Reviewerly/exHarmony huggingface dataset:
Occlusion-Aware Siamese Network for Human Pose Estimation

Pose estimation usually suffers from varying degrees of performance degeneration owing to occlusion. To conquer this dilemma, we propose an occlusion-aware siamese network to improve the performance. Specifically, we introduce scheme of feature erasing and reconstruction. Firstly, we utilize attention mechanism to predict the occlusion-aware attention map which is explicitly supervised and clean the feature map which is contaminated by different types of occlusions. Nevertheless, the cleaning procedure not only removes the useless information but also erases some valuable details. To overcome the defects caused by the erasing operation, we perform feature reconstruction to recover the information destroyed by occlusion and details lost in cleaning procedure. To make reconstructed features more precise and informative, we adopt siamese network equipped with OT divergence to guide the features of occluded images towards those of the un-occluded images. Algorithm is validated on MPII, LSP and COCO benchmarks and we achieve promising results.
Machine-learning model to predict the cause of death using a stacking ensemble method for observational data

Abstract Objective Cause of death is used as an important outcome of clinical research; however, access to cause-of-death data is limited. This study aimed to develop and validate a machine-learning model that predicts the cause of death from the patient’s last medical checkup. Materials and Methods To classify the mortality status and each individual cause of death, we used a stacking ensemble method. The prediction outcomes were all-cause mortality, 8 leading causes of death in South Korea, and other causes. The clinical data of study populations were extracted from the national claims (n = 174 747) and electronic health records (n = 729 065) and were used for model development and external validation. Moreover, we imputed the cause of death from the data of 3 US claims databases (n = 994 518, 995 372, and 407 604, respectively). All databases were formatted to the Observational Medical Outcomes Partnership Common Data Model. Results The generalized area under the receiver operating characteristic curve (AUROC) of the model predicting the cause of death within 60 days was 0.9511. Moreover, the AUROC of the external validation was 0.8887. Among the causes of death imputed in the Medicare Supplemental database, 11.32% of deaths were due to malignant neoplastic disease. Discussion This study showed the potential of machine-learning models as a new alternative to address the lack of access to cause-of-death data. All processes were disclosed to maintain transparency, and the model was easily applicable to other institutions. Conclusion A machine-learning model with competent performance was developed to predict cause of death.
A new secure image encryption algorithm based on a 5D hyperchaotic map

Image encryption is an effective method for protecting private images during communication. In this paper, a novel image encryption method is proposed based on a 5D hyperchaotic system. Since a 5D hyperchaotic system can generate more complex dynamic behavior than a low-dimensional system, it is used in this paper to generate pseudorandom number sequences. The generated sequences are processed to obtain new sequences. The randomness of the new sequences is improved by recombination and rearrangement. The experimental results and theoretical analysis show that the method possesses a large key space and can resist differential attacks, statistical analysis, entropy analysis, clipping attacks and noise attacks. Therefore, it is very secure and can be used for secure communication.
280-km experimental demonstration of a quantum digital signature with one decoy state

A quantum digital signature (QDS) guarantees the unforgeability, nonrepudiation, and transferability of signature messages with information-theoretic security, and hence has attracted much attention recently. However, most previous implementations of QDS showed relatively low signature rates and/or short transmission distance. In this Letter, we report a proof-of-principle phase-encoding QDS demonstration using only one decoy state. First, such a method avoids the modulation of the vacuum state, thus reducing experimental complexity and random number consumption. Moreover, incorporated with low-loss asymmetric Mach–Zehnder interferometers and a real-time polarization calibration technique, we have successfully achieved a higher signature rate, e.g., 0.98 bit/s at 103 km, and to date, a record-breaking, to the best of our knowledge, transmission distance of over 280-km installed fibers. Our work represents a significant step towards real-world applications of QDS.
Conditional Inference Trees and Random Forests

This chapter discusses popular non-parametric methods in corpus linguistics: conditional inference trees and conditional random forests. These methods, which allow the researcher to model and interpret the relationships between a numeric or categorical response variable and various predictors, are particularly attractive in ‘tricky’ situations, when the use of parametric methods (in particular, regression models) can be problematic, for example, in the situations of ‘small n, large p’, complex interactions, non-linearity and correlated predictors. For illustration, the chapter discusses a case study of T and V politeness forms in Russian based on a corpus of film subtitles.
Microblog sentiment analysis via embedding social contexts into an attentive LSTM

