Miracle-dz/UniFAR-FTUs huggingface dataset:
Compression-locked nailing of the humerus: A mechanical analysis

Background In the treatment of humeral fractures, reamed nailing and compression have been reported to give higher stability. In this cadaver study, we compared the Unreamed Humeral Nail (UHN) with the (reamed) Telescopic Locking Nail (TLN) to find out whether any differences exist concerning bending and rotational stability, both with and without compression. Methods Nails were tested in a paired set-up with 8 pairs of fresh frozen cadaveric humeri. The nail-bone constructs were submitted to axial distraction to test compression, four-point bending and torsion. After creating a bone defect simulating an unstable fracture, bending and torsional tests were run again Results After cyclic loading, distraction under compression with the TLN was significantly less than with the UHN: 0.10 (SD 0.06) vs. 0.31 (SD 0.18) mm (difference = –67%, 95% CI = –84% to –37%; p = 0.01). In bending, the constructs with TLN under compression were stiffer than those with the UHN: 0.96 (SD 0.25) vs. 0.80 (SD 0.25) kN/mm (difference = 0.16, 95% CI = 0.07 to 0.25; p = 0.01). In torsion and with a bone defect, no significant differences were found. Interpretation Both nails are capable of resisting physiological forces acting on the humerus. The constructs with the TLN under compression are more stable in bending. Compression with an axial set screw is the more stable option.
Botnet Command and Control Traffic Detection Challenges A Correlation based Solution

While high-speed computer networking and the Internet brought great convenience, a number of security challenges also emerged with these technologies. Amongst different computer network security threats, like viruses and worms, botnets have become one of the most malicious threats over the Internet. In this paper, we describe key research challenges in developing effective intrusion detection systems for botnet command and control traffic detection. Then, we outline a new approach to address such challenges, which is based on voting between intrusion detection methods to collaboratively identify command and control traffic. Each detection method analyzes the network traffic to detect one technique used for command and control communications. Four detection methods are initially investigated, these are: malicious IP address, malicious SSL certificate, domain flux and Tor connection detection. Initial analysis shows that the proposed voting-based intrusion detection significantly reduces the number of false positive alerts.
De-anonymization attack on geolocated datasets

With the advent of GPS-equipped devices, a massive amount of location data is being collected, raising the issue of the privacy risks incurred by the individuals whose movements are recorded. In this work, we focus on a specific inference attack called the de-anonymization attack, by which an adversary tries to infer the identity of a particular individual behind a set of mobility traces. More specifically, we propose an implementation of this attack based on a mobility model called Mobility Markov Chain (MMC). A MMC is built out from the mobility traces observed during the training phase and is used to perform the attack during the testing phase. We design two distance metrics quantifying the closeness between two MMCs and combine these distances to build de-anonymizers that can re-identify users in an anonymized geolocated dataset. Experiments conducted on real datasets demonstrate that the attack is both accurate and resilient to sanitization mechanisms such as downsampling. Keywords-Privacy, geolocation, inference attack, deanonymization.
The Madoff Chronicles: Inside the Secret World of Bernie and Ruth

If searched for a ebook The Madoff Chronicles: Inside the Secret World of Bernie and Ruth by Brian Ross in pdf format, then you've come to the correct website. We present utter variant of this ebook in ePub, doc, PDF, txt, DjVu forms. You may reading The Madoff Chronicles: Inside the Secret World of Bernie and Ruth online by Brian Ross either download. Further, on our website you can reading guides and different art eBooks online, or load their as well. We will draw on consideration what our website does not store the book itself, but we grant ref to site whereat you may downloading either read online. So if you have must to load pdf by Brian Ross The Madoff Chronicles: Inside the Secret World of Bernie and Ruth, then you've come to right site. We own The Madoff Chronicles: Inside the Secret World of Bernie and Ruth PDF, DjVu, txt, doc, ePub forms. We will be happy if you go back more.
Street Photography: From Atget to Cartier-Bresson

Street photography is perhaps the best-loved and most widely known of all photographic genres, with names like Cartier-Bresson, Brassai and Doisneau familiar even to those with a fleeting knowledge of the medium. Yet, what exactly is street photography? From what viewpoint does it present its subjects, and how does this viewpoint differ from that of documentary photography? Looking closely at the work of Atget, Kertesz, Bovis, Rene-Jacques, Brassai, Doisneau, Cartier- Bresson and more, this elegantly written book, extensively illustrated with both well-known and neglected works, unpicks Parisian street photography's affinity with Impressionist art, as well as its complex relationship with parallel literary trends and authors from Baudelaire to Philippe Soupault. Clive Scott traces street photography's origins, asking what really what happened to photography when it first abandoned the studio, and brings to the fore fascinating questions about the way the street photographer captures or frames those subjects - traders, lovers, entertainers - so beloved of the genre. In doing so, Scott reveals street photography to be a poetic, even 'picturesque' form, looking not to the individual, but to the type; not to the 'reality' of the street but to its 'romance'.
Autonomous discovery of temporal abstractions from interaction with an environment

