Reviewerly/exHarmony huggingface dataset:
Nonnegative self-representation with a fixed rank constraint for subspace clustering

A number of approaches to graph-based subspace clustering, which assumes that the clustered data points were drawn from an unknown union of multiple subspaces, have been proposed in recent years. Despite their successes in computer vision and data mining, most neglect to simultaneously consider global and local information, which may improve clustering performance. On the other hand, the number of connected components reflected by the learned affinity matrix is commonly inconsistent with the true number of clusters. To this end, we propose an adaptive affinity matrix learning method, nonnegative self-representation with a fixed rank constraint (NSFRC), in which the nonnegative self-representation and an adaptive distance regularization jointly uncover the intrinsic structure of data. In particular, a fixed rank constraint as a prior is imposed on the Laplacian matrix associated with the data representation coefficients to urge the true number of clusters to exactly equal the number of connected components in the learned affinity matrix. Also, we derive an efficient iterative algorithm based on an augmented Lagrangian multiplier to optimize NSFRC. Extensive experiments conducted on real-world benchmark datasets demonstrate the superior performance of the proposed method over some state-of-the-art approaches.
Recurrent reverse attention guided residual learning for saliency object detection

Deep convolutional neural networks (CNNs) have been widely applied to saliency object detection (SOD) with promising performance. However, the coarse feature resolutions due to a series of pooling and convolutional operations in CNNs considerably decrease the original image resolution, resulting in losing spatial details and fine structures, especially on salient object boundaries. To address this issue, we present a recurrent reverse attention based residual learning network for SOD. We first construct a pair of low-and high-level integrated feature representations by aggregating two groups of low-and high-level feature maps, respectively, among which we design a novel joint feature pyramid pooling module to increase the high-level feature resolution. Afterwards, we progressively learn to refine the residual between each side-output saliency prediction and the ground-truth in a cascaded fashion by alternatively using the high-level and low-level integrated features. Each residual learning module contains a recurrent reverse attention module that focuses on the residual region outside the current predicted salient regions, guiding the network to learn the missing salient parts and details. Finally, we develop a simple yet effective boundary detection loss to compensate the detailed texture loss. Extensive evaluations on six popular SOD benchmark datasets demonstrate remarkable performance boosting of our proposed approach compared with state-of-the-art methods. Especially, our approach runs in real-time with a speed of 32 fps.
Optimal PID control of an autonomous vehicle using Butterfly Optimization Algorithm BOA

This paper present an optimal PID controller based on Butterfly Optimization Algorithm (BOA) in order to control the lateral dynamics of autonomous vehicles. This is the first time that this new algorithm will be used to search the optimal values, of the PID controller. The proposed controller show good results in term of lateral acceleration or yaw rate can be compared to those of PID-PSO and PID-GA.
Switching Structured Prediction for Simple and Complex Human Activity Recognition

Automatic human activity recognition is an integral part of any interactive application involving humans (e.g., human-robot interaction systems). One of the main challenges for activity recognition is the diversity in the way individuals often perform activities. Furthermore, changes in any of the environment factors (i.e., illumination, complex background, human body shapes, viewpoint, etc.) intensify this challenge. In addition, there are different types of activities that robots need to interpret for seamless interaction with humans. Some activities are short, quick, and simple (e.g., sitting), while others may be detailed/complex, and spread throughout a long span of time (e.g., washing mouth). In this article, we recognize the activities within the context of graphical models in a sequence-labeling framework based on skeleton data. We propose a new structured prediction strategy based on probabilistic graphical models (PGMs) to recognize both types of activities (i.e., complex and simple). These activity types are often spanned in very diverse subspaces in the space of all possible activities, which would require different model parameterizations. In order to deal with these parameterization and structural breaks across models, a category-switching scheme is proposed to switch over the models based on the activity types. For parameter optimization, we utilize a distributed structured prediction technique to implement our model in a distributed setting. The method is tested on three widely used datasets (CAD-60, UT-Kinect, and Florence 3-D) that cover both activity types. The results illustrate that our proposed method is able to recognize simple and complex activities while the previous work concentrated on only one of these two main types.
Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks

In this work, a computer-aided tool is developed to predict relevant physical and mechanical properties that are involved in the selection tasks of metallic materials. The system is based on the use of artificial neural networks supported by big data collection of information about the technological characteristics of thousands of materials. Thus, the volume of data exceeds 43k. The system can access an open online material library (a website where material data are recorded), download the required information, read it, filter it, organise it and move on to the step based on artificial intelligence. An artificial neural network (ANN) is built with thousands of perceptrons, whose topology and connections have been optimised to accelerate the training and predictive capacity of the ANN. After the corresponding training, the system is able to make predictions about the material density and Young's modulus with average confidences greater than 99% and 98%, respectively.
Online Detection of Events With Low-Quality Synchrophasor Measurements Based on $i$Forest

