Reviewerly/exHarmony huggingface dataset:
EDCRA-IoT: Edge-based Data Conflict Resolution Approach for Internet of Things

In Internet of Things (IoT), data are collected from various sources (e.g., sensors and databases) to describe a phenomenon under-observation. Due to the lack of knowledge about the measurement environment and the limited accuracy of data sources, the IoT data inevitably appears uncertain, imperfect, and conflicting. This would lead to high data conflict among different data sources, and the final result of data fusion is degraded. This paper presents an edge-based data conflict resolution approach for IoT to tackle these issues while maintaining an accurate and reliable result. In the proposed approach, data conflict is significantly alleviated using two phases. The first is Faulty Data Detection and Correction (FDDC), which runs based on the confidence interval and estimated data. The second is Data Conflict Measure and Fusion (DCMF), which is developed to compute the degree of conflict among different data sources based on Fuzzy measures and calculate each data source's credibility degree. Then, data fusion is carried out based on the Minimum Mean Square Error (MMSE) criterion accordingly. The simulation results reveal the remarkable effectiveness and efficiency of the proposed approach for solving IoT conflicting data.
Fair Detection of Poisoning Attacks in Federated Learning

Federated learning is a decentralized machine learning technique that aggregates partial models trained by a set of clients on their own private data to obtain a global model. This technique is vulnerable to security attacks, such as model poisoning, whereby malicious clients submit bad updates in order to prevent the model from converging or to introduce artificial bias in the classification. Applying anti-poisoning techniques might lead to the discrimination of minority groups whose data are significantly and legitimately different from those of the majority of clients. In this work, we strive to strike a balance between fighting poisoning and accommodating diversity to help learning fairer and less discriminatory federated learning models. In this way, we forestall the exclusion of diverse clients while still ensuring detection of poisoning attacks. Empirical work on a standard machine learning data set shows that employing our approach to tell legitimate from malicious updates produces models that are more accurate than those obtained with standard poisoning detection techniques.
Metastatic Cancer Image Binary Classification Based on Resnet Model

With the rapid development of computers technology, computers have inherent advantages over human in terms of sample storage, data processing, and calculation speed. Computer assisted diagnosis achieves automatical disease detection and classification which is economical. In recent years, using deep learning methods to solve medical image classification has received much attention from researchers due to great performance. In this article, we propose a new metastatic cancer image classification approach based on Resent model. We aim to turn the digital pathology scan detection task into a binary classification task of cancer image. The ResNet model can effectively alleviate the gradient explosion and gradient disappearance problems and train a deeper network by using a skip connection with an identity transformation. The experiments were conducted on the PCam benchmark dataset which is provided by the Kaggle competition platform. The experimental results indicate that our method outperforms the other convolutional neural networks methods such as Vgg16 and Vgg19. Our proposed method has the highest scores on the Auc Roc and the Accuracy measures. Experimental results show that the use of deep learning method based on Resent can significantly improve the performance of metastatic cancer diagnosis.
Joint affine and deformable three‐dimensional networks for brain MRI registration

Purpose Volumetric medical image registration has important clinical significance. Traditional registration methods may be time‐consuming when processing large volumetric data due to their iterative optimizations. In contrast, existing deep learning‐based networks can obtain the registration quickly. However, most of them require independent rigid alignment before deformable registration; these two steps are often performed separately and cannot be end‐to‐end. Methods We propose an end‐to‐end joint affine and deformable network for three‐dimensional (3D) medical image registration. The proposed network combines two deformation methods; the first one is for obtaining affine alignment and the second one is a deformable subnetwork for achieving the nonrigid registration. The parameters of the two subnetworks are shared. The global and local similarity measures are used as loss functions for the two subnetworks, respectively. Moreover, an anatomical similarity loss is devised to weakly supervise the training of the whole registration network. Finally, the trained network can perform deformable registration in one forward pass. Results The efficacy of our network was extensively evaluated on three public brain MRI datasets including Mindboggle101, LPBA40, and IXI. Experimental results demonstrate our network consistently outperformed several state‐of‐the‐art methods with respect to the metrics of Dice index (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD). Conclusions The proposed network provides accurate and robust volumetric registration without any pre‐alignment requirement, which facilitates the end‐to‐end deformable registration.
Additive deep feature optimization for semantic image retrieval

