Miracle-dz/UniFAR-FTUs huggingface dataset:
Utility of Spinal Injury Diagnosis Using C-Spine Lateral X-Ray and Chest, Abdomen and Pelvis Computed Tomography in Major Trauma Patients with Impaired Consciousness

Purpose: The regional emergency medical centers manage the patients with major blunt trauma according to the process appropriate to each hospital rather than standardized protocol of the major trauma centers. The primary purpose of this study is to evaluate the effectiveness and influence on prognosis of additional cervical-thoracic- lumbar-spine computed tomography (CTL-spine CT) scan in diagnosis of spinal injury from the victim of major blunt trauma with impaired consciousness. Methods: The study included patients visited the urban emergency medical center with major blunt trauma who were over 18 years of age from January 2013 to December 2016. Data were collected from retrospective review of medical records. Sensitivity, specificity, positive predictive value, and negative predictive value were measured for evaluation of the performance of diagnostic methods. Results: One hundred patients with Glasgow coma scale ≤13 underwent additional CTL-spine CT scan. Mechanism of injury was in the following order: driver, pedestrian traffic accident, fall and passenger accident. Thirty-one patients were diagnosed of spinal injury, six of them underwent surgical management. The sensitivity of chest, abdomen and pelvis CT (CAP CT) was 72%, specificity 97%, false positive rate 3%, false negative rate 28% and diagnostic accuracy 87%. Eleven patients were not diagnosed of spinal injury with CAP CT and C-spine lateral view, but all of them were diagnosed of stable fractures. Conclusions: C-spine CT scan be actively considered in the initial examination process. When CAP CT scan is performed in major blunt trauma patients with impaired consciousness, CTL-spine CT scan or simple spinal radiography has no significant effect on the prognosis of the patient and can be performed if necessary.
Spectral-Efficient L/E-ACO-SCFDM-Based Dimmable Visible Light Communication System

In this paper, we propose a dimmable visible light communication (VLC) system based on spectral-efficient layered/enhanced asymmetrically clipped optical single-carrier frequency-division multiplexing (L/E-ACO-SCFDM). L/E-ACO-SCFDM superimposes multi-layer ACO-SCFDMs occupying different subcarriers for enhancing the spectral efficiency of the system. Both the adjustable layer polarities and the low peak-to-average-power ratio property of ACO-SCFDM make it better suited to the linearity-limited VLC system. Meanwhile, L/E-ACO-SCFDM has the signal-to-noise ratio averaging advantage, which can effectively resist the high-frequency distortion caused by the bandwidth limitation of the VLC system. Based on the maximization of spectral efficiency of the system, the layer amplitudes, layer spectral efficiencies, and bias voltage are jointly optimized for providing the efficient communication under different illumination requirements. Benefiting from the numerous strengths, the proposed L/E-ACO-SCFDM-based dimming scheme can achieve at least 22.0% average spectral efficiency gain compared with several state-of-the-art OFDM-based dimming schemes within the same dimming range, which shows the great superiority for future practical applications of VLC.
Engaging Students in Open Source: Establishing FOSS Development at a University

Open source is widely used for educational purposes in higher education around the world. While many educators use open source resources for teaching, there seems to be few contributions to such projects of students as part of their university courses. In this work we present our experience on establishing open source development from student contributors as part of their university curriculum. Since 2010 more than 300 students from Graz University of Technology have been involved in the presented Catrobat project and have gained knowledge about agile software development as well as several related domains, e.g., project management, marketing, or graphical design. In this paper we provide detailed insights into the project’s organization and evaluate in a study how students feel in this setting. As we conclude, bringing open source to university courses is an effective practical approach based on social learning and provides benefits for students and researchers.
Development of HAL for Lumbar Support

Low back pain (LBP) is one of severe modern diseases due to the overload onto lumbar. Populations of 70% in the world suffer from LBP. The purpose of this study is to propose and develop the HAL for lumbar support in order to reduce lumbar load, and to verify the effect of the HAL on lumbar load reduction through an experiment at the task lifting a heavy load. Compressive force of lumbosacral disk and muscle strength of erector spinae can be represented as the function of lumbar moment. We proposed and developed the HAL for lumbar support in order to reduce the lumbar load. The HAL fixes movement of lumbar vertebrae and trunk flexion/extension motion is alternated to hip flexion/extension movement. The HAL reduces moment by supporting hip flexion/extension movement. In the experiment at the task lifting the heavy load, seven subjects lift the heavy load of 10 [kg] by using Stoop Lifting, first unsupported, then supported by the HAL. After the experiment, we executed three questionnaires as subjective assessment. From measurement results, it was confirmed that the HAL reduces the lumbar load corresponding to trunk flexion and dynamic motion. Results of subjective assessment showed that they could lift the heavy load easily. We proposed and developed the HAL for lumbar support in order to reduce the lumbar load, and verified the effect of the HAL on lumbar load reduction.
Digital Photograph's Authentication Technique Based on Digital Watermark

