Reviewerly/exHarmony huggingface dataset:
A Color Calibration Method Based on Color Component Projection for Suppression of False Color Caused by Iterative Distribution Transfer

Different imaging conditions often result in different color reproductions. Hence, color reproductions must be calibrated when images are captured under different imaging conditions. Herein, a new color calibration method based on iterative distributed transfer (IDT) is proposed. IDT is used to preliminarily calibrate color reproductions, and the results are known as preliminary results. Because IDT may result in unnatural colors, namely false colors, the projected color components of the input image are used to suppress the false colors, and the results are the final results. To obtain the final results, a series of projection coefficients must be calculated. By minimizing the objective function, in which the preliminary results are used as the target for the final results, the projection coefficients are calculated. Simultaneously, the weight associated with the false color is incorporated into the objective function such that the final results do not depend significantly on the preliminary results when the IDT yields false colors. Meanwhile, to quantitatively evaluate the false color, a false color index is proposed herein. The proposed method can suppress false colors and offers high color-adjustment capabilities. The effectiveness of the method is verified based on evaluation indexes and the observation of experimental results.
Image and pattern reconstruction using differentiable plasticity

Abstract The brain is a substantial boon to humankind that adapts nature accordingly. The brain can learn and unlearn based on the situation. This singularity of human learning led to the research creating models using Artificial Intelligence (AI) to incorporate the brain’s behavior. The investigation opened up many new approaches to study AI with neural networks by adding new techniques to imitate the human brain’s functionalities. Many models can learn from experience like Recurrent Neural Network(RNN’s), Long Short Term Memory (LSTM) with the fixed network size. This paper describes the simple method of creating the model which will behave similar to the biological brain and recreates its differentiable plasticity to adopt the features of neural network connection. It also shows that applying plasticity and the Hebbian plastic connection rule can result in optimization in RNN. This new approach of reconstruction of images based on plastic neural network experiments shows that the above novel approach gives more optimized results than the traditionally used RNN techniques. In this paper, a proposal is made where models can memorize and reconstruct unseen sets of images by solving recurrent networks using plasticity rules.
Inertia weight strategies for task allocation using metaheuristic algorithm

To deal with huge amount of tasks that are allocated to suitable machine, we need an effective load balancing and task scheduling method. Therefore, most of researchers focus on particle swarm optimization (PSO)–based metaheuristic algorithm. But standard PSO shows local optimum problem and slows down the convergence rate and cannot effectively reduce makespan and improve the system performance. This problem can be solved by choosing the suitable inertia weight ( ω ). It is an important parameter in PSO which is used to make proper balance among global best and their own best. It improves searching capabilities of both local and global search according to the number of iterations to get optimum result. The range of inertia weight ( ω ) is in between 0 and 1. In this chapter 10 types of inertia weight strategies are taken and compared with their performance. In order to evaluate the performance, we analyze over different datasets utilizing CloudSim simulator. Simulation result shows that suitable inertia weight values can minimize the makespan. For comparison, we take three important algorithms such as RTPSO-B, PSOBTS, and L-PSO, respectively, and compare their makespan result on taking inertia weight values.
Diffusion based method for impulse noise removal using residual feedback

In the diffusion based processes, the gradient operator performs well in the case of Gaussian noise and can distinguish between edges (large gradient) and the noise-contaminated areas, while impulse noise can cause large gradients and consequently be preserved like the edges. Therefore, in this paper, the ENI operator which distinguishes well between impulse noise and signal is analyzed and using it, an efficient diffusion based method for impulse denoising is proposed. The proposed method is carried out in 2 phases using the residual feedback and the localization of the noisy pixels. The experimental results confirm the efficiency of the proposed method according to subjective and objective criteria.
Blockchain-Based Statistical Library for Telecom Security Fraud Cases

As Internet technology develops at a fast pace, telecom fraud cases happen more frequently, resulting in more and more people suffering great losses. This phenomenon should be kept under control, or it would bring bigger hazards to our society. However, here comes the blockchain technology, which has been used in lots of other fields out of its fabulous characteristics such as reliability and security, especially after the popularization of bitcoin. The study is trying to raise a blockchain-based statistical library for telecom security fraud cases, applying the technology to the prevention of Internet fraud.
Using Path Planning Algorithms and Digital Twin Simulators to Collect Synthetic Training Dataset for Drone Autonomous Navigation

