Reviewerly/exHarmony huggingface dataset:
Information traceability platforms for asset data lifecycle: blockchain-based technologies

Purpose The emergence of new digital technologies in the era of the Fourth Industrial Revolution presents a turning point that could change the fate of the traditional ways of designing, build and manage asset data. Disruptive technologies such as Blockchain and theInternet of things (IoT) are one of the main pillars that are driving this revolution. The integration of decentralised networks and automated workflows has the potential to become a pivotal factor in construction projects, especially in supply chain ecosystems within the off-site manufacturing field. Obstacles related to fragmented information, interoperability, transparency and “big data” management are the main drivers for change that the industry needs to address. Whilst organisations and users can automate workflows and processes by utilising IoT technology to transfer data without human-to-human or human-to-computer interaction, the interaction, storage and management of the data generated are not safe or reliable. Design/methodology/approach The approach outlined in this paper addresses the challenges that IoT and centralised networks present. Blockchain, a peer-to-peer distributed database, offers the possibility to support and maintain the asset information without interruptions in all the stages of the life cycle. The synergy between these technologies, along with other techniques, methods and platforms (such as building information modeling (BIM)), based on a single environment, will support information traceability from the strategic definition to end of life. Findings The framework of this study presents an excellent opportunity to apply new workflows and processes with the application of new technologies and protocols. It benefits from a well-established platform such as BIM to enable the coordination and management of digital assets as well as giving illustration and collaboration to the supply chain members. IoT and Blockchain are the other layers that work together with the third layer (BIM). This framework proposed the use of these platforms to ensure the information traceability of physical and digital assets, data automation and information management, in a dynamic supply chain ecosystem, bringing efficiency and transparency to stakeholders and users. Practical implications This study provides an exploratory framework to be used by the supply chain members in offsite manufacturing, and the architecture, engineering and construction (AEC) industry in general, to track asset information throughout their entire life cycle securely and transparently. Originality/value This paper contributes to the knowledge of IoT, Blockchain technology and BIM use in offsite manufacturing under the AEC industry. It provides a basis for future research by professionals, experts and academics regarding these technologies and their workflows.
The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers

Recently, many datasets have been proposed to test the systematic generalization ability of neural networks.The companion baseline Transformers, typically trained with default hyper-parameters from standard tasks, are shown to fail dramatically.Here we demonstrate that by revisiting model configurations as basic as scaling of embeddings, early stopping, relative positional embedding, and Universal Transformer variants, we can drastically improve the performance of Transformers on systematic generalization.We report improvements on five popular datasets: SCAN, CFQ, PCFG, COGS, and Mathematics dataset.Our models improve accuracy from 50% to 85% on the PCFG productivity split, and from 35% to 81% on COGS.On SCAN, relative positional embedding largely mitigates the EOS decision problem (Newman et al., 2020), yielding 100% accuracy on the length split with a cutoff at 26. Importantly, performance differences between these models are typically invisible on the IID data split.This calls for proper generalization validation sets for developing neural networks that generalize systematically.We publicly release the code to reproduce our results 1 .
A Comprehensive Review on 3D Object Detection and 6D Pose Estimation With Deep Learning

Nowadays, computer vision with 3D (dimension) object detection and 6D (degree of freedom) pose assumptions are widely discussed and studied in the field. In the 3D object detection process, classifications are centered on the object’s size, position, and direction. And in 6D pose assumptions, networks emphasize 3D translation and rotation vectors. Successful application of these strategies can have a huge impact on various machine learning-based applications, including the autonomous vehicles, the robotics industry, and the augmented reality sector. Although extensive work has been done on 3D object detection with a pose assumption from RGB images, the challenges have not been fully resolved. Our analysis provides a comprehensive review of the proposed contemporary techniques for complete 3D object detection and the recovery of 6D pose assumptions of an object. In this review research paper, we have discussed several proposed sophisticated methods in 3D object detection and 6D pose estimation, including some popular data sets, evaluation matrix, and proposed method challenges. Most importantly, this study makes an effort to offer some possible future directions in 3D object detection and 6D pose estimation. We accept the autonomous vehicle as the sample case for this detailed review. Finally, this review provides a complete overview of the latest in-depth learning-based research studies related to 3D object detection and 6D pose estimation systems and also points out a comparison between some popular frameworks. To be more concise, we propose a detailed summary of the state-of-the-art techniques of modern deep learning-based object detection and pose estimation models.
Healthchain: A Privacy Protection System for Medical Data Based on Blockchain

