Reviewerly/exHarmony huggingface dataset:
Extensible Cross-Modal Hashing

Cross-modal hashing (CMH) models are introduced to significantly reduce the cost of large-scale cross-modal data retrieval systems. In many real-world applications, however, data of new categories arrive continuously, which requires the model has good extensibility. That is the model should be updated to accommodate data of new categories but still retain good performance for the old categories with minimum computation cost. Unfortunately, existing CMH methods fail to satisfy the extensibility requirements. In this work, we propose a novel extensible cross-modal hashing (ECMH) to enable highly efficient and low-cost model extension. Our proposed ECMH has several desired features: 1) it has good forward compatibility, so there is no need to update old hash codes; 2) the ECMH model is extended to support new data categories using only new data by a well-designed ``weak constraint incremental learning'' algorithm, which saves up to 91\% time cost comparing with retraining the model with both new and old data; 3) the extended model achieves high precision and recall on both old and new tasks. Our extensive experiments show the effectiveness of our design.
Redes, tweets y engagement: análisis de las bibliotecas universitarias españolas en Twitter

The libraries of Spanish universities have significantly increased their presence and activity on social networks, especially on Twitter. This article analyzes the use that Spanish university libraries (member of the Red de Bibliotecas Universitarias Españolas, Rebiun) make of Twitter. Specifically, through the analysis of social networks, content analysis and non-parametric statistical tests, this research analyzes the network generated on Twitter by the 59 Spanish university libraries that have profiles on this platform as well as the purpose, content, the format and engagement of their tweets. The results indicate that the university libraries form a single-component network on Twitter with a certain degree of cohesion, but with several communities, in which the tweets about service information and the tweets with pictures are the most numerous. Tweets with pictures generate greater engagement independently of their content.
Densely Supervised Hierarchical Policy-Value Network for Image Paragraph Generation

Image paragraph generation aims to describe an image with a paragraph in natural language. Compared to image captioning with a single sentence, paragraph generation provides more expressive and fine-grained description for storytelling. Existing approaches mainly optimize paragraph generator towards minimizing word-wise cross entropy loss, which neglects linguistic hierarchy of paragraph and results in ``sparse" supervision for generator learning. In this paper, we propose a novel Densely Supervised Hierarchical Policy-Value (DHPV) network for effective paragraph generation. We design new hierarchical supervisions consisting of hierarchical rewards and values at both sentence and word levels. The joint exploration of hierarchical rewards and values provides dense supervision cues for learning effective paragraph generator. We propose a new hierarchical policy-value architecture which exploits compositionality at token-to-token and sentence-to-sentence levels simultaneously and can preserve the semantic and syntactic constituent integrity. Extensive experiments on the Stanford image-paragraph benchmark have demonstrated the effectiveness of the proposed DHPV approach with performance improvements over multiple state-of-the-art methods.
Linear <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mo stretchy="false">(</mml:mo><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> Secret Sharing Scheme with Reduced Number of Polynomials

Threshold secret sharing is concerned with the splitting of a secret into <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:math> shares and distributing them to some persons without revealing its information. Any <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:math> ≤ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:math> persons possessing the shares have the ability to reconstruct the secret, but any persons less than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:math> cannot do the reconstruction. Linear secret sharing scheme is an important branch of secret sharing. The purpose of this paper is to propose a new polynomial based linear (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:math>, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M7"><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:math>) secret sharing scheme, which is based on Shamir’s secret sharing scheme and ElGamal cryptosystem. Firstly, we withdraw some required properties of secret sharing scheme after reviewing the related schemes and ElGamal cryptosystem. The designed scheme provides the properties of security for the secret, recoverability of the secret, privacy of the secret, and cheating detection of the forged shares. It has half computation overhead than the previous linear scheme.
Storytelling from Structured Data and Knowledge Graphs : An NLG Perspective

Abhijit Mishra, Anirban Laha, Karthik Sankaranarayanan, Parag Jain, Saravanan Krishnan. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts. 2019.
Modular quantum computation in a trapped ion system

