Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Use of Haptic Tablets for UGV Teleoperation in Unstructured Environments: System Design and Evaluation

Teleoperation of unmanned ground vehicles (UGVs), particularly for inspection of unstructured and unfamiliar environments still raises important challenges from the point of view of the operator interface. One of these challenges is caused by the fact that all information available to the operator is presented to the operator through a computer interface, providing only a partial view of the robot situation. The majority of existing interfaces provides information using visual, and, less frequently, sound channels. The lack of situation awareness (SA), caused by this partial view, may lead to an incorrect and inefficient response to the current UGV state, usually confusing and frustrating the human operator. For instance, the UGV may become stuck in debris while the operator struggles to move the robot, not understanding the cause of the UGV lack of motion. We address this problem by studying the use of haptic feedback to improve operator SA. More precisely, improving SA with respect to the traction state of the UGV, using a haptic tablet for both commanding the robot and conveying traction state to the user by haptic feedback. We report 1) a teleoperating interface, integrating a haptic tablet with an existing UGV teleoperation interface and 2) the experimental results of a user study designed to evaluate the advantage of this interface in the teleoperation of a UGV, in a search and rescue scenario. Statistically significant results were found supporting the hypothesis that using the haptic tablet elicits a reduction in the time that the UGV spends in states without traction.
WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild

Pedestrian detection has achieved significant progress with the availability of existing benchmark datasets. However, there is a gap in the diversity and density between real world requirements and current pedestrian detection benchmarks: first, most existing datasets are taken from a vehicle driving through the regular traffic scenario, usually leading to insufficient diversity; second, crowd scenarios with highly occluded pedestrians are still underrepresented, resulting in low density. To narrow this gap and facilitate future pedestrian detection research, we introduce a large and diverse dataset named WiderPerson for dense pedestrian detection in the wild. This dataset involves five types of annotations in a wide range of scenarios, no longer limited to the traffic scenario. There are a total of 13 382 images with 399 786 annotations, that is, 29.87 annotations per image, which means this dataset contains dense pedestrians with various kinds of occlusions. Hence, pedestrians in the proposed dataset are extremely challenging due to large variations in the scenario and occlusion, which is suitable to evaluate pedestrian detectors in the wild. We introduce an improved Faster R-CNN and the vanilla RetinaNet to serve as baselines for the new pedestrian detection benchmark. Several experiments are conducted on previous datasets including Caltech-USA and CityPersons to analyze the generalization capabilities of the proposed dataset, and we achieve state-of-the-art performances on these previous datasets without bells and whistles. Finally, we analyze common failure cases and find the classification ability of pedestrian detector needs to be improved to reduce false alarm and misdetection rates. The proposed dataset is available at http://www.cbsr. ia.ac. cn/users/sfzhang/WiderPerson.
Understanding the determinants of guests’ behaviour to use green P2P accommodation

Purpose This study aims to develop and empirically test a comprehensive framework in which to understand the determinants of guests’ behavior to use green peer-to-peer (P2P) accommodation in the UK P2P context by emerging the theory of planed behavior, environmental commitment theory and the value-belief-norm (VBN) theory into one model. Design/methodology/approach Adopting a quantitative approach, the present study proposes an integrated model integrating theory of planed behavior, environmental commitment theory and the VBN theory and subsequently tests the model using structural equation modeling data analysis. Data collected from 721 respondents were analyzed through (AMOS) to test the proposed model. Findings The results indicate that our integrated framework demonstrates a favorable level of prediction power for guests’ behavior, which verified the superiority of the suggested framework. Furthermore, the findings verified the moderating impact of guest attributes on guests decision regarding the booking process. Practical implications This study contributes to the existing theory and practice by offering important insights about determinants of guests’ behavior to use green P2P accommodation in the UK P2P context. Originality/value This research was the first to explore the determinants of guests’ behavior to use green P2P accommodation in the UK P2P context.
Aerosol Loading and Its Implications on Atmospheric Corrosion over Tokoradi

Received: 11 January 2019 Accepted: 20 March 2019 Aerosol loading is now a veritable tool for understanding air quality over geographical regions. It also approximately evaluates the cumulative health risk from various pollutants in the atmosphere. In this research, the focus is to see the effect of aerosol loading on atmospheric corrosion. The dataset used for this study was obtained from satellite measurement. Fifteen years primary (aerosol optical depth) dataset was obtained from the Multi-angle Imaging Spectro-Radiometer (MISR). Using mathematical and computational experimentations, the reliability of the dataset was examined and applied in determining the aerosol loading as well as the atmospheric corrosion parameter i.e. corrosion rate. The results show that the atmospheric corrosion over the study area is high and there may be huge loss of uncoated metal surface due to corrosion.
A HISTOPATHOLOGICAL REVIEW OF SPLENECTOMIES IN HEMATOLOGICAL DISORDERS IN PAEDIATRIC AGE.

