Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovering Research Collaboration Networks from Scientific Digital Libraries and Repositories

Purpose: The purpose of the study is to outline a practical model for discovering research collaboration networks on the basis of data and information stored in scientific digital libraries and repositories. The discovered relationships between researchers, projects, scientific institutions and other scientific entities are used for identifying collaboration networks of researchers and research institutions interested in or working on a given subject. Afterwards, such networks can be subject to various types of network analysis in order to get in-depth knowledge on the networks and their components. Approach/methods: The method adopted in the study is twofold, that is: (i) it takes into consideration the way of discovering collaboration networks by means of simple tools that have been implemented within the ΩΨR system developed at Warsaw University of Technology; (ii) it develops an outline of a formal model of research collaboration networks that takes into account the specificity of scientific digital libraries and repositories and includes the network analysis techniques for discovering knowledge residing/hidden in the networks. Results and conclusions: The outcome of the research is the outline of a formal model of research collaboration networks that includes: (i) a discovery mechanism for identifying thematically related scientists, projects, research institutions, and other scientific entities; and (ii) a set of network analysis methods for getting in-depth knowledge residing in the networks. The model is implementable and scalable in terms of functionality it offers and the network analysis techniques it includes. The model is founded on a solid ground, which is the ΩΨR system functionality to discover simple collaboration networks, and it is being used for enhancing the ΩΨR system. Originality/value: The value of the research is the outline of a general research collaboration networks model that: (i) can help identify, build, and analyse research communities, and thereby increases the scope, value and impact of scientific endeavours on science and society; (ii) is used for enhancing the ΩΨR system.
Autonomous Navigation for Mobile Robots in Crowded Environments

The large diffusion of service robots in our life requires safe and efficient autonomous navigation in human environments. In particular, navigation in a crowded environment is a challenge, because the robot motion could compromise human safety. This Ph.D. dissertation focuses on two different scenarios: with aerial and ground robots, respectively. The first scenario focuses on safe navigation for Unmanned Aerial Systems (UASs) in urban areas. It is a critical scenario, because an impact of the UAS on the ground may cause casualties. To solve this problem, a novel approach is proposed, where a risk-based map and a risk-aware path planning strategy are used to determine a safe flight mission. The risk-based map quantifies the risk of flying over an urban area, defining areas where the flight is allowed or not, because of no-fly zones or high risk. The risk is defined with a probabilistic risk assessment approach and by combining several layers with information about population density, sheltering factor, obstacles at the flight altitude and coverage of the mobile network used to connect the UAS with the ground. Hence, a risk-aware path planning searches for a minimum risk path to reach a desired target position in the map and considering the risk-based map. In particular, the proposed risk-aware path planning strategy consists of two phases. First, an offline path planning searches for the globally optimal path based on a static risk-based map. Then, an online path planning updates the path according to a dynamic risk-based map. In this thesis, two different riskaware path planning strategies are proposed: (i) using riskA* and Borderland algorithms, able to provide an offline and an online path planning, respectively; and (ii) with riskRRT , a path planning and re-planning algorithm used to perform both offline and online phases. A simulation of a flight operation over a city is performed, demonstrating how the proposed approach is able to compute and maintain a safe flight mission, even in densely populated areas.
Interactive Digital Model of Assessing Energy Efficiency of Buildings

— The issues of enhancement of energy efficiency of buildings acquire the ever-greater significance in the recent decades, since a demand for utilization of alternative energy sources and reduction of energy consumption as a whole increases with acceleration of urbanization process and depletion of reserves of natural energy resources. This article gives consideration to the basic methods of digital modeling of energy efficiency of buildings and offers an interactive digital model for assessing energy efficiency developed on the basis of joint use of virtual reality means and BIM model of construction object. The digital model performs heat-balance calculations, finds a necessary quantity of energy for heating as well as calculates efficiency of utilization of alternative energy sources such as solar cell panels and wind turbines. The user of this model is granted a chance of independent selection of average temperature in the future object that is especially important for residential buildings. At that, it is not necessary for the user to possess any special knowledge owing to a simple and intuitive interface. Working with the digital model a user has a chance to freely move, perform manipulations with the specified set of energy sources as well as interact with light sources and change the interior by means of virtual reality capabilities in real-time mode.
Evaluation of Stacked Embeddings for Bulgarian on the Downstream Tasks POS and NERC

This paper reports on experiments with different stacks of word embeddings and evaluation of their usefulness for Bulgarian downstream tasks such as Named Entity Recognition and Classification (NERC) and Part-of-speech (POS) Tagging. Word embeddings stay in the core of the development of NLP, with several key language models being created over the last two years like FastText (CITATION), ElMo (CITATION), BERT (CITATION) and Flair (CITATION). Stacking or combining different word embeddings is another technique used in this paper and still not reported for Bulgarian NERC. Well-established architecture is used for the sequence tagging task such as BI-LSTM-CRF, and different pre-trained language models are combined in the embedding layer to decide which combination of them scores better.
Human Motion Trajectory Prediction in Human-Robot Collaborative Tasks

