Miracle-dz/UniFAR-FTUs huggingface dataset:
Maintaining the Presence of Remote Speakers on Telepresence Robots by Visual Morphing to Reduce Loneliness

Telepresence robots can provide the sense that a remote person is physically present. However, this sense disappears suddenly once a call ends. Therefore, the speaker may feel loneliness. This study explores “tele-vestige” in which speakers can perceive the weak social presence of a remote speaker after a call ended. To provide this tele-vestige, the remote speaker's face is morphed into a telepresence robot's face after the call ends. We conducted an experiment to investigate the effect of this morphing technique on loneliness.
PDDL+ Planning with Temporal Pattern Databases

The introduction of PDDL+ allowed more accurate representations of complex real-world problems of interest to the scientiﬁc community. However, PDDL+ problems are no-toriously challenging to planners, requiring more advanced heuristics. We introduce the Temporal Pattern Database (TPDB), a new domain-independent heuristic technique designed for PDDL+ domains with mixed discrete/continuous behaviour, non-linear system dynamics, processes, and events. The pattern in the TPDB is obtained through an abstraction based on time and state discretisation. Our approach combines constraint relaxation and abstraction techniques, and uses solutions to the relaxed problem, as a guide to solving the concrete problem with a discretisation ﬁne enough to satisfy the continuous model’s constraints.
A Sample-Based Approximate Ranking Method for Large Graphs

Graph centrality can be estimated by random walk algorithms such as PageRank. These algorithms are useful when ranking items with relationships, and are based on global structure of the graph. It needs to traverse the entire edge-set more than once to get a convergent result. However, when it comes to large graphs, traversing the whole graph is costly, especially when a single node's information is wanted. How to get a rough look at the rank of a specific node? Can the methods be simpler that don't bother to scan the entire graph? How to scale up the method to get more information of the graph? In this paper, we proposed a simple and light algorithm to get approximate rank value of a node. It could be simplified by sampling and expanded to distributed environment. Experiments on representative public graphs have shown that the method can provide an approximate ranking. And the higher ranking a node has by global algorithms, the more accurate the approximate rank will be. Our method also reduces the computation to several orders of magnitude than traditional global algorithms. With sampling techniques, it can outperforms the basic method by additionally reducing computation. It is useful in both estimation and edge computing.
Combinations of Independent Dominant Loci Conferring Clubroot Resistance in All Four Turnip Accessions (Brassica rapa) From the European Clubroot Differential Set

Clubroot disease is devastating to Brassica crop production when susceptible cultivars are planted in infected fields. European turnips are the most resistant sources and their resistance genes have been introduced into other crops such oilseed rape (Brassica napus L.), Chinese cabbage and other Brassica vegetables. The European clubroot differential (ECD) set contains four turnip accessions (ECD1–4). These ECD turnips exhibited high levels of resistance to clubroot when they were tested under controlled environmental conditions with Canadian field isolates. Gene mapping of the clubroot resistance genes in ECD1–4 were performed and three independent dominant resistance loci were identified. Two resistance loci were mapped on chromosome A03 and the third on chromosome A08. Each ECD turnip accession contained two of these three resistance loci. Some resistance loci were homozygous in ECD accessions while others showed heterozygosity based on the segregation of clubroot resistance in 20 BC1 families derived from ECD1 to 4. Molecular markers were developed linked to each clubroot resistance loci for the resistance gene introgression in different germplasm.
On the Use of Electromagnetic Inversion for Near-Field Antenna Measurements: A Review

This paper focuses on some features and developments pertaining to the source reconstruction method (SRM), an electromagnetic inverse source technique, for near-field antenna measurements. In particular, we describe the usefulness of the absence of restrictions on the measurement and reconstruction domains in the inverse source framework, which can lead to the development novel systems such as a pendulum-based measurement system. The application of the inverse source framework to phaseless (amplitude-only) near-field measurements using a multiplicative regularization scheme is reviewed, as well as the use of pre-scaled measurement data to improve the accuracy of the calculated far-field pattern.
IoT based Smart Cities