There is a major challenge in collecting real-world data for training the AI Agents of self-driving Cars, Drones, and Automated Guided Vehicles (AGVs). The process is slow and expensive, since the data must be reprocessed and correctly labeled before use. Furthermore, it is difficult to collect data for corner cases, especially dangerous scenarios that lead to accidents. Another challenge is the distribution of data that we use to train the model to guarantee optimal results without fitting problems or training with meaningless or redundant data. In this paper, we present a novel methodology to use path planning algorithms to generate and label the dataset needed for training AI agents. Our methodology is demonstrated with A* path planning algorithm and Microsoft Airsim Drone Simulator, which eases the obtainment of the required data for creating the obstacles grid and provides the tools needed for simulating the drone's movement.
Analysis of RNN Capsule Model for Multiclass Imbalanced Data

Jere, Shreekant Patil, Annapurna P. Muralidhar, AnanyaSentiment analysis, often known as opinion mining, is the process of mining text for views, attitudes, and emotions. Existing models are a tedious process as they require a significant amount of effort to develop the quality of instance representations and linguistic knowledge, which they depend on. Real-world datasets frequently lack training input for regular classifiers with some classes being overrepresented. With little data to learn from, imbalanced data raises issues in machine learning classification and outcome prediction becomes more challenging. This research analyzes a no-linguistic-knowledge RNN-based capsule model for sentiment analysis of a multiclass imbalanced emotion dataset. The RNN capsule model allows each capsule to produce words with sentiment inclinations that match the sentiment category to which it has been assigned. The RNN’s hidden vectors provide the instance representation of an input instance, from which each capsule outputs the state probability via its probability module and the reconstruction representation via its reconstruction module. Ensemble modeling is used to enhance the performance of the RNN capsule model, in which each classifier in the ensemble model evaluates a portion of the dataset comprising balanced data.
Using NooJ to Formalize French Cooking Expressions

Our study fits the teaching method FOS [] for Chinese cookers who come to work in French restaurants or who have chosen catering as a specialty. French gastronomy is internationally renowned, and the expressions of the cooking dishes often have informative and explanatory functions, and the large number of expressions of the cooking dishes that have the same structure (nominal group + à la/à l ’/ au/aux + name) attract our attention. Before teaching these expressions to our learners, the extraction of these expressions constitutes the problem of our study. Indeed, the extraction of multiple words sequences is also a problem in NLP (automatic language processing). In order to choose an appropriate software for our study, in this article, we first modeled the lexical data of these expressions of dishes having the same structure (nominal group + à la/à l ’/ au/aux + name) to choose NooJ as our appropriate software from among NLP extraction software. Then, we implemented the lexical data in NooJ by developing some grammars in the form of a transducer and will compile our data respecting the codes of the NooJ, in particular the operator + EXCLUDE (prefixed by the character “/”) and the colored nodes (prefixed by the character “:”). Finally, the result of our extraction with NooJ was satisfactory for teaching.
The Nature of Functions

This chapter introduces functions as an abstraction over expressions. It presents the first design recipes, top-down design, and bottom-up design.
Research on Off-line Handwritten Digit Recognition Algorithm Based on Binary Classification Tree

A handwritten numeric recognition algorithm based on holistic feature (concave and convex) is proposed in document [6], Although the effect is good, this algorithm is only suitable for the sample of the writing specification, in real life, the numbers have a variety of deformations by the randomness of people’s writing, it affects the recognition rate of this algorithm. This paper is proposed a novel off-line handwritten numeric recognition algorithm based on binary classification tree. It is improvement through background correction improvement, concave and convex features improvement and classification tree improvement. The experimental results show that the algorithm in this paper has been improved in the original recognition rate and is more adaptive to the deformation of handwritten numerals. The recognition of handwritten numbers is more extensive. At the same time, compared with other popular contour based features and neural network based algorithms, there is an obvious advantage in speed.
The Intelligent Scientific Research Management System Based on Cloud Computing

Informatization is the development trend of colleges and universities in China at this stage, and cloud computing is an effective technology to make rational use of resources and accelerate the pace of informatization in colleges and universities. This paper analyzes the current situation and problems in the informatization construction of domestic colleges and universities, discusses the role of cloud computing in the teaching, scientific research and campus management of colleges and universities, and according to the characteristics of cloud computing, puts forward the informatization construction strategy of colleges and universities based on cloud computing. The results show that cloud computing is very important in the application of information construction in colleges and universities can speed up the pace of teaching reform and information construction in colleges and universities and promote the whole process of education information construction.
Dynamic Convolution Pruning Using Pooling Characteristic in Convolution Neural Networks