In this article, we propose an online datadriven approach that leverages the isolation mechanism for fast event detection with low-quality data measurement. The proposed adaptive and online isolation forest (iForest)based detection (AOIFD) method adopts a hierarchical subspace feature selection scheme to design two levels of detectors. As such, it is capable of differentiating events from low-quality data measurements, preventing false alarms in the presence of low-quality data measurements. We further propose a data augmentation method to address the training data imbalance, which is caused by the rare occurrence of events. Moreover, we propose an adaptive training process to update the AOIFD method so that it can adapt to the time-varying operating conditions of power systems. The proposed AOIFD algorithm is practical in the sense that it is a fast-response method that requires no system modeling information and no global communications. Case studies with both synthetic and realistic PMU data are conducted to validate the effectiveness of the proposed method.
In- and out-of-equilibrium quantum metrology with mean-field quantum criticality

We study the influence that collective transition phenomena have on quantum metrological protocols. The single spherical quantum spin (SQS) serves as stereotypical toy model that allows analytical insights on a mean-field level. First, we focus on equilibrium quantum criticality in the SQS and obtain the quantum Fisher information analytically, which is associated with the minimum lower bound for the precision of estimation of the parameter driving the phase transition. We compare it with the Fisher information for a specific experimental scenario where photoncounting-like measurements are employed. We find how quantum criticality and squeezing are useful resources in the metrological scenario. Second, we obtain the quantum Fisher information for the out of equilibrium transition in the dissipative non-equilibrium steady state (NESS), and investigate how the presence of dissipation affects the parameter estimation. In this scenario, it is known that the critical point is shifted by an amount which depends on the dissipation rate. This is used here to design high precision protocols for a whole range of the transition-driving parameter in the ordered phase. In fact, for certain values of the parameter being estimated, dissipation may be used to obtain higher precision when compared to the equilibrium scenario.
Multi instance learning via deep CNN for multi-class recognition of Alzheimer's disease

In recent years, number of classification techniques for Alzheimer's disease (AD) have been developed that produced methods based on the use of hand-crafted machine learning and obscure deep learning models. This study proposed a new classification framework based on the combination of Unet-like 2D convolutional neural networks (CNN) and multinomial logistic regression classifier, which learns the intra-slice for multi-class classification after the selection of the 3D positron emission tomography (PET) image into a sequence of 2D slices. The CNNs are performed to generate the attention features of the brain while the logistic regression incorporated to learn those specifically localized features of various classes for AD classification. At the end of the network, we used a average pooling layer before the softmax for four-class classification problem. It can efficiently generate a flexible class of transformations and that can be trained end-to-end by back propagation. The results indicated that the proposed multi-instance learning (MIL) learns region of interest (ROI) itself and thus that could help to efficiently identify the precise patterns for AD. The proposed combined Unet-like CNN with multinomial regression classifier approach achieved highest accuracy of 97.9% and 96.7% on the classification of AD and MCI, respectively. It is much higher than the performances of the conventional methods in the literature.
Monitoring Children's Learning Through Wearable Eye-Tracking: The Case of a Making-Based Coding Activity

Learning activities for/with children include rich interactions with peers, tutors, and learning materials (in digital or physical form). During such activities, children gain new knowledge and master their skills. Automatized and continuous monitoring of children's learning is a complex task, but, if efficient, can greatly enrich teaching and learning. Wearable devices, such as eye-tracking glasses, have the capacity to continuously and unobtrusively monitor children's interactions, and such interactions might be capable of predicting children's learning. In this article, we set out to quantify the extent to which children's gaze, captured with eye-tracking glasses, can predict their learning. To do so, we collected data from a case study with 44 children (8–17 years old) during a making-based coding activity. Our analysis shows that children's gaze can predict their learning with 15.79% error. Our results also identify the most important gaze measures with respect to children's learning, and pave the way for new research in this area.
Educational Potentials of Flipped Learning in Intercultural Education as a Transversal Resource in Adolescents

