Reviewerly/exHarmony huggingface dataset:
Learning Semantics-Enriched Representation via Self-discovery, Self-classification, and Self-restoration

Medical images are naturally associated with rich semantics about the human anatomy, reflected in an abundance of recurring anatomical patterns, offering unique potential to foster deep semantic representation learning and yield semantically more powerful models for different medical applications. But how exactly such strong yet free semantics embedded in medical images can be harnessed for self-supervised learning remains largely unexplored. To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a semantics-enriched, general-purpose, pre-trained 3D model, named Semantic Genesis. We examine our Semantic Genesis with all the publicly-available pre-trained models, by either self-supervision or fully supervision, on the six distinct target tasks, covering both classification and segmentation in various medical modalities (i.e., CT, MRI, and X-ray). Our extensive experiments demonstrate that Semantic Genesis significantly exceeds all of its 3D counterparts as well as the de facto ImageNet-based transfer learning in 2D. This performance is attributed to our novel self-supervised learning framework, encouraging deep models to learn compelling semantic representation from abundant anatomical patterns resulting from consistent anatomies embedded in medical images. Code and pre-trained Semantic Genesis are available at https://github.com/JLiangLab/SemanticGenesis.
Look Through Masks: Towards Masked Face Recognition with De-Occlusion Distillation

Many real-world applications today like video surveillance and urban governance need to address the recognition of masked faces, where content replacement by diverse masks often brings in incomplete appearance and ambiguous representation, leading to a sharp drop in accuracy. Inspired by recent progress on amodal perception, we propose to migrate the mechanism of amodal completion for the task of masked face recognition with an end-to-end de-occlusion distillation framework, which consists of two modules. The de-occlusion module applies a generative adversarial network to perform face completion, which recovers the content under the mask and eliminates appearance ambiguity. The distillation module takes a pre-trained general face recognition model as the teacher and transfers its knowledge to train a student for completed faces using massive online synthesized face pairs. Especially, the teacher knowledge is represented with structural relations among instances in multiple orders, which serves as a posterior regularization to enable the adaptation. In this way, the knowledge can be fully distilled and transferred to identify masked faces. Experiments on synthetic and realistic datasets show the efficacy of the proposed approach.
Fusion of heterogeneous attention mechanisms in multi-view convolutional neural network for text classification

The rapid proliferation of user generated content has given rise to large volumes of text corpora. Increasingly, scholars, researchers, and organizations employ text classification to mine novel insights for high-impact applications. Despite their prevalence, conventional text classification methods rely on labor-intensive feature engineering efforts that are task specific, omit long-term relationships, and are not suitable for the rapidly evolving domains. While an increasing body of deep learning and attention mechanism literature aim to address these issues, extant methods often represent text as a single view and omit multiple sets of features at varying levels of granularity. Recognizing that these issues often result in performance degradations, we propose a novel Spatial View Attention Convolutional Neural Network (SVA-CNN). SVA-CNN leverages an innovative and carefully designed set of multi-view representation learning, a combination of heterogeneous attention mechanisms and CNN-based operations to automatically extract and weight multiple granularities and fine-grained representations. Rigorously evaluating SVA-CNN against prevailing text classification methods on five large-scale benchmark datasets indicates its ability to outperform extant deep learning-based classification methods in both performance and training time for document classification, sentiment analysis, and thematic identification applications. To facilitate model reproducibility and extensions, SVA-CNN’s source code is also available via GitHub.
Pathways for climate change adaptations in arid and semi-arid regions

Climate variability and change coupled with small landholdings, low land productivity and water scarcity in arid and semi-arid regions contribute to environmental degradation, reduced agricultural productivity, and increased vulnerability to the rural communities. With the aid of the fuzzy cognitive maps constructed by 427 community groups with 4–5 members in each group, drawn from 96 villages in 12 districts of arid and semi-arid India, the paper evaluates the effectiveness of various adaptation pathways. The ongoing adaptations in arid and semi-arid India face adaptation deficits. The FCM-based simulations revealed that integrated adaptation measures that embrace nature-based solutions, including integrated water resource management, natural farming-assisted soil rejuvenation, and improved agricultural productivity are most likely to enhance the resilience of small and marginalised farming communities to climate variability and change. Facilitation of such adaptation measures requires inclusive and adaptive local institutions, sufficient financial assistance, and climate information services. Besides, gender-nuanced, inclusive, and adaptive governance and processes would be helpful for the implementation of appropriate adaptation interventions in arid and semi-arid drylands worldwide. Hence policy-makers must enable polycentric and adaptive governance, and inclusive institutions and processes. The emphasis on multiple factors in a socio-ecological system often makes it difficult to understand the critical role of a particular factor. However, the FCM-based simulations in this study helped us overcome such limitations.
Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder

