Reviewerly/exHarmony huggingface dataset:
Learning Agile Flights Through Narrow Gaps with Varying Angles Using Onboard Sensing

This letter addresses the problem of traversing through unknown, tilted, and narrow gaps for quadrotors using Deep Reinforcement Learning (DRL). Previous learning-based methods relied on accurate knowledge of the environment, including the gap's pose and size. In contrast, we integrate onboard sensing and detect the gap from a single onboard camera. The training problem is challenging for two reasons: a precise and robust whole-body planning and control policy is required for variable-tilted and narrow gaps, and an effective Sim2Real method is needed to successfully conduct real-world experiments. To this end, we propose a learning framework for agile gap traversal flight, which successfully trains the vehicle to traverse through the center of the gap at an approximate attitude to the gap with aggressive tilted angles. The policy trained only in a simulation environment can be transferred into different domains with fine-tuning while maintaining the success rate. Our proposed framework, which integrates onboard sensing and a neural network controller, achieves a success rate of 87.36% in real-world experiments, with gap orientations up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$60^\circ$</tex-math></inline-formula> . To the best of our knowledge, this is the first letter that performs the learning-based variable-tilted narrow gap traversal flight in the real world, without prior knowledge of the environment.
JRCC-Net: A Segmentation Network With Joint Representation and Contrast Clustering for Surface Anomaly Detection

The goal of unsupervised surface anomaly detection is to detect areas of the image that are different from the normal pattern, which can be considered as a semantic segmentation problem oriented to anomalous patterns. However, this problem is challenging due to the lack of actual available anomaly samples. In this paper, we transform unsupervised anomaly detection into a self-supervised problem by the proposed anomaly simulation strategy. Using only normal samples for training, real anomalies appearing in the inference phase can be detected. Thus, we propose a segmentation network with joint representation and contrast clustering (JRCC-Net). The proposed method learns a joint representation between simulated samples and the magnitude of the differences in distance from their nearest memory samples, as well as a decision boundary between normal and anomalous samples. Moreover, we propose a novel contrast clustering based representation learning strategy, which allows the model to better learn general patterns from normal samples and mine the latent differences between simulated anomalous and normal samples. JRCC-Net is able to locate anomalies directly in an end-to-end manner and can be trained well with our elaborate anomaly simulation strategy. On the challenging MVTec AD dataset, JRCC-Net outperforms the state-of-the-art unsupervised methods, achieving 98.2 % image-level AUROC and 97.7 % pixel-level AUROC, respectively, and even on the extensively used DAGM dataset, its localization accuracy greatly exceeds that of fully-supervised methods.
Rank-based Decomposable Losses in Machine Learning: A Survey

Recent works have revealed an essential paradigm in designing loss functions that differentiate individual losses versus aggregate losses. The individual loss measures the quality of the model on a sample, while the aggregate loss combines individual losses/scores over each training sample. Both have a common procedure that aggregates a set of individual values to a single numerical value. The ranking order reflects the most fundamental relation among individual values in designing losses. In addition, decomposability, in which a loss can be decomposed into an ensemble of individual terms, becomes a significant property of organizing losses/scores. This survey provides a systematic and comprehensive review of rank-based decomposable losses in machine learning. Specifically, we provide a new taxonomy of loss functions that follows the perspectives of aggregate loss and individual loss. We identify the aggregator to form such losses, which are examples of set functions. We organize the rank-based decomposable losses into eight categories. Following these categories, we review the literature on rank-based aggregate losses and rank-based individual losses. We describe general formulas for these losses and connect them with existing research topics. We also suggest future research directions spanning unexplored, remaining, and emerging issues in rank-based decomposable losses.
An Improved Boykov’s Graph Cut-Based Segmentation Technique for the Efficient Detection of Cervical Cancer

The accurate and reliable derivation of the pap smear cell, which contains cytoplasm and nucleus regions, depends on the segmentation process employed in the cervical cancer detection mechanism. In this paper, an Improved Boykov’s Graph Cut-based Conditional Random Fields and Superpixel imposed Semantic Segmentation Technique (IBGC-CRF-SPSST) is proposed for efficient cervical cancer detection. This proposed IBGC-CRF-SPSST embeds the complete benefits of constraint association among pixels and superpixel edge data for accurate determination of the nuclei and cytoplasmic boundaries so as to ensure efficient differentiation of the healthy and unhealthy cancer cells. Finally, the pixel-level forecasting potential of Conditional Random Fields is included for enhancing the degree of semantic-based segmentation accuracy to a predominant level. The experimental evaluated results of the proposed IBGC-CRF-SPSST aim to produce an accuracy of 99.78%, a mean processing time of 2.18sec, a precision of 96%, a sensitivity of 98.92%, and a specificity of 99.32% value which is determined to be excellent and on par with the existing detection techniques used for investigating cervical cancer.
Predicting ICU Mortality Based on Generative Adversarial Nets and Ensemble Methods