The evolution of technology in the educational field has resulted in the emergence of new ways of teaching and learning, adapted to the formative characteristics of a digital era. This is the case of Flipped Learning (FL), an innovative teaching methodology that uses technology to carry out instructional action and change learning moments and spaces. The objective of this study is to verify the effectiveness of FL on a traditional methodology without the use of digital resources in teaching content related to intercultural education. A quantitative research method has been followed by an experimental design. Two study groups have been set up. With the control group a traditional training methodology has been used, and with the experimental group an innovative one, through FL. A total of 60 students of the 4th level of Secondary Education from an educational center in Ceuta (Spain) have participated. An ad hoc questionnaire derived from other validated instruments has been used for data collection. The findings show that the FL has achieved better results in the different dimensions analyzed. Therefore, it is concluded that the implementation of innovative training actions such as FL can obtain better results in academic indicators than a traditional methodology.
Tensor train rank minimization with hybrid smoothness regularization for visual data recovery

Recently, the tensor train (TT) rank has received much attention for tensor completion, due to its ability to explore the global low-rankness of tensors. However, existing methods still leave room for improvement, since the low-rankness itself is generally not sufficient to recover the underlying data. Inspired by this, we consider a novel tensor completion model by simultaneously exploiting the global low-rankness and local smoothness of visual data. In particular, we use low-rank matrix factorization to characterize the global TT low-rankness, and framelet and total variation regularization to enhance the local smoothness. We develop an efficient proximal alternating minimization algorithm to solve the proposed new model with guaranteed convergence. Extensive experiments on various data demonstrated that the proposed method outperforms compared methods in terms of visual and quantitative measures.
Modeling Analysis and Cost-Performance Ratio Optimization of Virtual Machine Scheduling in Cloud Computing

As an essential feature of cloud computing, dynamic scalability enables the cloud system to dynamically expand or shrink resources according to user needs at runtime. Effectively predicting and optimizing the cost and performance of cloud computing platforms have become one of the key research challenges in the field of cloud computing. In this article, to quantitatively predict the cost and performance of cloud computing platforms, we propose a cloud computing resource analysis model considering both hot/cold startup and hot/cold shutdown of virtual machines (VMs), and use the M/M/N/oo queuing model to analyze cloud computing platform and acquire accurate performance indicators, such as elasticity indicators, cost indicators, performance indicators, cost-performance ratios, etc. In addition, we establish a multi-objective optimization model to optimize both performance and cost of cloud computing platform. Then the optimal stopping and cost-performance optimization algorithm are applied to obtain the optimal configurations, including the number of hot startup VMs, the system service rate, the hot/cold startup rate of VMs, and the hot/cold shutdown rate. By comparing with existing optimization methods, we demonstrate the superiority of our cost-performance ratio optimization method.
Workload Transfer Strategy of Urban Neighboring Data Centers With Market Power in Local Electricity Market

This paper investigates how to minimize the operational cost of cloud service provider (CSP) that operates urban neighboring data centers (DCs) in the same electricity market and can conduct workload transfer among DCs. Due to the substantial electricity demand of DCs, their market power which can have impact on the locational marginal prices (LMPs) of the electricity market should be taken into consideration. We formulate a bilinear bilevel problem which regards the CSP as a price maker and explores cost-minimizing workload transfer strategies. The upper level is the operational cost minimization problem of CSP and the lower level corresponds to the economic dispatch problem of independent system operator (ISO) of electricity market which determines the electricity prices. It is challenging to directly solve the bilevel problem with bilinear term in the objective function. Hence, we first reformulate the original problem into a single level problem and then based on the property of the problem we develop a polytope cutting algorithm that attains the global optimal solution. The proposed algorithm solves linear optimizations iteratively by cutting the non-convex polytope feasible set into convex sets. In addition, considering the varying communication environment in practice, we analyze the impact of transfer price uncertainty on total cost of CSP, and show that the expected cost surprisingly decreases with the increasing uncertainty. Simulations based on the standard IEEE test cases show that the cost of CSP is significantly reduced and a win-win result for both the CSP and independent system operator (ISO) is possible.
Fast Anomaly Detection in Multiple Multi-Dimensional Data Streams

Multiple multi-dimensional data streams are ubiquitous in the modern world, such as IoT applications, GIS applications and social networks. Detecting anomalies in such data streams in real-time is an important and challenging task. It is able to provide valuable information from data and then assists decision-making. However, exiting approaches for anomaly detection in multi-dimensional data streams have not properly considered the correlations among multiple multi-dimensional streams. Moreover, for multi-dimensional streaming data, online detection speed is often an important concern. In this paper, we propose a fast yet effective anomaly detection approach in multiple multi-dimensional data streams. This is based on a combination of ideas, i.e., stream pre-processing, locality sensitive hashing and dynamic isolation forest. Experiments on real datasets demonstrate that our approach achieves a magnitude increase in its efficiency compared with state-of-the-art approaches while maintaining competitive detection accuracy.
Group-of-features relevance in multinomial kernel logistic regression and application to human interaction recognition

