Reviewerly/exHarmony huggingface dataset:
A Semisupervised Classification Approach for Multidomain Networks With Domain Selection

Multidomain network classification has attracted significant attention in data integration and machine learning, which can enhance network classification or prediction performance by integrating information from different sources. Despite the previous success, existing multidomain network learning methods usually assume that different views are available for the same set of instances, and thus, they seek a consistent classification result for all domains. However, in many real-world problems, each domain has its specific instance set, and one instance in one domain may correspond to multiple instances in another domain. Moreover, due to the rapid growth of data sources, different domains may not be relevant to each other, which asks for selecting domains relevant to the target/focused domain. A key challenge under this setting is how to achieve accurate prediction by integrating different data representations without losing data information. In this paper, we propose a semisupervised classification approach for a multidomain network based on label propagation, i.e., multidomain classification with domain selection (MCS), which can deal with the cross-domain information and different instance sets in domains. In particular, with sparse weight properties, the proposed MCS can automatically identify those domains relevant to our target domain by assigning them higher weights than the other irrelevant domains. This not only significantly improves a classification accuracy but also helps to obtain optimal network partition for the target domain. From the theoretical viewpoint, we equivalently decompose MCS into two simpler subproblems with analytical solutions, which can be efficiently solved by their computational procedures. Extensive experimental results on both synthetic and real-world data sets empirically demonstrate the advantages of the proposed approach in terms of both prediction performance and domain selection ability.
Sparse Proximal Reinforcement Learning via Nested Optimization

We consider the tasks of feature selection and policy evaluation based on linear value function approximation in reinforcement learning problems. High-dimension feature vectors and limited number of samples can easily cause over-fitting and computation expensive. To prevent this problem, ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -regularized method obtains sparse solutions and thus improves generalization performance. We propose an efficient ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -regularized recursive least squares-based online algorithm with O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) complexity per time-step, termed ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -RC. With the help of nested optimization decomposition, ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -RC solves a series of standard optimization problems and avoids minimizing mean squares projected Bellman error with ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -regularization directly. In ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -RC, we propose RC with iterative refinement to minimize the operator error, and we propose an alternating direction method of multipliers with proximal operator to minimize the fixed-point error. The convergence of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -RC is established based on ordinary differential equation method and some extensions are also given. In empirical computations, some state-of-the-art ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -regularized methods are chosen as the baselines, and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -RC are tested in both policy evaluation and learning control benchmarks. The empirical results show the effectiveness and advantages of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -RC.
Development of Deep Learning Framework for Mathematical Morphology

Mathematical morphology has been applied as a collection of nonlinear operations related to object features in images. In this paper, we present morphological layers in deep learning framework, namely MorphNet, to perform atomic morphological operations, such as dilation and erosion. For propagation of losses through the proposed deep learning framework, we approximate the dilation and erosion operations by differential and smooth multivariable functions of the softmax function, and therefore enable the neural network to be optimized. The proposed operations are analyzed by the derivative of approximation functions in the deep learning framework. Experimental results show that the output structuring element of a morphological neuron and the target structuring element are matched to confirm the efficiency and correctness of the proposed framework.
Bindings as bounded natural functors

We present a general framework for specifying and reasoning about syntax with bindings. Abstract binder types are modeled using a universe of functors on sets, subject to a number of operations that can be used to construct complex binding patterns and binding-aware datatypes, including non-well-founded and infinitely branching types, in a modular fashion. Despite not committing to any syntactic format, the framework is ``concrete'' enough to provide definitions of the fundamental operators on terms (free variables, alpha-equivalence, and capture-avoiding substitution) and reasoning and definition principles. This work is compatible with classical higher-order logic and has been formalized in the proof assistant Isabelle/HOL.
Learning Models of Sequential Decision-Making with Partial Specification of Agent Behavior

