Reviewerly/exHarmony huggingface dataset:
A modified-YOLOv5s model for detection of wire braided hose defects

High-pressure steel wire braided hoses have periodic braided steel wire structure and its productivity is too huge to refrain from making mistakes occasionally such as wire missed wire stacked and wire loosened. The major sticking points in detection are ultra-small, structural similarity and high reflectance of the steel wire braided hoses. Therefore, this paper proposed an automated method based on YOLOv5 to replace traditional naked eyes detection. The anchor boxes were optimized by K-means++ algorithm for defects dataset. Focal loss was used to mitigate the impact of samples imbalance. The predict heads were modified to increase the detection accuracy of ultra-small targets with guaranteeing speed. The effect of efficient channel attention mechanism (ECA) in different positions was analyzed to achieve a better network and increase detection performance. The experimental results showed that the accuracy and recognition efficiency of the proposed modified-YOLOv5s model could reach 92.2% and 23 frames per second.
Learned cardinality estimation

Cardinality estimation is core to the query optimizers of DBMSs. Non-learned methods, especially based on histograms and samplings, have been widely used in commercial and open-source DBMSs. Nevertheless, histograms and samplings can only be used to summarize one or few columns, which fall short of capturing the joint data distribution over an arbitrary combination of columns, because of the oversimplification of histograms and samplings over the original relational table(s). Consequently, these traditional methods typically make bad predictions for hard cases such as queries over multiple columns, with multiple predicates, and joins between multiple tables. Recently, learned cardinality estimators have been widely studied. Because these learned estimators can better capture the data distribution and query characteristics, empowered by the recent advance of (deep learning) models, they outperform non-learned methods on many cases. The goals of this paper are to provide a design space exploration of learned cardinality estimators and to have a comprehensive comparison of the SOTA learned approaches so as to provide a guidance for practitioners to decide what method to use under various practical scenarios.
A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm

Accurate and early detection of anomalies in peripheral white blood cells plays a crucial role in the evaluation of well-being in individuals and the diagnosis and prognosis of hematologic diseases. For example, some blood disorders and immune system-related diseases are diagnosed by the differential count of white blood cells, which is one of the common laboratory tests. Data is one of the most important ingredients in the development and testing of many commercial and successful automatic or semi-automatic systems. To this end, this study introduces a free access dataset of normal peripheral white blood cells called Raabin-WBC containing about 40,000 images of white blood cells and color spots. For ensuring the validity of the data, a significant number of cells were labeled by two experts. Also, the ground truths of the nuclei and cytoplasm are extracted for 1145 selected cells. To provide the necessary diversity, various smears have been imaged, and two different cameras and two different microscopes were used. We did some preliminary deep learning experiments on Raabin-WBC to demonstrate how the generalization power of machine learning methods, especially deep neural networks, can be affected by the mentioned diversity. Raabin-WBC as a public data in the field of health can be used for the model development and testing in different machine learning tasks including classification, detection, segmentation, and localization.
Adaptive cost-sensitive learning: Improving the convergence of intelligent diagnosis models under imbalanced data

The natural distribution of industrial data is imbalanced, which deteriorates the performance of intelligent fault diagnostic models. Although cost-sensitive learning is an effective method for solving the data imbalance problem, it suffers from the difficulty of setting optimal costs. Therefore, this paper proposes a strategy that considers the number distribution of samples, the convergence trend of classes, and the convergence trend of samples to calculate sample costs adaptively. Using costs to weigh the sample losses and applying them to different models and different loss functions, the diagnostic results under different sample sets show that the weighted losses can significantly improve the model's performance when using imbalanced data. By further analysing the training loss of the modified model, the angles between deep features, and the angles between deep features and classification weight vectors, it can be found that the dominance of majority classes in imbalanced data is suppressed in training, which is attributed to the loss of each class being coordinated by the proposed strategy. The comparison with various imbalanced learning methods demonstrates the advantages of the proposed method under conditions of large imbalance ratios and complex tasks.
Building a Quantum Engineering Undergraduate Program

