Reviewerly/exHarmony huggingface dataset:
MTSSP: Missing Value Imputation in Multivariate Time Series for Survival Prediction

In recent years, there has been a lot of research on deep learning for survival prediction in EHR (Electronic Health Record). At present, EHR usually contains multivariate time series data with missing values. How to better predict mortality based on such data is what many studies are currently doing. Most of the mortality prediction methods based on deep learning only pay attention to missing value filling or adjusting the model structure to enhance the mortality prediction performance, but they do not combine these two aspects well. In this paper, we propose MTSSP (Multivariate Time Series for Survival Prediction), a new method that combines missing value filling and time series classification. It takes two representations of the loss pattern: the mask and the time interval, which are combined into the recurrent neural network for the interpolation of the missing value of patient characteristics. When the missing data values are interpolated, the model combines bidirectional RNN and one-dimensional CNN to jointly capture the patient's medical behavior from a global and local perspective to enhance the representation ability of data information, thereby improving the prediction accuracy of the model. In the end, we conducted our mortality prediction experiments on the real-world emergency MIMIC-III dataset and MIMIC-IV dataset. The experimental results demonstrate that the proposed approach has been shown to significantly outperform other approaches.
Matching Patterns with Variables Under Edit Distance

A pattern $$\alpha $$ is a string of variables and terminal letters. We say that $$\alpha $$ matches a word w, consisting only of terminal letters, if w can be obtained by replacing the variables of $$\alpha $$ by terminal words. The matching problem, i.e., deciding whether a given pattern matches a given word, was heavily investigated: it is NP-complete in general, but can be solved efficiently for classes of patterns with restricted structure. If we are interested in what is the minimum Hamming distance between w and any word u obtained by replacing the variables of $$\alpha $$ by terminal words (so matching under Hamming distance), one can devise efficient algorithms and matching conditional lower bounds for the class of regular patterns (in which no variable occurs twice), as well as for classes of patterns where we allow unbounded repetitions of variables, but restrict the structure of the pattern, i.e., the way the occurrences of different variables can be interleaved. Moreover, under Hamming distance, if a variable occurs more than once and its occurrences can be interleaved arbitrarily with those of other variables, even if each of these occurs just once, the matching problem is intractable. In this paper, we consider the problem of matching patterns with variables under edit distance. We still obtain efficient algorithms and matching conditional lower bounds for the class of regular patterns, but show that the problem becomes, in this case, intractable already for unary patterns, consisting of repeated occurrences of a single variable interleaved with terminals.
DPWGAN: High-Quality Load Profiles Synthesis With Differential Privacy Guarantees

Smart meters have collected massive amounts of fine-grained load data from users, enabling various load profile analyses that can help improve the efficiency of smart grids. However, the smart meter data may leak private information, raising public concerns. To address this issue, current approaches typically employ data perturbation mechanisms or data generation mechanisms to ensure privacy when analyzing load profiles, but these approaches are either inflexible or not guaranteed to mitigate the leakage issue. To this end, we propose a differentially private Wasserstein Generative Adversarial Networks (DPWGAN) approach in this study. This approach can privately convert a real-world dataset into a high-quality synthetic load dataset so that studies and analyses conducted on the synthetic dataset can automatically satisfy user-level differential privacy guarantees. The extensive numerical studies highlight that our approach acts as an excellent substitute for the original dataset in real-world load profiling tasks.
TISE: Bag of Metrics for Text-to-Image Synthesis Evaluation

In this paper, we conduct a study on the state-of-the-art methods for text-to-image synthesis and propose a framework to evaluate these methods. We consider syntheses where an image contains a single or multiple objects. Our study outlines several issues in the current evaluation pipeline: (i) for image quality assessment, a commonly used metric, e.g., Inception Score (IS), is often either miscalibrated for the single-object case or misused for the multi-object case; (ii) for text relevance and object accuracy assessment, there is an overfitting phenomenon in the existing R-precision (RP) and Semantic Object Accuracy (SOA) metrics, respectively; (iii) for multi-object case, many vital factors for evaluation, e.g., object fidelity, positional alignment, counting alignment, are largely dismissed; (iv) the ranking of the methods based on current metrics is highly inconsistent with real images. To overcome these issues, we propose a combined set of existing and new metrics to systematically evaluate the methods. For existing metrics, we offer an improved version of IS named IS* by using temperature scaling to calibrate the confidence of the classifier used by IS; we also propose a solution to mitigate the overfitting issues of RP and SOA. For new metrics, we develop counting alignment, positional alignment, object-centric IS, and object-centric FID metrics for evaluating the multi-object case. We show that benchmarking with our bag of metrics results in a highly consistent ranking among existing methods that is well-aligned with human evaluation. As a by-product, we create AttnGAN++, a simple but strong baseline for the benchmark by stabilizing the training of AttnGAN using spectral normalization. We also release our toolbox, so-called TISE, for advocating fair and consistent evaluation of text-to-image models.
MRCAug: Data Augmentation via Machine Reading Comprehension for Document-Level Event Argument Extraction

