Reviewerly/exHarmony huggingface dataset:
CFM: a convolutional neural network for first-motion polarity classification of seismic records in volcanic and tectonic areas

First-motion polarity determination is essential for deriving volcanic and tectonic earthquakes’ focal mechanisms, which provide crucial information about fault structures and stress fields. Manual procedures for polarity determination are time-consuming and prone to human error, leading to inaccurate results. Automated algorithms can overcome these limitations, but accurately identifying first-motion polarity is challenging. In this study, we present the Convolutional First Motion (CFM) neural network, a label-noise robust strategy based on a Convolutional Neural Network, to automatically identify first-motion polarities of seismic records. CFM is trained on a large dataset of more than 140,000 waveforms and achieves a high accuracy of 97.4% and 96.3% on two independent test sets. We also demonstrate CFM’s ability to correct mislabeled waveforms in 92% of cases, even when they belong to the training set. Our findings highlight the effectiveness of deep learning approaches for first-motion polarity determination and suggest the potential for combining CFM with other deep learning techniques in volcano seismology.
Practical applications and limitations of basalt discrimination diagrams

Determining the tectonic setting of unknown volcanic rocks continues to be one of the key challenges in geoscience. While discrimination diagrams have been successfully employed due to their ease of use, recently, validation with big data has raised questions about their performance. In this study, the discrimination boundaries of Th/Yb versus (vs.) Nb/Yb and TiO2/Yb vs. Nb/Yb diagrams, which are the most used types of discrimination diagrams, were redefined based on a large amount of compiled data and support vector machine, a machine learning method. The effectiveness of discrimination diagrams was verified, and the limitations and conditions when using them were clarified. The results show that when using the Th/Yb vs. Nb/Yb diagram, only basalts with Th/Yb ratios higher than the discrimination boundary can be identified as volcanic arcs in origin. In contrast, a significant overlap occurs across boundaries in other cases when using these diagrams, particularly for enriched samples with Nb/Yb ratios higher than five. Therefore, when using these diagrams to determine the tectonic setting of unknown samples, their limitations must be considered when interpreting their results.
Endless ways to use computers to study narrative generation (part 1)

Abstract The purpose of this chapter and Chapter 13 is to illustrate how computers offer endless ways of approaching the study of narrative generation. Thus, rather than providing an exhaustive review of story-generator systems, these two chapters describe seven computer programs that exemplify the diversity of research interests and technical possibilities, in order to represent in computer terms some core aspects of narrative generation not covered earlier in this book. In this chapter we study four systems, MESSY, SCÉALEXTRIC, GRIOT, and the chess-game narrator, that show how general-purpose techniques commonly used in computer science for other purposes can be successfully handled for automatic narrative generation.
Generating a narrative step by step

Abstract This chapter provides a detailed example that illustrates how a computer program can employ the narrative structures defined in Chapter 4 to produce a narrative. Throughout the chapter we explain how the narrative structures are instantiated and how each of the sentences that make up the story arises. We also explain how the use of routines to manipulate text are useful for producing a better-quality output. Then we describe the main characteristics of TALE-SPIN, one of the most well-known story-generators. For instance, we explain that the system represents characters’ goals to meet physical needs, to move around the story-world, to persuade other characters to act in certain ways, and to communicate information. We end with some reflections about the scope and limitations of narrative generation based on problem-solving.
The story we always wanted to tell you

Abstract In this final chapter, we recapitulate the core features of the systems we have studied and draw some general conclusions that provide an overview of the field. We start by summarizing the five techniques studied in this book: templates, problem-solving, planning, engagement and reflection, and statistical methods. We explain how systems can be grouped into different categories, such as audience versus theory. Finally, we reflect on some of the implications for society from the development of automatic narrative generator systems and provide examples, such as how these systems might support intercultural diversity through storytelling, or the role of AI in education.
Face-tracking algorithm for large-amplitude head motions with a 7-DOF manipulator

