Reviewerly/exHarmony huggingface dataset:
FitCNN: A cloud-assisted and low-cost framework for updating CNNs on IoT devices

Recently convolutional neural networks (CNNs) have essentially achieved the state-of-the-art accuracies in image classification and recognition tasks. CNNs are usually deployed in the cloud to handle data collected from IoT devices, such as smartphones and unmanned systems. However, significant data transmission overhead and privacy issues have made it necessary to use CNNs directly in device side. Nevertheless, the trained model deployed on mobile devices cannot effectively handle the unknown data and objects in new environments, which could lead to low accuracy and poor user experience. Hence, it would be crucial to re-train a better model via future unknown data. However, with tremendous computing cost and memory usage, training a CNN on IoT devices with limited hardware resources is intolerable in practice. To solve this issue, using the power of cloud to assist mobile devices to train a deep neural network becomes a promising solution . Therefore, this paper proposes a cloud-assisted CNN framework, named FitCNN, with incremental learning and low data transmission, to reduce the overhead of updating CNNs deployed on devices. To reduce the data transmission during incremental learning, we propose a strategy, called Distiller, to selectively upload the data that is worth learning, and develop an extracting strategy, called Juicer, to choose light amount of weights from the new CNN model generated on the cloud to update the corresponding old ones on devices. Experimental results show that the Distiller strategy can reduce 39.4% data transmission of uploading based on a certain dataset, and the Juicer strategy reduces by more than 60% data transmission of updating with multiple CNNs and datasets.
Accelerating Flexible Manifold Embedding for Scalable Semi-Supervised Learning

In this paper, we address the problem of large-scale graph-based semi-supervised learning for multi-class classification. Most existing scalable graph-based semi-supervised learning methods are based on the hard linear constraint or cannot cope with the unseen samples, which limits their applications and learning performance. To this end, we build upon our previous work flexible manifold embedding (FME) [1] and propose two novel linear-complexity algorithms called fast flexible manifold embedding (f-FME) and reduced flexible manifold embedding (r-FME). Both of the proposed methods accelerate FME and inherit its advantages. Specifically, our methods address the hard linear constraint problem by combining a regression residue term and a manifold smoothness term jointly, which naturally provides the prediction model for handling unseen samples. To reduce computational costs, we exploit the underlying relationship between a small number of anchor points and all data points to construct the graph adjacency matrix, which leads to simplified closed-form solutions. The resultant f-FME and r-FME algorithms not only scale linearly in both time and space with respect to the number of training samples but also can effectively utilize information from both labeled and unlabeled data. Experimental results show the effectiveness and scalability of the proposed methods.
An optimized feature reduction based currency forecasting model exploring the online sequential extreme learning machine and krill herd strategies

For the prediction of exchange rate, this paper proposes a hybrid learning frame work model which is a joint estimation of On-Line Sequential Extreme Learning Machine (OS-ELM) along with optimized feature reduction using Krill Herd (KH). The proposed learning scheme is compared with Extreme Learning Machine (ELM) and Recurrent Back Propagation Neural Network (RBPNN), considering three factors such as; without feature reduction, with statistical based feature reduction using Principal Component Analysis (PCA) and with optimized feature reduction techniques such as KH, Bacteria Foraging Optimization (BFO) and Particle Swarm Optimization (PSO). The models are applied over USD/INR, USD/EURO, YEN/INR and SGD/INR, constructed using technical indicators and statistical measures considering 3, 5, 7, 12 and 15 as window sizes. The results of comparisons of different performance measures in testing phase and MSE in training process demonstrate that the proposed OSELM-KH exchange rate prediction model is potentiality superior compared to others.
Rehabilitation motion recognition based on the international biomechanical standards

