Reviewerly/exHarmony huggingface dataset:
Metareasoning Design Options

A robot’s object level can have many reasoning processes, so there might be many options for controlling object-level reasoning processes with metareasoning. Thus, we are faced with the following question: Which reasoning processes should the meta-level control? We might make this decision based on information about which reasoning processes are consuming the most resources or which ones are most likely to fail. This chapter discusses the options that are available for metareasoning approaches and policies, using examples from our own work and the literature to illustrate the techniques, and the advantages and drawbacks of different options. By making these decisions about the metareasoning approach, we are beginning to specify how the robot will perform metareasoning. Naturally, we might find that these choices do not work as expected, and we’ll have to iterate and revisit these decisions. This chapter describes many ways that a robot can use metareasoning to improve performance, reliability, and safety.
What Leads to Effective Online Physician-Patient Communication? the Power of Convergence

Online health communities (OHCs) provide patients with chronic diseases with an alternative physician-patient communication platform that offers social support and better management of their diseases. However, under the context of text-only communication, even though the part OHCs play in enabling patients' self-management of chronic conditions has attracted scholarly and practical attention, the role of linguistic features pertaining to online physician-patient communication in improving patient compliance has been significantly neglected. Based on communication accommodation theory, this study examines the effects of convergent linguistic features on patients' satisfaction and compliance, along with the contingent roles of physicians' titles and complications. According to the study results, physician-patient convergence of semantic and stylistic features positively affects patient satisfaction, in turn increasing compliance. Furthermore, the physician's title weakens the effect of the convergence of language concreteness but strengthens the effect of the convergence of emotional intensity on patient satisfaction. Additionally, complications negatively affect the relationship between satisfaction and compliance. These findings provide further insight into the literature regarding communication accommodation theory and physician-patient communication in OHCs and offer a practical guide to physician-patient communication skills.
A convolutional autoencoder and a neural gas model based on Bregman divergences for hierarchical color quantization

Color quantization (CQ) is one of the most common and important procedures to be performed on digital images. In this paper, a new approach to hierarchical color quantization is described, presenting a novel neural network architecture integrated by a convolutional autoencoder and a Growing Hierarchical Bregman Neural Gas (GHBNG). GHBNG is a CQ algorithm that allows the compression of an image by choosing a reduced set of the most representative colors to generate a high-quality reproduction of the original image. In the technique proposed here, an autoencoder is used to translate the image into a latent representation with higher per-pixel dimensionality but reduced resolution, and GHBNG is then used to quantize it. Experimental results confirm the performance of this technique and its suitability for tasks related to color quantization.
Extrinsic calibration of a camera and a 2D laser range finder using ping pong balls and the corner of a room

Three different methods for extrinsic calibration of a 2D laser range finder and a camera using ping pong balls and the corner of a room are presented and compared with four recent state-of-the-art methods. In the first method, a servo motor and gimbal were used to rotate the laser rangefinder to produce a 3D point cloud from the test field. In the second method, the laser range finder was solely used to get a 2D point cloud of the ping pong balls. In the third approach by employing the pyramid that was built in the room's corner and resolving the perspective-three-point problem, the 2D LRF calibration parameters with respect to the test field were determined with just one scan. In comparison to the estimated values produced by the third approach, the extrinsic calibration parameters derived with the 3D point cloud were more accurate. The second approach produced the worst outcomes, although it was the most straightforward approach.
Representation-centric approach for classification of Consumer Health Questions

Question Classification is the very first and pivotal step in Question Answering(QA) systems. It maps a given question into a predefined class. Classification of Consumer Health Questions contributes to answering medical queries of non-expert users. It plays a principal role in the performance of Consumer Health Question Answering systems. There are several approaches to the classification of questions. However, they do not consider the nature of Consumer Health Questions. In this study, we perform experiments focusing on the question representation methods to classify Consumer Health Questions accurately. These experiments provide an intuition of practical considerations for Consumer Health Question classification. The questions were represented through certain manually-designed features, word embedding, sentence embedding, and finetuning-based techniques. We create question classifiers that involve designing models for representing and categorizing questions. Based on the result, the fine-tuned Bidirectional Encoder Representations from Transformers(BERT)-large-based model with an accuracy of 86.00% on Genetic and Rare Diseases (GARD) and 80.40% on Yahoo! Answers-based datasets outperform previous works. In addition, the method based on A Robustly Optimized BERT Pretraining Approach(RoBERTa)-large model with 86.20% accuracy surpasses fine-tuned BERT-large model on the GARD dataset. Examining the outcomes of the models gives insight into decisive considerations for representing and classifying Consumer Health Questions.
Machine learning based geometry reconstruction for quality control of laser welding processes

