Reviewerly/exHarmony huggingface dataset:
Whale swarm algorithm with the mechanism of identifying and escaping from extreme points for multimodal function optimization

Most real-world optimization problems often come with multiple global optima or local optima. Therefore, increasing niching metaheuristic algorithms, which devote to finding multiple optima in a single run, are developed to solve these multimodal optimization problems. However, there are two difficulties urgently to be solved for most existing niching metaheuristic algorithms: how to set the optimal values of niching parameters for different optimization problems, and how to jump out of the local optima efficiently. These two difficulties limited their practicality largely. Based on Whale Swarm Algorithm (WSA) we proposed previously, this paper presents a new multimodal optimizer named WSA with Iterative Counter (WSA-IC) to address these two difficulties. In the one hand, WSA-IC improves the iteration rule of the original WSA for multimodal optimization, which removes the need of specifying different values of attenuation coefficient for different problems to form multiple subpopulations, without introducing any niching parameter. In the other hand, WSA-IC enables the identification of extreme point during iterations relying on two new parameters (i.e., stability threshold Ts and fitness threshold Tf), to jump out of the located extreme point. Moreover, the convergence of WSA-IC is proved. Finally, the proposed WSA-IC is compared with several niching metaheuristic algorithms on CEC2015 niching benchmark test functions and five additional classical multimodal functions with high dimensions. The experimental results demonstrate that WSA-IC statistically outperforms other niching metaheuristic algorithms on most test functions.
Discrete-time quantum walks as fermions of lattice gauge theory

It is shown that discrete-time quantum walks can be used to digitize, i.e., to time discretize fermionic models of continuous-time lattice gauge theory. The resulting discrete-time dynamics is thus not only manifestly unitary, but also ultralocal, i.e. the particle's speed is upper bounded, as in standard relativistic quantum field theories. The lattice chiral symmetry of staggered fermions, which corresponds to a translational invariance, is lost after the requirement of ultralocality of the evolution; this fact is an instance of Meyer's 1996 no-go lemma stating that no non-trivial one-dimensional scalar quantum cellular automaton can be translationally invariant [1]. All results are presented in a single-particle framework and for a (1+1)-dimensional spacetime.
Error-heralded generation and self-assisted complete analysis of two-photon hyperentangled Bell states through single-sided quantum-dot-cavity systems

Hyperentangled Bell-state analysis (HBSA) is critical for high-capacity quantum communication. Here we design two effective schemes for error-heralded deterministic generation and self-assisted complete analysis of hyperentangled Bell states for two-photon systems in both the polarization and spatial-mode degrees of freedom, assisted by single-sided quantum-dot-cavity systems. We construct an error-heralded block with a singly charged quantum dot inside a single-sided optical microcavity, two circular polarization beam splitters, one half-wave plate, and one single-photon detector, in which the errors due to imperfect interactions between photons and quantum dot systems can be heralded. With this error-heralded block, the fidelity of our two schemes for hyperentangled Bell-state generation and complete HBSA can reach unit one. What interesting is that by using the measured spatial-mode state to assist the analysis of the polarization state, our complete HBSA scheme works in a self-assisted way, which greatly simplifies the analysis process and largely relaxes the requirements on nonlinearities. These advantages make our schemes much easier to implement experimentally, and have more practical applications in long-distance high-capacity quantum communication.
An Improved Approach to Exposing JPEG Seam Carving Under Recompression

As a popular method for image and video retargeting, seam carving has been used for image/video forgery manipulation. Although significant advances have been made in detecting seam-carving forgery, there are very few contributions in exposing the forgery from recompressed JPEG images, especially the doctored images that are recompressed at the same or a lower quality. The detection is generally challenging because the recompression after tampering compromises the existing forgery traces. Aiming to address this problem, we propose a hybrid large-feature mining-based approach that contains multiple types of large features. Ensemble learning is used to deal with the high-feature dimensionality. This paper shows that the proposed approach effectively distinguishes the seam-carved JPEG images from untouched JPEG images and improves the detection accuracy. In our proposed multiple types of features, directional derivative-based feature set and Gabor residual-based feature set generally perform the best. This paper also indicates that feature selection may play an important role to greatly reduce the feature number while maintaining a better or comparable detection accuracy.
Blockchain and Smart Contracting for the Shareholder Community