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> A roadmap is provided for building a quantum engineering education program to satisfy U.S. national and international workforce needs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> The rapidly growing quantum information science and engineering (QISE) industry will require both quantum-aware and quantum-proficient engineers at the bachelor’s level. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research Question:</i> What is the best way to provide a flexible framework that can be tailored for the full academic ecosystem? <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methodology:</i> A workshop of 480 QISE researchers from across academia, government, industry, and national laboratories was convened to draw on best practices; representative authors developed this roadmap. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Findings:</i> 1) For quantum-aware engineers, design of a first quantum engineering course, accessible to all STEM students, is described; 2) for the education and training of quantum-proficient engineers, both a quantum engineering minor accessible to all STEM majors, and a quantum track directly integrated into individual engineering majors are detailed, requiring only three to four newly developed courses complementing existing STEM classes; 3) a conceptual QISE course for implementation at any postsecondary institution, including community colleges and military schools, is delineated; 4) QISE presents extraordinary opportunities to work toward rectifying issues of inclusivity and equity that continue to be pervasive within engineering. A plan to do so is presented, as well as how quantum engineering education offers an excellent set of education research opportunities; and 5) a hands-on training plan on quantum hardware is outlined, a key component of any quantum engineering program, with a variety of technologies, including optics, atoms and ions, cryogenic and solid-state technologies, nanofabrication, and control and readout electronics.
A weight initialization method based on neural network with asymmetric activation function

Weight initialization of neural networks has an important influence on the learning process, and the selection of initial weights is related to the activation interval of the activation function. It is proposed that an improved and extended weight initialization method for neural network with asymmetric activation function as an extension of the linear interval tolerance method (LIT), called 'GLIT' (generalized LIT), which is more suitable for higher-dimensional inputs. The purpose is to expand the selection range of the activation function so that the input falls in the unsaturated region, so as to improve the performance of the network. Then, a tolerance solution theorem based upon neural network system is given and proved. Furthermore, the algorithm is given about determining the initial weight interval. The validity of the theorem and algorithm is verified by numerical experiments. The input could fall into any preset interval in the sense of probability under the GLIT method. In another sense, the GLIT method could provide a theoretical basis for the further study of neural networks.
Fuzzy Neural Network Expert System with an Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm for Early Diagnosis of Breast Cancer in Saudi Arabia

Breast cancer is one of the common malignancies among females in Saudi Arabia and has also been ranked as the one most prevalent and the number two killer disease in the country. However, the clinical diagnosis process of any disease such as breast cancer, coronary artery diseases, diabetes, COVID-19, among others, is often associated with uncertainty due to the complexity and fuzziness of the process. In this work, a fuzzy neural network expert system with an improved gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia was proposed to address the uncertainty and ambiguity associated with the diagnosis of breast cancer and also the heavier burden on the overlay of the network nodes of the fuzzy neural network system that often happens due to insignificant features that are used to predict or diagnose the disease. An Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm was used to select the five fittest features of the diagnostic wisconsin breast cancer database out of the 32 features of the dataset. The logistic regression, support vector machine, k-nearest neighbor, random forest, and gaussian naïve bayes learning algorithms were used to develop two sets of classification models. Hence, the classification models with full features (32) and models with the 5 fittest features. The two sets of classification models were evaluated, and the results of the evaluation were compared. The result of the comparison shows that the models with the selected fittest features outperformed their counterparts with full features in terms of accuracy, sensitivity, and sensitivity. Therefore, a fuzzy neural network based expert system was developed with the five selected fittest features and the system achieved 99.33% accuracy, 99.41% sensitivity, and 99.24% specificity. Moreover, based on the comparison of the system developed in this work against the previous works that used fuzzy neural network or other applied artificial intelligence techniques on the same dataset for diagnosis of breast cancer using the same dataset, the system stands to be the best in terms of accuracy, sensitivity, and specificity, respectively. The z test was also conducted, and the test result shows that there is significant accuracy achieved by the system for early diagnosis of breast cancer.
Quantum Computing and Simulations for Energy Applications: Review and Perspective

Quantum computing and simulations are creating transformative opportunities by exploiting the principles of quantum mechanics in new ways to generate and process information. It is expected that a variety of areas ranging from day-to-day activities to making advanced scientific discoveries are going to benefit from such computations. Several early stage applications of quantum computing and simulation have already been demonstrated, and these preliminary results show that quantum computing and simulations could significantly accelerate the deployment of new technologies urgently needed to meet the growing demand for energy while safeguarding the environment. Exciting examples include developing new materials such as alloys, catalysts, oxygen carriers, CO2 sorbents/solvents, and energy storage materials; optimizing traffic flows and energy supply chains; locating energy generation facilities such as wind and solar farms and fossil and nuclear power plants; designing pipeline networks for transporting hydrogen, natural gas, and CO2; and speeding up tasks such as seismic imaging and inversion, reservoir simulation, and computational fluid dynamics. In this review, we introduce different aspects of quantum computing and simulations and discuss the status of theoretical and experimental approaches. We then specifically highlight a growing number of application areas in the energy sector. We conclude by providing an analysis of high-value application directions to address energy sector challenges.
Multi-Agent Motion Planning From Signal Temporal Logic Specifications

