Miracle-dz/UniFAR-FTUs huggingface dataset:
Methodological Guidelines for Publishing Library Data as Linked Data

Publishing data as Linked Data increases the interoperability and discoverability of resources over the web space. This process involves several design decisions and technologies. However, there is no one-size-fits-all formula for publishing data as Linked Data. Also, the quality of linked data published is a key issue to take into account. In the library domain, the quality of linked data is a crucial point for improving the retrieval and use of the data. In this paper, we propose a set of methodological guidelines based on five activities for publishing library data as Linked Data. The proposed guidelines consider the quality of published data as a key issue. In this line, our approach includes a preprocessing task for data cleansing and normalization. The proposed approach has been applied in a use case for publishing bibliographic data from Open Access journals in Cuba. The results obtained show the applicability of the methodological guidelines proposed in a real environment.
Simultaneous functional near-infrared spectroscopy and electroencephalography for monitoring of human brain activity and oxygenation: a review

Abstract. Multimodal monitoring has become particularly common in the study of human brain function. In this context, combined, synchronous measurements of functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) are getting increased interest. Because of the absence of electro-optical interference, it is quite simple to integrate these two noninvasive recording procedures of brain activity. fNIRS and EEG are both scalp-located procedures. fNIRS estimates brain hemodynamic fluctuations relying on spectroscopic measurements, whereas EEG captures the macroscopic temporal dynamics of brain electrical activity through passive voltages evaluations. The “orthogonal” neurophysiological information provided by the two technologies and the increasing interest in the neurovascular coupling phenomenon further encourage their integration. This review provides, together with an introduction regarding the principles and future directions of the two technologies, an evaluation of major clinical and nonclinical applications of this flexible, low-cost combination of neuroimaging modalities. fNIRS–EEG systems exploit the ability of the two technologies to be conducted in an environment or experimental setting and/or on subjects that are generally not suited for other neuroimaging modalities, such as functional magnetic resonance imaging, positron emission tomography, and magnetoencephalography. fNIRS–EEG brain monitoring settles itself as a useful multimodal tool for brain electrical and hemodynamic activity investigation.
Analysis of the Impact of Driver Behavior Models on Performance of Forward Collision Warning Systems

A critical aspect of vehicle safety analysis is understanding the impact of human behavior on the overall performance of the system. Since active safety systems (such as collision warning systems) are designed for escaping a life threatening situation, evaluation and validation of theircomponents and the underlying algorithms are often costly, and cannot be achieved without direct exposure of test subjects to hazards. As an alternative, researchers try to use simulation toolsto design and evaluate their safety algorithms prior to building test-beds for validation. This study proposes a comprehensive simulation framework for vehicular active safety system analysis. It comprises different domains of communication, transportation, and vehicular safety. The simulation framework supplies a driver model and allows for analysis of human reaction and behavior toan imminent warning. Modeling human behavior is specially challenging and requires an in-depth breakdown and investigation of the class of actions a human driver may select from. In thisstudy, we look at the primary form of a driver reaction, i.e. braking when a warning is issued. We investigate different levels of braking and complete our analysis by studying the effect of eachbraking level on a subject forward collision warning algorithm.
Optimizing NBA Player Selection Strategies Based on Salary and Statistics Analysis

In National Basketball Association (NBA), how to comprehensively measure a player's efficiency and how to sign talented players with reasonable contracts are two challenging issues. This research explored the key indicators widely used to measure player efficiency and team performance. Through data analysis, two indicators, namely Player Efficiency Rating and Player Defense Rating, were chosen to formulate the prediction of the team winning rate. Besides, different player selection strategies were proposed according to different objectives and constraints. Experimental results show that the developed team winning rate prediction models have high accuracy and the player selection strategies are effective.
Microdisk Resonator With Negative Thermal Optical Coefficient Polymer for Refractive Index Sensing With Thermal Stability

