Reviewerly/exHarmony huggingface dataset:
Efficient Ontological Query Answering by Rewriting into Graph Queries

The OWL 2 QL profile of the OWL 2 Web Ontology Language, based on the family of description logics called DL-Lite, allows for answering queries by rewriting, i.e. by reformulating a given query into another query that is then directly processed by a RDBMS system by pure querying, without materialising new data or updating existing data. In this paper we propose a new language whose expressive power goes beyond that of DL-Lite (in particular, our language extends both OWL 2 QL and linear $$\mathcal {ELH}$$ , two well known DL ontology languages) while still allowing query answering via rewriting of queries into conjunctive two-way regular path queries (C2RPQs). Our language is identified by a syntactic property that can be efficiently checked. After defining our new language, we propose a novel rewriting technique for conjunctive queries (CQs) that makes use of nondeterministic finite state automata. CQ answering in our setting is NLogSpace-complete in data complexity and NP-complete in combined complexity; answering instance queries is NLogSpace-complete in data complexity and in $$\textsc {PTime}$$ in combined complexity.
An integrated neural model of robust self-motion and object motion perception in visually realistic environments

Over the past decade, considerable progress has been made in understanding and modeling the neural mechanisms that support the perception of self-motion and object motion in humans. Building upon the neural architecture introduced in the STARS (Elder et al., 2009) and ViSTARS (Browning et al., 2009) models, Layton and Fajen have steadily developed a competitive dynamics model of MT/MST that uses optic flow to estimate heading and detect and perceive independently moving objects. The aim of this study was to systematically test the accuracy, stability, and robustness of model estimates in visually realistic environments. Our approach was to couple the model to Microsoft AirSim, a high-fidelity simulation platform built on the Unreal Engine. This allowed us to generate various scenarios involving self-motion in complex, visually realistic environments. We conducted a series of experiments to test the accuracy and robustness of model estimates in the presence of (1) globally discrepant motion by introducing blowing snow, (2) locally discrepant motion introduced by moving objects (e.g., people), (3) variations in lighting and contrast, (4) intermittent blackout, and (5) perturbations resulting from collisions that abruptly alter the direction of self-motion. The model generates accurate and stable heading estimates in static environments, and like humans, is weakly affected by locally and globally discrepant optic flow. Object motion estimation is more affected by discrepant optic flow and dependent on the location of objects relative to the focus of expansion. We will also discuss attempts to adapt the model for eventual use on-board small aerial robots, where constraints on payload and power supply encourage the use of vision-based solutions. In that context, we discuss strategies for speeding up model performance to enable self- and object-motion estimation from video in real time.
Evaluation of Cosine Similarity Feature for Named Entity Recognition on Tweets

In this paper, we present the Named Entity Recognition as a multi-class system and we evaluate baseline classifiers along with the technical features extracted from tweet datasets. Initially, we elaborate the conversion procedure of tweet data and we study three different datasets such that raw tweet data are compatible to the presented data model. The first dataset is well-known for benchmarking purposes and the other two datasets have been collected in the wild by using Twitter search API and given keywords. Then, we elaborate the feature vector constituted by 9 technical features. To reach at higher statistical metric values of the multi-class NER system, we seek the performance of the classifier subject to different combination of features. Finally, we elaborate the impact of the cosine similarity to the class centroid feature to the performance of the classifiers and we present the highest F1 score reached by using a particular set of features.
Hibiki: A Graph Visualization of Asian Music

Creating a visualization for a specific subdomain is an arduous task since most commercial visualization tools are often written in a way that allows them to be applicable to multiple subject domains. These tools are cer-tainly powerful, but inherently weaker since they were not written with a specific subject domain in mind. Thus, many researchers may want to create their own visualization. The goal of this project is to create a Neo4j database and an interactive web interface for a dataset that covers the intricacies of the East Asian Music scene, primarily focused on Japanese music. This paper serves as documentation to help other authors understand the processes involved when designing and creating similar tools. We break the project down into 3 separate components. First, we introduce the fundamentals of a Neo4j Graph Database and data mapping design decisions. Next, we explore what an ETL process looks like and how to implement it using Ruby libraries. Finally, we look at the design of the graph visualization software, it's components, and key design decisions. We end the discussion with some analysis of the visualization's effectiveness to provide information and how to improve computational efficiency of the visualization.
Perceiving Category Set Statistics On-the-fly

