Reviewerly/exHarmony huggingface dataset:
Matching Feature Sets for Few-Shot Image Classification

In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classification methods also mostly follow this trend. In this work, we depart from this established direction and instead propose to extract sets of feature vectors for each image. We argue that a set-based representation intrinsically builds a richer representation of images from the base classes, which can subsequently better transfer to the few-shot classes. To do so, we propose to adapt existing feature extractors to instead produce sets of feature vectors from images. Our approach, dubbed SetFeat, embeds shallow self-attention mechanisms inside existing encoder architectures. The attention modules are lightweight, and as such our method results in encoders that have approximately the same number of parameters as their original versions. During training and inference, a set-to-set matching metric is used to perform image classification. The effectiveness of our proposed architecture and metrics is demonstrated via thorough experiments on standard few-shot datasets-namely miniImageNet, tieredImageNet, and CUB-in both the 1- and 5-shot scenarios. In all cases but one, our method outperforms the state-of-the-art.
A Topic-Sensitive Method for Mashup Tag Recommendation Utilizing Multi-Relational Service Data

Tagging systems have been widely used as a major way of managing Web service resources. Many portals such as ProgrammableWeb and BioCatalogue allow users to create manual tags annotating Web services and their compositions (e.g., mashups). This is extremely helpful for managing and retrieving enormous Web service data. In the past few years, many tag recommendation approaches have been proposed for Web services that contain few or no tags. Most of them only exploit the textual content or tag service matrix information. Sometimes those approaches suffer from the data sparsity problem, especially when Web services have only few tags or their auxiliary textual contents are hard to be obtained. In real world, a plenty of relationships are available in recommendation systems, e.g., the composition relationship between services and the annotation relationship between mashups and tags. These multi-relational data can be utilized as additional features to improve the recommendation performance. In this paper, we exploit various types of relationships as features and propose a novel topic-sensitive approach based on the Factorization Machines for mashup tag recommendation. Factorization Machines is utilized to model the pair-wise interactions between all features and predict adequate tags for mashups. In this approach, we first obtain the latent topics of all tags as well as the description documents for mashups and APIs based on a novel probabilistic topic model. Then, a multi-relational network by mining various relationships from the Web service data is constructed. Various auxiliary informations are subsequently extracted from the network to train the Factorization Machines. The proposed model is evaluated on three real-world datasets and the experimental results show that it outperforms several state-of-the-art methods.
Experimental emulation of quantum non-Markovian dynamics and coherence protection in the presence of information backflow

We experimentally emulate, in a controlled fashion, the non-Markovian dynamics of a pure dephasing spin-boson model at zero temperature. Specifically, we use a randomized set of external radio-frequency fields to engineer a desired noise power spectrum to effectively realize a non-Markovian environment for a single NMR qubit. The information backflow, characteristic to the non-Markovianity, is captured in the nonmonotonicity of the decoherence function and von Neumann entropy of the system. Using such emulated non-Markovian environments, we experimentally study the efficiency of the Carr-Purcell-Meiboom-Gill dynamical decoupling (DD) sequence to inhibit the loss of coherence. Using the filter function formalism, we design optimized DD sequences that maximize coherence protection for non-Markovian environments and study their efficiencies experimentally. Finally, we discuss DD-assisted tuning of the effective non-Markovianity.
Optical flow for image-based river velocity estimation

We present a novel motion estimation technique for image-based river velocimetry. It is based on the so-called optical flow, which is a well developed method for rigid motion estimation in image sequences, devised in computer vision community. Contrary to PIV (Particle Image Velocimetry) techniques, optical flow formulation is flexible enough to incorporate physics equations that govern the observed quantity motion. Over the past years, it has been adopted by experimental fluid dynamics community where many new models were introduced to better represent different fluids motions, (see [18] for a review). Our optical flow is based on the scalar transport equation and is augmented with a weighted diffusion term to compensate for small scale (non-captured) contributions. Additionally, since there is no ground truth data for such type of image sequences, we present a new evaluation method to assess the results. It is based on trajectory reconstruction of few Lagrangian particles of interest and a direct comparison against their manually-reconstructed trajectories. The new motion estimation technique outperformed traditional optical flow and PIV-based methods.
A two-step calibration method of lenslet-based light field cameras