The ability to create and to use abstractions in complex environments, that is, to systematically ignore irrelevant details, is a key reason that humans are effective problem solvers. Although the utility of abstraction is commonly accepted, there has been relatively little research on autonomously discovering or creating useful abstractions. A system that can create new abstractions autonomously can learn and plan in situations that its original designer was not able to anticipate. This dissertation introduces two related methods that allow an agent to autonomously discover and create temporal abstractions from its accumulated experience with its environment. A temporal abstraction is an encapsulation of a complex set of actions into a single higher-level action that allows an agent to learn and plan while ignoring details that appear at finer levels of temporal resolution. The main idea of both methods is to search for patterns that occur frequently within an agent's accumulated successful experience and that do not occur in unsuccessful experiences. These patterns are used to create the new temporal abstractions. The two types of temporal abstractions that our methods create are (1) subgoals and closed-loop policies for achieving them, and (2) open-loop policies, or action sequences, that are useful “macros. ” We demonstrate the utility of both types of temporal abstractions in several simulated tasks, including two simulated mobile robot tasks. We use these tasks to demonstrate that the autonomously created temporal abstractions can both facilitate the learning of an agent within a task and can enable effective knowledge transfer to related tasks. As a larger task, we focus on the difficult problem of scheduling the assembly instructions for computers with multiple pipelines in such a manner that the reordered instructions will execute as quickly as possible. We demonstrate that the autonomously discovered action sequences can significantly improve performance of the scheduler and can enable effective knowledge transfer across similar processors. Both methods can extract the temporal abstractions from collections of behavioral trajectories generated by different processes. In particular, we demonstrate that the methods can be effective when applied to collections generated by reinforcement learning agents, heuristic searchers, and human tele-operators.
An outbreak of Marek's disease in broiler chickens: epidemiological, clinical and anatomopathological aspects.

Summary Marek’s disease, even if it is an infectious disease noted for a long time, continues to be loss/making in the farms of broiler chickens, although there are vaccines, which are administrated in the first day of life. The researches from this article aim to study the epidemiological characteristics, clinical findings and necropsy findings in Marek’s disease, which evolved in an effective of broiler chickens. The epidemiological exam of the effective revealed that the relative grave evolution in Marek’s disease was possible because the broiler chickens haven’t been vaccinated against Marek’s disease. The cumulative mortality of broiler chickens was of 12.06 % by beginning with the fourth week of life, from which 9 % pursuant to the evolution of Marek’s disease in the effective. In histopathological examination it was noted high cellular polymorphism: lymphocytes, limphoblasts, fibroblasts, and red cells and tumoral infiltrations focal or diffuse.
Serum Lipocalin-2 Predicts Cardiovascular Morbidity Risks in Subclinical Hypothyroid Rat Model = الليبوكالين 2 في مصل الدم يتنبأ بمخاطر اعتلال القلب والأوعية الدموية في نموذج الجرذان المصابة بقصور تحت السريري في الغدة الدرقية

Background: Subclinical hypothyroidism (SCH) and its associations with cardiovascular disease (CVD) remain controversial. Previous studies revealed that lipocalin -2 (LCN2) associated with increased cardiovascular mortality risk. Objective: Evaluation of the serum level of LCN2 and its association with some cardiovascular risk factors in SCH rat model. Material and methods: Twenty four adult male albino rats weighing 150-170 g were used and divided into two equal groups: control euthyroid group and SCH-induced group where SCH was induced by administration of methimazole (MMI) (10 mg/kg body weight) by gavage once daily for 3 months. Groups were compared for body mass index (BMI), serum thyroid-stimulating hormone (TSH), triiodothyronine (T3) thyroxin (T4), glucose, insulin, lipid profiles, C reactive protein (CRP), LCN2 , Homeostasis model assessment of insulin resistance (HOMA-IR), atherogenic index ,systolic (SBP) and diastolic blood pressure (DBP). Results: SCH induced group showed significantly higher mean serum levels of TSH, LCN2, insulin, total cholesterol, low density lipoprotein cholesterol (LDLC), triglycerides (TG), CRP than that of control. Also, BMI, calculated HOMA, atherogenic index, SBP and DBP were significantly higher than that of control and all these parameters were positively correlated to LCN2. However, high density lipoproteinscholesterol (HDLc) levels were significantly lower than that of control group and negatively correlated with serum LCN2 levels. Conclusion: Serum lipocalin-2 may be associated and can predict cardiovascular risk factors in SCH.
Discriminative methods for statistical spoken dialogue systems

