Reviewerly/exHarmony huggingface dataset:
Hybrid extreme learning machine approach for heterogeneous neural networks

In this paper, a hybrid learning approach, which combines the extreme learning machine (ELM) with a genetic algorithm (GA), is proposed. The utilization of this hybrid algorithm enables the creation of heterogeneous single layer neural networks (SLNNs) with better generalization ability than traditional ELM in terms of lower mean square error (MSE) for regression problems or higher accuracy for classification problems. The architecture of this method is not limited to traditional linear neurons, where each input participates equally to the neuron’s activation, but is extended to support higher order neurons which affect the network’s generalization ability. Initially, the proposed heterogeneous hybrid extreme learning machine (He-HyELM) algorithm creates a number of custom created neurons with different structure, which are used for the creation of homogeneous SLNNs. These networks are trained with ELM and an application specific GA evolves them into heterogeneous networks according to a fitness criterion utilizing the uniform crossover operator for the recombination process. After the completion of the evolution process, the network with the best fitness is selected as the most optimal. Experimental results demonstrate that the proposed learning algorithm can get better results than traditional ELM, homogeneous hybrid extreme learning machine (Ho-HyELM) and optimally pruned extreme learning machine (OP-ELM) for homogeneous and heterogeneous SLNNs.
Adversarial Deep Learning Models with Multiple Adversaries

We develop an adversarial learning algorithm for supervised classification in general and Convolutional Neural Networks (CNN) in particular. The algorithm's objective is to produce small changes to the data distribution defined over positive and negative class labels so that the resulting data distribution is misclassified by the CNN. The theoretical goal is to determine a manipulating change on the input data that finds learner decision boundaries where many positive labels become negative labels. Then we propose a CNN which is secure against such unforeseen changes in data. The algorithm generates adversarial manipulations by formulating a multiplayer stochastic game targeting the classification performance of the CNN. The multiplayer stochastic game is expressed in terms of multiple two-player sequential games. Each game consists of interactions between two players-an intelligent adversary and the learner CNN-such that a player's payoff function increases with interactions. Following the convergence of a sequential noncooperative Stackelberg game, each two-player game is solved for the Nash equilibrium. The Nash equilibrium finds a pair of strategies (learner weights and evolutionary operations) from which there is no incentive for either learner or adversary to deviate. We then retrain the learner over all the adversarial manipulations generated by multiple players to propose a secure CNN which is robust to subsequent adversarial data manipulations. The adversarial data and corresponding CNN performance is evaluated on MNIST handwritten digits data. The results suggest that game theory and evolutionary algorithms are very effective in securing deep learning models against performance vulnerabilities simulated as attack scenarios from multiple adversaries.
Benchmarking Neural Networks For Quantum Computations

The power of quantum computers is still somewhat speculative. Although they are certainly faster than classical ones at some tasks, the class of problems they can efficiently solve has not been mapped definitively onto known classical complexity theory. This means that we do not know for which calculations there will be a "quantum advantage," once an algorithm is found. One way to answer the question is to find those algorithms, but finding truly quantum algorithms turns out to be very difficult. In previous work, over the past three decades, we have pursued the idea of using techniques of machine learning to develop algorithms for quantum computing. Here, we compare the performance of standard real- and complex-valued classical neural networks with that of one of our models for a quantum neural network, on both classical problems and on an archetypal quantum problem: the computation of an entanglement witness. The quantum network is shown to need far fewer epochs and a much smaller network to achieve comparable or better results.
A Robust Modulation Classification Method for PSK Signals Using Random Graphs

In this paper, a modulation classification method is proposed for identifying phase shift-keying signals, and experiments are performed to confirm its validity. The method relies on the graph representation of the Fourier transform of the second and fourth powers of these signals. As the graph is fully connected only for certain modulation types, this represents a discriminating feature for classification. Unlike the existing techniques in the literature, the proposed method requires neither channel/ signal-to-noise ratio estimation nor timing/frequency offset correction. Experimental results show the applicability of the method in realistic radio fading channels.
mrMoulder: A recommendation-based adaptive parameter tuning approach for big data processing platform

