Reviewerly/exHarmony huggingface dataset:
A Deep Multi-kernel Uniform Capsule Approach for Hate Speech Detection

Hate Speech is an expression that expresses hatred towards people of a specific ethnic group or nationality and incites hatred. Even though many countries have anti-hate speech legislation, hate speech can spread in the native language on social media platforms, resulting in violent riots and protests that spiral out of control and result in anti-social events. Hence, hate speech has caused a crucial social issue. Thus, various intelligent mechanisms have been employed to classify hate speech, depending on the category. A deep learning model has certain limitations for providing n-gram features for text classification of the native language. As a result, in this paper, the Multi-kernel uniform capsule network for multilingual languages is proposed. The proposed method employs a Multi-kernel uniform capsule network to improve feature selection performance by utilizing the capsule network routing algorithm. The experiments were carried out on political, COVID-19 and vaccination, lockdown, and multilingual dataset. The experimental results demonstrate that the proposed methods achieve adequate results when compared with other machine learning models for hate speech detection.
HCC Recognition Within B-Mode and CEUS Images Using Traditional and Deep Learning Techniques

Hepatocellular Carcinoma (HCC) is the most often met malignant liver tumor and one of the most frequent causes of death worldwide. The most reliable method for HCC diagnosis is the needle biopsy, but this is invasive, dangerous, leading to the spread of the malignity through the body, as well as to infections. We develop noninvasive, computerized techniques for performing HCC diagnosis based on ultrasound images. In this research work, we combined the information provided by both B-mode ultrasound images and Contrast Enhanced Ultrasound (CEUS) images, in order to improve the HCC recognition performance. We computed classical and advanced textural features from both types of images, we applied dimensionality reduction methods, and then we provided the resulted features at the entrances of both traditional and deep learning classifiers. Similar experiments were conducted using the pixel values of the regions of interest. We obtained a recognition accuracy of 90%.
Markov Decision Processes with Discounted Rewards: Improved Successive Over-Relaxation Method

In mathematics, discrete-time Markov chains are stochastic control processes. They provide a mathematical framework for modeling decision making in situations where outcomes are partly governed by randomness and partly commanded under the control of a decision maker. In order to reduce the computational efforts of complexity, the action elimination procedures have been applied with considerable success and are appeared in the literature for solving this problem. We focus our study on the complexity of some classical problem of computing optimal policy computation in Markov decision problems (MDPs) with discounted reward criterion. It is well known that those problems are solvable in polynomial time via the dynamic programming tool. In this context, we give an overview on the main variants of the value iteration scheme which is a part of successive approximation methods. Then, we improve the successive over-relaxation algorithm (SOR) by establishing a new test of non-optimal decision. Finally we give an example for clarification and demonstrating our proposal.
Research on DeepFakes to Ensure Social Information Security

AI face replacement technology - DeepFakes is often maliciously used to tamper with video faces. Deepfakes forges the access rights of face authentication confidential information or gains the trust of information carriers, which will bring great hidden danger to the information security in the field of social engineering. In order to promote the reasonable application of AI face replacement technology, the DeepFakes source code is analyzed to define the key technologies. And investigate the current status of DeepFakes research. Based on the principle of DeepFakes tampering, this paper summarizes and proposes the method of detecting DeepFakes tampering attacks from two aspects of image feature and combining depth learning. The robustness and accuracy of different detection methods are compared through experiments.
The Role of Bio-Inspired Modularity in General Learning

One goal of general intelligence is to learn novel information without overwriting prior learning, i.e. catastrophic forgetting (CF). The utility of preserving knowledge across training tasks is twofold: first, the system can return to previously learned tasks after learning something new. In addition, bootstrapping previous knowledge may allow for faster learning of a novel task. Current approaches to learning without forgetting depend on strategically preserving weights that are critical to a previously learned task. However, another potential factor that has been largely overlooked is leveraging the initial network topology, or architecture. Here, we propose that the topology of biological brains likely evolved certain features that are designed to achieve knowledge preservation. In particular, we consider that the highly conserved property of anatomical modularity may offer a solution to weight-update learning methods that leverages learning without catastrophic forgetting for general bootstrapping to novel circumstances. Final considerations are made on how to combine these two objectives in a general learning system.
A new kind of encryption and decryption of RGB colour image using permutation matrix multiplication

