Reviewerly/exHarmony huggingface dataset:
Machine Learning of SPARQL Templates for Question Answering Over LinkedSpending

We present a Question Answering system aimed to answer natural language questions over the open RDF spending data provided by LinkedSpeding. We propose an original machine-learning approach to learn generalized SPARQL templates from an existing training set of (NL question, SPARQL query) pairs. In our approach, the generalized SPARQL templates are fed to an instance-based classifier that associates a given user-provided question to an existing pair that is used to answer the user question. We employ an external tagger, delegating the Named-Entity Recognition (NER) task to a service developed for the domain we want to query. The problem is particularly challenging due to the small training set size available, counting only 100 questions/SPARQL queries. We illustrate the results of our new approach using data provided by the Question Answering over Linked Data challenge (QALD-6) task 3, showing that we can provide a correct answer to 14 of the 50 questions of the test set. These results are then compared to existing systems, including our previous system, QA3, where templates were provided by an expert rather than being generated automatically from a training set.
A Particle Swarm Optimization with Filter-based Population Initialization for Feature Selection

Feature selection is an important research issue in classification. As an effective global optimization technique, Particle Swarm Optimization (PSO) algorithm has been widely employed to solve feature selection problems. Population initialization is an important stage in Evolutionary Computation (EC) techniques. Therefore, in EC research field, many works focus on population initialization and it has been verified that suitable population initialization methods can significantly improve the performance of EC techniques. The efficient of PSO significantly deteriorates when being used to solve the large-scale feature selection problems. At present, only a few works have been done to enhance the performance of PSO for feature selection problems through population initialization. Meanwhile, there are many excellent traditional filter feature selection methods. Their characteristic is quick in speed but low in effect. However, they can provide a lot of useful heuristic information. Therefore, this paper focuses on improving the performance of PSO for feature selection problems by designing filter-based population initialization methods. In the proposed method, the filters are firstly used to evaluate features. Based on the obtained heuristic information, the initialization method combining the mixed initialization and the threshold selection is designed. The experiments are carried out on several datasets and the proposed initialization method is compared to some related initialization methods. Besides, the K-Nearest Neighbour (KNN) and other compatible fitness learners are used for performance assessment for feature selection. The results show that the proposed method using KNN classifier is promising to improve the performance of PSO for feature selection problems.
Quantifying the adaptability of workflow-based service compositions

Service composition, which is the integration of heterogeneous Web-accessible services, has introduced an open phenomenon for creating software systems that are deployed in an unpredictable execution context, where changes in the requirements, user preferences, and component services frequently occur. The consideration of adaptability in designing such systems is necessary in this unstable and volatile environment. There have been intensive efforts on solutions to make service compositions adaptable, but little attention is given to the evaluation of such adaptability. This paper proposes a set of metrics to quantify the adaptability of service compositions that are specified from the adaptive WS-BPEL-based frameworks. The metrics are based on two adaptability dimensions: the structure and binding variabilities. Structure variability, enables runtime changes to the composition workflow while binding variability, allows dynamic binding to concrete partner services. We evaluate the metrics through a case study, utilising variants of a travel booking process specified from different adaptive WS-BPEL-based frameworks found in the literature. The metrics are applicable to a wide spectrum of adaptive service compositions and their frameworks. A support tool is also developed to automate metrics computation. Through the metrics, adaptability of service compositions and their frameworks can be assessed and compared. This would facilitate design decisions in building flexible and robust services where adaptability is a first class concern.
Method for Multimodal Recognition of One-Handed Sign Language Gestures Through 3D Convolution and LSTM Neural Networks

The paper presents an approach to the multimodal recognition of dynamic and static gestures of Russian sign language through 3D convolutional and LSTM neural networks. A set of data in color format and a depth map, consisting of 48 one-handed gestures of Russian sign language, is presented as well. The set of data was obtained with the use of the Kinect sensor v2 and contains records of 13 different native signers of Russian sign language. The obtained results are compared with these of other methods. The experiment on classification showed a great potential of neural networks in solving this problem. Achieved recognition accuracy was of 73.25%, and, compared to other approaches to the problem, this turns out to be the best result.
The Impact of Inter-Virtual Machine Traffic on Energy Efficient Virtual Machines Placement

