Reviewerly/exHarmony huggingface dataset:
Diffusion-weighted and spectroscopic MRI super-resolution using sparse representations

Diffusion-weighted magnetic resonance imaging (DW-MRI) and spectroscopic MRI (MRSI) are powerful diagnostic imaging tools as they provide complementary information over conventional MRI. Imaging is done at a low-resolution (LR) as the scanning time for high-resolution (HR) MR images would be very long and not practical besides being expensive for imaging. In this paper, we propose a novel single image super-resolution (SISR) scheme to improve spatial resolution of DW and MRS images. It is based on patch-wise sparse reconstruction of HR patches from LR feature patches utilizing a pair of learned overcomplete dictionaries. Reconstruction not only exploits the sparsity of MR image but also utilize the non-local self-similarity of patches of the input LR image as prior knowledge. Experiments are done using magnitude images of DW-MRI and MRSI along with a synthetic image. Performance evaluations based on different matrices besides visual analysis are carried out to validate and compare the obtained results with the state-of-the-art. It is observed that the proposed method clearly outperforms recent methods in terms of both quantitative and visual analysis. Finally, the proposed algorithm is also implemented using the GP-GPU based parallel hardware along with sequential implementations in order to showcase its potential for real clinical applications.
Research on Gait Recognition and Prediction of Exoskeleton Robot Based on Improved DTW Algorithm

Abstract In order to realize the follow-up control of the exoskeleton robot better, the gait phase of the human body should be accurately identified and the human body motion intention should be matched in real time. In this paper, a set of gait data measurement system is used to collect the gait information of the human body during the movement process. Then, the gait recognition of the six models is carried out by the support vector machine through the plantar pressure information. Then the human movement intention is divided into five kinds and the improved DTW algorithm was used to complete the work of matching human motion intentions. Ultimately, the BP neural network model was designed to accurately predict the gait data. The experimental results show that the exoskeleton robot can accurately realize the three functions of recognition, matching and prediction.
ExSample: Efficient Searches on Video Repositories through Adaptive Sampling

Capturing and processing video is increasingly com-mon as cameras become cheaper to deploy. At the same time, rich video-understanding methods have progressed greatly in the last decade. As a result, many organizations now have massive repositories of video data, with applications in mapping, navigation, autonomous driving, and other areas. Because state-of-the-art object-detection methods are slow and expensive, our ability to process even simple ad-hoc object search queries (“find 100 traffic lights in dashcam video”) over this accumulated data lags far behind our ability to collect the data. Processing video at reduced sampling rates is a reasonable default strategy for these types of queries; however, the ideal sampling rate is both data and query dependent. We introduce ExSample, a low cost framework for object search over un-indexed video that quickly processes search queries by adapting the amount and location of sampled frames to the particular data and query being processed. ExSample prioritizes the processing of frames in a video repository so that processing is focused in portions of video that most likely contain objects of interest. It approaches searching in a similar way to a multi-arm bandit problem where each arm corresponds to a portion of a video. On large, real-world datasets, ExSample reduces processing time by 1.9x on average and up to 6x over an efficient random sampling baseline. Moreover, we show ExSample finds many results long before sophisticated, state-of-the-art baselines based on proxy scores can begin producing their first results.
Multi-to-binary network (MTBNet) for automated multi-organ segmentation on multi-sequence abdominal MRI images

Fully convolutional neural network (FCN) has achieved great success in semantic segmentation. However, the performance of the FCN is generally compromised for multi-object segmentation. Multi-organ segmentation is very common while challenging in the field of medical image analysis, where organs largely vary with scales. Different organs are often treated equally in most segmentation networks, which is not quite optimal. In this work, we propose to divide a multi-organ segmentation task into multiple binary segmentation tasks by constructing a multi-to-binary network (MTBNet). The proposed MTBNet is based on the FCN for pixel-wise prediction. Moreover, we build a plug-and-play multi-to-binary block (MTB block) to adjust the influence of the feature maps from the backbone. This is achieved by parallelizing multiple branches with different convolutional layers and a probability gate (ProbGate). The ProbGate is set up for predicting whether the class exists, which is supervised clearly via an auxiliary loss without using any other inputs. More reasonable features are achieved by summing branches' features multiplied by the probability from the accompanying ProbGate and fed into a decoder module for prediction. The proposed method is validated on a challenging task dataset of multi-organ segmentation in abdominal MRI. Compared to classic medical and other multi-scale segmentation methods, the proposed MTBNet improves the segmentation accuracy of small organs by adjusting features from different organs and reducing the chance of missing or misidentifying these organs.
Noise Removal from Medical Images Using Hybrid Filters of Technique

