title: Effect of diet and exercise on body composition, energy intake and leptin levels in overweight women and men.

abstract: OBJECTIVE ::: To investigate the effect of diet alone (D), exercise alone (E), and a combination of diet and exercise (DE) on body weight, body composition, energy intake, blood pressure, serum lipid and leptin levels, and fitness levels in mildly obese sedentary women and men. ::: ::: ::: DESIGN ::: The three interventions were compared in a randomized longitudinal study design. The exercise programs were supervised for six months, after which participants in E and DE were provided with exercise equipment to take home. ::: ::: ::: SUBJECTS ::: 90 adult overweight women and men (age: 44.2 +/- 7.2 years; BMI = 30.5 +/- 2.7 kg/m(2)). ::: ::: ::: MEASUREMENTS ::: Body weight, body composition, waist and hip circumferences, blood pressure, serum lipid levels, and fitness levels were evaluated at 0, 3, 6, 9, and 12 months. Serum leptin concentrations were measured at 0 and 6 months only. ::: ::: ::: RESULTS ::: At 6 and 9 months in women, and 9 months in men, DE demonstrated a significant loss of body weight compared to both D and E (p < 0.05). Serum leptin levels significantly decreased from baseline to 6 months in women in D (p = 0.05) and DE (p = 0.0003) and men in E (p = 0.038). At one year, no significant differences existed among groups in any of the measures. ::: ::: ::: CONCLUSION ::: A combination of diet and exercise resulted in a significant decrease in body weight in women and men; but this decrease was not maintained at one year follow-up. Serum leptin concentrations showed significant within-group decreases, but were not different among groups. A supervised diet and exercise program is effective for weight loss; however, once intensive participant-investigator and participant-participant contact is discontinued, weight regain ensures.


title: Knowledge Sharing in the Workplace: A Social Networking Site Assessment

abstract: Enterprise executives are taking note that great potential exists for enhancing knowledge sharing and linking experts within the organization when incorporating social software technologies into their operations. But there are warnings of potential negative effects if ineffective use or even misuse of these technologies occurs. There is relatively little empirical research examining the business impact of social software implementations evolving from Web 2.0 applications when considering the effects of trust on knowledge sharing. This paper attempts to fill this gap in the research by proposing a theoretical framework to study the effects of trust, risk and benefits, critical mass, and social influence on knowledge sharing intentions of employees using social media technology in the organization.


title: A Bi-LSTM-RNN Model for Relation Classification Using Low-Cost Sequence Features

abstract: Relation classification is associated with many potential applications in the artificial intelligence area. Recent approaches usually leverage neural networks based on structure features such as syntactic or dependency features to solve this problem. However, high-cost structure features make such approaches inconvenient to be directly used. In addition, structure features are probably domain-dependent. Therefore, this paper proposes a bi-directional long-short-term-memory recurrent-neural-network (Bi-LSTM-RNN) model based on low-cost sequence features to address relation classification. This model divides a sentence or text segment into five parts, namely two target entities and their three contexts. It learns the representations of entities and their contexts, and uses them to classify relations. We evaluate our model on two standard benchmark datasets in different domains, namely SemEval-2010 Task 8 and BioNLP-ST 2016 Task BB3. In the former dataset, our model achieves comparable performance compared with other models using sequence features. In the latter dataset, our model obtains the third best results compared with other models in the official evaluation. Moreover, we find that the context between two target entities plays the most important role in relation classification. Furthermore, statistic experiments show that the context between two target entities can be used as an approximate replacement of the shortest dependency path when dependency parsing is not used.


title: Running head: MEASURING LEARNER ENGAGEMENT Measuring Learner Engagement 1 Measuring Learner Engagement in Computer-Equipped College Classrooms

abstract: Although engagement and learning seem linked, quantitatively measuring this relationship is challenging. New technologies offer a window into studying the interactions among classroom activity, student engagement, and positive learning outcomes in computer-equipped classrooms. A Classroom Behavioral Analysis System (CBAS) was developed to measure student engagement in a college writing class, and to test the hypothesis that an interactive lesson would increase student engagement levels in a computer-equipped classroom. Student computer-based behaviors (off-task and on-task Internet visits) were compared during a traditional, lecture-based lesson (no-simulation condition) and an interactive simulation-based lesson (simulation condition). The dependent variable was student engagement as measured by the number of off-task and on-task Internet activities during the lesson. Off-task Internet activities were operationalized as website visits that were not part of the classroom activity; on-task Internet activities included websites that related to the assigned class activity. CBAS recorded all student computer actions during the observed instructional periods. Students attending a simulation-based lesson performed more on-task Internet actions, and significantly fewer off-task Internet actions than did students attending a lecture-based lesson. These findings support the hypothesis that interactive lessons increase student engagement levels in computer-equipped classrooms, and demonstrate that CBAS is a promising tool for studying student engagement.


title: On Smoothing and Inference for Topic Models

abstract: Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling high-dimensional sparse count data. Various learning algorithms have been developed in recent years, including collapsed Gibbs sampling, variational inference, and maximum a posteriori estimation, and this variety motivates the need for careful empirical comparisons. In this paper, we highlight the close connections between these approaches. We find that the main differences are attributable to the amount of smoothing applied to the counts. When the hyperparameters are optimized, the differences in performance among the algorithms diminish significantly. The ability of these algorithms to achieve solutions of comparable accuracy gives us the freedom to select computationally efficient approaches. Using the insights gained from this comparative study, we show how accurate topic models can be learned in several seconds on text corpora with thousands of documents.


A Proposed Lexicon-Based Sentiment Analysis Approach for the Vernacular Algerian Arabic


title: An Approach in Security and Privacy for Service Governance Framework

abstract: Service Oriented Architecture (SOA) is a modern methodology for developing software. In a short period oftime, SOA is being adopted by many companies because of the extensive advantages it guarantees. Service governance plays a vital role in determining the success and failure of a SOA project. Service governance can be defined as the process which governs the mechanisms applied in SOA. It is a vast field that has various branches. This paper focuses on the current security and privacy issues which prevail in the governance of SOA projects. Security and privacy are critical aspects of SOA. SOA projects with good quality attributes but with weaknesses in these two areas still can make the project a failure. Thus security and privacy are vital factors that have to be considered while developing a SOA project. After identifying the security and privacy issues, we propose an approach to address these issues. This approach acts as a solution for security and privacy problems faced by SO A projects in the service governance context. The paper gives an approach for service governance framework that deals with the security an problems of SOA. Furthermore the derived approach is implemented in a healthcare website application for demonstrating how security issues are tacked.


title: The features, hardware, and architectures of data center networks

abstract: The rapid development of cloud computing in recent years has deeply affected our lifestyles. As core infrastructures of cloud computing, data centers have gained widespread attention from both the academia and industry. In a data center, the data center network (DCN) that plays a key role in computing and communication has attracted extensive interest from researchers. In this survey, we discuss the features, hardware, and architectures of DCN's, including their logical topological connections and physical component categorizations. We first give an overview of production data centers. Next, we introduce the hardware of DCN's, including switches, servers, storage devices, racks, and cables used in industries, which are highly essential for designing DCN architectures. And then we thoroughly analyze the topology designs and architectures of DCN's from various aspects, such as connection types, wiring layouts, interconnection facilities, and network characteristics based on the latest literature. Finally, the facility settings and maintenance issues for data centers that are important in the performance and the efficiency of DCN's are also briefly discussed. Specifically and importantly, we provide both qualitative and quantitative analyses on the features of DCN's, including performance comparisons among typical topology designs, connectivity discussion on average degree, bandwidth calculation, and diameter estimation, as well as the capacity enhancement of DCN's with wireless antennae and optical devices. The discussion of our survey can be referred as an overview of the ongoing research in the related area. We also present new observations and research trends for DCN's. We give a survey on the features and hardware of Data Center Networks.We thoroughly analyze the topology designs and architectures of DCNs.We provide both qualitative and quantitative analyses on the features of DCNs.


title: A study of flip-chip direct attachment LED COBs with metal core print circuit boards

abstract: One of the most critical reliability problems that the LED COBs are facing is the failure of wire bonding connections. The LED COBs would easily break down due to the mechanical shocks or material stress on the gold wire in conventional package roadmaps. Flip chip technologies are excellent options for solving this problem. This paper has been focused on the challenges during the design and manufacturing of flip chip LED COBs based on metal core print circuit boards (MCPCBs). The properties of solder paste, Au/Sn eutectic, and ceramic buffer die-bonding structures are studied. It is found out that the former two COBs have better thermal and optical performance, while the last COB shows good reliability with low leakage current.


title: OCMiner: A density-based overlapping community detection method for social networks

abstract: Community detection is an important task for identifying the structure and function of complex networks. The task is challenging as communities often show overlapping and hierarchical behavior, i.e., a node can belong to multiple communities, and multiple smaller communities can be embedded within a larger community. Moreover, real-world networks often contain communities of arbitrary size and shape, along with outliers. This paper presents a novel density-based overlapping community detection method, OCMiner, to identify overlapping community structures in social networks. Unlike other density-based community detection methods, OCMiner does not require the neighborhood threshold parameter (e) to be set by the users. Determining an optimal value for e is a longstanding and challenging task for density-based clustering methods. Instead, OCMiner automatically determines the neighborhood threshold parameter for each node locally from the underlying network. It also uses a novel distance function which utilizes the weights of the edges in weighted networks, besides being able to find communities in un-weighted networks. The efficacy of the proposed method has been established through experiments on various real-world and synthetic networks. In comparison to the existing state-of-the-art community detection methods, OCMiner is computationally faster, scalable to large-scale networks, and able to find significant community structures in social networks.


