Reviewerly/exHarmony huggingface dataset:
Discovering Leonardo with artificial intelligence and holograms: A user study

Cutting-edge visualization and interaction technologies are increasingly used in museum exhibitions, providing novel ways to engage visitors and enhance their cultural experience. Existing applications are commonly built upon a single technology, focusing on visualization, motion or verbal interaction (e.g., high-resolution projections, gesture interfaces, chatbots). This aspect limits their potential, since museums are highly heterogeneous in terms of visitors profiles and interests, requiring multi-channel, customizable interaction modalities. To this aim, this work describes and evaluates an artificial intelligence powered, interactive holographic stand aimed at describing Leonardo Da Vinci’s art. This system provides the users with accurate 3D representations of Leonardo’s machines, which can be interactively manipulated through a touchless user interface. It is also able to dialog with the users in natural language about Leonardo’s art, while keeping the context of conversation and interactions. Furthermore, the results of a large user study, carried out during art and tech exhibitions, are presented and discussed. The goal was to assess how users of different ages and interests perceive, understand and explore cultural objects when holograms and artificial intelligence are used as instruments of knowledge and analysis.
An interpretable fuzzy logic based data-driven model for the twin screw granulation process

In this research, a new framework based on fuzzy logic is proposed to model the twin screw granulation (TSG) process. First, various fuzzy logic systems (FLSs) having different structures are developed to define various rule bases. The extracted fuzzy rules are assessed and reduced accordingly into a single rule base by utilizing the singular value decomposition-QR factorization (SVD-QR) approach. The resulted reduced FLS is, then, implemented to describe the TSG process mathematically and linguistically via simple to understand IF-THEN rules. The linguistic output provides an accessible framework to increase the understanding of this complex process within an industrial context. Validated on laboratory-scale experiments, it is shown that the newly proposed model successfully predicts the granule size and enhances the understanding of the TSG process. Furthermore, the proposed framework outperforms the standard FLS and the Artificial Neural Network (ANN), with an overall improvement of approximately 16% and 29% in R2, respectively.
Fast 2D Convolution Algorithms for Convolutional Neural Networks

Convolutional Neural Networks (CNN) are widely used in different artificial intelligence (AI) applications. Major part of the computation of a CNN involves 2D convolution. In this paper, we propose novel fast convolution algorithms for both 1D and 2D to remove the redundant multiplication operations in convolution computations at the cost of controlled increase of addition operations. For example, when the 2D processing block size is 3×3, our algorithm has multiplication saving factor as high as 3.24, compared to direct 2D convolution computation scheme. The proposed algorithm can also process input feature maps and generate output feature maps with the same flexible block sizes that are independent of convolution weight kernel size. The memory access efficiency is also largely improved by the proposed method. These structures can be applied to different CNN layers, such as convolution with stride > 1, pooling and deconvolution by exploring flexible feature map processing tile sizes. The proposed algorithm is suitable for both software and hardware implementation.
SMART-KG: Hybrid Shipping for SPARQL Querying on the Web

While Linked Data (LD) provides standards for publishing (RDF) and (SPARQL) querying Knowledge Graphs (KGs) on the Web, serving, accessing and processing such open, decentralized KGs is often practically impossible, as query timeouts on publicly available SPARQL endpoints show. Alternative solutions such as Triple Pattern Fragments (TPF) attempt to tackle the problem of availability by pushing query processing workload to the client side, but suffer from unnecessary transfer of irrelevant data on complex queries with large intermediate results. In this paper we present smart-KG, a novel approach to share the load between servers and clients, while significantly reducing data transfer volume, by combining TPF with shipping compressed KG partitions. Our evaluations show that smart-KG outperforms state-of-the-art client-side solutions and increases server-side availability towards more cost-effective and balanced hosting of open and decentralized KGs.
An Online Coverage Path Planning Algorithm for Curvature-Constrained AUVs

The paper presents an algorithm for online coverage path planning of unknown environments using curvature-constrained AUVs. Unlike point vehicles, which can make quick maneuvers in any direction towards any goal, curvature-constrained AUVs need significant time to accelerate, decelerate, or turn towards the goal. Therefore, finding a feasible collision-free path to the waypoint in the presence of obstacles is a nontrivial task for curvature-constrained AUVs. In order to overcome this challenge, we develop a new algorithm that dynamically selects the shortest Dubins path from its current state to a neighboring region in a locally optimal manner while providing efficient global coverage. The proposed new algorithm is an extension of our recently developed algorithm called ε*, which utilizes an Exploratory Turing Machine (ETM) as a supervisor to guide the vehicle with adaptive navigation decisions. The performance of the proposed algorithm is validated on a high-fidelity underwater simulator called UWSim, where the collected terrain data is used offline for 3-D reconstruction of the seabed. The simulations show that the proposed algorithm generates feasible and safe coverage paths for curvature-constrained AUVs for accurate reconstruction of the underwater terrain.
Type Like a Man! Inferring Gender from Keystroke Dynamics in Live-Chats