Recently, with the great development of e-health, more and more countries have made certain achievements in the field of electronic medical treatment. The digitization of medical equipment and the structuralization of electronic medical records are the general trends. While bringing convenience to people, the explosive growth of medical data will further promote the value of mining medical data. Obviously, finding out how to safely store such a large amount of data is a problem that urgently needs to be solved. Additionally, the particularity of medical data makes it necessarily subject to great privacy protection needs. This reinforces the importance of designing a safe solution to ensure data privacy. Many existing schemes are based on single-server architecture, which have some natural defects (such as single-point faults). Although blockchain can help solve such problems, there are still some deficiencies in privacy protection. To solve these problems, this paper designs a medical data privacy protection system, which integrates blockchain, group signature, and asymmetric encryption to realize reliable medical data sharing between medical institutions and protect the data privacy of patients. This paper proves theoretically that it meets our security and privacy requirements, and proves its practicability through system implementation.
Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning

Breast cancer is one of the main causes of death among women worldwide. Early detection of this disease helps reduce the number of premature deaths. This research aims to design a method for identifying and diagnosing breast tumors based on ultrasound images. For this purpose, six techniques have been performed to detect and segment ultrasound images. Features of images are extracted using the fractal method. Moreover, k-nearest neighbor, support vector machine, decision tree, and Naïve Bayes classification techniques are used to classify images. Then, the convolutional neural network (CNN) architecture is designed to classify breast cancer based on ultrasound images directly. The presented model obtains the accuracy of the training set to 99.8%. Regarding the test results, this diagnosis validation is associated with 88.5% sensitivity. Based on the findings of this study, it can be concluded that the proposed high-potential CNN algorithm can be used to diagnose breast cancer from ultrasound images. The second presented CNN model can identify the original location of the tumor. The results show 92% of the images in the high-performance region with an AUC above 0.6. The proposed model can identify the tumor's location and volume by morphological operations as a post-processing algorithm. These findings can also be used to monitor patients and prevent the growth of the infected area.
Understanding and Resolving Performance Degradation in Deep Graph Convolutional Networks

A Graph Convolutional Network (GCN) stacks several layers and in each layer performs a PROPagation operation~(PROP) and a TRANsformation operation~(TRAN) for learning node representations over graph-structured data. Though powerful, GCNs tend to suffer performance drop when the model gets deep. Previous works focus on PROPs to study and mitigate this issue, but the role of TRANs is barely investigated. In this work, we study performance degradation of GCNs by experimentally examining how stacking only TRANs or PROPs works. We find that TRANs contribute significantly, or even more than PROPs, to declining performance, and moreover that they tend to amplify node-wise feature variance in GCNs, causing variance inflammation that we identify as a key factor for causing performance drop. Motivated by such observations, we propose a variance-controlling technique termed Node Normalization (NodeNorm), which scales each node's features using its own standard deviation. Experimental results validate the effectiveness of NodeNorm on addressing performance degradation of GCNs. Specifically, it enables deep GCNs to outperform shallow ones in cases where deep models are needed, and to achieve comparable results with shallow ones on 6 benchmark datasets. NodeNorm is a generic plug-in and can well generalize to other GNN architectures. Code is publicly available at https://github.com/miafei/NodeNorm.
A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion

The goal of voice conversion is to transform source speech into a target voice, keeping the content unchanged. In this paper, we focus on self-supervised representation learning for voice conversion. Specifically, we compare discrete and soft speech units as input features. We find that discrete representations effectively remove speaker information but discard some linguistic content - leading to mispronunciations. As a solution, we propose soft speech units. To learn soft units, we predict a distribution over discrete speech units. By modeling uncertainty, soft units capture more content information, improving the intelligibility and naturalness of converted speech. Samples available at https://ubisoft-laforge.github.io/speech/soft-vc/. Code available at https://github.com/bshall/soft-vc/.
Complete Coverage Path Planning of an Unmanned Surface Vehicle Based on a Complete Coverage Neural Network Algorithm

In practical applications, an unmanned surface vehicle (USV) generally employs a task of complete coverage path planning for exploration in a target area of interest. The biological inspired neural network (BINN) algorithm has been extensively employed in path planning of mobile robots, recently. In this paper, a complete coverage neural network (CCNN) algorithm for the path planning of a USV is proposed for the first time. By simplifying the calculation process of the neural activity, the CCNN algorithm can significantly reduce calculation time. To improve coverage efficiency and make the path more regular, the optimal next position decision formula combined with the covering direction term is established. The CCNN algorithm has increased moving directions of the path in grid maps, which in turn has further reduced turning-angles and makes the path smoother. Besides, an improved A* algorithm that can effectively decrease path turns is presented to escape the deadlock. Simulations are carried out in different environments in this work. The results show that the coverage path generated by the CCNN algorithm has less turning-angle accumulation, deadlocks, and calculation time. In addition, the CCNN algorithm is capable to maintain the covering direction and adapt to complex environments, while effectively escapes deadlocks. It is applicable for USVs to perform multiple engineering missions.
Certificateless Public Auditing Scheme With Data Privacy and Dynamics in Group User Model of Cloud-Assisted Medical WSNs

With the application of wireless sensor network (WSN) in healthcare field, online sharing of medical data has attracted more and more attention. However, wearable sensor nodes are limited in energy, storage space and data processing capacity, which largely restricts their deployment in resource demand application scenarios. Fortunately, cloud storage services can enrich the capabilities of wearable sensors and provide an effective method for people to share data within a group. However, as medical data directly relates to patients' health and privacy information, ensuring the integrity and privacy of medical records stored in cloud servers becomes a key issue to be urgently solved. Many public data auditing schemes have been put forward to address the above issues. Unfortunately, most of them have security vulnerabilities or poor functionality and performance. In this paper, we come up with a secure and efficient certificateless public auditing scheme for cloud-assisted medical WSNs, which not only supports dynamic data sharingand privacy protection, but also achieves efficient group user revocation. Security analysis and performance evaluation demonstrate that our scheme significantly reduce the total computation cost while achieving a higher security level. Compared with other related schemes, our new proposal is more suitable for group user data sharing in cloud-assisted medical WSNs.
Usage Experience and Capabilities of the VEGA-Science System

Currently, when satellite data volumes grow rapidly and exceed petabyte values and their quality provides reliable analysis of long-term time series, traditional data handling methods assuming local storage and processing may be impossible to implement for small or distributed research teams. Thus, new methods based on modern web technologies providing access to very large distributed data archives are gaining increasing importance. Furthermore, these new data handling solutions should provide not just access but also analysis and processing features, similar to desktop solutions. This paper describes the VEGA-Science web GIS—an open-access novel tool for satellite data processing and analysis. The overview of its architecture and basic technical components is given, but most attention is paid to examples of actual system application for various applied and research tasks. In addition, an overview of projects using the system is given to illustrate its versatility and further development directions are considered.
Interpretable deep learning approach for oral cancer classification using guided attention inference network

