Reviewerly/exHarmony huggingface dataset:
Calibration of Axial Fisheye Cameras Through Generic Virtual Central Models

Fisheye cameras are notoriously hard to calibrate using traditional plane-based methods. This paper proposes a new calibration method for large field of view cameras. Similarly to planar calibration, it relies on multiple images of a planar calibration grid with dense correspondences, typically obtained using structured light. By relying on the grids themselves instead of the distorted image plane, we can build a rectilinear Generic Virtual Central (GVC) camera. Instead of relying on a single GVC camera, our method proposes a selection of multiple GVC cameras which can cover any field of view and be trivially aligned to provide a very accurate generic central model. We demonstrate that this approach can directly model axial cameras, assuming the distortion center is located on the camera axis. Experimental validation is provided on both synthetic and real fisheye cameras featuring up to a 280° field of view. To our knowledge, this is one of the only practical methods to calibrate axial cameras.
Evaluating Artificial Neural Networks and Quantum Computing for Mechanics

Abstract The popularization of Machine Learning (ML) and the advent of Noisy Intermediate‐Scale Quantum (NISQ) devices for Quantum Computing (QC) sparked new inspiration for the search for techniques reducing computation time in mechanics. We evaluate artificial neural networks (ANNs) as candidates for creating computationally fast surrogate models for otherwise time‐consuming simulations, using a multiscale and multiphase model describing processes in the human liver. We also give a short overview of interesting quantum‐enhanced algorithms capable of reducing computational cost in parts of complex simulations.
Towards Privacy-preserving Recommender System with Blockchains

Data tampering is one of the most intriguing personal information security concerning issues in online business portals. For various individual or business purposes, clients need to share their personal information with these online business portals. Upon taking conveniences from this sharing of information about an individual, online business sites accumulate client data including client’s most sensitive information for running different data analysis without taking the clients’ authorization. In a view to proposing suggestions, data analysis may need to be done in the online business portals. A recommender system or framework creates an automated personalization on a rundown of items based on the users’ preference of searching any product over the portal. These days, the recommender system or framework is the part and parcel to the online marketing and business portals. However, secure control of client information is missing to some extent in such systems. Blockchain technology guarantees security in data manipulation for the clients in these online portals since it is a secure distributed ledger for storing data transaction. This paper presents a privacy-preserving or privacy-securing platform for recommender framework or system utilizing blockchain technology. The distributed ledger attribute of blockchain gives any client a verified domain where information is utilized for analysis with his/her required consents. Under this platform, clients get rewards (i.e., points, discounts) from the proposed online based company for sharing their information to figure out and propose relevant suggestions.
GF-Net: Improving machine reading comprehension with feature gates

Machine reading comprehension (MRC) is a field of question-answering in which computers understand given passages and answer related questions. Several previous models have tried to combine the use of linguistic and word embedding features to improve the performance of MRC; however, they could not obtain successful results because of feature interference problems caused by simple concatenation of the two. To resolve these problems, a machine reading comprehension model called gated feature network (GF-Net) is proposed in which linguistic features are selectively used according to their roles in the process of answer selection. In the GF-Net, the weights of the linguistic features are automatically controlled through gate mechanisms called feature gates. In the experiments with Stanford Question Answering Dataset SQuAD, the MRC models with feature gates showed a 0.67%p higher average of exact match (EM) and 0.64%p higher average of F1-score than models without feature gates. In addition, the GF-Net outperformed the previous MRC models to which feature gates were added. Based on these experimental results, it is concluded that the gate mechanism can contribute to an improvement in the performance of MRC models and the architecture of the GF-Net is suitable for the task of MRC.
Accelerating API-Based Program Synthesis via API Usage Pattern Mining

Program Synthesis is an exciting topic in software engineering which aims to generate programs satisfying user intent automatically. Although different approaches have been proposed in program synthesis, only small or domain-specific programs can be generated in practice, the main obstacle of which lies in the intractability of program space. With the rapid growth of reusable libraries, component-based synthesis provides a promising way, such as synthesizing Java programs that are only composed of APIs. However, the efficiency of searching for proper solutions for complex tasks is still a challenge. In certain scenarios, some API methods are frequently called together. The usage of these API methods always follows some usage patterns. Incorporating the information about API usage patterns will help to accelerate the speed of program synthesis. However, state-of-the-art synthesis tools do not capture the inherent relationships between API methods. Aiming at this problem, we propose a novel approach to accelerate the speed of API-based program synthesis via API usage pattern mining. It is a general approach that can be applied to any approach of API-based synthesis. We first collect open source projects of high quality from the Internet and use an off-the-shelf API-usage-pattern-mining tool to mine API usage patterns from these code snippets. We use two strategies to incorporate the information about API usage patterns with program synthesis, and either strategy can improve the efficiency of program synthesis. We evaluate our approach on 20 real programming tasks, which shows that our approach can accelerate the speed of program synthesis by 86% compared to the baseline.
Hematoxylin and Eosin (H&amp;E) Stained Liver Portal Area Segmentation Using Multi-Scale Receptive Field Convolutional Neural Network

