Query: The Theory of Planned Behavior and Parental Involvement : A Theoretical Framework for Narrowing the Achievement Gaps

Relevant passages:
Consumer attitudes and behavior: the theory of planned behavior applied to food consumption decisions <=SEP=> The author compares the multi-attribute and subjective expected utility (SEU) models popular in research on consumer behavior to the approach offered by the theory of planed behavior (TPB). Unlike the multi-attribute and SEU models, the TPB relies not on revealed preferences to infer the underlying decision process but instead on direct assessment of its theoretical constructs. According to the theory, the consumer’s behavior is a function of intention to perform the behavior in question; the intention is based on attitude, subjective norm, and perceived behavioral control with respect to the behavior; and these factors are determined, respectively, by behavioral, normative, and control beliefs. The theory allows us to predict intentions and behavior with respect to the purchase or use of a single brand or product as well as in relation to choice among different brands or products.

Parent – School Involvement and School Performance : Mediated Pathways Among Socioeconomically Comparable African American and Euro-American Families <=SEP=> Children’s academic and social competencies were examined as mediators to explain the often positive relation between parent–school involvement and achievement. Ethnic variations in the relation between parent–school involvement and early achievement and the mediated pathways were examined. Because much of the comparative research confounds ethnicity with socioeconomic status, the relations were examined among socioeconomically comparable samples of African American and Euro-American kindergarten children and their mothers. For reading achievement, academic skills mediated the relation between involvement and achievement for African Americans and Euro-Americans. For math achievement, the underlying process differed across ethnic groups. For African Americans, academic skills mediated the relation between school involvement and math performance. For Euro-Americans, social competence mediated the impact of home involvement on school achievement.

The framing of decisions and the psychology of choice. <=SEP=> The psychological principles that govern the perception of decision problems and the evaluation of probabilities and outcomes produce predictable shifts of preference when the same problem is framed in different ways. Reversals of preference are demonstrated in choices regarding monetary outcomes, both hypothetical and real, and in questions pertaining to the loss of human lives. The effects of frames on preferences are compared to the effects of perspectives on perceptual appearance. The dependence of preferences on the formulation of decision problems is a significant concern for the theory of rational choice.

The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. <=SEP=> In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.

Android Code Protection via Obfuscation Techniques: Past, Present and Future Directions <=SEP=> Mobile devices have become ubiquitous due to centralization of private user information, contacts, messages and multiple sensors. Google Android, an open-source mobile Operating System (OS), is currently the market leader. Android popularity has motivated the malware authors to employ set of cyber attacks leveraging code obfuscation techniques. Obfuscation is an action that modifies an application (app) code, preserving the original semantics and functionality to evade anti-malware. Code obfuscation is a contentious issue. Theoretical code analysis techniques indicate that, attaining a verifiable and secure obfuscation is impossible. However, obfuscation tools and techniques are popular both among malware developers (to evade anti-malware) and commercial software developers (protect intellectual rights). We conducted a survey to uncover answers to concrete and relevant questions concerning Android code obfuscation and protection techniques. The purpose of this paper is to review code obfuscation and code protection practices, and evaluate efficacy of existing code de-obfuscation tools. In particular, we discuss Android code obfuscation methods, custom app protection techniques, and various de-obfuscation methods. Furthermore, we review and analyze the obfuscation techniques used by malware authors to evade analysis efforts. We believe that, there is a need to investigate efficiency of the defense techniques used for code protection. This survey would be beneficial to the researchers and practitioners, to understand obfuscation and de-obfuscation techniques to propose novel solutions on Android.

Automated Plant Disease Analysis (APDA): Performance Comparison of Machine Learning Techniques <=SEP=> Plant disease analysis is one of the critical tasks in the field of agriculture. Automatic identification and classification of plant diseases can be supportive to agriculture yield maximization. In this paper we compare performance of several Machine Learning techniques for identifying and classifying plant disease patterns from leaf images. A three-phase framework has been implemented for this purpose. First, image segmentation is performed to identify the diseased regions. Then, features are extracted from segmented regions using standard feature extraction techniques. These features are then used for classification into disease type. Experimental results indicate that our proposed technique is significantly better than other techniques used for Plant Disease Identification and Support Vector Machines outperforms other techniques for classification of diseases.

