Reviewerly/exHarmony huggingface dataset:
Edge Server Deployment Scheme of Blockchain in IoVs

In the Internet of Vehicles (IoVs), vehicles generate and disseminate information, which makes the related vehicular services realized. However, the IoVs is an untrusted environment. Vehicles cannot evaluate the credibility of the received information, which makes it a challenge to implement data sharing in IoVs. Blockchain, constantly directed main attention, are considered as a feasible solution to address the challenge, due to its advantages of decentralization, unforgeability, and collective maintenance. The consensus mechanism of blockchain requires the miners in the system with strong computing power for mining, while the computing power of nodes in IoVs is limited, which restricts the application of blockchain in IoVs. In fact, the application of blockchain in IoVs can be implemented by employing edge computing. The key entity of edge computing is the edge servers(ESs). Roadside nodes (RSUs) can be deployed as ESs of edge computing in IoVs. In this article, we study the ESs deployment scheme for covering more vehicle nodes in IoVs, and propose a randomized algorithm to calculate approximation solutions. Finally, we simulated the performance of the proposed scheme and compared it with other deployment schemes.
Research on the evolution of the innovation ecosystem of the Internet of Things: A case study of Xiaomi(China)

With the support of industry trends and policies, the Internet of Things is seen as another revolution in the world's information technology industry. Many companies hope to establish a huge Internet of Things ecosystem through the business ecosystem to achieve rapid expansion and growth. This research selected Xiaomi, the world's largest consumer-level IoT platform, and tried to explore the growth and development mechanism of its IoT innovation ecosystem by analyzing the development logic of Xiaomi enterprises. The research conclusions show that the Xiaomi IoT innovation ecosystem has evolved through four stages of development, expansion, layout, and maturity. Through the four paths of building market entry, investing in incubating niche products, establishing a bamboo forest ecology, and building an IoT open platform, the ecological map of the Xiaomi IoT ecosystem is finally formed. This research aims to provide guidance and suggestions on business models, evolution strategies, and dynamic capabilities for other companies through path analysis of typical cases.
Blind and Compact Denoising Network Based on Noise Order Learning

A lightweight blind image denoiser, called blind compact denoising network (BCDNet), is proposed in this paper to achieve excellent trade-offs between performance and network complexity. With only 330K parameters, the proposed BCDNet is composed of the compact denoising network (CDNet) and the guidance network (GNet). From a noisy image, GNet extracts a guidance feature, which encodes the severity of the noise. Then, using the guidance feature, CDNet filters the image adaptively according to the severity to remove the noise effectively. Moreover, by reducing the number of parameters without compromising the performance, CDNet achieves denoising not only effectively but also efficiently. Experimental results show that the proposed BCDNet yields state-of-the-art or competitive denoising performances on various datasets while requiring significantly fewer parameters.
Mining behavioural and sentiment-dependent linguistic patterns from restaurant reviews for fake review detection

Online reviews are increasingly recognized as a key source of information influencing consumer behavior. This in turn implies that competitive advantage can be achieved by manipulating users' perceptions about restaurants. The hospitality industry is particularly susceptible to this issue because products and services in this industry can only be rated upon consumption. Therefore, many efforts have recently been dedicated to developing automatic methods for detecting fake reviews based on data intelligence in this sector. Recent studies suggest that both the semantic meaning of consumer reviews and the sentiment conveyed may be useful indicators of fake reviews. However, the semantic meaning may be context-sensitive and may also disregard sentiment information. Moreover, the content analysis approach should be integrated with the reviewer's behavior to reveal their true intentions. To address these problems, we propose a review representation model based on behavioural and sentiment-dependent linguistic features that effectively exploit the domain context. Using a large dataset of Yelp restaurant reviews, we demonstrate that the proposed review representation model is more effective than existing approaches in terms of detection accuracy. It furthermore accurately estimates the average rating assigned by legitimate reviewers, which has significant managerial implications for the hospitality industry.
CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation

Knowledge graph-based recommendation methods are a hot research topic in the field of recommender systems in recent years. As a mainstream knowledge graph-based recommendation method, the propagation-based recommendation method captures users’ potential interests in items by integrating the representations of entities and relations in the knowledge graph and the high-order connection patterns between entities to provide personalized recommendations. For example, the collaborative knowledge-aware attentive network (CKAN) is a typical state-of-the-art propagation-based recommendation method that combines user-item interactions and knowledge associations in the knowledge graph, and performs heterogeneous propagation in the knowledge graph to generate multi-hop ripple sets, thereby capturing users’ potential interests. However, existing propagation-based recommendation methods, including CKAN, usually ignore the complex relations between entities in the multi-hop ripple sets and do not distinguish the importance of different ripple sets, resulting in inaccurate user potential interests being captured. Therefore, this paper proposes a top-N recommendation method named collaborative knowledge-aware graph attention network (CKGAT). Based on the heterogeneous propagation strategy, CKGAT uses the knowledge-aware graph attention network to extract the topological proximity structures of entities in the multi-hop ripple sets and then learn high-order entity representations, thereby generating refined ripple set embeddings. CKGAT further uses an attention aggregator to perform weighted aggregation on the ripple set embeddings, the user/item initial entity set embeddings, and the original representations of items to generate accurate user embeddings and item embeddings for the top-N recommendations. Experimental results show that CKGAT, overall, outperforms three baseline methods and six state-of-the-art propagation-based recommendation methods in terms of recommendation accuracy, and outperforms four representative propagation-based recommendation methods in terms of recommendation diversity.
Mobile visual search model for Dunhuang murals in the smart library

Purpose The purpose of this paper is to build a mobile visual search service system for the protection of Dunhuang cultural heritage in the smart library. A novel mobile visual search model for Dunhuang murals is proposed to help users acquire rich knowledge and services conveniently. Design/methodology/approach First, local and global features of images are extracted, and the visual dictionary is generated by the k -means clustering. Second, the mobile visual search model based on the bag-of-words (BOW) and multiple semantic associations is constructed. Third, the mobile visual search service system of the smart library is designed in the cloud environment. Furthermore, Dunhuang mural images are collected to verify this model. Findings The findings reveal that the BOW_SIFT_HSV_MSA model has better search performance for Dunhuang mural images when the scale-invariant feature transform (SIFT) and the hue, saturation and value (HSV) are used to extract local and global features of the images. Compared with different methods, this model is the most effective way to search images with the semantic association in the topic, time and space dimensions. Research limitations/implications Dunhuang mural image set is a part of the vast resources stored in the smart library, and the fine-grained semantic labels could be applied to meet diverse search needs. Originality/value The mobile visual search service system is constructed to provide users with Dunhuang cultural services in the smart library. A novel mobile visual search model based on BOW and multiple semantic associations is proposed. This study can also provide references for the protection and utilization of other cultural heritages.
Learning multi-scale synergic discriminative features for prostate image segmentation

Although deep convolutional neural networks (DCNNs) have been proposed for prostate MR image segmentation, the effectiveness of these methods is often limited by inadequate semantic discrimination and spatial context modeling. To address these issues, we propose a Multi-scale Synergic Discriminative Network (MSD-Net), which includes a shared encoder, a segmentation decoder, and a boundary detection decoder. We further design the cascaded pyramid convolutional block and residual refinement block, and incorporate them and the channel attention block into MSD-Net to exploit the multi-scale spatial contextual information and semantically consistent features of the gland. We also fuse the features from two decoders to boost the segmentation performance, and introduce the synergic multi-task loss to impose the consistence constraint on the joint segmentation and boundary detection. We evaluated MSD-Net against several prostate segmentation methods on three public datasets and achieved an improved accuracy. Our results indicate that the proposed MSD-Net outperforms existing methods with setting the new state-of-the-art for prostate segmentation in magnetic resonance images.
Machine learning approach for longitudinal face recognition of children

The face of an individual is a popular and well-accepted biometric trait that can be used to perform identity recognition of adults as well as children. Children are the most valuable and jeopardy group in society; they should be under supervision continuously. According to a survey in India, children below 6 years of age comprise 13.12% of the total population. Security and healthcare of children is an important aspect of all countries. Automatic face recognition of children is a useful investigative tool to help identify the missing children. Though the development of the face of a child starts in the mother’s womb from 3 months, it is not proportional to the development of other parts of the body. Therefore, recognition of children below 6 year is still an open research problem. In this chapter, comparison of machine learning and deep learning techniques for the recognition of children is done. Highest recognition accuracy of 97% for the same-session and 94% for the cross-session database is achieved.
Differentially private self-normalizing neural networks for adversarial robustness in federated learning

The need for robust, secure and private machine learning is an important goal for realizing the full potential of the Internet of Things (IoT). Federated Learning has proven to help protect against privacy violations and information leakage. However, it introduces new risk vectors which make Machine Learning models more difficult to defend against adversarial samples. We consider the problem of improving the resilience of Federated Learning to adversarial samples without compromising the privacy preserving features which Federated Learning was primarily designed to provide.Common techniques such as adversarial training, in their basic form are not well suited for a Federated Learning environment. Our study shows that adversarial training, while improving adversarial robustness, comes at a cost of reducing the privacy guarantee of Federated Learning. We introduce DiPSeN, a Differentially Private Self-normalizing Neural Network which combines elements of differential privacy, self-normalization, and a novel optimization algorithm for adversarial client selection. Our empirical results on publicly available datasets for intrusion detection and image classification show that DiPSeN successfully improves both adversarial robustness in Federated Learning, while maintaining the privacy preserving characteristics of Federated Learning.
Cross-modal image–text search via Efficient Discrete Class Alignment Hashing

