Query: Improving Naive Bayes Classifier Using Conditional Probabilities

Relevant passages:
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss <=SEP=> The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containing clear attribute dependences suggest that the answer to this question may be positive. This article shows that, although the Bayesian classifier's probability estimates are only optimal under quadratic loss if the independence assumption holds, the classifier itself can be optimal under zero-one loss (misclassification rate) even when this assumption is violated by a wide margin. The region of quadratic-loss optimality of the Bayesian classifier is in fact a second-order infinitesimal fraction of the region of zero-one optimality. This implies that the Bayesian classifier has a much greater range of applicability than previously thought. For example, in this article it is shown to be optimal for learning conjunctions and disjunctions, even though they violate the independence assumption. Further, studies in artificial domains show that it will often outperform more powerful classifiers for common training set sizes and numbers of attributes, even if its bias is a priori much less appropriate to the domain. This article's results also imply that detecting attribute dependence is not necessarily the best way to extend the Bayesian classifier, and this is also verified empirically.

Learning Bayesian Networks is NP-Complete <=SEP=> Algorithms for learning Bayesian networks from data have two components: a scoring metric and a search procedure. The scoring metric computes a score reeecting the goodness-of-t of the structure to the data. The search procedure tries to identify network structures with high scores. Heckerman et al. (1995) introduce a Bayesian metric, called the BDe metric, that computes the relative posterior probability of a network structure given data. In this paper, we show that the search problem of identifying a Bayesian network|among those where each node has at most K parents|that has a relative posterior probability greater than a given constant is NP-complete, when the BDe metric is used. 12.1 Introduction Recently, many researchers have begun to investigate methods for learning Bayesian networks. Many of these approaches have the same basic components: a scoring metric and a search procedure. The scoring metric takes a database of observed cases D and a network structure B S , and returns a score reeecting the goodness-of-t of the data to the structure. A search procedure generates networks for evaluation by the scoring metric. These approaches use the two components to identify a network structure or set of structures that can be used to predict future events or infer causal relationships. Cooper and Herskovits (1992)|herein referred to as CH|derive a Bayesian metric, which we call the BD metric, from a set of reasonable assumptions about learning Bayesian networks containing only discrete variables. Heckerman et al. (1995)|herein referred to as HGC|expand upon the work of CH to derive a new metric, which we call the BDe metric, which has the desirable property of likelihood equivalence. Likelihood equivalence says that the data cannot help to discriminate equivalent structures. We now present the BD metric derived by CH. We use B h S to denote the hypothesis that B S is an I-map of the distribution that generated the database. 2 Given a belief-network structure B S , we use i to denote the parents of x i. We use r i to denote the number of states of variable x i , and q i = Q x l 2 i r l to denote the number of instances of i. We use the integer j to index these instances. That is, we write i = j to denote the observation of the jth instance of the parents of x i. 1996 Springer-Verlag. 2 There is an …

Probabilistic reasoning in intelligent systems - networks of plausible inference <=SEP=> Probabilistic methods to create the areas, of computational tools. But I needed to get canned, bayesian networks worked recently strongly. Recently I tossed this book was published. In intelligent systems is researchers in, ai operations research excellence award for graduate. Too concerned about how it i've been. Apparently daphne koller and learning structures evidential reasoning. Pearl is a language for i've. Despite its early publication date it, is not great give the best references.

Expert Systems and Probabilistic Network Models <=SEP=> Follow up what we will offer in this article about expert systems and probabilistic network models. You know really that this book is coming as the best seller book today. So, when you are really a good reader or you're fans of the author, it does will be funny if you don't have this book. It means that you have to get this book. For you who are starting to learn about something new and feel curious about this book, it's easy then. Just get this book and feel how this book will give you more exciting lessons.

Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches <=SEP=> The naïve Bayes classifier is built on the assumpti on of conditional independence between the attributes given the class. The algorithm has been shown to be surprisingly robust to obvious violations of this condition, but it is natural to ask if it is possib le to further improve the accuracy by relaxing this assumption. We examine an approach where naïve Bayes is augmented by the addition of correlation a rcs between attributes. We explore two methods for finding the set of augmenting arcs, a greedy hillclimbing search, and a novel, more computationally efficient algorithm that we call SuperParent. We compare these methods to TAN; a state-of the-art distribution-based approach to finding the augmenti ng arcs.

A novel three-phase utility interface minimizing line current harmonics of high-power telecommunications rectifier modules <=SEP=> Based on the combination of a three-phase diode bridge and a dc/dc boost converter, a new three-phase threeswitch three-level pulsewidth modulated (PWM) rectifier system is developed. It can be characterized by sinusoidal mains current consumption, controlled output voltage, and low-blocking voltage stress on the power transistors. The application could be, e.g., for feeding the dc link of a telecommunications power supply module. The stationary operational behavior, the control of the mains currents, and the control of the output voltage are analyzed. Finally, the stresses on the system components are determined by digital simulation and compared to the stresses in a conventional six-switch two-level PWM rectifier system.

Compressive Spectrum Sensing for Cognitive Radio Networks <=SEP=> ............................................................................................................................... 3 RÉSUME .................................................................................................................................... 5 ACKNOWLEDGEMENT .......................................................................................................... 7 TABLE OF CONTENTS ............................................................................................................ 9 LIST OF FIGURES .................................................................................................................. 12 LIST OF TABLES .................................................................................................................... 13 NOTATION .............................................................................................................................. 14 LIST OF ABBREVIATION ..................................................................................................... 15 Chapter I .................................................................................................................................... 16 INTRODUCTION .................................................................................................................... 16 I.1 Spectrum Management and Cognitive Radio .................................................................. 16 I.2 Cognitive Radio Cycle ..................................................................................................... 18 I.3 Compressive Sensing ....................................................................................................... 19 I.4 Dissertation Objectives .................................................................................................... 19 I.5 Dissertation Contributions ............................................................................................... 20 I.6 List of Publications .......................................................................................................... 21 I.7 Dissertation Organization ................................................................................................ 23 Chapter II .................................................................................................................................. 24 SPECTRUM SENSING ........................................................................................................... 24 II.1 Spectrum Sensing Model ............................................................................................... 24 II.2 Spectrum Sensing Techniques ....................................................................................... 26 II.2.1 Energy detection ...................................................................................................... 26 II.2.2 Autocorrelation based Detection ............................................................................. 28 II.2.3 Euclidian Distance based Detection ........................................................................ 30 II.2.4 Wavelet based Sensing ............................................................................................ 31 II.2.5 Matched Filter Detection ......................................................................................... 33 II.2.6 Evaluation Metrics .................................................................................................. 34 II.3 Conclusion ...................................................................................................................... 35 Chapter III ................................................................................................................................. 36 COMPRESSIVE SENSING ..................................................................................................... 36 III.

Event Detection in Crowded Videos <=SEP=> Real-world actions occur often in crowded, dynamic environments. This poses a difficult challenge for current approaches to video event detection because it is difficult to segment the actor from the background due to distracting motion from other objects in the scene. We propose a technique for event recognition in crowded videos that reliably identifies actions in the presence of partial occlusion and background clutter. Our approach is based on three key ideas: (1) we efficiently match the volumetric representation of an event against oversegmented spatio-temporal video volumes; (2) we augment our shape-based features using flow; (3) rather than treating an event template as an atomic entity, we separately match by parts (both in space and time), enabling robustness against occlusions and actor variability. Our experiments on human actions, such as picking up a dropped object or waving in a crowd show reliable detection with few false positives.

