Reviewerly/exHarmony huggingface dataset:
A Transferred Graph Laplacian Regularization Approach for Color Image Denoising

Image denoising is an ill-posed problem because noise interference during image acquisition frequently leads to serious image degradation. To mitigate this problem, we present a different definition of the normalized graph Laplacian matrix, which consists of a kernel similarity matrix and a related dual random symmetric matrix. Furthermore, we provide the transferred graph Laplacian regularization (TGLR) approach under the manifold structure, which assumes the smoothness of the graph frequencies on the data manifold and promotes the non-local self-similarity property that natural images exhibit. Specifically, the transferred graph Laplacian implicitly encapsulates the graph Fourier basis that interprets image denoising as a graph filtering problem based on data-adaptive Laplacian filters. Finally, an improved steepest descent (SD) algorithm is employed to solve effectively the presented color image denoising problem with symmetry and positive definiteness. Experimental results demonstrate that the proposed approach outperforms several state-of-the-art denoising methods in the objective and subjective qualities.
IMDENCLUE: An Improved DENCLUE Clustering Based On Kernel Density Estimation Optimisation And Cluster Merging Strategy

As a representative of density-based method, DENCLUE discovers the density-attractors of data using Hill Climbing (HC), if there exists a path between significant density-attractors, data points belonging to those attractors are grouped as one cluster. However, it is very difficult to select the appropriate influence parameter and the density-attractor significance for the DENCLUE. In this paper, we propose a kernel density estimation (KDE) optimization and a cluster merging strategy for improving the performance of the traditional DENCLUE. Asymptotic mean integrated square error (AMISE) of standard KDE is introduced to calculate the optimal window width. It can help to obtain the correct global possibility density, which will lead to accurate HC results. Fine reconstruction of data as well as outlier detection will be accomplished by an optimized threshold firstly. Then, boundary density between two fine clusters is evaluated for merging purposes, which can reorganize fine-grained groups. Experiment results show that we can improve the clustering performance by using the optimized KDE and the merging strategy. Compared with the traditional DENCLUE and DBSCAN, our method gains a higher clustering performance on the data with arbitrary shapes and sizes.
A Wide Analysis of Loss Functions for Image Classification Using Convolution Neural Network

Thanks to the humongous availability of data. The huge amount of data is helping the Deep Learning, continuously grow rapid in all kinds of domains and industries. Convolution Neural Network (CNN) is a class of Artificial Neural Network (ANN) which is a method of Deep Learning that has delighted across many fields that includes computer vision domain. The Deep Learning mimics the human brain as it memorizes the information and detect by patterns. Classifying the images is done by training the CNN model. As a human we make mistakes sometimes, not identifying the image due to similar features of a different object. Even CNN model makes mistakes which can be termed as ‘loss of detection’. A loss functions generally compares expected and predicted values during the training phase. Here the aim is to minimize the loss for a neural network model to have a better accuracy as it turns the model better resultant. In this paper, different kinds of loss functions have been analyzed deeply to understand which loss function performs better and/or improves accuracy for a multiclass classification particularly for images.
Author gender and text characteristics in contemporary Swedish fiction

The present study addresses the question of to what extent and how authors’ gender is reflected in the textual properties of bestselling fiction in Swedish during the period 2015–2020. The empirical material was a corpus of 235 female-authored books and 214 male-authored works. The analysis of the texts departed from text property measures targeting grammatical and lexical aspects of language use. Differences between the genders were analysed using the probability of superiority measure in combination with a threshold criterion. The results suggest that authors of bestselling fiction in the Swedish book market to a high degree engage in forms of gender performance when they compose their texts. The differences could in most cases be interpreted as conforming to patterns that have previously been reported for other languages and categories of language use. The gender performance to a large extent agreed with traditional stereotypes about the interests of women and men. There were also differences in grammar-related stylistic preferences. Among the female themes, positive emotion and social interaction were prominent. The male examples include weapons and animosity, as well as numerical quantification. A more grammar-related tendency is that male authors tend to package a larger fraction of their text into noun phrases.
Count Predictive Model with Mixed Categorical and Count Explanatory Variables

- The paper considers the problem of online prediction of a count variable based on real-time explanatory data of mixed count and categorical nature. The presented solution is based on (i) recursive Bayesian estimation of a mixture model of Poisson-distributed explanatory counts, using the categorical explanatory variable as a measurable pointer of the mixture, (ii) construction of a mixture of local Poisson regressions on the clustered data, and (iii) use of the pre-estimated mixtures for online prediction of the target count using actual measured explanatory data. The latter is one of the main contributions of the proposed approach. In addition, the dynamic model of the categorical explanatory variable preserves the functionality of the algorithm in case of its measurement failure. The experiments with simulations and real data report lower prediction errors compared to theoretical counterparts.
The Tracking System for E-Healthcare Employs IoT and Machine Learning Techniques