A bombardment of information overloads our sensory, perceptual and cognitive systems, which must integrate new information with memory of past scenes and events. Mechanisms employed to overcome sensory system bottlenecks include selective attention, Gestalt gist perception, categorization, and the recently investigated ensemble encoding of set summary statistics. We explore compensatory cognitive processes focusing on categorization and set ensemble summary statistics that relate objects sharing properties or function. Without encoding individual details of all individuals, (or as a shortcut to representing these details), observers perceive category prototype and boundaries or set mean and range, and perhaps higher order statistics as well. We found that observers perceive set mean and range, automatically, implicitly, and on-the-fly, for each presented set sequence, independently, and we found matching properties for category representation, suggesting a similar computational mechanism underlies the two phenomena. But categorization depends on a lifetime of learning about categories and their prototypes and boundaries. We now developed novel abstract “amoeba” forms, which are complex images similar to categories, but have simple ultrametric structure that observers can categorize on-the-fly (rather than depending on pre-learned categories). We find that, not only do observers learn the amoeba categories on-the-fly, they also build representations of their progenitor (related, but not equivalent, to set “mean” or category prototype), as well as category boundaries (related to set range and inter-category boundaries). These findings put set perception in a new light, related to object, scene and category representation.
Mean Spectral Normalization of Deep Neural Networks for Embedded Automation

Deep Neural Networks (DNNs) have begun to thrive in the field of automation systems, owing to the recent advancements in standardising various aspects such as architecture, optimization techniques, and regularization. In this paper, we take a step towards a better understanding of Spectral Normalization (SN) and its potential for standardizing regularization of a wider range of Deep Learning models, following an empirical approach. We conduct several experiments to study their training dynamics, in comparison with the ubiquitous Batch Normalization (BN) and show that SN increases the gradient sparsity and controls the gradient variance. Furthermore, we show that SN suffers from a phenomenon, we call the mean-drift effect, which mitigates its performance. We, then, propose a weight reparameterization called as the Mean Spectral Normalization (MSN) to resolve the mean drift, thereby significantly improving the network's performance. Our model performs $\sim$ 16% faster as compared to BN in practice, and has fewer trainable parameters. We also show the performance of our MSN for small, medium, and large CNNs - 3-layer CNN, VGG7 and DenseNet-BC, respectively and unsupervised image generation tasks using Generative Adversarial Networks (GANs) to evaluate its applicability for a broad range of embedded automation tasks.
Product Blind Area Assembly Method Based on Augmented Reality and Machine Vision

In the manual assembly of the blind area, the worker's line of sight is blocked, and the real-time state of the parts to be assembled cannot be seen, which has a great impact on the efficiency and accuracy of the assembly. Aiming at this problem, a blind zone assembly method based on machine vision and augmented reality(AR) is proposed. Firstly, the ellipse is used as the marker point. The object to be assembled in the blind area is indirectly tracked by the detection and positioning of the ellipse, and the AR visualization guide assembly is then performed by projection and the assembly is precisely guided using the principle of local error amplification. Finally, the blind zone assembly experiment based on machine vision and augmented reality is designed to verify the effectiveness of the method. The experimental results show that this method can significantly improve the efficiency of assembly work in blind areas and can effectively reduce the assembly error rate.
Identifying the number of components in Gaussian mixture models using numerical algebraic geometry

Using Gaussian mixture models for clustering is a statistically mature method for clustering in data science with numerous successful applications in science and engineering. The parameters for a Gaussian mixture model (GMM) are typically estimated from training data using the iterative expectation-maximization algorithm, which requires the number of Gaussian components a priori. In this study, we propose two algorithms rooted in numerical algebraic geometry (NAG), namely, an area-based algorithm and a local maxima algorithm, to identify the optimal number of components. The area-based algorithm transforms several GMM with varying number of components into sets of equivalent polynomial regression splines. Next, it uses homotopy continuation methods for evaluating the resulting splines to identify the number of components that is most compatible with the gradient data. The local maxima algorithm forms a set of polynomials by fitting a smoothing spline to a dataset. Next, it uses NAG to solve the system of the first derivatives for finding the local maxima of the resulting smoothing spline, which represent the number of mixture components. The local maxima algorithm also identifies the location of the centers of Gaussian components. Using a real-world case study in automotive manufacturing and extensive simulations, we demonstrate that the performance of the proposed algorithms is comparable with that of Akaike information criterion (AIC) and Bayesian information criterion (BIC), which are popular methods in the literature. We also show the proposed algorithms are more robust than AIC and BIC when the Gaussian assumption is violated.
3D Medical Image Segmentation in Virtual Reality

The possible achievements of accurate and intuitive 3D image segmentation are endless. For our specific research, we aim to give doctors around the world, regardless of their computer knowledge, a virtual reality (VR) 3D image segmentation tool which allows medical professionals to better visualize their patients&#x2019; data sets, thus attaining the best understanding of their respective conditions.We implemented an intuitive virtual reality interface that can accurately display MRI and CT scans and quickly and precisely segment 3D images, offering two different segmentation algorithms. Simply put, our application must be able to fit into even the most busy and practiced physicians&#x2019; workdays while providing them with a new tool, the likes of which they have never seen before.
Real time enhancement of low light images for low cost embedded platforms

