Reviewerly/exHarmony huggingface dataset:
Parallel trajectory planning for shipborne Autonomous collision avoidance system

Collision at sea is always a significant issue affecting the safety of ship navigation. The shipborne autonomous collision avoidance system (SACAS) has the great advantage to minimize collision accidents in ship navigation. A parallel trajectory planning architecture is proposed in this paper for SACAS system. The fully-coupled deliberative planner based on the modified RRT algorithm is developed to search for optimal global trajectory in a low re-planning frequency. The fully-coupled reactive planner based on the modified DW algorithm is developed to generate the optimal local trajectory in a high re-planning frequency to counteract the unexpected behavior of dynamic obstacles in the vicinity of the vessel. The obstacle constraints, ship maneuvering constraints, COLREGs rules, trajectory optimality, and real-time requirements are satisfied simultaneously in both global and local planning to ensure the collision-free optimal navigation in compliance with COLREGs rules. The on-water tests of a trimaran model equipped with a model-scale SACAS system are presented to demonstrate the effectiveness and efficiency of the proposed algorithm. The good balance between the computational efficiency and trajectory optimality is achieved in parallel trajectory planning.
Permissive Supervisor Synthesis for Markov Decision Processes Through Learning

This paper considers the permissive supervisor synthesis for probabilistic systems modeled as Markov Decision Processes (MDP). Such systems are prevalent in power grids, transportation networks, communication networks, and robotics. We propose a novel supervisor synthesis framework using automata learning and compositional model checking to generate the permissive local supervisors in a distributed manner. With the recent advances in assume-guarantee reasoning verification for MDPs, constructing the composed system can be avoided to alleviate the state space explosion. Our framework learns the supervisors iteratively using counterexamples from the verification and is guaranteed to terminate in finite steps and to be correct.
Neural machine translation of clinical texts between long distance languages

Abstract Objective To analyze techniques for machine translation of electronic health records (EHRs) between long distance languages, using Basque and Spanish as a reference. We studied distinct configurations of neural machine translation systems and used different methods to overcome the lack of a bilingual corpus of clinical texts or health records in Basque and Spanish. Materials and Methods We trained recurrent neural networks on an out-of-domain corpus with different hyperparameter values. Subsequently, we used the optimal configuration to evaluate machine translation of EHR templates between Basque and Spanish, using manual translations of the Basque templates into Spanish as a standard. We successively added to the training corpus clinical resources, including a Spanish-Basque dictionary derived from resources built for the machine translation of the Spanish edition of SNOMED CT into Basque, artificial sentences in Spanish and Basque derived from frequently occurring relationships in SNOMED CT, and Spanish monolingual EHRs. Apart from calculating bilingual evaluation understudy (BLEU) values, we tested the performance in the clinical domain by human evaluation. Results We achieved slight improvements from our reference system by tuning some hyperparameters using an out-of-domain bilingual corpus, obtaining 10.67 BLEU points for Basque-to-Spanish clinical domain translation. The inclusion of clinical terminology in Spanish and Basque and the application of the back-translation technique on monolingual EHRs significantly improved the performance, obtaining 21.59 BLEU points. This was confirmed by the human evaluation performed by 2 clinicians, ranking our machine translations close to the human translations. Discussion We showed that, even after optimizing the hyperparameters out-of-domain, the inclusion of available resources from the clinical domain and applied methods were beneficial for the described objective, managing to obtain adequate translations of EHR templates. Conclusion We have developed a system which is able to properly translate health record templates from Basque to Spanish without making use of any bilingual corpus of clinical texts or health records.
Software Defect Prediction Using Hybrid Distribution Base Balance Instance Selection and Radial Basis Function Classifier

Software is an important part of human life and with the rapid development of software engineering the demands for software to be reliable with low defects is increasingly pressing. The building of a software defect prediction model is proposed in this article by using various software metrics with publicly available historical software defect datasets collected from several projects. Such a prediction model can enable the software engineers to take proactive actions in enhancing software quality from the early stages of the software development cycle. This article introduces a hybrid classification method (DBBRBF) by combining distribution base balance (DBB) based instance selection and radial basis function (RBF) neural network classifier to obtain the best prediction compared to the existing research. The experimental results with post-hoc statistical significance tests shows the effectiveness of the proposed approach.
Bag similarity network for deep multi-instance learning

