Reviewerly/exHarmony huggingface dataset:
Incorporation of Structural Tensor and Driving Force Into Log-Demons for Large-Deformation Image Registration

Large-deformation image registration is important in theory and application in computer vision, but is a difficult task for non-rigid registration methods. In this paper, we propose a structural Tensor and Driving force-based Log-Demons algorithm for it, named TDLog-Demons for short. The structural tensor of an image is proposed to obtain a highly accurate deformation field. The driving force is proposed to solve the registration issue of large-deformation that often causes Log-Demons to trap into local minima. It is defined as a point correspondence obtained via multisupport-region-order-based gradient histogram descriptor matching on image's boundary points. It is integrated into an exponentially decreasing form with the velocity field of Log-Demons to move the points accurately and to speed up a registration process. Consequently, the driving force-based Log-Demons can well deal with large-deformation image registration. Extensive experiments demonstrate that the TDLog-Demons not only captures large deformations at a high accuracy but also yields a smooth deformation.
Toward data-driven production simulation modeling: dispatching rule identification by machine learning techniques

Production simulation is useful to predict and optimize future production. However, it requires effort and expertise to create accurate simulation models. For instance, operational control rules, such as job sequencing rules, are modeled based on interviews with shop-floor managers and some assumptions since those rules are tacit in general. In this paper, we consider a data-driven approach to model operational control rules. We develop job sequencing rule identification methods that model rules from production data using machine learning techniques. These methods are evaluated based on accuracy and robustness against uncertainty in human decision making using virtual and real production data.
Low Bit Rate 2D Seismic Image Compression with Deep Autoencoders

In this paper, we present a deep learning approach for very low bit rate seismic data compression. Our goal is to preserve perceptual and numerical aspects of the seismic signal whilst achieving high compression rates. The trade-off between bit rate and distortion is controlled by adjusting the loss function. 2D slices extracted from seismic 3D amplitude volumes feed the network for training two simultaneous networks, an autoencoder for latent space representation, and a probabilistic model for entropy estimation. The method benefits from the intrinsic characteristic of deep learning methods and automatically captures the most relevant features of seismic data. An approach for training different seismic surveys is also presented. To validate the method, we performed experiments in real seismic datasets, showing that the autoencoders can successfully yield compression rates up to 68:1 with an average PSNR around 40 dB.
Nuclear discrepancy for single-shot batch active learning

Active learning algorithms propose what data should be labeled given a pool of unlabeled data. Instead of selecting randomly what data to annotate, active learning strategies aim to select data so as to get a good predictive model with as little labeled samples as possible. Single-shot batch active learners select all samples to be labeled in a single step, before any labels are observed. We study single-shot active learners that minimize generalization bounds to select a representative sample, such as the maximum mean discrepancy (MMD) active learner. We prove that a related bound, the discrepancy, provides a tighter worst-case bound. We study these bounds probabilistically, which inspires us to introduce a novel bound, the nuclear discrepancy (ND). The ND bound is tighter for the expected loss under optimistic probabilistic assumptions. Our experiments show that the MMD active learner performs better than the discrepancy in terms of the mean squared error, indicating that tighter worst case bounds do not imply better active learning performance. The proposed active learner improves significantly upon the MMD and discrepancy in the realizable setting and a similar trend is observed in the agnostic setting, showing the benefits of a probabilistic approach to active learning. Our study highlights that assumptions underlying generalization bounds can be equally important as bound-tightness, when it comes to active learning performance. Code for reproducing our experimental results can be found at https://github.com/tomviering/NuclearDiscrepancy .
Verification-Oriented Process Ontology

This paper presents the ontology of the concurrent processes close to Hoare communicating sequential processes. It is a part of an intellectual system for supporting verification of behavioral properties of such processes. Our ontological representation of the processes is oriented both to the application of formal verification methods and to the extraction of information from technical documentation (by our previously developed system of information extraction from a natural language text). We describe the ontology classes and domains that define communicating concurrent processes. These processes are characterized by sets of local and shared variables, a list of actions on these variables which change their values, a list of channels for the process communication (which, in turn, are characterized by the type of reading messages, capacity, modes of writing and reading, and reliability), and also a list of communication actions for sending messages. In addition to the formal mathematical definition of classes and domains of the ontology, examples of descriptions of some ontological classes, as well as typical properties and axioms for them, are specified in the editor Protégé in the OWL language with the use of the inference rules in the SWRL language. We define the formal operational semantics of communicating processes for their ontological representation. This semantics is a labeled transition system. It is reduced to the local operational semantics of separate process instances in the interleaving model. We specialize several types of processes from the subject domain of automatic control systems that model the typical functional elements of the automatic control system (sensors, comparators and regulators), as well as their combinations. The concepts of the specialized ontology are illustrated by the example of a control part of a bottle-filling system.
Modified fuzzy c-mean for custom-sized clusters

