Reviewerly/exHarmony huggingface dataset:
Research and Evaluation of Robot Engineering Design Education in Science Leader Training Course

The authors held a training course to learn robotics and AI programming with a drone for junior high school students. This course is a part of the children's science and technology human resources development project in Okinawa Prefecture. Initially, this training course was conducted based on the “robots and control” subject of National Institute of Technology, Okinawa College. A lecture was conducted through team training as a practical engineering design education class. The contents of lecture were software training and demonstration of robot system realization. However, implementing a drone autonomous movement program for junior high school students was challenging. For this reason, we devised a method that uses human movements instead of full-autonomous programs (we used color-object following and human following to control the drone, which will be described later). As a result, we established a training course for making a drone control program for junior high school students in 3 to 4 days. This paper describes and evaluates the system used in the abovementioned training course. The evaluation was based on the ARCS model. We present this teaching system as a practical example of engineering design training in primary education.
Development and User Evaluation for a Robust Character Input Support System for Natural Images of Electricity Reading Slips

Text input, which is essential for a variety of applications, is one of the most difficult operations for older people. So, we propose a prototype to automatically detect and input text from a natural image taken from a user's smartphone. For the evaluation of the system, 232 photo data taken by the actual people were prepared. When all data was processed on the server, the average processing time was less than 10 seconds, and the overall recognition rate was over 94%. For user study, 20 healthy elderly people aged 65 years or older were collected. And, after conducting a simulation with the conventional typing system, the prototype in this proposal was asked to be used. The time required to complete each work was recorded, and the usability was evaluated using System Usability Scale (SUS).
Comparison of Processors for Real-time Homography Computation

Homography is one of the most fundamental image geometric transformation techniques. Real-time homography can be used in online learning as a support function. In this study, The homography computations were conducted using only one central processing unit (CPU) and a multiple-core CPU with a graphics processing unit and a field-programmable gate array, with or without a conversion table. We also compared the shooting angles to check whether the characters in the image were recognizable after homography. The angle of photography was changed from 10° to 40° in 2° increments, and optical character recognition technology was used for character recognition.
MASTER CARPENTER YAMADA GEN-ICHI’ S CAREER AND CHARACTERISTICS OF HIS WORKS

This paper aimed to discuss the career of the master carpenter (Touryou), Yamada Gen-ichi, who had a connection with HARA Sankei, and to consider characteristics of his works. He built houses and tea rooms. In particular, “Inakaya” (country cottage) were his specialty field. In addition, there was a common renovation method that seems to be Yamada's style. While based on“Minka”(Japanese Folk House), it adds an element of Sukiya as a tea room. In addition, modern elements such as new materials and earthquake resistance are actively incorporated to create a harmonious collection.
Prediction and Online Optimization of Strip Shape in Hot Strip Rolling Process Using Sparrow Search Algorithm‐Online Sequential‐Deep Multilayer Extreme Learning Machine Algorithm

In the hot strip process, the prediction of strip shape is a key factor to improve the quality of products. However, strip‐shape prediction models based on traditional machine learning algorithms with relatively simple network structures often rely on data preprocessing and feature selection. Herein, five strip‐shape prediction models are proposed by combining sparrow search algorithm (SSA), particle swarm optimization algorithm (PSO), extreme learning machine (ELM), and deep multilayer extreme learning machine (DELM). The prediction experiments show that the SSA‐DELM model has highest accuracy, and the determination coefficient ( R 2 ) of crown prediction and flatness prediction of SSA‐DELM model is 0.971 and 0.979. 100% of the samples have a prediction error of less than ±7 μm and ±7 IU. Furthermore, to solve the problem of concept drift affecting prediction accuracy in industrial processes, online optimization method is proposed. Especially, online sequential deep ELM model (OS‐DELM) optimized by SSA and guided by double window drift detection is proposed to fill the gap. Compared with other online optimization methods, the update time with this method can be reduced by up to 83.05% averagely, which is more suitable for online strip‐shape prediction in hot rolling process.
HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information

Learning disentangled representations requires either supervision or the introduction of specific model designs and learning constraints as biases. InfoGAN is a popular disentanglement framework that learns unsupervised disentangled representations by maximising the mutual information between latent representations and their corresponding generated images. Maximisation of mutual information is achieved by introducing an auxiliary network and training with a latent regression loss. In this short exploratory paper, we study the use of the Hilbert-Schmidt Independence Criterion (HSIC) to approximate mutual information between latent representation and image, termed HSIC-InfoGAN. Directly optimising the HSIC loss avoids the need for an additional auxiliary network. We qualitatively compare the level of disentanglement in each model, suggest a strategy to tune the hyperparameters of HSIC-InfoGAN, and discuss the potential of HSIC-InfoGAN for medical applications.
A Semantic Matching Method of E-Government Information Resources Knowledge Fusion Service Driven by User Decisions

