Reviewerly/exHarmony huggingface dataset:
Mixed-Supervised Dual-Network for Medical Image Segmentation

Deep learning based medical image segmentation models usually require large datasets with high-quality dense segmentations to train, which are very time-consuming and expensive to prepare. One way to tackle this difficulty is using the mixed-supervised learning framework, where only a part of data is densely annotated with segmentation label and the rest is weakly labeled with bounding boxes. The model is trained jointly in a multi-task learning setting. In this paper, we propose Mixed-Supervised Dual-Network (MSDN), a novel architecture which consists of two separate networks for the detection and segmentation tasks respectively, and a series of connection modules between the layers of the two networks. These connection modules are used to transfer useful information from the auxiliary detection task to help the segmentation task. We propose to use a recent technique called ‘Squeeze and Excitation’ in the connection module to boost the transfer. We conduct experiments on two medical image segmentation datasets. The proposed MSDN model outperforms multiple baselines.
Privacy-Preserving Support Vector Machine Computing Using Random Unitary Transformation

A privacy-preserving support vector machine (SVM) computing scheme is proposed in this paper. Cloud computing has been spreading in many fields. However, the cloud computing has some serious issues for end users, such as the unauthorized use of cloud services, data leaks, and privacy being compromised. Accordingly, we consider privacy-preserving SVM computing. We focus on protecting visual information of images by using a random unitary transformation. Some properties of the protected images are discussed. The proposed scheme enables us not only to protect images, but also to have the same performance as that of unprotected images even when using typical kernel functions such as the linear kernel, radial basis function (RBF) kernel and polynomial kernel. Moreover, it can be directly carried out by using well-known SVM algorithms, without preparing any algorithms specialized for secure SVM computing. In an experiment, the proposed scheme is applied to a face-based authentication algorithm with SVM classifiers to confirm the effectiveness.
Challenges of Integrative Disease Modeling in Alzheimer's Disease

Dementia-related diseases like Alzheimer’s Disease (AD) have a tremendous social and economic cost. A deeper understanding of its underlying pathophysiologies may provide an opportunity for earlier detection and therapeutic intervention. Previous approaches for characterizing AD were targeted at single aspects of the disease. Yet, due to the complex nature of AD, the success of these approaches was limited. However, in recent years, advancements in integrative disease modeling, built on a wide range of AD biomarkers, have taken a global view on the disease, facilitating more comprehensive analysis and interpretation. Integrative AD models can be sorted in two primary types, namely hypothetical models and data-driven models. The latter group split into two subgroups: i) Models that use traditional statistical methods such as linear models, ii) Models that take advantage of more advanced artificial intelligence approaches such as machine learning. While many integrative AD models have been published over the last decade, their impact on clinical practice is limited. There exist major challenges in the course of integrative AD modeling, namely data missingness and censoring, imprecise human-involved priori knowledge, model reproducibility, dataset interoperability, dataset integration, and model interpretability. In this review, we highlight recent advancements and future possibilities of integrative modeling in the field of AD research, showcase and discuss the limitations and challenges involved, and finally, propose avenues to address several of these challenges.
Privacy Adversarial Network

The remarkable success of machine learning has fostered a growing number of cloud-based intelligent services for mobile users. Such a service requires a user to send data, e.g. image, voice and video, to the provider, which presents a serious challenge to user privacy. To address this, prior works either obfuscate the data, e.g. add noise and remove identity information, or send representations extracted from the data, e.g. anonymized features. They struggle to balance between the service utility and data privacy because obfuscated data reduces utility and extracted representation may still reveal sensitive information. This work departs from prior works in methodology: we leverage adversarial learning to better balance between privacy and utility. We design a representation encoder that generates the feature representations to optimize against the privacy disclosure risk of sensitive information (a measure of privacy) by the privacy adversaries, and concurrently optimize with the task inference accuracy (a measure of utility) by the utility discriminator. The result is the privacy adversarial network (PAN), a novel deep model with the new training algorithm, that can automatically learn representations from the raw data. And the trained encoder can be deployed on the user side to generate representations that satisfy the task-defined utility requirements and the user-specified/agnostic privacy budgets. Intuitively, PAN adversarially forces the extracted representations to only convey information required by the target task. Surprisingly, this constitutes an implicit regularization that actually improves task accuracy. As a result, PAN achieves better utility and better privacy at the same time! We report extensive experiments on six popular datasets, and demonstrate the superiority of PAN compared with alternative methods reported in prior work.
Key rates for quantum key distribution protocols with asymmetric noise