title: Exploiting entities in social media

abstract: Over the past couple of years micro blogging platforms, such as Twitter, have become extremely popular for information generation and dissemination. Each day hundreds of millions of tweets are being published, containing fresh and trending information that is highly valuable for online users. However, discovering relevant information from such sources is becoming harder due to their rapid growth and the fact that social fragments are often short and noisy. Aggregation techniques such as clustering are often used for extracting this relevant information, since interesting signals begin to emerge when these fragments are grouped together. Clustering large amount of short tweets with limited features is however a challenging task in itself. In this paper, we propose to aggregate tweets by pivoting on entities and mapping them to topics that are already defined in websites such as Wikipedia and Freebase. This allows us to aggregate tweets in a more reliable and feasible way while providing interesting aggregated information about entities present in these fragments. Our analysis using large amounts of tweets shows that such an approach indeed works well. We present encouraging results and various interesting applications centered on entities.


title: Metagenomics - a guide from sampling to data analysis

abstract: Metagenomics applies a suite of genomic technologies and bioinformatics tools to directly access the genetic content of entire communities of organisms. The field of metagenomics has been responsible for substantial advances in microbial ecology, evolution, and diversity over the past 5 to 10 years, and many research laboratories are actively engaged in it now. With the growing numbers of activities also comes a plethora of methodological knowledge and expertise that should guide future developments in the field. This review summarizes the current opinions in metagenomics, and provides practical guidance and advice on sample processing, sequencing technology, assembly, binning, annotation, experimental design, statistical analysis, data storage, and data sharing. As more metagenomic datasets are generated, the availability of standardized procedures and shared data storage and analysis becomes increasingly important to ensure that output of individual projects can be assessed and compared.


title: Cochlear Nerve Aplasia and Hypoplasia: Predictors of Cochlear Implant Success

abstract: Objective To identify factors predicting performance outcomes following cochlear implantation in patients with cochlear nerve aplasia or hypoplasia. Data Sources Individual patient data extracted from published case series and reports. Review Methods The MEDLINE database, Cochrane Library, Embase, Web of Science, and Google Scholar were queried for "cochlear implant" in conjunction with "aplasia" or "hypoplasia" between 1985 and 2015. Eighteen studies were included describing 97 subjects with individual postimplant auditory data. Postimplant performance was categorized as follows: level 1, nonstimulation/minimal detection; level 2, improved detection; level 3, closed-set speech perception; or level 4, open-set speech perception. The subjects achieving speech perception (levels 3 and 4) were descriptively compared with those who did not. Results Subjects with a hypoplastic cochlear nerve on magnetic resonance imaging had higher reported rates of achieving speech perception than those with an aplastic nerve. Subjects with syndromic medical comorbidities had higher reported rates of nonstimulation than nonsyndromic subjects. The data showed that some children with an aplastic cochlear nerve or those with partial electrode insertion could obtain levels of speech discrimination. Reporting of patient characteristics and auditory outcomes was extremely variable across studies. Conclusion As previously shown, cochlear implant in patients with cochlear nerve aplasia or hypoplasia can provide meaningful hearing for select patients. The current study suggests that presence of a cochlear nerve on magnetic resonance imaging and lack of comorbid medical syndrome are associated with better auditory outcomes in such patients. Future efforts to report individual data in a consistent manner may allow better determination of predictive factors.


Some aspects on hybrid wideband transceiver design for mmWave communication systems.


title: Global change and species interactions in terrestrial ecosystems

abstract: The main drivers of global environmental change (CO2 enrichment, nitrogen deposition, climate, biotic invasions and land use) cause extinctions and alter species distributions, and recent evidence shows that they exert pervasive impacts on various antagonistic and mutualistic interactions among species. In this review, we synthesize data from 688 published studies to show that these drivers often alter competitive interactions among plants and animals, exert multitrophic effects on the decomposer food web, increase intensity of pathogen infection, weaken mutualisms involving plants, and enhance herbivory while having variable effects on predation. A recurrent finding is that there is substantial variability among studies in both the magnitude and direction of effects of any given GEC driver on any given type of biotic interaction. Further, we show that higher order effects among multiple drivers acting simultaneously create challenges in predicting future responses to global environmental change, and that extrapolating these complex impacts across entire networks of species interactions yields unanticipated effects on ecosystems. Finally, we conclude that in order to reliably predict the effects of GEC on community and ecosystem processes, the greatest single challenge will be to determine how biotic and abiotic context alters the direction and magnitude of GEC effects on biotic interactions.


title: A 14 b 35 MS/s SAR ADC Achieving 75 dB SNDR and 99 dB SFDR With Loop-Embedded Input Buffer in 40 nm CMOS

abstract: This paper presents a 14 bit 35 MS/s successive approximation register (SAR) ADC that achieves a nearly constant 74.5 dB peak SNDR up to Nyquist and an SFDR of 90/99 dB for inputs near Nyquist and at low-frequencies, respectively. The ADC employs a loop-embedded input buffer that shields the large sampling capacitor from the input and thereby eases the ADC drive requirements significantly. Since the buffer's nonlinearity is cancelled by the SAR operation, a pair of basic source followers can be used, adding only 12.5 mW (23% of the total power) to the power budget. The ADC includes a bandgap reference and a self-calibrated current steering DAC to close the SAR loop, which eliminates the need for a low-impedance off-chip reference. The design occupies 0.236 mm $^{2}$ in 40 nm CMOS and consumes a total power of 54.5 mW from its 1.2/2.5 V supplies, leading to an SNDR-based Schreier FOM of 159.5 dB at Nyquist.


Fast learning of scale-free networks based on Cholesky factorization


Life after App Uninstallation: Are the Data Still Alive? Data Residue Attacks on Android


title: Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence

abstract: Aesthetic quality prediction is a challenging task in the computer vision community because of the complex interplay with semantic contents and photographic technologies. Recent studies on the powerful deep learning based aesthetic quality assessment usually use a binary high-low label or a numerical score to represent the aesthetic quality. However the scalar representation cannot describe well the underlying varieties of the human perception of aesthetics. In this work, we propose to predict the aesthetic score distribution (i.e., a score distribution vector of the ordinal basic human ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs which aim to minimize the difference between the predicted scalar numbers or vectors and the ground truth cannot be directly used for the ordinal basic rating distribution. Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization). Experimental results on large scale aesthetic dataset demonstrate the effectiveness of our introduced CJS-CNN in this task.


title: Anytime State-Based Solution Methods for Decision Processes with non-Markovian Rewards

abstract: A popular approach to solving a decision process with non-Markovian rewards (NMRDP) is to exploit a compact representation of the reward function to automatically translate the NMRDP into an equivalent Markov decision process (MDP) amenable to our favorite MDP solution method. The contribution of this paper is a representation of non-Markovian reward functions and a translation into MDP aimed at making the best possible use of state-based anytime algorithms as the solution method. By explicitly constructing and exploring only parts of the state space, these algorithms are able to trade computation time for policy quality, and have proven quite effective in dealing with large MDPs. Our representation extends future linear temporal logic (FLTL) to express rewards. Our translation has the effect of embedding modelchecking in the solution method. It results in an MDP of the minimal size achievable without stepping outside the anytime framework, and consequently in better policies by the deadline.


title: Monocular Template-based Reconstruction of Inextensible Surfaces

abstract: We present a monocular 3D reconstruction algorithm for inextensible deformable surfaces. It uses point correspondences between a single image of the deformed surface taken by a camera with known intrinsic parameters and a template. The main assumption we make is that the surface shape as seen in the template is known. Since the surface is inextensible, its deformations are isometric to the template. We exploit the distance preservation constraints to recover the 3D surface shape as seen in the image. Though the distance preservation constraints have already been investigated in the literature, we propose a new way to handle them. Spatial smoothness priors are easily incorporated, as well as temporal smoothness priors in the case of reconstruction from a video. The reconstruction can be used for 3D augmented reality purposes thanks to a fast implementation. We report results on synthetic and real data. Some of them are compared to stereo-based 3D reconstructions to demonstrate the efficiency of our method.


title: Leaf size, specific leaf area and microhabitat distribution of chaparral woody plants: contrasting patterns in species level and community level analyses

abstract: We examined variation in leaf size and specific leaf area (SLA) in relation to the distribution of 22 chaparral shrub species on small-scale gradients of aspect and elevation. Potential incident solar radiation (insolation) was estimated from a geographic information system to quantify microclimate affinities of these species across north- and south-facing slopes. At the community level, leaf size and SLA both declined with increasing insolation, based on average trait values for the species found in plots along the gradient. However, leaf size and SLA were not significantly correlated across species, suggesting that these two traits are decoupled and associated with different aspects of performance along this environmental gradient. For individual species, SLA was negatively correlated with species distributions along the insolation gradient, and was significantly lower in evergreen versus deciduous species. Leaf size exhibited a negative but non-significant trend in relation to insolation distribution of individual species. At the community level, variance in leaf size increased with increasing insolation. For individual species, there was a greater range of leaf size on south-facing slopes, while there was an absence of small-leaved species on north-facing slopes. These results demonstrate that analyses of plant functional traits along environmental gradients based on community level averages may obscure important aspects of trait variation and distribution among the constituent species.


