Reviewerly/exHarmony huggingface dataset:
A Domain Agnostic Normalization Layer for Unsupervised Adversarial Domain Adaptation

We propose a normalization layer for unsupervised domain adaption in semantic scene segmentation. Normalization layers are known to improve convergence and generalization and are part of many state-of-the-art fully-convolutional neural networks. We show that conventional normalization layers worsen the performance of current Unsupervised Adversarial Domain Adaption (UADA), which is a method to improve network performance on unlabeled data sets and the focus of our research. Therefore, we propose a novel Domain Agnostic Normalization layer and thereby unlock the benefits of normalization layers for unsupervised adversarial domain adaptation. In our evaluation, we adapt from the synthetic GTA5 data set to the real Cityscapes data set, a common benchmark experiment, and surpass the state-of-the-art. As our normalization layer is domain agnostic at test time, we furthermore demonstrate that UADA using Domain Agnostic Normalization improves performance on unseen domains, specifically on Apolloscape and Mapillary.
Lifetime renormalization of weakly anharmonic superconducting qubits. I. Role of number nonconserving terms

The dynamics of a weakly anharmonic superconducting qubit in a complex electromagnetic environment is generally well-described by an effective multimode Kerr Hamiltonian at sufficiently weak excitation. This Hamiltonian can be embedded in a master equation with losses determined by the details of the electromagnetic environment. Recent experiments indicate, however, that when a superconducting circuit is driven with microwave signals the observed relaxation rates appear to be substantially different from expectations based on the electromagnetic environment of the qubit alone. This issue is a limiting factor in the optimization of superconducting qubit readout schemes. We claim here that an effective master equation with drive-power dependent parameters is an efficient approach to model such quantum dynamics. In this sequence of papers, we derive effective master equations, whose parameters exhibit nonlinear dependence on the excitation level of the circuit as well as the electromagnetic environment of the qubit. We show that the number non-conserving terms in the qubit nonlinearity generally lead to a renormalization of dissipative parameters of the effective master equation, while the number conserving terms give rise to a renormalization of the system frequencies. Here, in Part I, we consider the excitation-relaxation dynamics of a transmon qubit that is prepared in a certain initial state, but is not driven otherwise. A unitary transformation technique is introduced to study the renormalization of i) qubit relaxation due to coupling to a generic bath and ii) Purcell decay. Analytic expressions are provided for the dependence of the nonlinear dissipative terms on the details of the electromagnetic environment of the qubit. The perturbation technique based on unitary transformations developed here is generalized to the continuously driven case in Part II.
Adaptive differential evolution with a Lagrange interpolation argument algorithm

Differential evolution (DE) is a simple yet powerful evolutionary algorithm that has been used to solve various complex optimization problems in numerous engineering fields. However, DE has some problems, such as premature convergence and sensitivity to parameter settings. To improve the performance of DE and extend its application, an adaptive differential evolution with the Lagrange interpolation argument algorithm (ADELI) is proposed in this paper. To accelerate the convergence speed of DE, a local search with Lagrange interpolation (LSLI) is introduced into DE. LSLI performs a local search in the neighborhood of the best individual in the current generation to enhance the exploitation capability of DE. Meanwhile, an adaptive argument strategy is presented to adaptively determine whether to use LSLI in terms of its performance in the previous generation, which can balance the global exploration capability and the local exploitation capability of ADELI. To verify the feasibility and effectiveness of ADELI, 30 test functions in the CEC 2014 benchmark sets with different dimensions were simulated. Moreover, a path synthesis problem was also optimized. The results demonstrated that ADELI considerably outperforms other EAs in most functions and obtains the most accurate solution among the compared algorithms in the application of path generation.
New label noise injection methods for the evaluation of noise filters

Noise is often present in real datasets used for training Machine Learning classifiers. Their disruptive effects in the learning process may include: increasing the complexity of the induced models, a higher processing time and a reduced predictive power in the classification of new examples. Therefore, treating noisy data in a preprocessing step is crucial for improving data quality and to reduce their harmful effects in the learning process. There are various filters using different concepts for identifying noisy examples in a dataset. Their ability in noise preprocessing is usually assessed in the identification of artificial noise injected into one or more datasets. This is performed to overcome the limitation that only a domain expert can guarantee whether a real example is indeed noisy. The most frequently used label noise injection method is the noise at random method, in which a percentage of the training examples have their labels randomly exchanged. This is carried out regardless of the characteristics and example space positions of the selected examples. This paper proposes two novel methods to inject label noise in classification datasets. These methods, based on complexity measures, can produce more challenging and realistic noisy datasets by the disturbance of the labels of critical examples situated close to the decision borders and can improve the noise filtering evaluation. An extensive experimental evaluation of different noise filters is performed using public datasets with imputed label noise and the influence of the noise injection methods are compared in both data preprocessing and classification steps.
Distributed joint sensor registration and target tracking via sensor network

