Reviewerly/exHarmony huggingface dataset:
A Sharper Generalization Bound for Divide-and-Conquer Ridge Regression

We study the distributed machine learning problem where the n feature-response pairs are partitioned among m machines uniformly at random. The goal is to approximately solve an empirical risk minimization (ERM) problem with the minimum amount of communication. The divide-and-conquer (DC) method, which was proposed several years ago, lets every worker machine independently solve the same ERM problem using its local feature-response pairs and the driver machine combine the solutions. This approach is in one-shot and thereby extremely communication-efficient. Although the DC method has been studied by many prior works, reasonable generalization bound has not been established before this work.For the ridge regression problem, we show that the prediction error of the DC method on unseen test samples is at most ε times larger than the optimal. There have been constantfactor bounds in the prior works, their sample complexities have a quadratic dependence on d, which does not match the setting of most real-world problems. In contrast, our bounds are much stronger. First, our 1 + ε error bound is much better than their constant-factor bounds. Second, our sample complexity is merely linear with d.
Network Structure and Transfer Behaviors Embedding via Deep Prediction Model

Network-structured data is becoming increasingly popular in many applications. However, these data present great challenges to feature engineering due to its high non-linearity and sparsity. The issue on how to transfer the link-connected nodes of the huge network into feature representations is critical. As basic properties of the real-world networks, the local and global structure can be reflected by dynamical transfer behaviors from node to node. In this work, we propose a deep embedding framework to preserve the transfer possibilities among the network nodes. We first suggest a degree-weight biased random walk model to capture the transfer behaviors of the network. Then a deep embedding framework is introduced to preserve the transfer possibilities among the nodes. A network structure embedding layer is added into the conventional Long Short-Term Memory Network to utilize its sequence prediction ability. To keep the local network neighborhood, we further perform a Laplacian supervised space optimization on the embedding feature representations. Experimental studies are conducted on various real-world datasets including social networks and citation networks. The results show that the learned representations can be effectively used as features in a variety of tasks, such as clustering, visualization and classification, and achieve promising performance compared with state-of-the-art models.
Human Action Transfer Based on 3D Model Reconstruction

We present a practical and effective method for human action transfer. Given a sequence of source action and limited target information, we aim to transfer motion from source to target. Although recent works based on GAN or VAE achieved impressive results for action transfer in 2D, there still exists a lot of problems which cannot be avoided, such as distorted and discontinuous human body shape, blurry cloth texture and so on. In this paper, we try to solve these problems in a novel 3D viewpoint. On the one hand, we design a skeleton-to-3D-mesh generator to generate the 3D model, which achieves huge improvement on appearance reconstruction. Furthermore, we add a temporal connection to improve the smoothness of the model. On the other hand, instead of directly utilizing the image in RGB space, we transform the target appearance information into UV space for further pose transformation. Specially, unlike conventional graphics render method directly projects visible pixels to UV space, our transformation is according to pixel’s semantic information. We perform experiments on Human3.6M and HumanEva-I to evaluate the performance of pose generator. Both qualitative and quantitative results show that our method outperforms methods based on generation method in 2D. Additionally, we compare our render method with graphic methods on Human3.6M and People-snapshot. The comparison results show that our render method is more robust and effective.
Enhancing Lazy Grounding with Lazy Normalization in Answer-Set Programming

Answer-Set Programming (ASP) is an expressive rule-based knowledge-representation formalism. Lazy grounding is a solving technique that avoids the well-known grounding bottleneck of traditional ASP evaluation but is restricted to normal rules, severely limiting its expressive power. In this work, we introduce a framework to handle aggregates by normalizing them on demand during lazy grounding, hence relieving the restrictions of lazy grounding significantly. We term our approach as lazy normalization and demonstrate its feasibility for different types of aggregates. Asymptotic behavior is analyzed and correctness of the presented lazy normalizations is shown. Benchmark results indicate that lazy normalization can bring up-to exponential gains in space and time as well as enable ASP to be used in new application areas.
A Neural Network Approach to Verb Phrase Ellipsis Resolution

Verb Phrase Ellipsis (VPE) is a linguistic phenomenon, where some verb phrases as syntactic constituents are omitted and typically referred by an auxiliary verb. It is ubiquitous in both formal and informal text, such as news articles and dialogues. Previous work on VPE resolution mainly focused on manually constructing features extracted from auxiliary verbs, syntactic trees, etc. However, the optimization of feature representation, the effectiveness of continuous features and the automatic composition of features are not well addressed. In this paper, we explore the advantages of neural models on VPE resolution in both pipeline and end-to-end processes, comparing the differences between statistical and neural models. Two neural models, namely multi-layer perception and the Transformer, are employed for the subtasks of VPE detection and resolution. Experimental results show that the neural models outperform the state-of-the-art baselines in both subtasks and the end-to-end results.
Face recognition smart cane using haar-like features and eigenfaces

