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关键词： Computer science  

关键词： Computer science  

摘要： As one of the most important distance metrics, edit distance can reflect noise and errors in sequence data and thus has various applications in data cleaning and integration, databases, bioinformatics, collaborative filtering, and natural language processing. On the other hand, edit distance is also difficult to compute and estimate, which draws both theorists’ and practitioners’ interest for decades. In this thesis, we will investigate several of the most critical problems related to edit distance and propose algorithms that are efficient in practice and have provable theoretical guarantees. We summarize our contributions as follows: We propose a randomized algorithm EmbedJoin for the edit similarity joins problem. Our algorithm achieves significant advantages in running time and memory usage for datasets with long strings and large edit thresholds compared to all the state-of-the-art deterministic algorithms. We introduce another randomized algorithm MinJoin for edit similarity joins. The algorithm further improves the accuracy of EmbedJoin while maintaining EmbedJoin’s advantages in running time and memory usage. We believe it is the best scalable algorithm for edit similarity joins. We propose the MinSearch algorithm for edit similarity search, which is a problem closely related to edit similarity joins. Our algorithm works for both threshold queries and top-k queries, and obtains orders of magnitude improvements on query time compared to the best competitor. We propose the SmoothQGram algorithm for the overlap detection problem on long, error-prone sequencing reads generated by the Single-Molecule Real-Time (SMRT) technique. The overlap detection problem is the first and most critical step in the de Novo fragment assembly. Our algorithm is based on smooth q-gram, which is a variant of q-gram that can capture q-gram pairs within a small edit distance. Our algorithm improves both precision and recall of the existing algorithms. The main focus of the thesis is edi

关键词： Data mining   Itemsets   Upper bound   Heuristic algorithms   STEM   Computer science   High utility sequential pattern mining   sequence   SU-Chain structure   data mining  

摘要： High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, which considers both utility and sequence factors to derive the set of high-utility sequential patterns (HUSPs) from the quantitative databases. Several works have been presented to reduce the computational cost by variants of pruning strategies. In this paper, we present an efficient sequence-utility (SU)-chain structure, which can be used to store more relevant information to improve mining performance. Based on the SU-Chain structure, the existing pruning strategies can also be utilized here to early prune the unpromising candidates and obtain the satisfied HUSPs. Experiments are then compared with the state-of-the-art HUSPM algorithms and the results showed that the SU-Chain-based model can efficiently improve the efficiency performance than the existing HUSPM algorithms in terms of runtime and number of the determined candidates.

关键词： Medical imaging   Computer science   Oncology  

摘要： Purpose: To identify opportunities to improve automatic contouring of organs at risk for head and neck cancer patients by developing a convolutional neural network to conduct automatic contouring and comparing its performance to contemporary atlas-based methods. Methods: Using a cohort of 265 anonymized planning CT scans and their associated physician-defined structure set, with 19 set aside for evaluation, a convolutional neural network (CNN) was trained to predict contour data for eleven common organs at risk (OAR) in head and neck cancer patients. Once trained, the neural network predicted contours for the 19 CT scans that were in the evaluation dataset. A commercial deformable atlas-based automatic contour generation function was used to generate competing contour data for the same 19 CT scans. Each output was compared to approved physician-generated manual contours across three quantitative metrics: Dice similarity coefficient (DSC), mean surface distance (MSD), and 95th percentile Hausdorff distance (HD). Performance differences between the modalities on a given metric for each OAR were evaluated for statistical significance using a two-tailed t-test with a p-value threshold of 0.05 to define a statistically significant difference between the modalities. Results: The CNN scored better on all three metrics for both parotid glands and both submandibular glands. For the cochlea and brachial plexus, the CNN scored better on some metrics, and on some metrics, there was no statistically significant difference between the two modalities. For the brain, larynx, and spinal cord, the metrics showed no statistically superior modality. For the brainstem, the CNN contours scored statistically worse than the atlas-based method. The table below shows the percent improvement from the scores of the atlas-based contours to those of the CNN for each OAR category. Categories marked as “N/A” did not meet the significance threshold. [Table omitted] Conclusion: Except for the brains

