title: A Survey of Public Auditing for Secure Data Storage in Cloud Computing

abstract: Cloud computing has been popular as the IT architecture. Cloud service providers offer many services based on cloud computing. Cloud storage service is the cloud services which can provide a huge storage space to solve the bottleneck of the storage space of local end users. However, cloud storage service may have data security because the users' data is not stored in their own storage. In this paper, we will focus on data integrity in the cloud storage service. Public auditability is a model of outsourcing data integrity verification, which can achieve efficiency and security. Therefore, we survey the previous researches of data integrity based on public auditability which includes collecting the basic requirements and evaluation metrics, providing the representative with approaches to analyze security and efficiency. Finally, we propose some future developments.


title: Failure mechanisms of nano-silicon anodes upon cycling: an electrode porosity evolution model

abstract: With a specific capacity of 3600 mA h g(-1), silicon is a promising anode active material for Li-ion batteries (LIBs). However, because of the huge volume changes undergone by Si particles upon (de)alloying with lithium, Si electrodes suffer from rapid capacity fading. A deep understanding of the associated failure mechanisms is necessary to improve these electrochemical performances. To reach this goal, we investigate here nano-Si based electrodes by several characterization techniques. Thanks to all these techniques, many aspects, such as the behaviour of the active material or the solid electrolyte interphase (SEI) and the lithiation mechanisms, are studied upon cycling. A clear picture of the failure mechanisms of nano-Si based electrodes is provided. In particular, by combining Hg analyses, SEM observations of electrode cross-sections, and EIS measurements, we follow the evolution of the porosity within the electrode. For the first time, our results clearly show a real dynamic of the pore size distribution: the first cycles lead to the formation of a micrometric porosity which is not present initially. During the following cycles, these large pores are progressively filled up with SEI products which form continuously at the Si particle surface. Thus, from the 50th cycle, Li(+) ion diffusion is dramatically hindered leading to a strongly heterogeneous lithiation of the electrode and a rapid capacity fading.


title: On the variation of b-value for Karachi region, Pakistan through Gumbel’s extreme distribution method

abstract: The accurate estimation of b-value of Gutenberg–Richter (GR) frequency-magnitude relationship is very crucial in seismic hazard analysis of a region. In this study, we focused on the b-value estimated by using Gumbel’s extreme distribution and GR methods for Karachi region of Pakistan. The seismotectonic setting of Karachi region is of great significant because it is surrounded by many active faults systems such as Makran subduction, Run of Kutch, Kirthar, Pab fault etc. In the past, low to moderate seismicity has been occurred in Karachi region. However, because of its seismotectonic settings there is the possibility of occurrence of major seismic event. The probability of recurrence of potential earthquake is also investigated in Karachi region. An earthquake catalogue is developed by taking moment magnitude (M w ) as a homogenous magnitude for all events. The completeness of this catalogue is done through taking instrumental data of earthquakes from 1973 onwards. A total of 530 events, occurred in 300 km surrounding of Karachi city, are selected for analysis of b-value and probability of recurrence. The regression analysis is carried out by GR-relationship to analyze the pattern of earthquake distribution and b-value. The statistical analysis of past earthquakes has computed b-value 0.49 showing inadequate earthquake events. Therefore, to find out b-value for this region with low number of earthquake, lack of data and deficiency of relevant earthquake magnitude, GR methodology is not much appropriate. In this situation, Gumbel’s extreme distribution methodology is used, which have shown better results. The regression analysis through Gumbel’s method indicates b-value of 0.8. The probability of recurrence and return periods for different magnitudes is determined by using Gumbel’s extreme distribution value approach.


title: Future Segmentation Using 3D Structure

abstract: Predicting the future to anticipate the outcome of events and actions is a critical attribute of autonomous agents; particularly for agents which must rely heavily on real time visual data for decision making. Working towards this capability, we address the task of predicting future frame segmentation from a stream of monocular video by leveraging the 3D structure of the scene. Our framework is based on learnable sub-modules capable of predicting pixel-wise scene semantic labels, depth, and camera ego-motion of adjacent frames. We further propose a recurrent neural network based model capable of predicting future ego-motion trajectory as a function of a series of past ego-motion steps. Ultimately, we observe that leveraging 3D structure in the model facilitates successful prediction, achieving state of the art accuracy in future semantic segmentation.


title: A Survey on Security and Privacy Issues in Internet-of-Things

abstract: Internet-of-Things (IoT) are everywhere in our daily life. They are used in our homes, in hospitals, deployed outside to control and report the changes in environment, prevent fires, and many more beneficial functionality. However, all those benefits can come of huge risks of privacy loss and security issues. To secure the IoT devices, many research works have been conducted to countermeasure those problems and find a better way to eliminate those risks, or at least minimize their effects on the user’s privacy and security requirements. The survey consists of four segments. The first segment will explore the most relevant limitations of IoT devices and their solutions. The second one will present the classification of IoT attacks. The next segment will focus on the mechanisms and architectures for authentication and access control. The last segment will analyze the security issues in different layers.


title: Attitude of teenagers towards their grandparents.

abstract: Radical change occurred in the family structure value system adoption of western culture busy life style and independent views resulted in deteriorating the relationships between grandparents and grandchildren. Due to this adolescents treat their grandparents with less respect and less affection. They have less emotional intimate relationships with grandparents. And also in the age of group from sixteen onwards begin to view elderly as large and impersonal grey mass in the society something a social burden standing in their way. Boys and girls at this stage may respond to there grandparents with irritation distrust and suspicion. They are easily offended and are quick to complain that the elders do not understand them or they do not treat them fairly. Moreover boys have unfavorable and less intimate relationships with their grandparents this is due to sex role standard prevalent in our society which is bared on the differential ways boys and girls are treated. Boys are given greater freedom than girls they spend less time in the home while girls play a subordinate role and are encouraged to do household chores and thus spend maximum time in home than boys. More specifically study was undertaken to examine the attitude of male and female teenagers towards their grandparents. (excerpt)


title: Enhancement of photoprotection potential of catechin loaded nanoemulsion gel against UVA induced oxidative stress.

abstract: The present study was aimed to develop a catechin (CA) loaded nanoemulsion based nano-gel for the protection of skin against ultraviolet radiation (UV) induced photo-damage and to ensure its enhanced skin permeability as well as bioavailability through transdermal route. The optimized nanoemulsion (CA-NE4) was prepared by spontaneous nano-emulsification method. It was composed of oil (ethyl oleate), Smix [surfactant (span 80) and co-surfactant (transcutol CG)] and aqueous system in an appropriate ratio of 15:62:23% w/w respectively. The CA-NE4 was characterized through assessment of droplet size, zeta potential, refractive index, transmission electron microscopy (TEM), UV, high performance thin layer chromatography (HPTLC) and Fourier transform infrared spectroscopy (FTIR) analysis. The average droplet size and zeta potential of CA-NE4 were found to be 98.6 ± 1.01 nm and − 27.3 ± 0.20 mV respectively. The enhanced skin permeability was better with CA-NE4 based nano-gel (CA-NG4) [96.62%] compared to conventional gel (CA-CG) [53.01%] for a period of 24 h. The enhanced % relative bioavailability (F) of CA (894.73), Cmax (93.79 ± 6.19 ng mL− 1), AUC0 − t ∞ (2653.99 ± 515.02 ng h mL− 1) and Tmax (12.05 ± 0.02 h) was significantly obtained with CA-NG4 as compared to oral suspension for extended periods (72 h). CA-NG4 could improve the level of cutaneous antioxidant enzymes like superoxide dismutase (SOD), glutathione peroxidase (GPX) and catalase (CAT) and reduce the level of thiobarbituric acid reactive substances (TBRAS) against oxidative stress induced by UVA. Nano-gel formulation of CA showed sustained release profile and enhanced photoprotection potential due to its improved permeability as well as bioavailability (P < 0.05) compared to the conventional gel. Therefore, transdermal administration of nano-gel (CA-NG4) of CA offers a better way to develop the endogenous cutaneous protection system and thus could be an effective strategy for decreasing UV-induced oxidative damage in the skin tissues.


title: An investigation of the effects of reciprocal peer tutoring

abstract: This study investigated the effects of reciprocal peer tutoring (RPT) on student achievement, motivation, and attitudes. Four sections of an educational technology course were randomly assigned to one RPT and one non-RPT group. Participants in the RPT group were then randomly assigned to groups to tutor and support each other while participants in the non-RPT group worked individually. The results suggested that the RPT and non-RPT groups did not differ on student achievement and motivation. Findings concerning student attitudes revealed that what students liked about RPT were helpful group members, opportunities to work in groups, feedback from groups, the comfort that RPT provided, and knowledge sharing. What students disliked about RPT were the unnecessary work and lack of interaction.


title: Precision SAR processing using chirp scaling

abstract: A space-variant interpolation is required to compensate for the migration of signal energy through range resolution cells when processing synthetic aperture radar (SAR) data, using either the classical range/Doppler (R/D) algorithm or related frequency domain techniques. In general, interpolation requires significant computation time, and leads to loss of image quality, especially in the complex image. The new chirp scaling algorithm avoids interpolation, yet performs range cell migration correction accurately. The algorithm requires only complex multiplies and Fourier transforms to implement, is inherently phase preserving, and is suitable for wide-swath, large-beamwidth, and large-squint applications. This paper describes the chirp scaling algorithm, summarizes simulation results, presents imagery processed with the algorithm, and reviews quantitative measures of its performance. Based on quantitative comparison, the chirp scaling algorithm provides image quality equal to or better than the precision range/Doppler processor. Over the range of parameters tested, image quality results approach the theoretical limit, as defined by the system bandwidth. >


title: A new concept of microstrip Rotman lens design

abstract: This paper presents a methodology for design a more reliable printed microstrip Rotman lens with the support of electromagnetic simulation software. A lens with four beam ports and four array ports in the range of 4 to 6 GHz is developed and manufactured with simulated and measured results of reflection coefficient, isolation and radiation pattern, showing a good performance of this methodology.


