Reviewerly/exHarmony huggingface dataset:
Automatic lung segmentation based on image decomposition and wavelet transform

Accurately segmenting lungs from medical images is still a challenge due to some negative factors involved in the work, such as inhomogeneous intensities, juxta-pleural nodules, image noises and so on. To deal with the problem, in this paper, we present a novel algorithm to segment lungs from CT images in an accurate and automatical fashion. In our algorithm, an image decomposition based filtering strategy is first introduced to denoise lung CT images while preserving their lung contours. Lungs are then segmented from the CT images by wavelet transformation combining with a group of morphological operations. The segmentations are further refined by a contour correction approach, which is built on a fast corner detection technique, to correct and smooth the extracted lung contours. Experimental results show that our algorithm has better performance than a set of classical approaches, and it achieved an averaged Dice similarity coefficient of 98.04% and Jaccard's similarity index of 94.91% on lung CT image segmentation compared with ground truths. Our algorithm can correctly segment lung tissues from lung CT images and is helpful for radiologists' diagnosis of lung diseases.
Improve Person Re-Identification With Part Awareness Learning

Person re-identification (ReID) aims to predict whether two images from different cameras belong to the same person. Due to low image quality and variance in view point and body pose, it remains a difficult task. To solve the task, a model is supposed to appropriately capture features that describe body regions for identification. With the simple intuition that explicitly incorporating ReID model with part awareness could be beneficial for learning a more discriminative feature space, we propose part segmentation as an assistant body perception task during the training of a ReID model. Specifically, we add a lightweight segmentation head to the backbone of ReID model during training, which is supervised with part labels. Note that our segmentation head is only introduced during training and that it does not change network input or the way of extracting ReID feature. Experiments show that part segmentation considerably improves the performance of ReID. Through quantitative and qualitative analyses, we further reveal that body part perception helps ReID model to capture a set of more diverse features from the body, with decreased similarity between part features and increased focus on different body regions. We experiment with various representative ReID models and achieve consistent improvement on several large-scale datasets including Market1501, CUHK03, DukeMTMC-reID and MSMT17. E.g. on MSMT17, our method increases Rank-1 Accuracy of GlobalPool-ResNet-50, PCB and MGN by 2.3%, 2.9% and 3.9%, respectively. Incorporated with MGN, our model achieves state-of-the-art performance, with Rank-1 Accuracy 95.8%, 78.8%, 90.0% and 84.0% on four datasets, respectively.
Discriminative deep asymmetric supervised hashing for cross-modal retrieval

Due to the advantages of low storage cost and high retrieval efficiency, cross-modal hashing has received considerate attention. Most existing deep cross-modal hashing adopt a symmetric strategy to learn same deep hash functions for both query instances and database instances. However, the training of these symmetric deep cross-modal hashing methods is time-consuming, which makes them hard to effectively utilize the supervised information for cases with large-scale datasets. Inspired by the latest advance in the asymmetric hashing scheme, in this paper, we propose a discriminative deep asymmetric supervised hashing (DDASH) for cross-modal retrieval. Specifically, asymmetric hashing only learns hash codes of query instances by deep hash functions while learning the hash codes of the database instances by hand-crafted matrices. It cannot only make full use of the information in large-scale datasets, but also reduce the training time. Besides, we introduce discrete optimization to reduce the binary quantization error. Furthermore, a mapping matrix which maps generated hash codes into the corresponding labels is introduced to ensure that the hash codes are discriminative. We also calculate the level of similarity between instances as supervised information. Experiments on three common datasets for cross-modal retrieval show that DDASH outperforms state-of-the-art cross-modal hashing methods.
Scalable and secure product serialization for multi-party perishable good supply chains using blockchain

Product serialization aims to allocate unique serial numbers to products in a supply chain. The security challenges to product serialization are: • Valid serial numbers can be stolen and used to label fake products. Thus uniqueness of a serial number should be verifiable at any stage of its lifecycle in a supply chain. • A planned change of custody of a product in distribution can be corrupted by a few intimidatory nodes. Compliance with the planned change of custody should be verifiable. • The manufacturer and the consumer should be able to verify that perishable food products with expired shelf life are discarded. In this paper, we use blockchains to develop a product serialization method that solves the above security issues in a multi-party perishable good supply chain. Blockchains can revolutionize security and transparency in supply chains by providing a secure data-sharing platform in a multi-party environment. Although blockchains can provide a secure data storage of change of custody events of products in a supply chain, a high volume of such events poses scalability problems for blockchains. In this paper, we solve the product serialization problem using blockchain offline channels. Our solution significantly reduces the number of transactions needed to be recorded in the blockchain. We propose a secure serialization protocol to verify the authenticity of serial numbers despite not frequently engaging with the blockchain.
Developing a mobile application‐based particle image velocimetry tool for enhanced teaching and learning in fluid mechanics: A design‐based research approach