With the rise of microblogging services like Twitter and Sina Weibo, users are able to post various contents on breaking news, public events, or products conveniently and swiftly. These massive contents carry users’ mass sentiment and opinions on various topics, which are a kind of useful and timely source. Traditional microblog sentiment analysis methods often assume that microblogs are independent and identically distributed, they ignore the fact that the microblogs are networked data. Although some methods take the relations between microblogs into consideration, they only use shallow network features which are not sufficient, such as neighbors. Besides, these methods are content-based methods because they cannot use social context information in the prediction stage. To solve this problem, in this paper we use a deep learning method to fully capture the features of microblog relations including both the implicit and explicit ones and use these features to promote microblog sentiment analysis results. Specifically, we first construct a graph which models the relations between microblogs inspired by sentiment consistency and emotional contagion theories. Then we embed the microblog graph and get a continuous vector representation for social contexts of each microblog. After that, we propose a novel neural network to integrate social context knowledge with text information. To handle the problem that different words have different contributions to the classification result, we introduce the attention mechanism into our model. We conduct experiments on three publicly released datasets. The experimental results show that our proposed model can outperform state-of-the-art methods consistently and significantly.
Hybrid classical-quantum linear solver using Noisy Intermediate-Scale Quantum machines

We propose a realistic hybrid classical-quantum linear solver to solve systems of linear equations of a specific type, and demonstrate its feasibility using Qiskit on IBM Q systems. This algorithm makes use of quantum random walk that runs in $\mathcal{O}(N\log(N))$ time on a quantum circuit made of $\mathcal{O}(\log(N))$ qubits. The input and output are classical data, and so can be easily accessed. It is robust against noise, and ready for implementation in applications such as machine learning.
Sensitive periods in cognitive development: a mutualistic perspective

The theory of mutualism posits that general cognitive ability emerges, at least in part, due to positive reciprocal interaction between distinct cognitive abilities during development. In other words, cognitive abilities at a given developmental time point govern the rate of growth in other cognitive abilities. Moreover, this emerging field of work finds that the strength as well as the nature of these interactions differs during developmental time: In other words, there are sensitive periods when small individual differences may have especially pronounced, long-lasting consequences for cognitive development. Here, I review the literature on mutualistic effects, and show how it can shed new light on sensitive periods. I do so by considering sensitive periods as periods where interactions between (cognitive) domains differ in strength and/or in kind.
Deep Joint Source-channel Coding for Wireless Image Transmission

We propose a novel joint source and channel coding (JSCC) scheme for wireless image transmission that departs from the conventional use of explicit source and channel codes for compression and error correction, and directly maps the image pixel values to the complex-valued channel input signal. Our encoder-decoder pair form an autoencoder with a non-trainable layer in the middle, which represents the noisy communication channel. Our results show that the proposed deep JSCC scheme outperforms separation-based digital transmission at low signal-to-noise ratio (SNR) and low channel bandwidth regimes in the presence of additive white Gaussian noise (AWGN). More strikingly, deep JSCC does not suffer from the "cliff effect" as the channel SNR varies with respect to the SNR value assumed during training. In the case of a slow Rayleigh fading channel, deep JSCC can learn to communicate without explicit pilot signals or channel estimation, and significantly outperforms separation-based digital communication at all SNR and channel bandwidth values.
A Locality-Aware Energy-Efficient Accelerator for Graph Mining Applications

Graph mining is becoming increasingly important due to the ever-increasing demands on analyzing complex structures in graphs. Existing graph accelerators typically hold most of the randomly-accessed data in an on-chip memory to avoid off-chip communications. However, graph mining exhibits substantial random accesses from not only vertex dimension but also edge dimension (with the latter being excessively more complex than the former), leading to significant degradations in terms of both performance and energy efficiency.We observe that the most random memory requests arising in graph mining come from accessing a small fraction of valuable (vertex and edge) data when handling real-world graphs. To exploit this extension locality with maximum parallelism, we architect GRAMER, the first graph mining accelerator. GRAMER contains a specialized memory hierarchy, where the valuable data (precisely identified through a cost-efficient heuristic) is permanently resident in a high-priority memory while others are maintained in a cache-like memory under a lightweight replacement policy. The specific pipelined processing units are carefully designed to maximize computational parallelism. GRAMER is also equipped with a work-stealing mechanism to reduce load imbalance. We have implemented GRAMER on a Xilinx Alveo U250 accelerator card. Compared with two state-of-the-art CPU-based graph mining systems, Fractal and RStream, running on a 14-core Intel E5-2680 v4 processor, GRAMER achieves not only considerable speedups (1.11 × ~ 129.95 ) but also significant energy savings (5.79 × ~ 678.34×)
Multiplicative up-drift