Convolutional neural networks (CNNs) show the potential for automated classification of different cancer lesions. However, their lack of interpretability and explainability makes CNNs less than understandable. Furthermore, CNNs may incorrectly concentrate on other areas surrounding the salient object, rather than the network's attention focusing directly on the object to be recognized, as the network has no incentive to focus solely on the correct subjects to be detected. This inhibits the reliability of CNNs, especially for biomedical applications.Develop a deep learning training approach that could provide understandability to its predictions and directly guide the network to concentrate its attention and accurately delineate cancerous regions of the image.We utilized Selvaraju et al.'s gradient-weighted class activation mapping to inject interpretability and explainability into CNNs. We adopted a two-stage training process with data augmentation techniques and Li et al.'s guided attention inference network (GAIN) to train images captured using our customized mobile oral screening devices. The GAIN architecture consists of three streams of network training: classification stream, attention mining stream, and bounding box stream. By adopting the GAIN training architecture, we jointly optimized the classification and segmentation accuracy of our CNN by treating these attention maps as reliable priors to develop attention maps with more complete and accurate segmentation.The network's attention map will help us to actively understand what the network is focusing on and looking at during its decision-making process. The results also show that the proposed method could guide the trained neural network to highlight and focus its attention on the correct lesion areas in the images when making a decision, rather than focusing its attention on relevant yet incorrect regions.We demonstrate the effectiveness of our approach for more interpretable and reliable oral potentially malignant lesion and malignant lesion classification.
Mid-infrared hyperchaos of interband cascade lasers

Chaos in nonlinear dynamical systems is featured with irregular appearance and with high sensitivity to initial conditions. Near-infrared light chaos based on semiconductor lasers has been extensively studied and has enabled various applications. Here, we report a fully-developed hyperchaos in the mid-infrared regime, which is produced from interband cascade lasers subject to the external optical feedback. Lyapunov spectrum analysis demonstrates that the chaos exhibits three positive Lyapunov exponents. Particularly, the chaotic signal covers a broad frequency range up to the GHz level, which is two to three orders of magnitude broader than existed mid-infrared chaos solutions. The interband cascade lasers produce either periodic oscillations or low-frequency fluctuations before bifurcating to hyperchaos. This hyperchaos source is valuable for developing long-reach secure optical communication links and remote chaotic Lidar systems, taking advantage of the high-transmission windows of the atmosphere in the mid-infrared regime.
A novel outlier detection method for monitoring data in dam engineering

The accuracy of the dam monitoring data influences the evaluation of dam safety. However, the actual monitoring data may contain outliers due to sensor failure or operation mistakes, which consequences to an inaccurate evaluation. To tackle this issue, an universal and robust approach for outlier detection for monitoring data is proposed from the perspective of image processing and artificial intelligence in this work. The monitoring data series consists of isolated-pattern outliers, clustered-pattern outliers and normal points. We transform the monitoring data into a binary image of a scatter plot at first. With Gaussian blur, the gray scales of isolated points (outliers) are reduced and hence are eliminated after Otsu binarization. Then, Cuckoo Search (CS) algorithm is utilized to obtain the most possible connection of the pixel aggregations, distinguishing the clustered-pattern outliers and the process line automatically. To detect the outliers thoroughly, we implement the image processing techniques and CS algorithm cyclically until the detection results converge. The results indicate that the proposed method has the highest detection accuracy compared with other five outlier detection methods. Moreover, the monitoring model established based on the data pre-processed by the proposed method has better fitting and predicting ability.
Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators’ Disagreement

Since state-of-the-art approaches to offensive language detection rely on supervised learning, it is crucial to quickly adapt them to the continuously evolving scenario of social media. While several approaches have been proposed to tackle the problem from an algorithmic perspective, so to reduce the need for annotated data, less attention has been paid to the quality of these data. Following a trend that has emerged recently, we focus on the level of agreement among annotators while selecting data to create offensive language datasets, a task involving a high level of subjectivity. Our study comprises the creation of three novel datasets of English tweets covering different topics and having five crowd-sourced judgments each. We also present an extensive set of experiments showing that selecting training and test data according to different levels of annotators' agreement has a strong effect on classifiers performance and robustness. Our findings are further validated in cross-domain experiments and studied using a popular benchmark dataset. We show that such hard cases, where low agreement is present, are not necessarily due to poor-quality annotation and we advocate for a higher presence of ambiguous cases in future datasets, particularly in test sets, to better account for the different points of view expressed online.