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摘要： Here are the grammatical subjects for each of these five sentences: (1) "There" is a word that just waves at the wind, referring to no one nor no thing; (2) "A statement"; (3) "Little that is new"; (4) "The progress of our arms"-no people here, just arms; and (5) "No prediction." [...]when all of that is accomplished, then pay attention to how the rhythms of each of your sub units within a sentence are sending instructions to your readers about how to balance this unit with some other unit. To present conflict intellectually instead of emotionally, he relies once again on his techniques of balance and control, raising distinctions by a combination of (1) focused repetition and (2) a rhythmic, musical structure that we can see at work in the colometrics. First sentence: At virtually every moment, Lincoln is using rhetorical techniques that have great effect upon his audience, without the audience necessarily being aware of any of them.

关键词： Cloud computing   Oceans   Forensics   Computer science   Sun   Standards   Registers   Anonymous   attack   cloud computing   group signature   traceable  

摘要： In cloud environments, group data sharing has become a hot topic in recent years. How to share data securely and efficiently in cloud environments is an urgent problem to be solved. Recently, an anonymous and traceable group data sharing scheme was proposed by Shen et al. to address this issue. They constructed their scheme using a group signature scheme as the building block. In this comment, we discuss the security of their group signature scheme and point out that it does not achieve the anonymity which they claimed and give a corresponding attack.

关键词： Computer science   Machine learning   Machine learning--Mathematical models   Decision making--Computer programs  

摘要： This thesis is focused on a particular text-to-text generation problem, automatic summarization, where the goal is to map a large input text to a much shorter summary text. The research presented aims to both understand and tame existing machine learning models, hopefully paving the way for more reliable text-to-text generation algorithms. Somewhat against the prevailing trends, we eschew end-to-end training of an abstractive summarization model, and instead break down the text summarization problem into its constituent tasks. At a high level, we divide these tasks into two categories: content selection, or “what to say” and content realization, or “how to say it” (McKeown, 1985). Within these categories we propose models and learning algorithms for the problems of salience estimation and faithful generation. Salience estimation, that is, determining the importance of a piece of text relative to some context, falls into a problem of the former category, determining what should be selected for a summary. In particular, we experiment with a variety of popular or novel deep learning models for salience estimation in a single document summarization setting, and design several ablation experiments to gain some insight into which input signals are most important for making predictions. Understanding these signals is critical for designing reliable summarization models. We then consider a more difficult problem of estimating salience in a large document stream, and propose two alternative approaches using classical machine learning techniques from both unsupervised clustering and structured prediction. These models incorporate salience estimates into larger text extraction algorithms that also consider redundancy and previous extraction decisions. Overall, we find that when simple, position based heuristics are available, as in single document news or research summarization, deep learning models of salience often exploit them to make predictions, while ignoring the arguabl

关键词： 3D-ID   Craniometrics   Fordisc 3.1   Geographic ancestry   Population affiliation  

摘要： Research on current methods of ancestral estimation must reflect on biological heritage to aid in human identification. Using modern craniometrics methods, how do individuals with a varied biological history affect ancestral estimation? Today, the most used and reliable methods for craniometrics analysis for ancestral estimation in forensic anthropology are computer programs. Two programs are analyzed in this study, Fordisc 3.0 and 3D-ID. The analysis of these computer programs goes beyond the controlled environment provided by an osteological collection. These remains of individuals were unidentified, only to be identified later, through academic research, police work, and public outreach. The selection of samples occurred if they fit the following two criteria: 1) To evaluate known ancestry, the victim was initially unidentified but was later positively identified, and 2) both Linear and 3D coordinate craniometric data was available to test Fordisc 3.1 and 3D-ID. Humans adapt to their environment biologically and culturally, identifying with familiar cultures, foods, objects, events and, how we look. Thus, ancestral components to a person’s appearance can help outline the parameters in a search to return a lost loved one to their family and finish the last chapter in an individual’s life. We establish any trends in the correct and incorrect estimations by analyzing the posterior probability (pp) and typicalities (typ). Both computer programs struggled with the “Hispanic” cohort placement while finding higher reliability in European Americans’ estimations than any other ancestral group for both 3D-ID and Fordisc 3.1.

