Cluster Analysis of Simulated GravitationalWave Triggers Using S-MEANS and Constrained Validation Clustering

The fifth Science run of LIGO (S5) has been concluded recently. The data collected over two years of the run calls for a thorough analysis of the glitches seen in the gravitational wave channels, as well as in the auxiliary and environmental channels. The study presents two new techniques for cluster analysis of gravitational wave burst triggers. Traditional approaches to clustering treats the problem as an optimization problem in an “open” search space of clustering models. However, this can lead to problems with producing models that over-fit or under-fit the data as the search is stuck on local minima. The new algorithms tackle local minima by putting constraints in the search process. S-MEANS looks at similarity statistics of burst triggers and builds up clusters that have the advantage of avoiding local minima. Constrained Validation clustering tackles the problem by constraining the search in the space of clustering models that are “non-splittable” models in which centroids of the left and right child of a cluster (after splitting) are nearest to each other; the region of models that either over-fit or under-fit data (i.e. “splittable” models) can therefore be effectively avoided when assumptions about data are satisfied. These methods are demonstrated by using simulated data. The results on simulated data are promising and the methods are expected to be useful for LIGO S5 data analysis

Infusing Raspberry Pi in the Computer Science Curriculum for Enhanced Learning

With the advent of cloud computing, the Internet of Things (IoT), and mobile computing, CS faculty are continuously revamping the curriculum material to address such burgeoning set of technologies in practical and relatable ways. Raspberry Pi (RPi) devices represent an ideal hardware/software framework that embodies all these technologies through its simple architecture, small form factor (that minimizes the volume and footprint of a desktop computer), and ability to integrate various sensors that network together and connect to the Cloud. Therefore, one of the strategies of Computer Science Department, to enhance depth of learning concepts, has been to infuse Raspberry Pi (RPi) in computer science courses. RPi has been incorporated since 2016 in targeted courses, notably, Computer Organization & Assembly Language, Computer Architecture, Database Management Design & Implementation, Unix/Linux Programming, Internet Programming, and Senior Project. An inexpensive credit card sized computer, an RPi lends itself to allow depth of learning of concepts. From implementing firewalls, intrusion detection systems, scripting, client-server based computing, distributed computing, to interfacing with sensors and actuators, a student is guided to polish concepts taught in a class through RPi Project Based Learning (RPBL). Computer science curriculum already provides breadth of learning. The infusion of RPi in key courses provides depth in targeted concepts. There are peripheral desirable consequences as well, including a student learning prevalently used Linux environment even though a targeted course may have nothing directly to do with Linux. Furthermore, RPi provides an opportunity for students to realize that software programs can be interfaced with sensors and actuators to provide immersed experience in programming. From simply interfacing a switch and a Light Emitting Diode (LED) to getting data from sensors, buffering, and uploading to the cloud, a student already would have touched upon multiple disciplines in computer science. This paper provides a blueprint to infusing RPi in the targeted courses, and how each RPi based project provides depth to a targeted concept

An Accelerated Hierarchical Approach for Object Shape Extraction and Recognition

We present a novel automatic supervised object recognition algorithm based on a scale and rotation invariant Fourier descriptors algorithm. The algorithm is hierarchical in nature to capture the inherent intra-contour spatial relationships between the parent and child contours of an object. A set of distance metrics are introduced to go along with the hierarchical model. To test the algorithm, a diverse database of shapes is created and used to train standard classification algorithms, for shape-labeling. The implemented algorithm takes advantage of the multi-threaded architecture and GPU efficient image-processing functions present in OpenCV wherever possible, speeding up the running time and making it efficient for use in real-time applications. The technique is successfully tested on common traffic and road signs of real-world images, with excellent overall performance that is robust to moderate noise levels

A Holistic Approach for Enhancing Distributed Education with Multi-Campus Course Delivery Methods