The massive deployment of Internet of Things (IoT) is allowing Smart City projects and initiatives all over the world. The IoT is a modular approach to merge various sensors with all the ICT solutions. With over 50 billion objects will be connected and deployed in smart cities in 2020. The heart of smart cities operations is the IoT communications. IoT is designed to support Smart City concept, which aims at utilizing the most advanced communication technologies to promote services for the administration of the city and the citizens. This paper is presenting a comprehensive review of the concepts of IoT and smart cities and their motivations and applications. Moreover, this paper describes the main challenges and weaknesses of applying the IoT technologies based on smart city paradigms,
Economic outcomes associated with deep surgical site infection in patients with an open fracture of the lower limb

Aims The aim of this study was to estimate economic outcomes associated with deep surgical site infection (SSI) in patients with an open fracture of the lower limb. Patients and Methods A total of 460 patients were recruited from 24 specialist trauma hospitals in the United Kingdom Major Trauma Network. Preference‐based health‐related quality‐of‐life outcomes, assessed using the EuroQol EQ‐5D‐3L and the 6‐Item Short‐Form Health Survey questionnaire (SF‐6D), and economic costs (£, 2014/2015 prices) were measured using participant‐completed questionnaires over the 12 months following injury. Descriptive statistics and multivariate regression analysis were used to explore the relationship between deep SSI and health utility scores, quality‐adjusted life‐years (QALYs), and health and personal social service (PSS) costs. Results Deep SSI was associated with lower EQ‐5D‐3L derived QALYs (adjusted mean difference −0.102, 95% confidence interval (CI) ‐0.202 to 0.001, p = 0.047) and increased health and social care costs (adjusted mean difference £1950; 95% CI £1383 to £5285, p = 0.250) versus patients without deep SSI over the 12 months following injury. Conclusion Deep SSI may lead to significantly impaired health‐related quality of life and increased economic costs. Our economic estimates can be used to inform clinical and budgetary service planning and can act as reference data for future economic evaluations of preventive or treatment interventions. Cite this article: Bone Joint J 2018;100‐B:1506‐10.
Tunable Guided-Mode Resonance Filters for Multi-Primary Colors Based on Polarization Rotation

Tunable guided-mode resonance filters (GMRFs) for multi-primary colors based on a twisted nematic liquid crystal (TN-LC) polarization rotator with high peak efficiencies, narrow linewidths, and low sidebands are experimentally demonstrated. The proposed tunable color filter consists of a linear polarizer, the TN-LC polarization rotator, and the GMRF from top to bottom. The polarization of the linear polarized beam generated by the linear polarizer is controlled using a TN-LC. The resonance wavelengths and resonance peaks of the GMRFs are tuned following the orientation of the TN-LC directors. Thus, 3 ~ 6 high saturated primary colors with tunable intensities can be obtained by controlling the applied voltages on the TN-LC of every filter separately.
Null Distribution of Volume Under Ordered Three-Class ROC Surface (VUS) With Continuous Measurements

Receiver operating characteristic (ROC) analysis has become an indispensable tool in medical care, with a major application to characterizing the performance of binary diagnostic tests in clinical practice. In many circumstances, however, the diagnostic test has three outcomes, that is, the abnormalities are two sided. To deal with this scenario, this letter develops a recursive algorithm for computing the exact null distribution of the volume under the ordered three-class ROC surface (VUS) for samples following continuous distributions. Based on the asymptotic normality, an approximately normal distribution with exact mean and variance is also proposed, which is hoped to be useful for large-sample scenarios. Moreover, an efficient rank-based formula, in linearithmic time, is established for nonparametric estimation of VUS. Monte Carlo simulations verify the usefulness of the theoretical and algorithmic findings in this letter.
Verifying Vaccine Supply Chain System in Indonesia Using Linear-Time Temporal Logic

