OpenRaiser/Intern-Atlas huggingface dataset:
ProLFA: Representative Prototype Selection for Local Feature Aggregation
Venue: arXiv cs.CV 2019
Year: 2019

Given a set of hand-crafted local features, acquiring a global representation via aggregation is a promising technique to boost computational efficiency and improve task performance. Existing feature aggregation (FA) approaches, including Bag of Words and Fisher Vectors, usually fail to capture the desired information due to their pipeline mode. In this paper, we propose a generic formulation to provide a systematical solution (named ProLFA) to aggregate local descriptors. It is capable of producing compact yet interpretable representations by selecting representative prototypes from numerous descriptors, under relaxed exclusivity constraint. Meanwhile, to strengthen the discriminability of the aggregated representation, we rationally enforce the domain-invariant projection of bundled descriptors along a task-specific direction. Furthermore, ProLFA is also provided with a powerful generalization ability to deal flexibly with the semi-supervised and fully supervised scenarios in local feature aggregation. Experimental results on various descriptors and tasks demonstrate that the proposed ProLFA is considerably superior over currently available alternatives about feature aggregation.
Self-enhancement of automatic tunnel accident detection (TAD) on CCTV by AI deep-learning
Venue: arXiv cs.CV 2019
Year: 2019

The deep-learning-based tunnel accident detection (TAD) system (Lee 2019) has installed a system capable of monitoring 9 CCTVs at XX site in November, 2018. The initial deep-learning training was started by studying 70,914 labeled images and label data. However, sunlight, the tail light of a vehicle, and the warning light of the working vehicle were recognized as a fire, and many pedestrians were detected in the lane of the tunnel or a black elongated black object. To solve these problems, as shown in Fig. 1, the false detection data detected in the field were trained with labeled data and reapplied in the field. As a result, false detection of pedestrians and fire could be significantly reduced.
An application of a deep learning algorithm for automatic detection of unexpected accidents under bad CCTV monitoring conditions in tunnels
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, Object Detection and Tracking System (ODTS) in combination with a well-known deep learning network, Faster Regional Convolution Neural Network (Faster R-CNN), for Object Detection and Conventional Object Tracking algorithm will be introduced and applied for automatic detection and monitoring of unexpected events on CCTVs in tunnels, which are likely to (1) Wrong-Way Driving (WWD), (2) Stop, (3) Person out of vehicle in tunnel (4) Fire. ODTS accepts a video frame in time as an input to obtain Bounding Box (BBox) results by Object Detection and compares the BBoxs of the current and previous video frames to assign a unique ID number to each moving and detected object. This system makes it possible to track a moving object in time, which is not usual to be achieved in conventional object detection frameworks. A deep learning model in ODTS was trained with a dataset of event images in tunnels to Average Precision (AP) values of 0.8479, 0.7161 and 0.9085 for target objects: Car, Person, and Fire, respectively. Then, based on trained deep learning model, the ODTS based Tunnel CCTV Accident Detection System was tested using four accident videos which including each accident. As a result, the system can detect all accidents within 10 seconds. The more important point is that the detection capacity of ODTS could be enhanced automatically without any changes in the program codes as the training dataset becomes rich.
Shallow Art: Art Extension Through Simple Machine Learning
Venue: arXiv cs.CV 2019
Year: 2019

Shallow Art presents, implements, and tests the use of simple single-output classification and regression models for the purpose of art generation. Various machine learning algorithms are trained on collections of computer generated images, artworks from Vincent van Gogh, and artworks from Rembrandt van Rijn. These models are then provided half of an image and asked to complete the missing side. The resulting images are displayed, and we explore implications for computational creativity.
DCT Maps: Compact Differentiable Lidar Maps Based on the Cosine Transform
Venue: arXiv cs.CV 2019
Year: 2019