Document-level event argument extraction (EAE) is a critical event semantic understanding task that requires a model to identify an event's global arguments beyond the sentence level. Existing approaches to this problem are based on supervised learning, which require a large amount of labeled data for model training. However, due to the complicated structure of an event, human annotation for this task is costly, and the issue of inadequacy of training data has long hampered the study. In this study, we propose a novel approach to mitigating the data sparsity problem faced by document-level EAE, by linking the task with machine reading comprehension (MRC). Particularly, we devise two data augmentation regimes via MRC, including an implicit knowledge transfer method, which enables knowledge transfer from other tasks to the document-level EAE task, and an explicit data generation method, which can explicitly generate new training examples by treating a pre-trained MRC model as an annotator. Furthermore, we propose a self-training based noise reduction strategy that can effectively addresses the out-of-domain noise introduced by the data augmentation methods. The extensive assessments on three benchmarks have validated the effectiveness of our approach — it not only achieves state-of-the-art performance but also demonstrates superior results in the data-low scenario.
Optimizing the Prioritization of Compiled Quantum Circuits by Machine Learning Approaches

The performance of current quantum computers is limited by high error rates and few qubits. Nevertheless, more and more quantum computers are available in the cloud. Selecting a suitable quantum computer to execute a specific quantum circuit and receive precise results can be difficult. At the same time, it is crucial to choose an available quantum computer that offers the hardware characteristics required by the circuit to retrieve precise results, depending on the quantum computer’s last re-calibration and the quantum compiler that maps the circuit to the hardware. Furthermore, cloud providers regulate hardware access, so waiting times must be considered. To support the choice of a quantum computer, we introduced an automated framework in previous work. It enables the user to analyze and prioritize the compiled circuits of a given input circuit for different quantum computers based on their requirements. In this work, we extend the framework by automating the prioritization of compiled circuits targeting short waiting times and precise executions based on previous results. We present our framework’s prototype and case study to demonstrate and evaluate the practical feasibility.
D$$^3$$Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning and Visual Grounding

Recent work on dense captioning and visual grounding in 3D have achieved impressive results. Despite developments in both areas, the limited amount of available 3D vision-language data causes overfitting issues for 3D visual grounding and 3D dense captioning methods. Also, how to discriminatively describe objects in complex 3D environments is not fully studied yet. To address these challenges, we present D $$^3$$ Net, an end-to-end neural speaker-listener architecture that can detect, describe and discriminate. Our D $$^3$$ Net unifies dense captioning and visual grounding in 3D in a self-critical manner. This self-critical property of D $$^3$$ Net encourages generation of discriminative object captions and enables semi-supervised training on scan data with partially annotated descriptions. Our method outperforms SOTA methods in both tasks on the ScanRefer dataset, surpassing the SOTA 3D dense captioning method by a significant margin.
Building Semantic Cognitive Maps with Text Embedding and Clustering

Text embedding using vector space models has recently emerged as the leading way to represent text in natural language processing. These embeddings can be at the level of words, sentences, or larger textual units including entire documents. However, sentence embeddings are especially useful because sentences are the most explicitly specified elements of individual thoughts or ideas comprising a document, discussion, or conversation. Analyzing text at the sentence level thus allows access to its fine-grained semantics while preserving the semantic structure that is lost in bag-of-words approaches. Several deep learning-based models such as BERT and USE provide such contextual representations. However, the resulting embeddings are very high-dimensional, and the individual dimensions are not amenable to interpretable labels. Thus, these embeddings define a semantic space but not an explicitly useful cognitive map. In this paper, we show that an adaptive clustering approach applied to the embeddings produced by a neural network-based language model can produce much lower-dimensional, readily interpretable semantic representations, thus creating a usable cognitive map for applications such as semantic tracking and visualization of discussions, or discerning the sequential semantic structure of long documents.
Violence detection based on attention mechanism