Images captured at low light suffers from underexposure and noise. These poor-quality images act as hindrance for computer vision algorithms as well as human vision. While this problem can be solved by increasing the exposure time, it also introduces new problems. In applications like ADAS, where there are fast moving objects in the scene, increasing the exposure time will cause motion blur. In applications, that demand higher frame rate, increasing the exposure time is not an option. Increasing the gain will result in noise as well as saturation of pixels at higher end. So, a real time scene adaptive algorithm is required for the enhancement of low light images. We propose a real time low light enhancement algorithm with more detail preservation compared to existing global based enhancement algorithms for low cost embedded platforms. The algorithm is integrated to image signal processing pipeline of TI&#x2019;s TDA3x and achieved &#x2DC;50fps on c66x DSP for HD resolution video captured from Omnivision&#x2019;s OV10640 Bayer image sensor.
CDOT: Continuous Domain Adaptation using Optimal Transport.

Optimal transport aims to estimate a transportation plan that minimizes a displacement cost. This is realized by optimizing the scalar product between the sought plan and the given cost, over the space of doubly stochastic matrices. When the entropy regularization is added to the problem, the transportation plan can be efficiently computed with the Sinkhorn algorithm. Thanks to this breakthrough, optimal transport has been progressively extended to machine learning and statistical inference by introducing additional application-specific terms in the problem formulation. It is however challenging to design efficient optimization algorithms for optimal transport based extensions. To overcome this limitation, we devise a general forward-backward splitting algorithm based on Bregman distances for solving a wide range of optimization problems involving a differentiable function with Lipschitz-continuous gradient and a doubly stochastic constraint. We illustrate the efficiency of our approach in the context of continuous domain adaptation. Experiments show that the proposed method leads to a significant improvement in terms of speed and performance with respect to the state of the art for domain adaptation on a continually rotating distribution coming from the standard two moon dataset.
Automated Recognition of Numeric Display Based on Deep Learning

This paper proposes approach to the automatic recognition of digits from the displays of medical devices such as blood pressure meters, glucometers, analyzers of harmful substances content. A fully connected, convolutional model of deep learning with the use of tensor flow and keras deep learning libraries has been developed. A database was created for training and validation of the proposed network, which contains 4500 images. The evaluation of the proposed recognition system for a large number of images has been carried out. The optimization of the model for practical use with CPU and GPU is carried out and the average performance time is shown.
Similarity Driven Unsupervised Learning for Materials Science Terminology Extraction

Knowledge of material properties, microstructure, underlying material composition and manufacturing process parameters that the material has undergone is of significant interest to materials scientists and engineers. A large amount of information of this nature is present in the form of unstructured sources. To access the right information for a given problem at hand, various domain specific search systems have been developed. Domain terminologies, when available, can significantly improve the quality of such systems. In this paper, we propose a novel similarity driven learning approach for automatic terminology extraction for materials science domain. It first uses various intra-domain and inter-domain unsupervised corpus level features to score and rank candidate terminologies. For inter-domain features, we use British National Corpus (BNC) as the general purpose corpus. The ranked candidate terms are then used to generate training data for learning a similarity based scoring function. The parameters of this scoring function are learnt using a Siamese neural network which uses word embeddings learnt from both the domain as well as the general purpose corpora to leverage contrasting term features. The proposed similarity based learning approach consistently outperforms other reported classification approaches on the materials dataset.
Paleo-environmental Conditions, Paleoclimatic Significance and Effects of Weathering on Clay Deposits in the Lower Benue Trough, Nigeria. Mineralogical Approach

Combined methods for mineralogical identifications were used to characterise the clay deposits within the Lower Benue Trough of Nigeria to interpret paleo-environmental conditions, the paleoclimatic significance of the trough, and effects of weathering on the minerals as factors that favour its deposition/accumulation within the trough which host other important industrial minerals like coal, barite, limestone etc. Bulk-sample random-powder XRD data and data for clay fractions deposited onto zero-background quartz plates were measured. The samples contained kaolinite, vermiculite, and traces of smectite, and the non-clay phases included quartz, microcline, and muscovite. All samples were unaffected after glycolation, confirming the absence of significant smectite. Muscovite was characterized by the nature of its 10 Å basal peak with a width of &lt;0.10° 2θ, which was very sharp. DTA/TGA results support the presence of kaolinite, and the characteristic kaolinite O-H, Al-OH, Si-OH and SiO-Al FTIR bands also confirmed its presence. Vermicular and book-like morphologies were observed under the SEM, typical of kaolinitic clay from in situ alteration. High kaolinite abundance in these sediments is consistent with intense weathering of parent rocks rich in Al under wet/ tropical paleo-climatic conditions with fresh and/or brackish water conditions in a continental setting. The variety of observed morphologies suggests that the deposits suffered more of chemical weathering. The clay deposits in Lower Benue Trough are quartz-rich, kaolinitic and derived from the chemical weathering of Al-rich source rocks.
High-Performance Homomorphic Matrix Completion on GPUs

