Reviewerly/exHarmony huggingface dataset:
Joint Heterogeneous Feature Learning and Distribution Alignment for 2D Image-Based 3D Object Retrieval

2D image-based 3D object retrieval is a novel but challenging task for 3D object retrieval. In this paper, we propose a 2D image-based 3D object retrieval method via joint heterogeneous feature learning and distribution alignment. Specifically, we propose to learn a mapping function in the Grassmann manifold to reduce the divergence of heterogeneous features of 2D images and 3D objects. We further employ the data distribution alignment method to adaptively integrate both marginal and conditional distributions. We embed both terms into the objective function to learn a domain-invariant classifier based on structural risk minimization. The output domain-invariant features of 2D images and 3D objects can be utilized for 2D image-based 3D object retrieval. Since there is lack of large-scale dataset for the evaluation of this task, we build two new datasets, MI3DOR and MI3DOR-2. We compare the proposed method against the representative methods for domain adaption and explore the influence of different components of the objective functions and key parameter. Comparison experiments show the superiority of this method.
SLAs-Aware Online Task Scheduling Based on Deep Reinforcement Learning Method in Cloud Environment

As increasingly traditional applications migrate to the cloud, load balancing without sacrificing the performances of cloud-based applications is a highly challenging problem in cloud computing. Existing solutions focusing on online task scheduling have the inefficient issue, due to the highly dynamic nature of cloud workloads and the virtual machines (VMs) with heterogeneous hardware configurations. To address this issue, dependent on the strong perception and decision-making ability of deep reinforcement learning (DRL) in automatic control problems, we propose a deep reinforcement learning based task scheduling method, which can dynamic assign submitted tasks to different VMs. In details, we formulate the task scheduling as a dynamical optimal problem with constraints, and then adopt the deep deterministic policy gradients (DDPG) network to find the optimal task assignment solution with meeting the Service Level Agreements (SLAs) requirements. It makes the optimal scheduling decision only dependent on learning directly from its experience without any prior knowledge. The experimental results using the actual Alibaba cloud workload tracings show that compared with other existing solutions, our proposed task scheduling method can effectively reduce the average response time of tasks with guaranteeing the load balancing among VMs, when facing of dynamical workloads.
Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression

This paper presents a novel variational inference framework for deriving a family of Bayesian sparse Gaussian process regression (SGPR) models whose approximations are variationally optimal with respect to the full-rank GPR model enriched with various corresponding correlation structures of the observation noises. Our variational Bayesian SGPR (VBSGPR) models jointly treat both the distributions of the inducing variables and hyperparameters as variational parameters, which enables the decomposability of the variational lower bound that in turn can be exploited for stochastic optimization. Such a stochastic optimization involves iteratively following the stochastic gradient of the variational lower bound to improve its estimates of the optimal variational distributions of the inducing variables and hyperparameters (and hence the predictive distribution) of our VBSGPR models and is guaranteed to achieve asymptotic convergence to them. We show that the stochastic gradient is an unbiased estimator of the exact gradient and can be computed in constant time per iteration, hence achieving scalability to big data. We empirically evaluate the performance of our proposed framework on two real-world, massive datasets.
Healthcare data warehouse system supporting cross-border interoperability

The free movement of European citizens across member states of the European Union adds an important level of complexity to strategic efforts of health interoperability. The use of electronic health data has been marked as an important strategic activity and policy to improve healthcare in European countries. Cross-border healthcare depends on the ability to set up shared practices with respect to patient data exchange across the countries. Data flow must comply with demanding security, legal and interoperability requirements, as defined by the European Patients Smart Open Services project specifications. The aim of this article is to propose a novel design of healthcare data warehouse based on the restructured Extract–Transform–Load process. We describe a portal framework that offers a comprehensive set of interoperability services to enable national e-Health platforms to set up cross-border health information networks compliant with European Patients Smart Open Services. The presented approach incorporates the technical and organizational interoperability by interconnecting Health Level Seven standard and Open National Contact Points framework in order to provide a modular, scalable and inter-operating architecture.
WRIST