In this work, we investigate the energy efficiency of placing virtual machines (VMs) in geo-distributed data centers taking into account inter-VM traffic in addition to users traffic. The problem of VMs placement is formularized as a mixed integer linear programming (MILP) model with an objective to minimize the network and cloud power consumption taking into consideration cooperation traffic between different VMs and synchronization traffic between replicas of the same VM in addition to the download traffic from VMs to users. The model results show that the number of VMs replicas across geo-distributed clouds is limited by the existence of inter-VM traffic in the core network. The total power consumption can potentially increase by a factor of 39 if inter-VM traffic is not taken into consideration when optimizing the placement of VMs.
ProSign Everywhere - Addressing Communication Empowerment Goals for Deaf People

Sign language is recognized as one of the most popular means of communication for deaf people. Unfortunately, the knowledge of sign language by hearing people is not widespread and this may pose unpleasant barriers between deaf and their surrounding word. The analysis of the difficulties that deaf people are still encountering in their social life, led us to design ProSign, a mobile software solution which could be adopted everywhere both by deaf people as well as by hearing people as an effective communication and learning tool in different specific situations. The paper describes the current prototype and discusses the results of the usability testing activities carried out so far.
Using Machine Vision to Command a 6-axis Robot Arm to Act on a Randomly Placed Zinc Die Cast Product

The paper presents a method, documenting the machine vision techniques required, to automate a manual process at a local New Zealand manufacturing company. The system was required to monitor a conveyor belt for zinc die cast outputs. A robot arm was informed how to pick up the object. Die cast outputs appear face up or down and can be rotated 360°. Four experiments were conducted to determine how accurately the proposed system could detect if the cast was lying face up or down, determine the robot picking location, and determine the angle of direction while performing an error checking function. Circle Hough transform and principal component analysis, along with other object analysis techniques, were used on 2D datasets. Point cloud data was analysed to determine if the cast was face up or down when taken from a time-of-flight camera.
Overcoming fuzzy design practice: revealing potentials of user-centered design research and methodological concepts related to user involvement

As more and more research on involving users in design was published, the heterogeneity of design principles, methodologies and tools increased, leading to a variety of new terms being used to distinguish between the different types of solutions on how to support designers. Recently emerged research on human-centered or participatory design leads to small-scale differentiation, ending up in fuzziness. Influences from various disciplines form a conceptual landscape, in which the analytical boundaries become blurred and the conditions of certain design principles are characterized more by soft characteristics than systematic specifications. Consequently, practitioners feel challenged by the need of selecting the best suitable method for involving users into specific design tasks. Additionally, the term `method' is perceived differently and in quite flexible ways. Hereby, the relationship between methods, tools and design methodologies is not taken into considerations. By analyzing the different methodological approaches of user-centered development practice, this paper reveals that not only the exchange between academia and practice, but also between different research communities lacks a consistent communication basis. To overcome this gap, a clarification of the essential terms necessary for describing the different methodological concepts related to user involvement is necessary. A first proposal towards a collection of these terms is given.
Lazy Evaluation of Goal Specifications Guided by Motion Planning

Nowadays robotic systems are expected to share workspaces and collaborate with humans. In such collaborative environments, an important challenge is to ground or establish the correct semantic interpretation of a human request. Once such an interpretation is available, the request must be translated into robot motion commands in order to complete the desired task. It is not unusual that a human request cannot be grounded to a unique interpretation, thus leading to an ambiguous request. A simple example is to ask a robot to “put a cup on the table,” when there are multiple cups available. In order to deal with this kind of ambiguous request, we propose a delayed or lazy variable grounding. The focus of this paper is a motion planning algorithm that, given goal regions that represent different valid groundings, lazily finds a feasible path to any one valid grounding. This algorithm includes a reward-penalty strategy, which attempts to prioritize those goal regions that seem more promising to provide a solution. We validate our approach by solving requests with multiple valid alternatives in both simulation and real-world experiments.
Automatic first arrival picking for borehole seismic data using a pixel-level network

First arrival picking is the basis of the following seismic processing and imaging. The most widely used method, manual picking, is labor intensive, time-consuming as the seismic data volumes grows sharply. Robust and automatic picking methods need to be developed to meet the challenges. We propose a novel method to automatically pick the first breaks using an improved 2D pixel-wise convolutional network. Events picking is transferred into a problem of binary image segmentation through respectively tagging signal before and after first arrivals as one and zero. Field examples with borehole seismic data are used to demonstrate the effectiveness in reduce labor and superiority over traditional automatic picking methods. Presentation Date: Monday, September 16, 2019 Session Start Time: 1:50 PM Presentation Time: 3:55 PM Location: Poster Station 2 Presentation Type: Poster
Network Resource Isolation in Serverless Cloud Function Service