Adaptive content generation for games <=SEP=> From a simple entertainment activity to a learning tool, it is undeniable that video games are one of the most active and relevant areas in today's society. Since their inception, video games experienced an evolution unmatched by almost any other area and today's video games are complex pieces of software built by expensive multidisciplinary teams. Procedural Content Generation (PCG) offers an alternative to manual design of video game content, optimizing the process of development and thus reducing its cost. However, the content that is generated by traditional PCG techniques is usually very generic and it struggles to offer meaningful game experiences to a diverse player base. Recent years have brought some new PCG techniques that try to solve this problem by dynamically adjusting the generation of content to suit the needs of each individual player. The work presented in this paper focuses on the development of a new PCG methodology that aims to close the gap between game developers and their players. This is achieved by providing the developers with relevant real time player and playing context information and thus creating an easier way for developers to adjust their content generation process in run time to better suit the needs of each player. This was achieved by first designing a methodology that models the player, their context and the game. It was also developed a simple game to showcase the potential of such methodology. The end result was a game that adapts some of its content to different types of players and contexts.

eSOLHotel: Generación de un lexicón de opinión en español adaptado al dominio turístico <=SEP=> Since Web 2.0 is the largest container for subjective expressions about different topics or issues expressed in all languages, the study of Sentiment Analysis has grown exponentially. In this work, we focus on Spanish polarity classification of hotel reviews and a new domain-dependent lexical resource (eSOLHotel) is presented. This new lexicon has been compiled following a corpus-based approach. We have carried out several experiments using an unsupervised approach for the polarity classification over the category of hotels from corpus SFU. The results obtained with the new lexicon eSOLHotel outperform the results with other general purpose lexicon.

Exact matrix completion via convex optimization <=SEP=> Suppose that one observes an incomplete subset of entries selected from a low-rank matrix. When is it possible to complete the matrix and recover the entries that have not been seen? We demonstrate that in very general settings, one can perfectly recover all of the missing entries from most sufficiently large subsets by solving a convex programming problem that finds the matrix with the minimum nuclear norm agreeing with the observed entries. The techniques used in this analysis draw upon parallels in the field of compressed sensing, demonstrating that objects other than signals and images can be perfectly reconstructed from very limited information.

A Survey of Software-Defined Networking: Past, Present, and Future of Programmable Networks <=SEP=> The idea of programmable networks has recently re-gained considerable momentum due to the emergence of the Software-Defined Networking (SDN) paradigm. SDN, often referred to as a "radical new idea in networking", promises to dramatically simplify network management and enable innovation through network programmability. This paper surveys the state-of-the-art in programmable networks with an emphasis on SDN. We provide a historic perspective of programmable networks from early ideas to recent developments. Then we present the SDN architecture and the OpenFlow standard in particular, discuss current alternatives for implementation and testing of SDN-based protocols and services, examine current and future SDN applications, and explore promising research directions based on the SDN paradigm.

