Reviewerly/exHarmony huggingface dataset:
Liver Segmentation from Multimodal Images Using HED-Mask R-CNN

Precise segmentation of the liver is critical for computer-aided diagnosis such as pre-evaluation of the liver for living donor-based transplantation surgery. This task is challenging due to the weak boundaries of organs, countless anatomical variations, and the complexity of the background. Computed tomography (CT) scanning and magnetic resonance imaging (MRI) images have different parameters and settings. Thus, images acquired from different modalities differ from one another making liver segmentation challenging task. We propose an efficient liver segmentation with the combination of holistically-nested edge detection (HED) and Mask- region-convolutional neural network (R-CNN) to address these challenges. The proposed HED-Mask R-CNN approach is based on effective identification of edge map from multimodal images. The proposed system firstly applies a preprocessing step of image enhancement to get the ‘primal sketches’ of the abdomen. Then the HED network is applied to enhanced CT and MRI modality images to get better edge map. Finally, the Mask R-CNN is used to segment the liver from edge map images. We used a dataset of 20 CT patients and 9 MR patient from the CHAOS challenge. The system is trained on CT and MRI images separately and then converted to 2D slices. We significantly improved the segmentation accuracy of CT and MRI images on a database with Dice value of 0.94 for CT, 0.89 for T2-weighted MRI and 0.91 for T1-weighted MRI.
A novel block chain technology publication model proposal

Bitcoin is among the highest rated digital crypto-currency in financial investment markets. This technology relies on a backbone of distributed data architecture and peer-to-peer networking model called Blockchain. Unlike the current digital economy, which is governed centrally by financial institution or governments, Blockchain is fully autonomous without any third-party involvement. The exorbitant success of Bitcoin has attracted investors, scholars as well as organizations to peek into this lucrative technology for the possible application to other areas apart from crypto-currency. Blockchain can adopt Smart Contracts, which are digitally enabled contracts that can be executed and enforced fully or partially using pre-defined notions. The aim of this research is to investigate the synergy between Smart Contract and Blockchain to propose a digital framework for an academic paper publication model that has the capability to automate the entire process and challenge the existing system. It can also bring together all the stakeholders under the same system. The proposed model can further hold the stakeholders accountable for breach of contracts and/or reward them for executing the successes of terms pre-configured in the Smart Contract. The proposed model, called Digital Smart Publication or DSP (as referred in the document), is highly secure and ensures balance in distributing rewards to the involved stakeholders while keeping data integrity and security as paramount features.
Solar-TK

There has been significant prior work on solar performance modeling and forecasting that infers a site's current and future solar generation based on different factors including a site's location, time, weather, and physical attributes. Unfortunately, much of the prior work is not accessible to researchers, either because it has not been implemented and released as open-source, is too complex and time-consuming to re-implement, or requires access to proprietary data sources. To address the problem, we present Solar-TK, a data-driven toolkit for solar performance modeling and forecasting that is simple, extensible, and publicly accessible. Solar-TK's simple approach models and forecasts a site's solar output given only its location and a small amount of historical generation data. We plan to publicly release Solar-TK as open-source to enable research that requires realistic solar models and forecasts, and to serve as a baseline for comparing new solar modeling and forecasting techniques.
BDGAN: Image Blind Denoising Using Generative Adversarial Networks

In this paper, we present an end-to-end method for image blind denoising based on a conditional generative adversarial network (GAN). Discriminative learning-based methods, such as DnCNN, can achieve state-of-the-art denoising results but these methods usually focus on establishing noise model that resembles natural noisy images, thus neglecting to recover clean images from noisy images. Non-blind denoising methods are also limited since a precise noise level is hard to be obtained in the real world. Using multiple modified methods, we propose a novel end-to-end architecture which could directly generate clean images. A range of experiments have been done to show the convenience and superiority of our approach in image blind denoising.
A novel fuzzy logic algorithm for accurate fall detection of smart wristband

Falling is a major cause of serious injury or even death for the elderly population. To improve the safety of elderly people, a wide range of wearable fall detection devices have been developed over recent years, such as smart watches, waistbands and other wearable fall detectors. However, most of these fall detection devices are threshold-based and have a high rate of false alarm. This paper presents a novel fuzzy logic fall detection algorithm used in smart wristbands to reduce false alarms and achieve accurate fall detection. Experiments have been conducted in our laboratory and the results show that the proposed algorithm can accurately distinguish fall events from non-fall daily activities such as walking, jumping, clapping, and so forth. It shows good potential for commercial applications.
Estimation of photovoltaic generation forecasting models using limited information