A method is introduced to predict human motion trajectory in the process of human-robot collaboration (HRC). In the method, the human-robot distances are assumed to be a Gaussian Process (GP). To achieve this, a human-robot handover task is conducted by a human and a collaborative robot, while the positions of the human hand and the robot end-effector are recorded. Some of the recorded data are used for the Gaussian Process Regression (GPR), a GP and a 95% confidence convince about the GP are obtained by the GPR. Experimental results show that about 80% of the testing data are included in the 95% confidence convince. The method and results here are useful to other human-robot collaborative tasks where existing human-robot relative motions, especially, the method is able to predict the human motion trajectory with varying initial position of the human hand and varying locations of the robot end-effector.
AR for Maintenance: A Review of Augmented Reality Tools and Platforms for Training and Maintenance Procedures

Augmented reality (AR) is a widely accepted technology that can be exploited to provide mass-market users an effective and customizable support in a large spectrum of personal applications, by superimposing computer-generated hints to the real world. Mobile devices, such as smartphones and tablets, are playing a key role in the exponential growth of this kind of solutions. Maintenance, repair, and assembly have been considered as strategic fields for the application of the AR technology from the 1990s, but often only specialists using ad hoc hardware were involved in limited experimental tests. Nowadays, AR-based maintenance and repair procedures are available also for end-users on consumer electronics devices. This paper aims to explore use of this technology in maintenance and manufacturing industry, new trends in utilizing the technology, by also presenting the software framework to provide AR based maintenance solution
LTE network slicing and resource trading schemes for machine-to-machine communications

iv LIST OF ACRONYMS . vii LIST OF TABLES . ix LIST OF FIGURES . x Chapter 1: Introduction . 1 1.1 Network Slicing and Resource Trading . 1 1.2 Machine-to-Machine Communications . 2 1.3 Problem Statement . 3 1.4 Thesis Contribution and Structure . 4 Chapter 2: Background and Related Work . 6 2.
On Optimal Locally Repairable Codes With Multiple Disjoint Repair Sets

Locally repairable codes are desirable for distributed storage systems to improve the repair efficiency. In this paper, a new combination of codes with locality and codes with multiple disjoint repair sets (also called availability) is introduced. Accordingly, a Singleton-type bound is derived for the new code, which contains those bounds in <xref ref-type="bibr" rid="ref9">[9]</xref>, <xref ref-type="bibr" rid="ref20">[20]</xref>, <xref ref-type="bibr" rid="ref28">[28]</xref> as special cases. Optimal constructions are proposed with respect to the new bound. In addition, these constructions can also generate optimal codes with multiple disjoint repair sets with respect to the bound in <xref ref-type="bibr" rid="ref28">[28]</xref>, which to the best of our knowledge, are the first explicit constructions that can achieve the bound in <xref ref-type="bibr" rid="ref28">[28]</xref>.
Application and Thermal-reliability-aware Reinforcement Learning Based Multi-core Power Management

Power management through dynamic voltage and frequency scaling (DVFS) is one of the most widely adopted techniques. However, it impacts application reliability (due to soft errors, circuit aging, and deadline misses). However, increased power density impacts the thermal reliability of the chip, sometimes leading to permanent failure. To balance both application- and thermal-reliability along with achieving power savings and maintaining performance, we propose application- and thermal-reliability-aware reinforcement learning–based multi-core power management in this work. The proposed power management scheme employs a reinforcement learner to consider the power savings and variations in the application and thermal reliability caused by DVFS. To overcome the computational overhead, the power management decisions are determined at the application-level rather than per-core or system-level granularity. Experimental evaluation of proposed multi-core power management on a microprocessor with up to 32 cores, running PARSEC applications, was done to demonstrate the applicability and efficiency of the proposed technique. Compared to the existing state-of-the-art techniques, the proposed technique enables an average energy savings of up to ∼20%, up to 4.926°C temperature reduction without degradation in the application- and thermal-reliability.
A Review of Computational Methods for Aeroelastic Instabilities of Multi-Stage Compressor

Modern gas turbine design continues to move towards improved performance, reduced weight and reduced cost. As turbomachinery blade aerofoils are thinned to improve performance and reduce weight, aeroelastic issues such as flutter, forced response and stall driven vibrations become more predominant. Moreover, as the use of blisks (bladeintegrated-disks) with very low mechanical damping becomes more common in modern compressor designs, accurate prediction of compressor aeroelastic stability in a multi-row environment becomes vital. This paper presents a review of aeroelasticity research carried out at Rolls-Royce Vibration University Technology Centre (VUTC) at Imperial College over the past 20 years. The aim is to summarise the unusual aeroelastic issues observed in multi-stage compressors into one document so that it can be used by other researchers in the field. Blade passing forced response is not addressed here as their existence can be detected by a Campbell diagram. The results presented here are based on numerical methods but where possible data from experiments are used to verify the
Fault Diagnosis Based on Tree Heuristic Feature Selection and FS-DFV for Rolling Element Bearings