title: A 3D printed low profile magnetic dipole antenna

abstract: An electrically small, low profile magnetic dipole antenna is presented. The proposed antenna is composed of multiple small twisted loops connected in parallel. The simulated radiation efficiency is 87 % at the resonant frequency of 222.5 MHz. The corresponding electrical size ka is 0.276 with the height of 0.0016λ. The prototype is built using a selective laser sintering technology and silver paste painting. The measured result is discussed.


title: Learn to segment single cells with deep distance estimator and deep cell detector

abstract: Abstract Single cell segmentation is a critical and challenging step in cell imaging analysis. Traditional processing methods require time and labor to manually fine-tune parameters and lack parameter transferability between different situations. Recently, deep convolutional neural networks (CNN) treat segmentation as a pixel-wise classification problem and have become a general and efficient method for image segmentation. However, cell imaging data often possesses characteristics that adversely affect segmentation accuracy: absence of established training datasets, few pixels on cell boundaries, and ubiquitous blurry features. We developed a strategy that combines strengths of CNN and traditional watershed algorithm. First, we trained a CNN to learn Euclidean distance transform (EDT) of the mask corresponding to the input images (deep distance estimator). Next, we trained a faster R-CNN (Region with CNN) to detect individual cells in the EDT image (deep cell detector). Then, the watershed algorithm performed the final segmentation using the outputs of previous two steps. Tests on a library of fluorescence, phase contrast and differential interference contrast (DIC) images showed that both the combined method and various forms of the pixel-wise classification algorithm achieved similar pixel-wise accuracy. However, the combined method achieved significantly higher cell count accuracy than the pixel-wise classification algorithm did, with the latter performing poorly when separating connected cells, especially those connected by blurry boundaries. This difference is most obvious when applied to noisy images of densely packed cells. Furthermore, both deep distance estimator and deep cell detector converge fast and are easy to train.


A Character-Aware Encoder for Neural Machine Translation


abstract: Almost perfect autocorrelation sequences are defined as complex periodic sequences such that all the out-of-phase autocorrelation coefficients are zero except one. The study is restricted to (-1,+1)-sequences. In this case, such sequences exist only if the period n is a multiple of 4. After setting up theoretical results, several sequences are constructed for every period n multiple of 4 in the range 8 >


title: Successful Immediate Staged Breast Reconstruction with Intermediary Autologous Lipotransfer in Irradiated Patients

abstract: As indications for radiotherapy in mastectomized patients grow, the need for greater reconstructive options is critical. Preliminary research suggests an ameliorating impact of lipotransfer on irradiated patients with expander-to-implant reconstruction. Herein, we present our technique using lipotransfer during the expansion stage to facilitate implant placement. ::: ::: ::: Methods ::: A retrospective review of postmastectomy patients with expander-to-implant reconstruction by one reconstructive surgeon was performed. All patients were treated with immediate expander and ADM placement at the time of mastectomy. Irradiated patients underwent a separate lipotransfer procedure after completion of radiotherapy but prior to prosthesis exchange. Our study compared postoperative outcomes between non-radiated patients and irradiated patients who underwent this intermediary lipotransfer. Clinical endpoints of interest included: overall complications, infection, delayed wound healing, dehiscence, capsular contracture, implant failure, and reoperation. ::: ::: ::: Results ::: One hundred and thirty-one breast reconstructions were performed; 18 (13.74%) were irradiated and 113 (86.26%) were not. Overall complication risk (infection, implant failure, or reoperation) was no higher in irradiated breasts treated with lipotransfer than non-irradiated breasts (p=0.387). Fifteen patients who had one radiated and one non-radiated breast were separately analyzed; no difference in complication by radiotherapy exposure (p=1) was found. Age, BMI, smoking status, and nipple-sparing versus skin-sparing mastectomy did not vary significantly between study groups (p=0.182, p=0.696, p=0.489, p=1 respectively). ::: ::: ::: Conclusions ::: Comparable postoperative outcomes were found between non-radiated breasts and radiated breasts treated with intermediary lipotransfer. The ameliorating effects of autologous lipotransfer on radiotoxicity may therefore offer irradiated patients the option of expander-to-implant reconstruction with acceptable risk and cosmesis.


title: Radio signal propagation and attenuation measurements for modern residential buildings

abstract: This paper outlines problems that cellular network operators will face as energy-efficient housing becomes more popular. We report measurement results from houses made of modern construction materials that are required to achieve sufficient level of energy-efficiency, but that impact heavily also on radio signal propagation. Energy-efficiency is especially important in northern countries, where houses need to be properly isolated as heating generates a big share of the total energy consumption of households. However, the energy-efficiency trend will also reach rest of the Europe and other warmer countries as the tightening energy-efficiency requirements concern also cooling the houses. The measurement results indicate severe problems originating from radio signal attenuation as it increases up to 35 dB for individual construction materials for cellular frequencies around 2 GHz. From the perspective of actual building penetration losses in modern, energy-efficient houses, average attenuation values even up to 30 dB have been measured. Additional attenuation is very sensitive to buildings materials, but could jeopardize cellular coverage in the future.


title: Persistent detachment of the fovea after non-buckling repair of rhegmatogenous retinal detachment.

abstract: Persistent detachment of the fovea is increasingly recognised as a cause of delayed visual recovery after repair of rhegmatogenous retinal detachment (RRD).1–6 Often subfoveal fluid cannot be seen on biomicroscopy or indirect ophthalmoscopy. It has only been the recent introduction of optical coherence tomography (OCT) into clinical practice that has allowed a more detailed study of this fluid. Persistent subfoveal fluid has primarily been reported after scleral buckling (SB) surgery,7 though it has been documented after pneumatic retinopexy8,9 and pars plana vitrectomy (PPV).10 The persistent fluid can be very subtle and difficult to detect on fundus examination. We report three cases of delayed visual recovery caused by persistent neurosensory macular detachment after non-scleral buckling procedures. Despite a normal appearing fundus, OCT confirmed the presence of persistent subretinal fluid. ::: ::: A 58 year old myopic woman was referred for the treatment of a macula-off retinal detachment in the left eye. Her symptoms had begun 6 weeks earlier. Examination of the right eye was within normal limits. Best corrected visual acuity (BCVA) in the left eye was 20/400. Slit lamp examination (SLE) was unremarkable. Dilated fundus examination revealed a horseshoe retinal tear at 1 o’clock with macula-off temporal retinal detachment. Cryotherapy and pneumatic retinopexy with C3F8 gas were performed …


Learning Discriminative Affine Regions via


MEMS INERTIAL SENSOR 3-Axis - ±2g/±6g Digital Output Low Voltage Linear Accelerometer


title: A New Method to Visualize Deep Neural Networks

abstract: We present a method for visualising the response of a deep neural network to a specific input. For image data for instance our method will highlight areas that provide evidence in favor of, and against choosing a certain class. The method overcomes several shortcomings of previous methods and provides great additional insight into the decision making process of convolutional networks, which is important both to improve models and to accelerate the adoption of such methods in e.g. medicine. In experiments on ImageNet data, we illustrate how the method works and can be applied in different ways to understand deep neural nets.


title: Assessment of the state of food waste treatment in the United States and Canada.

abstract: Currently in the US, over 97% of food waste is estimated to be buried in landfills. There is nonetheless interest in strategies to divert this waste from landfills as evidenced by a number of programs and policies at the local and state levels, including collection programs for source separated organic wastes (SSO). The objective of this study was to characterize the state-of-the-practice of food waste treatment alternatives in the US and Canada. Site visits were conducted to aerobic composting and two anaerobic digestion facilities, in addition to meetings with officials that are responsible for program implementation and financing. The technology to produce useful products from either aerobic or anaerobic treatment of SSO is in place. However, there are a number of implementation issues that must be addressed, principally project economics and feedstock purity. Project economics varied by region based on landfill disposal fees. Feedstock purity can be obtained by enforcement of contaminant standards and/or manual or mechanical sorting of the feedstock prior to and after treatment. Future SSO diversion will be governed by economics and policy incentives, including landfill organics bans and climate change mitigation policies.


title: Content annotation for the semantic web: an automatic web-based approach

abstract: Semantic Annotation is required to add machine-readable content to natural language text. A global initiative such as the Semantic Web directly depends on the annotation of massive amounts of textual Web resources. However, considering the amount of those resources, a manual semantic annotation of their contents is neither feasible nor scalable. In this paper we introduce a methodology to partially annotate textual content of Web resources in an automatic and unsupervised way. It uses several well-established learning techniques and heuristics to discover relevant entities in text and to associate them to classes of an input ontology by means of linguistic patterns. It also relies on the Web information distribution to assess the degree of semantic co-relation between entities and classes of the input domain ontology. Special efforts have been put in minimizing the amount of Web accesses required to evaluate entities in order to ensure the scalability of the approach. A manual evaluation has been carried out to test the methodology for several domains showing promising results.


