Reviewerly/exHarmony huggingface dataset:
Marketing in the Metaverse: Conceptual understanding, framework, and research agenda

A hyper-connected digital universe referred to as the ‘metaverse’ bears the promise of fundamentally changing how consumers, brands, and firms will transact and interact in a seamlessly interconnected space of virtual realities. The potential of the metaverse is being accelerated by the increasing tendency of (i) consumers engaging and transacting in virtual spaces and (ii) firms investing millions of dollars in developing metaverse-related technologies. However, given the rapid evolution, there is a lack of clear understanding of the current scope of the metaverse and the consequent implications for marketing practice and research. This study integrates the findings from an extensive literature review of multiple disciplines and expert viewpoints of industry leaders to propose a definition and an organizing framework for the emergent metaverse. Subsequently, the authors discuss how metaverse-induced changes contribute to novel implications for marketing practice and propose a research agenda to guide future academic studies and marketing initiatives.
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning

Recently, large-scale pretrained language models have demonstrated impressive performance on several commonsense-reasoning benchmark datasets.However, building machines with commonsense to compose realistically plausible sentences remains challenging.In this paper, we present a constrained text generation task, COMMONGEN associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning.Given a set of common concepts (e.g., {dog, frisbee, catch, throw}); the task is to generate a coherent sentence describing an everyday scenario using these concepts (e.g., "a man throws a frisbee and his dog catches it").The COMMONGEN task is challenging because it inherently requires 1) relational reasoning with background commonsense knowledge, and 2) compositional generalization ability to work on unseen concept combinations.Our dataset, constructed through a combination of crowdsourced and existing caption corpora, consists of 77k commonsense descriptions over 35k unique concept-sets.Experiments show that there is a large gap between state-of-the-art text generation models (e.g., T5) and human performance (31.6% v.s.63.5% in SPICE metric).Furthermore, we demonstrate that the learned generative commonsense reasoning capability can be transferred to improve downstream tasks such as CommonsenseQA (76.9% to 78.4 in dev accuracy) by generating additional context.
TranAD

Efficient anomaly detection and diagnosis in multivariate time-series data is of great importance for modern industrial applications. However, building a system that is able to quickly and accurately pinpoint anomalous observations is a challenging problem. This is due to the lack of anomaly labels, high data volatility and the demands of ultra-low inference times in modern applications. Despite the recent developments of deep learning approaches for anomaly detection, only a few of them can address all of these challenges. In this paper, we propose TranAD, a deep transformer network based anomaly detection and diagnosis model which uses attention-based sequence encoders to swiftly perform inference with the knowledge of the broader temporal trends in the data. TranAD uses focus score-based self-conditioning to enable robust multi-modal feature extraction and adversarial training to gain stability. Additionally, model-agnostic meta learning (MAML) allows us to train the model using limited data. Extensive empirical studies on six publicly available datasets demonstrate that TranAD can outperform state-of-the-art baseline methods in detection and diagnosis performance with data and time-efficient training. Specifically, TranAD increases F1 scores by up to 17%, reducing training times by up to 99% compared to the baselines.
Masked Feature Prediction for Self-Supervised Visual Pre-Training

We present Masked Feature Prediction (MaskFeat) for self-supervised pre-training of video models. Our approach first randomly masks out a portion of the input sequence and then predicts the feature of the masked regions. We study five different types of features and find Histograms of Oriented Gradients (HOG), a hand-crafted feature descriptor, works particularly well in terms of both performance and efficiency. We observe that the local contrast normalization in HOG is essential for good results, which is in line with earlier work using HOG for visual recognition. Our approach can learn abundant visual knowledge and drive large-scale Transformer based models. Without using extra model weights or supervision, MaskFeat pretrained on unlabeled videos achieves unprecedented results of 86.7% with MViTv2-L on Kinetics-400, 88.3% on Kinetics 600, 80.4% on Kinetics-700, 38.8 mAP on AVA, and 75.0% on SSv2. MaskFeat further generalizes to image input, which can be interpreted as a video with a single frame and obtains competitive results on ImageN et.
Automated rationale generation

Automated rationale generation is an approach for real-time explanation generation whereby a computational model learns to translate an autonomous agent's internal state and action data representations into natural language. Training on human explanation data can enable agents to learn to generate human-like explanations for their behavior. In this paper, using the context of an agent that plays Frogger, we describe (a) how to collect a corpus of explanations, (b) how to train a neural rationale generator to produce different styles of rationales, and (c) how people perceive these rationales. We conducted two user studies. The first study establishes the plausibility of each type of generated rationale and situates their user perceptions along the dimensions of confidence, humanlike-ness, adequate justification, and understandability. The second study further explores user preferences between the generated rationales with regard to confidence in the autonomous agent, communicating failure and unexpected behavior. Overall, we find alignment between the intended differences in features of the generated rationales and the perceived differences by users. Moreover, context permitting, participants preferred detailed rationales to form a stable mental model of the agent's behavior.
Immersive virtual reality in the age of the Metaverse: A hybrid-narrative review based on the technology affordance perspective