One does not simply produce funny memes! - Explorations on the Automatic Generation of Internet humor


title: A Universally Unique IDentifier ( UUID ) URN Namespace

abstract: This specification defines a Uniform Resource Name namespace for UUIDs ::: (Universally Unique IDentifier), also known as GUIDs (Globally Unique ::: IDentifier). A UUID is 128 bits long, and can guarantee uniqueness ::: across space and time. UUIDs were originally used in the Apollo ::: Network Computing System and later in the Open Software ::: Foundation\'s (OSF) Distributed Computing Environment (DCE), and ::: then in Microsoft Windows platforms. This specification is derived ::: from the DCE specification with the kind permission of the OSF (now ::: known as The Open Group). Information from earlier versions of the DCE ::: specification have been incorporated into this document. [STANDARDS- ::: TRACK]


title: Cooperative Prediction-and-Sensing-Based Spectrum Sharing in Cognitive Radio Networks

abstract: This paper proposes prediction-and-sensing based spectrum sharing, a new spectrum-sharing model for cognitive radio networks, with a time structure for each resource block divided into a spectrum prediction-and-sensing phase and a data transmission phase. Cooperative spectrum prediction is incorporated as a sub-phase of spectrum sensing in the first phase. We investigate a joint design of transmit beamforming at the secondary base station (BS) and sensing time. The primary design goal is to maximize the sum rate of all secondary users (SUs) subject to the minimum rate requirement for all SUs, the transmit power constraint at the secondary BS, and the interference power constraints at all primary users. The original problem is difficult to solve since it is highly nonconvex. We first convert the problem into a more tractable form, then arrive at a convex program based on an inner approximation framework, and finally propose a new algorithm to successively solve this convex program. We prove that the proposed algorithm iteratively improves the objective while guaranteeing convergence at least to local optima. Simulation results demonstrate that the proposed algorithm reaches a stationary point after only a few iterations with a substantial performance improvement over existing approaches.


title: Bringing Transparency Design into Practice

abstract: Intelligent systems, which are on their way to becoming mainstream in everyday products, make recommendations and decisions for users based on complex computations. Researchers and policy makers increasingly raise concerns regarding the lack of transparency and comprehensibility of these computations from the user perspective. Our aim is to advance existing UI guidelines for more transparency in complex real-world design scenarios involving multiple stakeholders. To this end, we contribute a stage-based participatory process for designing transparent interfaces incorporating perspectives of users, designers, and providers, which we developed and validated with a commercial intelligent fitness coach. With our work, we hope to provide guidance to practitioners and to pave the way for a pragmatic approach to transparency in intelligent systems.


title: Building Hierarchical Representations for Oracle Character and Sketch Recognition

abstract: In this paper, we study oracle character recognition and general sketch recognition. First, a data set of oracle characters, which are the oldest hieroglyphs in China yet remain a part of modern Chinese characters, is collected for analysis. Second, typical visual representations in shape- and sketch-related works are evaluated. We analyze the problems suffered when addressing these representations and determine several representation design criteria. Based on the analysis, we propose a novel hierarchical representation that combines a Gabor-related low-level representation and a sparse-encoder-related mid-level representation. Extensive experiments show the effectiveness of the proposed representation in both oracle character recognition and general sketch recognition. The proposed representation is also complementary to convolutional neural network (CNN)-based models. We introduce a solution to combine the proposed representation with CNN-based models, and achieve better performances over both approaches. This solution has beaten humans at recognizing general sketches.


title: Promising directions in active vision

abstract: Active vision systems have mechanisms that can actively control camera parameters such as position, orientation, focus, zoom, aperture, and vergence (in a two-camera system) in response to the requirements of the task and external stimuli. They may also have features such as spatially variant (foveal) sensors. More broadly, active vision encompassesattention, selective sensing in space, resolution, and time, whether it is achieved by modifying physical camera parameters or the way data is processed after leaving the camera.


title: Image Completion Using Efficient Belief Propagation Via Priority Scheduling and Dynamic Pruning

abstract: In this paper, a new exemplar-based framework is presented, which treats image completion, texture synthesis, and image inpainting in a unified manner. In order to be able to avoid the occurrence of visually inconsistent results, we pose all of the above image-editing tasks in the form of a discrete global optimization problem. The objective function of this problem is always well-defined, and corresponds to the energy of a discrete Markov random field (MRF). For efficiently optimizing this MRF, a novel optimization scheme, called priority belief propagation (BP), is then proposed, which carries two very important extensions over the standard BP algorithm: ldquopriority-based message schedulingrdquo and ldquodynamic label pruning.rdquo These two extensions work in cooperation to deal with the intolerable computational cost of BP, which is caused by the huge number of labels associated with our MRF. Moreover, both of our extensions are generic, since they do not rely on the use of domain-specific prior knowledge. They can, therefore, be applied to any MRF, i.e., to a very wide class of problems in image processing and computer vision, thus managing to resolve what is currently considered as one major limitation of the BP algorithm: its inefficiency in handling MRFs with very large discrete state spaces. Experimental results on a wide variety of input images are presented, which demonstrate the effectiveness of our image-completion framework for tasks such as object removal, texture synthesis, text removal, and image inpainting.


title: Identification of a threshold value for the DEMATEL method using the maximum mean de-entropy algorithm to find critical services provided by a semiconductor intellectual property mall

abstract: To deal with complex problems, structuring them through graphical representations and analyzing causal influences can aid in illuminating complex issues, systems, or concepts. The DEMATEL method is a methodology which can confirm interdependence among variables and aid in the development of a chart to reflect interrelationships between variables, and can be used for researching and solving complicated and intertwined problem groups. The end product of the DEMATEL process is a visual representation-the impact-relations map-by which respondents organize their own actions in the world. In order to obtain a suitable impact-relations map, an appropriate threshold value is needed to obtain adequate information for further analysis and decision-making. In the existing literature, the threshold value has been determined through interviews with respondents or judged by the researcher. In most cases, it is hard and time-consuming to aggregate the respondents and make a consistent decision. In addition, in order to avoid subjective judgments, a theoretical method to select the threshold value is necessary. In this paper, we propose a method based on the entropy approach, the maximum mean de-entropy algorithm, to achieve this purpose. Using a real case to find the interrelationships between the services of a Semiconductor Intellectual Property Mall as an example, we will compare the results obtained from the respondents and from our method, and show that the impact-relations maps from these two methods could be the same.


title: Massive parallelization of approximate nearest neighbor search on KD-tree for high-dimensional image descriptor matching

abstract: To overcome the high computing cost associated with high-dimensional digital image descriptor matching, this paper presents a massively parallel approximate nearest neighbor search (ANNS) on K-dimensional tree (KD-tree) on the modern massively parallel architectures (MPA). The proposed algorithm is of comparable quality to traditional sequential counterpart on central processing unit (CPU). However, it achieves a high speedup factor of 121 when applied to high-dimensional real-world image descriptor datasets. The algorithm is also studied for factors that impact its performance to obtain the optimal runtime configurations for various datasets. The performance of the proposed parallel ANNS algorithm is also verified on typical 3D image matching scenarios. With the classical local image descriptor signature of histograms of orientations (SHOT), the parallel image descriptor matching can achieve speedup of up to 128. Our implementation will potentially benefit realtime image descriptor matching in high dimensions.


title: Commercial Block Detection in Broadcast News Videos

abstract: Automatic identification and extraction of commercial blocks in telecast news videos find a lot of applications in the domain of broadcast monitoring. Existing works in this domain have used channel specific assumptions, machine learning techniques and frequentist approaches for detecting commercial video segments. We note that in the Indian context, several channel specific assumptions do not hold and often news and commercials have comparable frequencies of occurrence. This motivates us to use the machine learning techniques for classifying commercials in news videos. Our main contribution lies in the proposal of two features which are shown to outperform the existing audio-visual features – first, the MFCC bag of words (BoW) as audio track feature and second, overlaid text distribution as video shot feature. The shot feature space is further extended by appending contextual features which are categorized by SVM based classifiers. Additionally, we have used a post-processing stage to suppress the false positives. We have experimented with 54 hours of video acquired from three different Indian English based news channels and have obtained a F-measure of around 97%.


title: Massive MIMO for Fifth Generation (5G): Opportunities and Challenges

abstract: MIMO is a technology that utilizes multiple antennas at transmitter/receiver to improve the throughput, capacity and coverage of wireless system. Massive MIMO where Base Station is equipped with orders of magnitude more antennas have shown over 10 times spectral efficiency increase over MIMO with simpler signal processing algorithms. Massive MIMO has benefits of enhanced capacity, spectral and energy efficiency and it can be built by using low cost and low power components. Despite its potential benefits, this paper also summarizes some challenges faced by massive MIMO such as antenna spatial correlation and mutual coupling as well as non-linear hardware impairments. These challenges encountered in massive MIMO uncover new problems that need further investigation.


title: Fuzzy Cognitive Map In Modeling Supervisory Control Systems

abstract: This paper investigates a hybrid methodology that combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for modeling and controlling Supervisory Control Systems. A mathematical description of Fuzzy Cognitive Maps (FCM) will be presented and new construction methods will be extensively examined. A Fuzzy Cognitive Map will be developed to model and control a process example and the Supervisor-FCM model characteristics will be discussed. There is a going need for more autonomous and intelligent systems, especially in Complex Systems area, the application of Fuzzy Cognitive Maps for modeling the Supervisor may contribute to develop more sophisticated systems.


