Query: Early Prediction of Students' Grade Point Averages at Graduation: A Data Mining Approach.

Relevant passages:
Data Mining : Concepts and Techniques <=SEP=> Association rule mining was first proposed by Agrawal, Imielinski, and Swami [AIS93]. The Apriori algorithm discussed in Section 5.2.1 for frequent itemset mining was presented in Agrawal and Srikant [AS94b]. A variation of the algorithm using a similar pruning heuristic was developed independently by Mannila, Tiovonen, and Verkamo [MTV94]. A joint publication combining these works later appeared in Agrawal, Mannila, Srikant, Toivonen, and Verkamo [AMS96]. A method for generating association rules from frequent itemsets is described in Agrawal and Srikant [AS94a].

Predicting and analyzing secondary education placement-test scores: A data mining approach <=SEP=> Understanding the factors that lead to success (or failure) of students at placement tests is an interesting and challenging problem. Since the centralized placement tests and future academic achievements are considered to be related concepts, analysis of the success factors behind placement tests may help understand and potentially improve academic achievement. In this study using a large and feature rich dataset from Secondary Education Transition System in Turkey we developed models to predict secondary education placement test results, and using sensitivity analysis on those prediction models we identified the most important predictors. The results showed that C5 decision tree algorithm is the best predictor with 95% accuracy on hold-out sample, followed by support vector machines (with an accuracy of 91%) and artificial neural networks (with an accuracy of 89%). Logistic regression models came out to be the least accurate of the four with and overall accuracy of 82%. The sensitivity analysis revealed that previous test experience, whether a student has a scholarship, student’s number of siblings, previous years’ grade point average are among the most important predictors of the placement test scores. 2012 Elsevier Ltd. All rights reserved.

Introduction to Data Mining <=SEP=> an introduction to data mining san jose state university introduction to data mining university of florida introduction to data mining exinfm introduction to data mining introduction to data mining and knowledge discovery lecture notes for chapter 3 introduction to data mining introduction to data mining introduction to data mining pennsylvania state university introduction to data mining tu/e introduction to data mining sas support session

Finding Interesting Rules from Large Sets of Discovered Association Rules <=SEP=> Association rules, introduced by Agrawal, Imielinski, and Swami, are rules of the form “for 90% of the rows of the relation, if the row has value 1 in the columns in set W, then it has 1 also in column B”. Efficient methods exist for discovering association rules from large collections of data. The number of discovered rules can, however, be so large that browsing the rule set and finding interesting rules from it can be quite difficult for the user. We show how a simple formalism of rule templates makes it possible to easily describe the structure of interesting rules. We also give examples of visualization of rules, and show how a visualization tool interfaces with rule templates.

A Data Mining view on Class Room Teaching Language <=SEP=> From ancient period in India, educational institution embarked to use class room teaching. Where a teacher explains the material and students understand and learn the lesson. There is no absolute scale for measuring knowledge but examination score is one scale which shows the performance indicator of students. So it is important that appropriate material is taught but it is vital that while teaching which language is chosen, class notes must be prepared and attendance. This study analyses the impact of language on the presence of students in class room. The main idea is to find out the support, confidence and interestingness level for appropriate language and attendance in the classroom. For this purpose association rule is used.

Artificial Diversity as Maneuvers in a Control Theoretic Moving Target Defense <=SEP=> Moving target cyber-defense systems encompass a wide variety of techniques in multiple areas of cyber-security. The dynamic system reconfiguration aspect of moving target cyber-defense can be used as a basis for providing an adaptive attack surface. The goal of this research is to develop novel control theoretic mechanisms by which a range of cyber maneuver techniques are provided such that when an attack is detected the environment can select the most appropriate maneuver to ensure a sufficient shift in the attack surface to render the identified attack ineffective. Effective design of this control theoretic cyber maneuver approach requires the development of two additional theories. First, algorithms are required for the estimation of security state. This will identify when a maneuver is required. Second, a theory for the estimation of the cost of performing a maneuver is required. This is critical for selecting the most cost-effective maneuver while ensuring that the attack is rendered fully ineffective. Finally, we present our moving target control loop as well as a detailed case study examining the impact of our proposed cyber maneuver paradigm on DHCP attacks.