Fuzzy c-mean (FCM) is one of the widely used data clustering methods. FCM method not only divides a data set into several clusters but also determines the potential belongingness of each data in different clusters. The size of clusters generated by FCM cannot be controlled by the inherent mechanism. However, sometimes real life situations demand that the clusters should have some pre-specified size. In this study, the FCM method is further extended to obtain clusters with specified size. In the first step of the proposed method, FCM algorithm is executed; later the potential belongingness matrix passes through an optimization model to yield clusters with specified sizes. In the proposed technique, the centres of the clusters obtained from FCM are considered but the boundary elements are redistributed to achieve equal or custom-sized clusters. The methodology has been explained further with examples.
Design and development of an automated web crawler used for building image databases

Every day people worldwide upload millions of images to social networks, personal blogs, community forums and other web-based applications. To increase impact and public popularity however all these images should be indexed by search engines. Building an efficient non-textual search engine is far from a trivial task. It should employ modern information retrieval and image processing techniques to extract, index and store proper metadata from images that allows fast subsequent processing and searching. Before any image processing to apply, the search engine should be able to find and process the large amount of data being constantly added to the Internet. The WWW represents an enormous directed weightless cyclic graph by nature. Blind crawling of such a structure is a pointless waste of time and may never end. To achieve any efficiency, the crawler itself should be able to determine if the current crawling direction is perspective and lead to desired resources or not. Thus calculating weights of graph components (vertices and edges) is absolutely necessary to allow the automated crawling tool to navigate through the web. This paper suggests various ways of calculating weights and proposes architecture of a web crawler designed for building image databases. Choosing the most appropriate search strategy seems to be the key point in building an efficient special purpose web crawler.
Using modern web frameworks when developing an education application: a practical approach

Nowadays the technology evolves rapidly and with it, the computing devices are becoming more powerful, flexible and capable of performing complicated and tangled tasks. This results in a significant shift in the way applications are designed and developed. Yet modern web frameworks manage to cope with this client-side complexity by adopting a mindset oriented towards the design of robust and complex applications that can be delivered quickly with a high level of security, scaled easily, and extended simply. As part of our research on developing an educational application, that supports university students enrolled in Web development course, we made an overview and comparison of the existing technologies, in terms of frameworks, libraries, programming languages and deployment options, adopting a practical approach. Apart from outlining the results, a sample blueprint of the application architecture of the educational application is also proposed in this paper.
Learning Graph Embeddings from WordNet-based Similarity Measures

We present path2vec, a new approach for learning graph embeddings that relies on structural measures of pairwise node similarities.The model learns representations for nodes in a dense space that approximate a given userdefined graph distance measure, such as e.g. the shortest path distance or distance measures that take information beyond the graph structure into account.Evaluation of the proposed model on semantic similarity and word sense disambiguation tasks, using various WordNetbased similarity measures, show that our approach yields competitive results, outperforming strong graph embedding baselines.The model is computationally efficient, being orders of magnitude faster than the direct computation of graph-based distances.
Towards evolutionary theorem proving for isabelle/HOL

Mechanized theorem proving is becoming the basis of reliable systems programming and rigorous mathematics. Despite decades of progress in proof automation, writing mechanized proofs still requires engineers' expertise and remains labor intensive. Recently, researchers have extracted heuristics of interactive proof development from existing large proof corpora using supervised learning. However, such existing proof corpora present only one way of proving conjectures, while there are often multiple equivalently effective ways to prove one conjecture. In this abstract, we identify challenges in discovering heuristics for automatic proof search and propose our novel approach to improve heuristics of automatic proof search in Isabelle/HOL using evolutionary computation.
Research on polarization dehazing through the coaxial and multi-aperture polarimetric camera