Today's unhealthy lifestyles are a result of a number of issues, including irregular eating patterns, a diet low in nutrition, environmental pollution, improper exercise, long hours at the office, raised stress levels along with restlessness, which are the cause of one's ill-health. Worldwide a maximum of 40% of youths, professional women and persons in their midlife suffer from an unhealthy lifestyle. In the medical sector, significant developments have been made by IoT, which is relatively a new technology that has seen significant advancements in the fields of medicine and health. Using new cutting-edge tools and resources IoT wearable devices are able to address new problems. The health status of in and out patients can be regularly and periodically checked using wearable medical equipment. In order to create an advanced automation system, this paper proposes an Internet of Things (IoT) architecture called the E-Healthcare Tracking System (EHTS) that has been implemented using machine learning (ML) methods. This system will allow for connections, monitoring, and decision-making for accurate diagnosis.
Understanding the Dynamics of Blockchain Technology and Its Impact on Environmental Sustainability in the Hospitality Industry

This research examines the possible contributions that blockchain technology could make to overcoming a variety of obstacles and accomplishing the objectives of sustainable tourism. Blockchain technology shows promise for the development of sustainable tourism that is aligned with the Sustainable Development Goals (SDGs). Blockchain technology has the potential to disrupt existing tourism operations, boost local economies, manage food supply chains, reduce food waste, improve tourists' satisfaction, influence sustainable behaviour, and address awareness-related concerns. In addition, this study makes suggestions for new lines of research that could be pursued in order to investigate the full potential of blockchain technology as it relates to sustainable tourism. This study covered Indian and International Cases of Multinational and Indian Companies with challenges and opportunities.
Monitoring mobility in older adults using a Global Positioning System (GPS) smartwatch and accelerometer: A validation study

There is growing interest in identifying valid and reliable methods for detecting early mobility limitations in aging populations. A multi-sensor approach that combines accelerometry with Global Positioning System (GPS) devices could provide valuable insights into late-life mobility decline; however, this innovative approach requires more investigation. We conducted a series of two experiments with 25 older participants (66.2±8.5 years) to determine the validity of a GPS enabled smartwatch (TicWatch S2 and Pro 3 Ultra GPS) and separate accelerometer (ActiGraph wGT3X-BT) to collect movement, navigation and body posture data relevant to mobility. In experiment 1, participants wore the TicWatchS2 and ActiGraph simultaneously on the wrist for 3 days. In experiment 2, participants wore the TicWatch Pro 2 Ultra GPS on the wrist and ActiGraph on the thigh for 3 days. In both experiments participants also carried a Qstarz data logger for trips outside the home. The TicWatch Pro 3 Ultra GPS performed better than the S2 model and was similar to the Qstarz in all tested trip-related measures, and it was able to estimate both passive and active trip modes. Both models showed similar results to the gold standard Qstarz in life-space-related measures. The TicWatch S2 demonstrated good to excellent overall agreement with the ActiGraph algorithms for the time spent in sedentary and non-sedentary activities, with 84% and 87% agreement rates, respectively. Under controlled conditions, the TicWatch Pro 3 Ultra GPS consistently measured step count in line with the participants' self-reported data, with a bias of 0.4 steps. The thigh-worn ActiGraph algorithm accurately classified sitting and lying postures (97%) and standing postures (90%). Our multi-sensor approach to monitoring mobility has the potential to capture both accelerometer-derived movement data and trip/life-space data only available through GPS. In this study, we found that the TicWatch models were valid devices for capturing GPS and raw accelerometer data, making them useful tools for assessing real-life mobility in older adults.
Weakly supervised phase unwrapping for fringe projection profilometry

Phase unwrapping is a vital task applied in fringe projection profilometry (FPP). In FPP, fast-speed and high-accuracy three-dimensional (3D) imaging has been the goal. One prominent approach to achieving this objective is the dualfrequency temporal phase unwrapping method (DF-TPU). However, the highest period number for the DF-TPU approach is usually constrained to no more than 16 or 32 by inevitable phase errors, thereby limiting reconstruction precision. For single-camera FPP systems, existing deep learning-based methods capable of unwrapping high-frequency phase maps require accurate labels. This paper proposes a novel deep-learning-based phase unwrapping method for single-camera FPP systems. The inaccurate unit-period phase map is used as the weakly supervised label to guide the convergence of the unwrapping of the high-frequency phase map. The trained network can unwrap the phase map of 64 periods. The proposed approach has been validated in multiple real-world scenarios, including motion blur, isolated objects, non-uniform reflectivity, and phase discontinuity.
Winning a noble race