Digital photograph's authenticity gets oppugned because it can be dealt with variously by computer software.Because of this,digital photograph can not act as evidence in court.In this paper,digital watermark technique based on wavelet transform is used for authenticating the authentication of digital photograph.According to the operation principle of the digital camera,an arithmetic is proposed in which digital watermark is embedded automatically when the digital camera is taking a shot.In the authentication process,the digital watermark is extracted from the photograph and the change is observed.The result of research points out: the digital photograph can be authenticated effectively by observing the change of the digital watermark,and the juggled place of the digital photograph als can be pointed out.
Decisions about Automated Scoring: What They Mean for Our Constructs

This paper discusses how decisions about the scoring criteria used in the automated scoring of constructed response items can affect the constructs that the test is intended to assess. It begins with a discussion of the benefits of automated scoring, followed by a brief overview of three general approaches to automated scoring—natural language processing (NLP), exact word matching, and keyword matching. The paper then focuses on the use of keyword matching in scoring comprehension items, and reasons why this approach is clearly superior to exact word matching, and can be preferable in some cases to the more powerful method of NLP. Using the classification scheme developed by Carr, Pan, and Xi (2002), the paper considers the ways in which decisions involving the implementation of automated scoring can affect the constructs of a test, dividing these effects into unintended alterations, purposeful/principled refinement, and mixed cases. It focuses on the effects of seven categories of decisions: exactness of responses, partial credit, “undesirable” responses, synonyms, paraphrases, misspellings, and penalizing for extraneous information. Examples from a reading comprehension test scored using keyword matching are provided in order to illustrate the potential results of various choices in each of the seven areas. This is accompanied by a discussion of ways in which to implement keyword matching approaches in the context of web-based testing (WBT), with an emphasis on low-budget approaches, including the author’s ongoing development of a keyword matching automated scoring program which runs in Microsoft Excel (Microsoft Corporation, 2003).
Real-time Full-view 3D Human Reconstruction using Multiple RGB-D Cameras

This manuscript presents a real-time solution for 3D human body reconstruction with multiple RGB-D cameras. The proposed system uses four consumer RGB/Depth (RGB-D) cameras, each located at approximately 90° from the next camera around a freely moving human body. A single mesh is constructed from the captured point clouds by iteratively removing the estimated overlapping regions from the boundary. A cell-based mesh construction algorithm is developed, recovering the 3D shape from various conditions, considering the direction of the camera and the mesh boundary. The proposed algorithm also allows problematic holes and/or occluded regions to be recovered from another view. Finally, calibrated RGB data is merged with the constructed mesh so it can be viewed from an arbitrary direction. The proposed algorithm is implemented with general-purpose computation on graphics processing unit (GPGPU) for real-time processing owing to its suitability for parallel processing.
Geologischer Atlas der Schweiz 1:25'000 Erfassungskonzept, GIS-Applikation und Struktur der Datenbank : mit 7 Figuren

Le developpement des outils informatiques ouvre desormais de nouvelles perspectives dans le domaine de la cartographie. Dans le cadre de l'Atlas geologique de la Suisse 1:2 25'000, le Service hydrologique et geologique national (SHGN) developpe et teste avec la feuille 1050 Zurzach un nouveau concept de saisie et de banque de donnees. Ce concept doit satisfaire la double exigence de produire des cartes imprimees repondant aux standards de qualite actuels, et de fournir des donnees digitales susceptibles d'etre exploitees dans un systeme d'informations geographiques (SIG). Pour ce faire, une saisie et un codage complexe des surfaces et des elements lineaires s'est avere necessaire. De plus, les donnees sont parametrees en termes de lithologie, chronologie et tectonique; des tables de references pour ces trois domaines ont ete etablies. Parallelement, des donnees sur le soubassement rocheux sont introduites, qui permettent la visualisation d'un ecorche geologique en faisant abstraction des formations superficielles meubles. L'experience montre la difficulte de realiser plusieurs produits simultanement avec les memes donnees. Les possibilites et contraintes rencontrees sont ici exposees.
Εθνικο Μετσοβιο Πολυτεχνειο