In this study, a coaxial and multi-aperture polarization imaging system was designed to obtain dehazed images under bad weather conditions, which could effectively utilize polarization information. The system can capture four polarization azimuth images simultaneously by integrating four polarizers with different transmission directions in front of the sub-apertures. The system can utilize energy of incident light effectively, as well as remain the image resolution of the camera. In order to solve the image registration problem, a translation registration method for sub-aperture polarized images was implemented, which was based on the phase-only correlation algorithm. Moreover, a polarization dehazing model suitable for this system is employed to remove the haze effect of the captured images. Further experimental data demonstrated that the system could achieve a pleasing visual effect, and its performance on image quality was certified by several objective evaluation means including the line spread function and quantified detail information. The core advantages of this system are real-time and the good image quality in terms of contrast, details, and clarity. The system can effectively adapt to the long-distance, high-resolution, and real-time imaging in bad weather conditions such as haze weather.
On the suitability of VMAF for quality assessment of medical videos: Medical ultrasound &amp; wireless capsule endoscopy

With the rapid evolution in modern multimedia networks and systems, services such as telemedicine and tele-surgery are becoming more popular. Quality estimation and monitoring of medical videos is becoming important not only in the field of research, but also in real-time applications and services. The state-of-the-art video quality metric (VQM) called Video Multimethod Assessment Fusion (VMAF) is a promising solution for quality estimation of videos impaired by compression and scaling artifacts. The metric was developed by Netflix for entertainment video content and its good performance does not necessarily extend to medical videos. This paper focuses on evaluating the performance of VMAF in the context of quality assessment (QA) for medical videos. We consider in this paper medical videos compressed via High Efficiency Video Coding (HEVC) and refer in particular to medical ultrasound videos and wireless capsule endoscopy (WCE) videos for the performance estimation of VMAF. The correlation between the subjective scores of these two datasets and VMAF's quality estimates is studied and presented. The results show that VMAF outperforms other state-of-the-art VQMs in the context of WCE videos, but this is not the case for medical ultrasound videos.
Is your function low dimensional

We study the problem of testing if a function depends on a small number of linear directions of its input data. We call a function $f$ a linear $k$-junta if it is completely determined by some $k$-dimensional subspace of the input space. In this paper, we study the problem of testing whether a given $n$ variable function $f : \mathbb{R}^n \to \{0,1\}$, is a linear $k$-junta or $\epsilon$-far from all linear $k$-juntas, where the closeness is measured with respect to the Gaussian measure on $\mathbb{R}^n$. Linear $k$-juntas are a common generalization of two fundamental classes from Boolean function analysis (both of which have been studied in property testing) $\textbf{1.}$ $k$- juntas which are functions on the Boolean cube which depend on at most k of the variables and $\textbf{2.}$ intersection of $k$ halfspaces, a fundamental geometric concept class. We show that the class of linear $k$-juntas is not testable, but adding a surface area constraint makes it testable: we give a $\mathsf{poly}(k \cdot s/\epsilon)$-query non-adaptive tester for linear $k$-juntas with surface area at most $s$. We show that the polynomial dependence on $s$ is necessary. Moreover, we show that if the function is a linear $k$-junta with surface area at most $s$, we give a $(s \cdot k)^{O(k)}$-query non-adaptive algorithm to learn the function up to a rotation of the basis. In particular, this implies that we can test the class of intersections of $k$ halfspaces in $\mathbb{R}^n$ with query complexity independent of $n$.
Feature Selection in Classification using Binary Max-Min Ant System with Differential Evolution

Ant Colony Optimization (ACO), an algorithm based on the natural foraging behavior of ants, has been used as a feature selection method to maximize the performance of the classifier or minimize the number of features in various classification problem applications. The multi-agent nature of the algorithm and indirect coordination mechanism make it attractive for searching a large feature space for an optimal feature subset. However, the iterative nature of the algorithm has a tendency to stagnate and unable to converge toward optimum solutions. This study presents a feature selection method based on the algorithmic variant of ACO called Max-Min Ant System combined with Differential Evolution (BMMASDEFS). Differential Evolution (DE) is introduced to the pheromone update mechanism of Max-Min Ant System (MMAS) to influence the search towards optimal solutions. The performance of the proposed algorithm is tested on well-known benchmark datasets from the UCI Machine Learning Repository and is compared with the results of similar feature selection methods. The proposed algorithm generated feature subsets that are 30% to 60% smaller than the original feature set. The experimental results show that the generated feature subsets improved the performance of the classifier than when all the available features are used. It is also observed that the performance of the classifier using the generated feature subsets is competitive with the results obtained using similar feature selection techniques.
Enhancing the Programmability and Performance Portability of GPU Tensor Operations