YouTube’s algorithm is often accused of putting users in filter bubbles and generating rabbit holes of radicalization. However, evidence on these issues is inconclusive. We conduct a systematic audit of the platform using 100,000 sock puppets ... Algorithms of social media platforms are often criticized for recommending ideologically congenial and radical content to their users. Despite these concerns, evidence on such filter bubbles and rabbit holes of radicalization is inconclusive. We conduct ...
Optical computing for neural ordinary differential equations

Increasing the layer number can improve the model performance of on-chip optical neural networks (ONNs). However, this results in larger integrated photonic chip areas due to the successive cascading of network hidden layers. We introduce a novel architecture for optical computing based on neural ordinary differential equations (ODEs) that employing optical ODE solvers to parameterize the continuous dynamics of hidden layers. The architecture comprises ONNs followed by a photonic integrator and an optical feedback loop, which can be configured to represent residual neural networks (ResNets) and implement the function of recurrent neural networks with effectively reduced chip area occupancy. For the interference-based optoelectronic nonlinear hidden layer, we demonstrate that the single hidden layer architecture can achieve approximately the same accuracy as the two-layer optical ResNets in image classification tasks. Furthermore, the architecture improves the model classification accuracy for the diffraction-based all-optical linear hidden layer. We also utilize the time-dependent dynamics property of architecture for trajectory prediction with high accuracy.
A Systematic Study of Central Bank Digital Currency

This chapter examines central bank digital currencies (CBDCs) and their possible impact on the Indian economy in an age of digitization. Commercial value, user experience, legal frameworks, and financial stability are crucial to understanding digital currencies. CBDCs are a new payment infrastructure frontier. The chapter emphasizes the significant investments needed to build a large user and merchant network, highlighting the far-reaching effects of digital currencies. This exploration aims to demystify CBDCs as electronic variants of fiat currency. CBDCs, issued by central banks as legal money, are versatile and available to the public, financial institutions, and businesses for savings and transactions. The study describes the rise of digital cash by tracing currency from early barter systems to physical notes and coins. It admits initial skepticism about digital currencies due to their volatility, diversity, and lack of official backing.
Block Overflow on Frequently Asked Questions Using Blockchain

This work shows how blockchain technology can evolve in our day-to-day lives and we can benefit from it. Much of the modern research work related to blockchain technology emphasizes its application for cryptocurrencies like Ethereum, bitcoin, etc. Blockchain technology is way more than cryptocurrency, and it can have various applications in the domain of finance, government, the banking industry, accounting, etc. Therefore, this study attempts to explore opportunities and their issues. The proposed system emphasizes the use of blockchain in frequently asked questions (FAQ) systems. The basic idea of the proposed system is that the questionnaire will post a question with an appropriate bounty amount and time so that others from the community can answer the question. The most upvoted answer is rewarded with a bounty amount. This transaction is carried out using blockchain technology. This helps to understand the level of contribution experience a person might have and is worthy in the eyes of the developer community for a reward.
Recognition and research of multifactor fuzzy patterns based on fuzzy algorithms

This article takes the recent notice from Guangdong Province on the "2022 Provincial Vocational College Satisfaction Survey" as the starting point and proposes that statistical analysis based on fuzzy mathematics theory and methods should be adopted to be more practical. After briefly introducing the importance of fuzzy mathematical methods and their applications, and aiming at the difficulties of multifactor fuzzy pattern recognition, we first start with the analysis of the fuzzy characteristics of the identified object, determine the mathematical model that conforms to the actual problem, adopt effective membership functions and reasonable model parameters, and then obtain the value of the comprehensive membership of each factor through the matrix operation of the membership of the relevant data in the fuzzy set, Finally, a comprehensive evaluation result is obtained.
Cultural Competency and Meaningful Online Relationships

Since their creation, online and distance-learning programs have afforded traditional and non-traditional students access to learning. Black, Indigenous, and People of Color (BIPOC) students represent many enrollments each year in online and distance-learning undergraduate, master's, and doctoral programs. While the absence of in-person and synchronous learning can impact the connections made by all students, this learning modality has also amplified some existing challenges for BIPOC students in these higher educational settings. This chapter will describe themes of isolation, a sense of belonging, and the quest for safe spaces amongst BIPOC students. This chapter will also discuss the role and responsibilities of mentorship; the importance of fostering meaningful relationships that combat isolation linked to BIPOC students' perception of support, safety, and belonging; and the position of culturally specific virtual affinity spaces in uncovering links to connectedness in online programs.
A two-phase submission algorithm using dynamic programming for Kafka consumer committing offset

