sequences of patients in MIMIC-III database.
Development of Architecture for Digital Supply Chain (DSC) using Blockchain, IoT and AI Technologies

To synchronize data and transactions in a supply chain network, blockchain technology was developed. This study illustrates how blockchain technology can increase the adaptability and agility of supply chain operations. With the use of blockchain and other recently developed technologies, supply chain uncertainty and other problems affecting the industry can be resolved. Issues in the supply chain are categorized, and a solution is offered by fusing blockchain technology with the Internet of Things (IoT), Artificial Intelligence (AI), and other technologies. Integration shows how tasks are completed utilizing blockchain and several supply chain technologies. Digital supply chains(DSC) are setting standards in a wide range of sectors because they are effective and lean. The creation of sustainable and environmentally friendly supply chains benefits from the use of intelligent technology. The study paper covers the challenges of implementing a digital supply chain. This will serve as a foundation for studying supply chain technology.
Electrically tunable optical spatial differentiation with graphene

In recent years, optical analog computing has experienced rapid development, among which optical differential operation has attracted great attention. Here, based on the unique optical properties of graphene, we propose an electrically tunable optical spatial differentiation by introducing a graphene layer at a quartz substrate. It is found that the output light field is sensitive to the graphene layer near the Brewster angle for small polarization output at the graphene-quartz substrate interface and can be modulated by changing the Fermi energy of graphene. In this case, the result of the optical differential operation can be dynamically regulated. Almost strict one-dimensional differential operations in different directions and almost perfect two-dimensional differential operations can be achieved. In addition, two-dimensional edge detection with different degrees of distortion in different directions can also be realized when applied to image processing. This new modulation method may provide more possibilities for tunable image edge detection and provide a potential way for developing more versatile optical simulators in the future.
A Study on the Application of Blockchain Technology in the Banking and Financial Sector in India

Modern technology is influencing our daily lives, from using a remote to control equipment to utilizing voice notes to give orders. Because of its properties of decentralization, enforceability, and sharing, blockchain technology is frequently used in banking, digital asset trade, and other sectors. Blockchain has emerged as a popular issue in fintech research, influencing the evolution of traditional financial forms. It is regarded as the foundation of the digital economy. Blockchain technology may be defined as a data structure that keeps transactional records and, by ensuring security, transparency, and decentralization, eliminates the possibility of fraudulent behaviour or transaction repetition without the use of a third party. This study examines the characteristics and challenges of blockchain technology, the impact of blockchain technology on the operation and administration of the banking and financial sector and the possibilities for blockchain technology implementation in banking and financial sector in India.
Quantum policy gradient algorithms

Understanding the power and limitations of quantum access to data in machine learning tasks is primordial to assess the potential of quantum computing in artificial intelligence. Previous works have already shown that speed-ups in learning are possible when given quantum access to reinforcement learning environments. Yet, the applicability of quantum algorithms in this setting remains very limited, notably in environments with large state and action spaces. In this work, we design quantum algorithms to train state-of-the-art reinforcement learning policies by exploiting quantum interactions with an environment. However, these algorithms only offer full quadratic speed-ups in sample complexity over their classical analogs when the trained policies satisfy some regularity conditions. Interestingly, we find that reinforcement learning policies derived from parametrized quantum circuits are well-behaved with respect to these conditions, which showcases the benefit of a fully-quantum reinforcement learning framework.
A Single-Stage Unsupervised Denoising Low-Illumination Enhancement Network Based on Swin-Transformer

Traditional low-light enhancement methods are often based on paired datasets for training. The training data is difficult to obtain and the resulting model has poor generalization. In unsupervised low-light enhancement networks, because paired data is not used for training, there is a lack of effective denoising methods, which often leads to noise problems. In this paper, we propose a low-light enhancement network based on Swin-Transformer and CNN, guided by brightness information. It is worth noting that it also has an unsupervised denoising module, which allows our network to achieve low-light restoration and noise removal goals with a single training session without any additional or fake samples. Our network is based on the idea of generative adversarial networks. We conducted extensive experiments on datasets such as LOL and VE-LOL-L, and compared our results with mainstream unsupervised learning, zero-shot learning, and even partially supervised learning low-light enhancement methods using a series of image indicators and visual effects such as PSNR, SSIM, NIQE, and LOE. The results show that our network is superior to mainstream unsupervised learning and partially supervised learning methods.
Investigation of Shadowing Effect and Electricity Generation in Seasonally Adjusted Solar Photo Voltaic Arrays in Indian Sub-Continent

Solar photovoltaic panels are very widely used these days across the country. Their usage is also being encouraged by the central government and there are many subsidies in place as well. The tilt angle of the solar photovoltaic panel with the horizontal along with module orientation determines the transformation of energy into electricity. The optimum tilt angle for solar photovoltaic panels is considered for incident annual solar energy, this angle is usually kept unchanged for the year-round cycle. In this research work, we examine the optimum tilt angles of panels for the incident energy over panels on an annual and seasonal basis in combination with the shading effect of these panels over rooftops of Delhi (latitude 28.7° N), Nagpur (latitude 21.15° N) and Ahmedabad (latitude 23.02° N). Solar radiation data for the investigation purpose has been classified under 3 seasons, i.e., summer (May, June, July, August), winter (November, December, January, February) and equinoxes (March, April, September, October). It is observed that when panels are arranged facing down south and adjusted seasonally for optimum tilt angle; on an annual basis, they generate higher electrical energy and, in the summer, cast a larger shadow; in the winter, cast a smaller shadow as compared to panels which have fixed tilt throughout the year. This arrangement helps in reducing the solar heat flux via the roof in summer and increasing it in winter, thus, favourably affecting air conditioning load levelling in the structure.
Automated Segmentation of Mitochondria in Virus-Infected Cells Using Deep Learning Models

