nils-herrmann/clean_scirepeval huggingface dataset:
A Model to Predict Low Academic Performance at a Specific Enrollment Using Data Mining

This paper presents the results of applying an educational data mining approach to model academic attrition (loss of academic status) at the Universidad Nacional de Colombia. Two data mining models were defined to analyze the academic and nonacademic data; the models use two classification techniques, naïve Bayes and a decision tree classifier, in order to acquire a better understanding of the attrition during the first enrollments and to assess the quality of the data for the classification task, which can be understood as the prediction of the loss of academic status due to low academic performance. The models aim to predict the attrition in the student's first four enrollments. First, considering any of these periods, and then, at a specific enrollment. Historical academic records and data from the admission process were used to train the models, which were evaluated using cross-validation and previously unseen records from a full academic period. Experimental results show that the prediction of the loss of academic status is improved when the academic data are added.
The fuzzy artificial immune system: motivations, basic concepts, and application to clustering and Web profiling

The human immune system can be seen as a complex network structure that is able to respond to an almost unlimited multitude of foreign invaders such as viruses and bacteria. Hence, this parallel and distributed adaptive system promises tremendous potential in many intelligent computing applications, including Web mining. Some of these immunity-based techniques involve the development and analysis of algorithms that can identify patterns in observed data in order to make predictions about unseen data. In this paper, we introduce several new enhancements to deal with some of the weaknesses of previous artificial immune system models. In particular, we address the uncertainty and fuzziness inherent in the matching process that takes place between antibodies and antigens. This problem is handled by introducing a fuzzy artificial immune system. A fuzzy artificial immune system mimicking the body's adaptive learning and defense mechanism in the face of invading biological agents is used as a monitoring and learning system for a Web site in the face of all incoming Web requests.
Unsupervised feature learning for content-based histopathology image retrieval

This paper proposes a strategy for content-based image retrieval, which combines unsupervised feature learning (UFL) with the classical bag-of-features (BOF) representation. In BOF, patches are usually represented using standard classical descriptors (i.e., SIFT, SURF, DCT, among others).We propose to use UFL to learn the patch representation itself. This is achieved by applying a topographic UFL method, which automatically learns visual invariance properties of color, scale and rotation from an image collection. The learned image representation is used as input for a multimodal latent semantic indexing system, which enriches the visual representation with semantics from image annotations. The overall strategy is evaluated in a particular histopathology image collection retrieval task, showing that the learned representation has a positive impact in retrieval performance for this particular task.
A comparison of geometric and energy-based point cloud semantic segmentation methods

The recent availability of inexpensive RGB-D cameras, such as the Microsoft Kinect, has raised interest in the robotics community for point cloud segmentation. We are interested in the semantic segmentation task in which the goal is to find some relevant classes for navigation, wall, ground, objects, etc. Several effective solutions have been proposed, mainly based on the recursive decomposition of the point cloud into planes. We compare such a solution to a non-associative MRF method inspired by some recent work in computer vision. The MRF yields interesting results that are however less good than those of a carefully tuned geometric method. Nevertheless, MRF still has some advantages and we suggest some improvements.
MOP2P: Peer-to-peer platform applied to multiobjective optimization problems

The resolution of multiobjective optimization problems (MOP) tipically requires a high demand of computational resources such as CPU and memory, finding candidate solutions at an acceptable response time, and with sufficient quality given a set of performance evaluation metrics. This paper presents a platform (denominated MOP2P) that allows the distributed execution of metaheuristic based algorithms for solving MOP using computers connected in a network in order to handle more computing power according to current demand. MOP2P implements Peer-to-Peer concepts to minimize the configuration effort and the management of a collection of computers. Also, a dynamic resource control mechanism is presented as a way to adapt the resource utilization levels intended for the optimization tasks executed on the different computers. Experimentally it was observed that the platform leverages the computing power available including desirable characteristics such as scalability, fault tolerance and non-intrusive use of computational resources.
An evolutionary approach to generate fuzzy anomaly (attack) signatures

We describe the generation of fuzzy signatures to detect some cyber attacks. This approach is an enhancement to our previous work, which was based on the principle of negative selection for generating anomaly detectors using genetic algorithms. The present work includes a different genetic representation scheme for evolving efficient fuzzy detectors. To determine the performance of the proposed approach, which is named Evolving Fuzzy Rule Detectors (EFR), experiments were conducted with three different data sets. One data set contains wireless data, generated using network simulator (NS2) while the other two data sets are publicly available (from Lincoln Lab). Results exhibited that the proposed approach outperformed the previous techniques.
Online multimodal matrix factorization for human action video indexing

This paper addresses the problem of searching for videos containing instances of specific human actions. The proposed strategy builds a multimodal latent space representation where both visual content and annotations are simultaneously mapped. The hypothesis behind the method is that such a latent space yields better results when built from multiple data modalities. The semantic embedding is learned using matrix factorization through stochastic gradient descent, which makes it suitable to deal with large-scale collections. The method is evaluated on a large-scale human action video dataset with three modalities corresponding to action labels, action attributes and visual features. The evaluation is based on a query-by-example strategy, where a sample video is used as input to the system. A retrieved video is considered relevant if it contains an instance of the same human action present in the query. Experimental results show that the learned multimodal latent semantic representation produces improved performance when compared with an exclusively visual representation.
Mining Coronal Loops in Solar Images from the SOHO collection

We present our preliminary findings as part of a new data mining application aiming at the automatic detection of images with coronal loops from one of NASA's solar image databases, known as EIT. Coronal loops are immense arches of hot gas on the surface of the Sun, thought to be jets of hot plasma flowing along in the alleys between the strong coronal magnetic fields. We use various data mining techniques including combining crisp and fuzzy classifiers for automated detection of blocks extracted from EIT solar images. Our data mining and retrieval system helps provide relevant data to astrophysicists who need such data to study the solar corona, and whose work is traditionally hindered by the need to manually sift through thousands of images in order to locate the very few that are useful for further analysis. Our data-driven approach is distinct from related image processing based approaches that cannot scale to large image databases because they rely mostly on semi-automated detection and on heavy and computationally intensive local shape analysis.
An immuno-fuzzy approach to anomaly detection

This paper presents a new technique for generating a set of fuzzy rules that can characterize the non-self space (abnormal) using only self (normal) samples. Because, fuzzy logic can provide a better characterization of the boundary between normal and abnormal, it can increase the accuracy in solving the anomaly detection problem. Experiments with synthetic and real data sets are performed in order to show the applicability of the proposed approach and also to compare with other works reported in the literature.
Complete expression trees for evolving fuzzy classifier systems with genetic algorithms and application to network intrusion detection

We propose a linear representation scheme for evolving fuzzy rules using the concept of complete binary tree structures. We also use special genetic operators such as gene addition, gene deletion, and variable length crossover. Results show that using these special operators along with the common mutation operator produce useful and minimal structure modifications to the fuzzy expression tree represented by the chromosome. The proposed method (representation and operators) is tested with a number of benchmark data sets including the KDDCup'99 Network Intrusion Detection data.
BIGS: A framework for large-scale image processing and analysis over distributed and heterogeneous computing resources

This paper presents BIGS the Big Image Data Analysis Toolkit, a software framework for large scale image processing and analysis over heterogeneous computing resources, such as those available in clouds, grids, computer clusters or throughout scattered computer resources (desktops, labs) in an opportunistic manner. Through BIGS, eScience for image processing and analysis is conceived to exploit coarse grained parallelism based on data partitioning and parameter sweeps, avoiding the need of inter-process communication and, therefore, enabling loosely coupled computing nodes (BIGS workers). It adopts an uncommitted resource allocation model where (1) experimenters define their image processing pipelines in a simple configuration file, (2) a schedule of jobs is generated and (3) workers, as they become available, take over pending jobs as long as their dependency on other jobs is fulfilled. BIGS workers act autonomously, querying the job schedule to determine which one to take over. This removes the need for a central scheduling node, requiring only access by all workers to a shared information source. Furthermore, BIGS workers are encapsulated within different technologies to enable their agile deployment over the available computing resources. Currently they can be launched through the Amazon EC2 service over their cloud resources, through Java Web Start from any desktop computer and through regular scripting or SSH commands. This suits well different kinds of research environments, both when accessing dedicated computing clusters or clouds with committed computing capacity or when using opportunistic computing resources whose access is seldom or cannot be provisioned in advance. We also adopt a NoSQL storage model to ensure the scalability of the shared information sources required by all workers, including within BIGS support for HBase and Amazon's DynamoDB service. Overall, BIGS now enables researchers to run large scale image processing pipelines in an easy, affordable and unplanned manner with the capability to take over computing resources as they become available at run time. This is shown in this paper by using BIGS in different experimental setups in the Amazon cloud and in an opportunistic manner, demonstrating its configurability, adaptability and scalability capabilities.
Understanding the relationships between self-regulated learning and students source code in a computer programming course

