A framework for easing the development of applications embedding answer set programming

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

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.


Evaluation of highly available and fault-tolerant middleware clustered architectures using RabbitMQ

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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.

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.


Scientific workflow management and the Kepler system

Many scientific disciplines are now data and information driven, and new scientific knowledge is often gained by scientists putting together data analysis and knowledge discovery “pipelines”. A related trend is that more and more scientific communities realize the benefits of sharing their data and computational services, and are thus contributing to a distributed data and computational community infrastructure (a.k.a. “the Grid”). However, this infrastructure is only a means to an end and scientists ideally should be bothered little with its existence. The goal is for scientists to focus on development and use of what we call scientific workflows. These are networks of analytical steps that may involve, e.g., database access and querying steps, data analysis and mining steps, and many other steps including computationally intensive jobs on high performance cluster computers. In this paper we describe characteristics of and requirements for scientific workflows as identified in a number of our application projects. We then elaborate on Kepler, a particular scientific workflow system, currently under development across a number of scientific data management projects. We describe some key features of Kepler and its underlying Ptolemy ii system, planned extensions, and areas of future research. Kepler is a communitydriven, open source project, and we always welcome related projects and new contributors to join. ∗Work supported by NSF/ITR 0225676 (SEEK), DOE SciDAC DE-FC02-01ER25486 (SDM), NSF/ITR CCR-00225610 (Chess), NSF/ITR 0225673 (GEON), NIH/NCRR 1R24 RR019701-01 Biomedical Informatics Research Network Coordinating Center (BIRN-CC), NSF/ITR 0325963 (ROADNet), NSF/DBI-0078296 (Resurgence) †San Diego Supercomputer Center, UC San Diego; ?Dept. of Computer Science & Genome Center, UC Davis; ‡National Center for Ecological Analysis and Synthesis, UC Santa Barbara; and §Department of Electrical Engineering and Computer Sciences, UC Berkeley


A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.


Computation for ChIP-seq and RNA-seq studies

Genome-wide measurements of protein-DNA interactions and transcriptomes are increasingly done by deep DNA sequencing methods (ChIP-seq and RNA-seq). The power and richness of these counting-based measurements comes at the cost of routinely handling tens to hundreds of millions of reads. Whereas early adopters necessarily developed their own custom computer code to analyze the first ChIP-seq and RNA-seq datasets, a new generation of more sophisticated algorithms and software tools are emerging to assist in the analysis phase of these projects. Here we describe the multilayered analyses of ChIP-seq and RNA-seq datasets, discuss the software packages currently available to perform tasks at each layer and describe some upcoming challenges and features for future analysis tools. We also discuss how software choices and uses are affected by specific aspects of the underlying biology and data structure, including genome size, positional clustering of transcription factor binding sites, transcript discovery and expression quantification.


Distributed computing in practice: the Condor experience

Since 1984, the Condor project has enabled ordinary users to do extraordinary computing. Today, the project continues to explore the social and technical problems of cooperative computing on scales ranging from the desktop to the world-wide computational Grid. In this paper, we provide the history and philosophy of the Condor project and describe how it has interacted with other projects and evolved along with the field of distributed computing. We outline the core components of the Condor system and describe how the technology of computing must correspond to social structures. Throughout, we reflect on the lessons of experience and chart the course travelled by research ideas as they grow into production systems. Copyright c © 2005 John Wiley & Sons, Ltd.


Effects of playing a violent video game as male versus female avatar on subsequent aggression in male and female players.

Previous research has shown that violent video games can increase aggression in players immediately after they play. The present research examines the effects of one subtle cue within violent video games that might moderate these effects-whether the avatar is male or female. One common stereotype is that males are more aggressive than females. Thus, playing a violent video game as a male avatar, compared to a female avatar, should be more likely to prime aggressive thoughts and inclinations in players and lead to more aggressive behavior afterwards. Male and female university students (N = 242) were randomly assigned to play a violent video game as a male or female avatar. After gameplay, participants gave an ostensible partner who hated spicy food hot sauce to eat. The amount of hot sauce given was used to measure aggression. Consistent with priming theory, results showed that both male and female participants who played a violent game as a male avatar behaved more aggressively afterwards than those who played as female avatar. The priming effects of the male avatar were somewhat stronger for male participants than for female participants, suggesting that male participants identified more with the male avatar than did the female participants. These results are particularly noteworthy because they are consistent with another recent experiment showing that playing a violent game as an avatar with a different stereotypically aggressive attribute (black skin color) stimulates more aggression than playing as an avatar without the stereotypically aggressive attribute (Yang et al., 2014, Social Psychological and Personality Science).


Meta BCI : Hippocampus-striatum network inspired architecture towards flexible BCI

Classifying neural signals is a crucial step in the brain-computer interface (BCI). Although Deep Neural Network (DNN) has been shown to be surprisingly good at classification, DNN suffers from long training time and catastrophic forgetting. Catastrophic forgetting refers to a phenomenon in which a DNN tends to forget previously learned task when it learns a new task. Here we argue that the solution to this problem may be found in the human brain, specifically, by combining functions of the two regions: the striatum and the hippocampus, which is pivotal for reinforcement learning and memory recall relevant to the current context, respectively. The mechanism of these brain regions provides insights into resolving catastrophic forgetting and long training time of DNNs. Referring to the hippocampus-striatum network we discuss design principles of combining different types of DNNs for building a new BCI architecture, called “Meta BCI”.


A smart parking system to minimize searching time, fuel consumption and CO2 emission

This paper proposes a Smart Parking System (SPS) based on Internet of Thing (IoT) and Wireless Sensors Network (WSN). It is assumed that Global Positioning System (GPS) and wireless communication system is placed in the vehicle. GPS gets the position of vehicle and wireless communication send it to server. A smartphone uses for user to remotely view the parking, status of spots and book a slot manually. The proposed system decreases the searching time of free slot by giving route to free slot. As the fuel consumption and carbon dioxide (CO2) emission decreases by decreasing the distance and wise versa because there is direct relationship between them. It is shown in simulations result at the end of the paper that the objectives of the paper to minimize searching time, fuel consumption and CO2 emission achieved.


MapReduce as a programming model for association rules algorithm on Hadoop

As association rules widely used, it needs to study many problems, one of which is the generally larger and multi-dimensional datasets, and the rapid growth of the mount of data. Single-processor's memory and CPU resources are very limited, which makes the algorithm performance inefficient. Recently the development of network and distributed technology makes cloud computing a reality in the implementation of association rules algorithm. In this paper we describe the improved Apriori algorithm based on MapReduce mode, which can handle massive datasets with a large number of nodes on Hadoop platform.


Design of a linear variable-stiffness mechanism using preloaded bistable beams

A machine with variable internal stiffness can meet the performance requirement in different working environments. Linear variable-stiffness mechanisms (LVSM) are rarely found compared with those producing rotary motion. This paper presents an approach to design a linear variable-stiffness mechanism. The idea is to parallel connect two lateral bistable beams with a vertical spring. Through the preload adjustment of the bistable beams, the output force to displacement curve can exhibit different stiffnesses. The merit of the proposed LVSM is that very large stiffness variation can be achieved in a compact space. The stiffness may even be tuned to zero by assigning a proper stiffness to the vertical spring. An optimization formulation is presented to design a LVSM with the largest stiffness variation. The effects of various parameters on the stiffness variation and linearity are discussed. The results are numerically verified with a prototype illustrated.