This paper deals with distributed registration of a sensor network for target tracking in the presence of false and/or missed measurements. Each sensor acquires measurements of the target position in local coordinates, having no knowledge about the relative positions (referred to as drift parameters) of its neighboring nodes. A distributed Bernoulli filter is run over the network to compute in each node a local posterior target density. Then a suitable cost function, expressing the discrepancy between the local posteriors in terms of averaged Kullback–Leibler divergence, is minimized with respect to the drift parameters for sensor registration purposes. In this way, a computationally feasible optimization approach for joint sensor registration and target tracking is devised. Finally, the effectiveness of the proposed approach is demonstrated through simulation experiments on both tree networks and networks with cycles, as well as with both linear and nonlinear sensors.
Object instance detection with pruned Alexnet and extended training data

Object instance detection has garnered much concern in many practical applications, especially in the field of intelligent service robot. Imagine robots working in real scenes, one may expect the instance detection system to be light-weighted to enable mobile or embedded system deployment. Focusing on reconstructing a smaller learning network from a noted deep model,we have pruned Alexnet to a compressed model with fewer parameters but equivalent accuracy, denoted as BING-Pruned Alexnet(B-PA). Our method first utilizes BING(Binarized Normed Gradient) to compute bounding boxes, then builds a pruned network for recognition by reducing neurons and cutting fully connected layers on the classic architecture Alexnet. Since the training samples for instance detection are limited and of small variation, we extend the training data by combining data augmentation with synthetic generation. In the end, our B-PA network occupies only 5MB, which is 50 times smaller than the original Alexnet, but can still achieve Alexnet-level accuracy when recognizing on GMU Kitchen dataset. Numerical experiments are conducted to compare our algorithm with the state-of-art instance detection algorithms on a self-made BHID database and two public database i.e.,WRGB-D dataset and GMU Kitchen dataset, which demonstrate that B-PA reduces the storage requirements of neural networks substantially while preserving generalization performance on object instance detection.
The effects of guided-unguided learning in 3d virtual environment on students' engagement and achievement

This study aims to find whether or not there are any differences between the behavioural, affective, cognitive engagement and achievement of student groups with and without instructor avatar guidance in winter sports learning environment developed in Second Life 3D virtual platform. It also analyses the correlations between the sub-factors of engagement and achievement according to guided and unguided groups. This study was conducted on basis of quasi-experimental and relational research method. The sample was determined by purposeful sampling method and composed of 104 secondary school students (54 guided, 50 unguided). The data collected through student engagement questionnaire and achievement test were analysed to descriptive and predictive statistics. According to the results, no significant differences were found between the groups in terms of behavioural, affective, cognitive student engagement and achievement. It was found that there were correlations between cognitive engagement and achievement in the guided group but that there were no significant correlations between behavioural engagement and achievement. In unguided group there were no significant correlations between behavioural, affective and cognitive engagement and achievement. This might have stemmed from the fact that the students had experienced learning in a different environment offered by 3D virtual platform-a new technology-for the first time.
A Swiss Army Infinitesimal Jackknife