We consider the asymptotic key rates achieved in the simplest quantum key distribution protocols, namely, the BB84 and the six-state protocols when nonuniform noise is present in the system. We first observe that higher qubit error rates do not necessarily imply lower key rates. Second, we consider protocols with advantage distillation and show that it can be advantageous to use the basis with higher quantum bit error rate for the key generation. We then discuss the relation between advantage distillation and entanglement distillation protocols. We show that applying advantage distillation to a string of bits formed by the outcomes of measurements in the basis with a higher quantum bit error rate is closely connected to the two-to-one entanglement distillation protocol of Deutsch-Ekert-Jozsa-Macchiavello-Popescu-Sanpera [Phys. Rev. Lett. 77, 2818 (1996)]. Finally, we discuss the implications of these results for implementations of quantum key distribution.
Robust Facial Landmark Detection via Heatmap-Offset Regression

Facial landmark detection aims at localizing multiple keypoints for a given facial image, which usually suffers from variations caused by arbitrary pose, diverse facial expression and partial occlusion. In this paper, we develop a two-stage regression network for facial landmark detection on unconstrained conditions. Our model consists of a Structural Hourglass Network (SHN) for detecting the initial locations of all facial landmarks based on heatmap generation, and a Global Constraint Network (GCN) for further refining the detected locations based on offset estimation. Specifically, SHN introduces an improved Inception-ResNet unit as basic building block, which can effectively improve the receptive field and learn contextual feature representations. In the meanwhile, a novel loss function with adaptive weight is proposed to make the whole model focus on the hard landmarks precisely. GCN attempts to explore the spatial contextual relationship between facial landmarks and refine the initial locations of facial landmarks by optimizing the global constraint. Moreover, we develop a pre-processing network to generate features with different scales, which will be transmitted to SHN and GCN for effective feature representations. Different from existing models, the proposed method realizes the heatmap-offset framework, which combines the outputs of heatmaps generated by SHN and coordinates estimated by GCN, to obtain an accurate prediction. The extensive experimental results on several challenging datasets, including 300W, COFW, AFLW, and 300-VW confirm that our method achieve competitive performance compared with the state-of-the-art algorithms.
Semantic and Generalized Entropy Loss Functions for Semi-Supervised Deep Learning

The increasing size of modern datasets combined with the difficulty of obtaining real label information (e.g., class) has made semi-supervised learning a problem of considerable practical importance in modern data analysis. Semi-supervised learning is supervised learning with additional information on the distribution of the examples or, simultaneously, an extension of unsupervised learning guided by some constraints. In this article we present a methodology that bridges between artificial neural network output vectors and logical constraints. In order to do this, we present a semantic loss function and a generalized entropy loss function (Rényi entropy) that capture how close the neural network is to satisfying the constraints on its output. Our methods are intended to be generally applicable and compatible with any feedforward neural network. Therefore, the semantic loss and generalized entropy loss are simply a regularization term that can be directly plugged into an existing loss function. We evaluate our methodology over an artificially simulated dataset and two commonly used benchmark datasets which are MNIST and Fashion-MNIST to assess the relation between the analyzed loss functions and the influence of the various input and tuning parameters on the classification accuracy. The experimental evaluation shows that both losses effectively guide the learner to achieve (near-) state-of-the-art results on semi-supervised multiclass classification.
The SECI Model in Knowledge Management Practices

The purpose of this study was to review the socialisation, externalisation, combination and internalisation (SECI) model of knowledge management (KM), and to explore the trends of its use, challenges, and best practices in the model’s application in KM practice in the period from 1995 to 2018. Using the methodology known as the preferred reporting items for systematic reviews and meta-analyses (PRISMA), we sought to do the following: establish whether the SECI model was more widely accepted than other KM models; examine specific demographic details of research studies on the SECI model; investigate the weaknesses of the model in practice; and generate a compendium of the use of the model in respect of the period we reviewed. Four databases were explored to search for open access, peer-reviewed, empirical journal articles. The study found that the SECI model continued to be widely accepted and used in spite of criticism levelled at it. Weaknesses that we noted in our review included the limitations of certain modes of the model in some areas of application and the non-linearity of the model in other areas. The study found evidence of exponential growth in the SECI model’s usage in the period reviewed. Also noted was the wide application of the model in knowledge creation studies in Africa. The study suggested that organisations could employ the model for their knowledge creation activities, but with the support of factors that enabled knowledge sharing. The model’s socialisation mode stood out as a mode of high impact, implying that the management teams of organisations needed to pay a lot of attention to it for effective organisational KM. This study generated a compendium for the practical application of the SECI model.&#x0D;
Calibrated model-based evidential clustering using bootstrapping

