Reviewerly/exHarmony huggingface dataset:
Real-time 3-D shape measurement based on radial spatial carrier phase shifting from circular fringe pattern

In this paper, we propose a method to realize real-time three-dimensional (3-D) measurement with a radial spatial carrier phase shifting algorithm. The circular fringe patterns are continuously projected onto the moving object; the distorted fringe patterns are recorded by CCD. In the 3-D reconstruction process, three patterns consisting of one recorded image and two artificial phase shifting fringe patterns are used to implement phase shifting, where the two artificial patterns are formed by numerically shifting the recorded image along the radial direction. Compared with traditional carrier phase shifting method, the proposed method possesses lower phase error and higher accuracy. The simulations and two experiments are carried out to testify the proposed method. From the experimental results of the static object, we find that the standard deviation of the proposed method is better than that of the traditional one. The dynamic experiment of a moving lampshade shows that the time resolution of our method is the same as the CCD. As a real-time quantitative dynamic measurement method, it can be applied in various fields.
Double-Head RCNN: Rethinking Classification and Localization for Object Detection

Two head structures (i.e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks. However, there is a lack of understanding of how does these two head structures work for these two tasks. To address this issue, we perform a thorough analysis and find an interesting fact that the two head structures have opposite preferences towards the two tasks. Specifically, the fully connected head (fc-head) is more suitable for the classification task, while the convolution head (conv-head) is more suitable for the localization task. Furthermore, we examine the output feature maps of both heads and find that fc-head has more spatial sensitivity than conv-head. Thus, fc-head has more capability to distinguish a complete object from part of an object, but is not robust to regress the whole object. Based upon these findings, we propose a Double-Head method, which has a fully connected head focusing on classification and a convolution head for bounding box regression. Without bells and whistles, our method gains +3.5 and +2.8 AP on MS COCO dataset from Feature Pyramid Network (FPN) baselines with ResNet-50 and ResNet-101 backbones, respectively.
Collaborative intelligence and gamification for on-line malaria species differentiation

Current World Health Organization recommendations for the management of malaria include the need for a parasitological confirmation prior to triggering appropriate treatment. The use of rapid diagnostic tests (RDTs) for malaria has contributed to a better infection recognition and a more targeted treatment. Nevertheless, low-density infections and parasites that fail to produce HRP2 can cause false-negative RDT results. Microscopy has traditionally been the methodology most commonly used to quantify malaria and characterize the infecting species, but the wider use of this technique remains challenging, as it requires trained personnel and processing capacity.In this study, the feasibility of an on-line system for remote malaria species identification and differentiation has been investigated by crowdsourcing the analysis of digitalized infected thin blood smears by non-expert observers using a mobile app.An on-line videogame in which players learned how to differentiate the young trophozoite stage of the five Plasmodium species has been designed. Images were digitalized with a smartphone camera adapted to the ocular of a conventional light microscope. Images from infected red blood cells were cropped and puzzled into an on-line game. During the game, players had to decide the malaria species (Plasmodium falciparum, Plasmodium malariae, Plasmodium vivax, Plasmodium ovale, Plasmodium knowlesi) of the infected cells that were shown in the screen. After 2 months, each player's decisions were analysed individually and collectively.On-line volunteers playing the game made more than 500,000 assessments for species differentiation. Statistically, when the choice of several players was combined (n > 25), they were able to significantly discriminate Plasmodium species, reaching a level of accuracy of 99% for all species combinations, except for P. knowlesi (80%). Non-expert decisions on which Plasmodium species was shown in the screen were made in less than 3 s.These findings show that it is possible to train malaria-naïve non-experts to identify and differentiate malaria species in digitalized thin blood samples. Although the accuracy of a single player is not perfect, the combination of the responses of multiple casual gamers can achieve an accuracy that is within the range of the diagnostic accuracy made by a trained microscopist.
List Decoding with Double Samplers

PREVIOUS ARTICLEAnalyzing Boolean functions on the biased hypercube via higher-dimensional agreement tests: [Extended abstract]
Detection of Salient Crowd Motion Based on Repulsive Force Network and Direction Entropy

This paper proposes a method for salient crowd motion detection based on direction entropy and a repulsive force network. This work focuses on how to effectively detect salient regions in crowd movement through calculating the crowd vector field and constructing the weighted network using the repulsive force. The interaction force between two particles calculated by the repulsive force formula is used to determine the relationship between these two particles. The network node strength is used as a feature parameter to construct a two-dimensional feature matrix. Furthermore, the entropy of the velocity vector direction is calculated to describe the instability of the crowd movement. Finally, the feature matrix of the repulsive force network and direction entropy are integrated together to detect the salient crowd motion. Experimental results and comparison show that the proposed method can efficiently detect the salient crowd motion.
Component Semantic Prior Guided Generative Adversarial Network for Face Super-Resolution

