LLukas22/scidocs huggingface dataset:
eLearning Tools Evaluation based on Quality Concept Distance Computing. A Case Study

Despite the extensive use of eLearning systems, there is no consensus on a standard framework for evaluating this kind of quality system. Hence, there is only a minimum set of tools that can supervise this judgment and gives information about the course content value. This paper presents two kinds of quality set evaluation indicators for eLearning courses based on the computational process of three known metrics, the Euclidian, Hamming and Levenshtein distances. The “distance” calculus is applied to standard evaluation templates (i.e. the European Commission Programme procedures vs. the AFNOR Z 76-001 Standard), determining a reference point in the evaluation of the e-learning course quality vs. the optimal concept(s). The case study, based on the results of project(s) developed in the framework of the European Programme “Leonardo da Vinci”, with Romanian contractors, try to put into evidence the benefits of such a method. Keywords—eLearning, European programme, metrics, quality evaluation
Conducting polymer nanowire arrays for high performance supercapacitors.

This Review provides a brief summary of the most recent research developments in the fabrication and application of one-dimensional ordered conducting polymers nanostructure (especially nanowire arrays) and their composites as electrodes for supercapacitors. By controlling the nucleation and growth process of polymerization, aligned conducting polymer nanowire arrays and their composites with nano-carbon materials can be prepared by employing in situ chemical polymerization or electrochemical polymerization without a template. This kind of nanostructure (such as polypyrrole and polyaniline nanowire arrays) possesses high capacitance, superior rate capability ascribed to large electrochemical surface, and an optimal ion diffusion path in the ordered nanowire structure, which is proved to be an ideal electrode material for high performance supercapacitors. Furthermore, flexible, micro-scale, threadlike, and multifunctional supercapacitors are introduced based on conducting polyaniline nanowire arrays and their composites. These prototypes of supercapacitors utilize the high flexibility, good processability, and large capacitance of conducting polymers, which efficiently extend the usage of supercapacitors in various situations, and even for a complicated integration system of different electronic devices.
Unmanned Air Vehicles for coastal and environmental research

PEREIRA, E., BENCATEL, R., CORREIA, J., FÉLIX, L., GONÇALVES, G., MORGADO, J. AND SOUSA, J., 2009. Unmanned Air Vehicles for coastal environmental research. Journal of Coastal Research, SI 56 (Proceedings of the 10th International Coastal Symposium), pg – pg. Lisbon, Portugal, ISBN (Leave the number of pages blank) In the last decade we have witnessed an unprecedented development of Unmanned Air Vehicles (UAVs) for missions with high societal impact. Future generations of UAV systems will reflect the major current trends: increased levels of autonomy, lower cost, longer endurance, and networking capabilities. Networking is one of the major trends for unmanned vehicle systems; it is also one of the enabling technologies for distributed cooperation (and computation). In mobile network systems, vehicles, sensors and operators interact through (inter-operated) communication networks. Dynamic networks of UAVs can be used in a broad range of missions like border patrol, tracking of pollution at sea, and oceanographic and environmental research. The Portuguese Air Force Academy and the School of Engineering at Porto University have been collaborating, since 2006, on the design, implementation and testing of different types of UAVs to demonstrate these technologies in a wide spectrum of military/civil missions. This is a multi-disciplinary cooperation encompassing several technological fields: advanced vision systems for identification and tracking of fixed and mobile features, cooperative control of teams of UAVs in mixed-initiative environments, sensor fusion and navigation systems. These technologies are being developed and integrated into UAV platforms for demonstration in military missions like reconnaissance, surveillance and target acquisition, and also in several civil missions like aerial gravimetry, aerial photography, surveillance and control of maritime traffic, fishing surveillance, and detection and control of coastal hazards. This paper reports on these developments and on the demonstrations that took place in the last two years. We report on video surveillance and environmental monitoring operations with six different types of fixed-wing UAVs (wingspans ranging from 1 to 6 meters), with autonomous take-off and landing capabilities. The smaller platforms can be easily deployed (hand-launched) while the others are more suitable for endurance and larger payloads. ADITIONAL INDEX WORDS: Mobile Sensor Network, Unmanned Air Vehicles
Combining One-Class Classifiers

In the problem of one-class classification target objects should be distinguished from outlier objects. In this problem it is assumed that only information of the target class is available while nothing is known about the outlier class. Like standard two-class classifiers, one-class classifiers hardly ever fit the data distribution perfectly. Using only the best classifier and discarding the classifiers with poorer performance might waste valuable information. To improve performance the results of different classifiers (which may differ in complexity or training algorithm) can be combined. This can not only increase the performance but it can also increase the robustness of the classification. Because for one-class classifiers only information of one of the classes is present, combining one-class classifiers is more difficult. In this paper we investigate if and how one-class classifiers can be combined best in a handwritten digit recognition problem.
Hybrid filtered error LMS algorithm: another alternative to filtered-x LMS