Accelerated singular value thresholding for matrix completion <=SEP=> Recovering a large matrix from a small subset of its entries is a challenging problem arising in many real world applications, such as recommender system and image in-painting. These problems can be formulated as a general matrix completion problem. The Singular Value Thresholding (SVT) algorithm is a simple and efficient first-order matrix completion method to recover the missing values when the original data matrix is of low rank. SVT has been applied successfully in many applications. However, SVT is computationally expensive when the size of the data matrix is large, which significantly limits its applicability. In this paper, we propose an Accelerated Singular Value Thresholding (ASVT) algorithm which improves the convergence rate from O(1/N) for SVT to O(1/N2), where N is the number of iterations during optimization. Specifically, the dual problem of the nuclear norm minimization problem is derived and an adaptive line search scheme is introduced to solve this dual problem. Consequently, the optimal solution of the primary problem can be readily obtained from that of the dual problem. We have conducted a series of experiments on a synthetic dataset, a distance matrix dataset and a large movie rating dataset. The experimental results have demonstrated the efficiency and effectiveness of the proposed algorithm.

Bot Classification for Real-Life Highly Class-Imbalanced Dataset <=SEP=> Botnets are networks formed with a number of machines infected by malware called bots. Detection of these malicious networks is becoming a major concern as they pose a serious threat to the network security. Most of the research on bot detection is based on particular botnet characteristics which fail to detect other types of botnets and bots. Furthermore, there are very few bot detection methods that considered real-life class-imbalanced dataset. A dataset is class-imbalanced if there are significantly more instances in one class than the other classes. In this paper, we develop three generic features to detect different types of bots regardless of their botnet characteristics. We develop five classification models based on those features to classify bots from a large, real-life, class-imbalanced network dataset. Results show that our methodology can detect bots more accurately than the existing methods. Experimental results also demonstrate that the developed methodology can successfully detect bots when the proportion of bots to normal activity is very small. We also provide a performance comparison of our methodology with a recent study on bot detection in a real-life, large, imbalanced dataset.

A 1.8-GHz CMOS Power Amplifier Using a Dual-Primary Transformer With Improved Efficiency in the Low Power Region <=SEP=> A 1.8-GHz CMOS power amplifier for a polar transmitter is implemented with a 0.18- RF CMOS process. The matching components, including the input and output transformers, were integrated. A dual-primary transformer is proposed in order to increase the efficiency in the low power region of the amplifier. The loss induced by the matching network for the low-output power region is minimized using the dual-primary transformer. The amplifier achieved a power-added efficiency of 40.7% at a maximum output power of 31.6 dBm. The dynamic range was 34 dB for a supply voltage that ranged from 0.5 to 3.3 V. The low power efficiency was 32% at the output power of 16 dBm.

A framework for easing the development of applications embedding answer set programming <=SEP=> Answer Set Programming (ASP) is a well-established declarative problem solving paradigm which became widely used in AI and recognized as a powerful tool for knowledge representation and reasoning (KRR), especially for its high expressiveness and the ability to deal also with incomplete knowledge.
 Recently, thanks to the availability of a number of robust and efficient implementations, ASP has been increasingly employed in a number of different domains, and used for the development of industrial-level and enterprise applications. This made clear the need for proper development tools and interoperability mechanisms for easing interaction and integration with external systems in the widest range of real-world scenarios, including mobile applications and educational contexts.
 In this work we present a framework for integrating the KRR capabilities of ASP into generic applications. We show the use of the framework by illustrating proper specializations for some relevant ASP systems over different platforms, including the mobile setting; furthermore, the potential of the framework for educational purposes is illustrated by means of the development of several ASP-based applications.

Behavioral Addiction versus Substance Addiction: Correspondence of Psychiatric and Psychological Views <=SEP=> INTRODUCTION
Behavioral science experts believe that all entities capable of stimulating a person can be addictive; and whenever a habit changes into an obligation, it can be considered as an addiction. Researchers also believe that there are a number of similarities as well as some differences between drug addiction and behavioral addiction diagnostic symptoms. The purpose of this study is to consider different approaches in this field.


METHODS
This is a descriptive research using content analysis method. First, differences and similarities of various perspectives on addiction and addiction behavior in different substances were obtained, thereafter, the data was coded and categorized, subjects were discussed and major issues were extracted.