In this paper, we propose a microdisk resonator with negative thermal optical coefficient (TOC) polymer for refractive index (RI) sensing with thermal stability. The transmission characteristics and sensing performances by using quasi-TE<sub>01</sub> and quasi-TM<sub>01</sub> modes are simulated by a three-dimensional finite element method. The influences of the TOC, RI, and thickness of the polymer on the sensing performances are also investigated. The simulation results show that the RI sensitivity <italic>S<sub>n</sub></italic> and temperature sensitivity <italic> S<sub>T</sub></italic> with different polymers are in the ranges of 25.1–26 nm/RIU and 67.3–75.2 pm/K for the quasi-TE<sub>01</sub> mode, and 94.5–110.6 nm/RIU and 1.2–51.3 pm/K for the quasi-TM<sub>01 </sub> mode, respectively. Moreover, figure-of-merit of the temperature sensing for the quasi-TM<sub>01</sub> mode is in the range of 2 × 10<sup>−4</sup>–8 × 10<sup>–3</sup>, which can find important application in the implementation of the adiabatic devices.
Verification of inverse planning and treatment delivery for segmental IMRT

With intensity‐modulated radiotherapy (IMRT), it is important that the inverse planning process yields the most appropriate dose distribution for the patient and that the delivered dose then corresponds to the planned dose. This paper presents methods by which the inverse planning and delivery of segmental (step‐and‐shoot) IMRT can be verified, and gives results for a typical treatment planning system (Pinnacle3 v6.2b, Philips Radiation Oncology Systems, Milpitas, CA). Inverse planning was assessed by observing the reduction in objective function as fields were successively added to three‐field prostate, esophagus, and thyroid plans. The ability of the treatment planning system to calculate dose for a segmented field was examined by creating a stepped field with five successively narrowing segments. The complete planning process was then investigated by using two orthogonal IMRT fields to create a homogeneous dose distribution in a cubic water phantom. Finally, a clinical situation was simulated by creating a five‐field segmental IMRT plan for a lung target in an anthropomorphic phantom. A conformal plan was also compared for context. Addition of fields to inverse plans generally resulted in a reduction of objective function, indicating consistency of inverse planning solutions. Planned dose for fields with stepped intensity agreed with ionization chamber measurements to within 5%. For orthogonal fields, planned dose distributions agreed well with dose measured using film and agreed with ionization chamber measurements to within 3%. For the anthropomorphic phantom, the standard deviation of difference between planned and measured dose was 4%. Although no consensus has yet been reached on what constitutes an acceptable IMRT plan, these results indicate that step‐and‐shoot IMRT can be planned and delivered using the system described with comparable accuracy to a standard conformal treatment. PACS numbers: 87.53. Dq, 87.53. Kn, 87.53. Mr, 87.53. Tf, 87.53. Xd
Catalytic Tubular Microjet Navigating in Confined Microfluidic Channels: Modeling and Optimization

This paper describes the propulsion of the catalytic tubular microjet in confined environments as microchannels and capillaries using the Stokes equations. Especially, the thrust capability of the microjet is outstanding compared with the other microsystems, but remains only partially understood. Studies have identified the internal precursory mechanisms of the propulsion of the microjet: its inner wall catalyzes the dismutation of the fuel and bubbles are then formed. Since the jet is conical, the bubble migrates towards its widest opening. This impulses the propulsion of the microjet towards the opposite direction. However, the precise propellant role of the liquid surrounding the jet remains misunderstood. The same goes for the inner wall of the vessel in which the jet navigates, especially in narrowed environment. This article discusses these aspects in a simplified theoretical framework. Calculations are performed by explicit computation of the Stokes equations. The obtained theoretical results are in good agreement with experimental results reported in the literature. [2017-0202]
New considerations on electromagnetic energy in antenna near-field by time-domain approach