This study focuses on the knowledge fusion model of e-government information resources that supports user decision-making information needs, it discusses the user decision-making information needs model, the knowledge fusion service model, and the relationship between them. The inter-layer mapping matching mechanism realizes the ultimate value of knowledge fusion. Therefore, this paper analyses and studies the mapping mechanism between the user information demand model and the knowledge fusion service model. A semantic, similarity-based knowledge fusion service matching method for e-government information resources is proposed to address the problem of lack of semantics in traditional web service matching methods. This method uses the ontology description language OWL-S to map information requirement documents of user decisions and knowledge fusion service function documents into an ontology tree structure. The authors then use this as the basis to calculate the concept similarity and relationship similarity measures, and the service matching based on semantic similarity can be realized.
Classification of English Educational Resources Information Based on Mobile Learning Using Cognitive Web Service

English proficiency is increasingly vital in the modern globalized and competitive world, especially for jobs that need cross-cultural communication. Using online educational tools wisely leads to improved English proficiency. Digital learning tools are constantly being improved owing to the emergence of new technologies. An essential problem in academic circles is categorizing and assisting in investigating different forms of digital learning resources. This research proposes an efficient intelligent system for the classification of English educational resources information based on mobile learning (CEERI-ML). The suggested design classifies English educational resources into different categories, applying a Classification based on Marzano and Kendall Taxonomy (CMKT). The system further implements a Classification based on Gagne Learning Categorization Theory (CGLCT) to classify the different levels of complexity in learning.
Collaborative Social Metric Learning in Trust Network for Recommender Systems

In this study, a novel top-K ranking recommendation method called collaborative social metric learning (CSML) is proposed, which implements a trust network that provides both user-item and user-user interactions in simple structure. Most existing recommender systems adopting trust networks focus on item ratings, but this does not always guarantee optimal top-K ranking prediction. Conventional direct ranking systems in trust networks are based on sub-optimal correlation approaches that do not consider item-item relations. The proposed CSML algorithm utilizes the metric learning method to directly predict the top-K items in a trust network. A new triplet loss is further proposed, called socio-centric loss, which represents user-user interactions to fully exploit the information contained in a trust network, as an addition to the two commonly used triplet losses in metric learning for recommender systems, which consider user-item and item-item relations. Experimental results demonstrate that the proposed CSML outperformed existing recommender systems for real-world trust network data.
Collaborative optimization with PSO for named entity recognition-based applications

Named entity recognition (NER) as a crucial technology is widely used in many application scenarios, including information extraction, information retrieval, text summarization, and machine translation assisted in AI-based smart communication and networking systems. As people pay more and more attention to NER, it has gradually become an independent and important research field. Currently, most of the NER models need to manually adjust their hyper-parameters, which is not only time-consuming and laborious, but also easy to fall into a local optimal situation. To deal with such problem, this paper proposes a machine learning-guided model to achieve NER, where the hyper-parameters of model are automatically adjusted to improve the computational performance. Specifically, the proposed model is implemented by using bi-directional encoder representation from transformers (BERT) and conditional random field (CRF). Meanwhile, the collaborative computing paradigm is also fused in the model, while utilizing the particle swarm optimization (PSO) to automatically search for the best value of hyper-parameters in a collaborative way. The experimental results demonstrate the satisfactory performance of our proposed model.
Constructing outlier-free histograms with variable bin-width based on distance minimization

We propose a new method of constructing a variable bin width histogram that can accommodate the unbalanced distribution of the samples yet retaining, as a whole, the good aspect of both equal width (EW) and equal-area (EA) histograms that are being used popularly for data visualization and analysis. We formulate this as an optimal change point detection problem in which the bin boundaries are determined by minimizing the sum of the absolute error or the squared error in each bin. The former is based on Distance Minimization (DM) and new, and the latter is based on Variance Minimization (VM) and is considered the state-of-the-art. The constructed histograms can effectively be used to detect and visualize hidden outliers/anomalies by applying the interquartile range method in each bin. The final histograms are obtained by adjusting bin boundaries and heights accordingly after removing the detected outliers/anomalies. We further propose a method to annotate the constructed bins if the data for annotation is given for each sample as a set of nominal variables, using z-score with respect to their distribution within each bin. We applied our method to both real vinyl greenhouse datasets and two different sets of three synthetic datasets, and confirmed that both DM and VM methods work as intended, both can represent the sample distribution with a smaller number of bins than those by EW and EA methods, The use of interquartile range method can detect anomalies as well as outliers, and the terms selected for annotation are interpretable and reasonable. EW and EA methods have contrasting properties. DM and VM methods lie in between, but the former is closer to EA method and the latter to EW method. DM method runs substantially faster than VM method and performs slightly better than VM method in outlier detection and annotation tasks.
fieldcompare: A Python package for regression testing simulation results