The intensive care unit (ICU) typically admits patients who require urgent medical intervention. Predicting ICU mortality is crucial for identifying those who are at higher risk. Traditional statistical methods, such as logistic regression, have been widely used for ICU survival prediction. However, these methods often have limitations in capturing complex nonlinear relationships between the clinical features. A prediction model based on ensemble learning was proposed for the ICU mortality prediction problem: MTX-stacking model. Firstly, the imbalanced data was processed based on the modified generative adversarial network method. This approach is more explanatory and more effective than traditional data generation methods. Secondly, XGBoost was optimized by tree-structured parzen estimator and stacking structure to prevent overfitting. The proposed MTX-stacking model was evaluated using 131,051 patients from MIT’s GOSSIS initiative. The results indicate that MTX-stacking outperforms the state-of-the-art approaches in terms of area under the receiver operator characteristic (ROC) curve (91.2% and 90.9%). These findings demonstrate the ability and efficiency of the MTX-stacking model to predict ICU mortality.
Secure Traffic Monitoring With Spatio-Temporal Metadata Protection Using Oblivious RAM

In a traffic monitoring system, users report their driving data (e.g., routes and timestamps) to a server, aggregating them to acquire the information of interest and provide services, e.g., traffic flow prediction. Such systems have considerable promise for improving the quality of life and decreasing environmental pollution and economic losses. However, the threat to individual privacy is a critical problem that hinders the further development of such systems; e.g., the server can abuse the spatio-temporal metadata in the reports to infer sensitive user information, such as the home address. In this paper, we propose a privacy-preserving aggregation framework called secure Publish Aggregate Subscribe Data ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> PASD) for traffic monitoring that can protect spatio-temporal metadata during driving data aggregation. The framework is built upon a trusted execution environment and two cryptographic tools: additive homomorphic encryption and oblivious RAM. It takes a hierarchical structure that boosts batch processing efficiency, making it more appropriate for traffic monitoring scenarios. Furthermore, we provide a cost model to derive the optimal settings from the efficiency perspective. Security analysis demonstrates that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> PASD can protect individual privacy by hiding spatio-temporal information during aggregation. Moreover, we implement the proposed framework in Python and evaluate it together with other competitive methods over a real-world dataset. The experiment results verify the accuracy of the proposed cost model and show that the proposed framework can gain a 51.39% saving on computation cost.
Noisy Label Learning With Provable Consistency for a Wider Family of Losses

Deep models have achieved state-of-the-art performance on a broad range of visual recognition tasks. Nevertheless, the generalization ability of deep models is seriously affected by noisy labels. Though deep learning packages have different losses, this is not transparent for users to choose consistent losses. This paper addresses the problem of how to use abundant loss functions designed for the traditional classification problem in the presence of label noise. We present a dynamic label learning (DLL) algorithm for noisy label learning and then prove that any surrogate loss function can be used for classification with noisy labels by using our proposed algorithm, with a consistency guarantee that the label noise does not ultimately hinder the search for the optimal classifier of the noise-free sample. In addition, we provide a depth theoretical analysis of our algorithm to verify the justifies' correctness and explain the powerful robustness. Finally, experimental results on synthetic and real datasets confirm the efficiency of our algorithm and the correctness of our justifies and show that our proposed algorithm significantly outperforms or is comparable to current state-of-the-art counterparts.
Application of Text Rank Algorithm Fused With LDA in Information Extraction Model

With the rapid development of network technology, a large amount of information fills the network world, and the performance of the current information extraction model to extract keyword information from a large number of data is insufficient. To solve the problem of insufficient extraction performance in traditional information extraction models, this paper combines text sorting algorithms with document topic generation models. A keyword information extraction model that combines the advantages of the two algorithms is proposed. The performance comparison experiment of this fusion algorithm shows that its accuracy and recall rates are 76.1% and 77.0%, respectively, which outperform the comparing algorithm. In the empirical analysis results of the information extraction model, it is found that the accuracy and precision rates of the proposed information extraction model are 80.16% and 77.54%, respectively, which are better than the comparing model. The proposed model of information extraction is of great importance for the development of the field of information extraction.
Analysis and Utilization of Hidden Information in Model Inversion Attacks