Since 1990, the International Society of Biomechanics (ISB) has made efforts to provide uniformity to motion description. However, the majority of user studies and technological systems that use motion recognition for rehabilitation, such as virtual and augmented reality (VR and AR), do not follows this standard. Typically, VR and AR techniques recognize motion by comparing human joint positions to a pre-recorded movement, direction, or position relative to an interactive object. Aiming to fill a lack of biomechanical reference on joint motion recognition and analysis for rehabilitation interactive systems, this paper proposes a movement description and recognition method following the ISB standard. The developed method aims to provide computational solutions which are clinically relevant and that uses clinical jargon known to the rehabilitation therapists. The proposed method will also allow motion analysis independent of user's height or its position relative to sensor. We conclude this paper by presenting a comparative analysis of the effectiveness of the Kinect sensors (versions 1 & 2) regarding the recognition of patient exercise motions related to the biomechanical standard. Our solution consists of biomechanical motion classification by identifying in which 3D plane the motion occurs. The motion recognition solution also provides patient posture, motion, and information about incorrect exercise performance. The method was developed by first implementing a body representation and Joint Coordinate System based on ISB standard and then motion angle and biomechanical classifications were performed. Specific methods to measure axial rotations were developed inspired by the traditional goniometry strategy. Also, experiments to evaluate if the method is able to classify the biomechanical motions and, thus, detect whether or not they are being performed correctly were performed using both Kinect v1 and Kinect v2. Motions were performed in correctly and wrongly to also verify false positives. Each classified motion was performed 100 times: 70 correct repetitions (35 at normal speed and 35 fast ones) and 30 wrong ones (out of their respective plane). The motion recognition method showed good efficacy in classifying the biomechanical motions correctly. Most of motions presented great success rate of recognition . higher than 80%, mainly using a 20 degrees tolerance.
Wavelet transform based on Meyer algorithm for image edge and blocking artifact reduction

In this study, a wavelet transform based on Meyer algorithm with edge-angle tracking capability is proposed for edge and blocking artifact reduction of an image, during image compression processes. This new proposed scheme is quite different from the conventional curvelet scheme, which can be viewed as using post-processing, after wavelet transform, to track only the edge of a high-frequency section of wavelet coefficients. However, the proposed wavelet transform scheme based on Meyer algorithm is with a complete wavelet design procedure, which is suitable for multiresolution analysis; it possesses the orthonormal property of conventional wavelet transforms. From computer simulation, we verify the merits of the proposed scheme over the conventional approaches, in terms of achieving better performance based on the subjective and objective (e.g., the peak signal-to-noise ratio) analysis.
Counter JPEG Anti-Forensic Approach Based on the Second-Order Statistical Analysis

The forensic investigation of JPEG compression generally relies on the analysis of first-order statistics based on image histogram. The JPEG compression detection methods based on such methodology can be effortlessly circumvented by adopting some anti-forensic attacks. This paper presents a counter JPEG anti-forensic method by considering the second-order statistical analysis based on the co-occurrence matrices (CMs). The proposed framework comprises three stages: selection of the target difference image, evaluation of CMs, and generation of second-order statistical feature based on CMs. In the first stage, we explore the effects of dithering operation of JPEG anti-forensics by analyzing the variance inconsistencies along the diagonals. Afterward, CMs are evaluated in the second stage to highlight the effects of grainy noise introduced during the dithering operation. The third stage is devoted to generate an optimal second-order statistical feature which is fed to the SVM classifier. The experimental results based on the uncompressed color image database and BOSSBase dataset images demonstrated that the proposed forensic detector based on CM is very efficient even in the presence of anti-forensic attacks. Moreover, the experimental results also confirm the competency of the proposed method in counter median filtering and contrast enhancement anti-forensics. The proposed scheme also provides satisfactory results in detecting other image processing operations such as mean filtering, Gaussian filtering, Weiner filtering, scaling, and rotation, thereby revealing its multi-purpose nature.
Cost-sensitive classifier chains: Selecting low-cost features in multi-label classification

Feature selection is one of the trending challenges in multi-label classification. In recent years a lot of methods have been proposed. However the existing approaches assume that all the features have the same cost. This assumption may be inappropriate when the acquisition of the feature values is costly. For example in medical diagnosis each diagnostic value extracted by a clinical test is associated with its own cost. In such cases it may be better to choose a model with an acceptable classification performance but a much lower cost. We propose a novel method which incorporates the feature cost information into the learning process. The method, named Cost-Sensitive Classifier Chains, combines classifier chains and penalized logistic regression with a modified elastic-net penalty which takes into account costs of the features. We prove the stability and provide a bound on generalization error of our algorithm. We also propose the adaptive version in which penalty factors are changing during fitting the consecutive models in the chain. The methods are applied on real datasets: MIMIC-II and Hepatitis for which the cost information is provided by experts. Moreover, we propose an experimental framework in which the features are observed with measurement errors and the costs depend on the quality of the features. The framework allows to compare the cost-sensitive methods on benchmark datasets for which the cost information is not provided. The proposed method can be recommended in a situation when one wants to balance low costs and high prediction performance.
A variational model with hybrid Hyper-Laplacian priors for Retinex

