Reviewerly/exHarmony huggingface dataset:
The Impact of VR/AR-Based Consumers’ Brand Experience on Consumer–Brand Relationships

This study aims to identify types of virtual/augmented reality–based brand experiences (VR/AR experiences) to understand their impacts on consumer–brand relationships. For this study, brand experiences were divided into four types: entertainment, aesthetic, educational, and real-escape experiences. Today, consumers can experience virtual/augmented reality–based brand experiences. In this study, a survey of consumers who participated in brand experiences was conducted. A total of 518 consumers participated in the survey. To verify the study hypotheses, structural equation modeling was conducting using the EQS 6 program. The results confirmed that entertainment, aesthetic, educational, and real-escape experiences were the main types of brand experiences that consumers accessed through AR/VR technologies. Additionally, brand experiences utilizing VR/AR technologies were found to affect consumer–brand relationship-building. Moreover, consumer–brand relationships were found to have a positive effect on consumer satisfaction, which, in turn, had a positive effect on purchase intentions. Furthermore, perceived brand authenticity played an important role in moderating the relationship between the brand experience types and consumer–brand relationship-building. It was found that there were interactions between study participants who perceived brand authenticity as high or low with all regulatory variables. These empirical studies of actual consumers’ AR and VR have the advantage of being able to present the most reliable analysis method for researchers to access their goals through various statistical data and analysis techniques. One of the most important factors in empirical research is sample size. This is because samples have a significant impact on generalizing the results. This means that the validity of the research results is high. The impact of VR and AR on sustainability examined in this study, the impact of VR and AR, and consumer-brand relationship building tried to deviate from the existing perspective in explaining sustainable behavior. It is judged that it contributed to grasping the causal relationship by establishing the relationship with the variables of brand experience with VR and AR and purchase intention. The fact that this study established the impact of VR and AR experience in predicting sustainable behavior of consumers contributed to the field of sustainable marketing.
ADMM-SRNet: Alternating Direction Method of Multipliers Based Sparse Representation Network for One-Class Classification

One-class classification aims to learn one-class models from only in-class training samples. Because of lacking out-of-class samples during training, most conventional deep learning based methods suffer from the feature collapse problem. In contrast, contrastive learning based methods can learn features from only in-class samples but are hard to be end-to-end trained with one-class models. To address the aforementioned problems, we propose alternating direction method of multipliers based sparse representation network (ADMM-SRNet). ADMM-SRNet contains the heterogeneous contrastive feature (HCF) network and the sparse dictionary (SD) network. The HCF network learns in-class heterogeneous contrastive features by using contrastive learning with heterogeneous augmentations. Then, the SD network models the distributions of the in-class training samples by using dictionaries computed based on ADMM. By coupling the HCF network, SD network and the proposed loss functions, our method can effectively learn discriminative features and one-class models of the in-class training samples in an end-to-end trainable manner. Experimental results show that the proposed method outperforms state-of-the-art methods on CIFAR-10, CIFAR-100 and ImageNet-30 datasets under one-class classification settings.
A blockchain‐based framework to optimize shipping container flows in the hinterland

Abstract We address two interrelated issues affecting the hinterland portion of the maritime container supply chain: reducing the movement of empty containers and reducing empty trips by trucks carrying these containers. In this paper, we show that empty container flow optimization can be implemented via a blockchain based on the proof‐of‐useful‐work concept where the proof of work requires the solution of an ‐hard optimization problem whose solution benefits the blockchain participants. Accordingly, we propose that anonymous miners compete to solve the container truck routing problem, which seeks to find the most efficient routes for trucks. We show that this problem is ‐hard. Miners must also solve the problem of optimally matching consignees and shippers, which will reduce transportation and storage costs for empty containers. In essence, the proposed framework turns blockchain into a massive optimization engine that directly benefits the hinterland container supply chain ecosystem.
Modeling Educational Discourse with Natural Language Processing

The broadening adoption of technology enhanced learning environments has substantially altered the manner in which educational communication takes place, with most people engaging in some form of online asynchronous or synchronous conversation every day. The language and discourse artifacts emerging from these technological environments is a rich source of information into learning processes and outcomes. This chapter describes the current landscape of natural language processing (NLP) tools and approaches available to researchers and practitioners to computationally discern patterns in large quantities of text-based conversations that take place across a variety of educational technology platforms. The capabilities of NLP are particularly important as, in the field of learning analytics, we desire to effectively and efficiently learn about the process of learning by observing learners, and then subsequently use that information to improve learning. We conclude the chapter with a discussion around the emerging applications (i.e., sensing technologies, breakthroughs in AI, and cloud computing) and challenges of NLP tools to educational discourse.
SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control

