Reviewerly/exHarmony huggingface dataset:
Deep Class-Wise Hashing: Semantics-Preserving Hashing via Class-Wise Loss

Deep supervised hashing has emerged as an effective solution to large-scale semantic image retrieval problems in computer vision. Convolutional neural network-based hashing methods typically seek pairwise or triplet labels to conduct similarity-preserving learning. However, complex semantic concepts of visual contents are hard to capture by similar/dissimilar labels, which limits the retrieval performance. Generally, pairwise or triplet losses not only suffer from expensive training costs but also lack sufficient semantic information. In this paper, we propose a novel deep supervised hashing model to learn more compact class-level similarity-preserving binary codes. Our model is motivated by deep metric learning that directly takes semantic labels as supervised information in training and generates corresponding discriminant hashing code. Specifically, a novel cubic constraint loss function based on Gaussian distribution is proposed, which preserves semantic variations while penalizes the overlapping part of different classes in the embedding space. To address the discrete optimization problem introduced by binary codes, a two-step optimization strategy is proposed to provide efficient training and avoid the problem of gradient vanishing. Extensive experiments on five large-scale benchmark databases show that our model can achieve the state-of-the-art retrieval performance.
Geometric Image Synthesis

The task of generating natural images from 3D scenes has been a long standing goal in computer graphics. On the other hand, recent developments in deep neural networks allow for trainable models that can produce natural-looking images with little or no knowledge about the scene structure. While the generated images often consist of realistic looking local patterns, the overall structure of the generated images is often inconsistent. In this work we propose a trainable, geometry-aware image generation method that leverages various types of scene information, including geometry and segmentation, to create realistic looking natural images that match the desired scene structure. Our geometrically-consistent image synthesis method is a deep neural network, called Geometry to Image Synthesis (GIS) framework, which retains the advantages of a trainable method, e.g., differentiability and adaptiveness, but, at the same time, makes a step towards the generalizability, control and quality output of modern graphics rendering engines. We utilize the GIS framework to insert vehicles in outdoor driving scenes, as well as to generate novel views of objects from the Linemod dataset. We qualitatively show that our network is able to generalize beyond the training set to novel scene geometries, object shapes and segmentations. Furthermore, we quantitatively show that the GIS framework can be used to synthesize large amounts of training data which proves beneficial for training instance segmentation models.
A Discrete Improved Artificial Bee Colony Algorithm for 0–1 Knapsack Problem

The present study integrated a discrete search equation to develop a discrete improved artificial bee colony (DiABC) algorithm. A random binary population was added in the initialization. The current optimal solution was then introduced into the search equations for worker and onlooker bees to effectively enhance the convergence accuracy of the algorithm. And a new selection method on scout bees was proposed. Eighteen benchmark knapsack problems (KPs) experiments were conducted, of which the results validated the effectiveness of the DiABC algorithm in optimizing the KPs.
Fine-Grained Entity Typing in Hyperbolic Space

How can we represent hierarchical information present in large type inventories for entity typing?We study the ability of hyperbolic embeddings to capture hierarchical relations between mentions in context and their target types in a shared vector space.We evaluate on two datasets and investigate two different techniques for creating a large hierarchical entity type inventory: from an expert-generated ontology and by automatically mining type co-occurrences.We find that the hyperbolic model yields improvements over its Euclidean counterpart in some, but not all cases.Our analysis suggests that the adequacy of this geometry depends on the granularity of the type inventory and the way hierarchical relations are inferred.
Combining Spans into Entities: A Neural Two-Stage Approach for Recognizing Discontiguous Entities

In medical documents, it is possible that an entity of interest not only contains a discontiguous sequence of words but also overlaps with another entity.Entities of such structures are intrinsically hard to recognize due to the large space of possible entity combinations.In this work, we propose a neural two-stage approach to recognizing discontiguous and overlapping entities by decomposing this problem into two subtasks: 1) it first detects all the overlapping spans that either form entities on their own or present as segments of discontiguous entities, based on the representation of segmental hypergraph, 2) next it learns to combine these segments into discontiguous entities with a classifier, which filters out other incorrect combinations of segments.Two neural components are designed for these subtasks respectively and they are learned jointly using a shared encoder for text.Our model achieves the state-of-the-art performance in a standard dataset, even in the absence of external features that previous methods used.
Self-organizing neural networks for universal learning and multimodal memory encoding

