Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of a Virtual Pointer on Trainees' Cognitive Load and Communication Efficiency in Surgical Training

We investigated the cognitive load effect of a trainer providing surgical instruction by pointing/drawing over laparoscopic video to a trainee. Results showed that while cognitive load is higher overall with the use of the instructional system, there is a decrease by the second experience of being instructed by the Virtual Pointer. Further analysis showed that trainees were more likely to perform the surgical task and watch/listen to the trainer's instruction at the same time when the instructional system was used. This is thought to be an indication of more efficient communication when using the instructional system. Thus, although there is a small cognitive overload with the instructional system initially, the more efficient communication allows trainees to better integrate the knowledge and instructions being conveyed into the actions they must perform - indicating a better learning environment.
Energy Efficient Computing for Smart Phones in Cloud Assisted Environment

In recent years, the employment of smart mobile phones has increased enormously and are concerned as an area of human life. Smartphones are capable to support immense range of complicated and intensive applications results shortened power capability and fewer performance. Mobile cloud computing is the newly rising paradigm integrates the features of cloud computing and mobile computing to beat the constraints of mobile devices. Mobile cloud computing employs computational offloading that migrates the computations from mobile devices to remote servers. In this paper, a novel model is proposed for dynamic task offloading to attain the energy optimization and better performance for mobile applications in the cloud environment. The paper proposed an optimum offloading algorithm by introducing new criteria such as benchmarking for offloading decision making. It also supports the concept of partitioning to divide the computing problem into various sub-problems. These sub-problems can be executed parallelly on mobile device and cloud. Performance evaluation results proved that the proposed model can reduce around 20% to 53% energy for low complexity problems and up to 98% for high complexity problems.
Enhancing Physical Layer Security in Internet of Things via Feedback: A General Framework

In this article, a general framework for enhancing the physical layer security (PLS) in the Internet of Things (IoT) systems via channel feedback is established. To be specific, first, we study the compound wiretap channel (WTC) with feedback, which can be viewed as an ideal model for enhancing the PLS in the downlink transmission of IoT systems via feedback. A novel feedback strategy is proposed and a corresponding lower bound on the secrecy capacity is constructed for this ideal model. Next, we generalize the ideal model (i.e., the compound WTC with feedback) by considering channel states and feedback delay, and this generalized model is called the finite state compound WTC with delayed feedback. The lower bounds on the secrecy capacities of this generalized model with or without delayed channel output feedback are provided, and they are constructed according to variations of the previously proposed feedback scheme for the ideal model. Finally, from a Gaussian fading example, we show that the delayed channel output feedback enhances the achievable secrecy rate of the finite state compound WTC with only delayed state feedback, which implies that feedback helps to enhance the PLS in the downlink transmission of the IoT systems.
Augmented Reality for Safer Pedestrian-Vehicle Interactions

Communicating the presence of pedestrians or bicyclists to drivers can lead to safer interactions with these vulnerable road users. In a connected vehicle environment, it is conceivable that when vehicle sensors detect a vulnerable road user along an unexpected location, that information can be communicated to other drivers. A full-scale driving simulator experiment was designed to study scenarios in which drivers receive advance knowledge of a pedestrian or bicyclist presence. Impact of Cues on Pedestrian Detection On average, cues were found to result in subjects detecting pedestrians sooner (in terms of distance). However, when the behaviors observed as a result of a cue were analyzed by group, no statistically significant difference was found.
Visible Light Positioning and Navigation with Noise Mitigation Using Allan Variance

Visible Light Positioning (VLP) has become an essential candidate for high-accurate positioning; however, its positioning accuracy is usually degraded by the noise in the VLP system. To solve this problem, a novel scheme of noise measurement and mitigation is proposed for VLPbased on the noise measurement from Allan Varianceand the noise mitigation from positioning algorithms such asAdaptive Least Squares (ALSQ)andExtended Kalman Filter (EKF). In this scheme, Allan Varianceis introduced for noise analysis in VLPfor the first time, which provides an efficient method for measuring the white noise in the VLPsystems. Meanwhile, we evaluate our noise reduction method under static testusing ALSQ and dynamic test using EKF. Furthermore, this article carefully discusses the relationship between positioning accuracy and Dilution of Precision (DOP) values. The preliminary field static tests demonstrate that the proposed scheme improves thepositioning accuracy by 16.5% and achieves the accuracy of 137mmwhile dynamic tests show an improvement of 60.4% and achieve the mean positioning accuracyof 153 mm.
Workplace Engagement of Librarians and Library Staff

