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关键词： Computer science   Data integrity   Intrusion detection systems   Network adapters   Algorithms   Clustering   Performance evaluation   Denial of service attacks   Access control   Semantics   Artificial intelligence  

关键词： Computer science  

关键词： Parents & parenting   Genealogy   Software   Principal components analysis   Siblings   Genetics   Chromosomes   Genomes   Genotype & phenotype   Performance evaluation   Parameter estimation   Computer science  

摘要： Recombination is one of the main forces that shapes genetic variation in populations. Recombination is an exchange of genomic sequence between two chromosomes that occurs during meiosis. In a diploid species, such as humans, each parent produces haploid gametes. During gamete production, crossovers may occur, resulting in a recombination event, and the single chromosome carried in the gamete is a combination of the parent’s two chromosomes. Therefore, although an o↵spring inherits only one chromosome from each parent, each chromosome is a combination of the parents’ two chromosomes due to the process of recombination. For example, siblings share a large proportion of their genomes, as they inherit chromosomes from the same parents. However, because of recombination, they inherit di↵erent combinations of their parents’ chromosomes. The contiguous stretches of siblings’ genomes that are identical, and therefore inherited from the same portion of one parent’s genome, are termed shared genomic segments. When a genomic segment in two individuals is inherited from the same recent common ancestor, that segment is said to be inherited identically-by-descent (IBD). The number of generations until the shared common ancestor is called the time to the most recent common ancestor (TMRCA). The length of a shared IBD segment is inversely proportional to the TMRCA because in each generation, there is the possibility that a recombination event shortens the shared segment. Two siblings share two common ancestors—their parents—just one generation in the past, and therefore share very long IBD segments. Conversely, two distantly related individuals whose shared ancestor is many generations in the past may share only a single short segment, or none at all, as recombination has many generations to shorten or eliminate a shared segment. In a population, the distribution of IBD segment length between pairs of individuals is inversely proportional to the distribution of TMRCAs between those

关键词： Astronomy   Optics   Materials science   Remote sensing   Computer science  

摘要： The current generation of radial-velocity spectrographs are at the precipice of discovering the first Earth-like exoplanets orbiting in the habitable zones of nearby stars. Such detections require Doppler precision of approximately 10 cm/s, an order of magnitude better than the typical best-case measurement from the previous generation of instruments. Therefore, the radial-velocity community requires research and innovation from all angles to push our technology over the brink. This thesis presents multiple contributions to this field---ranging from the development of precision laser equipment to the implementation of advanced statistical data analysis algorithms---all in support of the EXtreme PREcision Spectrograph (EXPRES) with the goal of improving instrument precision and exoplanet detection capability. In Chapter 2, we demonstrate the effectiveness of quasi-chaotic high-amplitude agitation as an optimal form of modal noise mitigation in the optical fibers that feed into radial-velocity spectrographs. This technique is shown to improve radial-velocity error for a single-wavelength laser line from more than 10 m/s-1 to less than 60 cm/s-1 without affecting focal ratio degradation within the fiber. After development of an agitator based on this method for use with EXPRES, we find that combined radial-velocity precision across an entire laser frequency comb improves from 32.8 cm/s-1 to 6.6 cm/s-1. In Chapter 3, I present aluminum nitride as a nonlinear optical material that can support frequency comb development from near-infrared to ultraviolet wavelengths. By injecting light from an aluminum nitride micro-ring into EXPRES, I demonstrate the material's viability of producing resolvable comb lines throughout the bandpass of the instrument. I also prototype a 16 GHz electro-optic modulation comb in combination with an aluminum nitride waveguide as a device that could become a cheap broadband visible-wavelength astro-comb for radial-velocity spectrograph wavelength

关键词： Computer science   Artificial intelligence  

摘要： The overwhelming number of recent data breaches reported that hundreds of terabytes of highly sensitive information, including national, financial, and personal, have been stolen from different organizations, indicating a clear asymmetric disadvantage that defenders face against cyber attackers. Modern attackers are well organized, highly stealthy, and stay persistent in the network for years; therefore, they are known as advanced persistent threats (APT). Existing detection and prevention based cyber defense techniques usually approach the target for specific, known attack signatures, descriptions, and behaviors. However, APT attackers can easily avoid such detection techniques by employing reconnaissance, fingerprinting, and social engineering. It is often very challenging and sometimes infeasible for defenders to prevent the information gathering of the adversary and patch all the vulnerabilities in the system. Therefore, a proactive defense approach is needed to break such asymmetry. Active Cyber Defense (ACD) is a promising paradigm to achieve this goal. ACD can proactively mislead adversaries and enables a unique opportunity to engage with them to learn new attack tactics and techniques. ACD enhances real-time detection, analysis, and mitigation of APT attacks. ACD can be achieved through cyber agility and cyber deception. Cyber Agility, such as moving target defense (MTD), enables cyber systems to defend proactively against sophisticated attacks by dynamically changing the system configuration parameters (called mutable parameters) in order to deter adversaries from reaching their goals. On the other hand, Cyber Deception is an intentional misrepresentation of the system's ground truth to manipulate adversaries' actions. Although cyber deception and MTD have been around for more than decades, static configurations and the lack of automation made many of the existing techniques easily discoverable by attackers and too expensive to manage, which diminishes the