Nonverbal communication is often referred to as body language, an expression that accounts for the major role that the body plays in interaction, especially when it comes to conveying socially and psychologically relevant information. Such a role is the result of a long evolutionary process that has shaped the brain to be sensitive to the signals sent by co -located others more than to any other signal in the environment (e.g., the human voice is one of the sounds that requires the lowest energy to be heard). Still, despite such an evolutionary history, people communicate increasingly more frequently through technologies that prevent, partially or totally, the use of nonverbal behavior. For example, phones allow one to use nonverbal vocal behavior (laughter, sobbing, intonation, pauses, etc.), but not facial expressions or gestures. In the context outlined above, it is important to investigate whether body language is still possible when the body cannot play its role. For this reason, this article has shown that there is a significant interplay between gender and keystroke dynamics at least in the case of interactions taking place through live -chat interfaces. In particular, the experiments have shown that it is possible to infer the gender of a person from her typing behavior with an accuracy higher than 95%. In addition, the experiments have shown that such a performance relies mostly on features (physical and machine detectable measures extracted through a key -logging platform) that account for implicit and explicit expression of affect, social presence, and planning problems. According to a recent survey, 36% of adults owning a smartphone use messaging systems (https://www.pewinternet.org/2015/08/19/mobilemessaging-and-social-media-2015D. In addition, The market for technologies supporting live -chats is expected to 40 50 60 grow with an average rate of 7.3% until 2023 when it is expected to reach a total volume close to one billion dollars per year (https://
Encrypted Traffic Classification Based on Text Convolution Neural Networks

Traffic classification is of great significance to network management. With the wide application of encryption technology, traditional traffic classification methods are no longer applicable. In recent years, the machine learning-based traffic classification method has become more and more popular. However, this method not only needs complex feature engineering but also is difficult to adapt to different network environments. A traffic classification method based on text convolution neural network is proposed in this paper, which represents traffic data as vectors, then use text convolution neural networks to extract key features for traffic classification. This method is validated on ISCX VPN-non VPN dataset and achieves better classification performance than the previous traffic classification method. For class imbalance problem, a new loss function and an appropriate method of class weight allocation are used in multi-class classification task, which can effectively deal with class imbalance problem.
OSVNet: Convolutional Siamese Network for Writer Independent Online Signature Verification

Online signature verification (OSV) is one of the most challenging tasks in writer identification and digital forensics. Owing to large intra-individual variability, there is a critical requirement to accurately learn the intrapersonal variations of the signature to achieve higher classification accuracy. To achieve this, in this paper, we propose an OSV framework based on deep convolutional Siamese network (DCSN). DCSN automatically extract robust feature descriptions based on metric-based loss function which decreases intra-writer variability (Genuine-Genuine) and increase inter-individual variability (Genuine-Forgery) and guides the DCSN for effective discriminative representation learning for online signatures. Experiments conducted on three widely accepted datasets MCYT-100 (DB1), MCYT-330 (DB2) and SVC-2004-Task2 emphasize the capability of our framework to distinguish the genuine and forgery samples. Experimental results confirm the efficiency of the proposed DCSN in one shot learning by achieving a lower error rate as compared to many recent and state-of-the art OSV models.
Performance of a Hybrid Demonstration Earthquake Early Warning System in the Sichuan–Yunnan Border Region

Abstract Earthquake early warning systems (EEWSs) are considered to be one of the most effective means for seismic risk mitigation, in terms of both losses and societal resilience, by releasing an alarm immediately after an earthquake occurs and before strong ground shaking arrives the target sites to be protected. To gain experience for the National System for Fast Seismic Intensity Report and Earthquake Early Warning project, we deployed a hybrid demonstration EEWS in the Sichuan–Yunnan border region with micro-electro-mechanical system-based sensors and broadband seismographs and low-latency data transmission. In this study, we described the structure of this EEWS and analyzed its performance in the first 2 yr from January 2017 to December 2018. During this test period, the EEWS detected and processed a total of 126 ML 3.0+ earthquakes, with excellent epicentral location and magnitude estimation. The average location and magnitude estimation errors for the first alert were 4.2±7.1 km and 0.2±0.31, respectively. For the earthquakes that occurred inside and outside the hybrid network, the first alert was generated 13.4±5.1 s and 26.3±13.5 s after the origin time (OT), respectively. We analyzed the performance of the EEWS for the 31 October 2018 M 5.1 earthquake, because it was the largest event that occurred inside the hybrid network during the test period. The first alert was obtained at 7.5 s after the OT, with a magnitude error of 0.1 magnitude unit, a location error of about 1 km, and a depth error of 8 km. Finally, we discussed the main differences between the EEWS’s estimates and the catalogs obtained by the China Earthquake Network Center, and proposed improvements to reduce the reporting time. This study demonstrated that we constructed a reliable, effective hybrid EEWS for the test region, which can provide sufficient support for the design of the National EEWS project.
Deep Neural Networks for Text Detection and Recognition in Historical Maps