Journal Article Automated Segmentation of Mitochondria in Virus-Infected Cells Using Deep Learning Models Get access Matthijs Schrage, Matthijs Schrage Delmic B.V., Life Sciences, Delft, The Netherlands Corresponding Author: schrage@delmic.com Search for other works by this author on: Oxford Academic Google Scholar Mario-Alin Rus, Mario-Alin Rus Delmic B.V., Life Sciences, Delft, The NetherlandsUniversity of Leiden, Leiden, The Netherlands Search for other works by this author on: Oxford Academic Google Scholar Marre Niessen, Marre Niessen Delmic B.V., Life Sciences, Delft, The Netherlands Search for other works by this author on: Oxford Academic Google Scholar Thomas Burgoyne, Thomas Burgoyne Institute of Ophthalmology, University College London, London, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Andreia Pinto, Andreia Pinto Royal Brompton Hospital, London, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Katherine Lau Katherine Lau Delmic B.V., Life Sciences, Delft, The Netherlands Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 979–981, https://doi.org/10.1093/micmic/ozad067.490 Published: 22 July 2023
Video Loop

Restricted accessAbstractFirst published online July 21, 2023Video LoopVolume 18, Issue 2_supplhttps://doi.org/10.1177/15569845231181442q
Analysis of the Application of the Principles of the Theories of Motivation in the Gamification of Learning

Recently, in the field of education, there has been a serious problem associated with the motivation for learning among schoolchildren and students. This is evidenced by the statistics of surveys of teachers and parents, which are similar in that there is a tendency to reduce the motivation to acquire new knowledge among children and adolescents, which, as a result, leads to a decrease in the overall level of education and training of specialists in various fields. The text analyzes theories of motivation, the concept of gamification and the principles of its use in the learning process are explored, as well as the relationship between motivation for learning and the gamification of the process. The author attemps to identify the main motivation theories and suggests options for using them in the development of software products for schools and higher educational institutions
The Ability to Learn – a Basic Prerequisite for Competence and Fulfilment

The dynamic development of the processes, which are realized in the modern societies – the diversity in the adolescent generations, the entering mass digitalization, the increased demands of the employers for knowledgeable, capable and experienced personnel, create serious challenges for the educational system and in particular for the higher education. In this environment, it is also necessary to rethink teaching methods in order to improve them.
Jejeran: Interactive Game-Based Flash Media as Social Studies Learning Media in Elementary Schools

Social studies learning in elementary schools has so far focused on conventional methods by giving verbal explanations. An innovation is needed that can help teachers carry out learning in an interesting, effective and efficient manner. One effort that can be done is to present an interactive flash technology-based learning media. The purpose of this study was to analyze the effectiveness of interactive game-based flash media as learning media in elementary schools. The research method used is research and development with the ADDIE model. This study involved teachers and 27 third grade students. Data collection techniques used are the results of unstructured interviews, observation, questionnaires, written questions (Pretest and Post-test). Product validity is obtained from expert validation and product trials. The results showed that the development of flash media based on interactive games was in the category of very feasible and very effective to use based on the feasibility test according to media experts with a total percentage of 90% and the due diligence according to material experts was 90.38%. This learning media was stated to be very effective based on the effectiveness test with the results of the small group test with a total percentage of 83.33% and the large group trial with a total percentage of 90.40%. In its application, the use of this media was able to increase the average value from 74.81 to 86.45 so that interactive game-based flash learning media was included in the category of very valid, very feasible and very effective.
Texture-Shape Optimized GAT for 3D Face Reconstruction

3D face reconstruction is widely used in face recognition research, online makeup, etc. However, texture and shape distortion regions usually exist in the reconstruction results. This paper proposes a novel framework named Texture-Shape optimized GAT for 3D face Reconstruction (TSGAT-3D), including data preprocessing and training phases. In the data preprocessing phase, we adopt the styleGAN2 to convert single-view images to multi-view images set. In the training phase, we design a novel Texture-Shape optimized Graph Attention Network, which can learn the facial prior knowledge from the multi-view images set, to improve the details of the initially reconstructed faces based on the auto-encoder module. This network can aggregate the features of face vertices according to the correlation of vertices, thereby improving the accuracy of the 3D reconstructed faces. Furthermore, we present a view loss function for this framework to constrain the shape and texture of the reconstructed face. Extensive experiments conducted on CelebA and Bosphorus show that the reconstruction results of our proposed method are closer to the real 3D faces.
Error and Correlation as fitness functions for Scaled Symbolic Regression in Grammatical Evolution

Linear scaling has greatly improved the performance of genetic programming when performing symbolic regression. Linear scaling transforms the output of an expression to reduce its error. Mean squared error and correlation have been used with scaling, often interchangeably and with assumed equivalence. We examine if this equivalence is justified by investigating the differences between an error-based metric and a correlation-based metric on 11 well-known symbolic regression benchmarks. We investigate the effect a change of fitness function has on performance, individuals size and diversity. Error-based scaling and Correlation were seen to attain equivalent performance and found solutions with very similar size and diversity on the majority of problem, but not all. In order to ascertain if the strengths of both approaches could be combined, we explored a double tournament selection strategy, where two tournaments are conducted sequentially to select individuals for recombination. Double tournament selection found smaller solutions and the best solution in five benchmarks, including finding the best solutions on both real-world dataset used in our experiments.