Reviewerly/exHarmony huggingface dataset:
Adaptive local-fitting-based active contour model for medical image segmentation

Due to the complex environments, characteristics of samples and limits of acquisition instruments, medical images are often acquired with intensity inhomogeneity and noise. These two consequences have brought challenges in medical image segmentation, especially when the background has similar intensity with the region of interest (ROI). Although lots of active-contour-model-based methods have appeared to extract ROI accurately, finer segmentation especially in tiny areas is still a difficult problem. In this paper, an adaptive local-fitting-based active contour model is proposed to separate the ROI accurately and robustly, which takes both adaptive local fitting energy and regularization energy to drive the initial contour to the object boundary. Unlike the traditional methods which assume the intensities in local region are constant, the proposed method takes an adaptive technique to fit the original image, which contributes to a more optimal solution. Experiments are conducted on lots of synthetic and real medical images. Results show that the proposed method not only obtains more accurate segmentation results than several well-known algorithms, while is robust to intensity inhomogeneity and noises. The source code is available at: https://github.com/madd2014/ALF.
Three Classes of Minimal Linear Codes Over the Finite Fields of Odd Characteristic

Minimal linear codes are a special subclass of linear codes and have significant applications in secret sharing and secure two-party computation. In this paper, we focus on constructing minimal linear codes with (w <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> )/(w <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> ) ≤ (p-1)/p for any odd prime p based on a generic construction of linear codes, where w <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> and w <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> denote the minimum and maximum nonzero weights in a code, respectively. First, we present two new infinite families of minimal linear codes with two or three weights by selecting suitable subcode of linear codes which are not minimal. Second, we also present an infinite family of minimal linear codes by employing partial spreads, which can be viewed as a generalization of the construction of Ding et al. In addition, we determine the weight distributions of all these minimal linear codes.
Challenges of Testing Machine Learning Based Systems

Machine learning is being increasingly deployed in many industrial and societal applications. Given its widespread use, it is of utmost importance to ensure the quality of software systems supported by machine learning. Although researchers have applied some concepts from traditional software testing to testing of machine learning based systems, the latter introduce a range of challenges not typical for traditional software systems, thus making traditional software testing techniques ineffective. In this paper, we discuss the challenges intrinsic to testing of machine learning based systems. We highlight the promising role of machine learning based testing to alleviate some of these challenges. We also discuss directions for future research in this domain. This paper focuses on the testing aspects of machine learning based systems from the quality assurance perspective, rather than model performance perspective.
Orthopartitions and soft clustering: Soft mutual information measures for clustering validation

In this work, we introduce the notion of orthopartition as a generalized partition with uncertainty. Several entropy-based measures are then developed to measure this intrinsic uncertainty, which are in turn applied to soft clustering. An application is explored: the use of the new Soft Mutual Information Measures to evaluate the performances of soft clustering algorithms. The new measures and methods are then tested on standard datasets, showing their applicability to rough clustering.
Classification of Red Blood Cells in Sickle Cell Anemia Using Deep Convolutional Neural Network

Sickle cell anemia is an abnormal red blood cell which leads to blood vessel obstruction joined by painful episodes and even death. It is also called abnormal hemoglobin. Hemoglobin is responsible for passing oxygen through the blood vessel for all over the body. Normal red blood cells are in a circular shape and they are compact and flexible, enabling them to move freely through small capillaries. On the other hand, abnormal red blood cells are in sickle shape and they are stiff and angular causing them to become stuck in small capillaries. Due to that, it will be a reason for pain to patients and lead to low oxygen and dehydration. The manual assessment, classification, and counting of biological cells require for an immense spending of time and it may lead to wrong classification and counting since red blood cells are millions in one smear. Also, cells classification is challenging due to heterogeneous and complex shapes, overlapped cells and a variety of colors. We overcome these drawbacks by introducing a new robust and effective deep Convolutional Neural Network to classify Red Blood Cells (RBCs) in three classes namely: normal ('N') abnormal (sickle cells anemia type ('S')) and miscellaneous ('M'). In order to improve the results further, we have used our model as features extractor then we applied an error-correcting output codes (ECOC) classifier for the classification task. Our model with ECOC showed outstanding performance and high accuracy of 92.06%.
An efficient Devanagari character classification in printed and handwritten documents using SVM

