Miracle-dz/UniFAR-FTUs huggingface dataset:
Control of Bio-Inspired Sprawling Posture Quadruped Robots with an Actuated Spine

Sprawling posture robots are characterized by upper limb segments protruding horizontally from the body, resulting in lower body height and wider support on the ground. Combined with an actuated segmented spine and tail, such morphology resembles that of salamanders or crocodiles. Although bio-inspired salamander-like robots with simple rotational limbs have been created, not much research has been done on kinematically redundant bio-mimetic robots that can closely replicate kinematics of sprawling animal gaits. Being bio-mimetic could allow a robot to have some of the locomotion skills observed in those animals, expanding its potential applications in challenging scenarios. At the same time, the robot could be used to answer questions about the animal’s locomotion. This thesis is focused on developing locomotion controllers for such robots. Due to their high number of degrees of freedom (DoF), the control is based on solving the limb and spine inverse kinematics to properly coordinate different body parts. It is demonstrated how active use of a spine improves the robot’s walking and turning performance. Further performance improvement across a variety of gaits is achieved by using model predictive control (MPC) methods to dictate the motion of the robot’s center of mass (CoM). The locomotion controller is reused on an another robot (OroBOT) with similar morphology, designed to mimic the kinematics of a fossil belonging to Orobates, an extinct early tetrapod. Being capable of generating different gaits and quantitatively measuring their characteristics, OroBOT was used to find the most probable way the animal moved. This is useful because understanding locomotion of extinct vertebrates helps to conceptualize major transitions in their evolution. To tackle field applications, e.g. in disaster response missions, a new generation of field-oriented sprawling posture robots was built. The robustness of their initial crocodile-inspired design was tested in the animal’s natural habitat (Uganda, Africa) and subsequently enhanced with additional sensors, cameras and computer. The improvements to the software framework involved a smartphone user interface visualizing the robot’s state and camera feed to improve the ease of use for the operator. Using force sensors, the locomotion controller is expanded with a set of reflex control modules. It is demonstrated how these modules improve the robot’s performance on rough and unstructured terrain. The robot’s design and its low profile allow it to traverse low passages. To also tackle narrow passages like pipes, an unconventional crawling gait is explored. While using it, the robot lies on
Correlation of Optic Nerve Sheath Diameter with Direct Measurement of Intracranial Pressure through an External Ventricular Drain

Background Early diagnosis and management of raised intracranial pressure (ICP) is essential for preventing brain damage and even death. Invasive monitoring is the gold standard to measure raised ICP but it may not be feasible in a heterogeneous group of patients. Noninvasively, a simple bedside ocular ultrasound can detect elevated ICP. The aim of our study was to evaluate the correlation between optic nerve sheath diameter (ONSD) and direct ICP measurements and to determine sensitivity and specificity of ONSD measurements to detect elevated ICP (>15 cm H2O). Methods This prospective study was conducted at the intensive care unit/high dependency units/wards of Aga Khan University Hospital. Patients with external ventricular drain (EVD) for intracranial hypertension were enrolled. Ocular ultrasound was performed with a 7.5 MHz linear probe. For each subject, three measurements on each eye were performed and the mean of the six measurements was determined. EVD was temporarily occluded and the ICP was recorded every minute for five minutes. A receiver operative characteristics (ROC) curve was constructed to determine the optimal ONSD cutoff to detect ICP above 15 cm H2O. Results A total of 35 adult patients were included in this study. The ONSD was linearly correlated with ICP in both right and left eyes (r = 0.662, p = 0.0005 and r = 0.449; p < 0.002) respectively. Pearson correlation of ONSD between two eyes (right and left) was 0.749; p = 0.0005 and 0.726; p = 0.005 at day 1 and day 2, respectively. ROC curve was created and observed that AUC of right and left eyes was 0.815 (95% CI: 0.61 to 0.99) and 0.69 (95% CI: 0.37 to 0.99). Conclusion According to this study, ventriculostomy measurements of ICP are directly correlated with ultrasound ONSD measurements. Hence, we conclude that ONSD measured by ocular ultrasound is a simple yet effective method to detect raised ICP.
A Two-Stage Clustering Based 3D Visual Saliency Model for Dynamic Scenarios