Visually impaired has the limitation in interacting with another human. They usually use the sense of hearing and touching their face to recognize human. Face recognition is a technology that can be used to solve this problem. This paper develops a smart cane function by integrated face recognition feature on the cane using Haar-Like features and Eigenfaces. This paper proposed a portable, real time, and wearable product. Raspberry Pi supports portability that affects the delay and computing speed of face recognition algorithms. Utilization of Raspi camera on the eyes glasses is for wearable purposes. Voice output provides information on whether the face is caught on camera or not. This prototype works well during the face detection and recognition process. It needs 3 seconds for one-face recognized in range 0.25 until 1.5 meters from the camera, until the sound and information are generated. It needs is me 5 second for two faces recognized and 10 seconds for 3 faces recognized by a system in the same range between face and the camera. The accuracy reaches 91.67% for the up-right position face but for other position the accuracy is only 18% until 32%.
A Hybrid Adaptive Neuro-Fuzzy Interface and Support Vector Machine Based Sentiment Analysis on Political Twitter Data

Twitter is a microblogging site where clients read and compose short messages on different points each day.Political investigation using social media is getting the consideration of numerous scientists to understand the general assessment and pattern, particularly at the time of elections.In this paper, a proficient approach with respect to fuzzy rules to dissect the general assessment and further anticipated poll results.The system contains mainly three modules in particular Data extraction, Pre-processing and characterization of sentiment.The initial two stages have undertakings, namely social media information extraction and data pre-processing in order to, expel all Uniform Resource Locator (URL) frame separated tweet.The last stage is a grouping stage where a strategy called Adaptive Neuro-Fuzzy Inference System (ANFIS) where the fuzzy based ontology is designed by actualizing Non-Linear Support Vector Machine (SVM) classifier analysis to improve the fuzzy principles.The result showed that the ANFIS-NonLinearSVM based Sentiment analysis of social network data is less complex and gives the high rate of accuracy compared to the existing methodologies.
Are Recent SISR Techniques Suitable for Industrial Applications at Low Magnification?

ΞIn recent years, many deep-network-based super-resolution techniques have been proposed and have achieved impressive results for 2× and higher magnification factors. However, lower magnification factors encountered in some industrial applications have not received special attention, such as 720P-to-1080P (1.5× magnification). Compared to traditional 2× or higher magnification factors, these lower magnifications are much simpler, but reconstructions of high-definition images are time-consuming and computationally complex. Hence, in this paper, a fast image upsampling method is designed specifically for industrial applications at low magnification. In the proposed method, edge and nonedge areas are first distinguished and then reconstructed via different fast approaches. For the edge area, a local edge pattern encoding-based method is presented to recover sharp edges. For the nonedge area, a global iterative reconstruction with texture constraint is utilized. Moreover, some acceleration strategies are also presented to further reduce the complexity. The experimental results demonstrate that the proposed method can obtain performance comparable to that of some state-of-the-art methods for 720P-to-1080P magnification, but the computational cost is much lower.
Classification of blast cell type on acute myeloid leukemia (AML) based on image morphology of white blood cells

AML is one type of cancer of the blood and spinal cord. AML has a number of subtypes including M0 and M1. Both subtypes are distinguished by the dominant blast cell type in the WBC, the myeloblast cells, promyelocyte, and myelocyte. This makes the diagnosis process of leukemia subtype requires identification of blast cells in WBC. Automatic blast cell identification is widely developed but is constrained by the lack of data availability, and uneven distribution for each type of blast cell, resulting in problems of data imbalance. This makes the system developed has poor performance. This study aims to classify blast cell types in WBC identified AML-M0 and AML-M1. The method used is divided into two stages, first pre-processing, image segmentation and feature extraction. The second stage, perform resample, which is continued over sampling with SMOTE. The process is done until the amount of data obtained is relatively the same for each blast cell, then the process of elimination of data duplication, randomize, classification and performance measurement. The validation method used is k-fold cross-validation with k=10. Performance parameters used are sensitivity, specifyicity, accuracy, and AUC. The average performance resulting from classification of cell types in AML with Random Forest algorithm obtained 82.9% sensitivity, 92.1% specificity, 89.6% accuracy and 87.5% AUC. These results indicate a significant improvement compared to the system model without using SMOTE. The performance generated by reference to the AUC value, the proposed system model belongs to either category, so it can be used for further stages of leukemia subtype AML-M0 and AML-M1.
Novel steganography scheme using Arabic text features in Holy Quran