Evaluation of Local Spatio-temporal Features for Action Recognition <=SEP=> Local space-time features have recently become a popular video representation for action recognition. Several methods for feature localization and description have been proposed in the literature, and promising recognition results were demonstrated for different action datasets. The comparison of those methods, however, is limited given the different experimental settings and various recognition methods used. The purpose of this paper is first to define a common evaluation setup to compare local space-time detectors and descriptors. All experiments are reported for the same bag-of-features SVM recognition framework. Second, we provide a systematic evaluation of different spatio-temporal features. We evaluate the performance of several space-time interest point detectors and descriptors along with their combinations on datasets with varying degree of difficulty. We also include a comparison with dense features obtained by regular sampling of local space-time patches. Feature detectors. In our experimental evaluation, we consider the following feature detectors. (1) The Harris3D detector [3] extends the Harris detector for images to image sequences. At each video point, a spatio-temporal second-moment matrix μ is computed using a separable Gaussian smoothing function and space-time gradients. Interest points are located at local maxima of H = det(μ)− k trace3(μ). (2) The Cuboid detector [1] is based on temporal Gabor filters. The response function has the form: R = (I ∗ g ∗ hev) + (I ∗ g ∗ hod), where g(x,y;σ) is the 2D Gaussian smoothing kernel, and hev and hod are 1D Gabor filters. Interest points are detected at local maxima of R. (3) The Hessian detector [6] is a spatio-temporal extension of the Hessian saliency measure. The determinant of the 3D Hessian matrix is used to measure saliency. The determinant of the Hessian is computed over several spatial and temporal scales. A non-maximum suppression algorithm selects extrema as interest points. (4) Dense sampling extracts multi-scale video blocks at regular positions in space and time and for varying scales. In our experiments, we sample cuboids with 50% spatial and temporal overlap. Feature descriptors. The following feature descriptors are investigated. (1) For the Cuboid descriptor [1], gradients computed for each pixel in a cuboid region are concatenated into a single vector. PCA projects vectors to a lower dimensional space. (2) The HOG/HOF descriptors [4] divide a cuboid region into a grid of cells. For each cell, 4-bin histograms of gradient orientations (HOG) and 5-bin histograms of optic flow (HOF) are computed. Normalized histograms are concatenated into HOG, HOF as well as HOG/HOF descriptor vectors. (3) The HOG3D descriptor [2] is based on histograms of 3D gradient orientations. Gradients are computed via an integral video representations. Regular polyhedrons are used to uniformly quantize the orientation of spatio-temporal gradients. A given 3D volume is divided into a grid of cells. The corresponding descriptor concatenates gradient histograms of all cells. (4) The extended SURF (ESURF) descriptor [6] extends the image SURF descriptor to videos. Again 3D cuboids are divided into a grid of cells. Each cell is represented by a weighted sum of uniformly sampled responses of Haar-wavelets aligned with the three axes. Experimental Setup. We represent video sequences as a bag of local spatio-temporal features [5]. Spatio-temporal features are first quantized HOG3D HOG/HOF HOG HOF Cuboids ESURF Harris3D 89.0% 91.8% 80.9% 92.1% – – Cuboids 90.0% 88.7% 82.3% 88.2% 89.1% – Hessian 84.6% 88.7% 7767% 88.6% – 81.4% Dense 85.3% 86.1% 79.0% 88.0% – – Table 1: Average accuracy on the KTH actions dataset.

Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation Learning <=SEP=> We introduce a method for following high-level navigation instructions by mapping directly from images, instructions and pose estimates to continuous low-level velocity commands for real-time control. The Grounded Semantic Mapping Network (GSMN) is a fully-differentiable neural network architecture that builds an explicit semantic map in the world reference frame by incorporating a pinhole camera projection model within the network. The information stored in the map is learned from experience, while the local-to-world transformation is computed explicitly. We train the model using DAGGERFM, a modified variant of DAGGER that trades tabular convergence guarantees for improved training speed and memory use. We test GSMN in virtual environments on a realistic quadcopter simulator and show that incorporating an explicit mapping and grounding modules allows GSMN to outperform strong neural baselines and almost reach an expert policy performance. Finally, we analyze the learned map representations and show that using an explicit map leads to an interpretable instruction-following model.

Gender Recognition Based on Sift Features <=SEP=> This paper proposes a robust approach for face detection and gender classification in color images. Previous researches about gender recognition suppose an expensive computational and time-consuming pre-processing step in order to alignment in which face images are aligned so that facial landmarks like eyes, nose, lips, chin are placed in uniform locations in image. In this paper, a novel technique based on mathematical analysis is represented in three stages that eliminates alignment step. First, a new color based face detection method is represented with a better result and more robustness in complex backgrounds. Next, the features which are invariant to affine transformations are extracted from each face using scale invariant feature transform (SIFT) method. To evaluate the performance of the proposed algorithm, experiments have been conducted by employing a SVM classifier on a database of face images which contains 500 images from distinct people with equal ratio of male and female.