title: Orienting attention in time: behavioural and neuroanatomical distinction between exogenous and endogenous shifts

abstract: Abstract Temporal orienting of attention is the ability to focus resources at a particular moment in time in order to optimise behaviour, and is associated with activation of left parietal and premotor cortex [Coull, J. T., Nobre, A. C. Where and when to pay attention: the neural systems for directing attention to spatial locations and to time intervals as revealed by both PET and fMRI. Journal of Neuroscience, 1998, 18, 7426–7435]. In the present experiment, we explored the behavioural and anatomical correlates of temporal orienting to foveal visual stimuli, in order to eliminate any spatial attention confounds. We implemented a two-way factorial design in an event-related fMRI study to examine the factors of trial validity (predictability of target by cue), length of delay (cue-target interval), and their interaction. There were two distinct types of invalid trial: those where attention was automatically drawn to a premature target and those where attention was voluntarily shifted to a delayed time-point. Reaction times for valid trials were shorter than those for invalid trials, demonstrating appropriate allocation of attention to temporal cues. All trial-types activated a shared system, including frontoparietal areas bilaterally, showing that this network is consistently associated with attentional orienting and is not specific to spatial tasks. Distinct brain areas were sensitive to cue-target delays and to trial validity. Long cue-target intervals activated areas involved in motor preparation: supplementary motor cortex, basal ganglia and thalamus. Invalid trials, where temporal expectancies were breached, showed enhanced activation of left parietal and frontal areas, and engagement of orbitofrontal cortex bilaterally. Finally, trial validity interacted with length of delay. Appearance of targets prematurely selectively activated visual extrastriate cortex; while postponement of target appearance selectively activated right prefrontal cortex. These findings suggest that distinct brain areas are involved in redirecting attention based upon sensory events (bottom-up, exogenous shifts) and based upon cognitive expectations (top-down, endogenous shifts).


title: Developmental Perception of the Self and Action

abstract: This paper describes a developmental framework for action-driven perception in anthropomorphic robots. The key idea of the framework is that action generation develops the agent's perception of its own body and actions. Action-driven development is critical for identifying changing body parts and understanding the effects of actions in unknown or nonstationary environments. We embedded minimal knowledge into the robot's cognitive system in the form of motor synergies and actions to allow motor exploration. The robot voluntarily generates actions and develops the ability to perceive its own body and the effect that it generates on the environment. The robot, in addition, can compose this kind of learned primitives to perform complex actions and characterize them in terms of their sensory effects. After learning, the robot can recognize manipulative human behaviors with cross-modal anticipation for recovery of unavailable sensory modality, and reproduce the recognized actions afterward. We evaluated the proposed framework in the experiments with a real robot. In the experiments, we achieved autonomous body identification, learning of fixation, reaching and grasping actions, and developmental recognition of human actions as well as their reproduction.


title: Evaluation of the Wound-healing Activity of Ethanolic Extract of Morinda citrifolia L. Leaf

abstract: Morinda citrifolia L. (noni) is one of the most important traditional Polynesian medicinal plants. The primary indigenous use of this plant appears to be of the leaves, as a topical treatment for wound healing. The ethanol extract of noni leaves (150 mg kg−1 day−1) was used to evaluate the wound-healing activity on rats, using excision and dead space wound models. Animals were randomly divided into two groups of six for each model. Test group animals in each model were treated with the ethanol extract of noni orally by mixing in drinking water and the control group animals were maintained with plain drinking water. Healing was assessed by the rate of wound contraction, time until complete epithelialization, granulation tissue weight and hydoxyproline content. On day 11, the extract-treated animals exhibited 71% reduction in the wound area when compared with controls which exhibited 57%. The granulation tissue weight and hydroxyproline content in the dead space wounds were also increased significantly in noni-treated animals compared with controls (P < 0.002). Enhanced wound contraction, decreased epithelialization time, increased hydroxyproline content and histological characteristics suggest that noni leaf extract may have therapeutic benefits in wound healing.


title: Boosting the Robustness Verification of DNN by Identifying the Achilles's Heel

abstract: Deep Neural Network (DNN) is a widely used deep learning technique. How to ensure the safety of DNN-based system is a critical problem for the research and application of DNN. Robustness is an important safety property of DNN. However, existing work of verifying DNN's robustness is time-consuming and hard to scale to large-scale DNNs. In this paper, we propose a boosting method for DNN robustness verification, aiming to find counter-examples earlier. Our observation is DNN's different inputs have different possibilities of existing counter-examples around them, and the input with a small difference between the largest output value and the second largest output value tends to be the achilles's heel of the DNN. We have implemented our method and applied it on Reluplex, a state-of-the-art DNN verification tool, and four DNN attacking methods. The results of the extensive experiments on two benchmarks indicate the effectiveness of our boosting method.


title: SSL/TLS Security Exploration Through X.509 Certificate’s Life Cycle Measurement

abstract: With the popular use of SSL/TLS, more and more web applications, such as online banking, e-mail, and ecommerce, turn to secured channels for communication, which rely on X.509 certificate for authentication. Generally, every certificate has a theoretical validity period when it is issued. However, the used period in practice is often different from the theoretical validity, namely, before or after the validity, for a long or short time. If a certificate is expired, it is easily to be exploited by cyber-attackers, leading to web users’ personal information at risk. To explore the security flaws of the SSL/TLS certificate, we conduct a large-scale measurement study of X.509 certificate life cycle from the view of leaf certificates. Based on a passive data set collected over one year, we investigate the certificate validity period in a fine-grained manner, and uncover that the actual usage of the certificates are not satisfactory. Meanwhile, we discover several security-related issues that may leave the web communication at risk. The recommendations are summarized to ensure the long-term security for certificate use in practice. We believe that the work will be beneficial to web security and improve the certificate utilization in the future.


Towards Incorporating Context Awareness into Business Process Management


title: Effects of a short-term pre-season training programme on the body composition and anaerobic performance of professional rugby union players

abstract: Abstract Pre-season rugby training develops the physical requisites for competition and consists of a high volume of resistance training and anaerobic and aerobic conditioning. However, the effects of a rugby union pre-season in professional athletes are currently unknown. Therefore, the purpose of this investigation was to determine the effects of a 4-week pre-season on 33 professional rugby union players. Bench press and box squat increased moderately (13.6 kg, 90% confidence limits ±2.9 kg and 17.6 ± 8.0 kg, respectively) over the training phase. Small decreases in bench throw (70.6 ± 53.5 W), jump squat (280.1 ± 232.4 W), and fat mass (1.4 ± 0.4 kg) were observed. In addition, small increases were seen in fat-free mass (2.0 ± 0.6 kg) and flexed upper-arm girth (0.6 ± 0.2 cm), while moderate increases were observed in mid-thigh girth (1.9 ± 0.5 cm) and perception of fatigue (0.6 ± 0.4 units). Increases in strength and body composition were observed in elite rugby union players after 4 weeks of intensiv...


title: MAGNETIC WHEELS OPTIMIZATION AND APPLICATION TO THE MagneBike CLIMBING ROBOT

abstract: Magnetic wheels are a powerful solution to design inspection climbing robots with excellent mobility. Magnetic wheels optimization based on simulations and the results that were obtained on prototypes are presented. The measured adhesion was doubled between the classic configuration and a novel multilayer one sharing exactly the same four magnets and the same total volume of iron. This know-how is then applied to optimize magnetic wheels for the existing robot called MagneBike. The adhesion force has been multiplied by 2 to 3 times depending on the conditions. Those amazing improvements open new possibilities for miniaturization of climbing robots or payload's increase.


title: Algorithmic Bayesian persuasion

abstract: Persuasion, defined as the act of exploiting an informational advantage in order to effect the decisions of others, is ubiquitous. Indeed, persuasive communication has been estimated to account for almost a third of all economic activity in the US. This paper examines persuasion through a computational lens, focusing on what is perhaps the most basic and fundamental model in this space: the celebrated Bayesian persuasion model of Kamenica and Gentzkow. Here there are two players, a sender and a receiver. The receiver must take one of a number of actions with a-priori unknown payoff, and the sender has access to additional information regarding the payoffs of the various actions for both players. The sender can commit to revealing a noisy signal regarding the realization of the payoffs of various actions, and would like to do so as to maximize her own payoff in expectation assuming that the receiver rationally acts to maximize his own payoff. When the payoffs of various actions follow a joint distribution (the common prior), the sender's problem is nontrivial, and its computational complexity depends on the representation of this prior. We examine the sender's optimization task in three of the most natural input models for this problem, and essentially pin down its computational complexity in each. When the payoff distributions of the different actions are i.i.d. and given explicitly, we exhibit a polynomial-time (exact) algorithmic solution, and a ``simple'' (1-1/e)-approximation algorithm. Our optimal scheme for the i.i.d. setting involves an analogy to auction theory, and makes use of Border's characterization of the space of reduced-forms for single-item auctions. When action payoffs are independent but non-identical with marginal distributions given explicitly, we show that it is #P-hard to compute the optimal expected sender utility. In doing so, we rule out a generalized Border's theorem, as defined by Gopalan et al, for this setting. Finally, we consider a general (possibly correlated) joint distribution of action payoffs presented by a black box sampling oracle, and exhibit a fully polynomial-time approximation scheme (FPTAS) with a bi-criteria guarantee. Our FPTAS is based on Monte-Carlo sampling, and its analysis relies on the principle of deferred decisions. Moreover, we show that this result is the best possible in the black-box model for information-theoretic reasons.


title: A duality based approach for realtime tv-l1 optical flow

abstract: Variational methods are among the most successful approaches to calculate the optical flow between two image frames. A particularly appealing formulation is based on total variation (TV) regularization and the robust L1 norm in the data fidelity term. This formulation can preserve discontinuities in the flow field and offers an increased robustness against illumination changes, occlusions and noise. In this work we present a novel approach to solve the TV-L1 formulation. Our method results in a very efficient numerical scheme, which is based on a dual formulation of the TV energy and employs an efficient point-wise thresholding step. Additionally, our approach can be accelerated by modern graphics processing units. We demonstrate the real-time performance (30 fps) of our approach for video inputs at a resolution of 320 × 240 pixels.


