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关键词： Computer science  

关键词： Computer science  

摘要： The article interprets the patriotic upbringing of children and youth as a systematic and purposeful activity of the subjects of educational activity to form a growing generation of love for the motherland, high patriotic consciousness, readiness to fulfill a civic duty and constitutional duties to protect the interests of the homeland, patriotically directed activities. The author believes that it is legitimate to understand the formation of a system of patriotic education as the procedure for its dynamic improvement, qualitative increments in the integrity and completeness of interrelationships between its structural components, which is externally manifested in an increase in the level of patriotic consciousness and activity of people. Patriotism as a social feeling has an individual, personal, deeply intimate character. As a significant, dear and sacred feeling, patriotism is filled with subjective meanings at the level of the unconscious and conscious and occupies one of the most important places in the human value hierarchy. The process approach involves a comprehensive and phased implementation of the components of patriotic education by subjects of pedagogical activity as a pedagogical process: target, subject-object, meaningful, technological, analytical and effective. Another characteristic of the process approach is the perception of the process as an element of the organization of activities of the subjects of activity, the identification of phases, stages and stages in patriotic education. © 2020. All Rights Reserved.

关键词： Computer science  

摘要： The field of computational complexity theory studies the inherent difficulties of performing certain computational tasks with limited resources. While characterizing the minimum time required for a computational task has received more attention, understanding the memory requirements is as fundamental and fascinating. The focus of this thesis is understanding the limits of space-bounded computation and implications for various algorithmic problems. 1. Implications of bounded space for learning algorithms: [SVW16] and [Sha14] started the study of online learning under memory constraints. In a breakthrough result, [Raz16] showed that learning an unknown n-bit vector from random linear equations (in F_2) requires either Ω(n^2) space (memory) or 2^Ω(n) samples. Work in this thesis extends these memory-sample tradeoffs to a larger class of learning problems through an extractor-based approach, to when the learner is allowed a second pass over the stream of samples and, to proving security of Goldreich’s local pseudorandom generator against memory-bounded adversaries in the streaming model. 2. Implications of bounded space for randomized algorithms: It is largely unknown if randomization gives space-bounded computation any advantage over deterministic computation. The current best hope of the community is to derandomize randomized log-space computation with one-sided error, that is, prove RL = L. A work presented in this thesis, in a step towards answering this question, improved upon the state-of-the-art constructions of hitting sets, which are tools for derandomization. 3. Implications of bounded space for mirror games: In this thesis, we show the impossibility of winning the following streaming game under memory constraints. Alice and Bob take turns saying numbers belonging to the set {1, 2, ..., 2N}. A player loses if they repeat a number that has already been said. Bob, who goes second, has a memory-less strategy to avoid losing. Alice, however, needs at least Ω(N) me

关键词： Computer Science   Computer Engineering   Telematics Data Analysis   Driving Behavior   Driving Risk   Usage Based Insurance   Urban Planning   Traffic Management   Accident Prediction   Traffic Prediction  

摘要： Analysis of telematics data collected from drivers in real-time, along with contextual data (such as traffic and weather, data), provides valuable insights regarding an individual's driving behavior, common driving habits, and characteristics of a road network. The primary focus in this dissertation is on predicting the risk in driving, with the risk being a combination of the risk of the driver, the risk in a route, and the risk caused by driving conditions. We propose several data-analytic techniques for driving risk prediction and related caused and apply them to different sources of telematics and contextual data to extract useful insights. We seek to answer the following research question: How can telematics data and their context be modeled to make a fair and sound prediction about driving risk? In answering this question, we take two concerns into account: 1) scoring of driving risk is not a context-agnostic process, and 2) driving risk is not independent of the personality of drivers and their driving skills. We propose a solution that consists of three parts: a) characterizing driving context, b) characterizing driving style, and c) context-aware driving risk prediction. The first two parts derive useful insights that we leverage to design the third part. Characterizing driving context is about exploring properties of different contexts. We propose two solutions for this task. The first solution, which we term segmentation and causality analysis, derives the characteristics of contexts from the aggregate behavior of drivers. We start by segmenting trajectories to identify meaningful driving patterns (e.g., a hard brake). Then we analyze each pattern with respect to contextual data to identify cause-and-effect patterns (e.g., traffic signal → hard-braking event). The second solution is a geo-spatiotemporal pattern discovery framework based on contextual data. We propose a new framework that explores two types of patterns, propagation and influential. Propaga

关键词： Computer science  

摘要： Cancer arises from the accumulation of somatic mutations and genetic alterations in cell division checkpoints and apoptosis, this often leads to abnormal tumor proliferation. Proper classification of cancer-linked driver mutations will considerably help our understanding of the molecular dynamics of cancer. In this study, we compared several cancer-specific predictive models for prediction of driver mutations in cancer-linked genes that were validated on canonical data sets of functionally validated mutations and applied to a raw cancer genomics data. By analyzing pathogenicity prediction and conservation scores, we have shown that evolutionary conservation scores play a pivotal role in the classification of cancer drivers and were the most informative features in the driver mutation classification. Through extensive comparative analysis with structure-functional experiments and multicenter mutational calling data from Pan-Cancer Atlas studies, we have demonstrated the robustness of our models and addressed the validity of computational predictions. We evaluated the performance of our models using the standard diagnostic metrics such as sensitivity, specificity, area under the curve and F-measure. To address the interpretability of cancer-specific classification models and obtain novel insights about molecular signatures of driver mutations, we have complemented machine learning predictions with structure-functional analysis of cancer driver mutations in several key tumor suppressor genes and oncogenes. Through the experiments carried out in this study, we found that evolutionary-based features have the strongest signal in the machine learning classification of driver mutations and provide orthogonal information to the ensembled-based scores that are prominent in the ranking of feature importance.