Most robot mapping techniques for lidar sensors tessellate the environment into pixels or voxels and assume uniformity of the environment within them. Although intuitive, this representation entails disadvantages: The resulting grid maps exhibit aliasing effects and are not differentiable. In the present paper, we address these drawbacks by introducing a novel mapping technique that does neither rely on tessellation nor on the assumption of piecewise uniformity of the space, without increasing memory requirements. Instead of representing the map in the position domain, we store the map parameters in the discrete frequency domain and leverage the continuous extension of the inverse discrete cosine transform to convert them to a continuously differentiable scalar field in the position domain, which we call DCT map. A DCT map assigns to each point in space a lidar decay rate, which models the local permeability of the space for laser rays. In this way, the map can describe objects of different laser permeabilities, from completely opaque to completely transparent. DCT maps represent lidar measurements significantly more accurate than grid maps, Gaussian process occupancy maps, and Hilbert maps, all with the same memory requirements, as demonstrated in our real-world experiments.
Speech Emotion Recognition via Contrastive Loss under Siamese Networks
Venue: arXiv cs.CV 2019
Year: 2019

Speech emotion recognition is an important aspect of human-computer interaction. Prior work proposes various end-to-end models to improve the classification performance. However, most of them rely on the cross-entropy loss together with softmax as the supervision component, which does not explicitly encourage discriminative learning of features. In this paper, we introduce the contrastive loss function to encourage intra-class compactness and inter-class separability between learnable features. Furthermore, multiple feature selection methods and pairwise sample selection methods are evaluated. To verify the performance of the proposed system, we conduct experiments on The Interactive Emotional Dyadic Motion Capture (IEMOCAP) database, a common evaluation corpus. Experimental results reveal the advantages of the proposed method, which reaches 62.19% in the weighted accuracy and 63.21% in the unweighted accuracy. It outperforms the baseline system that is optimized without the contrastive loss function with 1.14% and 2.55% in the weighted accuracy and the unweighted accuracy, respectively.
ÚFAL MRPipe at MRP 2019: UDPipe Goes Semantic in the Meaning Representation Parsing Shared Task
Venue: arXiv cs.CL 2019
Year: 2019

We present a system description of our contribution to the CoNLL 2019 shared task, Cross-Framework Meaning Representation Parsing (MRP 2019). The proposed architecture is our first attempt towards a semantic parsing extension of the UDPipe 2.0, a lemmatization, POS tagging and dependency parsing pipeline. For the MRP 2019, which features five formally and linguistically different approaches to meaning representation (DM, PSD, EDS, UCCA and AMR), we propose a uniform, language and framework agnostic graph-to-graph neural network architecture. Without any knowledge about the graph structure, and specifically without any linguistically or framework motivated features, our system implicitly models the meaning representation graphs. After fixing a human error (we used earlier incorrect version of provided test set analyses), our submission would score third in the competition evaluation. The source code of our system is available at https://github.com/ufal/mrpipe-conll2019.
Development and Implementation of a Dashboard for Diabetes Care Management in OpenMRS
Venue: arXiv cs.HC 2019
Year: 2019

A clinical dashboard for a patient's diabetes condition helps physicians to make better decisions based on readily available information. OpenMRS is a widely used open-source electronic health records system but does not provide a disease-specific dashboard. This project implemented a dashboard for displaying all diabetes-related lab measures at one place, when a physician accesses a patient record in OpenMRS. It summarizes a list of diabetes-related clinical measures through an intuitive, chart-based, customizable user experience. Gauge charts are used to display the most important lab values for Glucose, Renal Function, and Lipid Profile tests. Data Tables are used to display data of the lab values from the past and current visit in the table, including the ability to search for a specific visit date. Interactive line charts are used to display the trends of lab measures. Diabetes Dashboard may help physicians to make quicker decisions through this snapshot view. We took data for a few patients and demonstrated this to clinicians as a proof of concept, without performing a full-fledged user evaluation. Future work involves integrating this dashboard with clinical practice guidelines and alerting when measures are outside the guidelines.
Machine Translation from Natural Language to Code using Long-Short Term Memory
Venue: arXiv cs.CL 2019
Year: 2019