Abstract The increasing use of automated laser welding processes causes high demands on quality control. 2D or 3D sensor technology can be used for data acquisition to monitor the weld quality after laser welding. Compared to a 2D camera image, the 3D height data, e.g. acquired using optical coherence tomography, contains additional relevant information for quality inspection. However, the disadvantages are system complexity, higher costs, and longer acquisition times. Therefore, we compare image-based methods with the quality assessment based on height data. The first method uses feature vectors from grayscale images taken coaxially with the laser beam. The significant advantage is that a camera is often integrated into the laser system, so no additional hardware is required. In the second approach, we use an AI-based single-view 3D reconstruction method. The height profile is reconstructed from a camera image and used for further quality assessment. Thus, we combine the advantages of 2D data acquisition with higher accuracy in evaluating 3D data. In addition, we consider the usually low data availability in the industrial environment in the development of algorithms. We use a training data set with 95 samples and a test data set with 858 samples. The work uses the contracting process of copper wires to produce formed coil windings to illustrate the method. We analyze a data set with different defect types and compare the quality assessment using the height data acquired with OCT, the feature vectors from the camera images, and the reconstructed height data.
Shahmukhi named entity recognition by using contextualized word embeddings

Named Entity Recognition (NER) is an imperative Natural Language Processing (NLP) task which intents to identify and classify predefined named entities in a given span of text. For many Western and Asian languages, NER is a systematically premeditated and established task, however, a little work has been done for Shahmukhi. This paper presents Shahmukhi NER with four key contributions. First, a Bi-directional Long-Short Term Memory (BiLSTM) network based NER model has been developed by incorporating various features including character and word embeddings and Part of Speech (POS) tagging. Second, transfer learning has been employed by training context-free Word2Vec and contextualized Embeddings from Language Models (ELMo) word representations. The word representations have been trained using a Shahmukhi corpus of 14.9 million words. Third, we prepared a cleaner version of an existing Shahmukhi NER corpus by performing Unicode normalization and tokenization errands. The corpus has been deduplicated and results are reported on an unseen evaluation set which produced valid results. Fourth, we have studied the impact of two annotation schemes; Inside-Outside (IO) and Inside-Outside-Beginning (IOB) for Shahmukhi. Transfer learning was quite helpful to enhance the performance of NER models especially ELMo embeddings significantly improved the results by prompting contextualized embedding vectors. This is the first study to use character embeddings, POS tagging and transfer learning for Shahmukhi named entity recognition. The IO scheme based model achieved an accuracy of 98.60% with an f-score of 83.75. The IOB scheme based model performed with an accuracy of 98.43% and an f-score of 75.55. These scores are quite promising for an under-resourced morphologically-rich language.
A further step for efficient corrections of inconsistent probabilistic data sets

Partial conditional probability assessments are having renewed attention and the merging of several sources of information is one of the more compelling needs associated with them. We focus here on the consequent task of correcting inconsistent probabilistic databases. We propose an efficient method for correcting incoherent (i.e. inconsistent) conditional probability assessments, that has a polynomial space complexity, differently from methods based on probabilistic satisfiability problems (PSAT) which require an exponential amount of memory space. This method uses Mixed Integer Programming (MIP) procedure to minimize the L1 distance between probability assessments and exploits the presence of the so-called “zero layers”. Through a simple prototypical example, we illustrate the feasibility and the peculiarities of the proposed procedure. Finally, we show some experimental results obtained through randomly generated incoherent assessments.
Analysis of Korean Research Trends on Virtual Reality(VR) Programs In Practical Nursing Education

