Reviewerly/exHarmony huggingface dataset:
A local branching heuristic for solving a Graph Edit Distance Problem

The Graph Edit Distance (GED) problem is a very interesting problem that relates to Graph Matching (GM) problems, wherever a graph models a problem-defined pattern. Solving the GED problem leads to compute the matching between two given graphs by minimizing their GED, which is also called a dissimilarity measure between them. It has many application domains such as structural pattern recognition and chemical Structure-Activity Relationships (SAR). GEDEnA (Edges no Attributes) is a sub-problem of the GED problem that deals with a special type of graphs where edges do not carry attributes. They are both NP-hard problems and it is difficult to find the optimal solution in reasonable time. Many heuristics exist in the literature that give suboptimal solutions. Some other works have used mathematical programming tools to model and solve these two problems. The present work takes advantage of a powerful Mixed Integer Linear Program (MILP) formulation and proposes a heuristic called Local Branching to solve the GEDEnA problem. Mainly, a MILP model is iteratively modified by adding additional constraints to define neighborhoods in the solution space, which are explored using a black-box solver. A problem-dependent exploration is performed to find efficient solutions. Lastly, the proposed heuristic is evaluated considering two factors: its computational time and solution quality against the heuristics available in the literature and exact methods. The computational experiments reported in this paper show that the proposed local branching heuristic strongly outperforms the existing heuristics for the GEDEnA problem.
The Category TOF

We provide a complete set of identities for the symmetric monoidal category, TOF, generated by the Toffoli gate and computational ancillary bits.We do so by demonstrating that the functor which evaluates circuits on total points, is an equivalence into the full subcategory of sets and partial isomorphisms with objects finite powers of the two element set.The structure of the proof builds -and follows the proof of Cockett et al. -which provided a full set of identities for the cnot gate with computational ancillary bits.Thus, first it is shown that TOF is a discrete inverse category in which all of the identities for the cnot gate hold; and then a normal form for the restriction idempotents is constructed which corresponds precisely to subobjects of the total points of TOF.This is then used to show that TOF is equivalent to FPinj 2 , the full subcategory of sets and partial isomorphisms in which objects have cardinality 2 n for some n ∈ N.
Nonnegative matrix factorization with manifold structure for face recognition

Nonnegative matrix factorization (NMF) is a promising method to represent facial images using nonnegative features under a low-rank nonnegative basis-image matrix. The facial images usually reside on a low-dimensional manifold due to the variations of illumination, pose and facial expression. However, NMF has no ability to uncover the manifold structure of data embedded in a high-dimensional Euclidean space, while the manifold structure contains both local and nonlocal intrinsic features. These two kinds of features are of benefit to class discrimination. To enhance the discriminative power of NMF, this paper proposes a novel NMF algorithm with manifold structure (Mani-NMF). Two quantities related to adjacent graph and non-adjacent graph are incorporated into the objective function, which will be minimized by solving two convex suboptimization problems. Based on the gradient descent method and auxiliary function technique, we acquire the update rules of Mani-NMF and theoretically prove the convergence of the proposed Mani-NMF algorithm. Three publicly available face databases, Yale, pain expression and CMU databases, are selected for evaluations. Experiments results show that our algorithm achieves a better performance than some state-of-the-art algorithms.
Spectral test of the MIXMAX random number generators

An important statistical test on the pseudo-random number generators is called the spectral test. The test is aimed at answering the question of distribution of the generated pseudo-random vectors in dimensions d that are larger than the genuine dimension of a generator N. In particular, the default MIXMAX generators have various dimensions: N=8,17,240 and higher. Therefore the spectral test is important to perform in dimensions d > 8 for N=8 generator, d > 17 for N=17 and d > 240 for N=240 generator. These tests have been performed by L’Ecuyer and collaborators. When d > N the vectors of the generated numbers fall into the parallel hyperplanes and the distances between them can be larger than the genuine “resolution” of the MIXMAX generators, which is l=2−61. The aim of this article is to further study the spectral properties of the MIXMAX generators, to investigate the dependence of the spectral properties of the MIXMAX generators as a function of their internal parameters and in particular their dependence on the parameter m. We found that the best spectral properties are realized when m is between 224 and 236, a range which is inclusive of the value of the N=17 generator. We also provide the alternative parameters for the generators, N=8 and N=240 with m in this optimized range.
Improving quantum parameter estimation by monitoring quantum trajectories