Numerical software typically has a high inherent complexity as it aims at solving complex physical model equations by using advanced mathematical methods for solving partial differential equations.Beyond this, the model equations often involve parameters that are described by means of empirical constitutive relationships.Thus, a numerical simulation usually brings together various software components: for the domain discretization, the discretization method for the equations, the physics, and a non-linear and/or linear solver to obtain a solution for the discretized equations.
Seq2seq model for human action recognition based on skeleton and two-layer bidirectional LSTM

Human action recognition (HAR) plays an important role in social interaction in various fields. This study proposes a light-weight skeleton and two-layer bidirectional LSTM-based Seq2Seq model (SB2_Seq2Seq) for HAR to trade off recognition accuracy, users’ privacy and computer resource usage. An experiment was conducted to compare the proposed SB2_Seq2Seq with other skeleton-based Seq2Seq models and non-skeleton RGB video frame-based LSTM, CNN and seq2seq models. The UCF50 dataset was used for model evaluation, where 60%, 20% and 20% for model training, validation and testing, respectively. The experimental results show that the proposed model achieves 93.54% accuracy with 0.0214 Mean Square Error (MSE), suggesting that the proposed model outperforms all the other models. Besides, it also shows that the proposed model achieves state-of-the-art accuracy compared with state-of-the-arts methods in literature.
Is entanglement a unique resource in quantum illumination?

It is well known that quantum illumination with a two-mode squeezed vacuum state as an initial entangled bipartite state achieves 6 dB quantum advantage in the error probability compared to classical coherent-state illumination. Is entanglement the only resource responsible for the quantum advantage? We explore this issue by applying the various squeezing operators to the two-mode squeezed vacuum state. Even though the operations do not decrease the bipartite entanglement, it is shown that the quantum advantage drastically decreases with increasing the squeezing parameters. Based on the fact, we conclude that entanglement is not unique resource responsible for the quantum advantage.
Guided node graph convolutional networks for repository recommendation

Knowledge graph (KG) has been widely used in the field of recommender systems. There are some nodes in KG that guide the occurrence of interaction behaviors. We call them guided nodes. However, the current application doesn’t take into account the guided nodes in KG. We explore the utility of guided nodes in KG. It is applied in repository recommendations. In this paper, we propose an end-to-end framework, namely Guided Node Graph Convolutional Network (GNGCN), which effectively captures the connections between entities by mining the influence of related nodes. We extract samples of each entity in KG as their guided nodes and then combine the information and bias of the guided nodes when computing the representation of a given entity. The guided nodes can be extended to multiple hops. We evaluate our model on a real-world Github dataset named Github-SKG and music recommendation dataset, and the experimental results show that the method outperforms the recommendation baselines and our model is much lighter than others.
Line-Features-Based Pose Estimation Method for the Disc Cutter Holder of Shield Machine

To achieve automatic disc cutter replacement of shield machines, measuring the accurate pose of the disc cutter holder by machine vision is crucial. However, under polluted and restricted illumination conditions, achieving pose estimation by vision is a great challenge. This paper proposes a line-features-based pose estimation method for the disc cutter holder of the shield machine by using a monocular camera. For the blurring effect of rounded corners on the image edge, a rounded edge model is established to obtain edge points that better match the 3D model of the workpiece. To obtain the edge search box corresponding to each edge, a contour separation method based on an adaptive threshold region growing method is proposed. By preprocesses on the edge points of each edge, the efficiency and the accuracy of RANSAC linear fitting are improved. The experimental result shows that the proposed pose estimation method is highly reliable and can meet the measurement accuracy requirements in practical engineering applications.
Report on the 1st Workshop on Reaching Efficiency in Neural Information Retrieval (ReNeuIR 2022) at SIGIR 2022

