Reviewerly/exHarmony huggingface dataset:
Rethinking corporate governance in the digital economy: The role of stewardship

Digital technologies are increasingly changing the nature of competition and generating externalities that impact society. On the one hand, digitalization allows organizations to create substantial economic value in shorter periods of time through improved economies of scale, scope, and learning, resulting is increasing returns on capital and competitive concentration. On the other hand, ensuing societal concerns with inequities, regulatory lapses, and lack of transparency and truthfulness represent significant challenges for effective corporate governance. This article addresses the consequences to stakeholders that emerge from the digital economy, discussing how traditional governance mechanisms are ill-equipped to subvert negative externalities. We offer a stewardship-based model of corporate governance as a solution to the pressing problems plaguing consumers, employees, and other salient stakeholders of digital business excesses, with an emphasis on truthful disclosures, enhanced transparency, improvements in equitable allocation of organizational resources, and heightened trust relationships. The focus is on the positive role that organizational leaders can play as exemplars of virtuous stewardship in a highly networked society. The article redefines a modern view of stewardship in a digital economy and applies its principles to the four Ts of virtuous stewardship, a practical corporate governance model that encompasses truthfulness, transparency, trust, and technological equity.
TransAnomaly: Video Anomaly Detection Using Video Vision Transformer

Video anomaly detection is challenging because abnormal events are unbounded, rare, equivocal, irregular in real scenes. In recent years, transformers have demonstrated powerful modelling abilities for sequence data. Thus, we attempt to apply transformers to video anomaly detection. In this paper, we propose a prediction-based video anomaly detection approach named TransAnomaly. Our model combines the U-Net and the Video Vision Transformer (ViViT) to capture richer temporal information and more global contexts. To make full use of the ViViT for the prediction, we modified the ViViT to make it capable of video prediction. Experiments on benchmark datasets show that the addition of the transformer module improves the anomaly detection performance. In addition, we calculate regularity scores with sliding windows and evaluate the impact of different window sizes and strides. With proper settings, our model outperforms other state-of-the-art prediction-based video anomaly detection approaches. Furthermore, our model can perform anomaly localization by tracking the location of patches with lower regularity scores.
Reliable customer analysis using federated learning and exploring deep-attention edge intelligence

The Internet of Things (IoT) and smart cities are flourishing with distributed systems in mobile wireless networks. As a result, an enormous amount of data are being generated for devices at the network edge. This results in privacy concerns, sensor data management issues, and data utilization issues. In this research, we propose a collaborative clustering method where the exchange of raw data is not required. The attention-based model used with a federated learning framework. The edge devices compute the model updates using local data and send them to the server for aggregation. Repetition is performed in multiple rounds until a convergence point reached. The transaction data used to train the attention model that gives a low dimensional embedding. Afterwards, we share the convergence model among the client/stores. Then, efficient pattern mining methods known as a clustering-based dynamic method (CBDM) are applied. For experimentation, we used retail store data to cluster the customer based on purchase behaviour. The proposed clustering method used semantic embedding to extract and then cluster them by discovering relevant patterns. The method achieved the 0.75 ROC values for the random distribution and 0.70 for the fixed distribution. The clustering method can help to reduce communication costs while ensuring privacy.
Designing and Building Enterprise Knowledge Graphs

This book is a guide to designing and building knowledge graphs from enterprise relational databases in practice. It presents a principled framework centered on mapping patterns to connect relational databases with knowledge graphs, the roles within an organization responsible for the knowledge graph, and the process that combines data and people. The content of this book is applicable to knowledge graphs being built either with property graph or RDF graph technologies.
Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis

The wide application of smart devices enables the availability of multimodal data, which can be utilized in many tasks. In the field of multimodal sentiment analysis, most previous works focus on exploring intra- and inter-modal interactions. However, training a network with cross-modal information (language, audio and visual) is still challenging due to the modality gap. Besides, while learning dynamics within each sample draws great attention, the learning of inter-sample and inter-class relationships is neglected. Moreover, the size of datasets limits the generalization ability of the models. To address the afore-mentioned issues, we propose a novel framework HyCon for hybrid contrastive learning of tri-modal representation. Specifically, we simultaneously perform intra-/inter-modal contrastive learning and semi-contrastive learning, with which the model can fully explore cross-modal interactions, learn inter-sample and inter-class relationships, and reduce the modality gap. Besides, refinement term and modality margin are introduced to enable a better learning of unimodal pairs. Moreover, we devise pair selection mechanism to identify and assign weights to the informative negative and positive pairs. HyCon can naturally generate many training pairs for better generalization and reduce the negative effect of limited datasets. Extensive experiments demonstrate that our method outperforms baselines on multimodal sentiment analysis and emotion recognition.
Learning to Generate Scene Graph from Natural Language Supervision