The widely applications of deep learning have raised concerns about the privacy issues in deep neural networks. Model inversion attack aims to reconstruct specific details of each private training sample from a given neural network. However, limited to the availability of useful information, reconstructing distinctive private training samples still has a long way to go. In this paper, the requirements to reconstruct distinctive private training samples are investigated using information entropy. We find that more information is needed to reconstruct distinctive samples and propose to use the often ignored hidden information to achieve this goal. To better utilize this information, Amplified-MIA is proposed. In Amplified-MIA, a nonlinear amplification layer is inserted between the target network and the attack network. This nonlinear amplification layer further contains a nonlinear amplification function. The definition of the nonlinear amplification function is given and the effect of this nonlinear amplification function on the entropy of the hidden information is derived. The proposed nonlinear amplification function can amplify the small prediction vector entries and enlarge the differences between different prediction vectors in the same class. Thus, the hidden information can be better utilized by the attack network and distinctive private samples can be reconstructed. Various experiments are performed to empirically analyze the effects of the nonlinear amplification function on the reconstruction results. The reconstruction results on three different datasets show that the proposed Amplified-MIA outperforms existing works on almost all tasks. Especially, it achieves up to 68% performance gain of the Pixel Accuracy score over the direct inversion method on the hardest face reconstruction task.
Encoding Human Visual Perception Into Deep Hashing for Aerial Image Classification

Accurately calculating the labels of each high-resolution image is an unavoidable technique in remote sensing. In this paper, we propose a novel image assortment model that personate each aerial image by optimally encoding a gaze shifting path (GSP). At the same time, wrong semantic model can get absent with it. More specifically, for each aerial image, we reference visually/semantically noticeable representational rogue interiors. To encode their analysis attributes, we mean a small graph comprise of spatially conterminous motivational wall, and extract GSPs on it by active literature algorithm rules. GSP can accurately capture humans perception over many aerial image areas when the notice senses are placed in each image. Subsequently, a double deep learning framework is proposed to intelligently exploit the semantics of these GSPs, with three attributes: i) label noises reduction, ii) visual manner-unchanging semantics, and iii) adaptive data chart updates are seamlessly integrated. The proposed framework can iteratively solved, with each graphlet re-form into a base. Finally, the GSP-compliant summaries in each aerial have shown the quantized vectors for visual understanding. To qualitatively and quantitatively assess how GSP affects information aerial image classification. We notice that 1) the phantom copy of our progress classification is more accurate than its competitors, and 2) the GSPs propagated by Alzheimer's patients are discriminative from those produced by typical observers, making the classification competitive.
Arabic Diacritics Restoration Using Maximum Entropy Language Models

Restoring the input text's diacritics is crucial in developing Arabic text-to-speech systems. A few rules can be used to implement phonetic transcription when diacritics are present. The preferred method for recovering Arabic diacritics is based on scoring <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -gram language models. By utilizing maximum entropy (MaxEnt) language models, a novel procedure is presented in this work for restoring Arabic diacritics. The MaxEnt language models are used to score the diacritized words given previous words in a given sequence. Hence, dynamic programming (DP) efficient decoder retrieves the most probable word sequence conditioned on the input undiacritized word sequence. The Tashkeela corpus was used to evaluate the effectiveness of the suggested technique. Our methods were more efficient than the most sophisticated algorithms available at the time.
Machine Learning Techniques for Prognosis Estimation and Knowledge Discovery From Lab Test Results With Application to the COVID-19 Emergency

AI and Machine Learning (ML) offer powerful tools to support clinical decision making in emergency situations such as the COVID-19 pandemic. In this context, the application of ML requires to design predictive systems that have adequate accuracy and can effectively deal with issues concerning data quality, sensitive errors, uncertainty, and interpretability of the predictions. We present a methodology that deals with all these problems and a concrete study of its application to estimate the prognosis of hospitalised patients with COVID-19. In particular, we address the task of predicting the outcome (alive or deceased) of a patient at different times of her/his hospitalisation minimising false negatives (wrong survival predictions). The proposed methodology builds different optimised ML models to select those that perform the best to recognise, at different times of hospitalisation, patients who will have an unfavourable prognosis (decease). These models exploit a new algorithm, presented in the paper, that identifies an uncertainty threshold to rule out uncertain predictions with the purpose of making a ML model both more performing and more reliable. Moreover, we propose a general method for automatically extracting multi-variable prognostic rules from the available data. Such rules can provide possible new useful knowledge on the considered disease. We also show how they can be used effectively to explain the predictions made by the ML models. All proposed methods and techniques are experimentally evaluated in the context of our application task.
JEDI: Joint and Effective Privacy Preserving Outsourced Set Intersection and Data Integration Protocols

