Soft gelatin capsules (softgels).. It is estimated that more than 40% of new chemical entities (NCEs) coming out of the current drug discovery process have poor biopharmaceutical properties, such as low aqueous solubility and/or permeability. These suboptimal properties pose significant challenges for the oral absorption of the compounds and for the development of orally bioavailable dosage forms. Development of soft gelatin capsule (softgel) dosage form is of growing interest for the oral delivery of poorly water soluble compounds (BCS class II or class IV). The softgel dosage form offers several advantages over other oral dosage forms, such as delivering a liquid matrix designed to solubilize and improve the oral bioavailability of a poorly soluble compound as a unit dose solid dosage form, delivering low and ultra-low doses of a compound, delivering a low melting compound, and minimizing potential generation of dust during manufacturing and thereby improving the safety of production personnel. However, due to the very dynamic nature of the softgel dosage form, its development and stability during its shelf-life are fraught with several challenges. The goal of the current review is to provide an in-depth discussion on the softgel dosage form to formulation scientists who are considering developing softgels for therapeutic compounds.

Question: What is the shelf life of a softgel?


The COPS Security Checker System. In the past several years, there have been a large number of published works that have graphically described a wide variety of security problems particular to UNIX. Without fail, the same problems have been discussed over and over again, describing the problems with sum (set user ID) programs, improper file permissions, and bad passwords (to name a few). There are two common characteristics to each of these problems: first, they are usually simple to correct, if found; second, they are fairly easy to detect. Since almost all systems have fairly equivalent problems, it seems appropriate to create a tool to detect potential security problems as an aid to system administrators. This paper describes one such tool: Cops. (Computerized Oracle and Password System) is a freely-available,

Question: What is a freely available, open source tool?


Prepubertal hidradenitis suppurativa: two case reports and review of the literature.. Hidradenitis suppurativa (HS) is a chronic suppurative scarring disease of apocrine sweat gland-bearing skin in the axillary, anogenital, and, rarely, the breast and scalp regions. Females are more commonly affected than males and it is usually seen at puberty or later. We report two girls with prepubertal hidradenitis suppurativa whose initial presentation predated any signs of puberty. This early onset is very rare and its etiology remains unknown. Severe disease can be seen in prepubertal children and surgical intervention is effective in these cases.

Question: What is prepubertal hidradenitis suppurativa?


Risk Management: A Maturity Model Based on ISO 31000. Risk Management, according with the ISO Guide 73 is the set of "coordinated activities to direct and control an organization with regard to risk". In a nutshell, Risk Management is the business process used to manage risk in organizations. ISO 31000 defines a framework and process for risk management. However, implementing this standard without a detailed plan can become a burden on organizations. This paper presents a maturity model for the risk management process based on ISO 31000. The purpose of this model is to provide an assessment tool for organizations to use in order to get their current risk management maturity level. The results can then be used to create an improvement plan which will guide organizations to reach their target maturity level. This maturity model allows organizations to assess a risk management process according to the best practices defined in risk management references. The maturity model can also be used as a reference for improving this process since it sets a clear path of how a risk management process should be performed.

Question: What is the purpose of this model?


Android Malware Characterization Using Metadata and Machine Learning Techniques. Android Malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and meta-data to identify patterns in malware applications. Our experiments show that: (1) the permissions used by an application offer only moderate performance results; (2) other features publicly available at Android Markets are more relevant in detecting malware, such as the application developer and certificate issuer, and (3) compact and efficient classifiers can be constructed for the early detection of malware applications prior to code inspection or sandboxing.

Question: What is the difference between malware and a virus?


Reduced graphene oxide–silver nanoparticle nanocomposite: a potential anticancer nanotherapy. BACKGROUND Graphene and graphene-based nanocomposites are used in various research areas including sensing, energy storage, and catalysis. The mechanical, thermal, electrical, and biological properties render graphene-based nanocomposites of metallic nanoparticles useful for several biomedical applications. Epithelial ovarian carcinoma is the fifth most deadly cancer in women; most tumors initially respond to chemotherapy, but eventually acquire chemoresistance. Consequently, the development of novel molecules for cancer therapy is essential. This study was designed to develop a simple, non-toxic, environmentally friendly method for the synthesis of reduced graphene oxide-silver (rGO-Ag) nanoparticle nanocomposites using Tilia amurensis plant extracts as reducing and stabilizing agents. The anticancer properties of rGO-Ag were evaluated in ovarian cancer cells.   METHODS The synthesized rGO-Ag nanocomposite was characterized using various analytical techniques. The anticancer properties of the rGO-Ag nanocomposite were evaluated using a series of assays such as cell viability, lactate dehydrogenase leakage, reactive oxygen species generation, cellular levels of malonaldehyde and glutathione, caspase-3 activity, and DNA fragmentation in ovarian cancer cells (A2780).   RESULTS AgNPs with an average size of 20 nm were uniformly dispersed on graphene sheets. The data obtained from the biochemical assays indicate that the rGO-Ag nanocomposite significantly inhibited cell viability in A2780 ovarian cancer cells and increased lactate dehydrogenase leakage, reactive oxygen species generation, caspase-3 activity, and DNA fragmentation compared with other tested nanomaterials such as graphene oxide, rGO, and AgNPs.   CONCLUSION T. amurensis plant extract-mediated rGO-Ag nanocomposites could facilitate the large-scale production of graphene-based nanocomposites; rGO-Ag showed a signif

Question: What is the difference between graphene oxide and reduced graphene oxide?


Big Data Analytics for Earth Sciences: the EarthServer approach. Large-Scale Scientific Information Systems, Jacobs University, Bremen, Germany; Rasdaman GmbH, Bremen, Germany; CNR-IIA, National Research Council of Italy, Institute of Atmospheric Pollution Research, Florence, Italy; EOX IT Services GmbH, Vienna, Austria; Consorzio COMETA, Catania, Italy; Division of Catania, Italian National Institute for Nuclear Physics, 15 Catania, Italy; Department of Physics and Astronomy, University of Catania, Catania, Italy; MEEO S.r.l., Ferrara, Italy; Plymouth Marine Laboratory, Plymouth, UK; Fraunhofer IGD, Darmstadt, Germany; Athena Research and Innovation Center in Information Communication & Knowledge Technologies, Athens, Greece; British Geological Survey, Edinburgh, UK; Software Engineering Italia S.r.l., Catania, Italy; British Geological Survey, Keyworth, UK; NASA Ames 20 Research Center, Moffett Field, CA, USA

Question: What is the earthserver approach?


Multi-target tracking by online learning of non-linear motion patterns and robust appearance models. We describe an online approach to learn non-linear motion patterns and robust appearance models for multi-target tracking in a tracklet association framework. Unlike most previous approaches that use linear motion methods only, we online build a non-linear motion map to better explain direction changes and produce more robust motion affinities between tracklets. Moreover, based on the incremental learned entry/exit map, a multiple instance learning method is devised to produce strong appearance models for tracking; positive sample pairs are collected from different track-lets so that training samples have high diversity. Finally, using online learned moving groups, a tracklet completion process is introduced to deal with tracklets not reaching entry/exit points. We evaluate our approach on three public data sets, and show significant improvements compared with state-of-art methods.

Question: How to track multiple targets in a video?


Face Verification Across Age Progression. Human faces undergo considerable amounts of variations with aging. While face recognition systems have been proven to be sensitive to factors such as illumination and pose, their sensitivity to facial aging effects is yet to be studied. How does age progression affect the similarity between a pair of face images of an individual? What is the confidence associated with establishing the identity between a pair of age separated face images? In this paper, we develop a Bayesian age difference classifier that classifies face images of individuals based on age differences and performs face verification across age progression. Further, we study the similarity of faces across age progression. Since age separated face images invariably differ in illumination and pose, we propose preprocessing methods for minimizing such variations. Experimental results using a database comprising of pairs of face images that were retrieved from the passports of 465 individuals are presented. The verification system for faces separated by as many as nine years, attains an equal error rate of 8.5%

Question: How does age progression affect the similarity between a pair of face images of an individual?


Violent and nonviolent video games differentially affect physical aggression for individuals high vs. low in dispositional anger.. Although numerous experiments have shown that exposure to violent video games (VVG) causes increases in aggression, relatively few studies have investigated the extent to which this effect differs as a function of theoretically relevant individual difference factors. This study investigated whether video game content differentially influences aggression as a function of individual differences in trait anger. Participants were randomly assigned to play a violent or nonviolent video game before completing a task in which they could behave aggressively. Results showed that participants high in trait anger were the most aggressive, but only if they first played a VVG. This relationship held while statistically controlling for dimensions other than violent content on which game conditions differed (e.g. frustration, arousal). Implications of these findings for models explaining the effects of video games on behavior are discussed.

Question: What is the difference between violent and nonviolent video games?