关键词： Databases   Itemsets   Clustering algorithms   Data models   Prototypes   Computer science   Computational modeling   Multi-database mining   graph clustering   dual gradient descent   quasi-convex optimization   similarity measure  

摘要： Multinational corporations have multiple databases distributed throughout their branches, which store millions of transactions per day. For business applications, identifying disjoint clusters of similar and relevant databases contributes to learning the common buying patterns among customers and also increases the profits by targeting potential clients in the future. This process is called clustering, which is an important unsupervised technique for big data mining. In this article, we present an effective approach to search for the optimal clustering of multiple transaction databases in a weighted undirected similarity graph. To assess the clustering quality, we use dual gradient descent to minimize a constrained quasi-convex loss function whose parameters will determine the edges needed to form the optimal database clusters in the graph. Therefore, finding the global minimum is guaranteed in a finite and short time compared with the existing non-convex objectives where all possible candidate clusterings are generated to find the ideal clustering. Moreover, our algorithm does not require specifying the number of clusters a priori and uses a disjoint-set forest data structure to maintain and keep track of the clusters as they are updated. Through a series of experiments on public data samples and precomputed similarity matrices, we show that our algorithm is more accurate and faster in practice than the existing clustering algorithms for multi-database mining.

关键词： Computer science  

摘要： Paper-based data capture has long served as the primary means of collecting research data and continues to be the dominant means of data capture through the present day. Despite inertia with adopting information technology in clinical research, electronic methods of information capture have important benefits over traditional, paper-based methods. Electronic Data Capture (EDC) systems can provide integrated error checking, protocol enforcement, decision support, automated randomization, and quicker access to data and results. As EDC systems become more accessible and resourceful, EDC has begun to replace paper-based data capture. Meanwhile, mobile computing, utilizing smartphones and tablets, has become commonplace in business and our everyday lives. Many EDC solutions support mobile devices, yet few were conceived with a “mobile- first” design philosophy and fewer support extensive study protocol-support features. A significant amount of clinical research is conducted in geographic regions with limited or no Internet access such as impoverished and remote communities. Current EDC solutions remain challenging to use in these contexts. While EDC is an increasingly important tool for clinical research, when EDC solutions are built on web-centric architectures, the lack of Internet coverage means that researchers often need to fall back on paper-based data capture methods or build expensive, custom EDC tools. A customizable Mobile Electronic Data Capture (mEDC) framework with an asynchronous data transport layer will better meet the needs of distributed studies in resource- limited, geographical areas. I developed ConnEDCt, a full-featured mEDC application that is customizable for longitudinal study protocols, with regulatory-compliant security, auditability and an asynchronous data transport model. ConnEDCt is adaptable to different study protocols, has extensive study protocol-support built-in, and supports on- or off-line data synchronization to a central data repos

关键词： Data collection   Collaboration   Reliability   Personnel   Machine learning   Computer science   Standards   Collaborative filtering   religious congregation   social computing   empirical investigations   data collector integrity   reliability  

摘要： Mass gatherings (such as Hajj/Umrah), owing to their immensity, often present a variety of difficulties to the attendees. To have a comprehensive understanding of the difficulties as well as their potential remedies, surveying a good number of attendees is unavoidable and this can be facilitated through engaging data collectors. A crucial part here is identifying the integrity of the data collectors, which is yet to be explored in the literature to the best of our knowledge. To address this gap, in this study, we first perform a mass-scale data collection over Hajj/Umrah pilgrims through online (n = 236) and in-person (n = 752) surveys, where we cover a substantial part (n = 712) through paid data collectors (n = 53). We critically investigate data collection activities of the data collectors through focused group discussions involving expert reviewers. We explore two computing techniques to unveil the integrity of the data collectors from two different perspectives. Our study finds out influential (religious and socio-geographical) aspects that impact the data collection process. Besides, we find that the collaborative participation of expert reviewers is obligatory to scrutinize the data collectors' integrity. Additionally, our explored computing techniques, namely conflict analysis and learning-based analysis, can identify up to 74% and 99% of the unreliable data collectors respectively. We observe that, although these computing-based filtering can indicate the integrity up to a certain level, human-in-the-loop is unavoidable for concluding on the integrity. To the best of our knowledge, this is the first study of its kind (i.e., integrity analysis of the data collectors) in the literature.

