Reviewerly/exHarmony huggingface dataset:
Network-Specific Variational Auto-Encoder for Embedding in Attribute Networks

Network embedding (NE) maps a network into a low-dimensional space while preserving intrinsic features of the network. Variational Auto-Encoder (VAE) has been actively studied for NE. These VAE-based methods typically utilize both network topologies and node semantics and treat these two types of data in the same way. However, the information of network topology and information of node semantics are orthogonal and are often from different sources; the former quantifies coupling relationships among nodes, whereas the latter represents node specific properties. Ignoring this difference affects NE. To address this issue, we develop a network-specific VAE for NE, named as NetVAE. In the encoding phase of our new approach, compression of network structures and compression of node attributes share the same encoder in order to perform co-training to achieve transfer learning and information integration. In the decoding phase, a dual decoder is introduced to reconstruct network topologies and node attributes separately. Specifically, as a part of the dual decoder, we develop a novel method based on a Gaussian mixture model and the block model to reconstruct network structures. Extensive experiments on large real-world networks demonstrate a superior performance of the new approach over the state-of-the-art methods.
RoboCSE: Robot Common Sense Embedding

Autonomous service robots require computational frameworks that allow them to generalize knowledge to new situations in a manner that models uncertainty while scaling to real-world problem sizes. The Robot Common Sense Embedding (RoboCSE) showcases a class of computational frameworks, multi-relational embeddings, that have not been leveraged in robotics to model semantic knowledge. We validate RoboCSE on a realistic home environment simulator (AI2Thor) to measure how well it generalizes learned knowledge about object affordances, locations, and materials. Our experiments show that RoboCSE can perform prediction better than a baseline that uses pre-trained embeddings, such as Word2Vec, achieving statistically significant improvements while using orders of magnitude less memory than our Bayesian Logic Network baseline. In addition, we show that predictions made by RoboCSE are robust to significant reductions in data available for training as well as domain transfer to MatterPort3D, achieving statistically significant improvements over a baseline that memorizes training data.
Bisimulation and bisimilarity for fuzzy description logics under the Gödel semantics

Description logics (DLs) are a suitable formalism for representing knowledge about domains in which objects are described not only by attributes but also by binary relations between objects. Fuzzy extensions of DLs can be used for such domains when data and knowledge about them are vague and imprecise. One of the possible ways to specify classes of objects in such domains is to use concepts in fuzzy DLs. As DLs are variants of modal logics, indiscernibility in DLs is characterized by bisimilarity. The bisimilarity relation of an interpretation is the largest auto-bisimulation of that interpretation. In DLs and their fuzzy extensions, such equivalence relations can be used for concept learning. In this paper, we define and study fuzzy bisimulation and bisimilarity for fuzzy DLs under the Gödel semantics, as well as crisp bisimulation and strong bisimilarity for such logics extended with involutive negation. The considered logics are fuzzy extensions of the DL ALCreg (a variant of PDL) with additional features among inverse roles, nominals, (qualified or unqualified) number restrictions, the universal role, local reflexivity of a role and involutive negation. We formulate and prove results on invariance of concepts under fuzzy (resp. crisp) bisimulation, conditional invariance of fuzzy TBoxes/ABoxes under bisimilarity (resp. strong bisimilarity), and the Hennessy-Milner property of fuzzy (resp. crisp) bisimulation for fuzzy DLs without (resp. with) involutive negation under the Gödel semantics. Apart from these fundamental results, we also provide results on using fuzzy bisimulation to separate the expressive powers of fuzzy DLs, as well as results on using strong bisimilarity to minimize fuzzy interpretations.
Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment

Entity alignment aims to find entities in different knowledge graphs (KGs) that refer to the same real-world object.An effective solution for cross-lingual entity alignment is crucial for many cross-lingual AI and NLP applications.Recently many embedding-based approaches were proposed for cross-lingual entity alignment.However, almost all of them are based on TransE or its variants, which have been demonstrated by many studies to be unsuitable for encoding multi-mapping relations such as 1-N, N-1 and N-N relations, thus these methods obtain low alignment precision.To solve this issue, we propose a new embedding-based framework.Through defining dot productbased functions over embeddings, our model can better capture the semantics of both 1-1 and multi-mapping relations.We calibrate embeddings of different KGs via a small set of pre-aligned seeds.We also propose a weighted negative sampling strategy to generate valuable negative samples during training and we regard prediction as a bidirectional problem in the end.Experimental results (especially with the metric Hits@1) on real-world multilingual datasets show that our approach significantly outperforms many other embedding-based approaches with state-of-the-art performance.
Incorporating External Knowledge into Machine Reading for Generative Question Answering