UnTran: Recognizing Unseen Activities with Unlabeled Data Using Transfer Learning. The success and impact of activity recognition algorithms largely depends on the availability of the labeled training samples and adaptability of activity recognition models across various domains. In a new environment, the pre-trained activity recognition models face challenges in presence of sensing bias- ness, device heterogeneities, and inherent variabilities in human behaviors and activities. Activity Recognition (AR) system built in one environment does not scale well in another environment, if it has to learn new activities and the annotated activity samples are scarce. Indeed building a new activity recognition model and training the model with large annotated samples often help overcome this challenging problem. However, collecting annotated samples is cost-sensitive and learning activity model at wild is computationally expensive. In this work, we propose an activity recognition framework, UnTran that utilizes source domains' pre-trained autoencoder enabled activity model that transfers two layers of this network to generate a common feature space for both source and target domain activities. We postulate a hybrid AR framework that helps fuse the decisions from a trained model in source domain and two activity models (raw and deep-feature based activity model) in target domain reducing the demand of annotated activity samples to help recognize unseen activities. We evaluated our framework with three real-world data traces consisting of 41 users and 26 activities in total. Our proposed UnTran AR framework achieves ≈ 75% F1 score in recognizing unseen new activities using only 10% labeled activity data in the target domain. UnTran attains ≈ 98% F1 score while recognizing seen activities in presence of only 2-3% of labeled activity samples.

Question: How to recognize unseen activities using unlabeled data?


Regulating the new information intermediaries as gatekeepers of information diversity. Natali Helberger is Professor at the Institute for Information Law, University of Amsterdam, Amsterdam, The Netherlands. Katharina Kleinen-von Königslöw is Assistant Professor for Political Communication at the University of Zurich, Zurich, Switzerland. Rob van der Noll is Senior Researcher at SEO Economic Research, Amsterdam, The Netherlands. Abstract Purpose – The purposes of this paper are to deal with the questions: because search engines, social networks and app-stores are often referred to as gatekeepers to diverse information access, what is the evidence to substantiate these gatekeeper concerns, and to what extent are existing regulatory solutions to control gatekeeper control suitable at all to address new diversity concerns? It will also map the different gatekeeper concerns about media diversity as evidenced in existing research before the background of network gatekeeping theory critically analyses some of the currently discussed regulatory approaches and develops the contours of a more user-centric approach towards approaching gatekeeper control and media diversity.

Question: What is the evidence to substantiate the gatekeeper concerns?


Procedural facade variations from a single layout. We introduce a framework to generate many variations of a facade design that look similar to a given facade layout. Starting from an input image, the facade is hierarchically segmented and labeled with a collection of manual and automatic tools. The user can then model constraints that should be maintained in any variation of the input facade design. Subsequently, facade variations are generated for different facade sizes, where multiple variations can be produced for a certain size. Computing such new facade variations has many unique challenges, and we propose a new algorithm based on interleaving heuristic search and quadratic programming. In contrast to most previous work, we focus on the generation of new design variations and not on the automatic analysis of the input's structure. Adding a modeling step with the user in the loop ensures that our results routinely are of high quality.

Question: How to generate many variations of a facade design that look similar to a given facade layout?


Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.

Question: How do you register brain images?


A review of argumentation for the Social Semantic Web. Argumentation represents the study of views and opinions that humans express with the goal of reaching a conclusion through logical reasoning. Since the 1950's, several models have been proposed to capture the essence of informal argumentation in different settings. With the emergence of the Web, and then the Semantic Web, this modeling shifted towards ontologies, while from the development perspective, we witnessed an important increase in Web 2.0 human-centered collaborative deliberation tools. Through a review of more than 150 scholarly papers, this article provides a comprehensive and comparative overview of approaches to modeling argumentation for the Social Semantic Web. We start from theoretical foundational models and investigate how they have influenced Social Web tools. We also look into Semantic Web argumentation models. Finally we end with Social Web tools for argumentation, including online applications combining Web 2.0 and Semantic Web technologies, following the path to a global World Wide Argument Web.

Question: What is the social semantic web?


Gender Classification of Blog Authors: With Feature Engineering and Deep Learning using LSTM Networks. In this paper, we present two approaches to automatically classify the gender of blog authors: the first is a manual feature extraction based system incorporating two novel feature classes: variable length character sequence patterns and thirteen new word classes, along with an added class of surface features while the second is a first-ever application of a memory variant of Recurrent Neural Networks, i.e. Bidirectional Long Short Term Memory Networks (BLSTMs) on this task. We use two blog data sets to report our results: the first is a well-explored one used by the previous state-of-the-art model while the other is a 20 times larger corpus. For the first system, we use a voting of machine learning classifiers to obtain an improved accuracy with respect to the previous feature mining systems on the former data set. Using our second approach, we show that the accuracy obtained using such deep LSTMs is comparable to the current state-of-the-art deep learning system for the task of gender classification. Finally, we carry out a comparative study of performance of both the systems on the two data sets.

Question: How to classify the gender of blog authors?


Disgust is a factor in extreme prejudice.. Understanding intergroup prejudice is a dominant research focus for social psychology. Prejudice is usually conceptualized as a continuum of positive/negative affect, but this has limitations. It neither reflects people's ability to maintain simultaneous positive and negative stereotypes of others nor explains extreme prejudice (bigotry). Some researchers have proposed multidimensional models of prejudice in which different negative emotions are evoked depending on the situation. Extending this to bigotry raises the question of which emotions are most relevant. Therefore, this study looked at 'anti-group' texts--writings which promote extreme intergroup hostility--and analysed the frequency of emotive language. Findings suggest that bigotry may be distinguished by high levels of disgust.

Question: What is the difference between prejudice and bigotry?


Public opinion on automated driving : Results of an international questionnaire among 5000 respondents. This study investigated user acceptance, concerns, and willingness to buy partially, highly, and fully automated vehicles. By means of a 63-question Internet-based survey, we collected 5000 responses from 109 countries (40 countries with at least 25 respondents). We determined cross-national differences, and assessed correlations with personal variables, such as age, gender, and personality traits as measured with a short version of the Big Five Inventory. Results showed that respondents, on average, found manual driving the most enjoyable mode of driving. Responses were diverse: 22% of the respondents did not want to pay more than $0 for a fully automated driving system, whereas 5% indicated they would be willing to pay more than $30,000, and 33% indicated that fully automated driving would be highly enjoyable. 69% of respondents estimated that fully automated driving will reach a 50% market share between now and 2050. Respondents were found to be most concerned about software hacking/misuse, and were also concerned about legal issues and safety. Respondents scoring higher on neuroticism were slightly less comfortable about data transmitting, whereas respondents scoring higher on agreeableness were slightly more comfortable with this. Respondents from more developed countries (in terms of lower accident statistics, higher education, and higher income) were less comfortable with their vehicle transmitting data, with cross-national correlations between q = 0.80 and q = 0.90. The present results indicate the major areas of promise and concern among the international public, and could be useful for vehicle developers and other stakeholders. 2015 Elsevier Ltd. All rights reserved.

Question: What is the average age of the respondents?


Complete mesocolic excision with central vascular ligation produces an oncologically superior specimen compared with standard surgery for carcinoma of the colon.. PURPOSE The plane of surgery in colonic cancer has been linked to patient outcome although the optimal extent of mesenteric resection is still unclear. Surgeons in Erlangen, Germany, routinely perform complete mesocolic excision (CME) with central vascular ligation (CVL) and report 5-year survivals of higher than 89%. We aimed to further investigate the importance of CME and CVL surgery for colonic cancer by comparison with a series of standard specimens.   METHODS The fresh photographs of 49 CME and CVL specimens from Erlangen and 40 standard specimens from Leeds, United Kingdom, for primary colonic adenocarcinoma were collected. Precise tissue morphometry and grading of the plane of surgery were performed before comparison to histopathologic variables.   RESULTS CME and CVL surgery removed more tissue compared with standard surgery in terms of the distance between the tumor and the high vascular tie (median, 131 v 90 mm; P < .0001), the length of large bowel (median, 314 v 206 mm; P < .0001), and ileum removed (median, 83 v 63 mm; P = .003), and the area of mesentery (19,657 v 11,829 mm(2); P < .0001). In addition, CME and CVL surgery was associated with more mesocolic plane resections (92% v 40%; P < .0001) and a greater lymph node yield (median, 30 v 18; P < .0001).   CONCLUSION Surgeons in Erlangen routinely practicing CME and CVL surgery remove more mesocolon and are more likely to resect in the mesocolic plane when compared with standard excisions. This, along with the associated greater lymph node yield, may partially explain the high 5-year survival rates reported in Erlangen.

Question: What is the difference between CME and CVL surgery?


A Wideband Sequential-Phase Fed Circularly Polarized Patch Array. This communication presents a wideband circularly polarized (CP) 2 × 2 patch array using a sequential-phase feeding network. By combining three operating modes, both axial ratio (AR) and impedance bandwidths are enhanced and wider than those of previous published sequential-fed single-layer patch arrays. These three CP operating modes are tuned and matched by optimizing the truncated corners of patch elements and the sequential-phase feeding network. A prototype of the proposed patch array is built to validate the design experimentally. The measured -10-dB impedance bandwidth is 1.03 GHz (5.20-6.23 GHz), and the measured 3-dB AR bandwidth is 0.7 GHz (5.25-5.95 GHz), or 12.7% corresponding to the center frequency of 5.5 GHz. The measured peak gain is about 12 dBic and the gain variation is less than 3 dB within the AR bandwidth.

