Reviewerly/exHarmony huggingface dataset:
Towards Cross-Modality Medical Image Segmentation with Online Mutual Knowledge Distillation

The success of deep convolutional neural networks is partially attributed to the massive amount of annotated training data. However, in practice, medical data annotations are usually expensive and time-consuming to be obtained. Considering multi-modality data with the same anatomic structures are widely available in clinic routine, in this paper, we aim to exploit the prior knowledge (e.g., shape priors) learned from one modality (aka., assistant modality) to improve the segmentation performance on another modality (aka., target modality) to make up annotation scarcity. To alleviate the learning difficulties caused by modality-specific appearance discrepancy, we first present an Image Alignment Module (IAM) to narrow the appearance gap between assistant and target modality data. We then propose a novel Mutual Knowledge Distillation (MKD) scheme to thoroughly exploit the modality-shared knowledge to facilitate the target-modality segmentation. To be specific, we formulate our framework as an integration of two individual segmentors. Each segmentor not only explicitly extracts one modality knowledge from corresponding annotations, but also implicitly explores another modality knowledge from its counterpart in mutual-guided manner. The ensemble of two segmentors would further integrate the knowledge from both modalities and generate reliable segmentation results on target modality. Experimental results on the public multi-class cardiac segmentation data, i.e., MM-WHS 2017, show that our method achieves large improvements on CT segmentation by utilizing additional MRI data and outperforms other state-of-the-art multi-modality learning methods.
Role of non-linear data processing on speech recognition task in the framework of reservoir computing

Abstract The reservoir computing neural network architecture is widely used to test hardware systems for neuromorphic computing. One of the preferred tasks for bench-marking such devices is automatic speech recognition. This task requires acoustic transformations from sound waveforms with varying amplitudes to frequency domain maps that can be seen as feature extraction techniques. Depending on the conversion method, these transformations sometimes obscure the contribution of the neuromorphic hardware to the overall speech recognition performance. Here, we quantify and separate the contributions of the acoustic transformations and the neuromorphic hardware to the speech recognition success rate. We show that the non-linearity in the acoustic transformation plays a critical role in feature extraction. We compute the gain in word success rate provided by a reservoir computing device compared to the acoustic transformation only, and show that it is an appropriate bench-mark for comparing different hardware. Finally, we experimentally and numerically quantify the impact of the different acoustic transformations for neuromorphic hardware based on magnetic nano-oscillators.
Privacy-preserving Heterogeneous Federated Transfer Learning

Federated learning (FL) allows multiple parties to collaboratively train a machine learning model without sharing raw data. However, existing approaches are mainly designed for homogeneous feature spaces and fail to tackle covariate shift and feature heterogeneity without privacy leakage. In this paper, we propose a transfer learning approach to tackle the covariate shift of the overlapped homogeneous feature spaces, and bridge different data owners' heterogeneous feature spaces with stringent privacy preservation in FL. We propose an end-to-end privacy-preserving multi-party learning approach with two variants based on homomorphic encryption and secret sharing techniques, respectively, to build a heterogeneous federated transfer learning (HFTL) framework. Finally, we not only demonstrate experimentally that the HFTL is secure, effective and highly scalable on five benchmark datasets, but also apply it into a real application of in-hospital mortality prediction from MIMIC-III dataset, where privacy is of significant concern.
Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

The capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. The classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. This current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. The results are evaluated based on several statistical measures and graphical presentations. The river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.
Audio in VR: Effects of a Soundscape and Movement-Triggered Step Sounds on Presence

For effective virtual realities, “presence,” the feeling of “being there” in a virtual environment (VR), is deemed an essential prerequisite. Several studies have assessed the effect of the (non-)availability of auditory stimulation on presence, but due to differences in study design (e.g. virtual realities used, types of sounds included, rendering technologies employed), generalizing the results and estimating the effect of the auditory component is difficult. In two experiments, the influence of an ambient nature soundscape and movement-triggered step sounds were investigated regarding their effects on presence. In each experiment, approximately forty participants walked on a treadmill, thereby strolling through a virtual park environment reproduced via a stereoscopic head-mounted display (HMD), while the acoustical environment was delivered via noise-cancelling headphones. In Experiment 1, conditions with the ambient soundscape and the step sounds either present or absent were combined in a 2x2 within-subjects design, supplemented with an additional ‘no-headphones’ control condition. For the synchronous playback of step sounds, the probability of a step being taken was estimated by an algorithm using the head-mounted display’s sensor data. The results of Experiment 1 show that questionnaire-based measures of presence and realism were influenced by the soundscape but not by the reproduction of steps, which might be confounded with the fact that the perceived synchronicity of the sensor-triggered step sounds was rated rather low. Therefore, in Experiment 2, the step-reproduction algorithm was improved and judged to be more synchronous by participants. Consequently, large and statistically significant effects of both kinds of audio manipulations on perceived presence and realism were observed, with the effect of the soundscape being larger than that of including footstep sounds, possibly due to the remaining imperfections in the reproduction of steps. Including an appropriate soundscape or self-triggered footsteps had differential effects on subscales of presence, in that both affected overall presence and realism, while involvement was improved and distraction reduced by the ambient soundscape only.
Invisible Gold Paragenesis and Geochemistry in Pyrite from Orogenic and Sediment-Hosted Gold Deposits