The calibration of lenslet-based light field camera is the key issue to many applications, especially the three-dimensional shape measurement. In this paper, a two-step physically based calibration method is proposed, where every parameter is described with its own physical meaning. The parameters about the main lens of the light field camera are calibrated with the central sub-aperture image, and the parameters about the micro-lens array are calibrated with the epipolar images, subsequently. To improve the calibration result, the distortion correction of the main lens and a nonlinear optimization method are applied also. The calibration method is validated with a commercially available light field camera and compared to Dansereau's calibration method. Typical RMS reprojection errors are 0.0017, 0.0032 mm for 30.0, 35.1 mm calibration grids.
Biogeochemical mapping of metal contamination from mine tailings using field-portable XRF

Trace and major element composition of selected plant species and parts may be used to map geochemical dispersion from mineral deposits and contaminated areas. This study examines the application of field-portable X-ray fluorescence spectrometry (fpXRF) in obtaining real-time biogeochemical data. In situ analysis of parts of black and silver wattle (Acacia mearnsii De Wild. and Acacia dealbata Link) was conducted to map the extent of contamination surrounding the former Woodlawn base metal mine. High levels of ore-related elements were detected in the bark of these species in a zone extending up to 1 km down-drainage from the tailing ponds. Major elements are more elevated in bark on the side of the trees facing the tailings ponds and correlations between trace and major elements indicate dust contamination. The penetration distance for X-rays is dependent on the energy of the secondary X-rays measured, with the maximum depth of penetration in bark and leaf material <30 mm. There was a close correlation for most elements between the fpXRF and laboratory-based XRF analysis but with element-dependent attenuation by the organic matrix. Providing there is consistency in sampling and analytical methodology, in situ fpXRF analysis of vegetation is an effective method in both contamination surveys and biogeochemical mineral exploration for a range of trace and major elements.
Practical Collision Attacks against Round-Reduced SHA-3

The Keccak hash function is the winner of the SHA-3 competition (2008–2012) and became the SHA-3 standard of NIST in 2015. In this paper, we focus on practical collision attacks against round-reduced SHA-3 and some Keccak variants. Following the framework developed by Dinur et al. at FSE 2012 where 4-round collisions were found by combining 3-round differential trails and 1-round connectors, we extend the connectors to up to three rounds and hence achieve collision attacks for up to 6 rounds. The extension is possible thanks to the large degree of freedom of the wide internal state. By linearizing S-boxes of the first round, the problem of finding solutions of 2-round connectors is converted to that of solving a system of linear equations. When linearization is applied to the first two rounds, 3-round connectors become possible. However, due to the quick reduction in the degree of freedom caused by linearization, the connector succeeds only when the 3-round differential trails satisfy some additional conditions. We develop dedicated strategies for searching differential trails and find that such special differential trails indeed exist. To summarize, we obtain the first real collisions on six instances, including three round-reduced instances of SHA-3, namely 5-round SHAKE128, SHA3-224 and SHA3-256, and three instances of Keccak contest, namely Keccak[1440, 160, 5, 160], Keccak[640, 160, 5, 160] and Keccak[1440, 160, 6, 160], improving the number of practically attacked rounds by two. It is remarked that the work here is still far from threatening the security of the full 24-round SHA-3 family.
Softly Associative Transfer Learning for Cross-Domain Classification

The main challenge of cross-domain text classification is to train a classifier in a source domain while applying it to a different target domain. Many transfer learning-based algorithms, for example, dual transfer learning, triplex transfer learning, etc., have been proposed for cross-domain classification, by detecting a shared low-dimensional feature representation for both source and target domains. These methods, however, often assume that the word clusters matrix or the clusters association matrix as knowledge transferring bridges are exactly the same across different domains, which is actually unrealistic in real-world applications and, therefore, could degrade classification performance. In light of this, in this paper, we propose a softly associative transfer learning algorithm for cross-domain text classification. Specifically, we integrate two non-negative matrix tri-factorizations into a joint optimization framework, with approximate constraints on both word clusters matrices and clusters association matrices so as to allow proper diversity in knowledge transfer, and with another approximate constraint on class labels in source domains in order to handle noisy labels. An iterative algorithm is then proposed to solve the above problem, with its convergence verified theoretically and empirically. Extensive experimental results on various text datasets demonstrate the effectiveness of our algorithm, even with the presence of abundant state-of-the-art competitors.
Creating a Shared Reality with Robots

This paper outlines the system design, capabilities and potential applications of an Augmented Reality (AR) framework developed for Robot Operating System (ROS) powered robots. The goal of this framework is to enable high-level human-robot collaboration and interaction. It allows the users to visualize the robot's state in intuitive modalities overlaid onto the real world and interact with AR objects as a means of communication with the robot. Thereby creating a shared environment in which humans and robots can interact and collaborate.
Review Spam Detection Using Word Embeddings and Deep Neural Networks