The filtered-error LMS (FELMS) algorithms are widely used in multi-input and multi-output control (MIMO) active noise control (ANC) systems as an alternative to the filtered-x LMS (FXLMS) algorithms to reduce the computational complexity and memory requirements. However, the available FELMS algorithms introduce significant delays in updating the adaptive filter coefficients that slow the convergence rate. In this paper, we introduce a novel algorithm called the hybrid filtered-error LMS algorithm (HFELMS) which, while still a form of the FELMS algorithm, allows users to have some freedom to construct the error filter that guarantees its convergence with a sufficiently small step size. Without increasing the computational complexity, the proposed algorithm can improve the control system performance in one of several ways: 1) increasing the convergence rate without extra computation cost; 2) reducing the remaining noise mean square error (MSE); or 3) shaping the excess noise power. Simulation results show the effectiveness of the proposed method
Shared Scientific Thinking in Everyday Parent -- Child Activity

Current accounts of the development of scientific reasoning focus on individual children’s ability to coordinate the collection and evaluation of evidence with the creation of theories to explain the evidence. This observational study of parent-child interactions in a children’s museum demonstrated that parents shape and support children’s scientific thinking in everyday, non-obligatory activity. When children engaged an exhibit with parents, their exploration of evidence was observed to be longer, broader, and more focussed on relevant comparisons than children who engaged the exhibit without their parents. Parents were observed to talk to children about how to select and encode appropriate evidence and how to make direct comparisons between the most informative kinds of evidence. Parents also sometimes assumed the role of explainer by casting children’s experience in causal terms, connecting the experience to prior knowledge, or introducing abstract principles. We discuss these findings with respect to two dimensions of children’s scientific thinking: developments in evidence collection and developments in theory construction.
Early rehabilitation in patients with acute aneurysmal subarachnoid hemorrhage.

PURPOSE
The aim of this study was to describe and quantify the content of early rehabilitation adapted to patients with acute aneurysmal subarachnoid hemorrhage (aSAH) and to assess its feasibility.


METHODS
This was a prospective, observational study including 37 aSAH patients. Early rehabilitation was applied according to a mobilization algorithm. Clinical parameters, the time that rehabilitation team used on early rehabilitation and progression in mobilization were recorded. The patients' clinical conditions were graded according to the World Federation of Neurological Surgeons scale (WFNS).


RESULTS
Poor-grade patients (WFNS 3, 4, 5) (n = 12) received more rehabilitation (median 412 min) than did good-grade patients (WFNS 1, 2) (median 240 min). Mobilization to 60° of head elevation in good-grade patients began on day one after securing the aneurysm. Out-of-bed mobilization was possible on day three. Poor-grade patients were mobilized to 60° after two days and were out of bed on day seven. At discharge, 67% of poor-grade patients were mobilized to walking versus 78% of good-grade patients. No serious adverse effects to early rehabilitation were observed.


CONCLUSIONS
Early rehabilitation in aSAH patients is feasible from the first day after securing the aneurysm. The rehabilitation content varied according to the patient's clinical grade. Implications for Rehabilitation Early rehabilitation is feasible from the first day after securing the ruptured aneurysm in patients with aneurysmal subarachnoid hemorrhage (aSAH). Early rehabilitation requires close monitoring and continuous adjustment for the content and amount according to the patient's clinical condition. Interdisciplinary collaboration is recommended to match the rehabilitation needs to the medical condition on a daily basis.
Paternal mental health and socioemotional and behavioral development in their children.

OBJECTIVE
To examine the association between symptoms of psychological distress in expectant fathers and socioemotional and behavioral outcomes in their children at age 36 months.


METHODS
The current study is based on data from the Norwegian Mother and Child Cohort Study on 31 663 children. Information about fathers' mental health was obtained by self-report (Hopkins Symptom Checklist) in week 17 or 18 of gestation. Information about mothers' pre- and postnatal mental health and children's socioemotional and behavioral development at 36 months of age was obtained from parent-report questionnaires. Linear multiple regression and logistic regression models were performed while controlling for demographics, lifestyle variables, and mothers' mental health.


RESULTS
Three percent of the fathers had high levels of psychological distress. Using linear regression models, we found a small positive association between fathers' psychological distress and children's behavioral difficulties, B = 0.19 (95% confidence interval [CI] = 0.15-0.23); emotional difficulties, B = 0.22 (95% CI = 0.18-0.26); and social functioning, B = 0.12 (95% CI = 0.07-0.16). The associations did not change when adjusted for relevant confounders. Children whose fathers had high levels of psychological distress had higher levels of emotional and behavioral problems.


