Reviewerly/exHarmony huggingface dataset:
Synthesizing multi-dimensional excitation dynamics and localization transition in one-dimensional lattices

The geometric dimensionality of a physical system significantly impacts its fundamental characteristics. While experiments are fundamentally limited to the maximum of three spatial dimensions, there is a growing interest in harnessing additional synthetic dimensions. In our work, we introduce a new paradigm for the experimental realization of excitation dynamics associated with many-dimensional systems. Crucially, it relies solely on static one-dimensional equivalent structures with judiciously tailored parameters to faithfully reproduce the same optical spectrum and density of states of the high-dimensional system to be represented. In order to showcase the capabilities of our approach, we fabricate 1D photonic lattices that exhibit the characteristic non-monotonic excitation decays associated with quantum walks in up to 7D square lattices. Furthermore, we find that a new type of bound state at the edge of the continuum emerges in higher-than-three dimensions and gives rise to a sharp localisation transition at defect sites. In a series of experiments, we implement the mapped equivalent lattices of up to 5D systems and observe an extreme increase of sensitivity with respect to the detuning of the respective anchor sites. Our findings demonstrate the feasibility and applicative potential of harnessing high-dimensional effects in planar photonics for ultra-sensitive switching or sensing. Notably, our general approach is by no means limited to optics, and can readily be adapted to a variety of other physical contexts, including cold atoms and superconducting qubits with exclusively nearest-neighbour interactions, promising to drive significant advances in different fields including quantum simulations and information processing.
ExploroBOT: Rapid Exploration with Chart Automation

General-purpose visualization tools are used by people with varying degrees of data literacy. Often the user is not a professional analyst or data scientist and uses the tool infrequently, to support an aspect of their job. This can present difficulties as the user’s unfamiliarity with visualization practice and infrequent use of the tool can result in long processing time, inaccurate data representations or inappropriate visual encodings. To address this problem, we developed a visual analytics application called exploroBOT. The exploroBOT automatically generates visualizations and the exploration guidance path (an associated network of decision points, mapping nodes where visualizations change). These combined approaches enable users to explore visualizations based on a degree of “interestingness”. The user-driven approach draws on the browse/explore metaphor commonly applied in social media applications and is supported by guided navigation. In this paper we describe exploroBOT and present an evaluation of the tool.
A Content-aware Filtering for RGBD Faces

3D face models are widely used for several purposes, such as biometric systems, face verification, facial expression recognition, 3D visualization, and so on. They can be captured by using different kinds of devices, like plenoptic cameras, structured light cameras, time of flight, among others. Nevertheless, the models generated by all these consumer devices are quite noisy. In this work, we present a content-aware filtering for 2.5D meshes of faces that preserves their intrinsic features. This filter consists on an exemplar-based neighborhood matching where all models are in a frontal position avoiding rotation and perspective. We take advantage of prior knowledge of the models (faces) to improve the comparison. We first detect facial feature points, create the point correctors for regions of each feature, and only use the correspondent regions for correcting a point of the filtered mesh. The model is invariant to depth translation and scale. The proposed method is evaluated on a public 3D face dataset with different levels of noise. The results show that the method is able to remove noise without smoothing the sharp features of the face.
Second Language Vocabulary Learning While Walking

Second language (L2) learners often lack opportunities or motivation to dedicate their time to vocabulary learning over other daily activities. In this work, we introduce a mobile application that allows L2 learners to instead leverage their "dead time", such as when walking to and from school or work, to study new vocabulary items. The application combines audio learning and location-based contextually relevant L1-L2 word pairs to allow L2 learners to "discover" new foreign language words while walking. We report on the evaluation of the approach from three aspects: L2 vocabulary retention after 1 month, system usability and workload.
A Robust Page Frame Detection Method for Complex Historical Document Images

Document layout analysis is the most important part of converting scanned page images into search-able full text. An intensive amount of research is going on in the field of structured and semi-structured documents (journal articles, books, magazines, invoices) but not much in historical documents. Historical document digitization is a more challenging task than regular structured documents due to poor image quality, damaged characters, big amount of textual and non-textual noise. In the scientific community, the extraneous symbols from the neighboring page are considered as textual noise, while the appearances of black borders, speckles, ruler, different types of image etc. along the border of the documents are considered as non-textual noise. Existing historical document analysis method cannot handle all of this noise which is a very strong reason of getting undesired texts as a result from the output of Optical Character Recognition (OCR) that needs to be removed afterward with a lot of extra afford. This paper presents a new perspective especially for the historical document image cleanup by detecting the page frame of the document. The goal of this method is to find actual contents area of the document and ignore noises along the page border. We use morphological transforms, the line segment detector, and geometric matching algorithm to find an ideal page frame of the document. After the implementation of page frame method, we also evaluate our approach over 16th-19th century printed historical documents. We have noticed in the result that OCR performance for the historical documents increased by 4.49% after applying our page frame detection method. In addition, we are able to increase the OCR accuracy around 6.69% for contemporary documents too.
FPGA based Abnormality Classification in Kidney Ultrasound Images using KNN