Three-dimensional (3D) visual saliency is fundamental for vision-guided applications such as human–computer interaction in virtual reality, image quality assessment, object tracking, and event retrieval. Classical models for 3D visual saliency can draw an appropriate saliency map when the quality of the required depth maps or auxiliary cues is high enough. However, the depth map is usually impaired with artifacts (such as holes or noise) from faults in stereo matching or multipaths in range sensors. In these cases, challenges arise in those 3D visual saliency models because the core preliminary processes, such as the detection of low-level visual features, may fail. To solve this problem, we proposed a two-stage clustering-based 3D visual saliency model for human visual fixation prediction in dynamic scenarios. In this model, a two-stage clustering scheme is designed to handle the negative influence of impaired depth videos. With the help of this scheme, representative cues are selected for saliency modeling. After that, multimodal saliency maps are obtained from depth, color, and 3D motion cues. Finally, a cross-Bayesian model is designed for the pooling of multimodal saliency maps. The experimental results demonstrate that the proposed 3D saliency model based on two-stage clustering outperforms other state-of-the-art models on a variety of metrics. Furthermore, the consistency and robustness of our model are also verified.
Cardiogenic Differentiation of Murine Bone Marrow-Derived Mesenchymal Stem Cells by 5-Azacytidine: A Follow-up In vitro Study

Background: Cell-based therapy is a promising tool in the management of myocardial infarction. Aim of the Work: The aim of this study is to examine the in vitro potential differentiation of murine bone marrow (BM)-derived stem cells into cardiomyocytes using 5-azacytidine after 1, 3, and 5 weeks and follow it up after 8 weeks. Materials and Methods: BM-derived mesenchymal stem cells (MSCs) were extracted from the bones of adult albino rats. MSCs were induced with 10 μM 5-azacytidine for 24 h. The cells were examined after 1, 3, 5, and 8 weeks. Cell characterization with immunocytochemistry for detection of CD105, desmin, and T-troponin and transmission electron microscopy was performed. Results: The 5-azacytidine-induced MSCs showed light and electron microscopic histological characteristics resembling cardiomyocytes and progressively expressed the cardiac muscle-specific markers over the 1st, 3rd, and 5th weeks, yet by the 8th week, these parameters were significantly downregulated. Conclusion: Prolonged survival of 5-azacytidine-induced MSCs in culture beyond the 8th week resulted in loss of the newly acquired cardiomyocyte characteristics. It is not recommended to prolong the maintenance of 5-azacytidine-induced MSCs in culture on the hope of increasing its cardiogenic potentiality beyond 5 weeks.
Intelligent Parking Assist for Trucks with Prediction

Truck parking has been identified as a major issue both in the USA and E.U. and has been selected by the American Transportation Research Institute as the most important research need for the trucking industry in 2015. The lack of appropriate and convenient parking locations has been the cause of several safety issues over the past years as drivers might be forced to either drive while tired and increase the risk of accidents or park illegally in unsafe locations, which might also pose a safety hazard to them and other drivers. Additionally, the parking shortage also impacts the shipment costs and the environment as the drivers might spend more fuel looking for parking or idling for power when parked in inappropriate locations. This project’s objective was to study the truck parking problem, generate useful information and parking assist algorithms that could assist truck drivers in better planning their trips. By providing information about parking availability to truck drivers, the researchers expect to induce them to better distribute themselves among existing rest areas. This would decrease the peak demand in the most popular truck stops and attenuate the problems caused by the parking shortage. In this project, several parking availability prediction algorithms were tested using data from a company’s private truck stops reservation system. The prediction MSE (mean squared error) and classification (full/available) sensitivity and specificity plots were evaluated for different experiments. It is shown that none of the tested algorithms is absolutely better than the others and has superior performance in all situations. The results presented show that a more efficient way would be to combine them and use the most appropriate one according to the situation. A model assignment according to current time of the day and target time for prediction is proposed based on the experiment data. View the NCST Project Webpage
Iris Pattern Recognition

