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关键词： Computer science education   Concepts   K-8   Practice  

摘要： The consistent prevalence of computer science in our society and the urgent need for STEM professionals in the U.S. raised the need for computer science education at the K-8 level. Researchers have examined the challenges, possible approaches, and contents of teaching computer science. However, there has been limited research on teachers’ conceptualization and practice of teaching computer science in their classes at the K-8 level. This study aimed to examine K-8 teachers' conceptions and practices of teaching computer science in Indiana. To this end, the research question focused on how K-8 teachers conceptualize computer science and how K-8 teachers practice teaching computer science. This study utilized an explanatory sequential mixed method embedded with a multiple case design to answer the research questions. From kindergarten to eighth grade, nine teachers who had computer science teaching experience participated in this study. The data collected from the participants included questionnaires, semi-structured interviews, class observation, and teaching materials. Thematic analysis procedure and constant comparative analysis procedure were employed as the data analysis approaches. The results showed that the participants of this study used computers and technology indistinguishably. In addition, the participants viewed computer literacy and digital citizenship as the essential parts of computer science. However, the participants’ teaching practice emphasized coding and computer literacy more than digital citizenship, which was not alien to their concepts of computer science. The participants of this study have taught computer science as separate activities as opposed to a sustained curriculum. Moreover, the participants of this study relied on free and ready-to-use teaching materials, especially those that included hands-on activities. The participants of this study agreed that computer science should be a separate class and taught by specialists, even though th

关键词： Mining   Biochemistry   Bioengineering   Chemistry   Computer science   Engineering   Genetics   Pharmaceutical sciences   Pharmacology  

摘要： Since the initial discovery of CRISPR-Cas systems decades ago, efforts to elucidate the function of these mysterious systems ultimately revealed their biological role as novel prokaryotic defense systems. CRISPR-Cas systems represent the only known example of prokaryotic adaptive immunity, a unique and elegant solution to the continued challenge of predation by phage and other invasive mobile genetic elements. Functional research to further elucidate the ecology and activity of these systems has resulted in the adoption and pervasive use of RNA-guided Cas nucleases as powerful genome editing tools. Fueled by the promise of biotechnological application and the staggering accumulation of genomic sequencing data, discovery efforts have highlighted widespread diversity across CRISPR-Cas systems and continue to yield novel and divergent nucleases and genetic architectures, driving the growth and evolution of the nomenclature and associated classification paradigms. Despite the current maturity of the CRISPR-Cas genome editing platform, progress of applied molecular approaches has outpaced its in silico counterparts, providing tremendous opportunity and demand for effective computational methods and standard software packages to improve feature characterization and classification, to streamline basic biological elucidation, and to expedite discovery of rare, unknown, and novel systems. Development and use of these tools, informing the rational design and engineering of novel Cas effectors, is critical to deploying CRISPR-Cas systems in increasingly sophisticated environments while maintaining precise control of putative genetic modifications. Here, we developed a scalable high-throughput pipeline facilitating the extraction and visualization of the primary features of CRISPR-Cas systems: the repeat and spacer sequences of the CRISPR array. We employed a custom global alignment algorithm, enabling CRISPR-based genotyping across closely related strains, and applied this to

关键词： Materials science   Computer engineering   Computer science   Textile research  

关键词： Computer science   Artificial intelligence  

摘要： Verified compilers have the potential to greatly improve users’ trust in their code by providing machine-checked proofs of compiler correctness. In recent years they have become increasingly sophisticated and practical, compiling programs from convenient high-level programming languages to realistic target architectures and platforms. Ideally, compiler correctness requires every externally visible behavior of the compiled code to also be a valid behavior of the source it was compiled from. However, modern compilers often translate between languages with very different semantics; in particular, the target language may have error conditions, such as memory exhaustion, integer overflow, and process interruption, that are not expressed or modeled at the source level. In this work, we explore and evaluate the design space faced by verified compiler writers when adapting their systems for target-language errors. We provide template compiler correctness theorems that account for target-language errors in different ways. We consider a variety of target-language errors that appear in real-world target platforms, examining ways to modify each component of the verified compiler to adapt for them. Among these errors, we include failure modes of external components of the system that affect the running compiled code, such as process interruption and I/O device failure. We describe adaptations to a standard compiler correctness proof technique that allow verified compiler writers to prove one of our proposed correctness theorems when the target language contains limited nondeterminism introduced by target-language errors. Finally, we discuss ways to weaken a traditional compiler correctness statement to obtain guarantees that hold for all executions, including those that encounter target-language errors. Our systematization helps verified compiler writers decide how to appropriately account for target-language errors, allowing them to expose correctness guarantees that more accur

