Reviewerly/exHarmony huggingface dataset:
Deep3DSaliency: Deep Stereoscopic Video Saliency Detection Model by 3D Convolutional Networks

Stereoscopic saliency detection plays an important role in various stereoscopic video processing applications. However, conventional stereoscopic video saliency detection methods mainly use independent low-level features instead of extracting them automatically, and thus, they ignore the intrinsic relationship between the spatial and temporal information. In this paper, we propose a novel stereoscopic video saliency detection method based on 3D convolutional neural networks, namely, deep 3D video saliency (Deep3DSaliency). The proposed network consists of two sub-models: spatiotemporal saliency model (STSM) and stereoscopic saliency aware model (SSAM). STSM directly takes three consecutive video frames as the input to extract visual spatiotemporal features, while SSAM attempts to further infer the depth and semantic features from the left and right video frames by shared parameters from STSM. The visual spatiotemporal features from STSM and the depth and semantic features from SSAM are learned by an alternating optimization scheme. Finally, all these saliency-related features are combined together for the final stereoscopic saliency detection via 3D deconvolution. Experimental results show the superior performance of the proposed model over other existing ones in saliency estimation for 3D video sequences.
Quaternion Grassmann average network for learning representation of histopathological image

Abstract Histopathological image analysis works as ‘gold standard’ for cancer diagnosis. Its computer-aided approach has attracted considerable attention in the field of digital pathology, which highly depends on the feature representation for histopathological images. The principal component analysis network (PCANet) is a novel unsupervised deep learning framework that has shown its effectiveness for feature representation learning. However, PCA is susceptible to noise and outliers to affect the performance of PCANet. The Grassmann average (GA) is superior to PCA on robustness. In this work, a GA network (GANet) algorithm is proposed by embedding GA algorithm into the PCANet framework. Moreover, since quaternion algebra is an excellent tool to represent color images, a quaternion-based GANet (QGANet) algorithm is further developed to learn effective feature representations containing color information for histopathological images. The experimental results based on three histopathological image datasets indicate that the proposed QGANet achieves the best performance on the classification of color histopathological images among all the compared algorithms.
Visualization of Software Architectures in Virtual Reality and Augmented Reality

Software architecture is abstract and intangible. Tools for visualizing software architecture can help to comprehend the implemented architecture but they need an effective and feasible visual metaphor, which maps all relevant aspects of a software architecture and fits all types of software. We present the visualization of component-based software architectures in Virtual Reality (VR) and Augmented Reality (AR). We describe how to get all relevant data for the visualization by data mining on the whole source tree and on source code level of OSGi-based projects. The data is stored in a graph database for further analysis and visualization. The software visualization uses an island metaphor, which represents every module as a distinct island. The whole island is displayed in the confines of a virtual table, where users can explore the software visualization on multiple levels of granularity by performing navigational tasks. Our approach allows users to get a first overview about the complexity of an OSGi-based software system by interactively exploring its modules as well as the dependencies between them.
Hybrid mining

In today's cryptocurrencies, Hashcash proof of work is the most commonly-adopted approach to mining. In Hashcash, when a miner decides to add a block to the chain, she has to solve the difficult computational puzzle of inverting a hash function. While Hashcash has been successfully adopted in both Bitcoin and Ethereum, it has attracted significant and harsh criticism due to its massive waste of electricity, its carbon footprint and environmental effects, and the inherent lack of usefulness in inverting a hash function. Various other mining protocols have been suggested, including proof of stake, in which a miner's chance of adding the next block is proportional to her current balance. However, such protocols lead to a higher entry cost for new miners who might not still have any stake in the cryptocurrency, and can in the worst case lead to an oligopoly, where the rich have complete control over mining.
Real-Time 6DOF Pose Estimation of Endoscopic Instruments Using Printable Markers

To accurately navigate surgical instruments around in vivo environments in minimally invasive surgeries through flexible endoscopes or rigid scopes, real-time tracking of surgical instruments is required. It is even challenging to track regular-shaped rigid bodies on a less-distorted general-purpose monocular vision, while most of the surgical instruments or devices used are cylindrical and slim imaged with endoscope distortion in nature. Thus, more dedicated and accurate rigid-body tracking approach for cylindrical endoscopic instruments will be more helpful in intra-operative guidance. In this paper, we present an endoscopic instrument tracking approach with printable markers that consists of a green band and a square with four white and one black circle inscribed in it. The proposed approach can estimate the six degrees of freedom pose, i.e., X, Y, Z, Roll, Pitch, and Yaw, of the imaged instrument with respect to the endoscope. Detailed experiments justify that our proposed marker detection framework allows translation motions between 42 and 100 mm along depth direction and rotational motions around X and Y up to ±180°, with 90% detection rate in dynamic condition, 99% in static condition, and accurate pose estimation with a mean translation error of 1.286 mm with a standard deviation of 0.673 mm and a mean rotational error of 1.497° with a standard deviation of 0.873°. Our proposed marker can be easily printed and wrapped to the existing endoscopic instruments, which potentially can assist in the precise guidance in minimally invasive surgeries without changing the existing workflow or incurring significant cost burdens.
A Study of Feature Scattering in the Linux Kernel