To create an emerging research institution, a regional university was created that spans multiple campuses within a radius of more than one hundred miles by merging at least three current institutions. The merge allowed the university to pool its human and technical resources. Students can now pursue new degrees that were not available before at one campus or another, take a newly available technical or specialty courses, and even select their own preferred professor when a course is offered by many faculty. In order to serve students at multiple campuses that are geographically far a part, the university instituted policies to facilitate accessibility of courses to all students while meeting prerequisites and minimum enrollment requirements. This paper chronicles the policies, procedures, and faculty efforts in creating a sustainable framework for implementing a distributed campus course delivery that is acceptable by the university/college administration, the department, the faculty, and most importantly the student. Our experience shows that a successful framework should address many issues, including: - Logistics o Where to offer the courses; one campus, all campuses. o Is transportation provided for student at a convenient time o Etc. - Scheduling o Schedule classes so that student can attend all their classes on-time without conflicts o Coordinate scheduling among campuses - Faculty incentives o Maintain good faculty-to-student ratio o Provide formula for workload computation o Provide teaching/grading assistance o Home campus course Attribution - IT support o Provide Interactive TV with high bandwidth o Allow for faculty-to-student interaction o Provide state-of-the-art class podium o Allow for class recording o Allow for in-office tutorials or Q/A session through collaboration - Course Management System Delivery Methods o Enable many productive tools in the course management system o Allow proper notification for the student - Assessment and student participation o Maintain interaction with student on daily and weekly basis o Compare results from both campuses to avoid any emerging issues. The paper will present our efforts in each of the above areas, showing that despite the challenges faced, a distributed delivery system can be successful when the above issues/factors are adequately addressed. The results from our courses at the graduate and undergraduate levels show that students assessments don’t show any significant difference across campuses or based on where the home campus of the faculty is. By presenting our study, we hope that other institutions who are considering distributed education can benefit from our experience by adopting best practices while avoiding pitfalls

S-means: Similarity Driven Clustering and Its application in Gravitational-Wave Astronomy Data Mining

Clustering is to classify unlabeled data into groups. It has been well researched for decades in many disciplines. Clustering in massive amount of astronomical data generated by multi-sensor networks has become an emerging new challenge; assumptions in many existing clustering algorithms are often violated in these domains. For example, K means implicitly assumes that underlying distribution of data is Gaussian. Such an assumption is not necessarily observed in astronomical data. Another problem is the determination of K, which is hard to decide when prior knowledge is lacking. While there has been work done on discovering the proper value for K given only the data, most existing works, such as X-means, G-means and PG-means, assume that the model is a mixture of Gaussians in one way or another. In this paper, we present a similarity-driven clustering approach for tackling large scale clustering problem. A similarity threshold T is used to constrain the search space of possible clustering models such that only those satisfying the threshold are accepted. This forces the search to: 1) explicitly avoid getting stuck in local minima, and hence the quality of models learned has a meaningful lower bound, and 2) discover a proper value for K as new clusters have to be formed if merging them into existing ones will violate the constraint given by the threshold. Experimental results on the UCI KDD archive and realistic simulated data generated for the Laser Interferometer Gravitational Wave Observatory (LIGO) suggest that such an approach is promising

The Majority Rule: A General Protection on Recommender System

Recommender systems are widely used in a variety of scenarios, including online shopping, social network, and contents distribution. As users rely more on recommender systems for information retrieval, they also become attractive targets for cyber-attacks. The high-level idea of attacking a recommender system is straightforward. An adversary selects a strategy to inject manipulated data into the database of the recommender system to influence the recommendation results, which is also known as a profile injection attack. Most existing works treat attacking and protection in a static manner, i.e., they only consider the adversary’s behavior when analyzing the influence without considering normal users’ activities. However, most recommender systems have a large number of normal users who also add data to the database, the effects of which are largely ignored when considering the protection of a recommender system. We take normal users’ contributions into consideration and analyze popular attacks against a recommender system. We also propose a general protection framework under this dynamic setting

Search for gravitational-wave bursts in the first year of the fifth LIGO science run

We present the results obtained from an all-sky search for gravitational-wave (GW) bursts in the 64–2000 Hz frequency range in data collected by the LIGO detectors during the first year (November 2005—November 2006) of their fifth science run. The total analyzed live time was 268.6 days. Multiple hierarchical data analysis methods were invoked in this search. The overall sensitivity expressed in terms of the root-sum-square (rss) strain amplitude hrss for gravitational-wave bursts with various morphologies was in the range of 6×10−22  Hz−1/2 to a few×10−21  Hz−1/2. No GW signals were observed and a frequentist upper limit of 3.75 events per year on the rate of strong GW bursts was placed at the 90% confidence level. As in our previous searches, we also combined this rate limit with the detection efficiency for selected waveform morphologies to obtain event rate versus strength exclusion curves. In sensitivity, these exclusion curves are the most stringent to date

A Comparative Study on the Consistency of Features in On-Line Signature Verification

A large number of features have been proposed by researchers for on-line signature verification. However, little work has been done in measuring the consistency and discriminative power of these features. This paper presents a comparative study of features commonly used in on-line signature verification. A consistency model is developed by generalizing the existing feature-based measure to distance-based measure. Experimental results show that the simple features like X-, Y- coordinates, the speed of writing and the angle with the X-axis are among the most consistent. Key words: On-line Signature verification, Feature selection, Consistency