Abstract The collection of facial action data is essential for the accurate evaluation of a patient’s condition in the intensive care unit, such as pain evaluation. An automatic face-tracking system is demanded to reduce the burden of data collection on the medical staff. However, many previous studies assume that the optimal trajectory of a robotic tracking system is reachable which is inapplicable for large-amplitude head motions. To tackle this problem, we propose a region-based face-tracking algorithm for large-amplitude head motion with a 7-DOF manipulator. A configuration-based optimization algorithm is proposed to trade-off between theoretical optimal pose and workspace constraints through the assignment of importance weights. To increase the probability of recapturing the face exceeding the reachable workspace of the manipulator, the camera is directed toward the center of the head, named the facial orientation center (FOC) constraint. Furthermore, a region-based tracking approach is designed to stabilize the manipulator for small amplitude head motions and smooth the tracking trajectory by adjusting the joint angle in the null space of the 7-DOF manipulator. Experimental results demonstrate the effectiveness of the proposed algorithm in tracking performance and finding an appropriate configuration for the unreachable theoretical optimal configuration. Moreover, the proposed algorithm with FOC constraint can successfully follow the head motion as losing 33.2% of the face during the tracking.
Narrative generation from the author’s perspective

Abstract This chapter discusses how planning techniques are employed for the automatic generation of narratives. In this approach, authors’ goals and characters’ goals drive the tale’s development. An author goal is a computer representation of a goal of a human story-writer, e.g. choosing a theme to develop a story. Thus, this chapter complements and expands the notions about problem-solving studied in previous chapters. The chapter introduces concepts that include story-theme structure and knowledge base (a characterization of the authors’ knowledge). It explains how to represent the organization of a story (Introduction, Development, Resolution, and Closing) employing data structures. Finally, we consider the main steps to generate a narrative based on planning: choosing a theme structure, creating the characters’ structures of the theme, instantiating the Introduction, instantiating the Development, instantiating the Resolution, instantiating the Closing, and transforming the data structures into English.
T2RFIS: type-2 regression-based fuzzy inference system

Abstract This article discusses a novel type-2 fuzzy inference system with multiple variables in which no fuzzy rules are explicitly defined. By using a rule-free system, we avoid the serious disadvantage of rule-based systems, which are burdened with the curse of dimensionality. In the proposed system, Gaussian membership functions are used for its inputs, and linearly parameterized system functions are used to obtain its output. To obtain the system parameters, a genetic algorithm with multi-objective function is applied. In the presented method, the genetic algorithm is combined with a feature selection method and a regularized ridge regression. The objective functions consist of a pair in which one function is defined as the number of active features and the other as the validation error for regression models or the accuracy for classification models. In this way, the models are selected from the Pareto front considering some compromise between their quality and simplification. Compared to the author’s previous work on the regression-based fuzzy inference system, a new inference scheme with type-2 fuzzy sets has been proposed, and the quality has been improved compared to the system based on type-1 fuzzy sets. Four experiments involving the approximation of a function, the prediction of fuel consumption, the classification of breast tissue, and the prediction of concrete compressive strength confirmed the efficacy of the presented method.
PC-SC: A Predictive Channel-Based Semantic Communication System for Sensing Scenarios

Due to its significant efficiency, semantic communication emerges as a promising technique for sixth-generation (6G) networks. The wireless propagation channel plays a crucial role in system design, as it directly impacts transmission performance and capability. Given the increasingly complex communication scenarios, the channel exhibits high dynamism and poses challenges in acquisition. In such cases, sensing-based methods have drawn significant attention. To enhance system robustness, we propose a predictive channel-based semantic communication (PC-SC) system tailored for sensing scenarios. The PC-SC system is designed with an orientation toward applications by directly taking semantic targets into account. It comprises three modules: transmitter, predictive channel, and receiver. Firstly, at the transmitter, instead of employing global semantic coding, the scheme emphasizes preserving semantic information through target-based semantic extraction. Secondly, the channel prediction module predicts the dynamic wireless channel by utilizing the extracted target-based semantic information. Finally, at the receiver, the target-based semantic information can be utilized to meet specific application requirements. Alternatively, pre-captured background and semantic targets can be composited to fulfill complete image reconstruction needs. We evaluate the proposed approach by using a sensing image transmission scenario as a case study. Experimental results demonstrate the superiority of the PC-SC system in terms of image reconstruction performance and cost savings of bit. We employ beam prediction as a channel prediction task and find that the targets-based method outperforms the complete image-based approach in terms of efficiency and robustness, which can provide 32% time-saving.
Research on the Impact of Green Technology Innovation on Enterprise Financial Information Management Based on Compound Neural Network