The earlier experimental which were conducted has resulted in the biometric templates, which include the iris of human eye and the attributes of the individual, that are produced using different methods of recognition, which can be matched with the help of central rays in the convex polyhedral cones and that these templates are protected by a method which is extended from Iris-templates and it can be broken into. The results of these experiments clearly show that, the convex polyhedral cone template cannot be presumed secure without a thorough security analysis. If there is a contact lens present then it imposes challenge in the recognition of the iris pattern. It is more complicated when the lens is a textured lens used as cosmetics. These types of lenses are readily available in the market. This study also intends at analyzing the influence of these constraints in the recognition of iris of the human eye. The approach that is being proposed overtakes the other types of algorithms used for detection and improves the performance of the recognition process of the iris. When it comes to those who wear a contact lens, the identification of the presence of a lens is the initial task which should be taken in order to increase the reliability and usability of the recognition process. The astonishing success of Iris-templates depends deeply on its computational benefits and it includes the enormously high matching speed for identification of iris on a large-scale and automatic threshold adjustment which is based on the quality of the image. Many techniques which were modified from Iris-templates were suggested for iris recognition and the recognition based on the individuals attributes.
Multi-dialect Arabic broadcast speech recognition

Dialectal Arabic speech research suffers from the lack of labelled resources and standardised orthography. There are three main challenges in dialectal Arabic speech recognition: (i) finding labelled dialectal Arabic speech data, (ii) training robust dialectal speech recognition models from limited labelled data and (iii) evaluating speech recognition for dialects with no orthographic rules. This thesis is concerned with the following three contributions: Arabic Dialect Identification: We are mainly dealing with Arabic speech without prior knowledge of the spoken dialect. Arabic dialects could be sufficiently diverse to the extent that one can argue that they are different languages rather than dialects of the same language. We have two contributions: First, we use crowdsourcing to annotate a multi-dialectal speech corpus collected from Al Jazeera TV channel. We obtained utterance level dialect labels for 57 hours of high-quality consisting of four major varieties of dialectal Arabic (DA), comprised of Egyptian, Levantine, Gulf or Arabic peninsula, North African or Moroccan from almost 1,000 hours. Second, we build an Arabic dialect identification (ADI) system. We explored two main groups of features, namely acoustic features and linguistic features. For the linguistic features, we look at a wide range of features, addressing words, characters and phonemes. With respect to acoustic features, we look at raw features such as mel-frequency cepstral coefficients combined with shifted delta cepstra (MFCC-SDC), bottleneck features and the i-vector as a latent variable. We studied both generative and discriminative classifiers, in addition to deep learning approaches, namely deep neural network (DNN) and convolutional neural network (CNN). In our work, we propose Arabic as a five class dialect challenge comprising of the previously mentioned four dialects as well as modern standard Arabic. Arabic Speech Recognition: We introduce our effort in building Arabic automatic speech recognition (ASR) and we create an open research community to advance it. This section has two main goals: First, creating a framework for Arabic ASR that is publicly available for research. We address our effort in building two multi-genre broadcast (MGB) challenges. MGB-2 focuses on broadcast news using more than 1,200 hours of speech and 130M words of text collected from the broadcast domain. MGB-3, however, focuses on dialectal multi-genre data with limited non-orthographic speech collected from YouTube, with special
Improved simulation of virtual bone drilling surgery with a voxel-based model