Retinex aims at estimating real reflectance images by removing the effect of illumination. We propose a nonconvex variational model for Retinex with novel priors for reflectance and illumination. Based on the statistics of the gradients of reflectance and illumination, we use the hyper-Laplacian prior to characterize the gradients of reflectance, and the hybrid hyper-Laplacian and Tikhonov prior to characterize the gradients of illumination. An efficient alternating direction method of multipliers (ADMM) is developed to solve the proposed model. Extensive numerical experiments show that the proposed method is comparable to the state-of-the-art methods quantitatively and qualitatively.
Local polynomial contrast binary patterns for face recognition

We propose a novel face representation model, called the polynomial contrast binary patterns (PCBP), based on the polynomial filters, for robust face recognition. It is assumed that the discrete array of pixel values comes about by sampling an underlying smooth surface in an image. The proposed method efficiently estimates the underlying local surface information, which is approximately represented as linear projection coefficients of the pixels in a local patch. The decomposition using polynomial filters can capture rich image information at multiple orientations and frequency bands. This guarantees its robustness to illumination and expression variations. The weighting scheme embeds different discriminative powers of each filter response image. We also propose to carry out a subsequent Fisher linear Discriminant (FLD) on each decomposed image for dimension reduction of features. Our extensive experiments on the public FERET and LFW databases demonstrate that the non-weighted Polynomial contrast binary patterns performs better than most of methods and the weighting scheme further improves the recognition rates. WPCBP+FLD(CD) and WPCBP+FLD(HI) can achieve much competitive or even better recognition performance compared with the state-of-the-art face recognition methods.
Lyapunov Control of Quantum Systems Based on Energy-Level Connectivity Graphs

This paper proposes a new multilayer Lyapunov control scheme based on energy-level connectivity graphs of quantum systems, which enables a high-probability population transfer to any eigenstate in high-dimensional quantum systems. We provide a graph connectivity result for stability analysis and explore the selectivity characteristic of the Lyapunov control fields. A systematic layering method for the energy-level connectivity graph is presented to achieve a layer-by-layer population transfer to the target state. Several applications including a five-level quantum system, a three-qubit superconducting system, and a spin chain of five qubits are presented to demonstrate the effectiveness of the multilayer Lyapunov control method.
Split-Correctness in Information Extraction

Programs for extracting structured information from text, namely information extractors, often operate separately on document segments obtained from a generic splitting operation such as sentences, paragraphs, k-grams, HTTP requests, and so on. An automated detection of this behavior of extractors, which we refer to as split-correctness, would allow text analysis systems to devise query plans with parallel evaluation on segments for accelerating the processing of large documents. Other applications include the incremental evaluation on dynamic content, where re-evaluation of information extractors can be restricted to revised segments, and debugging, where developers of information extractors are informed about potential boundary crossing of different semantic components. We propose a new formal framework for split-correctness within the formalism of document spanners. Our preliminary analysis studies the complexity of split-correctness over regular spanners. We also discuss different variants of split-correctness, for instance, in the presence of black-box extractors with "split constraints".
Exploiting Machine Learning Against On-Chip Power Analysis Attacks: Tradeoffs and Design Considerations

Modern power analysis attacks (PAAs) and existing countermeasures pose unique challenges on the design of simultaneously secure, power efficient, and high-performance ICs. In a typical PAA, power information is collected with a monitoring circuit connected to the compromised device. The non-typical voltage variations induced on a power distribution network (PDN) by such a malicious probing are sensed with on-chip sensors and exploited in this paper for detecting PAAs in real-time using statistical analysis. A closed-form expression for the voltage variations caused by malicious probing is provided. Guidelines with respect to the PDN characteristics and number of sensors are proposed for securing power delivery. The PAA detection system is designed in a 45-nm standard CMOS process. Based on the simulation results, a PAA on an IBM benchmarked microprocessor is detected with the accuracy of 88% with 30 on-chip sensors. Power overhead of 0.34% and 14.3% is demonstrated in, respectively, the IBM microprocessor and a typical advanced encryption standard system. In a practical cryptographic device, security sensitive PDN regions can be identified, significantly reducing the number of the on-chip sensors.
Efficient and Distributed Generalized Canonical Correlation Analysis for Big Multiview Data