Making computer programming language more understandable and easy for the human is a longstanding problem. From assembly language to present day's object-oriented programming, concepts came to make programming easier so that a programmer can focus on the logic and the architecture rather than the code and language itself. To go a step further in this journey of removing human-computer language barrier, this paper proposes machine learning approach using Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) to convert human language into programming language code. The programmer will write expressions for codes in layman's language, and the machine learning model will translate it to the targeted programming language. The proposed approach yields result with 74.40% accuracy. This can be further improved by incorporating additional techniques, which are also discussed in this paper.
A comparable study: Intrinsic difficulties of practical plant diagnosis from wide-angle images
Venue: arXiv cs.CV 2019
Year: 2019

Practical automated detection and diagnosis of plant disease from wide-angle images (i.e. in-field images containing multiple leaves using a fixed-position camera) is a very important application for large-scale farm management, in view of the need to ensure global food security. However, developing automated systems for disease diagnosis is often difficult, because labeling a reliable wide-angle disease dataset from actual field images is very laborious. In addition, the potential similarities between the training and test data lead to a serious problem of model overfitting. In this paper, we investigate changes in performance when applying disease diagnosis systems to different scenarios involving wide-angle cucumber test data captured on real farms, and propose an effective diagnostic strategy. We show that leading object recognition techniques such as SSD and Faster R-CNN achieve excellent end-to-end disease diagnostic performance only for a test dataset that is collected from the same population as the training dataset (with F1-score of 81.5% - 84.1% for diagnosed cases of disease), but their performance markedly deteriorates for a completely different test dataset (with F1-score of 4.4 - 6.2%). In contrast, our proposed two-stage systems using independent leaf detection and leaf diagnosis stages attain a promising disease diagnostic performance that is more than six times higher than end-to-end systems (with F1-score of 33.4 - 38.9%) on an unseen target dataset. We also confirm the efficiency of our proposal based on visual assessment, concluding that a two-stage model is a suitable and reasonable choice for practical applications.
Descriptive Dimensionality and Its Characterization of MDL-based Learning and Change Detection
Venue: arXiv cs.LG 2019
Year: 2019

This paper introduces a new notion of dimensionality of probabilistic models from an information-theoretic view point. We call it the "descriptive dimension"(Ddim). We show that Ddim coincides with the number of independent parameters for the parametric class, and can further be extended to real-valued dimensionality when a number of models are mixed. The paper then derives the rate of convergence of the MDL (Minimum Description Length) learning algorithm which outputs a normalized maximum likelihood (NML) distribution with model of the shortest NML codelength. The paper proves that the rate is governed by Ddim. The paper also derives error probabilities of the MDL-based test for multiple model change detection. It proves that they are also governed by Ddim. Through the analysis, we demonstrate that Ddim is an intrinsic quantity which characterizes the performance of the MDL-based learning and change detection.
Metric Classification Network in Actual Face Recognition Scene
Venue: arXiv cs.CV 2019
Year: 2019

In order to make facial features more discriminative, some new models have recently been proposed. However, almost all of these models use the traditional face verification method, where the cosine operation is performed using the features of the bottleneck layer output. However, each of these models needs to change a threshold each time it is operated on a different test set. This is very inappropriate for application in real-world scenarios. In this paper, we train a validation classifier to normalize the decision threshold, which means that the result can be obtained directly without replacing the threshold. We refer to our model as validation classifier, which achieves best result on the structure consisting of one convolution layer and six fully connected layers. To test our approach, we conduct extensive experiments on Labeled Face in the Wild (LFW) and Youtube Faces (YTF), and the relative error reduction is 25.37% and 26.60% than traditional method respectively. These experiments confirm the effectiveness of validation classifier on face recognition task.
Non-Rectangular Convolutions and (Sub-)Cadences with Three Elements
Venue: arXiv cs.DS 2019
Year: 2019