Data integration driven ontology design, case study smart city <=SEP=> Methods to design of formal ontologies have been in focus of research since the early nineties when their importance and conceivable practical application in engineering sciences had been understood. However, often significant customization of generic methodologies is required when they are applied in tangible scenarios. In this paper, we present a methodology for ontology design developed in the context of data integration. In this scenario, a targeting ontology is applied as a mediator for distinct schemas of individual data sources and, furthermore, as a reference schema for federated data queries. The methodology has been used and evaluated in a case study aiming at integration of buildings' energy and carbon emission related data. We claim that we have made the design process much more efficient and that there is a high potential to reuse the methodology.

Hand Gesture Recognition using Computer Vision <=SEP=> The use of the gesture system in our daily life as a natural human-human interaction has inspired the researchers to simulate and utilize this gift in human-machine interaction which is appealing and can take place the bore interaction ones that existed such as television, radio, and various home appliances as well as virtual reality will worth and deserve its name. This kind of interaction ensures promising and satisfying outcomes if applied in systematic approach, and supports unadorned human hand when transferring the message to these devices which is easiest, comfort and desired rather than the communication that requires frills to deliver the message to such devices. With the rapid emergence of 3d applications and virtual environments in computer system the need for a new type of interaction device arises. This is because the traditional devices such as mouse, keyboard and joystick become inefficient and cumbersome within this virtual environments.in other words evolution of user interfaces shapes the change in the HumanComputer Interaction (HCI).Intuitive and naturalness characteristics of “Hand Gestures” in the HCI have been the driving force and motivation to develop an interaction device which can replace current unwieldy tools. A survey on the methods of analysing, modelling and recognizing hand gestures in the context of the HCI is provided in this paper.

A Theoretic Framework of K-Means-Based Consensus Clustering <=SEP=> Consensus clustering emerges as a promising solution to find cluster structures from data. As an efficient approach for consensus clustering, the Kmeans based method has garnered attention in the literature, but the existing research is still preliminary and fragmented. In this paper, we provide a systematic study on the framework of K-meansbased Consensus Clustering (KCC). We first formulate the general definition of KCC, and then reveal a necessary and sufficient condition for utility functions that work for KCC, on both complete and incomplete basic partitionings. Experimental results on various real-world data sets demonstrate that KCC is highly efficient and is comparable to the state-of-the-art methods in terms of clustering quality. In addition, KCC shows high robustness to incomplete basic partitionings with substantial missing values.

Pose estimation of anime/manga characters: a case for synthetic data <=SEP=> 2D articulated pose estimation is the task of locating the body joint positions of a human figure in an image. A pose estimator that works on anime/manga images could be an important component of an automatic system to create 3D animation from existing manga or anime, which could significantly the lower cost of media production. To create an accurate pose estimator, however, a sizable and high-quality dataset is needed, and such a dataset can be expensive to create.
 To alleviate data scarcity, we propose using a database of 3D character models and poses to generate synthetic training data for 2D pose estimators of anime/manga characters based on convolutional neural networks (CNN). We demonstrate that a high-performing estimator can be obtained by pretraining a network on a large synthetic dataset and then fine-tuning it on a small dataset of drawings. We also show that the approach yields a pose estimator competitive with many previous works when applied to a photograph-based dataset, establishing our synthetic data's usefulness beyond the intended domain.

A universal algorithm for sequential data compression <=SEP=> A universal algorithm for sequential data compression is presented. Its performance is investigated with respect to a nonprobabilistic model of constrained sources. The compression ratio achieved by the proposed universal code uniformly approaches the lower bounds on the compression ratios attainable by block-to-variable codes and variable-to-block codes designed to match a completely specified source.

RoboADS: Anomaly Detection Against Sensor and Actuator Misbehaviors in Mobile Robots <=SEP=> Mobile robots such as unmanned vehicles integrate heterogeneous capabilities in sensing, computation, and control. They are representative cyber-physical systems where the cyberspace and the physical world are strongly coupled. However, the safety of mobile robots is significantly threatened by cyber/physical attacks and software/hardware failures. These threats can thwart normal robot operations and cause robot misbehaviors. In this paper, we propose a novel anomaly detection system, which leverages physical dynamics of mobile robots to detect misbehaviors in sensors and actuators. We explore issues raised in real-world implementations, e.g., distinctive robot dynamic models, sensor quantity and quality, decision parameters, etc., for practicality purposes. We implement the detection system on two types of mobile robots and evaluate the detection performance against various misbehavior scenarios, including signal interference, sensor spoofing, logic bomb and physical jamming. The experiments show detection effectiveness and small detection delays.