LA-ICPMS analysis of pyrite in ten gold deposits is used to determine the precise siting of invisible gold within pyrite, and thus the timing of gold introduction relative to the growth of pyrite and related orogenic events. A spectrum of invisible gold relationships in pyrite has been observed which suggests that, relative to orogenic pyrite growth, gold introduction in some deposits is early at the start of pyrite growth; in other deposits, it is late toward the end of pyrite growth and in a third case, it may be introduced at the intermediate stage of orogenic pyrite growth. In addition, we report a distinct chemical association of invisible gold in pyrite in the deposits studied. For example, in the Gold Quarry (Carlin type), Mt Olympus, Macraes and Konkera, the invisible gold is principally related to the arsenic content of pyrite. In contrast, in Kumtor and Geita Hill, the invisible gold is principally related to the tellurium content of pyrite. Other deposits (Golden Mile, Bendigo, Spanish Mountain, Witwatersrand Carbon Leader Reef (CLR)) exhibit both the Au-As and Au-Te association in pyrite. Some deposits of the Au-As association have late orogenic Au-As-rich rims on pyrite, which substantially increase the value of the ore. In contrast, deposits of the Au-Te association are not known to have Au-rich rims on pyrite but contain nano- to micro-inclusions of Au-Ag-(Pb-Bi) tellurides.
Parity-Protected Superconductor-Semiconductor Qubit

Coherence of superconducting qubits can be improved by implementing designs that protect the parity of Cooper pairs on superconducting islands. Here, we introduce a parity-protected qubit based on voltage-controlled semiconductor nanowire Josephson junctions, taking advantage of the higher harmonic content in the energy-phase relation of few-channel junctions. A symmetric interferometer formed by two such junctions, gate-tuned into balance and frustrated by a half-quantum of applied flux, yields a cos(2{\phi}) Josephson element, reflecting coherent transport of pairs of Cooper pairs. We demonstrate that relaxation of the qubit can be suppressed tenfold by tuning into the protected regime.
DENet: A Universal Network for Counting Crowd With Varying Densities and Scales

Counting people or objects with significantly varying scales and densities has attracted much interest from the research community and yet it remains an open problem.In this paper, we propose a simple but efficient and effective network, named DENet, which is composed of two components, i.e., a detection network (DNet) and an encoder-decoder estimation network (ENet).We first run the DNet on the input image to detect and count individuals who can be segmented clearly.Then, the ENet is utilized to estimate the density maps of the remaining areas, typically with low resolution and high densities where individuals cannot be detected.For this purpose, we propose a modified Xception network as the encoder for feature extraction and a combination of dilated convolution and transposed convolution as the decoder.When evaluated on the ShanghaiTech Part A, UCF and WorldExpo'10 datasets, our DENet has achieved lower Mean Absolute Error (MAE) than those of the state-of-the-art methods.
Probabilistic Solar Power Forecasting Using Bayesian Model Averaging

There is rising interest in probabilistic forecasting to mitigate risks from solar power uncertainty, but the numerical weather prediction (NWP) ensembles readily available to system operators are often biased and underdispersed. We propose a Bayesian model averaging (BMA) post-processing method suitable for forecasting power from utility-scale photovoltaic (PV) plants at multiple time horizons up to at least the day-ahead timescale. BMA is a kernel dressing technique for NWP ensembles in which the forecast is a weighted sum of member-specific probability density functions. We tailor BMA for utility-scale PV forecasting by modeling power clipping at the AC inverter rating and advance the theory of BMA with a new beta kernel parameterization that accommodates theoretical constraints not previously addressed. BMA is demonstrated for a case study of 11 utility-scale PV plants in Texas, forecasting at hourly resolution for the complete year 2018. BMA's mixture-model approach mitigates underdispersion of the raw ensemble to significantly improve forecast calibration, while consistently outperforming an ensemble model output statistics (EMOS) parametric approach from the literature. At 4-hour lead time, the BMA post-processing achieves continuous ranked probability skill scores of 2-36% over the raw ensemble, with consistent performance at multiple lead times suitable for power system operations.
Attention-Driven Cropping for Very High Resolution Facial Landmark Detection