We propose an approach for human interaction recognition (HIR) in videos using multinomial kernel logistic regression with group-of-features relevance (GFR-MKLR). Our approach couples kernel and group sparsity modelling to ensure highly precise interaction classification. The group structure in GFR-MKLR is chosen to reflect a representation of interactions at the level of gestures, which ensures more robustness to intra-class variability due to occlusions and changes in subject appearance, body size and viewpoint. The groups consist of motion features extracted from tracking interacting persons joints over time. We encode group sparsity in GFR-MKLR through relevance weights reflecting each group (gesture) discrimination capability between different interaction categories. These weights are automatically estimated during GFR-MKLR training using gradient descent minimisation. Our model is computationally efficient and can be trained on a small training dataset while maintaining a good generalization and interpretation capabilities. Experiments on the well-known UT-Interaction dataset have demonstrated the performance of our approach by comparison with state-of-art methods.
A History of Audio Effects

Audio effects are an essential tool that the field of music production relies upon. The ability to intentionally manipulate and modify a piece of sound has opened up considerable opportunities for music making. The evolution of technology has often driven new audio tools and effects, from early architectural acoustics through electromechanical and electronic devices to the digitisation of music production studios. Throughout time, music has constantly borrowed ideas and technological advancements from all other fields and contributed back to the innovative technology. This is defined as transsectorial innovation and fundamentally underpins the technological developments of audio effects. The development and evolution of audio effect technology is discussed, highlighting major technical breakthroughs and the impact of available audio effects.
Data-Rate Driven Transmission Strategies for Deep Learning-Based Communication Systems

Deep learning (DL) based autoencoder is a promising architecture to implement end-to-end communication systems. One fundamental problem of such systems is how to increase the transmission rate. Two new schemes are proposed to address the limited data rate issue: adaptive transmission scheme and generalized data representation (GDR) scheme. In the first scheme, an adaptive transmission is designed to select the transmission vectors for maximizing the data rate under different channel conditions. The block error rate (BLER) of the first scheme is 80% lower than that of the conventional one-hot vector scheme. This implies that higher data rate can be achieved by the adaptive transmission scheme. In the second scheme, the GDR replaces the conventional one-hot representation. The GDR scheme can achieve higher data rate than the conventional one-hot vector scheme with comparable BLER performance. For example, when the vector size is eight, the proposed GDR scheme can double the date rate of the one-hot vector scheme. Besides, the joint scheme of the two proposed schemes can create further benefits. The effect of signal-to-noise ratio (SNR) is analyzed for these DL-based communication systems. Numerical results show that training the autoencoder using data set with various SNR values can attain robust BLER performance under different channel conditions.
Selective diffusion involving reaction for binarization of bleed-through document images

Binarization has always been a challenging problem in document image processing because of various types of degradation. In this paper, we present a nonlinear reaction–diffusion model for binarization of bleed-through document images, which is the Perona–Malik equation involving diffusion coefficient based on structure tensor along with a nonlinear reaction term. The Perona–Malik diffusion is utilized to selectively smooth document images with bleed-through removal. Meanwhile, the nonlinear reaction term takes the responsibility for the desired binarization. In order to solve our model numerically, we develop a parallel–series splitting algorithm by combining finite differencing with two kinds of splitting methods in the literature. Our algorithm is tested on seven publicly available datasets (DIBCO 2009 to 2014 and 2016). The experimental results show that our method averagely outperforms six relevant models for the nineteen document images with bleed-through in the DIBCO series datasets.
Near lossless image compression using parallel fractal texture identification

The most important parameters of image processing are resolution of the image and processing speed. The datasets of multimedia are compressed, which are rich in quality and quantity is challenging. This paper develops a novel approach to estimate the affine parameters of fractal texture identification, in order to minimize the complexity of computation. In this proposed NLICPFTI, a pattern dictionary is maintained to hold repeated fractal patterns. Different types of data chunks such as Fractal Pattern Chunk (FPC), Intermediate Raster Chunk (IRC), Intermediate Vector Chunk (IVC), Run Length Encode Chunk (RLEC) and Flood-fill Zone Chunk (FFZC). These chunks are used to store an image into compressed format. Experimental on standard images illustrate that our approach gives more significant improvements in Peak Signal to Noise Ratio, Structural Similarity Index Mode, Feature Similarity Index Mode at high compression ratio.