Assessing the Spoofing Threat: Development of a Portable GPS Civilian Spoofer


title: Robotic Tactile Perception of Object Properties: A Review

abstract: Touch sensing can help robots understand their surrounding environment, and in particular the objects they interact with. To this end, roboticists have, in the last few decades, developed several tactile sensing solutions, extensively reported in the literature. Research into interpreting the conveyed tactile information has also started to attract increasing attention in recent years. However, a comprehensive study on this topic is yet to be reported. In an effort to collect and summarize the major scientific achievements in the area, this survey extensively reviews current trends in robot tactile perception of object properties. Available tactile sensing technologies are briefly presented before an extensive review on tactile recognition of object properties. The object properties that are targeted by this review are shape, surface material and object pose. The role of touch sensing in combination with other sensing sources is also discussed. In this review, open issues are identified and future directions for applying tactile sensing in different tasks are suggested.


title: A simple approach for the estimation of circular arc center and its radius

abstract: Abstract A simple exact solution for estimating the location of the center of a circular arc and its radius is suggested. Its features are demonstrated with the help of a computer program.


title: Systematic mapping study on software engineering for sustainability (SE4S)

abstract: Background/Context: The objective of achieving higher sustainability in our lifestyles by information and communication technology has lead to a plethora of research activities in related fields. Consequently, Software Engineering for Sustainability (SE4S) has developed as an active area of research. Objective/Aim: Though SE4S gained much attention over the past few years and has resulted in a number of contributions, there is only one rigorous survey of the field. We follow up on this systematic mapping study from 2012 with a more in-depth overview of the status of research, as most work has been conducted in the last 4 years. Method: The applied method is a systematic mapping study through which we investigate which contributions were made, which knowledge areas are most explored, and which research type facets have been used, to distill a common understanding of the state-of-the-art in SE4S. Results: We contribute an overview of current research topics and trends, and their distribution according to the research type facet and the application domains. Furthermore, we aggregate the topics into clusters and list proposed and used methods, frameworks, and tools. Conclusion: The research map shows that impact currently is limited to few knowledge areas and there is need for a future roadmap to fill the gaps.


title: Nurse Staffing Levels: Impact of Organizational Characteristics and Registered Nurse Supply

abstract: In 2002, the Bureau of Health Professions at the Health Resources and Services Administration released a report on the supply of, demand for, and shortages of registered nurses (RNs) (Biviano et al. 2004). The report, based on data from state licensure boards, census data, labor statistics, National League for Nursing annual surveys, and the National Sample Survey of Registered Nurses, found a 6 percent shortage in the supply of RNs. The report further predicted the shortage would increase to 12 percent by 2010 and to 20 percent by 2020. However, the shortage was not spread evenly across the states; rather, in 2000 no shortage existed in 21 states while the study identified shortages ranging from 3.5 to 17 percent in the other 29 states. ::: ::: Nurses perceive a shortage in the practice setting and report that the quality of care provided is declining as a result. A majority of the respondents in a national survey of 4,108 randomly selected nurses working in inpatient units reported that the shortage of nurses has been a major problem, affecting the time nurses have to devote to each of their patients (89 percent), the quality of patient care provided by nurses (77 percent), the ability of nurses to maintain patient safety (64 percent), and the early detection of patient complications by nurses (64 percent) (NurseWeek/AONE 2002). ::: ::: Since Aiken's landmark study published in 1994, evidence has been building that higher nurse staffing levels in hospitals are associated with better quality of patient care (Aiken, Smith, and Lake 1994; Blegen, Goode, and Reed 1998; Blegen and Vaughn 1998; Kovner and Gergen 1998; Aiken et al. 2002; Kovner et al. 2002; Needleman et al. 2002; Unruh 2003a, b; Person et al. 2004; Needleman et al. 2006). Discussions of these research results uniformly include concerns about the impact of the nursing shortage on the quality of care and the safety of patients; however, only one research report in the last 15 years addressed the direct impact of the shortage on hospital staffing (Grumbach et al. 2001). ::: ::: Decisions about staffing levels in hospitals must balance personnel costs with the intensity of care required by the population of patients served by each hospital. The acuity of the patients, standards for staffing set by experts and professional groups, reimbursement rates, and the orientation of the hospital or hospital corporation (for-profit, not-for-profit) influence this balance. In addition, market and other environmental factors, such as the supply of nurses, influence the decisions about staffing and staffing standards. Little study has been done on the actual influence of these factors on staffing levels. ::: ::: This study is part of a larger project examining staffing and patient outcomes in a national random sample of hospitals. The purpose of this analysis was to assess the impact of nurse supply in the geographic areas surrounding the hospitals on patient care unit staffing levels, while taking into account hospital factors that influence nurse staffing levels. We analyzed several measures of patient care unit staffing in relation to the supply of RNs, the type of unit (intensive care, stepdown/telemetry, medical/surgical), unit size, and hospital complexity (size, technology, teaching status, patient case mix). Nurse staffing measures included RN hours per patient day (Hppd), LPN Hppd, Total Hppd, RN mix (proportion of total hours provided by RNs), and licensed mix (proportion provided by RNs and LPNs). ::: ::: ::: Two research questions were posed: ::: ::: ::: Controlling for patient care unit type, unit size, and hospital complexity, what is the impact of the supply of RNs in the surrounding geographic area on hospital nurse staffing? ::: ::: ::: What are the relationships between the supply of RNs and hospital RN vacancy rate, turnover rates, overtime hours, and the use of agency or traveler nurses? ::: ::: ::: ::: ::: While there has been a great deal of interest in the effect of nurse staffing levels on the quality of care in hospitals, there has been much less interest in determining the factors that affect the nurse staffing levels other than documenting the changes during the health care crisis of the past 15 years (Aiken, Sochalski, and Anderson 1996; Anderson and Kohn 1996; Spetz 1998; Buerhaus and Staiger 1999; Bond and Raehl 2000; Unruh 2002). Recent work has established that the supply of nurses is not meeting the demand for nurses and that this shortage will worsen in the decades to come (U.S. DHHS 2002). While the conclusion that a local shortage of nurses will decrease the level of RN staffing in hospitals is logical, there is little evidence actually linking them or estimating the magnitude of the effect. ::: ::: The determinants of RN staffing levels in hospitals have been examined directly or indirectly while investigating other questions. The type of patient care unit makes the biggest difference, with higher staffing levels on units with more acutely ill patients (Blegen, Goode, and Reed 1998; Blegen and Vaughn 1998; Brewer and Frazier 1998; Hodge et al. 2004). Unit level staffing is high in teaching hospitals (Mark, Salyer, and Wan 2000; Hodge et al. 2004; Seago, Spetz, and Mitchell 2004); hospitals with more acutely ill patients (Brewer and Frazier 1998; Mark, Salyer, and Wan 2000); and hospitals in rural areas (Brewer and Frazier 1998; Hodge et al. 2004). Not-for-profit hospitals and those located in areas with more competition and higher managed care penetration have higher staffing levels (Mark, Salyer, and Wan 2000; Seago, Spetz, and Mitchell 2004). ::: ::: Three studies examined hospital-level RN staffing using American Hospital Association (AHA) data from the early 1980s. Hospitals with higher staffing were located in areas with a more competitive market (Robinson 1988); were larger (Becker and Foster 1988; Bloom, Alexander, and Nuchols 1997); were private (Becker and Foster 1988); were located in urban areas (Bloom, Alexander, and Nuchols 1997); and were teaching hospitals (Bloom, Alexander, and Nuchols 1997). Bloom and colleagues added an RN supply factor to their model (ratio of number of RNs in the county to hospital beds in the county) and report that a higher supply of RNs in the county was associated with higher RN skill mix in the hospitals. ::: ::: Using more recent data from the AHA, Grumbach et al. (2001) examined the associations among hospitals' reports of shortage status, vacancy rate, RN turnover rate, and staffing levels and the relationship of these variables to the RN supply in the county. They defined shortage as a “situation in which hospitals are unable to hire nurses at prevailing wages to achieve the staffing desired” (p. 388). When hospital-reported statistics were aggregated to the health service area, RN supply (nurses in county in relation to population) was correlated with hospital-reported shortage, r= −0.18, p<.05; RN vacancy rate, r= −0.21, p<.05; but not with RN turnover rate, r= −0.30, not significant; and RN staffing, r= −0.04, not significant. Seago et al. (2001) found that the primary predictors of hospital-reported shortage are location in the South, proportion of nonwhite county residents, proportion of Medicaid or Medicare patients, higher patient acuity, and team or functional nursing care delivery model. However, they did not include supply of RNs in these analyses.