Question: What is a sequential-phase feeding network?


FiDO: A Community-based Web Browsing Agent and CDN for Challenged Network Environments. Homes located on tribal lands, particularly in rural areas of the United States, continue to lack access to broadband Internet and cellular connectivity [19]. Inspired by previous observations of community content similarity in tribal networks, we propose FiDO, a community-based Web browsing and content delivery system that takes advantage of user mobility, opportunistic connectivity, and collaborative filtering to provide relevant Web content to members of disconnected households via opportunistic contact with cellular base stations during a daily commute. We evaluate FiDO using trace-driven simulations with network usage data collected from a tribal-operated ISP that serves the Coeur d'Alene Indian Reservation in Western Idaho. By collecting data about household Web preferences and applying a collaborative filtering technique based on the Web usage patterns of the surrounding reservation community, we are able to opportunistically browse the Web on behalf of members of disconnected households, providing an average of 69.4 Web pages (all content from a specific URL, e.g., "http://gis.cdatribe-nsn.gov/LandBuyBack/") crawled from 73% of their top 10 most visited Web domains (e.g., "cdatribe-nsn.gov" or "cnn.com/") per day. Moreover, this content is able to be fetched and pushed to users even when the opportunistic data rate is limited to an average of only 0.99 Mbps (σ = 0.24 Mbps) and the daily opportunistic connection time is an average of 45.9 minutes (σ = 2.3 minutes). Additionally, we demonstrate a hybrid "search and browse" approach that allocates a percentage of opportunistic resources to the download of user-specified content. By dedicating only 10% of opportunistic windows of connectivity to the download of social media content, 51% of households were able to receive all of their daily expected social media content in addition to an average of 55.3 Web pages browsed on th

Question: What is the average number of Web pages that a household receives per day?


A Faster and More Realistic Flush+Reload Attack on AES. Cloud's unrivaled cost effectiveness and on the fly operation versatility is attractive to enterprise and personal users. However, the cloud inherits a dangerous behavior from virtualization systems that poses a serious security risk: resource sharing. This work exploits a shared resource optimization technique called memory deduplication to mount a powerful known-ciphertext only cache side-channel attack on a popular OpenSSL implementation of AES. In contrast to the other cross-VM cache attacks, our attack does not require synchronization with the target server and is fully asynchronous, working in a more realistic scenario with much weaker assumption. Also, our attack succeeds in just 15 seconds working across cores in the cross-VM setting. Our results show that there is strong information leakage through cache in virtualized systems and the memory deduplication should be approached with caution.

Question: What is a cross-VM cache attack?


An expert evaluation of word-sized visualizations for analyzing eye movement data. Word-sized visualizations for eye movement data allow analysts to compare a variety of experiment conditions or participants at the sametime. Weimplementedasetofsuchword-sizedvisualizations as part of an analysis framework. We want to find out which of the visualizations is most suitable for different analysis tasks. To this end, we applied the framework to data from an eye tracking study on the reading behavior of users studying metro maps. In anexpertevaluationwithfiveanalysts,weidentifieddistinguishing characteristics of the different word-sized visualizations.

Question: What is the difference between a word-sized visualization and a word-sized visualization?


A 5GHz Digital Fractional- $N$ PLL Using a 1-bit Delta–Sigma Frequency-to-Digital Converter in 65 nm CMOS. A highly digital two-stage fractional-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> phase-locked loop (PLL) architecture utilizing a first-order 1-bit <inline-formula> <tex-math notation="LaTeX">$ \Delta \!\Sigma $ </tex-math></inline-formula> frequency-to-digital converter (FDC) is proposed and implemented in a 65nm CMOS process. Performance of the first-order 1-bit <inline-formula> <tex-math notation="LaTeX">$ \Delta \!\Sigma $ </tex-math></inline-formula> FDC is improved by using a phase interpolator-based fractional divider that reduces phase quantizer input span and by using a multiplying delay-locked loop that increases its oversampling ratio. We also describe an analogy between a time-to-digital converter (TDC) and a <inline-formula> <tex-math notation="LaTeX">$ \Delta \!\Sigma $ </tex-math></inline-formula> FDC followed by an accumulator that allows us to leverage the TDC-based PLL analysis techniques to study the impact of <inline-formula> <tex-math notation="LaTeX">$ \Delta \!\Sigma $ </tex-math></inline-formula> FDC characteristics on <inline-formula> <tex-math notation="LaTeX">$ \Delta \!\Sigma $ </tex-math></inline-formula> FDC-based fractional-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> PLL (FDCPLL) performance. Utilizing proposed techniques, a prototype PLL achieves 1 MHz bandwidth, −101.6 dBc/Hz in-band phase noise, and <inline-formula> <tex-math notation="LaTeX">$ {\textrm {1.22 ps}_{\textrm {rms}}}$ </tex-math></inline-formula> (1 kHz–40 MHz) jitter while generating 5.031GHz output from 31.25MHz reference clock input. For the same output frequency, the stand-alone second-stage fractional-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> FDCPLL achieves 1MHz bandwidth, −106.1dBc/Hz in-band phase noise, and <inline-formula> <tex-math notation="LaTeX">$ {\t

Question: What is the jitter of a fractional-N PLL?


Lévy flights, non-local search and simulated annealing. We solve a problem of non-convex stochastic optimisation with help of simulated annealing of Lévy flights of a variable stability index. The search of the ground state of an unknown potential is non-local due to big jumps of the Levy flights process. The convergence to the ground state is fast due to a polynomial decrease rate of the temperature.

Question: What is simulated annealing?


Bootstrapping Fine-Grained Classifiers : ! Active Learning with a Crowd in the Loop. We propose an iterative crowd-enabled active learning algorithm for building high-precision visual classifiers from unlabeled images. Our method employs domain experts to identify a small number of examples of a specific visual event. These expert-labeled examples seed a classifier, which is then iteratively trained by active querying of a non-expert crowd. These non-experts actively refine the classifiers at every iteration by answering simple binary questions about the classifiers' detections. The advantage of this approach is that experts efficiently shepherd an unsophisticated crowd into training a classifier capable of fine-grained distinctions. This obviates the need to label an entire dataset to obtain high-precision classifiers. We find these classifiers are advantageous for creating a large vocabulary of visual attributes for specialized taxonomies. We demonstrate our crowd active learning pipeline by creating classifiers for attributes related to North American birds and fashion.

Question: How do you create a large vocabulary of visual attributes?


Comparison of high-speed switched reluctance machines with conventional and toroidal windings. This paper presents designs of 50,000 rpm 6/4 switched reluctance motors (SRM's) with a focus of the comparative study on conventional and toroidal windings. There are four different machines compared in this paper, while the first is a conventional SRM, and the other three are toroidal winding machines. The first toroidal SRM (TSRM1) employs the conventional asymmetric convert and the same switching sequence as conventional SRM (CSRM). Therefore, an equivalent magnetic performance is observed. The second toroidal SRM (TSRM2) introduces a 12-switch converter topology. With a proper coil connection and switching sequence, all the coils are active and contribute to the flux and torque generation at the same time. The analysis shows that for the same amount of copper losses, TSRM2 yields a 50% higher output torque and power at rated speed than CSRM, while TSRM1 only generates half the torque of CSRM. The third toroidal SRM is a resized TSRM2, which is presented with the same envelope dimension of CSRM (same volumetric comparison). The comparison shows it's competitive to CSRM, especially as toroidal-winding can achieve higher filling factor during the manufacture process of winding.

Question: What is the difference between a conventional SRM and a toroidal SRM?


A Semi-supervised Approach to Measuring User Privacy in Online Social Networks. During our digital social life, we share terabytes of information that can potentially reveal private facts and personality traits to unexpected strangers. Despite the research efforts aiming at providing efficient solutions for the anonymization of huge databases (including networked data), in online social networks the most powerful privacy protection is in the hands of the users. However, most users are not aware of the risks derived by the indiscriminate disclosure of their personal data. With the aim of fostering their awareness on private data leakage risk, some measures have been proposed that quantify the privacy risk of each user. However, these measures do not capture the objective risk of users since they assume that all user's direct social connections are close (thus trustworthy) friends. Since this assumption is too strong, in this paper we propose an alternative approach: each user decides which friends are allowed to see each profile item/post and our privacy score is defined accordingly. We show that it can be easily computed with minimal user intervention by leveraging an active learning approach. Finally, we validate our measure on a set of real Facebook users.

Question: How to measure user privacy in online social networks?


Defending the morality of violent video games. The effect of violent video games is among the most widely discussed topics in media studies, and for good reason. These games are immensely popular, but many seem morally objectionable. Critics attack them for a number of reasons ranging from their capacity to teach players weapons skills to their ability to directly cause violent actions. This essay shows that many of these criticisms are misguided. Theoretical and empirical arguments against violent video games often suffer from a number of significant shortcomings that make them ineffective. This essay argues that video games are defensible from the perspective of Kantian, Aristotelian, and utilitarian moral theories.