Modern computation relies crucially on modular architectures, breaking a complex algorithm into self-contained subroutines. A client can then call upon a remote server to implement parts of the computation independently via an application programming interface (API). Present APIs relay only classical information. Here we implement a quantum API that enables a client to estimate the absolute value of the trace of a server-provided unitary operation [Formula: see text]. We demonstrate that the algorithm functions correctly irrespective of what unitary [Formula: see text] the server implements or how the server specifically realizes [Formula: see text]. Our experiment involves pioneering techniques to coherently swap qubits encoded within the motional states of a trapped [Formula: see text] ion, controlled on its hyperfine state. This constitutes the first demonstration of modular computation in the quantum regime, providing a step towards scalable, parallelization of quantum computation.
Convolutional Neural Networks for Image Recognition in Mixed Reality Using Voice Command Labeling

In the context of the Industrial Internet of Things (IIoT), image and object recognition has become an important factor. Camera systems provide information to realize sophisticated monitoring applications, quality control solutions, or reliable prediction approaches. During the last years, the evolution of smart glasses has enabled new technical solutions as they can be seen as mobile and ubiquitous cameras. As an important aspect in this context, the recognition of objects from images must be reliably solved to realize the previously mentioned solutions. Therefore, algorithms need to be trained with labeled input to recognize differences in input images. We simplify this labeling process using voice commands in Mixed Reality. The generated input from the mixed-reality labeling is put into a convolutional neural network. The latter is trained to classify the images with different objects. In this work, we describe the development of this mixed-reality prototype with its backend architecture. Furthermore, we test the classification robustness with image distortion filters. We validated our approach with format parts from a blister machine provided by a pharmaceutical packaging company in Germany. Our results indicate that the proposed architecture is at least suitable for small classification problems and not sensitive to distortions.
A Semi-Automatic Data–Scraping Method for the Public Transport Domain

The growing amount of data on the Internet has led to a situation in which it is essential to process these data to generate new services with the specific aim of improving people's daily living conditions.Transport data is of the utmost importance, since everyday people have to move around to perform some daily tasks, such as going to work, studying and shopping, and this means that the number of journeys by public transport grows daily.People with special needs make a large number of these trips, but they do not have sufficient information about the accessibility of the routes they want to take.Although there are numerous websites and applications that provide information on public transport services, most do not provide detailed information on the accessibility of the routes.We are, therefore, developing a technological framework for the processing, management, and exploitation of open data to promote accessibility to urban public transport.This is taking place within the framework of the Access@City project.This paper specifically focuses on the data extraction and processing of the existing information on the web concerning public transport and its accessibility for the generation of an open data repository in which to store this information.We, therefore, propose a method for the semi-automatic generation of a data scraper for the public transport domain.This method allows the extraction of public transport data and the existing accessibility information from a selected website.We have additionally developed a web tool that employs the aforementioned method to generate a data scraper for the public transport domain.
Simultaneous spatiotemporal bias and state estimation for asynchronous multi‐sensor system

Elimination of spatiotemporal bias of asynchronous multiple sensors is the foundation of accurate multi-sensor data fusion. In this article, a sequential processing simultaneous spatiotemporal bias and state estimation algorithm for asynchronous multi-sensor system is proposed. The spatiotemporal bias is combined with target state to obtain an augmented state vector, and the augmented state model is formulated. For asynchronous multi-sensor with known but different sampling periods, a sequential processing method is proposed to handle the multiple measurements. The relationship of sensor measurements, target state and spatiotemporal bias is analysed, and the corresponding measurement model is formulated. A novel algorithm, in which the unscented Kalman filter is used to handle the nonlinearity between the augmented measurement and state vectors, is proposed to jointly estimate the spatiotemporal bias and target state. Simulation results verify the effectiveness of the proposed algorithm.
Entanglement distance for arbitrary <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>M</mml:mi></mml:math> -qudit hybrid systems

We propose a measure of entanglement that can be computed for any pure state of an $M$-qubit system. The entanglement measure has the form of a distance that we derive from an adapted application of the Fubini-Study metric. This measure is invariant under local unitary transformations and defined as trace of a suitable metric that we derive, the entanglement metric $\tilde{g}$. Furthermore, the analysis of the eigenvalues of $\tilde{g}$ gives information about the robustness of entanglement.
Latent Structure Models for Natural Language Processing

Latent structure models are a powerful tool for modeling compositional data, discovering linguistic structure, and building NLP pipelines. They are appealing for two main reasons: they allow incorporating structural bias during training, leading to more accurate models; and they allow discovering hidden linguistic structure, which provides better interpretability. This tutorial will cover recent advances in discrete latent structure models. We discuss their motivation, potential, and limitations, then explore in detail three strategies for designing such models: gradient approximation, reinforcement learning, and end-to-end differentiable methods. We highlight connections among all these methods, enumerating their strengths and weaknesses. The models we present and analyze have been applied to a wide variety of NLP tasks, including sentiment analysis, natural language inference, language modeling, machine translation, and semantic parsing. Examples and evaluation will be covered throughout. After attending the tutorial, a practitioner will be better informed about which method is best suited for their problem.
Deep Hierarchical Knowledge Tracing