Learning from image-text data has demonstrated recent success for many recognition tasks, yet is currently limited to visual features or individual visual concepts such as objects. In this paper, we propose one of the first methods that learn from image-sentence pairs to extract a graphical representation of localized objects and their relationships within an image, known as scene graph. To bridge the gap between images and texts, we leverage an off-the-shelf object detector to identify and localize object instances, match labels of detected regions to concepts parsed from captions, and thus create "pseudo" labels for learning scene graph. Further, we design a Transformer-based model to predict these "pseudo" labels via a masked token prediction task. Learning from only image-sentence pairs, our model achieves 30% relative gain over a latest method trained with human-annotated unlocalized scene graphs. Our model also shows strong results for weakly and fully supervised scene graph generation. In addition, we explore an open-vocabulary setting for detecting scene graphs, and present the first result for open-set scene graph generation.
BERT-based Dense Retrievers Require Interpolation with BM25 for Effective Passage Retrieval

The integration of pre-trained deep language models, such as BERT, into retrieval and ranking pipelines has shown to provide large effectiveness gains over traditional bag-of-words models in the passage retrieval task. However, the best setup for integrating such deep language models is still unclear. When BERT is used to re-rank passages (i.e., BERT re-ranker), previous work has empirically shown that, while in practice BERT re-ranker cannot act as initial retriever due to BERT's high query time costs, and thus a bag-of-words model such as BM25 is required. It is not necessary to interpolate BERT re-ranker and bag-of-words scores to generate the final ranking. In fact, the BERT re-ranker scores alone can be used by the re-ranker: the BERT re-ranker score appears to already capture the relevance signal provided by BM25. In this paper, we further investigate the topic of interpolating BM25 and BERT-based rankers. Unlike previous work that considered the BERT re-ranker, however, here we consider BERT-based dense retrievers (RepBERT and ANCE). Dense retrievers encode queries and documents into low dimensional BERT-based embeddings. These methods overcome BERT's high computational costs at query time, and can thus be feasibly used in practice as whole-collection retrievers, rather than just as re-rankers. Our novel empirical findings suggest that, unlike for BERT re-ranker, interpolation with BM25 is necessary for BERT-based dense retrievers to perform effectively; and the gains provided by the interpolation are significant. Further analysis reveals why this is so: dense retrievers are very effective at encoding strong relevance signals, but they fail in identifying weaker relevance signals -- a task that the interpolation with BM25 is able to make up for.
Dense Scale Network for Crowd Counting

Crowd counting has been widely studied by computer vision community in recent years. Due to the large scale variation, it remains to be a challenging task. Previous methods adopt either multi-column CNN or single-column CNN with multiple branches to deal with this problem. However, restricted by the number of columns or branches, these methods can only capture a few different scales and have limited capability. In this paper, we propose a simple but effective network called DSNet for crowd counting, which can be easily trained in an end-to-end fashion. The key component of our network is the dense dilated convolution block, in which each dilation layer is densely connected with the others to preserve information from continuously varied scales. The dilation rates in dilation layers are carefully selected to prevent the block from gridding artifacts. To further enlarge the range of scales covered by the network, we cascade three blocks and link them with dense residual connections. We also introduce a novel multi-scale density level consistency loss for performance improvement. To evaluate our method, we compare it with state-of-the-art algorithms on five crowd counting datasets (ShanghaiTech, UCF-QNRF, UCF_CC_50, UCSD and WorldExpo'10). Experimental results demonstrate that DSNet can achieve the best overall performance and make significant improvements.
UAV-Assisted Communication Efficient Federated Learning in the Era of the Artificial Intelligence of Things

