Reviewerly/exHarmony huggingface dataset:
Automated ICD coding for coronary heart diseases by a deep learning method

Automated ICD coding via machine learning that focuses on some specific diseases has been a hot topic. As one of the leading causes of death, coronary heart diseases (CHD) have seldom been specifically studied by related research, probably due to lack of data concretely targeting at the diseases. Based on Fuwai-CHD and MIMIC-III-CHD, which are a private dataset from Fuwai Hospital and the CHD-related subset of a public dataset named MIMIC-III respectively, this study aimed at automated CHD coding by a deep learning method, which mainly consists of three modules. The first is a BERT variant module responsible for encoding clinical text. In the module, we fine-tuned BERT variants with masked language model on clinical text, and proposed a truncation method to tackle the problem that BERT variants generally cannot handle sequences containing more than 512 tokens. The second is a word2vec module for encoding code titles and the third is a label-attention module for integrating the embeddings of clinical text and code titles. In short, we named the method BW_att. We compared BW_att against some widely studied baselines, and found that BW_att performed best in most of the coding missions. Specifically, BW_att reached a Macro-F1 of 96.2% and a Macro-AUC of 98.9% for the top-100 most frequent codes in Fuwai-CHD, which covered 89.2% of the total code occurrences. When predicting the top-50 most frequent codes in MIMIC-III-CHD, BW_att reached a Macro-F1 of 40.5% and a Macro-AUC of 66.1%. Moreover, BW_att was capable of locating informative tokens from clinical text for predicting the target codes. In summary, BW_att can not only suggest CHD codes accurately, but also possess robust interpretability, hence has great potential in facilitating CHD coding in practice.
A deeper look at Graph Embedding RetroFitting

Representation learning for graphs has attracted increasing attention in recent years. In particular, this work is focused on a new problem in this realm which is learning attributed graph embeddings. The setting considers how to update existing node representations from structural graph embedding methods when some additional node attributes are given. Recently, Graph Embedding RetroFitting (GERF) Bielak et al. was proposed to this end – a method that delivers a compound node embedding that follows both the graph structure and attribute space similarity. It uses existing structural node embeddings and retrofits them according to the neighborhood defined by the node attributes space (by optimizing the invariance loss and the attribute neighbor loss). In order to refine GERF method, we aim to include the simplification of the objective function and provide an algorithm for automatic hyperparameter estimation, whereas the experimental scenario is extended by a more robust hyperparameter search for all considered methods and a link prediction problem for evaluation of node embeddings.
Computing pullback function of second order differential operators by using their semi-invariants

In this paper, we present an algorithm that, given a differential operator of order 2, detects if this operator comes from a simpler operator under a non-trivial change of variables.
Marketing Through the Machine's Eyes: Image Analytics and Interpretability

The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.
Scaling Up “Sustainability Development”

Blockchains are currently recognized as a new area of digitization with difficulties in attaining sustainability. The research has not yet produced a unified framework for understanding the characteristics of blockchains that allow the development of financial trading markets over the long term. The topic of how blockchains support the growth of sustainable financial trading platforms and services still exists. A qualitative and iterative investigation was done to compile the basic concepts found in the literature to provide a conceptual framework with four sustainability themes and eleven types of affordances. The results are significant because they show how closely related these categories are, leading to conflicts between various sustainability-related concepts.
Waveguided sources of consistent, single-temporal-mode squeezed light: The good, the bad, and the ugly

We study theoretically how the brightness of pumps, with fixed profiles, affects the temporal mode structure of squeezed states generated by fixed parametric waveguided sources. We find that the temporal modes of these squeezed states can be partially mismatched and thus distinguishable, which is undesirable when using these states as resources for quantum computing or heralded state generation. By studying common frequency filtering techniques used experimentally, we find that although one can regain indistinguishability it comes at the price of potentially greatly reducing the purity of the state. We consider three different source configurations: unapodized single pass, apodized single pass, and apodized double pass. We find that the double pass configuration produces optimal results with almost perfectly indistinguishable states over varying degrees of brightness.
Semantic Relevance Learning for Video-Query Based Video Moment Retrieval