Portal area segmentation is an important step in the quantitative histological analysis process for hepatitis grading. However, portal areas often appear of different sizes and appearances due to the variations of surrounding components such as the ductule, bile duct, artery, and portal vein. The slim fibrosis expanding from the portal area further increases challenges of the portal area segmentation. A Multi-scale Receptive Field Convolutional Neural Network (MRF-CNN) for the segmentation of the liver portal areas in hematoxylin and eosin (H&E) stained whole slide images (WSIs) is proposed in this paper. The MRF-CNN adopts the atrous spatial pyramid pooling (ASPP) with multiple atrous rates and symmetric encoder-decoder with feature concatenation architecture. The atrous rates in ASPP are devised of receptive fields to extract features of meaningful tissue components in parallel in portal areas. Along with the MRFCNN, a small object sensitive loss function is also proposed to have the network focus on small portal areas and slim fibrosis. The results show that the proposed model achieves Intersection over Union (IOU) of 0.87 and sensitivity of 0.92. Compared to recent segmentation researches such as Fully Convolutional Network (FCN), U-Net and SegNet, the proposed network achieves an overall the best IOU and sensitivity performance. Experimental results also show that the designed ASPP block benefits in feature extraction, and the ability of identifying small objects in proposed small object sensitive loss has a significant improvement of the segmentation result comparing to the original cross entropy loss.
Research and analysis of deep learning image enhancement algorithm based on fractional differential

Aiming at the characteristics of the image processing technology course and the practicality, the deep learning and fractional differential wavelet algorithm are introduced into the image processing technology to solve the problem that the traditional algorithm loses the texture detail information during image enhancement. Through theoretical analysis, the fractional differential wavelet algorithm can greatly improve the high frequency components of the signal, enhance the IF component of the signal, and the very low frequency of the nonlinear retained signal. According to this, the application of fractional differential to image enhancement will make the edge of the image prominent. The enhanced image with clearer texture and image smoothing area information is preserved. Then, based on the classical fractional differential definition, the fractional difference equation is derived and the differential operator of the approximate depth learning method is constructed. Experiments with image enhancement show that the image enhancement method based on fractional differential operator is better than the traditional integer differential method.
An Efficient Image Encryption Scheme for Industrial Internet-of-Things Devices

Recently, industrial Internet-of-Things IoT has appeared and developed rapidly, and image data captured by IoT nodes or terminal devices is associated with user's personal privacy information. In order to protect image data, image encryption mechanism provides a flexible and secure approach in retaining the secrecy of images transformation and storage in the Internet-of-Things systems. In this paper, we propose an efficient image encryption algorithm that can encrypt a large number of images. First, initial conditions of hyperchaotic Chen system are computed by SHA-512 and chaotic sequences. Then, pixel scrambling, image encryption and cyclic shift are employed to permute the plain image. The XOR operation is also constructed to improve correlation between pixels. Due to the sensitivity of SHA algorithm for the original input, each image has its own unique encryption parameter value. Simulation results and theoretical analysis demonstrate that, compared with related schemes, the proposed scheme is efficient and practical, and it can be used in the requirement of sensitive image protections such as internet of vehicles (IoVs) and wireless body area networks (WBAN).
An FPGA Implementation of Real-Time Object Detection with a Thermal Camera