To better explore the incorporation of pointing and gesturing into ubiquitous computing, we introduce WRIST, an interaction and sensing technique that leverages the dexterity of human wrist motion. WRIST employs a sensor fusion approach which combines inertial measurement unit (IMU) data from a smartwatch and a smart ring. The relative orientation difference of the two devices is measured as the wrist rotation that is independent from arm rotation, which is also position and orientation invariant. Employing our test hardware, we demonstrate that WRIST affords and enables a number of novel yet simplistic interaction techniques, such as (i) macro-micro pointing without explicit mode switching and (ii) wrist gesture recognition when the hand is held in different orientations (e.g., raised or lowered). We report on two studies to evaluate the proposed techniques and we present a set of applications that demonstrate the benefits of WRIST. We conclude with a discussion of the limitations and highlight possible future pathways for research in pointing and gesturing with wearable devices.
Implementation of Optical Character Recognition using Tesseract with the Javanese Script Target in Android Application

Recognising characters from text have been a popular topic in the computer vision area. The application can benefit to many problems in the world. For example: recognising text in documents, classifying the text or scripts of documents, plate recognition, etc. Many researchers have been developed the methods for recognising characters in by using Optical Character Recognition methods. Although text recognition problem using Optical Character Recognition has been more or less solved, most of the Optical Character Recognition problem explored is belong to Latin alphabet texts. Meanwhile, there are several languages have non-Latin scripts as the written text. Recognising a non-Latin script is quite challenging as the contour and shape of the text are relatively different with a Latin script text. This research aims to collect datasets for OCR in Javanese characters. A total of 5880 characters were collected and trained with several methods with Tesseract OCR tools. The models then be implemented to a mobile phone (Android based). The highest accuracy (97,50%) achieved by the model was achieved by combining single boundary box for the whole parts of the character and the separate boundary boxes in main body and sandangan parts.
Quantum walks with sequential aperiodic jumps

We analyze a set of discrete-time quantum walks for which the displacements on a chain follow binary aperiodic jumps according to three paradigmatic sequences: Fibonacci, Thue-Morse, and Rudin-Shapiro. We use a generalized Hadamard coin, ${\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{C}}_{H}$, as well as a generalized Fourier coin, ${\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{C}}_{K}$. We verify the QW experiences a slowdown of the wave packet spreading, ${\ensuremath{\sigma}}^{2}(t)\ensuremath{\sim}{t}^{\ensuremath{\alpha}}$, by the aperiodic jumps whose exponent, $\ensuremath{\alpha}$, depends on the type of aperiodicity. Additional aperiodicity-induced effects also emerge, namely, (1) while the superdiffusive regime ($1<\ensuremath{\alpha}<2$) is predominant, $\ensuremath{\alpha}$ displays an unusual sensibility with the type of coin operator where the more pronounced differences emerge for the Rudin-Shapiro and random protocols and (2) even though the angle $\ensuremath{\theta}$ of the coin operator is homogeneous in space and time, there is a nonmonotonic dependence of $\ensuremath{\alpha}$ with $\ensuremath{\theta}$. Fingerprints of the aperiodicity in the hoppings are also found when distributional measures such as the Shannon and von Neumann entropies, the Inverse Participation Ratio, the Jensen-Shannon dissimilarity, and the kurtosis are computed, which allow assessing informational and delocalization features arising from these protocols and understanding the impact of linear and nonlinear correlations of the jump sequence in a quantum walk as well. Finally, we argue the spin-lattice entanglement is enhanced by aperiodic jumps.
Blind motion deblurring with cycle generative adversarial networks

Blind motion deblurring is one of the most basic and challenging problems in image processing and computer vision. It aims to recover a sharp image from its blurred version knowing nothing about the blurring process. Many existing methods use the maximum a posteriori or expectation maximization framework to tackle this problem, but they cannot handle well the natural images with high-frequency features. Most recently, deep neural networks have been emerging as a powerful tool for image deblurring. In this paper, we show that encoder–decoder architecture gives better results for image deblurring tasks. In addition, we propose a novel end-to-end learning model that refines the generative adversarial network by many novel strategies to tackle the problem of image deblurring. Experimental results show that our model can capture high-frequency features well, and achieve the competitive performance.
Interpretable Variational Autoencoders for Cognitive Models

One of the most used methodologies in the field of education assessment is Item Response Theory (IRT). In this work, we propose the use of a novel Variational Autoencoder (VAE) architecture for a multidimensional IRT model. Our approach combines the advantages of the IRT model while allowing us to model high latent trait dimensions, previously unattainable in prior work. Additionally, it has the advantage of interpretability in the domain of educational assessment.Our experiments show that, given enough data, the new model is competitive with the state-of-the-art methods with respect to predictive power and is much faster in runtime performance. In our experiments, we achieve competitive results on a sample size 20× larger in a runtime that is 40× faster than the state-of-the- art model.
Two-pass hashing feature representation and searching method for copy-move forgery detection