The effectiveness of multi-instance learning (MIL) has been demonstrated by its wide spectrum of applications in computer vision, biometrics, and natural language processing. Recently, solving MIL problems using deep neural networks has proven to be highly effective. However, in current multi-instance neural networks, the feature representation of each bag is learned individually, and the relations between bags are not considered. In this study, we propose a novel neural network for MIL that emphasizes modeling the affinities between bags. It achieves a more effective bag representation than previous methods. Specifically, a bag with multiple instances is modeled by its similarity to other bags, and the similarity calculation is carried out in a novel neural network, termed the bag similarity network (BSN). Training the BSN involves two representation learning problems: instance feature learning and bag similarity learning. To avoid the complex interdependence of these problems, we decouple the BSN training process by first training an instance feature learning network, and then construct a bag similarity network, each of which is optimized end-to-end by back-propagation. Experiments are conducted to demonstrate clearly the advantage of the proposed method over other state-of-the-art methods on various MIL datasets.
Constrained ensemble Kalman filter based on Kullback–Leibler divergence

Conventional recursive state estimation procedures cannot handle inequality constraints; therefore, they might result in physically meaningless state estimates. In this work, we develop a novel state estimation technique to incorporate inequality constraints in ensemble Kalman filters (EnKF). For this purpose, we first solve for the unconstrained EnKF state estimation, and then construct a constrained distribution based on the unconstrained one by formulating an optimization problem using the Kullback–Leibler (KL) divergence. The proposed constraint implementation technique is cast as a convex optimization problem involving second order cone constraints which can be solved to global optimality. This constraint implementation step can be integrated into any Gaussian filter. Demonstrative case studies of chemical reaction systems are presented to compare and illustrate the efficacy of the proposed state estimation methodology.
Automatic HCC Detection Using Convolutional Network with Multi-Magnification Input Images

Liver cancer postoperative pathologic examination of stained tissues is an important step in identifying prognostic factors for follow-up care. Traditionally, liver cancer detection would be performed by pathologists with observing the entire biological tissue, resulting in heavy work loading and potential misjudgment. Accordingly, the studies of the automatic pathological examination have been popular for a long period of time. Most approaches of the existing cancer detection, however, only extract cell level information based on single-scale high-magnification patch. In liver tissues, common cell change phenomena such as apoptosis, necrosis, and steatosis are similar in tumor and benign. Hence, the detection may fail when the patch only covered the changed cells area that cannot provide enough neighboring cell structure information. To conquer this problem, the convolutional network architecture with multi-magnification input can provide not only the cell level information by referencing high-magnification patches, but also the cell structure information by referencing low-magnification patches. The detection algorithm consists of two main structures: 1) extraction of cell level and cell structure level feature maps from high-magnification and low-magnification images respectively by separate general convolutional networks, and 2) integration of multi-magnification features by fully connected network. In this paper, VGG16 and Inception V4 were applied as the based convolutional network for liver tumor detection task. The experimental results showed that VGG16 based multi-magnification input convolutional network achieved 91% mIOU on HCC tumor detection task. In addition, with comparison between single-scale CNN (SSCN) and multi-scale CNN (MSCN) approaches, the MSCN demonstrated that the multi-scale patches could provide better performance on HCC classification task.
Taming Hyper-parameters in Deep Learning Systems

Deep learning (DL) systems expose many tuning parameters ("hyper-parameters") that affect the performance and accuracy of trained models. Increasingly users struggle to configure hyper-parameters, and a substantial portion of time is spent tuning them empirically. We argue that future DL systems should be designed to help manage hyper-parameters. We describe how a distributed DL system can (i) remove the impact of hyper-parameters on both performance and accuracy, thus making it easier to decide on a good setting, and (ii) support more powerful dynamic policies for adapting hyper-parameters, which take monitored training metrics into account. We report results from prototype implementations that show the practicality of DL system designs that are hyper-parameter-friendly.
The Logic of Action Lattices is Undecidable

We prove algorithmic undecidability of the (in)equational theory of residuated Kleene lattices (action lattices), thus solving a problem left open by D. Kozen, P. Jipsen, W. Buszkowski.
A Preliminary Study of Android Refactorings

Throughout the lifetime of a software system, developers continuously refactor the source code as a means of improving the quality of the code. Android apps, like traditional software systems, are not exempt from such refactoring activities. We conducted an empirical study to show the common refactoring operations in Android apps and discussed how they differ from traditional Java systems. Additionally, by contextualizing refactorings with commit messages, we identified the motivations for developers to perform these refactoring operations.
How we designed winning algorithms for abstract argumentation and which insight we attained