Feature code is often scattered across a software system. Scattering is not necessarily bad if used with care, as witnessed by systems with highly scattered features that evolved successfully. Feature scattering, often realized with a pre-processor, circumvents limitations of programming languages and software architectures. Unfortunately, little is known about the principles governing scattering in large and long-living software systems. We present a longitudinal study of feature scattering in the Linux kernel, complemented by a survey with 74, and interviews with nine Linux kernel developers. We analyzed almost eight years of the kernel's history, focusing on its largest subsystem: device drivers. We learned that the ratio of scattered features remained nearly constant and that most features were introduced without scattering. Yet, scattering easily crosses subsystem boundaries, and highly scattered outliers exist. Scattering often addresses a performance-maintenance tradeoff (alleviating complicated APIs), hardware design limitations, and avoids code duplication. While developers do not consciously enforce scattering limits, they actually improve the system design and refactor code, thereby mitigating pre-processor idiosyncrasies or reducing its use.
BRIGHT—Drift-Aware Demand Predictions for Taxi Networks

Massive data broadcast by GPS-equipped vehicles provide unprecedented opportunities. One of the main tasks in order to optimize our transportation networks is to build data-driven real-time decision support systems. However, the dynamic environments where the networks operate disallow the traditional assumptions required to put in practice many off-the-shelf supervised learning algorithms, such as finite training sets or stationary distributions. In this paper, we propose BRIGHT: a drift-aware supervised learning framework to predict demand quantities. BRIGHT aims to provide accurate predictions for short-term horizons through a creative ensemble of time series analysis methods that handles distinct types of concept drift. By selecting neighborhoods dynamically, BRIGHT reduces the likelihood of overfitting. By ensuring diversity among the base learners, BRIGHT ensures a high reduction of variance while keeping bias stable. Experiments were conducted using three large-scale heterogeneous real-world transportation networks in Porto (Portugal), Shanghai (China), and Stockholm (Sweden), as well as with controlled experiments using synthetic data where multiple distinct drifts were artificially induced. The obtained results illustrate the advantages of BRIGHT in relation to state-of-the-art methods for this task.
Defining IOCG signatures through compositional data analysis: A case study of lithogeochemical zoning from the Olympic Dam deposit, South Australia

The Olympic Dam Cu-U-Au-Ag deposit is dominantly composed of mineralised hematite-breccias and occurs entirely within the Roxby Downs Granite. Multivariate statistical analysis of a large whole-rock, 15 m-interval geochemical dataset (10,565 samples) was undertaken to identify geochemical signatures characteristic of iron-oxide copper gold (IOCG)-style mineralization and constrain the conspicuous lithogeochemical zonation observed at Olympic Dam. Statistical analyses include principal component analysis on centred logratio (clr)-transformed data coupled with hierarchical clustering. Certain groups of elements that can be interpreted in terms of an evolving hydrothermal system relative to host lithologies are derived from data analysis: granitophile (U-W-Sn-Mo); siderophile (Ni-Co); chalcophile (Ag-Bi) and related elements (As-Sb and Au-Te). The distributions of elements within each group are investigated through three vertical cross-sections and are compared with known lithological and Cu-(Fe)-sulphide zonation. Throughout the Olympic Dam Breccia Complex, the IOCG signature is defined by multi-element combinations of the commodity metals Cu, U, Au, and Ag, coupled with a range of trace elements. Overall, the IOCG signature overlaps well with Fe-metasomatism despite mismatch which is likely due to discrete styles of mineralisation found only on the margins of the deposit and also to the presence of mineralised domains within Fe-poor zones. The IOCG signature is composed of two geochemical associations, which exhibit distinct spatial distributions. The first group, Cu-U3O8-Se-S, shows concentric zonation whereas the second group, Au-W-Mo-Sb-As, forms a vertical ∼1800 m deep corridor in the southeastern lobe of the deposit. The specific Au-W-Mo-As-Sb signature could potentially be generic within IOCG systems across the Olympic Cu-Au province and if so, would provide a proxy model for near-mine exploration.
Data-Dependent Generalization Bounds for Multi-Class Classification