Question: What is the moral justification for violent video games?


Causality : Models , Reasoning , and Inference. For most researchers in the ever growing fields of probabilistic graphical models, belief networks, causal influence and probabilistic inference, ACM Turing award winner Dr. Pearl and his seminary papers on causality are well-known and acknowledged. Representation and determination of Causality, the relationship between an event (the cause) and a second event (the effect), where the second event is understood as a consequence of the first, is a challenging problem. Over the years, Dr. pearl has written significantly on both Art and Science of Cause and Effect. In this book on "Causality: Models, Reasoning and Inference", the inventor of Bayesian belief networks discusses and elaborates on his earlier workings including but not limited to Reasoning with Cause and Effect, Causal inference in statistics, Simpson's paradox, Causal Diagrams for Empirical Research, Robustness of Causal Claims, Causes and explanations, and Probabilities of causation Bounds and identification.

Question: What is the probability of causation?


Eleos: ExitLess OS Services for SGX Enclaves. Intel Software Guard extensions (SGX) enable secure and trusted execution of user code in an isolated enclave to protect against a powerful adversary. Unfortunately, running I/O-intensive, memory-demanding server applications in enclaves leads to significant performance degradation. Such applications put a substantial load on the in-enclave system call and secure paging mechanisms, which turn out to be the main reason for the application slowdown. In addition to the high direct cost of thousands-of-cycles long SGX management instructions, these mechanisms incur the high indirect cost of enclave exits due to associated TLB flushes and processor state pollution.  We tackle these performance issues in Eleos by enabling exit-less system calls and exit-less paging in enclaves. Eleos introduces a novel Secure User-managed Virtual Memory (SUVM) abstraction that implements application-level paging inside the enclave. SUVM eliminates the overheads of enclave exits due to paging, and enables new optimizations such as sub-page granularity of accesses.  We thoroughly evaluate Eleos on a range of microbenchmarks and two real server applications, achieving notable system performance gains. memcached and a face verification server running in-enclave with Eleos, achieves up to 2.2× and 2.3× higher throughput respectively while working on datasets up to 5× larger than the enclave's secure physical memory.

Question: What is the difference between secure and trusted execution?


Machine Learning for Reliable Network Attack Detection in SCADA Systems. Critical Infrastructures (CIs) use Supervisory Control And Data Acquisition (SCADA) systems for remote control and monitoring. Sophisticated security measures are needed to address malicious intrusions, which are steadily increasing in number and variety due to the massive spread of connectivity and standardisation of open SCADA protocols. Traditional Intrusion Detection Systems (IDSs) cannot detect attacks that are not already present in their databases. Therefore, in this paper, we assess Machine Learning (ML) for intrusion detection in SCADA systems using a real data set collected from a gas pipeline system and provided by the Mississippi State University (MSU). The contribution of this paper is two-fold: 1) The evaluation of four techniques for missing data estimation and two techniques for data normalization, 2) The performances of Support Vector Machine (SVM), and Random Forest (RF) are assessed in terms of accuracy, precision, recall and F 1 score for intrusion detection. Two cases are differentiated: binary and categorical classifications. Our experiments reveal that RF detect intrusions effectively, with an F_1 score of respectively > 99%.

Question: What is the contribution of this paper?


Python for Prototyping Computer Vision Applications. Python is a popular language widely adopted by the scientific community due to its clear syntax and an extensive number of specialized packages. For image processing or computer vision development, two libraries are prominently used: NumPy/SciPy and OpenCV with a Python wrapper. In this paper, we present a comparative evaluation of both libraries, assessing their performance and their usability. We also investigate the performance of OpenCV when accessed through a python wrapper versus directly using the native C implementation.

Question: What is the difference between NumPy and SciPy?


A comprehensive framework for testing graphical user interfaces. The widespread recognition of the usefulness of graphical user interfaces GUIs has established their importance as critical components of today's software. Although the use of GUIs continues to grow, GUI testing has remained a neglected research area. Since GUIs have c haracteristics that are diierent from those of conventional software, such as user events for input and graphical output, techniques developed to test conventional software cannot bedirectly applied to test GUIs. This thesis develops a uniied solution to the GUI testing problem with the particular goals of automation and integration of tools and techniques used in various phases of GUI testing. These goals are accomplished by developing a GUI testing framework with a GUI model as its central component. For eeciency and scalability, a GUI is represented as a hierarchy of components, each used as a basic unit of testing. The framework also includes a test coverage evaluator, test case generator, test oracle, test executor, and regression tester. The test coverage evaluator employs hierarchical, event-based coverage criteria to automatically specify what to test in a GUI and to determine whether the test suite has adequately tested the GUI. The test case generator employs plan generation techniques from artiicial intelligence to automatically generate a test suite. A test executor automatically executes all the test cases on the GUI. As test cases are being executed, a test oracle automatically determines the correctness of the GUI. The test oracle employs a model of the expected state of the GUI in terms of its constituent objects and their properties. After changes are made to a GUI, a regression tester partitions the original GUI test suite into valid test cases that represent correct inputtoutput for the modiied GUI and invalid test cases that no longer represent correct inputtoutput. The regression tester employs a new technique to reu

Question: What is a GUI testing framework?


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.

Question: What is a neural network model?


Calibration of Ultrawide Fisheye Lens Cameras by Eigenvalue Minimization. We present a new technique for calibrating ultrawide fisheye lens cameras by imposing the constraint that collinear points be rectified to be collinear, parallel lines to be parallel, and orthogonal lines to be orthogonal. Exploiting the fact that line fitting reduces to an eigenvalue problem in 3D, we do a rigorous perturbation analysis to obtain a practical calibration procedure. Doing experiments, we point out that spurious solutions exist if collinearity and parallelism alone are imposed. Our technique has many desirable properties. For example, no metric information is required about the reference pattern or the camera position, and separate stripe patterns can be displayed on a video screen to generate a virtual grid, eliminating the grid point extraction processing.

Question: How to calibrate ultrawide fisheye lens cameras?


Mutual Coupling Reduction Using F-Shaped Stubs in UWB-MIMO Antenna. A compact, high performance, and novel-shaped ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna with low mutual coupling is presented in this paper. The proposed antenna consists of two radiating elements with shared ground plane having an area of <inline-formula> <tex-math notation="LaTeX">$50\times 30$ </tex-math></inline-formula> mm2. F-shaped stubs are introduced in the shared ground plane of the proposed antenna to produce high isolation between the MIMO antenna elements. The designed MIMO antenna has very low mutual coupling of (S21 < −20 dB), low envelop correlation coefficient (ECC <0.04), high diversity gain (DG >7.4 dB), high multiplexing efficiency (<inline-formula> <tex-math notation="LaTeX">${\eta }_{\mathrm {Mux}}> -3.5$ </tex-math></inline-formula>), and high peak gain over the entire UWB frequencies. The antenna performance is studied in terms of S-Parameters, radiation properties, peak gain, diversity gain, envelop correlation coefficient, and multiplexing efficiency. A good agreement between the simulated and measured results is observed.

Question: What is the area of the antenna?


An ensemble classifier with random projection for predicting multi-label protein subcellular localization. In protein subcellular localization prediction, a predominant scenario is that the number of available features is much larger than the number of data samples. Among the large number of features, many of them may contain redundant or irrelevant information, causing the prediction systems suffer from overfitting. To address this problem, this paper proposes a dimensionality-reduction method that applies random projection (RP) to construct an ensemble multi-label classifier for predicting protein subcellular localization. Specifically, the frequencies of occurrences of gene-ontology terms are used as feature vectors, which are projected onto lower-dimensional spaces by random projection matrices whose elements conform to a distribution with zero mean and unit variance. The transformed low-dimensional vectors are classified by an ensemble of one-vs-rest multi-label support vector machine (SVM) classifiers, each corresponding to one of the RP matrices. The scores obtained from the ensemble are then fused for making the final decision. Experimental results on two recent datasets suggest that the proposed method can reduce the dimensions by six folds and remarkably improve the classification performance.

Question: How to predict protein subcellular localization?


A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification. Identifying discourse relations that are not overtly marked with discourse connectives remains a challenging problem. The absence of explicit clues indicates a need for the combination of world knowledge and weak contextual clues, which can hardly be learned from a small amount of manually annotated data. In this paper, we address this problem by augmenting the input text with external knowledge and context and by adopting a neural network model that can effectively handle the augmented text. Experiments show that external knowledge did improve the classification accuracy. On the other hand, contextual information provided no significant gain for implicit discourse relations while it worked for explicit ones.

Question: What is the difference between explicit and implicit discourse relations?


Prognostics and Health Management of Industrial Equipment. Prognostics and health management (PHM) is a field of research and application which aims at making use of past, present and future information on the environmental, operational and usage conditions of an equipment in order to detect its degradation, diagnose its faults, predict and proactively manage its failures. The present paper reviews the state of knowledge on the methods for PHM, placing these in context with the different information and data which may be available for performing the task and identifying the current challenges and open issues which must be addressed for achieving reliable deployment in practice. The focus is predominantly on the prognostic part of PHM, which addresses the prediction of equipment failure occurrence and associated residual useful life (RUL).