Hashing has produced enormous potentials in cross-modal image–text search, which learns compact binary codes by exploring the correlations between distinct modalities. However, there still exist some limitations. First, most existing methods neglect the relation between the data characteristics and supervised information. Second, a relaxation strategy results in large quantization errors. Third, constructing large n × n (a.k.a. training size) similarity graphs increases computational load. To address these issues, we propose a novel discrete supervised hashing method, termed Efficient Discrete Class Alignment Hashing (EDCAH), which integrates class alignment and matrix factorization for hashing learning. Specifically, it exploits the semantic consistency of data instances and informative labels to simultaneously learn the hash codes and hash functions. Meanwhile, a discrete optimization strategy is developed to solve the EDCAH, which is beneficial to generate high-quality hash codes. Furthermore, to improve the learning efficiency of EDCAH, we propose a fast and efficient variant dubbed EDCAH-t that utilizes a two-step hashing strategy. Extensive experiments demonstrate the superiority of EDCAH and EDCAH-t in both search accuracy and learning efficiency.
Virtual Classroom Design Model and Its Relation to Student Motivation and Performance in a Moodle Learning Environment During the Emergency of COVID-19

Virtual learning environments during the pandemic have made it possible to support the originally face-to-face modalities in education at all levels. The teaching and learning processes require educational strategies that make it possible to take classrooms to the virtual world and be as effective as in the face-to-face setting. The objective of this work is to determine a virtual classroom design model that generates a positive effect on the motivation and performance of the participants. A descriptive methodology with quantitative approach is developed using the survey technique in two courses of first academic cycle of engineering careers at the Salesian Polytechnic University in the city of Guayaquil, during two academic periods, one from May to September 2020, another from October 2020 to February 2021, and five courses during May to September 2021. The analysis determined the relationship of the virtual classroom design, with the order, graphic line, formats, and amount of resources, with the motivation and performance of the participants. It is concluded the positive effect that the learning activities have, making them more meaningful for the student, in relation to the model of their presentation, generating an intention of appropriation and belonging on the part of the participant, due to its immediate comprehension and ease of realization. This study presents a virtual classroom design model, as well as a set of digital educational media and meaningful learning activities for virtual environments on Moodle, which have proven to be effective in their application.
Qubit Regularization and Qubit Embedding Algebras

Qubit regularization is a procedure to regularize the infinite dimensional local Hilbert space of bosonic fields to a finite dimensional one, which is a crucial step when trying to simulate lattice quantum field theories on a quantum computer. When the qubit-regularized lattice quantum fields preserve important symmetries of the original theory, qubit regularization naturally enforces certain algebraic structures on these quantum fields. We introduce the concept of qubit embedding algebras (QEAs) to characterize this algebraic structure associated with a qubit regularization scheme. We show a systematic procedure to derive QEAs for the O(N) lattice spin models and the SU(N) lattice gauge theories. While some of the QEAs we find were discovered earlier in the context of the D-theory approach, our method shows that QEAs are far richer. A more complete understanding of the QEAs could be helpful in recovering the fixed points of the desired quantum field theories.
A Robust Edge-Preserving Stereo Matching Method for Laparoscopic Images

Stereo matching has become an active area of research in the field of computer vision. In minimally invasive surgery, stereo matching provides depth information to surgeons, with the potential to increase the safety of surgical procedures, particularly those performed laparoscopically. Many stereo matching methods have been reported to perform well for natural images, but for images acquired during a laparoscopic procedure, they are limited by image characteristics including illumination differences, weak texture content, specular highlights, and occlusions. To overcome these limitations, we propose a robust edge-preserving stereo matching method for laparoscopic images, comprising an efficient sparse-dense feature matching step, left and right image illumination equalization, and refined disparity optimization. We validated the proposed method using both benchmark biological phantoms and surgical stereoscopic data. Experimental results illustrated that, in the presence of heavy illumination differences between image pairs, texture and textureless surfaces, specular highlights and occlusions, our proposed approach consistently obtains a more accurate estimate of the disparity map than state-of-the-art stereo matching methods in terms of robustness and boundary preservation.
Nonlinear Schrödinger Kernel for Hardware Acceleration of Machine Learning