Learning and memory are two intertwined cognitive functions of the human brain. This paper shows how a family of biologically-inspired self-organizing neural networks, known as fusion Adaptive Resonance Theory (fusion ART), may provide a viable approach to realizing the learning and memory functions. Fusion ART extends the single-channel Adaptive Resonance Theory (ART) model to learn multimodal pattern associative mappings. As a natural extension of ART, various forms of fusion ART have been developed for a myriad of learning paradigms, ranging from unsupervised learning to supervised learning, semi-supervised learning, multimodal learning, reinforcement learning, and sequence learning. In addition, fusion ART models may be used for representing various types of memories, notably episodic memory, semantic memory and procedural memory. In accordance with the notion of embodied intelligence, such neural models thus provide a computational account of how an autonomous agent may learn and adapt in a real-world environment. The efficacy of fusion ART in learning and memory shall be discussed through various examples and illustrative case studies.
Text Detection and Recognition for Natural Scene Images Using Deep Convolutional Neural Networks

Words are the most indispensable information in human life. It is very important to analyze and understand the meaning of words. Compared with the general visual elements, the text conveys rich and high-level moral information, which enables the computer to better understand the semantic content of the text. With the rapid development of computer technology, great achievements have been made in text information detection and recognition. However, when dealing with text characters in natural scene images, there are still some limitations in the detection and recognition of natural scene images. Because natural scene image has more interference and complexity than text, these factors make the detection and recognition of natural scene image text face many challenges. To solve this problem, a new text detection and recognition method based on depth convolution neural network is proposed for natural scene image in this paper. In text detection, this method obtains high-level visual features from the bottom pixels by ResNet network, and extracts the context features from character sequences by BLSTM layer, then introduce to the idea of faster R-CNN vertical anchor point to find the bounding box of the detected text, which effectively improves the effect of text object detection. In addition, in text recognition task, DenseNet model is used to construct character recognition based on Kares. Finally, the output of Softmax is used to classify each character. Our method can replace the artificially defined features with automatic learning and context-based features. It improves the efficiency and accuracy of recognition, and realizes text detection and recognition of natural scene images. And on the PAC2018 competition platform, the experimental results have achieved good results.
The Na-Dene Languages

The Na-Dene languages are spoken in a large discontinuous area of western North America. They are known for highly complex, partly non-concatenative, predominately prefixal verb morphology that shows linearly alternating inflectional and derivational elements and has been the focus of extended theoretical investigation. Syntactically, they universally exhibit SOV constituent order but typically allow various kinds of scrambling. Other notable features are a two-copula system and doubly marked possession. All Na-Dene languages are endangered, and many communities are engaged in revitalization efforts.
A survey on vulnerability assessment tools and databases for cloud-based web applications

Due to its various offered benefits, an ever increasing number of applications are migrated to the cloud. However, such a migration should be carefully performed due to the cloud’s public nature. Further, due to the agile development cycle that applications follow, their security level might not be the best possible, exhibiting various sorts of vulnerability. As such, to better support application migration and runtime provisioning, this article supplies three main contributions. First, it attempts to connect vulnerability management to the application lifecycle so as to highlight the exact moments where application vulnerability assessment must be performed. Second, it analyses the state-of-the-art open-source tools and databases so as to enable developers to make an informed decision about which ones to select. In this sense, discovering such vulnerabilities will enable to better secure applications before or after migrating them to the cloud. The analysis conducted is quite rich, covering various aspects and a rich sets of criteria. Third, it explores the claim that vulnerability scanning tools need to be orchestrated to reach the highest possible vulnerability coverage, both in terms of extend and breadth. Finally, this article concludes with some challenges that current vulnerability tools and databases need to face to increase their added-value and applicability level.
Learning Cascade Attention for fine-grained image classification

Fine-grained image classification is a challenging task due to the large inter-class difference and small intra-class difference. In this paper, we propose a novel Cascade Attention Model using the Deep Convolutional Neural Network to address this problem. Our method first leverages the Spatial Confusion Attention to identify ambiguous areas of the input image. Two constraint loss functions are proposed: the Spatial Mask loss and the Spatial And loss; Second, the Cross-network Attention, applying different pre-train parameters to the two stream architecture. Also, two novel loss functions called Cross-network Similarity loss and Satisfied Rank loss are proposed to make the two-stream networks reinforce each other and get better results. Finally, the Network Fusion Attention merges intermediate results with the novel entropy add strategy to obtain the final predictions. All of these modules can work together and can be trained end to end. Besides, different from previous works, our model is fully weak-supervised and fully paralleled, which leads to easier generalization and faster computation. We obtain the state-of-the-art performance on three challenge benchmark datasets (CUB-200-2011, FGVC-Aircraft and Flower 102) with results of 90.8%, 92.1%, and 98.5%, respectively. The model will be publicly available at https://github.com/billzyx/LCA-CNN.
Using Priming to Uncover the Organization of Syntactic Representations in Neural Language Models