We tackle the challenging problem of multi-agent cooperative motion planning for complex tasks described using signal temporal logic (STL), where robots can have nonlinear and nonholonomic dynamics. Existing methods in multi-agent motion planning, especially those based on discrete abstractions and model predictive control (MPC), suffer from limited scalability with respect to the complexity of the task, the size of the workspace, and the planning horizon. We present a method based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">timed waypoints</i> to address this issue. We show that timed waypoints can help abstract nonlinear behaviors of the system as safety envelopes around the reference path defined by those waypoints. Then the search for waypoints satisfying the STL specifications can be inductively encoded as a mixed-integer linear program. The agents following the synthesized timed waypoints have their tasks automatically allocated, and are guaranteed to satisfy the STL specifications while avoiding collisions. We evaluate the algorithm on a wide variety of benchmarks. Results show that it supports multi-agent planning from complex specification over long planning horizons, and significantly outperforms state-of-the-art abstraction-based and MPC-based motion planning methods. The implementation is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/sundw2014/STLPlanning</uri> .
MsEmoTTS: Multi-Scale Emotion Transfer, Prediction, and Control for Emotional Speech Synthesis

Expressive synthetic speech is essential for many human-computer interaction and audio broadcast scenarios, and thus synthesizing expressive speech has attracted much attention in recent years. Previous methods performed the expressive speech synthesis either with explicit labels or with a fixed-length style embedding extracted from reference audio, both of which can only learn an average style and thus ignores the multi-scale nature of speech prosody. In this paper, we propose MsEmoTTS, a multi-scale emotional speech synthesis framework, to model the emotion from different levels. Specifically, the proposed method is a typical attention-based sequence-to-sequence model and with proposed three modules, including global-level emotion presenting module (GM), utterance-level emotion presenting module (UM), and local-level emotion presenting module (LM), to model the global emotion category, utterance-level emotion variation, and syllable-level emotion strength, respectively. In addition to modeling the emotion from different levels, the proposed method also allows us to synthesize emotional speech in different ways, i.e., transferring the emotion from reference audio, predicting the emotion from input text, and controlling the emotion strength manually. Extensive experiments conducted on a Chinese emotional speech corpus demonstrate that the proposed method outperforms the compared reference audio-based and text-based emotional speech synthesis methods on the emotion transfer speech synthesis and text-based emotion prediction speech synthesis respectively. Besides, the experiments also show that the proposed method can control the emotion expressions flexibly. Detailed analysis shows the effectiveness of each module and the good design of the proposed method.
Global growth of earthquake early warning

Public-private partnerships provide a method for vastly expanding sensor networks.
Risk management of supply chains in the digital transformation era: contribution and challenges of blockchain technology

Purpose The high degree of likely disruption challenges organizations at all levels to develop and implement innovative strategies. Ensuring supply chain continuity even during emergency and complex situations is critical for organizations. Therefore, this study explores some strategies adopted by firms based on innovation and blockchain-enabled digital transformation to reduce risk in their supply chain. Design/methodology/approach This study follows the qualitative form of enquiry. The authors interviewed 26 professionals from the supply chain domain. After three-layered coding and mapping multiple layers to the data of interviews, the authors identified emerging themes and sub-themes through a thematic analysis. Findings The authors identified type of risks that can affect global supply chains along with both the role of blockchain and innovation culture in minimizing the degree of such risks and the challenges in adopting blockchain technologies. This led us to develop a framework to address supply chain risk through digital transformation through innovation and blockchain. Practical implications This research offers exciting implications for practice by drawing on the insights gathered to facilitate supply chain risk management through innovation and blockchain applications for organizations that are strongly impacted by digital transformation practices around the world. The study also offers the utilization of a framework followed by propositions to reduce supply chain risks in the digital transformation era. Originality/value This study focuses on presenting a mechanism of supply chain risk management through the application of innovation and blockchain technology for the digital transformation of a value chain. Blockchain can offer an innovative platform to ready the supply chain for future dynamic situations.
Image captioning model using attention and object features to mimic human image understanding