Generalized canonical correlation analysis (GCCA) integrates information from data samples that are acquired at multiple feature spaces (or `views') to produce low-dimensional representations-which is an extension of classical two-view CCA. Since the 1960s, (G)CCA has attracted much attention in statistics, machine learning, and data mining because of its importance in data analytics. Despite these efforts, the existing GCCA algorithms have serious complexity issues. The memory and computational complexities of the existing algorithms usually grow as a quadratic and cubic function of the problem dimension (the number of samples / features), respectively-e.g., handling views with ≈1,000 features using such algorithms already occupies ≈10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> memory and the periteration complexity is ≈10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> flops-which makes it hard to push these methods much further. To circumvent such difficulties, we first propose a GCCA algorithm whose memory and computational costs scale linearly in the problem dimension and the number of nonzero data elements, respectively. Consequently, the proposed algorithm can easily handle very large sparse views whose sample and feature dimensions both exceed 100,000. Our second contribution lies in proposing two distributed algorithms for GCCA, which compute the canonical components of different views in parallel and thus can further reduce the runtime significantly if multiple computing agents are available. We provide detailed convergence analyses of the proposed algorithms and show that all the largescale GCCA algorithms converge to a Karush-Kuhn-Tucker (KKT) point at least sublinearly. Judiciously designed synthetic and realdata experiments are employed to showcase the effectiveness of the proposed algorithms.
A pooling based scene text proposal technique for scene text reading in the wild

Automatic reading texts in scenes has attracted increasing interest in recent years as texts often carry rich semantic information that is useful for scene understanding. In this paper, we propose a novel scene text proposal technique aiming for accurate reading texts in scenes. Inspired by the pooling layer in the deep neural network architecture, a pooling based scene text proposal technique is developed. A novel score function is designed which exploits the histogram of oriented gradients and is capable of ranking the proposals according to their probabilities of being text. An end-to-end scene text reading system has also been developed by incorporating the proposed scene text proposal technique where false alarms elimination and words recognition are performed simultaneously. Extensive experiments over several public datasets show that the proposed technique can handle multi-orientation and multi-language scene texts and obtains outstanding proposal performance. The developed end-to-end systems also achieve very competitive scene text spotting and reading performance.
LOGIC FOR EXACT ENTAILMENT

Abstract An exact truthmaker for A is a state which, as well as guaranteeing A ’s truth, is wholly relevant to it. States with parts irrelevant to whether A is true do not count as exact truthmakers for A . Giving semantics in this way produces a very unusual consequence relation, on which conjunctions do not entail their conjuncts. This feature makes the resulting logic highly unusual. In this paper, we set out formal semantics for exact truthmaking and characterise the resulting notion of entailment, showing that it is compact and decidable. We then investigate the effect of various restrictions on the semantics. We also formulate a sequent-style proof system for exact entailment and give soundness and completeness results.
A novel estimation approach for the solar radiation potential with its complex spatial pattern via machine-learning techniques

As a clean and sustainable energy resource with lower environmental impact, the Chinese government encourages the application of solar energy system. The global solar radiation on the horizontal surface in the specific site should be investigated in advance so that the solar energy system could be implemented properly and efficiently. However, the monthly average daily solar radiation (MADSR) in China has complex spatial patterns, and its observation stations are still lacking due to the high cost of equipment. To address these challenges, this study aimed to develop a novel estimation approach for the MADSR with its complex spatial pattern over a vast area in China via machine-learning techniques (i.e. a clustering method (k-means) and an advanced case-based reasoning (A-CBR) model). The MADSR and the relevant information were collected from 97 cities in China for 10 years (from 2006 to 2015). The average prediction accuracy of the proposed approach was determined at 93.23%, showing a promising way. The proposed novel approach is expected to be generalized via the interpolation methods (e.g. kriging method in a geographical information system) so that decision-makers (e.g. construction manager or facility manager) can determine the appropriate location, size and form in implementing the solar energy system.
A Local and Global Discriminative Framework and Optimization for Balanced Clustering

For many specific applications in data mining and machine learning, we face explicit or latent size constraint for each cluster that leads to the "balanced clustering" problem. Many existing clustering algorithms perform well in partitioning but fail in producing balanced clusters and preserving the naturally balanced structure of some data. In this paper, we propose a novel balanced clustering framework that flexibly utilizes local and global information of data. First, we propose the global balanced clustering (GBC), in which a global discriminative partitioning model is combined with the minimization of the distribution entropy of data. Then, we show that the proposed GBC can be further used to globally regularize some widely used local clustering models, so as to transform them into balanced clustering that simultaneously capture local and global data. We apply our global balanced regularization to spectral clustering (SC) and local learning (LL)-based clustering, respectively, and propose another two novel balanced clustering models: the local and global balanced SC (LGB-SC) and LGB-LL. Finding the optimal balanced partition is nondeterministic polynomial-time (NP)-hard in general. We adopt the method of augmented Lagrange multipliers to help optimize our model. Comprehensive experiments on several real world benchmarks demonstrate the advantage of our framework to yield balanced clusters while preserving good clustering quality. Our proposed LGB-SC and LGB-LL also outperform SC and LL as well as other classical clustering methods.