A syntactic language model based on incremental CCG parsing

Syntactically-enriched language models (parsers) constitute a promising component in applications such as machine translation and speech-recognition. To maintain a useful level of accuracy, existing parsers are non-incremental and must span a combinatorially growing space of possible structures as every input word is processed. This prohibits their incorporation into standard linear-time decoders. In this paper, we present an incremental, linear-time dependency parser based on Combinatory Categorial Grammar (CCG) and classification techniques. We devise a deterministic transform of CCGbank canonical derivations into incremental ones, and train our parser on this data. We discover that a cascaded, incremental version provides an appealing balance between efficiency and accuracy

A syntactified direct translation model with linear-time decoding

Recent syntactic extensions of statistical translation models work with a synchronous context-free or tree-substitution grammar extracted from an automatically parsed parallel corpus. The decoders accompanying these extensions typically exceed quadratic time complexity. This paper extends the Direct Translation Model 2 (DTM2) with syntax while maintaining linear-time decoding. We employ a linear-time parsing algorithm based on an eager, incremental interpretation of Combinatory Categorial Grammar (CCG). As every input word is processed, the local parsing decisions resolve ambiguity eagerly, by selecting a single supertag–operator pair for extending the dependency parse incrementally. Alongside translation features extracted from the derived parse tree, we explore syntactic features extracted from the incremental derivation process. Our empirical experiments show that our model significantly outperforms the state-of-the art DTM2 system

Supertagged phrase-based statistical machine translation

Until quite recently, extending Phrase-based Statistical Machine Translation (PBSMT) with syntactic structure caused system performance to deteriorate. In this work we show that incorporating lexical syntactic descriptions in the form of supertags can yield significantly better PBSMT systems. We describe a novel PBSMT model that integrates supertags into the target language model and the target side of the translation model. Two kinds of supertags are employed: those from Lexicalized Tree-Adjoining Grammar and Combinatory Categorial Grammar. Despite the differences between these two approaches, the supertaggers give similar improvements. In addition to supertagging, we also explore the utility of a surface global grammaticality measure based on combinatory operators. We perform various experiments on the Arabic to English NIST 2005 test set addressing issues such as sparseness, scalability and the utility of system subcomponents. Our best result (0.4688 BLEU) improves by 6.1% relative to a state-of-theart PBSMT model, which compares very favourably with the leading systems on the NIST 2005 task

Syntactic phrase-based statistical machine translation

Phrase-based statistical machine translation (PBSMT) systems represent the dominant approach in MT today. However, unlike systems in other paradigms, it has proven difficult to date to incorporate syntactic knowledge in order to improve translation quality. This paper improves on recent research which uses 'syntactified' target language phrases, by incorporating supertags as constraints to better resolve parse tree fragments. In addition, we do not impose any sentence-length limit, and using a log-linear decoder, we outperform a state-of-the-art PBSMT system by over 1.3 BLEU points (or 3.51% relative) on the NIST 2003 Arabic-English test corpus

Enhanced Traffic Congestion Management with Fog Computing: A Simulation-based Investigation using iFog-Simulator

Accurate latency computation is essential for the Internet of Things (IoT) since the connected devices generate a vast amount of data that is processed on cloud infrastructure. However, the cloud is not an optimal solution. To overcome this issue, fog computing is used to enable processing at the edge while still allowing communication with the cloud. Many applications rely on fog computing, including traffic management. In this paper, an Intelligent Traffic Congestion Mitigation System (ITCMS) is proposed to address traffic congestion in heavily populated smart cities. The proposed system is implemented using fog computing and tested in a crowded city. Its performance is evaluated based on multiple metrics, such as traffic efficiency, energy savings, reduced latency, average traffic flow rate, and waiting time. The obtained results are compared with similar techniques that tackle the same issue. The results obtained indicate that the execution time of the simulation is 4,538 seconds, and the delay in the application loop is 49.67 seconds. The paper addresses various issues, including CPU usage, heap memory usage, throughput, and the total average delay, which are essential for evaluating the performance of the ITCMS. Our system model is also compared with other models to assess its performance. A comparison is made using two parameters, namely throughput and the total average delay, between the ITCMS, IOV (Internet of Vehicle), and STL (Seasonal-Trend Decomposition Procedure based on LOESS). Consequently, the results confirm that the proposed system outperforms the others in terms of higher accuracy, lower latency, and improved traffic efficiency

Lexicalized semi-incremental dependency parsing

Even leaving aside concerns of cognitive plausibility, incremental parsing is appealing for applications such as speech recognition and machine translation because it could allow for incorporating syntactic features into the decoding process without blowing up the search space. Yet, incremental parsing is often associated with greedy parsing decisions and intolerable loss of accuracy. Would the use of lexicalized grammars provide a new perspective on incremental parsing? In this paper we explore incremental left-to-right dependency parsing using a lexicalized grammatical formalism that works with lexical categories (supertags) and a small set of combinatory operators. A strictly incremental parser would conduct only a single pass over the input, use no lookahead and make only local decisions at every word. We show that such a parser suffers heavy loss of accuracy. Instead, we explore the utility of a two-pass approach that incrementally builds a dependency structure by first assigning a supertag to every input word and then selecting an incremental operator that allows assembling every supertag with the dependency structure built so-far to its left. We instantiate this idea in different models that allow a trade-off between aspects of full incrementality and performance, and explore the differences between these models empirically. Our exploration shows that a semi-incremental (two-pass), linear-time parser that employs fixed and limited look-ahead exhibits an appealing balance between the efficiency advantages of incrementality and the achieved accuracy. Surprisingly, taking local or global decisions matters very little for the accuracy of this linear-time parser. Such a parser fits seemlessly with the currently dominant finite-state decoders for machine translation