Immersive virtual reality (VR) that utilizes head-mounted displays (HMD) is one of the key emerging technologies of the 21st century and has drawn keen attention from consumers, practitioners, and scholars in various disciplines. Nevertheless, the information systems (IS) discipline has neglected immersive VR, given that only a handful of studies have been published in mainstream IS journals. However, the recent advancements in immersive VR technology provide new opportunities for organizations and IS researchers. In light of these points, we reviewed the immersive VR literature to provide a holistic view of opportunities and challenges for organizations and future research directions for the IS field. By examining the technical capabilities of immersive VR and the previous literature, we identified five affordances: embodiment, interactivity, navigability, sense-ability, and create-ability. Our review of the 151 studies from the IS and related fields synthesized how these affordances were utilized in various research domains. Guided by the affordance-actualization theory, we also identified the strategic opportunities and challenges that come with implementing VR. The actualization of immersive VR affordances in organizations is indeed a fruitful area for IS scholars as there are various venues to move the IS field as well as the VR research in the organizational context forward.
MAXIM: Multi-Axis MLP for Image Processing

Recent progress on Transformers and multilayer perceptron (MLP) models provide new network architectural designs for computer vision tasks. Although these models proved to be effective in many vision tasks such as image recognition, there remain challenges in adapting them for lowlevel vision. The inflexibility to support high-resolution images and limitations of local attention are perhaps the main bottlenecks. In this work, we present a multi-axis MLP based architecture called MAXIM, that can serve as an efficient and flexible general-purpose vision backbone for image processing tasks. MAXIM uses a UNet-shaped hierarchical structure and supports long-range interactions enabled by spatially-gated MLPs. Specifically, MAXIM contains two MLP-based building blocks: a multi-axis gated MLP that allows for efficient and scalable spatial mixing of local and global visual cues, and a cross-gating block, an alternative to cross-attention, which accounts for cross-feature conditioning. Both these modules are exclusively based on MLPs, but also benefit from being both global and ‘fully-convolutional’, two properties that are desirable for image processing. Our extensive experimental results show that the proposed MAXIM model achieves state-of-the-art performance on more than ten benchmarks across a range of image processing tasks, including denoising, deblurring, de raining, dehazing, and enhancement while requiring fewer or comparable numbers of parameters and FLOPs than competitive models. The source code and trained models will be available at https://github.com/google-research/maxim.
Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study

<h2>Summary</h2><h3>Background</h3> We examined the potential change in cancer detection when using an artificial intelligence (AI) cancer-detection software to triage certain screening examinations into a no radiologist work stream, and then after regular radiologist assessment of the remainder, triage certain screening examinations into an enhanced assessment work stream. The purpose of enhanced assessment was to simulate selection of women for more sensitive screening promoting early detection of cancers that would otherwise be diagnosed as interval cancers or as next-round screen-detected cancers. The aim of the study was to examine how AI could reduce radiologist workload and increase cancer detection. <h3>Methods</h3> In this retrospective simulation study, all women diagnosed with breast cancer who attended two consecutive screening rounds were included. Healthy women were randomly sampled from the same cohort; their observations were given elevated weight to mimic a frequency of 0·7% incident cancer per screening interval. Based on the prediction score from a commercially available AI cancer detector, various cutoff points for the decision to channel women to the two new work streams were examined in terms of missed and additionally detected cancer. <h3>Findings</h3> 7364 women were included in the study sample: 547 were diagnosed with breast cancer and 6817 were healthy controls. When including 60%, 70%, or 80% of women with the lowest AI scores in the no radiologist stream, the proportion of screen-detected cancers that would have been missed were 0, 0·3% (95% CI 0·0–4·3), or 2·6% (1·1–5·4), respectively. When including 1% or 5% of women with the highest AI scores in the enhanced assessment stream, the potential additional cancer detection was 24 (12%) or 53 (27%) of 200 subsequent interval cancers, respectively, and 48 (14%) or 121 (35%) of 347 next-round screen-detected cancers, respectively. <h3>Interpretation</h3> Using a commercial AI cancer detector to triage mammograms into no radiologist assessment and enhanced assessment could potentially reduce radiologist workload by more than half, and pre-emptively detect a substantial proportion of cancers otherwise diagnosed later. <h3>Funding</h3> Stockholm City Council.
Multi-Task Consistency-Preserving Adversarial Hashing for Cross-Modal Retrieval