In this paper we illustrate the design choices that led to the development of ArgSemSAT, the winner of the preferred semantics track at the 2017 International Competition on Computational Models of Arguments (ICCMA 2017), a biennial contest on problems associated to the Dung's model of abstract argumentation frameworks, widely recognised as a fundamental reference in computational argumentation. The algorithms of ArgSemSAT are based on multiple calls to a SAT solver to compute complete labellings, and on encoding constraints to drive the search towards the solution of decision and enumeration problems. In this paper we focus on preferred semantics (and incidentally stable as well), one of the most popular and complex semantics for identifying acceptable arguments. We discuss our design methodology that includes a systematic exploration and empirical evaluation of labelling encodings, algorithmic variations and SAT solver choices. In designing the successful ArgSemSAT, we discover that: (1) there is a labelling encoding that appears to be universally better than other, logically equivalent ones; (2) composition of different techniques such as AllSAT and enumerating stable extensions when searching for preferred semantics brings advantages; (3) injecting domain specific knowledge in the algorithm design can lead to significant improvements.
Crowd Video Event Classification using Convolutional Neural Network

Crowd Event Classification in videos is an important and challenging task in computer vision based systems. The Crowd Event Classification system recognizes a large number of video events. The decisive of the model is a difficult task in the event classification. The event classification model has generalization capability on works with a higher number of videos. The embodiment of Deep Learning in video event classification derives powerful and distinguishes feature portrayals. The features of events are extracted from raw data through massive videos with effective and efficient detection. The Convolutional Neural Network (CNN) has been established as a powerful classification model for event recognition problems. A higher quality of new dataset of 3000 frames collected from youtube videos belonging to four classes of crowd events namely Marriage, Cricket, Jallikkattu, and Shopping mall The system has used two Deep CNN infrastructures are namely baseline and VGG16, which detects predefined events and provides temporal evidence. The CNN Model automatically tests input video frames and detects the events of centrality at the video. The CNN extracts the video events features from the video input frames and distinguishes the events name correctly. The system shows more improved 100% results compare with each other models.
Closure conversion is safe for space

We formally prove that closure conversion with flat environments for CPS lambda calculus is correct (preserves semantics) and safe for time and space, meaning that produced code preserves the time and space required for the execution of the source program. We give a cost model to pre- and post-closure-conversion code by formalizing profiling semantics that keep track of the time and space resources needed for the execution of a program, taking garbage collection into account. To show preservation of time and space we set up a general, "garbage-collection compatible", binary logical relation that establishes invariants on resource consumption of the related programs, along with functional correctness. Using this framework, we show semantics preservation and space and time safety for terminating source programs, and divergence preservation and space safety for diverging source programs. We formally prove that closure conversion with flat environments for CPS lambda calculus is correct (preserves semantics) and safe for time and space, meaning that produced code preserves the time and space required for the execution of the source program. We give a cost model to pre- and post-closure-conversion code by formalizing profiling semantics that keep track of the time and space resources needed for the execution of a program, taking garbage collection into account. To show preservation of time and space we set up a general, "garbage-collection compatible", binary logical relation that establishes invariants on resource consumption of the related programs, along with functional correctness. Using this framework, we show semantics preservation and space and time safety for terminating source programs, and divergence preservation and space safety for diverging source programs. This is the first formal proof of space-safety of a closure-conversion transformation. The transformation and the proof are parts of the CertiCoq compiler pipeline from Coq (Gallina) through CompCert Clight to assembly language. Our results are mechanized in the Coq proof assistant.
Commodifying Pointing in HRI: Simple and Fast Pointing Gesture Detection from RGB-D Images

We present and characterize a simple and reliable method for detecting pointing gestures suitable for human-robot interaction applications using a commodity RGB-D camera. We exploit an existing Deep CNN model to robustly find hands and faces in RGB images, then examine the corresponding depth channel pixels to obtain full 3D pointing vectors. We test several methods of estimating the hand end-point of the pointing vector. The system runs at better than 30Hz on commodity hardware: exceeding the frame rate of typical RGB-D sensors. An estimate of the absolute pointing accuracy is found empirically by comparison with ground-truth data from a VICON motion-capture system, and the useful interaction volume established. Finally, we show an end-to-end test where a robot estimates where the pointing vector intersects the ground plane, and report the accuracy obtained. We provide source code as a ROS node, with the intention of contributing a commodity implementation of this common component in HRI systems.
A Comprehensive View of Automatic Speech Recognition System - A Systematic Literature Review