Neural language models (LMs) perform well on tasks that require sensitivity to syntactic structure.Drawing on the syntactic priming paradigm from psycholinguistics, we propose a novel technique to analyze the representations that enable such success.By establishing a gradient similarity metric between structures, this technique allows us to reconstruct the organization of the LMs' syntactic representational space.We use this technique to demonstrate that LSTM LMs' representations of different types of sentences with relative clauses are organized hierarchically in a linguistically interpretable manner, suggesting that the LMs track abstract properties of the sentence.
Lightweight convolutional neural network for vehicle recognition in thermal infrared images

Nighttime vehicle recognition based on driver assistance systems (DAS), targeted at enhancing the driver perception of the environment, has been of interest to researchers in various disciplines. Research to date has primarily focused on the development of automatic, low-power, and small systems. Although popular in the strategic sector, thermal infrared cameras have not been considerably explored in traffic. This study demonstrates the use of thermal infrared cameras in the field of DAS and discusses their performance in nighttime vehicle recognition. In this work, a dataset of thermal infrared images comprising four vehicle classes was generated: bus, truck, van, and car. To achieve robust vehicle recognition, we designed a convolution neural network (CNN) called Net1 having four convolution layers for classifying four vehicle classes. To achieve compact and efficient performance, this paper applied the Fire module which was the core structure of SqueezeNet to build nine lightweight CNNs (Net2–Net10). Using Net1 as the reference network, we compared the performance of the above nine networks. Amongst all the networks, Net9 was found to be the optimal one with a 97% classification accuracy with 10.6% of the number of parameters of Net1. The recognition time of a single picture was only 0.52 ms on a desktop with a common CPU, thereby enabling its deployment in mobile terminal systems and embedded devices like DAS.
Using eye movements to measure intrinsic, extraneous, and germane load in a multimedia learning environment.

In a previous study, DeLeeuw and Mayer (2008) found support for the triarchic model of cognitive load (Sweller, Van Merrienboer, & Paas, 1998, 2019) by showing that three different metrics could be used to independently measure 3 hypothesized types of cognitive load: intrinsic, extraneous, and germane. However, 2 of the 3 metrics that the authors used were intrusive in nature because learning had to be stopped momentarily to complete the measures. The current study extends the design of DeLeeuw and Mayer (2008) by investigating whether learners’ eye movement behavior can be used to measure the three proposed types of cognitive load without interrupting learning. During a 1-hr experiment, we presented a multimedia lesson explaining the mechanism of electric motors to participants who had low prior knowledge of this topic. First, we replicated the main results of DeLeeuw and Mayer (2008), providing further support for the triarchic structure of cognitive load. Second, we identified eye movement measures that differentiated the three types of cognitive load. These findings were independent of participants’ working memory capacity. Together, these results provide further evidence for the triarchic nature of cognitive load (Sweller et al., 1998, 2019), and are a first step toward online measures of cognitive load that could potentially be implemented into computer assisted learning technologies. (PsycInfo Database Record (c) 2020 APA, all rights reserved)
Experimental quantum cloning in a pseudo-unitary system

Deterministically cloning (copying) nonorthogonal states is forbidden in quantum mechanics, but deterministic pseudo-unitary cloning is possible in a nonunitary system. We prove and show that, for any two nonorthogonal qubit states, we can find a linear, invertible Hermitian (metric) operator to make these states mutually orthogonal with respect to the corresponding pseudo-inner product. From this metric operator, we construct a pseudo-Hermitian Hamiltonian and its evolution operator as an ideal, deterministic pseudo-unitary cloner. For applicability, suppose Alice lives in our ordinary universe and Bob lives in a universe that has the desired pseudo-unitary evolution. Alice would send her nonorthogonal states to Bob and Bob would do the cloning deterministically and send back to Alice. The quantum channel will be lossy not due to Bob's universe's properties, but rather due to problems making a channel between the two universes. We experimentally demonstrate deterministic pseudo-unitary two-qubit cloning for a photonic pseudo-unitary two-qubit system. In our universe we have to demonstrate that universe by executing a postselected gate that effects loss-based pseudo-unitary evolution. Furthermore, we introduce an algorithmic method for designing experimental realizations of a generic class of nonunitary operators for pseudo-unitary cloning.
Robust support vector regression with generic quadratic nonconvex ε-insensitive loss