This work deals with the problem of estimating a photovoltaic generation forecasting model in scenarios where measurements of meteorological variables (i.e. solar irradiance and temperature) at the plant site are not available. A novel algorithm for the estimation of the parameters of the well-known PVUSA model of a photovoltaic plant is proposed. Such a method is characterized by a low computational complexity, and efficiently exploits only power generation measurements, a theoretical clear-sky irradiance model, and temperature forecasts provided by a meteorological service. An extensive experimental validation of the proposed method on real data is also presented.
Why project Q is more than the world's first nonbinary voice for technology

column Why project Q is more than the world's first nonbinary voice for technology Share on Author: Julie Carpenter California Polytechnic State University California Polytechnic State UniversityView Profile Authors Info & Claims InteractionsVolume 26Issue 6November - December 2019 pp 56–59https://doi.org/10.1145/3358912Published:30 October 2019 1citation971DownloadsMetricsTotal Citations1Total Downloads971Last 12 Months671Last 6 weeks103 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Improved entanglement detection with subspace witnesses

A ``subspace'' witness of entanglement, more robust than a typical state fidelity-based witness, is introduced and experimentally tested with a hybrid two-qubit system composed of electronic spins in diamond. Its straightforward implementation offers improved detection of specific genuine multipartite entangled states in noisy, intermediate-scale quantum processors.
Ground Metric Learning on Graphs

Optimal transport (OT) distances between probability distributions are parameterized by the ground metric they use between observations. Their relevance for real-life applications strongly hinges on whether that ground metric parameter is suitably chosen. The challenge of selecting it adaptively and algorithmically from prior knowledge, the so-called ground metric learning (GML) problem, has therefore appeared in various settings. In this paper, we consider the GML problem when the learned metric is constrained to be a geodesic distance on a graph that supports the measures of interest. This imposes a rich structure for candidate metrics, but also enables far more efficient learning procedures when compared to a direct optimization over the space of all metric matrices. We use this setting to tackle an inverse problem stemming from the observation of a density evolving with time; we seek a graph ground metric such that the OT interpolation between the starting and ending densities that result from that ground metric agrees with the observed evolution. This OT dynamic framework is relevant to model natural phenomena exhibiting displacements of mass, such as the evolution of the color palette induced by the modification of lighting and materials.
Face recognition system using Viola Jones, histograms of oriented gradients and multi-class support vector machine

Abstract This study aims to determine the best distance to install a CCTV camera in identifying a person’s face in the passenger inspection area at the airport, which can be developed for suspect detection systems. The training data used is in the form of an image with five different angles per person, while the testing data is in the form of video. The initial stage conducted is face detection based on local features (a pair of eyes, nose, and mouth) in the input data using the Viola-Jones method. The face detection results process with pre-processing, feature extraction, and classification. In the pre-processing stage, the Brightness Enhancement (BE), Grayscale and Contrast Limited Adaptive Histogram Equalization (CLAHE) methods are used to improve the quality of the detected face. Furthermore, the feature extraction and classification stages use the Histogram of Oriented Gradient (HOG) and the Multi-class Support Vector Machine (MSVM) methods, respectively. The result shows that the best accuracy obtained is 86.76% for a CCTV camera distance of 300 cm and a height of 250 cm.
Learning a Unified Named Entity Tagger from Multiple Partially Annotated Corpora for Efficient Adaptation

Named entity recognition (NER) identifies typed entity mentions in raw text. While the task is well-established, there is no universally used tagset: often, datasets are annotated for use in downstream applications and accordingly only cover a small set of entity types relevant to a particular task. For instance, in the biomedical domain, one corpus might annotate genes, another chemicals, and another diseases—despite the texts in each corpus containing references to all three types of entities. In this paper, we propose a deep structured model to integrate these “partially annotated” datasets to jointly identify all entity types appearing in the training corpora. By leveraging multiple datasets, the model can learn robust input representations; by building a joint structured model, it avoids potential conflicts caused by combining several models’ predictions at test time. Experiments show that the proposed model significantly outperforms strong multi-task learning baselines when training on multiple, partially annotated datasets and testing on datasets that contain tags from more than one of the training corpora.
Floquet Anderson localization of two interacting discrete time quantum walks

We study the interplay of two interacting discrete time quantum walks in the presence of disorder. Each walk is described by a Floquet unitary map defined on a chain of two-level systems. Strong disorder induces a novel Anderson localization phase with a gapless Floquet spectrum and one unique localization length ${\ensuremath{\xi}}_{1}$ for all eigenstates for noninteracting walks. We add a local contact interaction which is parametrized by a phase shift $\ensuremath{\gamma}$. A wave packet is spreading subdiffusively beyond the bounds set by ${\ensuremath{\xi}}_{1}$ and saturates at a new length scale ${\ensuremath{\xi}}_{2}\ensuremath{\gg}{\ensuremath{\xi}}_{1}$. In particular we find ${\ensuremath{\xi}}_{2}\ensuremath{\sim}{\ensuremath{\xi}}_{1}^{1.2}$ for $\ensuremath{\gamma}=\ensuremath{\pi}$. We observe a nontrivial dependence of ${\ensuremath{\xi}}_{2}$ on $\ensuremath{\gamma}$, with a maximum value observed for $\ensuremath{\gamma}$ values which are shifted away from the expected strongest interaction case $\ensuremath{\gamma}=\ensuremath{\pi}$. The novel Anderson localization regime indicates violation of single parameter scaling for both interacting and noninteracting walks.
Two linearities for quantum computing in the lambda calculus

We propose a way to unify two approaches of non-cloning in quantum lambda-calculi: logical and algebraic linearities. The first approach is to forbid duplicating variables, while the second is to consider all lambda-terms as algebraic-linear functions. We illustrate this idea by defining a quantum extension of first-order simply-typed lambda-calculus, where the type is linear on superposition, while allows cloning base vectors. In addition, we provide an interpretation of the calculus where superposed types are interpreted as vector spaces and non-superposed types as their basis.
Method for Fused Phase and PCA Direction Based on a SIFT Framework for Multi-Modal Image Matching

The high noise and local deformation of multi-modal images reduce the accuracy of scale invariant feature transform (SIFT) image matching.To solve this problem, a new method based on the SIFT framework, which fuses the phase consistency optimization strategy and the gradient direction of principal component analysis (PCA) with the 8 direction of latitude reduction, is proposed in this paper.This method fuses the histogram of the orientated phase congruency (HOPC) method to extract the direction of the image, and adopts PCA to extract the main direction, which effectively solves the problem that the matching accuracy decreases due to the inversion of the direction of the image.Using the image phase instead of gradient intensity, the difficult problem of direction extraction is effectively solved when image edge characteristics are not obvious.Finally, the random sample consistency (RANSAC) algorithm is used to eliminate false match points.Simulation and experiments show that compared with the SIFT algorithm and PCA-SIFT algorithm, the proposed method improved the number of match points and matching accuracy, significantly reduced the mismatching rate.The statistical results show that the number of match points raised in this paper increases by 20.1% and 200% respectively compared with the former two algorithms.
Pixelated Carrier Phase-Shifting Shearography Using Spatiotemporal Low-Pass Filtering Algorithm

Shearography has been widely used in non-destructive testing due to its advantages in providing full-field, high precision, real-time measurement. The study presents a pixelated carrier phase-shifting shearography using a pixelated micropolarizer array. Based on the shearography, a series of shearograms are captured and phase maps corresponding to deformation are measured dynamically and continuously. Using the proposed spatiotemporal filtering algorithm in the complex domain, the set of phase maps are simultaneously low-pass filtered in the spatial and temporal domains, resulting in better phase quality than spatial low-pass filtering. By accumulating the temporally adjacent phase, the phase corresponding to large deformation can be evaluated; thus, large deformations can be accurately measured and protected from speckle noise, allowing internal defects to be easily identified. The capability of the proposed shearography is described by theoretical discussions and experiments.
Absolute Eye Gaze Estimation With Biosensors in Hearing Aids

People with hearing impairment typically have difficulties following conversations in multi-talker situations. Previous studies have shown that utilizing eye gaze to steer audio through beamformers could be a solution for those situations. Recent studies have shown that in-ear electrodes that capture electrooculography in the ear (EarEOG) can estimate the eye-gaze relative to the head, when the head was fixed. The head movement can be estimated using motion sensors around the ear to create an estimate of the absolute eye-gaze in the room. In this study, an experiment was designed to mimic a multi-talker situation in order to study and model the EarEOG signal when participants attempted to follow a conversation. Eleven hearing impaired participants were presented speech from the DAT speech corpus (1), with three targets positioned at -30°, 0° and +30° azimuth. The experiment was run in two setups: one where the participants had their head fixed in a chinrest, and the other where they were free to move their head. The participants’ task was to focus their visual attention on an LED-indicated target that changed regularly. A model was developed for the relative eye-gaze estimation, taking saccades, fixations, head movement and drift from the electrode-skin half-cell into account. This model explained 90.5% of the variance of the EarEOG when the head was fixed, and 82.6% when the head was free. The absolute eye-gaze was also estimated utilizing that model. When the head was fixed, the estimation of the absolute eye-gaze was reliable. However, due to hardware issues, the estimation of the absolute eye-gaze when the head was free had a variance that was too large to reliably estimate the attended target. Overall, this study demonstrated the potential of estimating absolute eye-gaze using EarEOG and motion sensors around the ear.
Tensor Representation of Rank-Metric Codes

We present the theory of rank-metric codes with respect to the 3-tensors that generate them. We define the generator tensor and the parity check tensor of a matrix code, and describe the properties of a code through these objects. We define the tensor rank of a code to be the tensor rank of its generating tensors, and propose that this quantity is a significant coding theoretic parameter. By a result on the tensor rank of Kruskal from the 1970s, the tensor rank of a rank-metric code of dimension $k$ and minimum rank distance $d$ is at least $k+d-1$. We call codes that meet this bound minimal tensor rank (MTR) codes. It is known from results in algebraic complexity theory that an MTR code implies the existence of a maximum distance separable (MDS) code. In this paper, we also address the converse problem, that of the existence of an MTR code, given an MDS code. We identify several parameters for which the converse holds and give explicit constructions of MTR codes using MDS codes. We furthermore define generalized tensor ranks, which give a refinement of the tensor rank as a code invariant. Moreover, we use these to distinguish inequivalent rank-metric codes.
A Survey on Efficient Parallelization of Blockchain-based Smart Contracts

The main problem faced by smart contract platforms is the amount of time and computational power required to reach consensus. In a classical blockchain model, each operation is in fact performed by each node, both to update the status and to validate the results of the calculations performed by others. In this short survey we sketch some state-of-the-art approaches to obtain an efficient and scalable computation of smart contracts. Particular emphasis is given to sharding, a promising method that allows parallelization and therefore a more efficient management of the computational resources of the network.
Webshell Detection Based on the Word Attention Mechanism

Webshell is a backdoor web page-based program.Malicious attackers obtain some privileges through the Webshell so as to realize the operation and control of the website.However, due to confusion coding technology, Webshell detection becomes difficult.This paper presents a Webshell detection model based on the word attention mechanism.In the model, we mainly focus on intra-line word association.After using Word2vec to vectorize the words, we use GRU (Gated Recursive Unit) and the attention mechanism to train and detect the samples.The experimental results show that the model has a high detection rate and low loss function.
Capsules with Inverted Dot-Product Attention Routing

We introduce a new routing algorithm for capsule networks, in which a child capsule is routed to a parent based only on agreement between the parent's state and the child's vote. Unlike previously proposed routing algorithms, the parent's ability to reconstruct the child is not explicitly taken into account to update the routing probabilities. This simplifies the routing procedure and improves performance on benchmark datasets such as CIFAR-10 and CIFAR-100. The new mechanism 1) designs routing via inverted dot-product attention; 2) imposes Layer Normalization as normalization; and 3) replaces sequential iterative routing with concurrent iterative routing. Besides outperforming existing capsule networks, our model performs at-par with a powerful CNN (ResNet-18), using less than 25% of the parameters. On a different task of recognizing digits from overlayed digit images, the proposed capsule model performs favorably against CNNs given the same number of layers and neurons per layer. We believe that our work raises the possibility of applying capsule networks to complex real-world tasks.
Crowdsourcing of Hate Speech for Detecting Abusive Behavior on Social Media

Micro blogging sites nowadays provide a platform for a person to express their thoughts and opinions without risking anything. These social media platforms do not provide any religious or political restrictions for the user to restrict them from saying what they want. People use such platforms to express their opinions on current affairs, political campaigns, day to day activities, sports, and other services. One such platform is Twitter. This paper aims at targeting the tweets shared on twitter on the basis of emotion behind them-whether it falls under the category of hate speech or not. This paper identifies hate speech by employing various machine learning algorithms. The paper also derives the context in which various words are used and based on it identifies hate speech. It tries to keep the idea of freedom of speech intact and at the same time curtail hate speech which various other algorithms have failed to do. This paper focuses on applying the hate speech check on anti-national tweets. This can help in marking the users and to demote them to engage in such activities.
Sending-or-Not-Sending Twin-Field Quantum Key Distribution with Light Source Monitoring

Twin-field quantum key distribution (TF-QKD) is proposed to achieve a remote key distribution with a maximum secure transmission distance up to over 500 km. Although the security of TF-QKD in its detection part is guaranteed, there are some remaining problems in the source part. The sending-or-not-sending (SNS) protocol is proposed to solve the security problem in the phase post-selection process; however, the light source is still assumed to be an ideal coherent state. This assumption is not satisfied in real-life QKD systems, leading to practical secure issues. In this paper, we discuss the condition that the photon number distribution (PND) of the source is unknown for the SNS protocol, demonstrate that the security analysis is still valid under a source with unknown PND, and show that with light source monitoring, the performance of the SNS protocol can remain almost unchanged.