Reviewerly/exHarmony huggingface dataset:
An Improved Method to Recognize Bengali Handwritten Characters Using CNN

The process of attaching a symbolic identification to a character’s visual is known as character recognition. There are numerous handwritten characters of various languages, Bengali is one of them. Detecting handwritten documents is exceedingly challenging in today’s digital age due to the vast range of uses, unique shapes, and morphologically complicated formation. Besides, people’s handwritings are identical to them and vary according to sizes, shapes, and styles. Bangla has a lot of similar-looking characters that can be confusing to anyone who is not familiar with the language. In many circumstances, a single dot or mark distinguishes one character from another. Our paper describes an approach to recognize Bangla handwritten characters based on convolutional neural network. Here, we develop a new convolutional neural network model and test our model with CMATERdb 3.1.2 dataset. Our model obtains an average training accuracy of 98.78%, average validation accuracy of 98.33%, and average test accuracy of 98.21% on the alphabets of the dataset which outperforms the performances with the existing methods in the literature. This study paves the way for further advancements in the field of Bangla handwriting recognition by offering a framework for individual character recognition.
Development of a Linear-Scaling Consensus Mechanism of the Distributed Data Ledger Technology

The paper highlights the problem of scalability and security of distributed ledger systems. For this purpose, a reliable system consensus method is proposed, aimed for the linearly scalable consensus mechanism application. An appropriate consensus protocol was developed. Mathematical models are proposed to analyze the scalability and security of blockchain protocols created based on the sharding process. Approximate calculations of the failure probabilities of one shard were determined, and as a result, for each blockchain chain by probability estimates based on hypergeometric and binomial distributions. Based on tail inequalities, the fundamental problem of determining the failure probability for a given sharding protocol is less than a predetermined threshold is investigated. Given the network failure probability threshold, a procedure is proposed for calculating the average period during which the network can fail. Thus, to achieve a given level of security (in terms of the period to failure), an approach was developed allowing determining the minimum shard size that should be formed using blockchain protocols based on the segmentation process. A numerical experiment and a comparative analysis of the obtained estimates were carried out to show the mathematical models’ effectiveness.
A Hybrid Architecture for Action Recognition in Videos Using Deep Learning

Recognition of human actions in videos could be a difficult task that has received a great deal of importance within the analysis community. The actions of the people in the video sequence are symbols that describe the physical aspects and the displacement of people and objects in it. Human Activity Recognition is used in many applications including surveillance, anti-terrorism, anti-crime protection, and the cutting of health and assistance. The challenge is to find relevant details in the visual appearance of videos and motion between frames. The article presented is an integrated learning model that learns to differentiate human actions from videos that have been projected. The process begins by examining the effect of various existing neural network structures used to understand static frames in action perception, and then identifying the best and most appropriate model for action recognition is done using various neural network architectures such as VGG-16 and identifies the architecture that best suits our problem based on a variety of metrics. Finally, the selected architecture is trained using the UCF-101 dataset and the outputs obtained are tested for further improvement.
A metadata driven process for assessing stability and reusability based on risk of change of software systems

Abstract Measuring and estimating the reusability of software components are important steps toward finding reusable candidates. Reuse of software components can aid in the reduction of the development cost of new software or maintenance of existing ones. However, assessing the reusability of software is a challenging task. Even if reusable candidates can be identified, developers will need to decide on the reuse strategies, that is, reuse as it is without any modification, or introduce new changes to the reusable candidate to fulfill requirements in the new environment or releases. Thus, the stability of the reusable candidates plays a pivotal role because it will influence the ease of reuse. Assessing and predicting the stability of potential reusable candidates becomes a significant and important step in software reuse. In this research, we propose to leverage on risk of change as a proxy to measure software stability, where the latter is also a proxy measure for software reuse. Based on experiments conducted on 29 open‐source software with different sizes and application types hosted on GitHub, we found that classes with high impact and high risk of change should be avoided from being reused due to their instability, while those classes with low impact and risk of change should be given priority. The proposed work can aid in providing a better understanding of the ease of reuse for software systems and can be used as a tool to assess its overall quality.
D <sup>3</sup> T-GAN: Data-Dependent Domain Transfer GANs for Image Generation with Limited Data

