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关键词： Online graduate degree   Human capital   Graduate education   Adult students   Career motivation   Computer science  

摘要： Online graduate degree programs, particularly in technical disciplines, represent a fast-growing market for higher education institutions. Yet, it is unclear how online degree programs fit within students' educational investment decisions and career goals. Based on a foundation of human capital theory, and using data from a 2017 survey of students enrolled in an online master's degree program in computer science at a highly ranked US university, this study explores how motivation for seeking an online graduate degree in computer science varies by student demographics and professional background/preparation. Results show two student markets: one for career mobility, including job advancement and/or career change, and another for skill building without consideration of mobility. Our results also show that while students come from diverse backgrounds, the primary driving factor for seeking the degree is determined by relevant professional experience. Implications for future study and online instructional design are discussed.

关键词： Complex networks   Social networking (online)   Evolution (biology)   Computer science   Forecasting   Community detection   community evolution analysis   complex networks   event-based framework   weak events  

摘要： Community evolution remains a heavily researched and challenging area in the analysis of dynamic complex network structures. Currently, the primary limitation of traditional event-based approaches for community evolution analysis is the lack of strict constraint conditions for distinguishing evolutionary events, which entails that as the cardinality of discovered events increases, so does the number of redundant events. Another limitation of existing approaches is the lack of consideration for weak events. Weak events can be generated by small changes in communities, which are empirically prevalent, and are typically not captured by traditional events. To manage these two aforementioned limitations, this research aims to formalize a weak and strong events-based framework, which includes the following newly discovered events: "weak shrink," "weak expand," "weak merge," and "weak splity" predicated on the community overlapping degree and community degree membership, this article refines these traditional strong events, as well as new constraints for weak events. In addition, a community evolution mining framework, which is based on both strong and weak events, is proposed and denoted by a weak-event-based community evolution method (WECEM). The framework can be summarized by the following: 1) communities in complex networks with adjacent time-stamps are compared to determine the community overlapping degree and community membership degree;2) the values of the community overlapping degree and membership degree meet the definition of events;and 3) weak events are effectively identified. Extensive experimental results, on real and synthetic data sets consisting of dynamic complex networks and online social networks, demonstrate that WECEM is able to identify weak events more effectively than traditional frameworks. Specifically, WECEM outperforms traditional frameworks by 22.9% in the number of discovered strong events. The detection accuracy of evolutionary events is ap

关键词： User Interfaces and Human Computer Interaction   Computer Science   general   Personal Computing   Mobile Computing  

摘要： The development of cyberspace has brought about innumerable advantages for the mankind. However, it also came with several serious drawbacks;as cyberspace evolves, so does cybercrime. Since the birth of cyberspace, individuals, groups and whole nations have been engaging in computer-related offences of various significance and impact, trying to exploit systems' vulnerabilities, disseminate malicious software and steal data or funds. The concept of a hacker has entered the collective consciousness and become an intrinsic element of popular culture. However, there are hackers, or rather, cyberspace actors, who challenge this common view. This paper presents three types of such people, namely hacktivists, members of cyber militias and Internet trolls. Although they all use the Internet to break the laws or rules, their internal motivations are not always utterly sinister;actually, some of them firmly believe that their actions are for the greater good. This paper is structured as follows: Firstly, the general profile of a hacker is presented. Then, the state of the art is outlined, concerning other papers dealing with the motivations behind cyber threat actors. Following that, the three aforementioned groups of cyberspace actors are contrasted with the profile of a 'typical' hacker. Then, the profiles of a typical representative for each of the group and their motivations are indicated, followed by the final conclusions.

关键词： Hash functions   Image retrieval   Semantics   Training   Internet   Computer science   Concept drift   concept preserving   hashing   image retrieval   nonstationary environment  

摘要： Current hashing-based image retrieval methods mostly assume that the database of images is static. However, this assumption is not true in cases where the databases are constantly updated (e.g., on the Internet) and there exists the problem of concept drift. The online (also known as incremental) hashing methods have been proposed recently for image retrieval where the database is not static. However, they have not considered the concept drift problem. Moreover, they update hash functions dynamically by generating new hash codes for all accumulated data over time which is clearly uneconomical. In order to solve these two problems, concept preserving hashing (CPH) is proposed. In contrast to the existing methods, CPH preserves the original concept, that is, the set of hash codes representing a concept is preserved over time, by learning a new set of hash functions to yield the same set of hash codes for images (old and new) of a concept. The objective function of CPH learning consists of three components: 1) isomorphic similarity;2) hash codes partition balancing;and 3) heterogeneous similarity fitness. The experimental results on 11 concept drift scenarios show that CPH yields better retrieval precisions than the existing methods and does not need to update hash codes of previously stored images.

关键词： Computational thinking   STEAM   Game design   Computer science education  

摘要： This paper presents a game-design workshop built around a digital art installation featuring video games displayed over a real-world skyscraper to stimulate students' interest in computer science and a study testing its short-term effects on improving middle school students' computational thinking (CT) skills and attitudes towards computing. Following a STEAM approach, the workshop aimed to engage participants in age-appropriate activities that focus on CT skills through the lens of creating their own game. A web-based game design interface that allows students to code and play games as simulated on a skyscraper was developed to support the workshop's core activities. The web environment also featured step-by-step tutorials and fully functional games to promote the accessibility of the learning materials for a diverse body of students and educators around the globe. The results of the study indicated that the workshop helped students improve their CT skills and differentially influence their attitudes towards computing. In particular, the workshop experience led students from underserved community districts to lower their attitude ratings, whereas the reverse pattern was observed for students from more affluent districts. The workshop reportedly informed students' perception of computing as a profession and their appreciation of the analytical effort required for developing functional games. Qualitative analysis of artifact-based interviews indicated that students could begin to make abstractions and devise algorithms by associating variables through conditional statements while solving problems related to game development. Interview analysis also revealed that students took pride in the effort that they made during the workshop.