title: Rejection sensitivity and male violence in romantic relationships

abstract: Rejection sensitivity is the disposition to anxiously expect, readily perceive, and intensely react to rejection by significant others. A model of the role of this disposition in male violence toward romantic partners is proposed. Specifically, it is proposed that rejection sensitivity is a vulnerability factor for two distinct maladaptive styles of coping with intimate relationships. Rejection-sensitive men may attempt to prevent anticipated rejection by reducing their investment in intimate relationships. Alternatively, they may become highly invested in intimate relationships in search of an unconditionally supportive partner. Their low threshold for perceiving and overreacting to rejection, however, heightens their risk of responding aggressively to their partners’negative or ambiguous behavior. Cross-sectional data from 217 male college students supported predictions derived from the model. Among college men who reported relatively high investment in romantic relationships, anxious expectations of rejection predicted dating violence. Among men who reported relatively low investment in romantic relationships, anxious expectations of rejection predicted reduced involvement in discretionary close relationships with friends and romantic partners and, more generally, increased distress in and avoidance of social situations.


title: Burst and Tonic Spinal Cord Stimulation: Different and Common Brain Mechanisms.

abstract: OBJECTIVE ::: Spinal cord stimulation is commonly used to treat medically intractable pain. Different stimulation designs are used to obtain pain suppression such as tonic stimulation, high frequency stimulation, and burst stimulation. Preliminary analysis of the same data used in this study demonstrated that burst stimulation likely modulates the medial pain pathways in contrast to tonic stimulation. The question arises what different and common supraspinal mechanisms burst and tonic stimulation use. ::: ::: ::: MATERIALS AND METHODS ::: The clinical and electroencephalography (EEG) data of five patients undergoing tonic, burst, and sham stimulation were analyzed to look at the commonalities and differences between burst and tonic stimulation. A source-localized (sLORETA) EEG substraction and conjunction analysis is performed in each condition for both activity and functional connectivity. A ratio between the dorsal anterior cingulate cortex (dACC) and pregenual anterior cingulate cortex/ventromedial prefrontal cortex (pgACC/vmPFC) is calculated to reflect a balance between pain supporting and pain suppressing systems. ::: ::: ::: RESULTS ::: Differences are noted in the dACC, dorsolateral prefrontal cortex, the primary somatosensory cortex, and the posterior cingulate cortex (PCC). Burst and tonic stimulation share activation in the pgACC, inferior parietal area, which encompasses the inferior secondary somatosensory cortex, PCC, and the parahippocampus. Burst suppression normalizes the pain supporting/pain suppressing balance in contrast to tonic stimulation. ::: ::: ::: DISCUSSION AND CONCLUSION ::: These data suggest that burst and tonic stimulation both modulate the descending pain inhibitory system (via pgACC), as well as a self-referential contextual (via PCC) aversive memory system (via parahippocampus). However, burst normalizes the pain supporting/suppressing balance in contrast to tonic mode by a greater effect on the dACC.


title: The NoSQL Principles and Basic Application of Cassandra Model

abstract: This paper reveal the secret of NoSQL. The CAP theorem, the BASE theorem and the Eventual Consistency theorem construct the foundation stone of NoSQL Cassandra is one kind of NoSQL databases, It is used by Twitter, Facebook and some other famous corporations. Taking it for example, this online trading system is based on Cassandra database. I'll design and contrast the relational model and Cassandra-based model of this system, then construct the key space, the column family and do some other configuration. After these jobs done, I'll do some coding to implement this system.


title: Color-Image Quality Assessment: From Prediction to Optimization

abstract: While image-difference metrics show good prediction performance on visual data, they often yield artifact-contaminated results if used as objective functions for optimizing complex image-processing tasks. We investigate in this regard the recently proposed color-image-difference (CID) metric particularly developed for predicting gamut-mapping distortions. We present an algorithm for optimizing gamut mapping employing the CID metric as the objective function. Resulting images contain various visual artifacts, which are addressed by multiple modifications yielding the improved color-image-difference (iCID) metric. The iCID-based optimizations are free from artifacts and retain contrast, structure, and color of the original image to a great extent. Furthermore, the prediction performance on visual data is improved by the modifications.


title: Texture map generation for 3D reconstructed scenes

abstract: We present a novel method for generating texture maps for 3D geometric models reconstructed using consumer RGB-D sensors. Our method generates a texture map for a simplified 3D mesh of the reconstructed scene using spatially and temporally sub-sampled key frames of the input RGB stream. We acquire an accurate texture map by optimizing the texture coordinates of the 3D model to maximize the photometric consistency among multiple key frames. We show that the optimization can be performed efficiently using GPU by exploiting the locality of texture coordinate manipulation. Experimental results demonstrate that our method can generate a texture map in a few tens of seconds for a large 3D model, such as a whole room.


title: Recovering low-rank matrices from few coefficients in any basis

abstract: We present novel techniques for analyzing the problem of low-rank matrix recovery. The methods are both considerably simpler and more general than previous approaches. It is shown that an unknown matrix of rank can be efficiently reconstructed from only randomly sampled expansion coefficients with respect to any given matrix basis. The number quantifies the “degree of incoherence” between the unknown matrix and the basis. Existing work concentrated mostly on the problem of “matrix completion” where one aims to recover a low-rank matrix from randomly selected matrix elements. Our result covers this situation as a special case. The proof consists of a series of relatively elementary steps, which stands in contrast to the highly involved methods previously employed to obtain comparable results. In cases where bounds had been known before, our estimates are slightly tighter. We discuss operator bases which are incoherent to all low-rank matrices simultaneously. For these bases, we show that randomly sampled expansion coefficients suffice to recover any low-rank matrix with high probability. The latter bound is tight up to multiplicative constants.


title: A Machine Learning Approach To Pronoun Resolution In Spoken Dialogue

abstract: We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals with pronouns with NP-and non-NP-antecedents. We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron's (2002) manually tuned system.


title: A hybrid forecasting approach using ARIMA models and self-organising fuzzy neural networks for capital markets

abstract: Linear time series models, such as the autoregressive integrated moving average (ARIMA) model, are among the most popular statistical models used to forecast time series. In recent years non-linear computational models, such as artificial neural networks (ANN), have been shown to outperform traditional linear models when dealing with complex data, like financial time series. This paper proposes a novel hybrid forecasting model which exploits the linear modelling strengths of the ARIMA model, and the flexibility of a self-organising fuzzy neural network (SOFNN). The system's performance is evaluated using several datasets, and our results indicate that a hybrid system is an effective tool for time series forecasting.


title: From single cells to deep phenotypes in cancer

abstract: In recent years, major advances in single-cell measurement systems have included the introduction of high-throughput versions of traditional flow cytometry that are now capable of measuring intracellular network activity, the emergence of isotope labels that can enable the tracking of a greater variety of cell markers and the development of super-resolution microscopy techniques that allow measurement of RNA expression in single living cells. These technologies will facilitate our capacity to catalog and bring order to the inherent diversity present in cancer cell populations. Alongside these developments, new computational approaches that mine deep data sets are facilitating the visualization of the shape of the data and enabling the extraction of meaningful outputs. These applications have the potential to reveal new insights into cancer biology at the intersections of stem cell function, tumor-initiating cells and multilineage tumor development. In the clinic, they may also prove important not only in the development of new diagnostic modalities but also in understanding how the emergence of tumor cell clones harboring different sets of mutations predispose patients to relapse or disease progression.


Android Malware Detection Based on System Calls


title: Millimeter Wave Cellular Wireless Networks: Potentials and Challenges

abstract: Millimeter-wave (mmW) frequencies between 30 and 300 GHz are a new frontier for cellular communication that offers the promise of orders of magnitude greater bandwidths combined with further gains via beamforming and spatial multiplexing from multielement antenna arrays. This paper surveys measurements and capacity studies to assess this technology with a focus on small cell deployments in urban environments. The conclusions are extremely encouraging; measurements in New York City at 28 and 73 GHz demonstrate that, even in an urban canyon environment, significant non-line-of-sight (NLOS) outdoor, street-level coverage is possible up to approximately 200 m from a potential low-power microcell or picocell base station. In addition, based on statistical channel models from these measurements, it is shown that mmW systems can offer more than an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks at current cell densities. Cellular systems, however, will need to be significantly redesigned to fully achieve these gains. Specifically, the requirement of highly directional and adaptive transmissions, directional isolation between links, and significant possibilities of outage have strong implications on multiple access, channel structure, synchronization, and receiver design. To address these challenges, the paper discusses how various technologies including adaptive beamforming, multihop relaying, heterogeneous network architectures, and carrier aggregation can be leveraged in the mmW context.


title: Change management strategies for successful ERP implementation

abstract: The Array Processor of the present invention is comprised of a plurality of modular Elemental Processors, the modules being of a number of different functional types. These modules are associated so that the Elemental Processors are architecturally parallel to one another. The principal flow of data within the Array Processor, based on the simultaneous transfer of data words within the Elemental Processors, is thereby correspondingly parallel. The modules are also architecturally associated as functional planes that lie transverse to the Elemental Processors. Each functional plane is thereby comprised of an array of modules that are each otherwise associated with a separate Elemental Processor. Further, the modules of a given functional plane are of a single functional type. This allows the data of a two-dimensionally structured data set, present within the Array Processor, to be processed identically and in parallel by a common logical operation as provided and performed by a functional plane. The Array Processor is operatively connected to a Control Processor by an Array/Control Processor Interface. This interface allows the Control Processor to direct the operation of, and exchange data with, the Array Processor.