Virtual surgery simulation provides a safe, cost-effective and a repeatable alternative to traditional surgical training methods. The simulation is expected to not only simulate the graphical changes in the surgery, but also to provide a haptic response via a force feedback device. The objective of this study is to develop a virtual bone drilling simulation system and focus on the key issues that limit surgical simulations like the virtual bone drilling of spine surgery. A voxel-based bone model is developed and the algorithms (based on the model) are presented in this work. To provide operators with a realistic environment, dual thread technology is used for graphic and haptic rendering of the virtual bone drilling simulation. Such a surgical simulation system can be very helpful in surgical training.
EFFECT OF BLEED SYSTEM ON AIR INTAKE PERFORMANCE

Numerical studies are conducted to investigate the performance characteristics of the supersonic intake, considering the effects of the bleed system. In this study, the base model is a two-dimensional rectangular mixed type compression air intake. Numerical simulations are carried out by solving RANS equations with the standard k-ω turbulence model. A parametric study is conducted with different hole inclination angle and the number of holes.
THE EFFECTIVENESS OF COLD IN POST-EXERCISE RECOVERY IN SPRINT KAYAKING

4 RESUMO . 6 LIST OF ABBREVIATIONS . 8 INTRODUCTION . 9 MATERIALS AND METHODS . 13 RESULTS . 18 DISCUSSION . 24 Limitations . 26 CONCLUSION . 27 ACKNOWLEDGMENTS . 28 BIBLIOGRAFY . 29 ATTACHMENTS . 32
Understanding and detecting newly emerging attack vectors in cybercrimes

Numerous efforts have been devoted to securing computer systems in the past decade, which has rendered many previously popular attacks ineffective. In response to the arms race, cybercriminals are constantly seeking for new attack vectors. In this dissertation, we investigate several newly emerging attack vectors in cybercrime. Our research first focuses on embedded malware inside Adobe PDF (Portable Document Format) documents. Due to its widespread use and Javascript support, PDF has become the primary vehicle for delivering embedded exploits since 2008. Unfortunately, existing defenses are limited in effectiveness, prone to evasion, or computationally expensive to be employed as on-line protection systems. To this end, we propose a context-aware approach for detection and confinement of malicious Javascript in PDF documents. Based on more than twenty thousand benign and malicious samples, our experimental evaluation shows that our defense system can achieve very high detection accuracy with minor overhead. We further conduct the first comprehensive study on domain shadowing, a new strategy adopted by miscreants to build their attack infrastructures. We design a novel domain shadowing detector called Woodpecker, which characterizes shadowed domains based on a set of 17 novel features. By applying Woodpecker to the daily feeds of VirusTotal collected in two months, we can detect thousands of new domain shadowing campaigns. Our study highlights domain shadowing as an increasingly rampant threat since 2014. Moreover, we discover a new security threat caused by dangling records in DNS. In a dangling DNS record (Dare), the resources pointed to by the DNS record are invalid, but the record itself has not yet been purged from DNS. Our work reveals that Dare can be easily manipulated by adversaries for domain hijacking. In particular,
Secure and Privacy Preserving Management of Biometric Templates

Privacyand security are one of the most important challenges in biometrics. We propose in this paper an architecture for the secure storage and verification of the biometric template. We propose to use at the same time a trusted architecture based on Global Platform and an algorithmic solution for providing a cancelable biometric template. We illustrate this new technique on fingerprints. In the experimental results, we put into obviousness the benefit of the proposed architecture.
Customizing Stata graphs made even easier

In Jann (2017; University of Bern Social Sciences Working Paper No. 29) I presented a command called -grstyle- that simplifies changing the default look of Stata graphs. The command, however, still relies on idiosyncratic scheme file syntax, which may not be well known to many users. In this paper I therefore present an extension called -grstyle set- that automizes the creation of sets of scheme file entries for a number of potentially useful adjustments, without much typing and without requiring much knowledge about scheme file syntax. Covered topics include, for example, the rendering of the background and coordinate system, the placement and look of the legend, the assignment of colors, symbols, and line patterns, and the assignment of relative or absolute sizes.
Online Communication Platform Contributions to Internal Communication: A Lesson from Telkom Digital Service Division

