title: On power-law relationships of the Internet topology

abstract: Despite the apparent randomness of the Internet, we discover some surprisingly simple power-laws of the Internet topology. These power-laws hold for three snapshots of the Internet, between November 1997 and December 1998, despite a 45% growth of its size during that period. We show that our power-laws fit the real data very well resulting in correlation coefficients of 96% or higher.Our observations provide a novel perspective of the structure of the Internet. The power-laws describe concisely skewed distributions of graph properties such as the node outdegree. In addition, these power-laws can be used to estimate important parameters such as the average neighborhood size, and facilitate the design and the performance analysis of protocols. Furthermore, we can use them to generate and select realistic topologies for simulation purposes.


title: QoS aware Automatic Web Service Composition with Multiple objectives

abstract: With an increasing number of web services, providing an end-to-end Quality of Service (QoS) guarantee in responding to user queries is becoming an important concern. Multiple QoS parameters (e.g., response time, latency, throughput, reliability, availability, success rate) are associated with a service, thereby, service composition with a large number of candidate services is a challenging multi-objective optimization problem. In this paper, we study the multi-constrained multi-objective QoS aware web service composition problem and propose three different approaches to solve the same, one optimal, based on Pareto front construction and two other based on heuristically traversing the solution space. We compare the performance of the heuristics against the optimal, and show the effectiveness of our proposals over other classical approaches for the same problem setting, with experiments on WSC-2009 and ICEBE-2005 datasets.


title: Keep it stupid simple

abstract: Deep reinforcement learning can match and exceed human performance, but if even minor changes are introduced to the environment artificial networks often can't adapt. Humans meanwhile are quite adaptable. We hypothesize that this is partly because of how humans use heuristics, and partly because humans can imagine new and more challenging environments to learn from. We've developed a model of hierarchical reinforcement learning that combines both these elements into a stumbler-strategist network. We test transfer performance of this network using Wythoff's game, a gridworld environment with a known optimal strategy. We show that combining imagined play with a heuristic--labeling each position as "good" or "bad"'--both accelerates learning and promotes transfer to novel games, while also improving model interpretability.


title: Paracervical Block for Intrauterine Device Placement Among Nulliparous Women

abstract: OBJECTIVE:To investigate whether a 20-mL buffered 1% lidocaine paracervical block decreases pain during intrauterine device (IUD) placement.METHODS:In a randomized, single-blind, placebo-controlled trial, women were assigned to receive either a 20-mL buffered 1% lidocaine paracervical block or no bl


Finding Clues for Your Secrets: Semantics-Driven, Learning-Based Privacy Discovery in Mobile Apps


title: Level set hyperspectral image segmentation using spectral information divergence-based best band selection

abstract: We present a supervised hyperspectral segmentation procedure, consisting of best band analysis (BBA), an initial distance-based segmentation, and level set segmentation enhancement by forcing localized vicinities to be more homogeneous. BBA uses the spectral information divergence (SID) to reduce each pixel's high dimensional data to a scalar value, where the Bhattacharyya distance (BD) is maximized. The initial segmentation is based on feature vectors created from the SID metric. The level set segmentation then enhances areas that do not have spatially homogeneous ground cover. The proposed method is tested on a 72-band compact airborne spectrographic imager (CASI) image of a farm area in northern Mississippi, U.S.A. The proposed method is compared to a BBA-based maximum-likelihood (ML) method. Quantitative results are compared using segmentation and classification accuracies. Results show that both the initial classification using BBA features and the level set enhancement produced high-quality ground cover maps and outperformed the ML method, as well as previous studies by the authors. The ML method resulted in accuracies ges95.5%, whereas the level set segmentation approach resulted in accuracies as high as 99.7%.


title: Experimental investigation of machinability parameters in high-speed micro-end milling of titanium (grade-2)

abstract: This paper presents the experimental investigation of machinability parameters in high-speed micro-end milling of Ti alloy (grade-2) with uncoated tungsten carbide micro-end mills to understand the cutting mechanism and surface roughness formation. Micro-milling experiments are carried out by using a Taguchi method. The lower the better–quality characteristic in signal-to-noise ratio, analysis of variance and regression analysis are considered to analyze the effect of machining parameters on cutting forces and surface roughness. Model validation is performed by comparing analytical predicted results of cutting forces with experimental and Taguchi approach that are in good agreement. The investigation showed that micro-milling at high spindle speeds, low depth of cut, and low feed rate ensures the high quality of surface roughness and lower cutting forces.


title: Device Characteristics of Single-Gate Vertical Channel (SGVC) 3D NAND Flash Architecture

abstract: Device characteristics of single-gate vertical channel (SGVC) 3D NAND architecture are discussed in detail. The most important feature of SGVC is that the memory cell is a flat-channel device in 3D, different from the more often used gate-all-around (GAA) macaroni cell. Through various device optimizations, we have successfully produced excellent cell initial performances and more than 10V peak P/E window for each memory cell. In sharp contrast to the GAA cell, the SGVC flat cell naturally has superior layer-to-layer device uniformity that tolerates non-ideal vertical etching. Memory window managements in MLC and TLC applications are discussed. It is found that the major interference factor comes from the WL to WL interference. The root cause of WL interference is identified to be the channel potential interaction between the selected gate and neighbor WL in a junction-free 3D NAND. Random grain boundary trap effect further deteriorates the WL interference. It is found that the commonly adopted WL iterating algorithms in conventional FG NAND is also suitable for our SGVC 3D NAND to provide very tight Vt distribution for MLC and TLC applications.


title: High lithium anodic performance of highly nitrogen-doped porous carbon prepared from a metal-organic framework.

abstract: Theoretical and experimental results have revealed that the lithium-ion storage capacity for nitrogen-doped graphene largely depends on the nitrogen-doping level. However, most nitrogen-doped carbon materials used for lithium-ion batteries are reported to have a nitrogen content of approximately 10 wt% because a higher number of nitrogen atoms in the two-dimensional honeycomb lattice can result in structural instability. Here we report nitrogen-doped graphene particle analogues with a nitrogen content of up to 17.72 wt% that are prepared by the pyrolysis of a nitrogen-containing zeolitic imidazolate framework at 800 °C under a nitrogen atmosphere. As an anode material for lithium-ion batteries, these particles retain a capacity of 2,132 mA h g(-1) after 50 cycles at a current density of 100 mA g(-1), and 785 mAh g(-1) after 1,000 cycles at 5 A g(-1). The remarkable performance results from the graphene analogous particles doped with nitrogen within the hexagonal lattice and edges.


title: In Search of the Ideal Storage Configuration for Docker Containers

abstract: Containers are a widely successful technology today popularized by Docker. Containers improve system utilization by increasing workload density. Docker containers enable seamless deployment of workloads across development, test, and production environments. Docker's unique approach to data management, which involves frequent snapshot creation and removal, presents a new set of exciting challenges for storage systems. At the same time, storage management for Docker containers has remained largely unexplored with a dizzying array of solution choices and configuration options. In this paper we unravel the multi-faceted nature of Docker storage and demonstrate its impact on system and workload performance. As we uncover new properties of the popular Docker storage drivers, this is a sobering reminder that widespread use of new technologies can often precede their careful evaluation.


title: The implementation of ERP systems using ASAP methodology: (Case study: OpenERP 9.0 application on sales & distribution module at PT. XYZ)

abstract: PT. XYZ is a company that moves in glue manufacturing. The business activities includes purchases of raw materials, manufacturing process, and finished goods sales. The company implemented OpenERP which focused on Purchase and sales modules to integrate their business processes. OpenERP implementation uses Accelerated SAP (ASAP) methodology. Initial stages in the ASAP method are preparing schedules, purposes, and scope. Followed by, collecting data through interviews and observations. Then analyze business processes and problems, as well as the user requirements using difference table analysis. From the analysis, the consultant identified the problems that exist. After getting the requirement, conducted stages of planning new business processes or Blueprint. The Blueprint contains the system running now (as-is) and the design of the system that will be applied (to-be) which will be used in the realization stage to perform configuration, customization, data migration and testing interfaces. The next stage is the final preparation, where consultants will conduct final testing, create a user manual and conduct user training. Once this step is done, the implementation of the system will go live towards the stage and the system has been confirmed free of bugs and user needs are met.