Current shareholder engagement systems face large classical inefficiencies. First, due to the large chains of intermediaries in the current securities models, transaction costs are high and shareholder votes and other information are not always correctly transmitted between shareholders and issuers. Recent cases including DNick Holding and T. Rowe Price show the ‘absurdness’ of the current systems. The Shareholder Rights Directive II addresses these problems and the Implementing Regulation already hints at modern technologies to increase the transparency and verifiability of shareholder engagement. Next, the current shareholder engagement system enables different opportunities for different types of shareholders, creating inequalities and hindering shareholder democracy. The solution to these substantial problems lies in a state-of-the-art technology: in this contribution we argue that blockchain technology can solve these current inefficiencies that shareholders and companies face. Using a permissioned blockchain, information can be stored in a verifiable and immutable way, with a consensus mechanism tailored to its purpose. The large amount of initiatives and prototypes of blockchain proxy voting and trading, including the legislative initiatives that were initiated in the past 2 years, show the merits of using this state-of-the-art technology. The Europe Union should incorporate this technology in its legislation, like the CSD regulation, for remaining technology-proof in this globalized market.
Mixed-mode cohesive zone parameters from integrated digital image correlation on micrographs only

Mixed-mode loading conditions strongly affect the failure mechanisms of interfaces between different material layers as typically encountered in microelectronic systems, exhibiting complex material stacking and 3D microstructures. The integrated digital image correlation (IDIC) method is here extended to enable identification of mixed-mode cohesive zone model parameters under arbitrary levels of mode-mixity. Micrographs of a mechanical experiment with a restricted field of view and without any visual data of the applied far-field boundary conditions are correlated to extract the cohesive zone model parameters used in a corresponding finite element simulation. Reliable or accurate force measurement data is thereby not available, which constitutes a complicating factor. For proof-of-concept, a model system comprising a bilayer double cantilever beam specimen loaded under mixed-mode bending conditions is explored. Virtual experiments are conducted to assess the sensitivities of the technique with respect to mixed-mode loading conditions at the interface. The virtual experiments reveal the necessity of (1) optimizing the applied local boundary conditions in the finite element model and (2) optimizing the region of interest by analyzing the model’s kinematic sensitivity relative to the cohesive zone parameters. From a single test-case, exhibiting a range of mode-mixity values, the mixed-mode cohesive zone model parameters are accurately identified with errors below 1%. The IDIC-procedure is shown to be robust against large variations in the initial guess values for the parameters. Real mixed-mode bending experiments are conducted on bilayer specimens comprising two spring steel beams and an epoxy adhesive interface, under different levels of mode-mixity. The mixed-mode cohesive zone model parameters are identified, demonstrating that IDIC is a powerful technique for characterizing interface properties of interfaces, imaged with a limited field of view, as is typically the case in microelectronic applications.
Pseudo low rank video representation

Action recognition plays a fundamental role in computer vision and has drawn growing attention recently. This paper addresses this issue conditioned on extreme Low Resolution (abbreviated as eLR). Generally, eLR video is often susceptible to noise, thus extracting a robust representation is of great challenge. Besides, due to the limitation of video resolution, eLR video cannot be cropped or resized randomly, then it is inevitably complicated to design and to train a deep network for eLR video. This paper proposes a novel network for robust video representation by employing pseudo tensor low rank regularization. A new Video Low Rank Representation model (named VLRR) is first proposed to recover the inherent robust component of a given video, and then the recovered term is introduced to a convolutional Network (denoted pLRN) as an auxiliary pseudo Low Rank guidance. Benefitting from the auxiliary guidance, pLRN can learn an approximate low rank term end-to-end. Besides, this paper presents a new initialization strategy for eLR recognition neTwork based on Tensor factorization (dubbed TenneT). TenneT is data-driven and learns the convolutional kernels totally from the video distribution while without any back-propagation. It outperforms random initialization both in speed and accuracy. Experiments on benchmark datasets demonstrate the effectiveness and superiority of the proposed method.
Boosting collaborative filtering with an ensemble of co-trained recommenders

Abstract Collaborative Filtering (CF) is one of the best performing and most widely used approaches for recommender systems. Although significant progress has been made in this area, current CF methods still suffer from cold-start and sparsity problems. A primary issue is that the fraction of users willing to rate items tends to be very small in most practical applications, which causes the number of users and/or items with few or no interactions in recommendation databases to be large. As a direct consequence of ratings sparsity, recommender algorithms may provide poor recommendations (reducing accuracy) or decline recommendations (reducing coverage). This paper proposes an ensemble scheme based on a co-training approach, named ECoRec, that drives two or more recommenders to agree with each others’ predictions to generate their own. The experiments on eight real-life public databases show that better accuracy can be obtained when recommender algorithms are simultaneously trained from multiple views and combined into an ensemble to make predictions.
A sequential algorithm portfolio approach for black box optimization

