Reviewerly/exHarmony huggingface dataset:
Optimal recursive estimation for networked stochastic uncertain systems with fading measurements and time-correlated channel noises

This paper is concerned with the optimal linear estimation problem for discrete-time stochastic system with time-correlated channel noises, correlated multiplicative noises and fading measurements. The time-correlated channel noises are described by a seemingly ARMA (autoregressive moving average) model. By an equivalent transformation, the original system is transformed into the same-dimension system with finite-step correlated virtual noises and multiple measurement delays. The statistical properties of correlation functions among noise, state and measurement are analyzed. Further, the optimal linear state estimators including filter, predictor and smoother are derived by using an innovation analysis approach. A simulation example shows the effectiveness of the proposed algorithms.
An IoT Expert System Shell in Block-Chain Technology with ELM as Inference Engine

Knowledge society blockchain is one of the most powerful and recent tools to make the internet environment safer and reliable. Manufacturing has traditionally been dominated by standard designs that are mass-produced, due to the fact, that custom production causes additional costs that make it less affordable than mass production. This paper proposes to develop a designer expert system for IoT installation layout designs, using blockchain distributed system based on a machine learning, with users entering data to the expert system by a smart bot software. This expert system will work using extreme learning machine as inference engine; therefore, this is a shell to develop any expert system with fast learning. The whole system is represented by a smart contract with a value linked to the value of the expert system, the more this expert system be quoted on the web, the more the shares of the smart contract will cost.
Person Re-Identification by Semantic Region Representation and Topology Constraint

Person re-identification is a popular research topic which aims at matching the specific person in a multi-camera network automatically. Feature representation and metric learning are two important issues for person re-identification. In this paper, we propose a novel person re-identification method, which consists of a reliable representation called Semantic Region Representation (SRR), and an effective metric learning with Mapping Space Topology Constraint (MSTC). The SRR integrates semantic representations to achieve effective similarity comparison between the corresponding regions via parsing the body into multiple parts, which focuses on the foreground context against the background interference. To learn a discriminant metric, the MSTC is proposed to take into account the topological relationship among all samples in the feature space. It considers two-fold constraints: the distribution of positive pairs should be more compact than the average distribution of negative pairs with regard to the same probe, while the average distance between different classes should be larger than that between same classes. These two aspects cooperate to maintain the compactness of the intra-class as well as the sparsity of the inter-class. Extensive experiments conducted on five challenging person re-identification datasets, VIPeR, SYSU-sReID, QUML GRID, CUHK03, and Market-1501, show that the proposed method achieves competitive performance with the state-of-the-art approaches.
Locality-constrained least squares regression for subspace clustering

Low-rank representation (LRR) is a typical data representation method that has been developed in recent years. Based on the main idea of LRR, least squares regression (LSR) constructs a new optimization problem to group the highly correlated data together. Compared with other LRR algorithms that are sophisticated, LSR is simpler and more efficient. It is a linear representation method that captures the global structure of data with a low-rank constraint. Research has shown that locality constraints typically improve the discrimination of the representation and demonstrate better performance than those "non-local" methods for image recognition tasks. To combine LSR and the locality constraints, we propose a novel data representation method called locality-constrained LSR (LCLSR) for subspace clustering. LCLSR forces the representation to prefer the selection of neighborhood points. The locality constraint is calculated based on the Euclidean distances between data points. Under the locality constraint, the obtained affinity matrix captures the locally linear relationship for data points lie on a nonlinear manifold. We propose three approaches to obtain the locality constraint and compare their performance with other related work on subspace clustering. We conducted extensive experiments on several datasets for subspace clustering. The results demonstrated that the proposed DCLSR, LCLSR ε, and LCLSRk substantially outperformed state-of-the-art subspace clustering methods.
Recognizing white blood cells with local image descriptors

Automatic and reliable classification of images of white blood cells is desirable for inexpensive, quick and accurate health diagnosis worldwide. In contrast to previous approaches which tend to rely on image segmentation and a careful choice of ad hoc (geometric) features, we explore the possibilities of local image descriptors, since they are a simple approach, they require no explicit segmentation, and yet they have been shown to be quite robust against background distraction in a number of visual tasks. Despite its potential, this methodology remains unexplored for this problem. In this work, images are therefore characterized with the well-known visual bag-of-words approach. Three keypoint detectors and five regular sampling strategies are studied and compared. The results indicate that the approach is encouraging, and that both the sparse keypoint detectors and the dense regular sampling strategies can perform reasonably well (mean accuracies of about 80% are obtained), and are competitive to segmentation-based approaches. Two of the main findings are as follows. First, for sparse points, the detector which localizes keypoints on the cell contour (oFAST) performs somehow better than the other two (SIFT and CenSurE). Second, interestingly, and partly contrary to our expectations, the regular sampling strategies including hierarchical spatial information, multi-resolution encoding, or foveal-like sampling, clearly outperform the two simpler uniform-sampling strategies considered. From the broader perspective of expert and intelligent systems, the relevance of the proposed approach is that, since it is very general and problem-agnostic, it makes unnecessary human expertise to be elicited in the form of explicit visual cues; only the labels of the cell type are required from human domain experts.
Heterogeneous Hashing Network for Face Retrieval Across Image and Video Domains