Despite the growing success of diffusion models in continuous-valued domains (e.g., images), similar efforts for discrete domains such as text have yet to match the performance of autoregressive language models.In this work, we present SSD-LM-a diffusion-based language model with two key design choices.First, SSD-LM is semi-autoregressive, iteratively generating blocks of text, allowing for flexible output length at decoding time while enabling local bidirectional context updates.Second, it is simplex-based, performing diffusion on the natural vocabulary space rather than a learned latent space, allowing us to incorporate classifier guidance and modular control using offthe-shelf classifiers without any adaptation.We evaluate SSD-LM on unconstrained text generation benchmarks, and show that it matches or outperforms strong autoregressive GPT-2 models across standard quality and diversity metrics, while vastly outperforming diffusionbased baselines.On controlled text generation, SSD-LM also outperforms competitive baselines, with an extra advantage in modularity. 1
Overview of the MEDIQA-Chat 2023 Shared Tasks on the Summarization &amp; Generation of Doctor-Patient Conversations

Automatic generation of clinical notes from doctor-patient conversations can play a key role in reducing daily doctors' workload and improving their interactions with the patients.MEDIQA-Chat 2023 aims to advance and promote research on effective solutions through shared tasks on the automatic summarization of doctor-patient conversations and on the generation of synthetic dialogues from clinical notes for data augmentation.Seventeen teams participated in the challenge and experimented with a broad range of approaches and models.In this paper, we describe the three MEDIQA-Chat 2023 tasks, the datasets, and the participants' results and methods.We hope that these shared tasks will lead to additional research efforts and insights on the automatic generation and evaluation of clinical notes.
Gradient-based Constrained Sampling from Language Models

Large pretrained language models generate fluent text but are notoriously hard to controllably sample from.In this work, we study constrained sampling from such language models: generating text that satisfies user-defined constraints, while maintaining fluency and model's performance in a downstream task.We propose MUCOLA-a sampling procedure that combines the log-likelihood of the language model with arbitrary (differentiable) constraints in a single energy function, and then generates samples in a non-autoregressive manner.Specifically, it initializes the entire output sequence with noise and follows a Markov chain defined by Langevin Dynamics using the gradients of the energy function.We evaluate MUCOLA on text generation with soft and hard constraints as well as their combinations obtaining significant improvements over competitive baselines for toxicity avoidance, sentiment control, and keyword-guided generation. 1 cat Transformer Layers Transformer Layers Transformer Layers Transformer Layers Transformer Layers Transformer Layers Transformer Layers Transformer Layers Linear Pool & Linear … … … … … …
Generative Language Models for Paragraph-Level Question Generation

Powerful generative models have led to recent progress in question generation (QG).However, it is difficult to measure advances in QG research since there are no standardized resources that allow a uniform comparison among approaches.In this paper, we introduce QG-Bench, a multilingual and multidomain benchmark for QG that unifies existing question answering datasets by converting them to a standard QG setting.It includes generalpurpose datasets such as SQuAD (Rajpurkar et al., 2016) for English, datasets from ten domains and two styles, as well as datasets in eight different languages.Using QG-Bench as a reference, we perform an extensive analysis of the capabilities of language models for the task.First, we propose robust QG baselines based on fine-tuning generative language models.Then, we complement automatic evaluation based on standard metrics with an extensive manual evaluation, which in turn sheds light on the difficulty of evaluating QG models.Finally, we analyse both the domain adaptability of these models as well as the effectiveness of multilingual models in languages other than English.QG-Bench is released along with the fine-tuned models presented in the paper, 1 which are also available as a demo. 2
Flat design as visualization of flat ontologies

The object of this research is design as a semiotic embodiment of the modern worldview, the quintessence of which are flat ontologies. The subject of the study is parallelism in the philosophy of flat ontologies and modern flat design. The purpose of this study is to explicate the philosophical problem of modern design, which is a consequence of the unconscious expression by designers of the philosophy of flat ontologies. The philosophy of flat ontologies struggles with anthropocentrism, insisting that a person should not have a privileged ontological status compared to other objects. In the philosophy of flat ontologies, a machine or a neural network are agents of action in the same way as a person. Modern flat design is an expression of this position, since the modern visual information environment is less and less adapted to human organs of perception, more and more transhumanistic. Both flat design and flat ontologies are a consequence of the horizontally oriented, materialistic line of philosophy, which forms the worldview of modern society. Thanks to the explication of the problem of flat design, it can be understood that the horizontally oriented line of philosophy is problematic for the further existence of man. The main conclusion of the study is that the horizontally oriented line in philosophy – materialism and positivism – in their logical development lead to the philosophy of flat ontologies, which postulates the same ontological status for human and non-human agents. Such a position is antihuman and transhumanistic, and this becomes evident in the philosophical analysis of modern design. The main stylistics of modern design is a flat design, which uses fourth-order simulacra as the main type of signs. The use of simulacrum signs in design creates an environment that is increasingly less adapted to human organs of perception, a transhumanistic environment.
Computational pathology in cancer diagnosis, prognosis, and prediction – present day and prospects

