Reviewerly/exHarmony huggingface dataset:
Learn to Recover Visible Color for Video Surveillance in a Day

In silicon sensors, the interference between visible and near-infrared (NIR) signals is a crucial problem. For all-day video surveillance, commercial camera systems usually adopt NIR cut filter, and auxiliary NIR LED illumination to selectively block or enhance NIR signal according to the surrounding light conditions. This switching between the daytime and the nighttime mode inevitably involves mechanical parts, and thus requires frequent maintenance. Furthermore, images captured at nighttime mode are in shortage of chrominance, which might hinder human interpretation and high-level computer vision algorithms in succession. In this paper, we present a deep learning based approach that directly generates human-friendly, visible color for video surveillance in a day. To enable training, we capture well-aligned video pairs through a customized optical device and contribute a large-scale dataset, video surveillance in a day (VSIAD). We propose a novel multi-task deep network with state synchronization modules to better utilize texture and chrominance information. Our trained model generates high-quality visible color images and achieves state-of-the-art performance on multiple metrics as well as subjective judgment.
Multi-level Temporal Pyramid Network for Action Detection

Currently, one-stage frameworks have been widely applied for temporal action detection, but they still suffer from the challenge that the action instances span a wide range of time. The reason is that these one-stage detectors, e.g., Single Shot Multi-Box Detector (SSD), extract temporal features only applying a single-level layer for each head, which is not discriminative enough to perform classification and regression. In this paper, we propose a Multi-Level Temporal Pyramid Network (MLTPN) to improve the discrimination of the features. Specially, we first fuse the features from multiple layers with different temporal resolutions, to encode multi-layer temporal information. We then apply a multi-level feature pyramid architecture on the features to enhance their discriminative abilities. Finally, we design a simple yet effective feature fusion module to fuse the multi-level multi-scale features. By this means, the proposed MLTPN can learn rich and discriminative features for different action instances with different durations. We evaluate MLTPN on two challenging datasets: THUMOS’14 and Activitynet v1.3, and the experimental results show that MLTPN obtains competitive performance on Activitynet v1.3 and outperforms the state-of-the-art approaches on THUMOS’14 significantly.
Representation Based Meta-Learning for Few-Shot Spoken Intent Recognition

Spoken intent detection has become a popular approach to interface with various smart devices with ease. However, such systems are limited to the preset list of intents-terms or commands, which restricts the quick customization of personal devices to new intents. This paper presents a few-shot spoken intent classification approach with task-agnostic representations via meta-learning paradigm. Specifically, we leverage the popular representation-based meta-learning learning to build a task-agnostic representation of utterances, that then use a linear classifier for prediction. We evaluate three such approaches on our novel experimental protocol developed on two popular spoken intent classification datasets: Google Commands and the Fluent Speech Commands dataset. For a 5-shot (1-shot) classification of novel classes, the proposed framework provides an average classification accuracy of 88.6% (76.3%) on the Google Commands dataset, and 78.5% (64.2%) on the Fluent Speech Commands dataset. The performance is comparable to traditionally supervised classification models with abundant training samples.
A modified practical homodyne detector model for continuous-variable quantum key distribution: detailed security analysis and improvement by the phase-sensitive amplifier

Abstract The practical homodyne detector model of continuous-variable quantum key distribution models the inherent imperfections of the practical homodyne detector, namely the limited detection efficiency and the electronic noise, into trusted loss. However, the conventional practical homodyne detector model is valid only when both the imperfections of the practical homodyne detector are calibrated. In this paper, we show a modified practical homodyne detector model that can model the imperfections separately. The phase-sensitive amplifier (PSA) is further applied to compensate the imperfections of the practical homodyne detector. The feasibility of the modified practical homodyne detector model with the PSA is proved and the security analysis is provided in detail. Simulation results reveal that the PSA can be used to improve the performance of the modified practical homodyne detector model, and when the gain is infinitely high, the limited detection efficiency can be fully compensated.
BETULA: Numerically Stable CF-Trees for BIRCH Clustering

BIRCH clustering is a widely known approach for clustering, that has influenced much subsequent research and commercial products. The key contribution of BIRCH is the Clustering Feature tree (CF-Tree), which is a compressed representation of the input data. As new data arrives, the tree is eventually rebuilt to increase the compression. Afterward, the leaves of the tree are used for clustering. Because of the data compression, this method is very scalable. The idea has been adopted for example for k-means, data stream, and density-based clustering.
DR-Unet104 for Multimodal MRI Brain Tumor Segmentation