With advancements of deep learning techniques, it is now possible to generate super-realistic images and videos, i.e., deepfakes. These deepfakes could reach mass audience and result in adverse impacts on our society. Although lots of efforts have been devoted to detect deepfakes, their performance drops significantly on previously unseen but related manipulations and the detection generalization capability remains a problem. Motivated by the fine-grained nature and spatial locality characteristics of deepfakes, we propose Locality-Aware AutoEncoder (LAE) to bridge the generalization gap. In the training process, we use a pixel-wise mask to regularize local interpretation of LAE to enforce the model to learn intrinsic representation from the forgery region, instead of capturing artifacts in the training set and learning superficial correlations to perform detection. We further propose an active learning framework to select the challenging candidates for labeling, which requires human masks for less than 3% of the training data, dramatically reducing the annotation efforts to regularize interpretations. Experimental results on three deepfake detection tasks indicate that LAE could focus on the forgery regions to make decisions. The analysis further shows that LAE outperforms the state-of-the-arts by 6.52%, 12.03%, and 3.08% respectively on three deepfake detection tasks in terms of generalization accuracy on previously unseen manipulations.
Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys

We present a bootstrapping procedure for the full-RNS variant of the approximate homomorphic-encryption scheme of Cheon et al., CKKS (Asiacrypt 17, SAC 18). Compared to the previously proposed procedures (Eurocrypt 18 & 19, CT-RSA 20), our bootstrapping procedure is more precise, more efficient (in terms of CPU cost and number of consumed levels), and is more reliable and 128-bit-secure. Unlike the previous approaches, it does not require the use of sparse secret-keys. Therefore, to the best of our knowledge, this is the first procedure that enables a highly efficient and precise bootstrapping with a low probability of failure for parameters that are 128-bit-secure under the most recent attacks on sparse R-LWE secrets. We achieve this efficiency and precision by introducing three novel contributions: (i) We propose a generic algorithm for homomorphic polynomial-evaluation that takes into account the approximate rescaling and is optimal in level consumption. (ii) We optimize the key-switch procedure and propose a new technique for linear transformations (double hoisting). (iii) We propose a systematic approach to parameterize the bootstrapping, including a precise way to assess its failure probability. We implemented our improvements and bootstrapping procedure in the open-source Lattigo library. For example, bootstrapping a plaintext in $$\mathbb {C}^{32768}$$ takes 18 s, has an output coefficient modulus of 505 bits, a mean precision of 19.1 bits, and a failure probability of $$2^{-15.58}$$ . Hence, we achieve 14.1 $$\times $$ improvement in bootstrapped throughput (plaintext-bit per second), with respect to the previous best results, and we have a failure probability 468 $$\times $$ smaller and ensure 128-bit security.
A Survey on Machine Reading Comprehension—Tasks, Evaluation Metrics and Benchmark Datasets

Machine Reading Comprehension (MRC) is a challenging Natural Language Processing (NLP) research field with wide real-world applications. The great progress of this field in recent years is mainly due to the emergence of large-scale datasets and deep learning. At present, a lot of MRC models have already surpassed human performance on various benchmark datasets despite the obvious giant gap between existing MRC models and genuine human-level reading comprehension. This shows the need for improving existing datasets, evaluation metrics, and models to move current MRC models toward “real” understanding. To address the current lack of comprehensive survey of existing MRC tasks, evaluation metrics, and datasets, herein, (1) we analyze 57 MRC tasks and datasets and propose a more precise classification method of MRC tasks with 4 different attributes; (2) we summarized 9 evaluation metrics of MRC tasks, 7 attributes and 10 characteristics of MRC datasets; (3) We also discuss key open issues in MRC research and highlighted future research directions. In addition, we have collected, organized, and published our data on the companion website where MRC researchers could directly access each MRC dataset, papers, baseline projects, and the leaderboard.
Improved mechanical and ultraviolet shielding performances of hydroxyethyl cellulose film by using aramid nanofibers as additives

Recently, aramid nanofibers (ANFs) have drawn the attention of scientist due to the high mechanical strength, high-temperature resistance, and high electrical and thermal insulation properties. In this work, we aimed at improving the mechanical and ultraviolet shielding properties of hydroxyethyl cellulose (HEC) film by using ANFs as additives. Mechanical results show that the 1.0 % ANFs could improve the tensile strength of pure HEC film by 176.6 %. Meanwhile, the ANFs additives can also enable the HEC film excellent ultraviolet (UV) shielding and visible light transmittance, as well as high UV radiation resistance ability. It is believed that the high mechanical strength of the HEC/ANFs composites is derived from the rearrangement of HEC chains along the tensile direction after the addition of hard ANFs and the enhanced hydrogen bonds between HEC and ANFs.
Dynamics analysis of fractional-order Hopfield neural networks