title: Dual-mode compact microstrip antenna based on fundamental backward wave

abstract: In this paper, a dual-mode compact microstrip antenna based on the fundamental backward wave supported by a composite right/left-handed (CRLH) metamaterial transmission line (TL) is demonstrated. The physical size and operational frequencies of the antenna are determined by the CRLH metamaterial's unit-cell. This antenna is capable of monopolar radiation at one resonant frequency and patch-like radiation at another resonant frequency. Numerical and experimental results of a three unit-cell CRLH-based antenna are used to verify the authors' claims.


title: Analysis And Design Of Ptat Temperature Sensor In Digital CMOS VLSI Circuits

abstract: The paper presents theoretical analyses, simulations and design of a PTAT (proportional to absolute temperature) temperature sensor that is based on the vertical PNP structure and dedicated to CMOS VLSI circuits. Performed considerations take into account specific properties of materials that forms electronic elements. The electrothermal simulations are performed in order to verify the unwanted self-heating effect of the sensor


title: Semantic Scene Completion from a Single Depth Image

abstract: This paper focuses on semantic scene completion, a task for producing a complete 3D voxel representation of volumetric occupancy and semantic labels for a scene from a single-view depth map observation. Previous work has considered scene completion and semantic labeling of depth maps separately. However, we observe that these two problems are tightly intertwined. To leverage the coupled nature of these two tasks, we introduce the semantic scene completion network (SSCNet), an end-to-end 3D convolutional network that takes a single depth image as input and simultaneously outputs occupancy and semantic labels for all voxels in the camera view frustum. Our network uses a dilation-based 3D context module to efficiently expand the receptive field and enable 3D context learning. To train our network, we construct SUNCG - a manually created largescale dataset of synthetic 3D scenes with dense volumetric annotations. Our experiments demonstrate that the joint model outperforms methods addressing each task in isolation and outperforms alternative approaches on the semantic scene completion task. The dataset and code is available at http://sscnet.cs.princeton.edu.


title: Convolutional neural network compression for natural language processing

abstract: Convolutional neural networks are modern models that are very efficient in many classification tasks. They were originally created for image processing purposes. Then some trials were performed to use them in different domains like natural language processing. The artificial intelligence systems (like humanoid robots) are very often based on embedded systems with constraints on memory, power consumption etc. Therefore convolutional neural network because of its memory capacity should be reduced to be mapped to given hardware. In this paper, results are presented of compressing the efficient convolutional neural networks for sentiment analysis. The main steps are quantization and pruning processes. The method responsible for mapping compressed network to FPGA and results of this implementation are presented. The described simulations showed that 5-bit width is enough to have no drop in accuracy from floating point version of the network. Additionally, significant memory footprint reduction was achieved (from 85% up to 93%).


title: A Structured Model for Action Detection

abstract: A dominant paradigm for learning-based approaches in computer vision is training generic models, such as ResNet for image recognition, or I3D for video understanding, on large datasets and allowing them to discover the optimal representation for the problem at hand. While this is an obviously attractive approach, it is not applicable in all scenarios. We claim that action detection is one such challenging problem - the models that need to be trained are large, and labeled data is expensive to obtain. To address this limitation, we propose to incorporate domain knowledge into the structure of the model, simplifying optimization. In particular, we augment a standard I3D network with a tracking module to aggregate long-term motion patterns, and use a graph convolutional network to reason about interactions between actors and objects. Evaluated on the challenging AVA dataset, the proposed approach improves over the I3D baseline by 5.5% mAP and over the state-of-the-art by 4.8% mAP.


title: Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer

abstract: Transferring artistic styles onto everyday photographs has become an extremely popular task in both academia and industry. Recently, offline training has replaced online iterative optimization, enabling nearly real-time stylization. When those stylization networks are applied directly to high-resolution images, however, the style of localized regions often appears less similar to the desired artistic style. This is because the transfer process fails to capture small, intricate textures and maintain correct texture scales of the artworks. Here we propose a multimodal convolutional neural network that takes into consideration faithful representations of both color and luminance channels, and performs stylization hierarchically with multiple losses of increasing scales. Compared to state-of-the-art networks, our network can also perform style transfer in nearly real-time by performing much more sophisticated training offline. By properly handling style and texture cues at multiple scales using several modalities, we can transfer not just large-scale, obvious style cues but also subtle, exquisite ones. That is, our scheme can generate results that are visually pleasing and more similar to multiple desired artistic styles with color and texture cues at multiple scales.


title: A Fruit Size Detecting and Grading System Based on Image Processing

abstract: This paper presents a fruit size detecting and grading system based on image processing. After capturing the fruit side view image, some fruit characters is extracted by using detecting algorithms. According to these characters, grading is realized. Experiments show that this embedded grading system has the advantage of high accuracy of grading, high speed and low cost. It will have a good prospect of application in fruit quality detecting and grading areas.


title: Innovative enhancement of the Caesar cipher algorithm for cryptography

abstract: The Caesar Cipher algorithm for cryptography is one of the oldest algorithms. Now much newer algorithms have arrived that are much more secure, however in terms of speed of execution Caesar cipher algorithm is still the fastest owing to its simplicity. However the algorithm is extremely easy to crack. This is because in this algorithm each character of a message is always replaced by the same fixed character that has been predetermined. To improve the algorithm and enhance it's security feature, a few changes can be added. This paper proposes an enhancement to the existing algorithm by making use first of a simple Diffie-Hellman key exchange scenario to obtain a secret key and later using simple mathematics to ensure the encryption of data is much more safer. Once a private shared key is obtained by making use of the Diffie-Hellman method, the key is subject to the mod operation with 26 to obtain a value less than or equal to 26, then the current character is taken and to this the key value obtained is added to obtain a new character. For any character in the ‘x’ position the key is simply first multiplied with ‘x’ and then mod is done to obtain the encrypted character. So 2nd character of the message is multiplied with 2, third character with 3 and so on. This enhances the security and also does not increase the time of execution by a large margin.


title: Comparing citation contexts for information retrieval

abstract: In previous work, we have shown that using terms from around citations in citing papers to index the cited paper, in addition to the cited paper's own terms, can improve retrieval effectiveness. Now, we investigate how to select text from around the citations in order to extract good index terms. We compare the retrieval effectiveness that results from a range of contexts around the citations, including no context, the entire citing paper, some fixed windows and several variations with linguistic motivations. We conclude with an analysis of the benefits of more complex, linguistically motivated methods for extracting citation index terms, over using a fixed window of terms. We speculate that there might be some advantage to using computational linguistic techniques for this task.


Behavior-Based Control: Main Properties and Implications


title: CU-Net: Coupled U-Nets

abstract: We design a new connectivity pattern for the U-Net architecture. Given several stacked U-Nets, we couple each U-Net pair through the connections of their semantic blocks, resulting in the coupled U-Nets (CU-Net). The coupling connections could make the information flow more efficiently across U-Nets. The feature reuse across U-Nets makes each U-Net very parameter efficient. We evaluate the coupled U-Nets on two benchmark datasets of human pose estimation. Both the accuracy and model parameter number are compared. The CU-Net obtains comparable accuracy as state-of-the-art methods. However, it only has at least 60% fewer parameters than other approaches.


title: Alzheimer’s Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning

abstract: Alzheimer’s disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer’s disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests; therefore, an efficient approach for accurate prediction of the condition of the brain through the classification of magnetic resonance imaging (MRI) images is greatly beneficial and yet very challenging. In this paper, a novel approach is proposed for the diagnosis of very early stages of AD through an efficient classification of brain MRI images, which uses label propagation in a manifold-based semi-supervised learning framework. We first apply voxel morphometry analysis to extract some of the most critical AD-related features of brain images from the original MRI volumes and also gray matter (GM) segmentation volumes. The features must capture the most discriminative properties that vary between a healthy and Alzheimer-affected brain. Next, we perform a principal component analysis (PCA)-based dimension reduction on the extracted features for faster yet sufficiently accurate analysis. To make the best use of the captured features, we present a hybrid manifold learning framework which embeds the feature vectors in a subspace. Next, using a small set of labeled training data, we apply a label propagation method in the created manifold space to predict the labels of the remaining images and classify them in the two groups of mild Alzheimer’s and normal condition (MCI/NC). The accuracy of the classification using the proposed method is 93.86% for the Open Access Series of Imaging Studies (OASIS) database of MRI brain images, providing, compared to the best existing methods, a 3% lower error rate.


title: 3D Interpreter Networks for Viewer-Centered Wireframe Modeling

abstract: Understanding 3D object structure from a single image is an important but challenging task in computer vision, mostly due to the lack of 3D object annotations to real images. Previous research tackled this problem by either searching for a 3D shape that best explains 2D annotations, or training purely on synthetic data with ground truth 3D information. In this work, we propose 3D INterpreter Networks (3D-INN), an end-to-end trainable framework that sequentially estimates 2D keypoint heatmaps and 3D object skeletons and poses. Our system learns from both 2D-annotated real images and synthetic 3D data. This is made possible mainly by two technical innovations. First, heatmaps of 2D keypoints serve as an intermediate representation to connect real and synthetic data. 3D-INN is trained on real images to estimate 2D keypoint heatmaps from an input image; it then predicts 3D object structure from heatmaps using knowledge learned from synthetic 3D shapes. By doing so, 3D-INN benefits from the variation and abundance of synthetic 3D objects, without suffering from the domain difference between real and synthesized images, often due to imperfect rendering. Second, we propose a Projection Layer, mapping estimated 3D structure back to 2D. During training, it ensures 3D-INN to predict 3D structure whose projection is consistent with the 2D annotations to real images. Experiments show that the proposed system performs well on both 2D keypoint estimation and 3D structure recovery. We also demonstrate that the recovered 3D information has wide vision applications, such as image retrieval.


