Reviewerly/exHarmony huggingface dataset:
CHC-UNet: Cascaded Heterogeneous Convolution UNet Framework for Adaptive Image Dehazing

Haze is a phenomenon of degrading the visual quality of outdoor images by wet aerosols and particles suspended in the atmosphere under poor weather conditions. The incident light on to the object will be scattered and causes the distortion of color, contrast reduction and generate the halo artifacts. In this article, a Cascaded Heterogeneous Convolution UNet (CHC-UNet) with redefined Squeeze-and-Excitation (SE) mechanism framework is designed to reduce the adverse hazes’ effect in the images. The Heterogeneous Convolution kernels with size 3×3 and 1×1 are used in each block of UNet to extract the features at various image resolutions and to overcome excessive computation load. The adaptive residual connections preserve the original image features, prevent from overfitting problem and make the dehazing process more thorough. The SE Algorithm is re-defined to better represent the channel-wise image features with Local Feature Fusion (LFF) module. The qualitative and quantitative findings of the ablation experiments show that the suggested framework outperforms state-of-the-art methodologies in the examined literature using the benchmark I-Haze, O-Haze, and D-Hazy datasets. The proposed framework has an average improvement from 17.95 dB to 27.52 dB in Peak Signal-to-Noise Ratio (PSNR), from 0.56 to 0.87 in Structural SIMilarity (SSIM) index on retrieved images, from 4.98 to 3.12 in Naturalness Image Quality Evaluator (NIQE). Furthermore, the framework has varying degrees of improvement in the subjective visual effects of the dehazed output, and it also has the added advantage of optimum running time in comparison with the existing dehazing methods.
Measurement of Similarity (II)

This chapter continues the subject of Chap. 3 , considering the special case of the measurement of similarity between units of different cardinalities. The methodology adopted is described with the help of three examples in a growing complexity. The last section discusses some important issues arisen in the three cases.
Social Cybernetics in Human Resource Management

This paper explores the application of social cybernetics to human resource management. It is shown that social cybernetics is a social direction and its application refers to the field of social management. The content of social cybernetics and its stratification are disclosed. The distinction between cybernetics of the first, second, third and fourth order is shown. The factors of development of social cybernetics are described. It is shown that models of social systems differ from models of living systems. The main systems studied in social cybernetics are distinguished: observable systems, observational systems, cognitive systems, self-developing systems. It is shown that fourth-order cybernetics studies the mechanisms of information interaction and tacit knowledge. Fourth order cybernetics studies rational choice and rational behavior. The concept of rationality in social cybernetics is disclosed and its difference from the concept of optimality is shown. The connection between heuristics and social cybernetics is shown. Reflexivity as a factor of fourth-order cybernetics is described. Stratification of human resource management from the standpoint of social cybernetics is described. The specificity of human resource management for cybernetics of different levels is shown. A number of cybernetic models related to human resources is described. A cybernetic analysis of the Big Five model is given. #CSOC1120.
Interpreting the Incomprehensible: Benchmarking Visual Explanation Methods for Deep Convolutional Networks

Deep Convolutional Networks (CNNs) have revolutionized various fields, including computer vision, but their decision-making process remains largely opaque. To address this interpretability challenge, numerous visual explanation methods have been proposed. However, a comprehensive evaluation and benchmarking of these methods are essential to understand their strengths, limitations, and comparative performance. In this paper, we present a systematic study that benchmarks and compares various visual explanation techniques for deep CNNs. We propose a standardized evaluation framework consisting of benchmark explain ability methods. Through extensive experiments, we analyze the effectiveness, and interpretability of popular visual explanation methods, including gradient-based methods, activation maximization, and attention mechanisms. Our results reveal nuanced differences between the methods, highlighting their trade-offs and potential applications. We conduce a comprehensive evaluation of visual explanation methods on different deep CNNs, the results demonstrate the ability to achieve informed selection and adoption of appropriate techniques for interpretability in real-world applications.
The Road Toward Dependable AI Based Systems

With the advent of deep learning, AI components have achieved unprecedented performance on complex, human competitive tasks, such as image, video, text and audio processing. Hence, they are increasingly integrated into sophisticated software systems, some of which (e.g., autonomous vehicles) are required to deliver certified dependability warranties. In this talk, I will consider the unique features of AI based systems and of the faults possibly affecting them, in order to revise the testing fundamentals and redefine the overall goal of testing, taking a statistical view on the dependability warranties that can be actually delivered. Then, I will consider the key elements of a revised testing process for AI based systems, including the test oracle and the test input generation problems. I will also introduce the notion of runtime supervision, to deal with unexpected error conditions that may occur in the field. Finally, I will identify the future steps that are essential to close the loop from testing to operation, proposing an empirical framework that reconnects the output of testing to its original goals.
Proposal of ReLU activation function using III-V semiconductor membrane laser for optical neural network