An enhanced technique of colour image encryption based on permutation matrix key encoding is proposed. To encrypt the colour image, a separation into Red, Green and Blue (R,G,B) channels have been applied. Each channel is encrypted using a technique called double permutation key encoding to assigning a new colour to every pixels of RGB colour image. Also we introduce a new technique of partite block encryption method. The detail analysis of encryption of a image with 9-pixels including the number of possible encryptions are provided. Moreover, the decryption process is clearly expressed by Matrix representation.
Minimizing Response Time for MapReduce Applications on In-Storage Processing Architecture

Integrating In-Storage Processing architecture with the Hadoop model offers a potential solution for accelerating applications with large-scale data requirements. However, the data movement overhead among nodes and the task interference delay among multiple applications are getting serious with the increased data size of applications. To minimize the response time of applications while maintaining the throughput, we propose workload-aware scheduling and waiting-time-aware dispatcher to manage multiple MapReduce applications under the In-Storage Processing architecture.
Unsupervised Pixel-Wise Weighted Adversarial Domain Adaptation

Fully convolutional networks have been leveraged extensively in semantic segmentation tasks. While possessing demonstrated competency for dense prediction, such supervised learning networks are restricted to labeled data during training and hence show poor generalization while confronting unseen domains. As a pivotal transfer learning technique, domain adaptation aims to alleviate discrepancies between distinct domain distributions to improve the performance of generalization in unsupervised manners. Although a family of domain adaptation methods have demonstrated significant effectiveness on cross-domain semantic segmentation tasks, the overlook of pixel-wise domain divergences leads to over-adaptation. To deal with this problem, we investigate effective pixel-wise inter-domain discrepancy metrics to regularize the training of adaptation networks at a pixel-wise level. We first leverage generation confidence encoded from the output space as a weighting map to impose more adaptation emphasis on deeply shifted regions. Furthermore, we employ discrimination confidence on the feature space to refine generation confidence into a more reliable weighting map. The formulation of generation and discrimination confidence does not introduce additional computations over the fundamental DA framework. In our experiments, the proposed pixel-wise weighted adaptation approach outperforms state-of-the-art methods on two cross-domain segmentation tasks and demonstrates effective alleviation of over-adaptation.
Temporal and Sequential Models

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An Optimization for Convolutional Network Layers Using the Viola-Jones Framework and Ternary Weight Networks

Neural networks have the potential to be extremely powerful for computer vision related tasks, but can be computationally expensive. Classical methods, by comparison, tend to be relatively light weight, albeit not as powerful. In this paper, we propose a method of combining parts from a classical system, called the Viola-Jones Object Detection Framework, with a modern ternary neural network to improve the efficiency of a convolutional neural net by replacing convolutional filters with a set of custom ones inspired by the framework. This reduces the number of operations needed for computing feature values with negligible effects on overall accuracy, allowing for a more optimized network.
A Hybrid Framework Using the Boosting Technique for Efficient Rain Streak Removal During Daylight

Nowadays, images and videos with a rainy footprint will degrade the quality of images. The removal of such rain streaks from the images is considered as a challenging task. The layer priors and recurrent convolutional neural networks (RCNNs) are combined and applied for developing the best quality images. Here, the layer priors focus on superimposing the rain streak layer with respect to the background image and RCNN is focused on motion blur and eliminating such blur from the images. The final output of a valid and best quality image is obtained by using the combined principle of these two approaches. The proposed model is further enhanced by using boosting technique, which has an aim to fasten the principle and minimize the complexity. A quality image is achieved by separating rain streaks from the desired rainy image and focusing more on the background image. The integration of these approaches (layer priors + RCNN using boosting) along with boosting technique results in delivering a faster output with enhanced accuracy and performance. In the future, utilizing any of the innovative image enhancement approaches, it may be possible to improve the image quality without rain streaks.
Semantic Similarity Entity Disambiguation for Short Text

In the use of recommendation system and retrieval system, Named Entity Disambiguation (NED) is helpful to distinguish polysemy and synonym, so as to realize accurate retrieval and recommendation. Although existing statistics-based research methods achieve high accuracy, these studies have low accuracy when faced with short texts without context. To solve this problem, this essay proposes a Named Entity Disambiguation method based on Knowledge Graph similarity. The method is a semantic similarity model based on entity property similarity. The semantic similarity model based on entity property relies on embedding model and Knowledge Graph to learn the vector representation of words and features in the same shared vector space. Experimental results show that the Named Entity Disambiguation method proposed in this essay is more effective than the existing text-only statistical method when context information is scarce. In the expansibility experiment, this essay will discuss the influence of model parameter values on the precision of disambiguation results.
Fracture mechanics of additive manufactured crack-like notches by digital image correlation