We propose a formal approach to verify the safety of vaccine supply chain systems in Indonesia. The description of vaccine supply chain systems comes from PT. Bio Farma as the vaccine producer, and according to WHO regulation as well. Firstly, we describe the workflows of the system and model them using activity diagrams. Afterwards, we specify safety properties based on the WHO requirements as linear-time temporal logic formulas and translate the diagrams into temporal logic expressions in the form of NuSMV model. We verify them using NuSMV model checker to check whether the workflows conform to the requirements. In general, the result shows that the safety of the system is proven.
Target Selection in Head-Mounted Display Virtual Reality Environments

Target selection is one of the most common and important tasks in interactive systems. Within virtual reality environments, target selection can pose extra challenges to users because targets can be located far away, clustered together, and occluded from view. Although selection techniques have been explored, it is often unclear which techniques perform better across different environmental target density levels and which have higher levels of usability especially for recently released commercial head-mounted display (HMD) virtual reality systems and input devices. In this paper, we first review previous studies on target selection in HMD VR environments. We then compare the performances of three main techniques or metaphors (RayCasting, Virtual Hand, and Hand-Extension) using recently marketed VR headsets and input devices under different density conditions and selection areas. After, we select the best two techniques (RayCasting and Virtual Hand) for the second experiment to explore their relative performance and usability by adding different feedback to these two Journal of Universal Computer Science, vol. 24, no. 9 (2018), 1217-1243 submitted: 30/12/17, accepted: 30/6/18, appeared: 28/9/18 © J.UCS
Integrated Cloud Storage on Paperless Thesis Examination

The Internet is a technology that motivates the development of digitalization. The impact can distort all aspects of human work into automation. One of technology become a trend is cloud computing. The paperless thesis examination system is one of the systems developed in cloud computing. Nowadays, there are universities which developed and implemented paperless thesis examination system. The thesis documents are saving on the university's internal servers. The development of cloud storage can use as a storage media for thesis documents. Therefore, this paper discusses the design of paperless thesis examination information systems integrated with cloud storage. As a result, this paper proposes a business process that can use for the design of the system.
A Simple Distributed Channel Allocation Algorithm for D2D Communication Pairs

Device-to-device (D2D) communication is a promising technology to achieve high spectrum efficiency, low energy consumption, and enhanced system capacity. In D2D communications, the channel allocation plays a crucial role to avoid interference, which usually involves a centralized coordination. With the increasing number of devices or terminals as well as the dynamic network environment, the computation and information exchange during the centralized channel allocation would be a big burden for wireless networks. We tackle this problem by proposing a distributed channel allocation method with blind rendezvous to enable collision-free concurrent transmissions over multiple channels. Specifically, we first present a receiver-oriented channel allocation algorithm to reduce interference and then propose a sender-jump blind channel rendezvous algorithm based on channel hopping techniques. The proposed channel allocation method allows each sender–receiver pair to obtain an appropriate channel for collision-free transmissions requires no centralized coordination. Theoretical analysis, simulation results and test-bed experiments validate the proposed solution. Our findings reported in this paper may help network designers to contribute in the development of high-density Internet of Things.
The Shape Bias in Children With Autism Spectrum Disorder: Potential Sources of Individual Differences.