Abstract Image captioning spans the fields of computer vision and natural language processing. The image captioning task generalizes object detection where the descriptions are a single word. Recently, most research on image captioning has focused on deep learning techniques, especially Encoder-Decoder models with Convolutional Neural Network (CNN) feature extraction. However, few works have tried using object detection features to increase the quality of the generated captions. This paper presents an attention-based, Encoder-Decoder deep architecture that makes use of convolutional features extracted from a CNN model pre-trained on ImageNet (Xception), together with object features extracted from the YOLOv4 model, pre-trained on MS COCO. This paper also introduces a new positional encoding scheme for object features, the “importance factor”. Our model was tested on the MS COCO and Flickr30k datasets, and the performance is compared to performance in similar works. Our new feature extraction scheme raises the CIDEr score by 15.04%. The code is available at: https://github.com/abdelhadie-almalla/image_captioning
Hierarchical Blockchain Design for Distributed Control and Energy Trading Within Microgrids

Blockchain technology is recognized as a suitable tool to secure the energy trading because it could perfectly match the distributed structure of peer-to-peer (P2P) energy market. But its usage is stuck on the transaction level. Control systems are significant to the microgrid as they ensure a stable power delivery system and regulate the performance of parameters such as active power and frequency. This paper proves that the blockchain technology is also effective in securing the distributed control systems against the false data injection attack. A six-prosumer microgrid is tested with the implementation of the hierarchical blockchain system. The security of both the control system and energy trading system of the microgrid is ensured. Smart contracts are created to calculate the feedback measurements for the control system and execute the energy transactions. According to the hierarchical structure, the private blockchain with static nodes is implemented for the distributed control to match the sampling rate. A Proof-of-Authority based blockchain is utilized to support the energy trading. In addition, a double auction based simple iteration (DA-SI) pricing scheme is designed to improve the social welfare of the microgrid. Finally, case studies are presented to verify the proposed hierarchical blockchain system as an effective method to safeguard the control system and maximize the benefits of prosumers. Numerical results show the effectiveness and feasibility of the proposed approach.
SynFace: Face Recognition with Synthetic Data

With the recent success of deep neural networks, remarkable progress has been achieved on face recognition. However, collecting large-scale real-world training data for face recognition has turned out to be challenging, especially due to the label noise and privacy issues. Meanwhile, existing face recognition datasets are usually collected from web images, lacking detailed annotations on attributes (e.g., pose and expression), so the influences of different attributes on face recognition have been poorly investigated. In this paper, we address the above-mentioned issues in face recognition using synthetic face images, i.e., SynFace. Specifically, we first explore the performance gap between recent state-of-the-art face recognition models trained with synthetic and real face images. We then analyze the underlying causes behind the performance gap, e.g., the poor intraclass variations and the domain gap between synthetic and real face images. Inspired by this, we devise the SynFace with identity mixup (IM) and domain mixup (DM) to mitigate the above performance gap, demonstrating the great potentials of synthetic data for face recognition. Furthermore, with the controllable face synthesis model, we can easily manage different factors of synthetic face generation, including pose, expression, illumination, the number of identities, and samples per identity. Therefore, we also perform a systematically empirical analysis on synthetic face images to provide some insights on how to effectively utilize synthetic data for face recognition.
Stochastic Scene-Aware Motion Prediction

A long-standing goal in computer vision is to capture, model, and realistically synthesize human behavior. Specifically, by learning from data, our goal is to enable virtual humans to navigate within cluttered indoor scenes and naturally interact with objects. Such embodied behavior has applications in virtual reality, computer games, and robotics, while synthesized behavior can be used as training data. The problem is challenging because real human motion is diverse and adapts to the scene. For example, a person can sit or lie on a sofa in many places and with varying styles. We must model this diversity to synthesize virtual humans that realistically perform human-scene interactions. We present a novel data-driven, stochastic motion synthesis method that models different styles of performing a given action with a target object. Our Scene-Aware Motion Prediction method (SAMP) generalizes to target objects of various geometries while enabling the character to navigate in cluttered scenes. To train SAMP, we collected MoCap data covering various sitting, lying down, walking, and running styles. We demonstrate SAMP on complex indoor scenes and achieve superior performance than existing solutions. Code and data are available for research at https://samp.is.tue.mpg.de.