CONCLUSIONS
This study suggests that some risk of future child emotional, behavioral, and social problems can be identified during pregnancy. The findings are of importance for clinicians and policy makers in their planning of health care in the perinatal period because this represents a significant opportunity for preventive intervention.
Ask Me Anything: Free-Form Visual Question Answering Based on Knowledge from External Sources

We propose a method for visual question answering which combines an internal representation of the content of an image with information extracted from a general knowledge base to answer a broad range of image-based questions. This allows more complex questions to be answered using the predominant neural network-based approach than has previously been possible. It particularly allows questions to be asked about the contents of an image, even when the image itself does not contain the whole answer. The method constructs a textual representation of the semantic content of an image, and merges it with textual information sourced from a knowledge base, to develop a deeper understanding of the scene viewed. Priming a recurrent neural network with this combined information, and the submitted question, leads to a very flexible visual question answering approach. We are specifically able to answer questions posed in natural language, that refer to information not contained in the image. We demonstrate the effectiveness of our model on two publicly available datasets, Toronto COCO-QA [23] and VQA [1] and show that it produces the best reported results in both cases.
Synthesis and Characterization of Electromagnetic Materials.

Abstract : This report covers research on rare-earth magnetic materials and the electrical and optical properties of semiconductors. Studies were made of the magnetization reversal process in RCo5 particles and sintered permanent magnets and experimental verification of a model for the magnetization reversal in RCo5 particles were conducted. Research studies were conducted on both the optical and electrical properties of defect centers in semiconductors. A wavelength modulation spectrometer was designed to investigate the electrical characteristics of defects in Hg(x)Cd(1-x)Te.
Customizing the Datapath and ISA of Soft VLIW Processors

In this paper, we examine the trade-offs in performance and area due to customizing the datapath and instruction set architecture of a soft VLIW processor implemented in a high-density FPGA. In addition to describing our processor, we describe a number of microarchitectural optimizations we used to reduce the area of the datapath. We also describe the tools we developed to customize, generate, and program our processor. Our experimental results show that datapath and instruction set customization achieve high levels of performance, and that using onchip resources and implementing microarchitectural optimizations like selective data forwarding help keep FPGA resource utilization in check.
Tropical Cyclone Initialization and Prediction Based on Four-Dimensional Variational Data Assimilation

Issues on the initialization and simulation of tropical cyclones (TCs) have been studied here based on four-dimensional variational data assimilation. In particular, experiments have been carried out to assess 1) what the most critical parameters for the so-called bogus data assimilation are and 2) how the current procedures for the bogus data assimilation can be further improved. It is shown that the assimilation of wind fields is more successful than that of pressure fields in improving the initial structure and prediction of TCs. It is emphasized that the geostrophic adjustment favors the pressure field to adjust to the wind field because the TC vortex is much smaller than the radius of Rossby deformation. The results suggest that a better initial condition in the wind field is critical to the simulation of TCs. Experiments from this study also show that inclusion of the initial TC movement in the data assimilation window can help improve the track prediction, particularly during the early integration period. This method is able to shed light on the improvement of TC simulation based on the bogus data assimilation. In all, the results add a theoretical interpretation of the importance of the wind field to the sea level pressure field in terms of geostrophic adjustment, as well as a time dimension of the bogus data assimilation, by assimilating a movable vortex in the four-dimensional variational data assimilation.
A Multichannel MMSE-Based Framework for Speech Source Separation and Noise Reduction

We propose a new framework for joint multichannel speech source separation and acoustic noise reduction. In this framework, we start by formulating the minimum-mean-square error (MMSE)-based solution in the context of multiple simultaneous speakers and background noise, and outline the importance of the estimation of the activities of the speakers. The latter is accurately achieved by introducing a latent variable that takes N+1 possible discrete states for a mixture of N speech signals plus additive noise. Each state characterizes the dominance of one of the N+1 signals. We determine the posterior probability of this latent variable, and show how it plays a twofold role in the MMSE-based speech enhancement. First, it allows the extraction of the second order statistics of the noise and each of the speech signals from the noisy data. These statistics are needed to formulate the multichannel Wiener-based filters (including the minimum variance distortionless response). Second, it weighs the outputs of these linear filters to shape the spectral contents of the signals' estimates following the associated target speakers' activities. We use the spatial and spectral cues contained in the multichannel recordings of the sound mixtures to compute the posterior probability of this latent variable. The spatial cue is acquired by using the normalized observation vector whose distribution is well approximated by a Gaussian-mixture-like model, while the spectral cue can be captured by using a pre-trained Gaussian mixture model for the log-spectra of speech. The parameters of the investigated models and the speakers' activities (posterior probabilities of the different states of the latent variable) are estimated via expectation maximization. Experimental results including comparisons with the well-known independent component analysis and masking are provided to demonstrate the efficiency of the proposed framework.
Developing standards for reporting implementation studies of complex interventions (StaRI): a systematic review and e-Delphi

BACKGROUND
Dissemination and implementation of health care interventions are currently hampered by the variable quality of reporting of implementation research. Reporting of other study types has been improved by the introduction of reporting standards (e.g. CONSORT). We are therefore developing guidelines for reporting implementation studies (StaRI).


