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关键词： Computer science  

摘要： Graphs, which encode pairwise relations between entities, are a kind of universal data structure for many real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. For example, friend recommendation in social networks can be regarded as a link prediction task and predicting properties of chemical compounds can be treated as a graph classification task. An essential step to facilitate these tasks is to learn vector representations either for nodes or the entire graphs. Given its great success of representation learning in images and text, deep learning offers great promise for graphs. However, compared to images and text, deep learning on graphs faces immense challenges. Graphs are irregular where nodes are unordered and each of them can have a distinct number of neighbors. Thus, traditional deep learning models cannot be directly applied to graphs, which calls for dedicated efforts for designing novel deep graph models. To help meet this pressing demand, we developed and investigated novel GNN algorithms to generalize deep learning techniques to graph-structured data. Two key operations in GNNs are the graph filtering operation, which aims to refine node representations; and the graph pooling operation, which aims to summarize node representations to obtain a graph representation. In this thesis, we provide deep understandings or develop novel algorithms for these two operations from new perspectives. For graph filtering operations, we propose a unified framework from the perspective of graph signal denoising, which demonstrates that most existing graph filtering operations are conducting feature smoothing. Then, we further investigate what information typical graph filtering operations can capture and how they can be understood beyond feature smoothing. For graph pooling operations, we study the procedure of pooling from the perspective of g

关键词： Capital punishment   Death penalty   Prison   Penitentiary   Settler colonialism   Antebellum Wisconsin   Criminal law  

摘要： This dissertation explores the central importance of capital punishment in the development of a criminal justice system in Wisconsin – first as part of the Michigan Territory (1818-36), then as a separate U.S. territory (1836-48), and, finally, throughout its early years as a state (1848-60). During the 1820s and 1830s, capital cases involving both Indian and white defendants helped to define the boundaries of legal jurisdiction on a settler-colonial frontier. During the 1840s, challenges to capital punishment became intertwined with the politics of state building. During the early 1850s, a series of specific and often controversial developments, such as the execution of wife-murderer John McCaffary in August 1851 and the construction of the first state prison in February 1852, led to the abolition of capital punishment in July 1853. Rather than stemming from a rising tide of abolitionist sentiment inspired by the northeastern anti-capital punishment movement, the abolition of the death penalty in Wisconsin constituted a highly contingent modification of the state’s criminal code, enacted despite stubbornly persistent ambivalence on the issue among the people and politicians of Wisconsin. Abolition of the death penalty would not end debate over the state’s criminal justice system. Following a series of high-profile murders and lynchings in 1854 and 1855, a vocal coalition argued that the abolition experiment had already failed. Insisting that only a return of the gallows could restore law and order, these critics argued that the so-called enlightened reform – based on the premises that certainty of punishment was more important than severity and that reformation was more important than revenge – had proven unable to curb the spread of violence engulfing the state. Though calls for restoration would ultimately be unsuccessful, a growing fear of lawlessness convinced authorities in Milwaukee and other cities across Wisconsin to completely overhaul their law enforcemen

关键词： Image retrieval   Training   Semantics   Hamming distance   Computer science   Laplace equations   Image Retrieval   Hashing   Non-stationary Environment   Concept Drift   Re-ranking  

摘要： Concept drift is prevalent in non-stationary data environments but is rarely researched in image retrieval. Therefore, more research is needed on image retrieval in non-stationary data environments so that highly relevant images can still be retrieved when concept drifts happen. Hashing is a key technique to allow efficient image retrieval, so incremental hashing technique emerges in recent years for image retrieval in non-stationary environments. A state-of-the-art method is Incremental Hashing (ICH). ICH trains new hash tables on new data without considering the performance of previous hash tables, so the dependency of successive hash tables is ignored. To make use of this dependency in order to improve the performance of image retrieval in non-stationary environments, Complementary Incremental Hashing with query-adaptive Re-ranking (CIHR) is proposed in this paper. CIHR trains multiple hash tables incrementally, one for each data chunk of images. A new hash table is trained on a new data chunk of images as well as those images badly hashed by previous hash tables, thus the new hash table is complementary to the previous hash tables. To use the hash tables more effectively, a query-adaptive re-ranking method is used to weight all hash functions in each hash table according to their retrieval performance with respect to a given query. Weighted Hamming distance is finally used to evaluate the similarity between the query and the images in the database, as the basis of image retrieval. Experimental results on simulated non-stationary scenarios show that the proposed CIHR method achieves higher retrieval accuracy than all methods being compared, thus setting a new state of the art in image retrieval in non-stationary data environments.