Practical steganalysis of digital images: state of the art <=SEP=> Steganography is the art of hiding the very presence of communication by embedding secret messages into innocuous looking cover documents, such as digital images. Detection of steganography, estimation of message length, and its extraction belong to the field of steganalysis. Steganalysis has recently received a great deal of attention both from law enforcement and the media. In our paper, we classify and review current stego-detection algorithms that can be used to trace popular steganographic products. We recognize several qualitatively different approaches to practical steganalysis – visual detection, detection based on first order statistics (histogram analysis), dual statistics methods that use spatial correlations in images and higher-order statistics (RS steganalysis), universal blind detection schemes, and special cases, such as JPEG compatibility steganalysis. We also present some new results regarding our previously proposed detection of LSB embedding using sensitive dual statistics. The recent steganalytic methods indicate that the most common paradigm in image steganography – the bit-replacement or bit substitution – is inherently insecure with “safe capacities” far smaller than previously thought.

Loginson: a transform and load system for very large-scale log analysis in large IT infrastructures <=SEP=> Nowadays, most systems and applications produce log records that are useful for security and monitoring purposes such as debugging programming errors, checking system status, and detecting configuration problems or even attacks. To this end, a log repository becomes necessary whereby logs can be accessed and visualized in a timely manner. This paper presents Loginson, a high-performance log centralization system for large-scale log collection and processing in large IT infrastructures. Besides log collection, Loginson provides high-level analytics through a visual interface for the purpose of troubleshooting critical incidents. We note that Loginson outperforms all of the other log centralization solutions by taking full advantage of the vertical scalability, and therefore decreasing Capital Expenditure (CAPEX) and Operating Expense (OPEX) costs for deployment scenarios with a huge volume of log data.

Factors Influencing Consumers ’ E-Commerce Commodity Purchases <=SEP=> The 2002 North America Online Report published by eMarketer estimates that almost 24 million pre-college age students (ages 9-17) are already shopping online and gaining valuable e-commerce purchasing experience. Estimates of online shopping usage project steady growth, the number of young adults buying online will increase proportionally. This research seeks to develop a better understanding of the factors motivating young people to select e-commerce vendors for commodity purchases by exploring attitudes, demographic characteristics and purchase decision perceptions (i.e., the product, shopping experience, customer service, and consumer risk). Findings indicate that young adults with a history of e-commerce purchasing experience have a more positive attitude towards online buying than do young adults without e-commerce purchasing experience. In a related finding, a history of e-commerce purchasing experience serves as a good predictor of future e-commerce commodity purchases. Additionally, consumer risk and shopping experience perceptions were found to influence experienced e-commerce shoppers’ commodity purchase decisions more than customer service or consumer risk.

Orbitofrontal cortex, decision-making and drug addiction <=SEP=> The orbitofrontal cortex, as a part of prefrontal cortex, is implicated in executive function. However, within this broad region, the orbitofrontal cortex is distinguished by its unique pattern of connections with crucial subcortical associative learning nodes, such as basolateral amygdala and nucleus accumbens. By virtue of these connections, the orbitofrontal cortex is uniquely positioned to use associative information to project into the future, and to use the value of perceived or expected outcomes to guide decisions. This review will discuss recent evidence that supports this proposal and will examine evidence that loss of this signal, as the result of drug-induced changes in these brain circuits, might account for the maladaptive decision-making that characterizes drug addiction.

Dispersion characteristics of substrate integrated rectangular waveguide <=SEP=> Dispersion properties of the substrate integrated rectangular waveguide (SIRW) are rigorously obtained using the BI-RME method combined with the Floquet's theorem. Our analysis shows that the SIRW basically has the same guided-wave characteristics as the conventional rectangular waveguide. Empirical equations are derived from the calculated dispersion curves in order to estimate the cutoff frequency of the first two dominant modes of the SIRW To validate the analysis results, an SIRW guide was designed and measured. Very good agreements between the experimental and theoretical results were obtained.