Quantum-enhanced parameter estimation has widespread applications in many fields. An important issue is to protect the estimation precision against the noise-induced decoherence. Here we develop a general theoretical framework for improving the precision for estimating an arbitrary parameter by monitoring the noise-induced quantum trajectorie (MQT) and establish its connections to the purification-based approach to quantum parameter estimation. MQT can be achieved in two ways: (i) Any quantum trajectories can be monitored by directly monitoring the environment, which is experimentally challenging for realistic noises; (ii) Certain quantum trajectories can also be monitored by frequently measuring the quantum probe alone via ancilla-assisted encoding and error detection. This establishes an interesting connection between MQT and the full quantum error correction protocol. Application of MQT to estimate the level splitting and decoherence rate of a spin-1/2 under typical decoherence channels demonstrate that it can avoid the long-time exponential loss of the estimation precision and, in special cases, recover the Heisenberg scaling.
Smart and safe self‐adaption of connected devices based on discrete controllers

The Internet-of-things, which designates the interconnection of physical objects, is a growing research area with many challenges. One of these challenges is the management of failures and unforeseen situations when using connected devices in real world applications. To this end, the authors propose a self-adaptation framework to deal with changes and take into account storage, computational and communication constraints. Their self-adaptation framework relies on constraints expressed in terms of quality of service (QoS) and event-driven rules to specify control objectives. Internally, the framework generates labelled transitions systems and builds on the fly synchronous controllers to guarantee QoS properties. Moreover, their framework has capabilities to concurrently deal with dynamic control objectives, monitoring and self-adaptation. To prove the practicality of their framework, they present a healthcare case-study to remotely monitor a patient at risk of myocardial infarction recurrence. The objective control rules easily specify how wearable devices should coordinate their behaviours to ensure safety, resilience and health-awareness factors. The implementation of their framework is fully distributed and scalable and include their development of a wearable, remotely configurable, QoS aware cardiac activity sensor.
Hierarchical residual stochastic networks for time series recognition

Due to the complex spatio-temporal variations of data, time series recognition remains a challenging problem for the present deep networks. In this paper, we propose end-to-end hierarchical residual stochastic (HRS) networks to effectively and efficiently describe spatio-temporal variations. Specifically, we design stochastic kernelized filters based on a hierarchical framework with a new correlation residual (CorrRes) block to align the spatio-temporal features of a sequence. We further encode complex sequence patterns with a stochastic convolution residual (SConvRes) block, which employs the stochastic kernelized filters and a dropout strategy to reconfigure the convolution filters for large-scale computing in deep networks. Experiments on large-scale datasets, namely NTU RGB+D, SYSU-3D, UT-Kinect and Radar Behavior show that HRS networks significantly boost the performance of time series recognition and improve the state-of-the-art of skeleton, action, and radar behavior recognition performance.
Beam Focusing and Reduction of Quantum Uncertainty in Width at the Few-Photon Level via Multi-Spatial-Mode Squeezing

We show for the first time that it is possible to realize laser beam focusing at the few-photon level in the four-wave-mixing process, and at the same time reduce the quantum uncertainty in width. The reduction in quantum uncertainty results directly from the strong suppression of local intensity fluctuations. This surprising effect of simultaneous focusing and reduction of width uncertainty is enabled by multi-spatial-mode (MSM) squeezing, and is not possible via any classical optical approach or single-spatial-mode squeezing. Our results open promising possibilities for quantum-enhanced imaging and metrology; as an example, the limit on the measurement of very small beam displacement can be enhanced within feasible experimental parameters because of beam focusing and the noiseless amplification in the MSM squeezing process.
Oba Filter for Online-Adaptive Nonlinear State Estimation With Interference

Interference, such as jamming, is widely encountered in practical applications, such as target tracking. Kalman-based nonlinear filters may not be feasible for nonlinear state estimation with nonlinearly transformed interference. This paper presents an online-based adaptive (oba) filter, which we have named the oba filter (OF), for state filtering and prediction of nonlinear discrete dynamic models with estimate constraints, nonlinearly transformed interference and noise. In the OF, noises and interference are approximated by discrete noises and interference; states are quantized; some undesired state estimates, which are not in near vicinities of the actual state values, are detected by a confidence level, and then state quantization levels are updated at the times when undesired estimates occur by using the measurement model; and states are recursively estimated by a suboptimal implementation of multiple composite hypothesis testing. The OF can prevent some undesired state estimates and outperform the unscented Kalman, regularized, sampling importance resampling (SIR), and auxiliary SIR particle filters for some nonlinear models with noise and interference. It is also suitable for state estimation with either missing observations or estimate constraints.
A Unified View on Patch Aggregation