Dynamic pruning is an effective technique that has been widely applied to deep neural network compression. However, many existing dynamic methods involve network modification and time-consuming retraining. In this paper, we propose a dynamic region pruning method to skip partial regions in CNNs by considering the characteristic of pooling (max- or min-pooling). Specifically, kernels in a filter are ordered according to their \(\ell _{1}\)-norm so that a smaller left partial sum could be obtained, leading to more opportunities for pruning. Without fine-tuning or network retraining, scale factors approximating the left partial sum are efficiently and thoroughly explored by model inferencing on the validation dataset. The experimental results on the testing dataset show that we could achieve up to 43.6% operation reduction while keeping a negligible accuracy loss.
Research on Load Characteristic Data Clustering Based on Transfer Learning

Abstract As power system informatization developed, the distribution network side has accumulated a lot of load data. As an effective data processing method, cluster analysis is helpful to mine the valuable information hidden in the load data and extract accurate load characteristics. In order to extract intuitive and effective load characteristics, a load characteristic data clustering method based on transfer learning is proposed, which deeply studies the problem of ignoring the data distribution difference between training set and testing set in the traditional clustering process. Case studies verify the efficiency and effectiveness of the algorithm.
Construction of Composite Stereo Images of Scientific Results Using the Multi-View StereoMaker 2.0 Software Package

This paper presents the Multi-view StereoMaker software package. It allows the automatic construction of a stereo image for several objects and accompanying information to them using the multi-view technology. The Multi-view StereoMaker software package is designed to work with arbitrary 3D objects, which can be represented both by the results of calculations of mathematical modeling problems, and by images specified by the user himself. The software package is designed to work with an autostereoscopic monitor. The main purpose of the developed software is to realize the construction of several objects with different stereo bases in one stereo frame. It allows the user to quickly and efficiently build images by varying the stereo base for different objects in order to achieve the most expressive stereo effect. A description of the main functions and capabilities of the first version of the developed system is also presented and the real results of the operation of the Multi-view StereoMaker software complex are presented.
Summarization Algorithms for News: A Study of the Coronavirus Theme and Its Impact on the News Extracting Algorithm

Extract summarization algorithms help identify significant information from the news by extracting meaningful sentences from the original text. The information background existing at the time of the news release often significantly affects its content. Such background can distort the text summarization algorithm working results. The study was conducted with the example of the theme "coronavirus" (COVID-19), which at the time of the study was one of the main topics in news feeds. Experiments were carried out on sports news articles, concerned football. This news area was selected because it is not related to medical topics. The TextRank algorithm for sport news extraction was applied in two ways. First, the key information from the source text of news was extracted. Then, a list of the COVID related words was created and the key information from news without considering words from this list was extracted. Our approach showed that mentioning a popular theme such as COVID that is not related to sports can have a negative impact on the text summarization algorithm. We suggest that to obtain accurate results of the algorithm operation, it is necessary to first compile a dictionary of terms related to the coronavirus theme and then exclude them when identifying the main content of news texts.
Open Resources in the Process Management of an Educational Organization in the Context of Integration and Inclusion

The formation of a digital education space is an important moment for the development of the education system in the modern world. The article analyzes the problems of ensuring the security of information technologies in the management of the educational process of organizations, the risks and threats of open Internet resources in the context of integration and inclusion. It is proposed to identify the process of forming digital mentors in a pandemic Covid time as a safe expert space that can reduce risks and threats. Materials and Methods. Using the method of descriptive analysis of datasets voluntarily made publicly available on the Internet by educational organisations allowed the authors to investigate examples of large-scale projects of virtual schools in the formats of exchange models for information subscribers and system integrators. The issues of ensuring the safety of underage users, legal shortcomings of access and protection of Internet consumers of open educational resources were discussed. Results. The study has shown that the evidence from innovative processes of developing and producing open educational resources is complex, context-specific and difficult to generalise. Such initiatives require significant organisational change, including external partners of educational organisations and stakeholders with different cultures and educational practices. The authors highlight the use of benchmarking technology as a tool for the quality of intellectual integrations and the effectiveness of educational activities as a whole as an important condition for the success of the processes taking place. In the context of the research, the authors raise the issue of computer addiction among pupils and students and their safe interaction with multifaceted information online, drawing attention to the need for legal and educational measures for online safety, which should be planned and implemented in the educational organisation on a regular basis. Open educational resource OER initiatives are related to institutional change and require appropriate pedagogical and psychological approaches and legal support to help all actors in the educational space adapt to changes in the IT culture. The materials of this article will be useful to specialists and educational managers, including inclusive education, in developing prospective strategies for digital education and models for the development, production and maintenance of open educational resources.
Fundamental Theory and Algorithms of Edge Learning