摘要： Successful polices are usually implemented with the expectation of sustainment and education policies are no exception. In addition, education policies are often motivated by equity, even if the visions for equity are not explicitly described. The combination of ambiguously defined equity and the assumption of sustainability can lead to changes in, and dilution of, a policy’s original intents in ways that could leave a policy unrecognizable or ineffective in achieving its goals. This qualitative study sought to understand how equity is understood in the context of one state’s computer science (CS) policy, as well as expectations for sustaining the policy over time. The framework included examining equity using a distinction between inputs and outcomes, as well as a distinction between equal or differentiated inputs and outcomes. Conceptions of sustainability were explored by identifying what was sustained and how it was sustained. District CS leaders in one state were interviewed to understand how sustainment of the state’s CS policy was understood and how equity was conceptualized in the context of the policy. This study revealed that district leaders’ understanding of equity was dominated by beliefs that equalizing inputs would lead to differentiated, but proportional, student outcomes. Regarding sustainability, district leaders saw it as continuation of demand and supply of relevant CS courses in high schools. They described their role in sustainment as (1) increasing student enrollment, (2) fostering buy-in from families and school staff, and (3) recruiting, training, and retaining qualified teachers. The district leaders recognized that sustaining the policy required continual attention to it, particularly regarding buy-in and capacity in the form of qualified teachers and student interest. These findings highlight the value planning for sustainment by providing resources to foster buy-in and to maintaining teacher capacity, both with an emphasis on equity as t

关键词： Computer science  

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： Mobile applications   Probabilistic logic   Hidden Markov models   Recommender systems   Predictive models   Collaboration   Computer science   Mobile apps recommendation   matrix factorization   probabilistic matrix factorization   version sensitive recommendation   probabilistic evolution based version recommendation model  

摘要： Traditional recommendation approaches for the mobile Apps basically depend on the Apps related features. Now a days many users are in quench of Apps recommendation based on the version description. Earlier mobile Apps recommendation system do not handle the cold start problem and also lacks in time for recommending the related and latest version of Apps. To overcome this issues, a hybrid Apps recommendation framework which is considering the version of the mobile Apps is proposed. This novel framework named "Probabilistic Evolution based Version Recommendation Model (PEVRM)" integrates the principles of Probabilistic Matrix Factorization (PMF) with Version Evolution Progress Model (VEPM). With the help this novel recommendation algorithm, the mobile users easily identify the specific Apps for particular task based on its version progression. At same time, this framework helps in resolving cold start problems of new users. Evaluations of this framework utilize a benchmark dataset, i.e., Apple's iTunes App Store3, for revealing its promising performance.

关键词： Ciphers   Security   Encryption   Matrix converters   Streaming media   Performance analysis   Computer science   Security and privacy   substitution-box   block ciphers   encryption   image security   cyber physical systems  

摘要： For decades, the security and privacy of data are among the major challenges faced by service providers dealing with public data. To cope with these challenges, most of the organizations rely on the adoption of cryptographic methods for protecting data against any illegitimate access and attacks. Modern day cryptographic ciphers utilize one or more substitution-boxes (S-boxes) that facilitate the realisation of strong security of plain data during encryption and legal decoding of it during decryption process. Security of ciphers is directly proportional to the cryptographic strength of S-boxes. This study proposes an efficient and simple method based on some modular operations for the construction of dynamic S-boxes with high nonlinearity using a heuristic evolution strategy. A large number of strong S-boxes can be easily constructed using slight variations in the parameters of the anticipated method. A specimen S-box is constructed and its critical performance analysis against standard security criteria including nonlinearity, strict avalanche criterion, bit independence criterion, differential uniformity, linear probability, and fixed points are reported as justification for the proposed technique's high cryptographic strength. Furthermore, the generated S-box is also applied to encrypt digital images to assess its cryptographic application performance. The performance and comparison study validates that the proposed S-box has better performance strength, which makes it a viable candidate for cryptographic applications in different areas of image security.

关键词： Metadata   Collaborative filtering   Filtering   Feature extraction   Context modeling   Recommender systems   Computer science   Scientific paper recommendation   public contextual metadata   content-based filtering   collaborative filtering   hybrid approach  

摘要： Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest. Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles. Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly. However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users. Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions. Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module. Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy. In order to verify the effectiveness of the proposed approach, publicly available datasets were employed. Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications.