Reviewerly/exHarmony huggingface dataset:
A Novel Algorithm for Hyperspectral Image Denoising in Medical Application

The one of the preprocessing step for hyperspectral imagery is noise reduction. The images are received by the detector and this can be degraded by several factors like atmospherical things and device noises which emit temperature noise, processing noise and explosion noise. There are several strategies are developed already to cut back the signal to noise magnitude relation of the hyperspectral image. However, the stationary noise of the many denoising ways developed cannot be applied on to the gauge boson noise. Thus, the each gauge boson and thermal noise square measure gift within the captured hyperspectral image (HSI). during this paper, we tend to projected a replacement denoising framework known as tensor-based filtering employing a PARAFAC tensor decomposition methodology for scale back each noise. The proposed technique is performs higher in removing noise as compared with different strategies like Multiple linear regression (MLR) algorithm and combined algorithm called multidimensional wavelet transforms with multiway wiener filter (MWPT-MWF) technique. The performance analysis of the new denoising framework has more efficient for reducing signal dependent (PN) and signal independent noise (TN) as compared with other conventional method. Hence this novel denoising approach would be more beneficial for detection of skin allergy and also this algorithm will be very useful for detection of retinal exudates and diagnosis of diabetes mellitus and retinopathy disease in medical application.
Simple dynamic word embeddings for mapping perceptions in the public sphere

Word embeddings trained on large-scale historical corpora can illuminate human biases and stereotypes that perpetuate social inequalities. These embeddings are often trained in separate vector space models defined according to different attributes of interest. In this paper, we introduce a single, unified dynamic embedding model that learns attribute-specific word embeddings and apply it to a novel dataset—talk radio shows from around the US—to analyze perceptions about refugees. We validate our model on a benchmark dataset and apply it to two corpora of talk radio shows averaging 117 million words produced over one month across 83 stations and 64 cities. Our findings suggest that dynamic word embeddings are capable of identifying nuanced differences in public discourse about contentious topics, suggesting their usefulness as a tool for better understanding how the public perceives and engages with different issues across time, geography, and other dimensions.
Sensor Bias Estimation Based on Ridge Least Trimmed Squares

A robust sensor bias estimation approach, named as the ridge least trimmed squares (RLTS), is proposed. Combing the advantages of ridge regression and least trimmed squares, RLTS can solve the sensor bias estimation problem with the presence of misassociations and ill-conditioning. Simulation results verify the effectiveness of the proposed approach.
Stance Detection in Code-Mixed

Social media sites like Facebook, Twitter, and other microblogging forums have emerged as a platform for people to express their opinions and views on different issues and events. It is often observed that people tend to take a stance; in favor, against or neutral towards a particular topic. The task of assessing the stance taken by the individual became significantly important with the emergence in the usage of online social platforms. Automatic stance detection system understands the user’s stance by analyzing the standalone texts against a target entity. Due to the limited contextual information a single sentence provides, it is challenging to solve this task effectively. In this paper, we introduce a Multi-Task Learning (MTL) based deep neural network architecture for automatically detecting stance present in the code-mixed corpus. We apply our approach on Hindi-English code-mixed corpus against the target entity - “Demonetisation.” Our best model achieved the result with a stance prediction accuracy of 63.2% which is a 4.5% overall accuracy improvement compared to the current supervised classification systems developed using the benchmark dataset for code-mixed data stance detection.
A Dynamic Regret Analysis and Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning

On-policy imitation learning algorithms such as Dagger evolve a robot control policy by executing it, measuring performance (loss), obtaining corrective feedback from a supervisor, and generating the next policy. As the loss between iterations can vary unpredictably, a fundamental question is under what conditions this process will eventually achieve a converged policy. If one assumes the underlying trajectory distribution is static (stationary), it is possible to prove convergence for Dagger. Cheng and Boots (2018) consider the more realistic model for robotics where the underlying trajectory distribution, which is a function of the policy, is dynamic and show that it is possible to prove convergence when a condition on the rate of change of the trajectory distributions is satisfied. In this paper, we reframe that result using dynamic regret theory from the field of Online Optimization to prove convergence to locally optimal policies for Dagger, Imitation Gradient, and Multiple Imitation Gradient. These results inspire a new algorithm, Adaptive On-Policy Regularization (AOR), that ensures the conditions for convergence. We present simulation results with cart-pole balancing and walker locomotion benchmarks that suggest AOR can significantly decrease dynamic regret and chattering. To our knowledge, this the first application of dynamic regret theory to imitation learning.
Deep Convolution Neural Network Based Denoiser for Mammographic Images

Denoising is an important image pre-processing operation required to improve the image quality. In the past, several image denoising solutions have been put forth with varying performances. Recently, deep-learning based approaches have given better results than conventional algorithms. While these methods offer promising results on denoising of natural images, their application to medical imaging is yet to be fully explored. In this study, mammographic images, which are generally corrupted with Gaussian noise, have been effectively denoised using a deep convolution neural network. The model proposed in this work outshines various existing state-of-the-art solutions. Our model achieves a structural similarity index (SSIM) of 0.98 and value of 41.53 dB for peak signal to noise ratio (PSNR).
Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots

Learning for control can acquire controllers for novel robotic tasks, paving the path for autonomous agents. Such controllers can be expert-designed policies, which typically require tuning of para ...
Incorporating deep features in the analysis of tissue microarray images

Tissue microarray (TMA) images have been used increasingly often in cancer studies and the validation of biomarkers. TACOMA-a cutting-edge automatic scoring algorithm for TMA images-is comparable to pathologists in terms of accuracy and repeatability. Here we consider how this algorithm may be further improved. Inspired by the recent success of deep learning, we propose to incorporate representations learnable through computation. We explore representations of a group nature through unsupervised learning, e.g., hierarchical clustering and recursive space partition. Information carried by clustering or spatial partitioning may be more concrete than the labels when the data are heterogeneous, or could help when the labels are noisy. The use of such information could be viewed as regularization in model fitting. It is motivated by major challenges in TMA image scoring-heterogeneity and label noise, and the cluster assumption in semi-supervised learning. Using this information on TMA images of breast cancer, we have reduced the error rate of TACOMA by about 6%. Further simulations on synthetic data provide insights on when such representations would likely help. Although we focus on TMAs, learnable representations of this type are expected to be applicable in other settings.
Price Discovery and Efficiency in Bitcoin Markets

A later version of this paper has been published in the Journal of Financial Stability (Volume 50, October 2020, Article Number 100776).When trading volumes are high, bitcoin futures on the Chicago Mercantile Exchange (CME) and -- until recently -- the Chicago Board Options Exchange (CBOE) lead high-frequency bitcoin price formation. During periods of low trading volumes, the futures become decoupled from prices on centralised bitcoin spot exchanges. However, the trading volume on CME futures has been trending upwards and their price discovery role is now highly significant. Basis risk increases following jumps in either spot or futures prices but these are rapidly arbitraged away by high-frequency institutional traders in the futures.
A Comparison of Finger and Wrist Motion Tracking to Detect Bites during Food Consumption

This paper evaluates the use of a sensor mounted on a finger to detect bites during food consumption. Previous works have demonstrated the capability to detect bites by tracking wrist motion. The key motion is the roll of the wrist that occurs between picking up food and delivering it to the mouth. We hypothesize that the roll axes of the wrist, hand, and finger are congruent, and that therefore the roll motion measured at the wrist can be similarly measured at the finger. We collected data for 10 meals of subjects wearing sensors on both the wrist and finger. Results indicate that the bite counting algorithm obtains similar accuracy at both locations. The practical implication is that an eating activity monitor could be embedded in the form of a ring worn on the finger, and that this form factor could work as well as a watch-like device.
How One Library's Health Information Center Expanded Its Marketing Plan

When Preston Medical Library moved inside the Medical Center in September 2014, the new patient library, called the Health Information Center (HIC), was added. This addition is a patient focused, consumer health library that, among other things, offers health information and books. After the initial marketing plan was implemented, a task force was created to focus specifically on marketing the consumer health library and its resources. This article discusses how the task force revamped the marketing strategy to include outreach into the medical center's waiting rooms and other opportunities for collaboration.
Dichotomy for symmetric Boolean PCSPs

A PCSP is a combination of two CSPs defined by two similar templates; the computational question is to distinguish a YES instance of the first one from a NO instance of the second. The computational complexity of many PCSPs remains unknown. Even the case of Boolean templates (solved for CSP by Schaefer [STOC'78]) remains wide open. The main result of Brakensiek and Guruswami [SODA'18] shows that Boolean PCSPs exhibit a dichotomy (PTIME vs. NPC) when the clauses are and for of variables''. In this paper we remove the allow for of variables'' assumption from the theorem. The symmetric assumption means that changing the order of in a constraint does not change its satisfiability. The negation of variables means that both of the templates share a relation which can be used to effectively negate Boolean variables. The main result of this paper establishes dichotomy for all the boolean templates. The tractability case of our theorem and the theorem of Brakensiek and Guruswami are almost identical. The main difference, and the main contribution of this work, is the new reason for hardness and the reasoning proving the split.
Lower Bounds for Parallel and Randomized Convex Optimization

We study the question of whether parallelization in the exploration of the feasible set can be used to speed up convex optimization, in the local oracle model of computation. We show that the answer is negative for both deterministic and randomized algorithms applied to essentially any of the interesting geometries and nonsmooth, weakly-smooth, or smooth objective functions. In particular, we show that it is not possible to obtain a polylogarithmic (in the sequential complexity of the problem) number of parallel rounds with a polynomial (in the dimension) number of queries per round. In the majority of these settings and when the dimension of the space is polynomial in the inverse target accuracy, our lower bounds match the oracle complexity of sequential convex optimization, up to at most a logarithmic factor in the dimension, which makes them (nearly) tight. Prior to our work, lower bounds for parallel convex optimization algorithms were only known in a small fraction of the settings considered in this paper, mainly applying to Euclidean ($\ell_2$) and $\ell_\infty$ spaces. Our work provides a more general approach for proving lower bounds in the setting of parallel convex optimization.
Using chaos world cup optimization algorithm for medical images contrast enhancement

Summary In this paper, a chaos‐based optimization algorithm is employed for medical images contrast enhancement. Here, a weighted combined framework is suggested as the cost function for the contrast enhancement. The method utilizes the advantages of Gamma correction, histogram equalization, and edge information for decreasing the original image features losses. For more enhancements, a piecewise version of Gamma correction is also utilized to decrease the unnatural artifacts in the output image. A combined cost function is employed based on the three aforementioned features and the proposed chaos world cup optimization algorithm is used for maximizing the fitness function. The simulation results have been compared with five state‐of‐the‐art methods for presenting the method efficiency. To do this, contrast, homogeneity, weighted average peak SNR, a measure of enhancement, and contrast noise ratio are employed. The results also applied to two standard medical imaging datasets and compared with the other methods. Final results denoted that the presented multiobjective optimization algorithm improves the quality of the image contrast and can illustrate more details and information toward the other compared methods.
Support tensor data description

Many problems in pattern/image recognition, fault diagnosis, signal processing, anomaly detection, and machine learning generate massive amounts of multidimensional data with multiple aspects and high dimensionality. Multi-way arrays or tensors provide a natural and compact representation for such massive multidimensional data. Big data analytics require new technologies to efficiently deal with huge datasets within acceptable elapsed times. In some applications, we encounter a one-class classification problem due to availability of data of only one class. Recent advances in technology allow collecting data in high dimensional structure, namely tensors. Traditional vector-based algorithms have limitations when dealing with tensor input data. In this work, a new one-class classification method to deal with tensors as original input, support tensor data description (STDD), is introduced. This classifier works on tensor space. The advantage of this new proposal over one-class support tensor machine (OCSTM) is that support tensor data description does not use the alternating projection method of one-class support tensor machine. Consequently, support tensor data description is easier and faster to implement than one-class support tensor machine. The efficiency of the proposed method over one-class support tensor machine in some cases is illustrated through simulations. Also, this proposal is compared with a vector-based method and the experimental results illustrate the efficiency of the tensor-based algorithm over the vector-based one.
Solar Energy Distribution Using Blockchain and IoT Integration

This research focuses on the decentralised distribution of solar energy between networks using IoT (Internet of Things) enabled devices and Blockchain technology. IoT provides a medium for software part to interact with the physical part which is energy and transmission grid. IoT connects the physical battery containing the energy to the cloud database which is then used to interact with the software. IoT also enables the transfer of energy through the physical medium on the command given by the user through the user application. To make the system decentralized, blockchain is used, which helps to connect all the users in the network where they can transact without the need of knowing each other or having the obligation of any intermediary. Decentralisation helps in achieving a system which doesn't require any middleman and works on the cumulative trust of the nodes - often referred to as the Consensus-Based Algorithm. There are a number of such algorithms but Proof of Work (PoW) and Proof of Stake (PoS) are widely used for blockchains. We suggest using the PoW algorithm. We need to take care if the certain conditions meet before enabling any user to transact. For such condition, we used the application logic on the server side to enable a valid transaction (similar to smart contracts on Ethereum). Blockchain also offers security, transparency, immutability and customer centricity to develop the most advanced and automated platform.
Selection and Manipulation Whole-Body Gesture Elicitation Study In Virtual Reality

We present a whole-body gesture elicitation study using Head Mounted Displays, including a legacy bias reduction. The motivation for this study was to understand the type of gesture agreement rates for selection and manipulation interactions and to improve the user experience for whole-body interactions. We looked at 23 participants and 20 distinct referents (with multiple gestures per referent). We found that regardless of the production technique used to remove legacy bias, legacy bias was still found in some of the produced gestures. In some instances, gestures were derived from previous interactions but were still appropriate for the environment presented. This study provides a rich set of information and useful recommendations for future designers and/or developers.
Randomized Continuous Monitoring of a Target by Agents with Turn Radius Constraints

We consider a scenario where a target needs to be monitored continuously by a set of agents. The agents are equipped with sensors fixed to their bodies and the sensors have a finite footprint. The mission requires the target to lie in the sensor footprint of at least one agent at any time. Due to the finite footprint, an agent can monitor the target only when its range and orientation with respect to the target is within certain limits. The agents considered here have kinematic motion constraints and limits on their speed. This constraint mandates the use of multiple agents to monitor the target sequentially in order to achieve continuous monitoring. To reduce the predictability of their paths, the agents randomly generate successive monitoring positions. We present strategies for scheduling and path planning which assure continuous monitoring; the path planning is done using Bézier curves subject to curvature constraints, boundary constraints and length constraints. We also present an efficient computation methodology to enforce maximum curvature and path length constraints using only the control points of the curves.
Author Identification in Turkish Documents with Ridge Regression Analysis

The amount of documentation which increasing in a proportional manner with the increasing pace of technological development result the need for successful classification methods to categorize them to facilitate accessibility. In addition to printed documents, hundreds of thousands of texts are published on digital media every day, creating problems such as incorrect or anonymous transfer of text writers in a dirty information complex. In this study, for the solution of the author recognition problem, the features extracted by applying the Tf-Idf weighting method for word 1-3-ngrams and character 2-6-ngrams were combined and represented in vector space. Ridge Regression is trained for each author, and each trained model is provided with a predictive value on the test data set. The result with the highest value is then determined as the final estimate.This model, which was established in Hurriyet and Sabah national newspapers, has been trained in 100 different opinion columns of 237 different writers in the last 20 years and has been tested on a test set consisting of 20 different opinion columns for each author.This model, which has a accuracy of 89.6%, performed better than the best results in the literature on the same dataset.
Periodically manipulating the photonic spin Hall effect with an electric field

How to control the photonic spin Hall effect (PSHE) at will is a very interesting and significant task. In this letter, we propose an electrically driven method for modulating the spin-dependent splitting in the PSHE. When the light transmits through a liquid crystal variable retarder, the spin-dependent spatial and angular shifts can be periodically manipulated with an applied electric field. The weak measurement technique is introduced to experimentally observe the spatial and angular displacements by choosing different postselection states. These findings may have potential applications in spin-based nanophotonic devices.
Small and Unbalanced Data Set Problem in Classification

Classification of data is difficult in case of small and unbalanced data set and this problem directly affects the classification performance. Small and / or the imbalance dataset has become a major problem in data mining. Classification algorithms are developed based on the assumption that the data sets are balanced and large enough. The most of the algorithms ignore or misclassify examples of the minority class, focus on the majority class. Small and unbalanced data set problem is frequently encountered in medical data mining due to some limitations. Within the scope of the study, the public accessible data set, hepatitis, was divided into small and imbalanced data subsets, each of the data subsets were oversampled by distance based data generation methods. The oversampled data sets were classified by using four different machine learning algorithms (Artificial Neural Networks, Support Vector Machines, Naive Bayes and Decision Tree) and the classification scores were compared.
Towards a Bayesian Approach for Assessing Fault Tolerance of Deep Neural Networks

This paper presents Bayesian Deep Learning based Fault Injection (BDLFI), a novel methodology for fault injection in neural networks (NNs) and more generally differentiable programs. BDLFI uses (1) Bayesian Deep Learning to model the propagation of faults, and (2) Markov Chain Monte Carlo inference to quantify the effect of faults on the outputs of a NN. We demonstrate BDLFI on two representative networks and present our results that challenge pre-existing results in the field.
Statistical versus neural machine translation – a case study for a medium size domain-specific bilingual corpus

Abstract Neural Machine Translation (NMT) has recently achieved promising results for a number of translation pairs. Although the method requires larger volumes of data and more computational power than Statistical Machine Translation (SMT), it is believed to become dominant in near future. In this paper we evaluate SMT and NMT models learned on a domain-specific English-Polish corpus of a moderate size (1,200,000 segments). The experiment shows that both solutions significantly outperform a general-domain online translator. The SMT model achieves a slightly better BLEU score than the NMT model. On the other hand, the process of decoding is noticeably faster in NMT. Human evaluation carried out on a sizeable sample of translations (2,000 pairs) reveals the superiority of the NMT approach, particularly in the aspect of output fluency.