Question: What is the state of knowledge on the methods for PHM?


Smooth sensitivity and sampling in private data analysis. We introduce a new, generic framework for private data analysis.The goal of private data analysis is to release aggregate information about a data set while protecting the privacy of the individuals whose information the data set contains.Our framework allows one to release functions f of the data withinstance-based additive noise. That is, the noise magnitude is determined not only by the function we want to release, but also bythe database itself. One of the challenges is to ensure that the noise magnitude does not leak information about the database. To address that, we calibrate the noise magnitude to the smoothsensitivity of f on the database x --- a measure of variabilityof f in the neighborhood of the instance x. The new frameworkgreatly expands the applicability of output perturbation, a technique for protecting individuals' privacy by adding a smallamount of random noise to the released statistics. To our knowledge, this is the first formal analysis of the effect of instance-basednoise in the context of data privacy.  Our framework raises many interesting algorithmic questions. Namely,to apply the framework one must compute or approximate the smoothsensitivity of f on x. We show how to do this efficiently for several different functions, including the median and the cost ofthe minimum spanning tree. We also give a generic procedure based on sampling that allows one to release f(x) accurately on manydatabases x. This procedure is applicable even when no efficient algorithm for approximating smooth sensitivity of f is known orwhen f is given as a black box. We illustrate the procedure by applying it to k-SED (k-means) clustering and learning mixtures of Gaussians.

Question: What is the cost of the minimum spanning tree?


Rectal mucosal dissection commencing directly on the anorectal line versus commencing above the dentate line in laparoscopy-assisted transanal pull-through for Hirschsprung's disease: Prospective medium-term follow-up.. BACKGROUND In 2007, we began using the anorectal line (ARL) as the landmark for commencing rectal mucosal dissection (RMD) instead of the dentate line (DL) during laparoscopy-assisted transanal pull-through (L-TAPT) for Hirschsprung's disease (HD). We conducted a medium-term prospective comparison of postoperative fecal continence (POFC) between DL and ARL cases to follow our short-term study.   METHODS POFC is assessed by scoring frequency of motions, severity of staining, severity of perianal erosions, anal shape, requirement for medications, sensation of rectal fullness, and ability to distinguish flatus from stool on a scale of 0 to 2 (maximum: 14).   RESULTS Patient demographics were similar for ARL (2007-2014: n=33) and DL (1997-2006: n=41). There were no intraoperative complications and 2 cases of postoperative colitis in both ARL (6.1%) and DL (4.9%). Mean annual medium-term POFC scores for the 4-7 term of this study were consistently better in ARL: 9.7±1.4*, 10.1±1.6*, 10.6±1.6, and 11.3±1.4* in ARL and 8.6±1.5, 9.1±1.6, 9.8±1.9, 10.0±1.6 in DL (*: p<0.05).   CONCLUSIONS Medium-term POFC is better when the ARL is used as the landmark for RMD during L-TAPT for HD.

Question: What is the difference between the dentate line and the anorectal line?


Detecting human activities in retail surveillance using hierarchical finite state machine. Cashiers in retail stores usually exhibit certain repetitive and periodic activities when processing items. Detecting such activities plays a key role in most retail fraud detection systems. In this paper, we propose a highly efficient, effective and robust vision technique to detect checkout-related primitive activities, based on a hierarchical finite state machine (FSM). Our deterministic approach uses visual features and prior spatial constraints on the hand motion to capture particular motion patterns performed in primitive activities. We also apply our approach to the problem of retail fraud detection. Experimental results on a large set of video data captured from retail stores show that our approach, while much simpler and faster, achieves significantly better results than state-of-the-art machine learning-based techniques both in detecting checkout-related activities and in detecting checkout-related fraudulent incidents.

Question: How to detect human activities in retail surveillance using hierarchical finite state machine.


Relationship between procrastination and academic performance among a group of undergraduate dental students in India.. Procrastination, generally defined as a voluntary, irrational delay of behavior, is a prevalent phenomenon among college students throughout the world and occurs at alarmingly high rates. For this study, a survey was conducted of 209 second-, third-, and fourth-year undergraduate dental students of Bapuji Dental College and Hospital, Davangere, India, to identify the relationship between their level of procrastination and academic performance. A sixteen-item questionnaire was used to assess the level of procrastination among these students. Data related to their academic performance were also collected. Spearman's correlation coefficient test was used to assess the relationship between procrastination and academic performance. It showed a negative correlation of -0.63 with a significance level of p<0.01 (two-tailed test), indicating that students who showed high procrastination scores performed below average in their academics. In addition, analysis with the Mann-Whitney U test found a significant difference in procrastination scores between the two gender groups (p<0.05). Hence, among the Indian undergraduate dental students evaluated in this study, it appeared that individuals with above average and average academic performance had lower scores of procrastination and vice versa.

Question: What is the relationship between procrastination and academic performance among a group of undergraduate dental students in India?


Nonlinear Extensions of Reconstruction ICA. In a recent paper [1] it was observed that unsupervised feature learning with overcomplete features could be achieved using linear autoencoders (named Reconstruction Independent Component Analysis). This algorithm has been shown to outperform other well-known algorithms by penalizing the lack of diversity (or orthogonality) amongst features. In our project, we wish to extend and improve this algorithm to include other non-linearities. In this project we have considered three unsupervised learning algorithms: (a) Sparse Autoencoder (b) Reconstruction ICA (RICA), a linear autoencoder proposed in [1], and (c) Nonlinear RICA, a proposed extension of RICA for capturing nonlinearities in feature detection. Our research indicates that exploring non-linear extensions of RICA holds good promise; preliminary results with hyperbolic tangent function on the MNIST dataset showed impressive accuracy (comparable with sparse autoencoders), robustness, and required a fraction of the computational effort.

Question: What is the difference between sparse autoencoders and reconstruction independent component analysis?


Heterogeneity and plasticity of T helper cells. CD4 T helper (Th) cells play critical roles in adaptive immune responses. They recruit and activate other immune cells including B cells, CD8 T cells, macrophages, mast cells, neutrophils, eosinophils and basophils. Based on their functions, their pattern of cytokine secretion and their expression of specific transcription factors, Th cells, differentiated from naïve CD4 T cells, are classified into four major lineages, Th1, Th2, Th17 and T regulatory (Treg) cells, although other Th lineages may exist. Subsets of the same lineage may express different effector cytokines, reside at different locations or give rise to cells with different fates, whereas cells from different lineages may secrete common cytokines, such as IL-2, IL-9 and IL-10, resulting in massive heterogeneity of the Th cell population. In addition, the pattern of cytokine secretion may switch from that of one lineage toward another under certain circumstances, suggesting that Th cells are plastic. Tregs are also more heterogeneous and plastic than were originally thought. In this review, we summarize recent reports on heterogeneity and plasticity of Th cells, and discuss potential mechanisms and implications of such features that Th cells display.

Question: What is the difference between Th1 and Th2 cells?


Wideband and Low-Profile H-Plane Ridged SIW Horn Antenna Mounted on a Large Conducting Plane. This communication presents a wide-band and low-profile H-plane horn antenna based on ridged substrate integrated waveguide (SIW) with a large conducting ground. The horn antenna is implemented in a single substrate with a thickness of 0.13 λ0 at the center frequency. Despite its low profile, the new H-plane horn antenna achieves a very wide bandwidth by employing an arc-shaped copper taper printed on the extended dielectric slab and a three-step ridged SIW transition. The ridged SIW is critical for widening the operation bandwidth and lowering the characteristic impedance so that an excellent impedance matching from the coaxial probe to the narrow SIW can be obtained over a wide frequency range. Measured VSWR of the fabricated horn antenna is below 2.5 from 6.6 GHz to 18 GHz. The antenna also exhibits stable radiation beam over the same frequency range. It is observed that measured results agree well with simulated ones.

Question: What is a horn antenna?


Analysis of Flux Leakage in a Segmented Core Brushless Permanent Magnet Motor. This paper presents an analytical model to analyze the leakage flux associated with a brushless permanent magnet motor that utilizes segmented stator core. Due to the presence of a segmented stator structure, the leakage of flux through the tooth tips becomes worthy of analysis and consideration in such a motor. A leakage parameter alpha is developed to show the effects of various design parameters on tooth-tip flux leakage that is essential in accurately predicting the air gap flux density and other output parameters in the motor. Finite-element analysis and experimental verifications are also done to validate the analytical model.

Question: What is the purpose of the paper?