Abstract Computational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led to an explosion in innovation in computational pathology, ranging from the prospect of automation of routine diagnostic tasks to the discovery of new prognostic and predictive biomarkers from tissue morphology. Despite the promising potential of computational pathology, its integration in clinical settings has been limited by a range of obstacles including operational, technical, regulatory, ethical, financial, and cultural challenges. Here, we focus on the pathologists’ perspective of computational pathology: we map its current translational research landscape, evaluate its clinical utility, and address the more common challenges slowing clinical adoption and implementation. We conclude by describing contemporary approaches to drive forward these techniques. © 2023 The Authors. The Journal of Pathology published by John Wiley &amp; Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
An Explainable AI based Clinical Assistance Model for Identifying Patients with the Onset of Sepsis

The high mortality rate of sepsis, especially in Intensive Care Unit (ICU) makes it third-highest mortality disease globally. The treatment of sepsis is also time consuming and depends on multi-parametric tests, hence early identification of patients with sepsis becomes crucial. The recent rise in the development of Artificial Intelligence (AI) based models, especially in early prediction of sepsis, have improved the patient outcome. However, drawbacks like low sensitivity, use of excess features that leads to overfitting, and lack of interpretability limit their ability to be used in a clinical setting. So, in this research we have developed a smart, explainable and a highly accurate AI based model (called XAutoNet) that provides quick and early prediction of sepsis with a minimal number of features as input. An application based novel convolutional neural network (CNN) based autoencoder is also implemented that improves the performance of XAutoNet by dimensional reduction. Finally, to unbox the “Black Box” nature of these models, Gradient based Class Activation Map (GradCAM) and SHapley Additive exPlanations (SHAP) are implemented to provide interpretability of autoencoder and XAutoNet in the form of visualization graphs to assist clinicians in diagnosis and treatment.
Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model

As acquiring MRIs is expensive, neuroscience studies struggle to attain a sufficient number of them for properly training deep learning models. This challenge could be reduced by MRI synthesis, for which Generative Adversarial Networks (GANs) are popular. GANs, however, are commonly unstable and struggle with creating diverse and high-quality data. A more stable alternative is Diffusion Probabilistic Models (DPMs) with a fine-grained training strategy. To overcome their need for extensive computational resources, we propose a conditional DPM (cDPM) with a memory-efficient process that generates realistic-looking brain MRIs. To this end, we train a 2D cDPM to generate an MRI subvolume conditioned on another subset of slices from the same MRI. By generating slices using arbitrary combinations between condition and target slices, the model only requires limited computational resources to learn interdependencies between slices even if they are spatially far apart. After having learned these dependencies via an attention network, a new anatomy-consistent 3D brain MRI is generated by repeatedly applying the cDPM. Our experiments demonstrate that our method can generate high-quality 3D MRIs that share a similar distribution to real MRIs while still diversifying the training set. The code is available at https://github.com/xiaoiker/mask3DMRI_diffusion and also will be released as part of MONAI, at https://github.com/Project-MONAI/GenerativeModels .
Revealing University Students’ Attitudes toward Online Extensive Reading in Thailand

This study explores undergraduates’ attitudes toward extensive reading (ER) and reading graded readers online via Xreading.com in a large-scale ER program (ERP) involving over 5,000 students. Data were obtained from 356 students via an online questionnaire and 13 students in three focus groups. The quantitative findings showed that the students had positive attitudes toward ER. The top three reasons included (1) reading graded readers made them enjoy reading in English more than before, (2) they were more motivated to read graded readers because the stories were simple, and (3) they preferred to read shorter texts because they were simpler. Additionally, the qualitative findings revealed that all 13 students enjoyed freedom of choice in which they could choose their own graded readers that matched their interests and proficiency. Also, students had positive attitudes toward online ER through Xreading due to a variety of graded readers, an opportunity for students to practice their English and reading skills, enjoyment from reading, convenience and ease of use, and self-monitoring features. However, two difficulties were reported, including not knowing which graded readers to choose and eyestrain when reading online. Finally, pedagogical implications are discussed to guide ERP coordinators and teachers.
ポータブル蛍光エックス線分析装置による津波堆積物の簡易判別手法の検討