RESULTS
Behavioral addiction such as internet addiction is similar to drug addiction except that in the former, the individual is not addicted to a substance but the behavior or the feeling brought about by the relevant action. In addition, the physical signs of drug addiction, are absent in behavioral addiction. Others have stated that behaviorally addicted individuals have certain symptoms and will undergo the same consequences brought about by addiction to alcohol and drugs as well as other obsessive behaviors.


CONCLUSION
Similar to substance abuse prevention, programs aimed at addicted individuals and specialized training can educate adolescents about the warning signs of online addiction, in order to assist the early detection of this disorder. For prevention of behavioral addiction (such as internet addiction) authorities, cultural institutions and parents should monitor the use of internet and teach to the adolescent and children, the useful and appropriate methods of internet use.

A versatile FMCW Radar System Simulator for Millimeter-Wave Applications <=SEP=> For the successful design of low-cost and high-performance radar systems accurate and efficient system simulation is a key requirement. In this paper we present a new versatile simulation environment for frequency-modulated continuous-wave radar systems. Besides common hardware simulation it covers integrated system simulation and concept analysis from signal synthesis to baseband. It includes a flexible scenario generator, accurate noise modeling, and efficiently delivers simulation data for development and testing of signal processing algorithms. A comparison of simulations and measurement results for an integrated 77-GHz radar prototype shows the capabilities of the simulator on two different scenarios.

Evaluation of highly available and fault-tolerant middleware clustered architectures using RabbitMQ <=SEP=> The paper presents a performance evaluation of message broker system, Rabbit MQ in high availability - enabling and redundant configurations. Rabbit MQ is a message queuing system realizing the middleware for distributed systems that implements the Advanced Message Queuing Protocol. The scalability and high availability design issues are discussed. Since HA and performance scalability requirements are in conflict, scenarios for using clustered RabbitMQ nodes and mirrored queues are presented. The results of performance measurements are reported.

Compiling C/C++ SIMD Extensions for Function and Loop Vectorizaion on Multicore-SIMD Processors <=SEP=> SIMD vectorization has received significant attention in the past decade as an important method to accelerate scientific applications, media and embedded applications on SIMD architectures such as Intel® SSE, AVX, and IBM* AltiVec. However, most of the focus has been directed at loops, effectively executing their iterations on multiple SIMD lanes concurrently relying upon program hints and compiler analysis. This paper presents a set of new C/C++ high-level vector extensions for SIMD programming, and the Intel® C++ product compiler that is extended to translate these vector extensions and produce optimized SIMD instruction sequences of vectorized functions and loops. For a function, our main idea is to vectorize the entire function for callers instead of just vectorizing loops (if any) inside the function. It poses the challenge of dealing with complicated control-flow in the function body, and matching caller and callee for SIMD vector calls while vectorizing caller functions (or loops) and callee functions. Our compilation methods for automatically compiling vector extensions are described. We present performance results of several non-trivial visual computing, computational, and simulation workloads, utilizing SIMD units through the vector extensions on Intel® Multicore 128-bit SIMD processors, and we show that significant SIMD speedups (3.07x to 4.69x) are achieved over the serial execution.

The dawning era of polymer therapeutics <=SEP=> As we enter the twenty-first century, research at the interface of polymer chemistry and the biomedical sciences has given rise to the first nano-sized (5–100 nm) polymer-based pharmaceuticals, the 'polymer therapeutics'. Polymer therapeutics include rationally designed macromolecular drugs, polymer–drug and polymer–protein conjugates, polymeric micelles containing covalently bound drug, and polyplexes for DNA delivery. The successful clinical application of polymer–protein conjugates, and promising clinical results arising from trials with polymer–anticancer-drug conjugates, bode well for the future design and development of the ever more sophisticated bio-nanotechnologies that are needed to realize the full potential of the post-genomic age.

