Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Networks Based Level Set Framework for Pancreas Segmentation from CT Images

Pancreas segmentation in computed tomography (CT) is a challenging task because of the high inter-patient anatomical variability in both shape and size between patients. In this work, we proposed a convolutional neural networks based level set framework that can automatically segment a 3D images with the whole pancreas. Convolutional neural networks is applied to obtain an initial level set contour and then level set model is used to produce accurate segmentation results. Our method was compared with the state-of-the-art methods and evaluated on 20 CT images. The experiment results show that our approach achieves the highest dice scores than other methods.
Analysis of key performance indicators in a manufacturing plant via fuzzy cognitive maps

Companies have to follow key performance indicators (KPIs) for their key objectives, such as increasing of productivity and profit, and reducing cost. Performance measurement and management is also important in this context. The interaction between key performance indicators is often ignored and these interactions are expressed by the subjective evaluations of decision makers. In this study, fuzzy cognitive map (FCM) was trained based on historical data in a production system and connection matrix was formed/analyzed based on the data. KPIs and their values are taken from the company where the present application is performed.
Lightweight Federated Data Networks with Das2 Tools

Das2 describes a collection of cooperating programs originally created to support daily review and analysis activities of the Cassini Radio and Plasma Wave Science (RPWS) investigation. The system proved useful and is now relied on for rapid access to working data sets from many planetary missions including Mars Express and Juno, as well as ground-based radio astronomy results from the Nançay Decameter Array and Long Wave Array 1. As more teams have begun using das2 tools, the need for updates to what was largely an in-house protocol have become apparent. Though Autoplot continues to be the most flexible and commonly used das2 client program, interfacing with other tools would benefit the research community. We will provide an overview of recent updates to the das2 interface definition supporting redundant servers and federated data source catalogs. We will describe a cross site deployment between Southwest Research Institute and the University of Iowa to aid collaboration among ASPERA-3 and MARSIS investigators. We will give an update on the status of new client libraries for NumPy and IDL, as well as das2 stream adapters for HAPI and VOTables. Finally, we will describe how working data sets can be published in-place by registering das2 data sources with a VESPA EPN-TAP service.
Rational Delegation Computing Using Information Theory and Game Theory Approach

Delegation computing is a calculation protocol between non-cooperative participants, and its results are influenced by the participant’s choice of behavior. The goal of this paper is to solve the problem of high communication overhead in traditional delegation computing schemes. Combining the advantages of information theory and game theory, we propose a rational delegation computing scheme, which guarantees the correctness of the calculation results through the participant utility function. First, by analyzing the participant behavior strategy, we design the game model, which includes the participant set, information set, behavior strategy set and utility function. Second, according to the combination of Nash equilibrium and channel capacity limit in the game model, we construct a rational delegation computing scheme in this paper. Finally, we analyze and prove the scheme. When both the delegation party and computing party choose the honesty strategy, their utility reaches the maximum, that is, the global can reach the Nash equilibrium state, and the calculation efficiency has also been improved.
Comparison Study on Artificial Neural Network and Online Sequential Extreme Learning Machine in Regression Problem

At present, machine learning techniques is frequently used to extraction and/or prediction some information from enormous collected data. Traditional machine learning is performed batch learning like a popular one, Artificial Neural Network (ANN). That means it can’t integrate new information into already trained model but it is retrained from scratch. Online Sequential Extreme Learning Machine (OS-ELM) is a one of online incremental machine learning techniques that can learn and update model from new receive data without to retrain the model. This paper shows the result of comparison experiment between ANN and OS-ELM and found that, the OS-ELM has acceptable performance in situation of few initial data for training and/or statistic properties of data is changing while it working.
A new Tikhonov-TV regularization for optical flow computation

Tikhonov regularization and total variation (TV) are two famous smoothing techniques used in variational image processing problems and in particular for optical flow computation. We consider a new method that combines these two approaches in order to reconstruct piecewise-smooth optical flow. More precisely, we split the flow vector into the sum of its smooth and piecewise constant components, and then regularize the smooth part by quadratic Tikhonov regularization and the piecewise constant part by total variation. We solve the new variational optical flow problem through a discretize-optimize approach by applying a fast multilevel truncated Newton method. Experiments are performed on images from the Middlebury training benchmark to show the performance of our proposed method.
Movable camera and stereoscopic image system for teleoperation of aerial robot working in high place