Artificial agents that interact with other (human or artificial) agents require models in order to reason about those other agents’ behavior. In addition to the predictive utility of these models, maintaining a model that is aligned with an agent’s true generative model of behavior is critical for effective human-agent interaction. In applications wherein observations and partial specification of the agent’s behavior are available, achieving model alignment is challenging for a variety of reasons. For one, the agent’s decision factors are often not completely known; further, prior approaches that rely upon observations of agents’ behavior alone can fail to recover the true model, since multiple models can explain observed behavior equally well. To achieve better model alignment, we provide a novel approach capable of learning aligned models that conform to partial knowledge of the agent’s behavior. Central to our approach are a factored model of behavior (AMM), along with Bayesian nonparametric priors, and an inference approach capable of incorporating partial specifications as constraints for model learning. We evaluate our approach in experiments and demonstrate improvements in metrics of model alignment.
Does foreign aid help alleviate income inequality? New evidence from African countries

Purpose The purpose of this paper is to examine the relationship between foreign aid and income inequality (IIQ) reduction for 16 African countries using unbalanced panel data covering the period 1990–2011. This paper attempts to answer the critical question: does foreign aid lead to IIQ reduction? Design/methodology/approach To examine the effect of foreign aid on IIQ, this paper uses an RE model with robust OLS regression and system-GMM estimator, which are useful in dealing with the endogeneity problems. Findings Results of RE model indicate that foreign aid, foreign direct investment, trade openness as well as corruption have a positive and statistically significant effect on IIQ. Government spending and inflation have a negative and statistically significant effect on IIQ, while GDP per capita growth has a negative but statistically insignificant relationship with IIQ. The results are robust by using system-GMM dynamic panel model which confirms that the coefficients of all considered variables remain same sign and significance. Research limitations/implications This study implies that an increase in foreign aid is associated with an increase in IIQ. As an effective strategy to foreign aid, this paper suggests that improving of financial sector development, and institutional quality and policies can reduce income inequalities and stimulate economic growth. Originality/value This paper is the first of its kind to empirically explore the relationship between IIQ and foreign aid measured here by net aid transfers as a share of GDP in African countries, using modern econometric techniques, time period and a variety of control variables.
Cross-Lingual Non-ferrous Metals Related News Recognition Method Based on CNN with a Limited Bi-lingual Dictionary

To acquire non-ferrous metals related news from different countries’ internet, we proposed a cross-lingual non-ferrous metals related news recognition method based on CNN with a limited bilingual dictionary. Firstly, considering the lack of related language resources of non-ferrous metals, we use a limited bilingual dictionary and CCA to learn cross-lingual word vector and to represent news in different languages uniformly. Then, to improve the effect of recognition, we use a variant of the CNN to learn recognition features and construct the recognition model. The experimental results show that our proposed method acquires better results.
Quick response code scanning for children’s informal learning

Purpose Mobile technologies, such as QR codes, play a particularly important role in scaffolding the child user’s active learning in informal environments. The purpose of this paper is to examine the impact of QR code scanning on two informal learning outcomes: increased interest and greater knowledge understanding. Design/methodology/approach In total, 91 children and their families participated in the study as part of the iQ Zoo Project. Children in both the smartphone group and the control group completed were assessed qualitatively and quantitatively before and then after their zoo visits. Findings Qualitative findings suggest that most children’s interest in learning about animals was sustained as a result of the experience. Quantitative results reveal that QR code scanning was effective in promoting knowledge gains, especially on subjects that are challenging for the informal learner. Findings were comparable across the younger (5–8) and older (9–12) age groups. Originality/value This study provides empirical support for the value and usefulness of mobile technologies such as QR code scanning for children's learning in informal environments.
Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-Convex

Several recently proposed architectures of neural networks such as ResNeXt, Inception, Xception, SqueezeNet and Wide ResNet are based on the designing idea of having multiple branches and have demonstrated improved performance in many applications. We show that one cause for such success is due to the fact that the multi-branch architecture is less non-convex in terms of duality gap. The duality gap measures the degree of intrinsic non-convexity of an optimization problem: smaller gap in relative value implies lower degree of intrinsic non-convexity. The challenge is to quantitatively measure the duality gap of highly non-convex problems such as deep neural networks. In this work, we provide strong guarantees of this quantity for two classes of network architectures. For the neural networks with arbitrary activation functions, multi-branch architecture and a variant of hinge loss, we show that the duality gap of both population and empirical risks shrinks to zero as the number of branches increases. This result sheds light on better understanding the power of over-parametrization where increasing the number of branches tends to make the loss surface less non-convex. For the neural networks with linear activation function and $\ell_2$ loss, we show that the duality gap of empirical risk is zero. Our two results work for arbitrary depths, while the analytical techniques might be of independent interest to non-convex optimization more broadly. Experiments on both synthetic and real-world datasets validate our results.
Humans Vs. Algorithms: Assessment of Security Risks Posed by Facial Morphing to Identity Verification at Border Control