A large number of optimization algorithms have been proposed. However, the no free lunch (NFL) theorems inform us that no algorithm can solve all types of optimization problems. An approach, which can suggest the most suitable algorithm for different types of problems, is valuable. In this paper, we propose an approach called sequential algorithm portfolio (SAP) which belongs to the inter-disciplinary fields of algorithm portfolio and algorithm selection. It uses a pre-trained predictor to predict the most suitable algorithm and a termination mechanism to automatically stop the optimization algorithms. The SAP is easy to implement and can incorporate any optimization algorithm. We experimentally compare SAP with two state-of-the-art algorithm portfolio approaches and single optimization algorithms. The result shows that SAP is a well-performing algorithm portfolio approach.
An efficient parallel row enumerated algorithm for mining frequent colossal closed itemsets from high dimensional datasets

Mining colossal itemsets from high dimensional datasets have gained focus in recent times. The conventional algorithms expend most of the time in mining small and mid-sized itemsets, which do not enclose valuable and complete information for decision making. Mining Frequent Colossal Closed Itemsets (FCCI) from a high dimensional dataset play a highly significant role in decision making for many applications, especially in the field of bioinformatics. To mine FCCI from a high dimensional dataset, the existing preprocessing techniques fail to prune the complete set of irrelevant features and irrelevant rows. Besides, the state-of-the-art algorithms for the same are sequential and computationally expensive. The proposed work highlights an Effective Improved Parallel Preprocessing (EIPP) technique to prune the complete set of irrelevant features and irrelevant rows from high dimensional dataset and a novel efficient Parallel Frequent Colossal Closed Itemset Mining (PFCCIM) algorithm. Further, the PFCCIM algorithm is integrated with a novel Rowset Cardinality Table (RCT), an efficient method to check the closeness of a rowset and also an efficient pruning strategy to cut down the mining search space. The proposed PFCCIM algorithm is the first parallel algorithm to mine FCCI from a high dimensional dataset. The performance study shows the improved effectiveness of the proposed EIPP technique over the existing preprocessing techniques and the improved efficiency of the proposed PFCCIM algorithm over the existing algorithms.
Experimental quantum tomography assisted by multiply symmetric states in higher dimensions

High-dimensional quantum information processing has become a mature field of research with several different approaches being adopted for the encoding of $D$-dimensional quantum systems. Such progress has fueled the search of reliable quantum tomographic methods aiming for the characterization of these systems, being most of these methods specifically designed for a given scenario. Here, we report on a new tomographic method based on multiply symmetric states and on experimental investigations to study its performance in higher dimensions. Unlike other methods, it is guaranteed to exist in any dimension and provides a significant reduction in the number of measurement outcomes when compared to standard quantum tomography. Furthermore, in the case of odd dimensions, the method requires the least possible number of measurement outcomes. In our experiment we adopt the technique where high-dimensional quantum states are encoded using the linear transverse momentum of single photons and are controlled by spatial light modulators. Our results show that fidelities of $0.984\pm0.009$ with ensemble sizes of only $1.5\times10^5$ photons in dimension $D=15$ can be obtained in typical laboratory conditions, thus showing its practicability in higher dimensions.
Background subtraction with multi-scale structured low-rank and sparse factorization

Low-rank and sparse factorization, which models the background as a low-rank matrix and the foreground as the contiguously corrupted outliers, exhibits excellent performance in background subtraction, in which the structured constraints of the foreground usually play a very essential role. In this paper, we propose a novel approach with multi-scale structured low-rank and sparse factorization for background subtraction. Different from the conventional methods that only enforce the smoothness between the spatial neighbors, we propose to explore the structured smoothness with both appearance consistency and spatial compactness in the low-rank and sparse factorization framework. Moreover, we integrate structural information at different scales into the formulation for robustness. We also design a low-rank decomposition scheme to improve the computational efficiency of the optimization algorithm. Extensive experiments on benchmark datasets GTFD and CDnet suggest that our approach achieves big superior performance against the state-of-the-art methods.
A more decentralized vision for Linked Data

In this deliberately provocative position paper, we claim that more than ten years into Linked Data there are still (too?) many unresolved challenges towards arriving at a truly machine-readable and decentralized Web of data. We take a deeper look at key challenges in usage and adoption of Linked D ata from the ever-present "LOD cloud" diagram.1 Herein, we try to highlight and exemplify both key technical and non-technical challenges to the success of LOD, and we outline potential solution strategies. We hope that this paper will serve as a discussion basis for a fresh start towards more actionable, truly decentralized Linked Data, and as a call to the community to join forces.
Optimized dithering technique for three-dimensional shape measurement with projector defocusing