关键词： computer science   information technology   public health   health humanities   vaccines   machine learning  

摘要： Background: Social media has become an established platform for individuals to discuss and debate various subjects, including vaccination. With growing conversations on the web and less than desired maternal vaccination uptake rates, these conversations could provide useful insights to inform future interventions. However, owing to the volume of web-based posts, manual annotation and analysis are difficult and time consuming. Automated processes for this type of analysis, such as natural language processing, have faced challenges in extracting complex stances such as attitudes toward vaccination from large amounts of text. Objective: The aim of this study is to build upon recent advances in transposer-based machine learning methods and test whether transformer-based machine learning could be used as a tool to assess the stance expressed in social media posts toward vaccination during pregnancy. Methods: A total of 16,604 tweets posted between November 1, 2018, and April 30, 2019, were selected using keyword searches related to maternal vaccination. After excluding irrelevant tweets, the remaining tweets were coded by 3 individual researchers into the categories Promotional, Discouraging, Ambiguous, and Neutral or No Stance. After creating a final data set of 2722 unique tweets, multiple machine learning techniques were trained on a part of this data set and then tested and compared with the human annotators. Results: We found the accuracy of the machine learning techniques to be 81.8% (F score=0.78) compared with the agreed score among the 3 annotators. For comparison, the accuracies of the individual annotators compared with the final score were 83.3%, 77.9%, and 77.5%. Conclusions: This study demonstrates that we are able to achieve close to the same accuracy in categorizing tweets using our machine learning models as could be expected from a single human coder. The potential to use this automated process, which is reliable and accurate, could free valuable time a

关键词： Computer architecture   Encoding   Task analysis   Approximation algorithms   Training   Genetic algorithms   Computer science   Convolutional variational autoencoder   evolving deep learning   genetic algorithm (GA)   neural architecture search (NAS)  

摘要： Variational autoencoders (VAEs) have demonstrated their superiority in unsupervised learning for image processing in recent years. The performance of the VAEs highly depends on their architectures, which are often handcrafted by the human expertise in deep neural networks (DNNs). However, such expertise is not necessarily available to each of the end users interested. In this article, we propose a novel method to automatically design optimal architectures of VAEs for image classification, called evolving deep convolutional VAE (EvoVAE), based on a genetic algorithm (GA). In the proposed EvoVAE algorithm, the traditional VAEs are first generalized to a more generic and asymmetrical one with four different blocks, and then a variable-length gene encoding mechanism of the GA is presented to search for the optimal network depth. Furthermore, an effective genetic operator is designed to adapt to the proposed variable-length gene encoding strategy. To verify the performance of the proposed algorithm, nine variants of AEs and VAEs are chosen as the peer competitors to perform the comparisons on MNIST, street view house numbers, and CIFAR-10 benchmark datasets. The experiments reveal the superiority of the proposed EvoVAE algorithm, which wins 21 times out of the 24 comparisons and outperforms the best competitors by 1.39%, 14.21%, and 13.03% on the three benchmark datasets, respectively.

关键词： Computer Science   general   Computer Hardware   Computer Systems Organization and Communication Networks   Software Engineering/Programming and Operating Systems   Data Structures and Information Theory  

摘要： Zusammenfassung Entscheidungsbäume bereichern moderne künstliche Intelligenz durch vielseitige Weiterentwicklungen. Lernende Entscheidungsbäume sind seit Jahrzehnten ein tradiertes Werkzeug zur Erstellung von erklärbaren Modellen zu Klassifikation und Regression. Während moderne Trends wie Deep Learning die Aufmerksamkeit der KI-Entwicklung auf sich ziehen, werden Entscheidungsbäume kontinuierlich weiterentwickelt, in neuen Bereichen eingesetzt und mit scheinbar im Wettstreit stehenden Methoden verschmolzen. Der Artikel stellt die Funktionsweise von Entscheidungsbäumen dar und zeigt Entwicklungen der aktuellen Forschung auf.

关键词： cybersecurity   awareness   curriculum   computer science   information technology   education   framework   courses   content design  

摘要： Cybersecurity is a multifaceted global phenomenon representing complex socio-technical challenges for governments and private sectors. With technology constantly evolving, the types and numbers of cyberattacks affect different users in different ways. The majority of recorded cyberattacks can be traced to human errors. Despite being both knowledge- and environment-dependent, studies show that increasing users' cybersecurity awareness is found to be one of the most effective protective approaches. However, the intangible nature, socio-technical dependencies, constant technological evolutions, and ambiguous impact make it challenging to offer comprehensive strategies for better communicating and combatting cyberattacks. Research in the industrial sector focused on creating institutional proprietary risk-aware cultures. In contrast, in academia, where cybersecurity awareness should be at the core of an academic institution's mission to ensure all graduates are equipped with the skills to combat cyberattacks, most of the research focused on understanding students' attitudes and behaviors after infusing cybersecurity awareness topics into some courses in a program. This work proposes a conceptual Cybersecurity Awareness Framework to guide the implementation of systems to improve the cybersecurity awareness of graduates in any academic institution. This framework comprises constituents designed to continuously improve the development, integration, delivery, and assessment of cybersecurity knowledge into the curriculum of a university across different disciplines and majors;this framework would thus lead to a better awareness among all university graduates, the future workforce. This framework may be adjusted to serve as a blueprint that, once adjusted by academic institutions to accommodate their missions, guides institutions in developing or amending their policies and procedures for the design and assessment of cybersecurity awareness.