We describe a stereo camera and HMD system for teleoperation of aerial manipulation robot. The aerial robot in this study consists of a hexarotor platform, a top-mounted gripper and a manipulator mounted at the bottom of the body. After fixing the robot by grasping a bar-like object using the top-mounted gripper, the bottom-mounted manipulator is used to perform manipulation tasks.The proposed system consists of an on-board stereo camera that can rotate and slide according to the movement of operator’s head. During the manipulation process, the movable stereo camera eliminate an occlusion and provides stereo perception to operator.
How Compact Will My System Be? A Fully-Automated Way to Calculate LoC Reduced by Clone Refactoring

A code clone (in short, clone) is a code fragment that is identical or similar to other code fragments in source code. The presence of clone is known as bad smell, which is phenomena of source code to be refactored. A motivation of refactoring (merging) clones is to reduce the size of source code. An existing study proposed a technique to estimate reduced lines of code by merging clones; however, there are two issues in the existing technique: (1) the existing technique does not consider the refactorability of clones in spite that it is difficult or even impossible to merge some clones due to the limitation of programming languages; (2) in the case that multiple clones are overlapping, the existing technique only considers one of them can be merged. Due to the above issues, estimated reducible LoC is occasionally different from the actual number. Consequently, in this research, we propose a new technique to calculate a reducible LoC. The proposed technique is free from the two issues, and it calculates a reducible LoC fully automatically. The proposed technique performs a loop processing of (a) detecting clones, (b) merging them, (c) compiling the edited source files, and (d) testing them. After finishing the loop, reducible LoC is calculated from the edited source files. This paper also includes comparison results of the proposed technique and the existing one. In the comparisons, we confirmed that a reducible LoC which was calculated with considering refactorability is 25% of a reducible LoC which was estimated without considering refactorability. We also confirmed that the proposed technique was able to merge clones that were not counted in the existing technique.
An overview of multi-filters for eliminating impulse noise for digital images

An image through the digitization process is referred to as a digital image. The quality of the digital image may be degenerating due to interferences on the acquisition, transmission, extraction, etc. This attracted the attention of many researchers to study the causes of damage to the information in the image. In addition to finding cause of image damage, the researchers also looking for ways to overcome this problem. There are many filtering techniques that have been introduced to deal the damage to the information in the image. In addition to eliminating noise from the image, filtering techniques also aims to maintain the originality of the features in the image. Among the many research papers on image filtering there is a lack of review papers which are an important to facilitate researchers in understanding the differences in each filtering technique. Additionally, it helps researchers determine the direction of research conducted based on the results of previous research. Therefore, this paper presents a review of several filtering techniques that have been developed so far.
The Integrated Computational Environment for Optimization of Complex Systems

The concept, general structure and main components of the integrated computing environment (ICE) for optimization of complex systems based on the methods of conditional minimization of the objective functional characterizing the initial formulation of the problem of mathematical modeling on supercomputers are considered. The inverse problem is solved based on a sequence of direct initial-boundary value problems, each of which can be a resource-intensive interdisciplinary problem described by a system of multidimensional differential and/or integral (and/or discrete) equations in multi-scale computational domains with multi-connected piecewise smooth geometric configurations of boundaries and contrast material properties of multiphase media. This includes, in particular, the actual problems of surrogate optimization. The functional core of the ICE is focused on a high-performance support of all major stages of a large-scale computational experiment, including geometric and semantic modeling, generation of computational grids, approximation of initial equations, solution of algebraic problems, optimization algorithms for solving inverse problems, post-processing and visualization of results, decision-making on the results of the study. Technical requirements for the ICE involve a flexible expansion of the models and methods used, adaptation to the evolution of computer platforms, effective reuse of external software products, coordinated participation of different teams of developers and focus on a broad demand of the end users from different industries. These qualities are designed to provide a long life cycle and a qualitative increase in productivity in the development and application of the new generation software.
A Rule Induction Approach to Forecasting Critical Alarms in a Telecommunication Network

This paper proposes a white box method of predicting critical alarms so they can be mitigated and understood by engineers. Forecasting these alarms will avoid outages and maintain the agreed service level which is beneficial to both the provider of telecommunication services and the consumers. The paper evaluates several item set mining approaches on a set of alarms of the British Telecom (BT) national telecommunication network and proposes a novel transformation of the data to enable the discovery of patterns undetectable by current item set mining approaches. The result is a method for rule induction that predicts alarms with high precision using a wide range of features.
Modeling Dialogues with Hashcode Representations: A Nonparametric Approach