Commonsense and background knowledge is required for a QA model to answer many nontrivial questions.Different from existing work on knowledge-aware QA, we focus on a more challenging task of leveraging external knowledge to generate answers in natural language for a given question with context.In this paper, we propose a new neural model, Knowledge-Enriched Answer Generator (KEAG), which is able to compose a natural answer by exploiting and aggregating evidence from all four information sources available: question, passage, vocabulary and knowledge.During the process of answer generation, KEAG adaptively determines when to utilize symbolic knowledge and which fact from the knowledge is useful.This allows the model to exploit external knowledge that is not explicitly stated in the given text, but that is relevant for generating an answer.The empirical study on public benchmark of answer generation demonstrates that KEAG improves answer quality over models without knowledge and existing knowledge-aware models, confirming its effectiveness in leveraging knowledge.
Sensor network based PV power nowcasting with spatio-temporal preselection for grid-friendly control

The increasing penetration of photovoltaics (PV) systems introduces more uncertainties to the power system, and has drawn serious concern for maintaining the grid stability. Consequently, the PV power grid-friendly control (GFC) has been imposed by utilities to provide additional flexibilities for power system operations. Conventional GFC strategies show limitations to estimate real-time maximum available power, especially when fast moving clouds occur. In this regards, the spatio-temporal (ST) PV nowcasting using a sensor network provides a remedy to the above issue. However, current ST nowcasting methods suffer from the problems such as predictor mis-selection, inconsistent nowcasting, and poor model adaptability, which still hinder their practical use for GFC. In this paper, a novel ST PV power nowcasting method with predictor preselection is presented, which can be used for GFC. The proposed method enables a fast and precise predictor preselection in different scenarios, and provides consistent PV nowcasts with cloud information interpolated. The effectiveness of the proposed nowcasting method is evaluated in a real sensor network. The experimental results reveal that the proposed method has strong robustness in case of various weather conditions, with fewer training data used. Compared with the conventional methods, the proposed method shows an average nRMSE and nPMAE improvements over 13.5% and 41.3% respectively in the cloudy days. A practice of integrating the proposed nowcasting method to GFC operation is also demonstrated. The results show that the proposed method is promising to improve the performance of GFC.
Implications of Pooling Strategies in Convolutional Neural Networks: A Deep Insight

Abstract Convolutional neural networks (CNN) is a contemporary technique for computer vision applications, where pooling implies as an integral part of the deep CNN. Besides, pooling provides the ability to learn invariant features and also acts as a regularizer to further reduce the problem of overfitting. Additionally, the pooling techniques significantly reduce the computational cost and training time of networks which are equally important to consider. Here, the performances of pooling strategies on different datasets are analyzed and discussed qualitatively. This study presents a detailed review of the conventional and the latest strategies which would help in appraising the readers with the upsides and downsides of each strategy. Also, we have identified four fundamental factors namely network architecture, activation function, overlapping and regularization approaches which immensely affect the performance of pooling operations. It is believed that this work would help in extending the scope of understanding the significance of CNN along with pooling regimes for solving computer vision problems.
Automatic Gender Identification and Reinflection in Arabic

The impressive progress in many Natural Language Processing (NLP) applications has increased the awareness of some of the biases these NLP systems have with regards to gender identities.In this paper, we propose an approach to extend biased single-output genderblind NLP systems with gender-specific alternative reinflections.We focus on Arabic, a gender-marking morphologically rich language, in the context of machine translation (MT) from English, and for first-personsingular constructions only.Our contributions are the development of a system-independent gender-awareness wrapper, and the building of a corpus for training and evaluating firstperson-singular gender identification and reinflection in Arabic.Our results successfully demonstrate the viability of this approach with 8% relative increase in BLEU score for firstperson-singular feminine, and 5.3% comparable increase for first-person-singular masculine on top of a state-of-the-art gender-blind MT system on a held-out test set.
FakeSpotter: A Simple Baseline for Spotting AI-Synthesized Fake Faces