This paper proposes fractional-order systems for Hopfield Neural Network (HNN). The so-called Predictor–Corrector Adams–Bashforth–Moulton Method (PCABMM) has been implemented for solving such systems. Graphical comparisons between the PCABMM and the Runge–Kutta Method (RKM) solutions for the classical HNN reveal that the proposed technique is one of the powerful tools for handling these systems. To determine all Lyapunov exponents for them, the Benettin–Wolf algorithm has been involved in the PCABMM. Based on such algorithm, the Lyapunov exponents as a function of a given parameter and as another function of the fractional-order have been described, the intermittent chaos for these systems has been explored. A new result related to the Mittag–Leffler stability of some nonlinear Fractional-order Hopfield Neural Network (FoHNN) systems has been shown. Besides, the description and the dynamic analysis of those phenomena have been discussed and verified theoretically and numerically via illustrating the phase portraits and the Lyapunov exponents’ diagrams.
A boosted SVM classifier trained by incremental learning and decremental unlearning approach

The Support Vector Machines (SVM) classifier is a margin-based supervised machine learning method used for categorization and classification tasks. A Linear SVM classifier uses linear Kernels while a non-linear SVM classifier adopts non-linear Kernels. The Linear SVM classifier is considered an efficient technique, especially for big datasets of high dimensionality in various applications, such as document categorization, time-series classification, outliers' detection, to name a few. It is much faster to train a linear SVM classifier than a non-linear SVM classifier. For large-scale datasets with various shapes, configurations, and distributions, the computational complexity of training a non-linear SVM classifier is continuously evolving. Current research methods have introduced various problem formulations, solvers, and strategies for speeding up the training process of a non-linear SVM classifier. However, solving a quadratic programming (QP) problem is still challenging, especially for "Big Data", which poses a great challenge for traditional methods to train a non-linear classifier efficiently. In this paper, we are proposing a novel boosting algorithm to enhance the performance of weak non-linear SVM classifiers using the notion of incremental learning and decremental unlearning. Experimental results over artificial and real datasets with different sizes, shapes, and distributions show that the proposed ensemble boosting algorithm outperforms the individual SVM classifiers measured by the classification accuracy and the Speedup.
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction

At this moment, GAN-based image generation methods are still imperfect, whose upsampling design has limitations in leaving some certain artifact patterns in the synthesized image. Such artifact patterns can be easily exploited (by recent methods) for difference detection of real and GAN-synthesized images. However, the existing detection methods put much emphasis on the artifact patterns, which can become futile if such artifact patterns were reduced.
COMIC: Toward A Compact Image Captioning Model With Attention

Recent works in image captioning have shown very promising raw performance. However, we realize that most of these encoder-decoder style networks with attention do not scale naturally to large vocabulary size, making them difficult to be deployed on embedded system with limited hardware resources. This is because the size of word and output embedding matrices grow proportionally with the size of vocabulary, adversely affecting the compactness of these networks. To address this limitation, this paper introduces a brand new idea in the domain of image captioning. That is, we tackle the problem of compactness of image captioning models which is hitherto unexplored. We showed that, our proposed model, named COMIC for COMpact Image Captioning, achieves comparable results in five common evaluation metrics with state-of-the-art approaches on both MS-COCO and InstaPIC-1.1M datasets despite having an embedding vocabulary size that is 39x - 99x smaller. The source code and models are available at: https://github.com/jiahuei/COMIC-Compact-Image-Captioning-with-Attention
Feature detection and description for image matching: from hand-crafted design to deep learning

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Semi-Supervised Cyber Threat Identification in Dark Net Markets: A Transductive and Deep Learning Approach

Dark Net Marketplaces (DNMs), online selling platforms on the dark web, constitute a major component of the underground economy. Due to the anonymity and increasing accessibility of these platforms, they are rich sources of cyber threats such as hacking tools, data breaches, and personal account information. As the number of products offered on DNMs increases, researchers have begun to develop automated machine learning-based threat identification approaches. A major challenge in adopting such an approach is that the task typically requires manually labeled training data, which is expensive and impractical. We propose a novel semi-supervised labeling technique for leveraging unlabeled data based on the lexical and structural characteristics of DNMs using transductive learning. Empirical results show that the proposed approach leads to an approximately 3-5% increase in classification performance measured by F1-score, while increasing both precision and recall. To further improve the identification performance, we adopt Long Short-Term Memory (LSTM) as a deep learning structure on top of the proposed labeling method. The results are evaluated against a large collection of 79K product listings obtained from the most popular DNMs. Our method outperforms the state-of-the-art methods in threat identification and is considered as an important step toward lowering the human supervision cost in realizing automated threat detection within cyber threat intelligence organizations.
Genuine time-bin-encoded quantum key distribution over a turbulent depolarizing free-space channel