Serverless computing and function execution using cloud computing services are currently the subject considerable attention from both academia and industry. One of the reasons for the success of serverless computing is its straightforward interface that allows users to control the size of the memory allocated for the run-time of a function. However, this approach may result in the abstraction of too much information, and users cannot predict how their applications will perform, especially for the network resource. To address this issue, we evaluated several aspects of network resource performance. Despite the general belief, the variation of serverless applications' network performance is quite significant, and the ability to isolate network resource allocation during concurrent execution is rarely provided by service providers. Based on the results presented in this paper, we insist that network resource performance of functional execution models should be more visible and predictable, in order to expand the applications of serverless computing.
Distributed Estimation with Partially Overlapping States based on Deterministic Sample-based Fusion

Distributing workload between sensor nodes is a practical solution to monitor large-scale phenomena. In doing so, the system can be split into smaller subsystems that can be estimated and controlled more easily. While current state-of-the-art fusion methods for distributed estimation assume the fusion of estimates referring to the full dimension of the state, little effort has been made to account for the fusion of unequal state vectors referring to smaller subsystems of the full system. In this paper, a novel method to fuse overlapping state vectors using a deterministic sample-based fusion method is proposed. These deterministic samples can be used to account for the correlated and uncorrelated noise terms and are therefore able to reconstruct the joint covariance matrix in a distributed fashion. The performance of the proposed fusion method is compared to other state-of-the-art methods.
Indonesian Automatic Text Summarization Based on A New Clustering Method in Sentence Level

With the development of the Internet, the amount of information grows exponentially, and the automatic text summarization technology becomes more and more important. At present, the majority of researches on automatic summarization techniques are applied to common languages such as Chinese and English, but it is few in low resource language. In this paper, we constructed an automatic summary dataset of Indonesian language and conducts related research on Indonesian automatic abstracts. And in this paper, we propose a new and efficient extraction-based automatic text summarization method based on sentence similarity clustering. Based on the idea of clustering, this paper considers the semantics of sentences and we clusters sentences according to the similarity between sentences and sentences. According to the rules we extracts the abstracts and finally obtains the summarization results. This method not only ensures the integrity, criticality and importance of the summary, but also reduces the information redundancy of the summary. In the evaluation, our method achieved good results and exceeded all the baselines in the indexes of score of ROUGE-1, ROUGE-2, ROUGE-3.
A Play-Based Interactive Learning Approach for Fostering Counting and Numbers Learning Skills for Early Childhood Education Using QR Codes Mobile Technologies

Preschool education is vital to children’s social, physical, intellectual, cognitive, and motor skills. In this paper, we propose an interactive learning approach that allows preschool children to learn while they play based on a pedagogical concept for basic skills related to numbers and counting. To assess our proposed learning approach for basic skills related to the number core, we have developed a Quick Response (QR) code mobile application using the Android SDK that allows for children to navigate and count a set of QR tagged learning objects using a 1-to-1 counting correspondence and the cardinal counting principles. We evaluate the application with preschool children ages 3–5. Evaluation results indicate the viability of an interactive and entertaining learning experience that allows children to learn while they play. The results show that such a learning environment can have an influence on preschool children’s perception and learning related to numbers and counting.
Soter: Trust Discovery Framework for Internet of Medical Things (IoMT)

In this paper, we propose Soter a HIPAA compliance distributed personalized authentication mechanism for the Internet of Medical Things (IoMT), based on Multiparty Trust Negotiation. Multiparty Trust Negotiation (MTN)is a promising approach for establishing trust between multiparty (Things)demanding to share resources, by gradually exchanging digital credentials based on personalized Access Control Policies. Previous work has been proposed for authentication in IoMT environment, however the proposed approaches are not the best for IoMT as they are centralized and based on Identity Access Control Policies (IBAC)which have limitations make them unsuitable for open distributed environment such as the IoMT, we explain these limitation on the next section. Soter framework presents a number of innovation features, such as the use of Multiparty Trust Negotiation to establish trust between Medical Things, the support of Virtual Federations (VFs)and the Circle of Trust, and the use of personalized and dynamic access control policies (DACP).
GADE with Fitness-based Opposition and Tidal Mutation for Solving IEEE CEC2019 100-Digit Challenge