In recent years, generative adversarial networks (GANs) and its variants have achieved unprecedented success in image synthesis. They are widely adopted in synthesizing facial images which brings potential security concerns to humans as the fakes spread and fuel the misinformation. However, robust detectors of these AI-synthesized fake faces are still in their infancy and are not ready to fully tackle this emerging challenge. In this work, we propose a novel approach, named FakeSpotter, based on monitoring neuron behaviors to spot AI-synthesized fake faces. The studies on neuron coverage and interactions have successfully shown that they can be served as testing criteria for deep learning systems, especially under the settings of being exposed to adversarial attacks. Here, we conjecture that monitoring neuron behavior can also serve as an asset in detecting fake faces since layer-by-layer neuron activation patterns may capture more subtle features that are important for the fake detector. Experimental results on detecting four types of fake faces synthesized with the state-of-the-art GANs and evading four perturbation attacks show the effectiveness and robustness of our approach.
Improving Performance of End-to-End ASR on Numeric Sequences

Recognizing written domain numeric utterances (e.g. I need $1.25.) can be challenging for ASR systems, particularly when numeric sequences are not seen during training. This out-of-vocabulary (OOV) issue is addressed in conventional ASR systems by training part of the model on spoken domain utterances (e.g. I need one dollar and twenty five cents.), for which numeric sequences are composed of in-vocabulary numbers, and then using an FST verbalizer to denormalize the result. Unfortunately, conventional ASR models are not suitable for the low memory setting of on-device speech recognition. E2E models such as RNN-T are attractive for on-device ASR, as they fold the AM, PM and LM of a conventional model into one neural network. However, in the on-device setting the large memory footprint of an FST denormer makes spoken domain training more difficult. In this paper, we investigate techniques to improve E2E model performance on numeric data. We find that using a text-to-speech system to generate additional numeric training data, as well as using a small-footprint neural network to perform spoken-to-written domain denorming, yields improvement in several numeric classes. In the case of the longest numeric sequences, we see reduction of WER by up to a factor of 8.
Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments

Class attendance is an important means in the management of university students. Using face recognition is one of the most effective techniques for taking daily class attendance. Recently, many face recognition algorithms via deep learning have achieved promising results with large-scale labeled samples. However, due to the difficulties of collecting samples, face recognition using convolutional neural networks (CNNs) for daily attendance taking remains a challenging problem. Data augmentation can enlarge the samples and has been applied to the small sample learning. In this paper, we address this problem using data augmentation through geometric transformation, image brightness changes, and the application of different filter operations. In addition, we determine the best data augmentation method based on orthogonal experiments. Finally, the performance of our attendance method is demonstrated in a real class. Compared with PCA and LBPH methods with data augmentation and VGG-16 network, the accuracy of our proposed method can achieve 86.3%. Additionally, after a period of collecting more data, the accuracy improves to 98.1%.
A Finite Regime Analysis of Information Set Decoding Algorithms

Decoding of random linear block codes has been long exploited as a computationally hard problem on which it is possible to build secure asymmetric cryptosystems. In particular, both correcting an error-affected codeword, and deriving the error vector corresponding to a given syndrome were proven to be equally difficult tasks. Since the pioneering work of Eugene Prange in the early 1960s, a significant research effort has been put into finding more efficient methods to solve the random code decoding problem through a family of algorithms known as information set decoding. The obtained improvements effectively reduce the overall complexity, which was shown to decrease asymptotically at each optimization, while remaining substantially exponential in the number of errors to be either found or corrected. In this work, we provide a comprehensive survey of the information set decoding techniques, providing finite regime temporal and spatial complexities for them. We exploit these formulas to assess the effectiveness of the asymptotic speedups obtained by the improved information set decoding techniques when working with code parameters relevant for cryptographic purposes. We also delineate computational complexities taking into account the achievable speedup via quantum computers and similarly assess such speedups in the finite regime. To provide practical grounding to the choice of cryptographically relevant parameters, we employ as our validation suite the ones chosen by cryptosystems admitted to the second round of the ongoing standardization initiative promoted by the US National Institute of Standards and Technology.
Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images

Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart. Inspired by how experienced clinicians assess the cardiac morphology and function across multiple standard views (i.e. long- and short-axis views), we propose a novel approach which learns anatomical shape priors across different 2D standard views and leverages these priors to segment the left ventricular (LV) myocardium from short-axis MR image stacks. The proposed segmentation method has the advantage of being a 2D network but at the same time incorporates spatial context from multiple, complementary views that span a 3D space. Our method achieves accurate and robust segmentation of the myocardium across different short-axis slices (from apex to base), outperforming baseline models (e.g. 2D U-Net, 3D U-Net) while achieving higher data efficiency. Compared to the 2D U-Net, the proposed method reduces the mean Hausdorff distance (mm) from 3.24 to 2.49 on the apical slices, from 2.34 to 2.09 on the middle slices and from 3.62 to 2.76 on the basal slices on the test set, when only 10% of the training data was used.
EmIr-Auth: Eye Movement and Iris-Based Portable Remote Authentication for Smart Grid