To improve a method for geochemically discriminating paleotsunami deposits, a quantitative analysis of major and trace elements in boring cores from the Shizuoka plain on the Pacific coast of central Japan was performed using a portable energy dispersive X-ray fluorescence spectrometry system (portable XRF). Geochemical approaches using a portable XRF contribute to studies on simple and rapid methods for detecting paleotsunami deposits. Calibration curves for quantitative analyses were prepared using 25 geochemical standard samples. Geochemical characteristics of paleotsunami deposits (ca. 1000, 3500, and 4000 cal BP) from the Shizuoka plain were obtained. Most of the quantitative data collected with the portable XRF were good agreement with reported values measured with stationary-type energy dispersive XRF in previous studies. Therefore, a portable XRF was applied in geochemical studies of samples from the Shizuoka plain. Relatively high Si/Ti, Ca/Ti, and Sr/Ti atomic ratios were observed in paleotsunami deposits from layers aged ca. 1000, 3500, and 4000 cal BP. Moreover, relatively high Cr/Ti ratios were found in lower paleotsunami deposits (ca. 4000 cal BP). Fe/Ti and S/Ti ratios increased in peaty clay layers just below the paleotsunami deposits in the core. On the other hand, Cl was not detected in the cores. Most of the paleotsunami deposits were distinguished from other layers in the cores using a cluster analysis with the portable XRF data. Therefore, a portable XRF is useful for characterizing paleotsunami deposits from the Shizuoka plain.
Evaluating Object Hallucination in Large Vision-Language Models

Inspired by the superior language abilities of large language models (LLM), large vision-language models (LVLM) have been recently proposed by integrating powerful LLMs for improving the performance on complex multimodal tasks. Despite the promising progress on LVLMs, we find that they suffer from object hallucinations, i.e., they tend to generate objects inconsistent with the target images in the descriptions. To investigate it, this work presents the first systematic study on object hallucination of LVLMs. We conduct the evaluation experiments on several representative LVLMs, and show that they mostly suffer from severe object hallucination issues. We further discuss that the visual instructions may influence the hallucination, and find that: objects that frequently appear in the visual instructions or co-occur with the image objects are obviously prone to be hallucinated by LVLMs. Besides, we further design a polling-based query method called POPE for better evaluation of object hallucination. Experiment results show that our POPE can evaluate object hallucination in a more stable and flexible way.
Fast and consistent algorithm for the latent block model

The latent block model is used to simultaneously rank the rows and columns of a matrix to reveal a block structure. The algorithms used for estimation are often time consuming. However, recent work shows that the log-likelihood ratios are equivalent under the complete and observed (with unknown labels) models and the groups posterior distribution to converge as the size of the data increases to a Dirac mass located at the actual groups configuration. Based on these observations, the algorithm Largest Gaps is proposed in this paper to perform clustering using only the marginals of the matrix, when the number of blocks is very small with respect to the size of the whole matrix in the case of binary data. In addition, a model selection method is incorporated with a proof of its consistency. Thus, this paper shows that studying simplistic configurations (few blocks compared to the size of the matrix or very contrasting blocks) with complex algorithms is useless since the marginals already give very good parameter and classification estimates.
Well Quasi-orders and the Functional Interpretation

The purpose of this article is to study the role of Gödel’s functional interpretation in the extraction of programs from proofs in well quasi-order theory. The main focus is on the interpretation of Nash–Williams’ famous minimal bad sequence construction, and the exploration of a number of much broader problems which are related to this, particularly the question of the constructive meaning of Zorn’s lemma and the notion of recursion over the non-wellfounded lexicographic ordering on infinite sequences.
The SWITCH test for discriminating quantum evolutions

Abstract We study different quantum circuits that can discriminate between two arbitrary quantum evolution operators. These circuits can be used to check whether two quantum operators are equal or not and to estimate a fidelity measure telling how close the operators are. This operator comparison is related to the SWAP test for discriminating two quantum states. In terms of their practical realization, we comment possible laboratory implementations with light along the same lines of recent experimental realizations of quantum superpositions of causal orders exploiting the different degrees of freedom of photons. We also discuss hardware efficient realizations for noisy intermediate scale quantum computers. Finally, we comment potential applications to the discrimination of quantum communication channels and to the search for simpler quantum circuits in quantum compilers.
Visualization as assemblage

The Anti-Eviction Mapping Project is an example of critical visualization practice that interrogates both its own conditions of production and how who is represented is also affected by the representations. In order to describe and analyze this form of practice the notion of assemblage as well as tools from actor-network theory are employed. These concepts allow the researcher or designer to consider how visualizations operate beyond its existence as a discrete representation but rather as a process that weaves a network of humans and non-humans together which is especially relevant to a critical engagement in information visualization practice.