As Information Retrieval (IR) researchers, we not only develop algorithmic solutions to hard problems, but we also insist on a proper, multifaceted evaluation of ideas. The IR literature on the fundamental topic of retrieval and ranking, for instance, has a rich history of studying the effectiveness of indexes, retrieval algorithms, and complex machine learning rankers and, at the same time, quantifying their computational costs, from creation and training to application and inference. This is evidenced, for example, by more than a decade of research on efficient training and inference of large decision forest models in Learning to Rank (LtR). As we move towards even more complex, deep learning models in a wide range of applications, questions on efficiency have once again become relevant with renewed urgency. Indeed, efficiency is no longer limited to time- and space-efficiency; instead it has found new, challenging dimensions that stretch to resource-, sample- and energy-efficiency with ramifications for researchers, users, and the environment. As a step towards bringing together experts from industry and academia and creating a forum for a critical discussion and the promotion of efficiency in the era of Neural Information Retrieval (NIR), we held the ReNeuIR workshop on July 15 th , 2022 as a hybrid event---in person in Madrid, Spain along with online attendees---in conjunction with ACM SIGIR 2022. Recognizing the importance of this topic, approximately 80 participants answered our call and attended the workshop over three sessions. The event included a total of two keynotes and eight paper presentations, and concluded with a lively discussion where participants helped identify gaps in existing research and brainstormed future research directions. We had consensus in recognizing that efficiency is not simply latency, that a holistic, concrete definition of efficiency is needed to guide researchers and reviewers alike, and that more research is necessary in the development of efficiency-centered evaluation metrics and standard benchmark datasets, platforms, and tools. Date: 15 July, 2022. Website: https://ReNeuIR.org.
Riemannian Geometry for Scientific Visualization

This tutorial introduces the most important basics of Riemannian geometry and related concepts with a specific focus on applications in scientific visualization. The main concept in Riemannian geometry is the presence of a Riemannian metric on a differentiable manifold, comprising a second-order tensor field that defines an inner product in each tangent space that varies smoothly from point to point. Technically, the metric is what allows defining and computing distances and angles in a coordinate-independent manner. However, even more importantly, it in a sense is really the major structure (on top of topological considerations) that defines the space where scientific data, such as scalar, vector, and tensor fields live.
Intelligent Conversational Agents for Ambient Computing

We are in the midst of an AI revolution. Three primary disruptive changes set off this revolution: 1) increase in compute power, mobile internet, and advances in deep learning. The next decade is expected to be about the proliferation of Internet-of-Things (IoT) devices and sensors, which will generate exponentially larger amounts of data to reason over and pave the way for ambient computing. This will also give rise to new forms of interaction patterns with these systems. Users will have to interact with these systems under increasingly richer context and in real-time. Conversational AI has a critical role to play in this revolution, but only if it delivers on its promise of enabling natural, frictionless, and personalized interactions in any context the user is in, while hiding the complexity of these systems through ambient intelligence. However, current commercial conversational AI systems are trained primarily with a supervised learning paradigm, which is difficult, if not impossible, to scale by manually annotating data for increasingly complex sets of contextual conditions. Inherent ambiguity in natural language further complicates the problem. We need to devise new forms of learning paradigms and frameworks that will scale to this complexity. In this talk, we present some early steps we are taking with Alexa, Amazon's Conversational AI system, to move from supervised learning to self-learning methods, where the AI relies on customer interactions for supervision in our journey to ambient intelligence. Date: 14 July 2022.
CloudQ: A Secure AI / HPC Cloud Bursting System

As a method to optimize the investment for computational resources, cloud bursting is collecting a lot of attention, where the organizations utilize the cloud computing environment in on-demand fashion, while preserving the minimum amount of on-premise resources for sensitive data processing. For the practical cloud bursting, we need to achieve 1) secure job / data sharing, 2) uniform job execution environment for on-premise and cloud, and 3) on-demand automatic deployment of the execution environment on the cloud. To enable these items, we propose a meta-scheduling system called CloudQ. CloudQ 1) uses cloud object storage for data sharing, 2) utilizes container images to provide uniform job execution environment, and 3) automatically deploys an execution environment on the cloud.
Competitive Study on Public and Private Key Usage in Voice over Internet Protocol

The Internet was once a small world. However, an innumerable number of network nodes are now connected to personal computers and their servers. When Voice over Internet Protocol (VoIP) services became widespread, VoIP topology and infrastructure became vulnerable to attackers who attempt to gain access to VoIP servers. These attacks on VoIP servers are a serious concern. While cryptography has been used for thousands of years to secure messages during wartimes, today we face cybercrime hackers who attempt to crack public or private encryption keys using a wide range of tools and mathematical clues. Their aim is to break down the cryptographic algorithms that protect VoIP servers in order to get three things: calls, user client privileges. The previous research on VoIP security is insufficient to address the increasing number of attackers. The current research aims to secure packet telephony by using cryptography to increase confidentiality and enhance the crypto mathematical approach in order to reduce the cost of high encryption techniques. This paper intensively studied cryptographic usage, concentrating on public and private keys (though the former are more commonly used). Thus, this paper presents a basis for further research on VoIP encryption.