Performance Sensitivity of Space Sharing Processor Scheduling in Distributed-Memory Multicomputers

Processor scheduling in distributed-memory systems has received considerable attention in recent years. Several commercial distributed-memory systems use spacesharing processor scheduling. In space-sharing, the set of processors in a system is partitioned and each partition is assigned for the exclusive use of a job. Space-sharing policies can be divided into fixed, static, or dynamic categories. For distributed-memory systems, dynamic policies incur high overhead. Thus, static policies are considered as these policies provide a better performance than the fixed policies. Several static policies have been proposed in the literature. In a previously proposed adaptive static policy, the partition size is a function of the number of queued jobs. This policy, however, tends to underutilize the system resources. To improve the performance of this policy, we propose a new policy in which the partition size is a function of the total number of jobs in the system, as opposed to only the queued jobs. The results presented here demonstrate that the new policy performs substantially better than the original policy for the various workload and system parameters. Another major contribution is the evaluation of the performance sensitivity to job structure, variances in inter-arrival times and job service times, and network topology.


An Ontology-Based Approach to Text Summarization

Extractive text summarization aims to create a condensed version of one or more source documents by selecting the most informative sentences. Research in text summarization has therefore often focused on measures of the usefulness of sentences for a summary. We present an approach to sentence extraction that maps sentences to nodes of a hierarchical ontology. By considering ontology attributes we are able to improve the semantic representation of a sentence's information content. The classifier that maps sentences to the taxonomy is trained using search engines and is therefore very flexible and not bound to a specific domain. In our experiments, we train an SVM classifier to identify summary sentences using ontology-based sentence features. Our experimental results show that the ontology-based extraction of sentences outperforms baseline classifiers, leading to higher Rouge scores of summary extracts.


Assessing mental health issues on college campuses: preliminary findings from a pilot study

A significant fraction of college students suffer from serious mental health issues including depression, anxiety, self-harm and suicidal thought. The prevalence and severity of these issues among college students also appear to increase over time. However, most of these issues often remain undiagnosed, and as a result, untreated. One of the main reasons of this gap between illness and treatment results from the lack of reliable data over time. While health care services in college campuses have been focusing on detection of illness onset and appropriate interventions, their tools are mostly manual surveys which often fail to capture the granular details of contexts and behaviors which might provide important cues about illness onset. To overcome the limitations of these manual tools, we deployed a smartphone based tool or unobtrusive and continuous data collection from 22 students during an academic semester. In this paper, we present the preliminary findings from our study about assessing mental health on college campuses using passively sensed smartphone data.


Incremental multi-step Q-learning

This paper presents a novel incremental algorithm that combines Q-learning, a well-known dynamic-programming based reinforcement learning method, with the TD(λ) return estimation process, which is typically used in actor-critic learning, another well-known dynamic-programming based reinforcement learning method. The parameter λ is used to distribute credit throughout sequences of actions, leading to faster learning and also helping to alleviate the non-Markovian effect of coarse state-space quatization. The resulting algorithm.Q(λ)-learning, thus combines some of the best features of the Q-learning and actor-critic learning paradigms. The behavior of this algorithm has been demonstrated through computer simulations.


DTCTH: a discriminative local pattern descriptor for image classification

Despite lots of effort being exerted in designing feature descriptors, it is still challenging to find generalized feature descriptors, with acceptable discrimination ability, which are able to capture prominent features in various image processing applications. To address this issue, we propose a computationally feasible discriminative ternary census transform histogram (DTCTH) for image representation which uses dynamic thresholds to perceive the key properties of a feature descriptor. The code produced by DTCTH is more stable against intensity fluctuation, and it mainly captures the discriminative structural properties of an image by suppressing unnecessary background information. Thus, DTCTH becomes more generalized to be used in different applications with reasonable accuracies. To validate the generalizability of DTCTH, we have conducted rigorous experiments on five different applications considering nine benchmark datasets. The experimental results demonstrate that DTCTH performs as high as 28.08% better than the existing state-of-the-art feature descriptors such as GIST, SIFT, HOG, LBP, CLBP, OC-LBP, LGP, LTP, LAID, and CENTRIST.


Electrocardiogram (ECG) Signal Processing

Signal processing today is performed in the vast majority of systems for ECG analysis and interpretation. The objective of ECG signal processing is manifold and comprises the improvement of measurement accuracy and reproducibility (when compared with manual measurements) and the extraction of information not readily available from the signal through visual assessment. In many situations, the ECG is recorded during ambulatory or strenuous conditions such that the signal is corrupted by different types of noise, sometimes originating from another physiological process of the body. Hence, noise reduction represents another important objective of ECG signal processing; in fact, the waveforms of interest are sometimes so heavily masked by noise that their presence can only be revealed once appropriate signal processing has first been applied. Electrocardiographic signals may be recorded on a long timescale (i.e., several days) for the purpose of identifying intermittently occurring disturbances in the heart rhythm. As a result, the produced ECG recording amounts to huge data sizes that quickly fill up available storage space. Transmission of signals across public telephone networks is another application in which large amounts of data are involved. For both situations, data compression is an essential operation and, consequently, represents yet another objective of ECG signal processing. Signal processing has contributed significantly to a new understanding of the ECG and its dynamic properties as expressed by changes in rhythm and beat morphology. For example, techniques have been developed that characterize oscillations related to the cardiovascular system and reflected by subtle variations in heart rate. The detection of low-level, alternating changes in T wave amplitude is another example of oscillatory behavior that has been established as an indicator of increased risk for sudden, life-threatening arrhythmias. Neither of these two oscillatory signal properties can be perceived by the naked eye from a standard ECG printout. Common to all types of ECG analysis—whether it concerns resting ECG interpretation, stress testing, ambulatory monitoring, or intensive care monitoring—is a basic set of algorithms that condition the signal with respect to different types of noise and artifacts, detect heartbeats, extract basic ECG measurements of wave amplitudes and durations, and compress the data for efficient storage or transmission; the block diagram in Fig. 1 presents this set of signal processing algorithms. Although these algorithms are frequently implemented to operate in sequential order, information on the occurrence time of a heartbeat, as produced by the QRS detector, is sometimes incorporated into the other algorithms to improve performance. The complexity of each algorithm varies from application to application so that, for example, noise filtering performed in ambulatory monitoring is much more sophisticated than that required in resting ECG analysis. Once the information produced by the basic set of algorithms is available, a wide range of ECG applications exist where it is of interest to use signal processing for quantifying heart rhythm and beat morphology properties. The signal processing associated with two such applications—high-resolution ECG and T wave alternans—are briefly described at the end of this article. The interested reader is referred to, for example, Ref. 1, where a detailed description of other ECG applications can be found.