Dialogue promises a natural and effective method for users to interact with and obtain information from computer systems. Statistical spoken dialogue systems are able to disambiguate in the presence of errors by maintaining probability distributions over what they believe to be the state of a dialogue. However, traditionally these distributions have been derived using generative models, which do not directly optimise for the criterion of interest and cannot easily exploit arbitrary information that may potentially be useful. This thesis presents how discriminative methods can overcome these problems in Spoken Language Understanding (SLU) and Dialogue State Tracking (DST). A robust method for SLU is proposed, based on features extracted from the full posterior distribution of recognition hypotheses encoded in the form of word confusion networks. This method uses discriminative classifiers, trained on unaligned input/output pairs. Performance is evaluated on both an off-line corpus, and on-line in a live user trial. It is shown that a statistical discriminative approach to SLU operating on the full posterior ASR output distribution can substantially improve performance in terms of both accuracy and overall dialogue reward. Furthermore, additional gains can be obtained by incorporating features from the system’s output. For DST, a new word-based tracking method is presented that maps directly from the speech recognition results to the dialogue state without using an explicit semantic decoder. The method is based on a recurrent neural network structure that is capable of generalising to unseen dialogue state hypotheses, and requires very little feature engineering. The method is evaluated in the second and third Dialog State Tracking Challenges, as well as in a live user trial. The results demonstrate consistently high performance across all of the off-line metrics and a substantial increase in the quality of the dialogues in the live trial. The proposed method is shown to be readily applied to expanding dialogue domains, by exploiting robust features and a new method for online unsupervised adaptation. It is shown how the neural network structure can be adapted to output structured joint distributions, giving an improvement over estimating the dialogue state as a product of marginal distributions.
Analysing Artefacts Dependencies to Evolving Software Systems

Program maintenance accounts for the largest part of the costs of any program. During maintenance activities, developers implement changes (sometimes simultaneously) on artefacts to fix bugs and to implement new requirements. Thus, developers need knowledge to identify hidden dependencies among programs artefacts and detect correlated artefacts. As programs evolved, their designs become more complex over time and harder to change. In the absence of the necessary knowledge on artefacts dependencies, developers could introduce design defects and faults that causes development and maintenance costs to rise. Therefore, developers must understand the dependencies among program artefacts and take proactive steps to facilitate future changes and minimize fault proneness. On the one hand, maintaining a program without understanding the different dependencies between their artefacts may lead to the introduction of faults. On the other hand, when developers lack knowledge about the impact of their maintenance activities, they may introduce design defects, which have a negative impact on program evolution. Thus, developers need mechanisms to understand how a change to an artefact will impact the rest of the programs artefacts and tools to detect design defects impact. In this thesis, we propose three principal contributions. The first contribution is two novel change patterns to model new co-change and change propagation scenarios. We introduce the Asynchrony change pattern, corresponding to macro co-changes, i.e., of files that co-change within a large time interval (change periods), and the Dephase change pattern, corresponding to dephase macro co-changes, i.e., macro co-changes that always happen with the same shifts in time. We present our approach, named Macocha, and we show that such new change patterns provide interesting information to developers. The second contribution is proposing a novel approach to analyse the evolution of different classes in object-oriented programs and to link different evolution behavior to faults. In particular, we define an evolution model for each class to study the evolution and the co-evolution dependencies among classes and to relate such dependencies with fault-proneness.
Mobile-Edge Computing for Vehicular Networks: A Promising Network Paradigm with Predictive Off-Loading

Cloud-based vehicular networks are a promising paradigm to improve vehicular services through distributing computation tasks between remote clouds and local vehicular terminals. To further reduce the latency and the transmission cost of the computation off-loading, we propose a cloud-based mobileedge computing (MEC) off-loading framework in vehicular networks. In this framework, we study the effectiveness of the computation transfer strategies with vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication modes. Considering the time consumption of the computation task execution and the mobility of the vehicles, we present an efficient predictive combination-mode relegation scheme, where the tasks are adaptively off-loaded to the MEC servers through direct uploading or predictive relay transmissions. Illustrative results indicate that our proposed scheme greatly reduces the cost of computation and improves task transmission efficiency.
Paramor: from paradigm structure to natural language morphology induction