title: Hunting high and low: instruments to detect misconceptions related to algorithms and data structures

abstract: We present the result of assessing first-year students' misconceptions related to algorithms and data structures. Our study confirms findings from previous small-scale studies but additionally broadens the scope of the topics and methods investigated. The evaluation of our experiments sheds light on dependencies between active and passive knowledge as well as on the instruments used; in particular, we conclude that there is no "one size fits all" instrument but that instruments should be selected depending on the topic at hand.


title: Learning unified features from natural and programming languages for locating buggy source code

abstract: Bug reports provide an effective way for end-users to disclose potential bugs hidden in a software system, while automatically locating the potential buggy source code according to a bug report remains a great challenge in software maintenance. Many previous studies treated the source code as natural language by representing both the bug report and source code based on bag-of-words feature representations, and correlate the bug report and source code by measuring similarity in the same lexical feature space. However, these approaches fail to consider the structure information of source code which carries additional semantics beyond the lexical terms. Such information is important in modeling program functionality. In this paper, we propose a novel convolutional neural network NP-CNN, which leverages both lexical and program structure information to learn unified features from natural language and source code in programming language for automatically locating the potential buggy source code according to bug report. Experimental results on widely-used software projects indicate that NP-CNN significantly outperforms the state-of-the-art methods in locating the buggy source files.


title: C-KLAM: Constrained keyframe-based localization and mapping

abstract: In this paper, we present C-KLAM, a Maximum A Posteriori (MAP) estimator-based keyframe approach for SLAM. As opposed to many existing keyframe-based SLAM approaches, that discard information from non-keyframes for reducing the computational complexity, the proposed C-KLAM presents a novel, elegant, and computationally-efficient technique for incorporating most of this information, resulting in improved estimation accuracy. To achieve this, C-KLAM projects both proprioceptive and exteroceptive information from the nonkeyframes to the keyframes, using marginalization, while maintaining the sparse structure of the associated information matrix, resulting in fast and efficient solutions. The performance of CKLAM has been tested in both simulations and experimentally, using visual and inertial measurements, to demonstrate that it achieves performance comparable to that of the computationallyintensive batch MAP-based 3D SLAM, that uses all available measurement information.


title: A hybrid recommender system using artificial neural networks

abstract: Neural network based hybrid recommender system utilizing review metadata is proposed.The system optimizes model hyper-parameters to minimize log-loss.Validate predictive capability of model against heterogeneous business categories. In the context of recommendation systems, metadata information from reviews written for businesses has rarely been considered in traditional systems developed using content-based and collaborative filtering approaches. Collaborative filtering and content-based filtering are popular memory-based methods for recommending new products to the users but suffer from some limitations and fail to provide effective recommendations in many situations. In this paper, we present a deep learning neural network framework that utilizes reviews in addition to content-based features to generate model based predictions for the business-user combinations. We show that a set of content and collaborative features allows for the development of a neural network model with the goal of minimizing logloss and rating misclassification error using stochastic gradient descent optimization algorithm. We empirically show that the hybrid approach is a very promising solution when compared to standalone memory-based collaborative filtering method.


title: Propp's Morphology of the Folk Tale as a Grammar for Generation

abstract: The semi-formal analysis of Russian folk tales carried out by Vladimir Propp has often been used as theoretical background for the automated generation of stories. Its rigour and its exhaustive description of the constituent elements of Russian folk tales, and the enumeration of the patterns they follow, have acted as inspiration for several story generation systems, both sequential and interactive. Yet most of these efforts have attempted to generalize Propp’s account to types of stories beyond the corpus that it arose from. In the process, a number of the valuable intuitions present in the original work are lost. The present paper revisits Propp’s morphology to build a system that generates instances of Russian folk tales. Propp’s view of the folk tale as a rigid sequence of character functions is employed as a plot driver. Unification is used to incrementally build a conceptual representation of discourse by adding to an ongoing draft story actions that instantiate the character functions. Story actions are defined by pre and post conditions on the state of the plot to account for the causal relations crucial to narrative. The potential of the resulting system for providing a generic story generation system is discussed and possible lines of future work are discussed.


title: An efficient FPGA overlay for portable custom instruction set extensions

abstract: Custom instruction set extensions can substantially boost performance of reconfigurable softcore CPUs. While this approach is commonly tailored to one specific FPGA system, we are presenting a fine-grained FPGA-like overlay architecture which can be implemented in the user logic of various FPGA families from different vendors. This allows the execution of a portable application consisting of a program binary and an overlay configuration in a completely heterogeneous environment. Furthermore, we are presenting different optimizations for dramatically reducing the implementation cost of the proposed overlay architecture. In particular, this includes the mapping of the overlay interconnection network directly into the switch fabric of the hosting FPGA. Our case study demonstrates an overhead reduction of an order of magnitude as compared to related approaches.


abstract: Abstract Introduction Vaginismus is mostly unknown among clinicians and women. Vaginismus causes women to have fear, anxiety, and pain with penetration attempts. Aim To present a large cohort of patients based on prior published studies approved by an institutional review board and the Food and Drug Administration using a comprehensive multimodal vaginismus treatment program to treat the physical and psychologic manifestations of women with vaginismus and to record successes, failures, and untoward effects of this treatment approach. Methods Assessment of vaginismus included a comprehensive pretreatment questionnaire, the Female Sexual Function Index (FSFI), and consultation. All patients signed a detailed informed consent. Treatment consisted of a multimodal approach including intravaginal injections of onabotulinumtoxinA (Botox) and bupivacaine, progressive dilation under conscious sedation, indwelling dilator, follow-up and support with office visits, phone calls, e-mails, dilation logs, and FSFI reports. Main Outcome Measures Logs noting dilation progression, pain and anxiety scores, time to achieve intercourse, setbacks, and untoward effects. Post-treatment FSFI scores were compared with preprocedure scores. Results One hundred seventy-one patients (71%) reported having pain-free intercourse at a mean of 5.1 weeks (median = 2.5). Six patients (2.5%) were unable to achieve intercourse within a 1-year period after treatment and 64 patients (26.6%) were lost to follow-up. The change in the overall FSFI score measured at baseline, 3 months, 6 months, and 1 year was statistically significant at the 0.05 level. Three patients developed mild temporary stress incontinence, two patients developed a short period of temporary blurred vision, and one patient developed temporary excessive vaginal dryness. All adverse events resolved by approximately 4 months. One patient required retreatment followed by successful coitus. Conclusion A multimodal program that treated the physical and psychologic aspects of vaginismus enabled women to achieve pain-free intercourse as noted by patient communications and serial female sexual function studies. Further studies are indicated to better understand the individual components of this multimodal treatment program. Pacik PT, Geletta S. Vaginismus Treatment: Clinical Trials Follow Up 241 Patients. Sex Med 2017;5:e114–e123.


title: A Full Proof of the BGW Protocol for Perfectly Secure Multiparty Computation

abstract: In the setting of secure multiparty computation, a set of n parties with private inputs wish to jointly compute some functionality of their inputs. One of the most fundamental results of secure computation was presented by Ben-Or, Goldwasser, and Wigderson (BGW) in 1988. They demonstrated that any n-party functionality can be computed with perfect security, in the private channels model. When the adversary is semi-honest, this holds as long as $$t


Big Data Analytics in Healthcare


Larval cestodes in marine invertebrates and fishes with a discussion of the life cycles of the tetraphyllidea and the trypanorhyncha


title: A cloud robotics system for telepresence enabling mobility impaired people to enjoy the whole museum experience

abstract: We present a novel robotic telepresence platform composed by a semi-autonomous mobile robot based on a cloud robotics framework, which has been developed with the aim of enabling mobility impaired people to enjoy museums and archaeological sites that would be otherwise inaccessible. Such places, in fact, very often are not equipped to provide access for mobility impaired people, in particular because these aids require dedicated infrastructures that may not fit within the environment and large investments. For this reason, people affected by mobility impairments are often unable to enjoy a part or even the entire museum experience. Solutions allowing mobility impaired people to enjoy museum experience are often based on recorded tours, thus they do not allow active participation of the user. On the contrary, the presented platform is intended to allow users to enjoy completely the museum round. A robot equipped with a camera is placed within the museum and users can control it in order to follow predefined tours or freely explore the museum. Our solution ensures that users see exactly what the robot is seing in real-time. The cloud robotics platform controls both navigation capabilities and teleoperation. Navigation tasks are intended to let the robot reliably follow pre-defined tours, while main concern of teleoperation tasks is to ensure robot safety (e.g., by means of dynamic obstacle detection and avoidance software). Proposed platform has been optimized to maximize user experience.


title: Moral Complexity : The Fatal Attraction of Truthiness and the Importance of Mature Moral Functioning

abstract: Recently, intuitionist theories have been effective in capturing the academic discourse about morality. Intuitionist theories, like rationalist theories, offer important but only partial understand...