Nowadays the world has entered the big data era. Big data processing platforms, such as Hadoop and Spark, are increasingly adopted by many applications, in which there are numerous parameters that can be tuned to improve processing performance for big data platform operators. However, due to the large number of these parameters and the complex relationship among them, it is very time-consuming to manually tune parameters. Therefore, it is a challenge to automatically configure parameters as quickly as possible to optimize the performance of the current job. Existing auto-tuning methods often take a certain time before job runs to get the optimal configuration, which would increase the job’s total processing time and reduce the overall efficiency of cluster. In this paper, we propose an adaptive tuning framework, mrMoulder, to recommend a near-optimal configuration for the new job in a short time. mrMoulder sets a self-extending configuration repository and a collaborative filtering based recommendation engine, to speed up the process of optimizing parameter configuration. We have deployed mrMoulder in a Hadoop cluster, and the experiment results have demonstrated that, for a new big data application, the recommend time of mrMoulder is only 20% to 30% of that for the existing auto-tuning methods, while the recommendation quality remains almost unchanged.
Minimal Timing Jitter in Superconducting Nanowire Single-Photon Detectors

Using two-temperature model coupled with modified time-dependent Ginzburg-Landau equation we calculate the delay time $\tau_d$ in appearance of growing normal domain in the current-biased superconducting strip after absorption of the single photon. We demonstrate that $\tau_d$ depends on the place in the strip where photon is absorbed and monotonically decreases with increasing of the current. We argue, that the variation of $\tau_d$ (timing jitter), connected either with position-dependent response or Fano fluctuations could be as small as the lowest relaxation time of the superconducting order parameter $\sim \hbar/k_BT_c$ ($T_c$ is the critical temperature of the superconductor) when the current approaches the depairing current.
Online local pool generation for dynamic classifier selection

Dynamic Classifier Selection (DCS) techniques have difficulty in selecting the most competent classifier in a pool, even when its presence is assured. Since the DCS techniques rely only on local data to estimate a classifiers competence, the manner in which the pool is generated could affect the choice of the best classifier for a given instance. That is, the global perspective in which pools are generated may not help the DCS techniques in selecting a competent classifier for instances that are likely to be misclassified. Thus, it is proposed in this work an online pool generation method that produces a locally accurate pool for test samples in difficult regions of the feature space. The difficulty of a given area is determined by the estimated classification difficulty of the instances in it. That way, by using classifiers that were generated in a local scope, it could be easier for the DCS techniques to select the best one for those instances they would most probably misclassify. For the query samples surrounded by easy instances, a simple nearest neighbors rule is used in the proposed method. In order to identify in which cases the local pool is used in the proposed scheme, an analysis on the correlation between instance hardness and DCS techniques is performed in this work, and it is proposed the use of an instance hardness measure that conveys the degree of local class overlap near a given sample. Experimental results show that the DCS techniques were more able to select the most competent classifier for difficult instances when using the proposed local pool than when using a globally generated pool. Moreover, the proposed technique yielded significantly greater recognition rates in comparison to a Bagging-generated pool and two other global generation schemes for all DCS techniques evaluated. The performance of the proposed technique was also significantly superior to three state-of-the-art classification models and was statistically equivalent to five of them.
Joint CRF and Locality-Consistent Dictionary Learning for Semantic Segmentation

Semantic image segmentation can be accomplished by assigning a proper object category label to each meaningful region of an image. Beyond the original bottom-up models, the use of top-down categorization information has been applied to semantic segmentation to improve performance. An excellent example of such a top-down scheme is to integrate a Conditional Random Field (CRF) model with sparse dictionary learning. However, the existing solutions merely consider the discrimination of dictionaries to obtain better sparse codes, without considering the inherent data locality characteristics. In this paper, we explore such characteristics and propose a novel semantic segmentation framework based on an innovative CRF model with locality-consistent dictionary learning. In particular, we propose two new locality-consistent dictionary learning strategies by capturing the local consistencies in the feature space and the label space. In addition, we develop a joint dictionary and a CRF model parameter learning algorithm to seamlessly integrate the proposed locality-consistent dictionary learning strategies into the CRF model. Extensive experiments are conducted with two popular databases of different traits (i.e., Graz-02 and PASCAL-CONTEXT). The simulation results confirm the efficiency of the proposed scheme, especially when training data are limited.
Hypothesis testing and entropies of quantum channels

Hypothesis testing is an important task in mathematics and physics. Hypothesis testing of two random variables is related to the Kullback-Leibler divergence of the two corresponding distributions. Similarly, quantum hypothesis testing of two quantum states is characterized by the quantum relative entropy. While a quantum state can be abstracted as a device that only has outputs but no inputs, the most general quantum device is a quantum channel that also has inputs. In this work, we extend hypothesis testing to general quantum channels. In both the one-shot and asymptotic scenario, we study several quantifiers for hypothesis testing under different assumptions of how the channels are used. As the quantifiers are analogs to the quantum relative entropy of states, we call them the quantum relative entropy of channels. Then, we define the entropy of channels based on relative entropies from the target channel to the completely depolarizing channel. We investigate the properties that the quantum relative entropy of channels should satisfy, and we study its interplay with entanglement. With the broad applications of the quantum relative entropy of states, our results can be useful for understanding general properties of quantum channels.
Parallel Attentive Correlation Tracking

