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关键词： landfill compost   diversity   Firmicutes   Geobacillus   metagenome   SURF  

摘要： Firmicutes is almost a ubiquitous phylum. Several genera of this group, for instance, Geobacillus, are recognized for decomposing plant organic matter and for producing thermostable ligninolytic enzymes. Amplicon sequencing was used in this study to determine the prevalence and genetic diversity of the Firmicutes in two distinctly related environmental samples-South Dakota Landfill Compost (SDLC, 60 degrees C), and Sanford Underground Research Facility sediments (SURF, 45 degrees C). Although distinct microbial community compositions were observed, there was a dominance of Firmicutes in both the SDLC and SURF samples, followed by Proteobacteria. The abundant classes of bacteria in the SDLC site, within the phylum Firmicutes, were Bacilli (83.2%), and Clostridia (2.9%). In comparison, the sample from the SURF mine was dominated by the Clostridia (45.8%) and then Bacilli (20.1%). Within the class Bacilli, the SDLC sample had more diversity (a total of 11 genera with more than 1% operational taxonomic unit, OTU). On the other hand, SURF samples had just three genera, about 1% of the total population: Bacilli, Paenibacillus, and Solibacillus. With specific regard to Geobacillus, it was found to be present at a level of 0.07% and 2.5% in SURF and SDLC, respectively. Subsequently, culture isolations of endospore-forming Firmicutes members from these samples led to the isolation of a total of 117 isolates. According to colony morphologies, and identification based upon 16S rRNA and gyrB gene sequence analysis, we obtained 58 taxonomically distinct strains. Depending on the similarity indexes, a gyrB sequence comparison appeared more useful than 16S rRNA sequence analysis for inferring intra- and some intergeneric relationships between the isolates.

关键词： Breast cancer   Digital support   Patient reported outcome measures   Quality of life   Compliance   Adjuvant   Endocrine   Treatment  

摘要： Purpose The primary aim of the study was to develop and investigate a patient interactive digital support (an app) for patients on adjuvant endocrine breast cancer treatment. Patient's interactive digital applications are a fast-growing area for research and development. In general, patients want more information and support with regard to their diagnosis, treatment and self-care. At the same time, the health care system has limited resources for follow-up. Our primary endpoints were usability of the app and if it added any value to the patients. Methods We designed and constructed a prototype, in dialogue with patients, containing four main modules for registration of drug compliance, performed physical exercise, self-care activities, and questions on health and quality of life. The app was then tested by patients and improved further before we completed a pilot study in which 15 patients used the app for 3 months. Results Patients perceived the app easy to use with a very high median system usability score of 88.8, range 30-100. The 15 women registered in total 4251 times, range 118 to 372. The majority of registrations concerned compliance (adherence to treatment) and physical exercise. Conclusion The app was perceived easy to use and of support in every-day life of breast cancer survivors. How to best integrate electronically collected patient reported outcome measures in clinical routine needs to be further studied, and future research will show if it will be cost-effective in terms of better health outcome and less resource use.

关键词： Information technology   Computer science   Artificial intelligence   Software   Malware   Datasets  

摘要： Information assurance and computer system security involve many facets, one of which is intrusion detection. Traditional misuse-based intrusion detection systems involve examining certain files (e.g. executables) for known malware signatures, monitoring file accesses and alterations, and searching log records on the protected systems for known indicators of compromise. Encryption and polymorphism are used by malware authors to limit the effectiveness of such techniques. Newer intrusion detection approaches use anomaly based heuristics to look for aberrant user and system behaviors. However, both mechanisms are subject to subterfuge by computer memory (RAM) resident malware. Using the digital forensics techniques of memory capture and analysis can avoid this detection evasion capability. Some memory introspection research works have examined data in use and process execution (by interrogating operating system data structures and system call traces) to detect malicious activity. This dissertation provides a novel approach to host-based intrusion detection by analyzing data in RAM describing (a subset of) interprocess communications (IPC) exchanges. Network theory shows that information exchanges are well suited for modeling using graph structures. For this project, 634 memory captures were collected from physical and virtual Linux machines under varying loads (most benign, some malicious). Using digital forensics memory capture and analysis tools, processes and IPC resource use data were used to generate node features and were then formed into graphs. These systems graphs were aggregated and processed using machine learning techniques. Python was used to build graph neural network models which provided node embeddings that were processed using community detection, clustering, and classification algorithms. Evaluation of experimental results shows positive capability for classifying malicious nodes within the process graphs.

