Reviewerly/exHarmony huggingface dataset:
Acoustic Impedance Deblurring With a Deep Convolution Neural Network

Domain-specific methods for deblurring particular sorts of objects have gained increasing attention due to the ineffectiveness of generic methods. We present a simple and effective convolutional neural network that deblurs postinversion acoustic impedance images. The architecture of our model consists of a convolutional layer that highlights edges and contours related to interfaces between rock layers; a locally connected layer that performs a convolutional step with unshared weights; and, finally, two fully connected layers that perform a nonlinear estimation of acoustic impedance values. We use the updated Standford VI reservoir model as training data set, which is composed of 150 acoustic impedance sections, each section with 200 traces. In this letter, we adopt a strong supervised learning that exploit, trace by trace, the data set of the inverted and ground truth impedance images. We also present an analysis comparing the frequency bandwidth among the latent, blurry, and deblurred images. Furthermore, the peak signal-to-noise ratio is calculated and compared with a classical deblurring method. We additionally address the requirement of deep learning for the huge amount of training examples by inserting rectified linear units and keeping the network architecture simple. The experimental results demonstrate the efficacy of the proposed method.
On Convergence of the Class Membership Estimator in Fuzzy $k$-Nearest Neighbor Classifier

The fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -nearest neighbor classifier (F <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN) improves upon the flexibility of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -nearest neighbor classifier by considering each class as a fuzzy set and estimating the membership of an unlabeled data instance for each of the classes. However, the question of validating the quality of the class memberships estimated by F <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN for a regular multiclass classification problem still remains mostly unanswered. In this paper, we attempt to address this issue by first proposing a novel direction of evaluating a fuzzy classifier by highlighting the importance of focusing on the class memberships estimated by F <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN instead of its misclassification error. This leads us to finding novel theoretical upper bounds, respectively, on the bias and the mean squared error of the class memberships estimated by F <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN. Additionally the proposed upper bounds are shown to converge toward zero with increasing availability of the labeled data points, under some elementary assumptions on the class distribution and membership function. The major advantages of this analysis are its simplicity, capability of a direct extension for multiclass problems, parameter independence, and practical implication in explaining the behavior of F <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN in diverse situations (such as in presence of class imbalance). Furthermore, we provide a detailed simulation study on artificial and real data sets to empirically support our claims.
Multi-filters guided low-rank tensor coding for image inpainting

Image inpainting is a classical yet challenging inverse ill-posed problem. In this paper, we introduce the multi-filters guided low-rank tensor coding as a prior information to tackle it. The key innovation is to formulate multiple feature-domain tensors by convoluting the target image with multi-filters. Furthermore, by exploring a low-rank tensor coding, it can reduce the redundancy between sparse feature vectors at neighboring locations and improve the efficiency of the overall representation. The resulting non-convex model is iteratively tackled by gradient descent procedure for updating the image and by low-rank pursuit procedure for updating the multi-view features. Besides, we explore an aggregation version of proposed method for further improving the inpainting performance. Experimental results demonstrate that the proposed algorithms can faithfully recover image and outperform the current state-of-the-art approaches in terms of visual inspection, the quantitative peak signal-to-noise ratio (PSNR), and structural similarity (SSIM).
A Probabilistic Approach to Cross-Region Matching-Based Image Retrieval

With deep convolutional features, cross-region matching (CRM) has recently shown superior performance on image retrieval. It evaluates image similarity by comparing image regions at different locations and scales, and is, therefore, more robust to geometric variance of objects. This paper first scrutinizes CRM-based image retrieval to provide a rigorous probabilistic interpretation by following the probability ranking principle. In addition to manifesting the assumptions implicitly taken by CRM, our interpretation highlights a fundamental issue hindering the performance of CRM-when comparing two image regions, CRM ignores modeling the distribution of the visual concept class associated with an image region, making the similarity comparison less precise. Taking advantage of the unprecedented representation capability of deep convolutional features, this paper proposes one approach to tackle that issue. It treats locally clustered image regions as a pseudo-labeled class sharing the same visual concept and utilizes them to model the distribution of the visual concept class associated with an image region. Both non-parametric and parametric methods are developed for this purpose, with careful probabilistic justification. Extensive experimental study on multiple benchmark data sets demonstrates the superior performance of the proposed pseudo-label approach to CRM and other comparable methods, with the maximum improvement of more than 10 percentage points over CRM.
Life Model: A novel representation of life-long temporal sequences in health predictive analytics

