Reviewerly/exHarmony huggingface dataset:
Super-opposition spiral dynamic-based fuzzy control for an inverted pendulum system

This paper presents a hybrid spiral dynamic algorithm with a super-opposition spiral dynamic algorithm (SOSDA) strategy. An improvement on the spiral dynamic algorithm (SDA), this method uses a concept centered on opposition-based learning, which is used to evaluate the fitness of agents at the opposite location to the current solution. The SDA is a simple-structured and deterministic type of algorithm, which also performs competitively in terms of solution accuracy. However, its deterministic characteristic means the SDA suffers premature convergence caused by the unbalanced diversification and intensification during its search procedure. Thus, the algorithm fails to achieve highly accurate solutions. It is proposed that adopting super-opposition into the SDA would enable the deterministic and random techniques to complement one another. The SOSDA was tested on four benchmark functions and compared to the original SDA. To analyze the result statistically, the Friedman and Wilcoxon tests were conducted. Furthermore, the SOSDA was applied to optimize the parameters of an interval type-2 fuzzy logic control (IT2FLC) for an inverted pendulum (IP). The statistical results produced by the SOSDA for both benchmark functions and the IP show that the proposed algorithm significantly outperformed the SDA. The SOSDA-based IT2FLC scheme also produced better IP responses than the SDA-based IT2FLC.
Research on deep learning model of Chinese semantic classification

The short-term and long-term memory network divides English sentences into single words and numbers them according to the order in which the words appear. Due to the different order of words, the sequence number of each word is different. In semantic matching, the values of words that appear in training are random. This has little effect on English semantic matching. When used in Chinese, Chinese characters have a Chinese character library, and the location of each Chinese character in the Chinese character library is fixed. Therefore, we use the location code to compute the values of Chinese characters. Chinese characters and values are fixed values, and the semantics of Chinese characters and values are also one-to-one correspondence. When analyzing the semantics of Chinese characters, most researchers regard Chinese short sentences as the basis of semantic judgment, but the division of short sentences is difficult. Improper segmentation will damage the original semantics. Using LSTM algorithm for reference, we build a four layer CNN neural network and develop our own deep learning algorithm. Firstly, the sentence is cut according to the word, and then the location code is computed as the value of Chinese characters and sent to the neural network for training. Experiments show that after these two improvements, both LSTM and CNN algorithms can adapt to Chinese semantic matching and achieve good matching results.
Navigation Under Abrupt Motion Disturbances: A Convex Programming Perspective

An approach to account for sparse and abrupt motion disturbances in navigation applications is presented. Large impulsive disturbances force us to abandon the conventional Gaussian noise assumption in process noise modeling for dynamic systems. The non-Gaussian modeling approach taken in this research can estimate sparse motion outliers much more accurately than the classic Gaussian paradigm. The proposed estimation scheme is framed as a convex optimization problem. This allows for greater flexibility in problem formulation by enabling any finite number of domain dependent convex constraints to be included if desired. Illustration of the method is done for a discrete-time linear state space model for vehicle navigation. The results show that, ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> method, which considers heavy-tailed process noise models, has significant advantages over the classical ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> /Gaussian method. ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> method corresponds to the well known Kalman filter. Near perfect recovery of disturbance signals and improved kinematic state estimation is possible with the approach presented. Accurate disturbance recovery has benefits in enabling fault estimation and diagnosis for robust navigation applications. A simulation study is conducted to validate the proposed scheme.
Parallel Control System PD-SMCNN for Robust Autonomous Mini-Quadcopter

A parallel control system with proportional-derivative (PD) and sliding mode control-neural network (SMCNN) has been designed to control the position and attitude of mini-quadcopter parrot mambo mini-drone (PMD). This PDSMCNN control scheme is implemented in the PMD system through a 3D environment using MATLAB-Simulink, which represented the real conditions. The PD controller is used to reach the minimum value of the gain for the PMD getting to take-off, while the SMC as a robust controller is adjusted using a backpropagation neural network (NN) to make the PMD more robust regarding an external disturbance. The software and hardware simulation have been conducted to validate the proposed controller with a mission plan as an input. Based on the simulation results, the PD controller shows an overshoot of 26.7% then becomes 0% of overshoot when the PD-SMCNN controller is implemented. From the hardware simulation results, it is found that the PD controller has 45.1% of robustness and the PD-SMCNN controller has 100% of robustness in the absence of external disturbance. Next, it is found that the PD controller has 26.9% of robustness and the PD-SMCNN has 74.8% of robustness in the presence of external disturbance. Based on these results, indicates that the proposed PD-SMCNN controller is superior to the PD controller in terms of robustness.
Introduction