Rapid increase in the distribution of multimedia content in recent times presents a challenging problem for content-based image retrieval systems. Image contents, such as position and shape of objects alongside contextual features such as background can be used to retrieve visually similar images. Variations in contrast, color, intensity and texture of contextually similar images make it an interesting research problem. A deep convolutional neural network-based model called MaxNet for content-based image retrieval is presented in this paper. The proposed system bypasses the reliance on handcrafted features and extracts deep features directly from the images, which are then used to retrieve contextually similar images from the database. The proposed MaxNet model is built by stacking the updated inception module in a hierarchical fashion. Features extracted from various pipelines in the inception module are aggregated after each inception maximizing the feature values. This novel aggregation step generates a model that is able to adapt to variety of datasets. Various types of aggregations are discussed in this study. Model overcomes the over-fitting problem by using a dropout layer after each inception block and just before the output layer. The system outputs softmax probabilities, which are stored in the feature database and are used to compute the similarity index to retrieve images similar to the query image. The MaxNet model is evaluated using four popular image retrieval datasets namely, Corel-1k, Corel-5k, Corel-10k and Caltech-101, where it outperforms state-of-the-art methods in key performance indicators.
Analysis of Race and Gender Bias in Deep Age Estimation Models

Due to advances in deep learning and convolutional neural networks (CNNs) there has been significant progress in the field of visual age estimation from face images over recent years. While today's models are able to achieve considerable age estimation accuracy, their behaviour, especially with respect to specific demographic groups is still not well understood. In this paper, we take a deeper look at CNN-based age estimation models and analyze their performance across different race and gender groups. We use two publicly available off-the-shelf age estimation models, i.e., FaceNet and WideResNet, for our study and analyze their performance on the UTKFace and APPA-REAL datasets. We partition face images into sub-groups based on race, gender and combinations of race and gender. We then compare age estimation results and find that there are noticeable differences in performance across demographics. Specifically, our results show that age estimation accuracy is consistently higher for men than for women, while race does not appear to have consistent effects on the tested models across different test datasets.
Learning From Paired and Unpaired Data: Alternately Trained CycleGAN for Near Infrared Image Colorization

This paper presents a novel near infrared (NIR) image colorization approach for the Grand Challenge held by 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP). A Cycle-Consistent Generative Adversarial Network (CycleGAN) with cross-scale dense connections is developed to learn the color translation from the NIR domain to the RGB domain based on both paired and unpaired data. Due to the limited number of paired NIR-RGB images, data augmentation via cropping, scaling, contrast and mirroring operations have been adopted to increase the variations of the NIR domain. An alternating training strategy has been designed, such that CycleGAN can efficiently and alternately learn the explicit pixel-level mappings from the paired NIR-RGB data, as well as the implicit domain mappings from the unpaired ones. Based on the validation data, we have evaluated our method and compared it with conventional CycleGAN method in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and angular error (AE). The experimental results validate the proposed colorization framework.
A Modified jSO Algorithm for Solving Constrained Engineering Problems

Proposing new strategies to improve the optimization performance of differential evolution (DE) is an important research study. The jSO algorithm was the announced winner of the Congress on Evolutionary Computation (CEC) 2017 competition on numerical optimization, and is the state-of-the-art algorithm in the SHADE (Success-History based Adaptive Differential Evolution) algorithm series. However, the jSO algorithm converges prematurely in the search space with different dimensions and is prone to falling into local optimum during evolution, as well as the problem of decreasing population diversity. In this paper, a modified jSO algorithm (MjSO) is proposed which is based on cosine similarity with parameter adaptation and a novel opposition-based learning restart mechanism incorporated with symmetry to address the above problems, respectively. Moreover, it is well known that parameter setting has a significant impact on the performance of the algorithm and the search process can be divided into two symmetrical parts. Hence, a parameter control strategy based on a symmetric search process is introduced in the MjSO. The effectiveness of these designs is supported by presenting a population clustering analysis, along with a population diversity measure to evaluate the performance of the proposed algorithm, three state-of-the-art DE variant algorithms (EBLSHADE, ELSHADE-SPACMA, and SALSHADE-cnEPSin) and two original algorithms (jSO and LSHADE) are compared with it, for solving 30 CEC’17 benchmark functions and three classical engineering design problems. The experimental results and analysis reveal that the proposed algorithm can outperform other competitions in terms of the convergence speed and the quality of solutions. Promisingly, the proposed method can be treated as an effective and efficient auxiliary tool for more complex optimization models and scenarios.
Not Quite Yourself Today