To increase the success in computer programming courses, it is important to understand the learning process and common difficulties faced by students. Although several studies have investigated possible relationships between students performance and self-regulated learning characteristics in computer programming courses, little attention has been given to the source code produced by students in this regard. Such source code might contain valuable information about their learning process, specially in a context where practical programming assignments are frequent and students should write source code constantly during the course. This paper presents a strategy to support the correlation analysis among students performance, motivation, use of learning strategies, and source code metrics in computer programming courses. A comprehensive case study is presented to evaluate the proposed strategy through collected data (self-regulated learning characteristics and source code) from 205 undergrad students that accepted to participate voluntarily in the study during three semesters. Results show that the main features from source code which are significantly related to students performance and self-regulated learning features are: length-related metrics, with mainly positive correlations; and Halstead complexity measures, correlated negatively.
Analysis of PHOW representations for Alzheimer disease classification on brain structural MRI

Alzheimer's Disease (AD) is a neurodegenerative pathology characterized by progressive atrophy of brain and impairment of memory and cognitive functions. Physicians frequently use structural brain imaging to identify abnormal patterns in brain structure that may indicate probable AD. Thus, shape information is central for brain imaging analysis and AD diagnosis. This paper examines how three variants of Pyramid Histograms Of visual Words (PHOW) descriptions, a data-driven approach, handle the complex task of AD classification. 87 pathological cases and 87 controls from OASIS dataset were used to study the impact of shape and surface information. Best performance was 89.3%, a current mark for AD classification, and an increase (27.1%) in contrast to a naive approach. Additionally, controls were better classified than demented subjects (94.5% and 84.0%, respectively), while young, early-onset AD subjects, and elderly controls were the most difficult. Finally, dictionary word analysis revealed discriminative surface features. Also, local patterns induced by global word distribution appear to be more significant for classification than word location.
Visualizing multimodal image collections

This paper presents two different strategies for visualizing multimodal image collections, which are based on a representation strategy that fuses text and visual content in the same latent space. This latent space allows to find semantic groups of images, which are used to select image prototypes to build a semantic visualization. The first strategy is a graph-based visualization in which edges represent image similarities and vertices represent images. The second is a multimodal visualization in which a set of image prototypes surround a semantic tag cloud. Thus, we built a system prototype in order to evaluate the strategies. Results show that the propose strategy is promising and it could be used in a real image exploration system to improve the image collection exploration process.
TECNO-STREAMS: tracking evolving clusters in noisy data streams with a scalable immune system learning model

Artificial immune system (AIS) models hold many promises in the field of unsupervised learning. However, existing models are not scalable, which makes them of limited use in data mining. We propose a new AIS based clustering approach (TECNO-STREAMS) that addresses the weaknesses of current AIS models. Compared to existing AIS based techniques, our approach exhibits superior learning abilities, while at the same time, requiring low memory and computational costs. Like the natural immune system, the strongest advantage of immune based learning compared to other approaches is expected to be its ease of adaptation to the dynamic environment that characterizes several applications, particularly in mining data streams. We illustrate the ability of the proposed approach in detecting clusters in noisy data sets, and in mining evolving user profiles from Web clickstream data in a single pass. TECNO-STREAMS adheres to all the requirements of clustering data streams: compactness of representation, fast incremental processing of new data points, and clear and fast identification of outliers.
Generation of new OTA-C oscillator structures using network transposition

A simple but very useful approach based on network transposition principle is presented to derive new CMOS sinusoidal oscillators using operational transconductance amplifiers and capacitors (OTA-C). The principle of mirroring to convert a three-terminal two-port network to a four-terminal fully differential network is utilized to generate fully differential OTA-C oscillator structures. The theoretical work is verified by using discrete resistors, capacitors and OTA devices.
Network properties of a pair of generalized polynomials

In this article, it is shown that there exists an intimate relationship between the network functions of certain ladder one-port and two-port networks, and a set of generalized two-variable polynomials defined by U/sub n/(x,y)=xU/sub n-1/(x,y)+yU/sub n-2/(x,y), n/spl ges/2, U/sub 0/(x,y)=0, U/sub 1/(x,y)=1, and V/sub n/(x,y)=xV/sub n-1/(x,y)+yV/sub n-2/(x,y), n/spl ges/2, V/sub 0/(x,y)=2, V/sub 1/(x,y)=x. Observing that well-known polynomials such as Fibonacci, Chebyshev, Jacobsthal, Pell and Morgan-Voyce polynomials are special cases of these generalized polynomials, it is shown how using these polynomials we can derive elegant relations amongst these various polynomials. Also, using the well-established properties of two-element-kind one-and two-port networks, we then obtain a number of interesting results regarding the location of the zeros of these polynomials, as well as their derivatives.
Robust Shrinkage Affine-Projection Sign Adaptive-Filtering Algorithms for Impulsive Noise Environments