Gray level image processing using contrast enhancement and watershed segmentation with quantitative evaluation <=SEP=> Both image enhancement and image segmentation are most practical approaches among virtually all automated image recognition systems. Feature extraction and recognition have numerous applications on telecommunication, weather forecasting, environment exploration and medical diagnosis. The adaptive image contrast stretching is a typical image enhancement approach and watershed segmentation is a typical image segmentation approach. Under conditions of an improper or disturbed illumination, the adaptive contrast stretching should be conducted, which adapts to intensity distributions. Watershed segmentation is a feasible approach to separate different objects automatically, where watershed lines separate the catchment basins. The erosion and dilation operations are essential procedures involved in watershed segmentation. To avoid over-segmentation, the markers for foreground and background can be selected accordingly. Quantitative measures (gray level energy, discrete entropy, relative entropy and mutual information) are proposed to evaluate the actual improvement via two techniques. These methodologies can be easily expanded to many other image processing approaches.

Grassmannian beamforming for multiple-input multiple-output wireless systems <=SEP=> Transmit and receive beamforming is an attractive, low-complexity technique for exploiting the significant diversity that is available in multiple-input and multiple-output (MIMO) wireless systems. Unfortunately, optimal performance requires either complete channel knowledge or knowledge of the optimal beamforming vector which is difficult to realize in practice. In this paper, we propose a quantized maximum signal-to-noise ratio beamforming technique where the receiver only sends the label of the best weight vector in a predetermined codebook to the transmitter. We develop optimal codebooks for i.i.d. Rayleigh fading matrix channels. We derive the distribution of the optimal transmit weight vector and use this result to bound the SNR degradation as a function of the quantization. A codebook design criterion is proposed by exploiting the connections between the quantization problem and Grassmannian line packing. The design criterion is flexible enough to allow for side constraints on the codebook vectors. Bounds on the maximum distortion with the resulting Grassmannian codebooks follow naturally from the nature of the code. A proof is given that any system using an overcomplete codebook for transmit diversity and maximum ratio combining obtains a diversity on the order of the product of the number of transmit and receive antennas. Bounds on the loss in capacity due to quantization are derived. Monte Carlo simulations are presented that compare the symbol error probability for different quantization strategies.

Efficient Memory Disaggregation with Infiniswap <=SEP=> Memory-intensive applications suffer large performance loss when their working sets do not fully fit in memory. Yet, they cannot leverage otherwise unused remote memory when paging out to disks even in the presence of large imbalance in memory utilizations across a cluster. Existing proposals for memory disaggregation call for new architectures, new hardware designs, and/or new programming models, making them infeasible. This paper describes the design and implementation of INFINISWAP, a remote memory paging system designed specifically for an RDMA network. INFINISWAP opportunistically harvests and transparently exposes unused memory to unmodified applications by dividing the swap space of each machine into many slabs and distributing them across many machines’ remote memory. Because one-sided RDMA operations bypass remote CPUs, INFINISWAP leverages the power of many choices to perform decentralized slab placements and evictions. We have implemented and deployed INFINISWAP on an RDMA cluster without any modifications to user applications or the OS and evaluated its effectiveness using multiple workloads running on unmodified VoltDB, Memcached, PowerGraph, GraphX, and Apache Spark. Using INFINISWAP, throughputs of these applications improve between 4× (0.94×) to 15.4× (7.8×) over disk (Mellanox nbdX), and median and tail latencies between 5.4× (2×) and 61× (2.3×). INFINISWAP achieves these with negligible remote CPU usage, whereas nbdX becomes CPU-bound. INFINISWAP increases the overall memory utilization of a cluster and works well at scale.