A Business Intelligence System

ระบบ Business Intelligence เป็นซอฟแวร์ที่เข้าไปช่วยอ านวยความสะดวกในด้านการวเิคราะห์ข้อมลู และการตัดสินใจใน ข้อมูลทางธุรกิจทั้งหมดของในหนว่ยงาน ซึ่งช่วยให้ผู้ใช้งานระบบงานในองค์กรท าการตัดสินใจทางธุรกิจที่ดียิ่งข้ึน มีฟังก์ช่ันการใช้ งานต่างๆครบถ้วนไม่ว่าจะเป็น Data Integration, Reporting, ad-hoc Query, Analysis Dashboard, Data Mining ทั้งนี้เพื่อลดการท างานท่ี ซ้ าซ้อน ลดความผิดพลาดและความขัดแย้งท่ีอาจจะเกิดขึ้นเนื่องจากการสื่อสารระหว่างหน่วยงานภายในกันเอง หรือกับหน่วยงาน ภายนอก


Understanding and Evaluating User Satisfaction with Music Discovery

We study the use and evaluation of a system for supporting music discovery, the experience of finding and listening to content previously unknown to the user. We adopt a mixed methods approach, including interviews, unsupervised learning, survey research, and statistical modeling, to understand and evaluate user satisfaction in the context of discovery. User interviews and survey data show that users' behaviors change according to their goals, such as listening to recommended tracks in the moment, or using recommendations as a starting point for exploration. We use these findings to develop a statistical model of user satisfaction at scale from interactions with a music streaming platform. We show that capturing users' goals, their deviations from their usual behavior, and their peak interactions on individual tracks are informative for estimating user satisfaction. Finally, we present and validate heuristic metrics that are grounded in user experience for online evaluation of recommendation performance. Our findings, supported with evidence from both qualitative and quantitative studies, reveal new insights about user expectations with discovery and their behavioral responses to satisfying and dissatisfying systems.


Dual-Band and Polarization-Flexible Cavity Antenna Based on Substrate Integrated Waveguide

In this letter, a novel dual-band and polarization-flexible substrate integrated waveguide (SIW) cavity antenna is proposed. The SIW cavity used for the antenna is excited by a conventional TE120 mode for its first resonance. With the intervention of the slot, a second resonance excited by a modified- TE120 mode is also generated, thereby providing a broadside radiation pattern at two resonant frequencies. In addition, the proposed antenna has two orthogonal feeding lines. Therefore, it is possible to provide any six major polarization states. In this letter, three major polarization cases are simulated and compared to measured results. Since modern communication systems require multifunctional antennas, the proposed antenna concept is a promising candidate.


EEVi –Framework and Guidelines to Evaluate the Effectiveness of Cyber-Security Visualization

Cyber-security visualization aims to reduce security analysts’ workload by presenting information as visual analytics instead of a string of text and characters. However, the adoption of the resultant visualizations by security analysts, is not widespread. The literature indicates a lack of guidelines and standardized evaluation techniques for effective visualization in cyber-security, as a reason for the low adoption rate. Consequently, this article addresses the research gap by introducing a framework called EEVi for effective cyber-security visualizations for the performed task. The term ‘effective visualization’ is defined as the features of visualization that are critical for an analyst to competently perform a certain task. EEVi has been developed by analyzing qualitative data which led to the formation of cognitive relationships (called links) between data. These relationships acted as guidelines for effective cyber-security visualization to perform tasks. The methodology to develop this framework can be applied to other fields to understand cognitive relationships between data. Additionally, the analysis of the framework presented, demonstrates how EEVi can be put into practice using the guidelines for effective cybersecurity visualization. The guidelines can be used to guide visualization developers to create effective visualizations for security analysts based on their requirements.


Google's PageRank and beyond - the science of search engine rankings

Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other web pages always appear at the top? What creates these powerful rankings? And how? The first book...


Videogame Addiction and its Treatment

For many, the concept of videogame addiction seems far-fetched, particularly if their concepts and definitions of addiction involve the taking of drugs. This paper overviews the small, but growing area of videogame addiction and then examines the treatment options available for those affected by excessive videogame playing. An overview of the available empirical literature appears to indicate that adverse effects are likely to affect only a relatively small subgroup of players and that frequent players are the most at-risk for developing problems. Worldwide, there are relatively few practitioners that specialise in the treatment of videogame addiction and this may be because there are so few players who are genuinely addicted to playing videogames. However, the Internet may be facilitating excessive online game playing as evidenced by the increasing number of specialist addiction treatment clinics for online videogame addiction. This paper overviews the various approaches that have been used as an intervention in treating videogame addicts, including online support groups, 12-step programmes, behavioural and cognitive-behavioural therapies, and motivational interviewing.


Activity Forecasting

We address the task of inferring the future actions of people from noisy visual input. We denote this task activity forecasting. To achieve accurate activity forecasting, our approach models the effect of the physical environment on the choice of human actions. This is accomplished by the use of state-of-the-art semantic scene understanding combined with ideas from optimal control theory. Our unified model also integrates several other key elements of activity analysis, namely, destination forecasting, sequence smoothing and transfer learning. As proof-of-concept, we focus on the domain of trajectory-based activity analysis from visual input. Experimental results demonstrate that our model accurately predicts distributions over future actions of individuals. We show how the same techniques can improve the results of tracking algorithms by leveraging information about likely goals and trajectories.


Anticipating Visual Representations from Unlabeled Video

Anticipating actions and objects before they start or appear is a difficult problem in computer vision with several real-world applications. This task is challenging partly because it requires leveraging extensive knowledge of the world that is difficult to write down. We believe that a promising resource for efficiently learning this knowledge is through readily available unlabeled video. We present a framework that capitalizes on temporal structure in unlabeled video to learn to anticipate human actions and objects. The key idea behind our approach is that we can train deep networks to predict the visual representation of images in the future. Visual representations are a promising prediction target because they encode images at a higher semantic level than pixels yet are automatic to compute. We then apply recognition algorithms on our predicted representation to anticipate objects and actions. We experimentally validate this idea on two datasets, anticipating actions one second in the future and objects five seconds in the future.


Autoencoding beyond pixels using a learned similarity metric

We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic.


Unsupervised Learning for Physical Interaction through Video Prediction

A core challenge for an agent learning to interact with the world is to predict how its actions affect objects in its environment. Many existing methods for learning the dynamics of physical interactions require labeled object information. However, to scale real-world interaction learning to a variety of scenes and objects, acquiring labeled data becomes increasingly impractical. To learn about physical object motion without labels, we develop an action-conditioned video prediction model that explicitly models pixel motion, by predicting a distribution over pixel motion from previous frames. Because our model explicitly predicts motion, it is partially invariant to object appearance, enabling it to generalize to previously unseen objects. To explore video prediction for real-world interactive agents, we also introduce a dataset of 50, 000 robot interactions involving pushing motions, including a test set with novel objects. In this dataset, accurate prediction of videos conditioned on the robot’s future actions amounts to learning a “visual imagination” of different futures based on different courses of action. Our experiments show that our proposed method not only produces more accurate video predictions, but also more accurately predicts object motion, when compared to prior methods.