This paper introduces a novel hybrid evolutionary algorithm to solve the 2019 IEEE CEC Competition on 100-Digit Challenge on Single Objective Numerical Optimization. The proposed algorithm, named GADE, employs a genetic algorithm (GA) to explore the search space. If a complete solution is not found with GA, differential evolution (DE) exploits the search space using the latest GA solution candidates. We present two techniques to improve GA’s exploration capabilities: fitness-based opposition and tidal mutation. Simulations on the ten challenge problems indicate that fitness-based opposition allows more GA simulations to find the correct digits. Also while GA and DE can fully solve four of the problems on their own, the hybrid algorithm allows for higher scores in at least three of the remaining problems. Furthermore, we provide analysis on the contribution of each EA to the score based on their cost function evaluations.
Mechatronic design and implementation of a bicycle virtual reality system

The aim of this study is to design and implement a virtual reality bicycle system based on a functional-based mechatronic design approach. The development of virtual reality technologies with haptic systems demands a proper integration of the involved disciplines to provide immerse experiences for users. The proposed design approach provides a formal manner to gather the subsystems in the mechatronic device. The developed system is divided in a Virtual Reality System (VRS) and a Physical System (PS) for the design process. The former includes an interactive virtual environment in which an Avatar is animated using a simple kinematic bicycle model. The latter includes an adapted mountain bicycle with haptic feedback mechanisms to interact with the user and to produce the corresponding inputs for the bicycle model. Both systems are integrated by a control behavior system that works under two operation modes, where the user carries out virtual tours and gets feedbacks from a stereoscopic display system, audio cues, and haptic mechanisms. A multibody simulation validates the consistency and the integration of the physical system. In addition, a set of experimental results show the performance of instrumentation elements, control strategies, and feedback mechanisms, to provide the user with an immersive experience in the virtual environment. A brief survey was carried out to assess the opinion of users about the virtual bicycle tours, providing feedback for future improvements. The different designed modules and sub-systems allow modifying and enhancing the VRS without major modifications of the PS, or allow enhancing the physical platform without affecting the functionality of the virtual environment.
Learning from Few Samples: Lexical Substitution with Word Embeddings for Short Text Classification

Text classification helps to categorize large number of documents in Digital Libraries. Text classification results highly depend on the quality of labeled training data. In practice, the process of manually annotating documents is a hidden cost that is often overlooked. We propose a general preprocessing method for scenarios in which training data is scarce. It clusters semantically similar terms by including both a semantic distance measure and a probabilistic model of any task-specific term distributions. By preprocessing the training data with our method, one increases the mean classification performance of all tested classification approaches in text classification tasks having 500 or 1000 training samples. The largest observed increase is 15%. When more training samples are available, we report significant improvements in most scenarios as well.
CAPSO: A Parallelized Multiobjective Cultural Algorithm Particle Swarm Optimizer

CAPSO is a parallelized hybrid optimization system designed for solving multi-objective problems. CAPSO combines elements from Cultural Algorithms (CA), Particle Swarm Optimization (PSO), and Vector-Evaluated Genetic Algorithms (VEGA). CAPSO works by dividing a large search space between multiple particle swarms joined by the sharing of CA knowledge amongst themselves. In this paper we investigate the relative contribution of different CA knowledge sources in the deployment of PSO swarms in a search for the Pareto Optimum in Constrained multi-objective optimization problems. We show that depending upon certain symmetries in the search space, certain categories of knowledge sources are able to dominate others in the search process. While exploratory knowledge sources tend to dominate search in unconstrained problems, exploitative knowledge sources are able to exploit search space patterns and symmetries in constrained problems. The dominance hierarchy that emerged for each of the example problems was different for each. That suggests the flexibility of such a knowledge driven approach to handle a variety of constrained problem types.
Nonclassical properties and polarization degree of photon-subtracted entangled nonlinear coherent states

As in two previous papers where nonclassical properties and entanglement dynamics were studied for entangled nonlinear coherent states (ENCS) [D. Afshar and A. Anbaraki, J. Opt. Soc. Am. B 33, 558 (2016).] and for photon-added entangled nonlinear coherent states (PAENCS) [A. Anbaraki, D. Afshar and M. Jafarpour, Eur. Phys. J. Plus 133, 2 (2018).], we study quantum mechanical treatments of photon-subtracted entangled nonlinear coherent states (PSENCS) introduced through applying annihilation operators on the ENCS, where the related nonlinearity functions are assumed to be harmonious. To gain insight into the effectiveness of photon subtracting from ENCS and comparing with the case already discussed as the PAENCS, we present a general analysis of nonclassical properties such as photon statistics and degree of polarization. We also derive the concurrence measure to quantify the entanglement of these states and look for conditions that provide information on whether they become maximally entangled. As a result, we can see that the photon depletion number m plays an important role in nonclassical effects. Especially, depending on whether the photon depletion number m is even or odd, one can observe different nonclassical effects.