关键词： Computer science  

关键词： Fluid mechanics   Law   Computer science  

摘要： This qualitative modified Delphi study identifies the acceptable digital forensic model elements. These elements are required to create a widely accepted digital forensic model with a high degree of usefulness, a low degree of difficulty, and the capability of organizational acclimation through adaptation. The study consisted of elemental theme generation using a United States sample population of 20 experts in the field of digital forensics from the private and public sectors. Participants have all been through a certification process, have more than five years’ experience, and are considered forensic experts by U.S. courts. Participants evaluated potential forensic model elements researched from formerly suggested models and best practices plus open-ended questions they answered. The analysis of the captured participant data involved the use of *** Qualitative Data Analysis Software and the use of Apache OpenOffice. The Delphi method yielded 6 themes centered on the required elements of an adoptable and adaptive digital forensic model. Participant responses produced the following 6 elemental themes: authority, preparation, triage, examination, chain of custody, and post examination. Only 6 participants indicated they use a forensic model of any type. The identified elemental themes provide a foundation for future research into establishing a widely accepted digital forensic model with a high degree of usefulness, a low degree of difficulty, and the capability of organizational acclimation through adaptation.

关键词： Computer science  

摘要： Sequence fragments generated from targeted regions of phylogenetic marker genes provide valuable insight in identifying and classifying organisms and inferring taxonomic hierarchies. In recent years, significant development in targeted gene fragment sequencing through Next Generation Sequencing (NGS) technologies has increased the necessity of efficient sequence similarity computation methods for very large numbers of pairs of NGS sequences. The edit distance has been widely used to determine the dissimilarity between pairs of strings. All the known methods for the edit distance calculation run in near quadratic time with respect to string lengths, and it may take days or weeks to compute distances between such large numbers of pairs of NGS sequences. To solve the performance bottleneck problem, faster edit distance approximation and bounded edit distance calculation methods have been proposed. Despite these efforts, the existing edit distance calculation methods are not fast enough when computing larger numbers of pairs of NGS sequences. In order to further reduce the computation time, many NGS sequence similarity methods have been proposed using matching k-mers. These methods extract all possible k-mers from NGS sequences and compare similarity between pairs of sequences based on the shared k-mers. However, these methods reduce the computation time at the cost accuracy. In this dissertation, our goal is to compute NGS sequence similarity using edit distance based methods while reducing the computation time. We propose a few edit distance prediction methods using dataset independent reference sequences that are distant from each other. These reference sequences convert sequences in datasets into feature vectors by computing edit distances between the sequence and each of the reference sequences. Given sequences A, B and a reference sequence r, the edit distance, ed(A.B) ≥ (ed(A, r) ~ ed(B, r)). Since each reference sequence is significantly different from each othe

关键词： Information technology   Computer science  

摘要： The continuing innovation of computer systems and the ambiguity associated with strategic information systems require increased control of information to enable information security success. The purpose of this qualitative multiple case study was to identify the factors that prevented Florida hospitals from obtaining information security (InfoSec) benefits to prevent crypto-malware attacks when implementing strategic information systems plans (SISP). Agency theory served as the theoretical foundation for the study. Four research questions were designed to collect information on the constructs of SISP concerning InfoSec. The four constructs included top management support, the usefulness of information systems plans, the degree of information technology infrastructure flexibility, and the degree of SISP success. The research included face-to-face interviews with managers, senior managers, and directors from for-profit and nonprofit hospitals in Florida. Analysis of the data yielded four themes: the role of top management support in SISP and InfoSec, planning for InfoSec, flexibility, and top management support in SISP success. Practical implications include directors developing SISP plans based on obtaining InfoSec benefits and allowing top management to monitor the execution phases of SISPs. Future researchers should consider pursuing a follow-up study to investigate the relationship between SISP, InfoSec success, and their impact on policy and procedure in Florida hospitals. Future research should also focus on the integration of the four constructs in business strategy, regulatory mandates, and corporate information technology management strategies on hospitals outside of Florida.

关键词： Information technology   Computer science  

摘要： With technological advancement in embedded system design, powerful cameras have been embedded within smart phones, and wireless cameras can be easily deployed at street corners, traffic lights, big stadiums, train stations, etc. Besides, the growth of online media, surveillance, and mobile cameras have resulted in an explosion of videos being uploaded to social media sites such as Facebook and YouTube. The availability of such a vast volume of videos has attracted the computer vision community to conduct much research on human activity recognition since people are arguably the most interesting subjects of such videos. Automatic human activity recognition allows engineers and computer scientists to design smarter surveillance systems, semantically aware video indexes and also more natural human-computer interfaces. Despite the explosion of video data, the ability to automatically recognize and understand human activities is still rather limited. This is primarily due to multiple challenges inherent to the recognition task, namely large variability in human execution styles, the complexity of the visual stimuli in terms of camera motion, background clutter, viewpoint changes, etc., and the number of activities that can be recognized. In addition, the ability to predict future actions of objects based on past observed video frames is very useful. Therefore, in this thesis, we explore four designs to solve the problems we discussed earlier, namely (1) A semantics-based deep learning model, namely SBGAR, is proposed to do group activity recognition. This model achieves higher accuracy and efficiency than existing group activity recognition methods. (2) Despite its high accuracy, SBGAR has some limitations, namely (i) it requires a large dataset with caption information, (ii) activity recognition model is independent of the caption generation model and hence SBGAR may not perform well in some cases. To remove such limitations, we design ReHAR, a robust and efficient human