Reviewerly/exHarmony huggingface dataset:
An evolutionary algorithm for automated machine learning focusing on classifier ensembles: An improved algorithm and extended results

A large number of classification algorithms have been proposed in the machine learning literature. These algorithms have different pros and cons, and no algorithm is the best for all datasets. Hence, a challenging problem consists of choosing the best classification algorithm with its best hyper-parameter settings for a given input dataset. In the last few years, Automated Machine Learning (Auto-ML) has emerged as a promising approach for tackling this problem, by doing a heuristic search in a large space of candidate classification algorithms and their hyper-parameter settings. In this work we propose an improved version of our previous Evolutionary Algorithm (EA) – more precisely, an Estimation of Distribution Algorithm – for the Auto-ML task of automatically selecting the best classifier ensemble and its best hyper-parameter settings for an input dataset. The new version of this EA was compared against its previous version, as well as against a random forest algorithm (a strong ensemble algorithm) and a version of the well-known Auto-ML method Auto-WEKA adapted to search in the same space of classifier ensembles as the proposed EA. In general, in experiments with 21 datasets, the new EA version obtained the best results among all methods in terms of four popular predictive accuracy measures: error rate, precision, recall and F-measure.
KLog-Home: A Holistic Approach of In-Situ Monitoring in Elderly-Care Home

The advent of superior research and advances in medicine and healthcare have led to an increase in life expectancy of humans increasing the aging population in many countries. Due to the rapid cost-to-performance ratio of the Internet of Things (IoT), many sectors are adopting IoT from agriculture to healthcare and daily life applications; monitoring elderly patients pervasively with IoT can improve elderly care while minimizing caregivers' effort. Furthermore transparent and uninterrupted monitoring by caregivers helps them finding patterns in elderly patients before precarious incidents, costing much more to care. Ambient Assisted Living (AAL) is a term that indicates the concept of improving wellness and health conditions of older adults. In the current scenario, AAL extends to learning the elderly behavior by caregivers to avoid accidents in the future. In this context, KLog-Home project is being carried out as an initiative deployed to an elderly care home to monitor the elderly inhabitants continuously to help caregivers get notified of critical events and act instantly to avoid fatal incidents. KLog-Home enriches the concept of AAL with videos enabling, Video Assisted Ambient Living (VAAL). This multi-modal scenario is achieved by leveraging the technological benefits from IoT and communication technologies and envisioning a solution to provide ubiquitous care to senior people while enabling them to live an independent, safe and secure life.
An Adaptive Control Algorithm for Stable Training of Generative Adversarial Networks

Generative adversarial networks (GANs) have shown significant progress in generating highquality visual samples, however they are still well known both for being unstable to train and for the problem of mode collapse, particularly when trained on data collections containing a diverse set of visual objects.In this paper, we propose an Adaptive k-step Generative Adversarial Network (Ak-GAN), which is designed to mitigate the impact of instability and saturation in the original by dynamically adjusting the ratio of the training steps of both the generator and discriminator.To accomplish this, we track and analyze stable training curves of relatively narrow datasets and use them as the target fitting lines when training more diverse data collections.Furthermore, we conduct experiments on the proposed procedure using several optimization techniques (e.g., supervised guiding from previous stable learning curves with and without momentum) and compare their performance with that of state-of-the-art models on the task of image synthesis from datasets consisting of diverse images.Empirical results demonstrate that Ak-GAN works well in practice and exhibits more stable behavior than regular GANs during training.A quantitative evaluation has been conducted on the Inception Score (IS) and the relative inverse Inception Score (RIS); compared with regular GANs, the former has been improved by 61% and 83%, and the latter by 21% and 60%, on the CelebA and the Anime datasets, respectively.
Low-rank graph optimization for multi-view dimensionality reduction

Graph-based dimensionality reduction methods have attracted substantial attention due to their successful applications in many tasks, including classification and clustering. However, most classical graph-based dimensionality reduction approaches are only applied to data from one view. Hence, combining information from different data views has attracted considerable attention in the literature. Although various multi-view graph-based dimensionality reduction algorithms have been proposed, the graph construction strategies utilized in them do not adequately take noise and different importance of multiple views into account, which may degrade their performance. In this paper, we propose a novel algorithm, namely, Low-Rank Graph Optimization for Multi-View Dimensionality Reduction (LRGO-MVDR), that overcomes these limitations. First, we construct a low-rank shared matrix and a sparse error matrix from the graph that corresponds to each view for capturing potential noise. Second, an adaptive nonnegative weight vector is learned to explore complementarity among views. Moreover, an effective optimization procedure based on the Alternating Direction Method of Multipliers scheme is utilized. Extensive experiments are carried out to evaluate the effectiveness of the proposed algorithm. The experimental results demonstrate that the proposed LRGO-MVDR algorithm outperforms related methods.
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives

Reinforcement learning agents that operate in diverse and complex environments can benefit from the structured decomposition of their behavior. Often, this is addressed in the context of hierarchical reinforcement learning, where the aim is to decompose a policy into lower-level primitives or options, and a higher-level meta-policy that triggers the appropriate behaviors for a given situation. However, the meta-policy must still produce appropriate decisions in all states. In this work, we propose a policy design that decomposes into primitives, similarly to hierarchical reinforcement learning, but without a high-level meta-policy. Instead, each primitive can decide for themselves whether they wish to act in the current state. We use an information-theoretic mechanism for enabling this decentralized decision: each primitive chooses how much information it needs about the current state to make a decision and the primitive that requests the most information about the current state acts in the world. The primitives are regularized to use as little information as possible, which leads to natural competition and specialization. We experimentally demonstrate that this policy architecture improves over both flat and hierarchical policies in terms of generalization.
Toward Amortized Ranking-Critical Training For Collaborative Filtering

We investigate new methods for training collaborative filtering models based on actor-critic reinforcement learning, to more directly maximize ranking-based objective functions. Specifically, we train a critic network to approximate ranking-based metrics, and then update the actor network to directly optimize against the learned metrics. In contrast to traditional learning-to-rank methods that require re-running the optimization procedure for new lists, our critic-based method amortizes the scoring process with a neural network, and can directly provide the (approximate) ranking scores for new lists. We demonstrate the actor-critic's ability to significantly improve the performance of a variety of prediction models, and achieve better or comparable performance to the state-of-the-art on three large-scale datasets.
Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks

Current artificial neural networks (ANNs) can perform and excel at a variety of tasks ranging from image classification to spam detection through training on large datasets of labeled data. While the trained network usually performs well on similar testing data, certain inputs that differ even slightly from the training data may trigger unpredictable behavior. Due to this limitation, it is possible to generate inputs with very small designed perturbations that can result in misclassification. These adversarial attacks present a security risk to deployed ANNs and indicate a divergence between how ANNs and humans perform classification. Humans are robust at behaving in the presence of noise and are capable of correctly classifying objects that are occluded, blurred, or otherwise distorted. It has been hypothesized that sleep promotes generalization and improves robustness against noise in animals and humans. In this work, we utilize a biologically inspired sleep phase in ANNs and demonstrate the benefit of sleep on defending against adversarial attacks as well as increasing ANN classification robustness. We compare the sleep algorithm's performance on various robustness tasks with two previously proposed adversarial defenses, defensive distillation and fine-tuning. We report an increase in robustness after sleep to adversarial attacks as well as to general image distortions for three datasets: MNIST, CUB200, and a toy dataset. Overall, these results demonstrate the potential for biologically inspired solutions to solve existing problems in ANNs and guide the development of more robust, human-like ANNs.
SVQN: Sequential Variational Soft Q-Learning Networks

Partially Observable Markov Decision Processes (POMDPs) are popular and flexible models for real-world decision-making applications that demand the information from past observations to make optimal decisions. Standard reinforcement learning algorithms for solving Markov Decision Processes (MDP) tasks are not applicable, as they cannot infer the unobserved states. In this paper, we propose a novel algorithm for POMDPs, named sequential variational soft Q-learning networks (SVQNs), which formalizes the inference of hidden states and maximum entropy reinforcement learning (MERL) under a unified graphical model and optimizes the two modules jointly. We further design a deep recurrent neural network to reduce the computational complexity of the algorithm. Experimental results show that SVQNs can utilize past information to help decision making for efficient inference, and outperforms other baselines on several challenging tasks. Our ablation study shows that SVQNs have the generalization ability over time and are robust to the disturbance of the observation.
Countermodel Construction via Optimal Hypersequent Calculi for Non-normal Modal Logics

We develop semantically-oriented calculi for the cube of non-normal modal logics and some deontic extensions. The calculi manipulate hypersequents and have a simple semantic interpretation. Their main feature is that they allow for direct countermodel extraction. Moreover they provide an optimal decision procedure for the respective logics. They also enjoy standard proof-theoretical properties, such as a syntactical proof of cut-admissibility.
Enhanced Online Sequential Parallel Extreme Learning Machine and its Application in Remaining Useful Life Prediction of Integrated Modular Avionics