Violence detection task is very useful in video surveillance and other scenes, such as prisons, schools and other public places. In order to fully extract the temporal and spatial features and temporal features in video surveillance, and accurately detect and classify violence, a violence detection model combining the advantages of two-stream network, 3D convolutional neural network and convolutional LSTM network network is proposed. In order to better extract human motion features, the model uses YOLO target detection method to segment human targets and extract local features, integrates CA attention mechanism into 3D convolution network, so as to improve the feature extraction of video channel and space, and replaces part of 3D convolution layer in neural network with convlstm layer, so as to obtain better video timing relationship. The experiment was carried out on RWF-2000 data set. The results show that the proposed model has high accuracy in violence detection.
Research on trusted sharing and privacy computing technology of energy data in supply chain based on alliance chain

This paper proposes an evaluation mechanism of supply chain credit that integrates federated learning and block chain technology. In order to ensure the effective use of energy data, a set of credit evaluation index is designed for supply chain enterprises data and their energy data. Meanwhile, under the condition that data privacy is guaranteed, a federated learning algorithm is designed to improve the accuracy of federated learning training with the data of supply chain enterprises. Block chain and federated learning technology are integrated to assess credit. On the basis of ensuring data security, the credit evaluation results stored on the alliance chain are used for a second time, which not only ensures the interests of data requesters, but also ensures the risk control of external enterprises participating in supply chain finance.
Style Your Hair: Latent Optimization for Pose-Invariant Hairstyle Transfer via Local-Style-Aware Hair Alignment

Editing hairstyle is unique and challenging due to the complexity and delicacy of hairstyle. Although recent approaches significantly improved the hair details, these models often produce undesirable outputs when a pose of a source image is considerably different from that of a target hair image, limiting their real-world applications. HairFIT, a pose-invariant hairstyle transfer model, alleviates this limitation yet still shows unsatisfactory quality in preserving delicate hair textures. To solve these limitations, we propose a high-performing pose-invariant hairstyle transfer model equipped with latent optimization and a newly presented local-style-matching loss. In the StyleGAN2 latent space, we first explore a pose-aligned latent code of a target hair with the detailed textures preserved based on local style matching. Then, our model inpaints the occlusions of the source considering the aligned target hair and blends both images to produce a final output. The experimental results demonstrate that our model has strengths in transferring a hairstyle under larger pose differences and preserving local hairstyle textures. The codes are available at https://github.com/Taeu/Style-Your-Hair .
Privacy-preserving time series prediction with temporal convolutional neural networks

Designing and developing machine and deep learning solutions able to guarantee the privacy of users' data is a novel and promising research area. Homomorphic Encryption (HE) is playing a primary role in this area thanks to its ability to support the processing of machine and deep learning solutions directly on encrypted data. Currently, the research in this field focuses on HE-based machine and deep learning solutions for the processing of images and text, while the privacy-preserving processing of time series has been mostly left unattended due to the strong constraints imposed by HE on the machine and deep learning forecasting models. This paper introduces, for the first time in the literature, a general privacy-preserving solution for time series prediction based on HE and Temporal Convolutional Neural Networks. The novel content brought by the paper is twofold. From the algorithmic point of view, this paper introduces a family of Temporal Convolutional Neural Networks, called PINPOINT, which is integrated with a HE scheme to support the privacy-preserving time series prediction. From the technical point of view, this paper introduces and details a Cloud-based privacy-preserving system for the forecasting-as-a-service based on the proposed PINPOINT models. Experimental results on publicly available benchmarks show the effectiveness of the proposed solution for privacy-preserving time series prediction.
A Scheme for News Article Classification in a Low-Resource Language

This paper proposes a scheme for classifying news articles in Tigrinya, a language spoken in northern Ethiopia and Eritrea, and is known for its lack of extensive and readily available data. We present the first publicly available news article dataset for Tigrinya, containing 2396 articles. In addition, we propose a data augmentation method for text classification. Furthermore, we explore the performance in text classification using traditional machine learning methods (support vector machine, logistic regression, random forest, linear discriminant analysis, decision tree, and Naive Bayes), a neural network-based model (bidirectional long short term memory), and a transformer-based model (TigRoBERTa). The experimental results show that the proposed method performs better in accuracy than the comparative methods up to nine points. The codes and the dataset are open to the research community ( https://github.com/mehari-eng/Article-News-Categorization ).
A Development of Kohs Block Design Test in Virtual Reality with Eye Tracking and Hand Tracking