Deep Underwater Image Enhancement <=SEP=> In an underwater scene, wavelength-dependent light absorption and scattering degrade the visibility of images, causing low contrast and distorted color casts. To address this problem, we propose a convolutional neural network based image enhancement model, i.e., UWCNN, which is trained efficiently using a synthetic underwater image database. Unlike the existing works that require the parameters of underwater imaging model estimation or impose inflexible frameworks applicable only for specific scenes, our model directly reconstructs the clear latent underwater image by leveraging on an automatic end-to-end and data-driven training mechanism. Compliant with underwater imaging models and optical properties of underwater scenes, we first synthesize ten different marine image databases. Then, we separately train multiple UWCNN models for each underwater image formation type. Experimental results on real-world and synthetic underwater images demonstrate that the presented method generalizes well on different underwater scenes and outperforms the existing methods both qualitatively and quantitatively. Besides, we conduct an ablation study to demonstrate the effect of each component in our network.

A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection <=SEP=> We review accuracy estimation methods and compare the two most commonmethods cross validation and bootstrap Recent experimen tal results on arti cial data and theoretical re sults in restricted settings have shown that for selecting a good classi er from a set of classi ers model selection ten fold cross validation may be better than the more expensive leave one out cross validation We report on a large scale experiment over half a million runs of C and a Naive Bayes algorithm to estimate the e ects of di erent parameters on these al gorithms on real world datasets For cross validation we vary the number of folds and whether the folds are strati ed or not for boot strap we vary the number of bootstrap sam ples Our results indicate that for real word datasets similar to ours the best method to use for model selection is ten fold strati ed cross validation even if computation power allows using more folds

Natural language Interface for Database: A Brief review <=SEP=> Information is playing an important role in our lives. One of the major sources of information is databases. Databases and database technology are having major impact on the growing use of computers. Almost all IT applications are storing and retrieving information from databases. Retrieving information database requires knowledge of database languages like SQL. The Structured Query Language (SQL) norms are been pursued in almost all languages for relational database systems. However, not everybody is able to write SQL queries as they may not be aware of the structure of the database. So this has led to the development of Intelligent Database System ( IDBS) . There is an overwhelming need for non-expert users to query relational databases in their natural language instead of working with the values of the attributes. As a result many intelligent natural language interfaces to databases have been developed, which provides flexible options for manipulating queries. The idea of using Natural Language instead of SQL has prompted the development of new type of processing called Natural language Interface to Database. NLIDB is a step towards the development of intelligent database systems (IDBS) to enhance the users in performing flexible querying in databases. This paper is an introduction to Intelligent Database System and Natural Language Interface to Databases. Then a brief overview of NLIDB subcomponents is given and then discussion then moves on to NLIDB architectures and various approaches for the development of NLIDB systems.

Dynamic circular work-stealing deque <=SEP=> The non-blocking work-stealing algorithm of Arora, Blumofe, and Plaxton (henceforth ABP work-stealing) is on its way to becoming the multiprocessor load balancing technology of choice in both industry and academia. This highly efficient scheme is based on a collection of array-based double-ended queues (deques) with low cost synchronization among local and stealing processes. Unfortunately, the algorithm's synchronization protocol is strongly based on the use of fixed size arrays, which are prone to overflows, especially in the multiprogrammed environments for which they are designed. We present a work-stealing deque that does not have the overflow problem.The only ABP-style work-stealing algorithm that eliminates the overflow problem is the list-based one presented by Hendler, Lev and Shavit. Their algorithm indeed deals with the overflow problem, but it is complicated, and introduces a trade-off between the space and time complexity, due to the extra work required to maintain the list.Our new algorithm presents a simple lock-free work-stealing deque, which stores the elements in a cyclic array that can grow when it overflows. The algorithm has no limit other than integer overflow (and the system's memory size) on the number of elements that may be on the deque, and the total memory required is linear in the number of elements in the deque.

Information Systems and Healthcare XXXII: Understanding the Multidimensionality of Information Systems Use: A Study of Nurses' Use of a Mandated Electronic Medical Record System <=SEP=>