As an important and challenging problem, image generation with limited data aims at generating realistic images through training a GAN model given few samples. A typical solution is to transfer a well-trained GAN model from a data-rich source domain to the data-deficient target domain. In this paper, we propose a novel self-supervised transfer scheme termed D 3 T-GAN, addressing the cross-domain GANs transfer in limited image generation. Specifically, we design two individual strategies to transfer knowledge between generators and discriminators, respectively. To transfer knowledge between generators, we conduct a data-dependent transformation, which projects target samples into the latent space of source generator and reconstructs them back. Then, we perform knowledge transfer from transformed samples to generated samples. To transfer knowledge between discriminators, we design a multi-level discriminant knowledge distillation from the source discriminator to the target discriminator on both the real and fake samples. Extensive experiments show that our method improves the quality of generated images and achieves the state-of-the-art FID scores on commonly used datasets.
How to approximate fuzzy sets: mind-changes and the Ershov Hierarchy

Computability theorists have introduced multiple hierarchies to measure the complexity of sets of natural numbers. The Kleene Hierarchy classifies sets according to the first-order complexity of their defining formulas. The Ershov Hierarchy classifies limit computable sets with respect to the number of mistakes that are needed to approximate them. Biacino and Gerla extended the Kleene Hierarchy to the realm of fuzzy sets, whose membership functions range in a complete lattice. In this paper, we combine the Ershov Hierarchy and fuzzy set theory, by introducing and investigating the Fuzzy Ershov Hierarchy.
Predicting processing effort during L1 and L2 reading: The relationship between text linguistic features and eye movements

Abstract Researchers have taken great interest in the assessment of text readability. This study expands on this research by developing readability models that predict the processing effort involved during first language (L1) and second language (L2) text reading. Employing natural language processing tools, the study focused on assessing complex linguistic features of texts, and these features were used to explain the variance in processing effort, as evidenced by eye movement data for L1 or L2 readers of English that were extracted from an open eye-tracking corpus. Results indicated that regression models using the indices of complex linguistic features provided better performance in predicting processing effort for both L1 and L2 reading than the models using simple linguistic features (word and sentence length). Furthermore, many of the predictive variables were lexical features for both L1 and L2 reading, emphasizing the importance of decoding for fluent reading regardless of the language used.
Apollo: Adaptive Polar Lattice-Based Local Obstacle Avoidance and Motion Planning for Automated Vehicles

The motion planning module is the core module of the automated vehicle software system, which plays a key role in connecting its preceding element, i.e., the sensing module, and its following element, i.e., the control module. The design of an adaptive polar lattice-based local obstacle avoidance (APOLLO) algorithm proposed in this paper takes full account of the characteristics of the vehicle's sensing and control systems. The core of our approach mainly consists of three phases, i.e., the adaptive polar lattice-based local search space design, the collision-free path generation and the path smoothing. By adjusting a few parameters, the algorithm can be adapted to different driving environments and different kinds of vehicle chassis. Simulations show that the proposed method owns strong environmental adaptability and low computation complexity.
Digital Accounting Practices and Financial Performance of Listed Deposit Money Banks in Nigeria

The study investigated the relationship between digital accounting practices and financial performance of Nigerian deposit money banks. Both primary and secondary sources were used in the study's examination of the research on Nigerian deposit money banks. The collected data was subjected to regression analysis. Regression analysis was used to predict the value of the dependent variables based on the knowledge about the explanatory factors used to assess the effect on the dependent variables. The eleven (11) deposit money banks registered on the Nigerian Exchange Group (NXG) were the population for this research; however, only nine (9) of such banks were actually sampled and included in the analysis. Among the study's results were positive connections between cloud accounting (CLA) and return on assets; also, blockchain technology had a positive relationship with return on assets. Therefore, the study concluded that the adoption of electronic accounting practices and financial performance of Nigerian deposit money banks. As a result, we propose digital activities in the banking industry and call for more research into digital activities in businesses since doing so may reveal even bigger investment prospects for investors among other things.
Polarization Imaging Method for Underwater Low-Visibility Metal Target Using Focus Dividing Plane