Ultrasound imaging has been widely used for diagnosing the internal abnormalities. Tele-radiographers are lacking nowadays to provide accurate diagnosis of the presence of abnormalities. To overcome this issue, Teleradiology paved a new way for the online access of the doctors around all parts of the world. Frequent access through online is a bottleneck problem. So, CAD based technique is implemented in VIRTEX-6 FPGA platform in order to clearly classify the presence of the abnormality. Manual intervention is reduced in this case. The proposed algorithm involves five steps: pre-processing segmentation, feature extraction, feature selection and classification. Based on selected features, the classification is made using KNN Classifier. Good range of specificity is achieved which is comparable with the existing algorithms. The specificity for normal, cystic and stone kidneys is 95, 80 and 75 respectively. The proposed method produces far higher accuracy when compared with SVM using linear, RBF and polynomial kernel. But it shows a bit lesser value when compared with SVM using MLP kernel.
Spatial integration of radiology and pathology images to characterize breast cancer aggressiveness on pre-surgical MRI

The widespread use of screening mammography has resulted in a remarkable rise in the diagnosis of Ductal Carcinoma In Situ (DCIS). A resultant challenge is the early screening of these patients to identify those with concurrent invasive breast cancer (IBC), as one in five DCIS at biopsy, are upgraded to IBC following surgery. Both x-ray mammography and multi-parametric Magnetic Resonance Imaging (MRI) lack the ability to distinguish DCIS from IBC reliably. Our robust methodology for 3D alignment of histopathology images and MRI provides a unique opportunity to spatially map digitized histopathology slides on pre-surgical MRI which is particularly important in the tumors where DCIS and IBC co-occur as well as for the study of tumor heterogeneity. In this proof-of-concept study, we developed and evaluated a methodological framework for the 3D spatial alignment of MRI and histopathology slices, using x-ray radiographs as intermediate modality. Our methodology involves (1) the co-registration of 2D x-ray radiographs showing macrosections and corresponding 2D histology slices, (2) the 3D reconstruction of the ex vivo specimen based on the x-ray images, and aligned histology slices, and (3) the registration of the 3D reconstructed ex vivo specimen with the 3D MRI. The spatially co-registered MRI and histopathology images may enable the identification of MRI features that distinguish aggressive from indolent disease on in vivo MRI.
Subspace-based non-blind deconvolution

In this paper, we develop a novel subspace-based recovery algorithm for non-blind deconvolution (named SND). With considering visual importance difference between image structures and smoothing areas, we propose subspace data fidelity for protecting image structures and suppressing both noise and artifacts. Meanwhile, with exploiting the difference of subspace priors, we put forward differentiation modelings on different subspace priors for improving deblurring performance. Then we utilize the least square integration method to fuse deblurred estimations and to compensate information loss of subspace deblurrings. In addition, we derive an efficient optimization scheme for addressing the proposed objective function by employing the methods of least square and fast Fourier transform. Final experimental results demonstrate that the proposed method outperforms several classical and state-of-the-art algorithms in both subjective and objective assessments.
Visual Exploration Tools for Ensemble Clustering Analysis

Uncertainty Analysis is essential to support decisions, and it has been gaining attention in both visualization and machine learning communities —in the latter case, mainly because ensemble methods are becoming a robust approach in several applications. In particular, for unsupervised learning, there are several ensemble clustering methods that generate a co-association matrix, i.e., a matrix whose element (i, j) represents the estimated probability that the given sample pair is on the same cluster. This work studies the following decision problem: “Given a similarity function, which groups of elements of a set form robust clusters?” Robust here means that all elements of each cluster are connected with a probability within a given interval. Our main contribution is a prototype that helps decision makers, through visual exploration, to have insights to solve this task. To do so, we provide visual tools for ensemble clustering analysis. Such tools are grounded in the co-association matrix generated by the ensemble. With these tools we are better equipped to recommend the group of elements that form each cluster, considering the uncertainty generated by ensemble clustering methods.
Annealing by Increasing Resampling in the Unified View of Simulated Annealing