To enhance the early warning level of financial risks in enterprises, mitigate the financial risks arising from diverse adversities, and drive green technological innovation and sustainable development, this study proposes a financial risk prediction model (MS-BGRU) that amalgamates multi-scale convolution and two-way GRU. Firstly, a multi-scale feature extraction module is devised that assimilates financial information from various scales by leveraging hole convolution with distinct expansion rates. This assimilated information is then fused to obtain richer context information. Secondly, the BGRU network is employed to discern the sequence characteristics and time information of financial indicators. The empirical results showcase that the model proposed in this paper exhibits a high identification accuracy, surging up to 98.03%, which surpasses other benchmark models. The model can accurately prophesize the financial risk of enterprises and offer guidance to management decision-makers in averting financial risk.
DALMIG: Matching-Based Data Center Allocation and Dual Live VM Migration in Cluster-Based Federated Cloud

The modern era has been ruled by federated cloud extensively owing to the upward popularity of real-time applications. However, balancing the load among the different service providers in the federation is a challenging task. This is because of the enlarged load of each service provider and the demand for Service Level Agreement (SLA) requirements. Live VM migration is an effective mechanism to balance the load among the different Cloud Service Providers (CSP). Yet, none of the works has focused on both inter- and intra- CSP-based live VM migration in the federated cloud. Besides, they utilized traditional techniques (Pre and Post-copy) to perform live VM migration. This increases migration and downtime in a federated cloud environment. Motivated by this, we propose matching-based data center allocation and Duple live VM migration in clustered federated cloud environment (DALMIG). The proposed DALMIG approach tackles the downsides faced in the federated cloud as yet. In this regard, three processes are contributed that are Matching-based Data Center Allocation, Two-Fold Clustering, and Duple live VM migration. Initially, brokers allocate apt data center to each incoming task using Auction-based Bipartite One to N (A-BON) matching algorithm. And then, we execute two-fold clustering process: Here, the data center is clustered using the Kernel Neutrosophic C-Means (kernel NCM) algorithm. And, we cluster VMs in each data center using the Credal C-Means (CCM) algorithm. To maintain the load of each CSP, we maintain two entities, such as a load monitor and a migrator. Here, the load monitor estimates a load of each data center and the migrator performs a live VM migration process. Here, the live VM migration process is performed using the Hybrid Copy technique. We execute both intra- and inter-CSP live VM migration. Here, intra-live VM migration is performed using the State Action Reward State Action (SARSA) algorithm. Inter-CSP live VM migration is accomplished using the Black Widow Optimization (BWO) algorithm. We show our DALMIG performance in the federation using the Cloudsim tool. The simulation results acquired from Cloudsim are auspicious in the context of metrics including latency, response time, number of VM migrations, Migration time, load fairness, and communication overhead.
The Optimization of Face Detection Technology Based on Neural Network and Deep Learning

Face detection is a biometric technology that automatically contains facial feature information. It integrates digital image processing, pattern recognition, and other technologies and collects images or video streams containing human faces by cameras or cameras for automatic detection and tracking. Starting from the idea of local features and deep learning, aiming at the problem that traditional convolutional neural network (CNN) only extracts features from the whole image and ignores practical local details, this article proposes a deep CNN model based on the fusion of global and local features. It explores the face detection algorithm with better performance under the interference of illumination, expression, and other internal or external factors. This method designs a suitable network structure according to the size of the training data set, and the core technology is the debugging of super parameters. The simulation results show that compared with SVM, the improved CNN has obvious advantages in the later stage of operation, and the error is reduced by 36.85%. Compared with the traditional face detection method, it can automatically extract image features and also automatically learn its model and get a higher recognition rate.
Bio-inspired machine learning: programmed death and replication

We analyze algorithmic and computational aspects of biological phenomena, such as replication and programmed death, in the context of machine learning. We use two different measures of neuron efficiency to develop machine learning algorithms for adding neurons to the system (i.e., replication algorithm) and removing neurons from the system (i.e., programmed death algorithm). We argue that the programmed death algorithm can be used for compression of neural networks and the replication algorithm can be used for improving performance of the already trained neural networks. We also show that a combined algorithm of programmed death and replication can improve the learning efficiency of arbitrary machine learning systems. The computational advantages of the bio-inspired algorithms are demonstrated by training feedforward neural networks on the MNIST dataset of handwritten images.
Decoupled Edge Guidance Network for Automatic Checkout

