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摘要： Four CS faculty members from different primarily undergraduate institutions (PUIs) will share the joys and challenges of such careers. The media has broadcasted the need for educating more students in computer science, especially PUIs with liberal arts [2, 3, 4]. In order to educate the growing CS student enrollment at PUIs [1], we need to expand and support the pipeline of potential faculty members to teach at PUIs. The primary audience for this panel includes graduate students pursuing a computing PhD and those with PhDs who are considering a career change. If the panel is virtual or offered remotely, it can be advertised to graduate students across the USA. The goals of this panel include: 1) provide an overview of joys and opportunities of PUI careers, 2) provide an overview of the challenges of this type of career, 3) invite other attendees to contribute from their experiences, and 4) answer attendees' questions, and 5) create a community of current and future PUI faculty among attendees and the panelists. In the sections that follow, we describe the joys and challenges of faculty careers at institutions that focus on undergraduate education.

关键词： Predictive models   Neural networks   Social networking (online)   Computational modeling   Deep learning   Feature extraction   Computer science   click prediction   user behavior   recurrent neural network   online advertising   social networks  

摘要： People connected by huge-size socialnetworks are highly dependent on recommendation systems to discover interesting persons, contents, and commodities. Extracting user interests and modeling their evolutions from user historical behavior are vital for algorithms to judge whether users are interested in given persons and items. The existing methods have two shortcomings: firstly, they do not have the high-order ability to process temporal sequences, which leads to their inability to mine the evolution of user interests;secondly, the models take full large-scale information of items as input, which causes a severe problem of overfitting. Thus, we propose a new hybrid neural network, DGRU, which integrates Factorization-Machine Based Neural Network (DeepFM) and Gated Recurrent Unit Neural Network (GRU). The DeepFM component is responsible for performing the autonomic feature combination, and the GRU component is designed to model user interests and evolutions. The GRU component is fed with a series of 1 and 0 representing user click behaviors. It contains information on what users like and dislike. Moreover, the conciseness of the format is helpful to avoid the problem of overfitting. Experiments on three real datasets demonstrated that the proposed model has a better CTR prediction performance and robustness than the state-of-the-art models.

摘要： Teaching introductory Artificial Intelligence (AI) courses in undergraduate institutions presents a unique set of challenges due to the ever increasing content areas of AI. The goal of an introductory course is to provide a broad introduction to the field, but in an undergraduate setting where this may be the only AI course a student encounters, equipping students with tangible AI skills to apply in their careers after graduation is also highly desirable. This paper presents an experience report that discusses the rationale and methodology used to redesign a standard, introductory course into a more versatile version, and provides high level student evaluation data over an eight year period to assess the efficacy of this course redesign. The key idea in this redesign is to allow a significant portion of the content area to vary from one course offering to another.

摘要： Computer science education is more important than ever. The COVID-19 pandemic has highlighted our society's reliance on computing and its power to help businesses innovate and adapt, yet at the same time has surfaced greater disparities for students studying computer science. Computing is the number one source of all new wages in our economy, and there are currently 400,000 open computing jobs across the United States. However, unequal access to computer science instruction and opportunities to engage in computational thinking and practices remain prevalent (Google & Gallup, 2015). In the 2018-19 school year, only 15 percent of Maryland graduates took at least one computing related course, and there were significant gaps in the course taking patterns by gender and race (Maryland Center for Computing Education, 2021). To grow students' competencies with computational thinking and computer science, it is essential to build the capacity of teachers to integrate computational thinking competencies and standards into their practice. The Frostburg State University College of Education's Maryland Accelerates (MA) Program provides aspiring teachers the opportunity to earn an accelerated Master of Arts in Teaching (Elementary or Secondary track) degree along with an innovative yearlong teacher residency in partnership with Frederick and Garrett County Public Schools. One of the primary goals of the program is to integrate mathematical problem solving and computational thinking to promote scientific inquiry in partnering elementary and secondary schools. To this end, Frostburg State University has developed an exciting opportunity for Master of Arts in Teaching (MAT) students to earn a microcredential in Computational Thinking that is aligned to the ISTE Educator Standards, the High Leverage Practice Standards from TeachingWorks, and the Interstate Teacher Assessment and Support Consortium InTASC Model as part of their teacher preparation program. In this panel, faculty from

关键词： Special issues and sections   Logic programming   History  

摘要： The articles in this special section offer a new perspectives on the question of how and why logic became intertwined with computing and programming. It focuses on the locality of programming and computing practices, highlighting their relation to formalist practices and concepts to provide a deeper understanding of the historical relations between these different practices. The theoretical and technical considerations that tie logic and computing together have been historically contingent on the local and national structures inwhich they unfold.

摘要： A longitudinal study (2014-2021) of the Bachelor of Science in Computer Science degree requirements specified by 380 computer science programs in the United States is presented. Specifically, the study data surveyed the average number of required and elective computer science course credits and the average percentage of fifteen computer science courses required to complete the degree. An analysis of the significant and trending requirements changes between the start and end of the survey is also presented with a discussion or what factors may be accounting for these changes.

摘要： In this tutorial we will demonstrate the use of SageMath Interacts ([2, 3]) to as a great way to implement various numerical methods in a class consisting of mostly undergraduate Computer Science majors (although students can also be non-CS majors). The use of Interacts (SageMath Interacts) is a great and fun way to introduce Computer Science students various Math concepts such as: variables, functions, solving non-linear equations, approximation, polynomial and spline interpolation, etc. Such Interacts help students visualize the problems they are working on, as well as give them a chance to easily program nice Graphical User Interface applications. SageMath is an open-source Python-based tool, so learners will get exposed to the syntax of Python and will also make use of the symbolic computation capabilities provided by SageMath. In this tutorial we will explore all the above topics through hands-on applications. While the tutorial will focus on programming examples used in a Numerical Methods course ([1]), they can be used in other courses such as: Introduction to Computer Science, Discrete Mathematics, and other.

关键词： Privacy   Mobile applications   Computer science   Mobile computing   User interfaces   Security   Semantics   Security and privacy   information flow controls   mobile code security   network-level security and protection  

摘要： While mobile apps often need to transmit sensitive information out to support various functionalities, they may also abuse the privilege by leaking the data to unauthorized third parties. This makes us question: Is the given transmission required to fulfill the app functionality? In this paper, we make the first attempt to automatically identify suspicious transmissions from app visual interfaces, including app names, descriptions, and user interfaces. We design and implement a novel framework called FlowIntent to detect nonfunctional transmissions at both software and network levels. During the exercising of the given apps, FlowIntent automatically detects privacy-sharing transmissions and determines their purposes by utilizing the fact that mobile users rely on visible app interface to perceive the functionality of the app at certain context. The characterizations of nonfunctional network traffic are then summarized to provide network level protection. FlowIntent not only reduces the false alarms caused by traditional taint analysis, but also captures the sensitive transmissions missed by widely-used taint analysis system TaintDroid. Evaluation using 2125 sharing flows collected from more than a thousand running instances shows that our approach achieves about 94 percent accuracy in detecting nonfunctional transmissions.