Evidential clustering is an approach to clustering in which cluster-membership uncertainty is represented by a collection of Dempster-Shafer mass functions forming an evidential partition. In this paper, we propose to construct these mass functions by bootstrapping finite mixture models. In the first step, we compute bootstrap percentile confidence intervals for all pairwise probabilities (the probabilities for any two objects to belong to the same class). We then construct an evidential partition such that the pairwise belief and plausibility degrees approximate the bounds of the confidence intervals. This evidential partition is calibrated, in the sense that the pairwise belief-plausibility intervals contain the true probabilities "most of the time", i.e., with a probability close to the defined confidence level. This frequentist property is verified by simulation, and the practical applicability of the method is demonstrated using several real datasets.
Edge Detection Technique using Binary Particle Swarm Optimization

Edge detection is long established in computer eyesight applications such as article identification, shape matching, medical image classification, etc. For this reason, many edge detectors like LOG, Prewitt, Canny, etc. have been developed in the past in order to boost the grouping correctness of edge pixels. All these approaches work fine on images having minimum variation in intensity, however, their performance is not consistent on images having high-intensity variation. Therefore, in this paper “Binary Particle Swarm Optimization (BPSO)” based edge detection methodology minimizing multi-objective fitness function is proposed. Multi-objective fitness function is formulated by considering the weighted sum of five cost factors and all these cost factors are associated with four techniques of edge validation. The proposed approach is examined on 500 “BSD” images and results are compared with classical edge detectors (Canny, Prewitt) as well as computational intelligent techniques (ACO, GA) using the F score performance parameter. Performance of the proposed approach are consistent on all testing images and outperform all classical edge detectors, ACO and GA having average F score 0.2901 and have little standard deviation (0.0401).
Object-Oriented Software for Functional Data

This paper introduces the funData R package as an object-oriented implementation of functional data. It implements a unified framework for dense univariate and multivariate functional data on one- and higher dimensional domains as well as for irregular functional data. The aim of this package is to provide a user-friendly, self-contained core toolbox for functional data, including important functionalities for creating, accessing and modifying functional data objects, that can serve as a basis for other packages. The package further contains a full simulation toolbox, which is a useful feature when implementing and testing new methodological developments. Based on the theory of object-oriented data analysis, it is shown why it is natural to implement functional data in an object-oriented manner. The classes and methods provided by funData are illustrated in many examples using two freely available datasets. The MFPCA package, which implements multivariate functional principal component analysis, is presented as an example for an advanced methodological package that uses the funData package as a basis, including a case study with real data. Both packages are publicly available on GitHub and CRAN.
Impossibility of masking a set of quantum states of nonzero measure

We study quantum information masking based on isometric linear operators that distribute the information encoded in pure states to the correlations in bipartite states. It is shown that an isometric linear operator cannot mask any nonzero measure set of pure states. We present a geometric characterization of the maskable sets and show that any maskable set must be on a spherical circle in certain Euclidean spaces. Detailed examples and potential applications such as secret sharing and quantum cryptography are analyzed.
One-thumb Text Acquisition on Force-assisted Miniature Interfaces for Mobile Headsets

Touchscreen interfaces are shrinking and even dis-appearing on mobile headsets. The existing approaches for text acquisition on mobile headsets, for instance, speech commands and hand gestures, are cumbersome and coarse. In this paper, we show the feasibility of interaction on a miniature area as small as 12 * 13 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> that offers an input alternative on small form-factor devices such as smartwatches, smart rings, or the spectacles frames of mobile headsets. To this end, we propose and implement two interaction approaches, namely FRS and DupleFR, for acquiring textual contents on mobile headsets. Both approaches leverage force-assisted interaction on a miniature-size interface. They enable the user to acquire textual content with various granularities such as characters, words, sentences, paragraphs, and the entire text. After 8 sessions, 22 participants with FRS and DupleFR achieve the peak performance of respectively 11.455 and 10.611 seconds per textual acquisition with accuracy rates of 91.41% and 94.95%. Although FRS and DupleFR as indirect manipulations are disadvantageous, they are at least 37.06% faster than the commercial standards designated to direct manipulation on touchscreens.
Comparison Study of Sentiment Analysis of Tweets using Various Machine Learning Algorithms