Abstract Very little has been written about engagement in the library workplace. Engagement and burnout are two poles on the workplace behavior spectrum. Burnout can be defined as exhaustion, cynicism, and reduced efficacy in daily work. Engagement can be defined as the opposite of burnout: energy, involvement, and high efficacy. Actively engaged employees are more satisfied in their jobs, and they are more likely to innovate and move into leadership positions. This study sought to understand the level of engagement of librarians and library staff. The Utrecht Work Engagement Scale (UWES) uses 17 questions to determine an individual’s engagement in their work by measuring the three factors of vigor, dedication, and absorption. The scores (on a 0–6 scale) of participants (n = 1100) for engagement was 4.25; vigor 4.15; dedication 4.52; and absorption 4.11. All of these scores are within the “average” range. Analysis found differences in levels of engagement by type of library (academic, public, special, and school) and work performed (administrative responsibility and patron interaction), but the differences were small. Open-ended responses revealed the main influences on participant engagement were workload, work fit, and work expectations; recognition; culture and environment; leadership; health; and meaning.
PSS/E based placement wind/PV hybrid system to improve stability of Iraqi grid

Proper employment of Hybrid Wind/ PV system is often implemented near the load, and it is linked with the grid to study dynamic stability analysis. Generally, instability is because of sudden load demand variant and variant in renewable sources generation. As well as, weather variation creates several factors that affect the operation of the integrated hybrid system. So this paper introduces output result of a PV /wind via power electronic technique; DC chopper; that is linked to Iraqi power system to promote the facilitating achievement of Wind/ PV voltage. Moreover, PSS/E is used to study dynamic power stability for hybrid system which is attached to an effective region of Iraqi Network. The hybrid system is connected to Amara Old bus and fault bus is achieved to that bus and the stability results reflects that settling time after disturbance is not satisfactory. But, it is found that PV/wind generation system influences Iraqi grid stability to be better than that with only PV generation and the latter is better than stability of the grid that is enhanced with only wind generation. These results represent an important guideline for Iraqi power system planner.
SCAROS: A Scalable and Robust Self-Backhauling Solution for Highly Dynamic Millimeter-Wave Networks

Millimeter-wave (mmWave) backhauling is key to ultra-dense deployments in beyond-5G networks because providing every base station with a dedicated fiber-optic backhaul link to the core network is technically too complicated and economically too costly. Self-backhauling allows the operators to provide fiber connectivity only to a small subset of base stations (Fiber-BSs), whereas the rest of the base stations reach the core network via a (multi-hop) wireless link towards the Fiber-BS. Although a very attractive architecture, self-backhauling is proven to be an NP-hard route selection and resource allocation problem. The existing self-backhauling solutions lack practicality because: <inline-formula> <tex-math notation="LaTeX">$(i)$ </tex-math></inline-formula> they require solving a fairly complex combinatorial problem every time there is a change in the network (e.g., channel fluctuations), or <inline-formula> <tex-math notation="LaTeX">$(ii)$ </tex-math></inline-formula> they ignore the impact of network dynamics which are inherent to mobile networks. In this article, we propose SCAROS which is a semi-distributed learning algorithm that aims at minimizing the end-to-end latency as well as enhancing the robustness against network dynamics including load imbalance, channel variations, and link failures. We benchmark SCAROS against state-of-the-art approaches under a real-world deployment scenario in Manhattan and using realistic beam patterns obtained from off-the-shelf mmWave devices. The evaluation demonstrates that SCAROS achieves the lowest latency, at least <inline-formula> <tex-math notation="LaTeX">$1.8\times $ </tex-math></inline-formula> higher throughput, and the highest flexibility against variability or link failures in the system.
Techniques for the Publication of Accessible Multimedia Content on the Web

Multimedia has become one of the most important sources of information and communication on the web. However, despite recent technological progress, people with disabilities and the elderly face difficulties accessing multimedia on the web. In some cases, these difficulties are impossible to overcome and are a fundamental cause of digital exclusion. Given the importance of this topic, several investigations on the problems of accessing multimedia resources have been carried out. Some organizations have also proposed certain standards to guide the creation and publication of accessible web content. Nevertheless, the authoring tools used in the process of publishing multimedia on the web do not offer all the accessibility features required. Authoring tools can also be used by people who do not have knowledge about web accessibility or programming, resulting in web publications lacking accessibility. This research proposes 278 novel techniques to guide authors, designers, programmers, and testers in the publication of accessible and inclusive multimedia on the web. These techniques are designed to guarantee the compliance with the recommended success criteria of Authoring Tools Accessibility Guidelines (ATAG) 2.0 of the World Wide Web Consortium. Moreover, these techniques can be used to evaluate the accessibility of the existing authoring tools used to create multimedia for the web. Additionally, we present 80 possible failures that can cause the non-fulfillment of ATAG 2.0. These failures can help authors discern what to avoid and help evaluators check whether particular multimedia is accessible.
An electric vehicle operation optimization method based on demand‐side management