Review spam (fake review) detection is increasingly important taking into consideration the rapid growth of internet purchases. Therefore, sophisticated spam filters must be designed to tackle the problem. Traditional machine learning algorithms use review content and other features to detect review spam. However, as demonstrated in related studies, the linguistic context of words may be of particular importance for text categorization. In order to enhance the performance of review spam detection, we propose a novel content-based approach that considers both bag-of-words and word context. More precisely, our approach utilizes n-grams and the skip-gram word embedding method to build a vector model. As a result, high-dimensional feature representation is generated. To handle the representation and classify the review spam accurately, a deep feed-forward neural network is used in the second step. To verify our approach, we use two hotel review datasets, including positive and negative reviews. We show that the proposed detection system outperforms other popular algorithms for review spam detection in terms of accuracy and area under ROC. Importantly, the system provides balanced performance on both classes, legitimate and spam, irrespective of review polarity.
Learning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs

Geert Heyman, Bregt Verreet, Ivan Vulić, Marie-Francine Moens. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Improving real-time CNN-based pupil detection through domain-specific data augmentation

Deep learning is a promising technique for real-world pupil detection. However, the small amount of available accurately-annotated data poses a challenge when training such networks. Here, we utilize non-challenging eye videos where algorithmic approaches perform virtually without errors to automatically generate a foundational data set containing subpixel pupil annotations. Then, we propose multiple domain-specific data augmentation methods to create unique training sets containing controlled distributions of pupil-detection challenges. The feasibility, convenience, and advantage of this approach is demonstrated by training a CNN with these datasets. The resulting network outperformed current methods in multiple publicly-available, realistic, and challenging datasets, despite being trained solely with the augmented eye images. This network also exhibited better generalization w.r.t. the latest state-of-the-art CNN: Whereas on datasets similar to training data, the nets displayed similar performance, on datasets unseen to both networks, ours outperformed the state-of-the-art by ≈27% in terms of detection rate.
SAPIENS

We present SAPIENS, a software architecture designed to support engineering of interactive systems featuring peripheral interaction in the context of smart environments. SAPIENS introduces dedicated components for user and device tracking, attention detection, priority management for devices, tasks, and notifications, context-awareness inference, user interruptibility prediction, and device interchangeability that can be instantiated at will according to the needs of the application. To implement these components effectively, SAPIENS employs event-based processing by reusing the core engine of a recently introduced software architecture, Euphoria (Schipor et al., 2019), that was specifically designed for engineering interactions in smart environments with heterogeneous I/O devices, and relies entirely on web standards, protocols, and open data-interchange formats, such as JavaScript, WebSockets, HTTP, and JSON. This inheritance makes SAPIENS flexible and adaptable to support implementation of diverse application scenarios for peripheral interaction and for a wide variety of smart environments, devices, platforms, data formats, and contexts of use. We present our design criteria for SAPIENS regarding (1) event handling techniques, (2) quality, (3) contextual, and (4) attention-related properties, and describe its components and dataflows that make SAPIENS a specialized software architecture for peripheral interaction scenarios. We also demonstrate SAPIENS with a practical application, inspired and adapted from Bakker's (2013) classical example for peripheral interaction, for which we provide an online simulation tool that researchers and practitioners can readily use to consult actual JavaScript code implementing the inner logic of selected components of our architecture as well as to observe live JSON messages exchanged by the various components of SAPIENS.
Multi-layer competitive-cooperative framework for performance enhancement of differential evolution

Differential evolution (DE) is recognized as one of the most powerful optimizers in the evolutionary algorithm (EA) family. Many DE variants were proposed in recent years, but significant differences in performances between them are hardly observed. Therefore, this paper suggests a multi-layer competitive-cooperative (MLCC) framework to facilitate the competition and cooperation of multiple DEs, which in turns, achieve a significant performance improvement. Unlike other multi-method strategies which adopt a multi-population based structure, with individuals only evolving in their corresponding subpopulations, MLCC implements a parallel structure with the entire population simultaneously monitored by multiple DEs assigned to their corresponding layers. An individual can store, utilize and update its evolution information in different layers based on an individual preference based layer selecting (IPLS) mechanism and a computational resource allocation bias (RAB) mechanism. In IPLS, individuals connect to only one favorite layer. While in RAB, high-quality solutions are evolved by considering all the layers. Thus DEs associated in the layers work in a competitive and cooperative manner. The proposed MLCC framework has been implemented on several highly competitive DEs. Experimental studies show that the MLCC variants significantly outperform the baseline DEs as well as several state-of-the-art and up-to-date DEs on CEC benchmark functions.
Transfer Learning for Named Entity Recognition in Financial and Biomedical Documents