In this paper, we present a heterogeneous hashing network to generate effective and compact hash representations of both face images and face videos for face retrieval across image and video domains. The network contains an image branch and a video branch to project face images and videos into a common space, respectively. Then, the non-linear hash functions are learned in the common space to obtain the corresponding binary hash representations. The network is trained with three loss functions: 1) the Fisher loss; 2) the softmax loss; and 3) the triplet ranking loss. The Fisher loss uses the difference form of within-class and between-class scatter and is appropriate for the mini-batch-based optimization method. The Fisher loss together with the softmax loss is exploited to enhance the discriminative power of the common space. The triplet ranking loss is enforced on the final binary hash representations to improve retrieval performance. Experiments on a large-scale face video dataset and two challenging TV-series datasets demonstrate the effectiveness of the proposed method.
Speech-acts based analysis for requirements discovery from online discussions

Online discussions about software applications and services that take place on web-based communication platforms represent an invaluable knowledge source for diverse software engineering tasks, including requirements elicitation. The amount of research work on developing effective tool-supported analysis methods is rapidly increasing, as part of the so called software analytics. Textual messages in App store reviews, tweets, online discussions taking place in mailing lists and user forums, are processed by combining natural language techniques to filter out irrelevant data; text mining and machine learning algorithms to classify messages into different categories, such as bug report and feature request. Our research objective is to exploit a linguistic technique based on speech-acts for the analysis of online discussions with the ultimate goal of discovering requirements-relevant information. In this paper, we present a revised and extended version of the speech-acts based analysis technique, which we previously presented at CAiSE 2017, together with a detailed experimental characterisation of its properties. Datasets used in the experimental evaluation are taken from a widely used open source software project (161120 textual comments), as well as from an industrial project in the home energy management domain. We make them available for experiment replication purposes. On these datasets, our approach is able to successfully classify messages into Feature/Enhancement and Other, with F-measure of 0.81 and 0.84 respectively. We also found evidence that there is an association between types of speech-acts and categories of issues, and that there is correlation between some of the speech-acts and issue priority, thus motivating further research on the exploitation of our speech-acts based analysis technique in semi-automated multi-criteria requirements prioritisation.
Tight Proofs of Space and Replication

We construct a concretely practical proof-of-space (PoS) with arbitrarily tight security based on stacked depth robust graphs and constant-degree expander graphs. A proof-of-space (PoS) is an interactive proof system where a prover demonstrates that it is persistently using space to store information. A PoS is arbitrarily tight if the honest prover uses exactly N space and for any $$\epsilon > 0$$ the construction can be tuned such that no adversary can pass verification using less than $$(1-\epsilon ) N$$ space. Most notably, the degree of the graphs in our construction are independent of $$\epsilon $$ , and the number of layers is only $$O(\log (1/\epsilon ))$$ . The proof size is $$O(d/\epsilon )$$ . The degree d depends on the depth robust graphs, which are only required to maintain $$\varOmega (N)$$ depth in subgraphs on 80% of the nodes. Our tight PoS is also secure against parallel attacks. Tight proofs of space are necessary for proof-of-replication (PoRep), which is a publicly verifiable proof that the prover is dedicating unique resources to storing one or more retrievable replicas of a specified file. Our main PoS construction can be used as a PoRep, but data extraction is as inefficient as replica generation. We present a second variant of our construction called ZigZag PoRep that has fast/parallelizable data extraction compared to replica generation and maintains the same space tightness while only increasing the number of levels by roughly a factor two.
Bisimilarity in Fuzzy Description Logics Under the Zadeh Semantics