The purpose of this study was to evaluate the effectiveness of the VR Kohs Block Design Test and the relationship between eye movements and hand movements by reproducing the Kohs Block Design Test in a VR space and measuring the subjects’ eye and hand movements during the test.Using the developed system, we conducted the actual Kohs Block Design Test and the Kohs Block Design Test on VR with three healthy adult male subjects.As a result, it was found that the current VR Kohs Block Design Test is very difficult to perform grasping movements, and that it is necessary to construct a highly realistic system. The subject’s gaze and hand movements during the test were generally consistent, indicating that the subject was simultaneously performing grasping and gazing.
Path planning of mobile robots based on improved A* algorithm

Mobile robots are an important branch of service robots, and path planning is an important research part of mobile robots. For complex environments, traditional A* algorithms tend to be more restrictive in their path planning, resulting in less search efficiency. In this regard, this paper proposes to improve the A* algorithm to improve the search efficiency and path security, and realize the optimal path planning of mobile robots. Through simulation experiments, the average planning time is 0. 05s, which is more time-sensitive and efficient than other algorithms, which promotes the development process of “smart city” and promotes the innovative development of mobile robots.
Improving Closed and Open-Vocabulary Attribute Prediction Using Transformers

We study recognizing attributes for objects in visual scenes. We consider attributes to be any phrases that describe an object’s physical and semantic properties, and its relationships with other objects. Existing work studies attribute prediction in a closed setting with a fixed set of attributes, and implements a model that uses limited context. We propose TAP, a new Transformer-based model that can utilize context and predict attributes for multiple objects in a scene in a single forward pass, and a training scheme that allows this model to learn attribute prediction from image-text datasets. Experiments on the large closed attribute benchmark VAW show that TAP outperforms the SOTA by 5.1% mAP. In addition, by utilizing pretrained text embeddings, we extend our model to OpenTAP which can recognize novel attributes not seen during training. In a large-scale setting, we further show that OpenTAP can predict a large number of seen and unseen attributes that outperforms large-scale vision-text model CLIP by a decisive margin. The project page is available at https://vkhoi.github.io/TAP .
Image Aesthetics Assessment Using Graph Attention Network

Aspect ratio and spatial layout are two of the principal factors influencing the aesthetic value of a photograph. However, incorporating these into the traditional convolution-based frameworks for the task of image aesthetics assessment is problematic. The aspect ratio of the photographs gets distorted while they are resized/cropped to a fixed dimension to facilitate training batch sampling. On the other hand, the convolutional filters process information locally and are limited in their ability to model the global spatial layout of a photograph. In this work, we present a two-stage framework based on graph neural networks and address both these problems jointly. First, we propose a feature-graph representation in which the input image is modelled as a graph, maintaining its original aspect ratio and resolution. Second, we propose a graph neural network architecture that takes this feature-graph and captures the semantic relationship between different regions of the input image using visual attention. Our experiments show that the proposed framework advances the state-of-the-art results in aesthetic score regression on the Aesthetic Visual Analysis (AVA) benchmark. Our code is publicly available for comparisons and further explorations. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Folding Knots Using a Team of Aerial Robots

From ancient times, humans have been using cables and ropes to tie, carry, and manipulate objects by folding knots. However, automating knot folding is challenging because it requires dexterity to move a cable over and under itself. In this paper, we propose a method to fold knots in midair using a team of aerial vehicles. We take advantage of the fact that vehicles are able to fly in between cable segments without any re-grasping. So the team grasps the cable from the floor, and releases it once the knot is folded. Based on a composition of catenary curves, we simplify the complexity of dealing with an infinite-dimensional configuration space of the cable, and formally propose a new knot representation. Such representation allows us to design a trajectory that can be used to fold knots using a leader-follower approach. We show that our method works for different types of knots in simulations. Additionally, we show that our solution is also computationally efficient and can be executed in real-time.
Elementary Number Theory Problems. Part III

Summary In this paper problems 11, 16, 19–24, 39, 44, 46, 74, 75, 77, 82, and 176 from [10] are formalized as described in [6], using the Mizar formalism [1], [2], [4]. Problems 11 and 16 from the book are formulated as several independent theorems. Problem 46 is formulated with a given example of required properties. Problem 77 is not formulated using triangles as in the book is.
Numerical Data Imputation: Choose kNN over Deep Learning

Artificial neural networks (ANNs) are now ubiquitous in data science. In this respect, Deep-Learning (DL) methods have been developed to address missing data problems. The present study compares state-of-the-art DL Generative Adversarial Network (GAN) models with the well-established kNN algorithm (1951) for numerical data imputation. Using real-world and generated datasets in various missing data scenarios, we show that the good old kNN algorithm is still competitive with powerful DL algorithms for numerical data imputation. This review consolidates the emerging consensus that numerical data imputation does not necessarily require powerful or heavy DL tools.