title: From Citation Network to Study Map: A Novel Model to Reorganize Academic Literatures

abstract: As the number of academic papers and new technologies soars, it has been increasingly difficult for researchers, especially beginners, to enter a new research field. Researchers often need to study a promising paper in depth to keep up with the forefront of technology. Traditional Query-Oriented study method is time-consuming and even tedious. For a given paper, existent academic search engines like Google Scholar tend to recommend relevant papers, failing to reveal the knowledge structure. The state-of-the-art Map-Oriented study methods such as AMiner and AceMap can structure scholar information, but they're too coarse-grained to dig into the underlying principles of a specific paper. To address this problem, we propose a Study-Map Oriented method and a novel model called RIDP (Reference Injection based Double-Damping PageRank) to help researchers study a given paper more efficiently and thoroughly. RIDP integrates newly designed Reference Injection based Topic Analysis method and Double-Damping PageRank algorithm to mine a Study Map out of massive academic papers in order to guide researchers to dig into the underlying principles of a specific paper. Experiment results on real datasets and pilot user studies indicate that our method can help researchers acquire knowledge more efficiently, and grasp knowledge structure systematically.


title: Effect of body massage on increase of low birth weight neonates growth parameters: A randomized clinical trial

abstract: Background: Admission of low birth-weight (LBW) neonates in neonatal intensive care unit (NICU) causes their deprivation of tactile and sensory stimulation. Objective: The purpose of this study was to evaluate efficacy of body massage on growth parameters (weight, height and head circumference) gain velocity of LBW in Yazd, Iran. Materials and Methods: A randomized clinical trial study was conducted on LBW neonates whom were admitted to NICU of Shahid Sadoughi Hospital, Yazd, Iran from March to December 2011. Neonates were randomly assigned to two groups. In group one, 20 neonates were received massage three times in a day for consecutive 14 days by their mothers. In group two, intervention consisted of standard and routine care as control group. The primary endpoints were efficacy in increase of mean of weight, height and head circumference that were evaluated 14 days after intervention, at ages one and two months. Secondary outcome was clinical side effects. Results: 17 girls and 23 boys with mean gestational age of 34.4±1.22 weeks were evaluated. In the body massage group, only weight at the age of two months was significantly higher than the control group (mean±SD: 3250±305 vs. 2948±121 gr, p=0.005). No adverse events were seen in the two groups. Conclusion: Body massage might be used as an effective and safe non-medical intervention for increasing of weight gain velocity in LBW preterm neonates.


title: An integrated scale for measuring an organizational learning system

abstract: Purpose – The purpose of this paper is to develop an integrated measurement scale for an organizational learning system by capturing the learning enablers, learning results and performance outcome in an organization.Design/methodology/approach – A new measurement scale was developed by integrating and modifying two existing scales, identified through a literature survey. The new scale was validated through the responses of 502 managerial employees from 14 thermal power plants of the largest power company in India.Findings – There were scales, which measure learning enablers or learning achieved or learning in general or the relation between some forms of learning with performance. This study could integrate learning enablers at three levels, learning achieved at three levels, and the organizational outcome in the form of performance in a single scale.Research limitations/implications – The research study took place within the largest organization in one industry to control for potential industry effects a...


title: The use of linked line segments for cluster representation and data reduction

abstract: Abstract Regardless of how one searches for clusters in data, the resulting cluster structures are often modelled as their respective centroids. However, the use of centroids in this manner implies the clusters are hyperspherical in shape. If the cluster shape is not hyperspherical, using the distance between individual observations and the centroid as a metric of an observation's cluster membership can be misleading. This report proposes the use of a linked line segment based model of cluster structure which is not biased towards any particular cluster shape. The effectiveness of the linked line segment approach is demonstrated in a data reduction exercise using simulated and real world data.


title: A microwave synthesized CuxS and graphene oxide nanoribbon composite as a highly efficient counter electrode for quantum dot sensitized solar cells.

abstract: To boost the photoconversion efficiency (PCE) of ever promising quantum dot sensitized solar cells (QDSSCs), and to improve the design of photoanodes, the ability of the counter electrode (CE) to effectively reduce the oxidized electrolyte needs special attention. A composite of a 15 wt% graphene oxide nanoribbon (GOR), obtained by unzipping multi-walled carbon nanotubes (MWCNTs), and CuxS intersecting hexagonal nanoplates, synthesized by a low cost, facile and scalable microwave synthesis route, is reported as a fascinating CE for QDSSCs. The best performing Cu1.18S-GOR CE could notably achieve a record PCE of ∼3.55% for CdS sensitized QDSSCs, ∼5.42% for in situ deposited CdS/CdSe co-sensitized QDSSCs and ∼6.81% for CdTe/CdS/CdS dual sensitized QDSSCs, apart from increasing the PCE of previously reported QDSSCs. A systematic investigation of the CE design revealed the high electrocatalytic activity of GOR due to the presence of organic functional groups, graphitic edge sites and a quasi-one-dimensional (quasi-1D) structure, which increases the interfacial charge transfer kinetics from the CE to the polysulfide electrolyte. The highly stable Cu1.18S-GOR CE has the added advantage of a favourable energy band alignment with the redox potential of the polysulfide electrolyte, which reduces the loss of charge carriers and thus can increase the PCE of QDSSCs.


title: Calcium hydroxylapatite filler: an overview of safety and tolerability.

abstract: Abstract Soft tissue fillers are becoming increasingly important as nonsurgical treatment options for facial rejuvenation. Calcium hydroxylapatite (CaHA) is an injectable dermal filler that contains uniform CaHA microspheres suspended in an aqueous carboxymethylcellulose gel carrier. It is considered a long-lasting, but non-permanent filler, and is highly biocompatible with human tissue. No osteogenesis has been reported in extensive literature describing the use of CaHA in a variety of soft tissue applications. Injection of CaHA into the oral mucosa and the lips is an unapproved indication and may result in nodule formation. This occurs soon after injection and is a result of accumulated particles and not a granulomatous reaction. As with all biodegradable dermal fillers, CaHA can be associated with rare incidences of foreign body reactions, but only a handful of case reports have been documented in 10 years of clinical use. CaHA can be associated with local, short-term, injection-related adverse events, which are generally mild and resolve within a few days. Clinical trials that have followed patients for up to 3 years post-injection report no long-term or delayed-onset adverse events. CaHA is an effective and safe treatment option for a variety of aesthetic indications. This paper focuses on common safety concerns of patients and aesthetic physicians, including unfounded fears of osteogenesis and foreign body granulomas, providing an up-to-date overview of the tolerability and long-term safety of CaHA for aesthetic indications.


title: Inventive progress measured by multi-stage patent citation analysis

abstract: Abstract Studies of technological change constitute a field of growing importance and sophistication. In this paper we contribute to the discussion with a methodological reflection and application of multi-stage patent citation analysis for the measurement of inventive progress. Investigating specific patterns of patent citation data, we conclude that single-stage citation analysis cannot reveal technological paths or lineages. Therefore, one should also make use of indirect citations and bibliographical coupling. To measure aspects of cumulative inventive progress, we develop a “shared specialization measure” of patent families. We relate this measure to an expert rating of the technological value added in the field of variable valve actuation for internal combustion engines. In sum, the study presents promising evidence for multi-stage patent citation analysis in order to explain aspects of technological change.


Honeyd: A Virtual Honeypot Daemon (Extended Abstract)


title: Fast charging stations for electric vehicle: The impact on the mv distribution grids of the milan metropolitan area

abstract: In the next 20 years the number of electric vehicles (EV) will exponentially increase and will reach 40 millions in EU. The energy required for charging EV, usually recovering in private parking places for many hours, will be mainly provided through smart charging boxes (“wall box”) managed by the (smart) grid control system of the LV network. In all other cases, EVs will be charged in fast charging stations or battery swap stations connected to the MV network. Such charging stations require high power during the day, especially when the network could be overloaded, hence there is the need to model and analyze the impact on the grid. This paper investigates the potential impact of fast charging stations on distribution MV grid and the use of storages to shave peak power demand and providing additional network services.