An AOI algorithm for PCB based on feature extraction. With the development of the micro-electronic industry, electronical components assembled on the printed circuit board (PCB) become more and more microsize and its mounted density is increasing. It is out of date to depend on manual inspection to assure the joints quality. Instead, automated optical inspection (AOI) for solder joints based on the machine vision has become more and more important. In this paper, based on the image acquired from a 3-CCD color camera and a 3-color hemispherical LED arrays light resource (red, green, and blue), the place, shape, and logical features of solder joints of chip components are extracted. The place features are related to solder joint place. As for shape features, we divide solder joint into several regions and extract its shape features by its color, occupancy ratio of area, center of gravity and continuous pixels. The logical features come from their close relationships of different regions of shape features, color distributing and place features. On the basis of the features, an AOI algorithm is developed. The defects of lacking solder, surplus solder, no solder, pseudo joints, wrong component, damaged component, component absent, shift, tomb stone, wrong polarity, etc. can be identified properly by using the proposed algorithm. Finally, some experiment results are presented to show the validity of the algorithm.

Question: What is the AOI algorithm for PCB based on feature extraction?


Neural correlates of mindfulness meditation-related anxiety relief.. Anxiety is the cognitive state related to the inability to control emotional responses to perceived threats. Anxiety is inversely related to brain activity associated with the cognitive regulation of emotions. Mindfulness meditation has been found to regulate anxiety. However, the brain mechanisms involved in meditation-related anxiety relief are largely unknown. We employed pulsed arterial spin labeling MRI to compare the effects of distraction in the form of attending to the breath (ATB; before meditation training) to mindfulness meditation (after meditation training) on state anxiety across the same subjects. Fifteen healthy subjects, with no prior meditation experience, participated in 4 d of mindfulness meditation training. ATB did not reduce state anxiety, but state anxiety was significantly reduced in every session that subjects meditated. Meditation-related anxiety relief was associated with activation of the anterior cingulate cortex, ventromedial prefrontal cortex and anterior insula. Meditation-related activation in these regions exhibited a strong relationship to anxiety relief when compared to ATB. During meditation, those who exhibited greater default-related activity (i.e. posterior cingulate cortex) reported greater anxiety, possibly reflecting an inability to control self-referential thoughts. These findings provide evidence that mindfulness meditation attenuates anxiety through mechanisms involved in the regulation of self-referential thought processes.

Question: What is the neural correlate of mindfulness meditation-related anxiety relief?


Learning grammatical structure with Echo State Networks. Echo State Networks (ESNs) have been shown to be effective for a number of tasks, including motor control, dynamic time series prediction, and memorizing musical sequences. However, their performance on natural language tasks has been largely unexplored until now. Simple Recurrent Networks (SRNs) have a long history in language modeling and show a striking similarity in architecture to ESNs. A comparison of SRNs and ESNs on a natural language task is therefore a natural choice for experimentation. Elman applies SRNs to a standard task in statistical NLP: predicting the next word in a corpus, given the previous words. Using a simple context-free grammar and an SRN with backpropagation through time (BPTT), Elman showed that the network was able to learn internal representations that were sensitive to linguistic processes that were useful for the prediction task. Here, using ESNs, we show that training such internal representations is unnecessary to achieve levels of performance comparable to SRNs. We also compare the processing capabilities of ESNs to bigrams and trigrams. Due to some unexpected regularities of Elman's grammar, these statistical techniques are capable of maintaining dependencies over greater distances than might be initially expected. However, we show that the memory of ESNs in this word-prediction task, although noisy, extends significantly beyond that of bigrams and trigrams, enabling ESNs to make good predictions of verb agreement at distances over which these methods operate at chance. Overall, our results indicate a surprising ability of ESNs to learn a grammar, suggesting that they form useful internal representations without learning them.

Question: What is the difference between a recurrent neural network and an echo state network?


Generative Adversarial Networks in Estimation of Distribution Algorithms for Combinatorial Optimization. Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Generative Ad-versarial Networks (GAN) are generative neural networks which can be trained to implicitly model the probability distribution of given data, and it is possible to sample this distribution. We integrate a GAN into an EDA and evaluate the performance of this system when solving combinatorial optimization problems with a single objective. We use several standard benchmark problems and compare the results to state-of-the-art multivariate EDAs. GAN-EDA doe not yield competitive results – the GAN lacks the ability to quickly learn a good approximation of the probability distribution. A key reason seems to be the large amount of noise present in the first EDA generations.

Question: What is a generative adversarial network?


Measuring problem video game playing in adolescents.. AIMS Some researchers suggest that for some people, video game playing is an addictive behaviour similar to substance dependence. Our aim was to design and validate a scale to measure the problems associated with the apparently addictive use of all types of video games and video game systems, because there is no instrument at the present time that can be used for this purpose.   DESIGN We reviewed the DSM-IV criteria for substance dependence and for pathological gambling, as well as the literature on the addictions in order to design a short scale (PVP; problem video game playing) that is quick and easy to apply.   PARTICIPANTS The scale was administered to 223 Spanish adolescents aged between 13 and 18 years. The study was carried out in Granada and Algeciras, Spain.   FINDINGS Psychometric analyses show that the PVP seems to be unidimensional and has acceptable internal consistency (Cronbach's alpha) at 0.69. The pattern of associations between the scale scores and alternative measures of problem play supports its construct validity (higher total scores in the scale were associated with higher frequency of play, mean and longest times per session, self and parents' perception of playing to excess, and scores in the Severity of Dependence Scale).   CONCLUSIONS Our results confirm that the excessive use of video games is associated with a number of problems which resemble a dependence syndrome, and the PVP appears as a useful instrument for the measurement of such problems.

Question: What is the difference between a substance dependence and a pathological gambling?


Baby Talk : Understanding and Generating Image Descriptions. We posit that visually descriptive language offers computer vision researchers both information about the world, and information about how people describe the world. The potential benefit from this source is made more significant due to the enormous amount of language data easily available today. We present a system to automatically generate natural language descriptions from images that exploits both statistics gleaned from parsing large quantities of text data and recognition algorithms from computer vision. The system is very effective at producing relevant sentences for images. It also generates descriptions that are notably more true to the specific image content than previous work.

Question: How can we generate descriptions from images?


Software engineering for big data projects: Domains, methodologies and gaps. Context: Big data has become the new buzzword in the information and communication technology industry. Researchers and major corporations are looking into big data applications to extract the maximum value from the data available to them. However, developing and maintaining stable and scalable big data applications is still a distant milestone. Objective: To look at existing research on how software engineering concepts, namely the phases of the software development project life cycle (SDPLC), can help build better big data application projects. Method: A literature survey was performed. A manual search covered papers returned by search engines resulting in approximately 2,000 papers being searched and 170 papers selected for review. Results: The search results helped in identifying data rich application projects that have the potential to utilize big data successfully. The review helped in exploring SDPLC phases in the context of big data applications and performing a gap analysis of the phases that have yet to see detailed research efforts but deserve attention.

Question: What is the difference between a big data project and a big data application?


Small form factor PIFA antenna design at 28 GHz for 5G applications. This paper presents the design and analysis of lowest form factor planar inverted-F Antenna at 28GHz for 5G Mobile applications. Feeding and shorting of the antenna is done using metallic strips. The Important properties of this antenna are its small footprint (0.25λg), good gain of 4.5dBi, 10dB impedance bandwidth of 1.55 GHz and radiation efficiency of 94%. The total size of the PIFA antenna is 0.25λg. PIFA antenna is one of the antennas whose performance greatly depends on ground plane size and its position on the ground plane. It shows good radiation pattern when placed at the corner or edge.

Question: What is the size of the antenna?


A scalable, commodity data center network architecture. Today's data centers may contain tens of thousands of computers with significant aggregate bandwidth requirements. The network architecture typically consists of a tree of routing and switching elements with progressively more specialized and expensive equipment moving up the network hierarchy. Unfortunately, even when deploying the highest-end IP switches/routers, resulting topologies may only support 50% of the aggregate bandwidth available at the edge of the network, while still incurring tremendous cost. Non-uniform bandwidth among data center nodes complicates application design and limits overall system performance.  In this paper, we show how to leverage largely commodity Ethernet switches to support the full aggregate bandwidth of clusters consisting of tens of thousands of elements. Similar to how clusters of commodity computers have largely replaced more specialized SMPs and MPPs, we argue that appropriately architected and interconnected commodity switches may deliver more performance at less cost than available from today's higher-end solutions. Our approach requires no modifications to the end host network interface, operating system, or applications; critically, it is fully backward compatible with Ethernet, IP, and TCP.

Question: What is a scalable, commodity data center network architecture?


DroidMat: Android Malware Detection through Manifest and API Calls Tracing. Recently, the threat of Android malware is spreading rapidly, especially those repackaged Android malware. Although understanding Android malware using dynamic analysis can provide a comprehensive view, it is still subjected to high cost in environment deployment and manual efforts in investigation. In this study, we propose a static feature-based mechanism to provide a static analyst paradigm for detecting the Android malware. The mechanism considers the static information including permissions, deployment of components, Intent messages passing and API calls for characterizing the Android applications behavior. In order to recognize different intentions of Android malware, different kinds of clustering algorithms can be applied to enhance the malware modeling capability. Besides, we leverage the proposed mechanism and develop a system, called Droid Mat. First, the Droid Mat extracts the information (e.g., requested permissions, Intent messages passing, etc) from each application's manifest file, and regards components (Activity, Service, Receiver) as entry points drilling down for tracing API Calls related to permissions. Next, it applies K-means algorithm that enhances the malware modeling capability. The number of clusters are decided by Singular Value Decomposition (SVD) method on the low rank approximation. Finally, it uses kNN algorithm to classify the application as benign or malicious. The experiment result shows that the recall rate of our approach is better than one of well-known tool, Androguard, published in Black hat 2011, which focuses on Android malware analysis. In addition, Droid Mat is efficient since it takes only half of time than Androguard to predict 1738 apps as benign apps or Android malware.