关键词： Feature extraction   Training   Metaheuristics   Intrusion detection   Classification algorithms   Computer science   Artificial neural networks   Intrusion detection system (IDS)   bat algorithm (BAT)   metaheuristic algorithm (MA)   feature selection (FS)   multi-objective optimization (MOO)   multilayer perceptron (MLP)  

摘要： The staggering development of cyber threats has propelled experts, professionals and specialists in the field of security into the development of more dependable protection systems, including effective intrusion detection system (IDS) mechanisms which are equipped for boosting accurately detected threats and limiting erroneously detected threats simultaneously. Nonetheless, the proficiency of the IDS framework depends essentially on extracted features from network traffic and an effective classifier of the traffic into abnormal or normal traffic. The prime impetus of this study is to increase the performance of the IDS on networks by building a two-phase framework to reinforce and subsequently enhance detection rate and diminish the rate of false alarm. The initial stage utilizes the developed algorithm of a proficient wrapper-approach-based feature selection which is created on a multi-objective BAT algorithm (MOBBAT). The subsequent stage utilizes the features obtained from the initial stage to categorize the traffic based on the newly upgraded BAT algorithm (EBAT) for training multilayer perceptron (EBATMLP), to improve the IDS performance. The resulting methodology is known as the (MOB-EBATMLP). The efficiency of our proposition has been assessed by utilizing the mainstream benchmarked datasets: NLS-KDD, ISCX2012, UNSW-NB15, KDD CUP 1999, and CICIDS2017 which are established as standard datasets for evaluating IDS. The outcome of our experimental analysis demonstrates a noteworthy advancement in network IDS above other techniques.

关键词： Deep learning   Standards   Measurement   Computer science   Data mining   Computer architecture   Complexity theory   Text summarization   arabic natural language processing   machine learning   extractive text summarization   abstractive text summarization   and deep learning models  

摘要： The explosion of online and offline data has changed how we gather, evaluate, and understand data. It is frequently difficult and time-consuming to comprehend large text documents and extract crucial information from them. Text summarization techniques address the mentioned problems by compressing long texts while retaining their essential contents. These techniques rely on the fast delivery of filtered, high-quality content to their users. Due to the massive amounts of data generated by technology and various sources, automated text summarization of large-scale data is challenging. There are three types of automatic text summarization techniques: extractive, abstractive, and hybrid. Regardless of these previous techniques, the generated summaries are a long way from the summarization produced by human experts. Although Arabic is a widely spoken language that is frequently used for content sharing on the web, Arabic text summarization of Arabic content is limited and still immature because of several problems, including the Arabic language's morphological structure, the variety of dialects, and the lack of adequate data sources. This paper reviews text summarization approaches and recent deep learning models for this approach. Additionally, it focuses on existing datasets for these approaches, which are also reviewed, along with their characteristics and limitations. The most often used metrics for summarization quality evaluation are ROUGE1, ROUGE2, ROUGE L, and Bleu. The challenges that are encountered during Arabic text summarizing methods and approaches and the solutions proposed in each approach are analyzed. Many Arabic text summarization methods have problems, such as the lack of golden tokens during testing, being out of vocabulary (OOV) words, repeating summary sentences, lack of standard systematic methodologies and architectures, and the complexity of the Arabic language. Finally, providing the required corpora, improving evaluation using semantic represe

关键词： Risk assessment   Software testing   Training   Random forests   Predictive models   Machine learning   Computer science   Risk in requirements   risk dataset for requirements   tree family machine learning technique  

摘要： Risk prediction is the most sensitive and critical activity in the Software Development Life Cycle (SDLC). It might determine whether the project succeeds or fails. To increase the success probability of a software project, the risk should be predicted at the early stages. This study proposed a novel model based on the requirement risk dataset to predict software requirement risks using Tree-Family -Machine-Learning (TF-ML) approaches. Moreover, the proposed model is compared with the state-of-the-art models to determine the best-suited methodology based on the nature of the dataset. These strategies are assessed and evaluated using a variety of metrics. The findings of this study may be reused as a baseline for future studies and research, allowing the results of any proposed approach, model, or framework to be benchmarked and easily checked.