In this paper, the problem of determining electromagnetic energy in the near-field of antennas is critically revisited. It is emphasized why one must look beyond the traditional antenna-Q factor and investigate near-field EM energy from a time-domain approach. A finite-difference time-domain (FDTD) simulation strategy for calculating reactive energy near radiating structures is described briefly. Finally, some new aspects pertaining to time-evolution and visualization of the near-field energy distributions are described, that can be tackled with this FDTD-based methodology.
The Effects of Intense Pulsed Light on Tear Osmolarity in Dry Eye Disease

Purpose: In this prospective study we show the influence of Intense Pulsed Light Therapy (IPL) on tear osmolarity, an increasingly important metric of dry eye disease. Previous studies have measured the effectiveness IPL has had on other metrics including tear break up time (TBUT), lipid layer grade (LLG), tear evaporation rate (TER), tear meniscus height (TMH), and subjective responses from patients.Methods: Single center prospective study included 16 patients and 32 eyes. Patient ages ranged from 18 to 90 years old with 75% of participants being female. All patients had an at least one eye with a tear osmolarity of 308 mOsm/L or greater, or had an inter-eye difference in tear osmolarity of 11 mOsm/L or greater. Tear osmolarity was measured bilaterally before a single IPL treatment followed by one drop of topical NSAID. Bilateral tear osmolarity was then measured again one month later.Results: Average tear osmolarity pre-treatment OD was 309 mOsm/L and OS was 306.75 mOsm/L. Average tear osmolarity post-treatment was OD 296.75 mOsm/L and OS 296.06 mOsm/L. Paired t tests were performed showing the change in tear osmolarity to be statistically significant in each eye, OD p=0.0029 and OS p=0.0011. Average inter-eye difference in osmolarity went from 11.81 mOsm/L to 6.81 mOsm/L, statistically significant with p=0.0148. Conclusions: A statistically significant decrease in tear osmolarity from abnormal to normal range as well as a reduction in inter-eye difference demonstrates an improvement in tear film quality after one treatment with IPL, thus suggesting IPL's effectiveness at treating dry eye disease.
Face Recognition Using Composite Features Based on Discriminant Analysis

Extracting holistic features from the whole face and extracting the local features from the sub-image have pros and cons depending on the conditions. In order to effectively utilize the strengths of various types of holistic features and local features while also complementing each weakness, we propose a method to construct a composite feature vector for face recognition based on discriminant analysis. We first extract the holistic features and the local features from the whole face image and various types of local images using the discriminant feature extraction method. Then, we measure the amount of discriminative information in the individual holistic features and local features and construct composite features with only discriminative features for face recognition. The composite features from the proposed method were compared with the holistic features, local features, and others prepared by hybrid methods through face recognition experiments for various types of face image databases. The proposed composite feature vector displayed better performance than the other methods.
"Speak, Friend, and Enter" - Secure, Spoken One-Time Password Authentication

User authentication is a long-standing problem in need of a comprehensive solution. As technology progresses towards more ubiquitous connectivity and digital services, there is a need for new methods that are usable in these new contexts. One direction of progress has been in virtual and augmented reality technologies. Augmented reality provides tools also for better user authentication. We have developed a system, where user authentication can be done with the help of augmented reality, visual cryptography and speech recognition. This type of authentication can be used in untrusted environments and in scenarios, where the hands of the user are not available for inputting a password with some keyboard.
Evolution of Repertoire-Based Control for Robots With Complex Locomotor Systems

The evolution of task-oriented control for robots with complex locomotor systems is currently out of reach for traditional evolutionary computation techniques, as the coordination of a high number of locomotion parameters in response to the robot’s sensory inputs is extremely challenging. Evolutionary techniques have therefore mainly been applied to the optimization of specific locomotion patterns, such as forward motion. In this paper, we explore the evolutionary repertoire-based control (EvoRBC) approach, which divides the synthesis of control into two steps: 1) the evolution of a repertoire of locomotion primitives using a quality diversity algorithm and 2) the evolution of a high-level arbitrator that leverages the locomotion primitives in the repertoire to solve a given task. We comprehensively study the main components of the EvoRBC approach using a four-wheel steering robot. We then conduct a set of experiments in simulation using a hexapod robot. Our results show that EvoRBC is robust to parameter variations, and for all the robots tested, it is able to evolve controllers for a maze navigation task and significantly outperforms the traditional evolutionary robotics approach.
Adaptive closed-loop bladder neuromodulation

