Secondary structure elements in protein molecules refer to local sub-conformational regions stabilized by hydrogen bonding. Secondary structure elements can be divided into helical, sheet, or loop. Secondary structure elements bolster the folding and topology of the protein. They are important for modern structural bioinformatics such as protein modeling and functional analysis. Therefore, assigning the types of secondary structures in proteins is crucial. Many methods have been developed to address the problem. Methods can be categorized into two approaches. One approach uses the information about hydrogen bonding and energy while the other approach uses protein trace geometry. If the information of some atoms is missing, the second approach is more feasible. In this paper, we develop a machine learning method that belongs to the second approach to assign secondary structure elements. We develop a 3-state machine learning classifier. The classifier uses protein's Ca information only. The classifier ensembles four (4) machine learning models: Random Forest, Support Vector Machine, Multilayer Perceptron, and eXtreme Gradient Boosting. The classifier is trained with 600K amino acids. We tested our classifier at two different data sets. One data set contains 150K amino acids. The accuracy of our system was 94.6%. In addition, the classifier was tested on a set of 20 protein structures and compared with PCASSO from the same category. The information from Protein Data Bank was used as a reference. The comparison shows that our method can produce assignments that are more aligned with PDB at 93% accuracy while PCASSO achieved 84% accuracy.

We developed a hands-on lab for students to learn macro malware detection using decision trees on an open sourced data analytics software, HPCC, running on a virtual machine. The labware along with required software and documents are freely available at our project website. Our design is low cost, customizable and can be easily replicated to other institutions. Our experience shows that students have difficulties in configuring software environment for the lab. The virtual machine approach allows pre-configurations for datasets and required software installations and setting to facilitate students' learning and instructors' teaching. Since students are working on malware, the virtual machine provides an isolated network environment for testing without affecting normal operating network. This lab design can also be integrated into online courses by simply downloading and installing the customized virtual machine.

In this paper we present an innovative authentic learning approach of mobile security through real-world-scenario case studies. in computing field. The primary goal of this learning approach is to create an engagement and motivating learning environment that encourages all students in learning emerging technologies such as mobile software developments and its security concepts and practices. This approach provides students with hands-on laboratory practices on real-world mobile app developments and security. The laboratory consists of multiple modules covering Mobile App development, mobile security on cryptography, secure programming, network, database, Web app, mobile malware and spyware analysis. Each topic consists of a series of progressive sub-labs: a pre-lab, lab activities, and a student add-on post-lab. The preliminary feedback from students is positive. Students have gained hands-on real world experiences on mobile security with Android mobile devices, which also greatly promoted students' self-efficacy and confidences in their mobile security learning.

The popularity and the large market share of mobile devices such as smart phones and tablets have had a significant impact on our daily lives. The full-fledged computing platforms render a ubiquitous means to deliver information and knowledge. This project aims to utilize such platforms to enhance computer science education, especially in curricula such as programming, mobile application development and information assurance and security. Meanwhile, there is an urgent need to produce more qualified computer professionals in mobile application development and information assurance and security to meet the workforce shortage. In this paper, we describe the design and implementation of our hands-on pedagogical model for learning information assurance and security based on mobile devices. Not only is this model feasible, but also it is affordable. The lab ware consists of hands-on real-world relevant self-contained learning modules, and is designed to be ready for wide scale implementation.

Mobile computing has become extremely popular in our daily lives [1] and hence it is an important platform for software developers. Mobile platforms enable students to learn in a modern context when they are used as teaching tools in Computer Science (CS) or Information Technology (IT) education. Meanwhile, mobile security is an important topic in security curricula partly due to the popularity of consumer mobile devices and a shift in computing landscape towards mobile devices' apps development. Due to the rapid demand and popularity of mobile devices [2-3], the security of mobile computing is vital to the growing army of users and for the future of our social, economic and political systems. Hence, it is important and needed to develop hands-on learning materials on mobile security that produce a well-educated and trained workforce, who understands the many concerns of security, privacy, integrity and reliability, and is able to develop strong protection mechanisms and apply fundamental principles and best practices in solving real world problems [4-6]. Many colleges and universities are developing and offering courses on mobile programming and development. However, pedagogical materials based on real world applications running on a portable isolated network are still in infant stage.

This paper presents a new inexpensive portable learning platform for information assurance and security (IAS) education. Unique features such as affordable settings, portability, and isolated network are discussed. This project is developed based on open sourced or free software to minimize the implementation cost. A dedicated repository that hosts our findings and deliverables is freely available online. This portable learning platform makes it possible to offer hands-on lab intensive curriculum online and to enhance research productivity on IAS.