The task of video-query based video moment retrieval (VQ-VMR) aims to localize the segment in the reference video, which matches semantically with a short query video. This is a challenging task due to the rapid expansion and massive growth of online video services. With accurate retrieval of the target moment, we propose a new metric to effectively assess the semantic relevance between the query video and segments in the reference video. We also develop a new VQ-VMR framework to discover the intrinsic semantic relevance between a pair of input videos. It comprises two key components: a Fine-grained Feature Interaction (FFI) module and a Semantic Relevance Measurement (SRM) module. Together they can effectively deal with both the spatial and temporal dimensions of videos. First, the FFI module computes the semantic similarity between videos at a local frame level, mainly considering the spatial information in the videos. Subsequently, the SRM module learns the similarity between videos from a global perspective, taking into account the temporal information. We have conducted extensive experiments on two key datasets which demonstrate noticeable improvements of the proposed approach over the state-of-the-art methods.
Outlier-Robust Gaze Signal Filtering Framework Based on Eye-Movement Modality Recognition and Set-Membership Approach

High-quality gaze signals are crucial in many biomedical fields that utilize them. However, the limited studies on gaze signal filtering can hardly address the outliers and non-Gaussian noise in gaze data simultaneously. Our objective is to design a generic filtering framework capable of reducing the noise and eliminating outliers of the gaze signal.In this study, we design an eye-movement modality-based zonotope set-membership filtering framework (EM-ZSMF) to suppress the noise and outliers of the gaze signal. This framework consists of an eye-movement modality recognition model (EG-NET), an eye-movement modality-based gaze movement model (EMGM), and a zonotope set-membership filter (ZSMF). The eye-movement modality determines the EMGM, and the ZSMF combined with the EMGM completes the filtering of the gaze signal. Moreover, this study establishes an eye-movement modality and gaze filtering dataset (ERGF) that can be utilized for the evaluation of future work integrating eye-movement modality with gaze signal filtering.The eye-movement modality recognition experiments demonstrated that our proposed EG-NET achieved the best Cohen's kappa compared with previous studies. The gaze data filtering experiments showed that the proposed EM-ZSMF reduced the gaze signal noise and eliminated outliers effectively, and achieved the best performance (RMSEs and RMS) compared with previous methods.The proposed EM-ZSMF effectively recognizes eye-movement modalities, reduces gaze signal noise and, eliminates outliers.To the best of the authors' knowledge, this is the first attempt to simultaneously solve the problem of non-Gaussian noise and outliers in gaze signals. The proposed framework has the potential for application in any eye image-based eye trackers and contributes to the development of eye-tracking technology.
Geometric Searchable Encryption for Privacy-Preserving Location-Based Services

Location data play an important role in location-based services (LBS) since they can help a service provider analyze users’ daily activities and further derive users’ behavioral patterns. However, in the meanwhile the use of the location data in LBS can also enable the service provider to track users’ journeys which will reveal their personal information, such as house address and places of interest they visited, all of which users are usually unwilling to disclose. Therefore, security and privacy incidents can and do occur often. In this article, aiming at how to hide location privacy and meanwhile provide an accurate search for LBS, we design a new geometric searchable encryption scheme under a public-key system (a.k.a. asymmetric system) for different types of geometric range queries, which enables the service provider to respond to the range queries from users accurately without learning the information about users’ location. Towards this goal, we first exploit a novel encoding, and then develop some sophisticated transformations in combination with a special pairing function, which eventually converts different types of geometric range queries in LBS into different inner product problems. Finally, we present our construction by virtue of the techniques above with the inner product encryption technique. In addition, a comprehensive experimental analysis running on a specific application of privacy-preserving LBS and comparison demonstrate, our proposal is able to guarantee a higher accuracy for users’ range queries without any false positive, and meanwhile express richer queries. Furthermore, we show another application of our proposal to privacy-preserving remote medical diagnosis at the end of this article.
Demographic Bias in Low-Resolution Deep Face Recognition in the Wild