Mode division multiplexing using an orbital angular momentum mode sorter and MIMO-DSP over a graded-index few-mode optical fibre


Mortality in vegetarians and comparable nonvegetarians in the United Kingdom123


The Impact on Family Functioning of Social Media Use by Depressed Adolescents: A Qualitative Analysis of the Family Options Study


title: Narrative, Identity, and the Politics of Exclusion: Social Change and the Gay and Lesbian Life Course

abstract: The social and political context of sexual identity development in the United States has changed dramatically since the mid twentieth century. Same-sex attracted individuals have long needed to reconcile their desire with policies of exclusion, ranging from explicit outlaws on same-sex activity to exclusion from major social institutions such as marriage. This paper focuses on the implications of political exclusion for the life course of individuals with same-sex desire through the analytic lens of narrative. Using illustrative evidence from a study of autobiographies of gay men spanning a 60-year period and a study of the life stories of contemporary same-sex attracted youth, we detail the implications of historic silence, exclusion, and subordination for the life course.


title: The risk and prevention of internet finance

abstract: Internet Finance promote the innovation and development of financial field, at the same time, it also has its own risks, such as system security risk, capital security risk, higher liquidity risk, market selection risk, monetary policy risk. So the internet financial supervision is very necessary, regulators should pay attention to the risk characteristics of the Internet financial regulation, establish and perfect the Internet financial laws and regulations system, effort to achieve targeted risk prevention.


title: eFuse Design and Reliability

abstract: Summary form only given. Programmable eFuse designs present an integration challenge in modern CMOS processing. The power level to program a fuse, and the programming methodologies leverage reliability mechanisms which all other elements in a design avoid. A high degree of eFuse process control and circuit design is required in order to guarantee operation. Almost all eFuse types are one time programmable and are limited to "one chance" programmable. This tutorial discussed selected eFuse technologies describing the design philosophy electrical programming and characterization, the physics of failure, and some of the many applications an on chip programmable element provides.


title: Automatic abstraction for verification of cyber-physical systems

abstract: Models of cyber-physical systems are inherently complex since they must represent hardware, software, and the physical environment. Formal verification of these models is often precluded by state explosion. Fortunately, many important properties may only depend upon a relatively small portion of the system being accurately modeled. This paper presents an automatic abstraction methodology that simplifies the model accordingly. Preliminary results on a fault-tolerant temperature sensor are encouraging.


title: Formative assessment and the design of instructional systems

abstract: The theory of formative assessment outlined in this article is relevant to a broad spectrum of learning outcomes in a wide variety of subjects. Specifically, it applies wherever multiple criteria are used in making judgments about the quality of student responses. The theory has less relevance for outcomes in which student responses may be assessed simply as correct or incorrect. Feedback is defined in a particular way to highlight its function in formative assessment. This definition differs in several significant respects from that traditionally found in educational research. Three conditions for effective feedback are then identified and their implications discussed. A key premise is that for students to be able to improve, they must develop the capacity to monitor the quality of their own work during actual production. This in turn requires that students possess an appreciation of what high quality work is, that they have the evaluative skill necessary for them to compare with some objectivity the quality of what they are producing in relation to the higher standard, and that they develop a store of tactics or moves which can be drawn upon to modify their own work. It is argued that these skills can be developed by providing direct authentic evaluative experience for students. Instructional systems which do not make explicit provision for the acquisition of evaluative expertise are deficient, because they set up artificial but potentially removable performance ceilings for students.


title: Practical design strategy for two-phase step up DC-DC Fibonacci Switched-Capacitor converter

abstract: The Fibonacci Switched-Capacitor (SC) converter demonstrates the highest performance by using minimum number of capacitors. However, as the Fibonacci SC requires a wide range of voltage rating of the devices, its implementation is difficult. This paper presents two gate driving techniques for designing and implementing two-phase Fibonacci SC converter for both low and high step-up conversion ratios. The proposed gate driving techniques only require few auxiliary transistors to provide the required boosted voltages for turning the charge transfer switches in the converter on and off. As a result, the proposed gate driving techniques reduce the design complexity and increase the reliability of the Fibonacci SC converter. Practical 8X and 5X Fibonacci SC converters are simulated and constructed based on the proposed techniques. The high conversion efficiencies achieved prove the effectiveness of the proposed techniques.


title: Web Service SWePT: A Hybrid Opinion Mining Approach

abstract: This work has been partially supported by the Sectorial Fund CONACyT-INEGI: Project with ref. 208471, INFOTEC, Mexico. And, also by the project CNDT-PYR2015-0016, CENIDET, Mexico. The work of the third author was in the framework of the SomEMBED MINECO TIN2015-71147-C2-1-P research project. The National Council for Science and Technology (CONACyT Mexico) has funded the research work of Delia Irazu Hernandez Farias (Grant No. 218109/313683 CVU-369616).


title: Scene Text Recognition: No Country for Old Men?

abstract: It is a generally accepted fact that Off-the-shelf OCR engines do not perform well in unconstrained scenarios like natural scene imagery, where text appears among the clutter of the scene. However, recent research demonstrates that a conventional shape-based OCR engine would be able to produce competitive results in the end-to-end scene text recognition task when provided with a conveniently preprocessed image. In this paper we confirm this finding with a set of experiments where two off-the-shelf OCR engines are combined with an open implementation of a state-of-the-art scene text detection framework. The obtained results demonstrate that in such pipeline, conventional OCR solutions still perform competitively compared to other solutions specifically designed for scene text recognition.


title: Three-way joins on MapReduce: An experimental study

abstract: We study three-way joins on MapReduce. Joins are very useful in a multitude of applications from data integration and traversing social networks, to mining graphs and automata-based constructions. However, joins are expensive, even for moderate data sets; we need efficient algorithms to perform distributed computation of joins using clusters of many machines. MapReduce has become an increasingly popular distributed computing system and programming paradigm. We consider a state-of-the-art MapReduce multi-way join algorithm by Afrati and Ullman and show when it is appropriate for use on very large data sets. By providing a detailed experimental study, we demonstrate that this algorithm scales much better than what is suggested by the original paper. However, if the join result needs to be summarized or aggregated, as opposed to being only enumerated, then the aggregation step can be integrated into a cascade of two-way joins, making it more efficient than the other algorithm, and thus becomes the preferred solution.


title: Ocular ischemia with hypotony after injection of hyaluronic acid gel.

abstract: The authors experienced a case with ocular ischemia with hypotony following injection of a dermal filler for augmentation rhinoplasty. Immediately after injection, the patient demonstrated a permanent visual loss with typical fundus features of central retinal artery occlusion. Multiple crusted ulcerative patches around the nose and left periorbit developed, and the left eye became severely inflamed, ophthalmoplegic, and hypotonic. Signs of anterior and posterior segment ischemia were observed including severe cornea edema, iris atrophy, and chorioretinal swelling. The retrograde arterial embolization of hyaluronic acid gel from vascular branches of nasal tip to central retinal artery and long posterior ciliary artery was highly suspicious. After 6 months of follow up, skin lesions and eyeball movement became normalized, but progressive exudative and tractional retinal detachment was causing phthisis bulbi.


title: FOCUS PAPER Microbial co-operation in the rhizosphere

abstract: Soil microbial populations are immersed in a framework of interactions known to affect plant fitness and soil quality. They are involved in fundamental activities that ensure the stability and productivity of both agricultural systems and natural ecosystems. Strategic and applied research has demonstrated that certain co-operative microbial activities can be exploited, as a low-input biotechnology, to help sustainable, environmentally-friendly, agro-technological practices. Much research is addressed at improving understanding of the diversity, dynamics, and significance of rhizosphere microbial populations and their co-operative activities. An analysis of the co-operative microbial activities known to affect plant development is the general aim of this review. In particular, this article summarizes and discusses significant aspects of this general topic, including (i) the analysis of the key activities carried out by the diverse trophic and functional groups of micro-organisms involved in co-operative rhizosphere interactions; (ii) a critical discussion of the direct microbe-microbe interactions which results in processes benefiting sustainable agro-ecosystem development; and (iii) beneficial microbial interactions involving arbuscular mycorrhiza, the omnipresent fungus-plant beneficial symbiosis. The trends of this thematic area will be outlined, from molecular biology and ecophysiological issues to the biotechnological developments for integrated management, to indicate where research is needed in the future.


title: Assessment of environmental and economic feasibility of Enhanced Landfill Mining.