Abstract The rapid development of a variety of Additive Manufacturing (AM) techniques is witnessed by the large interest of the scientific community. Among the numerous new features offered by these techniques, very lately the possibility of including cracks of any shape, direction, and position inside AM samples to validate FEM models was explored. Therefore, the necessity arose of full-field measurement techniques that would allow the evaluation of the fracture mechanics parameters in the cases of both linear elastic and partially plastic materials, as a function of the fracture modes. Invented in the ’80 of the XX century, the Digital Image Correlation (DIC) in recent years has taken place in nearly every laboratory. It is used to measure displacements, and by numerical differentiation or coupling with Finite Element Method, to calculate strains at different spatial scales. In particular, two procedures that were successfully employed on a specific kind of specimens, based on DIC at a small scale near the crack-tip, in this paper are shown in detail.
A New Structure Of Handwritten Number Recognition : ––Based on MNIST

MNIST handwritten number recognition is processed by building and using a single linear feedforward neuron. Ten handwritten digital images of 0–9 are input into the specified position as samples, and then the model generated by the machine learning is allowed to automatically distinguish the numbers displayed by the current image, and the corresponding numbers are correctly identified and outputted. The main thing is to process the image through unique thermal coding and label, and then to get a set of data after processing the data by a specific function. Then a linear feedforward [1] model is defined. After simple processing of the image through a series of functions and specific coding, the data obtained by the specified function can get a similarity result between the corresponding predicted value and the label value through the specified function. Finally, the output similarity results are classified by the main function softmax to achieve the recognition results. Similar to the neurons in the human brain neural network, neurons, as the main existence basic structure of the human brain, play a self-evident and important role in the human body. It can be seen that the existence and status of neurons can’t be replaced in the profound change of machine artificial intelligence, and its further improvement and optimization also has great research value and significance.
Building 3D Virtual Worlds from Monocular Images of Urban Road Traffic Scenes

Three-dimensional (3D) modeling of urban road scenes has warranted well-deserved interest in entertainment, urban planning, and autonomous vehicle simulation. Modeling such realistic scenes is still predominantly a manual process, relying mainly on 3D artists. Cameras mounted on vehicles can now provide images of road scenes, which can be used as references for automating scene layout. Our goal is to use the information from such images from a single camera sensor on a moving vehicle to build an approximate 3D virtual world. We propose a workflow that takes the human out of the loop through the use of deep learning to generate a dense depth map, an inverse projection to correct for perspective distortion in the image, collision detection, and a rendering engine. The engine loads and displays 3D models belonging to a particular type, at accurate relative positions, thus building and rendering a virtual world corresponding to the image. This virtual world can then be edited and animated. Our proposed workflow can potentially speed up the process of modeling virtual environments significantly when integrated with a modeling tool. We have tested the efficacy of our 3D virtual world-building and rendering using user studies with image-to-image similarity and video-to-image correspondences. Even with limited photorealistic rendering, our user study results demonstrate that 3D world-building can be effectively done, with minimal human intervention, using our workflow with monocular images from moving vehicles as inputs.
Discrimination of two nonorthogonal states against amplitude‐damping noise via feed‐forward control

Abstract Guo et al. introduced the quantum feed‐forward control (QFFC) before the minimum‐error discrimination (MED) measurement to effectively discriminate between two nonorthogonal states via an amplitude‐damping (AD) noise. However, the discrimination is inversely proportional to the success probability. To solve the problem, we propose an improved scheme where only preweak measurement and feed‐forward operations are retained before the MED measurement. Interestingly, we find the improved scheme achieves a similar effect of discrimination as the scheme proposed by Guo et al. Most importantly, the value of success probability is constant to 1.
Compositional Committees of Tiny Networks

Deep neural networks tend to be accurate but computationally expensive, whereas ensembles tend to be fast but do not capitalize on hierarchical representations. This paper proposes an approach that attempts to combine the advantages of both approaches. Hierarchical ensembles represent an effort in this direction, however they are not compositional in a representational sense, since they only combine classifier decisions and/or outputs. We propose to take this effort one step further in the form of compositional ensembles, which exploit the composition of the hidden representations of classifiers, here defined as tiny networks on account of being neural networks of significantly limited scale. As such, our particular instance of compositional ensembles is called Compositional Committee of Tiny Networks (CoCoTiNe). We experimented with different CoCoTiNe variants involving different types of composition, input usage, and ensemble decisions. The best variant demonstrated that CoCoTiNe is more accurate than standard hierarchical committees, and is relatable to the accuracy of vanilla Convolutional Neural Networks, whilst being 25.7 times faster in a standard CPU setup. In conclusion, the paper demonstrates that compositional ensembles, especially in the context of tiny networks, are a viable and efficient approach for combining the advantages of deep networks and ensembles.
Disaster mitigation-based land suitability for settlements using Multi-Criteria Evaluation (MCE) method in The Northern Part of South Barito Regency