关键词： Computer science   Astrophysics   Artificial intelligence  

摘要： Solar flare events are explosions of energy and radiation from the Sun’s surface. These events occur due to the tangling and twisting of magnetic fields associated with sunspots. When Coronal Mass ejections accompany solar flares, solar storms could travel towards earth at very high speeds, disrupting all earthly technologies and posing radiation hazards to astronauts. For this reason, the prediction of solar flares has become a crucial aspect of forecasting space weather. Our thesis utilized the time-series data consisting of active solar region magnetic field parameters acquired from SDO that span more than eight years. The classification models take AR data from an observation period of 12 hours as input to predict the occurrence of flare in next 24 hours. We performed preprocessing and feature selection to find optimal feature space consisting of 28 active region parameters that made our multivariate time series dataset (MVTS). For the first time, we modeled the flare prediction task as a 4-class problem and explored a comprehensive set of machine learning models to identify the most suitable model. This research achieved a state-of-the-art true skill statistic (TSS) of 0.92 with a 99.9% recall of X-/M- class flares on our time series forest model. This was accomplished with the augmented dataset in which the minority class is over-sampled using synthetic samples generated by SMOTE and the majority classes are randomly under-sampled. This work has established a robust dataset and baseline models for future studies in this task, including experiments on remedies to tackle the class imbalance problem such as weighted cost functions and data augmentation. Also the time series classifiers implemented will enable shapelets mining that can provide interpreting ability to domain experts.

关键词： Engineering   Alternative Energy   Applied Mathematics   Computer science   Energy   Mathematics   Thermodynamics  

关键词： Computer science   Artificial intelligence  

摘要： Feature selection is a key process for supervised learning algorithms. It involves discarding irrelevant attributes from the training dataset from which the models are derived. One of the vital feature selection approaches is Filtering, which often uses mathematical models to compute the relevance for each feature in the training dataset and then sorts the features into descending order based on their computed scores. However, most Filtering methods face several challenges including, but not limited to, merely considering feature-class correlation when defining a feature’s relevance; additionally, not recommending which subset of features to retain. Leaving this decision to the end-user may be impractical for multiple reasons such as the experience required in the application domain, care, accuracy, and time. In this research, we propose a new hybrid Filtering method called Class Association Rule Filter (CARF) that deals with the aforementioned issues by identifying relevant features through the Class Association Rule Mining approach and then using these rules to define weights for the available features in the training dataset. More crucially, we propose a new procedure based on mutual information within the CARF method which suggests the subset of features to be retained by the end-user, hence reducing time and effort. Empirical evaluation using small, medium, and large datasets that belong to various dissimilar domains reveals that CARF was able to reduce the dimensionality of the search space when contrasted with other common Filtering methods. More importantly, the classification models devised by the different machine learning algorithms against the subsets of features selected by CARF were highly competitive in terms of various performance measures. These results indeed reflect the quality of the subsets of features selected by CARF and show the impact of the new cut-off procedure proposed.

摘要： What would happen to the world if we took all the computers out of it tomorrow? What would happen if the Internet, which is called the World Wide Web for a very good reason, stopped working?Do you have a computer?

关键词： Computer science   Artificial intelligence  

摘要： Recommender systems are intelligent information retrieval applications, and have been leveraged in numerous domains such as e-commerce, movies, music, books, and point-of-interests. They play a crucial role in the users' information-seeking process, and overcome the information overload issue by recommending personalized items (products, services, or information) that best match users' needs and preferences. Driven by the recent advances in machine learning theories and the prevalence of deep learning techniques, there have been tremendous interests in developing deep learning based recommender systems. They have unprecedentedly advanced effectiveness of mining the non-linear user-item relationships and learning the feature representations from massive datasets, which produce great vitality and improvements in recommendations from both academic and industry communities. Despite above prominence of existing deep recommender systems, their adaptiveness and automation still remain under-explored. Thus, in this dissertation, we study the problem of adaptive and automated deep recommender systems. Specifically, we present our efforts devoted to building adaptive deep recommender systems to continuously update recommendation strategies according to the dynamic nature of user preference, which maximizes the cumulative reward from users in the practical streaming recommendation scenarios. In addition, we propose a group of automated and systematic approaches that design deep recommender system frameworks effectively and efficiently from a data-driven manner. More importantly, we apply our proposed models into a variety of real-world recommendation platforms and have achieved promising enhancements of social and economic benefits.