Predictive analytics in healthcare can prevent patients’ emergency health conditions, and reduce costs in the long term. Moreover, accurate and timely anomaly predictions by focusing on recent events can save lives. In real-time IoT predictive analytics, modeling historical temporal health records with missing values in diagnosis prediction is a major challenge. Recent studies have started using deep learning and data abstraction techniques to model health data. However, it is difficult to train a model to predict anomalies based on temporal sparse data, especially to classify all disease diagnosis classes. Modeling a lifetime of an individual’s medical history in a short, concise sequence is a challenge. Moreover, the model should be robust and preserve the concept of time for variety of examples despite the missing values; especially in an IoT system, in which real-time prediction depends on both recent data and historical records. The proposed solution in this research for modelingtemporal pattern sequences is called as Life Model (LM). LM provides a concise sequence to represent the history or future, using the novel intensity temporal sequence (ITS) tensors. LM algorithms and properties enable ITS tensors to train long short-term memory (LSTM) recurrent neural networks (RNN) efficiently in order to predict anomalies and diagnosis in real-time, even in the absence of some values. LM is used to predict mortality of 10,000 patients from MIMIC III dataset based on their diagnosis and procedures codes. The results show improvement in the model trained by LM-mapped data compared to fixed-sized intervals which achieved an accuracy of 99.6% with AUROC and brier score of 99.5% and of 0.00 respectively. In addition, the LM model can predict the approximate time of activities, with different granularity of seconds and up to years; tested on an activity dataset. Furthermore, a new LM-powered predictive health analytics and real-time monitoring schema (PHARMS) is proposed to enable design and implementation of predictive health analytic systems. PHARMS uses deep learning for real-time minimally-invasive intelligent activity monitoring and predictive analysis in a medical IoT scheme.
Computer Aided Detection of Clustered Microcalcification: A Survey

This paper attempts to pinpoint different techniques for Pectoral Muscle (PM) segmentation, Microcalcification (MC) detection and classification in digital mammograms. The segmentation of PM and detection of MC and its classification are mostly based on image processing and data mining techniques.The review centered on major techniques in image processing and data mining that is employed for PM segmentation, MC detection and classification in digital mammograms. Breast cancer is one of the significant causes of death among women aged above 40. Mammography is considered the most successful means for prompt and timely detection of breast cancers. One notable visual indication of the malignant growth is the appearance of Masses, Architectural Distortions, and Microcalcification Clusters (MCCs). There are some disadvantages and hurdles for mankind viewers, and it is hard for radiologists to supply both precise and steady assessment for a large number of mammograms created in extensive screening. Computer Aided Detection has been employed to help radiologists in detecting MC and MCCs. The automatic recognition of malignant MCCs could be very helpful for diagnostic purpose. In this paper, we summarize the methods of automatic detection and classification of MCs in digitized mammograms. Pectoral muscle segmentation techniques are also summarized.The techniques used for segmentation of PM, MC detection and classification in a digitized mammogram are reviewed.
FSB-EA: Fuzzy search bias guided constraint handling technique for evolutionary algorithm

Constraint handling technique (CHT) is a critical task for the evolutionary algorithms (EAs) on solving the constrained optimization problems (COPs). In the CHTs development, the definition of the search bias in the search process of an EA is one of the important issues. Based on the different search bias definitions, this study divides the CHTs into two categories: deterministic and non-deterministic techniques. The deterministic techniques define the search bias with an artificial rule. These techniques generally perform well for several scenarios. However, they may have poor performance for some others. This is mainly due to the difficulty to establish an accurate mapping between the complex search scenarios and the uncertain search biases. In contrast with the deterministic techniques, the non-deterministic techniques apply stochastic based methods to escape this mapping establishment which will improve the robustness of the algorithm. However, the probability-based blind random selection can affect negatively on the search efficiency of the algorithm. To compensate the deficiencies of these two categories, this paper proposes a fuzzy search bias guided CHT, namely FSB. The search bias in the FSB is guided by a membership function. It is used to meet the fuzzy knowledge that a solution with large constraint violation biases the search toward solutions with smaller constraint violation and a solution with small constraint violation biases the search toward solutions with better objective function value. The experimental results show that FSB is more robust and more efficient than the other compared techniques.
Topic representation: Finding more representative words in topic models