Self-Supervised Visual Planning with Temporal Skip Connections

In order to autonomously learn wide repertoires of complex skills, robots must be able to learn from their own autonomously collected data, without human supervision. One learning signal that is always available for autonomously collected data is prediction. If a robot can learn to predict the future, it can use this predictive model to take actions to produce desired outcomes, such as moving an object to a particular location. However, in complex open-world scenarios, designing a representation for prediction is difficult. In this work, we instead aim to enable self-supervised robot learning through direct video prediction: instead of attempting to design a good representation, we directly predict what the robot will see next, and then use this model to achieve desired goals. A key challenge in video prediction for robotic manipulation is handling complex spatial arrangements such as occlusions. To that end, we introduce a video prediction model that can keep track of objects through occlusion by incorporating temporal skipconnections. Together with a novel planning criterion and action space formulation, we demonstrate that this model substantially outperforms prior work on video prediction-based control. Our results show manipulation of objects not seen during training, handling multiple objects, and pushing objects around obstructions. These results represent a significant advance in the range and complexity of skills that can be performed entirely with self-supervised robot learning.


Integrated staffing and scheduling for an aircraft line maintenance problem

This paper presents a method for constructing the workforce schedules of an aircraft maintenance company. The method integrates both the staffing and the scheduling decision. We formulate the optimization problem using a mixed integer linear programming approach and solve it heuristically using a branch-and-bound enumeration framework. Each node of the tree represents a combination of teams and team sizes. An extensive computational experiment is added to illustrate the algorithmic performance using a set of test instances that are derived from a real-life setting. We conclude this paper by discussing some results and by addressing interesting future extensions to the model.


Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals

Background and objective: According to the World Health Organization (WHO) epilepsy affects approximately 45–50 million people. Electroencephalogram (EEG) records the neurological activity in the brain and it is used to identify epilepsy. Visual inspection of EEG signals is a time-consuming process and it may lead to human error. Feature extraction and classification are two main steps that are required to build an automated epilepsy detection framework. Feature extraction reduces the dimensions of the input signal by retaining informative features and the classifier assigns a proper class label to the extracted feature vector. Our aim is to present effective feature extraction techniques for automated epileptic EEG signal classification. Methods: In this study, two effective feature extraction techniques (Local Neighbor Descriptive Pattern [LNDP] and One-dimensional Local Gradient Pattern [1D-LGP]) have been introduced to classify epileptic EEG signals. The classification between epileptic seizure and non-seizure signals is performed using different machine learning classifiers. The benchmark epilepsy EEG dataset provided by the University of Bonn is used in this research. The classification performance is evaluated using 10-fold cross validation. The classifiers used are the Nearest Neighbor (NN), Support Vector Machine (SVM), Decision Tree (DT) and Artificial Neural Network (ANN). The experiments have been repeated for 50 times. Results: LNDP and 1D-LGP feature extraction techniques with ANN classifier achieved the average classification accuracy of 99.82% and 99.80%, respectively, for the classification between normal and epileptic EEG signals. Eight different experimental cases were tested. The classification results were better than those of some existing methods. Conclusions: This study suggests that LNDP and 1D-LGP could be effective feature extraction techniques for the classification of epileptic EEG signals. . Introduction Epilepsy is a central nervous system disorder and it has been eported that approximately 45–50 million people suffer from this isorder [1]. EEG captures the neurological activity inside the brain y placing electrodes on the scalp and helps in detection of epilepic seizure [2,3]. Epileptic seizure detection can be considered as classification problem where the task is to classify an input sigal either as an epileptic seizure signal or as a non-seizure signal. EG signals are usually recorded for long durations to carry out n analysis. Detection of epileptic seizure in these long duration EG signals requires expertise. In the absence of expert, particuarly in emergencies, seizure detection becomes a challenging task. herefore, providing a framework for automated epileptic seizure ∗ Corresponding author: E-mail address: abegiitdhanbad@gmail.com (A.K. Jaiswal). ttp://dx.doi.org/10.1016/j.bspc.2017.01.005 746-8094/© 2017 Elsevier Ltd. All rights reserved. © 2017 Elsevier Ltd. All rights reserved. detection is of great significance. A number of methods have been proposed in literature for the classification of epileptic EEG signals. The methods can be categorized into following domains of signal analysis: Time domain analysis: Some techniques in the field of epileptic EEG signal classification belong to this category. Techniques like linear prediction [4], Fractional linear prediction [5], Principal component analysis based radial basis function neural network [6], etc. have been proposed for epileptic seizure detection. Frequency domain analysis: With the assumption that the EEG signals are stationary signals, Polat and Gunes [7] introduced a hybrid framework based on frequency domain analysis with Fourier transform and decision tree. In the hybrid model, Fourier transform was used for feature extraction and decision tree was used for the classification. Srinivasan et al. [8] used features extracted from time domain and frequency domain for seizure detection in EEG signals. The extracted features were fed to the Elman neural network to detect epileptic seizure. 82 A.K. Jaiswal, H. Banka / Biomedical Signal Processing and Control 34 (2017) 81–92 0 50


Obtaining accurate trajectories from repeated coarse and inaccurate location logs

Context awareness is a key property for enabling context aware services. For a mobile device, the user's location or trajectory is one of the crucial contexts. One common challenge for detecting location or trajectory by mobile devices is to manage the tradeoff between accuracy and power consumption. Typical approaches are (1) controlling the frequency of usage of sensors and (2) sensor fusion technique. The algorithm proposed in this paper takes a different approach to improve the accuracy by merging repeatedly measured coarse and inaccurate location data from cell tower. The experimental result shows that the mean error distance between the detected trajectory and the ground truth is improved from 44m to 10.9m by merging data from 41 days' measurement.


A flame detection algorithm based on Bag-of-Features in the YUV color space

Computer vision-based fire detection involves flame detection and smoke detection. This paper proposes a new flame detection algorithm, which is based on a Bag-of-Features technique in the YUV color space. Inspired by that the color of flame in image and video will fall in certain regions in the color space, models of flame pixels and non-flame pixels are established based on code book in the training phase in our proposal. In the testing phase, the input image is split into some N×N blocks and each block is classified respectively. In each N×N block, the pixels values in the YUV color space are extracted as features, just as in the training phase. According to the experimental results, our proposed method can reduce the number of false alarms greatly compared with an alternative algorithm, while it also ensures the accurate classification of positive samples. The classification performance of our proposed method is better than that of alternative algorithms.


Multi sensor system for automatic fall detection

To reduce elderly falling risk in the area where video surveillance system is unavailable due to privacy reason and so on, we need a monitor system which does no recognize what they are doing. This paper presents an elderly-falling detection system using ultrasonic sensors. The ultrasonic technology-based multi sensors are couple of receiver and transmitter together, which are connected to Arduino microcontroller in order to send the elderly person's fall related signal using WiFi to the processing unit. The sensors are positioned as an array on the roof and wall in the room. The signal is analyzed to recognize human by sensing distance, and detect the action such as standing, sitting, and falling by pattern matching with the standard templates of top and side signal. In experiments, the proposed system can recognize accuracy of falling detection is 93% approximately.


Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification

We address the problem of visual category recognition by learning an image-to-image distance function that attempts to satisfy the following property: the distance between images from the same category should be less than the distance between images from different categories. We use patch-based feature vectors common in object recognition work as a basis for our image-to-image distance functions. Our large-margin formulation for learning the distance functions is similar to formulations used in the machine learning literature on distance metric learning, however we differ in that we learn local distance functions¿a different parameterized function for every image of our training set¿whereas typically a single global distance function is learned. This was a novel approach first introduced in Frome, Singer, & Malik, NIPS 2006. In that work we learned the local distance functions independently, and the outputs of these functions could not be compared at test time without the use of additional heuristics or training. Here we introduce a different approach that has the advantage that it learns distance functions that are globally consistent in that they can be directly compared for purposes of retrieval and classification. The output of the learning algorithm are weights assigned to the image features, which is intuitively appealing in the computer vision setting: some features are more salient than others, and which are more salient depends on the category, or image, being considered. We train and test using the Caltech 101 object recognition benchmark.


A demonstration of the GUINNESS: A GUI based neural NEtwork SyntheSizer for an FPGA

The GUINNESS is a tool flow for the deep neural network toward FPGA implementation [3,4,5] based on the GUI (Graphical User Interface) including both the binarized deep neural network training on GPUs and the inference on an FPGA. It generates the trained the Binarized deep neural network [2] on the desktop PC, then, it generates the bitstream by using standard the FPGA CAD tool flow. All the operation is done on the GUI, thus, the designer is not necessary to write any scripts to descript the neural network structure, training behaviour, only specify the values for hyper parameters. After finished the training, it automatically generates C++ codes to synthesis the bitstream using the Xilinx SDSoC system design tool flow. Thus, our tool flow is suitable for the software programmers who are not familiar with the FPGA design.


DR(eye)VE: A Dataset for Attention-Based Tasks with Applications to Autonomous and Assisted Driving

Autonomous and assisted driving are undoubtedly hot topics in computer vision. However, the driving task is extremely complex and a deep understanding of drivers' behavior is still lacking. Several researchers are now investigating the attention mechanism in order to define computational models for detecting salient and interesting objects in the scene. Nevertheless, most of these models only refer to bottom up visual saliency and are focused on still images. Instead, during the driving experience the temporal nature and peculiarity of the task influence the attention mechanisms, leading to the conclusion that real life driving data is mandatory. In this paper we propose a novel and publicly available dataset acquired during actual driving. Our dataset, composed by more than 500,000 frames, contains drivers' gaze fixations and their temporal integration providing task-specific saliency maps. Geo-referenced locations, driving speed and course complete the set of released data. To the best of our knowledge, this is the first publicly available dataset of this kind and can foster new discussions on better understanding, exploiting and reproducing the driver's attention process in the autonomous and assisted cars of future generations.


Tooth structure removal associated with various preparation designs for anterior teeth.

STATEMENT OF PROBLEM
The conservation of sound tooth structure helps preserve tooth vitality and reduce postoperative sensitivity. Innovative preparation designs, like those for porcelain laminate veneers, are much less invasive than conventional complete-coverage crown preparations. However, no study has quantified the amount of tooth structure removed during these preparations.

PURPOSE
The purpose of this study was to quantify and compare the amount of tooth structure removed when various innovative and conventional tooth preparation designs were completed on different teeth.

MATERIAL AND METHOD
. A new comprehensive tooth preparation design classification system was introduced. Typodont resin teeth representing the maxillary left central incisor, maxillary left canine, and mandibular left central incisor were prepared with the following designs: partial (V1), traditional (V2), extended (V3), and complete (V4) porcelain laminate veneer preparations; resin-bonded retainer preparation with grooves (A1) and with wing/grooves (A2); all-ceramic crown preparation with 0.8 mm axial reduction and tapering chamfer finish line (F1), all-ceramic crown preparation with 1.0 mm axial reduction and rounded shoulder finish line (F2), and metal-ceramic crown with 1.4 mm axial reduction and facial shoulder finish line (F3). After tooth preparations (10 per group), the crown was separated from the root at the CEJ. The removed coronal tooth structure was measured with gravimetric analysis. Means and standard deviations for tooth structure removal with different preparation designs were calculated and analyzed with analysis of variance at a significance level of P<.05.

RESULTS
Significant differences in the amount of tooth structure removal were noted between preparation designs. Ceramic veneers and resin-bonded prosthesis retainers were the least invasive preparation designs, removing approximately 3% to 30% of the coronal tooth structure by weight. Approximately 63% to 72% of the coronal tooth structure was removed when teeth were prepared for all-ceramic and metal-ceramic crowns. For a single crown restoration, the tooth structure removal required for an F3 preparation (metal-ceramic crown) was 4.3 times greater than for a V2 preparation (porcelain laminate veneer, facial surface only) and 2.4 times greater than for a V4 preparation (more extensive porcelain laminate veneer).

CONCLUSION
Within the limitations of this study, tooth preparations for porcelain laminate veneers and resin-bonded prostheses required approximately one-quarter to one-half the amount of tooth reduction of conventional complete-coverage crowns.


InTouch Tactile Tales: Haptic Feedback and Long-Distance Storytelling

The squeeze of an arm during a tense moment in a story -- an absentminded caress on the back of the hand while listening to an engaging tale -- the physical presence and interpersonal touch of a loved one can be an important part of reading to a child. However the thrill of the tale is often lost and the intimacy diluted when separated families have to resort to the flatness of a video-call or mobile app to share a bedtime story with their loved one. This project is setting out to create a physical story portal, a magical object which uses technologies to link teller and listener through sound, light and touch to help bridge this gap when families are separated. While the development of the InTouch reading system is still in the preliminary stages, a prototype handheld device has been created to allow adults and children to share touch-based messages during story time. It has been trialled with several families in the lab, and the initial results have pointed to both guidance and design considerations that should be taken into account for such a system to be successfully deployed.


Quaternion Zernike moments and their invariants for color image analysis and object recognition

Moments and moment invariants have become a powerfu l tool in pattern recognition and image analysis. C onventional methods to deal with color images are based on RGB decomposition or graying, which may lose some signi ficant color information. In this paper, by using the algebra of quaternions, we introduce the quaternion Zernike m oments (QZMs) to deal with the color images in a holistic manner. It is s hown that the QZMs can be obtained from the convent ional Zernike moments of each channel. We also provide the theore tical framework to construct a set of combined inva riants with respect to rotation, scaling and translation (RST) transfor mation. Experimental results are provided to illust rate the efficiency of the proposed descriptors.


Bilayer Phosphorene: Effect of Stacking Order on Bandgap and Its Potential Applications in Thin-Film Solar Cells.

Phosphorene, a monolayer of black phosphorus, is promising for nanoelectronic applications not only because it is a natural p-type semiconductor but also because it possesses a layer-number-dependent direct bandgap (in the range of 0.3 to 1.5 eV). On basis of the density functional theory calculations, we investigate electronic properties of the bilayer phosphorene with different stacking orders. We find that the direct bandgap of the bilayers can vary from 0.78 to 1.04 eV with three different stacking orders. In addition, a vertical electric field can further reduce the bandgap to 0.56 eV (at the field strength 0.5 V/Å). More importantly, we find that when a monolayer of MoS2 is superimposed with the p-type AA- or AB-stacked bilayer phosphorene, the combined trilayer can be an effective solar-cell material with type-II heterojunction alignment. The power conversion efficiency is predicted to be ∼18 or 16% with AA- or AB-stacked bilayer phosphorene, higher than reported efficiencies of the state-of-the-art trilayer graphene/transition metal dichalcogenide solar cells.