Owing to the advantages of low storage cost and high query efficiency, cross-modal hashing has received increasing attention recently. As failing to bridge the inherent modality gap between modalities, most existing cross-modal hashing methods have limited capability to explore the semantic consistency information between different modality data, leading to unsatisfactory search performance. To address this problem, we propose a novel deep hashing method named Multi-Task Consistency-Preserving Adversarial Hashing (CPAH) to fully explore the semantic consistency and correlation between different modalities for efficient cross-modal retrieval. First, we design a consistency refined module (CR) to divide the representations of different modality into two irrelevant parts, i.e., modality-common and modality-private representations. Then, a multi-task adversarial learning module (MA) is presented, which can make the modality-common representation of different modalities close to each other on feature distribution and semantic consistency. Finally, the compact and powerful hash codes can be generated from modality-common representation. Comprehensive evaluations conducted on three representative cross-modal benchmark datasets illustrate our method is superior to the state-of-the-art cross-modal hashing methods.
Data sharing practices and data availability upon request differ across scientific disciplines

Abstract Data sharing is one of the cornerstones of modern science that enables large-scale analyses and reproducibility. We evaluated data availability in research articles across nine disciplines in Nature and Science magazines and recorded corresponding authors’ concerns, requests and reasons for declining data sharing. Although data sharing has improved in the last decade and particularly in recent years, data availability and willingness to share data still differ greatly among disciplines. We observed that statements of data availability upon (reasonable) request are inefficient and should not be allowed by journals. To improve data sharing at the time of manuscript acceptance, researchers should be better motivated to release their data with real benefits such as recognition, or bonus points in grant and job applications. We recommend that data management costs should be covered by funding agencies; publicly available research data ought to be included in the evaluation of applications; and surveillance of data sharing should be enforced by both academic publishers and funders. These cross-discipline survey data are available from the plutoF repository.
Weakly supervised mitosis detection in breast histopathology images using concentric loss

Developing new deep learning methods for medical image analysis is a prevalent research topic in machine learning. In this paper, we propose a deep learning scheme with a novel loss function for weakly supervised breast cancer diagnosis. According to the Nottingham Grading System, mitotic count plays an important role in breast cancer diagnosis and grading. To determine the cancer grade, pathologists usually need to manually count mitosis from a great deal of histopathology images, which is a very tedious and time-consuming task. This paper proposes an automatic method for detecting mitosis. We regard the mitosis detection task as a semantic segmentation problem and use a deep fully convolutional network to address it. Different from conventional training data used in semantic segmentation system, the training label of mitosis data is usually in the format of centroid pixel, rather than all the pixels belonging to a mitosis. The centroid label is a kind of weak label, which is much easier to annotate and can save the effort of pathologists a lot. However, technically this weak label is not sufficient for training a mitosis segmentation model. To tackle this problem, we expand the single-pixel label to a novel label with concentric circles, where the inside circle is a mitotic region and the ring around the inside circle is a "middle ground". During the training stage, we do not compute the loss of the ring region because it may have the presence of both mitotic and non-mitotic pixels. This new loss termed as "concentric loss" is able to make the semantic segmentation network be trained with the weakly annotated mitosis data. On the generated segmentation map from the segmentation model, we filter out low confidence and obtain mitotic cells. On the challenging ICPR 2014 MITOSIS dataset and AMIDA13 dataset, we achieve a 0.562 F-score and 0.673 F-score respectively, outperforming all previous approaches significantly. On the latest TUPAC16 dataset, we obtain a F-score of 0.669, which is also the state-of-the-art result. The excellent results quantitatively demonstrate the effectiveness of the proposed mitosis segmentation network with the concentric loss. All of our code has been made publicly available at https://github.com/ChaoLi977/SegMitos_mitosis_detection.
Holistic CNN Compression via Low-Rank Decomposition with Knowledge Transfer