The top word list, i.e., the top-M words with highest marginal probabilities in a given topic, is the standard topic representation in topic models. Most of recent automatical topic labeling algorithms and popular topic quality metrics are based on it. However, we find, empirically, words in this type of top word list are not always representative. The objective of this paper is to find more representative top word lists for topics. To achieve this, we rerank the words in a given topic by further considering marginal probabilities on words over every other topic. The reranking list of top-M words is used to be a novel topic representation for topic models. We investigate three reranking methodologies, using (1) standard deviation weight, (2) standard deviation weight with topic size and (3) Chi Square χ2 statistic selection. Experimental results on real-world collections indicate that our representations can extract more representative words for topics, agreeing with human judgements.
Deterministic Concept Drift Detection in Ensemble Classifier Based Data Stream Classification Process

The data in streaming environment tends to be non-stationary. Hence, frequent and irregular changes occur in data, which usually denotes as a concept drift related to the process of classifying data streams. Depiction of the concept drift in traditional phase of data stream mining demands availability of labelled samples; however, incorporating the label to a streamlining transaction is infeasible in terms of process time and resource utilization. In this article, deterministic concept drift detection (DCDD) in ensemble classifier-based data stream classification process is proposed, which can depict a concept drift regardless of the labels assigned to samples. The depicted model of DCDD is evaluated by experimental study on dataset called poker-hand. The experimental result showing that the proposed model is accurate and scalable to detect concept drift with high drift detection rate and minimal false alarming and missing rate that compared to other contemporary models.
SegMo: CT volume segmentation using a multi-level Morse complex

The analysis of existing objects for developing new products is called reverse engineering and is a common process in industrial manufacturing. X-ray CT scanning is a powerful reverse-engineering tool that can acquire the entire geometry of an assembled object, including its inner structure, without disassembly. Before X-ray CT scanning can be used to accurately extract the parts of an assembled object from its CT volume, several challenges remain: CT artifacts, the non-uniqueness of the CT values, and limited resolution. Currently, an operator usually manually segments the target parts of the CT volume at voxel-level accuracy, which takes a long time. Hence, this paper proposes SegMo, a segmentation system which can perform segmentation of an assembled object to sub-voxel accuracy more quickly and with less effort than conventional methods. These advantages are achieved by suggesting the part boundary candidates that are coded as a hierarchical decomposition of the CT volume based on a Morse complex and polygonization at sub-voxel accuracy. SegMo consists of three steps: the generation of a hierarchical decomposition of a CT volume, segmentation using this hierarchy, and polygonization of the segmentation result. In this paper, we demonstrate how SegMo can achieve accurate and efficient segmentation on CT volumes of several complex assembled objects.
Visual appearance or functional complementarity: Which aspect affects your decision making?

A personalized product recommendation can significantly help customers find their preferred items and assist business owners in obtaining more income. However, due to the complexity of the users’ decision-making process, which is influenced by various aspects in different item domains such as functional complementarity and visual compatibility, item recommendations usually suffer from many challenging problems. Although several visually-aware recommendation methods have been proposed, most do not simultaneously incorporate both the influence of functional complementarity and the effect of visual compatibility into latent factor models. To address this issue, we first build an item-complementarity network using “frequently-bought-together” item information. Then, a Visual and Relational Probabilistic Matrix Factorization (VRPMF) model is proposed, which models a user’s preference for a given item as the combination of visual contents and item-complementarity relationships. Due to the dynamic nature of online data, such a comprehensive model poses efficiency challenge in learning parameters of the model. To overcome this scalability issue, we present a novel and Fast Alternating Least Squares (FALS) algorithm, to efficiently optimize the proposed model. Finally, to evaluate the VRPMF method, we conduct comprehensive experiments with several state-of-the-art competitors and evaluation metrics on multiple real-world datasets. The empirical results show that our method achieves significant improvements in terms of both rating prediction accuracy and running time. Our implementation of VRPMF is publicly available at: https://github.com/wubin7019088/VRPMF
Model-Based Digital Pianos: From Physics to Sound Synthesis