Abstract A robust and intuitive understanding of fluid mechanics—the applied science of fluid motion—is foundational within many engineering disciplines, including aerospace, chemical, civil, mechanical, naval, and ocean engineering. In‐depth knowledge of fluid mechanics is critical to safe and economical design of engineering applications employed globally everyday, such as automobiles, aircraft, and sea craft, and to meeting global 21st century engineering challenges, such as developing renewable energy sources, providing access to clean water, managing the environmental nitrogen cycle, and improving urban infrastructure. Despite the fundamental nature of fluid mechanics within the broader undergraduate engineering curriculum, students often characterize courses in fluid mechanics as mathematically onerous, conceptually difficult, and aesthetically uninteresting; anecdotally, undergraduates may choose to opt‐out of fluids engineering‐related careers based on their early experiences in fluids courses. Therefore, the continued development of new frameworks for engineering instruction in fluid mechanics is needed. Toward that end, this paper introduces mobile instructional particle image velocimetry (mI‐PIV), a low‐cost, open‐source, mobile application‐based educational tool under development for smartphones and tablets running Android. The mobile application provides learners with both technological capability and guided instruction that enables them to visualize and experiment with authentic flow fields in real time. The mI‐PIV tool is designed to generate interest in and intuition about fluid flow and to improve understanding of mathematical concepts as they relate to fluid mechanics by providing opportunities for fluids‐related active engagement and discovery in both formal and informal learning contexts.
Incremental Learning of Multi-Domain Image-to-Image Translations

Current multi-domain image-to-image translation models assume a fixed set of domains and that all the data are always available during training. However, over time, we may want to include additional domains to our model. Existing methods either require re-training the whole model with data from all domains or require training several additional modules to accommodate new domains. To address these limitations, we present IncrementalGAN, a multi-domain image-to-image translation model that can incrementally learn new domains using only a single generator. Our approach first decouples the domain label representation from the generator to allow it to be re-used for new domains without any architectural modification. Next, we introduce a distillation loss that prevents the model from forgetting previously learned domains. Our model compares favorably against several state-of-the-art baselines.
Overview of the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

This overview introduces the task of parsing into enhanced universal dependencies, describes the datasets used for training and evaluation, and evaluation metrics.We outline various approaches and discuss the results of the shared task.
Refinement and Universal Approximation via Sparsely Connected ReLU Convolution Nets

We construct a highly regular and simple structured class of sparsely connected convolutional neural networks with rectifier activations that provide universal function approximation in a coarse-to-fine manner with increasing number of layers. The networks are localized in the sense that local changes in the function to be approximated only require local changes in the final layer of weights. At the core of the construction lies the fact that the characteristic function can be derived from a convolution of characteristic functions at the next coarser resolution via a rectifier passing. The latter refinement result holds for all higher order univariate B-splines.
Verification Plan Using Neural Algorithm Blockchain Smart Contract for Secure P2P Real Estate Transactions

Blockchain and artificial intelligence are the most important keywords in the Fourth Industrial Revolution. This study sought to apply these core technologies to future validated algorithms that make real estate transactions secure to come up with an encryption algorithm. In addition, the real estate transaction is being paid a large fee by the middlemen, the real estate agent. Furthermore and recently, P2P (peer-to-peer) real estate exchange is used a lot. However, these P2P real estate exchanges also have problems that have not been identified by each other between landlords and tenants. In particular, a research model was established to compare and verify the PBFT (practical Byzantine fault tolerance) algorithm of Hyperledger through the blockchain agreement process. Subsequently, a process for verifying the real estate contract was established. Through VM (virtual machine) research methodology for the verification of blockchain real estate contracts, ElGamal communication was provided to prove quantum cryptography. We also automated lightweight encryption test verification tools and blockchain smart contract VM (virtual machine) models using artificial intelligence. Verification was performed through a reservation server and a monitoring server using a test verification tool for network-based lightweight security IoT (Internet of things) GW (gateway). It presents important ECP (elastic curve program) and elastic curve Qu-Vanstone (ECQV) models among the main functions of the blockchain smart contract, and it is equipped with quantum-based encryption algorithm. In addition, the necessary UML (unified modeling language) source code and performance data were calculated according to the actual experimental environment, and the average value for blockchain for administrative or government authorized assets—4000 TPS (transaction per second) were tested. In the future, we want to use this technology for real estate transactions.
TSK Fuzzy System for Multi-View Data Discovery Underlying Label Relaxation and Cross-Rule &amp; Cross-View Sparsity Regularizations