&lt;p&gt;With the rapid growth of the Internet and mobile devices, the need for hidden communications has significantly increased. Steganography is a technique introduced for establishing hidden communication, Most steganography techniques have been applied to audio, images, videos, and text. Many researchers used steganography in Arabic texts to take advantage of adding, editing or changing letters or diacritics, but lead to notable and suspicious text. In this paper, we propose two novel steganography algorithms for Arabic text using the Holy Quran as cover text. The fact that it is forbidden to add, edit or change any letter or diacritics in the Holy Quran provides the valuable feature of its robustness and difficulty as a cover in steganography. The algorithms hide secret messages elements within Arabic letters benefiting from the existence of sun letters (Arabic: ḥurūf shamsīyah) and moon letters (ḥurūf qamarīyah). Also, we consider the existence of some Arabic language characteristics represented as small vowel letters (Arabic Diacritics). Our experiments using the proposed two algorithms demonstrate high capacity for text files. The proposed algorithms are robust against attack since the changes in the cover text are imperceptible, so our contribution offers a more secure algorithm that provides good capacity.&lt;/p&gt;
Semi-supervised cross-modal common representation learning with vector-valued manifold regularization

While cross-media data, like text, image, audio, video and 3D model, has been the main form of big data, there is a current dearth of research on cross-media retrieval. In this paper, we focus on how to learn the common representation of heterogeneous data which is a key challenge for cross-media retrieval. Most existing approaches linearly project original low-level feature into a joint feature space for isomorphic data representation. However, linear projection cannot capture most complex cross-modal correlation with high nonlinearity. In this paper, we propose a novel feature learning algorithm, which is semi-supervised cross-modal vector-valued manifold regularization (SCVM), to explore common representation of heterogeneous data. SCVM jointly explores low-level feature correlation and semantic information in a unified framework. Based on manifold regularization, we learn cross-media features from vector-valued reproducing kernel Hilbert spaces (RKHS) by kernel transformation on both labeled and unlabeled samples; moreover, we impose smoothness constraints of possible solutions to improve retrieval accuracy. Comparing with the current state-of-the-art approaches on two public datasets, comprehensive experimental results show superior performance of our SCVM. The method is more robust and stable when extended from two media types to five media types, which is very attractive in practical application.
Image blind deblurring using an adaptive patch prior

Image blind deblurring uses an estimated blur kernel to obtain an optimal restored original image with sharp features from a degraded image with blur and noise artifacts. This method, however, functions on the premise that the kernel is estimated accurately. In this work, we propose an adaptive patch prior for improving the accuracy of kernel estimation. Our proposed prior is based on local patch statistics and can rebuild low-level features, such as edges, corners, and junctions, to guide edge and texture sharpening for blur estimation. Our prior is a nonparametric model, and its adaptive computation relies on internal patch information. Moreover, heuristic filters and external image knowledge are not used in our prior. Our method for the reconstruction of salient step edges in a blurry patch can reduce noise and over-sharpening artifacts. Experiments on two popular datasets and natural images demonstrate that the kernel estimation performance of our method is superior to that of other state-of-the-art methods.
Optimal pulse width modulation technique combined with stair phase-coding method for absolute phase retrieval with projector defocusing

The accurate three-dimensional shape measurement of a complex surface is significantly important in industrial testing. However, three-dimensional profilometry by conventional sinusoidal fringe projection using a phase-shifting algorithm performs suboptimally because of the nonlinear intensity response of projectors. To overcome this problem, the present paper proposes using a combined optimizing sinusoidal pulse width modulation (SPWM) technique and stair phase-coding approach to obtain the unwrapped phase. Properly optimizing SPWM with a small degree of a defocusing projector to generate sinusoidal fringe patterns can overcome the undesired harmonics and nonlinear gamma effect. Two groups of four-step phase-shifting fringe patterns are used. One group of the stripes contains four sinusoidal patterns generated by the SPWM technique, which is used to determine the wrapped phase. The other group of stripes contains four sinusoidal patterns with the codeword embedded into the stair phase, whose stair changes are perfectly aligned with the 2π discontinuities of the sinusoidal fringe phase, which is used to determine the fringe order for the phase unwrapping. Moreover, for the defocusing projection, because the frequency of the phase-coding fringe increases, the fringe order decision becomes less reliable. Thus, a self-correction phase unwrapping method is applied for phase retrieval. Experiments were conducted to verify the performance of the proposed method.
Multi-Scale Quantum Harmonic Oscillator Algorithm With Truncated Mean Stabilization Strategy for Global Numerical Optimization Problems