Patch-based methods are widely used in various topics of image processing, such as image restoration or image editing and synthesis. Patches capture local image geometry and structure and are much easier to model than whole images: in practice, patches are small enough to be represented by simple multivariate priors. An important question arising in all patch-based methods is the one of patch aggregation. For instance, in image restoration, restored patches are usually not compatible, in the sense that two overlapping restored patches do not necessarily yield the same values to their common pixels. A standard way to overcome this difficulty is to see the values provided by different patches at a given pixel as independent estimators of a true unknown value and to aggregate these estimators. This aggregation step usually boils down to a simple average, with uniform weights or with weights depending on the trust we have on these different estimators. In this paper, we propose a probabilistic framework aiming at a better understanding of this crucial and often neglected step. The key idea is to see the aggregation of two patches as a fusion between their models rather than a fusion of estimators. The proposed fusion operation is pretty intuitive and generalizes previous aggregation methods. It also yields a novel interpretation of the Expected Patch Log Likelihood (EPLL) proposed in Zoran and Weiss (in: 2011 IEEE international conference on computer vision (ICCV), 2011).
Two new results about quantum exact learning

We present two new results about exact learning by quantum computers. First, we show how to exactly learn a $k$-Fourier-sparse $n$-bit Boolean function from $O(k^{1.5}(\log k)^2)$ uniform quantum examples for that function. This improves over the bound of $\widetilde{\Theta}(kn)$ uniformly random \emph{classical} examples (Haviv and Regev, CCC'15). Additionally, we provide a possible direction to improve our $\widetilde{O}(k^{1.5})$ upper bound by proving an improvement of Chang's lemma for $k$-Fourier-sparse Boolean functions. Second, we show that if a concept class $\mathcal{C}$ can be exactly learned using $Q$ quantum membership queries, then it can also be learned using $O\left(\frac{Q^2}{\log Q}\log|\mathcal{C}|\right)$ \emph{classical} membership queries. This improves the previous-best simulation result (Servedio and Gortler, SICOMP'04) by a $\log Q$-factor.
Multi‐task learning for captioning images with novel words

Recent captioning models are limited in their ability to describe concepts unseen in paired image–sentence pairs. This study presents a framework of multi‐task learning for describing novel words not present in existing image‐captioning datasets. The authors’ framework takes advantage of external sources‐labelled images from image classification datasets, and semantic knowledge extracted from the annotated text. They propose minimising a joint objective which can learn from these diverse data sources and leverage distributional semantic embeddings. When in the inference step they change the BeamSearch step by considering both the caption model and language model enabling the model to generalise novel words outside of image‐captioning datasets. They demonstrate that in the framework by adding an annotated text data which can help the image captioning model to describe images with the right corresponding novel words. Extensive experiments are conducted on both AI Challenger and Microsoft coco (MSCOCO) image captioning datasets of two different languages, demonstrating the ability of their framework to describe novel words such as scenes and objects.
Analisis Pengaruh Sistem Keamanan Informasi Perbankan Pada Nasabah Pengguna Internet Banking

&#x0D; A secure banking world service is an absolute requirement to compete competitively in today's digital age. Information system security in banking services transaction especially Internet Banking features become very important to support accountable service. In the information security system or computer there are 3 dimensions are assessed, namely confidentiality, integrity, and availability. The three dimensions applied to the respondents of employees of PT. XYZ Salatiga using Forum Group Discussion (FGD) technique. The results of this study show that most of the employees of PT. XYY Salatiga of 80%, stated that the availability dimension with the access speed indicator is the most affect the security of information system of Internet Banking feature. This can be a recommendation for Bank BCA in terms of increasing data security especially related to Internet Banking.
The Implementation of School Literacy Movement at the Senior High School

Literacy skill is an individual’s ability to access, understand, and organize information that he or she has to be used in various activities in his or her life. By having good literacy skill, a student is expected to maximize the development of critical, creative, and social-empathy skills as well as knowledge affection. Therefore, the School Literacy Movement (GLS) program appears to help expedite the development of the students’ literacy skill. However, in its implementation, the GLS program still needs to be refined so that it can be in accordance with the condition in the field. This research uses qualitative research methodology. The purpose of this research is to help schools solve problems in implementing GLS program in the field. By means of this research, it is expected that the implementation of GLS program can run optimally and bring about students with good literacy skill.
Classical access structures of ramp secret sharing based on quantum stabilizer codes

Abstract In this paper, we consider to use the quantum stabilizer codes as secret sharing schemes for classical secrets. We give necessary and sufficient conditions for qualified and forbidden sets in terms of quantum stabilizers. Then, we give a Gilbert–Varshamov-type sufficient condition for existence of secret sharing schemes with given parameters, and by using that sufficient condition, we show that roughly 19% of participants can be made forbidden independently of the size of classical secret, in particular when an n -bit classical secret is shared among n participants having 1-qubit share each. We also consider how much information is obtained by an intermediate set and express that amount of information in terms of quantum stabilizers. All the results are stated in terms of linear spaces over finite fields associated with the quantum stabilizers.
X-ray image global enhancement algorithm in medical image classification