In this paper we propose a 2D deep residual Unet with 104 convolutional layers (DR-Unet104) for lesion segmentation in brain MRIs. We make multiple additions to the Unet architecture, including adding the ‘bottleneck’ residual block to the Unet encoder and adding dropout after each convolution block stack. We verified the effect of including the regularization of dropout with small rate (e.g. 0.2) on the architecture, and found a dropout of 0.2 improved the overall performance compared to no dropout, or a dropout of 0.5. We evaluated the proposed architecture as part of the Multimodal Brain Tumor Segmentation (BraTS) 2020 Challenge and compared our method to DeepLabV3+ with a ResNet-V2–152 backbone. We found the DR-Unet104 achieved a mean dice score coefficient of 0.8862, 0.6756 and 0.6721 for validation data, whole tumor, enhancing tumor and tumor core respectively, an overall improvement on 0.8770, 0.65242 and 0.68134 achieved by DeepLabV3+. Our method produced a final mean DSC of 0.8673, 0.7514 and 0.7983 on whole tumor, enhancing tumor and tumor core on the challenge’s testing data. We produce a competitive lesion segmentation architecture, despite only using 2D convolutions, having the added benefit that it can be used on lower power computers than a 3D architecture. The source code and trained model for this work is openly available at https://github.com/jordan-colman/DR-Unet104 .
The New DBpedia Release Cycle: Increasing Agility and Efficiency in Knowledge Extraction Workflows

Abstract Since its inception in 2007, DBpedia has been constantly releasing open data in RDF, extracted from various Wikimedia projects using a complex software system called the DBpedia Information Extraction Framework (DIEF). For the past 12 years, the software received a plethora of extensions by the community, which positively affected the size and data quality. Due to the increase in size and complexity, the release process was facing huge delays (from 12 to 17 months cycle), thus impacting the agility of the development. In this paper, we describe the new DBpedia release cycle including our innovative release workflow, which allows development teams (in particular those who publish large, open data) to implement agile, cost-efficient processes and scale up productivity. The DBpedia release workflow has been re-engineered, its new primary focus is on productivity and agility , to address the challenges of size and complexity. At the same time, quality is assured by implementing a comprehensive testing methodology. We run an experimental evaluation and argue that the implemented measures increase agility and allow for cost-effective quality-control and debugging and thus achieve a higher level of maintainability. As a result, DBpedia now publishes regular (i.e. monthly) releases with over 21 billion triples with minimal publishing effort .
Iterative Compression of End-to-End ASR Model Using AutoML

Increasing demand for on-device Automatic Speech Recognition (ASR) systems has resulted in renewed interests in developing automatic model compression techniques. Past research have shown that AutoML-based Low Rank Factorization (LRF) technique, when applied to an end-to-end Encoder-Attention-Decoder style ASR model, can achieve a speedup of up to 3.7x, outperforming laborious manual rank-selection approaches. However, we show that current AutoML-based search techniques only work up to a certain compression level, beyond which they fail to produce compressed models with acceptable word error rates (WER). In this work, we propose an iterative AutoML-based LRF approach that achieves over 5x compression without degrading the WER, thereby advancing the state-of-the-art in ASR compression.
Towards GFO 2.0: Architecture, Modules and Applications

The General Formal Ontology (GFO) is a top-level ontology that has been developed by the Onto-Med Research Group since the early 2000s. Since that time several new theoretical results have been achieved as well as numerous projects have utilized the ontology, especially in complex domains such as bioinformatics and medical computer science. This leads to the need for an up-to-date overview of GFO and access to its applications. This paper represents the first step towards introducing the GFO 2.0 framework, which aims at the integration of the work that is already present, but scattered in various publications, and its provision as a ready-to-use and reusable framework. For this purpose we summarize key features of GFO so far, outline a novel modular architecture and survey first modules for GFO 2.0, linking to applications. Finally, a rigorous and systematic development process is indicated.
DIGITAL SKILLS OF A MODERN SCHOOLCHILD AND OPPORTUNITIES FOR THEIR FORMATION IN THE SCHOOL INFORMATICS COURSE

The article analyzes some of the problems that arose during the transition to learning in self-isolation mode, including the lack of the required level of skills for the majority of schoolchildren to use e-learning and distance learning technologies. The European model "The Digital Competence Framework for Citizens" and the levels of digital skills allocated within this model — Foundation, Intermediate, Advanced and Highly Specialized, are considered. The requirements for the ICT competencies of schoolchildren, fixed in the FSES of General Education, are presented. The role and capabilities of the school informatics course in the formation of students' digital skills are shown. The directions of work on the effective formation of digital skills of schoolchildren are indicated.
Could a Decentralized Onsite Earthquake Early Warning System Help in Mitigating Seismic Risk in Northeastern Italy? The Case of the 1976 Ms 6.5 Friuli Earthquake