Psychological and cognitive findings indicate that human visual perception is attentive and selective, which may process spatial and appearance selective attentions in parallel. By reflecting some aspects of these attentions, this paper presents a novel correlation filter (CF)-based tracking approach, corresponding to processing a local and a semi-local background domains, respectively. In the local domain, inspired by the Gestalt principle of figure-ground segregation, we leverage an efficient Boolean map representation, which characterizes an image by a set of Boolean maps via randomly thresholding its color channels, yielding a location response map as a weighted sum of all Boolean maps. The Boolean maps capture the topological structures of target and its scene with different granularities, thereby enabling to effectively improve tracking of non-rectangular objects. Alternatively, in the semi-local domains, we introduce a novel distractor-resilient metric regularization into CF, which acts as a force to push distractors into negative space. Consequently, the unwanted boundary effects of CF can be effectively alleviated. Finally, both models associated with the local and the semi-local domains are seamlessly integrated into a Bayesian framework, and the tracked location is determined by maximizing its likelihood function. Extensive evaluations on the OTB50, OTB100, VOT2016, and VOT2017 tracking benchmarks demonstrate that the proposed method achieves favorable performance against a variety of state-of-the-art trackers with a speed of 45 fps on a single CPU.
An Energy-Efficient Accelerator Based on Hybrid CPU-FPGA Devices for Password Recovery

Password recovery tools are needed to recover lost and forgotten passwords so as to regain access to valuable information. As the process of password recovery can be extremely compute-intensive, hardware accelerators are often needed to expedite the recovery process. This paper thus presents a high performance, energy-efficient accelerator built upon modern hybrid CPU-FPGA SoC devices. The proposed password recovery accelerator relies on the development of a set of intellectual property (IP) cores for implementing variety of encryption algorithms with vastly different characteristics and complexities. To keep the resource requirements of each IP core running on a resource-strapped FPGA to the minimum, while achieving the highest throughput possible, the most performance critical computational hash functions are mapped to the FPGA with two specific optimization techniques, namely the fixed message padding for hashing and loop transformation for deep pipelining. The proposed password recovery accelerator implements a non-blocking deep pipeline design that does not incur any data and structural hazards, which is made possible by applying a task scheduling scheme through the use of block RAMs. Synchronization between tasks that are mapped to run separately on CPU and FPGA is achieved through task reordering and a communication protocol for maximum parallelism and low overhead. The proposed design is evaluated on Xilinx XC7Z030-3 device, and it is compared much favorably with other known implementations. The proposed hardware accelerator design is found 12.5 and 3.1 times more resource-efficient than the pure FPGA-based password recovery accelerators for TrueCrypt and WPA-2, respectively. The proposed implementation also shows more than 200 percent improvement in energy efficiency over a state-of-the-art implementation on NVIDIA GTX 750 Ti GPU.
Parallel approach to incremental co-location pattern mining

Co-location pattern mining is an important area of spatial data mining. In many real word applications, new data is continuously arriving to the system and is stored in spatial databases. As co-location discovery is computationally demanding task, it is crucial to maintain co-location patterns for such dynamic databases without recalculating them from scratch. In this paper we present the first GPU-parallelized solution to this problem. Our contribution is threefold: 1) we present a modified version of EUCOLOC algorithm using the iCPI-tree method called iCPI-EUCOLOC, 2) we modify state-of-the-art MGPUCPM algorithm to implement iCPI-EUCOLOC algorithm on GPUs and 3) we present experimental results showing large performance improvements over the original MGPUCPM algorithm. Our solution allows to reduce user waiting times and is economically beneficial due to the reduced overall computation time.
Automatic Ensemble Diffusion for 3D Shape and Image Retrieval