Comparing a Clipmap to a Sparse Voxel Octree for Global Illumination <=SEP=> Voxel cone tracing is a real-time method that approximates global illumination using a voxel approximation of the original scene. However, a high-resolution voxel approximation, which is necessary for good quality, consumes much memory, and a compact data structure for storing the voxels is necessary. In this thesis, as a primary contribution, we provide a comparison of two such data structures: a Sparse Voxel Octree, and a Clipmap. We implement these two data structures, and provide detailed descriptions of both with many important implementation details. These descriptions are much more complete than what exists in the current literature, and it is the secondary contribution of this thesis. In the comparison, we find that the octree performs worse than the clipmap with respect to memory consumption and performance, due to the overhead introduced by the complex octree data structure. However, with respect to visual quality, the octree is the superior choice, since the clipmap does not provide the same voxel resolution everywhere.

SuBiC: A Supervised, Structured Binary Code for Image Search <=SEP=> For large-scale visual search, highly compressed yet meaningful representations of images are essential. Structured vector quantizers based on product quantization and its variants are usually employed to achieve such compression while minimizing the loss of accuracy. Yet, unlike binary hashing schemes, these unsupervised methods have not yet benefited from the supervision, end-to-end learning and novel architectures ushered in by the deep learning revolution. We hence propose herein a novel method to make deep convolutional neural networks produce supervised, compact, structured binary codes for visual search. Our method makes use of a novel block-softmax nonlinearity and of batch-based entropy losses that together induce structure in the learned encodings. We show that our method outperforms state-of-the-art compact representations based on deep hashing or structured quantization in single and cross-domain category retrieval, instance retrieval and classification. We make our code and models publicly available online.

A methodology for power characterization of associative memories <=SEP=> Content Addressable Memories (CAM) have become increasingly more important in applications requiring high speed memory search due to their inherent massively parallel processing architecture. We present a complete power analysis methodology for CAM systems to aid the exploration of their power-performance trade-offs in future systems. Our proposed methodology uses detailed transistor level circuit simulation of power behavior and a handful of input data types to simulate full chip power consumption. Furthermore, we applied our power analysis methodology on a custom designed associative memory test chip. This chip was developed by Fermilab for the purpose of developing high performance real-time pattern recognition on high volume data produced by a future large-scale scientific experiment. We applied our methodology to configure a power model for this test chip. Our model is capable of predicting the total average power within 4% of actual power measurements. Our power analysis methodology can be generalized and applied to other CAM-like memory systems and accurately characterize their power behavior.

A data-driven approach to exploring similarities of tourist attractions through online reviews <=SEP=> The motivation for tourists to visit a city is often driven by the uniqueness of the attractions accessible within the region. The draw to these locations varies by visitor as some travelers are interested in a single specific attraction while others prefer thematic travel. Tourists today have access to detailed experiences of other visitors to these locations in the form of user-contributed text reviews, opinions, photographs, and videos, all contributed through online tourism platforms. The data available through these platforms offer a unique opportunity to examine the similarities and difference between these attractions, their cities, and the visitors that contribute the reviews. In this work we take a data-driven approach to assessing similarity through textual analysis of user-contributed reviews, uncovering nuanced differences and similarities in the ways that reviewers write about attractions and cities.

An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective <=SEP=> Given a question and a set of answer candidates, answer triggering determines whether the candidate set contains any correct answers. If yes, it then outputs a correct one. In contrast to existing pipeline methods which first consider individual candidate answers separately and then make a prediction based on a threshold, we propose an end-to-end deep neural network framework, which is trained by a novel group-level objective function that directly optimizes the answer triggering performance. Our objective function penalizes three potential types of error and allows training the framework in an end-to-end manner. Experimental results on the WIKIQA benchmark show that our framework outperforms the state of the arts by a 6.6% absolute gain under F1 measure1.