In the era of big data with an ever-increasing amount of data, the demand for data processing is increasing, and various computing service agreements have also been created. While data are widely used, data providers worry that their privacy will be infringed, so the security of the data has also received more attention. Private set intersection (PSI) allows two data-owning parties to jointly compute the intersection of private datasets. The party cannot obtain any other information except for the intersection results. However, Bloom filter (BF) is a naive solution that can be used to potentially obtain PSI, but the verifiability of using BF is usually a concern for effectiveness. Hence, garbled Bloom filter is indeed expected to overcome this concern in two-party setting. We aim for the outsourced scenario where there are many data-owning parties in distributed setting. These parties would like to jointly obtain the set intersection in a private manner supported by an outsourced cloud, and further to obtain data integration. The main challenges are that the previous solutions cannot be directly transformed into an outsourced scenario. In this paper, the proposed joint and effective privacy preserving outsourced set intersection and data integration protocols are called JEDI. They are generic constructions. We start by introducing a basic outsourced PSI protocol from the cryptographic building blocks and new BF and GBF techniques. Then, we modify the basic protocol and propose the full-fledged scheme to achieve an outsourced multi-party PSI protocol. Finally, we analyze the security and execution efficiency aspects to ensure usability.
Multiscale Coarse-to-Fine Guided Screenshot Demoiréing

In this letter, we propose a multiscale coarse-to-fine guided screenshot demoireing algorithm. We first extract the multiscale features of the input image. Then, we develop the multiscale guided restoration block (MGRB), which removes moire patterns with the guidance of multiscale information by exploiting the correlation between moire frequencies. To this end, we design two blocks for feature modulation and moire pattern removal. In addition, to further improve the performance, we develop an adaptive reconstruction loss to direct the network to focus on regions that are difficult to restore. Experimental results on multiple datasets demonstrate that the proposed algorithm provides comparable or even better demoireing performance than state-of-the-art algorithms.
The Postdigital University Teacher

University teachers have a complex history with digital (and other) technologies, which can offer opportunities for creative approaches to teaching but are also associated with instrumental teaching and constraints on practice. The COVID-19 pandemic ensured that all university teachers have engaged at some level with technology for teaching; shared experiences from this time have fed into the research on postdigital knowledge ecologies. A key message for university teachers from the postdigital literature is that there are alternative approaches to the way things are. Postdigital writers and editors are themselves using alternative methods to make this point.
AI-Aided Disease Prediction in Visualized Medicine

Artificial intelligence (AI) is playing a vitally important role in promoting the revolution of future technology. Healthcare is one of the promising applications in AI, which covers medical imaging, diagnosis, robotics, disease prediction, pharmacy, health management, and hospital management. Numbers of achievements that made in these fields overturn every aspect in traditional healthcare system. Therefore, to understand the state-of-art AI in healthcare, as well as the chances and obstacles in its development, the applications of AI in disease detection and outlook and the future trends of AI-aided disease prediction were discussed in this chapter.
Would People Mumble Rap to Alexa?

Unclear speech, like mumbling, is difficult to understand for people, and even harder for conversational user interfaces (CUI) to process. Yet, there are multiple reasons why unintelligible speech is meaningful between humans, playing a critical role in social dynamics and status signaling that evolved in humans over time allowing us to form cohesive social groups for survival. For example in modern times, humans often use such changed speech in order to make themselves understandable only by in-group members, e.g. “mumble rap”, while subtly excluding out-group members. As such, we argue here that future CUIs must be attentive to how people use various forms of non-standard changed speech (e.g. mumbling, dialect, slang, inflection) to express themselves, lest CUIs be socially inept. Based on psychological, linguistic, and cross-cultural research, we point out several major challenges for researchers: 1) current CUIs typically omit non-standard speech like mumbling which are critical to human social communication, and 2) in the future humans may innately form ingroups with their personal CUIs resulting in speech behaviors meant to exclude outgroup members (both humans and machines). Both of those challenges require more research to address. Moreover, the use of changed speech for status signaling and ingroup/outgroup (IG/OG) signaling appears to be a phenomenon that varies across diverse cultures, languages, and situations, which CUI designers and engineers need to be mindful of going forward.
MoDEsT: a Modular Dialogue Experiments and Demonstration Toolkit

We present a modular dialogue experiments and demonstration toolkit (MoDEsT) that assists researchers in planning tailored conversational AI-related studies. The platform can: 1) assist users in picking multiple templates based on specific task needs; 2) allow users to create their experiment/demo interface by selecting different components; and 3) support playback from dialogue history (i.e. logs). MoDEsT is an open-source platform, hosted on a university-managed publicly accessible server.