Annealing by Increasing Resampling (AIR) is a stochastic hill-climbing optimization by resampling with increasing size for evaluating an objective function. In this paper, we introduce a unified view of the conventional Simulated Annealing (SA) and AIR. In this view, we generalize both SA and AIR to a stochastic hill-climbing for objective functions with stochastic fluctuations, i.e., logit and probit, respectively. Since the logit function is approximated by the probit function, we show that AIR is regarded as an approximation of SA. The experimental results on sparse pivot selection and annealing-based clustering also support that AIR is an approximation of SA. Moreover, when an objective function requires a large number of samples, AIR is much faster than SA without sacrificing the quality of the results.
Affective Analysis of Chinese Sentences Based on Word2vec and SVC

This Paper was based on word2vec, Using Chinese Encyclopedia Corpus of Wikipedia as the training set to generate Chinese word vector and Chinese sentence vector, and using SVC (support vector classification) to classify the text of 16542 comments in a hotel industry, that is, to realize the affective analysis of Chinese sentences. The results show that the sentence vectors generated by the voting SVC model are better than those generated by the mean SVC model.
Generic Framework for Evaluating Commutativity of Multi-Variant Model Transformations

Multi-variant model transformations (MVMTs) aim at automatically propagating variability annotations present in software product lines (SPL) when executing state-of-the-art model transformations. Variability annotations are boolean expressions used in annotative SPL engineering (SPLE) for expressing in which products model elements are visible. Developing the SPL in a model-driven way requires various model representations, e.g., database schemata for data storage or Java models for the code generation. Although model transformations are the key essence of model-driven software engineering (MDSE) and can be used to generate these representations from already existing (model) artifacts, they suffer from not being able to handle the variability annotations. Thus, the developer is forced to annotate target models manually contradicting the goal of both disciplines, MDSE and SPLE, to increase productivity. Recently, approaches have been proposed to solve the problem using, e.g., traces, to propagate annotations without changing the transformation itself. For evaluating the outcome all of the approaches require the transformation to commute w.r.t. the derived products. Although the criterion is the same, a common framework for testing it does not exist. Therefore, we contribute a generic framework allowing to evaluate whether the target model of arbitrary (reuse-based) MVMTs was correctly annotated according to the shared commutativity criterion.
Off-line Text-independent Writer Identification Using Local Convex Micro-Structure Patterns

Feature extraction is fundamental in writer identification to characterize a given handwritten text by a set of discriminative measures, which are used to capture the large variability between handwritten samples. This paper presents an effective learning-based approach for off-line text-independent writer identification using Local Convex Micro-Structure Patterns (LCxMSP) descriptor. The LCxMSP algorithm, which characterizes the writing style of each writer, is applied to a set of connected component sub-images extracted from the handwriting samples (documents or set of words/text lines). Histogram features computed from all the labeled components in all the writing samples are fed to the 1NN (Nearest Neighbor) classifier in order to identify the writer of the tested handwritten samples. Experimental results show that our proposed system combined with the LCxMSP descriptor demonstrate superior performance on the Hybrid-language ICDAR2011 database compared to the state-of-the-art writer identification systems.
A hierarchical Bayesian state trace analysis for assessing monotonicity while factoring out subject, item, and trial level dependencies

State trace analyses assess the latent dimensionality of a cognitive process by asking whether the means of two dependent variables conform to a monotonic function across a set of conditions. Using an assumption of independence between the measures, recently proposed statistical tests address bivariate measurement error, allowing both frequentist and Bayesian analyses of monotonicity (e.g., Davis-Stober, Morey, Gretton, & Heathcote, 2016; Kalish, Dunn, Burdakov, & Sysoev, 2016). However, statistical inference can be biased by unacknowledged dependencies between measures, particularly when the data are insufficient to overwhelm an incorrect prior assumption of independence. To address this limitation, we developed a hierarchical Bayesian model that explicitly models the separate roles of subject, item, and trial-level dependencies between two measures. Assessment of monotonicity is then performed by fitting separate models that do or do not allow a non-monotonic relation between the condition effects (i.e., same versus different rank orders). The Widely Applicable Information Criterion (WAIC) and Pseudo Bayesian Model Averaging – both cross validation measures of model fit – are used for model comparison, providing an inferential conclusion regarding the dimensionality of the latent psychological space. We validated this new state trace analysis technique using model recovery simulation studies, which assumed different ground truths regarding monotonicity and the direction/magnitude of the subject- and trial-level dependence. We also provide an example application of this new technique to a visual object learning study that compared performance on a visual retrieval task (forced choice part recognition) versus a verbal retrieval task (cued recall).
Histopathological Image Segmentation Using Modified Kernel-Based Fuzzy C-Means and Edge Bridge and Fill Technique