Hate Source : White Supremacist Hate Groups and Hate Crime <=SEP=> The relationship between hate group activity and hate crime is theoretically ambiguous. Hate groups may incite criminal behavior in support of their beliefs. On the other hand, hate groups may reduce hate crime by serving as a forum for members to verbally vent their frustrations or as protection from future biased violence. I nd that the presence of an active white supremacist hate group chapter is associated with an 18.7 percent higher hate crime rate. White supremacist groups are not associated with the level of anti-white hate crimes committed by non-whites, nor do they form in expectation of future hate crimes by non-whites. JEL Codes: K14, J15, D71

Relating Newcomer Personality to Survival and Activity in Recommender Systems <=SEP=> In this work, we explore the degree to which personality information can be used to model newcomer retention, investment, intensity of engagement, and distribution of activity in a recommender community. Prior work shows that Big-Five Personality traits can explain variation in user behavior in other contexts. Building on this, we carry out and report on an analysis of 1008 MovieLens users with identified personality profiles. We find that Introverts and low Agreeableness users are more likely to survive into the second and subsequent sessions compared to their respective counterparts; Introverts and low Conscientiousness users are a significantly more active population compared to their respective counterparts; High Openness and High Neuroticism users contribute (tag) significantly more compared to their counterparts, but their counterparts consume (browse and bookmark) more; and low Agreeableness users are more likely to rate whereas high Agreeableness users are more likely to tag. These results show how modeling newcomer behavior from user personality can be useful for recommender systems designers as they customize the system to guide people towards tasks that need to be done or tasks the users will find rewarding and also decide which users to invest retention efforts in.

Stealthy Denial of Service Strategy in Cloud Computing <=SEP=> The success of the cloud computing paradigm is due to its on-demand, self-service, and pay-by-use nature. According to this paradigm, the effects of Denial of Service (DoS) attacks involve not only the quality of the delivered service, but also the service maintenance costs in terms of resource consumption. Specifically, the longer the detection delay is, the higher the costs to be incurred. Therefore, a particular attention has to be paid for stealthy DoS attacks. They aim at minimizing their visibility, and at the same time, they can be as harmful as the brute-force attacks. They are sophisticated attacks tailored to leverage the worst-case performance of the target system through specific periodic, pulsing, and low-rate traffic patterns. In this paper, we propose a strategy to orchestrate stealthy attack patterns, which exhibit a slowly-increasing-intensity trend designed to inflict the maximum financial cost to the cloud customer, while respecting the job size and the service arrival rate imposed by the detection mechanisms. We describe both how to apply the proposed strategy, and its effects on the target system deployed in the cloud.

Multibiometric Cryptosystems Based on Feature-Level Fusion <=SEP=> Multibiometric systems are being increasingly de- ployed in many large-scale biometric applications (e.g., FBI-IAFIS, UIDAI system in India) because they have several advantages such as lower error rates and larger population coverage compared to unibiometric systems. However, multibiometric systems require storage of multiple biometric templates (e.g., fingerprint, iris, and face) for each user, which results in increased risk to user privacy and system security. One method to protect individual templates is to store only the secure sketch generated from the corresponding template using a biometric cryptosystem. This requires storage of multiple sketches. In this paper, we propose a feature-level fusion framework to simultaneously protect multiple templates of a user as a single secure sketch. Our main contributions include: (1) practical implementation of the proposed feature-level fusion framework using two well-known biometric cryptosystems, namery,fuzzy vault and fuzzy commitment, and (2) detailed analysis of the trade-off between matching accuracy and security in the proposed multibiometric cryptosystems based on two different databases (one real and one virtual multimodal database), each containing the three most popular biometric modalities, namely, fingerprint, iris, and face. Experimental results show that both the multibiometric cryptosystems proposed here have higher security and matching performance compared to their unibiometric counterparts.

A virtual translation technique to improve current regulator for salient-pole AC machines <=SEP=> This paper presents a synchronous frame current regulator for salient-pole AC machines, which is insensitive to both parameters and synchronous frequencies. The transformation from stationary to synchronous frame is known to produce a frame-dependent cross-coupling term, which results in degradation in current response at high speeds and is represented as an asymmetric pole in the complex vector form of such systems. The asymmetric pole close to right half plane (RHP) in the complex vector s-domain cannot be perfectly canceled by parameter-based design of PI controllers due to parameter variation. Complex vector design synchronous frame PI current regulator with a virtually translated asymmetric pole is shown in this paper to reduce the parameter sensitivity and to improve disturbance rejection. The current regulator with the virtually translated pole have been analyzed in complex vector form for nonsalient-pole AC machine and in scalar form for salient-pole AC machines. It has been shown to yield comparable performances in both cases. This paper includes experimental verification of the performance of the proposed current regulator for an IPMSM.