In this paper, we study data-dependent generalization error bounds that exhibit a mild dependency on the number of classes, making them suitable for multi-class learning with a large number of label classes. The bounds generally hold for empirical multi-class risk minimization algorithms using an arbitrary norm as the regularizer. Key to our analysis is new structural results for multi-class Gaussian complexities and empirical ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -norm covering numbers, which exploit the Lipschitz continuity of the loss function with respect to the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> - and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -norm, respectively. We establish data-dependent error bounds in terms of the complexities of a linear function class defined on a finite set induced by training examples, for which we show tight lower and upper bounds. We apply the results to several prominent multi-class learning machines and show a tighter dependency on the number of classes than the state of the art. For instance, for the multi-class support vector machine of Crammer and Singer (2002), we obtain a data-dependent bound with a logarithmic dependency, which is a significant improvement of the previous square-root dependency. The experimental results are reported to verify the effectiveness of our theoretical findings.
Blind quality assessment for screen content images via convolutional neural network

With the wide propagation of cloud and mobile computing, screen content images (SCIs) have become more indispensable in our daily lives. Compared to natural scene images (NSIs), SCIs possess many particular characteristics, like mixed contents, extremely sharp edges, and text graphics. Consequently, more challenges occur in the feature extraction, which is used to reflect the distortion, during the quality assessment of SCIs. Recently, some convolutional neural network (CNN) models have been designed by automatically learning feature to evaluate the quality. In this paper, we develop a novel blind quality assessment method for SCIs via the CNN. First, compared with existing CNN-based methods, the proposed method avoids the disadvantage of training with image patches, and it is the pioneering attempt that takes the entire image as inputs. Second, instead of the image gray value, the original image is decomposed into two portions, i.e., the predicted and unpredicted portions, according to the internal generative mechanism (IGM) theory as the input of CNN. Through the CNN, all features of the image are learned automatically from beginning to end, and the network finally outputs the predicted score. Since existing SCI database is too small, to fully train the network, we collected 30000 SCIs and employed a high-accuracy full-reference quality assessment metric of SCI to compute scores as the training labels. Experimental results on SIQAD database demonstrate that the proposed method is comparable to reference-based SCI quality assessment metrics and is superior to the state-of-the-art NSI quality assessment metrics.
Energy-aware dynamic resource management in elastic cloud datacenters

In clouds, placement of on-demand applications on heterogeneous machines, has turned out to be a crucial research problem, particularly, in terms of performance and energy consumption. Techniques like Dynamic Voltage and Frequency Scaling (DVFS), processor speed adjustment and features such as turning off displays, activating sleep modes, etc. are only useful for decreasing the energy consumption of a single machine, at marginal loss in performance. They cannot be used to achieve significant power optimization in High Performance Computing (HPC) systems such as grids, and cloud datacenters; because power saved by scaling down the processor voltage is far less than switching off a machine. Resource management, using dynamic consolidation of VMs, allows cloud service providers to optimize resource usability, performance and decrease power consumption. This paper investigates various resource management techniques, and suggests several heuristic approaches to optimise energy consumption and performance in elastic datacenters. Using real workload datasets, our evaluation suggests that a combination of the proposed VM allocation and consolidation with migration control technique could save approximately 1.96%–9.38% energy, and improve 0.32%–5.96% performance, as compared to its closest rivals.
Significance-based discriminative sequential pattern mining

Discriminative sequential patterns are sub-sequences whose occurrences exhibit significant differences across sequential data sets with different class labels. The discovery of such types of patterns has many practical applications in different fields. To date, various algorithms for mining discriminative sequential patterns have been proposed. However, the reported patterns from these methods usually contain many false positives that only hold in the sample data by chance. To alleviate this issue, we put forward the concept of significance-based discriminative sequential pattern mining and a corresponding algorithm DSPM-MTC (Discriminative Sequential Pattern Mining with Multiple Testing Correction). The key idea of DSPM-MTC is to integrate the multiple hypothesis testing correction procedure into the pattern mining process to generate a pattern set with error rate control. To demonstrate the effectiveness of DSPM-MTC, we conduct a series of experiments on real sequential data sets and simulation data sets. The experimental results show that DSPM-MTC can effectively recognize false discoveries to generate a pattern set with statistical quality control.
Joint Embedding Learning and Low-Rank Approximation: A Framework for Incomplete Multiview Learning