With the increased demand, exploration and globalization of digitized Devanagari documents, many printed and handwritten mono-lingual character recognition techniques have evolved since last two decades. This paper presents an efficient Devanagari character classification model using SVM for printed and handwritten mono-lingual Hindi, Sanskrit and Marathi documents, which first preprocesses the image, segments it through projection profiles, removes shirorekha, extracts features, and then classifies the shirorekha-less characters into pre-defined character categories. The experiments performed on proposed system obtained average classification accuracies of 99.54% and 98.35% for printed and handwritten images, respectively, and showed better performance than other techniques.
Image Super-Resolution Using Knowledge Distillation

The significant improvements in image super-resolution (SR) in recent years is majorly resulted from the use of deeper and deeper convolutional neural networks (CNN). However, both computational time and memory consumption simultaneously increase with the utilization of very deep CNN models, posing challenges to deploy SR models in realtime on computationally limited devices. In this work, we propose a novel strategy that uses a teacher-student network to improve the image SR performance. The training of a small but efficient student network is guided by a deep and powerful teacher network. We have evaluated the performance using different ways of knowledge distillation. Through the validations on four datasets, the proposed method significantly improves the SR performance of a student network without changing its structure. This means that the computational time and the memory consumption do not increase during the testing stage while the SR performance is significantly improved.
An Improved Convergence Particle Swarm Optimization Algorithm with Random Sampling of Control Parameters

Although particle swarm optimization (PSO) has been widely used to address various complicated engineering problems, it still needs to overcome the several shortcomings of PSO, e.g., premature convergence and low accuracy. Its final optimization result is related to the control parameters selection; therefore, an improved convergence particle swarm optimization algorithm with random sampling of control parameters is proposed. For the proposed algorithm, the random sampling strategy of control parameters is designed, which can promote the flexibility of algorithm parameters and simultaneously enhance the updating randomness for both particle velocity and position. According to the convergence analysis of PSO, the sampling range for inertial weight is determined after both the acceleration factors have already been sampled in their respective value interval, to ensure convergence for every evolution step of algorithm. Besides that, in order to make full use of dimension information of some better particles, the stochastic correction approach on each dimension for the population optimum value has been adopted. The final experiments results demonstrate that the proposed algorithm further improves the convergence rate while maintaining higher convergence accuracy, compared with basic particle swarm optimization and other variants.
Dephasing Free Photon Entanglement with a Quantum Dot

Generation of photon pairs from quantum dots with near-unity entanglement fidelity has been a long-standing scientific challenge. It is generally thought that the nuclear spins limit the entanglement fidelity through spin flip dephasing processes. However, this assumption lacks experimental support. Here, we show two-photon entanglement with negligible dephasing from an indium rich single quantum dot comprising a nuclear spin of 9/2 when excited quasi-resonantly. This finding is based on a significantly close match between our entanglement measurements and our model that assumes no dephasing and takes into account the detection system's timing jitter and dark counts. We suggest that neglecting the detection system is responsible for the degradation of the measured entanglement fidelity in the past and not the nuclear spins. Therefore, the key to unity entanglement from quantum dots comprises a resonant excitation scheme and a detection system with ultralow timing jitter and dark counts.
Photon-photon interactions in dynamically coupled cavities

We study theoretically the interaction between two photons in a nonlinear cavity. The photons are absorbed into the cavity by an effective tuning of its input-output coupling via external control of a coupling to a second, strongly output-coupled cavity mode. Such ``dynamically coupled'' cavities, which can be implemented using bulk ${\ensuremath{\chi}}^{(2)}$ and ${\ensuremath{\chi}}^{(3)}$ nonlinearities, enable incoming photon wave packets to be absorbed into the cavity with high fidelity when the duration of the control is similar to that of the wave packets. Further, this configuration can be used to avoid limitations in the photon-photon interaction time set by the delay-bandwidth product of passive cavities and enables the elimination of wave-packet distortions caused by dispersive cavity transmission and reflection. We consider three kinds of nonlinearities, two arising from ${\ensuremath{\chi}}^{(2)}$ and ${\ensuremath{\chi}}^{(3)}$ materials and one due to an interaction with a two-level emitter. To analyze the input and output of few-photon wave packets, we use a Schr\"odinger-picture formalism in which traveling-wave fields are discretized into infinitesimal time bins. We suggest that dynamically coupled cavities provide a very useful tool for improving the performance of quantum devices relying on cavity-enhanced light-matter interactions such as single-photon sources and atomlike quantum memories with photon interfaces. As an example, we present simulation results showing that high-fidelity two-qubit entangling gates may be constructed using any of the considered nonlinear interactions.
Customized Multi-person Tracker