Deep-learning models with convolutional networks are widely used for many artificial-intelligence tasks, thanks to the increasing adoption of high-throughput GPUs, even in mobile phones. CUDA and OpenCL are the two largely used programming interfaces for accessing the computing power of GPUs. However, attaining code portability has always been a challenge, until the introduction of the Vulkan API. Still, performance portability is not necessarily provided. In this paper, we investigate the unique characteristics of CUDA, OpenCL, and Vulkan kernels and propose a method for abstracting away syntactic differences. Such abstraction creates a single-source kernel which we use for generating code for each GPU programming interface. In addition, we expose auto-tuning parameters to further enhance performance portability. We implemented a selection of convolution operations, covering the core operations needed for deploying three common image-processing neural networks, and tuned them for NVIDIA, AMD, and ARM Mali GPUs. Our experiments show that we can generate deep-learning kernels with minimal effort for new platforms and achieve reasonable performance. Specifically, our Vulkan backend is able to provide competitive performance compared to vendor deep-learning libraries.
Computer-aided diagnosis system for breast ultrasound images using deep learning

IntroductionUltrasonography has been recommended as an adjunctive modality to mammography, which is insufficient for accurate diagnosis of breast cancer in women with dense breast tissue. However, ultrasonography has the disadvantage of being operator dependent, requiring proficiency in reading ultrasound (US) images and increasing the false positive rate. To overcome these problems, we aimed to develop a computer-aided diagnosis (CAD) system for classification of breast malignant and benign masses on ultrasonography based on the convolutional neural network (CNN), a state-of-the-art deep learning technique.MethodsFrom a large ultrasound (US) image database, managed by Japan Association of Breast and Thyroid Sonology (JABTS), we collected images of 1536 breast masses (897 malignant and 639 benign) confirmed by pathological examinations, with each breast mass captured from various angles using the US imaging probe. We constructed an ensemble network by combining two CNN models (VGG192 and ResNe1523) fine-tuned on balanced training data with the data augmentation which is a popular technique for synthetically generating new samples from the original and used the mass level classification method enabling the CNN to classify a given mass using all views. We explored the grounds of classification by generating a heatmap capable of presenting important regions used by the CNN for classification in humans.ResultsOn an independent 154 test masses (77 malignant and 77 benign), our network showed outstanding classification performance with a sensitivity of 90.9%, a specificity of 87.0%, and an AUC of 0.951 compared with the two CNN models. In addition, our study indicated that not only breast masses but also surrounding tissues are important regions for correct classification.ConclusionThis CNN-based CAD system is expected to assist doctors from the viewpoint of second opinion and improve the diagnosis of breast cancer in clinical practice.
A Study on Different Types of Web Crawlers

The world wide web is a global information medium in which as many people as possible explore the information around the world. Search engine is a place where internet users search for the required content and the results are returned to users through websites, images or videos. Here web crawlers emerged that browses the web to gather and download pages relevant to user topics and store them in a large repository that makes the search engine more efficient. These web crawlers are becoming more important and growing daily. This paper presents the various web crawler types and their architectures. Comparisons are analyzed between these crawlers.
Machine Translation With Weakly Paired Documents

Neural machine translation, which achieves near human-level performance in some languages, strongly relies on the large amounts of parallel sentences, which hinders its applicability to low-resource language pairs.Recent works explore the possibility of unsupervised machine translation with monolingual data only, leading to much lower accuracy compared with the supervised one.Observing that weakly paired bilingual documents are much easier to collect than bilingual sentences, e.g., from Wikipedia, news websites or books, in this paper, we investigate training translation models with weakly paired bilingual documents.Our approach contains two components.1) We provide a simple approach to mine implicitly bilingual sentence pairs from document pairs which can then be used as supervised training signals.2) We leverage the topic consistency of two weakly paired documents and learn the sentence translation model by constraining the word distribution-level alignments.We evaluate our method on weakly paired documents from Wikipedia on six tasks, the widely used WMT16 German↔English, WMT13 Spanish↔English and WMT16 Romanian↔English translation tasks.We obtain 24.1/30.3,28.1/27.6 and 30.1/27.6 BLEU points separately, outperforming previous results by more than 5 BLEU points in each direction and reducing the gap between unsupervised translation and supervised translation up to 50%.
UdS-DFKI Participation at WMT 2019: Low-Resource (en-gu) and Coreference-Aware (en-de) Systems

This paper describes the UdS-DFKI submission to the WMT2019 news translation task for Gujarati-English (low-resourced pair) and German-English (document-level evaluation).Our systems rely on the on-line extraction of parallel sentences from comparable corpora for the first scenario and on the inclusion of coreference-related information in the training data in the second one.