The discrete acyclic convolution computes the 2n-1 sums sum_{i+j=k; (i,j) in [0,1,2,...,n-1]^2} (a_i b_j) in O(n log n) time. By using suitable offsets and setting some of the variables to zero, this method provides a tool to calculate all non-zero sums sum_{i+j=k; (i,j) in (P cap Z^2)} (a_i b_j) in a rectangle P with perimeter p in O(p log p) time. This paper extends this geometric interpretation in order to allow arbitrary convex polygons P with k vertices and perimeter p. Also, this extended algorithm only needs O(k + p(log p)^2 log k) time. Additionally, this paper presents fast algorithms for counting sub-cadences and cadences with 3 elements using this extended method.
Open Access -- Towards a non-normative and systematic understanding
Venue: arXiv cs.DL 2019
Year: 2019

The term Open Access not only describes a certain model of scholarly publishing -- namely in digital format freely accessible to readers -- but often also implies that free availability of research results is desirable, and hence has a normative character. Together with the large variety of presently used definitions of different Open Access types, this normativity hinders a systematic investigation of the development of open availability of scholarly literature. In this paper, we propose a non-normative definition of Open Access and its usage as a neutral, descriptive term in bibliometric studies and research on science. To this end, we first specify what normative figures are commonly associated with the term Open Access and then develop a neutral definition. We further identify distinguishing characteristics of openly accessible literature, called dimensions, and derive a classification scheme into Open Access categories based on these dimensions. Additionally, we present an operationalisation method to assign scientific publications to the respective categories in practice. Here, we describe useful data sources, which can be employed to gather the information needed for the classification of scholarly works according to the presented classification scheme.
A Simple Dynamic Learning Rate Tuning Algorithm For Automated Training of DNNs
Venue: arXiv cs.LG 2019
Year: 2019

Training neural networks on image datasets generally require extensive experimentation to find the optimal learning rate regime. Especially, for the cases of adversarial training or for training a newly synthesized model, one would not know the best learning rate regime beforehand. We propose an automated algorithm for determining the learning rate trajectory, that works across datasets and models for both natural and adversarial training, without requiring any dataset/model specific tuning. It is a stand-alone, parameterless, adaptive approach with no computational overhead. We theoretically discuss the algorithm's convergence behavior. We empirically validate our algorithm extensively. Our results show that our proposed approach \emph{consistently} achieves top-level accuracy compared to SOTA baselines in the literature in natural as well as adversarial training.
Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection
Venue: arXiv cs.CL 2019
Year: 2019

Event detection (ED), a sub-task of event extraction, involves identifying triggers and categorizing event mentions. Existing methods primarily rely upon supervised learning and require large-scale labeled event datasets which are unfortunately not readily available in many real-life applications. In this paper, we consider and reformulate the ED task with limited labeled data as a Few-Shot Learning problem. We propose a Dynamic-Memory-Based Prototypical Network (DMB-PN), which exploits Dynamic Memory Network (DMN) to not only learn better prototypes for event types, but also produce more robust sentence encodings for event mentions. Differing from vanilla prototypical networks simply computing event prototypes by averaging, which only consume event mentions once, our model is more robust and is capable of distilling contextual information from event mentions for multiple times due to the multi-hop mechanism of DMNs. The experiments show that DMB-PN not only deals with sample scarcity better than a series of baseline models but also performs more robustly when the variety of event types is relatively large and the instance quantity is extremely small.
Learning to Localize Temporal Events in Large-scale Video Data
Venue: arXiv cs.CV 2019
Year: 2019