Truncating Temporal Differences: On the Efficient Implementation of TD(lambda) for Reinforcement Learning <=SEP=> Temporal diierence (TD) methods constitute a class of methods for learning predictions in multi-step prediction problems, parameterized by a recency factor. Currently the most important application of these methods is to temporal credit assignment in reinforcement learning. Well known reinforcement learning algorithms, such as AHC or Q-learning, may be viewed as instances of TD learning. This paper examines the issues of the eecient and general implementation of TD() for arbitrary , for use with reinforcement learning algorithms optimizing the discounted sum of rewards. The traditional approach, based on eligibility traces, is argued to suuer from both ineeciency and lack of generality. The TTD (Truncated Temporal Diierences) procedure is proposed as an alternative, that indeed only approximates TD(), but requires very little computation per action and can be used with arbitrary function representation methods. The idea from which it is derived is fairly simple and not new, but probably unexplored so far. Encouraging experimental results are presented, suggesting that using > 0 with the TTD procedure allows one to obtain a signiicant learning speedup at essentially the same cost as usual TD(0) learning.

Cooperative learning in neural networks using particle swarm optimizers <=SEP=> This paper presents a method to employ particle swarms optim izers in a cooperative configuration. This is achieved by splitting the input vector into several sub-vectors, each w hich is optimized cooperatively in its own swarm. The applic ation of this technique to neural network training is investigate d, with promising results.

CAPTCHAs: An Artificial Intelligence Application to Web Security <=SEP=> Nowadays, it is hard to find a popular Web site with a registration form that is not protected by an automated human proof test which displays a sequence of characters in an image, and requests the user to enter the sequence into an input field. This security mechanism is based on the Turing Test—one of the oldest concepts in Artificial Intelligence—and it is most often called Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). This kind of test has been conceived to prevent the automated access to an important Web resource, for example, a Web mail service or a Social Network. There are currently hundreds of these tests, which are served millions of times a day, thus involving a huge amount of human work. On the other side, a number of these tests have been broken, that is, automated programs designed by researchers, hackers, and spammers have been able to automatically serve the correct answer. In this chapter, we present the history and the concept of CAPTCHAs, along with their applications and a wide review of their instantiations. We also discuss their evaluation, both from the user and the security perspectives, including usability, attacks, and countermeasures. We expect this chapter provides to the reader a good overview of this interesting field. CES IN COMPUTERS, VOL. 83 109 Copyright © 2011 Elsevier Inc. 65-2458/DOI: 10.1016/B978-0-12-385510-7.00003-5 All rights reserved. 110 J.M. GOMEZ HIDALGO AND G. ALVAREZ MARAÑON 1. I ntroduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 1 .1. T he Turing Test and the Origin of CAPTCHAs . . . . . . . . . . . . . . . 111 2. M otivation and Applications . . . . . . . . . . . . . . . . . . . . . . . . 116 2 .1. G eneral Description of CAPTCHAs . . . . . . . . . . . . . . . . . . . . . 116 2 .2. D esirable Properties of CAPTCHAs . . . . . . . . . . . . . . . . . . . . . 117 2 .3. I m plementation and Deployment . . . . . . . . . . . . . . . . . . . . . . 119 2 .4. A pplications and the Rise of the Robots . . . . . . . . . . . . . . . . . . . 121 3. T ypes of CAPTCHAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 3 .1. O CR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 3 .2. I m age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 3 .3. A udio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 3 .4. C ognitive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 4. E valuation of CAPTCHAs . . . . . . . . . . . . . . . . . . . . . . . . . 146 4 .1. E fficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 4 .2. A ccessibility Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 4 .3. P ractical Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 5. S ecurity and Attacks on CAPTCHAs . . . . . . . . . . . . . . . . . . . . 156 5 .1. A ttacks on CAPTCHAs . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 5 .2. S ecurity Requirements on CAPTCHAs . . . . . . . . . . . . . . . . . . . 169 6. A lternatives to CAPTCHAs . . . . . . . . . . . . . . . . . . . . . . . . . 171 7. C onclusions and Future Trends . . . . . . . . . . . . . . . . . . . . . . . 173 R eferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173