Abstract Settlements are indispensable to humans as a place of residence and for other activities. Moreover, there is an increase in land requirements for settlement construction due to the high population. The development of cities and settlements in the context of SDGs needs consideration to mitigate, adapt to climate change, and resist disasters. Therefore, this study aims to identify, analyze, and describe disaster mitigation-based land suitability and availability for settlements in the Northern Part of South Barito Regency. The method used was Multi-Criteria Evaluation (MCE), while the inclusion criteria are slope, soil type, rocks, drainage, soil texture, elevation, land use, and distance to the rivers. Each criterion was weighted using an AHP, while scoring and map overlaying were carried out using a GIS to determine the class of land suitability. The flood hazard map was used to obtain the area of free flooding. The results showed that actual land suitability for the most extensive settlements is in the S2 (Moderately Suitable) class. Meanwhile, the land that is used without flood hazards is approximately 18.9% of the Northern Part of South Barito Regency landmass. This analysis is used as consideration by policymakers to develop spatial planning in South Barito Regency.
Performance Evaluation Of Video Surveillance System For Use In Automatic Recognition Application

Automatic Recognition image analysis plays a major role in supporting the advancement of modern safety and security protections. Correct and reliable detection of human and object in the video is one of the biggest challenges in the recognition system. This paper presents a systematic testing method to help evaluate the performance of the black box models used in machine learning systems. Computational image analysis technology and a standardized test plan are among of the best solutions to the video surveillance system used in recognition applications.
Identification of malignancy in lung using artificial neural network

Earlier diagnosis of cancer cell growth leads to save lots of precious human lives. It is necessary to develop some automated tool, in order to detect malignant state at the beginning stage itself. Many algorithms had been proposed earlier by many researchers in the past, but, the accuracy of prediction is always a challenging task. In this work, an artificial neural network based methodology is proposed to find the abnormal growth of lung tissues. Higher probability of detection is taken as an objective to get an automated tool, with great accuracy. Manual interpretation always leads to misdiagnosis. Optimal feature sets derived from Haralick grey level co occurrence matrix and used as the dimension reduction way for feeding neural network. In this work, a binary classifier neural network has been proposed to identify the normal images out of all the images. The capability of the proposed neural network has been quantitatively computed using confusion matrix.
Conclusions and Resources for Next Steps

Abstract The final chapter provides recap of the main takeaways from the rest of the book, including the high and rising global disparities, headwinds going forward, and the required policy response. The book synthesized the theory, empirical findings, and policy experiences on the many dimensions of inclusive growth. But despite the voluminous literature on most of the topics, there are still substantial gaps in our knowledge. The chapter then summarizes some of the key areas in which additional research could be helpful in further expanding our knowledge to improve inclusive growth. Finally, the chapter provides an extensive set of resources for more information and policy support for readers interested in delving deeper into specific topics.
Continuing education as the value basis of professional activity of the pedagogue

Professional socialization of future graduates of the pedagogical university is aimed at the formation of competencies essential for the practical activity and solution of vital tasks. One of the components of successful professional activity is the willingness for continuing education. The subject of this research is the phenomenon of continuous pedagogical education as the value. Globalization has encompasses all segments of life of the modern society, including education. The rapidly changing world with its uncertainty imposes new requirements on the system of vocational training. A modern specialist is no longer narrow-focused, but is capable of solving complex and versatile tasks, which is facilitated by constant striving for new knowledge using various forms of learning. However, this is only possible with realization by the individual of the need for continuing education and its utility in different axiological coordinates. For the analysis of axiological basis of continuing education, the author surveyed the students of pedagogical university to determine their perception of continuing education as a personal meaningful value that contributes to the achievement of certain career results. The acquired results on the one hand demonstrate the the vagueness of the concept of &amp;ldquo;continuing education&amp;rdquo; for the university students; while on the other hand, prove the hypothesis on the need and importance of continuing education for the pedagogues, but at the same time the unwillingness of the future specialists to consider it as the value of life. The presented materials allow adjusting the educational process of future pedagogues and their motivation for continuing education, since the pedagogical classes to Master&amp;rsquo;s Degree.