Artificial Intelligence (AI) based models are increasingly deployed in the Internet of Things (IoT), paving the evolution of the IoT into the AI of things (AIoT). Currently, the predominant approach for AI model training is cloud-centric and involves the sharing of data with external parties. To preserve privacy while enabling collaborative model training across distributed IoT devices, the machine learning paradigm called Federated Learning (FL) has been proposed. The future FL network is envisioned to involve up to millions of distributed IoT devices involved in collaborative learning. However, communication failures and dropouts by nodes can lead to inefficient FL. Inspired by the UAV-assisted communications in 5G heterogeneous networks (HetNet), we propose the UAV-assisted FL in this article. The FL model owner may employ UAVs to provide the intermediate model aggregation in the sky and mobile relay of the updated model parameters from data owners to the model owner. This therefore increases the reach of FL to data owners that face uncertain network conditions and improves the communication efficiency. To incentivize the UAV service providers, we adopt the multi-dimensional contract incentive design as a case study. The incentive compatibility of the contract ensures that the UAVs only choose an incentive package corresponding to its type, for example, traveling cost. The simulation results show that the UAV-assisted FL achieves significant improvement in communication efficiency and validates the incentive compatibility of our contract design.
Multimodal sentiment analysis with unidirectional modality translation

Multimodal Sentiment Analysis (MSA) is a challenging research area that investigates sentiment expressed from multiple heterogeneous sources of information. To integrate multimodal information including text, visual and audio modalities, state-of-the-art models focus on developing various fusion strategies, such as attention and outer product. However, the inferior quality of visual and audio features that is commonly observed in this area has not aroused much attention. We argue that this issue will obstruct the performance of the fusion strategies to a considerable extent. Therefore, in this paper, we propose Multimodal Translation for Sentiment Analysis (MTSA), a multimodal framework that improves the quality of visual and audio features by translating them to text features extracted by Bidirectional Encoder Representations from Transformers (BERT). Experiments on two benchmark datasets CMU-MOSI and CMU-MOSEI show that our model performs better than the state-of-the-art methods on both datasets across all the metrics, which illustrates the effectiveness of our method.
Applications of Blockchain in the Medical Field: Narrative Review

As a distributed technology, blockchain has attracted increasing attention from stakeholders in the medical industry. Although previous studies have analyzed blockchain applications from the perspectives of technology, business, or patient care, few studies have focused on actual use-case scenarios of blockchain in health care. In particular, the outbreak of COVID-19 has led to some new ideas for the application of blockchain in medical practice.This paper aims to provide a systematic review of the current and projected uses of blockchain technology in health care, as well as directions for future research. In addition to the framework structure of blockchain and application scenarios, its integration with other emerging technologies in health care is discussed.We searched databases such as PubMed, EMBASE, Scopus, IEEE, and Springer using a combination of terms related to blockchain and health care. Potentially relevant papers were then compared to determine their relevance and reviewed independently for inclusion. Through a literature review, we summarize the key medical scenarios using blockchain technology.We found a total of 1647 relevant studies, 60 of which were unique studies that were included in this review. These studies report a variety of uses for blockchain and their emphasis differs. According to the different technical characteristics and application scenarios of blockchain, we summarize some medical scenarios closely related to blockchain from the perspective of technical classification. Moreover, potential challenges are mentioned, including the confidentiality of privacy, the efficiency of the system, security issues, and regulatory policy.Blockchain technology can improve health care services in a decentralized, tamper-proof, transparent, and secure manner. With the development of this technology and its integration with other emerging technologies, blockchain has the potential to offer long-term benefits. Not only can it be a mechanism to secure electronic health records, but blockchain also provides a powerful tool that can empower users to control their own health data, enabling a foolproof health data history and establishing medical responsibility.
Securing matrix counting-based secret-sharing involving crypto steganography

The privacy and confidentiality of our data are more fundamental to our existence than ever, especially with our growing dependence on online services and the transfer of information, be it for convenience or the unfortunate COVID 19 pandemic. Traditional security measures such as cryptography and steganography are essential. However, they are not sufficient anymore as when one party maintains complete control over the data, they may intentionally, or not, lose or reveal it. Thus, secret sharing schemes were introduced to address applications that require collective agreement by authorized personnel to access or modify, such as missile launching, sophisticated medical agreements, or high-level bank transfers. In standard secret sharing, the target key is distributed among several authorized participants in a way that only the intended group of them are needed to reconstruct the original target key. These schemes became popular as they fortified the security of both cryptography and steganography and accomplished extraordinary results combined with each. This work focuses on two particular secret sharing techniques known as counting-based secret sharing and matrix-based secret sharing, which is based on the former. These methods are simple and intuitive. Consequently, they are prone to attacks that may result in the successful guessing of the key. In this work, not only are the shares hidden but they are also encrypted beforehand so that should they be intercepted, the adversary cannot decipher them. In other words, two layers of security are added to the secret-sharing method: steganography and cryptography. We studied two image steganography methods: least significant bit (LSB) and discrete wavelet transform (DWT), each combined with XOR encryption for security and robustness acceptability verification. The research results demonstrated that the use of steganography and encryption along with the matrix-based secret sharing did not affect the quality of operation nor compromised the security of information presenting attractive remarks.
On-chip photonic diffractive optical neural network based on a spatial domain electromagnetic propagation model