Imatinib a Tyrosine Kinase Inhibitor: a potential treatment for SARS- COV-2 induced pneumonia

Introduction: As the coronavirus disease (COVID-19) spreads worldwide, awaiting the development of a vaccine, researchers are looking among the arsenal of available drugs, for a potential cure or medication to improve patients’ outcome. A highly elevated levels of cytokines in COVID-19 patients requiring ICU admission, has suggested that a “cytokine storm” was associated with disease severity. Methods: We summarize published key findings about imatinib, aiming to rationalize its use as a potential pharmacologic treatment for COVID-19. Results: Data from cellular, animal models and clinical trials, showed a beneficial role of tyrosine kinase inhibitors in the regulation of inflammation, the maintenance of endothelial barrier integrity, as well as the expression of antiviral properties. This data is especially derived from imatinib, the most studied Abl family kinase inhibitor, that is currently in clinical use for multiple medical conditions. Discussion: Based on this encouraging data, we hypothesize that imatinib might be beneficial for the treatment of patients with SARS-CoV-2 pneumonia, in the aim of preventing disease progression into the severe phenotype of hypoxic respiratory failure and acute respiratory distress syndrome. This concept can be considered for evaluation in a randomized controlled study

Revolutionizing Healthcare Image Analysis in Pandemic-Based Fog-Cloud Computing Architectures

The emergence of pandemics has significantly emphasized the need for effective solutions in healthcare data analysis. One particular challenge in this domain is the manual examination of medical images, such as X-rays and CT scans. This process is time-consuming and involves the logistical complexities of transferring these images to centralized cloud computing servers. Additionally, the speed and accuracy of image analysis are vital for efficient healthcare image management. This research paper introduces an innovative healthcare architecture that tackles the challenges of analysis efficiency and accuracy by harnessing the capabilities of Artificial Intelligence (AI). Specifically, the proposed architecture utilizes fog computing and presents a modified Convolutional Neural Network (CNN) designed specifically for image analysis. Different architectures of CNN layers are thoroughly explored and evaluated to optimize overall performance. To demonstrate the effectiveness of the proposed approach, a dataset of X-ray images is utilized for analysis and evaluation. Comparative assessments are conducted against recent models such as VGG16, VGG19, MobileNet, and related research papers. Notably, the proposed approach achieves an exceptional accuracy rate of 99.88% in classifying normal cases, accompanied by a validation rate of 96.5%, precision and recall rates of 100%, and an F1 score of 100%. These results highlight the immense potential of fog computing and modified CNNs in revolutionizing healthcare image analysis and diagnosis, not only during pandemics but also in the future. By leveraging these technologies, healthcare professionals can enhance the efficiency and accuracy of medical image analysis, leading to improved patient care and outcomes

A Systemic Risk Management Model to Manage the Equipment Maintenance System in Oil and Gas Companies

The risk management is significant when managing the equipment maintenance system (EMS) which is very important to maintain equipment operations and is fundamental for achieving business objectives. With the advent of risk-based thinking in industry, there was a need for introducing the risk culture within the organization, including maintenance, in order to reduce business losses. Analysis of equipment failures data showed a relation between the failures types with their consequences, and all interaction with system maintenance components. The ineffective maintenance system may cause multiple losses for the organization and therefore affects the whole business. This paper introduces a systemic risk management model to manage the maintenance system undesired events and control the impact on the organization and the consequences on business. Using systemic risk management model, the maintenance professional can manage the whole maintenance system through risk analysis, assessment, and management by creating the different risk scenarios to develop proper types of control

Epidemiological study of risk factors in pediatric asthma

Background: Childhood asthma is a major public health problem in Egypt and worldwide. Epidemiologic, physiologic, and social factors appear to be associated with an increased risk of asthma. Objective: The aim of the study was to determine the most frequent risk factors of childhood asthma exacerbation and severity in our community. Methods: This cross sectional study involved 206 asthmatic children, 5 to 15 years old. They were enrolled from the School Students Health Insurance facility of El-Matareya Teaching Hospital and from the Pediatric Outpatient Clinic of Saint Mark Charity Hospital representing several social and residential classes. They were assessed clinically and by peak expiratory flow rate (PEFR). Parents of children were interviewed for symptoms and some demographic, social, environmental, housing and familial data as well as asthma triggers through a comprehensive detailed questionnaire. Results: Residential distribution and social status were significantly associated with asthma severity as most moderate persistent asthmatics lived in semi-urban areas (70.8%) and belonged to the low-level segment of social classification (47.9%). In the majority of the study population (69.9%), a family member or more had a positive history of bronchial asthma, and this was especially evident in moderate persistent cases (70.8%). Passive smoking and dust triggered exacerbations in 48.6% and 65% of the studied sample respectively and in most moderate persistent asthmatics (83.4% and 93.7%). Most houses of moderate asthmatics were infested with cockroaches (91.7%) and domestic animals were present in 56.2%. Recurrent chest infections and cold/flu attacks were strongly associated with asthma exacerbation and severity (93.8% and 93.7% of moderate persistent cases respectively). Most moderate persistent asthmatics (91.7%) reported exercise-induced asthma while 64.6% stated that emotional stress triggered their symptoms. Indoor pollutants such as insecticides, household chemicals and odors were strongly associated with asthma severity and exacerbation especially in moderate persistent cases (triggered symptoms in 66.7%, 52.1% and 58.3% of cases respectively). Conclusion: Smoking, emotional stress and dust were the most significant triggers of asthma exacerbation and severity in our series. Identification and avoidance of risk factors for persistent asthma, combined with early institution of pharmacologic and other intervention strategies, may lead to a better outcome.Keywords: asthma severity; asthma triggers; children; residence; risk factors; smoking; social statusEgypt J Pediatr Allergy Immunol 2007; 5(1): 11-1