BACKGROUND: The spleen was considered by Galen as "an organ of mystery," by Aristotle as unnecessary, by Pliny as an organ that might hinder the speed of runners and also as an organ that produced laughter and mirth, a notion reaffirmed in the Babylonian Talmud. Splenectomy as a therapeutic modality is widely used in management of hematological disorders. Splenectomy is specifically indicated for children with Thalassemia, Hereditary spherocytosis and Chronic Idiopathic thrombocytopenic purpura refractory to medical treatment considering its advantages of being an effective procedure. AIMS AND OBJECTIVES: The objectives of the present study was to determine various indications, age and sex incidence for splenectomies in hematological diseases and to study the histopathological changes of the splenectomy specimens in detail. MATERIAL AND METHODS: The present retrospective study was undertaken at Department of Pathology, Paediatric Referral Hospital. We reviewed 17 specimens that were received over a span of 5 years. Routine hematological, biochemical, serological laboratory investigations and bone marrow aspirates were evaluated. The splenectomy specimens were examined after routine processing and staining with Haematoxylin and Eosin. RESULTS: The most common indication for splenectomy was Thalassemia (41.1%). Splenectomy was commonly done in the age group 18 months - 12 years. The incidence of splenectomy was 1.8 times more common in boys than in girls. Histopathological picture of spleen in various lesions was consistent with the changes secondary to the disease. CONCLUSION: Thalassemia proved to be the most common indication for splenectomy in paediatric age group followed by hereditary spherocytosis and refractory Idiopathic thrombocytopenic purpura. Splenectomy has been the safest and most effective procedure for children with Chronic or Refractory Idiopathic thrombocytopenic purpura. Rarely splenectomy has shown to be beneficial in patients with lymphoma, Kasabach-Merritt syndrome and Gaucher's disease.
Audio speech enhancement using masks derived from visual speech

The aim of the work in this thesis is to explore how visual speech can be used within monaural masking based speech enhancement to remove interfering noise, with a focus on improving intelligibility. Visual speech has the advantage of not being corrupted by interfering noise and can therefore provide additional information within a speech enhancement framework. More specifically, this work considers audio-only, visual-only and audio-visual methods of mask estimation within deep learning architectures with application to both seen and unseen noise types. To estimate masks from audio and visual speech information, models are developed using deep neural networks, specifically feed-forward (DNN) and recurrent (RNN) neural networks for temporal modelling and convolutional neural networks (CNN) for visual feature extraction. It was found that the proposed layer normalised bi-directional feed-forward hybrid network using gated recurrent units (LNBiGRUDNN) provided best performance across all objective measures for temporal modelling. Also, extracting visual features using both pre-trained and end-to-end trained CNNs outperform traditional active appearance model (AAM) feature extraction across all noise types and SNRs tested. End-to-end CNNs trained on images focused on mouth-only regions-of-interest provided best performance for both audio-visual and visual-only models. The best performing audio-visual masking method outperformed both audio-only and visual-only masking methods in both matched and unseen noise type and SNR dependent conditions. For example, in unseen cafeteria babble noise at -10 dB, audio-visual masking had an ESTOI of 46.8, while audio-only and visual-only masking scored 15.0 and 42.4, and the unprocessed audio scored 9.3. Formal tests show that visual information is critical for improving intelligibility at low SNRs and for generalisation to unseen noise conditions. Experiments in large unconstrained vocabulary speech confirm that the model architectures and approaches developed can generalise to unconstrained speech across noise independent conditions and can be considered for monaural speaker dependent real-world applications.
An Empirical Study on the Spreading of Fault Revealing Test Cases in Prioritized Suites

Code edits are very common during software development. Specially for agile development, these edits need constant validation to avoid functionality regression. In this context, regression test suites are often used. However, regression testing can be very costly. Test case prioritization (TCP) techniques try to reduce this burden by reordering the tests of a given suite aiming at fastening the achievement of a certain testing goal. The literature presents a great number of TCP techniques. Most of the work related to prioritization evaluate the performance of TCP techniques by calculating the rate of test cases that fail per fault (the APFD metric). However, other aspects should be considered when evaluating prioritization results. For instance, the ability to reduce the spreading of failing test cases, since a better grouping often provides more information regarding faults. This paper presents an empirical investigation for evaluating the performance of a set of prioritization techniques comparing APFD and spreading results. Our results show that prioritization techniques generate different APFD and spreading results, being total-statement prioritization the one with the lowest spreading.
Dynamic PET Image Denoising Using Deep Convolutional Neural Networks Without Prior Training Datasets