Purpose Children with autism spectrum disorder (ASD) demonstrate many mechanisms of lexical acquisition that support language in typical development; however, 1 notable exception is the shape bias. The bases of these children's difficulties with the shape bias are not well understood, and the current study explored potential sources of individual differences from the perspectives of both attentional and conceptual accounts of the shape bias. Method Shape bias performance from the dataset of Potrzeba, Fein, and Naigles (2015) was analyzed, including 33 children with typical development (M = 20 months; SD = 1.6), 15 children with ASD with high verbal abilities (M = 33 months; SD = 4.6), and 14 children with ASD with low verbal abilities (M = 33 months; SD = 6.6). Lexical predictors (shape-side noun percentage from the MacArthur-Bates Communicative Development Inventory; Fenson et al., 2007) and social-pragmatic predictors (joint attention duration during play sessions) were considered as predictors of subsequent shape bias performance. Results For children in the low verbal ASD group, initiation of joint attention (positively) and passive attention (negatively) predicted subsequent shape bias performance, controlling for initial language and developmental level. Proportion of child's known nouns with shape-defined properties correlated negatively with shape bias performance in the high verbal ASD group but did not reach significance in regression models. Conclusions These findings suggest that no single account sufficiently explains the observed individual differences in shape bias performance in children with ASD. Nonetheless, these findings break new ground in highlighting the role of social communicative interactions as integral to understanding specific language outcomes (i.e., the shape bias) in children with ASD, especially those with low verbal abilities, and point to new hypotheses concerning the linguistic content of these interactions. Presentation Video https://doi.org/10.23641/asha.7299581.
Fuzzy Clustering and Genetic Algorithm for Clinical Pratice Guideline Execution Engines

The clinical practice guideline (CPG) is used to support the decision system using general practitioner. The patient symptoms are recommended and followed as per CPG. Medical knowledge is framed on the clinical decision making and clinical practice. The NLP is important data source for clinical decision. The NLP system is included in part of speech tagging. Fuzzy c means are used to group the symptoms. The fuzzy membership function is used to find the Fuzzy c means clustering approach to determine the centroid correctly. OWL Language is used to map (onto map) symptoms and disease in the clinical evaluation. The healthcare, semantic web and ontology's for the patients clinical practice is based on CPG. Ontology is potentially integrated with health care. Ontology's are accessed through the user interface and frame work. The gaps between the users requirement is suggested. Genetic Algorithms (GA) via clustering is used for posting the optimization issues. GA provides optimal results when used in complex issues. Genetic Algorithm is optimized for stomach disease prediction as well.
Development of Background Radiation Monitoring System Based on Arduino Platform

In the paper, the background radiation monitoring system has been developed. It has been built on the Arduino Uno microcontroller board, Geiger-Mueller counters, alphanumeric LCD module and high-voltage power supply module for counters. The system measures power of the equivalent radiation dose and accumulated dose and outputs the information via the serial port to PC, and also sends warning signals when the equivalent dose rate is exceeded.
The Application of AGNES Algorithm to Optimize Knowledge Base for Tourism Chatbot

Most tourists have limited time and schedule when they are visiting a particular tourism area. One of the main issues faced by those tourists is that they can not make simple queries to search engines about, for example, which sites or places to visit optimally if we only have one day or two-day visit? Search engines are better and better in replying users' queries with relevant answers. However, search engines are not always able to respond queries suited to users' characteristics such as their styles, personalities, knowledge backgrounds, etc. Also, search engines are not as flexible as chatbots in enabling dialogical-communication-queries. This case study aims to respond aformentioned issue by applying hierarchical cluster analysis on a set of tourism sites around Malang city, Malang regency, and Batu city based on AGNES (Agglomerative Nesting) algorithm. The result, then, is fed into chatbot's knowledge base. This chatbot will assist tourists to get information on which possible sites they have to visit optimally if they are under limited vacancy-time constraints.
A Universal Input CrCM Luo Converter With Low-Cost Pilot-Line Dimming Concept for General Purpose LED Lighting Applications

This paper deals with the design and implementation of a Luo converter operating in two different modes with an improved power quality (PQ) and flickerless operation over the universal ac mains for general purpose lighting applications. A cost-effective analog pilot-line dimming concept is incorporated and discussed in detail. The proposed dimming concept offers good PQ, even during dimming, with low cost for the energy efficient lighting solutions. The Bode plot analysis of this proposed Luo converter is presented using state-space analysis to ensure its stability with closed-loop control. The prototype is developed and tested in the laboratory for a 36-W light-emitting diode lamp load with rated current of 700 mA. The measured power factor in case of 30% dimming is 0.97 with total harmonic distortion of 8.7% only. The measured efficiency is 91% at rated ac mains voltage of 230 V.
A Preliminary Study of a Control Framework for Forearm Contact During Robot Navigation