Facial landmark detection is a fundamental task for many consumer and high-end applications and is almost entirely solved by machine learning methods today. Existing datasets used to train such algorithms are primarily made up of only low resolution images, and current algorithms are limited to inputs of comparable quality and resolution as the training dataset. On the other hand, high resolution imagery is becoming increasingly more common as consumer cameras improve in quality every year. Therefore, there is need for algorithms that can leverage the rich information available in high resolution imagery. Naively attempting to reuse existing network architectures on high resolution imagery is prohibitive due to memory bottlenecks on GPUs. The only current solution is to downsample the images, sacrificing resolution and quality. Building on top of recent progress in attention-based networks, we present a novel, fully convolutional regional architecture that is specially designed for predicting landmarks on very high resolution facial images without downsampling. We demonstrate the flexibility of our architecture by training the proposed model with images of resolutions ranging from 256 x 256 to 4K. In addition to being the first method for facial landmark detection on high resolution images, our approach achieves superior performance over traditional (holistic) state-of-the-art architectures across ALL resolutions, leading to a general-purpose, extremely flexible, high quality landmark detector.
Multi-Granularity Interaction Network for Extractive and Abstractive Multi-Document Summarization

In this paper, we propose a multi-granularity interaction network for extractive and abstractive multi-document summarization, which jointly learn semantic representations for words, sentences, and documents.The word representations are used to generate an abstractive summary while the sentence representations are used to produce an extractive summary.We employ attention mechanisms to interact between different granularity of semantic representations, which helps to capture multi-granularity key information and improves the performance of both abstractive and extractive summarization.Experiment results show that our proposed model substantially outperforms all strong baseline methods and achieves the best results on the Multi-News dataset.
Dynamic Sampling Networks for Efficient Action Recognition in Videos

The existing action recognition methods are mainly based on clip-level classifiers such as two-stream CNNs or 3D CNNs, which are trained from the randomly selected clips and applied to densely sampled clips during testing. However, this standard setting might be suboptimal for training classifiers and also requires huge computational overhead when deployed in practice. To address these issues, we propose a new framework for action recognition in videos, called {\em Dynamic Sampling Networks} (DSN), by designing a dynamic sampling module to improve the discriminative power of learned clip-level classifiers and as well increase the inference efficiency during testing. Specifically, DSN is composed of a sampling module and a classification module, whose objective is to learn a sampling policy to on-the-fly select which clips to keep and train a clip-level classifier to perform action recognition based on these selected clips, respectively. In particular, given an input video, we train an observation network in an associative reinforcement learning setting to maximize the rewards of the selected clips with a correct prediction. We perform extensive experiments to study different aspects of the DSN framework on four action recognition datasets: UCF101, HMDB51, THUMOS14, and ActivityNet v1.3. The experimental results demonstrate that DSN is able to greatly improve the inference efficiency by only using less than half of the clips, which can still obtain a slightly better or comparable recognition accuracy to the state-of-the-art approaches.
The Application of Augmented Reality in the Automotive Industry: A Systematic Literature Review

Augmented reality (AR) is a fairly new technology enabling human machine interaction by superimposing virtual information on a real environment. Potential applications can be found in many areas of research from recent years. This study presents a systematic review of existing AR systems in the automotive field, synthesizing 55 studies from 2002 to 2019. The main research questions are: where AR technology has been applied within the automotive industry, what is the purpose of its application, what are the general characteristics of these systems, and what are the emphasized benefits and challenges of using AR in this field? The aim of this paper is to provide an insight into the AR applications and technologies in the automotive field.
Risk Assessment Model for UAV Cost-Effective Path Planning in Urban Environments