Face super-resolved (SR) images aid human perception. The state-of-the-art face SR methods leverage the spatial location of facial components as prior knowledge. However, it remains a great challenge to generate natural textures. In this paper, we propose a component semantic prior guided generative adversarial network (CSPGAN) to synthesize faces. Specifically, semantic probability maps of facial components are exploited to modulate features in the CSPGAN through affine transformation. To compensate for the overly smooth performance of the generative network, a gradient loss is proposed to recover the high-frequency details. Meanwhile, the discriminative network is designed to perform multiple tasks which predict semantic category and distinguish authenticity simultaneously. The extensive experimental results demonstrate the superiority of the CSPGAN in reconstructing photorealistic textures.
Effective Video Stabilization via Joint Trajectory Smoothing and Frame Warping

Video stabilization is usually composed of three stages: feature trajectory extraction, trajectory smoothing, and frame warping. Most previous approaches view them as three separate stages. This paper proposes a method combining the last two stages, namely the trajectory smoothing and frame warping stages, into a single optimization framework. The novelty exists in the way of how we combine them: the trajectory smoothing part plays a major role while the frame warping part plays an auxiliary role. With this kind of design, we can conveniently increase the strength of the trajectory smoothing part by a robust first-order derivative term, which makes it possible to produce very aggressive stabilization effects. On the other hand, we adopt adaptive weighting mechanisms in the frame warping part, to follow the smoothed trajectories as much as possible while regularizing other places as similar as possible. Our method is robust to utilize both foreground and background features, and very short trajectories. The utilization of all these information in turn increases the accuracy of the proposed method. We also provide a simplified implementation of our method, which is less accurate but more efficient. Experiments on various kinds of videos demonstrate the effectiveness of our method.
Multi-Precision Quantized Neural Networks via Encoding Decomposition of {-1,+1}

The training of deep neural networks (DNNs) requires intensive resources both for computation and for storage performance. Thus, DNNs cannot be efficiently applied to mobile phones and embedded devices, which seriously limits their applicability in industry applications. To address this issue, we propose a novel encoding scheme of using {−1, +1} to decompose quantized neural networks (QNNs) into multibranch binary networks, which can be efficiently implemented by bitwise operations (xnor and bitcount) to achieve model compression, computational acceleration and resource saving. Based on our method, users can easily achieve different encoding precisions arbitrarily according to their requirements and hardware resources. The proposed mechanism is very suitable for the use of FPGA and ASIC in terms of data storage and computation, which provides a feasible idea for smart chips. We validate the effectiveness of our method on both large-scale image classification tasks (e.g., ImageNet) and object detection tasks. In particular, our method with lowbit encoding can still achieve almost the same performance as its full-precision counterparts.
Discrete Sliding Mode Control for Chaos Synchronization and Its Application to an Improved El-Gamal Cryptosystem

This paper is concerned with the design of an improved El-Gamal cryptosystem based on chaos synchronization. The El-Gamal cryptosystem is an asymmetric encryption algorithm that must use the public and private keys, respectively, in the encryption and decryption processes. However, in our design, the public key does not have to appear in the public channel. Therefore, this proposed improved El-Gamal cryptosystem becomes a symmetric-like encryption algorithm. First, a discrete sliding mode controller is proposed to ensure the synchronization of master and slave chaotic systems; next, a novel improved El-Gamal cryptosystem is presented. In the traditional El-Gamal cryptosystem, the public key is static and needs to be open which provides an opportunity to attack. However, in this improved design, due to the chaos synchronization, the public key becomes dynamic and does not appear in public channels. As a result, drawbacks of long cipher text and time-consuming calculation in the traditional El-Gamal cryptosystem are all removed. Finally, several performance tests and comparisons have shown the efficiency and security of the proposed algorithm.
Medical Augmented-Reality Visualizer for Surgical Training and Education in Medicine

This paper presents a projection-based augmented-reality system (MARVIS) that supports the visualization of internal structures on the surface of a liver phantom. MARVIS is endowed with three key features: tracking of spatial relationship between the phantom and the operator’s head in real time, monoscopic projection of internal liver structures onto the phantom surface for 3D perception without additional head-mounted devices, and phantom internal electronic circuit to assess the accuracy of a syringe guidance system. An initial validation was carried out by 25 medical students (12 males and 13 females; mean age, 23.12 years; SD, 1.27 years) and 3 male surgeons (mean age, 43.66 years; SD, 7.57 years). The validation results show that the ratio of failed syringe insertions was reduced from 50% to 30% by adopting the MARVIS projection. The proposed system suitably enhances a surgeon’s spatial perception of a phantom internal structure.
A Wearable Fall Detection System Using Deep Learning