Data loss and privacy exposure are two major issues in many applications such as trajectory tracking and online recommendations. The homomorphic matrix completion (HMC) is a promising approach to address these two issues simultaneously and to exploit the computing power of cloud servers. However, HMC comprises a compute-intensive matrix completion procedure, making it impractical for a cloud server to host large-scale parallel requests in real-time. In this paper, we propose a high-performance homomorphic matrix completion approach by offloading the matrix completion task from CPUs to commodity GPUs (Graphics Processing Units) widely installed in cloud servers. We propose optimization strategies for efficient memory access, reduced memory consumption, and batched least squares (LS) minimization to improve performance. Experiment results with similar recovery errors on both CPUs and GPUs demonstrate the homomorphism property for the matrix completion task. For small-or medium-scale matrices of various sizes, the proposed approach achieves an average of 66.84X and up to 116.23X speedups over dual 10-core Xeon CPUs. We further propose a method for computing large-scale matrices exceeding the GPU memory capacity and it achieves an average of 71.85X and up to 174.92X speedups versus dual 10-core Xeon CPUs.
Generalized PatternAttribution for Neural Networks with Sigmoid Activations

Explanation methods for deep neural networks (DNN) such as LRP [1], PatternAttribution [2], LIME [3], DeepLIFT [4] have promoted the explanation of convolutional neural networks (CNN). Results are obtained mostly using ReLU activation function. In this paper, we investigate the performance of explanation methods on neural networks with sigmoid activations like sigmoid and tanh. PatternAttribution is a recent approach which allows learning explanation patterns from data. We show that the saturated zones of sigmoids pose difficulties for PatternAttribution. In order to solve these issues, as a first novelty, we generalize global explanations to piece-wise dependent explanations. In a second contribution, we learn the parameters of PatternAttribution in near-linear activation zones of the sigmoids, while replacing it with LRP in the saturated zones. Finally, we introduce and evaluate direct layer-wise Taylor approximation to show that LRP as a deep-Taylor-motivated approach outperforms the ad-hoc application of Taylor approximation. We show results on MNIST and also on LSTM and GRU networks used for two sentiment classification tasks as important application cases of models using sigmoids. Our results demonstrate that the proposed method, Piece-wise PAtternLRP (PPAP), outperforms PatternAttribution as well as LRP for networks with sigmoids, thus combining their strengths effectively.
Regular Expression Learning with Evolutionary Testing and Repair

Regular expressions are widely used to describe and document regular languages, and to identify a set of (valid) strings. Often they are not available or known, and they must be learned or inferred. Classical approaches like L* make strong assumptions that normally do not hold. More feasible are testing approaches in which it is possible only to generate strings and check them with the underlying system acting as oracle. In this paper, we devise a method that starting from an initial guess of the regular expression, it repeatedly generates and feeds strings to the system to check whether they are accepted or not, and it tries to repair consistently the alleged solution. Our approach is based on an evolutionary algorithm in which both the population of possible solutions and the set of strings co-evolve. Mutation is used for the population evolution in order to produce the offspring. We run a set of experiments showing that the string generation policy is effective and that the evolutionary approach outperforms existing techniques for regular expression repair.
Track‐before‐detect algorithm for airborne radar system

This study considers the detection of dim target via track-before-detect (TBD) in airborne radar system. Dim target detection by TBD is well-studied while aircraft ego-motion is not taken into account. Ego-motion of the aircraft (platform) causes traditional TBD method useless in airborne radar system by two reasons. On one hand, the movement of the aircraft causes measurement misaligned among different frames. On the other hand, state space and measurement space is non-one-to-one correspondence. Considering these two issues, the authors proposed a TBD algorithm for airborne radar based on plots (AR-P-TBD). First, they derived coordinate rotation and motion compensation formula, and convert each frame of measurement into a unified coordinate system to make these measurements aligned. Then, TBD processing in measurement space is implemented. It avoids the non-one-to-one correspondence between state space and measurement space. Simulation results demonstrate the effectiveness of AR-P-TBD.