Reviewerly/exHarmony huggingface dataset:
Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder

Abstract Obsessive and Compulsive Symptoms (OCS) or Obsessive Compulsive Disorder (OCD) in the context of schizophrenia or related disorders are of clinical importance as these are associated with a range of adverse outcomes. Natural Language Processing (NLP) applied to Electronic Health Records (EHRs) presents an opportunity to create large datasets to facilitate research in this area. This is a challenging endeavour however, because of the wide range of ways in which these symptoms are recorded, and the overlap of terms used to describe OCS with those used to describe other conditions. We developed an NLP algorithm to extract OCS information from a large mental healthcare EHR data resource at the South London and Maudsley NHS Foundation Trust using its Clinical Record Interactive Search (CRIS) facility. We extracted documents from individuals who had received a diagnosis of schizophrenia, schizoaffective disorder, or bipolar disorder. These text documents, annotated by human coders, were used for developing and refining the NLP algorithm (600 documents) with an additional set reserved for final validation (300 documents). The developed NLP algorithm utilized a rules-based approach to identify each of symptoms associated with OCS, and then combined them to determine the overall number of instances of OCS. After its implementation, the algorithm was shown to identify OCS with a precision and recall (with 95% confidence intervals) of 0.77 (0.65–0.86) and 0.67 (0.55–0.77) respectively. The development of this application demonstrated the potential to extract complex symptomatic data from mental healthcare EHRs using NLP to facilitate further analyses of these clinical symptoms and their relevance for prognosis and intervention response.
Publishers’ Responsibilities in Promoting Data Quality and Reproducibility

Scholarly publishers can help to increase data quality and reproducible research by promoting transparency and openness. Increasing transparency can be achieved by publishers in six key areas: (1) understanding researchers’ problems and motivations, by conducting and responding to the findings of surveys; (2) raising awareness of issues and encouraging behavioural and cultural change, by introducing consistent journal policies on sharing research data, code and materials; (3) improving the quality and objectivity of the peer-review process by implementing reporting guidelines and checklists and using technology to identify misconduct; (4) improving scholarly communication infrastructure with journals that publish all scientifically sound research, promoting study registration, partnering with data repositories and providing services that improve data sharing and data curation; (5) increasing incentives for practising open research with data journals and software journals and implementing data citation and badges for transparency; and (6) making research communication more open and accessible, with open-access publishing options, permitting text and data mining and sharing publisher data and metadata and through industry and community collaboration. This chapter describes practical approaches being taken by publishers, in these six areas, their progress and effectiveness and the implications for researchers publishing their work.
High-resolution image reconstruction for portable ultrasound imaging devices

Abstract Pursuing better imaging quality and miniaturizing imaging devices are two trends in the current development of ultrasound imaging. While the first one leads to more complex and expensive imaging equipment, poor image quality is a common problem of portable ultrasound imaging systems. In this paper, an image reconstruction method was proposed to break through the imaging quality limitation of portable devices by introducing generative adversarial network (GAN) model into the field of ultrasound image reconstruction. We combined two GAN generator models, the encoder-decoder model and the U-Net model to build a sparse skip connection U-Net (SSC U-Net) to tackle this problem. To produce more realistic output, stabilize the training procedure, and improve spatial resolution in the reconstructed ultrasound images, a new loss function which combines adversarial loss, L1 loss, and differential loss was proposed. Three datasets including 50 pairs of simulation, 40 pairs of phantom, and 72 pairs of in vivo images were used to evaluate the reconstruction performance. Experimental results show that our SSC U-Net is able to reconstruct ultrasound images with improved quality. Compared with U-Net, our SSC U-Net is able to preserve more details in the reconstructed images and improve full width at half maximum (FWHM) of point targets by 3.23%.
Scalable Attentive Sentence Pair Modeling via Distilled Sentence Embedding

Recent state-of-the-art natural language understanding models, such as BERT and XLNet, score a pair of sentences (A and B) using multiple cross-attention operations – a process in which each word in sentence A attends to all words in sentence B and vice versa. As a result, computing the similarity between a query sentence and a set of candidate sentences, requires the propagation of all query-candidate sentence-pairs throughout a stack of cross-attention layers. This exhaustive process becomes computationally prohibitive when the number of candidate sentences is large. In contrast, sentence embedding techniques learn a sentence-to-vector mapping and compute the similarity between the sentence vectors via simple elementary operations. In this paper, we introduce Distilled Sentence Embedding (DSE) – a model that is based on knowledge distillation from cross-attentive models, focusing on sentence-pair tasks. The outline of DSE is as follows: Given a cross-attentive teacher model (e.g. a fine-tuned BERT), we train a sentence embedding based student model to reconstruct the sentence-pair scores obtained by the teacher model. We empirically demonstrate the effectiveness of DSE on five GLUE sentence-pair tasks. DSE significantly outperforms several ELMO variants and other sentence embedding methods, while accelerating computation of the query-candidate sentence-pairs similarities by several orders of magnitude, with an average relative degradation of 4.6% compared to BERT. Furthermore, we show that DSE produces sentence embeddings that reach state-of-the-art performance on universal sentence representation benchmarks. Our code is made publicly available at https://github.com/microsoft/Distilled-Sentence-Embedding.
Image Enhanced Event Detection in News Articles