Face biometrics play a primary role in smart cities, from consumer- to organizational-level applications. This class of technologies has been recently shown to emphasize performance disparities across gender and ethnic groups. Prior work on demographic bias in deep face recognition has tended to focus exclusively on high-resolution images, leaving out low-resolution images captured, for example, by a certain surveillance camera at a distance. Notable reasons for this focus include the lack of low-resolution face image data sets that report user's demographic attributes. Therefore, demographic bias in low-resolution deep face recognition in the wild still remains under-explored. In this paper, we propose a framework for exploring demographic disparities in low-resolution face recognition at a distance. To this end, we devised a deep generative approach to turn high-resolution face images to their low-resolution counterpart. We then trained state-of-the-art face recognition models on different combinations of high- and low-resolution images. Finally, we assessed the model demographic disparities on artificially degraded images from five data sets. Our results show that face images artificially degraded through our generative approach are more realistic than those obtained with other statistical changes. Key disparities across gender and ethnic groups exist and urge timely interventions.
Self-Supervised Global Spatio-Temporal Interaction Pre-Training for Group Activity Recognition

This paper focuses on exploring distinctive spatio-temporal representation in a self-supervised manner for group activity recognition. Firstly, previous networks treat spatial- and temporal-aware information as a whole, limiting their abilities to represent complex spatio-temporal correlations for group activity. Here, we propose the Spatial and Temporal Attention Heads (STAHs) to extract spatial- and temporal-aware representations independently, which generate complementary contexts for boosting group activity understanding. Then, we propose the Global Spatio-Temporal Contrastive (GSTCo) loss to aggregate these two kinds of features. Unlike previous works focusing on the individual temporal consistency while overlooking the correlations between actors, i.e., in a local perspective, we explore the global spatial and temporal dependency. Moreover, GSTCo could effectively avoid the trivial solution faced in contrastive learning by achieving the right balance between spatial and temporal representations. Furthermore, our method imports affordable overhead during pre-training, without additional parameters or computational costs in inference, guaranteeing efficiency. By evaluating on widely-used datasets for group activity recognition, our method achieves good performance. State-of-the-art performance is achieved when applying our pre-trained backbone to existing networks. Extensive experiments verify the generalizability of our method.
Multi-User Collusion-Resistant Searchable Encryption for Cloud Storage

The continued development of cloud computing requires technologies protecting users’ data privacy even from the cloud providers themselves, such as Multi-user searchable encryption. It allows data owners to selectively enable users to perform keyword searches over their encrypted data stored at a cloud server. For privacy purposes, it is important to limit what an adversarial server can infer about the encrypted data, even if it colludes with some users. Clearly, in this case it can learn the content of data shared with these “corrupted” users, however, it is important to ensure this collusion does not reveal information about parts of the dataset that are only shared with “uncorrupted” users via <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cross-user</i> leakage. In this work, we propose three novel multi-user searchable encryption schemes eliminating cross-user leakage. Compared to previous ones, our first two schemes are the first to achieve asymptotically <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">optimal search time</i> . Our third scheme achieves minimal user storage and forward privacy with respect to data sharing, but slightly slower search performance. We formally prove the security of our schemes under reasonable assumptions. Moreover, we implement them for textual documents and tabular databases and evaluate their computation and communication performance with encouraging results.
Blind Image Denoising via Deep Unfolding Network With Degradation Information Guidance

Blind denoising is an active research area in image processing. In recent years, denoising methods based on deep learning have achieved outstanding results. However, directly designing more complex networks is very challenging and often lacks interpretability. Besides, existing methods also often ignore the guidance of degradation information. Therefore, how to guide the design of deep neural networks by combining traditional algorithms while predicting and utilizing degradation information is an open problem. In this brief, based on the maximum a posterior (MAP) framework, we first estimate the degradation information, and design corresponding operators to obtain initial restoration estimation in high-dimensional mapping space. On this basis, the initial estimation is substituted into the denoising problem. According to the splitting algorithm and momentum based gradient descent method, the iterative optimization of the substituted problem is carried out, and the proposed algorithm framework is obtained. Furthermore, according to the proposed algorithm framework, we design the corresponding network structure, and a novel blind unfolding network named BDUNet is introduced. Experimental results show that our network not only outperforms blind methods but also has advantages over non-blind methods.
A Novel Robust Variational Bayesian Filter for Unknown Time-Varying Input and Inaccurate Noise Statistics

Considering that, in many practical applications, the unknown time-varying input and heavy-tailed process and measurement noises induced by some unpredictable anomalous behaviors may degrade the performance of conventional filters seriously, this letter proposes a new robust variational Bayesian (VB) filter. First, the modified one-step prediction and measurement likelihood probability density function are constructed. Then, the VB method is utilized to jointly infer the system state, unknown time-varying input, and inaccurate noise covariance matrices. Finally, a new robust filter is derived, and its effectiveness is verified by the numerical simulations.
Dual-Branch TransV-Net for 3-D Echocardiography Segmentation