Objectives This study was attempted to confirm the current status and effects of Korean research on virtual reality (VR) education programs for nursing students in nursing practice education and to suggest future research directions.&#x0D; Methods We searched the KERIS, DBpia, KISS, KMbase, NDSL and e-search databases using key words regardless of the period of publication. Among 248 searched papers, we selected 25 papers after excluding literature that does not satisfy the selection criteria.&#x0D; Results Of 25 papers, 24 have been published since 2020. There were 13 one-group pretest-posttest design, 10 non-equivalent control group pretest-posttest design, 1 non-equivalent control group posttest design, 1 randomized control-group pre-post test design. The VR education program varied depending on the type of practical subject, and the effect of VR education program was found to have some effect on self-efficacy, clinical nursing performance, performance confidence, critical thingking problem-solving ability, and knowledge increase.&#x0D; Conclusions This study comprehensively confirmed the current status and effectiveness of VR education program for nursing students. Therefore, it is significant in that it presented the direction of follow-up research. Non-face-to-face education using VR education programs has been expanded due to COVID-19, and it can be settled as an alternative teaching method in a limited practical education environment even after post-COVID-19. However, efforts are needed for high-quality evidence-based educational interventions, such as research on a randomized control group design and development of a VR program suitable for the Korean nursing education situation.
Structure Enhanced Path Reasoning for Knowledge Graph Completion

Knowledge graphs are crucial foundations for building intelligent systems, such as question answering and recommendation. However, their performance is hampered by the incompleteness of KGs, so the knowledge graph completion arises to infer whether a triple of the form (head entity, relation, tail entity) is a missing fact. The path-based approach that encodes paths from the head entity to the tail entity for reasoning achieves good performance. Previous work suggests that entity type is beneficial for learning path representations. Nevertheless, the semantics of entities are not captured accurately, as many entities are not typed or loosely typed. In addition, previous methods tend to model paths only from the forward direction but fail to capture new path patterns from the reverse direction (i.e., tail entity to head entity). In this paper, we introduce a structure enhanced path reasoning (SPR) framework to address the above-given problems. First, the model uilizes the structure of entities, i.e., their relational contexts (the relations linked from the given entity), to obtain a reliable path representation that captures correct entity semantics. This information is accessible to all nonisolated entities in all KGs, so that it can compensate the semantics for entities or KGs that have no type available. Second, we leverage the structure of paths to derive their reverse paths, so as to enhance the path representation by additionally encoding the new patterns embedded in them through a dual path encoding method. In order to verify the effectiveness of the proposed methods, we design different architectures based on LSTM and Transformer, respectively. Experimental results on two benchmark datasets, WN18RR, and FB15k-237, show that our approach apparently outperforms state-of-the-art methods on fact prediction task and relation prediction task. Furthermore, extensive experiments illustrate the benefits of enhancing path reasoning by exploiting structure information from entity relational contexts and the dual path encoding method.
Zero-shot learning via visual feature enhancement and dual classifier learning for image recognition

Zero-shot image recognition attempts to simulate the zero-shot learning mechanism of humans and recognizes the images of novel classes. It is crucial to learn transferable knowledge from seen classes and generalize it to unseen classes for image recognition in zero-shot learning (ZSL). Most existing ZSL methods extract visual features with pretrained backbone networks and learn transferable knowledge with the extracted visual features. However, the backbone networks are not pretrained for a special task, and the extracted visual features usually contain some distractive information for the ZSL task, which causes some discriminative information to be ignored or weakened and degrades the quality of knowledge learned from seen classes. Moreover, since visual samples of unseen classes are not obtainable, domain shift is another challenging problem. In this paper, we propose visual feature enhancement to learn more discriminative visual features via a graph convolutional network (GCN) and an attention mechanism for improving the quality of the learned transferable knowledge. Different from previous works, we explore the correlations between different latent visual patterns of an image and introduce GCN to enhance visual features. On the other hand, we take advantage of different learning mechanisms of GCN and MLP and propose dual classifier learning for improving the generalization and inference capabilities of our model. In end-to-end model training, the module of visual feature enhancement and the module of dual classifier learning are beneficial to each other via joint optimization. Finally, we perform extensive experiments in the ZSL setting and GZSL setting. The extensive experimental results verify the effectiveness and superiority of our method.
Explainability in image captioning based on the latent space

This paper focuses on the representation/latent space in neural architectures to develop an end-to-end explanation approach for Image Captioning (IC) models. By injecting Gaussian perturbations into the latent space of each component of the architecture, we first analyze and identify the parts of the model likely to be the most decisive/influential in the caption generation. The results show that the visual part, mainly composed of visual encoding and attention mechanism, is more decisive than the language part, which could lead to more subtle explanations. We then follow this approach with an in-depth explanation protocol that also utilizes the latent space and focuses on the visual modality to design and compare two explanation methods with different scopes; (1) a surrogate-based method with Local Interpretable Model-Agnostic Explanations (LIME), with local scope. (2) a backpropagation-based method with Layer-wise Relevance Propagation (LRP) for global explanations. To assess the quality of the obtained explanations, we propose the new concept of Latent Ablation, which proves to be more consistent than classical Ablation, which usually leads to inconsistencies and truncated information. Extensive experiments show that both methods achieve comparable results and that their scope has no explicit impact on the quality of the explanations.
A critical review of research on EFL writing errors in search of ways to utilize the analysis results