We demonstrate a sparse YOLOv2-based object detector with a thermal camera. A thermal camera outputs pixel values which represent heat (temperature), and the output is gray-scale images. Since the thermal cameras do not depend on whether there is the light or not unlike other visible range cameras, object detection using the thermal camera is reliable without ambient surrounding. This topic is of a broad interest in object surveillance and action recognition. However, since it is challenging to extract informative features from the thermal images, the implementation challenges of the object detector with high accuracy remain. In recent works, convolutional neural networks (CNNs) outperform conventional techniques, and a variety of object detectors based on the CNNs have been proposed. The representative networks are single-shot detectors that consist of a CNN and infer locations and classes simultaneously (e.g., SSD and YOLOv2). Although the primary advantage of the type is that it enables to train detection and classification simultaneously, the resulting increased computation time and area requirements can cause problems of implementation on an FPGA. Also, as for the proposed networks on RGB three channel images, one of the problems is false positive; the realization of more reliable object detector is required. To realize a real-time reliable object detector, we investigate an FPGA implementation of a sparse YOLOv2-based one whose inputs are four-channel images that consist of both the RGB and the thermal ones. In this demonstration, we show a performance comparison between an RGB-based detector and our proposed one on FPGAs.
Doing thinking: revisiting computing with artistic research and technofeminism

In this paper, we explore how artistic research and its methodologies can create new modes of thinking and research on computational issues. We draw on experiences with the research project ‘Reconfiguring Computing Through Cyberfeminism and New Materialism’ (CF+), during which we collaboratively investigated modes and methods of knowledge production in computing. Cyberfeminist legacies and new materialism provided a historically informed and theoretically rigorous set of concerns around which artistic research and computing were brought together. We discuss collaborative experiments and three hybrid methodologies – creating situations for intra-action, diffractive reading/making and material speculation – that we employed and suggest that what emerged is a kind of ‘doing thinking’ as a material-speculative practice that re-positions research as a critical open-ended space for non-standard ways of knowing. The paper explores implications of this for ethics and responsibility in computing, materiality of knowledge production and modes of interdisciplinary collaborations.
Fostering Historical Research in CSCW &amp; HCI

This day-long workshop aims to support and grow the community of CSCW and HCI scholars that investigate the past to inform the design, critique and conceptualization of technology. At this workshop, we will learn from examples of historically-based CSCW and HCI work, explore issues in historical method that come up in such work, share methods and techniques, provide feedback and support to ongoing investigations; and define a shared agenda for future research on this topic. The workshop will also highlight research and methods that focus on non-Western contexts and that give voice to historically marginalized groups. Based on the workshop, we will develop a white paper and a website that will collect resources to support CSCW based historical investigations.
Attention-based bidirectional gated recurrent unit neural networks for sentiment analysis

Sentiment analysis is an important research direction of natural language processing. In-depth exploration of online textual emotional information has great social significance social and commercial value for market research, online public opinion discovery and early warning. In this paper, the gated recurrent unit neural network and the attention mechanism are combined to propose a text sentiment analysis model---Attention-BGRU. The attention mechanism was added to the gated recurrent unit neural network, and the model was implemented under the Keras deep learning framework. According to the experimental results, the comparison with the existing models shows that the proposed model has obvious advantages over the general deep learning method.
Training Deep Learning Models for Geographic Feature Recognition from Historical Maps

Historical map scans contain valuable information (e.g., historical locations of roads, buildings) enabling the analyses that require long-term historical data of the natural and built environment. Many online archives now provide public access to a large number of historical map scans, such as the historical USGS (United States Geological Survey) topographic archive and the historical Ordnance Survey maps in the United Kingdom. Efficiently extracting information from these map scans remains a challenging task, which is typically achieved by manually digitizing the map content. In computer vision, the process of detecting and extracting the precise locations of objects from images is called semantic segmentation. Semantic segmentation processes take an image as input and classify each pixel of the image to an object class of interest. Machine learning models for semantic segmentation have been progressing rapidly with the emergence of Deep Convolutional Neural Networks (DCNNs or CNNs). A key factor for the success of CNNs is the wide availability of large amounts of (labeled) training data, but these training data are mostly for daily images not for historical (or any) maps. Today, generating training data needs a significant amount of manual labor that is often impractical for the application of historical map processing. One solution to the problem of training data scarcity is by transferring knowledge learned from a domain with a sufficient amount of labeled data to another domain lacking labeled data (i.e., transfer learning). This chapter presents an overview of deep-learning semantic segmentation models and discusses their strengths and weaknesses concerning geographic feature recognition from historical map scans. The chapter also examines a number of transfer learning strategies that can reuse the state-of-the-art CNN models trained from the publicly available training datasets for the task of recognizing geographic features from historical maps. Finally, this chapter presents a comprehensive experiment for extracting railroad features from USGS historical topographic maps as a case study.
Automatic modulation recognition of DVB-S2X standard-specific with an APSK-based neural network classifier