As a result of their complexity and versatility, pianos are arguably one of the most important instruments in Western music. The size, weight, and price of grand pianos as well as their relatively simple control surface (i.e., the keyboard), have led to the development of digital counterparts that mimic the sound of acoustic pianos as closely as possible. While most commercial digital pianos are based on sample playback, it is also possible to reproduce a piano's sound by modeling the physics of the instrument. The process of physical modeling starts with first understanding the physical principles, followed by creating accurate numerical models, and finally, finding numerically optimized signal processing models that allow sound synthesis in real time by excluding inaudible phenomena and adding some perceptually important features by using signal processing tricks. Accurate numerical models can be used by physicists and engineers to understand how the instrument functions or to help piano makers with instrument development. On the other hand, efficient real-time models are geared toward composers and musicians who perform at home or onstage. This article provides an overview of a physics-based piano synthesis beginning with computationally heavy, physically accurate approach followed by a discussion of the approaches that are designed to produce the best possible sound quality for real-time synthesis.
Encryption Switching Service: Securely Switch Your Encrypted Data to Another Format

Big data analytics has been regarded as a promising technology to yield better insights into future development by government and industry. Data collection and aggregation are necessary pre-steps to enable data analysis. However, data may be dispersed across multiple places and in different formats. Even worse, data can be encrypted under various encryption mechanisms when data owners try to secure the confidentiality of the data. This makes data aggregation extremely challenging, if not impossible, especially when the encryption keys cannot be shared for various reasons. In this paper, we take the first step in addressing this problem. More specifically, we propose a new notion of cross-domain encryption switching service that securely bridges two well-studied encryption mechanisms, namely traditional public key encryption and identity-based encryption. As of independent interest, our notion supports keyword search over encrypted data, i.e., after encryption switching one may search over the (outsourced) data without loss of data and query secrecy. We provide a provably-secure instantiation satisfying the notion, and further present the efficiency analysis to show the scalability. Our proposed scheme may be applicable in multi-domain cloud storage system.
On Power-Peak-Aware Scheduling for Large-Scale Shared Clusters

Recent studies have reported that big data analytics clusters, such as Hadoop, can create substantial power peaks, bringing instability and inflexibility issues to the power grid. Substantial power peaks also lead to high penalty charges from electric utility companies, accounting for more than 30 percent of the electricity bill for a cluster operator according to empirical studies. To this end, in this paper, we present a framework that schedules computing jobs in large-scale data analytics clusters to mitigate power peaks. The scheduling model captures important properties of modern distributed data analytics clusters, including bundled resource provisioning and job-to-task decomposition with distributed processing. The scheduling problem is formulated as a nonlinear integer program. Its solution is derived by decomposing it into two classes of sub-problems and solving each class with an exact and efficient solution method. As a direct application, we detail the implementation of our proposed scheduling framework on a Hadoop cluster, and demonstrate its efficacy by extensive trace-driven simulations based on the CloudSim simulator.
A new support vector machine with an optimal additive kernel

Although the kernel support vector machine (SVM) outperforms linear SVM, its application to real world problems is limited because the evaluation of its decision function is computationally very expensive due to kernel expansion. On the other hand, additive kernel (AK) SVM enables fast evaluation of a decision function using look-up tables (LUTs). The AKs, however, assume a specific functional form for kernels such as the intersection kernel (IK) or χ2 kernel, and are problematic in that their performance is seriously degraded when a given problem is highly nonlinear. To address this issue, an optimal additive kernel (OAK) is proposed in this paper. The OAK does not assume any specific kernel form, but the kernel is represented by a quantized Gram table. The training of the OAK SVM is formulated as semi-definite programming (SDP), and it is solved by convex optimization. In the experiment, the proposed method is tested with 2D synthetic datasets, UCI repository datasets and LIBSVM datasets. The experimental results show that the proposed OAK SVM has better performance than the previous AKs and RBF kernel while maintaining fast computation using look-up tables.
Multi-Modal Mention Topic Model for mentionee recommendation

As one of the most commonly used communication functions in Twitter-like social media systems, mention is playing an important role in users’ online interactions. With the dramatic increase in the number of social media users, the problem of mentionee recommendation, i.e., recommending mentionees (mentioned users) when mentioners (mentioning users) attempt to mention others, has received considerable attention in recent years. While an increasing line of work has studied this problem, the existing efforts focus only on the contribution of the non-visual data like post text. In fact, many social media posts contain not only textual but also visual contents like images, and these two heterogeneous data sources both describe users’ mentioning tendencies. In this work, we proposed a novel generative model, named Multi-modal Mention Topic Model (MMTM), to tackle the mentionee recommendation problem by learning users’ semantic patterns and the correlations between contents in different modalities of users’ multi-modal mentioning documents in a unified way. Extensive experiments were conducted on a real-world dataset to evaluate the performance of our method. The experiment results demonstrated the superiority of our method in terms of making more effective recommendations compared with other state-of-the-art methods.