Urinary retention affects millions of people, and is characterized by a weak flow of urine or high post-void residual urine volume in the bladder. In severe neurogenic bladder conditions, such as that resulting from spinal cord injury, sensation from the bladder and voluntary control of micturition are lost. Micturition can be driven by using the sacral anterior root stimulator (SARS). However, commercially available SARS devices are not equipped with a closed-loop regulator for adaptive and automated control of bladder contractions. In this study, we developed a closed-loop control strategy for bladder emptying in a rat model, achieving 86% void efficiency. Our results provide a basis for developing an implantable closed-loop neural prosthesis for treating underactive bladder in the future.
Understand What Happened under the Surface: Tracing Dynamic Deep Data

Data mining has been an active research area for a couple of decades, yet the complicated nature of data mining is still not fully understood. In this paper, we discuss the need for studying dynamic deep data, which may not only provide a better understanding on the nature of data mining, but also offer an effective way in real-world problem solving at the era of big data. We present a descriptive framework to conduct the proposed research. A case study is also presented.
An interpretation of system F through bar recursion

There are two possible computational interpretations of second-order arithmetic: Girard's system F or Spector's bar recursion and its variants. While the logic is the same, the programs obtained from these two interpretations have a fundamentally different computational behavior and their relationship is not well understood. We make a step towards a comparison by defining the first translation of system F into a simply-typed total language with a variant of bar recursion. This translation relies on a realizability interpretation of second-order arithmetic. Due to Gödel's incompleteness theorem there is no proof of termination of system F within second-order arithmetic. However, for each individual term of system F there is a proof in second-order arithmetic that it terminates, with its realizability interpretation providing a bound on the number of reduction steps to reach a normal form. Using this bound, we compute the normal form through primitive recursion. Moreover, since the normalization proof of system F proceeds by induction on typing derivations, the translation is compositional. The flexibility of our method opens the possibility of getting a more direct translation that will provide an alternative approach to the study of polymorphism, namely through bar recursion.
Task Offloading in Heterogeneous Mobile Cloud Computing: Modeling, Analysis, and Cloudlet Deployment

In this paper, we consider a heterogeneous mobile cloud computing (HMCC) system that consists of remote cloud servers, local cloudlets, task offloading mobile devices (TMDs), non-task offloading MDs (NTMDs), and radio access networks such as cellular networks and WLANs. TMDs have the capability of task offloading to remote cloud servers or cloudlets, whereas NTMDs are conventional cellular users that do not have such capability. By using stochastic geometry, we analyze the outage probability of task offloading in the MCC system with only remote cloud servers and that in the HMCC with both remote cloud servers and cloudlets. The analysis provides useful information, i.e., how the varying system parameters affect the outage probability. From the analysis, we show that there is an intrinsic limitation in reducing the outage probability in the MCC system due to the outage when accessing remote cloud servers. In addition, we show that the use of cloudlets is a promising solution to overcome this limitation. However, a tradeoff exists in using cloudlets due to their deployment and operation costs. Thus, to address this tradeoff, we also study the optimal cloudlet deployment to maximize the cloud service provider’s profit while guaranteeing maximum outage probability requirements.
Deep Spectral-Spatial Feature Extraction Based on DCGAN for Hyperspectral Image Retrieval