As an important middleware of distributed computing, message queue enables us to address issues like decoupling applications, asynchronous communication, fluctuating data traffic, and others. Kafka is a popular open-source message queue for collecting, processing, storing, and analyzing messages at scale, which is famous for its excellent performance, low latency, fault tolerance, and high throughput. In some scenarios like electronic trading, reliable consumption is the utmost concern. However, the native consumer client provided by Kafka cannot guarantee that messages are consumed successfully at least once. This paper proposes a two-phase submission algorithm using dynamic programming which can improve reliability of message consuming. This algorithm utilizes a two-phase submission to ensure that every message is consumed successfully at least once, and uses dynamic programming to improve submission efficiency. We conduct experiments to demonstrate that this algorithm is more efficient in contrast with the original submission strategy. In experiments, this algorithm must make sure that every message is consumed successfully at least once while various consuming failure rates are mocked.
Automatic speech recognition with efficient transformer

Automatic Speech Recognition (ASR) is an important technology in modern society, since it acts as a great tool for humans to communicate with computers, visually impaired people, and deaf people. However, existing speech recognition methods are still facing many problems. Some of the methods require a large model and excessive parameters, others cannot achieve reliable accuracy. Therefore, our study utilizes an Efficient Transformer Model with Convolutional Network to conduct an ASR task. Our model significantly improves the accuracy of speech recognition while does not become a huge model with a large number of parameters.
A joint defogging network based on image characteristics

Deep learning has emerged as a pivotal tool in the domain of image dehazing. Deep learning models can learn the mapping between foggy and haze-free images from extensive datasets, thereby achieving the dehazing effect. However, in contemporary deep learning-based dehazing algorithms, the presence of low-quality images engenders challenges, giving rise to issues such as spatial distortions during the dehazing process. Consequently, deep learning-based image dehazing has evolved into a formidable challenge within the realm of computer vision. In order to address this issue, we propose a two-stage conceptual network termed JDN. In the first stage, we employ the image restoration algorithm BOPBL for defect rectification. Subsequently, in the second stage, we employ the non-paired dehazing algorithm RefineDnet. Through experimental validation, we ascertain a significant enhancement in dehazing efficacy. This enhancement can be attributed to the propensity of dehazing algorithms to yield erroneous mappings, there by culminating in distortions. When incorporating image restoration algorithms as a preprocessing step, the acquisition of mappings becomes more amenable, consequently leading to superior dehazing effects. The introduced JDN network is capable of end-to-end training. Moreover, we demonstrate the efficacy of this approach in both image dehazing and quality enhancement. Experimental results on the SOTS-Outdoor dataset reveal that, in comparison to paired algorithms such as FFANet, hardGAN, and PSD, as well as unpaired algorithms including DCP, CycleDehaze, YOLY, and RefineDNet, the JDN exhibits superior performance.
Non-uniform haze removal from polarized images based on generative adversarial networks

Environmental conditions with smoke severely degrade the image quality, obscuring numerous features and fine details in polarimetric images. Hence, the process of smoke removal and the recovery of polarized information in polarimetric images are of paramount importance. To address this issue, the paper proposes a novel smoke removal algorithm for polarimetric images based on Generative Adversarial Networks. It successfully restores the polarized intensity responses of four distinct polarization angles, captured by a focused plane polarimetric camera in a smoke-free environment, making the recovered images indistinguishable from those captured without smoke. Experimental validation demonstrates the superiority of the proposed method over existing classical smoke removal algorithms in terms of both subjective visual quality evaluation and objective performance metrics. The method achieves remarkable results on a natural visible light polarimetric smoke image dataset.
A multi-objective optimization based doherty power amplifier and its matching network optimization method

In the actual design process of traditional power amplifiers, there is a problem of being cumbersome and unable to simultaneously meet low power and saturation modes. Therefore, an improved multi-objective optimization algorithm proposed by decomposition is introduced to optimize its matching network to achieve overall optimization design of power amplifiers. The algorithm, matching network, and optimized power amplifier performance are simulated and verified. The experimental outcomes denote that on the logic function with Zener diode transistor, the proposed algorithm has a mean generation distance index of 5.03E-3, which is lower than most algorithms. Its overall comprehensive performance is better than the comparison algorithm, and compared to the comparison algorithm, it converges more quickly in the early stage of iteration on 1 and 2, and tends to stabilize in the 40th generation, and completes convergence in the 80th generation. In addition, the optimal solution has already begun to appear around the 25th generation and reached saturation around the 70th generation. At the same time, in the actual working bandwidth, the optimized power amplifier saturation efficiency reaches 51.5%~61.9%, and the efficiency at 6dB power backoff is about 44.4%~56.5%. Overall, the algorithm proposed in the study is effective in optimizing power amplifiers and their matching networks, effectively solving the problem of insufficient efficiency in low power modes in traditional designs.