Increasing use of Unmanned Aerial Vehicle (UAV) in urban environments poses to an increased risk of fallen UAVs impacting people and vehicles on the ground, as well as colliding with manned aircraft in the vicinity of airports. Risk management of UAV flights for safe operations is essential. We proposed a comprehensive risk assessment model for UAV operation in urban environments. Three risk categories (people, vehicles, and manned aircraft) were considered and each risk cost was quantified using collision probability. We adjusted the risk costs in various magnitudes to a same scale and conducted a sensitivity analysis to determine the optimal coefficients of the three risk cost models. We then computed the total risk and generated a risk cost map for path planning. Modified path planning algorithms were used to produce a cost-effective path, and we compared their performances in terms of total risk cost and computational time. Lastly, we performed simulations to validate the feasibility and effectiveness of our proposed risk assessment model. The results show that the risk-cost-based path planning method can generate safer path for UAV operations than the traditional shortest-distance-based method. Our proposed model can be extended to complex urban environments by including more relevant parameters and data.
Detecting misogyny in Spanish tweets. An approach based on linguistics features and word embeddings

Online social networks allow powerless people to gain enormous amounts of control over particular people’s lives and profit from the anonymity or social distance that the Internet provides in order to harass other people. One of the most frequently targeted groups comprise women, as misogyny is, unfortunately, a reality in our society. However, although great efforts have recently been made to identify misogyny, it is still difficult to distinguish as it can sometimes be very subtle and deep, signifying that the use of statistical approaches is not sufficient. Moreover, as Spanish is spoken worldwide, context and cultural differences can complicate this identification. Our contribution to the detection of misogyny in Spanish is two-fold. On the one hand, we apply Sentiment Analysis and Social Computing technologies for detecting misogynous messages in Twitter. On the other, we have compiled the Spanish MisoCorpus-2020, a balanced corpus regarding misogyny in Spanish, and classified it into three subsets concerning (1) violence towards relevant women, (2) messages harassing women in Spanish from Spain and Spanish from Latin America, and (3) general traits related to misogyny. Our proposal combines a classification based on average word embeddings and linguistic features in order to understand which linguistic phenomena principally contribute to the identification of misogyny. We have evaluated our proposal with three machine-learning classifiers, achieving the best accuracy of 85.175%. Finally the proposed approach is also validated with existing corpora for misogyny and aggressiveness detection such as AMI and HatEval obtaining good results
A fuzzy TOPSIS based analysis toward selection of effective security requirements engineering approach for trustworthy healthcare software development

Abstract Background Today’s healthcare organizations want to implement secure and quality healthcare software as cyber-security is a significant risk factor for healthcare data. Considering security requirements during trustworthy healthcare software development process is an essential part of the quality software development. There are several Security Requirements Engineering (SRE) methodologies, framework, process, standards available today. Unfortunately, there is still a necessity to improve these security requirements engineering approaches. Determining the most suitable security requirements engineering method for trustworthy healthcare software development is a challenging process. This study is aimed to present security experts’ perspective on the relative importance of the criteria for selecting effective SRE method by utilizing the multi-criteria decision making methods. Methods The study was planned and conducted to identify the most appropriate SRE approach for quality and trustworthy software development based on the security expert’s knowledge and experience. The hierarchical model was evaluated by using fuzzy TOPSIS model. Effective SRE selection criteria were compared in pairs. 25 security experts were asked to response the pairwise criteria comparison form. Results The impact of the recognized selection criteria for effective security requirements engineering approaches has been evaluated quantitatively. For each of the 25 participants, comparison matrixes were formed based on the scores of their responses in the form. The consistency ratios (CR) were found to be smaller than 10% (CR = 9.1% &lt; 10%). According to pairwise comparisons result; with a 0.842 closeness coefficient (Ci), STORE methodology is the most effective security requirements engineering approach for trustworthy healthcare software development. Conclusions The findings of this research study demonstrate various factors in the decision-making process for the selection of a reliable method for security requirements engineering. This is a significant study that uses multi-criteria decision-making tools, specifically fuzzy TOPSIS, which used to evaluate different SRE methods for secure and trustworthy healthcare application development.
Deep learning for procedural content generation

Procedural content generation in video games has a long history. Existing procedural content generation methods, such as search-based, solver-based, rule-based and grammar-based methods have been applied to various content types such as levels, maps, character models, and textures. A research field centered on content generation in games has existed for more than a decade. More recently, deep learning has powered a remarkable range of inventions in content production, which are applicable to games. While some cutting-edge deep learning methods are applied on their own, others are applied in combination with more traditional methods, or in an interactive setting. This article surveys the various deep learning methods that have been applied to generate game content directly or indirectly, discusses deep learning methods that could be used for content generation purposes but are rarely used today, and envisages some limitations and potential future directions of deep learning for procedural content generation.