As a post-processing procedure, the diffusion process has demonstrated its ability of substantially improving the performance of various visual retrieval systems. Whereas, great efforts are also devoted to similarity (or metric) fusion, seeing that only one individual type of similarity cannot fully reveal the intrinsic relationship between objects. This stimulates a great research interest of considering similarity fusion in the framework of the diffusion process (i.e., fusion with diffusion) for robust retrieval. In this paper, we first revisit representative methods about fusion with diffusion and provide new insights which are ignored by previous researchers. Then, observing that existing algorithms are susceptible to noisy similarities, the proposed regularized ensemble diffusion (RED) is bundled with an automatic weight learning paradigm, so that the negative impacts of noisy similarities are suppressed. Though formulated as a convex optimization problem, one advantage of RED is that it converts back into the iteration-based solver with the same computational complexity as the conventional diffusion process. At last, we integrate several recently-proposed similarities with the proposed framework. The experimental results suggest that we can achieve new state-of-the-art performances on various retrieval tasks, including 3D shape retrieval on the ModelNet data set, and image retrieval on the Holidays and Ukbench data sets.
A White-Noise-on-Jerk Motion Prior for Continuous-Time Trajectory Estimation on &lt;italic&gt;SE(3)&lt;/italic&gt;

Simultaneous trajectory estimation and mapping (STEAM) offers an efficient approach to continuous-time trajectory estimation, by representing the trajectory as a Gaussian process (GP). Previous formulations of the STEAM framework use a GP prior that assumes white-noise-on-acceleration, with the prior mean encouraging constant body-centric velocity. We show that such a prior cannot sufficiently represent trajectory sections with nonzero acceleration, resulting in a bias to the posterior estimates. This letter derives a novel motion prior that assumes white-noise-on-jerk, where the prior mean encourages constant body-centric acceleration. With the new prior, we formulate a variation of STEAM that estimates the pose, body-centric velocity, and body-centric acceleration. By evaluating across several datasets, we show that the new prior greatly outperforms the white-noise-on-acceleration prior in terms of the solution accuracy.
Reduction of systematic errors in structured light metrology at discontinuities in surface reflectivity

In measuring 3D shape with structured light techniques, systematic errors arise in the neighbourhood of discontinuities in reflectivity or geometry. A mechanism for this phenomenon is proposed, based on the finite size of the imaging system's point spread function. A theoretical analysis for the phase errors in a phase-shifting projected fringe system is given, from which a correction algorithm to minimise the systematic errors is presented. In this algorithm, a closed form expression for the errors based on the intensity values and the phase values in a neighbourhood excluding the affected region is used to remove the estimated error from the measured phase values within the affected region. Experiments on samples with both linear and circular discontinuities in reflectivity demonstrated respective reductions in systematic errors by factors of 2.5× and 3×.
Collision Avoidance for Redundant Robots in Position-Based Visual Servoing

To tackle the problem on trajectory planning or the design of control law, this paper introduces a visual servoing system for a manipulator with redundant joints that the trajectory of the manipulator approaching the target is determined spontaneously by the visual control law. The proposed method resolves joint solution for visual servoing and obstacle avoidance. The work comprises of two procedures, feature extraction for position-based visual servoing (PBVS) and collision avoidance within the working envelope. In the PBVS control, the target pose must be reconstructed with respect to the robot and this results in a Cartesian motion-planning problem. Once the geometric relationship between the target and the end effector is determined, a secure inverse kinematics method incorporating trajectory planning is used to solve the solution of the redundant manipulator by the virtual repulsive torque method. Therefore, the links of the manipulator can always maintain a safe distance from obstacles while approaching the target smoothly. The proposed method is verified with its applicability in experiments using an eye-in-hand manipulator with seven joints. For reusability and extensibility, the system has been coded and constructed in the framework of the Robot Operating System so as that the developed algorithms can be disseminated to different platforms.
Flexible updating of dynamic knowledge structures

Abstract Schemas are knowledge structures that allow us to make efficient judgments about the world without the cost of memorizing every detail of previous experiences. It has long been known that schemas can enhance long-term memory for related information. The usefulness of schemas, however, critically depends on their adaptability: how flexibly a schema can be updated according to changing environmental conditions. Prior consolidation of a schema supports new learning of schema-consistent information. Yet, the effect of consolidation on inconsistent information, and how schemas may be subsequently updated, are not well understood. It is difficult to track the dynamic updating of knowledge structures with traditional memory measures. Here, using a continuous-report paradigm, we were able to show that schematization increases incrementally with consolidation and that the strength with which schemas are initially established predicts schema-guided responding in a later test. Critically, schema updating in response to inconsistent information was more pronounced in a group which was given time to consolidate compared to a group that was not given time to consolidate. Importantly, the later group reverted back to the no longer relevant schema, indicating that systematic bias towards old information, rather than increased forgetting, underlies reduced memory for schema-inconsistent information.