We propose programmable low-power-consumption optical ReLU activation function for the fully optical neural network. The optical-to-optical nonlinearity is realized using characteristics of semiconductor membrane laser on Si-platform, and it generates ReLU function with controllable threshold input power. Optical ReLU function using a GaInAsP membrane distributed reflector laser on Si was experimentally demonstrate.
Soft Time Stretch: Boosting the Stretch Factor by Deep Learning

We propose the concept of Soft Time Stretch, a technique that boosts the stretch factor of classic time-stretch systems by exploiting deep learning and nonlinear optics.
The Talk: Speculative Conversation with Everyday Objects

Communication between humans and everyday objects is often implicit. In this paper, we create speculative scenarios where users could have a conversation, as explicit communication, with everyday objects that usually may not have conversational artificial intelligence (AI) installed, such as a book. We present a design fiction narrative and a conversational system about conversations with everyday objects. Our user test showed a positive acceptance of conversation with everyday objects, and users tried to build a human-like relationship with them.
Gumball Dreams: Live Theatre in VR

Since 2020 Ferryman Collective has been pushing the boundaries of what is possible in theater by using VR technology to give audiences the ability to become a part of the story in a fully immersive way. VR theatre offers the feeling of embodiment and immersion that closely mimics the feeling of going to a theater in person, with the added interactivity of gaming, and the cinematic qualities of film. Ferryman Collective productions have been well received at the most prestigious festivals in the world and garnered many awards along the way.
Social VR

While many VR demos involve two users or one using a virtual agent, few contemplate using social VR. Many live demos at industry conferences do not adequately address the social aspect of VR and will not explore its possibilities. Given that Immersive experiences are moving more and more to social VR experiences, it is crucial to discuss its development and implications in various applications, such as arts, games, entertainment, health, and education.
PhysicsAR: Problem Set Reality

PhysicsAR is an augmented reality (AR) application that transcends traditional teaching methods by enabling users to delve into the intricacies of physics through interactions with real-world objects. By seamlessly integrating AR technology with advanced physics concepts, PhysicsAR offers an unparalleled learning experience. This paper presents a comprehensive overview of PhysicsAR, emphasizing its ability to turn real world objects into interactive problems, and the potential it holds for physics education.
Detection of ovarian follicles cancer cells using hybrid optimization technique with deep convolutional neural network classifier

Having one’s life threatened by a disease like ovarian cancer is the single most crucial thing in the whole world. It is difficult to achieve high performance without sacrificing computational efficiency; the results of the denoising process are not as good as they could be; the proposed models are nonconvex and involve several manually chosen parameters, which provides some leeway to boost denoising performance; the methods generally involve a complex optimisation problem in the testing stage; Here at DnCNN, we’ve developed our own version of the deep ii learning model, a discriminative learning technique. The goal was to eliminate the need for the iterative optimisation technique at the time it was being evaluated. The goal was to avoid having to go through testing altogether, thus this was done. It is highly advised to use a Deep CNN model, the efficacy of which can be evaluated by comparing it to that of more traditional filters and pre-trained DnCNN. The Deep CNN strategy has been shown to be the best solution to minimise noise when an image is destroyed by Gaussian or speckle noise with known or unknown noise levels. This is because Deep CNN uses convolutional neural networks, which are trained using data. This is because convolutional neural networks, which are the foundation of Deep CNN, are designed to learn from data and then use that learning to make predictions. Deep CNN achieves a 98.45% accuracy rate during testing, with an error rate of just 0.002%.
Metaverse-oriented visual art quality enhancement strategies: a field architecture design and fuzzy assessment theory perspective