abstract: This paper addresses the environmental and economic performance of Enhanced Landfill Mining (ELFM). Based on life cycle assessment and life cycle costing, a detailed model is developed and is applied to a case study, i.e. the first ELFM project in Belgium. The environmental and economic analysis is performed in order to study the valorisation of different waste types in the landfill, such as municipal solid waste, industrial waste and total waste. We found that ELFM is promising for the case study landfill as greater environmental benefits are foreseen in several impact categories compared to the landfill's current situation (the 'Do-nothing' scenario). Among the considered processes, the thermal treatment process dominates both the environmental and economic performances of ELFM. Improvements in the electrical efficiency of thermal treatment process, the calorific value of refuse derived fuel and recovery efficiencies of different waste fractions lead the performance of ELFM towards an environmentally sustainable and economically feasible direction. Although the environmental and economic profiles of ELFM will differ from case to case, the results of this analysis can be used as a benchmark for future ELFM projects.


title: Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling

abstract: Recent approaches for instance-aware semantic labeling have augmented convolutional neural networks (CNNs) with complex multi-task architectures or computationally expensive graphical models. We present a method that leverages a fully convolutional network (FCN) to predict semantic labels, depth and an instance-based encoding using each pixel’s direction towards its corresponding instance center. Subsequently, we apply low-level computer vision techniques to generate state-of-the-art instance segmentation on the street scene datasets KITTI and Cityscapes. Our approach outperforms existing works by a large margin and can additionally predict absolute distances of individual instances from a monocular image as well as a pixel-level semantic labeling.


title: Speaker Dependent and Independent Isolated Hindi Word Recognizer using Hidden Markov Model (HMM)

abstract: Hindi is very complex language with large number of phonemes and being used with various ascents in different regions in India. In this manuscript, speaker dependent and independent isolated Hindi word recognizers using the Hidden Markov Model (HMM) is implemented, under noisy environment. For this study, a set of 10 Hindi names has been chosen as a test set for which the training and testing is performed. The scheme instigated here implements the Mel Frequency Cepstral Coefficients (MFCC) in order to compute the acoustic features of the speech signal. Then, K-means algorithm is used for the codebook generation by performing clustering over the obtained feature space. Baum Welch algorithm is used for re-estimating the parameters, and finally for deciding the recognized Hindi word whose model likelihood is highest, Viterbi algorithm has been implemented; for the given HMM. This work resulted in successful recognition with 98.6% recognition rate for speaker dependent recognition, for total of 10 speakers (6 male, 4 female) and 97.5% for speaker independent isolated word recognizer for 10 speakers (male). General Terms Speech recognition, Isolated Hindi words.


title: Using static analysis for Ajax intrusion detection

abstract: We present a static control-flow analysis for JavaScript programs running in a web browser. Our analysis tackles numerous challenges posed by modern web applications including asynchronous communication, frameworks, and dynamic code generation. We use our analysis to extract a model of expected client behavior as seen from the server, and build an intrusion-prevention proxy for the server: the proxy intercepts client requests and disables those that do not meet the expected behavior. We insert random asynchronous requests to foil mimicry attacks. Finally, we evaluate our technique against several real applications and show that it protects against an attack in a widely-used web application.


title: Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams

abstract: Clusters in document streams, such as online news articles, can be induced by their textual contents, as well as by the temporal dynamics of their arriving patterns. Can we leverage both sources of information to obtain a better clustering of the documents, and distill information that is not possible to extract using contents only? In this paper, we propose a novel random process, referred to as the Dirichlet-Hawkes process, to take into account both information in a unified framework. A distinctive feature of the proposed model is that the preferential attachment of items to clusters according to cluster sizes, present in Dirichlet processes, is now driven according to the intensities of cluster-wise self-exciting temporal point processes, the Hawkes processes. This new model establishes a previously unexplored connection between Bayesian Nonparametrics and temporal Point Processes, which makes the number of clusters grow to accommodate the increasing complexity of online streaming contents, while at the same time adapts to the ever changing dynamics of the respective continuous arrival time. We conducted large-scale experiments on both synthetic and real world news articles, and show that Dirichlet-Hawkes processes can recover both meaningful topics and temporal dynamics, which leads to better predictive performance in terms of content perplexity and arrival time of future documents.


title: Item popularity and recommendation accuracy

abstract: Recommendations from the long tail of the popularity distribution of items are generally considered to be particularly valuable. On the other hand, recommendation accuracy tends to decrease towards the long tail. In this paper, we quantitatively examine this trade-off between item popularity and recommendation accuracy. To this end, we assume that there is a selection bias towards popular items in the available data. This allows us to define a new accuracy measure that can be gradually tuned towards the long tail. We show that, under this assumption, this measure has the desirable property of providing nearly unbiased estimates concerning recommendation accuracy. In turn, this also motivates a refinement for training collaborative-filtering approaches. In various experiments with real-world data, including a user study, empirical evidence suggests that only a small, if any, bias of the recommendations towards less popular items is appreciated by users.


title: Assessing the impact of video game based design projects in a first year engineering design course

abstract: Introductory engineering design courses are an opportunity to engage and encourage first-year engineering students. In one such course, we implemented a novel student design project using a commercial video game. The game, Kerbal Space Program, is a simulation of rocket travel and provides a reasonably realistic representation of rocket propulsion and orbital mechanics. Teams of students were tasked with designing a rocket that could fly to the home planet's moon and return safely. The efficacy of the project was assessed using a pre- and post-activity survey, and results are compared with those from a larger-focus research project on the effectiveness of toys in the classroom.


title: Geometric Kinematics and Applications of a Mobile Robot

abstract: In this paper, the simple geometric kinematics of a three-wheeled holonomic mobile robot is proposed. Wheel architecture is developed for the holonomic mobile platform in order to provide omni-directional motions by three individually driven and steered wheels. Three types of basic motions are proposed for the path generation of the developed mobile robot. All paths of the mobile robot can be achieved through a combination of the proposed basic motion trajecto- ries. The proposed method is verified through computer simulations and the developed mobile robot.


title: Companion apps for long arc TV series: supporting new viewers in complex storyworlds with tightly synchronized context-sensitive annotations

abstract: As television merges with digital technology, storytelling is becoming increasingly complex. Use of a second screen has become common, but academic research has focused on social applications and commercial applications have stressed community-building and trivia questions. Our survey of viewers reveals an unmet need for tightly synchronized second screen applications that can help them to enter and keep track of dramatic series with multiple recurring characters and multi-episode story arcs. We employed an iterative design process to create a second screen companion application for the critically acclaimed series Justified that was tightly synchronized, context-sensitive, and character-focused. Our usability testing indicated that use of the companion app enhanced comprehension for first-time viewers of a late season episode, and was especially effective in supporting understanding of character relationships, while also surfacing design considerations for future applications.


title: A federated architecture approach for Internet of Things security

abstract: Internet of Things (IoT) refers to the capability to connect, communicate and remotely manage a large number of networked, automated devices via the Internet. IoT is becoming as part of daily life and aims to extend pervasive communication and networking anytime, anywhere with any device. In this context security requirements and architectures must be properly formulated, implemented in order to enforce the security policies during their life-cycle. This paper provides a survey and analysis of security in the area of IoT introducing an approach addressed to overcome the conventional security solutions and deploy a federated architecture for dynamic prevention, detection, diagnosis, isolation, and countermeasures against cyber attacks. Based on the analysis of the most common web services, the paper defines the security needs proposing a federated model to design an architecture for secure exchange of services in IoT paradigm.


title: SeqFace: Make full use of sequence information for face recognition

abstract: Deep convolutional neural networks (CNNs) have greatly improved the Face Recognition (FR) performance in recent years. Almost all CNNs in FR are trained on the carefully labeled datasets containing plenty of identities. However, such high-quality datasets are very expensive to collect, which restricts many researchers to achieve state-of-the-art performance. In this paper, we propose a framework, called SeqFace, for learning discriminative face features. Besides a traditional identity training dataset, the designed SeqFace can train CNNs by using an additional dataset which includes a large number of face sequences collected from videos. Moreover, the label smoothing regularization (LSR) and a new proposed discriminative sequence agent (DSA) loss are employed to enhance discrimination power of deep face features via making full use of the sequence data. Our method achieves excellent performance on Labeled Faces in the Wild (LFW), YouTube Faces (YTF), only with a single ResNet. The code and models are publicly available on-line (this https URL).


