FPGA based computing platform with temporal partitioning mechanism

In the presented work the FPGA based run-time reconfigurable platform with temporal partitioning of hardware resources is proposed. This platform is based on the Field Programmable Gate Array (FPGA) device that can be reconfigured "on-fly" to provide the optimal adaptation of a processing architecture to the algorithm and data structure by utilization of developed mechanisms of temporal partitioning of computational / logic resources. It was shown that the proposed approach allows reaching very high cost-effectiveness of the computing platform oriented on processing of framed data-streams. On the other hand, the hardware programming and compilation processes could be simplified by utilization of library of precompiled Virtual Hardware Components stored in the on-board FLASH memory. Paper presents theoretical proof of the proposed approach by analytical comparison of the performance that could be reached on the conventional processors and FPGA platform with Temporal Partitioning Mechanism (TPM) of hardware resources. The implementation of the proposed TPM on the basis of Xilinx Spartan-3 and Xilinx Virtex II FPGA devices is described. Experimental results gained on the prototype of the FPGA based platform with TPM are discussed and analyzed. Keywords: reconfigurable computing, data-stream processing, FPGA, run-time reconfiguration, temporal partitioning.

FPGA based computing platform with temporal partitioning mechanism

In the presented work the FPGA based run-time reconfigurable platform with temporal partitioning of hardware resources is proposed. This platform is based on the Field Programmable Gate Array (FPGA) device that can be reconfigured "on-fly" to provide the optimal adaptation of a processing architecture to the algorithm and data structure by utilization of developed mechanisms of temporal partitioning of computational / logic resources. It was shown that the proposed approach allows reaching very high cost-effectiveness of the computing platform oriented on processing of framed data-streams. On the other hand, the hardware programming and compilation processes could be simplified by utilization of library of precompiled Virtual Hardware Components stored in the on-board FLASH memory. Paper presents theoretical proof of the proposed approach by analytical comparison of the performance that could be reached on the conventional processors and FPGA platform with Temporal Partitioning Mechanism (TPM) of hardware resources. The implementation of the proposed TPM on the basis of Xilinx Spartan-3 and Xilinx Virtex II FPGA devices is described. Experimental results gained on the prototype of the FPGA based platform with TPM are discussed and analyzed. Keywords: reconfigurable computing, data-stream processing, FPGA, run-time reconfiguration, temporal partitioning.

Initial Suspicion on Detecting Code Plagiarism and Collusion in Academia: Case Study of Algorithm and Data Structure Courses

In engineering education, some assessments require the students to submit program code, and since that code might be a result of plagiarism or collusion, a similarity detection tool is often used to filter excessively similar programs. To improve the scalability of such a tool, it is suggested to initially suspect some programs and only compare those programs to others (instead of exhaustively compare all programs one another). This paper compares the ef-fectiveness of two common techniques to raise such initial suspicion: focusing on the submissions of smart students (as they are likely to be copied), or the submissions of slow-paced students (since those students are likely to breach academic integrity to get higher assessment mark). Our study shows that the latter statistically outperforms the former by 13% in terms of precision; slow-paced students are likely to be the perpetrators, but they fail to get the submissions of smart students.

A new local causal learning algorithm applied in learning analytics

PurposeThe paper aims to present a new two stage local causal learning algorithm – HEISA. In the first stage, the algorithm discoveries the subset of features that better explains a target variable. During the second stage, computes the causal effect, using partial correlation, of each feature of the selected subset. Using this new algorithm, the study aims to identify the actions that lead a student succeed or failure in a course.Design/methodology/approachThe paper presents a brief review of main concepts used in this study: Causal Learning and Causal effects. The paper also discusses the results of applying the algorithm in education data set. Data used in this study was extracted from the log of actions of a Learning Management System, Moodle. These actions represent the behavior of 229 engineering students that take Algorithm and Data Structure course offered in a blended model.FindingsThe algorithm proposed in the paper identifies that features with weak relevance to a target may become relevant when computing the direct effect.Research limitations/implicationsThe algorithm needs to be improved to automatically discard attributes that are under a specific threshold of direct effect. Researchers are also encouraged to test the proposed propositions further.Practical implicationsThe algorithm presented in this paper can be used to identify the mostly relevant features given a classification task.Originality/valueThis paper computes the direct effect of a selected subset of features in a target variable to evaluate if a variable in this subset is really a cause of the target or if it is a spurious correlation.

Pyramid Search: Skip Ring Data Structure-Based New Searching Algorithm

Our new algorithm and data structure, pyramid search (PS) and skip ring, were created with the help of circular linked list and skip list algorithms and data structures. In circular linked list, operations were performed on a single circular list. Our new data structure consists of circular linked lists formed in layers which were linked in a pyramid way. Time complexity of searching, insertion and deletion algorithms equal to [Formula: see text] (lg[Formula: see text]) in an [Formula: see text]-element skip ring data structure. Therefore, skip ring data structure is employed more effectively ([Formula: see text](lg[Formula: see text])) in circumstances where circular linked lists ([Formula: see text]) are used. The priority is determined based on the searching frequency in PS which was developed in this study. Thus, the time complexity of searching is almost [Formula: see text](1) for [Formula: see text] records data set. In this paper, the applications of searching algorithms like linear search (LS), binary search (BS) and PS were realized and the obtained results were compared. The obtained results demonstrated that the PS algorithm is superior to the BS algorithm.

The Match: A Case Study In Algorithm Analysis Of The National Resident Matching Program

There are rare opportunities when solving an easily-understood problem can bring together application of skills taught in diverse courses in a Computer Science (CS) or Management Information Systems (MIS) program. This paper presents such an opportunity in the typical database management systems course taught at the junior or senior level. Specifically, we describe the case study of solving the classical Hospitals/Residents problem in Microsoft Access. The solution, based on classical Gale-Shapely algorithm for the Stable Marriage problem, offers pedagogical opportunities in data modeling, algorithm and data structure considerations for program development, Visual Basic for Applications (VBA) and embedded SQL (Structured Query Language) programming, and empirical analysis of running time complexity of algorithms that work remarkably well in teaching students the value of each tool in the toolset they take away from required courses as a part of their undergraduate education in CS or MIS.