This work addresses the task of multi-person tracking in crowded street scenes, where long-term occlusions pose a major challenge. One popular way to address this challenge is to re-identify people before and after occlusions using Convolutional Neural Networks (CNNs). To achieve good performance, CNNs require a large amount of training data, which is not available for multi-person tracking scenarios. Instead of annotating large training sequences, we introduce a customized multi-person tracker that automatically adapts its person re-identification CNNs to capture the discriminative appearance patterns in a test sequence. We show that a few high-quality training examples that are automatically mined from the test sequence can be used to fine-tune pre-trained CNNs, thereby teaching them to recognize the uniqueness of people's appearance in the test sequence. To that end, we introduce a hierarchical correlation clustering (HCC) framework, in which we utilize an existing robust correlation clustering tracking model, but with different graph structures to generate local, reliable tracklets as well as globally associated tracks. We deploy intuitive physical constraints on the local tracklets to generate the high-quality training examples for customizing the person re-identification CNNs. Our customized multi-person tracker achieves state-of-the-art performance on the challenging MOT16 tracking benchmark.
DeepFusion

In recent years, significant research efforts have been spent towards building intelligent and user-friendly IoT systems to enable a new generation of applications capable of performing complex sensing and recognition tasks. In many of such applications, there are usually multiple different sensors monitoring the same object. Each of these sensors can be regarded as an information source and provides us a unique "view" of the observed object. Intuitively, if we can combine the complementary information carried by multiple sensors, we will be able to improve the sensing performance. Towards this end, we propose DeepFusion, a unified multi-sensor deep learning framework, to learn informative representations of heterogeneous sensory data. DeepFusion can combine different sensors' information weighted by the quality of their data and incorporate cross-sensor correlations, and thus can benefit a wide spectrum of IoT applications. To evaluate the proposed DeepFusion model, we set up two real-world human activity recognition testbeds using commercialized wearable and wireless sensing devices. Experiment results show that DeepFusion can outperform the state-of-the-art human activity recognition methods.
Fluid

Recently, crowdsourcing has emerged as a new computing paradigm to solve problems that need human intrinsic, such as image annotation. However, there are two limitations in existing crowdsourcing platforms, i.e. non-transparent incentive mechanism and isolated profiles of workers, which harms the interests of both requesters and workers. Meanwhile, Blockchain technology introduces a solution to build a transparent, immutable data model in the Byzantine environment. Moreover, Blockchain systems (e.g. Ethereum) can also support the Tuning-complete script called smart contracts. Thus, we are motivated to use the feature of the transparent data model and smart contract in Blockchain to address the two limitations. Based on the proposed solutions, we have designed a Blockchain based framework which supports foundations of general crowdsourcing platforms. In addition, our framework also has following novel features: (1) it provides the transparent incentive mechanisms; (2) it supports a trusted worker's profile sharing in a cross-platform mode.
Slim DensePose: Thrifty Learning From Sparse Annotations and Motion Cues

DensePose supersedes traditional landmark detectors by densely mapping image pixels to body surface coordinates. This power, however, comes at a greatly increased annotation cost, as supervising the model requires to manually label hundreds of points per pose instance. In this work, we thus seek methods to significantly slim down the DensePose annotations, proposing more efficient data collection strategies. In particular, we demonstrate that if annotations are collected in video frames, their efficacy can be multiplied for free by using motion cues. To explore this idea, we introduce DensePose-Track, a dataset of videos where selected frames are annotated in the traditional DensePose manner. Then, building on geometric properties of the DensePose mapping, we use the video dynamic to propagate ground-truth annotations in time as well as to learn from Siamese equivariance constraints. Having performed exhaustive empirical evaluation of various data annotation and learning strategies, we demonstrate that doing so can deliver significantly improved pose estimation results over strong baselines. However, despite what is suggested by some recent works, we show that merely synthesizing motion patterns by applying geometric transformations to isolated frames is significantly less effective, and that motion cues help much more when they are extracted from videos.
Mutual Learning of Complementary Networks via Residual Correction for Improving Semi-Supervised Classification