Aiming at the problems of brightness attenuation and contrast reduction in the target image caused by underwater low-visibility environments, a metal target detection method based on split-focus plane polarization imaging is proposed. Firstly, a hybrid enhancement method is proposed to clarify the degraded polarization image. In this study, the GrayWorld method is improved to compensate the attenuation difference of the total light intensity of the polarization image. Variational contrast and saturation enhancement algorithms are used to reduce the underwater scattering effect; secondly, a split-focus plane polarization imaging system is built to complete the control of camera parameters, polarization image acquisition and information processing. Under different underwater low-visibility conditions, polarization imaging of targets with different materials can be realized; finally, an image quality evaluation system is constructed to compare the light intensity and degree of polarization images that are collected by the focal plane polarization imaging system. The polarization characteristics of metal and nonmetal target plates are analyzed. The results show that under the condition of low visibility, the obtained polarization image contrast of the metal target is relatively high, and its EME, information entropy and average gradient are increased by 183.82%, 53.46% and 586.22% on average relative to the image of light intensity. In an underwater low-visibility scene, the method of focal plane polarization imaging proposed in this paper can reduce the difficulty of metal target detection.
Unsupervised Embedding Learning for Large-Scale Heterogeneous Networks Based on Metapath Graph Sampling

How to learn the embedding vectors of nodes in unsupervised large-scale heterogeneous networks is a key problem in heterogeneous network embedding research. This paper proposes an unsupervised embedding learning model, named LHGI (Large-scale Heterogeneous Graph Infomax). LHGI adopts the subgraph sampling technology under the guidance of metapaths, which can compress the network and retain the semantic information in the network as much as possible. At the same time, LHGI adopts the idea of contrastive learning, and takes the mutual information between normal/negative node vectors and the global graph vector as the objective function to guide the learning process. By maximizing the mutual information, LHGI solves the problem of how to train the network without supervised information. The experimental results show that, compared with the baseline models, the LHGI model shows a better feature extraction capability both in medium-scale unsupervised heterogeneous networks and in large-scale unsupervised heterogeneous networks. The node vectors generated by the LHGI model achieve better performance in the downstream mining tasks.
An empowerment-based solution to robotic manipulation tasks with sparse rewards

Abstract In order to provide adaptive and user-friendly solutions to robotic manipulation, it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals. To address the issues reinforcement learning algorithms face when task rewards are sparse, this paper proposes an intrinsic motivation approach that can be easily integrated into any standard reinforcement learning algorithm and can allow robotic manipulators to learn useful manipulation skills with only sparse extrinsic rewards. Through integrating and balancing empowerment and curiosity, this approach shows superior performance compared to other state-of-the-art intrinsic exploration approaches during extensive empirical testing. When combined with other strategies for tackling the exploration challenge, e.g. curriculum learning, our approach is able to further improve the exploration efficiency and task success rate. Qualitative analysis also shows that when combined with diversity-driven intrinsic motivations, this approach can help manipulators learn a set of diverse skills which could potentially be applied to other more complicated manipulation tasks and accelerate their learning process.
Distortion Detection of Lithographic Projection Lenses Based on Wavefront Measurement

As with the decreasing feature size prompted by Moore’s law and the continuous technological advancements in the semiconductor industry, the distortion of the projection lens is an important factor that affects the overlay. In this paper, we propose a wavefront-measurement-based method to detect the projection lens distortion in the lithographic system. By normalizing the coordinates of the Shack–Hartmann system with the image displacements represented in the Z2 and Z3 terms of Zernike coefficients, the offsets between the actual image points and the ideal image points can be determined. By offset collection at an array of 7 × 7 field points to establish an overdetermined system of equations, the proposed method can simultaneously detect the distortions of translation, magnification, rotation, decentering distortion, thin prism distortion, and third-order radial distortion. This distortion measurement method is highly flexible for distortion measurement with portable and compactly integrated sensors, enabling the real-time and cost-efficient measurement of wave aberration and distortion. For proof-of-concept experiments, a projection lens with a numerical aperture (NA) of 0.58 for i-line (365 nm) is used for experimental testing. The results reveal that the repeatability accuracy of distortion detection is 51 nm and the 72 h long-term reproducibility is 143 nm.
Toward Smart Energy Systems: The Case of Relevance Vector Regression Models in Hourly Solar Power Forecasting

This chapter places itself in the framework of renewable power systems. More specifically it introduces the utilization of machine learning for forecasting the hourly solar power generation. Getting into details, the relevance vector machine is used to implement data driven forecasting of solar power. Relevance vector regression (RVR) is a learning tool that has the ability to provide a predictive distribution (mean and variance values) over the future variables of interest. In this work, RVR is exposed on a set that encompasses historical values of the solar power and then is utilized for making predictions over the hourly generated power from solar for a day ahead horizon. RVR is tested on real world datasets taken from New England ISO and is benchmarked against a simple linear regression model. Results exhibit the superiority of RVR over linear regression with respect to forecasting accuracy.
Introduction to Fusion of Machine Learning Paradigms