Modulation recognition of transmitted signals remains a significant concern in smart, modern communication systems such software-defined radios. Applying machine learning algorithms to interesting features extracted from the input signals are widely used for classification of such signals. Here, we propose leveraging novel higher order spectra features (HOSF) to a classification algorithm based on neural network properties to deal with modulation recognition problems specified for the M-APSK DVB-S2X modulation signals standard. The HOSF characteristics of signals are extracted under Additive white Gaussian noise (AWGN) channel, and four individual parameters are defined for distinguishing modulation signals from the set, {16, 32, 64} -APSK. This approach makes the recogniser more intelligent and improves its success rate of classification. The results illustrate excellent classification accuracy obtained at a low SNR of 0 dB, which demonstrates the potential of combining these proposed features with a neural network classifier for the application of M-APSK modulation classification.
An Archetype Query Language interpreter into MongoDB: Managing NoSQL standardized Electronic Health Record extracts systems

The fast development of today’s healthcare and the need to extract new medical knowledge from exponentially-growing volumes of standardized Electronic Health Records data, as required by studies in Precision Medicine, brings up a challenge that may probably only be addressed using NoSQL DBMSs, due to the non-optimal performance of traditional relational DBMSs on standardized data; and these database systems operated by semantic archetype-based query languages, because of the expected generalized extension of standardized EHR systems. An AQL into MongoDB interpreter has been developed to its first version. It translates system-independent AQL queries posed on ISO/EN 13606 standardized EHR extracts into the NoSQL MongoDB query language. The new interpreter has the advantages of both the archetype-based system-independent AQL queries and the dual-model-based standardized EHR extracts stored on document-centric NoSQL DBMSs, such as MongoDB. AQL queries are independent of applications, programming languages and system environments due to the use of the dual model, but EHR extracts featuring this model are best persisted on document-based NoSQL databases. Consequently, the interpreter allows us to query standardized EHR extracts semantically, and also affording optimal performance.
Deep Convolutional and LSTM Neural Network Architectures on Leap Motion Hand Tracking Data Sequences

This paper focuses on the hand gesture recognition problem, in which input is a multidimensional time series signal acquired from a Leap Motion Sensor and output is a predefined set of gestures. In the present work, we propose the adoption of Convolutional Neural Networks (CNNs), either in combination with a Long Short-Term Memory (LSTM) neural network (i.e. CNN-LSTM), or standalone in a deep architecture (i.e. dCNN) to automate feature learning and classification from the raw input data. The learned features are considered as the higher level abstract representation of low level raw time series signals and are employed in a unified supervised learning and classification model. The proposed CNN-LSTM and deep CNN models demonstrate recognition rates of 94% on the Leap Motion Hand Gestures for Interaction with 3D Virtual Music Instruments dataset, which outperforms previously proposed models of handcrafted and automated learned features on LSTM networks.
Informed component label algorithm for robust identification of connected components with volume-of-fluid method

The connected component labeling technique (CCL), which labels regions of connected Eulerian field data, will inaccurately identify closely spaced components when applied to the volume-of-fluid function. We present two modifications to the CCL that improve its robustness and accuracy. This Informed Component Labeling algorithm (ICL) incorporates the normal and uses multilevel thresholding to improve and refine connectivity decisions for components with spacing just larger than the grid size. Through detailed verification and validation using synthetic volume fraction data, we show that the ICL algorithm removes the bias to larger components, provide guidelines for its use, and estimate its error bounds for the smallest components. The ICL produces zero standard deviation in the number of components identified for those with radius larger than twice the grid size and can reduce it by ∼ 38% for smaller components. The modifications that comprise the ICL can be applied to any existing CCL algorithm with a known increase in computational cost. It enables robust identification of connected components for accurate transfer of information in mixed Eulerian-Lagrangian methods and statistical analysis that use the volume-of-fluid function.
Design and experimental performance of local entanglement witness operators

Entanglement is a central concept in quantum information and a key resource for many quantum protocols. In this work we propose and analyze a class of entanglement witnesses that detect the presence of entanglement in subsystems of experimental multi-qubit stabilizer states. The witnesses we propose can be decomposed into sums of Pauli operators and can be efficiently evaluated by either two measurement settings only or at most a number of measurements that only depends on the size of the subsystem of interest. We provide two constructive methods to design the local witness operators, the first one based on the local unitary equivalence between graph and stabilizer states, and the second one based on sufficient and necessary conditions that the respective set of constituent Pauli operators needs to fulfill. We theoretically establish the noise tolerance of the proposed witnesses and benchmark their practical performance by analyzing the local entanglement structure of an experimental seven-qubit quantum error correction code.