News-based trading strategies <=SEP=> The marvel of markets lies in the fact that dispersed information is instantaneously processed and used to adjust the price of goods, services and assets. Financial markets are particularly efficient when it comes to processing information; such information is typically embedded in textual news that is then interpreted by investors. Quite recently, researchers have started to automatically determine news sentiment in order to explain stock price movements. Interestingly, this so-called news sentiment works fairly well in explaining stock returns. In this paper, we design trading strategies that utilize textual news in order to obtain profits on the basis of novel information entering the market. We thus propose approaches for automated decisionmaking based on supervised and reinforcement learning. Altogether, we demonstrate how news-based data can be incorporated into an investment system.

Handcrafted features with convolutional neural networks for detection of tumor cells in histology images <=SEP=> Detection of tumor nuclei in cancer histology images requires sophisticated techniques due to the irregular shape, size and chromatin texture of the tumor nuclei. Some very recently proposed methods employ deep convolutional neural networks (CNNs) to detect cells in H&E stained images. However, all such methods use some form of raw pixel intensities as input and rely on the CNN to learn the deep features. In this work, we extend a recently proposed spatially constrained CNN (SC-CNN) by proposing features that capture texture characteristics and show that although CNN produces good results on automatically learned features, it can perform better if the input consists of a combination of handcrafted features and the raw data. The handcrafted features are computed through the scattering transform which gives non-linear invariant texture features. The combination of handcrafted features with raw data produces sharp proximity maps and better detection results than the results of raw intensities with a similar kind of CNN architecture.

Inverse procedural modeling of 3D models for virtual worlds <=SEP=> This course presents a collection of state-of-the-art approaches for modeling and editing of 3D models for virtual worlds, simulations, and entertainment, in addition to real-world applications. The first contribution of this course is a coherent review of inverse procedural modeling (IPM) (i.e., proceduralization of provided 3D content). We describe different formulations of the problem as well as solutions based on those formulations. We show that although the IPM framework seems under-constrained, the state-of-the-art solutions actually use simple analogies to convert the problem into a set of fundamental computer science problems, which are then solved by corresponding algorithms or optimizations. The second contribution includes a description and categorization of results and applications of the IPM frameworks. Moreover, a substantial part of the course is devoted to summarizing different domain IPM frameworks for practical content generation in modeling and animation.

Lack of sleep affects the evaluation of emotional stimuli <=SEP=> Sleep deprivation (SD) negatively affects various cognitive performances, but surprisingly evidence about a specific impact of sleep loss on subjective evaluation of emotional stimuli remains sparse. In the present study, we assessed the effect of SD on the emotional rating of standardized visual stimuli selected from the International Affective Picture System. Forty university students were assigned to the sleep group (n=20), tested before and after one night of undisturbed sleep at home, or to the deprivation group, tested before and after one night of total SD. One-hundred and eighty pictures (90 test, 90 retest) were selected and categorized as pleasant, neutral and unpleasant. Participants were asked to judge their emotional reactions while viewing pictures by means of the Self-Assessment Manikin. Subjective mood ratings were also obtained by means of Visual Analog Scales. No significant effect of SD was observed on the evaluation of pleasant and unpleasant stimuli. On the contrary, SD subjects perceived the neutral pictures more negatively and showed an increase of negative mood and a decrease of subjective alertness compared to non-deprived subjects. Finally, an analysis of covariance on mean valence ratings of neutral pictures using negative mood as covariate confirmed the effect of SD. Our results indicate that sleep is involved in regulating emotional evaluation. The emotional labeling of neutral stimuli biased toward negative responses was not mediated by the increase of negative mood. This effect can be interpreted as an adaptive reaction supporting the "better safe than sorry" principle. It may also have applied implications for healthcare workers, military and law-enforcement personnel.