This paper is addressing the challenges of incorporating networking and security concepts into effective teaching and learning platform (PLab) that highlights real-world technical issues. PLab is an innovative portable learning platform that allows network applications to be safely tested. The isolated network without the need for a server promotes learning at anytime and anywhere. The strong connection between academic subjects and reality and digital-native students' everyday lives engages students in learning this emerging field, and better prepares students for the high industrial demands in mobile application development workforce. Moreover, this pedagogical model will help faculty develop expertise in the latest development of networking and security. The modular labware is designed to offer faculty the flexibility to implement it in many existing courses. Curricular materials will be delivered on Google cloud for its sustainability. This "ready-to-adopt" model will greatly save resources and time for enhancing modern networking and security education to meet the emerging workforce.

In this paper, we have proposed a time efficient, energy saving and robust broadcast/multicast protocol for reconfigurable cluster-based sensor network. In our broadcast protocol, a broadcast can be executed in O(hd2+ D2) rounds and each node needs to be awake in O(D2) rounds, where D and d are the degrees of G and the sub-network induced by the network backbone, respectively, and h is the height of the backbone. When k channels are available, the broadcast can be executed in O((hd2+ D2)/k) rounds and each. We show that our broadcast protocol can be readily modified to the one for multicast. The cluster-based architecture used in this paper for a sensor network is an improved version. The proposed network architecture is self-constructible and self-reconfigurable by using two topological management operations: node-move-in and node-move-out. Details of the protocol along with experimental results are discussed. Simulation results show that the protocol performance is much better than that in the theoretical analysis.

We consider the problem of distributed deterministic broadcasting in synchronous radio networks whose topology and size are unknown. Radio networks can be modeled by directed graphs. It has been shown that there does not exist any algorithm of acknowledged radio broadcasting (ARB) on the model without a collision detection even if graphs are restricted to symmetric ones, where ARB is a broadcasting task in which a distinguished node(called source) transmits a source message to all nodes and it confirms that all nodes have received the source message. In this paper, on the model of radio networks with a collision detection, we show an O(r + ecc) time deterministic ARB algorithm for symmetric graphs, where r is the length of the source message and ecc is the largest distance from the source to any other node.

Given a collection of segments in the plane, if we regard the segments as opaque barriers, their upper envelope consists of the portions of the segments visible from point (0, +/spl infin/). In this paper, we present deterministic parallel methods for constructing the upper envelope of segments on the weakest shared-memory model, the EREW PRAM. We show that we can find the upper envelope of n line segments optimally in 0(logn) time using 0(n) processors. Furthermore, if the segments are nonintersecting and their endpoints are sorted in x-coordinate, then we can reduce the number of processors to 0(n/ logn). Our method implies that we can find the upper envelope sequentially in 0(n log log n) time, which improves previous results. We also show that we can find the upper envelope of n k-intersecting segments (any pair of the segments intersects at most k times) with a slightly larger time and processor bound.

Given a collection of segments in the plane that intersect pairwise at most k times, regarding the segments as opaque barriers, their upper envelope consists of the portions of the segments visible from point (0,+/spl infin/). We give efficient parallel methods for finding the upper envelope of k-intersecting segments for any integer k/spl ges/0, in the weakest shared memory model, the EREW PRAM. We show that the upper envelope of n k-intersecting segments can be found in 0(log/sup 1+/spl epsiv//n) time using 0(/spl lambda//sub k+1/(n)/log/sup /spl epsiv//n) processors for any /spl epsiv/>0, where /spl lambda//sub k+2/(n)/sup 1/ is the size of the upper envelope. In particular, for line segments we show the following optimal algorithms: the upper envelope of n line segments can be found in O(log n) time using O(n) processors, and if the line segments are nonintersecting and sorted, the envelope can be found in O(log n) time using O(n/log n) processors. We also show that our methods imply a fast sequential result: the upper envelope of n sorted line segments can be found in O(n log log n) time sequentially, which improves the known lowest upper bound O(n log n).

Article Parallel robust algorithms for constructing strongly convex hulls Share on Authors: Wei Chen Department of Electrical and Computer Engineering, Nagoya Institute of Technology, Showa, Nagoya 466, Japan Department of Electrical and Computer Engineering, Nagoya Institute of Technology, Showa, Nagoya 466, JapanView Profile , Koichi Wada Department of Electrical and Computer Engineering, Nagoya Institute of Technology, Showa, Nagoya 466, Japan Department of Electrical and Computer Engineering, Nagoya Institute of Technology, Showa, Nagoya 466, JapanView Profile , Kimio Kawaguchi Department of Electrical and Computer Engineering, Nagoya Institute of Technology, Showa, Nagoya 466, Japan Department of Electrical and Computer Engineering, Nagoya Institute of Technology, Showa, Nagoya 466, JapanView Profile Authors Info & Claims SCG '96: Proceedings of the twelfth annual symposium on Computational geometryMay 1996 Pages 133–140https://doi.org/10.1145/237218.237329Online:01 May 1996Publication History 2citation465DownloadsMetricsTotal Citations2Total Downloads465Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access