AbstractIn recent times, digital information on the Internet has been an essential resource for users in many aspects of their life. However, although digital information management has enabled knowledge accessibility to Internet users, its proliferation causes information overload that has been addressed by Automatic Text Summarization (ATS). Within ATS, the Evaluation of Text Summaries (ETS) is crucial to determine the performance of proposed ATS methods. This chapter introduces the reader to the basic notions that involve the ETS. We introduce the main challenges of the ETS, the research problems, and the expectations of this book about linear optimization of content metrics.
Could an Artificial Intelligence be a Ghostwriter?

Advanced technologies, such as Artificial Intelligence (AI) systems, have been pushing nowadays societies toward new ethical and legal challenges, including copyright law dilemmas.The contemporary development of innovative machines and cognitive technologies raises the need to rethink basic concepts such as ownership and accountability.In light of the rules of copyright law, this paper argues that innovative algorithms, such as GPT-3 (an autoregressive language model developed by Open AI to produce human-like text via deep learning), could be considered a modern form of ghostwriting brought forward by the Third Industrial Revolution, as defined by Jeremy Rifkin. The phenomenon of ghostwriting has been notorious sinceantiquity. Although ghost writing is also today, neither national nor international legal systems have yet fully regulated it. Based on the assumption that AI systems operate like ghost writers in terms of their creativity, this paper asks whether AI’s creation should be subject to copyright regulations soon, and if so, to what extent.
A Deep Reinforcement Learning Based Emotional State Analysis Method for Online Learning

With the development of artificial intelligence technology, the basic judgment of students' learning state can be realized through the comprehensive analysis of students' face, expression, behavior posture and other multi-modal data.However, due to the lack of end-to-end recognition model and complete data sets, it is impossible to achieve accurate analysis of learning status.In this paper, based on deep reinforcement learning, an online learning state analysis method based on affective computing is proposed.On the basis of student identity recognition, face recognition is carried out through an unsupervised expression recognition model based on Siam-RCNN, and then 3D CNNs is used to recognize the feature data set for timing extraction.The state of collaborative awareness learning is analyzed by using HMM model.After verification, the accuracy of emotional state recognition can reach 98.88%, which is in the leading level in the industry.
Leadership in English Education

The Covid-19 pandemic has changed the way people live, including in Indonesia. Schools to make changes to the learning system so that teachers and students feel comfortable in the classroom and at home in carrying out learning activities at school. The school leadership was not spared from this problem. School leaders must quickly make the right decisions about what must be done in response to the changes that have occurred due to this pandemic. The purpose of this study is to collect various data to then be analyzed and reviewed regarding the decisions taken by the principal so that this abnormal condition becomes normal by conditioning several things at school. The research method used is literature review by reviewing various library sources which are the results of various studies. The result of this study is the principal's leadership in increasing teacher creativity and innovation, which occurs because the principal is always proactive in dealing with various contextual problems in schools. The principal must also be ready to make strategic decisions so that school stakeholders do not have to wait too long in carrying out the next activities in the school so that the learning process continues.
Design of Automatic Verification System for Electronic Balance Based on Machine Vision

Based on the current situation of the application of electronic balance, the method of manual verification is not only inefficient, but also prone to Angle problems. Therefore, researchers put forward an automatic verification system based on machine vision in practice. Through the use of machine vision system to display LCD display character recognition, calculation error, judgment results and other content, and the use of servo electric cylinder to load or unload weight step by step, according to the cylinder, the detection of product material transport and backward is achieved. The final experimental results show that the machine vision system can quickly and accurately complete the character recognition, and the constructed detection system can carry out automatic metrological verification of electronic balance, which has the advantages of intelligence and high efficiency in practice.
Business and Economic Motivation for Blockchain

Since the mid-1960s, technology innovations such as relational databases, enterprise resource planning systems, the internet and digitization have contributed to improving business productivity and enabling growth. This chapter describes those aspects of a business that have not yet been significantly impacted by existing technology because these are the areas that blockchain has the potential to impact.
Overview of Core Technologies Supporting Blockchain

The promise of blockchain is achieved by innovatively integrating existing technology concepts from distributed systems, cryptography, and peer-to-peer networking. These technologies also impose limitations that drive engineering design decisions for blockchain implementations.
Fund a Project: Tokens and Gas Fees