Deep learning has attracted growing interest for application to medical imaging, such as positron emission tomography (PET), due to its excellent performance. Convolutional neural networks (CNNs), a facet of deep learning requires large training-image datasets. This presents a challenge in a clinical setting because it is difficult to prepare large, high-quality patient-related datasets. Recently, the deep image prior (DIP) approach has been devised, based on the fact that CNN structures have the intrinsic ability to solve inverse problems such as denoising without pre-training and do not require the preparation of training datasets. Herein, we proposed the dynamic PET image denoising using a DIP approach, with the PET data itself being used to reduce the statistical image noise. Static PET data were acquired for input to the network, with the dynamic PET images being handled as training labels, while the denoised dynamic PET images were represented by the network output. We applied the proposed DIP method to computer simulations and also to real data acquired from a living monkey brain with 18F-fluoro-2-deoxy-D-glucose (18F-FDG). As a simulation result, our DIP method produced less noisy and more accurate dynamic images than the other algorithms. Moreover, using real data, the DIP method was found to perform better than other types of post-denoising method in terms of contrast-to-noise ratio, and also maintain the contrast-to-noise ratio when resampling the list data to 1/5 and 1/10 of the original size, demonstrating that the DIP method could be applied to low-dose PET imaging. These results indicated that the proposed DIP method provides a promising means of post-denoising for dynamic PET images.
Dynamic Spectrum Access for Multimedia Transmission Over Multi-User, Multi-Channel Cognitive Radio Networks

The optimal spectrum access strategy is investigated for multi-user multi-channel scenario in cognitive radio networks. At first, an online learning method based on Dirichlet Process is adopted to predict the channel usage based on ACK/NACK feedbacks, which can avoid frequent information exchange among users. Based on the prediction result, the delay performance can be computed when a user transmits certain percentage of multimedia packets on a specific channel. Second, the packet delivery ratio (PDR) is derived from the prediction result of channel usage to reflect the accessing competition among multiple users. Finally, the quality of service (QoS) of multimedia applications is defined as the joint delay and throughput performances. Moreover, a dynamic spectrum access scheme is proposed to optimize the QoS metrics. The simulation results demonstrate that the QoS and the peak-signal-to-noise ratio (PSNR) of the proposed spectrum access algorithm outperform the three existing spectrum access algorithms, i.e., cognitive cross-layer algorithm, dynamic learning algorithm, and dynamic least interference algorithm. The proposed algorithm achieves more than 21.8%, 5.4%, and 3.9% PDR enhancement and over 3.23 dB, 0.82 dB, and 0.50 dB PSNR gains, compared with those three algorithms, given the transmission power as 10, 20, and 30 units, respectively.
Integration of social sustainability in software requirements using requirements pattern: a case of equality

Software is taking an increasingly prominent role in all of the areas relevant to social sustainability; e.g. by creating and enabling communities, facilitating connectedness between groups of people, enforcing standards, rules, and laws over individuals’ and business’ interactions. It enables the sharing of information and facilitate cooperation. Presently, there is no solution, framework, or method that supports software and requirements engineers in accounting for social sustainability requirements during software development. Most requirement engineers don’t even understand what social sustainability is or what it may have to do with software. Thus, the main aim of this thesis is to deliver a method that de-mystifies the notion of social sustainability, and enables requirements engineers to integrate (otherwise implicit) social sustainability requirements into a software systems specification. This thesis tackles this issue by eliciting the structure of the social sustainability concern, as reported within in the current scientific literature (systematic literature review), and delivering a generic, repeatable approach through which each of the identified sub-concerns could be operationalised into reusable requirements. The proposed approach is instantiated for the equality concern, for which a value-based pattern is derived (qualitative analysis), which is operationalised through templates. An elicitation method is also derived using the pattern and usage guideline is proposed. Several targeted studies are designed to evaluate the developed pattern and method with both requirements engineering experts and general software users. The studies demonstrate the utility and (re-)usability of the method, pattern along with its template and requirements. This thesis reveals that the generic social sustainability requirements can be used (but contextualised) across all countries and cultures. It also declares that each social sustainability concern can be represented by a value pattern. For equality, this pattern is related to equal support of: stakeholders variability, goal achievement and access to services that will facilitate goal achievement.
A 14 DEGREES OF FREEDOM VEHICLE DYNAMICS MODEL TO PREDICT THE BEHAVIOR OF A GOLF CAR