Abstract In May 1976, a devastating earthquake of magnitude Ms 6.5 occurred in Friuli, Italy, resulting in 976 deaths, 2000 injured, and 60,000 homeless. It is notable that, at the time of the earthquake, only one station was installed in the affected region. The resulting lack of information, combined with a dearth of mitigation planning for responding to such events, lead to a clear picture of the impact of the disaster being available only after a few days. This region is now covered by nearly 100 seismological and strong-motion stations operating in real time. Furthermore, 30 average-cost strong-motion stations have been recently added, with the goals of improving the density of real-time ground-motion observations and measuring the level of shaking recorded at selected buildings. The final goal is to allow rapid impact estimations to be made to improve the response of civil protection authorities. Today, considering the higher density seismological network, new efforts in terms of the implementation and testing of earthquake early warning systems as a possible tool for mitigating seismic risk are certainly worthwhile. In this article, we show the results obtained by analyzing in playback and using an algorithm for decentralized onsite earthquake early warning, broadband synthetic strong-motion data calculated at 18 of the stations installed in the region, while considering the magnitude and location of the 1976 Friuli earthquake. The analysis shows that the anisotropy of the lead times is related not only to the finite nature of the source but also to the slip distribution. A reduction of 10% of injured persons appears to be possible if appropriate mitigating actions are employed, such as the development of efficient automatic procedures that improve the safety of strategic industrial facilities.
Model-Driven Development of Web APIs to Access Integrated Tabular Open Data

More and more governments around the world are publishing tabular open data, mainly in formats such as CSV or XLS(X).These datasets are mostly individually published, i.e. each publisher exposes its data on the Web without considering potential relationships with other datasets (from its own or from other publishers).As a result, reusing several open datasets together is not a trivial task, thus requiring mechanisms that allow data consumers (as software developers or data scientists) to integrate and access tabular open data published on the Web.In this paper, we propose a model-driven approach to automatically generate Web APIs that homogeneously access multiple integrated tabular open datasets.This work focuses on data that can be integrated by means of join and union operations.As a first step, our approach detects unionable and joinable tabular open data by using a table similarity measure based on word embeddings.Then, an APIfication process is developed to create APIs that access the previously integrated datasets through a single endpoint.A running example is presented throughout the article, as well as a set of experiments for performance evaluation to show the feasibility of our approach.
A pedestrian introduction to coherent and squeezed states

This review is intended for readers who want to have a quick understanding on the theoretical underpinnings of coherent states and squeezed states which are conventionally generated from the prototype harmonic oscillator but not always restricting to it. Noting that the treatments of building up such states have a long history, we collected the important ingredients and reproduced them from a fresh perspective but refrained from delving into detailed derivation of each topic. By no means we claim a comprehensive presentation of the subject but have only tried to re-capture some of the essential results and pointed out their inter-connectivity.
Reachability in Infinite-Dimensional Unital Open Quantum Systems with Switchable GKS–Lindblad Generators

In quantum systems theory one of the fundamental problems boils down to: given an initial state, which final states can be reached by the dynamic system in question. Here we consider infinite-dimensional open quantum dynamical systems following a unital Kossakowski–Lindblad master equation extended by controls. More precisely, their time evolution shall be governed by an inevitable potentially unbounded Hamiltonian drift term H 0 , finitely many bounded control Hamiltonians H j allowing for (at least) piecewise constant control amplitudes [Formula: see text] plus a bang-bang (i.e., on-off) switchable noise term Г V in Kossakowski–Lindblad form. Generalizing standard majorization results from finite to infinite dimensions, we show that such bilinear quantum control systems allow to approximately reach any target state majorized by the initial one as up to now it only has been known in finite dimensional analogues. The proof of the result is currently limited to the bounded control Hamiltonians H j and for noise terms Г V with compact normal V.
Feature-Free Explainable Data Mining in SAR Images Using Latent Dirichlet Allocation

In this article, we propose a promising approach for the application-oriented content classification of spaceborne radar imagery that presents an interesting alternative to popular current machine learning algorithms. In the following, we consider the problem of unsupervised feature-free satellite image classification with already known classes as an explainable data mining problem for regions with no prior information. Three important issues are addressed here: explainability, feature independence, and unsupervision. There is an increasing demand toward explainable machine learning models as they strive to meet the “right to explanation.” The importance of feature-free classification stems from the problem that different classification outcomes are obtained from using different features and the complexity of computing sophisticated image primitive features. Developing unsupervised discovery techniques helps overcome the limitations in object discovery due to the lack of labeled data and the dependence on features. In this article, we demonstrate the applicability of a latent Dirichlet allocation (LDA) model, one of the most established unsupervised probabilistic methods, in discovering the latent structure of synthetic aperture radar data. The idea is to use LDA as an explainable data mining tool to discover scientifically explainable semantic relations. The suitability of the approach as an explainable model is discussed and interpretable topic representation maps are produced, which practically demonstrate the idea of “interpretability” in an explainable machine learning paradigm. LDA discovers the latent structures in the data as a set of topics. We create the interpretable visualizations of the data utilizing these topics and compute the topic distributions for each land-cover class. Our results show that each class has a distinct topic distribution that represents that particular class. Then these classes can be grouped based on their similarity of topic composition. Both the topic composition and grouping are explainable by domain experts.
Report on the First Knowledge Graph Reasoning Challenge 2018