Event detection is a crucial and challenging sub-task of event extraction, which suffers from a severe ambiguity issue of trigger words. Existing works mainly focus on using textual context information, while there naturally exist many images accompanied by news articles that are yet to be explored. We believe that images not only reflect the core events of the text, but are also helpful for the disambiguation of trigger words. In this paper, we first contribute an image dataset supplement to ED benchmarks (i.e., ACE2005) for training and evaluation. We then propose a novel Dual Recurrent Multimodal Model, DRMM, to conduct deep interactions between images and sentences for modality features aggregation. DRMM utilizes pre-trained BERT and ResNet to encode sentences and images, and employs an alternating dual attention to select informative features for mutual enhancements. Our superior performance compared to six state-of-art baselines as well as further ablation studies demonstrate the significance of image modality and effectiveness of the proposed architecture. The code and image dataset are avaliable at https://github.com/shuaiwa16/image-enhanced-event-extraction.
Learning End-to-End Scene Flow by Distilling Single Tasks Knowledge

Scene flow is a challenging task aimed at jointly estimating the 3D structure and motion of the sensed environment. Although deep learning solutions achieve outstanding performance in terms of accuracy, these approaches divide the whole problem into standalone tasks (stereo and optical flow) addressing them with independent networks. Such a strategy dramatically increases the complexity of the training procedure and requires power-hungry GPUs to infer scene flow barely at 1 FPS. Conversely, we propose DWARF, a novel and lightweight architecture able to infer full scene flow jointly reasoning about depth and optical flow easily and elegantly trainable end-to-end from scratch. Moreover, since ground truth images for full scene flow are scarce, we propose to leverage on the knowledge learned by networks specialized in stereo or flow, for which much more data are available, to distill proxy annotations. Exhaustive experiments show that i) DWARF runs at about 10 FPS on a single high-end GPU and about 1 FPS on NVIDIA Jetson TX2 embedded at KITTI resolution, with moderate drop in accuracy compared to 10× deeper models, ii) learning from many distilled samples is more effective than from the few, annotated ones available.
Integrating Cognition with an Affective Lens to Better Understand Information Security Policy Compliance

Information systems security behavioral research has primarily focused on individual cognitive processes and their impact on information security policy noncompliance. However, affective processes (operationalized by affective absorption and affective flow) may also significantly contribute to misuse or information security policy noncompliance. Our research study evaluated the impact of affective absorption (i.e., the trait or disposition to allow one’s emotions to drive decision-making) and affective flow (i.e., a state of immersion with one’s emotions) on cognitive processes in the context of attitude toward and compliance with information security policies. Our conceptual model was evaluated using a laboratory research design. We found that individuals who were frustrated by work-related tasks experienced negative affective flow and violated information security policies. Furthermore, perceptions of organizational injustice increased negative affective flow. Our findings underscore the need for understanding affective processes as well as cognitive processes which may lead to a more holistic understanding regarding information security policy compliance.
Optimization of parabolic trough power plant operations in variable irradiance conditions using all sky imagers

The continuously growing penetration of intermittent electricity sources will increase the future demand for dispatchable power plants, which balance out fluctuations within the electrical grids. Parabolic trough power plants with thermal energy storages could be one renewable solution for regions with a high yearly direct normal irradiance (DNI) sum, but in order to compete against other renewable as well as non-renewable technologies, the economic competitiveness must be ensured. Price reductions can be achieved for example by optimizing the plant operation. One shortcoming of state of the art plant controllers is that they only use the DNI measured at one or a few positions in the solar field. Due to the spatial variability of the DNI throughout the solar field this DNI information can be misleading. In this paper, we investigate the optimization potential of solar field control strategies with access to spatially resolved DNI information from all sky imagers (ASI). Uncertainties of the ASI system are considered by introducing additional independent spatial DNI information from a shadow camera system. The spatial and temporal DNI variability of the DNI seen by the controller is classified in distinct DNI variability classes. Two new control strategies are developed, with optimized control parameters for distinct combinations of the spatial and temporal DNI variability classes. These new variability class dependent control strategies are benchmarked in a simulation environment. A relative increase in revenue in excess of 1.9% is observed over a test period with 22 days, compared to a state of the art reference controller.
An Improved Crow Search Algorithm Based on Spiral Search Mechanism for Solving Numerical and Engineering Optimization Problems