We address temporal localization of events in large-scale video data, in the context of the Youtube-8M Segments dataset. This emerging field within video recognition can enable applications to identify the precise time a specified event occurs in a video, which has broad implications for video search. To address this we present two separate approaches: (1) a gradient boosted decision tree model on a crafted dataset and (2) a combination of deep learning models based on frame-level data, video-level data, and a localization model. The combinations of these two approaches achieved 5th place in the 3rd Youtube-8M video recognition challenge.
Rare Event Simulation for non-Markovian repairable Fault Trees
Venue: arXiv cs.DC 2019
Year: 2019

Dynamic Fault Trees (DFT) are widely adopted in industry to assess the dependability of safety-critical equipment. Since many systems are too large to be studied numerically, DFTs dependability is often analysed using Monte Carlo simulation. A bottleneck here is that many simulation samples are required in the case of rare events, e.g. in highly reliable systems where components fail seldomly. Rare Event Simulation (RES) provides techniques to reduce the number of samples in the case of rare events. We present a RES technique based on importance splitting, to study failures in highly reliable DFTs. Whereas RES usually requires meta-information from an expert, our method is fully automatic: by cleverly exploiting the fault tree structure we extract the so-called importance function. We handle DFTs with Markovian and non-Markovian failure and repair distributions (for which no numerical methods exist) and show the efficiency of our approach on several case studies.
Breaking an image encryption scheme based on Arnold map and Lucas series
Venue: arXiv cs.CR 2019
Year: 2019

Fairly recently, a novel image encryption based on Arnold scrambling and Lucas series has been proposed in the literature. The scheme design is based on permutation-substitution operations, where Arnold map is used to permute pixels for some T rounds, and Lucas sequence is used to mask the image and substitute pixel's values. The authors of the cryptosystem have claimed, after several statistical analyses, that their system is "with high efficiency" and resists chosen and known plaintext attacks. Negatively, in this paper we showed the opposite. The key space of the scheme under study could be reduced considerably after our equivalent keys analysis, and thus the system is breakable under reasonable brute force attack. After all, the design of the scheme has several weaknesses that make it weak against chosen and known plaintext attacks. Consequently, we do not recommend the use of this system for any cryptographic concern or security purpose.
Selective Lambda Lifting
Venue: arXiv cs.PL 2019
Year: 2019

Lambda lifting is a well-known transformation, traditionally employed for compiling functional programs to supercombinators. However, more recent abstract machines for functional languages like OCaml and Haskell tend to do closure conversion instead for direct access to the environment, so lambda lifting is no longer necessary to generate machine code. We propose to revisit selective lambda lifting in this context as an optimising code generation strategy and conceive heuristics to identify beneficial lifting opportunities. We give a static analysis for estimating impact on heap allocations of a lifting decision. Performance measurements of our implementation within the Glasgow Haskell Compiler on a large corpus of Haskell benchmarks suggest modest speedups.
What Smartphones, Ethnomethodology, and Bystander Inaccessibility Can Teach Us About Better Design?
Venue: arXiv cs.HC 2019
Year: 2019

Smartphones, the ubiquitous mobile screens now normal parts of everyday social situations, have created a kind of ongoing natural experiment for social scientists. According to Garfinkel's ethnomethodology social action gets its meaning not only from its content but also through its context. Mobility, small screen size, and the habitual way of using smartphones ensure that, while offering the biggest variety of activities for the user, in comparison to other everyday items, smartphones offer the least cues to bystanders on what the user is actually doing and how long it might take. This 'bystander inaccessibility' handicaps shared understanding of the social context that the user and collocated others find themselves in. Added considerations and interactive effort in managing the situation is therefore required. Future design needs to relate to this basic building block of collocated interaction to not be met with discontent.
Implementing choreography extraction
Venue: arXiv cs.PL 2019
Year: 2019

Choreography extraction deals with the generation of a choreography (a global description of communication behaviour) from a set of local process behaviours. In this work, we implement a previously proposed theory for extraction and show that, in spite of its theoretical exponential complexity, it is usable in practice. We discuss the data structures needed for an efficient implementation, introduce some optimizations, and perform a systematic practical evaluation.