The shortcomings of the traditional password-based authentication mechanism are becoming increasingly apparent as we transition from "one user - one device" to a richer "multiple users - multiple devices" computing paradigm. The currently dominant research direction focuses on on-device biometrics, which require sensitive information, such as images of a user's face, to be constantly streamed from a single recording source, often the device on which a user is getting authenticated. Instead, in this work we explore the possibilities offered by heterogeneous devices that opportunistically collect non-sensitive data in smart environments. We construct an IoT testbed in which we gather data pertaining to a person's movement in space, interaction with certain physical objects, PC terminal usage, and keyboard typing, and construct machine learning models capturing the person's behaviour traits. We commence our examination with models constructed from data sensed during a previously-completed task run and with such models we achieve up to 68% user identification accuracy (c.f. 7% baseline) among up to 20 individuals. Taking into account the limits of behaviour persistence we then revise our approach to continuously refine the model with the most recently sampled sensor data. This method allows us to achieve 99.3% user verification accuracy and successfully prevent a session takeover attack within 12 seconds with less than 1% of false attack detection.
“Can NLP techniques be utilized as a reliable tool for medical science?” - Building a NLP Framework to Classify Medical Reports

Artificial intelligence persists on being a right-hand tool for many branches of biology. From preliminary advices and treatments, such as understanding if symptoms related to fever or cold, to critical detection of cancerous cell or classification of X-rays, traditional machine learning and deep learning techniques achieved remarkable feats. However, total dependency on machine-based prediction is yet a far fetched concept. In this paper, we provide a framework utilizing several Natural Language Processing (NLP) algorithms to construct a comparative analysis. We create an ensemble of top-performing algorithms to accomplish classification task on medical reports. We compare both the traditional machine learning and deep learning techniques and evaluate their probabilities of being reliable on analyzing medical diagnosis. We concluded that an ensemble approach can provide reliable outcomes with accuracy over 92% and that the current state of the art is unequipped to provide the result with the standard needed for health sectors but an ensemble of these techniques can be a pathway for future research direction.
Proposal and Experimental Results of a DNN Based Real-Time Recognition Method for Ohsone Style Fingerspelling in Static Characters Environment

In Japan, the Employment Promotion Act for Persons with Disabilities was amended in April 2020. The employers are required to hire person with disabilities. On the other hand, the hearing-impaired person have hard in working due to communication problems with the not hearing-impaired person. In order to solve this problem, the not hearing-impaired person needs to learn the fingerspelling. However, fingerspelling is difficult to learn because of limited learning opportunities. In this paper, we propose a real-time recognition method for learning support the Ohsone style fingerspelling.
Learning-to-Fly: Learning-based Collision Avoidance for Scalable Urban Air Mobility

With increasing urban population, there is global interest in Urban Air Mobility (UAM), where hundreds of autonomous Unmanned Aircraft Systems (UAS) execute missions in the airspace above cities. Unlike traditional human-inthe-loop air traffic management, UAM requires decentralized autonomous approaches that scale for an order of magnitude higher aircraft densities and are applicable to urban settings. We present Learning-to-Fly (L2F), a decentralized on-demand airborne collision avoidance framework for multiple UAS that allows them to independently plan and safely execute missions with spatial, temporal and reactive objectives expressed using Signal Temporal Logic. We formulate the problem of predictively avoiding collisions between two UAS without violating mission objectives as a Mixed Integer Linear Program (MILP). This however is intractable to solve online. Instead, we develop L2F, a two-stage collision avoidance method that consists of: 1) a learning-based decision-making scheme and 2) a distributed, linear programming-based UAS control algorithm. Through extensive simulations, we show the real-time applicability of our method which is ≈6000× faster than the MILP approach and can resolve 100% of collisions when there is ample room to maneuver, and shows graceful degradation in performance otherwise. We also compare L2F to two other methods and demonstrate an implementation on quad-rotor robots.
Blockchain based Data Marketplace System

Blockchain technologies have given lots of impact on the existing market trends during the past few years. In particular, smart contract mechanism based on blockchain can guarantee the automatic and secure trading. In this paper, we propose a novel approach for blockchain-based data marketplace system. It can facilitate effective data retrieval and automatic transactions between various data providers and consumers. Also, we provide the named and secure data communication at the network layer, based on NDN. We present the system architecture and describe the service flows that show functionality of the marketplace.
RETRACTED: Wearable exercise monitoring equipment for physical exercise teaching process based on wireless sensor