METHODS
Using established methodology for developing health research reporting guidelines, we systematically reviewed the literature to generate items for a checklist of reporting standards. We then recruited an international, multidisciplinary panel for an e-Delphi consensus-building exercise which comprised an initial open round to revise/suggest a list of potential items for scoring in the subsequent two scoring rounds (scale 1 to 9). Consensus was defined a priori as 80% agreement with the priority scores of 7, 8, or 9.


RESULTS
We identified eight papers from the literature review from which we derived 36 potential items. We recruited 23 experts to the e-Delphi panel. Open round comments resulted in revisions, and 47 items went forward to the scoring rounds. Thirty-five items achieved consensus: 19 achieved 100% agreement. Prioritised items addressed the need to: provide an evidence-based justification for implementation; describe the setting, professional/service requirements, eligible population and intervention in detail; measure process and clinical outcomes at population level (using routine data); report impact on health care resources; describe local adaptations to the implementation strategy and describe barriers/facilitators. Over-arching themes from the free-text comments included balancing the need for detailed descriptions of interventions with publishing constraints, addressing the dual aims of reporting on the process of implementation and effectiveness of the intervention and monitoring fidelity to an intervention whilst encouraging adaptation to suit diverse local contexts.


CONCLUSIONS
We have identified priority items for reporting implementation studies and key issues for further discussion. An international, multidisciplinary workshop, where participants will debate the issues raised, clarify specific items and develop StaRI standards that fit within the suite of EQUATOR reporting guidelines, is planned.


REGISTRATION
The protocol is registered with Equator: http://www.equator-network.org/library/reporting-guidelines-under-development/#17 .
Adaptive Learning by Using SCOs Metadata

This paper presents an adaptation scenario for tailoring instructional content towards individual learner characteristics taking into consideration his/her learning style type and subject matter motivation level. Learning resources are organized through shareable content objects (SCOs) – a small digital chunks of knowledge, independent and self described pieces of instructional material delivered via Learning Management System (LMS). We use an ontology based student model for storing student information. The scenario of designing lesson content is presented as a cross section of learning style and motivation level, based on the learning object’s educational metadata. Adaptation is made through discovering those SCO’s whose educational category metadata implies that SCO is to be delivered for the learning style of user. Our future work will be to provide experiment and to test our proposed guidelines in order to get feedback on how learners see the adaptive learning environments tailored to their individual learning style and motivation characteristics.
Sigmoid-weighted linear units for neural network function approximation in reinforcement learning

In recent years, neural networks have enjoyed a renaissance as function approximators in reinforcement learning. Two decades after Tesauro's TD-Gammon achieved near top-level human performance in backgammon, the deep reinforcement learning algorithm DQN achieved human-level performance in many Atari 2600 games. The purpose of this study is twofold. First, we propose two activation functions for neural network function approximation in reinforcement learning: the sigmoid-weighted linear unit (SiLU) and its derivative function (dSiLU). The activation of the SiLU is computed by the sigmoid function multiplied by its input. Second, we suggest that the more traditional approach of using on-policy learning with eligibility traces, instead of experience replay, and softmax action selection can be competitive with DQN, without the need for a separate target network. We validate our proposed approach by, first, achieving new state-of-the-art results in both stochastic SZ-Tetris and Tetris with a small 10 × 10 board, using TD(λ) learning and shallow dSiLU network agents, and, then, by outperforming DQN in the Atari 2600 domain by using a deep Sarsa(λ) agent with SiLU and dSiLU hidden units.
Seminar in Biodiversity Conservation: Towards New Paradigms

Course goals: This course will use a graduate-student seminar format to examine the current state of knowledge about the goals and methods of biodiversity conservation, especially in reference to global changes, both environmental and social. Students will be expected to read the relevant scientific literature, to actively participate in class discussions, and to write three essays. The seminar is crafted to expose participants to the means of research and communication concerning the environmental consequences of global change, and especially in regards the management of species, ecosystems, and landscapes. New approaches to biodiversity conservation will be evaluated over the course of the semester.