Drift analysis aims at translating the expected progress of an evolutionary algorithm (or more generally, a random process) into a probabilistic guarantee on its run time (hitting time). So far, drift arguments have been successfully employed in the rigorous analysis of evolutionary algorithms, however, only for the situation that the progress is constant or becomes weaker when approaching the target.
Class-Aware Domain Adaptation for Semantic Segmentation of Remote Sensing Images

Unsupervised domain adaptation (UDA) for the semantic segmentation of remote sensing images is challenging since the same class of objects may have different spectra while the different class of objects may have the same spectrum. To address this issue, we propose a class-aware generative adversarial network (CaGAN) for UDA semantic segmentation of multisource remote sensing images, which explicitly models the discrepancies of intraclass and the interclass between the source domain images with labels and the target domain images without labels. Specifically, first, to enhance the global domain alignment (GDA), we propose a transferable attention alignment (TAA) procedure to add more fine-grained features into the adversarial learning framework. Then, we propose a novel class-aware domain alignment (CDA) approach in semantic segmentation. CDA mainly includes two parts: the first one is adaptive category selection, which is to alleviate the class imbalance and select the reliable per-category centers in the source and target domains; the second one is adaptive category alignment, which is to model the intraclass compactness and interclass separability from source-only, target-only, and joint source and target images. Finally, the CDA plays as a penalty of GDA to train GaGAN in an alternating and iterative manner. Experiments on domain adaptation of space to space, spectrum to spectrum, both space-to-space and spectrum-to-spectrum data sets demonstrate that CaGAN outperforms the current state-of-the-art methods, which may serve as a starting point and baseline for the comprehensive applications of semantic segmentation in cross-space and cross-spectrum remote sensing images.
Brain tumor segmentation in MR images using a sparse constrained level set algorithm

Brain tumor segmentation using Magnetic Resonance (MR) Imaging technology plays a significant role in computer-aided brain tumor diagnosis. However, when applying classic segmentation methods, limitations such as inhomogeneous intensity, complex physiological structure and blurred tissues boundaries in brain MR images usually lead to unsatisfactory results. To address these issues, this paper proposes an automatic sparse constrained level set method to realize the brain tumor segmentation in MR images. By studying brain tumor images, this method finds out common characteristics of brain tumors’ shape and constructs a sparse representation model. By considering this model as a prior constraint, an energy function based on level set method is constructed. In experiments, the proposed method can achieve an average accuracy of 96.20% for the MR images from the dataset Brats2017 and performs better than the others. With lower false positive rate and stronger robustness, the experimental results show that the proposed method can segment brain tumor from MR image accurately and stably.
Beyond Monocular Deraining: Stereo Image Deraining via Semantic Understanding

Rain is a common natural phenomenon. Taking images in the rain however often results in degraded quality of images, thus compromises the performance of many computer vision systems. Most existing de-rain algorithms use only one single input image and aim to recover a clean image. Few work has exploited stereo images. Moreover, even for single image based monocular deraining, many current methods fail to complete the task satisfactorily because they mostly rely on per pixel loss functions and ignore semantic information. In this paper, we present a Paired Rain Removal Network (PRRNet), which exploits both stereo images and semantic information. Specifically, we develop a Semantic-Aware Deraining Module (SADM) which solves both tasks of semantic segmentation and deraining of scenes, and a Semantic-Fusion Network (SFNet) and a View-Fusion Network (VFNet) which fuse semantic information and multi-view information respectively. We also propose new stereo based rainy datasets for benchmarking. Experiments on both monocular and the newly proposed stereo rainy datasets demonstrate that the proposed method achieves the state-of-the-art performance.
Explanations of Black-Box Model Predictions by Contextual Importance and Utility

The significant advances in autonomous systems together with an immensely wider application domain have increased the need for trustable intelligent systems. Explainable artificial intelligence is gaining considerable attention among researchers and developers to address this requirement. Although there is an increasing number of works on interpretable and transparent machine learning algorithms, they are mostly intended for the technical users. Explanations for the end-user have been neglected in many usable and practical applications. In this work, we present the Contextual Importance (CI) and Contextual Utility (CU) concepts to extract explanations that are easily understandable by experts as well as novice users. This method explains the prediction results without transforming the model into an interpretable one. We present an example of providing explanations for linear and non-linear models to demonstrate the generalizability of the method. CI and CU are numerical values that can be represented to the user in visuals and natural language form to justify actions and explain reasoning for individual instances, situations, and contexts. We show the utility of explanations in car selection example and Iris flower classification by presenting complete (i.e. the causes of an individual prediction) and contrastive explanation (i.e. contrasting instance against the instance of interest). The experimental results show the feasibility and validity of the provided explanation methods.
Neural Manipulation Planning on Constraint Manifolds