Industry 4.0 places special emphasis on the use of intelligent models to discover patterns in data. In this article, we propose a novel Takagi-Sugeno-Kang (TSK) fuzzy system with low model complexity for multiview data pattern discovery. Compared with the classic TSK fuzzy systems, the proposed one has three merits: First, we introduce a transformation matrix to relax the strict binary label matrix of the training set so that the margins between classes become more discriminative. Second, we introduce two kinds of sparsity regularizations, i.e., cross-rule and cross-view, to reduce indiscriminative fuzzy rules and consequent parameters so that the model complexity is significantly reduced. Third, we introduce the alternating direction method of multipliers to optimize the objective function so that we have compact closed-form solutions in each iteration. Extensive experiments on different kinds of multiview image datasets indicate the promising performance for data pattern discovery with low model complexity.
Preserving Privacy in WiFi Localization With Plausible Dummy Locations

Benefiting from the development of wireless communication, WiFi localization plays a vital role in various mobile applications. However, users' locations are breached by untrusted localization servers, thus disclosing users' location privacy and other sensitive personal information. Existing works on WiFi localization preservation employed homomorphic encryption, which incurs extremely high overheads. On the other hand, lightweight methods like k-anonymity and dummy-based approaches are hardly used for privacy preservation in WiFi localization scenarios due to their inherent drawbacks, i.e., k-anonymity approaches rely on trusted third parties while dummy-based approaches are susceptible to spatio-temporal correlation attacks. To design an effective yet lightweight WiFi localization privacy algorithm, this paper proposes to reinforce dummy techniques with plausible dummy location to resist the attacks. This study presents a novel algorithm, referred to as Location Preservation Algorithm with Plausible Dummies (LPPD), which is the first attempt towards WiFi localization privacy preservation using dummy techniques. The benefits of the algorithm are three-fold: First, it is lightweight. Second, it does not rely on trusted third parties. Third, it can resist spatio-temporal correlation attacks. Moreover, this study implements a WiFi localization system that integrates the proposed privacy-preserving algorithm with a WiFi fingerprint based indoor localization algorithm. Experiments results are presented to demonstrate the effectiveness and efficiency of our approach when compared with existing algorithms.
Individual predictors of the susceptibility for motion-related sickness: A systematic review

BACKGROUND:Individuals seem to be differently susceptible to motion-related sickness (motion sickness, visually induced sickness etc.). Investigations of the reasons for these different susceptibilities have revealed many potential factors that could predict individual susceptibility to motion-rela ted sickness. OBJECTIVE:This paper attempts to conduct a comprehensive literature review on inter-individual predictors of susceptibility to motion-related sickness using systematic approaches. METHODS:After a systematic literature research, titles and abstracts of 1778 publications were screened for relevance. Reference lists of selected publications were searched for additional studies. This procedure yielded 184 relevant publications. RESULTS:The identified predictors were clustered into demographic, physiological and psychological aspects. Among these predictors, the factors gender, length of velocity storage and anxiety showed the greatest predictive power. In addition, individual susceptibility to motion-related sickness is also to a large extent dependent on the degree of habituation to the aversive stimulus. CONCLUSIONS:Some of the identified influencing factors seem to have different effects on physically and visually induced motion sickness. More research is needed to close gaps, especially on predictive factors of visually induced motion sickness.
Multi-scale deep relational reasoning for facial kinship verification

In this paper, we propose a deep relational network which exploits multi-scale information of facial images for kinship verification. Unlike most existing deep learning based facial kinship verification methods which employ convolutional neural networks to extract holistic features, we present a deep model to exploit facial kinship relationship from local regions. For each given pair of face images, our method uses two convolutional neural networks which share parameters to extract different scales of features, which are expected to provide global contextual information of face images. We split a set of features at the same scale into multiple groups, where different groups capture information of different local regions. For each pair of local feature groups which are extracted from the same scale and position, we propose a relation network to reason their relationship, and use a verification network to infer the kin relation based on the results of local relations from different facial regions. We conduct experiments on two widely used facial kinship datasets: KinFaceW-I and KinFaceW-II, and our experimental results are presented to demonstrate the effectiveness of our approach.
Trusted data infrastructure for smart cities: a blockchain perspective