In Mechanism Design, the goal is to come up with mechanisms that achieve (or closely approximate) the optimal social welfare achievable. In order to truthfully extract private information from the strategic entities holding them, the mechanism is allowed to impose monetary transfers upon them. In the majority of research in algorithmic mechanism design, payments are treated solely as a means to extract this private information. However, in many cases there are limits to the amount of monetary tranfers allowed, thus limiting the space of allowed mechanisms. In this thesis, we study the effect of imposing budget constraints upon the entities of the mechanism (the agents or the auctioneer) and the possible ways of circumventing the obstacles created by them. We survey recent results in auctions with budget constrained bidders and procurement auctions with a budget constrained center. We then develop a novel approach to reducing payments in order to respect payment bounds, and present applications to existing settings.
Health-Related Optimism and Quality of Life among Diabetes Patients: the Moderating Role of Clinical Factors in a Nigerian Sample

This study examined severity of comorbidity and adherence to treatment as contextual moderators in the link between health-related optimism and quality of life among diabetic patients. The study adopted a cross-sectional survey design in which questionnaires were used for data collection from diabetic patients in two major hospitals within the Ibadan metropolis. A multistage sampling technique was employed in selecting a sample of 385 participants for the study. The sample consisted of 232 males and 153 females with a mean age of 64.69 years, and a standard deviation of 10.56. Results showed that the first model of treatment adherence and health related optimism explained 11.94% of the variance in quality of life among diabetes patients, while the interaction term accounted for a significant change in R2 by 3.45%. The results implied that health related optimism positively predicted quality of life when treatment adherence was optimum (β=2.0295; p<.01); while there was no influence of health related optimism on quality of life when treatment adherence was poor (β=.2126; p>.05). Similarly, the second model of comorbidity severity and health related optimism explained 9.30% of the variance in quality of life among diabetes patients; while the interaction term accounted for a significant change in R2 by 3.45%. The result implied that health related optimism positively predicted quality of life when severity of comorbidity was high (β=.6746; p<.01) or low (β=3.6161; p<.01); however, a higher effect was recorded at the latter than the former. Implications of the results were highlighted and discussed.
Tunisian Dialect Sentiment Analysis: A Natural Language Processing-based Approach

Social media platforms have been witnessing a significant increase in posts written in the Tunisian dialect since the uprising in Tunisia at the end of 2010. Most of the posted tweets or comments reflect the impressions of the Tunisian public towards social, economical and political major events. These opinions have been tracked, analyzed and evaluated through sentiment analysis systems. In the current study, we investigate the impact of several preprocessing techniques on sentiment analysis using two sentiment classification models: Supervised and lexicon-based. These models were trained on three Tunisian datasets of different sizes and multiple domains. Our results emphasize the positive impact of preprocessing phase on the evaluation measures of both sentiment classifiers as the baseline was significantly outperformed when stemming, emoji recognition and negation detection tasks were applied. Moreover, integrating named entities with these tasks enhanced the lexicon-based classification performance in all datasets and that of the supervised model in medium and small sized datasets.
ScatterNet hybrid frameworks for deep learning

Image understanding is the task of interpreting images by effectively solving the individual tasks of object recognition and semantic image segmentation. An image understanding system must have the capacity to distinguish between similar looking image regions while being invariant in its response to regions that have been altered by the appearance-altering transformation. The fundamental challenge for any such system lies within this simultaneous requirement for both invariance and specificity. Many image understanding systems have been proposed that capture geometric properties such as shapes, textures, motion and 3D perspective projections using filtering, non-linear modulus, and pooling operations. Deep learning networks ignore these geometric considerations and compute descriptors having suitable invariance and stability to geometric transformations using (end-to-end) learned multi-layered network filters. These deep learning networks in recent years have come to dominate the previously separate fields of research in machine learning, computer vision, natural language understanding and speech recognition. Despite the success of these deep networks, there remains a fundamental lack of understanding in the design and optimization of these networks which makes it difficult to develop them. Also, training of these networks requires large labeled datasets which in numerous applications may not be available. In this dissertation, we propose the ScatterNet Hybrid Framework for Deep Learning that is inspired by the circuitry of the visual cortex. The framework uses a handcrafted front-end, an unsupervised learning based middle-section, and a supervised back-end to rapidly learn hierarchical features from unlabelled data. Each layer in the proposed framework is automatically optimized to produce the desired computationally efficient architecture. The term ‘Hybrid’ is coined because the framework uses both unsupervised as well as supervised learning. We propose two hand-crafted front-ends that can extract locally invariant features from the input signals. Next, two ScatterNet Hybrid Deep Learning (SHDL) networks (a generative and a deterministic) were introduced by combining the proposed frontends with two unsupervised learning modules which learn hierarchical features. These
Object-oriented modeling support system: model representation, and incremental modeling