We introduce deep convolutional and recurrent neural networks for end-to-end, open-vocabulary text reading on historical maps. A text detection network predicts word bounding boxes at arbitrary orientations and scales. The detected word images are then normalized for a robust recognition network. Because accurate recognition requires large volumes of training data but manually labeled data is relatively scarce, we introduce a dynamic map text synthesizer providing a practically infinite stream of training data. Results are evaluated on a labeled data set of 30 maps featuring over 30,000 text labels.
Digital pathology for primary diagnosis of screen‐detected breast lesions – experimental data, validation and experience from four centres

Aim The rate of deployment of digital pathology (DP) systems for primary diagnosis in the UK is accelerating. The flexibility and resilience of digital versus standard glass slides could be of great benefit in the NHS breast screening programme (NHSBSP). This study aims to document the safety and benefits of DP for preoperative tissue diagnosis of screen‐detected breast lesions. Methods and results Concordance data for glass and digital slides of the same cases from four sites were subjected to detailed concordance–discordance analysis. A literature review of DP in the primary diagnosis of breast lesions is presented, making this the most comprehensive synthesis of digital breast cancer histopathological diagnostic data to date. Detailed concordance analysis of experimental data from two histopathology departments reveals clinical concordance rates for breast biopsies of 96% (216 of 225) and 99.6% (249 of 250). Data from direct comparison validation studies in two histopathology departments, utilising the protocol recommended by the Royal College of Pathologists, found concordance rates for breast histology cases of 99.4% (180 of 181) and 99.0% (887 of 896). An intraobserver variation study for glass versus digital slides for difficult cases from the NHSBSP yielded a kappa statistic of 0.80, indicating excellent agreement. Discordances encountered in the studies most frequently concerned discrepancies in grading attributable to mitotic count‐scoring and identification of weddelite. Conclusions The experience of four histopathology laboratories and our review of pre‐existing literature suggests that DP is safe for the primary diagnosis of NHSBSP breast histology specimens, and does not increase the risk of misclassification.
Arbitrary style transfer using neurally-guided patch-based synthesis

We present a new approach to example-based style transfer combining neural methods with patch-based synthesis to achieve compelling stylization quality even for high-resolution imagery. We take advantage of neural techniques to provide adequate stylization at the global level and use their output as a prior for subsequent patch-based synthesis at the detail level. Thanks to this combination, our method keeps the high frequencies of the original artistic media better, thereby dramatically increases the fidelity of the resulting stylized imagery. We show how to stylize extremely large images (e.g., 340 Mpix) without the need to run the synthesis at the pixel level, yet retaining the original high-frequency details. We demonstrate the power and generality of this approach on a novel stylization algorithm that delivers comparable visual quality to state-of-art neural style transfer while completely eschewing any purpose-trained stylization blocks and only using the response of a feature extractor as guidance for patch-based synthesis.
Temporal super-resolution of 2D/3D echocardiography using cubic B-spline interpolation

Echocardiography is an important imaging modality in daily clinical practices. Lack of image quality and limited number of frames per second are the major problems of this modality. Frame rate limitation becomes worse in 3D echocardiography, because it has to scan a volume instead of a plane and acquisition of a volume consumes more time compared to its two-dimensional counterpart. Higher frame rates can help improve medical diagnostics like tracking movements of cardiac valves and position of ventricular endocardial, myocardial and epicardial by tracing the small transient motions in order to better characterize the functionality of cardiac activities. There are a few numbers of temporal super-resolution methods to improve the frame rate. Here, we have proposed a temporal interpolation method using splines. Comparison of the results with that of the state-of-the-art methods shows promising outcomes to enhance echocardiography frame sequences in both two and three-dimensional clinical echocardiography images. Results show that proposed method is doing interpolation more precisely and it also performs about ten times faster in the sense of processing time compared to the latest works to increase the frame rate. We have also shown that interpolation accuracy depends greatly on how many frames per second we have initially and how many frames we want to add to the original sequence. This frame rate increase is also applied to the color Doppler in both 2D and 3D echocardiography and results are presented.
Neural Embedding Propagation on Heterogeneous Networks