A multi-scale quantum harmonic oscillator algorithm (MQHOA) is a quantum population-based algorithm proposed recently. It utilizes the quantum wave function to locate the global optimum of a global numerical optimization problem. As the MQHOA employs the elitism to replace the worst particle in each iteration cycle, it reduces one of the particles in each run, which will cripple the diversity of the population and slow down the convergence speed. Therefore, the particles will be easily trapped into local optima. In this paper, we suggest a new MQHOA with truncated mean stabilization (TS-MQHOA) policy to alleviate the above-mentioned problems. The theoretical and experimental analyses indicate that the truncated mean stabilization strategy helps to diversify the populations and improve the convergence efficiency. The proposed TS-MQHOA is evaluated on a number of dimension-wise unimodal and multimodal CEC benchmark functions, and the computational results are compared with several popular population-based algorithms. The experimental results on complex test problems demonstrate that the proposed TS-MQHOA, in most function evaluations, is able to obtain better convergence toward the global optimum compared with several renowned heuristic algorithms based on swarm intelligence. Meanwhile, the comparative results reveal the competitiveness and superiority of the proposed algorithm, especially on high-dimensional function evaluations.
Local Parallel Cross Pattern: A Color Texture Descriptor for Image Retrieval

Riding the wave of visual sensor equipment (e.g., personal smartphones, home security cameras, vehicle cameras, and camcorders), image retrieval (IR) technology has received increasing attention due to its potential applications in e-commerce, visual surveillance, and intelligent traffic. However, determining how to design an effective feature descriptor has been proven to be the main bottleneck for retrieving a set of images of interest. In this paper, we first construct a six-layer color quantizer to extract a color map. Then, motivated by the human visual system, we design a local parallel cross pattern (LPCP) in which the local binary pattern (LBP) map is amalgamated with the color map in "parallel" and "cross" manners. Finally, to reduce the computational complexity and improve the robustness to image rotation, the LPCP is extended to the uniform local parallel cross pattern (ULPCP) and the rotation-invariant local parallel cross pattern (RILPCP), respectively. Extensive experiments are performed on eight benchmark datasets. The experimental results validate the effectiveness, efficiency, robustness, and computational complexity of the proposed descriptors against eight state-of-the-art color texture descriptors to produce an in-depth comparison. Additionally, compared with a series of Convolutional Neural Network (CNN)-based models, the proposed descriptors still achieve competitive results.
A Unified Framework for Decision Tree on Continuous Attributes

The standard algorithms of decision trees and their derived methods are usually constructed on the basis of the frequency information.However, they still suffer from a dilemma or multichotomous question for continuous attributes when two or more candidate cut points have the same or similar splitting performance with the optimal value, such as the maximal information gain ratio or the minimal Gini index.In this paper, we propose a unified framework model to deal with this question.We then design two algorithms based on Splitting Performance and the number of Expected Segments, called SPES1 and SPES2, which determine the optimal cut point, as follows.First, several candidate cut points are selected based on their splitting performances being the closest to the optimal.Second, we compute the number of expected segments for each candidate cut point.Finally, we combine these two measures by introducing a weighting factor α to determine the optimal one from several candidate cut points.To validate the effectiveness of our methods, we perform them on 25 benchmark datasets.The experimental results demonstrate that the classification accuracies of the proposed algorithms are superior to the current state-of-the-art methods in tackling the multichotomous question, about 5% in some cases.In particular, according to the proposed methods, the number of candidate cut points converges to a certain extent.
Continuous Leakage-Resilient Identity-Based Encryption with Tight Security

Abstract In the actual applications, an adversary can break the security of cryptography scheme through various leakage attacks (e.g. side-channel attacks, cold-boot attacks, etc.), even the continuous leakage attacks. That is, a practical cryptography scheme must maintain its claimed security in the continuous leakage setting. However, the previous constructions on the leakage-resilient identity-based encryption (IBE) scheme could tolerate a leakage that is bounded, and cannot resist the continuous leakage attacks. In order to further achieve the better security, a novel method to build the continuous leakage-resilient IBE scheme with tight security is presented in this paper, and the scheme’s security is proved, in the standard model, based on a stronger security assumption that depends on the number of queries made by the adversary. In addition, our proposal has several advantages over previous such constructions, e.g. shorter public parameters, higher communication efficiency, tight security, etc.