Integrated modular avionics is one of the most advanced systems which has been widely applied in modern aircraft. The performance of integrated modular avionics deeply impacts flight mission. Remaining useful life prediction is the critical manner which can efficiently improve the safety and reliability of aircraft. Since integrated modular avionics is a real-time system, the prediction algorithm should have fast learning speed to satisfy the real-time requirement. In this paper, an enhanced online sequential method is proposed to predict the remaining useful life of integrated modular avionics. Firstly, this paper proposes an online sequential network which is based on the architecture of parallel layer network. Secondly, to enhance the learning capability, this paper adopts the extreme learning machine denosing autoencoder to determine the input weights of the network. Thirdly, an adaptive forgetting factor is added to further improve the performance of the proposed method. The effectiveness and the superiority of the proposed method are verified in comparison with three online sequential algorithms on the standard datasets. Finally, a degradation model is built to depict the deteriorated process of integrated modular avionics. The prediction results confirms that the proposed method can effectively address remaining useful life of integrated modular avionics.
TRICE: Mining Frequent Itemsets by Iterative TRimmed Transaction LattICE in Sparse Big Data

Sparseness is often witnessed in big data emanating from a variety of sources, including IoT, pervasive computing, and behavioral data. Frequent itemset mining is the first and foremost step of association rule mining, which is a distinguished unsupervised machine learning problem. However, techniques for frequent itemset mining are least explored for sparse real-world data, showing somewhat comparable performance. On the contrary, the methods are adequately validated for dense data and stand apart from each other in terms of performance. Hence, there arises an immense need for evaluating these techniques as well as proposing new ones for large sparse real-world datasets. In this study, a novel method: Mining Frequent Itemsets by Iterative TRimmed Transaction lattICE (TRICE) is proposed. TRICE iteratively generates combinations of varying-sized trimmed subsets of I, where I denote the set of distinct items in a database. Extensive experiments are conducted to assess TRICE against HARPP, FP-Growth, optimized SaM, and optimized RElim algorithms. The experimental results show that TRICE outperforms all these algorithms both in terms of running time and memory consumption. TRICE maintains a substantial performance gap for all sparse real-world datasets on all minimum support thresholds. Moreover, assessment of memory use of optimized SaM and RElim algorithms has been performed for the first time.
Real Color Image Denoising Using t-Product- Based Weighted Tensor Nuclear Norm Minimization

Color images can be seen as third-order tensors with column, row and color modes. Considering two inherent characteristics of a color image including the non-local self-similarity (NSS) and the cross-channel correlation, we extract non-local similar patch groups from a color image and treat these groups as tensors with each color channel corresponding to the frontal slice of the tensor to exploit the information within and cross channel correlation. Inspired by recently proposed tensor-tensor product (t-product), t-SVD, tensor tubal rank and rigorously deduced tensor nuclear norm, a novel t-product based weighted tensor nuclear norm minimization (WTNNM) is proposed to model the extracted non-local similar patch group tensor (NPGT). Considering the NPGT is of low tubal rank, we formulate real color image denoising as a low tubal rank tensor recovery problem and solve it with the weighted tensor nuclear norm minimization. Experiments on both simulated and realistic noisy images verify the effectiveness of our method.
Assessing Mistake Potential in Writing German Passive Sentences (An Approach of Language Error Analysis)

Abstract. The purpose of this study is to identify the types of errors experienced by students in writing German language passivation. The study uses a qualitative approach, especially the language error analysis approach. The data in this study are in the form of the writings of German students. The results showed that there were 4 types of errors experienced by students: 1) werden conjugation was wrong, 2) did not use Partizip Perfekt, 3) errors in the process of declination of articles, 4) errors in writing subjects or objects. One of the mistakes caused by one of them is the interference of the Indonesian language in learning German. Keywords: Passive Voice, Interference, Error Analysis, German Language.
Coping Strategies and Paradoxes Related to BYOD Information Security Threats in France

Bring Your Own Device (BYOD) refers to the provision and use of personal mobile devices by employees for both private and business purposes. Although there has been research on BYOD, little attention has been paid to employees' perception of threats to their personal information security (ISS) when using a BYOD, especially in a professional context. This article investigates employee coping strategies related to BYOD ISS threats in France. The results of a survey of 223 employees indicate that while perceived behavioral control exerts only direct effects on problem-focused (i.e., disturbance handling) and emotion-focused (i.e., self-preservation) coping strategies, ISS concern exhibits significant direct and moderating influences. Several security paradoxes could be identified, namely, discrepancies between the respondents' ISS concern and the adopted coping strategies. This article offers the first insights into the French context and can serve as a basis for comparisons in future research and to help improve employees' personal ISS in the professional context.
Neural Networks Multiobjective Learning With Spherical Representation of Weights