Classification is one of the most important problems in machine learning. To address label scarcity, semi-supervised learning (SSL) has been intensively studied over the past two decades, which mainly leverages data affinity modeled by networks. Label propagation (LP), however, as the most popular SSL technique, mostly only works on homogeneous networks with single-typed simple interactions. In this work, we focus on the more general and powerful heterogeneous networks, which accommodate multi-typed objects and links, and thus endure multi-typed complex interactions. Specifically, we propose neural embedding propagation (NEP), which leverages distributed embeddings to represent objects and dynamically composed modular networks to model their complex interactions. While generalizing LP as a simple instance, NEP is far more powerful in its natural awareness of different types of objects and links, and the ability to automatically capture their important interaction patterns. Further, we develop a series of efficient training strategies for NEP, leading to its easy deployment on real-world heterogeneous networks with millions of objects. With extensive experiments on three datasets, we comprehensively demonstrate the effectiveness, efficiency, and robustness of NEP compared with state-of-the-art network embedding and SSL algorithms.
Landing of a Multirotor Aerial Vehicle on an Uneven Surface Using Multiple On-board Manipulators

We describe the concept, design and implementation of a unique manipulator system for a multirotor aerial vehicle. The proposed manipulator system consists of three robotic arms attached to a multirotor airframe with the objective to provide the ability to manipulate single or multiple objects as well as aid in complex navigation tasks by doing contact based obstacle avoidance and acting as adaptive landing gear in uneven terrain. In this paper we primarily focus on the description and experimentation of one of the tasks achievable by the proposed aerial multi-manipulator system: landing a multirotor aerial vehicle on an uneven surface. Deploying the on-board manipulator as landing gears reduces the hardware carried by the vehicle while additionally providing the ability to land in unstructured and commonly difficult to land terrain.
The multiple model multi-Bernoulli filter based track-before-detect using a likelihood based adaptive birth distribution

In this paper, a novel multi-Bernoulli filter based track-before-detect (MB-TBD) algorithm is proposed, to solve the tracking problem of multiple maneuvering targets. We incorporate the multiple motion models into the basic MB-TBD filtering, and then derive the closed-form recursive equations including both prediction and update steps based on optimal Bayesian filtering. Moreover, to accommodate the case of unknown prior knowledge for target births, a likelihood based adaptive birth distribution for the MB-TBD is proposed. The implementation of the proposed algorithm with adaptive birth distribution is presented using sequential Monte Carlo (SMC) technique. The performance of the proposed algorithm is demonstrated in challenging scenarios including multiple highly maneuvering objects.
Deep Learning based Automatic Image Caption Generation

The paper aims at generating automated captions by learning the contents of the image. At present images are annotated with human intervention and it becomes nearly impossible task for huge commercial databases. The image database is given as input to a deep neural network (Convolutional Neural Network (CNN)) encoder for generating "thought vector" which extracts the features and nuances out of our image and RNN (Recurrent Neural Network) decoder is used to translate the features and objects given by our image to obtain sequential, meaningful description of the image. In this paper, we systematically analyze different deep neural network-based image caption generation approaches and pretrained models to conclude on the most efficient model with fine-tuning. The analyzed models contain both with and without `attention' concept to optimize the caption generating ability of the model. All the models are trained on the same dataset for concrete comparison.
Backpressure Flow Control

Effective congestion control in a multi-tenant data center is becoming increasingly challenging with rapidly increasing workload demand, ever faster links, small average transfer sizes, extremely bursty traffic, limited switch buffer capacity, and one-way protocols such as RDMA. Existing deployed algorithms, such as DCQCN, are still far from optimal in many plausible scenarios, particularly for tail latency. Many operators compensate by running their networks at low average utilization, dramatically increasing costs.
Combining Global and Local Similarity for Cross-Media Retrieval

This paper mainly studies the problem of image-text matching in order to make image and text better match. Existing cross-media retrieval methods only make use of the information of image and part of text, that is, matching the whole image with the whole sentence, or matching some image areas with some words. In order to better reveal the potential connection between image and text semantics, this paper proposes a fusion of two levels of similarity across media images-text retrieval method, constructed the cross-media two-level network to explore the better matching between images and texts, it contains two subnets for dealing with global features and local characteristics. Specifically, in this method, the image is divided into the whole picture and some image area, the text is divided into the whole sentences and words, to study respectively, to explore the full potential alignment of images and text, and then use a two-level alignment framework is used to promote each other, fusion of two kinds of similarity can learn to complete representation of cross-media retrieval. Through the experimental evaluation on Flickr30K and MS-COCO datasets, the results show that the method in this paper can make the semantic matching of image and text more accurate, and is superior to the international popular cross-media retrieval method in various evaluation indexes.