High-efficiency multi-junction solar cells : Current status and future potential


abstract: Motivated by navigation and tracking applications within sensor networks, we consider the problem of performing Kalman filtering with intermittent observations. When data travel along unreliable communication channels in a large, wireless, multihop sensor network, the effect of communication delays and loss of information in the control loop cannot be neglected. We address this problem starting from the discrete Kalman filtering formulation, and modeling the arrival of the observation as a random process. We study the statistical convergence properties of the estimation error covariance, showing the existence of a critical value for the arrival rate of the observations, beyond which a transition to an unbounded state error covariance occurs. We also give upper and lower bounds on this expected state error covariance.


title: Object separation in x-ray image sets

abstract: In the segmentation of natural images, most algorithms rely on the concept of occlusion. In x-ray images, however, this assumption is violated, since x-ray photons penetrate most materials. In this paper, we introduce SATISφ, a method for separating objects in a set of x-ray images using the property of additivity in log space, where the log-attenuation at a pixel is the sum of the log-attenuations of all objects that the corresponding x-ray passes through. Our method leverages multiple projection views of the same scene from slightly different angles to produce an accurate estimate of attenuation properties of objects in the scene. These properties can be used to identify the material composition of these objects, and are therefore crucial for applications like automatic threat detection. We evaluate SATISφ on a set of collected x-ray scans, showing that it outperforms a standard image segmentation approach and reduces the error of material estimation.


title: Distance Metric Learning with Application to Clustering with Side-Information

abstract: Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as K-means initially fails to find one that is meaningful to a user, the only recourse may be for the user to manually tweak the metric until sufficiently good clusters are found. For these and other applications requiring good metrics, it is desirable that we provide a more systematic way for users to indicate what they consider "similar." For instance, we may ask them to provide examples. In this paper, we present an algorithm that, given examples of similar (and, if desired, dissimilar) pairs of points in ℝn, learns a distance metric over ℝn that respects these relationships. Our method is based on posing metric learning as a convex optimization problem, which allows us to give efficient, local-optima-free algorithms. We also demonstrate empirically that the learned metrics can be used to significantly improve clustering performance.


Assistive technologies for pain management in people with amputation: a literature review


title: Faster and More Focused Control-Flow Analysis for Business Process Models through SESE Decomposition

abstract: We present a technique to enhance control-flow analysis of business process models. The technique considerably speeds up the analysis and improves the diagnostic information that is given to the user to fix control-flow errors. The technique consists of two parts: Firstly, the process model is decomposed into single-entry-single-exit (SESE) fragments, which are usually substantially smaller than the original process. This decomposition is done in linear time. Secondly, each fragment is analyzed in isolation using a fast heuristic that can analyze many of the fragments occurring in practice. Any remaining fragments that are not covered by the heuristic can then be analyzed using any known complete analysis technique. ::: ::: We used our technique in a case study with more than 340 real business processes modeled with the IBM WebSphere Business Modeler. The results suggest that control-flow analysis of many real process models is feasible without significant delay (less than a second). Therefore, control-flow analysis could be used frequently during editing time, which allows errors to be caught at earliest possible time.


title: OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement

abstract: Dynamic simulations of movement allow one to study neuromuscular coordination, analyze athletic performance, and estimate internal loading of the musculoskeletal system. Simulations can also be used to identify the sources of pathological movement and establish a scientific basis for treatment planning. We have developed a freely available, open-source software system (OpenSim) that lets users develop models of musculoskeletal structures and create dynamic simulations of a wide variety of movements. We are using this system to simulate the dynamics of individuals with pathological gait and to explore the biomechanical effects of treatments. OpenSim provides a platform on which the biomechanics community can build a library of simulations that can be exchanged, tested, analyzed, and improved through a multi-institutional collaboration. Developing software that enables a concerted effort from many investigators poses technical and sociological challenges. Meeting those challenges will accelerate the discovery of principles that govern movement control and improve treatments for individuals with movement pathologies.


title: Web page segmentation based on the hough transform and vision cues

abstract: Web page segmentation has extensive value in web applications. As the traditional method which only based on Document Object Model (Dom) tree poorly reflects the actual semantic structure of a page, the vision-based measures which attempt to understand the perception of user have attracted great concern in recently. Moreover, the visual page layout structuring is more suitable to suggest a semantic partitioning of a page. Vision-based Page Segmentation (VIPS) algorithm is a notable technique which improves the situation that the visual effect is not consistent with the corresponding DOM tree of web pages. However, due to the increasing complicated structure of web pages and ever-changing of web design, the rules in VIPS become numerous and are no longer fully applicable. To alleviate the shortcoming of VIPS, this paper introduces Hough transform in image processing and takes advantage from DOM trees and visual cues. Our proposed method first extracts the visual separator in web pages according to the perceptive of web designers, then adjusts the segmented information blocks by Hough transformation in the hope of enhancing the VIPS algorithm compatibility and improving the performance of information extraction in applications. The quality of our proposed method is evaluated by subjective and objective measurements, and the experiment results show that this method has come to an anticipant result which even better than the classical one.


title: The Relation Between Cognitive Development and Anxiety Phenomena in Children

abstract: We examined the relation between cognitive development and fear, anxiety, and behavioral inhibition in a non-clinical sample of 226 Dutch children aged 4–9 years. To assess cognitive development, children were tested with Piagetian conservation tasks and a Theory-of-Mind (TOM) test. Fears were measured by means of a self-report scale completed by the children, while anxiety symptoms and behavioral inhibition were indexed by rating scales that were filled out by parents. Significant age trends were observed for some anxiety phenomena. For example, younger children displayed higher fear scores, whereas older children exhibited higher levels of generalized anxiety. Most importantly, results of regression analyses (in which we controlled for age) indicated that cognitive development, and in particular TOM ability, made a unique and significant contribution to various domains of behavioral inhibition. In all cases, higher levels of TOM were associated with lower levels of behavioral inhibition. In general, percentages of explained variance were rather small (i.e., <6%), indicating that the role of cognitive development in various anxiety phenomena is limited.


title: Short-range FMCW X-band radar platform for millimetric displacements measurement

abstract: A frequency modulated continuous wave (FMCW) X-band radar platform for millimetric displacements measurement of short-range targets is presented in this paper. The platform's transceiver is based on a heterodyne architecture because the beat frequency is relatively small for short-range targets and it can be placed in the frequency range influenced by the specific homodyne architecture problems: DC offset, self-mixing and 1/f noise. The platform's displacement measurement capability was tested on range profiles and SAR images acquired for various targets. The displacements were computed from the interferometric phase. The measurement errors were situated below 0.1 mm for metal bar targets placed at a few meters from the radar.


Preliminary phytochemical , total phenolics and flavonoid content analysis of Vitex negundo and Calatropis gigantea leaf ethanolic extracts


title: A roll stabilization system for a monohull ship: modeling, identification, and adaptive control

abstract: In this paper some results regarding the implementation of an automatic roll reduction system for a new monohull ship are presented. To attenuate the roll due to the waves, two auxiliary automatically controlled wings are proposed. These auxiliary wings which are variably dipped into the water introduce a torque that reduces the oscillations of the ship. Dynamic models based on experimental data of the ship, of the hydraulic actuators, and of the disturbances due to the waves are computed, both from physical data and by using identification techniques from experimental measurements. Two different compensators for the roll effect have been designed. The first one was designed by using classical frequency domain techniques, whereas the second was an adaptive linear quadratic compensator. For this compensator, adaptation was performed by using a multilayer perceptron neural network. For the classical controller both simulations and experimental tests performed on the real ship have shown the capabilities of the proposed control system, while for the adaptive controller the results obtained revealed the superior performance that can be reached by using more sophisticated controllers.


title: Pmomo: Projection Mapping on Movable 3D Object

abstract: We introduce Pmomo (acronym of projection mapping on movable object), a dynamic projection mapping system that tracks the 6-DOF position of real-world object, and shades it with virtual 3D contents by projection. The system can precisely lock the projection on the moving object in real-time, even the one with complex geometry. Based on depth camera, we developed a novel and robust tracking method that samples the structure of the object into low-density point cloud, then performs an adaptive searching scheme for the registration procedure. As a fully interactive system, our method can handle both internal and external complex occlusions, and can quickly track back the object even when losing track. In order to further improve the realism of the projected virtual textures, our system innovatively culls occlusions away from projection, which is achieved by a facet-covering method. As a result, the Pmomo system enables the possibility of new interactive Augmented Reality applications that require high-quality dynamic projection effect.