In real-world applications, not all instances in multi-view data are fully represented. To deal with incomplete data, Incomplete Multi-view Learning (IML) rises. In this paper, we propose the Joint Embedding Learning and Low-Rank Approximation (JELLA) framework for IML. The JELLA framework approximates the incomplete data by a set of low-rank matrices and learns a full and common embedding by linear transformation. Several existing IML methods can be unified as special cases of the framework. More interestingly, some linear transformation based complete multi-view methods can be adapted to IML directly with the guidance of the framework. Thus, the JELLA framework improves the efficiency of processing incomplete multi-view data, and bridges the gap between complete multi-view learning and IML. Moreover, the JELLA framework can provide guidance for developing new algorithms. For illustration, within the framework, we propose the Incomplete Multi-view Learning with Block Diagonal Representation (IML-BDR) method. Assuming that the sampled examples have approximate linear subspace structure, IML-BDR uses the block diagonal structure prior to learn the full embedding, which would lead to more correct clustering. A convergent alternating iterative algorithm with the Successive Over-Relaxation optimization technique is devised for optimization. Experimental results on various datasets demonstrate the effectiveness of IML-BDR.
Studying the plasticity in deep convolutional neural networks using random pruning

Recently, there has been a lot of work on pruning filters from deep convolutional neural networks (CNNs) with the intention of reducing computations. The key idea is to rank the filters based on a certain criterion (say, $$l_1$$ -norm, average percentage of zeros, etc.) and retain only the top-ranked filters. Once the low-scoring filters are pruned away, the remainder of the network is fine-tuned and is shown to give performance comparable to the original unpruned network. In this work, we report experiments which suggest that the comparable performance of the pruned network is not due to the specific criterion chosen, but due to the inherent plasticity of deep neural networks which allows them to recover from the loss of pruned filters once the rest of the filters are fine-tuned. Specifically, we show counterintuitive results wherein by randomly pruning 25–50% filters from deep CNNs we are able to obtain the same performance as obtained by using state-of-the-art pruning methods. We empirically validate our claims by doing an exhaustive evaluation with VGG-16 and ResNet-50. Further, we also evaluate a real-world scenario where a CNN trained on all 1000 ImageNet classes needs to be tested on only a small set of classes at test time (say, only animals). We create a new benchmark dataset from ImageNet to evaluate such class-specific pruning and show that even here a random pruning strategy gives close to state-of-the-art performance. Lastly, unlike existing approaches which mainly focus on the task of image classification, in this work we also report results on object detection and image segmentation. We show that using a simple random pruning strategy, we can achieve significant speedup in object detection (74% improvement in fps) while retaining the same accuracy as that of the original Faster-RCNN model. Similarly, we show that the performance of a pruned segmentation network is actually very similar to that of the original unpruned SegNet.
Observer-Based Leader-Following Formation Control for Multi-Robot With Obstacle Avoidance

This paper focuses on the observer-based leader-following formation tracking control for multi-robot with obstacle avoidance. First, based on the leader-follower formation control method, setting the trajectory of the leader robot, then the follower robots track the leader robot. Second, an observer for the followers is designed to estimate the state of the leader robot, which can limit the estimation errors to a small value at short time-frame. Moreover, the gain controller and corrective controller are presented to complete the formation task and implement the trajectory transformation, respectively. In addition, an improved obstacle avoidance algorithm is devised to avoid the obstacles by adjusting the angle between the leader robot and the followers. Finally, the simulation results are presented to verify the effectiveness of the proposed control scheme. The main advantages of this paper are both an observer-based controller includes gain control and corrective control is developed for the nonlinear multi-robot system, and the obstacle avoidance problem during movement is solved with the optimal method.
Analog programmable neuron and case study on VLSI implementation of Multi-Layer Perceptron (MLP)

In this work, a programmable neuron is proposed to approximate the following activation functions: sigmoid, hyperbolic tangent and linear. In other words, the neural network designer can use several control bits to choose the type of activation function without any physical change. The proposed neuron which is simulated in a 0.18 μm CMOS technology, shows a good approximation with maximum error from the ideal hyperbolic tangent function and the ideal sigmoid function by 29.33% and 7.4%, respectively. In order to evaluate the functionality of the neuron, it is applied in two Multi-Layer Perceptron (MLP) neural networks. The first one which is trained to implement XOR gate, is capable of processing signals in the frequency range from 2.5 mHz to 50 MHz. The accuracy of the network is more than 99.9%. The second one is a pattern recognition neural network. Comparison with previous work reveals that there is 48% decrease in the network's power consumption. Moreover, the proposed neuron has been applied in the fully connected (FC) layers of a convolutional neural network (CNN) and an experiment has been conducted on a benchmark dataset MNIST.