Two robust affine projection sign (RAPS) algorithms, both of which minimize the mixed norm of l1 and l2 of the error signal, are proposed. The direction vector of the RAPS algorithms is obtained from the gradient of an l1 norm-based objective function, while two related l2 norm-based minimization problems are solved to obtain the line search of the two RAPS algorithms. The l1 norm-based direction vector reduces the impact of impulsive noise, whereas the l2 norm-based line search produces an unbiased solution in the proposed algorithms. In addition, one of the two RAPS algorithms shares the data selective adaptation used in the set-membership (SM) affine projection (SMAP) algorithm. The proposed algorithms are shown to offer a significant improvement in the convergence speed as well as a significant reduction in the steady-state misalignment relative to the pseudo affine projection sign (PAPS) algorithm. In addition, the proposed algorithms offer robust performance with respect to impulsive noise and improved tracking of the unknown system in comparison to that provided by the PAPS and Affine projection sign (APS) algorithms. These features of the proposed algorithms are demonstrated using simulation results in system-identification and echo-cancellation applications.
An efficient split-radix FHT algorithm

In this paper, a new efficient split-radix FHT algorithm is proposed for computing a length-2/sup m/ DHT by using a mixture of radix-2 and radix-8 index maps in the decomposition of the DHT. An efficient indexing process and a technique for grouping the twiddle factors are incorporated. Introduction of these three features in the proposed algorithm results in significant savings in terms of the data transfer, address generation, and twiddle factor evaluation or access to the lookup table, which contribute significantly to the execution time of the FHT algorithms. These savings are achieved with the arithmetic complexity (multiplications+additions) being no more than that of the existing split-radix FHT algorithms.
A biometric identification system based on the fusion of palmprint and speech signal

Biometric systems allow automatic person recognition based on physical or behavioral features which belong to a certain person. Each biometric feature has its limits and no biometric system is perfect so unimodal biometric systems raise a variety of problems. To over fulfilling some of the mentioned inconvenient and limitations and to increase the level of security the multimodal biometric systems are used. This paper proposes the multimodal biometrics system for identity verification using two traits, i.e., speech signal and palmprint. The proposed system is designed for applications where the training data contains a speech signal and palmprint. The matching score level architecture uses weighted sum of score technique. The features are extracted from the pre-processed palm image and pre-processed speech signal. The features of a query image and speech signal are compared with those of a database images and speech signal to obtain matching scores. The individual scores generated after matching are passed to the fusion module. This module consists of three major steps i.e., normalization, generation of similarity score and fusion of weighted scores. The final score is then used to declare the person as genuine or an impostor. The system is tested on database collected by the authors for 120 subjects and gives an overall accuracy of 98.63% with FAR of 1.67% and FRR of 0.84%.
A low-power systolic array-based adaptive Viterbi decoder and its FPGA implementation

In this paper, the design and FPGA implementation of a low-power adaptive Viterbi decoder with a constraint length of 9 and code rate of 1/2 is presented. In this design, a novel systolic array-based architecture with time multiplexing and arithmetic pipelining for implementing the adaptive Viterbi algorithm is used. A scheme for providing a tolerance to clock-to-data skew to avoid timing violation is proposed. A process of eliminating spurious toggles, for reducing power consumption, is also developed. It is shown that the total power consumption in the implementation of the adaptive algorithm can be reduced by up to 43% compared to that in the implementation of a corresponding non-adaptive Viterbi algorithm, with a negligible increase in the hardware.
8-bit partial sums of 16 luminance values for fast block motion estimation

Fast block motion estimation algorithms are needed for real-time implementations of video coding standards due to the high computational complexity of the full-search algorithm for block motion estimation. In this paper, an algorithm using 8-bit partial sums of 16 luminance values for a fast block motion estimation is proposed. The technique of using the partial sums is employed to reduce the computational complexity of not only the full-search algorithm but also some of the fast block motion estimation algorithms while maintaining their accuracy. Furthermore, it is shown that the byte-type data-parallelism on an SIMD architecture can be utilized to access and process these partial sums concurrently to accelerate the process of motion estimation. Simulation results are presented to demonstrate that the use of the partial sums can accelerate the execution of the full-search, three-step search, and four-step search algorithms on an SIMD architecture significantly.