A General Empirical Solution to the Macro Software Sizing and Estimating Problem

Application software development has been an area of organizational effort that has not been amenable to the normal managerial and cost controls. Instances of actual costs of several times the initial budgeted cost, and a time to initial operational capability sometimes twice as long as planned are more often the case than not.


Transfer of mitochondria via tunneling nanotubes rescues apoptotic PC12 cells

Tunneling nanotubes (TNTs) are F-actin-based membrane tubes that form between cells in culture and in tissues. They mediate intercellular communication ranging from electrical signalling to the transfer of organelles. Here, we studied the role of TNTs in the interaction between apoptotic and healthy cells. We found that pheochromocytoma (PC) 12 cells treated with ultraviolet light (UV) were rescued when cocultured with untreated PC12 cells. UV-treated cells formed a different type of TNT with untreated PC12 cells, which was characterized by continuous microtubule localized inside these TNTs. The dynamic behaviour of mCherry-tagged end-binding protein 3 and the accumulation of detyrosinated tubulin in these TNTs indicate that they are regulated structures. In addition, these TNTs show different biophysical properties, for example, increased diameter allowing dye entry, prolonged lifetime and decreased membrane fluidity. Further studies demonstrated that microtubule-containing TNTs were formed by stressed cells, which had lost cytochrome c but did not enter into the execution phase of apoptosis characterized by caspase-3 activation. Moreover, mitochondria colocalized with microtubules in TNTs and transited along these structures from healthy to stressed cells. Importantly, impaired formation of TNTs and untreated cells carrying defective mitochondria were unable to rescue UV-treated cells in the coculture. We conclude that TNT-mediated transfer of functional mitochondria reverse stressed cells in the early stages of apoptosis. This provides new insights into the survival mechanisms of damaged cells in a multicellular context.


Business Analytics in the Context of Big Data: A Roadmap for Research

This paper builds on academic and industry discussions from the 2012 and 2013 pre-ICIS events: BI Congress III and the Special Interest Group on Decision Support Systems (SIGDSS) workshop, respectively. Recognizing the potential of “big data” to offer new insights for decision making and innovation, panelists at the two events discussed how organizations can use and manage big data for competitive advantage. In addition, expert panelists helped to identify research gaps. While emerging research in the academic community identifies some of the issues in acquiring, analyzing, and using big data, many of the new developments are occurring in the practitioner community. We bridge the gap between academic and practitioner research by presenting a big data analytics framework that depicts a process view of the components needed for big data analytics in organizations. Using practitioner interviews and literature from both academia and practice, we identify the current state of big data research guided by the framework and propose potential areas for future research to increase the relevance of academic research to practice.


The Price of Anarchy in Supply Chains: Quantifying the Efficiency of Price-Only Contracts

I this paper, we quantify the efficiency of decentralized supply chains that use price-only contracts. With a price-only contract, a buyer and a seller agree only on a constant transaction price, without specifying the amount that will be transferred. It is well known that these contracts do not provide incentives to the parties to coordinate their inventory/capacity decisions. We measure efficiency with the price of anarchy (PoA), defined as the largest ratio of profits between the integrated supply chain (that is, fully coordinated) and the decentralized supply chain. We characterize the efficiency of various supply chain configurations: push or pull inventory positioning, two or more stages, serial or assembly systems, single or multiple competing suppliers, and single or multiple competing retailers.


Learning Semantic Script Knowledge with Event Embeddings

Induction of common sense knowledge about prototypical sequences of events has recently received much attention (e.g., (Chambers & Jurafsky, 2008; Regneri et al., 2010)). Instead of inducing this knowledge in the form of graphs, as in much of the previous work, in our method, distributed representations of event realizations are computed based on distributed representations of predicates and their arguments, and then these representations are used to predict prototypical event orderings. The parameters of the compositional process for computing the event representations and the ranking component of the model are jointly estimated from texts. We show that this approach results in a substantial boost in ordering performance with respect to previous methods.


Experimental study platform of the automatic transfer switch used to power supplies back-up

This paper presents the practical implementation of an experimental platform used for the automatic transfer switch to the back-up power source. The automatic transfer switch concept represents the procedure of fast connection of the electrical consumers to a back-up electric power source. The connection to the back-up power source is necessary in the event of failure of the main power source. The experimental platform is designed to operate in automatic standard mode, manual mode and through a programmable logic controller. The selection's mode of operation is determined by the operation's key. The simulation of grid faults on the power supply is done by means of the controls and the variable voltage regulators located on the experimental platform. The automatic transfer switch is conditioned by the effective status of the power supply and minimum voltage allowed by devices. The signaling status of the power supply is performed based on the information received from relays of interface and programmable logic controllers. The platform presents to users two technologies able to achieve the same function. Thus, in case of the classic mode of the automatic transfer switch, the wired technology is used and this involves many components and related links. In case of the automatic transfer switch based on programmable logic controller, a technology based on programming is also implemented. Using this platform, the advantage of implementation of programmable logic controllers versus wired technology in industrial applications can be observed.


Power to the people? The evolving recognition of human aspects of security

It is perhaps unsurprising to find much of the focus in IT and computer security being drawn towards the technical aspects of the discipline. However, it is increasingly recognised that technology alone cannot deliver a complete solution, and there is also a tangible need to address human aspects. At the core, people must understand the threats they face and be able to use the protection available to them, and although this has not been entirely ignored, it has not received the level of attention that it merits either. Indeed, security surveys commonly reveal that the more directly user-facing aspects such as policy, training and education are prone to receiving significantly less attention than technical controls such as firewalls, antivirus and intrusion detection. The underlying reason for such disparity is that the human aspects are in many ways a more challenging problem to approach, not least because they cannot be easily targeted with a product-based solution. There is also a direct overlap into the technical area, with issues such as the usability and acceptability of technology solutions having a direct impact upon the actual protection that they are able to deliver. This paper explores these themes, highlighting the need for human aspects to form part of a holistic security strategy alongside the necessary technologies. Taking the specific examples of security awareness and two user-facing technical controls (user authentication and antivirus), the discussion examines how things have evolved to the present day and considers how they need to be positioned for the future. a 2012 Elsevier Ltd. All rights reserved.


Modeling Label Ambiguity for Neural List-Wise Learning to Rank

List-wise learning to rank methods are considered to be the stateof-the-art. One of the major problems with these methods is that the ambiguous nature of relevance labels in learning to rank data is ignored. Ambiguity of relevance labels refers to the phenomenon that multiple documents may be assigned the same relevance label for a given query, so that no preference order should be learned for those documents. In this paper we propose a novel sampling technique for computing a list-wise loss that can take into account this ambiguity. We show the e ectiveness of the proposed method by training a 3-layer deep neural network. We compare our new loss function to two strong baselines: ListNet and ListMLE. We show that our method generalizes better and signi cantly outperforms other methods on the validation and test sets. CCS CONCEPTS •Computingmethodologies→Neural networks; •Information systems →Learning to rank;


The incredible queen of green: Nutritive value and therapeutic potential of Moringa oleifera Lam.