In previous chapters, we went through the technical aspects of smart contract coding, development, and deployment as well as blockchain security and scalability. In this chapter, we will discuss how to fund a project from both the business and technical aspects of smart contracts and tokens.
Security Considerations

We went through many aspects of Solidity smart contract programming already in previous chapters. We covered Solidity programming syntax, using Remix or Truffle to compile source code, and deploying bytecode to an embedded EVM or development blockchain. We also mentioned the token economy and token design such as choosing fungible or nonfungible tokens and using the tokens to represent assets and elements for business use cases. In this chapter, we are going to cover the security of Ethereum blockchain and smart contracts.
Blockchain Business Applications

Applications developed using blockchain technology have the potential to create a step increase in productivity. However, all business problems do not require a blockchain solution. Blockchain applications are more complex to build than traditional applications and should only be built when dictated by the business problem.
Blockchain Components and Architecture

The four components of blockchain that working together create its enormous potential to alleviate economic inefficiencies in our current system are distributed ledgers, privacy preservation, algorithms for distributed systems consensus, and smart contracts.
Secure Boolean Masking of Gimli

Gimli is a highly secure permutation with high performance across a broad range of platforms. However, side-channel analysis poses a threat to the Gimli without any masking protection. To resist side-channel analysis, the current state of the art of Boolean masking in software proposes an efficient scheme of bitwise logic operations. In practice, a software implementation of masked Gimli may leak information due to pipeline registers and also due to other effects. To avoid unintentional leakage, costly overheads are required, such as more randomness and higher-order share implementation. For our implementation, we present two efficient optimal masked Gimli implementations for the ARM Cortex-M4 on the STM32F407 Discovery(a common Cortex-M4 board) and evaluate their security using TVLA. In 3-shared scenarios, our approach performs with high security with a t-statistic value bounded by a threshold of 4.5 standard deviations, which implies that leakage information cannot be detected. Furthermore, our results promise significant performance improvement for the implementation on Cortex-M processors, with a reduction of the amount of overhead for masking by 61% and 76% for 2 and 3 shared scenarios, respectively.
SimCGE: Simple Contrastive Learning of Graph Embeddings for Cross-Version Binary Code Similarity Detection

Binary code similarity detection (BCSD) has many applications in computer security, whose task is to detect the similarity of two binary functions without having access to the source code. Recently deep learning methods have shown better efficiency, accuracy, and potential in BCSD. Most of them reduce losses by the Siamese network, and they ignore some shortcomings of the Siamese network. In this paper, we introduce the idea of contrastive learning into graph neural networks and experimentally demonstrate that the way of training graph models by contrastive learning is significantly better than Siamese. In addition, we found that Principal Neighbourhood Aggregation for Graph Nets (PNA) has the best ability to extract structural information of control flow graph (CFG) among various graph neural networks.
A Blockchain-Based approach in Healthcare Supply Chain using Smart Contracts and Decentralized Storage Systems

Despite substantial advancements in healthcare systems and management, little progress has been achieved in addressing supply chain constraints such as, inefficient purchasing, ordering, predicting, and administration procedures. Healthcare authorities needs to effectively manage the healthcare supply chain processes not just during pandemics like COVID-19, but also in their everyday operations to guarantee best possible services. Product recalls, product supply shortage monitoring, expiry, and counterfeiting are some of the critical healthcare supply chain operations which must be ensured. Our suggested solution combines blockchain technology and distributed storage to increase transparency, improve stakeholder communication, and shorten product procurement timelines while eliminating crucial disparities and faults. We present a generic framework for the healthcare supply chain, equipped with comprehensive algorithms showing various stakeholder interactions. Our system uses the Ethereum network to link all parties, including the healthcare authority, manufacturer, wholesaler, retailer, and healthcare provider. Remix IDE platform was used to develop and test the smart contract codes. As decentralized storage, we have used the combination of InterPlanetary File System (IPFS) and Hyperledger Fabric. We have also examined a variety of security issues, provided a cost analysis of the various transactions proposed in our solution while making comparison with similar blockchain-based solutions.
Unpaired Face Restoration via Learnable Cross-Quality Shift