关键词： Computer science   Law enforcement   Artificial intelligence  

摘要： Face recognition is a classic problem in the field of computer vision and pattern recognition due to its wide applications in real-world problems such as access control, identity verification, physical security, surveillance, etc. Recent progress in deep learning techniques and the access to large-scale face databases has lead to a significant improvement of face recognition accuracy under constrained and semi-constrained scenarios. Deep neural networks are shown to surpass human performance on Labeled Face in the Wild (LFW), which consists of celebrity photos captured in the wild. However, in many applications, e.g. surveillance videos, where we cannot assume that the presented face is under controlled variations, the performance of current DNN-based methods drop significantly. The main challenges in such an unconstrained face recognition problem include, but are not limited to: lack of labeled data, robust face normalization, discriminative representation learning and the ambiguity of facial features caused by information loss. In this thesis, we propose a set of methods that attempt to address the above challenges in unconstrained face recognition systems. Starting from a classic deep face recognition pipeline, we review how each step in this pipeline could fail on low-quality uncontrolled input faces, what kind of solutions have been studied before, and then introduce our proposed methods. The various methods proposed in this thesis are independent but compatible with each other. Experiment on several challenging benchmarks, e.g. IJB-C and IJB-S show that the proposed methods are able to improve the robustness and reliability of deep unconstrained face recognition systems. Our solution achieves state-of-the-art performance, i.e. 95.0% TAR@FAR=0.001% on IJB-C dataset and 61.98% Rank1 retrieval rate on the surveillance-to-booking protocol of IJB-S dataset.

关键词： Game theory   Linear programming   Rationality   Algorithms   Neighborhoods   Artificial intelligence   Elections   Computer science  

摘要： This thesis is concerned with investigating elements of computational social choice in the light of real-world applications. We contribute to a better understanding of the areas of fair allocation and multiwinner voting. For both areas, inspired by real-world scenarios, we propose several new notions and extensions of existing models. Then, we analyze the complexity of answering the computational questions raised by the introduced concepts. To this end, we look through the lens of parameterized complexity. We identify different parameters which describe natural features specific to the computational problems we investigate. Exploiting the parameters, we successfully develop efficient algorithms for spe- cific cases of the studied problems. We complement our analysis by showing which parameters presumably cannot be utilized for seeking efficient algorithms. Thereby, we provide comprehensive pictures of the computational complexity of the studied problems. Specifically, we concentrate on four topics that we present below, grouped by our two areas of interest. For all but one topic, we present experimental studies based on implementations of newly developed algorithms. We first focus on fair allocation of indivisible resources. In this setting, we consider a collection of indivisible resources and a group of agents. Each agent reports its utility evaluation of every resource and the task is to “fairly” allocate the resources such that each resource is allocated to at most one agent. We concentrate on the two following issues regarding this scenario. The social context in fair allocation of indivisible resources. In many fair allocation settings, it is unlikely that every agent knows all other agents. For example, consider a scenario where the agents represent employees of a large corporation. It is highly unlikely that every employee knows every other employee. Motivated by such settings, we come up with a new model of graph envy-freeness by adapting the classical envy

关键词： Computer science   Software   Accuracy   Dynamic programming   Artificial intelligence   Sensors   Classification   Malware   Art   Methods   Computer viruses   Algorithms   Engines   Dynamic link libraries   Visualization   Engineers  

摘要： Machine Learning allows computers to learn from experience and to understand the world in terms of a hierarchy of concepts, with each being defined through its relations to simpler concepts. Extensive and beautiful prospect for the global machine learning market spurs the development of various applications in language translation, image recognition, social media, speech recognition, malware detection, etc. It also stimulates the potential of sophisticated adversaries to attack them. Nevertheless, there is no practical adversarial malware example to successfully attack a collection of third-party black-box malware detectors in the real world since they combine machine learning techniques with classical techniques. Additionally, existing methods for malware classification, whether static or dynamic, are non-trivial tasks when stubborn knowledge barriers, limited computing resources, and conservative feasibility need to be addressed. In this thesis, we first propose a convolutional generative adversarial network-based (Conv-GAN) framework titled MalFox, targeting adversarial malware example generation against third-party black-box malware detectors. Motivated by the rival game between malware authors and malware detectors, MalFox adopts a confrontational approach to produce perturbation paths, with each formed by up to three methods (namely Obfusmal, Stealmal, and Hollowmal) to generate practical adversarial malware examples. We then propose a reinforcement learning-based framework called MalInfo, which could generate adversarial malware examples via an adaptive selection of a perturbation path for each malware while MalFox is trained on a collection of malware. To cope with limited computation, MalInfo applies either dynamic programming or temporal difference learning to choose the optimal perturbation path. Finally, we propose a visualization malware classification framework called VisMal, which could provide highly efficient categorization with acceptable accuracy

关键词： Genomics   Bioinformatics   Sequential analysis   Tumors   Computer science   Computational modeling   Next generation networking   Copy number variation   local outlier factor   next generation sequencing   boxplot procedure  