In order to make up for the deficiency of traditional single diagnosis in rolling element bearing fault diagnosis application, eliminate a large amount of redundant information and improve the classification effect of the aliasing mode, based on comprehensive analysis of the respective advantages of fuzzy set and tree search, this paper presents a joint rolling bearing fault diagnosis method based on tree-inspired feature selection and FS-DFV (Fuzzy Set and Dependent Feature Vector). The dependent feature vectors (DFV) can dig deeper the essential differences of the faults and improve the fault accuracy. By establishing the heuristic tree model, the tree type heuristic feature search strategy is designed, and the excellent feature selection criteria based on the density clustering with noise are proposed, and the conventional feature selection model is improved. In addition, fuzzy set are used to process the problem of extracting aliasing patterns in the DFV, and fuzzy membership is used to guide subsequent feature extraction of the alias modes. The proposed method is compared with the other four fault diagnosis methods. The experimental results show that the proposed method can effectively improve the diagnostic efficiency of the rolling bearing.
Accurate 3-D Localization of Selected Smart Objects in Optical Internet of Underwater Things

Localization is a fundamental task for the optical Internet of Underwater Things (O-IoUT) to enable various applications, such as data tagging, routing, navigation, and maintaining link connectivity. The accuracy of the localization techniques for O-IoUT greatly relies on the location of the anchors. Therefore, recently, the localization techniques for O-IoUT which optimize the anchor’s location have been proposed. However, the optimization of the anchors’ location for all the smart objects in the network is not a useful solution. Indeed, in a network of densely populated smart objects, the data collected by some sensors are more valuable than the data collected from other sensors. Therefore, in this article, we propose a 3-D accurate localization technique by optimizing the anchor’s location for a set of smart objects. Spectral graph partitioning is used to select the set of valuable sensors. The numerical results show that the proposed technique of optimizing anchor’s location for a set of selected sensors provides a better location accuracy.
Design of frequency selective rasorber with high in-band transmission and wideband absorption properties

A low-frequency transmissive frequency selective rasorber (FSR) with high in-band transmission and wideband absorption characteristics is proposed and experimentally demonstrated. It comprises the top resistive sheet and the bottom bandpass FSS. For resistive sheet, the tiny strip-type parallel LC resonator composed of square spiral inductor and its two-sided capacitors is inserted between the resistor-loaded crosses to achieve lower insertion loss (IL) by cutting off the lossy path. The bandpass FSS with high selectivity consists of Jerusalem cross slot array and capacitive metallic patches. An IL of 0.3 dB is obtained at 3.55 GHz, and the 10-dB-absorption band is 5.4-13.2 GHz.
CARING EXPERIENCES AND CHALLENGES OF FAMILIES WITH PATIENTS IN VEGETATIVE STATE

زمینه و هدف: داشتن یک بیمار نباتی در خانواده تجربه پیچیده و استرس زایی است و مراقبت از چنین بیمارانی با ناتوانی کامل در منزل بسیار چالش زا می باشد. مطالعه حاضر با هدف تبیین تجارب و چالش های مراقبتی خانواده های دارای بیمار در وضعیت نباتی صورت گرفته است. روش بررسی: در مطالعه کیفی حاضر که به صورت مقطعی از مرداد ماه سال 1392 تا شهریور ماه سال 1393 در برخی استان های ایران (خوزستان، تهران و لرستان) صورت گرفت، 17 مشارکت کننده شامل 14 مراقب خانگی و 3 مراقب حرفه ای به مطالعه وارد شدند. جهت جمع آوری داده ها از مصاحبه های بدون ساختار و مشاهده استفاده شد. جمع آوری داده ها تا رسیدن به اشباع داده ها و پدیدار شدن الگوهای اصلی ادامه یافت. تحلیل داده ها با استفاده از روش تحلیل محتوایی صورت گرفت. یافته ها: چالش های مراقبتی که خانواده های دارای بیمار نباتی با آن مواجه بودند در قالب پنج الگوی اصلی شامل ولع آموزش، عهده داری مراقبتی دشوار، هزینه های فرساینده، برزخ حرف و حدیث ها و پسرفت روحی پدیدار گردید. نتیجه گیری: یافته های مطالعه حاضر نشان داد که خانواده های دارای بیماران نباتی طی دوره مراقبت از بیمار خود با چالش های متعددی روبرو هستند که بخش عمده ی این چالش ها مرتبط با سیاست های سیستم سلامت و برخی نیز مرتبط با سطح آگاهی عموم جامعه می باشد، لذا توجه بیشتر به این چالش ها از سوی مسئولان مرتبط پیشنهاد می گردد.
Knowledge audit process framework for inter-agencies collaboration