Abstract In this paper noise removal from the medical images using the hybrid filter of technique is presented. From the last couple of decades, medical image processing and analysis techniques based on computing algorithms acquired prominence as an alternate skillset for medical experts in disease diagnosis and prevention. As the number of patients are increasing yearly, doctors don’t have enough time to calculate the actual information from the medical images, as most of the medical images are affected by the noise. Medical images contain a different kind of noises because several machines are operating for data acquisition and transmission, so in order to reduce the complexity from the radiologist point of view we were very much interested in the design of an algorithm which can be beneficial and useful at the convenient level. Image Processing has become a very prominent technique in medical image analysis and medical image processing. The proposed architecture is the amalgamation of morphological operations, A modified form of Median Filters and Wiener Filters. The boundary and the shape of the image is extracted through Morphological operation. For noise removal and enhancement purpose modified median filter and wiener filters were used. The parameters like Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Root Mean Square Error (RMSE) are determined through proposed algorithm. Overall results indicate that the enhancement quality is performing well in proposed technique.
An image-based method for evaluating local deformations of saturated sand in undrained torsional shear tests

An image-based measurement system was developed to evaluate the induced local deformations of saturated sand specimens in undrained torsional shear tests. The images of specimens taken by a camera in front of a pressure chamber were noticeably distorted. To correct the errors caused by the curvature of the specimens and by the refraction of rays traveling through water, acrylic resin, and the atmosphere, a coordinate correction method was proposed to convert the apparent coordinates obtained from the distorted specimen image into the real coordinates under planar conditions. This method was verified using a rigid dummy specimen and was illustrated using a sand specimen as an example. The results obtained indicate that the resolution of displacement measurements is approximately 0.01 mm, representing approximately 3.3e-5 of the specimen height. Moreover, high measurement accuracy is obtained even if the dummy specimen has undergone a rotation of up to 26.4°. This measurement system was further extended to directly evaluate the local deformation of sand specimens through a transparent membrane. The direct and indirect evaluations indicate that the direct evaluation is more effective for quantitatively representing specimen deformation than indirect evaluation since membrane wrinkles and vertical slippage between the specimen and membrane occurred during liquefaction.
Track: a Multi-Modal Deep Architecture for Head Motion Prediction in 360<sup>°</sup> Videos

Head motion prediction is an important problem with 360° videos, in particular to inform the streaming decisions. Various methods tackling this problem with deep neural networks have been proposed recently. In this article, we introduce a new deep architecture, named TRACK, that benefits both from the history of past positions and knowledge of the video content. We show that TRACK achieves state-of-the-art performance when compared against all recent approaches considering the same datasets and wider prediction horizons: from 0 to 5 seconds.
Research and Analysis on the Distributed Database of Blockchain and Non- Blockchain

In the Data Technology (DT) era, data have become the most important means of production. With the rapid development of cloud computing, the database technology has become the most basic data technology facility. Especially, the Distributed Databases have been widely used to store and process large-scale data, which are applied in different scenarios than relational databases. In addition, blockchain technology has led to changes in decentralized architecture. The BigchainDB database is based on a distributed database with the features of blockchain. It has not only the advantages of distributed database, such as high throughput, low latency and easy expansion, but also has the characteristics of blockchain, such as decentralized control, tamper-proof and traceability. This paper mainly analyzes the comparison between the BigchainDB and other distributed databases in terms of execution performance. In practice, this paper focuses on the characteristics of BigchainDB, then compares the performance of creating and querying transactions between the BigchainDB and two important distributed databases: HadoopDB and Hive. The experiments in this paper show three things. Firstly, the BigchainDB is obviously better than HadoopDB and Hive in transaction creation. Secondly, the transaction query performance of the BigchainDB is one order higher than them. Thirdly, the BigchainDB is three orders of magnitude higher than them in fuzzy query performance. These results demonstrate that BigchainDB is significantly better than HadoopDB and Hive in both the performance of read and write.
Twitter Sentiment Analysis on Activities of Saudi General Entertainment Authority