A new challenge for knowledge graph reasoning started in 2018. Deep learning has promoted the application of artificial intelligence (AI) techniques to a wide variety of social problems. Accordingly, being able to explain the reason for an AI decision is becoming important to ensure the secure and safe use of AI techniques. Thus, we, the Special Interest Group on Semantic Web and Ontology of the Japanese Society for AI, organized a challenge calling for techniques that reason and/or estimate which characters are criminals while providing a reasonable explanation based on an open knowledge graph of a well-known Sherlock Holmes mystery story. This paper presents a summary report of the first challenge held in 2018, including the knowledge graph construction, the techniques proposed for reasoning and/or estimation, the evaluation metrics, and the results. The first prize went to an approach that formalized the problem as a constraint satisfaction problem and solved it using a lightweight formal method; the second prize went to an approach that used SPARQL and rules; the best resource prize went to a submission that constructed word embedding of characters from all sentences of Sherlock Holmes novels; and the best idea prize went to a discussion multi-agents model. We conclude this paper with the plans and issues for the next challenge in 2019.
Ghmerti at SemEval-2019 Task 6: A Deep Word- and Character-based Approach to Offensive Language Identification

This paper presents the models submitted by Ghmerti team for subtasks A and B of the OffensEval shared task at SemEval 2019. OffensEval addresses the problem of identifying and categorizing offensive language in social media in three subtasks; whether or not a content is offensive (subtask A), whether it is targeted (subtask B) towards an individual, a group, or other entities (subtask C). The proposed approach includes character-level Convolutional Neural Network, word-level Recurrent Neural Network, and some preprocessing. The performance achieved by the proposed model for subtask A is 77.93% macro-averaged F1-score.
Semantic Foreground Inpainting From Weak Supervision

Semantic scene understanding is an essential task for self-driving vehicles and mobile robots. In our work, we aim to estimate a semantic segmentation map, in which the foreground objects are removed and semantically inpainted with background classes, from a single RGB image. This semantic foreground inpainting task is performed by a single-stage convolutional neural network (CNN) that contains our novel max-pooling as inpainting (MPI) module, which is trained with weak supervision, i.e., it does not require manual background annotations for the foreground regions to be inpainted. Our approach is inherently more efficient than the previous two-stage state-of-the-art method, and outperforms it by a margin of 3% IoU for the inpainted foreground regions on Cityscapes. The performance margin increases to 6% IoU, when tested on the unseen KITTI dataset. The code and the manually annotated datasets for testing are shared with the research community at https://github.com/Chenyang-Lu/semantic-foreground-inpainting.
A polynomial-time algorithm for learning nonparametric causal graphs

We establish finite-sample guarantees for a polynomial-time algorithm for learning a nonlinear, nonparametric directed acyclic graphical (DAG) model from data. The analysis is model-free and does not assume linearity, additivity, independent noise, or faithfulness. Instead, we impose a condition on the residual variances that is closely related to previous work on linear models with equal variances. Compared to an optimal algorithm with oracle knowledge of the variable ordering, the additional cost of the algorithm is linear in the dimension $d$ and the number of samples $n$. Finally, we compare the proposed algorithm to existing approaches in a simulation study.
Neural Conversational QA: Learning to Reason vs Exploiting Patterns

Neural Conversational QA tasks like ShARC require systems to answer questions based on the contents of a given passage.On studying recent state-of-the-art models on the ShARC QA task, we found indications that the models learn spurious clues/patterns in the dataset.Furthermore, we show that a heuristic-based program designed to exploit these patterns can have performance comparable to that of the neural models.In this paper we share our findings about four types of patterns found in the ShARC corpus and describe how neural models exploit them.Motivated by the aforementioned findings, we create and share a modified dataset that has fewer spurious patterns, consequently allowing models to learn better.
Mimic and Conquer: Heterogeneous Tree Structure Distillation for Syntactic NLP

Syntax has been shown useful for various NLP tasks, while existing work mostly encodes singleton syntactic tree using one hierarchical neural network.In this paper, we investigate a simple and effective method, Knowledge Distillation, to integrate heterogeneous structure knowledge into a unified sequential LSTM encoder.Experimental results on four typical syntax-dependent tasks show that our method outperforms tree encoders by effectively integrating rich heterogeneous structure syntax, meanwhile reducing error propagation, and also outperforms ensemble methods, in terms of both the efficiency and accuracy.