关键词： Drones   Security   Computer science   Servers   Receivers   Computational modeling   Real-time systems   Internet of drones   proxy signcryption   security   privacy   edge computing   HECC   random oracle model  

摘要： Internet of Drones (IoD) is a decentralized networking architecture that makes use of the internet for uniting drones to enter controlled airspace in a coordinated manner. On the one hand, this new clan of interconnected drones has ushered in a new era of real-world applications;Small drones, on the other hand, are generally not designed with security in mind, making them exposed to fundamental security and privacy concerns. Limited computing capabilities, along with communication over an open wireless channel, exacerbate these challenges, making the IoD unfeasible for secure operations. In this article, we propose an identity-based proxy signcryption scheme to address these issues. During data transfer between drones and to the cloud server, the proposed scheme supports outsourcing decryption and member revocation. The proposed scheme is based on the notion of Hyper Elliptic Curve Cryptography (HECC), which improves network computation efficiency. We use formal security analysis with the Random Oracle Model (ROM) to evaluate security toughness. The performance analysis of the proposed scheme has also been reviewed in terms of computation and communication costs with the relevant existing schemes. The results obtained from both the security and performance analyses affirm the superiority of the proposed scheme.

关键词： Career and technical education   Computing education   Social cognitive career theory   STEM Education   Career pathways  

摘要： Science, Technology, Engineering and Mathematics (STEM), including Computer Science (CS) are fields that are in great demand globally. This study’s purpose was to explore the nature of the educational pathways, critical factors and commonalities/differences leading to CS undergraduate enrollment through the male and female perspectives focusing on personal/home, academic/attitude and psychological factors underlying the Social Cognitive Career Theory factors. Purposive sampling method was used for this multi-case study, comprised of CS undergraduate upperclassman. Participants shared their perspectives on their CS educational pathway via three interviews and journals. Thematic analysis of narrative for both individual and cumulative group analysis, plus researcher journal and drawings, enabled an interpretivist lens on the participant’s voices. Five themes were identified in connection the participants educational pathways to and within CS: family role model, sense of belonging, growth mindset, good teachers, and you have to want it. Students’ early exposure is essential and most influential within family connections. Students need a sense of belonging; and need to have educational experiences and strong foundations in STEM core disciplines in order to be successful and progress through more technically oriented educational pathways. My findings align with much of the relevant literature: early exposure and a sense of belonging is essential; students need strong core STEM experiences to pursue successfully STEM educational pathways. Recommendations for policy, practice and research are highlighted in the manuscript.

关键词： Visualization   Shape   Visual perception   Clustering algorithms   Deep learning   Splines (mathematics)   Computer science  

摘要： Cluster analysis is an important technique in data analysis. However, there is no encompassing theory on scatterplots to evaluate clustering. Human visual perception is regarded as a gold standard to evaluate clustering. The cluster analysis based on human visual perception requires the participation of many probands, to obtain diverse data, and hence is a challenge to do. We contribute an empirical and data-driven study on human perception for visual clustering of large scatterplot data. First, we systematically construct and label a large, publicly available scatterplot dataset. Second, we carry out a qualitative analysis based on the dataset and summarize the influence of visual factors on clustering perception. Third, we use the labeled datasets to train a deep neural network for modeling human visual clustering perception. Our experiments show that the data-driven model successfully models the human visual perception, and outperforms conventional clustering algorithms in synthetic and real datasets.