title: Nonradiochemical DNase I footprinting by capillary electrophoresis.

abstract: A new application for DNase I footprinting using capillary electrophoresis (CE) has been developed in order to decrease analysis time and to eliminate the use of radiochemicals. An additional advantage of the new method over the traditional radioactive methods is that the DNA probe can be labeled on both ends with different fluorescein dyes. This provides an internal check of the identification of protein-binding sites on DNA, because the binding region can be observed from both DNA strands. The initial parameters for the CE method were developed using the Promega Core Footprinting Kit for analysis of AP-2 binding sites in the SV40 enhancer sequence. After optimization of the method, the protocol was found to be effective for footprint analysis of the immediate upstream region (bases -1 to -370) of the rat glutathione peroxidase (GPX) and it permitted identification of a previously unknown binding site in the upstream sequence of the GPX gene.


title: Enabling Software Defined Networking with QoS Guarantee for Cloud Applications

abstract: Due to the centralized control, network-wide monitoring and flow-level scheduling of Software-Defined-Networking (SDN), it can be utilized to achieve Quality of Service (QoS) for cloud applications and services, such as voice over IP, video conference and online games, etc. However, most existing approaches stay at the QoS framework design and test level, while few works focus on studying the basic QoS techniques supported by SDN. In this paper, we enable SDN with QoS guaranteed abilities, which could provide end-to-end QoS routing for each cloud user service. First of all, we implement an application identification technique on SDN controller to determine required QoS levels for each application type. Then, we implement a queue scheduling technique on SDN switch. It queues the application flows into different queues and schedules the flows out of the queues with different priorities. At last, we evaluate the effectiveness of the proposed SDN-based QoS technique through an experimental analysis. Results show that when the output interface has sufficiently available bandwidth, the delay can be reduced by 28% on average. In addition, for the application flow with the highest priority, our methods can reduce 99.99% delay and increase 90.17% throughput on average when the output interface utilization approaches to the maximum bandwidth limitation.


title: The Applicability of Spike Time Dependent Plasticity to Development

abstract: Spike time dependent plasticity (STDP) has been observed in both developing and adult animals. Theoretical studies suggest that it implicitly leads to both competition and homeostasis in addition to correlation-based plasticity, making it a good candidate to explain developmental refinement and plasticity in a number of systems. However, it has only been observed to play a clear role in development in a small number of cases. Because the fast time scales necessary to elicit STDP, it would likely be inefficient in governing synaptic modifications in the absence of fast correlations in neural activity. In contrast, later stages of development often depend on sensory inputs that can drive activity on much faster time scales, suggesting a role in STDP in many sensory systems after opening of the eyes and ear canals. Correlations on fast time scales can be also be present earlier in developing microcircuits, such as those produced by specific transient “teacher” circuits in the cerebral cortex. By reviewing examples of each case, we suggest that STDP is not a universal rule, but rather might be masked or phased in, depending on the information available to instruct refinement in different developing circuits. Thus, this review describes selected cases where STDP has been studied in developmental contexts, and uses these examples to suggest a more general framework for understanding where it could be playing a role in development.


title: Kalman Filter Sensor Fusion for Mecanum Wheeled Automated Guided Vehicle Localization

abstract: The Mecanum automated guided vehicle (AGV), which can move in any direction by using a special wheel structure with a LIM-wheel and a diagonally positioned roller, holds considerable promise for the field of industrial electronics. A conventional method for Mecanum AGV localization has certain limitations, such as slip phenomena, because there are variations in the surface of the road and ground friction. Therefore, precise localization is a very important issue for the inevitable slip phenomenon situation. So a sensor fusion technique is developed to cope with this drawback by using the Kalman filter. ENCODER and StarGazer were used for sensor fusion. StarGazer is a position sensor for an image recognition device and always generates some errors due to the limitations of the image recognition device. ENCODER has also errors accumulating over time. On the other hand, there are no moving errors. In this study, we developed a Mecanum AGV prototype system and showed by simulation that we can eliminate the disadvantages of each sensor. We obtained the precise localization of the Mecanum AGV in a slip phenomenon situation via sensor fusion using a Kalman filter.


title: High School Students’ Perceptions of Motivations for Cyberbullying: An Exploratory Study

abstract: OBJECTIVES: Internet usage has increased in recent years resulting in a growing number of documented reports of cyberbullying. Despite the rise in cyberbullying incidents, there is a dearth of research regarding high school students' motivations for cyberbullying. The purpose of this study was to investigate high school students' perceptions of the motivations for cyberbullying. METHOD: We undertook an exploratory qualitative study with 20 high school students, conducting individual interviews using a semi-structured interview protocol. Data were analyzed using Grounded Theory. RESULTS: The developed coding hierarchy provides a framework to conceptualize motivations, which can be used to facilitate future research about motivations and to develop preventive interventions designed to thwart the negative effects of cyberbullying. The findings revealed that high school students more often identified internally motivated reasons for cyberbullying (e.g., redirect feelings) than externally motivated (no consequences, non-confrontational, target was different). CONCLUSION: Uncovering the motivations for cyberbullying should promote greater understanding of this phenomenon and potentially reduce the interpersonal violence that can result from it. By providing a framework that begins to clarify the internal and external factors motivating the behavior, there is enhanced potential to develop effective preventive interventions to prevent cyberbullying and its negative effects. Language: en


Assessing Executive Functions in Preschoolers Using Shape School Task


title: Using human brain activity to guide machine learning

abstract: Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.


title: Capturing the Religious Spirit: A Challenge for the Secular State

abstract: Critical engagement with the politics of the Christian Right in Australia and, more important, analysis of the values underpinning their political activism has been the focus of some recent scholarship. 1 Yet as John Warhurst notes, religion and politics more broadly remain an under-researched field in Australia.2 Perhaps as a consequence, Australian experiences rarely appear in international scholarship tackling the rise of religion. Of the limited academic scholarship that has been undertaken, the subject of religion in contemporary Australia has been addressed as a sociological foray, cast as a confrontation between church and state, or treated as an investigation of political partisanship and shifting denominational relations.


title: Absence of Ndn, encoding the Prader-Willi syndrome-deleted gene necdin, results in congenital deficiency of central respiratory drive in neonatal mice.

abstract: necdin (Ndn) is one of a cluster of genes deleted in the neurodevelopmental disorder Prader-Willi syndrome. necdin is upregulated during neuronal differentiation and is thought to play a role in cell cycle arrest in terminally differentiated neurons. Most necdin-deficient Ndn(tm2Stw) mutant pups carrying a targeted replacement of Ndn with a lacZ reporter gene die in the neonatal period of apparent respiratory insufficiency. We now demonstrate that the defect can be explained by abnormal neuronal activity within the putative respiratory rhythm-generating center, the pre-Bötzinger complex. Specifically, the rhythm is unstable with prolonged periods of depression of respiratory rhythmogenesis. These observations suggest that the developing respiratory center is particularly sensitive to loss of necdin activity and may reflect abnormalities of respiratory rhythm-generating neurons or conditioning neuromodulatory drive. We propose that necdin deficiency may contribute to observed respiratory abnormalities in individuals with Prader-Willi syndrome through a similar suppression of central respiratory drive.


title: PANFIS: A Novel Incremental Learning Machine

abstract: Most of the dynamics in real-world systems are compiled by shifts and drifts, which are uneasy to be overcome by omnipresent neuro-fuzzy systems. Nonetheless, learning in nonstationary environment entails a system owning high degree of flexibility capable of assembling its rule base autonomously according to the degree of nonlinearity contained in the system. In practice, the rule growing and pruning are carried out merely benefiting from a small snapshot of the complete training data to truncate the computational load and memory demand to the low level. An exposure of a novel algorithm, namely parsimonious network based on fuzzy inference system (PANFIS), is to this end presented herein. PANFIS can commence its learning process from scratch with an empty rule base. The fuzzy rules can be stitched up and expelled by virtue of statistical contributions of the fuzzy rules and injected datum afterward. Identical fuzzy sets may be alluded and blended to be one fuzzy set as a pursuit of a transparent rule base escalating human's interpretability. The learning and modeling performances of the proposed PANFIS are numerically validated using several benchmark problems from real-world or synthetic datasets. The validation includes comparisons with state-of-the-art evolving neuro-fuzzy methods and showcases that our new method can compete and in some cases even outperform these approaches in terms of predictive fidelity and model complexity.