The presence of task constraints imposes a significant challenge to motion planning. Despite all recent advancements, existing algorithms are still computationally expensive for most planning problems. In this paper, we present Constrained Motion Planning Networks (CoMPNet), the first neural planner for multimodal kinematic constraints. Our approach comprises the following components: i) constraint and environment perception encoders; ii) neural robot configuration generator that outputs configurations on/near the constraint manifold(s), and iii) a bidirectional planning algorithm that takes the generated configurations to create a feasible robot motion trajectory. We show that CoMPNet solves practical motion planning tasks involving both unconstrained and constrained problems. Furthermore, it generalizes to new unseen locations of the objects, i.e., not seen during training, in the given environments with high success rates. When compared to the state-of-the-art constrained motion planning algorithms, CoMPNet outperforms by order of magnitude improvement in computational speed with a significantly lower variance.
Deep learning side-channel attack against hardware implementations of AES

In the field of image recognition, machine learning technologies, especially deep learning, have been rapidly advancing alongside the advances of hardware such as GPUs. In image recognition, in general, large numbers of labeled images to be identified are input to a neural network, and repeatedly learning the images enables the neural network to identify objects with high accuracy. A new profiling side-channel attack method, the deep learning side-channel attack (DL-SCA), utilizes the neural network’s high identifying ability to unveil a cryptographic module’s secret key from side-channel information. In DL-SCAs, the neural network is trained with power waveforms captured from a target cryptographic module, and the trained network extracts the leaky part that depends on the secret. However, at this stage, the main target of investigation has been software implementation, and studies regarding hardware implementation, such as ASIC, are somewhat lacking. In this paper, we first depict deep learning techniques, profiling side-channel attacks, and leak models to clarify the relation between secret and side channels. Next, we investigate the use of DL-SCA against hardware implementations of AES and discuss the problem derived from the Hamming distance model and ShiftRow operation of AES. To solve the problem, we propose a new network training method called “mixed model dataset based on round-round XORed value.” We prove that our proposal solves the problem and gives the attack capability to neural networks. We also compare the attack performance and characteristics of DL-SCA to conventional analysis methods such as correlation power analysis and conventional template attack. In our experiment, a dedicated ASIC chip for side-channel analysis is utilized and the chip is also equipped with a side-channel countermeasure AES. We show how DL-SCA can recover secret keys against the side-channel countermeasure circuit. Our results demonstrate that DL-SCA can be a more powerful option against side-channel countermeasure implementations than conventional SCAs.
An empirical analysis of the use of agricultural mobile applications among smallholder farmers in Myanmar

Abstract Mobile phone applications (apps) designed to assist smallholder farmers improve decision‐making have been revolutionizing the agriculture sector. These apps offer solutions to farmer information needs by providing weather information, crop market trends, pest and disease damage identification, and advice on pesticide and fertilizer use. They also facilitate interaction with fellow farmers, extension workers and other stakeholders in the value chain who are interested in information exchange. Much previous research has investigated the contribution of mobile apps to agricultural production. This study explored the agricultural mobile apps available in Myanmar, analyzed factors affecting their use and assessed the potential for farm‐based decision support. Our findings indicate that when introducing mobile‐based tools, focus should be given to younger, more educated farmers growing more specialized crops. The main constraints to adopt agricultural apps are lack of access to smartphone and/or internet (63%) and lack of digital knowledge (20%). However, smallholder farmers in Myanmar were optimistic and positive toward agricultural apps for effective utilization. We also found that majority of the surveyed farmers were familiar with information received through Facebook groups. Incorporating useful information and functions from an agricultural mobile app to a Facebook Page could have a more useful and sustainable impact.
TP-LSD: Tri-Points Based Line Segment Detector

This paper proposes a novel deep convolutional model, Tri-Points Based Line Segment Detector (TP-LSD), to detect line segments in an image at real-time speed. The previous related methods typically use the two-step strategy, relying on either heuristic post-process or extra classifier. To realize one-step detection with a faster and more compact model, we introduce the tri-points representation, converting the line segment detection to the end-to-end prediction of a root-point and two endpoints for each line segment. TP-LSD has two branches: tri-points extraction branch and line segmentation branch. The former predicts the heat map of root-points and the two displacement maps of endpoints. The latter segments the pixels on straight lines out from background. Moreover, the line segmentation map is reused in the first branch as structural prior. We propose an additional novel evaluation metric and evaluate our method on Wireframe and YorkUrban datasets, demonstrating not only the competitive accuracy compared to the most recent methods, but also the real-time run speed up to 78 FPS with the $$320\times 320$$ input.