It is very important to improve the operation and management efficiency of data and network for the construction of smart cities. The current trust infrastructure in smart cities shows obvious shortcomings, accordingly, this paper proposed the idea of applying blockchain to smart city systems and as their trust infrastructure. The research method is: firstly, the network architecture and data architecture of smart cities were proposed, and the database was classified as a shared database, authorized access database or private database. Secondly, the overall architecture model of the blockchain in a smart city system was constructed, and the blockchain was classified as a public blockchain, consortium blockchain or private blockchain. Thirdly, the realization principle of the cross-blockchain interoperability of different blockchains, the construction principle of dynamic smart blockchains, and the selection method of blockchain authentication nodes were designed, and the supervision method and supervision content of blockchain system operation were discussed. The research result is: took the smart transportation subsystem as an object, through algorithms, the operation principle of the blockchain was constructed. This research can provide a reference for the construction of trust infrastructure in smart cities and can promote the application research progress of blockchain technology.
Penalty and partitioning techniques to improve performance of QUBO solvers

Quadratic Unconstrained Binary Optimization (QUBO) modeling has become a unifying framework for solving a wide variety of both unconstrained as well as constrained optimization problems. More recently, QUBO (or equivalent −1/+1 Ising Spin) models are a requirement for quantum annealing computers. Noisy Intermediate-Scale Quantum (NISQ) computing refers to classical computing preparing or compiling problem instances for compatibility with quantum hardware architectures. The process of converting a constrained problem to a QUBO compatible quantum annealing problem is an important part of the quantum compiler architecture and specifically when converting constrained models to unconstrained the choice of penalty magnitude is not trivial because using a large penalty to enforce constraints can overwhelm the solution landscape, while having too small a penalty allows infeasible optimal solutions. In this paper we present NISQ approaches to bound the magnitude of the penalty scalar M and demonstrate efficacy on a benchmark set of problems having a single equality constraint and present a QUBO partitioning approach validated by experimentation.
Prediction Lung Cancer– In Machine Learning Perspective

Past years have experienced increasing mortality rate due to lung cancer and thus it becomes crucial to predict whether the tumor has transformed to cancer or not, if the prediction is made at an early stage then many lives can be saved and accurate prediction also can help the doctors start their treatment. Computed tomography plays a vital role in ensuring the condition of tumor that by checking the size of tumor, location of tumor, etc. In this paper, we have proposed a framework for prediction of cancer at an early stage so that many lives that are in an endangered situation could be revived. Basically, our focus is on two domains of computer science that is Digital Image Processing acronymed DIP and Machine Learning. Digital image processing is well-known for the phase of preprocessing the image. In the further stage, the pre-processed image is exposed to segmentation phase and then the segmented image is passed for feature extraction and finally the extracted features are trained using machine learning classification algorithms like SVM (Support Vector Machines), Random Forest, ANN (Artificial Neural Network) . Based on the classification results obtained, prediction is made whether the tumor is benign or malignant. The inevitable parameters such as accuracy, Recall and precision are calculated for determining which algorithm has the highest predictive accuracy.
Depth Sequential Information Entropy Maps and Multi-Label Subspace Learning for Human Action Recognition

Human action recognition plays a key role in human-computer interaction in complex environments. However, similar actions will lead to poor feature sequence extraction and result in a reduction in recognition accuracy. This paper proposes a method (Action-Fusion: Multi-label subspace Learning (MLSL)) from depth maps called Depth Sequential Information Entropy Maps (DSIEM) and skeleton data for human action recognition in multiple modal features. The DSIEM describe the spatial information of human motion with information entropy, and describe the temporal information through stitching. DSIEM can reduce the redundancy of depth sequences and effectively capture spatial motion states. MLSL studies the relationship between different modalities and the inherent connection between different labels. The method is evaluated on three public datasets: Microsoft action 3D dataset (MSR Action3D), University of Texas at Dallas-multimodal human action dataset (UTD-MHAD), UTD MHAD- Kinect Version-2 (UTD-MHAD-Kinect V2). Experimental results show that the proposed MLSL model obtains new state-of-the-art results, including achieving the average rate of the MSR Action3D to 93.55%, the average rate of the UTD-MHAD to 88.37% and the average rate of the UTD-MHAD-Kinect V2 to 90.66%.
Pseudoinverse learning of Fuzzy Cognitive Maps for multivariate time series forecasting

Forecasting multivariate time series is an important problem considered in many real-world scenarios. To deal with that problem, several forecasting models have already been proposed, where Fuzzy Cognitive Maps (FCMs) are proved to be a suitable alternative. The key limitation of the existing FCM-based forecasting models is the lack of time-efficient learning algorithms. In this paper, we plug that gap by proposing a new FCM learning algorithm which is based on Moore–Penrose inverse. Moreover, we propose an innovative approach that equips FCM with long-term, multistep prediction capabilities. A huge advantage of our method is the lack of parameters which in the case of competitive approaches require laborious adjustment or tuning. The other added value of our method is the reduction of the processing time required to train FCM. The performed experiments revealed that FCM trained using our method outperforms the best FCM-based forecasting model reported in the literature.