Fuzzy description logics (DLs) are extensions of DLs for dealing with imprecise and vague concepts. They found the logical basis for fuzzy ontologies, which are useful for practical applications. Bisimilarity is a natural notion of equivalence between individuals in DLs. In this paper, for the first time, we introduce the notion of bisimilarity in fuzzy DLs under the Zadeh semantics. It is defined using our notion of p-cut simulation between fuzzy interpretations. The considered logics are fuzzy DLs that extend the fuzzy version of the DL ALC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reg</sub> (a variant of propositional dynamic logic) with features among inverse roles, the universal role, qualified number restrictions, nominals, and local reflexivity of a role. We provide results on preservation of information by the mentioned simulations, conditional invariance of ABoxes and TBoxes by bisimilarity between witnessed interpretations, as well as the Hennessy-Milner property for fuzzy DLs under the Zadeh semantics.
EOG-based eye movement detection and gaze estimation for an asynchronous virtual keyboard

This work aims to develop a novel electrooculography (EOG)-based virtual keyboard with a standard QWERTY layout which, unlike similar state-of-the-art systems, allows users to reach any icon from any location directly and asynchronously. The saccadic EOG potential displacement is mapped to angular gaze displacement using a novel two-channel input linear regression model, which considers features extracted from both the horizontal and vertical EOG signal components jointly. Using this technique, a gaze displacement estimation error of 1.32 ± 0.26° and 1.67 ± 0.26° in the horizontal and vertical directions respectively was achieved, a performance which was also found to be generally statistically significantly better than the performance obtained using one model for each EOG component to model the relationship in the horizontal and vertical directions separately, as typically used in the literature. Furthermore, this work also proposes a threshold-based method to detect eye movements from EOG signals in real-time, which are then classified as saccades or blinks using a novel cascade of a parametric and a signal-morphological classifier based on the EOG peak and gradient features. This resulted in an average saccade and blink labelling accuracy of 99.92% and 100.00% respectively, demonstrating that these two eye movements could be reliably detected and discriminated in real-time using the proposed algorithms. When these techniques were used to interface with the proposed asynchronous EOG-based virtual keyboard, an average writing speed across subjects of 11.89 ± 4.42 characters per minute was achieved, a performance which has been shown to improve substantially with user experience.
A Structure-Enriched Neural Network for network embedding

Recent years have witnessed the importance of network embedding in many fields, as well as increased attention in academia. Although a number of algorithms have been proposed in this area, most existing models which only utilize the structure topology information of networks often suffer performance losses because of their insufficiency with regard to selecting structure similar patterns, handling noise data, and/or capturing non-linear or high-order structure information. To address these challenges, in this paper, we present a novel Structure-Enriched Neural Network (SENN) for network embedding. Specifically, SENN can not only capture the complex structure similar patterns observed in networks by introducing direction adjustment parameters of the transition probability, but also introduce a stacked denoise autoencoder to perform the dimension reduction for each order matrix independently. Therefore, SENN can preserve more useful structure information and make the embeddings more robust. Moreover, SENN can effectively integrate the multi-order structure information by the combining layer with attention mechanism. Finally, to compare with other state-of-the-art methods, we conduct extensive experiments with both synthetic and real-world datasets on various tasks (e.g.,node classification, visualization). The experimental results clearly demonstrate the effectiveness of our proposed model for network embedding.
A binary level set variational model with L1 data term for image segmentation

In this paper, we propose a variational model in binary level set framework for segmenting two-phase images with impulse noise. In this model, the contour is implicitly represented by the discontinuity of a binary level set function instead of the zero-level set. The energy functional consists of three terms, i.e., the data term that is defined by L1-norm metric, the regularization term that is defined by Dirichlet energy of level set function, and the penalty term that punishes the deviation of level set function from binary function. We design a three-step time-splitting scheme to solve the gradient descent flow of the proposed model efficiently, in which the flow equation is divided into three differential equations that are solved sequentially and alternatively until convergence. We present some experimental results that are performed on simulated and real images, which demonstrate that our model is very robust to impulse noise and has the best performance compared with five relevant models.
T-shaped grouping: Expert finding models to agile software teams retrieval

Agile team formation is an important requirement of software companies. Since the members of an agile team should be generalizing specialists (i.e. T-shaped experts), we need members who are specialist in a specific topic and have general knowledge in other topics of the team. Selecting such members results in an ideal team which is flexible, high-performing and low-cost. In this paper, we define the problem of agile team formation in which given a set of required skills of an agile team, the ideal output is a set of low-cost candidates who can collectively cover the required skills while they can effectively communicate with each other. We propose two retrieval models to address this problem and then we introduce three evaluation measures for assessment. These measures are coverage, communication and optimality. Our experiments on two test collections extracted from StackOverflow demonstrate the efficiency of our proposed models in comparison with several strong baselines.
SingleGAN: Image-to-Image Translation by a Single-Generator Network Using Multiple Generative Adversarial Learning