An Extended Resource Information Layer for BPMN


title: E-healthcare and data management services in a cloud

abstract: E-Healthcare data management is a core issue in a modern hospital. The data generated is enormous due to newer diagnostic techniques. The medical doctors and patients demand a greater life time for medical data records, they require fast data retrieval, and presentation services for Clinical Decision Support Systems (CDSS). The cost on maintaining a data service centre in an average hospital may be substantial both on infrastructure and qualified ICT staff. The availability of large storage capacities and computing resources from Cloud Service Providers (CSP) is seen as an opportunity to save investment by outsourcing information services. Electronic Patient Record (EPR) management is sensitive issue which requires a foolproof security. It is extremely important to develop methods to relieve the clients from problems of security, integrity, availability and cross CSP mobility. We present E-Healthcare models for ubiquitous services for data acquisition archiving and presentation in Cloud. The management issues and security concerns in cloud domains are addressed by a services architecture proposed. The model includes Wireless Sensor Networks besides communication and storage systems for a typical hospital taking advantage of the Cloud Services Architecture (CSA).


title: Reality-Based Models for Vibration Feedback in Virtual Environments

abstract: Reality-based modeling of vibrations has been used to enhance the haptic display of virtual environments for impact events such as tapping, although the bandwidths of many haptic displays make it difficult to accurately replicate the measured vibrations. We propose modifying reality-based vibration parameters through a series of perceptual experiments with a haptic display. We created a vibration feedback model, a decaying sinusoidal waveform, by measuring the acceleration of the stylus of a three degree-of-freedom haptic display as a human user tapped it on several real materials. A series of perceptual experiments, where human users rated the realism of various parameter combinations, were performed to further enhance the realism of the vibration display for impact events. The results provided different parameters than those derived strictly from acceleration data. Additional experiments verified the effectiveness of these modified model parameters by showing that users could differentiate between materials in a virtual environment.


title: Iris Recognition: On the Segmentation of Degraded Images Acquired in the Visible Wavelength

abstract: Iris recognition imaging constraints are receiving increasing attention. There are several proposals to develop systems that operate in the visible wavelength and in less constrained environments. These imaging conditions engender acquired noisy artifacts that lead to severely degraded images, making iris segmentation a major issue. Having observed that existing iris segmentation methods tend to fail in these challenging conditions, we present a segmentation method that can handle degraded images acquired in less constrained conditions. We offer the following contributions: 1) to consider the sclera the most easily distinguishable part of the eye in degraded images, 2) to propose a new type of feature that measures the proportion of sclera in each direction and is fundamental in segmenting the iris, and 3) to run the entire procedure in deterministically linear time in respect to the size of the image, making the procedure suitable for real-time applications.


title: Efficient Dynamic Searchable Encryption with Forward Privacy

abstract: Searchable symmetric encryption (SSE) enables a client to perform searches over its outsourced encrypted files while preserving privacy of the files and queries. Dynamic schemes, where files can be added or removed, leak more information than static schemes. For dynamic schemes, forward privacy requires that a newly added file cannot be linked to previous searches. We present a new dynamic SSE scheme that achieves forward privacy by replacing the keys revealed to the server on each search. Our scheme is efficient and parallelizable and outperforms the best previous schemes providing forward privacy, and achieves competitive performance with dynamic schemes without forward privacy. We provide a full security proof in the random oracle model. In our experiments on the Wikipedia archive of about four million pages, the server takes one second to perform a search with 100,000 results.


Nonlinear Programming 2


title: Home telehealth by Internet of Things (IoT)

abstract: Home based Telehealth is a combination of communications, imaging, sensing and human computer interaction technologies targeted at diagnosis, treatment and monitoring patients without disturbing the quality of lifestyle. This paper proposes development of a low cost medical sensing, communication and analytics device that is real-time monitoring internet enabled patients physiological conditions. Internet of Things (IoT) network will provide active and real-time engagement of patient, hospitals, caretaker and doctors. Massaging and synchronising the system has been the based focus in this paper, where it applies the suggested algorithm to predict the minimum time period that separates two consecutive bursts of messages and measures the minimum queue sizes for the health care personals nods, to manage the traffic and avoid the dropping of messages. NS2 simulator was employed to simulate the Telehealth environment algorithm.


title: Rapid and sustained symptom reduction following psilocybin treatment for anxiety and depression in patients with life-threatening cancer: a randomized controlled trial

abstract: Background:Clinically significant anxiety and depression are common in patients with cancer, and are associated with poor psychiatric and medical outcomes. Historical and recent research suggests a role for psilocybin to treat cancer-related anxiety and depression.Methods:In this double-blind, placebo-controlled, crossover trial, 29 patients with cancer-related anxiety and depression were randomly assigned and received treatment with single-dose psilocybin (0.3 mg/kg) or niacin, both in conjunction with psychotherapy. The primary outcomes were anxiety and depression assessed between groups prior to the crossover at 7 weeks.Results:Prior to the crossover, psilocybin produced immediate, substantial, and sustained improvements in anxiety and depression and led to decreases in cancer-related demoralization and hopelessness, improved spiritual wellbeing, and increased quality of life. At the 6.5-month follow-up, psilocybin was associated with enduring anxiolytic and anti-depressant effects (approximately 60–80...


title: Leveraging virtual business model innovation: a framework for designing business model development tools

abstract: This paper presents a framework for developing tool support for the design and management of new business models. Existing IT tools supporting the process of designing, innovating, and evaluating a company's business model are currently not leveraging the full potential of tool support, because they do not make use of theoretical and empirical knowledge around business model development. Against this backdrop, we analyze existing knowledge on business model design and management, resulting in a first systematization of the activities that are necessary for developing and managing new business models. In order to complement this knowledge and to identify the requirements for supporting these activities, a series of expert interviews is conducted. Based on the results of the interview series, a new business model development tool is created and evaluated. The learnings of this development process are then consolidated in a unified framework. This framework constitutes a new solution for systematically designing tool support for business model development and extends existing literature by highlighting the importance of collaboration between participants in a business model development project. It also provides designers of new business model development tool with an empirically based conceptualization to guide their efforts.


title: Counting Everyday Objects in Everyday Scenes

abstract: We are interested in counting the number of instances of object classes in natural, everyday images. Previous counting approaches tackle the problem in restricted domains such as counting pedestrians in surveillance videos. Counts can also be estimated from outputs of other vision tasks like object detection. In this work, we build dedicated models for counting designed to tackle the large variance in counts, appearances, and scales of objects found in natural scenes. Our approach is inspired by the phenomenon of subitizing - the ability of humans to make quick assessments of counts given a perceptual signal, for small count values. Given a natural scene, we employ a divide and conquer strategy while incorporating context across the scene to adapt the subitizing idea to counting. Our approach offers consistent improvements over numerous baseline approaches for counting on the PASCAL VOC 2007 and COCO datasets. Subsequently, we study how counting can be used to improve object detection. We then show a proof of concept application of our counting methods to the task of Visual Question Answering, by studying the `how many?' questions in the VQA and COCO-QA datasets.


title: Parallel in-situ data processing with speculative loading

abstract: Traditional databases incur a significant data-to-query delay due to the requirement to load data inside the system before querying. Since this is not acceptable in many domains generating massive amounts of raw data, e.g., genomics, databases are entirely discarded. External tables, on the other hand, provide instant SQL querying over raw files. Their performance across a query workload is limited though by the speed of repeated full scans, tokenizing, and parsing of the entire file. In this paper, we propose SCANRAW, a novel database physical operator for in-situ processing over raw files that integrates data loading and external tables seamlessly while preserving their advantages: optimal performance across a query workload and zero time-to-query. Our major contribution is a parallel super-scalar pipeline implementation that allows SCANRAW to take advantage of the current many- and multi-core processors by overlapping the execution of independent stages. Moreover, SCANRAW overlaps query processing with loading by speculatively using the additional I/O bandwidth arising during the conversion process for storing data into the database such that subsequent queries execute faster. As a result, SCANRAW makes optimal use of the available system resources -- CPU cycles and I/O bandwidth -- by switching dynamically between tasks to ensure that optimal performance is achieved. We implement SCANRAW in a state-of-the-art database system and evaluate its performance across a variety of synthetic and real-world datasets. Our results show that SCANRAW with speculative loading achieves optimal performance for a query sequence at any point in the processing. Moreover, SCANRAW maximizes resource utilization for the entire workload execution while speculatively loading data and without interfering with normal query processing.


title: The Role of Early Auditory Discrimination Deficits in Language Disorders

abstract: Language impairments can have a devastating effect on the individual’s life. Brain damage such as stroke may cause varying degrees of impaired language. Even milder language problems, such as developmental dyslexia or specific language impairment, can have long-lasting detrimental effects on the individual’s life, affecting both success at school as well as motivation and even self-esteem. In recent years, the mismatch negativity (MMN) has been intensively applied to study the neural basis of language impairments. These studies have shown that the MMN, which reflects the early stages of cortical sound discrimination, is abnormal in a large variety of language impairments. Furthermore, a close relationship between the MMN and some language disorders is suggested by significant correlations between the MMN and language test results. Further support is provided by follow-up studies suggesting that the MMN parameters may predict future language problems in children and by investigations indicating that interv...