A MODERN TAKE <=SEP=> We revisit the bias-variance tradeoff for neural networks in light of modern empirical findings. The traditional bias-variance tradeoff in machine learning suggests that as model complexity grows, variance increases. Classical bounds in statistical learning theory point to the number of parameters in a model as a measure of model complexity, which means the tradeoff would indicate that variance increases with the size of neural networks. However, we empirically find that variance due to training set sampling is roughly constant (with both width and depth) in practice. Variance caused by the non-convexity of the loss landscape is different. We find that it decreases with width and increases with depth, in our setting. We provide theoretical analysis, in a simplified setting inspired by linear models, that is consistent with our empirical findings for width. We view bias-variance as a useful lens to study generalization through and encourage further theoretical explanation from this perspective.

A Foundation for the Study of IT Effects: A New Look at DeSanctis and Poole's Concepts of Structural Features and Spirit <=SEP=> Special Issue

Visibility estimation and joint inpainting of lidar depth maps <=SEP=> This paper presents a novel variational image inpainting method to solve the problem of generating, from 3-D lidar measures, a dense depth map coherent with a given color image, tackling visibility issues. When projecting the lidar point cloud onto the image plane, we generally obtain a sparse depth map, due to undersampling. Moreover, lidar and image sensor positions generally differ during acquisition, such that depth values referring to objects that are hidden from the image view point might appear with a naive projection. The proposed algorithm estimates the complete depth map, while simultaneously detecting and excluding those hidden points. It consists in a primal-dual optimization method, where a coupled total variation regularization term is included to match the depth and image gradients and a visibility indicator handles the selection of visible points. Tests with real data prove the effectiveness of the proposed strategy.

Review of Electromagnetic Techniques for Breast Cancer Detection <=SEP=> Breast cancer is anticipated to be responsible for almost 40,000 deaths in the USA in 2011. The current clinical detection techniques suffer from limitations which motivated researchers to investigate alternative modalities for the early detection of breast cancer. This paper focuses on reviewing the main electromagnetic techniques for breast cancer detection. More specifically, this work reviews the cutting edge research in microwave imaging, electrical impedance tomography, diffuse optical tomography, microwave radiometry, biomagnetic detection, biopotential detection, and magnetic resonance imaging (MRI). The goal of this paper is to provide biomedical researchers with an in-depth review that includes all main electromagnetic techniques in the literature and the latest progress in each of these techniques.

Mapping CMMI Project Management Process Areas to SCRUM Practices <=SEP=> Over the past years, the capability maturity model (CMM) and capability maturity model integration (CMMI) have been broadly used for assessing organizational maturity and process capability throughout the world. However, the rapid pace of change in information technology has caused increasing frustration to the heavyweight plans, specifications, and other documentation imposed by contractual inertia and maturity model compliance criteria. In light of that, agile methodologies have been adopted to tackle this challenge. The aim of our paper is to present mapping between CMMI and one of these methodologies, Scrum. It shows how Scrum addresses the Project Management Process Areas of CMMI. This is useful for organizations that have their plan-driven process based on the CMMI model and are planning to improve its processes toward agility or to help organizations to define a new project management framework based on both CMMI and Scrum practices.

DepSky: Dependable and Secure Storage in a Cloud-of-Clouds <=SEP=> The increasing popularity of cloud storage services has lead companies that handle critical data to think about using these services for their storage needs. Medical record databases, large biomedical datasets, historical information about power systems and financial data are some examples of critical data that could be moved to the cloud. However, the reliability and security of data stored in the cloud still remain major concerns. In this work we present DepSky, a system that improves the availability, integrity, and confidentiality of information stored in the cloud through the encryption, encoding, and replication of the data on diverse clouds that form a cloud-of-clouds. We deployed our system using four commercial clouds and used PlanetLab to run clients accessing the service from different countries. We observed that our protocols improved the perceived availability, and in most cases, the access latency, when compared with cloud providers individually. Moreover, the monetary costs of using DepSky in this scenario is at most twice the cost of using a single cloud, which is optimal and seems to be a reasonable cost, given the benefits.