Sentiment analysis can be defined as a natural language process to determine the individual's sentiment or opinion towards something. It helps institutions, companies and governments to gain a deeper understanding and supports decision-making. This paper aims to analyse individuals' opinions in Twitter on the activities of the Saudi General Entertainment Authority (GEA) using machine and deep learning techniques. To achieve this aim, 3,817 tweets were collected using RapidMiner. To classify tweets into supporters and opposers, three machine learning algorithms were used namely, Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), and one deep learning algorithm, which is Recurrent Neural Network (RNN). Two test options were applied to evaluate the classification model, percentage split and K-fold validation tests. The results show that the people are happy and agree with the GEAs' activities. As for the gender, the support rate of females was higher than males. In addition, RF algorithm outperforms other algorithms in terms of the classification accuracy and the error rate.
Substitution matrix based color schemes for sequence alignment visualization

Visualization of multiple sequence alignments often includes colored symbols, usually characters encoding amino acids, according to some (physical) properties, such as hydrophobicity or charge. Typically, color schemes are created manually, so that equal or similar colors are assigned to amino acids that share similar properties. However, this assessment is subjective and may not represent the similarity of symbols very well.In this article we propose a different approach for color scheme creation: We leverage the similarity information of a substitution matrix to derive an appropriate color scheme. Similar colors are assigned to high scoring pairs of symbols, distant colors are assigned to low scoring pairs. In order to find these optimal points in color space a simulated annealing algorithm is employed.Using the substitution matrix as basis for a color scheme is consistent with the alignment, which itself is based on the very substitution matrix. This approach allows fully automatic generation of new color schemes, even for special purposes which have not been covered, yet, including schemes for structural alphabets or schemes that are adapted for people with color vision deficiency.
Two Hilbert Schemes in Computer Vision

We study multiview moduli problems that arise in computer vision. We show that these moduli spaces are always smooth and irreducible, in both the calibrated and uncalibrated cases, for any number of views. We also show that these moduli spaces always admit open immersions into Hilbert schemes for more than two views, extending and refining work of Aholt, Sturmfels, and Thomas. We use these moduli spaces to study and extend the classical twisted pair covering of the essential variety.
Static and dynamic coherent robust control for a class of uncertain quantum systems

This paper concerns a class of uncertain linear quantum systems subject to quadratic perturbations in the system Hamiltonian. In order to obtain improved control performance, we propose two methods to design coherent robust controllers for the system. One is to formulate a static quantum controller by adding a controller Hamiltonian to the given system, and the other is to build a dynamic quantum controller which is directly coupled to the given system.
Restoration of Marker Occluded Hematoxylin and Eosin Stained Whole Slide Histology Images Using Generative Adversarial Networks

It is common for pathologists to annotate specific regions of the tissue, such as tumor, directly on the glass slide with markers. Although this practice was helpful prior to the advent of histology whole slide digitization, it often occludes important details which are increasingly relevant to immuno-oncology due to recent advancements in digital pathology imaging techniques. The current work uses a generative adversarial network with cycle loss to remove these annotations while still maintaining the underlying structure of the tissue by solving an image-to-image translation problem. We train our network on up to 300 whole slide images with marker inks and show that 70% of the corrected image patches are indistinguishable from originally uncontaminated image tissue to a human expert. This portion increases 97% when we replace the human expert with a deep residual network. We demonstrated the fidelity of the method to the original image by calculating the correlation between image gradient magnitudes. We observed a revival of up to 94,000 nuclei per slide in our dataset, the majority of which were located on tissue border.
A novel approach for automatic and robust segmentation of the 3D liver in computed tomography images

Liver segmentation is important for the computer-assisted diagnosis of liver disease and liver volume measurement. Because of the complexity of liver computed tomography (CT) images, multiple image segmentation techniques are required to robustly segment the liver. Many liver segmentation approaches have been proposed in the past few decades, however the liver segmentation problem remains. More robust approaches are needed and new possibilities should be investigated. In this paper, we propose a novel 3D liver segmentation approach that contains four major parts: (1) image pre-processing, (2) edge-constrained liver segmentation by 3D region growing, (3) separation of 3D liver and other organs, and (4) post-processing. In the first step, an anisotropic diffusion filter removes noise effectively while keeping the image edges almost unchanged. In the second step, both 3D and 2D canny edges are combined to constrain the region growing process. The seeds of region growing are generated by slope difference distribution threshold selection of the 3D CT image and morphological erosion. In the third step, 3D random walk and slope difference distribution threshold selection are combined to separate the adhered organs. In the fourth step, a novel 2D vascular region correction method is proposed for post-processing. Two open accessible CT liver datasets are used to evaluate the proposed approach and the achieved average DICE measure accuracy is 96.83%.
Dynamic Neighbourhood Identification Based on Multi-clustering in Collaborative Filtering Recommender Systems