Question: Android Malware Detection through Manifest and API Calls Tracing


Deep Voice 2 : Multi-Speaker Neural Text-to-Speech. We introduce a technique for augmenting neural text-to-speech (TTS) with lowdimensional trainable speaker embeddings to generate different voices from a single model. As a starting point, we show improvements over the two state-ofthe-art approaches for single-speaker neural TTS: Deep Voice 1 and Tacotron. We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1. We improve Tacotron by introducing a post-processing neural vocoder, and demonstrate a significant audio quality improvement. We then demonstrate our technique for multi-speaker speech synthesis for both Deep Voice 2 and Tacotron on two multi-speaker TTS datasets. We show that a single neural TTS system can learn hundreds of unique voices from less than half an hour of data per speaker, while achieving high audio quality synthesis and preserving the speaker identities almost perfectly.

Question: How many voices can a single neural TTS system learn?


Pacific kids DASH for health (PacDASH) randomized, controlled trial with DASH eating plan plus physical activity improves fruit and vegetable intake and diastolic blood pressure in children.. BACKGROUND Pacific Kids DASH for Health (PacDASH) aimed to improve child diet and physical activity (PA) level and prevent excess weight gain and elevation in blood pressure (BP) at 9 months.   METHODS PacDASH was a two-arm, randomized, controlled trial (ClinicalTrials.gov: NCT00905411). Eighty-five 5- to 8-year-olds in the 50th-99th percentile for BMI were randomly assigned to treatment (n=41) or control (n=44) groups; 62 completed the 9-month trial. Sixty-two percent were female. Mean age was 7.1±0.95 years. Race/ethnicity was Asian (44%), Native Hawaiian or Other Pacific Islander (28%), white (21%), or other race/ethnicity (7%). Intervention was provided at baseline and 3, 6 and 9 months, with monthly supportive mailings between intervention visits, and a follow-up visit at 15 months to observe maintenance. Diet and PA were assessed by 2-day log. Body size, composition, and BP were measured. The intervention effect on diet and PA, body size and composition, and BP by the end of the intervention was tested using an F test from a mixed regression model, after adjustment for sex, age, and ethnic group.   RESULTS Fruit and vegetable (FV) intake decreased less in the treatment than control group (p=0.04). Diastolic BP (DBP) was 12 percentile units lower in the treatment than control group after 9 months of intervention (p=0.01). There were no group differences in systolic BP (SBP) or body size/composition.   CONCLUSIONS The PacDASH trial enhanced FV intake and DBP, but not SBP or body size/composition.

Question: What is the difference between diastolic and systolic blood pressure?


Frontal cortex function as derived from hierarchical predictive coding. The frontal lobes are essential for human volition and goal-directed behavior, yet their function remains unclear. While various models have highlighted working memory, reinforcement learning, and cognitive control as key functions, a single framework for interpreting the range of effects observed in prefrontal cortex has yet to emerge. Here we show that a simple computational motif based on predictive coding can be stacked hierarchically to learn and perform arbitrarily complex goal-directed behavior. The resulting Hierarchical Error Representation (HER) model simulates a wide array of findings from fMRI, ERP, single-units, and neuropsychological studies of both lateral and medial prefrontal cortex. By reconceptualizing lateral prefrontal activity as anticipating prediction errors, the HER model provides a novel unifying account of prefrontal cortex function with broad implications for understanding the frontal cortex across multiple levels of description, from the level of single neurons to behavior.

Question: What is the function of the frontal cortex?


May I help you? - Design of Human-like Polite Approaching Behavior-. When should service staff initiate interaction with a visitor? Neither simply-proactive (e.g. talk to everyone in a sight) nor passive (e.g. wait until being talked to) strategies are desired. This paper reports our modeling of polite approaching behavior. In a shopping mall, there are service staff members who politely approach visitors who need help. Our analysis revealed that staff members are sensitive to "intentions" of nearby visitors. That is, when a visitor intends to talk to a staff member and starts to approach, the staff member also walks a few steps toward the visitors in advance to being talked. Further, even when not being approached, staff members exhibit "availability" behavior in the case that a visitor's intention seems uncertain. We modeled these behaviors that are adaptive to pedestrians' intentions, occurred prior to initiation of conversation. The model was implemented into a robot and tested in a real shopping mall. The experiment confirmed that the proposed method is less intrusive to pedestrians, and that our robot successfully initiated interaction with pedestrians.

Question: How to design human-like polite approaching behavior?


A framework for evaluating quality-driven self-adaptive software systems. Over the past decade the dynamic capabilities of self-adaptive software-intensive systems have proliferated and improved significantly. To advance the field of self-adaptive and self-managing systems further and to leverage the benefits of self-adaptation, we need to develop methods and tools to assess and possibly certify adaptation properties of self-adaptive systems, not only at design time but also, and especially, at run-time. In this paper we propose a framework for evaluating quality-driven self-adaptive software systems. Our framework is based on a survey of self-adaptive system papers and a set of adaptation properties derived from control theory properties. We also establish a mapping between these properties and software quality attributes. Thus, corresponding software quality metrics can then be used to assess adaptation properties.

Question: What is a self-adaptive software system?


Common mode noise cancellation for electrically non-contact ECG measurement system on a chair.. Electrically non-contact ECG measurement system on a chair can be applied to a number of various fields for continuous health monitoring in daily life. However, the body is floated electrically for this system due to the capacitive electrodes and the floated body is very sensitive to the external noises or motion artifacts which affect the measurement system as the common mode noise. In this paper, the Driven-Seat-Ground circuit similar to the Driven-Right-Leg circuit is proposed to reduce the common mode noise. The analysis of this equivalent circuit is performed and the output signal waveforms are compared between with Driven-Seat-Ground and with capacitive ground. As the results, the Driven-Seat-Ground circuit improves significantly the properties of the fully capacitive ECG measurement system as the negative feedback.

Question: How to reduce the common mode noise?


An Attention-based Regression Model for Grounding Textual Phrases in Images. Grounding, or localizing, a textual phrase in an image is a challenging problem that is integral to visual language understanding. Previous approaches to this task typically make use of candidate region proposals, where end performance depends on that of the region proposal method and additional computational costs are incurred. In this paper, we treat grounding as a regression problem and propose a method to directly identify the region referred to by a textual phrase, eliminating the need for external candidate region prediction. Our approach uses deep neural networks to combine image and text representations and refines the target region with attention models over both image subregions and words in the textual phrase. Despite the challenging nature of this task and sparsity of available data, in evaluation on the ReferIt dataset, our proposed method achieves a new state-of-the-art in performance of 37.26% accuracy, surpassing the previously reported best by over 5 percentage points. We find that combining image and text attention models and an image attention area-sensitive loss function contribute to substantial improvements.

Question: What is the best way to ground a textual phrase in an image?


Robustness beyond shallowness: incremental deep parsing. Robustness is a key issue for natural language processing in general and parsing in particular, and many approaches have been explored in the last decade for the design of robust parsing systems. Among those approaches is shallow or partial parsing, which produces minimal and incomplete syntactic structures, often in an incremental way. We argue that with a systematic incremental methodology one can go beyond shallow parsing to deeper language analysis, while preserving robustness. We describe a generic system based on such methodology and designed for building robust analyzers that tackle deeper linguistic phenomena than those traditionally handled by the now widespread shallow parsers. The rule formalism allows the recognition of n-ary linguistic relations between words or constituents on the basis of global or local structural, topological and/or lexical conditions. It offers the advantage of accepting various types of inputs, ranging from raw to chunked or constituent-marked texts, so for instance it can be used to process existing annotated corpora, or to perform a deeper analysis on the output of an existing shallow parser. It has been successfully used to build a deep functional dependency parser, as well as for the task of co-reference resolution, in a modular way.

Question: What is a deep parser?


Structure, culture and Simmelian ties in entrepreneurial firms. This article develops a cultural agreement approach to organizational culture that emphasizes how clusters of individuals reinforce potentially idiosyncratic understandings of many aspects of culture including the structure of network relations. Building on recent work concerning Simmelian tied dyads (defined as dyads embedded in three-person cliques), the research examines perceptions concerning advice and friendship relations in three entrepreneurial firms. The results support the idea that Simmelian tied dyads (relative to dyads in general) reach higher agreement concerning who is tied to whom, and who are embedded together in triads in organizations. © 2002 Elsevier Science B.V. All rights reserved.

Question: What is the structure of the network of advice relations in an entrepreneurial firm?