关键词： Computer science  

摘要： The literature on bias in NLP has chiefly focused on the extent to which an algorithm produces outputs that can be differentiated along demographic lines. This is universally framed as undesirable and generally assumed to manifest in similar ways across different models/datasets. NLP models are claimed to play an essential role in shaping this societal bias, but it is equally important to understand how societal norms shape this bias as bias in NLP models originates from either the training corpus or the word embeddings or the algorithm. This thesis aims to find if the manifestation of bias, specifically gender bias, is different across different communities with different norms. The hypothesis is that the exhibition of gender bias is in sync with community norms, and changes along with it. We study gender bias in three different online communities - r/RoastMe, r/ToastMe, and r/RateMe. r/RoastMe roasts people who upload an image whereas in r/ToastMe, we find comments complimenting or appreciating the users. r/RateMe tries to rate a person “objectively” based on the uploaded image. Given such contrasting norms, We use NLP models to check if the data from these subreddits indeed has bias by trying to predict the gender of the person for whom a comment is made. We then see if the biases reflected in the models are in sync with the norms of the three communities by comparing the coefficients obtained from the models. The biases are also compared and contrasted across gender and communities by ranking the words associated with each gender. The evaluations show that gender bias exhibited in different communities is indeed different and depends on the context and the norms of the community. On comparing the biases in r/RoastMe and word embeddings, we also see that some words are universally gendered whereas others are context-specific.

关键词： Computer science  

摘要： Ethereum is a unique offshoot of blockchain technologies that incorporates the use of what are called smart contracts or DApps – small-sized programs that orchestrate financial transactions on the Ethereum blockchain. With this fairly new paradigm in blockchain, however, comes a host of security concerns and a track record that reveals a history of losses in the range of millions of dollars. Since Ethereum is a decentralized entity, these concerns are not allayed as they are in typical financial institutions. For example, there is no Federal Deposit Insurance Corporation (FDIC) to back the investors of these contracts from financial loss as there is with bank depositors. Furthermore, there is also no Better Business Bureau (BBB) or Consumer Reports organization to offer any sort of ratings on these contracts. However, there exists a well-known method for verifying a program’s integrity; a method called symbolic execution. Such an examination promises to give not only a perspective on the security of Ethereum, but also highlight areas where security experts may need to target to more quickly improve upon the security of this blockchain. This paper proposes a solution to ensuring security and increasing end user confidence -- a digital registry of smart contracts that have security flaws in them. A rating system for contracts is proposed and the capabilities one has with knowledge of these vulnerabilities is examined. This research attempts to give a picture of the current state of security of Ethereum Smart Contracts by employing symbolic analysis on a portion of the Smart Contracts up until approximately the 8.4 millionth block. Vulnerabilities in Smart Contracts may be prevalent and, if they are, a registry for enumerating which ones are can be built and potentially used to easily enumerate them.

关键词： anti-Semitism   Auschwitz traumas   existentialism   Holocaust   memoir literature   The Lost generation  

摘要： The study deals with existential motifs in selected fiction and memoir works of Slovak literature, which thematically focus on World War II. and the Holocaust of the Slovak Jewish minority. As we considered the fact that many works of Slovak provenance with this issue were published (especially after 1989), we focused only on a selection of those works, which contains fiction and non-fiction literature and memoir works written by surviving Jews, in which they gave valuable testimony about the Holocaust and anti-Semitism of the war-tom Slovak state and post-war society. Based on the interpretation and analysis of Shoah-themed works, we detect recurring existential motifs: degradation of human dignity, feelings of guilt for one's own survival, Auschwitz traumas, and the motif of The Lost Generation.