There have been active studies on optimized dithering techniques to improve 3D shape measurement quality with image sensor and defocusing projector. These techniques can be classified into intensity-based optimization technology and phase-based optimization technology. However, those phase-based optimization methods are sensitive to the amount of defocusing while intensity-based optimization methods cannot reduce phase errors efficiently. This paper presents an optimization framework. This framework combines structural similarity index measurement and intensity residual error optimization. By applying this optimization framework to patches and tiling the best patch, high quality fringe pattern can be generated for three-dimensional measurement. Both simulation and experimental results show that this proposed algorithm can achieve phase quality improvements and it is robust to various defocusing levels.
Exploring mission planning method for a team of carrier aircraft launching

High-level efficiency and safety are of great significance for improving the fighting capability of an aircraft carrier. One way to enhance efficiency and safety level is to organize the carrier aircraft into combat effectively. This paper studies the mission planning problem for a team of carrier aircraft launching, and a novel distributed mission planning architecture is proposed. The architecture is hierarchical and is comprised of four levels, namely, the input level, the coordination level, the path planning level and the execution level. Realistic constraints in each level of the distributed architecture, such as the vortex flow effect, the crowd effect and the motion of aircraft, are considered in the model. To solve this problem, a distributed path planning algorithm based on the asynchronous planning strategy is developed. The proposed Mission Planning Approach for Carrier Aircraft Launching (MPACAL) is validated using the setups of the Nimitz-class aircraft carrier. Compared to the isolated planning architecture and the centralized planning architecture, the proposed distributed planning architecture has advantages in coordinating the launch tasks not only belonging to the same catapult but also when all different catapults are considered. The proposed MPACAL provides a modeling method for the flight deck operation on aircraft carrier.
An Adaptive Multi-Projection Metric Learning for Person Re-Identification Across Non-Overlapping Cameras

Person re-identification is one of the most important and challenging problems in video analytics systems; it aims to match people across non-overlapping camera views. For person re-identification, metric learning is introduced to improve the performance by providing a metric adapted for cross-view matching. The essence of metric learning is to search for an optimal projection matrix to project the original features into a new feature space. However, most existing metric learning methods overlook the inconsistency of feature distributions in multiple cameras. In this paper, we propose a multi-projection metric learning (MPML) method to overcome the inconsistency among multiple cameras in person re-identification. Our solution is to jointly learn multiple projection matrices using paired samples from different cameras to project features from different cameras into a common feature space. To make our method adaptive to newly added cameras without affecting the learned projection matrices, we further propose an adaptive MPML method, which can learn new camera projection matrices without having to update any of the obtained projection matrices. The proposed methods are evaluated on four major person re-identification data sets, with comprehensive experiments showing the effectiveness of the proposed methods and notable improvements over the state-of-the-art approaches.
Short-term cognitive networks, flexible reasoning and nonsynaptic learning

While the machine learning literature dedicated to fully automated reasoning algorithms is abundant, the number of methods enabling the inference process on the basis of previously defined knowledge structures is scanter. Fuzzy Cognitive Maps (FCMs) are recurrent neural networks that can be exploited towards this goal because of their flexibility to handle external knowledge. However, FCMs suffer from a number of issues that range from the limited prediction horizon to the absence of theoretically sound learning algorithms able to produce accurate predictions. In this paper we propose a neural system named Short-term Cognitive Networks that tackle some of these limitations. In our model, used for regression and pattern completion, weights are not constricted and may have a causal nature or not. As a second contribution, we present a nonsynaptic learning algorithm to improve the network performance without modifying the previously defined weight matrix. Besides, we derive a stop condition to prevent the algorithm from iterating without significantly decreasing the global simulation error.
Modeling Camera Effects to Improve Visual Learning from Synthetic Data

Recent work has focused on generating synthetic imagery to increase the size and variability of training data for learning visual tasks in urban scenes. This includes increasing the occurrence of occlusions or varying environmental and weather effects. However, few have addressed modeling variation in the sensor domain. Sensor effects can degrade real images, limiting generalizability of network performance on visual tasks trained on synthetic data and tested in real environments. This paper proposes an efficient, automatic, physically-based augmentation pipeline to vary sensor effects – chromatic aberration, blur, exposure, noise, and color temperature – for synthetic imagery. In particular, this paper illustrates that augmenting synthetic training datasets with the proposed pipeline reduces the domain gap between synthetic and real domains for the task of object detection in urban driving scenes.