Web-based statistical fact checking of textual documents. User generated content has been growing tremendously in recent years. This content reflects the interests and the diversity of online users. In turn, the diversity among internet users is also reflected in the quality of the content being published online. This increases the need to develop means to gauge the support available for content posted online. In this work, we aim to make use of the web-content to calculate a statistical support score for textual documents. In the proposed algorithm, phrases representing key facts are extracted to construct basic elements of the document. Search is used thereon to validate the support available for these elements online, leading to assigning an overall score for each document. Experimental results have shown a difference between the score distribution of factual news data and false facts data. This indicates that the approach seems to be a promising seed for distinguishing different articles based on the content.

Question: What is the difference between the score distribution of factual news data and false facts data?


Systematic review of the research evidence examining the effectiveness of interventions using a sensory integrative approach for children.. Twenty-seven studies were systematically reviewed to identify, evaluate, and synthesize the research literature on the effectiveness of sensory integration (SI) intervention on the ability of children with difficulty processing and integrating sensory information to engage in desired occupations and to apply these findings to occupational therapy practice. Results suggest the SI approach may result in positive outcomes in sensorimotor skills and motor planning; socialization, attention, and behavioral regulation; reading-related skills; participation in active play; and achievement of individualized goals. Gross motor skills, self-esteem, and reading gains may be sustained from 3 mo to 2 yr. Findings may be limited by Type II error because of small sample sizes, variable intervention dosage, lack of fidelity to intervention, and selection of outcomes that may not be meaningful to clients and families or may not change with amount of treatment provided. Replication of findings with methodologically and theoretically sound studies is needed to support current findings.

Question: What is the effectiveness of sensory integrative approach on children?


Stability of photovoltaic and wind turbine grid-connected inverters for a large set of grid impedance values. The aim of this paper is to analyze the stability problems of grid connected inverters used in distributed generation. Complex controllers (e.g., multiple rotating dq-frames or resonant-based) are often required to compensate low frequency grid voltage background distortion and an LCL-filter is usually adopted for the high frequency one. The possible wide range of grid impedance values (distributed generation is suited for remote areas with radial distribution plants) challenge the stability and the effectiveness of the LCL-filter-based current controlled system. It has been found out and it will be demonstrated in this paper that the use of active damping helps to stabilise the system in respect to many different kinds of resonances. The use of active damping results in an easy plug-in feature of the generation system in a vast range of grid conditions and in a more flexible operation of the overall system able to manage sudden grid changes. In the paper, a vast measurement campaign made on a single-phase system and on a three-phase system used as scale prototypes for photovoltaic and wind turbines, respectively, validate the analysis.

Question: How to stabilize a grid connected inverter?


A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm. Missing values in datasets should be extracted from the datasets or should be estimated before they are used for classification, association rules or clustering in the preprocessing stage of data mining. In this study, we utilize a fuzzy c-means clustering hybrid approach that combines support vector regression and a genetic algorithm. In this method, the fuzzy clustering parameters, cluster size and weighting factor are optimized and missing values are estimated. The proposed novel hybrid method yields sufficient and sensible imputation performance results. The results are compared with those of fuzzy c-means genetic algorithm imputation, support vector regression genetic algorithm imputation and zero imputation. 2013 Elsevier Inc. All rights reserved.

Question: What is a hybrid method for imputation of missing values?


DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be repurposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and fine-grained recognition challenges. We compare the efficacy of relying on various network levels to define a fixed feature, and report novel results that significantly outperform the state-of-the-art on several important vision challenges. We are releasing DeCAF, an open-source implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.

Question: What is a deep convolutional activation feature?


Viewpoint - Why "open source" misses the point of free software. Decoding the important differences in terminology, underlying philosophy, and value systems between two similar categories of software.

Question: What is the difference between open source and free software?


Step Climbing Omnidirectional Mobile Robot with Passive Linkages. In this paper, we develop a holonomic omni-directional mobile vehicle with step-climbing ability. This system realizes omni-directional motion on flat floor using special wheels and passes over the step in forward or backward direction using the passive linkage mechanism. Our key ideas are the following topics. First topic is new omnidirectional mobile mechanism which consists of special wheels and passive linkage mechanism. Second topic is new passive linkage mechanism which can change its body configuration easily when the vehicle passes over the step. Third topic is wheel control reference derivation based on the body configuration, which changes passively during step climbing for reducing wheel slippage. Last topic is wheel control method which keeps the rotation velocity coordination among the wheels for reducing wheel slippage and increasing the step-climbing performance. We verified the effectiveness of our proposed method by the computer simulations and experiments. Utilizing our proposed mechanism and control systems, the vehicle has both omnidirectional mobility and step overcoming function. Furthermore, our developing vehicle can pass over the 128[mm] height step using the wheel which radius is 66[mm]. Its performance is three times larger than one of general wheeled vehicle.

Question: What is the height of the step?


Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach. This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model

Question: What is the difference between a neural network and a fuzzy neural network?


Testing Unconstrained Optimization Software. Much of the testing of optimization software is inadequate because the number of test functmns is small or the starting points are close to the solution. In addition, there has been too much emphasm on measurmg the efficmncy of the software and not enough on testing reliability and robustness. To address this need, we have produced a relatwely large but easy-to-use collection of test functions and designed gmdelines for testing the reliability and robustness of unconstrained optimization software.

Question: What is a test function?


Improving Dermoscopic Image Segmentation With Enhanced Convolutional-Deconvolutional Networks. Automatic skin lesion segmentation on dermoscopic images is an essential step in computer-aided diagnosis of melanoma. However, this task is challenging due to significant variations of lesion appearances across different patients. This challenge is further exacerbated when dealing with a large amount of image data. In this paper, we extended our previous work by developing a deeper network architecture with smaller kernels to enhance its discriminant capacity. In addition, we explicitly included color information from multiple color spaces to facilitate network training and thus to further improve the segmentation performance. We participated and extensively evaluated our method on the ISBI 2017 skin lesion segmentation challenge. By training with the 2000 challenge training images, our method achieved an average Jaccard Index of 0.765 on the 600 challenge testing images, which ranked itself in the first place among 21 final submissions in the challenge.

Question: How to improve the segmentation of skin lesions?


Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis. Twitter sentiment analysis offers organizations ability to monitor public feeling towards the products and events related to them in real time. The first step of the sentiment analysis is the text pre-processing of Twitter data. Most existing researches about Twitter sentiment analysis are focused on the extraction of new sentiment features. However, to select the pre-processing method is ignored. This paper discussed the effects of text pre-processing method on sentiment classification performance in two types of classification tasks, and summed up the classification performances of six pre-processing methods using two feature models and four classifiers on five Twitter datasets. The experiments show that the accuracy and F1-measure of Twitter sentiment classification classifier are improved when using the pre-processing methods of expanding acronyms and replacing negation, but barely changes when removing URLs, removing numbers or stop words. The Naive Bayes and Random Forest classifiers are more sensitive than Logistic Regression and support vector machine classifiers when various pre-processing methods were applied.

Question: What is the difference between the pre-processing methods of expanding acronyms and replacing negation?


Wireless Network Virtualization: A Survey, Some Research Issues and Challenges. Since wireless network virtualization enables abstraction and sharing of infrastructure and radio spectrum resources, the overall expenses of wireless network deployment and operation can be reduced significantly. Moreover, wireless network virtualization can provide easier migration to newer products or technologies by isolating part of the network. Despite the potential vision of wireless network virtualization, several significant research challenges remain to be addressed before widespread deployment of wireless network virtualization, including isolation, control signaling, resource discovery and allocation, mobility management, network management and operation, and security as well as non-technical issues such as governance regulations, etc. In this paper, we provide a brief survey on some of the works that have already been done to achieve wireless network virtualization, and discuss some research issues and challenges. We identify several important aspects of wireless network virtualization: overview, motivations, framework, performance metrics, enabling technologies, and challenges. Finally, we explore some broader perspectives in realizing wireless network virtualization.

Question: What is wireless network virtualization?


A Client-Driven Approach for Channel Management in Wireless LANs. We propose an efficient client-based approach for channel management (channel assignment and load balancing) in 802.11-based WLANs that lead to better usage of the wireless spectrum. This approach is based on a "conflict set coloring" formulation that jointly performs load balancing along with channel assignment. Such a formulation has a number of advantages. First, it explicitly captures interference effects at clients. Next, it intrinsically exposes opportunities for better channel re-use. Finally, algorithms based on this formulation do not depend on specific physical RF models and hence can be applied efficiently to a wide-range of inbuilding as well as outdoor scenarios. We have performed extensive packet-level simulations and measurements on a deployed wireless testbed of 70 APs to validate the performance of our proposed algorithms. We show that in addition to single network scenarios, the conflict set coloring formulation is well suited for channel assignment where multiple wireless networks share and contend for spectrum in the same physical space. Our results over a wide range of both simulated topologies and in-building testbed experiments indicate that our approach improves application level performance at the clients by upto three times (and atleast 50%) in comparison to current best-known techniques.

Question: What is the client-based approach for channel management in wireless LANs?