摘要： Copy number variation (CNV) is a major type of genomic structural variations that play an important role in human disorders. Next generation sequencing (NGS) has fueled the advancement in algorithm design to detect CNVs at base-pair resolution. However, accurate detection of CNVs of low amplitudes remains a challenging task. This paper proposes a new computational method, CNV-LOF, to identify CNVs of full-range amplitudes from NGS data. CNV-LOF is distinctly different from traditional methods, which mainly consider aberrations from a global perspective and rely on some assumed distribution of NGS read depths. In contrast, CNV-LOF takes a local view on the read depths and assigns an outlier factor to each genome segment. With the outlier factor profile, CNV-LOF uses a boxplot procedure to declare CNVs without the reliance of any distribution assumptions. Simulation experiments indicate that CNV-LOF outperforms five existing methods with respect to F1-measure, sensitivity, and precision. CNV-LOF is further validated on real sequencing samples, yielding highly consistent results with peer methods. CNV-LOF is able to detect CNVs of low and moderate amplitudes where the other existing methods fail, and it is expected to become a routine approach for the discovery of novel CNVs on whole sequencing genome.

关键词： Design   Computer science   Health care management  

摘要： Asthma is a chronic disease that cannot be cured. Every attack of asthma is a physical and mental destruction to patients, bringing pain and economic burden to patients, and seriously affecting the quality of life and happiness index of patients. But the vast majority of asthma cases can be completely controlled with appropriate treatment and intervention. Investigations at home and abroad have found that the self-health management level of asthma patients is still at a relatively low level, and more than half of asthma patients are in a state of out-of-control. Currently, there is no online software that can effectively help people with allergies such as asthma check the ambient air quality and keep track of changes in the air quality. So of asthma patients need a simple and convenient mobile phone software to use, which can through to the reality and the existing network, more quickly and accurately provide people with more air quality information, makes the asthma patients can pass on these information collection, to respond to their surrounding environment, to avoid detrimental to their own conditions of the environment, so as to ease the development of the disease. Based on the above analysis, an application that is connected with the air quality sensor network of the world's major cities need to be designed. By taking advantage of the advantages of sensors everywhere in the city, it can detect the surrounding environment and provide the effective air quality information obtained to the users more quickly and accurately. To help asthmatic patients monitor changes in their surroundings in real time, and using AR technology feature could allow people to monitor local air quality through real-time image processing. Thus, the incidence of respiratory diseases caused by changes in the air environment can be reduced.

摘要： The aim of this thesis is to show the impact that Black and African culture has on the entertainment industry specifically in the USA. The first portion of the discussion retraces Black and African cultural influences in the 1920’s, beginning with Black-owned newspapers, then on to comic books, until arriving in the 21st century with an exploration of such influences in the film and musical *** introduction of relevant African mythology figures will follow this historical look at cultural influences on entertainment. We will discuss the adaptations of these mythological figures in modern-day heroic beings. Providing detailed information about each figure, the discussion will then highlight how these personal creations are portrayed in heroic narratives. This thesis also aims to show that, more often than not, most portrayals of people of color in the entertainment industry are negative and *** conclusion, this thesis argues for the desperate need for accurate and positive portrayals of Black people in the entertainment industry. This thesis intends to open our eyes to the major impact that Black and African culture has had on society in the USA.

关键词： Computer science   Petrology   Geology   Pacific Rim studies  

摘要： Traditional methods of collecting quantitative petrographic data from thin sections (modal mineralogy, size distribution, shapes, etc.) are time- and labor-intensive, and rarely have sample sizes adequate to statistically describe complex rocks (i.e. volcanic rocks). Although manual counting and measurements are now routinely supplemented by digital image analysis, the majority of quantitative petrographic studies still go through a manual digitization stage where object classes are traced before further analyses. This is a major rate-limiting step that reproduces the same problems of small n-values resulting from significant effort. We have valuated the potential and limitations of using the Trainable Weka Segmentation (TWS) plugin within the commonly used ImageJ / Fiji digital image analysis and processing environment. Specifically, we have assessed their capacity to classify, segment, and threshold user-defined petrographic features from a suite of images of progressively more complex volcanic rocks to accelerate the collection of quantitative petrographic data. TWS uses a fast-random-forest algorithm to classify an image based on a set of training pixels selected by the user - in this case different mineral phases, vesicles, etc. Training of the classifier is intuitive and fast. For example, three classes each with eleven training spots are classified in less than 1 minute for a medium to high-resolution image. Eight plane polarized light photomicrographs with increasing crystallinity and complexity were classified (i.e. trained) and automatically segmented using TWS. Samples where the assigned classes have distinct, homogeneous RGB values and sharp boundaries are successfully classified with TWS. However, samples where the classes are heterogeneous but similar, as a result of alteration for example, are not adequately classified. Once classified, two major efficiency gains are possible: (1) the classifier can be saved and applied again to any similar sample, an