title: Randomized Controlled Field Trials of Predictive Policing

abstract: The concentration of police resources in stable crime hotspots has proven effective in reducing crime, but the extent to which police can disrupt dynamically changing crime hotspots is unknown. Police must be able to anticipate the future location of dynamic hotspots to disrupt them. Here we report results of two randomized controlled trials of near real-time epidemic-type aftershock sequence (ETAS) crime forecasting, one trial within three divisions of the Los Angeles Police Department and the other trial within two divisions of the Kent Police Department (United Kingdom). We investigate the extent to which (i) ETAS models of short-term crime risk outperform existing best practice of hotspot maps produced by dedicated crime analysts, (ii) police officers in the field can dynamically patrol predicted hotspots given limited resources, and (iii) crime can be reduced by predictive policing algorithms under realistic law enforcement resource constraints. While previous hotspot policing experiments fix treatme...


title: 0.23-V Sample-Boost-Latch-Based Offset Tolerant Sense Amplifier

abstract: An offset tolerant SRAM sense amplifier (SA) deployed with sample-boost-latch technique to facilitate both common mode and differential mode boosting is proposed. The common mode boosting enables proposed SA, SBLSA to operate deeper into the subthreshold regime whereas the differential mode boosting helps tolerate undesirable mismatch conditions by driving cross-coupled inverters with boosted differential voltages. The proposed boosting circuit first samples the bitline voltages and then isolates the sampled voltages from the highly capacitive bitlines followed by the boosting phase on relatively much less internal input capacitances of the latch. Arrays of SBLSA and conventional voltage latch SA (VLSA) were fabricated in 65-nm-GP CMOS (512 each). At 25 °C, SBLSA achieves 23.3% reduction in directly measured standard deviation of input-referred offset (across 16 ICs, 8192 SAs) and 38.5% improvement (typical IC) in sensing delay at 0.3 V. SBLSA offers reliable operation across entire temperature range of −25 °C to 85 °C at 0.35 V whereas it is 0.41 V for VLSA. Finally, SBLSA operates reliably at the minimum supply of 0.23 V at 25 °C which is 30 mV less compared to VLSA.


title: Application of stationary wavelet entropy in pathological brain detection

abstract: Labeling brain images as healthy or pathological cases is an important procedure for medical diagnosis. Therefore, we proposed a novel image feature, stationary wavelet entropy (SWE), to extract brain image features. Meanwhile, we replaced the feature extraction procedure in state-of-the-art approaches with the proposed SWE. We found the classification performance improved after replacing wavelet entropy (WE), wavelet energy (WN), and discrete wavelet transform (DWT) with the proposed SWE. This proposed SWE is superior to WE, WN, and DWT.


title: From Partition-Based Clustering to Density-Based Clustering: Fast Find Clusters With Diverse Shapes and Densities in Spatial Databases

abstract: Spatial data clustering has played an important role in the knowledge discovery in spatial databases. However, due to the increasing volume and diversity of data, conventional spatial clustering methods are inefficient even on moderately large data sets, and usually fail to discover clusters with diverse shapes and densities. To address these challenges, we propose a two-phase clustering method named KMDD (clustering by combining K-means with density and distance-based method) to fast find clusters with diverse shapes and densities in spatial databases. In the first phase, KMDD uses a partition-based algorithm (K-means) to cluster the data set into several relatively small spherical or ball-shaped subclusters. After that, each subcluster is given a local density; to merge subclusters, KMDD utilizes the idea that genuine cluster cores are characterized by a higher density than their neighbor subclusters and by a relatively large distance from subclusters with higher densities. Extensive experiments on both synthetic and real-world data sets demonstrate that the proposed algorithm has a near-linear time complexity with respect to the data set size and dimension, and has the capability to find clusters with diverse shapes and densities.


title: Improved Wi-Fi RSSI Measurement for Indoor Localization

abstract: Indoor localization based on Wi-Fi received signal strength indication (RSSI) has the advantage of low cost and easy implementation compared with a range of other localization approaches. However, Wi-Fi RSSI suffers from multipath interference in indoor dynamic environments, resulting in significant errors in RSSI observations. To handle this issue, a number of different methods have been proposed in the literature, including the mean method, Kalman filter algorithm, and the particle filter algorithm. It is observed that these existing methods may not perform sufficiently well in ever-changing dynamic indoor environments. This paper presents an algorithm to improve RSSI observations by using the average of a number of selected maximum RSSI observations. Smoothness index is employed to evaluate the quality of RSSI so as to select an appropriate number of RSSI observations. Experiments were conducted in four rooms and a corridor within an office building and the results demonstrate that the proposed method considerably outperforms the existing algorithms in terms of positioning accuracy, which is defined as the cumulative distribution function of position error.


Signal to Noise Ratio Calculation Model in Radar Detection System


Note 65 10 January 2014 Measurement of Complex Permittivity of Large Concrete Samples with an Open-Ended Coaxial Line


title: Scene Coordinate and Correspondence Learning for Image-Based Localization

abstract: Scene coordinate regression has become an essential part of current camera re-localization methods. Different versions, such as regression forests and deep learning methods, have been successfully applied to estimate the corresponding camera pose given a single input image. In this work, we propose to regress the scene coordinates pixel-wise for a given RGB image by using deep learning. Compared to the recent methods, which usually employ RANSAC to obtain a robust pose estimate from the established point correspondences, we propose to regress confidences of these correspondences, which allows us to immediately discard erroneous predictions and improve the initial pose estimates. Finally, the resulting confidences can be used to score initial pose hypothesis and aid in pose refinement, offering a generalized solution to solve this task.


title: Tactile Texture Recognition with a 3-Axial Force MEMS integrated Artificial Finger

abstract: Recently, several three-axial MEMS-based force sensors have been developed. This kind of force micro sensor is also called tactile sensor in literature for its similarities in size and sensitivity with a human mechanoreceptors. Therefore, we believe these three-axial force sensors being able to analyse textures properties while sliding on a surface, as would do a person with his finger. In this paper, we present one of these sensors packaged as an artificial finger, with a hard structure for the bone and a soft rubber for the skin. Preliminary experiments show a good sensitivity of the finger, as its ability to sense the periodic structure of fabrics or to differentiate papers from fabrics calculating a friction coefficient. Its performance for discrimination of different surfaces is then estimated on fine textures of 10 kinds of paper. Supervised classification methods are tested on the data. They lead to an automatic classifier of the 10 papers showing good performances.


title: The IsoMetrics Usability Inventory - An operationalisation of ISO 9241-10 supporting summative and formative evaluation of software systems

abstract: Aiming at a user-oriented approach in software evaluation on the basis of ISO 9241 Part 10, we present a questionnaire (IsoMetrics) which collects usability data for summative and formative evaluation, and document its construction. The summative version of IsoMetrics shows a high reliability of its subscales and gathers valid information about differences in the usability of different software systems. Moreover, we show that the formative version of IsoMetrics is a powerful tool for supporting the identification of software weaknesses. Finally, we propose a procedure to categorize and prioritize weak points, which subsequently can be used as basic input to usability reviews.


title: A Comparison of learning algorithms on the Arcade Learning Environment

abstract: Reinforcement learning agents have traditionally been evaluated on small toy problems. With advances in computing power and the advent of the Arcade Learning Environment, it is now possible to evaluate algorithms on diverse and difficult problems within a consistent framework. We discuss some challenges posed by the arcade learning environment which do not manifest in simpler environments. We then provide a comparison of model-free, linear learning algorithms on this challenging problem set.


title: Self-contained Position Tracking of Human Movement Using Small Inertial/Magnetic Sensor Modules

abstract: Numerous applications require a self-contained personal navigation system that works in indoor and outdoor environments, does not require any infrastructure support, and is not susceptible to jamming. Posture tracking with an array of inertial/magnetic sensors attached to individual human limb segments has been successfully demonstrated. The "sourceless" nature of this technique makes possible full body posture tracking in an area of unlimited size with no supporting infrastructure. Such sensor modules contain three orthogonally mounted angular rate sensors, three orthogonal linear accelerometers and three orthogonal magnetometers. This paper describes a method for using accelerometer data combined with orientation estimates from the same modules to calculate position during walking and running. The periodic nature of these motions includes short periods of zero foot velocity when the foot is in contact with the ground. This pattern allows for precise drift error correction. Relative position is calculated through double integration of drift corrected accelerometer data. Preliminary experimental results for various types of motion including walking, side stepping, and running document accuracy of distance and position estimates.


title: Ovarian Huge Serous Cystadenoma in Adolescent Girl: A Case Report -

abstract: Ovarian cysts are an extremely common gynecological problem in adolescent. Majority of ovarian cysts are benign with few cases being malignant. Ovarian serous cystadenoma are rare in children. A 14-year-old presented with abdominal pain and severe abdominal distention. She underwent laparotomy and after surgical removal, the mass was found to be ovarian serous cystadenoma on histology. ::: In conclusions, germ cell tumors the most important causes for the giant ovarian masses in children. Epithelial tumors should not be forgotten in the differential diagnosis.


title: How iris recognition works

abstract: The principle that underlies the recognition of persons by their iris patterns is the failure of a test of statistical independence on texture phase structure as encoded by multiscale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 bits/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. Algorithms first described by the author in 1993 have now been tested in several independent field trials and are becoming widely licensed. This presentation reviews how the algorithms work and presents the results of 9.1 million comparisons among different eye images acquired in trials in Britain, the USA, Korea, and Japan.


title: Further Optimal Regret Bounds for Thompson Sampling

abstract: Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems. It is a randomized algorithm based on Bayesian ideas, and has recently generated significant interest after several studies demonstrated it to have better empirical performance compared to the state of the art methods. In this paper, we provide a novel regret analysis for Thompson Sampling that simultaneously proves both the optimal problem-dependent bound of $(1+\epsilon)\sum_i \frac{\ln T}{\Delta_i}+O(\frac{N}{\epsilon^2})$ and the first near-optimal problem-independent bound of $O(\sqrt{NT\ln T})$ on the expected regret of this algorithm. Our near-optimal problem-independent bound solves a COLT 2012 open problem of Chapelle and Li. The optimal problem-dependent regret bound for this problem was first proven recently by Kaufmann et al. [ALT 2012]. Our novel martingale-based analysis techniques are conceptually simple, easily extend to distributions other than the Beta distribution, and also extend to the more general contextual bandits setting [Manuscript, Agrawal and Goyal, 2012].