Humans have always attempted to correspond with objects in a natural language. Communications have been the essential feature of human life, a powerful tool for sharing and building the information that is passed from generation to generation. Among speech processing problems, automatic speech recognition mechanisms of converting the recorded speech signals into the text are one of the most challenging tasks. The signals are typically processed in a digital representation, so speech processing can be observed as a particular case of digital signal processing. The overall performance of an automatic speech recognition system greatly depends upon the acoustic modeling. Hence, building a precise and robust acoustic model holds the key to a suitable recognition performance. People have used different methods for automated speech recognition system. For recognizing the speech people always choose the English language in the majority of the research and implementation but very less work is done in other languages. Our analysis presents the study of the different speech recognition systems present in Indian and foreign languages in the systematic review of speech recognition paper. This paper gives the review of different aspects related to Automatic Speech recognition. We have elaborated the recent advancement in the speech recognition system, robust method for the development of an automatic speech recognition system and application of automatic speech recognition system in different fields.
A novel optimization booster algorithm

In this paper, a novel meta-heuristic method called the Optimization Booster Algorithm (OBA) is presented. It incorporates existing optimization methods with human-inspired intelligence, which applies particularly while conducting business in exchange markets. A key objective in exchange markets is to increase wealth over time, which is typically the same objective when performing optimizations. Moreover, optimization is about finding a way to increase the fitness value of a system, by spending adequate computation time. The OBA is founded on the core idea that a key reason behind the rapid evolution of human societies compared to the tortoise-like natural evolution is the forward-looking approach. In exchange markets, analysts have learned to make decisions based on forecasted prices, rather than the current prices; and this is an illustrative example of the application of such forward-looking approaches, which form the essence of the OBA. Following extensive numerical experiments, and applications of fourteen well-known heuristic and meta-heuristic methods to solve seventy-one non-linear unconstrained and constrained, single-objective and multi-objective benchmarks, before and after receiving a boost, the OBA performance is investigated. It has proven — in both theory and practice — to quite significantly improve existing optimization methods. In most cases, boosting resulted in much better quality of outputs, while requiring less computation time.
The iPAC Scale: A Survey to Measure Distinctive Mobile Pedagogies

This paper develops and examines a scale to capture teachers' views of the mobile pedagogies they are adopting. Mobile pedagogies refer to the practices and approaches teachers use that involve mobile technologies to support learning. A mobile technology is any portable, handheld technology that potentially supports learning and includes laptops, tablets and smartphones. This study develops a rigorous scale to capture mobile pedagogies adopted in mobile learning tasks designed by teachers. In particular, the scale focuses on measuring pedagogies related to the iPAC framework which comprises three overarching dimensions of m-learning: personalisation, authenticity and collaboration (PAC). Each dimension has a pair of sub-components associated with it (for personalisation: agency and customisation; for authenticity: context and task; for collaboration: conversation and co-creation). The reliability and validity of a 20-item scale is assessed. A measure of overall m-learning experiences was also established and found to be significantly predicted by m-learning pedagogy. We also report on differences between teachers in m-learning practice with respect to subject area and year of schooling.
Logic-driven autoencoders

Autoencoders are computing architectures encountered in various schemes of deep learning and realizing an efficient way of representing data in a compact way by forming a set of features. In this study, a concept, architecture, and algorithmic developments of logic-driven autoencoders are presented. In such structures, encoding and the decoding processes realized at the consecutive layers of the autoencoder are completed with the aid of some fuzzy logic operators (namely, OR, AND, NOT operations) and the ensuing encoding and decoding processing is carried out with the aid of fuzzy logic processing. The optimization of the autoencoder is completed through a gradient-based learning. The transparent knowledge representation delivered by autoencoders is facilitated by the involvement of logic processing, which implies that the encoding mechanism comes with the generalization abilities delivered by OR neurons while the specialization mechanism is achieved by the AND-like neurons forming the decoding layer. A series of illustrative examples is also presented.
A Latent Variable Model for Learning Distributional Relation Vectors

Recently a number of unsupervised approaches have been proposed for learning vectors that capture the relationship between two words. Inspired by word embedding models, these approaches rely on co-occurrence statistics that are obtained from sentences in which the two target words appear. However, the number of such sentences is often quite small, and most of the words that occur in them are not relevant for characterizing the considered relationship. As a result, standard co-occurrence statistics typically lead to noisy relation vectors. To address this issue, we propose a latent variable model that aims to explicitly determine what words from the given sentences best characterize the relationship between the two target words. Relation vectors then correspond to the parameters of a simple unigram language model which is estimated from these words.