title: A Virtual Model of Spring Reverberation

abstract: The digital emulation of analog audio effects and synthesis components, through the simulation of lumped circuit components has seen a large amount of activity in recent years; electromechanical effects have seen rather less, primarily because they employ distributed mechanical components, which are not easily dealt with in a rigorous manner using typical audio processing constructs such as delay lines and digital filters. Spring reverberation is an example of such a system-a spring exhibits complex, highly dispersive behavior, including coupling between different types of wave propagation (longitudinal and transverse). Standard numerical techniques, such as finite difference schemes are a good match to such a problem, but require specialized design and analysis techniques in the context of audio processing. A model of helical spring vibration is introduced, along with a family of finite difference schemes suitable for time domain simulation. Various topics are covered, including numerical stability conditions, tuning of the scheme to the response of the model system, numerical boundary conditions and connection to an excitation and readout, implementation details, as well as computational requirements. Simulation results are presented, and full energy-based stability analysis appears in an ????? .


Improved Bat Algorithm (IBA) on Continuous Optimization Problems


Atypical Mole ( Dysplastic Nevus ) Author :


title: Pigmented "black" pheochromocytoma of the adrenal gland: a case report and review of the literature.

abstract: A pigmented "black" mass was discovered incidentally in a 72-year-old woman. The tumor, which was detected near the upper pole of the left kidney, was cystic and intensely black throughout with the presence of abundant, coarse, dark brown to black pigment granules. Histologic examination showed a proliferation of monomorphous neoplastic cells arranged in a typical zellballen growth pattern. These features indicated a diagnosis of pheochromocytoma. Immunostains for chromogranin A, synaptophysin, and neuron-specific enolase were positive. The immunostain for HMB-45 was negative. Electron microscopy showed neurosecretory-type granules and larger pleomorphic granules, which were considered most consistent with neuromelanin, a waste product of catecholamine metabolism. The evidence of melanin or melanin-like pigment in the adrenal gland has been described in only 10 cases in the literature and is always a microscopic finding. The case reported here is unique for the characteristic black macroscopic appearance and the abundance of pigment.


title: An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model

abstract: Attention-based sequence-to-sequence models for automatic speech recognition jointly train an acoustic model, language model, and alignment mechanism. Thus, the language model component is only trained on transcribed audio-text pairs. This leads to the use of shallow fusion with an external language model at inference time. Shallow fusion refers to log-linear interpolation with a separately trained language model at each step of the beam search. In this work, we investigate the behavior of shallow fusion across a range of conditions: different types of language models, different decoding units, and different tasks. On Google Voice Search, we demonstrate that the use of shallow fusion with a neural LM with wordpieces yields a 9.1% relative word error rate reduction (WERR) over our competitive attention-based sequence-to-sequence model, obviating the need for second-pass rescoring.


title: A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions

abstract: We aim to shed light on the strengths and weaknesses of the newly introduced neural machine translation paradigm. To that end, we conduct a multifaceted evaluation in which we compare outputs produced by state-of-the-art neural machine translation and phrase-based machine translation systems for 9 language directions across a number of dimensions. Specifically, we measure the similarity of the outputs, their fluency and amount of reordering, the effect of sentence length and performance across different error categories. We find out that translations produced by neural machine translation systems are considerably different, more fluent and more accurate in terms of word order compared to those produced by phrase-based systems. Neural machine translation systems are also more accurate at producing inflected forms, but they perform poorly when translating very long sentences.


title: Design, Optimization and Fabrication of a 28.3 THz Nano-Rectenna for Infrared Detection and Rectification

abstract: The increasing energy demands of the world's population and the quickly diminishing fossil fuel reserves together suggest the urgent need to secure long-lasting alternative and renewable energy resources. Here, we present a THz antenna integrated with a rectifier (rectenna) for harvesting infrared energy. We demonstrate a resonant bowtie antenna that has been optimized to produce highly enhanced localized fields at the bow tip. To benefit from this enhancement, the rectifier is realized between the overlapped antenna's arms using a 0.7 nm copper oxide. The thin film diode offers low zero bias resistance of 500 Ω, thus improving the impedance matching with the antenna. In addition, the rectenna prototype demonstrates high zero bias responsivity (4 A/W), which is critical in producing DC current directly from THz signals without the application of an external electric source, particularly for energy harvesting applications.


title: Can we make operating systems reliable and secure?

abstract: Microkernels long discarded as unacceptable because of their lower performance compared with monolithic kernels might be making a comeback in operating systems due to their potentially higher reliability, which many researchers now regard as more important than performance. Each of the four different attempts to improve operating system reliability focuses on preventing buggy device drivers from crashing the system. In the Nooks approach, each driver is individually hand wrapped in a software jacket to carefully control its interactions with the rest of the operating system, but it leaves all the drivers in the kernel. The paravirtual machine approach takes this one step further and moves the drivers to one or more machines distinct from the main one, taking away even more power from the drivers. Both of these approaches are intended to improve the reliability of existing (legacy) operating systems. In contrast, two other approaches replace legacy operating systems with more reliable and secure ones. The multiserver approach runs each driver and operating system component in a separate user process and allows them to communicate using the microkernel's IPC mechanism. Finally, Singularity, the most radical approach, uses a type-safe language, a single address space, and formal contracts to carefully limit what each module can do.


title: Document Summarization via Guided Sentence Compression

abstract: Joint compression and summarization has been used recently to generate high quality summaries. However, such word-based joint optimization is computationally expensive. In this paper we adopt the ‘sentence compression + sentence selection’ pipeline approach for compressive summarization, but propose to perform summary guided compression, rather than generic sentence-based compression. To create an annotated corpus, the human annotators were asked to compress sentences while explicitly given the important summary words in the sentences. Using this corpus, we train a supervised sentence compression model using a set of word-, syntax-, and documentlevel features. During summarization, we use multiple compressed sentences in the integer linear programming framework to select salient summary sentences. Our results on the TAC 2008 and 2011 summarization data sets show that by incorporating the guided sentence compression model, our summarization system can yield significant performance gain as compared to the state-of-the-art.


title: Privacy in Recommender Systems

abstract: In many online applications, the range of content that is offered to users is so wide that a need for automated recommender systems arises. Such systems can provide a personalized selection of relevant items to users. In practice, this can help people find entertaining movies, boost sales through targeted advertisements, or help social network users meet new friends. To generate accurate personalized recommendations, recommender systems rely on detailed personal data on the preferences of users. Examples are ratings, consumption histories, and personal profiles. Recommender systems are useful, however the privacy risks associated to gathering and processing personal data are often underestimated or ignored. Many users are not sufficiently aware if and how much of their data is collected, if such data is sold to third parties, or how securely it is stored and for how long. This chapter aims to provide insight into privacy in recommender systems. First, we discuss different types of existing recommender systems. Second, we give an overview of the data that is used in recommender systems. Third, we examine the associated risks to data privacy. Fourth, relevant research areas for privacy-protection techniques and their applicability to recommender systems are discussed. Finally, we conclude with a discussion on applying and combining different privacy-protection techniques in real-world settings, making clear mappings to reflect typical relations between recommender system types, information types, particular privacy risks, and privacy-protection techniques.


title: Lazy collision checking in asymptotically-optimal motion planning

abstract: Asymptotically-optimal sampling-based motion planners, like RRT*, perform vast amounts of collision checking, and are hence rather slow to converge in complex problems where collision checking is relatively expensive. This paper presents two novel motion planners, Lazy-PRM* and Lazy-RRG*, that eliminate the majority of collision checks using a lazy strategy. They are sampling-based, any-time, and asymptotically complete algorithms that grow a network of feasible vertices connected by edges. Edges are not immediately checked for collision, but rather are checked only when a better path to the goal is found. This strategy avoids checking the vast majority of edges that have no chance of being on an optimal path. Experiments show that the new methods converge toward the optimum substantially faster than existing planners on rigid body path planning and robot manipulation problems.