Alternative machine learning approaches that have extremely low latency and can work with only a small training dataset are needed for applications where the insatiable demands of deep learning methods for computing power and large training data cannot be met. Here we report a new optical accelerator for AI that exploits femtosecond pulses for both data acquisition and computing enabling classification at short time scales for fast optical imaging, sensing, and metrology without increasing data dimensions. Modulation of data onto the spectrum of femtosecond optical pulses followed by projection into a new space using nonlinear optics reduces the latency in the nonlinear classification of certain data by orders of magnitude. This Femtocomputing approach is validated by the classification of various datasets, including brain intracranial pressure, cancer cell imaging, spoken digit recognition, and the classic exclusive OR benchmark for nonlinear operation. The concept is demonstrated by seeding the nonlinear effect that is responsible for many fascinating natural phenomena, such as optical rogue waves. Stimulation of nonlinear optical interactions with spectrally modulated data transforms the data such that a computationally-light linear algorithm can learn a nonlinear decision boundary that separates the data into the correct classes. Since the optical kernel is not trained, its performance is inevitably data-dependent. Quantitative comparison with a popular numerical kernel offers insights into how this physical technique accelerates inference. Single-shot operation is demonstrated using time stretch data acquisition.
On the Depth of Earthquakes in the Delaware Basin: A Case Study along the Reeves–Pecos County Line

Abstract Increased rates of seismicity in the Delaware basin, Texas, accompanying unconventional petroleum development have created intensive interest in determining their cause. Detailed and accurate spatial distribution of seismicity and focal mechanisms are critical components for understanding the underlying industrial processes responsible for inducing seismicity. We focus on a highly seismically active area straddling the Reeves–Pecos County line where two TexNet stations sit atop the seismicity, which includes 21 ML 3+ events from 2017 to 2020 (Advanced National Seismic System Comprehensive Earthquake Catalog). Short epicentral distance enables us to reliably estimate the hypocentral depth using seismic phase picks and standard location methods. We use a deep-learning-based method to detect earthquakes and time the phase arrivals. Hypocentral locations computed in a velocity model constrained by local well data reveal that the seismicity concentrates between 1.5 and 2.5 km below ground in the Delaware Mountain Group, the primary wastewater disposal zone at this location. Waveform inversions for the moment tensor and focal depth independently confirm the shallow depths. The moment tensor solutions define critically stressed high-angle normal faults, suggesting a causal connection between injection and seismicity.
Stochasticity of the magnon parametron

Unconventional computing schemes based on bistable systems (``Ising spins'') may supersede conventional computing paradigms. The ``magnon parametron'' is an Ising spin that forms as a result of parametric excitation of a ferromagnetic particle by microwaves beyond a certain threshold. This Ising spin becomes unstable at a second threshold power at which a high-frequency telegraph noise emerges. We explain the experimentally observed stochastic switching (``p-bit'' characteristics) at room temperature by the Suhl instability of the uniform magnetization precession.
ACORT: A compact object relation transformer for parameter efficient image captioning

Recent research that applies Transformer-based architectures to image captioning has resulted in state-of-the-art image captioning performance, capitalising on the success of Transformers on natural language tasks. Unfortunately, though these models work well, one major flaw is their large model sizes. To this end, we present three parameter reduction methods for image captioning Transformers: Radix Encoding, cross-layer parameter sharing, and attention parameter sharing. By combining these methods, our proposed ACORT models have 3.7× to 21.6× fewer parameters than the baseline model without compromising test performance. Results on the MS-COCO dataset demonstrate that our ACORT models are competitive against baselines and SOTA approaches, with CIDEr score ⩾126. Finally, we present qualitative results and ablation studies to demonstrate the efficacy of the proposed changes further. Code and pre-trained models are publicly available at https://github.com/jiahuei/sparse-image-captioning.
A Study of Transducer Based End-to-End ASR with ESPnet: Architecture, Auxiliary Loss and Decoding Strategies

In this study, we present recent developments of models trained with the RNN-T loss in ESPnet. It involves the use of various archi-tectures such as recently proposed Conformer, multi-task learning with different auxiliary criteria and multiple decoding strategies, in-cluding our own proposition. Through experiments and benchmarks, we show that our proposed systems can be competitive against other state-of-art systems on well-known datasets such as LibriSpeech and AISHELL-1. Additionally, we demonstrate that these models are promising against other already implemented systems in ESPnet in regards to both performance and decoding speed, enabling the pos-sibility to have powerful systems for a streaming task. With these additions, we hope to expand the usefulness of the ESPnet toolkit for the research community and also give tools for the ASR industry to deploy our systems in realistic and production environments.