In this paper, we propose a two-pass hashing feature representation and searching method for copy-move forgery detection with a good score and high efficiency. First, the normalized moment transformation is presented to extract the corresponding block features from multiple frequency images. The multiple-dimensional features of each pixel are projected into the corresponding hashing bin to obtain the corresponding hashing features. Then, a novel two-pass hashing feature representation is proposed to concatenate multiple hashing features as the bit sequence. Based on the two-pass hashing feature representations, the two-pass hashing searching algorithm searches and updates the nearest pixel matches in high efficiency. Finally, post-processing operations are proposed to accurately identify the forgery regions. The experimental results show that the proposed copy-move forgery detection method can achieve the best scores among the state-of-the-art methods, even under various attacks. In addition, the proposed method has a very high detection efficiency without iterations.
Gaining Extra Supervision via Multi-task learning for Multi-Modal Video Question Answering

This paper proposes a method to gain extra supervision via multi-task learning for multi-modal video question answering. Multi-modal video question answering is an important task that aims at the joint understanding of vision and language. However, establishing large scale dataset for multi-modal video question answering is expensive and the existing benchmarks are relatively small to provide sufficient supervision. To overcome this challenge, this paper proposes a multi-task learning method which is composed of three main components: (1) multi-modal video question answering network that answers the question based on the both video and subtitle feature, (2) temporal retrieval network that predicts the time in the video clip where the question was generated from and (3) modality alignment network that solves metric learning problem to find correct association of video and subtitle modalities. By simultaneously solving related auxiliary tasks with hierarchically shared intermediate layers, the extra synergistic supervisions are provided. Motivated by curriculum learning, multi-task ratio scheduling is proposed to learn easier task earlier to set inductive bias at the beginning of the training. The experiments on publicly available dataset TVQA shows state-of-the-art results, and ablation studies are conducted to prove the statistical validity.
Intrusion-resilient public cloud auditing scheme with authenticator update

Key-exposure resilient cloud storage auditing can create a secure cloud storage during before and after the key-exposure period. However, the malicious cloud server can still tamper with and even discard the client's files which are uploaded during the key-exposure period without being detected. So as to cope with this problem, we propose an intrusion-resilient public cloud auditing scheme, in which auditing authenticators are updated periodically to prevent the malicious cloud from tampering with these files using the exposed key. In addition, our scheme is secure unless the client and TPA (Third Party Auditor) are compromised in the same time period. This is different from Yu et al.'s scheme proposed in TIFS 2017, which is not secure if the client and TPA are compromised in different periods. Finally, the scheme can protect the client's file privacy, and prevent a curious TPA from recovering the file. The security analysis and the results of the experiment indicate that the security and performance of the scheme are acceptable.
Personality Traits Classification Using Deep Visual Activity-Based Nonverbal Features of Key-Dynamic Images

This paper addresses nonverbal behavior analysis for the classification of perceived personality traits using novel deep visual activity (VA)-based features extracted only from key-dynamic images. Dynamic images represent short-term VA. Key-dynamic images carry more discriminative information i.e., nonverbal features (NFs) extracted from them contribute to the classification more than NFs extracted from other dynamic images. Dynamic image construction, learning long-term VA with CNN+LSTM, and detecting spatio-temporal saliency are applied to determine key-dynamic images. Once VA-based NFs are extracted, they are encoded using covariance, and resulting representation is used for classification. This method was evaluated on two datasets: small group meetings and vlogs. For the first dataset, proposed method outperforms not only the state-of-the-art VA-based methods but also multi-modal approaches for all personality traits. For extraversion classification, it performs better than i) the most popular key-frames selection algorithm, ii) random and uniform dynamic image selection, and iii) NFs extracted from all dynamic images. Furthermore, the ablation study proves the superiority of proposed method. For the further dataset, it performs as well as the state-of-the-art visual-NFs on average, while showing improved performance for agreeableness classification. Proposed method can be adapted to any application based on nonverbal behavior analysis, thanks to being data-driven.
Core‐sets: An updated survey