In this paper, we propose a robust support vector regression with a novel generic nonconvex quadratic ε-insensitive loss function. The proposed method is robust to outliers or noise since it can adaptively control the loss value and decrease the negative influence of outliers or noise on the decision function by adjusting the elastic interval parameter and adaptive robustification parameter. Given the nature of the nonconvexity of the optimization problem, a concave-convex programming procedure is employed to solve the proposed problem. Experimental results on two artificial data sets and three real-world data sets indicate that the proposed method outperforms support vector regression, L1-norm support vector regression, least squares support vector regression, robust least squares support vector regression, and support vector regression with the Huber loss function on both robustness and generalization ability.
Experimental Investigation of Demographic Factors Related to Phishing Susceptibility

This paper reports on a simulated phishing experiment targeting 6,938 faculty and staff at George Mason University.The study examined various possible predictors of phishing susceptibility.The focus of the present paper is on demographic factors (including age, gender and position/employment). Since previous studies of age and gender have yielded discrepant results, one purpose of the study was to disambiguate these findings.A second purpose was to compare different types of email phishing exploits.A third objective was to compare the effect of different types of feedback given to those who clicked on one or more of three simulated phishing exploits that were deployed over a three-week period.Our analysis of demographic factors, effects of phishing email content, and effects of repeated exposure to phishing exploits revealed significant age effects, marginally significant gender differences, and significant differences in email type.A multi-level model estimated effects of multiple variables simultaneously.
Design and implementation of a mobile device app for network-based earthquake early warning systems (EEWSs): application to the PRESTo EEWS in southern Italy

Abstract. A fundamental feature of any earthquake early warning system is the ability of rapidly broadcast earthquake information to reach a wide audience of potential end users and stakeholders, in an intuitive, customizable way. Smartphones and other mobile devices are nowadays continuously connected to the Internet and represent the ideal tools for earthquake alerts dissemination to inform a large number of users about the potential damaging shaking of an impending earthquake. Here we present a mobile app (named ISNet EWApp or simply EWApp) for Android devices which can receive the alerts generated by a network-based Early Warning system. Specifically, the app receives the earthquake alerts generated by the PRESTo EEWS, which is currently running on the accelerometric stations of the Irpinia Seismic Network (ISNet) in southern Italy. In the absence of alerts, EWApp displays the standard bulletin of seismic events that have occurred within the network. In the event of a relevant earthquake, the app has a dedicated module to predict the expected ground-shaking intensity and the available lead time at the user's position and to provide customized messages to inform the user about the proper reaction to adopt during the alert. We first present the architecture of both the network-based system and EWApp and then describe its essential operational modes. The app is designed in a way that is easily exportable to any other network-based early warning system.
The Role of Blockchain in Internet of Vehicles (IoV): Issues, Challenges and Opportunities

Internet of Vehicles (IoV) is a dynamic network of vehicles connected together through sensors and communicating devices embedded in them. These embedded devices work in internal and external synchronization and consistency with each other. The functionality of the vehicles can be controlled, tracked and monitored through a remote controlling station. In recent years we have seen an exponential growth in the automobile sector where things are moving towards automation. Driverless cars and drones are no longer fictions, rather a reality now. There are several pertinent benefits of automation in the automobile industry like better designs, better safety features, better fuel efficiency, better pickup, reduced maintenance, safer driving experience etc. However, with these advantages and benefits, there are several issues and challenges associated with the internet of vehicles. This paper discusses the existing state of the Internet of Vehicles ecosystem and the important issues and challenges faced by the manufacturer as well as the consumers in the IoV systems. Furthermore, the paper also discusses the potential use of blockchain based solutions to overcome these limitations and challenges. The concept of Blockchain technology along with its key characteristics is discussed focusing on the integration of “Blockchain” technology with the “Internet of Things (IoT)” technology in the urban transportation system. The work presented focuses on the applications of blockchain in the IoV ecosystem. More specifically, the paper delves into the blockchain technology for securing the IoV network. Finally, the paper highlights some of the issues, challenges and opportunities associated with the blockchain based IoV systems.
Plug-and-play continuous-variable quantum key distribution for metropolitan networks

We report a plug-and-play continuous variable quantum key distribution system (CV-QKD) with Gaussian modulated quadratures and a true local oscillator. The proposed configuration avoids the need for frequency locking two narrow line-width lasers. To minimize Rayleigh back-scattering, we utilize two independent fiber strands for the distribution of the laser and the transmission of the quantum signals. We further demonstrate the quantum-classical co-existing capability of our system by injecting high-power classical light in both fibers. A secret key rate up to 0.88 Mb/s is obtained by using two fiber links of 13 km and up to 0.3 Mb/s when adding 4 mW of classical light in the optical fiber used for transmitting the quantum signal. The reported performance indicates that the proposed QKD scheme has the potential to become an effective low-cost solution for metropolitan optical networks.