Although error analysis (EA) research on English learners is one of the most pivotal areas in English education, the research findings of a few decades are not effectively utilized. To answer research questions triggered by the awareness of the problem, this study critically synthesizes domestic studies over the past 20 years that analyzed English writing errors of college students. First, the current study identified common patterns of English writing errors made by college students. Second, the study examined the difficulties involved in summarizing the accumulated research results for practical use, including the differences in the error classification system and the differing criteria for defining English proficiency of learners. The implications for future research are discussed as well as strategies for providing easier access to EA research findings for those outside of academia.
ELAMD: An ensemble learning framework for adversarial malware defense

Machine learning-based methods have been widely used in malware detection. However, recent studies show that models based on machine learning (or deep learning) are vulnerable to adversarial attacks. For example, slight perturbation to input can cause the models to produce false detection results with high confidence. Although some research efforts have been made to defend against adversarial attacks, the existing methods suffer from limitations in terms of detection accuracy and labeling cost. To address this problem, we propose an ensemble learning framework for Windows malware adversarial defense that contains two methods. The first one is an adversarial sample detection method to defeat specific adversarial attacks. This method takes malware features into groups and uses ensemble learning to detect the adversarial sample. The second one is an anomaly detection method to defend against agnostic adversarial attacks. This method regards adversarial samples as outliers and utilizes unsupervised and semi-supervised learning to construct anomaly detection models. We use the adversarial defense methods proposed as supplementary modules to the original malware detection models. Experiments show that our methods can improve malware detection model robustness against adversarial attacks. Moreover, comparison experiments indicate that our methods outperform traditional adversarial training by about 11% on detection accuracy.
Conclusion

This chapter revisits each of the design principles, summarizing and drawing connections between them. Many of the principles are based on empirical evidence from traditional learning environments; a discussion on the boundary conditions of the design principles explores the extrapolations of this evidence to training recognition skills in dynamic, high stakes environments. The chapter closes with a discussion of the contributions and challenges of augmented reality to training.
IEEE ISMAR 2022 Report: Toward Better Mixed Realities

On October 21, 2022, the 21st IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2022) was successfully completed in Singapore. ISMAR is the leading international conference in the fields of augmented reality, mixed reality, and virtual reality. This was the first time that ISMAR was held in Southeast Asia and the first time in hybrid mode. ISMAR 2022 achieved a historically high number of papers and attendees, witnessing the steady growth of the community and the scientific contributions. In this article, we report the key outcomes, impressions, research trends, and lessons learned from the conference.
Enhancement of PCA-Based Dimensionality Reduction Using BB-BC Optimization Algorithm

Dimensionality reduction is an important step for various applications where usage of the internet and multimedia systems is involved which requires huge bandwidth and storage space. This paper presents Big Bang-Big Crunch (BB-BC) optimization algorithm based two new approaches to feature selection for dimensionality reduction. In first approach, PCA is used to extract the features (eigenvectors) and defines feature set based on computation of knee point and BB-BC optimization algorithm, in turn selects an optimal subset from the predefined feature set. In the second approach, PCA is used only for feature extraction and BB-BC optimization algorithm is used for optimal feature selection from all extracted features. Olivetti Research Laboratory (ORL) face database has been used for performing the experiment and recognition rate is the parameter to be optimized for face recognition as an application area. The experimentation proves BB-BC as a powerful soft computing technique to solve such NP hard problems.
A Practical Approach to Quantum Resilient Cloud Usage Obtaining Data Privacy

Cloud computing has emerged as an attractive and fastest-growing technology. The Cloud model encompasses a highly scalable infrastructure environment and guarantees the daily business processes for data-driven business cases protected by quantum vulnerable cryptography and no preserving of data privacy. This paper proposes a practical quantum-safe approach to guarantee data privacy even if a quantum computer can break classical public-key exchange algorithms. We present a quantum-safe cloud use based on post-quantum cryptographic and privacy-focused applications via homomorphic encryption and secure multiparty computation.
The Birth of the Barcode

Abstract 50 years ago, the barcode was born. Millions of barcodes are read around the world every hour of the day – but how did they come to be? Derrick Brown explains.