Incremental modeling is the ability to incrementally add new and more specialized modeling capabilities to existing models, thus creating larger, more complex, and more specialized models. Some of the basic functions of the object-oriented integrated modeling support (IMS) system are described. Elemental modeling constructs are presented, and the creation of the models is discussed. Capabilities are based on the object-oriented principles of abstraction, encapsulation, class-subclass structure, inheritance, polymorphism, and specialization upon which the design and operation of the IMS system is built. The benefits of incremental modeling include stepwise model building, code, object, and model reusability; fast model prototyping; and an evolutionary approach to model creation and model-base design. <<ETX>>
Network Performance Open Source Toolkit: Using Netperf, tcptrace, NISTnet, and SSFNet

From the Publisher: In these times of reduced corporate budgets, this book shows how to test for performance problems and how to install and run freely available tools, instead of buying costly new hardware and software testing packages Network performance is the #1 headache for network operators; this book gives IT professionals straightforward guidance Author is a troubleshooting practitioner, a hands-on networking computer specialist for the Department of Defense
HQTimer: A Hybrid  ${Q}$ -Learning-Based Timeout Mechanism in Software-Defined Networks

Software-defined networking (SDN) has enabled flexible control over the network by leveraging data plane programming languages such as OpenFlow. However, this fine-grained control is potentially at odds with data plane performance due to the high storage load and limited flow table space of SDN switches. Wildcard rules and timeout mechanisms are the main approaches to relieve the load. However, wildcard rules introduce the rule dependency problem, which poses obstacles to preserve the semantics of network policies and design the timeout mechanism. Therefore, exploiting the limited flow table effectively as well as designing a safe timeout mechanism become the main challenge. In this paper, we propose HQTimer: a hybrid <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning-based timeout mechanism in SDN. HQTimer employs a hybrid timeout mechanism and a <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning-based adaptation logic. HQTimer is safe, as its timeout mechanism ensures the forwarding logic is not violated by the rule dependency problem. HQTimer is adaptive, as it assigns different timeout values to different rules according to the traffic dynamics and the data plane performance based on <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning. The extensive experiments based on real and synthetic workloads show that HQTimer achieves both a higher table-hit rate and a lower overflow number compared with existing timeout mechanisms. Specifically, in contrast to a well-tuned, static idle timeout mechanism, HQTimer improves the table-hit rate from 97.6% to 99.4% while decreasing the overflow number by 83.8%.
Grundriss der deutschen Grammatik. Band 2: Der Satz. Stuttgart: Metzler 1999. XII + 551 S.

L'A. commente et critique le deuxieme volume d'une grammaire de l'allemand, consacre a la thematique de la phrase : Grundriss der deutschen Grammatik. Band 2 : Der Satz, redige par P. Eisenberg (1997). L'A. discute des fondements theoriques qui sous-tendent ce volume, s'arrete sur la structure et l'articulation de l'ouvrage et discute de la maniere dont Eisenberg a traite le systeme verbal (temps, mode d'action, valence, structure argumentale, flexion)
Parallel Implementation of Single-Source Shortest Path Algorithm Based on Haloop

As a typical problem of graph theory, Single-Source Shortest Path(SSSP) has a wide range of applications and research. The traditional MapReduce framework such as Hadoop has been applied to SSSP problem. However, in this way, it may need writing and reading the disk frequently, transmitting data largely, beacuse of SSSP’s iterative. Haloop is a parallel programming framework which makes an improvement based on Hadoop framework, and adapts itself to iterative programming. Hence, this article represents the SSSP problem with an iterative way firstly, and then we put forward the implementation mechanism of SSSP algorithm based on Haloop. Though testing and analyzing, the implementation based on Haloop obviously improves the efficiency of program execution, compared with the traditional realization mechanism.
Suitability of Danish Bilharziasis Laboratory Technique (DBL) as Detection Test for Trematode Infection in Buffaloes

Trematodes are one of the devastating parasites in ruminants with significant public health and economic importance. Continued evaluation and development of novel detection methods for accurate diagnosis are highly warranted. This study aimed to assess the suitability of the Danish Bilharziasis Laboratory (DBL) technique as a detection test for Fasciola spp. and Paramphistomum spp. infection in buffaloes. Sensitivity and specificity were compared to post-mortem recovery of parasites from specific organs. A total of 246 buffaloes were examined in this study, of which 125 (50.81%) were infected with Fasciola spp. and 109 (44.31%) with Paramphistomum spp. by post-mortem detection. In contrast, 107 (43.50%) were positive for Fasciola spp. and 92 (37.40%) for Paramphistomum spp. by the DBL method. Sensitivity of DBL was high in detecting Fasciola spp. (85.6%) and Paramphistomum spp. (84.4%). The specificity was 100% for both parasites. Kappa coefficients (к) indicated an almost perfect agreement of the two tests in detecting Fasciola spp. (0.8539) and Paramphistomum spp. (0.8577). Based on these findings, DBL is a suitable technique for the detection of trematodes in buffaloes.