The current global enhancement algorithm for medical X-ray image has problems of poor de-noising and enhancement effect and low reduction of the enhanced medical X-ray image. To address the problems, a global enhancement algorithm for X-ray image in medical image classification is proposed in this paper. The medical X-ray image is gray scaled, which provides the basis for the further processing of the image. The noise in medical X-ray image is removed by using multi-wavelet transform to improve the enhancement effect of the method. With the curve-let domain the medical X-ray image is enhanced, the reduction degree of medical X-ray image is improved and the global enhancement of the medical X-ray image is completed. Experimental results show that the de-noising effect of the proposed method is effective, the enhanced medical X ray image is better, and the reduction degree of medical X-ray image is high.
Frequency estimation algorithm without reconstruction based on MWC CS structure

A carrier frequency estimation algorithm for the real signal based on the modulated wideband converter compressed sampling (CS) wideband digital receiver without signal reconstruction is proposed. First, the weighted coefficients matrix can be obtained by mixing function in the receiver. Then, between every two branches of the CS receiver, the results of CS signals multiplied by the inverse of their corresponding weighted coefficients sub-matrix are all equal. According to the equivalent relationship above, the weighted coefficients sub-matrix corresponding received signal can be searched in a weighted coefficients matrix. In addition, the weighted coefficients sub-matrix corresponding received signal includes the information of signal-band index which represents the frequency support. Finally, the carrier frequency estimation of the received signal can be obtained by signal-band index and frequency estimation of CS signals. The simulation results demonstrate the efficiency of the proposed algorithm.
Particle Probability Hypothesis Density Filter Based on Pairwise Markov Chains

Most multi-target tracking filters assume that one target and its observation follow a Hidden Markov Chain (HMC) model, but the implicit independence assumption of the HMC model is invalid in many practical applications, and a Pairwise Markov Chain (PMC) model is more universally suitable than the traditional HMC model. A set of weighted particles is used to approximate the probability hypothesis density of multi-targets in the framework of the PMC model, and a particle probability hypothesis density filter based on the PMC model (PF-PMC-PHD) is proposed for the nonlinear multi-target tracking system. Simulation results show the effectiveness of the PF-PMC-PHD filter and that the tracking performance of the PF-PMC-PHD filter is superior to the particle PHD filter based on the HMC model in a scenario where we kept the local physical properties of nonlinear and Gaussian HMC models while relaxing their independence assumption.
KSVD-Based Multiple Description Image Coding

In this paper, we present a new multiple description coding scheme, which is based on a sparse dictionary training method called K singular value decomposition (KSVD). In the proposed scheme, each description encodes one source subset with a small quantization stepsize, and other subsets are predictively coded with a large quantization stepsize. The source processed by the KSVD becomes sparse, which can improve the coding efficiency. The proposed scheme is then applied to lapped transform-based multiple description image coding. Finally, image coding results show that the proposed scheme achieves a better performance than the current state-of-the-art multiple description coding methods.
Fast Reconstruction for Monte Carlo Rendering Using Deep Convolutional Networks

Denoising the Monte Carlo (MC) rendering images is different from denoising the natural images since low-sampled MC renderings have a higher noise level and there are inexpensive by-products (e.g., feature buffers) we can leverage. However, the main challenge is designing a model to fast fuse these feature buffers and reconstruct perceptually noise-free images from noisy MC renderings. In recent years, supervised learning methods remove the noise and reconstruct clean images, but most of them cannot handle MC noise well. In this paper, we introduce an end-to-end CNN model to fuse feature buffers and predict a clean image directly. In addition, we devise a new high-dynamic range (HDR) image normalization method to help us to train the model on HDR images in a more efficient and stable way. We setup a series of experiments for selecting the hyperparameter of our deep learning model, network depth, which can promote our network’s performance and avoid overfitting. We demonstrate that our model is robust on a wide range of scenes and can generate satisfactory results in a significantly faster way.
Robustness Envelopes for Temporal Plans

To achieve practical execution, planners must produce temporal plans with some degree of run-time adaptability. Such plans can be expressed as Simple Temporal Networks (STN), that constrain the timing of action activations, and implicitly represent the space of choices for the plan executor.A first problem is to verify that all the executor choices allowed by the STN plan will be successful, i.e. the plan is valid. An even more important problem is to assess the effect of discrepancies between the model used for planning and the execution environment.We propose an approach to compute the “robustness envelope” (i.e., alternative action durations or resource consumption rates) of a given STN plan, for which the plan remains valid. Plans can have boolean and numeric variables as well as discrete and continuous change. We leverage Satisfiability Modulo Theories (SMT) to make the approach formal and practical.