An integrated physical diffractive optical neural network (DONN) is proposed based on a standard silicon-on-insulator (SOI) substrate. This DONN has compact structure and can realize the function of machine learning with whole-passive fully-optical manners. The DONN structure is designed by the spatial domain electromagnetic propagation model, and the approximate process of the neuron value mapping is optimized well to guarantee the consistence between the pre-trained neuron value and the SOI integration implementation. This model can better ensure the manufacturability and the scale of the on-chip neural network, which can be used to guide the design and manufacturing of the real chip. The performance of our DONN is numerically demonstrated on the prototypical machine learning task of prediction of coronary heart disease from the UCI Heart Disease Dataset, and accuracy comparable to the state-of-the-art is achieved.
Automatic Speech Recognition: Systematic Literature Review

A huge amount of research has been done in the field of speech signal processing in recent years. In particular, there has been increasing interest in the automatic speech recognition (ASR) technology field. ASR began with simple systems that responded to a limited number of sounds and has evolved into sophisticated systems that respond fluently to natural language. This systematic review of automatic speech recognition is provided to help other researchers with the most significant topics published in the last six years. This research will also help in identifying recent major ASR challenges in real-world environments. In addition, it discusses current research gaps in ASR. This review covers articles available in five research databases that were completed according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. The search strategy yielded 45 articles related to the study’s scope for the period 2015–2020. The results presented in this review shed light on research trends in the area of ASR and also suggest new research directions.
Bridge inspection named entity recognition via BERT and lexicon augmented machine reading comprehension neural model

As an important data source in the field of bridge management, bridge inspection reports contain large-scale fine-grained data, including information on bridge members and structural defects. However, due to insufficient research on automatic information extraction in this field, valuable bridge inspection information has not been fully utilized. Particularly, for Chinese bridge inspection entities, which involve domain-specific vocabularies and have obvious nesting characteristics, most of the existing named entity recognition (NER) solutions are not suitable. To address this problem, this paper proposes a novel lexicon augmented machine reading comprehension-based NER neural model for identifying flat and nested entities from Chinese bridge inspection text. The proposed model uses the bridge inspection text and predefined question queries as input to enhance the ability of contextual feature representation and to integrate prior knowledge. Based on the character-level features encoded by the pre-trained BERT model, bigram embeddings and weighted lexicon features are further combined into a context representation. Then, the bidirectional long short-term memory neural network is used to extract sequence features before predicting the spans of named entities. The proposed model is verified by the Chinese bridge inspection named entity corpus. The experimental results show that the proposed model outperforms other mainstream NER models on the bridge inspection corpus. The proposed model not only provides a basis for automatic bridge inspection information extraction but also supports the downstream tasks such as knowledge graph construction and question answering systems.
Reduced density matrix sampling: Self-consistent embedding and multiscale electronic structure on current generation quantum computers

We investigate fully self-consistent multiscale quantum-classical algorithms on current generation superconducting quantum computers, in a unified approach to tackle the correlated electronic structure of large systems in both quantum chemistry and condensed matter physics. In both of these contexts, a strongly correlated quantum region of the extended system is isolated and self-consistently coupled to its environment via the sampling of reduced density matrices. We analyze the viability of current generation quantum devices to provide the required fidelity of these objects for a robust and efficient optimization of this subspace. We show that with a simple error mitigation strategy and optimization of compact tensor product bases to minimize the number of terms to sample, these self-consistent algorithms are indeed highly robust, even in the presence of significant noises on quantum hardware. Furthermore, we demonstrate the use of these density matrices for the sampling of non-energetic properties, including dipole moments and Fermi liquid parameters in condensed phase systems, achieving a reliable accuracy with sparse sampling. It appears that uncertainties derived from the iterative optimization of these subspaces is smaller than variances in the energy for a single subspace optimization with current quantum hardware. This boosts the prospect for routine self-consistency to improve the choice of correlated subspaces in hybrid quantum-classical approaches to electronic structure for large systems in this multiscale fashion.