Due to the growing aging of the population and the impact of falls on the health and autonomy of the older people, the development of cost-effective non-invasive automatic fall detection systems (FDS) has gained much attention. This work proposes and analyzes the capability of convolutional deep neural networks to detect fall events based on the measurements captured by wearable tri-axial accelerometers that are transported by the user to characterize the mobility of the body. The study is performed on a long public data repository containing the traces obtained from a wide group of experimental users during the execution of a predetermined set of Activities of the Daily Living (ADLs) and mimicked falls. The system is evaluated in term of accuracy, sensitivity and specificity when the network is alternatively fed with the module of the acceleration and the with the tri-axial components of the acceleration.
IELE: A Rigorously Designed Language and Tool Ecosystem for the Blockchain

This paper proposes IELE, an LLVM-style language, together with a tool ecosystem for implementing and formally reasoning about smart contracts on the blockchain. IELE was designed by specifying its semantics formally in the K framework. Its implementation, a IELE virtual machine (VM), as well as a formal verification tool for IELE smart contracts, were automatically generated from the formal specification. The automatically generated formal verification tool allows us to formally verify smart contracts without any gap between the verifier and the actual VM. A compiler from Solidity, the predominant high-level language for smart contracts, to IELE has also been (manually) implemented, so Ethereum contracts can now also be executed on IELE.
New one-step model of breast tumor locating based on deep learning

Breast cancer has the highest cancer prevalence rate among the women worldwide. Early detection of breast cancer is crucial for successful treatment and reducing cancer mortality rate. However, tumor detection of breast ultrasound (US) image is still a challenging work in computer-aided diagnosis (CAD).This study aims to develop a novel automated algorithm for breast tumor detection based on deep learning.We proposed a new deep learning network named One-step model which have one input and two outputs, the first one was the segmentation result and the other one was used for false-positive reduction. The proposed One-step model includes three key components: Base-net, Seg-net, and Cls-net based on Anchor Box. The model chose DenseNet to construct Base-net, the decoder part of RefineNet as Seg-net, and connected several middle layers of Base-net and Seg-net to Cls-net. From the first output acquired by Base-net and Seg-net, the model detected a series of suspicious lesion regions. Then the second output from the Cls-net was used to recognize and reduce the false-positive regions.Experimental results showed that the new model achieved competitive detection result with 90.78% F1 score, which was 8.55% higher than Single Shot MultiBox Detector (SSD) method. In addition, running new model is also computational efficient and has comparative cost effect as SSD.We established a novel One-step model which improves location accuracy by generating more precise bounding box via Seg-net and removing false targets by another object detection network (Cls-net). On the other hand, a real-time detection of tumor is achieved by sharing the common Base-net. The experimental results showed that the new model performed well on various irregular and blurred ultrasound images. As a result, this study demonstrated feasibility of applying deep learning scheme to detect breast lesions depicting on US image.
To Transfer or Not: An Online Cost Optimization Algorithm for Using Two-Tier Storage-as-a-Service Clouds

Currently, Storage-as-a-Service (StaaS) clouds offer multiple data storage and access pricing options which usually consist of hot and cold tiers. The cold tier storage option offers a lower storage price while the hot tier storage option offers a lower access price. Cloud users need to choose an optimal tier to store their data objects economically based on the frequency of accesses to their data objects. Besides, StaaS cloud users can transfer data objects between these two tiers to save cost according to the varying frequency of accesses to their data objects. Therefore, in order to make optimal transferring decisions, future access curves are needed to be predicted. However, for cloud users, it is difficult to precisely predict future access frequencies for their data objects. In this paper, we propose an online algorithm to guide StaaS cloud users in making decisions on whether and when to transfer their data objects between cold and hot tiers for achieving cost optimizations, while users do not need to have any prior knowledge of future access frequencies. We prove theoretically that the proposed online algorithm can achieve guaranteed competitive ratios for data objects stored in a two-tier StaaS cloud. Finally, through extensive experiments, we validate the effectiveness of our proposed online algorithm and show that it can save costs significantly compared with always keeping data objects in one tier or always transferring data objects from one tier to the other when their access frequencies begin to vary.
Improving GAN with Neighbors Embedding and Gradient Matching