We propose a novel dialogue modeling framework, the first-ever nonparametric kernel functions based approach for dialogue modeling, which learns hashcodes as text representations; unlike traditional deep learning models, it handles well relatively small datasets, while also scaling to large ones. We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses. As demonstrated on three real-life datasets, including prominently psychotherapy sessions, the proposed approach significantly outperforms several state-of-art neural network based dialogue systems, both in terms of computational efficiency, reducing training time from days or weeks to hours, and the response quality, achieving an order of magnitude improvement over competitors in frequency of being chosen as the best model by human evaluators.
A predictive model to identify Kanban teams at risk

Kanban, which is an agile process methodology as well as a means to implement lean principles, has been growing as a project management framework across a range of domains, including manufacturing, software development and data science. This paper explores, for teams using Kanban, the ability to pr edict low team performance. The prediction is based on an analytical model that uses specific project metrics that can be collected via the team’s visual Kanban board. Specifically, data from 80 teams was used to build and test machine learning models that predict teams at risk for delivering low quality results. The model developed was significantly better than the baseline situation of thinking that all teams were at risk. While this analysis was done within a data science project context, the results are likely applicable across a range of information system projects.
Ant colony systems optimization applied to BNF grammars rule derivation (ACORD algorithm)

Ant colony systems have been widely employed in optimization issues primarily focused on path finding optimization, such as travelling salesman problem. The main advantage lies in the choice of the edge to be explored, defined using pheromone trails. This paper proposes the use of ant colony systems to explore a Backus–Naur form grammar whose elements are solutions to a given problem. Similar models, without using ant colonies, have been used to solve optimization problems or to automatically generate programs such as grammatical swarm (based on particle swarm optimization) and grammatical evolution (based on genetic algorithms). This work presents the application of proposed ant colony rule derivation algorithm and benchmarks this novel approach in a well-known deceptive problem, the Santa Fe Trail. Proposed algorithm opens the way to a new branch of research in swarm intelligence, which until now has been almost nonexistent, using ant colony algorithms to generate solutions of a given problem described by a BNF grammar with the advantage of genotype/phenotype mapping, described in grammatical evolution. In this case, such mapping is performed based on the pheromone concentration for each production rule. The experimental results demonstrate proposed algorithm outperforms grammatical evolution algorithm in the Santa Fe Trail problem with higher success rates and better solutions in terms of the required steps.
Adaptive Neural Dynamic Surface Control for the Chaotic PMSM System with External Disturbances and Constrained Output

Background: This article studies the issue of adaptive neural dynamic surface control for the chaotic permanent magnet synchronous motor system with constrained output, external disturbances and parameter perturbations. Methods: Firstly, a virtual controller and two practical controllers are created based on the backstepping framework. In the process of creating controllers, adaptive technique and radial basis function neural networks are used to handle unknown parameters and nonlinearities, respectively. The nonlinear damping items are applied to overcome external disturbances. The barrier Lyapunov function is used to prevent the violation of system output constraint. Meanwhile, the first-order filter to eliminate the “explosion of complexity” of traditional back stepping has been introduced. Then, it is proved that all the closed-loop signals are uniform ultimate asymptotic stability and the tracking error converges to a small set of origin. Results: The effectiveness and robustness of the developed approach are illustrated by numerical simulations. Conclusion: The raised control scheme is a useful tool for enhancing the performance of the chaotic PMSM system with external disturbances, constrained output and parameter perturbations.
Collision-Free Trajectory Planning for Multi-UAV Coordinated Ground Attack Mission Under Uncertainties

To cope with the uncertainties of position and arrival time of UAVs during trajectory planning, a trajectory optimization method considering timing constraints to eliminate collisions in the multi-UAV cooperated ground attack mission. Firstly, characteristics of multi-UAV coordinated ground attack missions are analyzed, and a mission model of coordinated attacking is established. Secondly, a trajectory collision degree function is introduced. Collisions can be eliminated by changing control variables of flight time, trajectory and flight height. Simulation results show feasibility and applicability of the proposed method in solving the collision-free trajectory planning problem.
GlassMasq: Adversarial Examples Masquerading in Face Identification Systems with Feature Extractor