Visual art was originally measured by viewing and appreciating graphic works, and there was no previous research into ways to improve the quality of visual art. With the rapid development of visual arts and technology, the question of how to improve quality has become an urgent one. As the most cutting-edge and hottest concept in the international arena today, the development and application of metaverse technology has widely drawn the close attention of various industries, including management, economy, education, and art. However, there is no in-depth and clear research on the concept of metaverse in the field of art, especially in the field of visual art. We believe that the creation of visual art in the context of metaverse will be an important direction for art development in the future, and can also greatly contribute to the improvement of the quality of metaverse visual art presentation. Therefore, we focus on the issue of visual art quality assessment in our research, and propose a theory and method of metaverse-oriented future visual art quality assessment. The method focuses on the G1-entropy value method to calculate the weights in visual arts, combines qualitative research with quantitative research, and proposes the improvement path and countermeasures for visual arts. In summary, our research aims to address the theoretical approaches to the design of the metaverse field architecture and the assessment of art quality for the future introduction of the metaverse. The main contributions of our research are focused on the following three aspects: 1. The construction of the visual art field architecture draws on the functional requirements analysis method of system science simulation, considering that the entire visual art metaverse field architecture is constructed at three levels: the bottom data support layer, the middle technical support layer and the upper technical application layer. 2. The G1-entropy combination weighting method is used to derive the importance ranking of visual art quality indicators and identify key factors, and to derive suggestions for quality improvement based on the key indicator factors. More importantly, we also build a field architecture for future-oriented visual arts in this study, which bridges the gap in the structural design of visual arts after the introduction of the future concept. Our present study makes a great contribution to the application of visual art quality enhancement, focusing on the analysis of new concepts and the improvement of old methods, building a new scene of organic combination of new technologies and traditional visual art, with practical research theoretical support for the promotion and progress of the disciplinary field.
An adaptive approach for compression format based on bagging algorithm

The traditional parallel Sparse matrix vector multiplication (SpMV) method has been optimized by an application-specific or compression format-specific. However, a single compression format cannot deal with all sparse matrix types effectively in practical applications. To solve this problem, an adaptive compression format based on Bagging ensemble learning algorithm is proposed in this paper. Experiments show that the adaptive compression format has higher prediction and computational performance on NVIDIA V100 and NVIDIA RTX 2080Ti. Compared with SpMV of the four compression formats, SpMV based on adaptive compression format reduces the execution time of 1.5×, 6.6×, 9× and 1.1×, respectively.
Teachers' Perception on Giving Reinforcement to the Students with Special Needs

This study aimed at describing the teachers' perception about the advantages, disadvantages, difficulties, strategy, and the types of reinforcement used on giving reinforcement to the students with special needs at SLB Negeri 1 Singaraja. The subjects of this research were 4 English teachers at SLB Negeri 1 Singaraja. The data were collected through questionnaire and interview. The result of this research showed that: (1) English teachers at SLB N 1 Singaraja have a very positive perception about the advantages of reinforcement, (2) English teachers at SLB Negeri 1 Singaraja have a positive perception about the difficulties on giving reinforcement to the students with special needs (3) English teachers at SLB Negeri 1 Singaraja have a very positive perception about the types of reinforcement used by English teachers (4). English teachers at SLB Negeri 1 Singaraja have a positive perception about the strategy in giving reinforcement to the students with special needs.
Cameroon: John Fru Ndi Dies

Africa Research Bulletin: Political, Social and Cultural SeriesVolume 60, Issue 6 p. 24059B-24059B Internal Developments Cameroon: John Fru Ndi Dies First published: 14 July 2023 https://doi.org/10.1111/j.1467-825X.2023.11119.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume60, Issue6July 2023Pages 24059B-24059B RelatedInformation
MEDIA

Africa Research Bulletin: Political, Social and Cultural SeriesVolume 60, Issue 6 p. 24080A-24081C Social and Cultural MEDIA First published: 14 July 2023 https://doi.org/10.1111/j.1467-825X.2023.11143.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume60, Issue6July 2023Pages 24080A-24081C RelatedInformation
APPLICATION OF THE REAL-TIME FAN SCHEDULING IN THE EXPLORATION-EXPLOITATION TO OPTIMIZE MINIMUM FUNCTIONS OBJECTIVES

This paper presents the application of a task scheduling algorithm called Fan on an artificial intelligence technique as genetic algorithms for the problem of finding minima in objective functions, where the equations are predefined to measure the return on an investment. This work combines the methodologies of exploration and exploitation of a population, obtaining results with good aptitudes until finding a better learning based on conditions of not ending until an individual delivers a better aptitude, complying with the established restrictions, exhausting all possible options and fulfilling a stop condition. A real-time task planning algorithm was applied based on consensus techniques. A software tool was developed, and the scheduler called FAN was adapted that contemplates the execution of periodic, aperiodic, and sporadic tasks focused on controlled environments, considering that strict time restrictions are met. In the first phase of the work, it is shown how convergence precipitates to an evolution, this is done in few iterations. In a second stage, exploitation was improved, giving the algorithm a better performance in convergence and feasibility. As a result, there is the exploitation of the population and applying iterations with the fan algorithm and better aptitudes were obtained that occur through asynchronized processes under real-time planning concurrently.
AUTOMATIC IDENTIFICATION OF GLAUCOMA USING MATHEMATICALY MORPHOLOGY