The error or variability of machine learning algorithms is often assessed byrepeatedly refitting a model with different weighted versions of the observeddata. The ubiquitous tools of cross-validation (CV) and the bootstrap areexamples of this technique. These methods are powerful in large part due totheir model agnosticism but can be slow to run on modern, large data sets due tothe need to repeatedly re-fit the model. In this work, we use a linearapproximation to the dependence of the fitting procedure on the weights,producing results that can be faster than repeated re-fitting by an order ofmagnitude. This linear approximation is sometimes known as the “infinitesimaljackknife” in the statistics literature, where it is mostly used as atheoretical tool to prove asymptotic results. We provide explicit finite-sampleerror bounds for the infinitesimal jackknife in terms of a small number ofsimple, verifiable assumptions. Our results apply whether the weights and dataare stochastic or deterministic, and so can be used as a tool for proving theaccuracy of the infinitesimal jackknife on a wide variety of problems. As acorollary, we state mild regularity conditions under which our approximationconsistently estimates true leave k-out cross-validation for any fixed k. Thesetheoretical results, together with modern automatic differentiation software,support the application of the infinitesimal jackknife to a wide variety ofpractical problems in machine learning, providing a “Swiss Army infinitesimaljackknife.” We demonstrate the accuracy of our methods on a range of simulatedand real datasets.
Anomalous Behaviors Detection in Moving Crowds Based on a Weighted Convolutional Autoencoder-Long Short-Term Memory Network

We propose an anomaly detection approach by learning a generative model of moving pedestrians to guarantee public safety. To resolve the existing challenges of anomaly detection in complicated definitions, complex backgrounds, and local occurrence, a weighted convolutional autoencoder-long short-term memory network is proposed to reconstruct raw data and their corresponding optical flow and then perform anomaly detection based on reconstruction errors. Unlike equally treating raw data and optical flow, a novel two-stream framework is proposed to take the reconstructed optical flow as supplementary cues that encode pedestrian motions. A weighted Euclidean loss is proposed to enable the network to concentrate on moving foregrounds, thus restraining background influence. Global-local analysis is used to jointly detect and localize local anomaly in reconstructed raw data. Final detection of anomalous events is achieved by jointly considering the results of the global-local analysis and reconstructed optical flow. Qualitative evaluations verify the effectiveness of our two-stream framework, the weighted Euclidean loss, and the global-local analysis. Moreover, comparisons with state-of-the-art approaches indicate the superiority of our approach in anomaly detection.
An efficient algorithm for graph edit distance computation

The graph edit distance (GED) is a well-established distance measure widely used in many applications, such as bioinformatics, data mining, pattern recognition, and graph classification. However, existing solutions for computing the GED suffer from several drawbacks: large search spaces, excessive memory requirements, and many expensive backtracking calls. In this paper, we present BSS_GED, a novel vertex-based mapping method that calculates the GED in a reduced search space created by identifying invalid and redundant mappings. BSS_GED employs the beam-stack search paradigm, a widely utilized search algorithm in AI, combined with two specially designed heuristics to improve the GED computation, achieving a trade-off between memory utilization and expensive backtracking calls. Through extensive experiments, we demonstrate that BSS_GED is highly efficient on both sparse and dense graphs and outperforms the state-of-the-art methods. Furthermore, we apply BSS_GED to solve the well-investigated graph similarity search problem. The experimental results show that this method is dozens of times faster than state-of-the-art graph similarity search methods.
Large scale continuous global optimization based on micro differential evolution with local directional search

Over the years, many optimization algorithms have been developed to solve large-scale optimization problems accurately and efficiently. In this regard, Memetic Algorithms offer robust and efficient framework that hybridizes the Evolutionary Algorithms with a local heuristic search. In this work, we propose micro Differential Evolution with a Directional Local Search (µDSDE) algorithm using a small population size to solve large scale continuous optimization problems. In this technique, the best individual retains its position, the second best individual undergoes mutation and crossover processes of DE, and the rest are reinitialized on the search space. Exploration of the search is carried out with the dispersal of the worst individuals whereas exploitation is performed through DE operators and Directional Local Search (DLS). We conducted extensive empirical studies using two test suites on Large Scale Global Optimization benchmark with up to 5000 dimensions. The results show that µDSDE considerably outperforms existing solutions in terms of the convergence rate and solution quality.
Learning of fuzzy cognitive maps using a niching-based multi-modal multi-agent genetic algorithm