This article presents a novel representation of artificial neural networks (ANNs) that is based on a projection of weights into a new spherical space defined by a radius r and a vector of angles Θ. This spherical representation of ANNs further simplifies the multiobjective learning problem, which is usually treated as a constrained optimization problem that requires great computational effort to maintain the constraints. With the proposed spherical representation, the constrained optimization problem becomes unconstrained, which simplifies the formulation and computational effort required. In addition, it also allows the use of any nonlinear optimization method for the multiobjective learning of ANNs. Results presented in this article show that the proposed spherical representation of weights yields more accurate estimates of the Pareto set than the classical multiobjective approach. Regarding the final solution selected from the Pareto set, our approach was effective and outperformed some state-of-the-art methods on several data sets.
Forgetting an Argument

The notion of forgetting, as considered in the famous paper by Lin and Reiter in 1994 has been extensively studied in classical logic and more recently, in non-monotonic formalisms like logic programming. In this paper, we convey the idea of forgetting to another major AI formalism, namely Dung-style argumentation frameworks. Our approach is axiomatic-driven and not limited to any specific semantics: we propose semantical and syntactical desiderata encoding different criteria for what forgetting an argument might mean; analyze how these criteria relate to each other; and check whether the criteria can be satisfied in general. The analysis is done for a number of widely used argumentation semantics. Our investigation shows that almost all desiderata are individually satisfiable. However, combinations of semantical and/or syntactical conditions reveal a much more interesting landscape. For instance, we found that the ad hoc approach to forgetting an argument, i.e., by the syntactical removal of the argument and all of its associated attacks, is too restrictive and only compatible with the two weakest semantical desiderata. Amongst the several interesting combinations identified, we showed that one satisfies a notion of minimal change and presented an algorithm that given an AF F and argument x, constructs a suitable AF G satisfying the conditions in the combination.
Education Systems as Contextual Factors in the Technological Pedagogical Content Knowledge Framework

Digital competences should be considered in teacher education and professional development. The Technological Pedagogical Content Knowledge (TPACK) framework represents the knowledge required by teachers in order to successfully integrate technology into the teaching process. The Survey of Pre-service Teachers’ Knowledge of Teaching and Technology (SPTKTT) is one of the inventories developed to measure the TPACK framework. The aim of this study is to validate the SPTKTT inventory in the context of the Croatian education system by the use of exploratory and confirmatory factor analysis. Results showed that the new context structure of the SPTKTT inventory consists of nine factors, while confirmatory factor analysis established that the two Content Knowledge factors can actually be unified. This can be explained by differing content organization in education systems. There were also differences in the distribution of some of the remaining factors. Internal consistency of the inventory shows high reliability.
Enhancing the Credit Card Fraud Detection Through Ensemble Techniques

Nowadays people prefer to use e-commerce because of easiness, timesaving, convenience, etc. By the increase in e-commerce use, credit card fraud increases. The fraudsters get the benefit of online payments and stealing the card details. Therefore, it is essential to improve the detection methods to overcome with the fraudster’s activity and secure the card transactions. The purpose of this study is to investigate the performance of several individual different classifiers and the combination of classifiers using ensemble methods for credit card fraud detection. The study is organized as initially the three well-known classifiers i.e., Decision Tree, Naïve Bayes and SVM have been applied. Afterwards the ensemble learning module have been applied using the boosting technique with the previously mentioned classification algorithms. The dataset used is open source credit card transaction dataset containing 3075 transactions. The performance of the classification techniques is evaluated based on accuracy, sensitivity, specificity, precision, ROC value and F -measure. The result shows that Boosting with Decision Tree outperforms the other techniques.
End-to-end optical backpropagation for training neural networks.

Backpropagation through nonlinear neurons is an outstanding challenge to the field of optical neural networks and the major conceptual barrier to all-optical training schemes. Each neuron is required to exhibit a directionally dependent response to propagating optical signals, with the backwards response conditioned on the forward signal, which is highly non-trivial to implement optically. We propose a practical and surprisingly simple solution that uses saturable absorption to provide the network nonlinearity. We find that the backward propagating gradients required to train the network can be approximated in a pump-probe scheme that requires only passive optical elements. Simulations show that, with readily obtainable optical depths, our approach can achieve equivalent performance to state-of-the-art computational networks on image classification benchmarks, even in deep networks with multiple sequential gradient approximations. This scheme is compatible with leading optical neural network proposals and therefore provides a feasible path towards end-to-end optical training.