Recent deep learning approaches have shown promising results for named entity recognition (NER). A reasonable assumption for training robust deep learning models is that a sufficient amount of high-quality annotated training data is available. However, in many real-world scenarios, labeled training data is scarcely present. In this paper we consider two use cases: generic entity extraction from financial and from biomedical documents. First, we have developed a character based model for NER in financial documents and a word and character based model with attention for NER in biomedical documents. Further, we have analyzed how transfer learning addresses the problem of limited training data in a target domain. We demonstrate through experiments that NER models trained on labeled data from a source domain can be used as base models and then be fine-tuned with few labeled data for recognition of different named entity classes in a target domain. We also witness an interest in language models to improve NER as a way of coping with limited labeled data. The current most successful language model is BERT. Because of its success in state-of-the-art models we integrate representations based on BERT in our biomedical NER model along with word and character information. The results are compared with a state-of-the-art model applied on a benchmarking biomedical corpus.
WSLLN:Weakly Supervised Natural Language Localization Networks

We propose weakly supervised language localization networks (WSLLN) to detect events in long, untrimmed videos given language queries.To learn the correspondence between visual segments and texts, most previous methods require temporal coordinates (start and end times) of events for training, which leads to high costs of annotation.WSLLN relieves the annotation burden by training with only video-sentence pairs without accessing to temporal locations of events.With a simple end-to-end structure, WSLLN measures segment-text consistency and conducts segment selection (conditioned on the text) simultaneously.Results from both are merged and optimized as a video-sentence matching problem.Experiments on ActivityNet Captions and DiDeMo demonstrate that WSLLN achieves state-of-the-art performance.
A System to Monitor Cyberbullying based on Message Classification and Social Network Analysis

Social media platforms like Twitter and Instagram face a surge in cyberbullying phenomena against young users and need to develop scalable computational methods to limit the negative consequences of this kind of abuse. Despite the number of approaches recently proposed in the Natural Language Processing (NLP) research area for detecting different forms of abusive language, the issue of identifying cyberbullying phenomena at scale is still an unsolved problem. This is because of the need to couple abusive language detection on textual message with network analysis, so that repeated attacks against the same person can be identified. In this paper, we present a system to monitor cyberbullying phenomena by combining message classification and social network analysis. We evaluate the classification module on a data set built on Instagram messages, and we describe the cyberbullying monitoring user interface.
Medical knowledge embedding based on recursive neural network for multi-disease diagnosis

The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic representation enables quantitative reasoning with statistical probability, it is difficult to utilize with machine learning models as they perform numerical operations. In contrast, knowledge embedding (i.e., high-dimensional and continuous vectors) is a feasible approach to complex reasoning that can not only retain the semantic information of knowledge, but also establish the quantifiable relationship among embeddings. In this paper, we propose a recursive neural knowledge network (RNKN), which combines medical knowledge based on first-order logic with a recursive neural network for multi-disease diagnosis. After the RNKN is efficiently trained using manually annotated Chinese Electronic Medical Records (CEMRs), diagnosis-oriented knowledge embeddings and weight matrixes are learned. The experimental results confirm that the diagnostic accuracy of the RNKN is superior to those of four machine learning models, four classical neural networks and Markov logic network. The results also demonstrate that the more explicit the evidence extracted from CEMRs, the better the performance. The RNKN gradually reveals the interpretation of knowledge embeddings as the number of training epochs increases.
2nd Global Enterprise Blockchain Benchmarking Study

The second edition of the Global Enterprise Blockchain Benchmarking Study provides new insights into the current state of the enterprise blockchain ecosystem. The study gathers survey data from 160 start-ups, established companies, central banks, and other public-sector institutions from 49 different countries worldwide. The empirical analysis specifically focuses on enterprise blockchain networks that have entered in production, drawing on data from a unique sample of 67 live networks.
Deep Visual Template-Free Form Parsing

The following topics are dealt with: learning (artificial intelligence); document image processing; feature extraction; text analysis; convolutional neural nets; image segmentation; handwritten character recognition; image classification; text detection; optical character recognition.