This article presents a new approach to collaborative filtering recommender systems that focuses on the problem of an active user’s (a user to whom recommendations are generated) neighbourhood modelling. Precise identification of neighbours of an active user is one of the essential problems that recommender systems encounter due to its direct impact on the quality of generated recommendation lists. In classical algorithm, the neighbourhood is modelled by nearest k neighbours, but this approach has poor scalability. Clustering techniques, although improving time efficiency of recommender systems, can negatively affect the quality (precision) of recommendations. It results from inaccurate modelling of object neighbourhood in the case of data located on borders of clusters. A new algorithm based on multi-clustering is proposed in this article. Instead of one clustering scheme, it works on a set of ones, therefore it selects the clustering scheme that models the neighbourhood more precisely. This article presents the experiments confirming these advantages with respect to recommendation quality as well as time efficiency.
Cancer Sensitive Cascaded Networks (CSC-Net) for Efficient Histopathology Whole Slide Image Segmentation

Automatic segmentation of histopathological whole slide images (WSIs) is challenging due to the high resolution and large scale. In this paper, we proposed a cascade strategy for fast segmentation of WSIs based on convolutional neural networks. Our segmentation framework consists of two U-Net structures which are trained with samples from different magnifications. Meanwhile, we designed a novel cancer sensitive loss (CSL), which is effective in improving the sensitivity of cancer segmentation of the first network and reducing the false positive rate of the second network. We conducted experiments on ACDC-LungHP dataset and compared our method with 2 state-of-the-art segmentation methods improved from U-Net. The experimental results have demonstrated that the proposed method can improve the segmentation accuracy and meanwhile reduce the amount of computation. The dice score coefficient and precision of lung cancer segmentation are 0.694 and 0.947, respectively, which are superior to the compared methods.
3D Mapping of TAU Neurofibrillary Tangle Pathology in the Human Medial Temporal Lobe

Tau protein neurofibrillary tangles (NFT) are linked to neuronal and synaptic loss and cognitive decline in Alzheimer's disease (AD) and related dementias. In AD, NFT pathology is known to spread through the cortex in a characteristic pattern, starting in the medial temporal lobe. However, the exact 3D pattern of NFT progression has not been described, and capturing this pattern quantitatively can help inform in vivo AD imaging biomarkers. We present a computational framework for generating 3D maps of NFT load from ex vivo MRI and serial histology. Weakly supervised deep learning is used to detect NFTs on histology slides prepared with an anti-tau immunohistochemistry stain, and a multi-stage registration pipeline that leverages 3D printing is used for histology-MRI alignment. Derived maps of NFT density are strongly concordant with manual NFT counting, as well as categorical NFT severity ratings used for clinical diagnosis.
A new prediction method for recommendation system based on sampling reconstruction of signal on graph

Recommendation technology is widely used in various e-commerce platforms. Accurately predicting user’s preference is the most important goal of recommendation technology. One of the core difficulties of recommendation technology that the rating matrices are seriously sparse. However, the unknown entries in the rating matrix actually contain a lot of useful information for prediction, which are usually discarded in traditional methods. Based on the idea of semi-supervised learning, this paper models the recommendation problem as a signal reconstruction problem on a graph. The new model utilizes both the information of the unlabeled samples and the location information, and thus achieves an excellent predictive performance. Meanwhile, to reduce the computational complexity a strategy is designed skillfully to approximately solve the model. Experimental results shows that the proposed method significantly outperforms the reference methods in predictive accuracy and is robust to the diversity of data sets.
In Search of Forms for Evocative and Generative Reflection

Today an increasing number of personal informatics tools and platforms support intended behavior change and goal achievement through data-based self-reflection. The scope of self-reflection expands with emerging sources, goals, and challenges of human well-being, demanding for reframing recent computer-mediated reflective practice. This study investigates a broader range of contexts and forms of self-reflection that support navigating one's mind and goals beyond achieving preset goals. This paper describes contemporary issues on human well-being and two exploratory studies-one conducted in a traveling artists' residency and the other in a design studio class-which surveyed various triggers, contexts, and forms of self-reflection. By connecting the insights from the two studies, I propose evocative and generative reflection as an alternative perspective to tracking-based, goal-oriented reflection and discuss implications for the design for reflection with a focus on the creative dimension of human well-being.