The hyperspectral images are represented as an image cube, which image the same ground object with several tens to hundreds of spectral bands from ultraviolet to the microwave range. Content-Based Image Retrieval (CBIR) for hyperspectral images has been explored in recent years. However, it is still a concern and challenging task to extract a highly descriptive feature to contribute to improve the performance of hyperspectral image retrieval. At present, deep learning is a new research area of machine learning, which can extract more effective deep features by using a cascade of many layers of nonlinear processing units. In this paper, a deep spectral-spatial feature extraction method is proposed based on Deep Convolutional Generative Adversarial Networks (DCGAN) for hyperspectral image retrieval. Firstly, spectral vector is extracted by selecting manually pure pixels from hyperspectral image. Then spatial vector is extracted by selecting manually the neighbor pixels of pure pixels from principal bands after reducing the dimensionality of hyperspectral images with one-bit (1BT) transform. Spectral-spatial vector as training samples is obtained by combining spectral vector with spatial vector by using Vector Stacking (VS) approach. After training DCGAN model, deep spectral-spatial feature is extracted to further apply into hyperspectral retrieval. Experiments are conducted among our method and the three other state-of-the-art methods including endmember extraction using improved Automatic Pixel Purity Index (APPI), spectral and spatial features extraction, and endmember signatures extraction using Endmember Induction Algorithm (EIA). Experimental results on AVIRIS data show that our method can achieve a higher accuracy for hyperspectral image retrieval and further prove our extracted deep spectral-spatial feature has stronger descriptive ability.
Addendum to brachytherapy dose‐volume histogram commissioning with multiple planning systems

The process for validating dose‐volume histogram data in brachytherapy software is presented as a supplement to a previously published article. Included is the DVH accuracy evaluation of the Best NOMOS treatment planning system called “Best TPS VohvmePlan” As done previously in other software, a rectangular cuboid was contoured in the treatment planning system. A single radioactive 125I source was positioned coplanar and concentric with one end. Calculations were performed to estimate dose deposition in partial volumes of the cuboid structure, using the brachytherapy dosimetry formalism defined in AAPM Task Group 43. Hand‐calculated, dose‐volume results were compared to TPS‐generated, point‐source‐approximated dose‐volume histogram data to establish acceptance. The required QA for commissioning was satisfied for the DVH as conducted previously for other software, using the criterion that the DVH %VolTPS “actual variance” calculations should differ by no more than 5% at any specific radial distance with respect to %VolTG‐43, and the “average variance” DVH %VolTPS calculations should differ by no more than 2% over all radial distances with respect to %VolTG‐43. The average disagreement observed between hand calculations and treatment planning system DVH was less than 0.5% on average for this treatment planning system and less than 1.1% maximally for 1≤r≤5 cm. PACS number(s): 87.10. +e, 87.55. ‐x, 87.53. Jw, 07.05. Tp
Human-Guided Evolutionary Story Narration

Stories are useful tools with which we can exchange experience learnt in social contexts, ways to communicate futures in strategic planning, and unique building blocks that connect meanings in a movie or a virtual environment. Evolutionary computation (EC) techniques have the potential to overcome existing limitations in automated storytelling, whereby evolution can provide a process of innovation. However, one source of complexity lies in the transformation of a story in a natural language into a representation that EC can evolve easily. Another complexity arises from the fact that the ultimate judge for the quality of a story is a human being, and humans are diverse in their taste. This paper attempts to tackle the above complexities through an automatic story narration application. We present a methodology which can transform a story written in English into an event-level and hierarchical-level grammar using a network representation. This approach makes it possible to devise an encoding scheme that translates a story narration with flashback into a chromosome and vice versa. We then discuss different metrics for the evolutionary narration problem and use 42 human participants to evaluate the generated narrations. To incorporate diversified human opinions, we propose to build individual human-surrogate models from the human-evaluation experiment and further fuse them into an ensemble. The ensembles of human surrogate models serve as the objective functions of multi-objective EC to guide the generation of desirable stories from human perspectives. We demonstrate that this approach is successful in evolving better narrations as assessed by 31 human participants.