ABSTRACTA14 Degrees Of Freedom vehicle handling dynamics model is presented andimplemented using MATLAB/SIMULINK software. The model is used to predict thebehavior of a golf car in different steering conditions. This work is focusing on thevehicle heading modeling as a part of “implementation and control of an autonomouscar project” adopted by Autotronics Research Lab (ARL). The vehicle handlingrelated degrees of freedom are calculated. The vehicle yaw rate and lateralacceleration for step and sinusoidal steering wheel excitation are computed to predictthe heading of the vehicle. The model results are verified against similar workstudying the steering and handling behavior of different cars. The results show goodagreement with the literature results.
Toward Understanding the Impacts of Role Model Avatars on Engagement in Computer Science Learning

Studies show that using role models can boost academic performance of learners (Lockwood, 2006; Marx & Roman, 2002). In this paper, we describe an experiment (N=1067) exploring the impacts of varying types of avatar on engagement in an educational game. The different conditions include role models and (c) the non-role model case of simple geometric shapes (for baseline comparison). Using the Game Experience Questionnaire (GEQ) (IJsselsteijn, Kort, Poels, Jurgelionis, & Bellotti, 2007), we find that female participants using role model case (scientist avatars) had significantly higher engagement than female participants using non-scientist or shape avatars. This result suggests that STEM role model avatars have the potential to enhance engagement in educational games, which could in turn influence learning outcomes (Blumenfeld, Kempler, & Krajcik, 2005).
A Validation of a Pivoted Point Absorber Type Wave Energy Converter Using CFD

Wave energy is sustainable and clean energy, so it has great potential to be an eco-friendly and lasting renewable energy resource in the future. Recently, a number of researchers have investigated different types of wave energy converters (WECs) using numerical models such as potential theory and Computational Fluid Dynamics (CFD) to enhance the efficiency of such devices. In this paper, a validation of a point absorber type WECs is investigated to capture the movement of the WEC system and to measure the moment on the WEC system. The WEC consists of a lever and a buoy. The geometry is the same as the existing experimental geometry of the reference in order to validate the present numerical simulation. The buoy is connected to the lever and has a hinge on the connection point. Besides, another hinge is installed in the middle of the lever, and the WEC system rotates in the pitch direction. The commercial CFD package Star-CCM+, which solves Reynolds-Averaged Navier-Stokes equations, is employed in this study. In the initial stages of this research, a validation study against published experimental results was conducted. The rotational displacement and the moment on the buoy were compared with the existing experimental data of the reference. The result shows good agreement. In the near future, a study on a new pivoted point absorber WEC device regarding the buoy shape of the WEC device and an operation principle will be performed based on this numerical study.
Sampling methods for the recovery of offender cellular material from victim skin surfaces

Sexual assault can cause major health and welfare issues and is considered a severe and inhuman criminal offence. During an assault, minute quantities of trace DNA can be transferred between a perpetrator, victim and/or crime scene. Transfer of DNA often occurs through strong physical skin-to-skin contact or oral acts resulting in saliva being deposited onto the skin of a victim. Understandably, the skin of a victim can be a critical scene for gathering biological material from the perpetrator to produce a DNA profile, so it is imperative that trace DNA evidence is correctly collected. This literature review aims to address the current research on sampling collection methods available and identify potential problems and factors influencing the recovery of DNA from skin-to-skin or saliva-to-skin contact. Several factors have been identified to potentially influence the recovery of biological material from skin, including, but not limited to, the effect of background DNA and shedder status. Whether or not an individual consistently deposits the same amount of DNA every time, particularly after washing hands, is still an area of constant debate. Nevertheless, background DNA has been found to impact the conclusiveness of a profile, and while it is unavoidable, a sampling method that collects the least quantity of background DNA would be extremely beneficial. There are several sample collection methods available including the single swab, double swab, tape-lift and mini-tape. Unfortunately, the use of adhesive tapes is more common for the recovery of cellular material from textiles, with little research focusing on skin as a target surface. Presently, the double swabbing method is considered the gold standard technique for sampling skin, with numerous studies utilising this procedure. While the idea behind this technique is promising, the support for this method was found to be far from universal. There is a lack of overwhelming support for any single sample collection technique, therefore a new method could be introduced using alcohol wipes. Not only are alcohol wipes pre-moistened making for a faster application, but they are also cheap and easily accessible to medical and healthcare workers. There is a need for a single study to compare all the available and potential collection methods, focusing specifically on the recovery of offender DNA from victim skin.