Knowledge tracing is an essential and challenging task in intelligent tutoring systems, whose goal is to estimate students’ knowledge state based on their responses to questions. Although many models for knowledge tracing task are developed, most of them depend on either concepts or items as input and ignore the hierarchical structure of items, which provides valuable information for the prediction of student learning results. In this paper, we propose a novel deep hierarchical knowledge tracing (DHKT) model exploiting the hierarchical structure of items. In the proposed DHKT model, the hierarchical relations between concepts and items are modeled by the hinge loss on the inner product between the learned concept embeddings and item embeddings. Then the learned embeddings are fed into a neural network to model the learning process of students, which is used to make predictions. The prediction loss and the hinge loss are minimized simultaneously during training process.
The fog on: Generalized teleportation by means of discrete-time quantum walks on <i>N</i>-lines and <i>N</i>-cycles

The recent paper entitled “Generalized teleportation by means of discrete-time quantum walks on [Formula: see text]-lines and [Formula: see text]-cycles” by Yang et al. [Mod. Phys. Lett. B 33(6) (2019) 1950069] proposed the quantum teleportation by means of discrete-time quantum walks on [Formula: see text]-lines and [Formula: see text]-cycles. However, further investigation shows that the quantum walk over the one-dimensional infinite line can be based over the [Formula: see text]-cycles and cannot be based on [Formula: see text]-lines. The proofs of our claims on quantum walks based on finite lines are also provided in detail.
SOTagger - Towards Classifying Stack Overflow Posts through Contextual Tagging (S)

There is an ever increasing growth in the use of Q&A websites such as Stack Overflow (SO), so are the number of posts on them.These websites serve as knowledge sharing platforms where Subject Matter Experts (SMEs) and developers answer questions posted by other users.It is effort intensive for developers to navigate to right posts because of the large volume of posts on the platform, despite the presence of existing tags, that are based on technologies.Tagging these posts based on their context and purpose might help developers and SMEs in easily identifying questions they wish to answer and also in identifying contextually similar posts.To support this idea, we propose SOTagger as a prototype plug-in for Stack Overflow to tag questions contextually.We have considered SO data provided on SOTorrent and automated the identification of 6 categories of questions using Latent Dirichlet Allocation.We have also manually verified relevance of these categories.Using these categories and dataset, we have built a classification model to classify a post into one of these six categories using Support Vector Machine.We have evaluated SOTagger by conducting a user survey with 32 developers.The preliminary results are promising with about 80% developers recommending the plugin to others.
X.509-Compliant Hybrid Certificates for the Post-Quantum Transition

We provide an X.509-standard-compliant Java implementation of hybrid certificates, which enable the parallel usage of two independent cryptographic schemes within public key infrastructures and related applications.This enables a stepwise transition to post-quantum secure and hybrid algorithms without the risk of incompatibility problems.
A Concave Optimization-Based Approach for Joint Multi-Target Track Initialization

The performance of track initialization methods based on real-time filtering depends heavily on the state estimation accuracy of the track head, which cannot be accurately obtained in many cases. This paper proposes the joint optimization problem of multi-target track initialization, in which the data association and track parameters of targets are obtained simultaneously. To this end, the target trajectory is first modeled as a weighted sum of a set of continuous time basis functions, and the corresponding track initialization is to determine discrete-value decision related to data association and continuous-value estimate related to function weights (i.e., track parameters). Such binary optimization is further transformed into the equivalent quadratic concave optimization of the data association vector by track parameter elimination, while each target definitely corresponds to one of the local minima that satisfy the target existence condition. In implementation, a modified normal rectangular algorithm is presented to obtain such minima, instead of the global minimum gotten by the standard normal rectangular algorithm. Finally, simulation results show the effectiveness of the proposed algorithm.
Reasoning Over Paragraph Effects in Situations