Face identification systems based on deep neural networks (DNNs) have been widely introduced in various areas. The key component of the systems is a feature extractor trained to map a face image into a feature space to distinguish a large number of unspecified individuals. Several works revealed that the feature extractor is vulnerable to carefully crafted adversarial examples. These works indicate that we could manipulate the outputs of the feature extractor by perturbing a source image. However, they might require large perturbations to ensure that the perturbed image is recognized incorrectly. This raises a question: how can we craft adversarial examples which leads miss-identification into a target identity with high-confidence and small perturbation? In this paper, to obtain adversarial examples with high confidence and small perturbation, we first introduce a condition which adversarial examples against the face identification systems should satisfy, then introduce a new method called GlassMasq to create adversarial examples based on the condition. In our evaluations, we demonstrate that our generated adversarial examples can deceive the face identification systems with higher confidence and at most 62.8% smaller perturbation than existing methods. The proposed method enables us to evaluate robustness of the face identification system against adversarial examples in a more appropriate manner.
SWRL Parallel Reasoning Implementation with Spark SQL

Abstract With the rapid development of semantic Web and big data technology, ontology data has the characteristics of large-scale, high-speed growth and diversity which big data has. On one hand, the conventional ontology reasoners do not scale well for large amounts of ontologies because they are designed for run on a single machine. On the other hand, the existing scalable reasoners are not perfect enough, for example, to completely support the widely used Semantic Web Rule Language (SWRL) rules. This paper presents an implementation for SWRL scalable parallel reasoning using the Spark SQL programming model, and optimizes and processes some of the problems in the implementation.
A Study on the Development of Class Observation Tools for attending Korean classes

The purpose of the study aims to review the contents of the class observations discussed in the precedent researches and develop Korean language class observation tools based on the Korean language teachers' awareness survey. Based on the precedent studies, the detailed items of classroom observation were derived from the upper category of “teacher quality and characteristics, class contents, class operation”. The survey was based on what Korean teachers attach importance to in classroom observations and what items they value based on the observations developed in this study. In a subjective survey on class observation items, there were differences between prospective and experienced teachers; prospective teachers considered ‘teacher quality and characteristics’ and ‘class content’ important, while experienced teachers thought ‘teacher quality and characteristics’ and ‘class operation’ were important. However not many significant differences were perceived between these two groups when the observation items were presented classroom observation tools developed by this study. Yet, in detail, ‘teacher’s creating for class atmosphere’ among the details of ‘teacher's qualities and characteristics,’ ‘teacher's ability to respond to unexpected questions or conditions’ among the details of ‘class operation’, ‘review of past classes in the introduction stage,’ ‘clear instruction of exercise activities’ and ‘suitable evaluation or confirmation at the conclusion of the class’ among the details in ‘class content.’ showed significant differences. The results suggest that it will be essential to provide class observation tools and training for prospective and experienced teachers. Seohyung Kim(Kyonggi University), Sun Hee Park(Ewha Womans University), Mira Jang(Kyung Hee Cyber University)
Re-purposing computers as a cluster for academic institutions

Over the past few years, MCAST has been increasing its commitment towards research, yet a common limitation is the availability of processing power by our researchers. Whilst funding is available for the setting up of classroom computers, there isn’t enough funding for high-end processing infrastructure for research purposes. We have thus undertaken a research project to explore the possibility of repurposing existing computer systems and have them work in a cluster during out-of-office hours to address just this need. In this initial phase of our research, we are presenting our findings on a simple proof of concept using seven single-board computers benchmarked as a Beowulf and Dispy clusters. The intention is to further this research by testing up to 25 single-board computers then to migrate our setup and findings to 120 computers. Our findings in this research are very encouraging and justify the next phases of this project.
Curiosity Driven Deep Reinforcement Learning for Motion Planning in Multi-agent Environment

Motion planning in dynamic multi-agent environment tends to be a challenging feat by itself. If the agent is also required to complete an elaborate task the complexity of the problem substantially increases. Heuristically designing policies for unstructured environments can be unfeasible and time consuming for certain scenarios. We propose a Deep Reinforcement Learning approach for a continuous multi-agent setting that is robust enough to handle high-level task accomplishment and low-level collision avoidance control. To alleviate disadvantages of a sparse reward environment we introduce intrinsic reward inspired by the curious behavior of animals and humans. As for the low-level, collision avoidance segment of control, we introduce a general reward function. Furthermore, we formulate an agent-centric state space and implement robust Reinforcement Learning algorithm that is capable of handling reward signal from both sources. Suitable 3D physical environment is constructed to examine the feasibility of our approach. Subsequently, case study is performed and effectiveness of our method is validated when compared with the state-of-the-art Reinforcement Learning algorithm.