title: A Solar Power-Assisted Battery Balancing System for Electric Vehicles

abstract: This paper proposes a solar power-assisted electric vehicle battery balancing system. There are three operation modes of the system: solar-balancing, storage-balancing, and charge balancing. The solar-balancing mode charges the battery module with the lowest state of charge (SOC) using the solar power during vehicle driving; the charge-balancing mode is operated when the vehicle is parked and being charged by the conventional charger. Under this mode, the balancing circuit discharges the battery module with the highest SOC by transferring the energy to an additional storage cell while the solar panel also charges the storage cell independently at the same time if solar power is available. When the solar power is low, the storage-balancing mode will be selected to charge the battery module with the lowest SOC using energy stored in the storage cell. This system eliminates the energy loss that would otherwise happen in conventional active and passive balancing schemes by equalizing the battery using solar/stored energy in the storage cell. A 48-V battery pack with four 12-V battery modules system is simulated and tested. A prototype system is developed to prove the concept. The simulation and experimental results verify that the proposed system not only achieves the same balancing performance as conventional balancing circuits, but also effectively increases the overall usable battery energy by 2.1%–3.3% every 13.2 km.


title: Trajectory based forwarding and its applications

abstract: Trajectory based forwarding (TBF) is a novel methodto forward packets in a dense ad hoc network that makes it possible to route a packet along a predefined curve. It is a hybrid between source based routing and Cartesian forwarding in that the trajectory is set by the source, but the forwarding decision is based on the relationship to the trajectory rather than names of intermediate nodes. The fundamental aspects of TBF are: it decouples path naming from the actual path; it provides cheap path diversity; it trades off communication for computation. These aspects address the double scalability issue with respect to mobility rate and network size. In addition, TBF provides a common framework for many services such as: broadcasting, discovery, unicast, multicast and multipath routing in ad hoc networks. TBF requires that nodes know their position relative to a coordinate system. While a global coordinate system afforded by a system such as GPS would be ideal, approximate positioning methods provided by other algorithms are also usable.


title: Enhancing Pediatric Oncology Nursing Care Through Research, Quality Improvement, and Evidence-Based Practice

abstract: Research, quality improvement (QI), and evidence-based practice (EBP) are knowledge-based methods used to enhance nursing practice. Nurses need to conduct studies to fill empirical gaps of knowledge (research), continually monitor health care practices and processes (QI), and systematically compile and review evidence (EBP). Research, QI, and EBP are valuable instruments within the health care setting as they can benefit the patient, families, health care team members, and the nursing profession, but it is important to understand when and how to use each method. This article will differentiate between and provide examples of research, QI, and EBP by focusing on the characteristics and purposes of each method. Clinical issues and concerns can transform practices based on evidence through the proper use of these methods. Nurses are highly motivated to create the best possible environment of healing for their patients. Therefore, it is essential that nurses use research, QI, and EBP so pediatric oncology knowledge can continue to evolve.


title: Theory and Design of Octave Tunable Filters With Lumped Tuning Elements

abstract: This paper presents octave-tunable resonators and filters with surface mounted lumped tuning elements. Detailed theoretical analysis and modeling in terms of tuning range and unloaded quality factor (Qu) are presented in agreement with simulated and measured results. Based on the models, a systematic design method to maximize tuning ratio and optimize Qu of the resonator is suggested. A resonator tuning from 0.5 to 1.1 GHz with Qu ranging from 90 to 214 is demonstrated using solid-state varactors. A two-pole filter with a tuning range of 0.5-1.1 GHz with a constant 3-dB fractional bandwidth (FBW) of 4±0.1% and insertion loss of 1.67 dB at 1.1 GHz is demonstrated along with a three-pole filter with a tuning range of 0.58-1.22 GHz with a constant 3-dB FBW of 4±0.2% and insertion loss of 2.05 dB at 1.22 GHz. The measured input third-order intermodulation is better than 17 dBm over the frequency range for the two-pole filter.


title: HyperFlow: A Distributed Control Plane for OpenFlow

abstract: OpenFlow assumes a logically centralized controller, which ideally can be physically distributed. However, current deployments rely on a single controller which has major drawbacks including lack of scalability. We present HyperFlow, a distributed event-based control plane for OpenFlow. HyperFlow is logically centralized but physically distributed: it provides scalability while keeping the benefits of network control centralization. By passively synchronizing network-wide views of OpenFlow controllers, HyperFlow localizes decision making to individual controllers, thus minimizing the control plane response time to data plane requests. HyperFlow is resilient to network partitioning and component failures. It also enables interconnecting independently managed OpenFlow networks, an essential feature missing in current OpenFlow deployments. We have implemented HyperFlow as an application for NOX. Our implementation requires minimal changes to NOX, and allows reuse of existing NOX applications with minor modifications. Our preliminary evaluation shows that, assuming sufficient control bandwidth, to bound the window of inconsistency among controllers by a factor of the delay between the farthest controllers, the network changes must occur at a rate lower than 1000 events per second across the network.


title: A study of student behavior in accepting the Blackboard Learning System: A Technology Acceptance Model (TAM) approach

abstract: This paper tried to examine the students' acceptances for the use of Blackboard Learning System using Technology Acceptance Model (TAM) as an analysis approach. Four factors of TAM namely Perceived Ease of Use, Perceived Usefulness, Attitude, Behavior Intention, and Perceived Interactivity as an additional factor were used as the evaluation model factors to measure the students' technology acceptance and a total of six hypotheses were proposed. An online questionnaire was conducted to extract the information from students of National Taiwan University of Science and Technology (NTUST) and a total of 302 respondents were collected. A Structural Equation Modeling (SEM) is used to analyze the model. The analysis result explained that a value of 36 percent of student' acceptance to use Blackboard Learning System can be described by the evaluated factors. Our findings illustrated that the five factors mentioned have intercorrelated effect to explain the students' behavior and Perceived Interactivity was determined as the key factor.


title: Solution-Processed Metal Nanowire Mesh Transparent Electrodes

abstract: Transparent conductive electrodes are important components of thin-film solar cells, light-emitting diodes, and many display technologies. Doped metal oxides are commonly used, but their optical transparency is limited for films with a low sheet resistance. Furthermore, they are prone to cracking when deposited on flexible substrates, are costly, and require a high-temperature step for the best performance. We demonstrate solution-processed transparent electrodes consisting of random meshes of metal nanowires that exhibit an optical transparency equivalent to or better than that of metal-oxide thin films for the same sheet resistance. Organic solar cells deposited on these electrodes show a performance equivalent to that of devices based on a conventional metal-oxide transparent electrode.


title: Indoor Scene Understanding with Geometric and Semantic Contexts

abstract: Truly understanding a scene involves integrating information at multiple levels as well as studying the interactions between scene elements. Individual object detectors, layout estimators and scene classifiers are powerful but ultimately confounded by complicated real-world scenes with high variability, different viewpoints and occlusions. We propose a method that can automatically learn the interactions among scene elements and apply them to the holistic understanding of indoor scenes from a single image. This interpretation is performed within a hierarchical interaction model which describes an image by a parse graph, thereby fusing together object detection, layout estimation and scene classification. At the root of the parse graph is the scene type and layout while the leaves are the individual detections of objects. In between is the core of the system, our 3D Geometric Phrases (3DGP). We conduct extensive experimental evaluations on single image 3D scene understanding using both 2D and 3D metrics. The results demonstrate that our model with 3DGPs can provide robust estimation of scene type, 3D space, and 3D objects by leveraging the contextual relationships among the visual elements.


title: Zigbee Based Centralised Patient Monitoring System

abstract: Centralised patient monitoring systems are in huge demand as they not only reduce the labour work and cost but also the time of the clinical hospitals. Earlier wired communication was used but now Zigbee which is a wireless mesh network is preferred as it reduces the cost. Zigbee is also preferred over Bluetooth and infrared wireless communication because it is energy efficient, has low cost and long distance range (several miles). In this paper we proposed wireless transmission of data between a patient and centralised unit using Zigbee module. The paper is divided into two sections. First is patient monitoring system for multiple patients and second is the centralised patient monitoring system. These two systems are communicating using wireless transmission technology i.e. Zigbee. In the first section we have patient monitoring of multiple patients. Each patient's multiple physiological parameters like ECG, temperature, heartbeat are measured at their respective unit. If any physiological parameter value exceeds the threshold value, emergency alarm and LED blinks at each patient unit. This allows a doctor to read various physiological parameters of a patient in real time. The values are displayed on the LCD at each patient unit. Similarly multiple patients multiple physiological parameters are being measured using particular sensors and multiple patient's patient monitoring system is made. In the second section centralised patient monitoring system is made in which all multiple patients multiple parameters are displayed on a central monitor using MATLAB. ECG graph is also displayed on the central monitor using MATLAB software. The central LCD also displays parameters like heartbeat and temperature. The module is less expensive, consumes low power and has good range.