Status of humidification dehumidification desalination technology


title: Spearcons (Speech-Based Earcons) Improve Navigation Performance in Advanced Auditory Menus

abstract: Objective:The goal of this project is to evaluate a new auditory cue, which the authors call spearcons, in comparison to other auditory cues with the aim of improving auditory menu navigation.Backg...


title: Touch-less interactive augmented reality game on vision-based wearable device

abstract: This is the preprint version of our paper on Personal and Ubiquitous Computing. There is an increasing interest in creating pervasive games based on emerging interaction technologies. In order to develop touch-less, interactive and augmented reality games on vision-based wearable device, a touch-less motion interaction technology is designed and evaluated in this work. Users interact with the augmented reality games with dynamic hands/feet gestures in front of the camera, which triggers the interaction event to interact with the virtual object in the scene. Three primitive augmented reality games with eleven dynamic gestures are developed based on the proposed touch-less interaction technology as proof. At last, a comparing evaluation is proposed to demonstrate the social acceptability and usability of the touch-less approach, running on a hybrid wearable framework or with Google Glass, as well as workload assessment, user's emotions and satisfaction.


title: Sigma-Point Kalman Filters For Integrated Navigation

abstract: Core to integrated navigation systems is the concept of fusing noisy observations from GPS, Inertial Measurement Units (IMU), and other available sensors. The current industry standard and most widely used algorithm for this purpose is the extended Kalman filter (EKF) [6]. The EKF combines the sensor measurements with predictions coming from a model of vehicle motion (either dynamic or kinematic), in order to generate an estimate of the current navigational state (position, velocity, and attitude). This paper points out the inherent shortcomings in using the EKF and presents, as an alternative, a family of improved derivativeless nonlinear Kalman filters called sigma-point Kalman filters (SPKF). We demonstrate the improved state estimation performance of the SPKF by applying it to the problem of loosely coupled GPS/INS integration. A novel method to account for latency in the GPS updates is also developed for the SPKF (such latency compensation is typically inaccurate or not practical with the EKF). A UAV (rotor-craft) test platform is used to demonstrate the results. Performance metrics indicate an approximate 30% error reduction in both attitude and position estimates relative to the baseline EKF implementation.


title: Simple, Fast Noise-Contrastive Estimation for Large RNN Vocabularies

abstract: We present a simple algorithm to efficiently train language models with noise-contrastive estimation (NCE) on graphics processing units (GPUs). Our NCE-trained language models achieve significantly lower perplexity on the One Billion Word Benchmark language modeling challenge, and contain one sixth of the parameters in the best single model in Chelba et al. (2013). When incorporated into a strong Arabic-English machine translation system they give a strong boost in translation quality. We release a toolkit so that others may also train large-scale, large vocabulary LSTM language models with NCE, parallelizing computation across multiple GPUs.


title: Mathematical model for LTE system dimensioning

abstract: The objective of this paper is to define a mathematic model for the dimensioning of LTE Rel-8 network, after describing the mechanisms influencing the dimensioning such us the Scheduling, QOS and LTE capacity, we define the strategy of the planning and the objective function which aim to minimize the number of the base stations and the capacities of the wired links to deploy. The mathematical formulation of the model leads to a linear and mixed problem. PLEX method based on "Branch and Cut" algorithm has been used to solve this problem.


title: Hip-spine syndrome: cross-sectional-study of spinal alignment in patients with coxalgia

abstract: Introduction:Patients presenting with hip diseases often have coexisting spine disorders, a condition that is termed “hip-spine syndrome.” However, few reports have evaluated total spinal alignment...


title: Improving Human Action Recognition through Hierarchical Neural Network Classifiers

abstract: Automatic understanding of videos is one of the complex problems in machine learning and computer vision. An important area in the field of video analysis is human action recognition (HAR). Though a large number of HAR systems have already been developed, there is plenty of daily life actions that are difficult to recognize, due to several reasons, such as recording on different devices, poor video quality and similarities among actions. Development in the field of deep learning, especially in convolutional neural networks (CNN), has provided us with methods that are well-suited for the tasks of image and video recognition. This work implements a CNN-based hierarchical recognition approach to recognize 20 most difficult-to-recognize actions from the Kinetics dataset. Experimental results have shown that the application of our method significantly improves the quality of recognition for these actions.


title: Keyword extraction and clustering for document recommendation in conversations

abstract: This paper addresses the problem of keyword extraction from conversations, with the goal of using these keywords to retrieve, for each short conversation fragment, a small number of potentially relevant documents, which can be recommended to participants. However, even a short fragment contains a variety of words, which are potentially related to several topics; moreover, using an automatic speech recognition (ASR) system introduces errors among them. Therefore, it is difficult to infer precisely the information needs of the conversation participants. We first propose an algorithm to extract keywords from the output of an ASR system (or a manual transcript for testing), which makes use of topic modeling techniques and of a submodular reward function which favors diversity in the keyword set, to match the potential diversity of topics and reduce ASR noise. Then, we propose a method to derive multiple topically separated queries from this keyword set, in order to maximize the chances of making at least one relevant recommendation when using these queries to search over the English Wikipedia. The proposed methods are evaluated in terms of relevance with respect to conversation fragments from the Fisher, AMI, and ELEA conversational corpora, rated by several human judges. The scores show that our proposal improves over previous methods that consider only word frequency or topic similarity, and represents a promising solution for a document recommender system to be used in conversations.


title: A Hybrid Multi-criteria Semantic-Enhanced Collaborative Filtering Approach for Personalized Recommendations

abstract: Recommender systems aim to assist web users to find only relevant information to their needs rather than an undifferentiated mass of information. Collaborative filtering (CF) techniques are probably the most popular and widely adopted techniques in recommender systems. Despite of their success in various applications, CF-based techniques still encounter two major limitations, namely sparsity and cold-start problems. More recently, semantic information of items has been successfully used in recommender systems to alleviate such problems. Moreover, the incorporation of multi-criteria ratings in recommender systems can help to produce more accurate recommendations. Thereby, in this paper, we propose a hybrid Multi-Criteria Semantic-enhanced CF (MC-SeCF) approach. The MC-SeCF approach integrates the enhanced MC item-based CF and the item-based semantic filtering approaches to alleviate current limitations of the item-based CF techniques. Experimental results demonstrate the effectiveness of the proposed MC-SeCF approach in terms of improving accuracy, as well as in dealing with very sparse data sets or cold-start items compared to benchmark item-based CF techniques.


title: My Smartphone Knows What You Print: Exploring Smartphone-based Side-channel Attacks Against 3D Printers

abstract: Additive manufacturing, also known as 3D printing, has been increasingly applied to fabricate highly intellectual property (IP) sensitive products. However, the related IP protection issues in 3D printers are still largely underexplored. On the other hand, smartphones are equipped with rich onboard sensors and have been applied to pervasive mobile surveillance in many applications. These facts raise one critical question: is it possible that smartphones access the side-channel signals of 3D printer and then hack the IP information? To answer this, we perform an end-to-end study on exploring smartphone-based side-channel attacks against 3D printers. Specifically, we formulate the problem of the IP side-channel attack in 3D printing. Then, we investigate the possible acoustic and magnetic side-channel attacks using the smartphone built-in sensors. Moreover, we explore a magnetic-enhanced side-channel attack model to accurately deduce the vital directional operations of 3D printer. Experimental results show that by exploiting the side-channel signals collected by smartphones, we can successfully reconstruct the physical prints and their G-code with Mean Tendency Error of 5.87% on regular designs and 9.67% on complex designs, respectively. Our study demonstrates this new and practical smartphone-based side channel attack on compromising IP information during 3D printing.


title: Characteristics of professional development that effect change in secondary science teachers' classroom practices

abstract: We studied the outcome of a professional development opportunity that consisted of 2- week-long resident institutes for teams consisting of a secondary science teacher and two students. The science content of the National Science Foundation (NSF)-funded professional development institute was monarch butterfly ecology. The first institute took place in Minnesota during the summer, and the second in Texas during the fall. Staff scientists provided intense instruction in inquiry, with numerous opportunities for participants to conduct short inquiry-based research projects. Careful attention was paid to introducing each step of the full inquiry process, from asking questions to presenting research findings. All participants conducted independent team full inquiry projects between the two institutes. Project findings show that the number of teachers providing opportunities for their students to conduct full inquiry increased significantly after their participation. A mixed-methodology analysis that included qualitative and quantitative data from numerous sources, and case studies of 20 teachers, revealed that the characteristics of the program that helped teachers successfully translate inquiry to their classrooms were: deep science content and process knowledge with numerous opportunities for practice; the requirement that teachers demonstrate competence in a tangible and assessable way; and providers with high expectations for learning and the capability to facilitate multifaceted inquiry experiences. 2005 Wiley Periodicals, Inc. J Res Sci Teach 42: 668-690, 2005