Against the background of energy restriction and environmental pollution, in recent years, new energy electric vehicles have developed rapidly with their advantages of energy saving and environmental protection. To realize the benign interaction between electric vehicle charging and switching power stations and power grid, and the demand‐side management of electric vehicle charging and switching power stations, the Monte Carlo random sampling method is used to collect the battery‐charged state of batteries in one day. Considering the constraints of charging and discharging times of charging stations, the operation model of charging and switching stations for electric vehicles is established, and the interior point optimization method is used to solve the model; thus, the optimal orderly charging and discharging strategy for electric vehicles can be obtained. Finally, simulation results show that the optimized charging mode can effectively reduce the peak‐valley difference of the system load and improve the stability of the system compared with the stochastic charging mode. In addition, this mode of charging can effectively develop the economic potential of the electric‐vehicle batteries without affecting the use needs of the owners, and it has good prospects for engineering popularization.
Transceiver Design for Wireless Power Transfer for Multiuser MIMO Communication Systems

This chapter describes transceiver design methods for simultaneous wireless power transmission (WPT) and information transmission in two typical multiuser MIMO networks, that is, the MIMO broadcasting channel (BC) and interference channel (IC) networks. The design problems are formulated to minimize the transmit power consumption at the transmitter(s) while satisfying the quality of service (QoS) requirements of both the information decoding (ID) and WPT of all users. The mean-square error (MSE) and the signal-to-interference-noise ratio (SINR) criteria are adopted to characterize the ID performance of the BC network and the IC network, respectively. The designs are cast as nonconvex optimization problems due to the coupling of multiple variables with respect to transmit precoders, ID receivers, and power splitting factors, which are difficult to solve directly. The feasibility conditions of these deign problems are discussed, and effective solving algorithms are developed through alternative optimization (AO) framework and semidefinite programming relaxation (SDR) techniques. Low-complexity algorithms are also developed to alleviate the computation burden in solving the semidefinite programming (SDP) problems. Finally, simulation results validating those proposed algorithms are included.
Nature‐inspired waveform optimisation for range spread target detection in cognitive radar

The waveform optimisation problem in cognitive radar is non-convex and will have sub-optimal solutions when solved by the semi-definite relaxation (SDR) technique. Here, a novel nature-inspired waveform optimisation framework is proposed for range-spread target detection in cognitive radar. First, the waveform optimisation problem is formulated using maximum a posteriori probability and Kalman filtering to estimate the target scattering coefficients. To solve this problem more accurately and efficiently, three nature-inspired algorithms (modified particle swarm optimisation algorithm, Bat Algorithm, and Beetle Antennae Search algorithm), as a nature-inspired waveform optimisation (NIWO) approach is proposed. It is demonstrated through computer simulations that the proposed NIWO approach significantly outperforms the SDR approach, showing a promising tool for waveform optimisation in cognitive radar.
Various Techniques Used in Wireless Sensor Networks for Data Transfer

Nowadays the wireless sensor networking is the common way for data transfer. So, it is a b ig task to upload and download data securely without any data loss. As we know in the wireless sensor network there are several nodes which are used to transfer the data. To perform this task there are various data transport protocols available. In this paper, we analyze the various protocol which is used for data transfer in WSN. We also discuss the various challenges which are faced during the data transfer in WSN. Keywords— Wireless sensor network, Challenge in WSN for data transfer, protocols for data transfer in
Deep Learning For Mri Reconstruction Using A Novel Projection Based Cascaded Network

After their triumph in various classification, recognition and segmentation problems, deep learning and convolutional networks are now making great strides in different inverse problems of imaging. Magnetic resonance image (MRI) reconstruction is an important imaging inverse problem, where deep learning methodologies are starting to make impact. In this work we will develop a new Convolutional Neural Network (CNN) based variant for MRI reconstruction. The developed algorithm is based on the recently proposed deep cascaded CNN (DC-CNN) structure. In the original DCCNN network, the regular data consistency (DC) layer acts as a periodic enforcer of data fidelity. Here, we introduce a novel DC layer structure which also calculates the projection of the intermediary image estimate onto the unobserved subspace of the Fourier domain. These intermediary innovation images are saved and reutilized in the final stage of the overall structure via skip connections. This enhanced cascaded deep network results in improved reconstruction performance when compared to not only the original DC-CNN structure but also another recent deep network approach, where similar number of parameters get utilized in the competing deep methods.
Monte-Carlo Tree Search and Reinforcement Learning for Reconfiguring Data Stream Processing on Edge Computing