Image translation is a burgeoning field in computer vision where the goal is to learn the mapping between an input image and an output image. However, most recent methods require multiple generators for modeling different domain mappings, which are inefficient and ineffective on some multi-domain image translation tasks. In this paper, we propose a novel method, SingleGAN, to perform multi-domain image-to-image translations with a single generator. We introduce the domain code to explicitly control the different generative tasks and integrate multiple optimization goals to ensure the translation. Experimental results on several unpaired datasets show superior performance of our model in translation between two domains. Besides, we explore variants of SingleGAN for different tasks, including one-to-many domain translation, many-to-many domain translation and one-to-one domain translation with multimodality. The extended experiments show the universality and extensibility of our model.
Moving object detection via segmentation and saliency constrained RPCA

Robust Principal Component Analysis (RPCA) is inherently a promising approach for detecting the moving objects in video frames: Under certain conditions, the low-rank and sparse components in RPCA can identify the background and moving objects, respectively. However, it is arguable that RPCA cannot work well without the following two assumptions: (1) The background is static or quasi-static and (2) the moving of the foreground objects is not slow. These two assumptions, unfortunately, are often invalid in reality. To overcome the drawbacks of RPCA, we propose in this work a novel method termed Segmentation and Saliency constrained RPCA (SSC-RPCA), the solution of which is regularized by both segmentation and saliency constraints. In general, SSC-RPCA can effectively overcome the two aforementioned flaws: The segmentation constraint gives SSC-RPCA the ability to cope with the dynamic background, while the saliency constraint makes SSC-RPCA be able to detect the objects moving slowly. Experiments on 14 videos of CDCNET 2014 show the superior performance of SSC-RPCA.
Optimal distillation of quantum coherence with reduced waste of resources

We present an optimal probabilistic protocol to distill quantum coherence. Inspired by a specific entanglement distillation protocol, our main result yields a strictly incoherent operation that produces one of a family of maximally coherent states of variable dimension from any pure quantum state. We also expand this protocol to the case where it is possible, for some initial states, to avert any waste of resources as far as the output states are concerned, by exploiting an additional transformation into a suitable intermediate state. These results provide practical schemes for efficient quantum resource manipulation.
Plasmon-assisted two-photon Rabi oscillations in a semiconductor quantum dot – metal nanoparticle heterodimer

Tho-photon Rabi oscillations hold potential for quantum computing and quantum information processing, because during a Rabi cycle a pair of entangled photons may be created. We theoretically investigate the onset of this phenomenon in a heterodimer comprising a semiconductor quantum dot strongly coupled to a metal nanoparticle. Two-photon Rabi oscillations in this system occur due to a coherent two-photon process involving the ground-to-biexciton transition in the quantum dot. The presence of a metal nanoparticle nearby the quantum dot results in a self-action of the quantum dot via the metal nanoparticle, because the polatization state of the latter depends on the quantum state of the former. The interparticle interaction gives rise to two principal effects: (i) - enhancement of the external field amplitude and (ii) - renormalization of the quantum dot's resonance frequencies and relaxation rates of the off-diagonal density matrix elements, both depending on the populations of the quantum dot's levels. Here, we focus on the first effect, which results in interesting new features, in particular, in an increased number of Rabi cycles per pulse as compared to an isolated quantum dot and subsequent growth of the number of entangled photon pairs per pulse. We also discuss the destructive role of radiative decay of the excitonic states on two-photon Rabi oscillations for both an isolated quantum dot and a heterodimer.
A cascade-learning approach for automated segmentation of tumour epithelium in colorectal cancer

Automated segmentation of tumor epithelial tissue from histological images is a fundamental aspiration of digital pathology to improve biomarker assessment and tissue diagnosis. Accurate tumour segmentation is an important step in many automated digital image analysis applications to be used in clinical practice. In particular, segmentation of tumour, non-tumour epithelium and stromal tissue compartments on immunohistochemistry images presents a challenge. Many artifacts, such as staining and/or illumination variations, can confound image analysis. In this paper, we propose a cascade-learning approach which can diminish the impact of these artifacts. It consists of (a) a set of novel invariant features that encodes meaningful information about the appearance and shape of the region of interest and (b) a novel level set formulation where contour evolution is driven by a probabilistic model of the appearance of the region (based on fuzzy c-means). The merit of our approach is that it exploits both appearance and shape information and combines them in the tissue classification framework. We evaluate the performance of our approach on the segmentation of tumour epithelium in colorectal cancer. The experimental results show that our approach is robust to staining differences, additive noise, intensity inhomogeneities, and can cope with a limited number of training samples, when compared to the state-of-the-art tumour epithelial segmentation methods.