Abstract In optimization or machine learning problems we are given a set of items, usually points in some metric space, and the goal is to minimize or maximize an objective function over some space of candidate solutions. For example, in clustering problems, the input is a set of points in some metric space, and a common goal is to compute a set of centers in some other space (points, lines) that will minimize the sum of distances to these points. In database queries, we may need to compute such a some for a specific query set of k centers. However, traditional algorithms cannot handle modern systems that require parallel real‐time computations of infinite distributed streams from sensors such as GPS, audio or video that arrive to a cloud, or networks of weaker devices such as smartphones or robots. Core‐set is a “small data” summarization of the input “big data,” where every possible query has approximately the same answer on both data sets. Generic techniques enable efficient coreset maintenance of streaming, distributed and dynamic data. Traditional algorithms can then be applied on these coresets to maintain the approximated optimal solutions. The challenge is to design coresets with provable tradeoff between their size and approximation error. This survey summarizes such constructions in a retrospective way, that aims to unified and simplify the state‐of‐the‐art. This article is categorized under Algorithmic Development &gt; Structure Discovery Fundamental Concepts of Data and Knowledge &gt; Big Data Mining Technologies &gt; Machine Learning Algorithmic Development &gt; Scalable Statistical Methods
LSTM-CNN Deep Learning Model for Sentiment Analysis of Dialectal Arabic

In this paper we investigate the use of Deep Learning (DL) methods for Dialectal Arabic Sentiment Analysis. We propose a DL model that combines long-short term memory (LSTM) with convolutional neural networks (CNN). The proposed model performs better than the two baselines. More specifically, the model achieves an accuracy between 81% and 93% for binary classification and 66% to 76% accuracy for three-way classification. The model is currently the state of the art in applying DL methods to Sentiment Analysis in dialectal Arabic.
GMM-based Undersampling and Its Application for Credit Card Fraud Detection

The class imbalance problem exists in many real-world applications such as fraud detection, medical diagnosis and spam filtering, and seriously influences the performance of learning algorithms. Randomly undersampling is a famous method to solve the problem. However, it cannot well extract the samples nearby the cross-edge of majority and minority classes due to its randomness, while these samples are very important for a classifier since they influence the classification performance. In this paper, we propose a novel Gaussian Mixture Undersampling (GMUS for short). GMUS mainly contains three steps. Firstly, a Gaussian Mixture Model (GMM) is applied to fit the majority samples. Secondly, considering the probability density function (PDF) of predicted minority samples on the well-fitted GMM, the maximum of PDF is selected as the cross-edge of two classes. Finally, we undersample the majority samples near the cross-edge. We do experiments on 16 public datasets and the results demonstrate that GMUS can sample more informative instances and thus improve the performance of classifiers compared with the state-of-the-art undersampling methods. We also apply GMUS to the credit card fraud detection and obtain a good performance.
Investigation on the net cascade using Ant Colony optimization algorithm

In the present work, the analysis of the net cascades with recirculation has been investigated. Therefore, survey on performance of some configurations of the net cascade has been conducted. It was demonstrated that the lozenge net cascades with recycled flows to different layers indicate the same function. Also, it is found that they perform as well as their equivalent conventional matched abundance ratio cascade and corresponding matched abundance ratio net cascade. Moreover, based on the metaheuristic algorithms, the Ant Colony optimization for continuous domain is used to optimize the net cascade parameters. For this purpose, the code ACORNET is developed and the waste and the product concentrations of a target component and the separation power of single machine were selected as the fitness function main items. It has been revealed that the application of the algorithm to this problem guarantees finding the acceptable solution and from an evolutionary point of view the performance of it is superior.
Nemesyst: A hybrid parallelism deep learning-based framework applied for internet of things enabled food retailing refrigeration systems

Deep learning has attracted considerable attention across multiple application domains, including computer vision, signal processing and natural language processing. Although quite a few single node deep learning frameworks exist, such as tensorflow, pytorch and keras, we still lack a complete processing structure that can accommodate large scale data processing, version control, and deployment, all while staying agnostic of any specific single node framework. To bridge this gap, this paper proposes a new, higher level framework, i.e. Nemesyst, which uses databases along with model sequentialisation to allow processes to be fed unique and transformed data at the point of need. This facilitates near real-time application and makes models available for further training or use at any node that has access to the database simultaneously. Nemesyst is well suited as an application framework for internet of things aggregated control systems, deploying deep learning techniques to optimise individual machines in massive networks. To demonstrate this framework, we adopted a case study in a novel domain; deploying deep learning to optimise the high speed control of electrical power consumed by a massive internet of things network of retail refrigeration systems in proportion to load available on the UK National Grid (a demand side response). The case study demonstrated for the first time in such a setting how deep learning models, such as Recurrent Neural Networks (vanilla and Long-Short-Term Memory) and Generative Adversarial Networks paired with Nemesyst, achieve compelling performance, whilst still being malleable to future adjustments as both the data and requirements inevitably change over time.