Facial morphing, if applied to a biometric portrait intended for an identity document application, compromises further identity verification by means of the issued document. An electronic machine readable travel document is a prime target of a face morphing attack because a successful attack allows a wanted criminal for illicit border crossing. The open question is whether human examiners and algorithms can be fooled only by professionally created manual morphs or even by automatically generated morphs with evident visual artifacts. In this paper, we introduce a border control simulation to examine the ability of humans in recognizing morphed passport photographs as well as in mismatching morphed passport photographs against live faces of travelers. The error rates of humans are compared with those of algorithms to emphasize the necessity for computer-aided support of border guards.
What Kind of Natural Language Inference are NLP Systems Learning: Is this Enough?

In this paper, we look at Natural Language Inference, arguing that the notion of inference the current NLP systems are learning is much narrower compared to the range of inference patterns found in human reasoning. We take a look at the history and the nature of creating datasets for NLI. We discuss the datasets that are mainly used today for the relevant tasks and show why those are not enough to generalize to other reasoning tasks, e.g. logical and legal reasoning, or reasoning in dialogue settings. We then proceed to propose ways in which this can be remedied, effectively producing more realistic datasets for NLI. Lastly, we argue that the NLP community could have been too hasty to altogether dismiss symbolic approaches in the study of NLI, given that these might still be relevant for more fine-grained cases of reasoning. As such, we argue for a more pluralistic take on tackling NLI, favoring hybrid rather than non-hybrid approaches.
Natural Answer Generation With Attention Over Instances

Natural answer generation (NAG) is more and more popular in real-world knowledge base question answering (KBQA) systems for being able to automatically generate natural language answers with structured KB. Large-scale community QA-pairs crawled from the Internet could be directly used to train NAG models. However, it is pervasive in these datasets that one question may contain multiple answers of varied quality, and NAG models suffer from the simple principle of equal treatment of these answers. To address this problem, we propose two kinds of attention-based algorithms to handle all answers to a question at a time. Selective attention and self-attention mechanisms are used to dynamically weight the answers to one question during the training process. Specifically, selective attention methods weight the answers using the relationships between all the KB objects the question needs and the generated answers, and self-attention methods weight them according to the generating difficulty. The experiments on the public open-domain community QA dataset demonstrate that Selective-ATT outperforms the state-of-the-art by 10.53% in the entity accuracy, 9.34% in the BLEU score, and 1.19% in the Rouge score.
Article Users Activity Gesture Recognition on Kinect Sensor Using Convolutional Neural Networks and FastDTW for Controlling Movements of a Mobile Robot

In this paper, we use data from the Microsoft Kinect sensor that processes the captured imageof a person using and extracting the joints information on every frame. Then, we propose the creation ofan image derived from all the sequential frames of a gesture the movement, which facilitates training in aconvolutional neural network. We trained a CNN using two strategies: combined training and individualtraining. The strategies were experimented in the convolutional neural network (CNN) using theMSRC-12 dataset, obtaining an accuracy rate of 86.67% in combined training and 90.78% of accuracyrate in the individual training.. Then, the trained neural network was used to classify data obtained fromKinect with a person, obtaining an accuracy rate of 72.08% in combined training and 81.25% inindividualized training. Finally, we use the system to send commands to a mobile robot in order to controlit.
Toward a big data approach for indexing encrypted data in Cloud Computing

Searchable encryption provides encryption schemes allowing search on encrypted data without decrypting the whole collection. However, due to the huge amount of data that exists in the Cloud, it is required to review such encryption schemes to decrease search and indexation times. In this paper, we propose a big data approach called MR‐Index (MapReduce Indexation) that makes the indexation mechanism parallel. In fact, the proposed MR‐Index is composed of two phases: Map phase and Reduce phase. In the Map phase, MR‐Index processes documents in parallel to build for each keyword, the list of identifiers of documents containing it. Access control is ensured through attribute based encryption. In the reduce phase of MR‐Index, the data structures of the index are constructed. The MR‐Index is performed by a trusted party called searchable encryption authority. Experimental results, shown that the proposed solution decreases the indexation time for large documents, compared to the original sequential indexation mechanism. In terms of scalability of the solution, we ameliorated the MR‐Index using the Hadoop archive files.
E-learning within the Field of Andragogy