In the recent years, Machine Learning research has been exploring integrated or hybrid learning methodologies, i.e. methodologies that fuse together two or more Machine Learning approaches and promise to achieve higher performance and better efficiency when compared to those of their constituent components. The book at hand, consisting of this editorial note and an additional eight chapters, aims at updating the relevant computer science-related research communities, including professors, researchers, scientists, engineers and students, as well as the general reader from other disciplines, on the most recent advances in applications of methods based on Fusing Machine Learning Paradigms.
UltrasonicGS: A Highly Robust Gesture and Sign Language Recognition Method Based on Ultrasonic Signals

With the global spread of the novel coronavirus, avoiding human-to-human contact has become an effective way to cut off the spread of the virus. Therefore, contactless gesture recognition becomes an effective means to reduce the risk of contact infection in outbreak prevention and control. However, the recognition of everyday behavioral sign language of a certain population of deaf people presents a challenge to sensing technology. Ubiquitous acoustics offer new ideas on how to perceive everyday behavior. The advantages of a low sampling rate, slow propagation speed, and easy access to the equipment have led to the widespread use of acoustic signal-based gesture recognition sensing technology. Therefore, this paper proposed a contactless gesture and sign language behavior sensing method based on ultrasonic signals-UltrasonicGS. The method used Generative Adversarial Network (GAN)-based data augmentation techniques to expand the dataset without human intervention and improve the performance of the behavior recognition model. In addition, to solve the problem of inconsistent length and difficult alignment of input and output sequences of continuous gestures and sign language gestures, we added the Connectionist Temporal Classification (CTC) algorithm after the CRNN network. Additionally, the architecture can achieve better recognition of sign language behaviors of certain people, filling the gap of acoustic-based perception of Chinese sign language. We have conducted extensive experiments and evaluations of UltrasonicGS in a variety of real scenarios. The experimental results showed that UltrasonicGS achieved a combined recognition rate of 98.8% for 15 single gestures and an average correct recognition rate of 92.4% and 86.3% for six sets of continuous gestures and sign language gestures, respectively. As a result, our proposed method provided a low-cost and highly robust solution for avoiding human-to-human contact.
A New Method for Training CycleGAN to Enhance Images of Cold Seeps in the Qiongdongnan Sea

Clear underwater images can help researchers detect cold seeps, gas hydrates, and biological resources. However, the quality of these images suffers from nonuniform lighting, a limited range of visibility, and unwanted signals. CycleGAN has been broadly studied in regard to underwater image enhancement, but it is difficult to apply the model for the further detection of Haima cold seeps in the South China Sea because the model can be difficult to train if the dataset used is not appropriate. In this article, we devise a new method of building a dataset using MSRCR and choose the best images based on the widely used UIQM scheme to build the dataset. The experimental results show that a good CycleGAN could be trained with the dataset using the proposed method. The model has good potential for applications in detecting the Haima cold seeps and can be applied to other cold seeps, such as the cold seeps in the North Sea. We conclude that the method used for building the dataset can be applied to train CycleGAN when enhancing images from cold seeps.
THE IMPACT OF EIGHTH GRADERS’ SELF-REPORTED METACOGNITIVE READING STRATEGIES ON 2019 NAEP INFORMATIONAL READING SCORES

This study presented a secondary analysis of the National Assessment of Educational Progress (NAEP) dataset. It examined the impact of eighth graders’ self-reported metacognitive reading strategies on their 2019 NAEP informational reading scores. A quantitative descriptive research design was utilized to analyze secondary data extracted from the 2019 NAEP dataset. The findings are: (1) The average subscale score of students who feel that they definitely can recognize when they don’t understand something they are reading is significantly higher than those who reported a lower ability level. (2) The average subscale score of students who feel that they definitely can figure out the meaning of a word they don’t know by using other words in the text is significantly higher than those who reported a lower ability level. (3) The average subscale score of students who feel that they definitely can identify the main idea of a text is significantly higher than those who reported a lower ability level. These findings indicate that metacognitive reading strategy instruction could be beneficial to students’ informational reading comprehension, and therefore, the need for further educator professional development on metacognitive reading instruction is warranted.