The main objective of medical image processing field is to design computational tools which will assist quantification and visualization of remarkable pathology and anatomical structure .A medical condition known as diabetic retinopathy occurs when fluids from blood vessels seep into there in a of the human eye , causing damage to the retina.The detection of the optic disc in pictures of the retinal fundus quantitative study of the evolution of its figure the next entplaya significant part in diagnosing different pathologies, and the abnormalities related to the retina of human eye.Maximum of there abnormalities which are related to optic disc may leads to a structural changes in theinner and the outer area the optical disc.The precise border of the optical disc is obtained by calculating the region of interest and applying an in no vative morphological transformation based adaptive thresholding.The presented technique helps to reduce the process area needed for segmentation techniques leading to a distinguished performance enhancement and reducing the amount of the needed computational cost for each retinal fundus image.The proposed technique has been evaluated on publicly available data sets of retinal images which are DIARETDB1, DRIVE, HRF, DRIONS-DB, IDRiD and STARE, and a remarkable improvement has been found over the existing techniques in terms of accuracy and processing time.
Smatable: A System to Transform Furniture into Interface using Vibration Sensor

Recently, with the spread of smart houses, the smartness of housing equipment and home appliances has progressed, and the functionality and usability of interfaces between people and equipment and between people and home appliances have become important factors. Currently, the main interfaces are remote controls, smartphone applications, and even voice recognition. Furthermore, research is also being conducted on interfaces that can be operated without having the device at hand using cameras and radio waves. However, special equipment must be installed for operation, and compatibility with room design has become an issue. In this research, we proposed a system to transform existing furniture into an interface rather than providing a new interface. The proposed system focused on vibration sensors that are small, inexpensive, and can be attached to existing furniture or hidden from view. To evaluate the proposed system, an experiment was conducted to transform existing furniture into an interface for swiping by simply attaching the vibration sensor to the existing furniture. Specifically, the system attaches four vibration sensors with synchronized output signals to a table and uses a CNN to learn the vibration data obtained from the sensors to predict the direction of the swipe. As a result, when the table and person swiping were fixed, the system could predict the swipe with an accuracy of over 0.86.
Machine Learning Based Framework for Cryptocurrency Price Prediction

Cryptocurrency, a digital currency, acts as a medium of exchange through the Internet. The main agenda behind cryptocurrency being so popular these days is the desire for reliable, long-term value without the involvement of any central authority like banks. The power lies in the hands of the currency holders which resolve the problems of the traditional currencies by adopting a decentralized system. Predicting the future price of different cryptocurrencies is a prominent area of interest for individuals or investors. In this work, we use a dataset collected from the coinmarketcap website for the duration of September 2014 to March 2022. The outcome of this work is compared to the existing algorithms for time series data analysis namely the Auto Regressive Moving Average Model (ARIMA), FbProphet, and several ensemble models on the basis of their accuracy in predicting the future price. We also create different ensemble frameworks for the prediction of the cryptocurrency price. To form the ensemble models, we initially select the three best-performing regression models on the dataset, namely Extra Trees, Random Forest, and Decision Trees Regressors. Our findings indicate that the ARIMA model performs better than the ensemble model with the lowest RMSE MAE and MSE.
The Entangle of Posthuman New Materialism in Baek Hee-na's Picture Book

Based on the posthuman new materialism, we would like to examine the characteristics of Baek Hee-na or picture books and the entanglement between humans and more-than-human’ world. After selecting four picture books: “Strange Mom,” “A Strange Guest ,” “Magic Candies” and “I'm a Dog,”, diffraction analysis was conducted. Baek Hee-na picture books have the characteristics of polystyled entanglement of text-picture texts, the lively and related material (world) system, and various emotions in daily life. Next, Baek Hee-na's picture books were intertwined with post-human new materialism, first with 'acting background: assemblage relationship among all', second with 'cross-existence breaking boundaries: strange hybrid', and third with 'response to non-human beings: ability: relationship and ethics of care'. The discourse on posthuman new materialism became materialized with the world of picture books. This paper’s proposition may broaden the academy to discuss the theoretical picture books foundations and directions of early childhood education in the posthuman era.