A key component of successfully reading a passage of text is the ability to apply knowledge gained from the passage to a new situation. In order to facilitate progress on this kind of reading, we present ROPES, a challenging benchmark for reading comprehension targeting Reasoning Over Paragraph Effects in Situations. We target expository language describing causes and effects (e.g., "animal pollinators increase efficiency of fertilization in flowers"), as they have clear implications for new situations. A system is presented a background passage containing at least one of these relations, a novel situation that uses this background, and questions that require reasoning about effects of the relationships in the background passage in the context of the situation. We collect background passages from science textbooks and Wikipedia that contain such phenomena, and ask crowd workers to author situations, questions, and answers, resulting in a 14,322 question dataset. We analyze the challenges of this task and evaluate the performance of state-of-the-art reading comprehension models. The best model performs only slightly better than randomly guessing an answer of the correct type, at 61.6% F1, well below the human performance of 89.0%.
Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective

Developing successful sign language recognition, generation, and translation systems requires expertise in a wide range of fields, including computer vision, computer graphics, natural language processing, human-computer interaction, linguistics, and Deaf culture. Despite the need for deep interdisciplinary knowledge, existing research occurs in separate disciplinary silos, and tackles separate portions of the sign language processing pipeline. This leads to three key questions: 1) What does an interdisciplinary view of the current landscape reveal? 2) What are the biggest challenges facing the field? and 3) What are the calls to action for people working in the field? To help answer these questions, we brought together a diverse group of experts for a two-day workshop. This paper presents the results of that interdisciplinary workshop, providing key background that is often overlooked by computer scientists, a review of the state-of-the-art, a set of pressing challenges, and a call to action for the research community.
New Results in Perceptually Lossless Compression of Hyperspectral Images

Hyperspectral images (HSI) have hundreds of bands, which impose heavy burden on data storage and transmission bandwidth.Quite a few compression techniques have been explored for HSI in the past decades.One high performing technique is the combination of principal component analysis (PCA) and JPEG-2000 (J2K).However, since there are several new compression codecs developed after J2K in the past 15 years, it is worthwhile to revisit this research area and investigate if there are better techniques for HSI compression.In this paper, we present some new results in HSI compression.We aim at perceptually lossless compression of HSI.Perceptually lossless means that the decompressed HSI data cube has a performance metric near 40 dBs in terms of peak-signal-to-noise ratio (PSNR) or human visual system (HVS) based metrics.The key idea is to compare several combinations of PCA and video/image codecs.Three representative HSI data cubes were used in our studies.Four video/image codecs, including J2K, X264, X265, and Daala, have been investigated and four performance metrics were used in our comparative studies.Moreover, some alternative techniques such as video, split band, and PCA only approaches were also compared.It was observed that the combination of PCA and X264 yielded the best performance in terms of compression performance and computational complexity.In some cases, the PCA + X264 combination achieved more than 3 dBs than the PCA + J2K combination.
A Value Set Analysis Refinement Approach Based on Conditional Merging and Lazy Constraint Solving

Value set analysis is a common static binary program analysis approach. Value set analysis attempts to identify a tight over-approximation of the program state at any given point in the program and can be used to detect vulnerability. Existing memory corruption detection analysis technologies based on value set analysis have a high false positive rate, because value set analysis suffers from a lack of accuracy. We observed that two main sources of imprecision in value set analysis are merge operation and failed branch conditions tracking. In order to address above problems, in this paper, we propose a value set analysis refinement approach based on conditional merging and lazy constraint solving. We propose a variable dependence analysis algorithm to divide program paths into subsets and only merge the states which satisfy the condition that the states are from the same subset, which reduces the imprecision from the merging operation. We collect path predicates as path constraint and solve the path constraint using Satisfiability Modulo Theories (SMT) solver lazily to get a tighter number range of the variable when a variable need be refined, which reduces the imprecision from the failed branch conditions tracking. We implement a prototype system RVSA based on the proposed approach and verify its effectiveness according to experimentation. Compared with state-of-the-art approach, the experimental results demonstrate that the false positive rate is reduced by 12.9%. Furthermore, using our proposed approach, 25 zero-day vulnerabilities are found in the Netgear httpd binary.
Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles

This paper describes preliminary work in the recent promising approach of generating synthetic training data for facilitating the learning procedure of deep learning (DL) models, with a focus on aerial photos produced by unmanned aerial vehicles (UAV). The general concept and methodology are described, and preliminary results are presented, based on a classification problem of fire identification in forests as well as a counting problem of estimating number of houses in urban areas. The proposed technique constitutes a new possibility for the DL community, especially related to UAV-based imagery analysis, with much potential, promising results, and unexplored ground for further research.