Deep mutual learning jointly trains multiple essential networks having similar properties to improve semi-supervised classification. However, the commonly used consistency regularization between the outputs of the networks may not fully leverage the difference between them. In this paper, we explore how to capture the complementary information to enhance mutual learning. For this purpose, we propose a complementary correction network (CCN), built on top of the essential networks, to learn the mapping from the output of one essential network to the ground truth label, conditioned on the features learnt by another. To make the second essential network increasingly complementary to the first one, this network is supervised by the corrected predictions. As a result, minimizing the prediction divergence between the two complementary networks can lead to significant performance gains in semi-supervised learning. Our experimental results demonstrate that the proposed approach clearly improves mutual learning between essential networks, and achieves state-of-the-art results on multiple semi-supervised classification benchmarks. In particular, the test error rates are reduced from previous 21.23% and 14.65% to 12.05% and 10.37% on CIFAR-10 with 1000 and 2000 labels, respectively.
Crime base: Towards building a knowledge base for crime entities and their relationships from online news papers

In the current era of internet, information related to crime is scattered across many sources namely news media, social networks, blogs, and video repositories, etc. Crime reports published in online newspapers are often considered as reliable compared to crowdsourced data like social media and contain crime information not only in the form of unstructured text but also in the form of images. Given the volume and availability of crime-related information present in online newspapers, gathering and integrating crime entities from multiple modalities and representing them as a knowledge base in machine-readable form will be useful for any law enforcement agencies to analyze and prevent criminal activities. Extant research works to generate the crime knowledge base, does not address extraction of all non-redundant entities from text and image data present in multiple newspapers. Hence, this work proposes Crime Base, an entity relationship based system to extract and integrate crime related text and image data from online newspapers with a focus towards reducing duplicity and loss of information in the knowledge base. The proposed system uses a rule-based approach to extract the entities from text and image captions. The entities extracted from text data are correlated using contextual as-well-as semantic similarity measures and image entities are correlated using low-level and high-level image features. The proposed system also presents an integrated view of these entities and their relations in the form of a knowledge base using OWL. The system is tested for a collection of crime related articles from popular Indian online newspapers.
A robust active contour model driven by pre-fitting bias correction and optimized fuzzy c-means algorithm for fast image segmentation

Active contour model (ACM) is an effective method for image segmentation that has been widely used in various research fields. For images with severe intensity inhomogeneity, most existing ACMs show a poor segmentation performance. Moreover, robustness of these models to initial contour and noise is unsatisfactory. To seek better approaches to these issues, this paper proposes an ACM driven by pre-fitting bias correction and optimized fuzzy c-means (FCM) algorithm, which is robust and achieves a fast segmentation. Firstly, an optimized FCM algorithm is presented, by which bias field is pre-estimated. Secondly, a criterion function for local intensity is defined, then integrated with respect to the center for a global criterion. Thirdly, the above theory is introduced into ACM according to the property of level set function. Fourthly, a novel regularization method is proposed for variational level set. Experiments on real and synthetic images prove that the proposed model can effectively segment images with severe intensity inhomogeneity. Compared with the bias correction model, there is no more time-consuming convolutions in iterations so that computational amount of our model is enormously reduced. Furthermore, the model has better robustness to both noise and initialization, segmentation efficiency and accuracy than most region-based models.
Subjective quality evaluation of tile-based streaming for omnidirectional videos

In viewport-adaptive streaming of omnidirectional video, only the field of view is streamed in high quality. While this has significant benefits over streaming the entire 360 sphere, no standard test method for perceived quality and simulator sickness is available to evaluate the quality of experience (QoE) of such streaming approaches. QoE testing is important as tile-based viewport-adaptive streaming technologies are replacing classical approaches because of significant bandwidth savings and increase in viewing quality. In this work, we propose a testbed, a test method, as well as test metrics for QoE tests of viewport-adaptive streaming approaches. The proposed method is validated in two different test setups, using a specific tile-based streaming technology available in the market. The chosen input variables (videos sequences, resolution, bandwidth, and network round-trip delay) are tested for their statistical significance. We found that our test method is suitable for QoE testing of viewport-adaptive streaming technologies. We also found that simulator sickness scores increase with test duration, but that breaks between tests reduce this effect. With our systematic test approach, it is possible to compare metrics among different test setups. On the tested technology, we found that a typical network delay (47 ms) only has a minimal effect on the quality ratings. Furthermore, the magnitude of the network delay does not influence simulator sickness for the system we have tested.