With the development of Industry 4.0, the communication of smart grid has recently been taken seriously to ensure secure communication between operator and control center. However, the authentication process between them faces many challenges. Once the attacker successfully authenticated in the control center, the privacy data in the smart grid may leak and cause irreparable damage to the user. In addition, operator authentication is one of the most basic and crucial processes. Therefore, we propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eye-movement and iris recognition based authentication (</i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><monospace>EmIr-Auth</monospace></i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">)</i> , a novel biometrics-based remote operator authentication scheme. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EmIr-Auth</monospace> uses the recorded eye-movement trajectory and randomly selected iris image to authenticate operators, which is beneficial in that it is able to get rid of many cryptographic computations, as well as the need to minimize message exchange. Furthermore, except for a high-resolution camera, we do not require any additional biometric sensors in this scheme. Using the Burrows–Abadi–Needham logic, in this article, we demonstrate that our scheme provides secure authentication. Moreover, we analyze the attacks that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EmIr-Auth</monospace> can resist by informal security analysis. Experimental results show that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EmIr-Auth</monospace> is efficient enough to deploy on portable devices and reduce the overhead of authentication procedure.
A New Chaotic Image Steganography Technique Based on Huffman Compression of Turkish Texts and Fractal Encryption With Post-Quantum Security

As the digital age is becoming an unavoidable part of our daily life people are becoming more concern about their privacy and security of their digital communications.Steganography and cryptography are the two most frequently used merits of digital life to fulfill these needs.In this study, a new spatial domain chaotic steganography scheme is presented utilizing also new fractal stream encryption algorithm to hide Huffman encoded and compressed Turkish texts.The study composes of four main phases.Firstly, a sample of Turkish newspaper columnist corpus is gathered to obtain not only the frequencies of letters including special Turkish characters but also punctuations, spaces, newlines, quotations, etc.As a result of the first phase, a static Huffman encoding dictionary is obtained for 102 encountered characters.Secondly, super Mandelbrot sets are used with the Logistic map to obtain one-time-pad stream keys to encrypt compressed texts.Thirdly, the LSB (Least Significant Bit) plain of the cover image is analyzed morphologically to find low entropy pixel locations so that in steganography phase spotted locations can be avoided and scheme can become more resistant against stego analysis.Lastly, the chaotic steganography phase is the final phase in which data hiding pixel is selected according to logistic map chaotic function then LSB of the selected pixel is updated.Many tests are carried out on many images to show the image quality namely PSNR, SNR, SSMI, UIQI, Entropy, MSE, and histogram.Results indicate that proposed schemes are successful for encryption and offer robust steganography.
GWO-BP Neural Network Based OP Performance Prediction for Mobile Multiuser Communication Networks

The complexity and variability of wireless channels makes reliable mobile multiuser communications challenging. As a consequence, research on mobile multiuser communication networks has increased significantly in recent years. The outage probability (OP) is commonly employed to evaluate the performance of these networks. In this paper, exact closed-form OP expressions are derived and an OP prediction algorithm is presented. Monte-Carlo simulation is used to evaluate the OP performance and verify the analysis. Then, a grey wolf optimization back-propagation (GWO-BP) neural network based OP performance prediction algorithm is proposed. Theoretical results are used to generate training data. We also examine the extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), BP neural network, and wavelet neural network methods. Compared to the wavelet neural network, LWLR, SVM, BP, and ELM methods, the results obtained show that the GWO-BP method provides the best OP performance prediction.
Blockchain Technology: Financial Sector Applications Beyond Cryptocurrencies

Blockchain technology was initially employed as the public transaction ledger for cryptocurrencies. However, beyond cryptocurrencies, blockchain technology has been recently considered for a plethora of other applications as it encapsulates unique properties including decentralization, security, transparency and anti-tampering. Such properties are particularly advantageous for variety of prominent issues experienced in the financial sector. As a result, blockchain technology holds the potential to revolutionize the financial industry by altering the way in which different services are conducted in the financial industry. In this paper, we outline five different financial industry use cases that are expected to be radically transformed by the use of blockchain technology.