Segmentation of left and right ventricles from 3-D echocardiographic images is premised and key for the quantitative analysis of cardiac function, which is important for pediatric cardiac diagnosis. Compared with 2-D echocardiography, 3-D echocardiography can fully represent a ventricular structure without geometric inference. However, it usually takes experts several hours to obtain a 3-D segmentation mask of the left and right ventricles. Therefore, a fast and automatic segmentation method is highly desired. Unfortunately, 3-D echocardiography suffers from low contrast, unclear left and right ventricles borders, and blind zone. Existing segmentation methods usually have poor performance at ventricular boundaries. To deal with these problems, we propose a novel dual-branch TransV-Net (DBTV). DBTV comprises two parallel, interleaved, and relatively independent V-shaped encoder窶電ecoder branches. The main branch acts on the original data to extract image features, and the auxiliary branch acts on edge maps to extract the additional edge features. To suppress noise and enhance the edge information, extra concatenations are added to bridge the features from the main and auxiliary branches. To reduce object missing caused by blind zone, a 3-D transformer-based module is proposed in the bottom layer of the dual-branch structure to extract the global contexts. We do experiments on a self-collected dataset with 120 3-D echocardiographic images from 60 cardiac sequences, and the dice scores of 0.913 and 0.880 are obtained in the left and right ventricle segments, respectively. Each inference takes about two-thirds of a second for a single 3-D frame.
Attention-Based Knowledge Distillation in Scene Recognition: The Impact of a DCT-Driven Loss

Knowledge Distillation (KD) is a strategy for the definition of a set of transferability gangways to improve the efficiency of Convolutional Neural Networks. Feature-based Knowledge Distillation is a subfield of KD that relies on intermediate network representations, either unaltered or depth-reduced via maximum activation maps, as the source knowledge. In this paper, we propose and analyze the use of a 2D frequency transform of the activation maps before transferring them. We pose that—by using global image cues rather than pixel estimates, this strategy enhances knowledge transferability in tasks such as scene recognition, defined by strong spatial and contextual relationships between multiple and varied concepts. To validate the proposed method, an extensive evaluation of the state of the art in scene recognition is presented. Experimental results provide strong evidence that the proposed strategy enables the student network to better focus on the relevant image areas learnt by the teacher network, hence leading to better descriptive features and higher transferred performance than every other state-of-the-art alternative. We publicly release the training and evaluation framework used in this paper at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www-vpu.eps.uam.es/publications/DCTBasedKDForSceneRecognition</uri> .
A Graph Convolutional Network Feature Learning Framework for Interpretable Geometry Problem Solving

Geometry problem solving is a long-standing problem in artificial intelligence. The task requires generating explainable solving sequences based on text and diagram descriptions. Existing approaches have made great progress in geometry formal language extraction and interpretable solving. However, they neglect the graph structure information in formal language. This leads to poor prediction effect of the theorem, and too long reasoning time for problem solving and affects the accuracy of problem solving. In this paper, we construct the formal language graph and use a graph convolutional network to encode structure information of formal language. We propose an improved diagram parser for better diagram relation set extraction. The experimental results show that our method achieves better performance in interpretable geometry problem solving.
Interpretation and Analysis of Angular Framework

In today's world of rapidly evolving technologies and a heightened focus on customer satisfaction, it is crucial for numerous web application frameworks to undergo constant upkeep and update in order to meet the demands of the modern market. There are a plethora of web frameworks to choose from, and it's up to the developer to find one that works best for their project. The history, development, features, and implications of Angular, a web framework developed by Google that is widely used for Single Page Applications, are the subject of this study. Among the many popular frameworks used to create modern, single-page web applications, Angular's primary purpose is to facilitate the creation of dynamic views within the app itself. Rather than spending so much time wrangling with the underlying technology, developers should be able to focus on the user experience (such as HTML5, CSS, and JavaScript). Angular's out-of-the-box features are excellent, and it has the potential to make nontrivial jQuery-based projects much more robust while still being easy to maintain. This study discusses the various Angular versions that can be used for single-page front-end web development, and it provides guidance for developers selecting the best one to use.