Automatic checkout (ACO) aims at correctly generating complete shopping lists from checkout images. However, the domain gap between the single product in training data and multiple products in checkout images endows ACO tasks with a major difficulty. Despite remarkable advancements in recent years, resolving the significant domain gap remains challenging. It is possibly because networks trained solely on synthesized images may struggle to generalize well to realistic checkout scenarios. To this end, we propose a decoupled edge guidance network (DEGNet), which integrates synthesized and checkout images via a supervised domain adaptation approach and further learns common domain representations using a domain adapter. Specifically, an edge embedding module is designed for generating edge embedding images to introduce edge information. On this basis, we develop a decoupled feature extractor that takes original images and edge embedding images as input to jointly utilize image information and edge information. Furthermore, a novel proposal divide-and-conquer strategy (PDS) is proposed for the purpose of augmenting high-quality samples. Through experimental evaluation, DEGNet achieves state-of-the-art performance on the retail product checkout (RPC) dataset, with checkout accuracy (cAcc) results of 93.47% and 95.25% in the average mode of faster RCNN and cascade RCNN frameworks, respectively. Codes are available at https://github.com/yourbikun/DEGNet.
Development of E-learning technologies in blended learning

This article provides an overview of e-learning technologies, highlights the effectiveness of blended learning compared to traditional teaching methods. The results of studies that confirm the effectiveness of blended learning compared to traditional methods are presented, as well as new E-learning technologies that can significantly improve learning outcomes, the didactic functions of digital educational resources are noted. The materials of the article will be useful to students who are interested in new technologies in education. The article notes the importance of innovative approaches to the organization of modern education.
Inspired by the brain

Abstract For years, the brain has been an inspiration for the creation of artificial intelligent systems. In this chapter, we start our study of neural networks by considering the performance of a single artificial neuron. We introduce essential concepts, such as weight, activation function, training, and learning. We describe the main features of a neuron and provide an example that illustrates how it works. For instance, we show how a single neuron is able to detect statistical regularities in a dataset and how those regularities can be employed to correctly classify data that the system has never seen before. We end the chapter describing the main limitation of individual neurons and how such a limitation has been overcome.
MULTI-STRATEGY IMPROVED TUNA SWARM OPTIMIZATION (TSO)-BASED HYBRID KERNEL EXTREME LEARNING MACHINE MODEL FOR EARLY DIAGNOSIS OF PARKINSON’S DISEASE

Parkinson’s disease (PD) is a neurodegenerative disease that progresses slowly. In this study, a tuna swarm optimization (TSO)-based hybrid kernel extreme learning machine (HKELM) method was proposed for early phonetic recognition of PD. Herein, HKELM models were optimized using multi-strategy (reverse learning and greedy selection swarm initialization, sin–cos strategy disturbance, and Weibull variation disturbance) improved TSO for early diagnosis of PD. The HKELM was employed for early diagnosis of PD and its classification accuracy was evaluated. The results of the experiments reveal that, as compared to the unimproved tuna technique for optimizing ELM, the suggested algorithm converges faster, can leap out of the local optimal, and has higher optimization accuracy.
Semi-quantum key distribution using qudit systems and security proof

Most prior semi-quantum key distribution (SQKD) protocols are designed in the context of qubits. In this paper, we proposed an SQKD protocol using ([Formula: see text]+1) quantum states in two mutually unbiased bases in the context of qudits without invoking the classical party’s measurement capability. It not only decreases the quantum states sent by quantum party by half, but also improves the efficiency of key rate than qubits do. To prove the security of present protocol, we first illustrate that the present protocol is completely robust, and then prove its unconditional security from information theoretical perspective by computing its key rate and noise tolerance against cloning-based individual attack. The unconditional security proof demonstrates that the higher dimension of quantum system is, the higher key rate can be obtained and the higher noise tolerance is allowed in depolarization channel.
All-in-one picture: visual summary of items in a recommender system

Abstract Navigation through large volumes of images is a complex and tedious task that requires tools to facilitate the exploration and discovery of visual information. Photo summaries are one of these tools, which consist of selecting a reduced set of images that best represent the original data source. However, creating photo summaries in the context of recommender systems poses several challenges: How to select the most relevant images for each item? How to encode each image? How to evaluate the quality of the generated summary? In this manuscript, we propose a clustering-based method to create a visual summary in the context of a restaurant recommender system, which includes the photos taken by users who visited the restaurants (items) in a given city. These photos are encoded using a deep neural network that takes into account not only their content but also the relationships between users and restaurants. This encoding will allow us to create a visual summary that captures the essence of user tastes and illustrates the gastronomic offer of the city. We also propose a similarity measure between items based on the users who have visited them and an evaluation method that calculates to what extent the summary obtained represents the original data source. The experimentation carried out includes five datasets and the obtained results demonstrate the adequacy of our proposal for the construction of these summaries.