Distributed Stream Processing (DSP) applications are increasingly used in new pervasive services that process enormous amounts of data in a seamless and near real-time fashion. Edge computing has emerged as a means to minimise the time to handle events by enabling processing (i.e., operators) to be offloaded from the Cloud to the edges of the Internet, where the data is often generated. Deciding where to execute such operations (i.e., edge or cloud) during application deployment or at runtime is not a trivial problem. In this work, we employ Reinforcement Learning (RL) and Monte-Carlo Tree Search (MCTS) to reassign operators during application runtime. Experimental results show that RL and MCTS algorithms perform better than traditional placement techniques. We also introduce an optimisation to a MCTS algorithm, called MCTS-Best-UCT, that achieves similar latency with fewer operator migrations and faster execution time. In certain scenarios, the time needed by MCTS-Best-UCT to find the best end-to-end latency is at least 33% smaller than the time required by the other algorithms.
Attempts at using Bayesian neural networksfor uncertainty assessments of temperature forecasts

This thesis describes attempts at estimating the uncertainty of the 2-metre temperature forecast error from a probabilistic point of view, utilizing Bayesian neural networks. Bayesian neural networks are a type of machine-learning algorithms used to find patterns in data and make probabilistic predictions. Multiple fields of output data from the ECMWF IFS global model, along with temperature measurements from two meteorological observation stations for a period of six years are used for training of the networks. Attempts are made to assess probability distributions for the error as a continuous variable and through approaching the task as a binary classification problem. None of the attempts described were successful in terms of producing useful predictions, but may serve as a starting point for further investigations. (Less)
Comparing treatment programmes for anorexia

Objectives: To compare end-of-treatment outcomes for patients receiving either inpatient or day patient specialist treatment for anorexia nervosa. Outcomes included body mass index (BMI), symptom severity (EDE), general psychopathology (CORE), and treatment cost. Methods: A naturalistic study design was used. Participants were asked to complete validated questionnaires at the start and end of treatment, and weight data were also collected at these points. Cost of treatment episode was calculated at the end of treatment. These variables were compared between units. Results: Patients attending the two different treatment modalities had comparable treatment outcomes across weight measures, and global scores on the EDE and CORE questionnaires—all considered key treatment outcome variables. Those attending day patient treatment had treatment costs of on average £17 753 less than those in inpatient treatment. Discussion: Day patient treatment can produce similar treatment outcomes to its more intensive—and expensive—inpatient alternative. This is the first study to examine this in eating disorders, and warrants further examination.
Air-Ground Heterogeneous Networks for 5G and Beyond via Integrating High and Low Altitude Platforms

UAVs are expected to be an important complementary component for 5G (and beyond) communication systems to achieve the goal of global access to the Internet for all. To fully exploit the benefits of the distinct features of various UAVs, this article proposes a novel hierarchical network architecture enabled by software defined networking, which integrates cross-layer high and low altitude platforms into conventional terrestrial cellular networks to inject additional capacity and expand the coverage for underserved areas in a flexible, seamless, and cost-effective manner. Specifically, we first present a comprehensive comparison and review of different types of UAVs for communication services. Then, we propose an integrated airground heterogeneous network architecture and outline its characteristics and potential advantages. Next, several key enabling techniques for the integrated system are discussed in detail. In addition, we identify the potential application scenarios where the system can further enhance the performance of traditional terrestrial networks, followed by a case study to demonstrate the effectiveness of the proposed architecture. Finally, the discussions on challenges and open research issues are given.
Registro de pacientes con hepatitis C en las Unidades Centinela para hepatitis virales en Argentina, 2007 - 2014. Distribución por año de nacimiento

Las recomendaciones para el tamizaje de la hepatitis C basadas en factores de riesgo para la transmision demostraron ser ineficientes. En concordancia con ello, el CDC recomendo para EE.UU. el tamizaje a todos los individuos nacidos entre los anos 1945-1965, basado en informacion de prevalencia poblacional de la infeccion. La conveniencia de aplicar la recomendacion en otros contextos y/o poblaciones se puede estimar, en principio, conociendo la distribucion etaria de los individuos infectados. No se dispone de prevalencia poblacional en nuestro pais, pero se puede conocer la distribucion etaria de casos de hepatitis C que accedieron al sistema de salud. El objetivo de este trabajo es analizar la distribucion por cohorte de nacimiento de los casos registrados como “hepatitis C” en las Unidades Centinela para hepatitis virales en el periodo 2007-2014 para contribuir a la identificacion, si la hubiera, de una cohorte hacia la cual pueda dirigirse la recomendacion de tamizaje, basada en factores de riesgo propios de nuestro pais y nuestra realidad epidemiologica. La distribucion etaria de nuestros casos resulto mas amplia y joven que la de la poblacion que sustenta la recomendacion del CDC, lo que puede hipotetizarse, mas alla de la diferencia en las poblaciones que se comparan, se debe a una distribucion de factores de riesgo y edad al momento de la infeccion dife­rente entre EE.UU. y Argentina. Con estos resultados, se avala la recomendacion del Consenso Argentino de hepatitis C del ano 2013 de tamizar a todos los individuos una vez en la vida