Despite its widespread use in fiber optics, encoding quantum information in photonic time-bin states is usually considered impractical for free-space quantum communication as turbulence-induced spatial distortion impedes the analysis of time-bin states at the receiver. Here, we demonstrate quantum key distribution using time-bin photonic states distorted by turbulence and depolarization during free-space transmission. Utilizing a novel analyzer apparatus, we observe stable quantum bit error ratios of 5.32 %, suitable for generating secure keys, despite significant wavefront distortions and polarization fluctuations across a 1.2 km channel. This shows the viability of time-bin quantum communication over long-distance free-space channels, which will simplify direct fiber/free-space interfaces and enable new approaches for practical free-space quantum communication over multi-mode, turbulent, or depolarizing channels.
In-Bed Pose Estimation: Deep Learning With Shallow Dataset

This paper presents a robust human posture and body parts detection method under a specific application scenario known as in-bed pose estimation. Although the human pose estimation for various computer vision (CV) applications has been studied extensively in the last few decades, the in-bed pose estimation using camera-based vision methods has been ignored by the CV community because it is assumed to be identical to the general purpose pose estimation problems. However, the in-bed pose estimation has its own specialized aspects and comes with specific challenges, including the notable differences in lighting conditions throughout the day and having pose distribution different from the common human surveillance viewpoint. In this paper, we demonstrate that these challenges significantly reduce the effectiveness of the existing general purpose pose estimation models. In order to address the lighting variation challenge, the infrared selective (IRS) image acquisition technique is proposed to provide uniform quality data under various lighting conditions. In addition, to deal with the unconventional pose perspective, a 2- end histogram of oriented gradient (HOG) rectification method is presented. The deep learning framework proves to be the most effective model in human pose estimation; however, the lack of large public dataset for in-bed poses prevents us from using a large network from scratch. In this paper, we explored the idea of employing a pre-trained convolutional neural network (CNN) model trained on large public datasets of general human poses and fine-tuning the model using our own shallow (limited in size and different in perspective and color) in-bed IRS dataset. We developed an IRS imaging system and collected IRS image data from several realistic life-size mannequins in a simulated hospital room environment. A pre-trained CNN called convolutional pose machine (CPM) was fine-tuned for in-bed pose estimation by re-training its specific intermediate layers. Using the HOG rectification method, the pose estimation performance of CPM improved significantly by 26.4% in the probability of correct key-point (PCK) criteria at PCK0.1 compared to the model without such rectification. Even testing with only well aligned in-bed pose images, our fine-tuned model still surpassed the traditionally tuned CNN by another 16.6% increase in pose estimation accuracy.
Quantum SDP-Solvers: Better upper and lower bounds

Brand\~ao and Svore very recently gave quantum algorithms for approximately solving semidefinite programs, which in some regimes are faster than the best-possible classical algorithms in terms of the dimension $n$ of the problem and the number $m$ of constraints, but worse in terms of various other parameters. In this paper we improve their algorithms in several ways, getting better dependence on those other parameters. To this end we develop new techniques for quantum algorithms, for instance a general way to efficiently implement smooth functions of sparse Hamiltonians, and a generalized minimum-finding procedure. We also show limits on this approach to quantum SDP-solvers, for instance for combinatorial optimizations problems that have a lot of symmetry. Finally, we prove some general lower bounds showing that in the worst case, the complexity of every quantum LP-solver (and hence also SDP-solver) has to scale linearly with $mn$ when $m\approx n$, which is the same as classical.
Toward Practical-Scale Quantum Annealing Machine for Prime Factoring

We propose a prime factorizer operated in a frame work of quantum annealing (QA).The idea is inverse operation of a multiplier implemented with QA-based Boolean logic circuits.We designed the QA machine on an application-specific-annealing-computing architecture which efficiently increases available hardware budgets at the cost of restricted functionality.The invertible operation of QA logic gates consisting of superconducting flux qubits was confirmed by circuit simulation with classical noise sources.The circuits were implemented and fabricated by using superconducting integrated circuit technologies with Nb/AlO x /Nb Josephson junctions.We also propose a 2.5-dimensional packaging scheme of a qubit-chip / interposer / package-substrate structure for realizing practically large-scale QA systems.