Fuzzy cognitive maps (FCMs) are generally applied to model and analyze complex dynamical systems. To learn the FCM weight matrix, various efficient learning algorithms have been proposed. However, those algorithms only learn one FCM from data once. Learning only one FCM is not enough for modeling and analyzing complex dynamical systems because the learned FCM may be just a local optimum. To solve this problem, we tend to learn multiple FCMs simultaneously. To this end, the FCM learning problem is modeled as a multi-modal optimization problem. So far, niching is the most adopted method to deal with multi-modal optimization. Thus, a multi-agent genetic algorithm (MAGA), which is a popular numerical optimization algorithm, is combined with current niching methods. Then, a niching-based multi-modal multi-agent genetic algorithm is proposed for learning FCM, termed as NMMMAGA-FCM. In this paper, NMMMAGA-FCM is adopted to learn several FCMs at the same time, then chooses the optimal FCM from all candidates. In the experiments, NMMMAGA-FCM is applied to learn the FCMs from synthetic data with varying sizes and densities. The experimental results show that NMMMAGA-FCM can learn FCMs with high accuracy. In addition, NMMMAGA-FCM is validated on the benchmark datasets DREAM3 and DREAM4. The experimental results show that NMMMAGA-FCM outperforms other learning algorithms obviously, which illustrates that NMMMAGA-FCM can reconstruct gene regulatory networks (GRNs) effectively.
Systematic literature review of mobile application development and testing effort estimation

In the recent years, the advances in mobile technology have brought an exorbitant change in daily lifestyle of individuals. Smartphones/mobile devices are rampant in all aspects of human life. This has led to an extreme demand for developing software that runs on mobile devices. The developers have to keep up with this high demand and deliver high-quality app on time and within budget. For this, estimation of development and testing of apps play a pivotal role. In this paper, a Systematic Literature Review (SLR) is conducted to highlight development and testing estimation process for software/application. The goal of the present literature survey is to identify and compare existing test estimation techniques for traditional software (desktop/laptop) and for mobile software/application. The characteristics that make mobile software/application different from traditional software are identified in this literature survey. Further, the trend for developing the software is towards agile, thus this study also presents and compares estimation techniques used in agile software development for mobile applications. The analysis of literature review suggests filling a research gap to present formal models for estimating mobile application considering specific characteristics of mobile software.
Through the looking glass: Seeing events in power systems dynamics

This paper presents a new method to identify classes of events, by processing phasor measurement units (PMU) frequency data through deep neural networks. Deep tapered Multi-layer Perceptrons of the half-autoencoder type, Deep Belief Networks and Convolutional Neural Networks (CNN) are compared, using real data from Brazil. A sound success is obtained by a transformation of time-domain signals, from dynamic events recorded, into 2D images; these images wee processed with a CNN, taking advantage of the strong dependency existing among neighboring pixels in images. The training, computing and processing was achieved with a GPU (Graphics Processing Unit), allowing speeding-up of the process up to 30 times and rendering the process suitable to increase the online situational awareness of network operators.
Recurrent Poisson Factorization for Temporal Recommendation

Poisson Factorization (PF) is the gold standard framework for recommendation systems with implicit feedback whose variants show state-of-the-art performance on real-world recommendation tasks. However, they do not explicitly take into account the temporal behavior of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback. RPF treats time as a natural constituent of the model, and takes important factors for recommendation into consideration to provide a rich family of time-sensitive factorization models. They include Hierarchical RPFthat captures the consumption heterogeneity among users and items, Dynamic RPF that handles dynamic user preferences and item specifications, Social RPF that models the social-aspect of product adoption, Item-Item RPFthat considers the inter-item correlations, and eXtended Item-Item RPF that utilizes items' metadata to better infer the correlation among engagement patterns of users with items. We also develop an efficient variational algorithm for approximate inference that scales up to massive datasets. We demonstrate RPF's superior performance over many state-of-the-art methods on synthetic dataset, and wide variety of large scale real-world datasets.
Abstraction-safe effect handlers via tunneling

Algebraic effect handlers offer a unified approach to expressing control-flow transfer idioms such as exceptions, iteration, and async/await. Unfortunately, previous attempts to make these handlers type-safe have failed to support the fundamental principle of modular reasoning for higher-order abstractions. We demonstrate that abstraction-safe algebraic effect handlers are possible by giving them a new semantics. The key insight is that code should only handle effects it is aware of. In our approach, the type system guarantees all effects are handled, but it is impossible for higher-order, effect-polymorphic code to accidentally handle effects raised by functions passed in; such effects tunnel through the higher-order, calling procedures polymorphic to them. By contrast, the possibility of accidental handling threatens previous designs for algebraic effect handlers. We prove that our design is not only type-safe, but also abstraction-safe. Using a logical-relations model that we prove sound with respect to contextual equivalence, we derive previously unattainable program equivalence results. Our mechanism offers a viable approach for future language designs aiming for effect handlers with strong abstraction guarantees.