关键词： Computer science  

摘要： Large organizations have multiple networks that are subject to attacks, which can be detected by continuous monitoring and analyzing the network traffic by Intrusion Detection Systems. Collaborative Intrusion Detection Systems (CIDS) are used for efficient detection of distributed attacks by having a global view of the traffic events in large networks. However, CIDS are vulnerable to internal attacks, and these internal attacks decrease the mutual trust among the nodes in CIDS required for sharing of critical and sensitive alert data in CIDS. Without the data sharing, the nodes of CIDS cannot collaborate efficiently to form a comprehensive view of events in the networks monitored to detect distributed attacks. The compromised nodes will further decrease the accuracy of CIDS by generating false positives and false negatives of the traffic event classifications. In this thesis, an approach based on a trust score system is presented to detect and suspend the compromised nodes in CIDS to improve the trust among the nodes for efficient collaboration. This trust score-based approach is implemented as a consensus model on a private blockchain because private blockchain has the features to address the accountability, integrity and privacy requirements of CIDS. In this approach, the trust scores of malicious nodes are decreased with every reported false negative or false positive of the traffic event classifications. When the trust scores of any node falls below a threshold, the node is identified as compromised and suspended. The approach is evaluated for the accuracy of identifying malicious nodes in CIDS.

关键词： Computer science  

摘要： Advancement in computing technologies made malware development easier for malware authors. Unconventional computing paradigms such as cloud computing, the internet of things, In-memory computing, etc. introduced new ways to develop more complex and effective malware. To demonstrate this, we designed and implemented a fileless malware that could infect any device that supports JavaScript and HTML5. In addition, another proof-of-concept is implemented that signifies the security threat of in-memory malware for in-memory data storage and computing platforms. Furthermore, a detailed analysis of unconventional malware has been performed using current state-of-the-art malware analysis and detection techniques. Our analysis shows that, by utilizing the unique characteristics of emerging technologies, malware attacks could easily deceive the anti-malware tools and evade themselves from detection. This clearly demonstrates the need for an innovative and effective detection mechanism. Because of the limitations of existing techniques, we propose a hybrid approach using specification-based and behavioral analysis techniques together as an effective solution against unconventional and emerging malware instances. Our approach begins with the specification development where we present the way of writing it in a succinct manner to describe the expected behavior of the application. Moreover, the behavior monitoring component of our approach makes the detection mechanism effective enough by matching the actual behavior with pre-defined specifications at run-time and alarms the system if any action violates the expected behavior. We demonstrate the effectiveness of the proposed approach by applying it for the detection of in-memory malware that threatens the HazelCast in-memory data grid platform. In our experiments, we evaluated the performance and effectiveness of the approach by considering the possible use cases where in-memory malware could affect the data present in the storage

关键词： Aphasia   Neurosciences   Computer science   Auditory cortex   Oral communication   Phonemics   Electrical engineering  

摘要： Communicating through speech is an important part of everyday life, and losing that ability can be devastating. Millions of patients around the globe have lost the ability to hear or speak due to auditory cortex deficits. Doctor’s ability to help these patients has been hindered by a lack of understanding of the speech processing mechanisms in the human auditory cortex. This dissertation focuses on enhancing our understanding of the mechanisms of speech encoding in human primary and secondary auditory cortices using two methods of electroencephalography (EEG) and electrocorticography (ECoG). Phonemes are the smallest linguistic elements that can change a word’s meaning. I characterize EEG responses to continuous speech by obtaining the time-locked responses to phoneme instances (phoneme-related potential). I show that responses to different phoneme categories are organized by phonetic features, and each instance of a phoneme in continuous speech produces multiple distinguishable neural responses occurring as early as 50 ms and as late as 400 ms after the phoneme onset. Comparing the patterns of phoneme similarity in the neural responses and the acoustic signals confirms a repetitive appearance of acoustic distinctions of phonemes in the neural data. Analysis of the phonetic and speaker information in neural activations reveals that different time intervals jointly encode the acoustic similarity of both phonetic and speaker categories. These findings provide evidence for a dynamic neural transformation of low-level speech features as they propagate along the auditory pathway, and form an empirical framework to study the representational changes in learning, attention, and speech disorders. Later in this dissertation, I use ECoG neural recordings to explore mechanisms of speech communication in real-world environments that require adaptation to changing acoustic conditions. I explore how the human auditory cortex adapts as a new noise source appears in or disappears f