The article analyzes a new education paradigm based on the concept of e-learning, which relates to the ideas of andragogy. The article deals with the openness of education for each individual, that is confirmed by the fact that each individual is provided with the conditions for free development of his/her educational, intellectual and activity opportunities throughout his/her life. The article presents the main differences between the traditional education and e-learning. The most appropriate andragogical platform means continuous, student-centered adult education, there are significant changes in the purpose and the content of teaching, social roles, forms and nature of communication between the teacher and the student, based on e-learning.
Scalable Syntax-Aware Language Models Using Knowledge Distillation

Prior work has shown that, on small amounts of training data, syntactic neural language models learn structurally sensitive generalisations more successfully than sequential language models. However, their computational complexity renders scaling difficult, and it remains an open question whether structural biases are still necessary when sequential models have access to ever larger amounts of training data. To answer this question, we introduce an efficient knowledge distillation (KD) technique that transfers knowledge from a syntactic language model trained on a small corpus to an LSTM language model, hence enabling the LSTM to develop a more structurally sensitive representation of the larger training data it learns from. On targeted syntactic evaluations, we find that, while sequential LSTMs perform much better than previously reported, our proposed technique substantially improves on this baseline, yielding a new state of the art. Our findings and analysis affirm the importance of structural biases, even in models that learn from large amounts of data.
Behavior Modeling and Individual Recognition of Sonar Transmitter for Secure Communication in UASNs

It is necessary to improve the safety of the underwater acoustic sensor networks (UASNs) since it is mostly used in the military industry. Specific emitter identification is the process of identifying different transmitters based on the radio frequency fingerprint extracted from the received signal. The sonar transmitter is a typical low-frequency radiation source and is an important part of the UASNs. Class D power amplifier, a typical nonlinear amplifier, is usually used in sonar transmitters. The inherent nonlinearity of power amplifiers provides fingerprint features that can be distinguished without transmitters for specific emitter recognition. First, the nonlinearity of the sonar transmitter is studied in-depth, and the nonlinearity of the power amplifier is modeled and its nonlinearity characteristics are analyzed. After obtaining the nonlinear model of an amplifier, a similar amplifier in practical application is obtained by changing its model parameters as the research object. The output signals are collected by giving the same input of different models, and, then, the output signals are extracted and classified. In this paper, the memory polynomial model is used to model the amplifier. The power spectrum features of the output signals are extracted as fingerprint features. Then, the dimensionality of the high-dimensional features is reduced. Finally, the classifier is used to recognize the amplifier. The experimental results show that the individual sonar transmitter can be well identified by using the nonlinear characteristics of the signal. By this way, this method can enhance the communication safety of the UASNs.
Logistic Regression and Random Forest for Effective Imbalanced Classification

Nowadays, the application of data mining and machine learning techniques continues to be common in many fields. There are many imbalanced datasets with much less significant samples than unimportance ones in real-life because it is hard to collect representative positive examples. Under these circumstances, the conventional aim of reducing overall classification accuracy and most of the standard machine learning methods may not be suitable for the imbalanced problem. In this work, we compare the performance of random forest and logistic regression on the prediction of an imbalanced dataset. We propose several ways to enhance two models based on cost-sensitive learning to provide more accurate predictions when dealing with imbalanced datasets.
Adaptive Histogram Thresholding-Based Leukocyte Image Segmentation

To improve the accuracy of leukocyte segmentation, this paper presents a novel method based on adaptive histogram thresholding (AHT). The proposed method first employs color component combination and AHT to extract the nucleus of leukocyte and utilizes image color features to remove the complex backgrounds such as red blood cells (RBCs) and substantial dyeing impurities. Then, Canny edge detection is performed to extract the entire leukocyte. Finally, the cytoplasm of the leukocyte is obtained by subtracting the nucleus with the entire leukocyte. Experimental results on an image dataset containing 60 leukocyte images show that the proposed method generates more accurate segmentation results than the counterparts.