In this chapter, we first provide convergence results of Stochastic Gradient Descent (SGD) methods that are usually adopted to solve the machine learning problem. Then, we introduce advanced training algorithms including momentum SGD, Hyper-parameter-based algorithms, and optimization algorithms for deep learning models. At last, we give theoretical frameworks about how to deal with the staleness gradient incurred by ASP or SSP.
FACTOR AFFECTING GOOD UNIVERSITY GOVERNANCE IN FINANCIAL INFORMATION SYSTEM: EVIDENCE FROM UNIVERSITY OF INDONESIA

This study aims to determine the factors that affect the realization of Good University Governance in financial information governance at the University of Indonesia. The variables used in this study are transformational leadership, system quality, information quality, service quality, individual impact, and information system user satisfaction. The approach used in this research is quantitative research. Data obtained from 94 users of financial information systems were collected through questionnaires and analyzed using partial-least squares structural equation modeling (PLS-SEM). The results show that transformational leadership and service quality have a positive effect on Good University Governance. The quality of the system and the quality of information have a positive effect on the impact of individuals, and the quality of information and the quality of service affect the satisfaction of users of financial information systems. Good University Governance can be realized by integrating the vision and mission of the organization and its leaders, human resources, and quality infrastructure. Another important thing is the monitoring, evaluation, and adaptation of these three aspects according to the needs and current developments.
Which Phonemes Will Distinguish the Different Regions Within the Same Dialect?

The work of finding which phonemes distinguish the different regions within the same dialect will be of great significance to the improvement the recognition technology, national and information security and dialect protection. To address this issue, this paper investigates the refinement recognition of different regions of the same dialect based on the corpus designed and recorded by CASIA (CASIA Dialect Corpus, CASIA DC) firstly and then finds the distinguishing phonemes by the probabilistic accumulation of phonemes method. Based on i-vector model, the recognition results indicate that the recognition rates of different regions of the same dialect are different from one dialect to another. The recognition rate of the Mandarin dialects is lower than that of other dialects. Through the probabilistic accumulation of phonemes, we find that the phonemes with significant difference can distinguish different regions of the same dialect, which will provide significance for the synthesis and recognition of dialects in the future.
SCAN: Spatial and Channel Attention Normalization for Image Inpainting

Image inpainting focuses on predicting contents with shape structure and consistent details in damaged regions. Recent approaches based on convolutional neural network (CNN) have shown promising results via adversarial learning, attention mechanism, and various loss functions. This paper introduces a novel module named Spatial and Channel Attention Normalization (SCAN), combining attention mechanisms in spatial and channel dimension and normalization to handle complex information of known regions while avoiding its misuse. Experiments on the varies datasets indicate that the performance of the proposed method outperforms the current state-of-the-art (SOTA) inpainting approaches.
Fine Grainded Sentiment Analysis on COVID-19 Vaccine

The most talked about topic of interest in the medical realm as of today, is the debate on the impact that COVID-19 vaccine has on individuals, and their response in encountering the virus. While there are quite a few vaccine variants that have been developed, there has always been a lingering ambiguity in declaring that an individual can be completely immune to the virus. There have been many studies whilom this cognition of analysing the sentiment perception of vaccines, however the data utilization from various sources and the apropos implementation using the language processing methodologies have lagged a great deal. This paper pivots on the data drawn from social media platforms, and optimizes the sentiments using the Natural Language processing Toolkit (NLTK). The process of word embedding, with TFIDF vectorizer commingled with data unsheathing through fine-grained sentiment analysis and machine learning algorithms such as Linear SVC, SVM and Naïve bayes on the covid19 dataset have aided in stratifying the public tweet sentiments based on their polarity, precision, recall, f1-score value and support. The simulations have been implemented using the lexicon, rubric-based analytical tool VADER (Valence Aware Dictionary and sentiment Reasoner) incorporated in Python specifically for optimized extraction of sentiments from data.