The crow search algorithm (CSA) is a new intelligent optimization algorithm based on the behavior of the crow population, which has been proven to perform well.However, its simple search mechanism also leads to the algorithm's slow convergence speed and its ease of falling into local optimization when solving complex optimization problems.In order to overcome these problems, this paper proposes an improved CSA (ISCSA) based on a spiral search mechanism.By introducing a weight coefficient, an optimal guidance position and a spiral search mechanism, the position equation was updated to accelerate the convergence of the algorithm and make the exploration and exploitation of CSA more balanced.Meanwhile, adding Gaussian variation and random perturbation strategy made it difficult for the algorithm to fall into local optimization.The advantages of the proposed ISCSA were evaluated using 23 benchmark functions and four classical engineering design problems.The experimental and statistical results of 23 test functions showed that the proposed ISCSA could escape from the local optima with higher accuracy and faster convergence than both the CSA and other meta-heuristic optimization algorithms.The calculation results of the four engineering optimization problems showed that compared with other algorithms, ISCSA can solve the practical optimization problem well and has been proved to have strong competitiveness and good performance.
An Efficient Commutative Encryption and Data Hiding Scheme for HEVC Video

In this paper, an improved commutative encryption and data hiding scheme for bit rate preservation of High Efficiency Video Coding (HEVC) is proposed. To achieve commutative property, one set of syntax elements within the HEVC standard is utilized for data hiding, while another set is exploited for encryption. Specifically, the syntax elements including the sign of quantized transform coefficient (QTC), the sign of motion vector difference (MVD), and the magnitude of MVD are encrypted to perceptually distort the video. On the other hand, an improved QTC modification method is employed for data hiding to increase the embedding capacity. The proposed framework produces the format compliant bitstream and allows the hidden information to be extracted regardless of the video being in the encrypted or decrypted from. Experimental results demonstrate that the proposed scheme keeps the bit rate unchanged while increasing the embedding capacity.
Geochemical mapping in stream sediments of the Carajás Mineral Province: Background values for the Itacaiúnas River watershed, Brazil

Multi-elemental analysis of high-density (regional-scale) geochemical surveys is an important strategy for multi-purpose applications, particularly in addressing geochemical background concentrations in different sampling media. This approach was applied to the Itacaiúnas River Watershed (IRW), which is situated in the most prominent mining area of Brazil, the Carajás Mineral Province. Microcatchment-based mapping (~50 km2 each) covering the whole extent of IRW was delimited using remote sensing techniques and targeted for sampling. A total of 788 samples, including 27 duplicates, were collected in 2017. The <0.177 mm fraction of all samples was digested by aqua regia and 51 elements were analyzed by inductively coupled plasma atomic emission spectroscopy (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS). Geochemical maps for Fe and potentially toxic elements (PTE; As, Ba, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Sn, V, and Zn) and microcatchment-based distribution maps based on threshold values were constructed for the whole IRW. The stream sediment geochemistry is mainly controlled by the local geologic setting and underlying lithology. Geochemical background (GB) values for 43 elements in stream sediments of the IRW were determined by a variety of methods (Tukey's inner fences -TIF, median ± 2*median absolute deviation - MAD, and percentile-based techniques). The results provided from the different methods show a wide range of values, with the MAD method being considered the most appropriate for deriving GB concentrations. A comparison of reference levels for PTE contemplated in the Brazilian regulation in stream sediments, the threshold concentrations obtained for the IRW area, and different study cases around the world is presented in detail. Our findings provide not only valuable information for selecting potential areas for mineral exploration surveys, but also for evaluating geochemical contaminant effects with time-varying treatments. Studies conducted to determine background values at regional scale are needed for environmental decision making, as well as to attest actions in cases of potential contamination. In the absence of these studies, misleading interpretations of the magnitude of contamination levels in a certain area may cause under- or overestimation of ecological and/or human-health risks of PTE.
Switching intention to crypto-currency market: Factors predisposing some individuals to risky investment