Abstract Histopathological lung cancer segmentation using region of interest is one of the emerging research area in the field of health monitoring system. In this paper, the histopathological images were collected from the database Stanford Tissue Microarray Database (TMAD). After image collection, pre-processing was performed using a normalization technique, which enhances the quality of the histopathological image by eliminating unwanted noise. After pre-processing, segmentation was carried out using the modified kernel-based fuzzy c-means clustering (KFCM) approach along with the edge bridge and fill technique (EBFT). It was a flexible high-level machine learning technique to localize the object in a complex template. The experimental result shows that the proposed approach segments the normal and abnormal cancer regions by means of precision, recall, specificity, accuracy, and Jaccard coefficient. The proposed methodology improved the classification accuracy in lung cancer segmentation up to 2.5–5% compared to the existing methods deep convolutional neural network (DCNN) and diffusion-weighted approach.
TABLING WITH INTERNED TERMS ON CONTEXTUAL ABDUCTION

Abduction (also called abductive reasoning) is a form of logical inference which starts with an observation and is followed by finding the best explanations. In this paper, we improve the tabling in contextual abduction technique with an advanced tabling feature of XSB Prolog, namely tabling with interned terms. This feature enables us to store the abductive solutions as interned ground terms in a global area only once so that the use of table space to store abductive solutions becomes more efficient. We implemented this improvement to a prototype, called as TABDUAL+INT. Although the experiment result shows that tabling with interned terms is relatively slower than tabling without interned terms when used to return first solutions from a subgoal, tabling with interned terms is relatively faster than tabling without interned terms when used to returns all solutions from a subgoal. Furthermore, tabling with interned terms is more efficient in table space used when performing abduction both in artificial and real world case, compared to tabling without interned terms.
Robust Image Watermarking via Perceptual Structural Regularity-based JND Model

A better tradeoff between robustness and invisibility will be realized by using the just noticeable (JND) model into the quantization-based watermarking scheme.The JND model is usually used to describe the perception characteristics of human visual systems (HVS).According to the research of cognitive science, HVS can adaptively extract the structure features of an image.However, the existing JND models in the watermarking scheme do not consider the structure features.Therefore, a novel JND model is proposed , which includes three aspects: contrast sensitivity function, luminance adaptation, and contrast masking (CM).In this model, the CM effect is modeled by analyzing the direction features and texture complexity, which meets the human visual perception characteristics and matches well with the spread transform dither modulation (STDM) watermarking framework by employing a new method to measure edge intensity.Compared with the other existing JND models, the proposed JND model based on structural regularity is more efficient and applicable in the STDM watermarking scheme.In terms of the experimental results, the proposed scheme performs better than the other watermarking scheme based on the existing JND models.
Experimental realization of a quantum autoencoder via a universal two-qubit unitary gate

As a ubiquitous aspect of modern information technology, data compression has a wide range of applications. Therefore, a quantum autoencoder which can compress quantum information into a low-dimensional space is fundamentally important to achieve automatic data compression in the field of quantum information. Such a quantum autoencoder can be implemented through training the parameters of a quantum device using classical optimization algorithms. In this article, we analyze the condition of achieving a perfect quantum autoencoder and theoretically prove that a quantum autoencoder can losslessly compress high-dimensional quantum information into a low-dimensional space (also called latent space) if the number of maximum linearly independent vectors from input states is no more than the dimension of the latent space. Also, we experimentally realize a universal two-qubit unitary gate and design a quantum autoencoder device by applying machine learning method. Experimental results demonstrate that our quantum autoencoder is able to compress two two-qubit states into two one-qubit states. Besides compressing quantum information, the quantum autoencoder is used to experimentally discriminate two groups of nonorthogonal states.
An Analysis of the Reliability of a Teaching Model for Presenting a Computer-based Cash Flow Report

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Context-aware Dynamic Block.

Although deeper and larger neural networks have achieved better performancenowadays, the complex network structure and increasing computational costcannot meet the demands of many resource-constrained applications. An effectiveway to address this problem is to make use of dynamic inference mechanism.Existing methods usually choose to execute or skip an entire specific layerthrough a switch structure, which can only alter the depth of the network. Inthis paper, we propose a dynamic inference method called Context-aware DynamicBlock (CDB), which provides more path selection choices in terms of networkwidth and depth during inference. The execution of CDB is determined by acontext-aware group controller, which can take into account both historical andobject category information. The proposed method can be easily incorporatedinto most modern network architectures. Experimental results on ImageNet andCIFAR-100 demonstrate the superiority of our method on both efficiency andoverall classification quality. To be specific, we integrate CDB block intoResNet-101 and find that our method significantly outperforms theircounterparts and saves 45.1% FLOPs.