Internet Jones and the Raiders of the Lost Trackers: An Archaeological Study of Web Tracking from 1996 to 2016.


title: Players and Ball Detection in Soccer Videos Based on Color Segmentation and Shape Analysis

abstract: This paper proposes a scheme to detect and locate the players and the ball on the grass playfield in soccer videos. We put forward a shape analysis-based approach to identify the players and the ball from the roughly extracted foreground, which is obtained by a trained, color histogram-based playfield detector and connected component analysis. We employ Euclidean distance transform to extract skeletons for every foreground blob, and then perform shape analysis to remove false alarms (non-player and non-ball blobs) and cut-off the artifacts (mostly due to playfield lines) based on skeleton pruning and reverse Euclidean distance transform. Results are given to demonstrate the proposed algorithm works well in soccer video clips.


Predicting Stock Volatility from Quarterly Earnings Calls and Transcript Summaries using Text Regression


title: Question Generation from a Knowledge Base

abstract: When designing the natural language question asking interface for a formal knowledge base, managing and scoping the user expectations regarding what questions the system can answer is a key challenge. Allowing users to type ask arbitrary English questions will likely result in user frustration, because the system may be unable to answer many questions even if it correctly understands the natural language phrasing. We present a technique for responding to natural language questions, by suggesting a series of questions that the system can actually answer. We also show that the suggested questions are useful in a variety of ways in an intelligent textbook to improve student learning.


title: Comparison of Different Models and Simulation Approaches for the Energetic Study of a Subway

abstract: This paper compares different models and simulation approaches for an energetic simulation of an automatic subway. For this purpose, several models are carried out from a dynamic model, which is validated by comparison with experimental measurements. Furthermore, two different simulation approaches are compared, i.e., backward and forward approaches. A simplified model is obtained and allows the reduction of the simulation time by 96 compared with the dynamic model by keeping an accuracy value of more than 99%.


Comparative Study of Chronic Kidney Disease Prediction using KNN and SVM


Automatic Code Completion


title: Data Processing Requirements of Industry 4.0 - Use Cases for Big Data Applications

abstract: Industry 4.0 stands for the 4 th Industrial revolution and the new paradigm of autonomous and decentralized control in production. Products and production systems are enhanced to Cyber Physical Systems which have the capability to communicate with each other, to build ad-hoc networks and for self-control and self-optimization. From the IT-perspective this involves a new level of networking, data integration and data processing in production. Established technologies like Internet of Things, Cloud or Big Data are propagated solution-components of Industry 4.0. So far, there is no founded elaboration of IT-requirements and no differentiated discussion on how solution-components fulfil these requirements. This research uses the method of content analysis to extract requirements of Industry 4.0 from current research publications. Objective of analysis is a structured compilation of requirements regarding data processing. The resulting category scheme enables further development of solution-components in the application domain of Industry 4.0. Furthermore, this paper shows how the requirements can be matched to the capabilities of Big Data software solutions. As a result, two general use cases for Big Data applications in Industry 4.0 were identified and characterized.


title: Hybrid Modulation Technique for Grid-Connected Cascaded Photovoltaic Systems

abstract: In this paper, a control method based on the derivative of power is introduced for single-stage single-phase grid-connected cascaded H-bridge (CHB) photovoltaic inverters. The proposed scheme uses solar arrays derivative of power with respect to current to track maximum power point of each array individually. Using this method along with hybrid modulation technique allows utilization of CHB cells up to their full potential. The control system has a simple structure, and is inherently able to stabilize the system under heavy mismatching conditions where the modulation index goes higher than unity. The effectiveness of the proposed technique is verified by simulation and experimental results.


title: SVMs for the Blogosphere: Blog Identification and Splog Detection

abstract: Weblogs, or blogs have become an important new way to publish information, engage in discussions and form communities. The increasing popularity of blogs has given rise to search and analysis engines focusing on the 'blogosphere'. A key requirement of such systems is to identify blogs as they crawl the Web. While this ensures that only blogs are indexed, blog search engines are also often overwhelmed by spam blogs (splogs). Splogs not only incur computational overheads but also reduce user satisfaction. In this paper we first describe our experiments on blog identification using Support Vector Machines (SVM). We compare results of using different feature sets and introduce new features for blog identification. We then report preliminary results on splog detection and identify future work.


Timing and awareness of movement decisions: does consciousness really come too late?


1. Introduction Ecological Psychoacoustics and Auditory Displays: Hearing, Grouping, and Meaning Making


title: Deep Recurrent and Convolutional Networks for Accelerated Fault Tolerant Adaptive Flight Control Under Severe Failures

abstract: Design of fault tolerant systems is a popular subject in flight control system design. In particular, adaptive control approach has been successful in recovering aircraft in a wide variety of different actuator/sensor failure scenarios. However, if the aircraft goes under a severe actuator failure, control system might not be able to adapt fast enough to changes in the dynamics, which would result in performance degradation or even loss of the aircraft. Inspired by the recent success of deep learning applications, this work builds a hybrid recurren-t/convolutional neural network model to estimate adaptation parameters for aircraft dynamics under actuator/engine faults. The model is trained offline from a database of different failure scenarios. In case of an actuator/engine failure, the model identifies adaptation parameters and feeds this information to the adaptive control system, which results in significantly faster convergence of the controller coefficients. Developed control system is implemented on a nonlinear 6-DOF F-16 aircraft, and the results show that the proposed architecture is especially beneficial in severe failure scenarios.


title: IDS rainStorm: visualizing IDS alarms

abstract: The massive amount of alarm data generated from intrusion detection systems is cumbersome for network system administrators to analyze. Often, important details are overlooked and it is difficult to get an overall picture of what is occurring in the network by manually traversing textual alarm logs. We have designed a novel visualization to address this problem by showing alarm activity within a network. Alarm data is presented in an overview where system administrators can get a general sense of network activity and easily detect anomalies. They then have the option of zooming and drilling down for details. The information is presented with local network IP (Internet Protocol) addresses plotted over multiple yaxes to represent the location of alarms. Time on the x-axis is used to show the pattern of the alarms and variations in color encode the severity and amount of alarms. Based on our system administrator requirements study, this graphical layout addresses what system administrators need to see, is faster and easier than analyzing text logs, and uses visualization techniques to effectively scale and display the data. With this design, we have built a tool that effectively uses operational alarm log data generated on the Georgia Tech campus network. The motivation and background of our design is presented along with examples that illustrate its usefulness.


title: High-resolution image reconstruction from multiple differently exposed images

abstract: Super-resolution reconstruction is the process of reconstructing a high-resolution image from multiple low-resolution images. Most super-resolution reconstruction methods assume that exposure time is fixed for all observations, which is not necessarily true. In reality, cameras have limited dynamic range and nonlinear response to the quantity of light received, and exposure time might be adjusted automatically or manually to capture the desired portion of the scene's dynamic range. In this letter, we propose a Bayesian super-resolution algorithm based on an imaging model that includes camera response function, exposure time, sensor noise, and quantization error in addition to spatial blurring and sampling.


title: Facial Age Group Classification

abstract: Estimating human age group automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, It is still a challenging problem for the existing computer vision systems to automatically and effectively estimate human age group. The aging process is determined by not only the person’s gene, but also many external factors, such as health, living style, living location, and weather conditions. Males and females may also age differently. An age group classification system for facial images is proposed in this paper. Five age groups including babies, children, young adults, middle-aged adults, and old adults, are used in the classification system. The process of the system is divided into threephases: location, feature extraction, and age classification. Geometric features are used to distinguish whether the face is baby or child. Wrinkle features are used to classify the image into one of three adult groupsyoung adults, middle-aged adults, and old adults.


title: Automatic suggestion of phrasal-concept queries for literature search

abstract: Abstract Both general and domain-specific search engines have adopted query suggestion techniques to help users formulate effective queries. In the specific domain of literature search (e.g., finding academic papers), the initial queries are usually based on a draft paper or abstract, rather than short lists of keywords. In this paper, we investigate phrasal-concept query suggestions for literature search. These suggestions explicitly specify important phrasal concepts related to an initial detailed query. The merits of phrasal-concept query suggestions for this domain are their readability and retrieval effectiveness: (1) phrasal concepts are natural for academic authors because of their frequent use of terminology and subject-specific phrases and (2) academic papers describe their key ideas via these subject-specific phrases, and thus phrasal concepts can be used effectively to find those papers. We propose a novel phrasal-concept query suggestion technique that generates queries by identifying key phrasal-concepts from pseudo-labeled documents and combines them with related phrases. Our proposed technique is evaluated in terms of both user preference and retrieval effectiveness. We conduct user experiments to verify a preference for our approach, in comparison to baseline query suggestion methods, and demonstrate the effectiveness of the technique with retrieval experiments.