Nowadays, computer-mediated communication (CMC) enables the employees to ease work coordination and communication within an organization. An organization, in which hierarchy exists among its employees, must keep the communication flow in order to maintain activities leading to reach the organizational goals. Telkom Digital Service, as a part of Telekomunikasi Indonesia, Tbk utilizes a CMC tool called Slack.com as an integrated internal communication platform for employees to communicate and coordinate inand off-working hours. Using qualitative approach, three participants were involved in in-depth interviews to gain their insights and understanding on how the platform contributed to the internal communication within the company. Recently, the researchers found that the CMC platform helped them in attaining direct information, uploading and downloading important documents, and organizing multi-channels for multi-purposes. Moreover, the participants admitted that the platform had allowed them to express emotions without any barrier caused by the hierarchy within the organization. Keywords—computer-mediated communication; internal communication; organizational communication; slack.com
Virtual Network Recognition and Optimization in SDN-Enabled Cloud Environment

Cloud computing is a scalable and efficient technology for providing different services. For better reconfigurability and other purposes, users build virtual networks in cloud environments. Since some applications bring heavy pressure to cloud datacenter networks, it is necessary to recognize and optimize virtual networks with different applications. In some cloud environments, cloud providers are not allowed to monitor user private information in cloud instances. Therefore, in this paper, we present a virtual network recognition and optimization method to improve quality-of-service (QoS) of cloud services. We first introduce a community detection method to recognize virtual networks from the cloud datacenter network. Then, we design a scheduling strategy by combining SDN-based network management and instance placement to improve the service-level agreements (SLA) fulfillment. Our experimental result shows that we can achieve a recognition accuracy as high as 80 percent to find out the virtual networks, and the scheduling strategy increases the number of SLA fulfilled virtual networks.
Evaluation of a health system intervention to improve virological management in an antiretroviral programme at a municipal clinic in central Durban

Background With the largest antiretroviral therapy (ART) programme globally, demand for effective HIV management is increasing in South Africa. While viral load (VL) testing is conducted, VL follow-up and management are sub-optimal. Objectives The objective of this study was to address gaps in the VL cascade to improve VL testing and management. Methods Antiretroviral therapy records were sampled for an in-depth review. The study team then reviewed individual records, focusing on ART management, virological suppression and retention. Multifaceted interventions focused on virological control, including a clinical summary chart for ART care; streamlining laboratory results receipt and management; monitoring VL suppression, flagging virological failure and missed visits for follow-up; down-referral of stable patients eligible for the chronic club system; and training of personnel and patients. Results Pre-intervention, 78% (94/120) of eligible patients had VL tests, versus 92% (145/158) post-intervention (p = 0.0009). Pre-intervention, 59% (71/120) of patients accessed their VL results, versus 86% (136/158) post-intervention (p < 0.0001). Post-intervention, 73% (19/26) of patients eligible for ART change were appropriately managed, versus 11% (4/36) pre-intervention (p < 0.0001). Only 27% had no regimen changes (7/26) post-intervention, versus 81% (29/36) pre-intervention (p < 0.0001). Conclusion Service delivery was streamlined to facilitate HIV services by focusing on VL test monitoring, protocol training and accessibility of results, thereby improving clinical management.
Machine Translation of Speech-Like Texts: Strategies for the Inclusion of Context

Whilst the focus of Machine Translation (MT) has for a long time been the translation of planned, written texts, more and more research is being dedicated to translating speech-like texts (informal or spontaneous discourse or dialogue). To achieve high quality and natural translation of speechlike texts, the integration of context is needed, whether it is extra-linguistic (speaker identity, the interaction between speaker and interlocutor) or linguistic (coreference and stylistic phenomena linked to the spontaneous and informal nature of the texts). However, the integration of contextual information in MT systems remains limited in most current systems. In this paper, we present and critique three experiments for the integration of context into a MT system, each focusing on a different type of context and exploiting a different method: adaptation to speaker gender, cross-lingual pronoun prediction and the generation of tag questions from French into English.