Today's advancement in the design of web technology, has made enormous data available for internet operators and also the numerous data is being created Web technology networking sites like Twitter, Facebook, Google+ has become a platform for sharing and exchanging the knowledge, for discussing and expressing their views about different themes with, unlike groups. The proposed system emphasizes largely on opinion investigation of twitter facts that support the investigation of the data in the tweets that show ideas are extremely unstructured, varied and in some instances positive or negative, and also emotions are taken into account for the sentiment analysis. The proposed system provides a performance analysis of machine learning algorithms such as Support Vector Machine, Navie Bayes, Max Entropy, LSTM, CNN, Random forest. Among all these techniques SVM provides an accuracy of 79.90%
Optical computation of a spin glass dynamics with tunable complexity

Significance Optics is a unique platform to speed up computation, providing advantages especially for inherently parallel operations. Here we demonstrate an optical experiment enabling the simulation of a paradigmatic prototype model for statistical mechanics: the spin glass (SG). SGs are systems presenting all the aspects of complexity, such as many equilibrium states producing multiple relaxation times and nontrivial phenomenology. With the optical approach, the spin state is mapped on the mirrors of a fast adaptive optics device illuminated by a laser, and the energy calculation is performed by collecting the intensity after all the controlled light rays interfered in strongly scattering medium. The optical simulation enables beating the digital calculation in terms of speed for large-sized SG.
Optimal Detection of Rotations about Unknown Axes by Coherent and Anticoherent States

Coherent and anticoherent states of spin systems up to spin <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>2</mml:mn></mml:math> are known to be optimal in order to detect rotations by a known angle but unknown rotation axis. These optimal quantum rotosensors are characterized by minimal fidelity, given by the overlap of a state before and after a rotation, averaged over all directions in space. We calculate a closed-form expression for the average fidelity in terms of anticoherent measures, valid for arbitrary values of the quantum number <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>j</mml:mi></mml:math>. We identify optimal rotosensors (i) for arbitrary rotation angles in the case of spin quantum numbers up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>7</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:math> and (ii) for small rotation angles in the case of spin quantum numbers up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>5</mml:mn></mml:math>. The closed-form expression we derive allows us to explain the central role of anticoherence measures in the problem of optimal detection of rotation angles for arbitrary values of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>j</mml:mi></mml:math>.
Handwritten Kazakh and Russian (HKR) database for text recognition

In this paper, we introduce a large scale dataset, called HKR, to address challenging detection and recognition problems of handwritten Russian and Kazakh text in the scanned documents. We present a new Russian and Kazakh database (with about 95% of Russian and 5% of Kazakh words/sentences respectively) for offline handwriting recognition. A few pre-processing and segmentation procedures have been developed together with the database. The database is written in Cyrillic and shares the same 33 characters. Besides these characters, the Kazakh alphabet also contains 9 additional specific characters. This dataset is a collection of forms. The sources of all the forms in the datasets were generated by LaTeXwhich subsequently was filled out by persons with their handwriting. The database consists of more than 1500 filled forms. There are approximately 63000 sentences, more than 715699 symbols produced by approximately 200 different writers. It can serve researchers in the field of handwriting recognition tasks by using deep and machine learning. For experiments, we used several popular text recognition methods for word and line recognition like CTC-based and attention-based methods. The results indicate the diversity of HKR. The dataset is available at https://github.com/abdoelsayed2016/HKR_Dataset .
A Hybrid Controller for Multi-Agent Collision Avoidance via a Differential Game Formulation

We consider the multi-agent collision avoidance problem for a team of wheeled mobile robots. Recently, a local solution to this problem, based on a game-theoretic formulation, has been provided and validated via numerical simulations. Due to its local nature, the result is not well-suited for online applications. In this article, we propose a novel hybrid implementation of the control inputs that yields a control strategy suited for the online navigation of mobile robots. Moreover, subject to a certain dwell time condition, the resulting trajectories are globally convergent. The control design is demonstrated both via simulations and experiments.