Efficient navigation in a congested environment is immensely difficult for the current state-of-the-art mobile robots owing to the challenges related to both sensing and actuation. Consequently, conventional approaches in mobile robotics research prioritize safety by slowing down or stopping robot's movement under challenging circumstances. This paper considers an alternative approach in which the robot utilizes forearm contact to create space for itself and to act as a safety buffer, during very close or congested navigation interactions. First, two categories of contact methods are defined based on different navigation scenarios. These contact methods are analyzed through different contact positions and force directions in a set of comparative experiments. The results of the comparative experiments are then incorporated into a contact-based framework that outputs the necessity, and the appropriate form of forearm contact. Finally, a set of evaluation experiments are performed to test the usability and effectiveness of the constructed framework. The results indicate that the proposed framework effectively outputs appropriate contact according to the situation, and aids the robot while navigating through space-constrained scenarios.
Invitation or Bait? Detecting Malicious URLs in Facebook Events

With 2.2 billion monthly active users, Facebook is the most popular Online Social Network. Given its huge popularity and diverse features such as pages, events, groups etc., it is potentially the most attractive platform for cybercriminals to launch various attacks. In this paper, we study the role of Facebook Events in disseminating malicious URLs in the network. Here, we focus our analysis on Facebook Events which are created by Facebook Pages. The existing services like Web of Trust (WOT) and other blacklists follow crowdsourcing models. Thus, malicious URLs can only be detected once they are widespread on the network and has done significant damage. Therefore, we train a supervised machine learning model on our labeled dataset to create an efficient classifier for automatic detection of malicious Facebook events, independent of blacklists and third-party reputation services. Our model is able to classify malicious events with 75% accuracy using Support Vector Machine. To the best of our knowledge, this is the first paper to study the presence of malicious URLs on Facebook Events.
An Effective Natural Language Understanding Model Using Deep Learning and PyDial Toolkit

In the current world, where we want spoken dialogue systems, to understand natural language utterances by human beings in a very much human way, an effective natural language understanding model plays a crucial role. In the model, we have proposed and used bi-directional LSTM (Long short-term memory network) based Recurrent neural network for slot identification, and we have developed an ontology for a subset of ATIS airlines dataset, describing the slots under different categories (request able, user-request able, in formable) to guide the Spoken dialogue system to help the user finish the task. The novelty in this research comes from the fact that a complete methodology has been proposed to build the NLU model for any domain based on deep learning which can understand utterances in simpler context and understand multi-word slots. With the following methodology, a natural language understanding model can be built for a spoken dialogue system for any domain. In the proposed model, the bi directional lstm model obtained had an accuracy of over 86%. The proposed technique is easy to use and can be helpful in building basic voice based search agents for any domain with good performance.
New Five Level Resonant DC/DC Buck Converter

This paper introduces a new five level resonant DC/DC converter and examines its operation in steady-state symmetrical conditions in discontinous current conduction mode. The converter has four outputs, which can provide four equivalent voltage levels or four different ones as well. It has several advantages such as improved performance at high efficiency and lower EMI. By using this converter, zero voltage and zero current switching is possible which helps reducing the swtiching losses. The most important relations among the input, output, and control variables will be derived and verified by simulation results. Three control variables will be examined in the paper, switching angle $\alpha_{p}[{}^{\circ}]$, capacitor peak voltage $V_{c}^{*}[pu]$ and switching frequency $f_{s}^{*}$ [pu]. Parameters $V_{c}^{*}$ and $\alpha_{p}$ are dependent variables, while $f_{s}$ is independent of them. This converter can be applied in UPS systems or in multi-level inverter drives as well.