Face restoration aims to recover high-quality (HQ) face images from low-quality (LQ) ones with various unknown degradations. Unpaired face restoration approaches focus on the adaptation to unseen degradations, which is a more challenging setting. Recently, generative facial priors of StyleGAN are used to improve the restoration capability of paired face restoration methods. For unpaired methods, however, using face priors is a challenge due to the lack of paired supervision. To address this issue, we take advantage of the editing capabilities of StyleGAN&#x2019;s latent code and propose a novel learnable cross-quality shift. The proposed learnable cross-quality shift not only introduces the generative facial priors into the unpaired framework, but also enables the straight-forward addition/subtraction in the latent feature space to achieve quality conversion. Furthermore, we design a two-branch framework with the proposed cross-quality shift to deal with unpaired data and improve the fidelity of restoration. With the unpaired framework, our method can be fine-tuned on images with unseen degradation. Experimental results show that (i) compared to state-of-the-art methods, our method improves performances under moderate and severe degradation situations; and (ii) both the proposed learnable cross-quality shift and the two-branch framework benefit the restoration performance.
Persistent-Transient Duality in Human Behavior Modeling

We propose to model the persistent-transient duality in human behavior using a parent-child multi-channel neural network, which features a parent persistent channel that manages the global dynamics and children transient channels that are initiated and terminated on-demand to handle detailed interactive actions. The short-lived transient sessions are managed by a proposed Transient Switch. The neural framework is trained to discover the structure of the duality automatically. Our model shows superior performances in human-object interaction motion prediction.
Enterprise intelligent risk management information system based on data analysis algorithm

With the gradual improvement of modern enterprise internal control, the comprehensive risk management system plays a more and more important role in ensuring the sustainable development of enterprises, promoting the construction of comprehensive legal system, strengthening enterprise risk control and management, and improving the level of enterprise risk prevention. This paper summarizes the fuzzy clustering theory, risk management and AFDD algorithm, puts forward the risk identification management system, and analyzes the risk evaluation results. The results show that H company analyzes database permission risk (0.357), platform and system permission risk (0.637) and database account risk (0.725). These three risks exceed 0.1, which belong to the category of high risk and need to be paid attention to and solved first.
Motion blur image restoration based on generative adversarial networks

During the image capture process, it is difficult to maintain relative stillness between the camera and the subject, resulting in blurred images. In order to solve this problem, based on the generative adversarial network, this paper introduces a residual module to solve the gradient dispersion problem generated during the training process. The multi-scale theory is introduced to process the image to better extract the detailed information of the image. Finally, the experiment shows that the scheme has achieved good results.
A construction technology of automatic reasoning system based on knowledge graph

With the rapid development of artificial intelligence, the research on perceptual intelligence is becoming more and more mature. For the next stage of cognitive intelligence research, knowledge graph (KG) is one of the key directions. Knowledge reasoning is an important part of KG and has a wide range of application requirements. At present, knowledge reasoning methods in large-scale KG still have problems of poor interpretability, low reasoning accuracy and efficiency. Knowledge reasoning methods based on deep reinforcement learning have better interpretability and stronger reasoning ability. This paper introduces the research progress of knowledge reasoning based on KG, and makes an analysis of the current knowledge reasoning methods based on knowledge representation, relational path and deep reinforcement learning. At the same time, this paper conducts link prediction and fact prediction experiments on related knowledge reasoning methods on NELL-995 and FB15K-237 datasets, and summarizes the datasets, evaluation methods and indicators involved in these experiments. In conclusion, the proposed future knowledge reasoning model is based on the fusion knowledge representation, relational path with deep reinforcement learning methods.
Infinite Mixtures of Multivariate Normal-Inverse Gaussian Distributions for Clustering of Skewed Data

Mixtures of multivariate normal inverse Gaussian (MNIG) distributions can be used to cluster data that exhibit features such as skewness and heavy tails. For cluster analysis, using a traditional finite mixture model framework, the number of components either needs to be known a priori or needs to be estimated a posteriori using some model selection criterion after deriving results for a range of possible number of components. However, different model selection criteria can sometimes result in different numbers of components yielding uncertainty. Here, an infinite mixture model framework, also known as Dirichlet process mixture model, is proposed for the mixtures of MNIG distributions. This Dirichlet process mixture model approach allows the number of components to grow or decay freely from 1 to $$\infty$$ (in practice from 1 to N) and the number of components is inferred along with the parameter estimates in a Bayesian framework, thus alleviating the need for model selection criteria. We run our algorithm on simulated as well as real benchmark datasets and compare with other clustering approaches. The proposed method provides competitive results for both simulations and real data.