Ultrasound-Guided Fenestration of the Carpal Ligament Using a Double-Needle Approach. <=SEP=> Injection techniques for carpal tunnel syndrome have evolved from landmark palpation injection techniques to more accurate ultrasound-guided approaches. Presented is a case report describing a technique serendipitously discovered during a carpal ligament fenestration. The case involved a 66-year-old man with a diagnosis of carpal tunnel syndrome. After a failed attempt at treatment using a wrist splint and activity modification, he was treated by median nerve hydrodissection with 100% temporary pain relief. When his symptoms recurred, a carpal tunnel combined hydrodissection/fenestration technique was performed. Because of difficulty extricating the carpal ligament from the median nerve with the first needle, which was placed longitudinal to the median nerve, a second needle was placed transverse to the median nerve to aid in hydrodissection. The second needle was left in because it was found to be helpful in maintaining a safe distance between the median nerve and the carpal ligament by intermittent injection through the second needle. The patient reported 70% relief of his symptoms at 2-week follow-up and 50% sustained relief at 3 months. A 2-needle technique is feasible and can be helpful during median nerve hydrodissection/carpal ligament fenestration when technical or anatomical issues arise preventing treatment using the traditional single needle approach. This double needle approach allows for use of injectate to maintain separation between the median nerve and the ligament during the fenestration.

When does Consumer Empowerment Lead to Satisfied Customers? Some Mediating and Moderating Effects of the Empowerment-Satisfaction Link <=SEP=> Technological advances increasingly provide marketers with the opportunity to empower consumers. Consumer empowerment is a positive subjective state evoked by consumer perceptions of increasing control. As a positive state, increasing consumer empowerment should be associated with increasing consumer satisfaction. If such a relationship exists, it may be influenced by a number of contextual variables. Knowing in what contexts empowerment has a greater impact on satisfaction would help marketers decide when they could more effectively use such a strategy. This study has two purposes: 1) to investigate the relationship between consumer empowerment and satisfaction and 2) to investigate a set of potential influences on that relationship. Marketers will be in a better position to decide when to empower consumers if they have guidance on the relationships between these variables.

Linear codes over F2+uF2+vF2+uvF2 <=SEP=> In this work, we investigate linear codes over the ring F2 + uF2 + vF2 + uvF2. We first analyze the structure of the ring and then define linear codes over this ring which turns out to be a ring that is not finite chain or principal ideal contrary to the rings that have hitherto been studied in coding theory. Lee weights and Gray maps for these codes are defined by extending on those introduced in works such as Betsumiya et al. (Discret Math 275:43–65, 2004) and Dougherty et al. (IEEE Trans Inf 45:32–45, 1999). We then characterize the F2 +uF2 +vF2 +uvF2-linearity of binary codes under the Gray map and give a main class of binary codes as an example of F2 + uF2 + vF2 + uvF2-linear codes. The duals and the complete weight enumerators for F2 + uF2 + vF2 + uvF2-linear codes are also defined after which MacWilliams-like identities for complete and Lee weight enumerators as well as for the ideal decompositions of linear codes over F2 + uF2 + vF2 + uvF2 are obtained.

AIMED- A Personalized TV Recommendation System <=SEP=> Previous personalized DTV recommendation systems focus only on viewers’ historical viewing records or demographic data. This study proposes a new recommending mechanism from a user oriented perspective. The recommending mechanism is based on user properties such as Activities, Interests, Moods, Experiences, and Demographic information—AIMED. The AIMED data is fed into a neural network model to predict TV viewers’ program preferences. Evaluation results indicate that the AIMED model significantly increases recommendation accuracy and decreases prediction errors compared to the conventional model.

Proportional smile design using the recurring esthetic dental (red) proportion. <=SEP=> Dentists have needed an objective way in which to evaluate a smile. A method for determining the ideal size and position of the anterior teeth has been presented here. Use of the FIVE to evaluate the RED proportion and the width-to-height ratio, tempered with sound clinical judgment, gives pleasing and consistent results. With the diversity that exists in nature, rarely does the final result follow all the mathematical rules of proportional smile design. This approach may serve as a foundation on which to base initial smile design, however. When one begins to understand the relationship between beauty, mathematics, and the surrounding world, one begins to appreciate their interdependence.