Living a healthy, fulfilling life or carrying out daily activities inevitably depend on several different physical activity scales. Therefore, physical exercise is needed to evaluate both healthy subjects and patients to plan their welfare needs. Monitoring Efficacy of physical activity and range of exercise wearable monitoring techniques to assess physical exercise treatment, safety, physical fitness and disability of wireless sensor. There seems to be no surveyed review of the impact of wearable movement monitoring on existing systems' heart population. Physical activity and proper balance between intensity exercises, it is important to maximize the positive effects monitoring in wireless sensor. Auxiliary tools are very important, as it is not easy to get this balance. Therefore, training in the balance between Ambient Assisted Monitoring (AAM) system support and incentives is desirable for wearable fitness equipment. The present of the proposal system, AAM's method teaches the level and provides this, focusing on the level of physical activity monitoring of the platform skills of the elderly population. Monitoring (1): The main focus is on personalized education methods and wireless sensor monitoring to determine the type, intensity, frequency and duration of activity according to its boosting method to provide athletic training estimates. Explaining how to do this Time is given the physical exercise (2) of the director or coach's intensity activities. Here, this method is to describe the user's parameters, which should provide useful feedback in real time to understand how to use the correct technology safely.
A divide-and-conquer approach to neural natural language generation from structured data

Current approaches that generate text from linked data for complex real-world domains can face problems including rich and sparse vocabularies as well as learning from examples of long varied sequences. In this article, we propose a novel divide-and-conquer approach that automatically induces a hierarchy of “generation spaces” from a dataset of semantic concepts and texts. Generation spaces are based on a notion of similarity of partial knowledge graphs that represent the domain and feed into a hierarchy of sequence-to-sequence or memory-to-sequence learners for concept-to-text generation. An advantage of our approach is that learning models are exposed to the most relevant examples during training which can avoid bias towards majority samples. We evaluate our approach on two common benchmark datasets and compare our hierarchical approach against a flat learning setup. We also conduct a comparison between sequence-to-sequence and memory-to-sequence learning models. Experiments show that our hierarchical approach overcomes issues of data sparsity and learns robust lexico-syntactic patterns, consistently outperforming flat baselines and previous work by up to 30%. We also find that while memory-to-sequence models can outperform sequence-to-sequence models in some cases, the latter are generally more stable in their performance and represent a safer overall choice.
Monitoring the 2020 Magna, Utah, Earthquake Sequence with Nodal Seismometers and Machine Learning

Abstract Immediately following the 18 March 2020 Mww 5.7 Magna, Utah, earthquake, work began on installing a network of three-component, 5 Hz geophones throughout the Salt Lake Valley. After six days, 180 geophones had been sited within 35 km of the epicenter. Each geophone recorded 250 samples per second data onsite for ∼40 days. Here, we integrate the geophone data with data from the permanent regional seismic network operated by the University of Utah Seismograph Stations (UUSS). We use machine learning (ML) methods to create a new catalog of arrival time picks, earthquake locations, and P-wave polarities for 18 March 2020–30 April 2020. We train two deep-learning U-Net models to detect P waves and S waves, assigning arrival times to maximal posterior probabilities, followed by a two-step association process that combines deep learning with a grid-based interferometric approach. Our automated workflow results in 142,000 P picks, 188,000 S picks, and over 5000 earthquake locations. We recovered 95% of the events in the UUSS authoritative catalog and more than doubled the total number of events (5000 vs. 2300). The P and S arrival times generated by our ML models have near-zero biases and standard deviations of 0.05 s and 0.09 s, respectively, relative to corresponding analyst times picked at backbone stations. We also use a deep-learning architecture to automatically determine 70,000 P-wave first motions, which agree with 93% of 5876 hand-picked up or down first motions from both the backbone and nodal stations. Overall, the use of ML led to large increases in the number of arrival times, especially S times, that will be useful for future tomographic studies, as well as the discovery of thousands more earthquakes than exist in the UUSS catalog.
Legacy Photo Editing with Learned Noise Prior

There are quite a number of photographs captured under undesirable conditions in the last century. Thus, they are of-ten noisy, regionally incomplete, and grayscale formatted. Conventional approaches mainly focus on one point so that those restoration results are not perceptually sharp or clean enough. To solve these problems, we propose a noise prior learner NEGAN to simulate the noise distribution of real legacy photos using unpaired images. It mainly focuses on matching high-frequency parts of noisy images through discrete wavelet transform (DWT) since they include most of noise statistics. We also create a large legacy photo dataset for learning noise prior. Using learned noise prior, we can easily build valid training pairs by degrading clean images. Then, we propose an IEGAN framework performing image editing including joint denoising, inpainting and colorization based on the estimated noise prior. We evaluate the proposed system and compare it with state-of-the-art image enhancement methods. The experimental results demonstrate that it achieves the best perceptual quality. Please see the webpage https://github.com/zhaoyuzhi/Legacy-Photo-Editing-with-Learned-Noise-Prior for the codes and the proposed LP dataset.