Most of the world's natural languages have complex morphology. But the expense of building morphological analyzers by hand has prevented the development of morphological analysis systems for the large majority of languages. Unsupervised induction techniques, that learn from unannotated text data, can facilitate the development of computational morphology systems for new languages. Such unsupervised morphological analysis systems have been shown to help natural language processing tasks including speech recognition (Creutz, 2006) and information retrieval (Kurimo and Turunen, 2008). This thesis describes ParaMor, an unsupervised induction algorithm for learning morphological paradigms from large collections of words in any natural language Paradigms are sets of mutually substitutable morphological operations that organize the inflectional morphology of natural languages. ParaMor focuses on the most common morphological process, suffixation. ParaMor learns paradigms in a three-step algorithm. First, a recall-centric search scours a space of candidate partial paradigms for those which possibly model suffixes of true paradigms. Second, ParaMor merges selected candidates that appear to model portions of the same paradigm. And third, ParaMor discards those clusters which most likely do not model true paradigms. Based on the acquired paradigms, ParaMor then segments words into morphemes. ParaMor, by design, is particularly effective for inflectional morphology, while other systems, such as Morfessor (Creutz, 2006), better identify derivational morphology. This thesis leverages the complementary strengths of ParaMor and Morfessor by adjoining the analyses from the two systems. ParaMor and its combination with Morfessor participated in Morpho Challenge, a peer operated competition for morphology analysis systems (Kurimo, Turunen, and Varjokallio, 2008). The Morpho Challenge competitions held in 2007 and 2008 evaluated each system's morphological analyses in five languages, English, German, Finnish, Turkish, and Arabic. When ParaMor's morphological analyses are merged with those of Morfessor, the resulting morpheme recall in all five languages is higher than that of any system which competed in either year's Challenge; in Turkish, for example, ParaMor's recall of 52.1% is twice that of the next highest system. This strong recall leads to F1 scores for morpheme identification above that of all systems in all languages but English.
Rational use of azithromycin in the control of trachoma : using quantitative PCR to assess distribution of infection and impact of treatment

Trachoma is a chronic keratoconjunctivits caused by ocular infection with Chlamdydia trachomatis (CT). It is a major cause of blindness. This thesis addresses issues important for the rational use of the antibiotic azithromycin, one of the cornerstones of WHO's strategy for trachoma elimination. An entire sub-village in Rombo District, Tanzania was invited to participate. Consenting individuals were examined and had swabs taken for quantitative PCR (directed against the single-copy CT gene ompl), at baseline (before treatment). and two, six, twelve and eighteen months after, mass distribution of single-dose azithromycin. At the time of drug distribution, both weight and height of each treated individual was recorded. Four findings are highlighted. (1) Before treatment, and at each post-treatment time point, children below the age of ten years harboured the bulk of the community's ocular CT. Control programmes should view this age group as their principal target in antibiotic distribution campaigns. (2) Signs of active trachoma were much less useful than age thresholds for predicting population subsets with heavy infections. (3) Based on a summary statistic referred to as the community ocular CT load, or COCTL, the overall community burden of organism fell dramatically following azithromycin distribution, and stayed low for the entire eighteen months of follow-up. The COCTL was 13% of its baseline level at two months. 8% of baseline at six months, and 4% at both twelve and eighteen months. This may have been due to the high treatment coverage (98%) achieved. (4) In the population studied, height was a good proxy for weight for determining azithromycin dose. These studies contribute new information about the epidemiology of ocular CT infection, provide dramatic evidence of the potential effect of azithromycin when uptake is high, and should help to streamline azithromycin distribution efforts by simplifying determination of dose in the field.
The Group Affect and Performance (GAP) Corpus

In this paper, we present the Group Affect and Performance (GAP) corpus, a publicly available dataset of thirteen small group meetings. The GAP corpus contains meeting audio, transcriptions, annotations, decision-making performance, as well as group member influence, post-meeting ratings of satisfaction, and demographics. In this paper, we discuss all aspects of data collection and preparation. We also present preliminary analyses and findings concerning decision-making performance, group member influence, group member satisfaction, and additional meeting characteristics. We conclude with future directions. In creating and releasing this corpus, it is our goal to stimulate research on the computational analysis of small group meetings, and to supplement the relatively small amount of currently available group interaction data.
From data to development : using qualitative data to create ideas and solutions

During autumn 2015 we conducted a large qualitative user study on our library as a learning space. The purpose of the study was to find evidence regarding user needs, in order to be able to make well-informed decisions for a development project.The paper will discuss both the usefulness of qualitative methods in understandingstudents’ and researchers’ needs and requirements when using the library, and how you can work with the collected data as a base for development.
Atlas of Clinical Fungi

The atlas compiled by these editors is a commendable effort and welcome addition to the mycology textbook sector. Up until now, the publication of medical mycology textbooks has been sparse and those that have been published are either too detailed for a resident in training or practicing physician or do not provide sufficient photographs or illustrations of the main features of the mycotic organisms. As a lecturer in mycology for the dermatology residents at my local teaching hospital and program, there are 3 key objectives of my mycology lectures: (1) to provide some type of organizational approach to mycotic organisms, (2) to provide a concise clinical history, and (3) to provide as many photographs and illustrations of mycotic organisms as possible. This atlas provides an exemplary addition to my book collection on medical mycology textbooks and sources for illustrations of mycotic organisms. The electron photomicrographs, photoplates, and line drawings of