Knowledge audit is a process to determine an organization’s knowledge asset. In the case of government inter-agencies special joint-operations, knowledge audit helps government agencies to better leverage knowledge for improving services and make better decisions based on what they know of their own knowledge. At present, there is no knowledge audit process specially catered for inter-agencies collaboration. This has hindered agencies from identifying their knowledge asset, and resulted in difficulties for agencies to collaborate efficiently. This research developed a Knowledge Audit Process for Inter-agencies (KAP-I) Framework. A Design Science Research methodology was carried out to ensure the rigor and relevance of the framework. Qualitative data were collected from a joint-operation of selected agencies involved in flood management as the case study. Data gathered from nineteen respondents of flood management agencies were analysed using knowledge sharing theory for inter-agencies. Theoretically, the technological, organizational and managerial, and political and policy perspectives were used to derive the knowledge audit elements. Using a deductive content analysis, twelve applicable knowledge audit elements for inter-agencies collaboration were discovered and later manifested in the framework. To ensure the efficacy and utility of the framework, formative and summative evaluations were conducted to iterate the artefact design. The framework was verified by practitioners who agreed with the logic and content, and they proposed suggestions for improvements. For verification, the framework was tested on the Independence Day Celebrations as a case study using structured walkthrough technique. Based on the findings, KAP-I Framework was formulated and it consists of three components: KAP-I Process, KAP-I Guide, and KAP-I Template that complement each other as a whole. The framework can be used to guide agencies through the inter-agencies knowledge audit process as it assists in the identification of special joint-operations related knowledge asset, as well as capture the experiential knowledge gained from the special joint-operations.
Learning about perception of temporal fine structure by building audio codecs

The goal of audio coding is to efficiently describe an auditory experience while enabling a faithful reconstruction to the listener. The subjective quality compared to the original is measured by established psychoacoustic tests (BS.1116, 2015; BS.1534, 2015) and the description cost is measured in number of bits. As it is much cheaper to describe coarse scale signal properties than temporal fine structure (TFS), tools like noise fill, spectral extension, binaural cue coding, and machine learning have increased performance of audio codecs far beyond the first generation based on masking principles (e.g., mp3). In this evolution, implicit knowledge on hearing has been acquired by codec developers, but it has become increasingly difficult to construct tools to predict subjective quality. For example, it is yet unknown which aspects of the TFS that are essential for the listening impression to be preserved. To explore these issues, we study models of auditory representations with the mindset from audio coding. Given a method to solve the inverse problem of creating a signal with a specified representation, evaluating by listening can immediately reveal strengths and weaknesses of a candidate model.
Different Feature Selection of Soil Attributes Influenced Clustering Performance on Soil Datasets

Feature selection is very important to obtain meaningful and interpretive clustering results from a clustering analysis. In the application of soil data clustering, there is a lack of good understanding of the response of clustering performance to different features subsets. In the present paper, we analyzed the performance differences between k-means, fuzzy c-means, and spectral clustering algorithms in the conditions of different feature subsets of soil data sets. The experimental results demonstrated that the performances of spectral clustering algorithm were generally better than those of k-means and fuzzy c-means with different features subsets. The feature subsets containing environmental attributes helped to improve clustering performances better than those having spatial attributes and produced more accurate and meaningful clustering results. Our results demonstrated that combination of spectral clustering algorithm with the feature subsets containing environmental attributes rather than spatial attributes may be a better choice in applications of soil data clustering.
Automobile Driver Fingerprinting: A New Machine Learning Based Authentication Scheme

Advanced technologies are constantly emerging in automobile industry, which not only provides drivers with a comfortable driving experience, but also enhances the safety of passengers. However, there are still some security issues need to be solved in automobiles, such as automobile driver fingerprinting. At present, identification technologies, such as fingerprint recognition and iris recognition, cannot monitor the driver's identity in real-time manner. Therefore, it is of great significance to design a real-time automobile driver fingerprinting scheme to ensure the safety of people's properties and even lives. Different from previous work concerning automobile driver fingerprinting, in this article, we conduct a comprehensive study on behavioral characteristics of drivers in two vehicles, namely Luxgen U5 SUV and Buick Regal. We exploit the actual data of the controller area network to construct a driver identity comparison library by extracting and processing the feature data. Then, we construct a combined model based on convolutional neural network and support vector domain description to achieve efficient automobile driver fingerprinting. Extensive experimental results show that the proposed driver fingerprinting scheme can dynamically match the driver's identity in real time without affecting the normal driving.