We propose two new techniques for training Generative Adversarial Networks (GANs) in the unsupervised setting. Our objectives are to alleviate mode collapse in GAN and improve the quality of the generated samples. First, we propose neighbor embedding, a manifold learning-based regularization to explicitly retain local structures of latent samples in the generated samples. This prevents generator from producing nearly identical data samples from different latent samples, and reduces mode collapse. We propose an inverse t-SNE regularizer to achieve this. Second, we propose a new technique, gradient matching, to align the distributions of the generated samples and the real samples. As it is challenging to work with high-dimensional sample distributions, we propose to align these distributions through the scalar discriminator scores. We constrain the difference between the discriminator scores of the real samples and generated ones. We further constrain the difference between the gradients of these discriminator scores. We derive these constraints from Taylor approximations of the discriminator function. We perform experiments to demonstrate that our proposed techniques are computationally simple and easy to be incorporated in existing systems. When Gradient matching and Neighbour embedding are applied together, our GN-GAN achieves outstanding results on 1D/2D synthetic, CIFAR-10 and STL-10 datasets, e.g. FID score of 30.80 for the STL-10 dataset. Our code is available at: https://github.com/tntrung/gan
Multiple Organ Localization in CT Image Using Triple-Branch Fully Convolutional Networks

Organ localization is an essential prerequisite for many computed tomography (CT) image analysis tasks such as organ segmentation, lesion detection, and image registration.However, it is a challenging problem due to various causes such as the low-contrast organ boundaries, the large variations of organ appearance, the truncated organs, and the abnormalities.In this work, we propose an automatic method for multiple organ localization in CT image using a novel triple-branch fully convolutional network (FCN).First, the input CT image is extended to a three-channel image that contains both the original image and the enhanced organ structures using a density enhancement filter.Then, the extended three-channel image is fed to a 3D triple-branch FCN to parallelly predict the organs' presence probability curves along axial, coronal and sagittal directions.Finally, the bounding boxes of the target organs are computed based on these presence probability curves.The triple-branch FCN is fully implemented in 3D manner, thus it can take full advantage of the spatial context information in CT image to perform accurate organ localization.Only several basic convolutional neural networks (ConvNet) operations (such as convolution, pooling and softmax regression) are involved in this triple-branch FCN, thus it is easy-implemented and flexible to serve as a preprocessing module in other medical image analysis tasks.By enhancing the structural information in the CT image using the density enhancement filter, the performance of the triple-branch FCN can be further improved.The proposed method is evaluated on a challenging public dataset with 201 clinical abdominal/torso CT images to locate 11 body organs.As the experimental results showed, the proposed method achieves higher localization accuracy than the current state-of-the-art methods.
Towards instance-optimal private query release

We study efficient mechanisms for the query release problem in differential privacy: given a workload of m statistical queries, output approximate answers to the queries while satisfying the constraints of differential privacy. In particular, we are interested in mechanisms that optimally adapt to the given workload. Building on the projection mechanism of Nikolov, Talwar, and Zhang, and using the ideas behind Dudley's chaining inequality, we propose new efficient algorithms for the query release problem, and prove that they achieve optimal sample complexity for the given workload (up to constant factors, in certain parameter regimes) with respect to the class of mechanisms that satisfy concentrated differential privacy. We also give variants of our algorithms that satisfy local differential privacy, and prove that they also achieve optimal sample complexity among all local sequentially interactive private mechanisms.
DPASF: a flink library for streaming data preprocessing

Data preprocessing techniques are devoted to correcting or alleviating errors in data. Discretization and feature selection are two of the most extended data preprocessing techniques. Although we can find many proposals for static Big Data preprocessing, there is little research devoted to the continuous Big Data problem. Apache Flink is a recent and novel Big Data framework, following the MapReduce paradigm, focused on distributed stream and batch data processing. In this paper, we propose a data stream library for Big Data preprocessing, named DPASF, under Apache Flink. The library is composed of six of the most popular and widely used data preprocessing algorithms. It contains three algorithms for discretization, and three algorithms for performing feature selection. The algorithms have been tested using two Big Data datasets. Experimental results show that preprocessing can not only reduce the size of the data, but also maintain or even improve the original accuracy in a short period of time. DPASF contains algorithms that are useful when dealing with Big Data data streams. The preprocessing algorithms included in the library are able to tackle Big Datasets efficiently and to correct imperfections in the data.
Visual Story Post-Editing

We introduce the first dataset for human edits of machine-generated visual stories and explore how these collected edits may be used for the visual story post-editing task.The dataset, VIST-Edit 1 , includes 14,905 humanedited versions of 2,981 machine-generated visual stories.The stories were generated by two state-of-the-art visual storytelling models, each aligned to 5 human-edited versions.We establish baselines for the task, showing how a relatively small set of human edits can be leveraged to boost the performance of large visual storytelling models.We also discuss the weak correlation between automatic evaluation scores and human ratings, motivating the need for new automatic metrics.