关键词： Design   Agronomy   Bioinformatics   Computer science   Food Science   Genetics   Logic  

摘要： Blueberries (Vaccinium corymbosum L.) benefit from increased fruit firmness because of consumer preference and machine harvestability. However, the genetic component of fruit texture and skin thickness and their relationship to firmness have yet to be deciphered. This study used bulked segregant RNA-seq (BSR-seq) for differential gene expression analysis. Previously an F1 population of a cross between firm-fruited southern highbush cv. 'Reveille' and soft-fruited cv. 'Arlen' was developed in our laboratory. The total RNA of the parents, the two softest, and the two firmest progenies, were extracted at the breaker and fully ripe stages. Next-generation sequencing cDNA libraries were constructed and subjected to Illumina short-read sequencing. Subsequently, the short reads were mapped to a blueberry genome, and differentially expressed genes (DEGs) were identified in immature and mature fruit coded for potentially biologically significant proteins such as expansins, polygalacturonase, polygalacturonase-inhibiting protein, and mannosidase. Additionally, DEGs corresponding to cysteine proteases and S-adenyl methyltransferases (SAM-MTases) that were previously reported as candidate genes for blueberry firmness were identified in this study. Our results indicated that BSR-seq is a promising method for identifying major candidate genes controlling complex traits in blueberry.

关键词： General \u0026 Introductory Computer Science  

ISBN: (数字)9781394163830;9781394163823 ISBN: (纸本)9781786307552

摘要： This book serves as a guide to help the reader develop an awareness of security vulnerabilities and attacks, and encourages them to be circumspect when using the various computer resources and tools available today. For experienced users, Computer Science Security presents a wide range of tools to secure legacy software and hardware. Computing has infiltrated all fields nowadays. No one can escape this wave and be immune to security attacks, which continue to evolve, gradually reducing the level of expertise needed by hackers. It is high time for each and every user to acquire basic knowledge of computer security, which would enable them to mitigate the threats they may face both personally and professionally. It is this combined expertise of individuals and organizations that will guarantee a minimum level of security for families, schools, the workplace and society in general.

关键词： Chemistry   Alternative Energy   Atmospheric Chemistry   Atmospheric sciences   Computer science   Design   Energy   Mathematics   Nuclear engineering   Sustainability  

摘要： Elevated operational costs of platinum group metal (PGM) based environmental catalytic systems shift the focus of catalysis research towards cost-effective materials. In search for PGM-free alternative catalytic materials for NOx removal, high catalytic performance and long catalyst lifetime emerge as two important technical challenges. Within the scope of this dissertation, novel B-site mixed perovskites LaCoxMn1-xO3 (x = 0.1-0.9) and Fe and/or Co based CeO2 catalysts were synthesized, investigated and optimized as high performance, PGM-free, and durable catalyst alternatives for NOx removal systems. The perovskite based catalytic architectures can be utilized as diesel oxidation catalyst (DOC) oxidizing NO/CO to NO2/CO2, which can be coupled with selective catalytic reduction (SCR) catalysts to reduce NOx species to N2. On the other hand, Fe/Co based CeO2 systems can be exploited as catalyst candidates in SCR of NOx. In both of these NOx aftertreatment systems, NO activation is required. A simple and reproducible synthetic protocol was utilized to obtain perovskite-based DOC catalysts whose comprehensive structural characterization was carried out via XRD, N2 adsorption-desorption isotherm, ICP-MS, TEM, H2-TPR, ex-situ and in-situ XANES, EXAFS, in-situ FTIR, XPS, and TPD techniques. The oxidative catalytic performance of the perovskites for CO and NO oxidation was determined in flow-mode catalytic activity tests. It was demonstrated that bulk-oxygen vacancies have a strong influence on the redox activity of the B-site mixed perovskites with the ABO3 structure (where A = La, B = Co, Mn) allowing them to efficiently switch between high and low oxidation states in a reversible fashion under relatively moderate redox conditions without requiring elevated temperatures for regeneration, unlike conventional LaMnO3 and LaCoO3-based simple perovskite systems. La1.01Co0.75Mn0.24O2.97 and La1.04Co0.65Mn0.31O2.97 were found to reveal the best NO and CO oxidation performances