Convolutional neural networks (CNNs) have achieved remarkable success in various computer vision tasks, which are extremely powerful to deal with massive training data by using tens of millions of parameters. However, CNNs often cost significant memory and computation consumption, which prohibits their usage in resource-limited environments such as mobile or embedded devices. To address the above issues, the existing approaches typically focus on either accelerating the convolutional layers or compressing the fully-connected layers separatedly, without pursuing a joint optimum. In this paper, we overcome such a limitation by introducing a holistic CNN compression framework, termed LRDKT, which works throughout both convolutional and fully-connected layers. First, a low-rank decomposition (LRD) scheme is proposed to remove redundancies across both convolutional kernels and fully-connected matrices, which has a novel closed-form solver to significantly improve the efficiency of the existing iterative optimization solvers. Second, a novel knowledge transfer (KT) based training scheme is introduced. To recover the accumulated accuracy loss and overcome the vanishing gradient, KT explicitly aligns outputs and intermediate responses from a teacher (original) network to its student (compressed) network. We have comprehensively analyzed and evaluated the compression and speedup ratios of the proposed model on MNIST and ILSVRC 2012 benchmarks. In both benchmarks, the proposed scheme has demonstrated superior performance gains over the state-of-the-art methods. We also demonstrate the proposed compression scheme for the task of transfer learning, including domain adaptation and object detection, which show exciting performance gains over the state-of-the-arts. Our source code and compressed models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ShaohuiLin/LRDKT</uri> .
Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study

The whale optimization algorithm (WOA) is a prominent problem solver which is broadly applied to solve NP-hard problems such as feature selection. However, it and most of its variants suffer from low population diversity and poor search strategy. Introducing efficient strategies is highly demanded to mitigate these core drawbacks of WOA particularly for dealing with the feature selection problem. Therefore, this paper is devoted to proposing an enhanced whale optimization algorithm named E-WOA using a pooling mechanism and three effective search strategies named migrating, preferential selecting, and enriched encircling prey. The performance of E-WOA is evaluated and compared with well-known WOA variants to solve global optimization problems. The obtained results proved that the E-WOA outperforms WOA's variants. After E-WOA showed a sufficient performance, then, it was used to propose a binary E-WOA named BE-WOA to select effective features, particularly from medical datasets. The BE-WOA is validated using medical diseases datasets and compared with the latest high-performing optimization algorithms in terms of fitness, accuracy, sensitivity, precision, and number of features. Moreover, the BE-WOA is applied to detect coronavirus disease 2019 (COVID-19) disease. The experimental and statistical results prove the efficiency of the BE-WOA in searching the problem space and selecting the most effective features compared to comparative optimization algorithms.
Federated learning review: Fundamentals, enabling technologies, and future applications

Federated Learning (FL) has been foundational in improving the performance of a wide range of applications since it was first introduced by Google. Some of the most prominent and commonly used FL-powered applications are Android’s Gboard for predictive text and Google Assistant. FL can be defined as a setting that makes on-device, collaborative Machine Learning possible. A wide range of literature has studied FL technical considerations, frameworks, and limitations with several works presenting a survey of the prominent literature on FL. However, prior surveys have focused on technical considerations and challenges of FL, and there has been a limitation in more recent work that presents a comprehensive overview of the status and future trends of FL in applications and markets. In this survey, we introduce the basic fundamentals of FL, describing its underlying technologies, architectures, system challenges, and privacy-preserving methods. More importantly, the contribution of this work is in scoping a wide variety of FL current applications and future trends in technology and markets today. We present a classification and clustering of literature progress in FL in application to technologies including Artificial Intelligence, Internet of Things, blockchain, Natural Language Processing, autonomous vehicles, and resource allocation, as well as in application to market use cases in domains of Data Science, healthcare, education, and industry. We discuss future open directions and challenges in FL within recommendation engines, autonomous vehicles, IoT, battery management, privacy, fairness, personalization, and the role of FL for governments and public sectors. By presenting a comprehensive review of the status and prospects of FL, this work serves as a reference point for researchers and practitioners to explore FL applications under a wide range of domains.
DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

We present in this paper a novel denoising training method to speedup DETR (DEtection TRansformer) training and offer a deepened understanding of the slow convergence issue of DETR-like methods. We show that the slow convergence results from the instability of bipartite graph matching which causes inconsistent optimization goals in early training stages. To address this issue, except for the Hungarian loss, our method additionally feeds ground-truth bounding boxes with noises into Transformer decoder and trains the model to reconstruct the original boxes, which effectively reduces the bipartite graph matching difficulty and leads to a faster convergence. Our method is universal and can be easily plugged into any DETR-like methods by adding dozens of lines of code to achieve a remarkable improvement. As a result, our DN-DETR results in a remarkable improvement (+1.9AP) under the same setting and achieves the best result (AP 43.4 and 48.6 with 12 and 50 epochs of training respectively) among DETR-like methods with ResNet-50 backbone. Compared with the baseline under the same setting, DN-DETR achieves comparable performance with 50% training epochs. Code is available at https://github.com/FengLi-ust/DN-DETR.