A descent modified <=SEP=> In this paper, we propose a modified Polak–Ribière–Polyak (PRP) conjugate gradient method. An attractive property of the proposed method is that the direction generated by the method is always a descent direction for the objective function. This property is independent of the line search used. Moreover, if exact line search is used, the method reduces to the ordinary PRP method. Under appropriate conditions, we show that the modified PRP method with Armijo-type line search is globally convergent. We also present extensive preliminary numerical experiments to show the efficiency of the proposed method.

Secure Border Gateway Protocol (Secure-BGP) <=SEP=> The Border Gateway Protocol (BGP), which is used to distribute routing information between autonomous systems (ASes), is a critical component of the Internet’s routing infrastructure. It is highly vulnerable to a variety of malicious attacks, due to the lack of a secure means of verifying the authenticity and legitimacy of BGP control traffic. This document describes a secure, scalable, deployable architecture (S-BGP) for an authorization and authentication system that addresses most of the security problems associated with BGP. The paper discusses the vulnerabilities and security requirements associated with BGP, describes the S-BGP countermeasures, and explains how they address these vulnerabilities and requirements. In addition, this paper provides a comparison of this architecture with other approaches that have been proposed, analyzes the performance implications of the proposed countermeasures, and addresses operational issues.

Empowering personalized medicine with big data and semantic web technology: Promises, challenges, and use cases <=SEP=> In healthcare, big data tools and technologies have the potential to create significant value by improving outcomes while lowering costs for each individual patient. Diagnostic images, genetic test results and biometric information are increasingly generated and stored in electronic health records presenting us with challenges in data that is by nature high volume, variety and velocity, thereby necessitating novel ways to store, manage and process big data. This presents an urgent need to develop new, scalable and expandable big data infrastructure and analytical methods that can enable healthcare providers access knowledge for the individual patient, yielding better decisions and outcomes. In this paper, we briefly discuss the nature of big data and the role of semantic web and data analysis for generating “smart data” which offer actionable information that supports better decision for personalized medicine. In our view, the biggest challenge is to create a system that makes big data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare costs. We highlight some of the challenges in using big data and propose the need for a semantic data-driven environment to address them. We illustrate our vision with practical use cases, and discuss a path for empowering personalized medicine using big data and semantic web technology.

Series No . 16 Profiting from Innovation in the Digital Economy : Standards , Complementary Assets , and Business Models In the Wireless World <=SEP=> 

10 user interface elements for mobile learning application development <=SEP=> Mobile learning application is a new fashionable trend. More than a thousand learning applications have been designed and developed. Therefore, the focus on mobile learning applications needs to be refined and improved in order to facilitate users' interaction and response towards the learning process. To develop an effective mobile learning application, one should start from the User Interface (UI) because an effective UI can play a role in constantly making the user focus on an object and subject. The purpose of this study is to investigate and identify the best UI elements to use in mobile learning application development. Four existing UI guidelines and 12 selected learning applications were analysed, and we identified 10 UI Elements in order to develop the next mobile learning applications. All the 10 elements are described accordingly and they have implications for those designing and implementing UI in mobile learning applications.

Automated Accessibility Testing of Mobile Apps <=SEP=> It is important to make mobile apps accessible, so as not to exclude users with common disabilities such as blindness, low vision, or color blindness. Even when developers are aware of these accessibility needs, the lack of tool support makes the development and assessment of accessible apps challenging. Some accessibility properties can be checked statically, but user interface widgets are often created dynamically and are not amenable to static checking. Some accessibility checking frameworks analyze accessibility properties at runtime, but have to rely on existing thorough test suites. In this paper, we introduce the idea of using automated test generation to explore the accessibility of mobile apps. We present the MATE tool (Mobile Accessibility Testing), which automatically explores apps while applying different checks for accessibility issues related to visual impairment. For each issue, MATE generates a detailed report that supports the developer in fixing the issue. Experiments on a sample of 73 apps demonstrate that MATE detects more basic accessibility problems than static analysis, and many additional types of accessibility problems that cannot be detected statically at all. Comparison with existing accessibility testing frameworks demonstrates that the independence of an existing test suite leads to the identification of many more accessibility problems. Even when enabling Android's assistive features like contrast enhancement, MATE can still find many accessibility issues.