Moringa oleifera Lam. (synonym: Moringa pterygosperma Gaertn.) (M. oleifera) known in 82 countries by 210 different names is well known by the name of the miracle tree. It is one of the extensively cultivated and highly valued members of Moringaceae, a monogeneric family, comprising of thirteen perennial angiosperm shrubs and trees[1-3]. Moringa tree is endemic to the Himalayan foothills of Pakistan, Afghanistan, Bangladesh and India, and is cultivated throughout tropics. It is recognized by a mixture of vernacular names, among of them, drumstick tree, horseradish tree, ben oil tree and malunggay are the most commonly reported in the history of this plant[4]. In Pakistan, Sohanjna is the vernacular name of M. oleifera[5,6]. It yields low quality timber, as it is a softwood tree, but it is belived for centuries that this plant possesses a number of industrial, traditional and medicinal benefits[7]. Fertilizer (seed cake), green manure (leaves), blue dye (wood), fencing (living trees), domestic cleaning agent (crushed leaves), alley cropping, animal feed (leaves and seed cake), medicine (all plant parts), foliar nutrient (juice expressed from the leaves), gum (tree trunks), biogas (leaves), biopesticide, ornamental plantings, water purifier (powdered seeds), honey (flower nectar) are different uses of this plant reported in literature[2,6,8-20]. M. oleifera is a good source of aminoacids and contains a number of important minerals, β-carotene, various phenolics and vitamins[21,22]. M. oleifera is also an important vegetable food article of trade, particularly in Pakistan, Hawaii, Philippines, Africa and India which has a huge deliberation as the natural nutrition[1,23]. In South Asia, various plant parts, including leaves, bark, root, gum, flowers, pods, seeds and seed oil are used for the variety of infectious and inflammatory disorders along with hepatorenal, gastrointestinal, hematological and cardiovascular diseases[22,24-26]. Various therapeutic potentials are also credited to different parts of ARTICLE INFO ABSTRACT


Should a firm charge on a per-use basis or sell subscriptions when its service experiences congestion? Queueing-based models of pricing primarily focus on charging a fee per use for the service, in part because per-use pricing enables the firm to regulate congestion—raising the per-use price naturally reduces how frequently customers use a service. The firm has less control over usage with subscription pricing (by definition, with subscription pricing customers are not charged proportional to their actual usage), and this is a disadvantage when customers dislike congestion. However, we show that subscription pricing is more effective at earning revenue. Consequently, the firm may be better off with subscription pricing, even, surprisingly, when congestion is intuitively most problematic for the firm: e.g., as congestion becomes more disliked by consumers. We show that the absolute advantage of subscription pricing relative to per-use pricing can be substantial, whereas the potential advantage of per-use pricing is generally modest. Subscription pricing becomes relatively more attractive if consumers become more heterogeneous in their service rates (e.g., some know they are “heavy” users and others know they are “light” users) as long as capacity is fixed, the potential utilization is high, and the two segments have substantially different usage rates. Otherwise, heterogeneity in usage rates makes subscription pricing less attractive relative to per-use pricing. We conclude that subscription pricing can be effective even if congestion is relevant for the overall quality of a service. Keywords service operations, operations strategy, pricing and revenue management, game theory, queueing theory Disciplines Business | Business Administration, Management, and Operations | Business Analytics | Management Sciences and Quantitative Methods | Marketing | Operations and Supply Chain Management | Sales and Merchandising This technical report is available at ScholarlyCommons: https://repository.upenn.edu/marketing_papers/320 Pricing Services Subject to Congestion: Charge Per-Use Fees or Sell Subscriptions? Gérard P. Cachon Pnina Feldman Operations and Information Management, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA Operations and Information Technology Management, Haas School of Business, University of California, Berkeley, California 94720, USA cachon@wharton.upenn.edu feldman@haas.berkeley.edu June 17, 2008; revised July 14, 2009; May 21, 2010; August 10, 2010 Should a rm charge on a per-use basis or sell subscriptions when its service experiences congestion? Queueing-based models of pricing primarily focus on charging a fee per use for the service, in part because per-use pricing enables the rm to regulate congestion raising the per-use price naturally reduces how frequently customers use a service. The rm has less control over usage with subscription pricing (by denition, with subscription pricing customers are not charged proportional to their actual usage), and this is a disadvantage when customers dislike congestion. However, we show that subscription pricing is more e¤ective at earning revenue. Consequently, the rm may be better o¤ with subscription pricing, even, surprisingly, when congestion is intuitively most problematic for the rm: e.g., as congestion becomes more disliked by consumers. We show that the absolute advantage of subscription pricing relative to per-use pricing can be substantial whereas the potential advantage of per-use pricing is generally modest. Subscription pricing becomes relatively more attractive if consumers become more heterogeneous in their service rates (e.g., some know they are heavyusers and others know they are lightusers) as long as capacity is xed, the potential utilization is high and the two segments have substantially di¤erent usage rates. Otherwise, heterogeneity in usage rates makes subscription pricing less attractive relative to per-use. We conclude that subscription pricing can be e¤ective even if congestion is relevant for the overall quality of a service. How should a rm price its service when congestion is an unavoidable reality? Customers dislike congestion, so a rm has an incentive to ensure it provides reasonably fast service. At the same time, the rm needs to earn an economic prot, so the rms pricing scheme must generate a su¢ cient amount of revenue. Furthermore, these issues are closely linked: the chosen pricing scheme inuences how frequently customers use a service, which dictates the level of congestion; congestion correlates with the customersperceived value for the service, and that determines the amount of revenue the rm can generate. A natural option is to charge customers a per-use fee or toll: customers pay a per-transaction fee each time they withdraw money from the ATM; beauty shops and hair salons price on a peruse basis; and car maintenance companies, such as Pep Boys Auto, charge each time a service is


Bayesian models for Large-scale Hierarchical Classification

A challenging problem in hierarchical classification is to leverage the hierarchical relations among classes for improving classification performance. An even greater challenge is to do so in a manner that is computationally feasible for large scale problems. This paper proposes a set of Bayesian methods to model hierarchical dependencies among class labels using multivariate logistic regression. Specifically, the parent-child relationships are modeled by placing a hierarchical prior over the children nodes centered around the parameters of their parents; thereby encouraging classes nearby in the hierarchy to share similar model parameters. We present variational algorithms for tractable posterior inference in these models, and provide a parallel implementation that can comfortably handle largescale problems with hundreds of thousands of dimensions and tens of thousands of classes. We run a comparative evaluation on multiple large-scale benchmark datasets that highlights the scalability of our approach and shows improved performance over the other state-of-the-art hierarchical methods.


A critical survey of state-of-the-art image inpainting quality assessment metrics

7 Image inpainting is the process of restoring missing pixels in digital images in a plausible way. Research in image inpainting has received considerable attention in di erent areas, including restoration of old and damaged documents, removal of undesirable objects, computational photography, retouching applications, etc. The challenge is that the recovery processes themselves introduce noticeable artifacts within and around the restored image regions. As an alternative to subjective evaluation by humans, a number of approaches have been introduced to quantify inpainting processes objectively. Unfortunately, existing objective metrics have their own strengths and weaknesses as they use di erent criteria. This paper provides a thorough insight into existing metrics related to image inpainting quality assessment, developed during the last few years. The paper provides, under a new framework, a comprehensive description of existing metrics, their strengths, their weaknesses, and a detailed performance analysis on real images from public image inpainting database. The paper also outlines future research directions and applications of inpainting and inpainting-related quality assessment measures.