We investigate factors affecting individual investors' switching intention from traditional financial market to crypto-currency financial market. By sampling factors of individual investors related with crypto-currency (CC), the study applies structural equation modeling method (SEM) to investigate their effects on switching intention by integrating PPM and Reinforcement Sensitivity theories (RST) to form a pulling, pushing and mooring effects model. The investigation indicates that crypto-currency market can be regarded as a kind of beneficial supplement of tradition investment market for those individual investors who are with high innovativeness, reward sensitivity, knowledge and perceived risk. This study proves that the individual investors are not only attracted by significant expected return from crypto-currency but also relevant knowledge and risks disclosed by crypto-currency market regulators and distributors. The findings reinforce major roles for both market regulators and individual investors in considering and providing insights for future policy, management and investigations.
Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting

Unsupervised machine translation (MT) has recently achieved impressive results with monolingual corpora only.However, it is still challenging to associate source-target sentences in the latent space.As people speak different languages biologically share similar visual systems, the potential of achieving better alignment through visual content is promising yet under-explored in unsupervised multimodal MT (MMT).In this paper, we investigate how to utilize visual content for disambiguation and promoting latent space alignment in unsupervised MMT.Our model employs multimodal back-translation and features pseudo visual pivoting in which we learn a shared multilingual visual-semantic embedding space and incorporate visuallypivoted captioning as additional weak supervision.The experimental results on the widely used Multi30K dataset show that the proposed model significantly improves over the state-ofthe-art methods and generalizes well when images are not available at the testing time.
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples

Despite achieving prominent performance on many important tasks, it has been reported that neural networks are vulnerable to adversarial examples.Previously studies along this line mainly focused on semantic tasks such as sentiment analysis, question answering and reading comprehension.In this study, we show that adversarial examples also exist in dependency parsing: we propose two approaches to study where and how parsers make mistakes by searching over perturbations to existing texts at sentence and phrase levels, and design algorithms to construct such examples in both of the black-box and white-box settings.Our experiments with one of state-of-the-art parsers on the English Penn Treebank (PTB) show that up to 77% of input examples admit adversarial perturbations, and we also show that the robustness of parsing models can be improved by crafting high-quality adversaries and including them in the training stage, while suffering little to no performance drop on the clean input data.
Rotation Equivariant Graph Convolutional Network for Spherical Image Classification

Convolutional neural networks (CNNs) designed for low-dimensional regular grids will unfortunately lead to non-optimal solutions for analyzing spherical images, due to their different geometrical properties from planar images. In this paper, we generalize the grid-based CNNs to a non-Euclidean space by taking into account the geometry of spherical surfaces and propose a Spherical Graph Convolutional Network (SGCN) to encode rotation equivariant representations. Specifically, we propose a spherical graph construction criterion showing that a graph needs to be regular by evenly covering the spherical surfaces in order to design a rotation equivariant graph convolutional layer. For the practical case where the perfectly regular graph does not exist, we design two quantitative measures to evaluate the degree of irregularity for a spherical graph. The Geodesic ICOsahedral Pixelation (GICOPix) is adopted to construct spherical graphs with the minimum degree of irregularity compared to the current popular pixelation schemes. In addition, we design a hierarchical pooling layer to keep the rotation-equivariance, followed by a transition layer to enforce the invariance to the rotations for spherical image classification. We evaluate the proposed graph convolutional layers with different pixelations schemes in terms of equivariance errors. We also assess the effectiveness of the proposed SGCN in fulfilling rotation-invariance by the invariance error of the transition layers and recognizing the spherical images and 3D objects.
DFD: Adversarial Learning-based Approach to Defend Against Website Fingerprinting

The Onion Router (Tor) is designed to support an anonymous communication through end-to-end encryption. To prevent vulnerability of side channel attacks (e.g. website fingerprinting), dummy packet injection modules have been embedded in Tor to conceal trace patterns that are associated with the individual websites. However, recent study shows that current Website Fingerprinting (WF) defenses still generate patterns that may be captured and recognized by the deep learning technology. In this paper, we conduct in-depth analyses of two state-of-the-art WF defense approaches. Then, based on our new observations and insights, we propose a novel defense mechanism using a per-burst injection technique, called Deep Fingerprinting Defender (DFD), against deep learning-based WF attacks. The DFD has two operation modes, one-way and two-way injection. DFD is designed to break the inherent patterns preserved in Tor user's traces by carefully injecting dummy packets within every burst. We conducted extensive experiments to evaluate the performance of DFD over both closed-world and open-world settings. Our results demonstrate that these two configurations can successfully break the Tor network traffic pattern and achieve a high evasion rate of 86.02% over one-way client-side injection rate of 100%, a promising improvement in comparison with state-of-the-art adversarial trace's evasion rate of 60%. Moreover, DFD outperforms the state-of-the-art alternatives by requiring lower bandwidth overhead; 14.26% using client-side injection.