Using Smart Contracts and Blockchains to Support Consumer Trust Across Distributed Clouds


title: An adaptive matching pursuit algorithm for sparse channel estimation

abstract: This paper examines the problem of compressed sensing-based sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. In particular, we present an improved estimation algorithm based on the sparsity adaptive matching pursuit (SAMP), which is referred to as the adaptive step size SAMP (AS-SAMP), and compare it with the existing algorithms. Without requiring a priori knowledge of the sparsity, the proposed algorithm adjusts the step size adaptively to approach the true sparsity, thus improving the estimation accuracy. Simulation results show that the proposed algorithm provides a better trade-off between the mean squared error (MSE) performance and complexity when compared with conventional methods.


title: Automatic test case generation for UML activity diagrams

abstract: The test case generation from design specifications is an important work in testing phase. In this paper, we use UML activity diagrams as design specifications, and present an automatic test case generation approach. The approach first randomly generates abundant test cases for a JAVA program under testing. Then, by running the program with the generated test cases, we can get the corresponding program execution traces. Last, by comparing these traces with the given activity diagram according to the specific coverage criteria, we can get a reduced test case set which meets the test adequacy criteria. The approachcan also be used to check the consistency between the program execution traces and the behavior of UML activity diagrams.


title: Personality and substance use disorders: A prospective study.

abstract: The personality systems of Cloninger (as measured by the Tridimensional Personality Questionnaire [TPQ]) and Eysenck (as measured by the Eysenck Personality Questionnaire [EPQ]) both have been linked to substance use and abuse. The current study examined the predictive utility of both systems for substance use disorder (SUD) diagnoses, both cross-sectionally and prospectively. Participants (N = 489 at baseline) completed the EPQ and TPQ and were assessed via structured diagnostic interview at baseline and 6 years later (N = 457 at follow-up). Both the EPQ and TPQ scales demonstrated bivariate cross-sectional and prospective associations with SUDs. Within each system, those dimensions marking a broad impulsive sensation-seeking or behavioral disinhibition trait were the best predictors prospectively, although the 2 systems were differentially sensitive to specific diagnoses. These relations remained significant even with autoregressivity, other concurrent SUD diagnoses, and multiple personality dimensions statistically controlled.


title: The dawning of the autonomic computing era by

abstract: This issue of the IBM Systems Journal explores a broad set of ideas and approaches to autonomic computing--some first steps in what we see as a journey to create more self-managing computing systems. Autonomic computing represents a collection and integration of technologies that enable the creation of an information technology computing infrastructure for IBM's agenda for the next era of computing--e-business on demand. This paper presents an overview of IBM's autonomic computing initiative. It examines the genesis of autonomic computing, the industry and marketplace drivers, the fundamental characteristics of autonomic systems, a framework for how systems will evolve to become more self-managing, and the key role for open industry standards needed to support autonomic behavior in heterogeneous system environments. Technologies explored in each of the papers presented in this issue are introduced for the reader.


title: 40 Years of Formal Methods Some Obstacles and Some Possibilities ?

abstract: In this "40 years of formal methods" essay we shall first delineate, Sect. 1, what we mean by method, formal method, computer science, computing science, software engineering, and model-oriented and algebraic methods. Based on this, we shall characterize a spectrum from specification-oriented methods to analysis-oriented methods. Then, Sect. 2, we shall provide a "survey": which are the 'prerequisite works' that have enabled formal methods, Sect. 2.1, and which are, to us, the, by now, classical 'formal methods', Sect. 2.2. We then ask ourselves the question: have formal methods for software development, in the sense of this paper been successful? Our answer is, regretfully, no! We motivate this answer, in Sect. 3.2, by discussing eight obstacles or hindrances to the proper integration of formal methods in university research and education as well as in industry practice. This "looking back" is complemented, in Sect. 3.4, by a "looking forward" at some promising developments -- besides the alleviation of the eighth or more hindrances!


THE RELATIONSHIP BETWEEN BOARD SIZE, BOARD COMPOSITION, CEO DUALITY AND FIRM PERFORMANCE: EXPERIENCE FROM GHANA Abstract


title: Advances in vaccine adjuvants

abstract: Currently, aluminum salts and MF59 are the only vaccine adjuvants approved for human use. With the development of new-generation vaccines (including recombinant subunit and mucosal vaccines) that are less immunogenic, the search for more potent vaccine adjuvants has intensified. Of the novel compounds recently evaluated in human trials, immunostimulatory molecules such as the lipopolysaccharide derived MPL and the saponin derivative QS21 appear most promising, although doubts have been raised as to their safety in humans. Preclinical work with particulate adjuvants, such as the MF59 microemulsion and lipid-particle immune-stimulating complexes (Iscoms), suggest that these molecules are also potent elicitors of humoral and cellular immune responses. In addition, preclinical data on CpG oligonucleotides appear to be encouraging, particularly with respect to their ability to selectively manipulate immune responses. While all these adjuvants show promise, further work is needed to better define the mechanisms of adjuvant action. Ultimately, the development of more potent adjuvants may allow vaccines to be used as therapeutic, rather than prophylactic, agents.


title: Experiencing Service-Learning

abstract: A unique resource for students and professors alike, this book reveals the important practical, educational, and emotional benefits provided by college programs that allow students to help others through service work in inner-city classrooms, clinics, and other challenging environments. Filled with vivid first-person reflections by students, "Experiencing Service-Learning" emphasizes learning by doing, getting into the field, sharing what one sees with colleagues, and interpreting what one learns. As the authors make clear, service-learning is not a spectator sport. It takes students away from the routines and comfort zones of lecture, test, term paper, exam and puts them into the world. Service-learning requires them to engage actively with cultures that may be unfamiliar to them and to be introspective about their successes and their mistakes. At the same time, it demands of their instructors something other than Power-Point slides or an eloquently delivered lecture, as no teacher can predict in advance the questions their students experiences will raise. In service-learning, students and teacher must act together as a team of motivators, problem solvers, and change agents. While most of its personal vignettes come from service-learners who have worked as mentors in elementary schools, the book also includes a chapter in which coauthor Michele Gourley describes at length her experiences at a faith-based health clinic in Honduras. In offering such stories along with a succinct introduction to basic concepts, an assessment of how service-learners can effect transformational change, and project examples this text will not only prepare students for the adventures of service-learning but also aid professors and administrators tasked with developing service-learning courses and programs. Robert F. Kronick is a professor of educational psychology and counseling at the University of Tennessee Knoxville and the author of "Full Service Community Schools." Robert B. Cunningham is a professor emeritus of political science at the University of Tennessee Knoxville. His books include "Agendas and Decisions: How State Government Executives and Middle Managers Make and Administer Policy, " coauthored with Dorothy F. Olshfski. Michele Gourley is a physician and public health professional with a background in rural community health and state health policy."


title: Design and implementation of ISA card for rotary inductosyn signal processing based on AD2S1210 and CPLD

abstract: The rotary inductosyn is widely used as an angle measuring instrument in a control system. In order to obtain the absolute angle from the original output signals of the inductosyn, an ISA card for inductosyn signal processing based on the resolver-to-digital converter AD2S1210 and CPLD is designed and implemented in this paper. The sinusoidal oscillator and some other circuits, such as the one for adjusting the bandwidth, are integrated within AD2S1210. Therefore, the excitation generation circuit could be gotten rid of and few peripheral circuits would be adopted. This reduces the design complexity and improves the circuit integration. Meanwhile, a binary counter in the range of 360 degrees is implemented by CPLD to calculate the integer angle value, and a zero position sensor is set to obtain the absolute angle. Finally, the ISA card is tested with an industrial computer. The results indicate the real-time angle curve is smooth and the angle value is accurate. Consequently, the practicability and stability are verified. Compared with the traditional method of adding another one-pair pole resolver, the proposed approach simplifies the structure as well as reduces the costs of the system. The circuit is suitable for application requiring high resolution and fast conversion speed in industrial automation.


title: An analysis of mobile credit card usage intentions

abstract: Purpose - Many banks consider mobile-based technologies have improved the banking services through introduction of new banking facilities. One of the latest facilities developed in this area is the "mobile credit card." The purpose of this study is to examine the factors that determine intention to use mobile credit card among Malaysia bank customers, as their new way in conducting payment transactions. Design/methodology/approach - The technology acceptance model (TAM) was used as the base model in order to develop the modified version of TAM to better reflect mobile credit card. In the modified model, perceived credibility and the amount of information on mobile credit card were added, in addition to perceived usefulness and perceived ease of use. Findings - Results suggest that perceived usefulness, perceived ease of use, perceived credibility and the amount of information on mobile credit cards are important determinants to predict Malaysia bank customers' intentions to use mobile credit card. Needless to say, the paper is exploratory in nature. Research limitations/implications - This study suffers from two limitations. The discussion of these limitations is provided in the last part of this paper. Practical implications - Useful to Islamic banking institutions planning further mobile credit card services for their customers. Originality/value - Extends the understanding of TAM to newly emerging context of mobile credit.


title: The memory effect and its implications on Web QoE modeling

abstract: Quality of Experience (QoE) has gained enormous attention during the recent years. So far, most of the existing QoE research has focused on audio and video streaming applications, although HTTP traffic carries the majority of traffic in the residential broadband Internet. However, existing QoE models for this domain do not consider temporal dynamics or historical experiences of the user's satisfaction while consuming a certain service. This psychological influence factor of past experience is referred to as the memory effect. The first contribution of this paper is the identification of the memory effect as a key influence factor for Web QoE modeling based on subjective user studies. As second contribution, three different QoE models are proposed which consider the implications of the memory effect and imply the required extensions of the basic models. The proposed Web QoE models are described with a) support vector machines, b) iterative exponential regressions, and c) two-dimensional hidden Markov models.


title: Color compatibility from large datasets

abstract: This paper studies color compatibility theories using large datasets, and develops new tools for choosing colors. There are three parts to this work. First, using on-line datasets, we test new and existing theories of human color preferences. For example, we test whether certain hues or hue templates may be preferred by viewers. Second, we learn quantitative models that score the quality of a five-color set of colors, called a color theme. Such models can be used to rate the quality of a new color theme. Third, we demonstrate simple proto-types that apply a learned model to tasks in color design, including improving existing themes and extracting themes from images.