Programming your network at run-time for big data applications

Recent advances of software defined networking and optical switching technology make it possible to program the network stack all the way from physical topology to flow level traffic control. In this paper, we leverage the combination of SDN controller with optical switching to explore the tight integration of application and network control. We particularly study the run-time network configuration for big data applications to jointly optimize application performance and network utilization. We use Hadoop as an example to discuss the integrated network control architecture, job scheduling, topology and routing configuration mechanisms for Hadoop jobs. Our analysis suggests that such an integrated control has great potential to improve application performance with relatively small configuration overhead. We believe our study shows early promise of achieving the long-term goal of tight network and application integration using SDN.


A Fundamental Storage-Communication Tradeoff in Distributed Computing with Straggling Nodes

The optimal storage-computation tradeoff is characterized for a MapReduce-like distributed computing system with straggling nodes, where only a part of the nodes can be utilized to compute the desired output functions. The result holds for arbitrary output functions and thus generalizes previous results that restricted to linear functions. Specifically, in this work, we propose a new information-theoretical converse and a new matching coded computing scheme, that we call coded computing for straggling systems (CCS).


Language, cognition, and short-term memory in individuals with Down syndrome.

The developmentally emerging phenotype of language and cognition in individuals with Down syndrome is summarized on the basis of the project's prior work. Identified are a) the emerging divergence of expressive and receptive language, b) the emerging divergence of lexical and syntactic knowledge in each process, and c) the emerging divergence within cognitive skills of auditory short-term memory and visuospatial short-term memory from other visuospatial skills. Expressive syntax and auditory short-term memory are identified as areas of particular difficulty. Evidence for the continued acquisition of language skills in adolescence is presented. The role of the two components of working memory, auditory and visual, in language development is investigated in studies of narrative and longitudinal change in language skills. Predictors of individual differences during six years of language development are evaluated through hierarchical linear modelling. Chronological age, visuospatial short-term memory, and auditory-short term memory are identified as key predictors of performance at study entry, but not individual change over time, for expressive syntax. The same predictors account for variation in comprehension skill at study outset; and change over the six years can be predicted by chronological age and the change in visuospatial short-term memory skills.


The MIT Encyclopedia of Cognitive Sciences: Sign Languages

Sign languages (alternatively, signed languages) are human languages whose forms consist of sequences of movements and configurations of the hands and arms, face, and upper torso. Typically, sign languages are perceived through the visual mode. Sign languages thus contrast, of course, with spoken languages, whose forms consist of sounds produced by sequences of movements and configurations of the mouth and vocal tract. More informally, then, sign languages are visual-gestural languages, whereas spoken languages are auditory-vocal languages.


Optimal Multi-taxi Dispatch for Mobile Taxi-Hailing Systems

Traditional taxi-hailing systems through wireless networks in metropolitan areas allow taxis to compete for passengers chaotically and accidentally, which generally result in inefficiencies, long waiting time and low satisfaction of taxi-hailing passengers. In this paper, we propose a new Mobile Taxi-hailing System (called MTS) based on optimal multi-taxi dispatch, which can be used by taxi service companies (TSCs). Different from the competition modes used in traditional taxi-hailing systems, MTS assigns vacant taxis to taxi-hailing passengers proactively. For the taxi dispatch problem in MTS, we define a system utility function, which involves the total net profits of taxis and waiting time of passengers. Moreover, in the utility function, we take into consideration the various classes of taxis with different resource configurations, and the cost associated with taxis' empty travel distances. Our goal is to maximize the system utility function, restricted by the individual net profits of taxis and the passengers' requirements for specified classes of taxis. To solve this problem, we design an optimal algorithm based on the idea of Kuhn-Munkres (called KMBA), and prove the correctness and optimality of the proposed algorithm. Additionally, we demonstrate the significant performances of our algorithm through extensive simulations.


Learning Multilingual Word Representations using a Bag-of-Words Autoencoder

Recent work on learning multilingual word representations usually relies on the use of word-level alignements (e.g. infered with the help of GIZA++) between translated sentences, in order to align the word embeddings in different languages. In this workshop paper, we investigate an autoencoder model for learning multilingual word representations that does without such word-level alignements. The autoencoder is trained to reconstruct the bag-of-word representation of given sentence from an encoded representation extracted from its translation. We evaluate our approach on a multilingual document classification task, where labeled data is available only for one language (e.g. English) while classification must be performed in a different language (e.g. French). In our experiments, we observe that our method compares favorably with a previously proposed method that exploits word-level alignments to learn word representations.


Digital receivers and transmitters using polyphase filter banks for wireless communications

This paper provides a tutorial overview of multichannel wireless digital receivers and the relationships between channel bandwidth, channel separation, and channel sample rate. The overview makes liberal use of figures to support the underlying mathematics. A multichannel digital receiver simultaneously down-convert a set of frequency-division-multiplexed (FDM) channels residing in a single sampled data signal stream. In a similar way, a multichannel digital transmitter simultaneously up-converts a number of baseband signals to assemble a set of FDM channels in a single sampled data signal stream. The polyphase filter bank has become the architecture of choice to efficiently accomplish these tasks. This architecture uses three interacting processes to assemble or to disassemble the channelized signal set. In a receiver, these processes are an input commutator to effect spectral folding or aliasing due to a reduction in sample rate, a polyphase -path filter to time align the partitioned and resampled time series in each path, and a discrete Fourier transform to phase align and separate the multiple baseband aliases. In a transmitter, these same processes operate in a related manner to alias baseband signals to high order Nyquist zones while increasing the sample rate with the output commutator. This paper presents a sequence of simple modifications to sampled data structures based on analog prototype systems to obtain the basic polyphase structure. We further discuss ways to incorporate small modifications in the operation of the polyphase system to accommodate secondary performance requirements. MATLAB simulations of a 10-, 40-, and 50-channel resampling receiver are included in the electronic version of this paper. An animated version of the ten-channel resampling receiver illustrates the time and frequency response of the filter bank when driven by a slowly varying linear FM sweep.


A PASSIVE MILLIMETER-WAVE IMAGER USED FOR CONCEALED WEAPON DETECTION

An 8mm-band passive millimeter-wave imager BHU-2D has been developed by Beihang University. This imager is designed for detecting concealed weapons on human body. The imager adopts two-dimensional synthetic aperture interferometric radiometer (SAIR) technique which avoids the radiation risk to human body. Compared with scanning technique, SAIR technique could obtain images of a larger field of view (FOV) while achieving high imaging rate, which are necessary for security monitoring. In this paper, the imaging principle of SAIR is stated firstly. Secondly, background cancellation method designed for BHU-2D is interpreted. The technique is used to reduce the complexity as well as the dimensional requirements of the receiving elements. Thirdly, system configuration is illustrated in detail. Then external point source calibration method is introduced and discussed specifically for security check applications. Finally, imaging experiments on a person with concealed weapon are conducted by which the design and image calibration algorithms are verified. To conclude, experimental results prove that BHU-2D could be employed in security check applications.