title: DriveSafe: An app for alerting inattentive drivers and scoring driving behaviors

abstract: This paper presents DriveSafe, a new driver safety app for iPhones that detects inattentive driving behaviors and gives corresponding feedback to drivers, scoring their driving and alerting them in case their behaviors are unsafe. It uses computer vision and pattern recognition techniques on the iPhone to assess whether the driver is drowsy or distracted using the rear-camera, the microphone, the inertial sensors and the GPS. We present the general architecture of DriveSafe and evaluate its performance using data from 12 drivers in two different studies. The first one evaluates the detection of some inattentive driving behaviors obtaining an overall precision of 82% at 92% of recall. The second one compares the scores between DriveSafe vs the commercial AXA Drive app obtaining a better valuation to its operation. DriveSafe is the first app for smartphones based on inbuilt sensors able to detect inattentive behaviors evaluating the quality of the driving at the same time. It represents a new disruptive technology because, on the one hand, it provides similar ADAS features that found in luxury cars, and on the other hand, it presents a viable alternative for the “blackboxes” installed in vehicles by the insurance companies.


title: Asynchronous Stereo Vision for Event-Driven Dynamic Stereo Sensor Using an Adaptive Cooperative Approach

abstract: This paper presents an adaptive cooperative approach towards the 3D reconstruction tailored for a bio-inspired depth camera: the stereo dynamic vision sensor (DVS). DVS consists of self-spiking pixels that asynchronously generate events upon relative light intensity changes. These sensors have the advantage to allow simultaneously high temporal resolution (better than 10μs) and wide dynamic range (>120dB) at sparse data representation, which is not possible with frame-based cameras. In order to exploit the potential of DVS and benefit from its features, depth calculation should take into account the spatiotemporal and asynchronous aspect of data provided by the sensor. This work deals with developing an appropriate approach for the asynchronous, event-driven stereo algorithm. We propose a modification of the cooperative network in which the history of the recent activity in the scene is stored to serve as spatiotemporal context used in disparity calculation for each incoming event. The network constantly evolves in time - as events are generated. In our work, not only the spatiotemporal aspect of the data is preserved but also the matching is performed asynchronously. The results of the experiments prove that the proposed approach is well suited for DVS data and can be successfully used for our efficient passive depth camera.


title: LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares

abstract: An iterative method is given for solving Ax ~ffi b and minU Ax b 112, where the matrix A is large and sparse. The method is based on the bidiagonalization procedure of Golub and Kahan. It is analytically equivalent to the standard method of conjugate gradients, but possesses more favorable numerical properties. Reliable stopping criteria are derived, along with estimates of standard errors for x and the condition number of A. These are used in the FORTRAN implementation of the method, subroutine LSQR. Numerical tests are described comparing I~QR with several other conjugate-gradient algorithms, indicating that I~QR is the most reliable algorithm when A is ill-conditioned.


title: SoundTrak: Continuous 3D Tracking of a Finger Using Active Acoustics

abstract: The small size of wearable devices limits the efficiency and scope of possible user interactions, as inputs are typically constrained to two dimensions: the touchscreen surface. We present SoundTrak, an active acoustic sensing technique that enables a user to interact with wearable devices in the surrounding 3D space by continuously tracking the finger position with high resolution. The user wears a ring with an embedded miniature speaker sending an acoustic signal at a specific frequency (e.g., 11 kHz), which is captured by an array of miniature, inexpensive microphones on the target wearable device. A novel algorithm is designed to localize the finger’s position in 3D space by extracting phase information from the received acoustic signals. We evaluated SoundTrak in a volume of space (20cm × 16cm × 11cm) around a smartwatch, and show an average accuracy of 1.3 cm. We report on results from a Fitts’ Law experiment with 10 participants as the evaluation of the real-time prototype. We also present a set of applications which are supported by this 3D input technique, and show the practical challenges that need to be addressed before widespread use.


title: The resilience renaissance? Unpacking of resilience for tackling climate change and disasters

abstract: The term 'resilience' is increasingly used in the context of discussion, policies and programming around climate change adaptation ('adaptation') and disaster risk reduction (DRR). It has become particularly popular to describe the intersection between these two fields and those of poverty and development as 'climate resilient development', and 'climate resilient development' is rapidly becoming a catch-all for tackling climate change impacts in a development context'. However, despite this growth in popularity, there has been little attempt to scrutinise the literature to examine how it might underpin an operational approach to resilience. This working paper reviews academic conceptualisation of the concept of 'resilience' in social, ecological and socio-ecological systems. It reviews 16 overlapping conceptualisations of resilience from the literature, outlining key characteristics and indicators of resilience. A metatable captures the key findings of the paper, including detail on indicators


Arabic Light Stemmer : Anew Enhanced Approach


title: Location based reminder Android application using Google Maps API

abstract: The popular reminders are based on electronic calendar in mobile phones. These reminders are purely time specific i.e. this will give notification only on at that particular time. Many times it is not confirmed that we will be present at the specific location for the work for which we have set the reminder. Instead it is beneficial if the notification or alarm triggers when we are actually present near or at that specific location. To remind modern people of something at a specific time and location, Smart Location Reminder is a boon. To serve the purpose, implementing an application for Android-based smartphones and tablets which is not only time based but also location based. The system uses free, public API service from Google Maps. Timely reminder reduces chances of missing the location of interest & task to be reminded can be performed on desired time and at desired location. Also Ease of search is achieved by finding nearby places of interest.


title: A system dynamics model for mobile banking adoption

abstract: Adoption of technology and developed products is always one of the favorite issues for innovator organizations' major managers. Developing products and new services, follows high amounts of money that is needed for researches and developing. Anyway in some cases, new services and products, are not accepted by target market and customers and also doesn't serves our expectances regarding benefits. The purpose of this research is to provide a good intuition regarding effective reasons for mobile banking adoption by banks' customers. In this research, we will review last researches about acceptance of technology and recognizing effective variables in mobile banking adoption in order to know effective variables and perceiving the dynamics on this process, by using systems dynamics methodology and drawing cause and effects' diagram, and also we will give a model for analysis of dynamic of mobile banking adoption. Finally, the results of proposed scenarios would be done and be analyzed.


Context and Dialogue Control


title: Robot navigation: Review of techniques and research challenges

abstract: Robot navigation has multi-spectral applications across a plethora of industries. Being one of the fastest evolving field of Artificial intelligence, it has garnered a lot of speculation and the problem of efficient Robot navigation has become a talking point in the “Artificial Intelligence research community”. Robot Navigation can be thought of as a collection of sub-problems that when solved efficiently will (most likely) give the complete and the most efficient solution. While the main Navigation problem can be divided into numerous smaller ones some of the prevalent (sub)-tasks associated with Robot Navigation are Path Planning, Collision Prevention, Search Algorithm and an appropriate pictorial (graphical) representation of the given environment. Path Planning gives us the most optimal path finding approach while keeping the dynamism of the environment and the objects in mind. Collision Prevention ensures the Robot reaches its goal without colliding with any of the objects present in the environment. Collision Prevention techniques' vitality increases considerably when the number of objects in the input space is large (>10, approximately). Search Algorithms are the functional units of this problem as they determine the path the Robot follows while traveling to its destination. Since the environment greatly affects the choice of technique for all the other tasks it is vital to have a clear, concise and a non-ambiguous representation of the same. The main aim of the paper is to summarise the development of various techniques in the multiple domains of the Robot Navigation problem in the last fifteen years (2000–2015) while stating the scope and the restriction of each of them.


title: Programming by Examples: PL Meets ML

abstract: Programming by Examples (PBE) involves synthesizing intended programs in an underlying domain-specific language from example-based specifications. PBE systems are already revolutionizing the application domain of data wrangling and are set to significantly impact several other domains including code refactoring.


title: TouchDevelop: app development on mobile devices

abstract: Mobile devices are becoming the prevalent computing platform for most people. TouchDevelop is a new mobile development environment that enables anyone with a Windows Phone to create new apps directly on the smartphone, without a PC or a traditional keyboard. At the core is a new mobile programming language and editor that was designed with the touchscreen as the only input device in mind. Programs written in TouchDevelop can leverage all phone sensors such as GPS, cameras, accelerometer, gyroscope, and stored personal data such as contacts, songs, pictures. Thousands of programs have already been written and published with TouchDevelop.


title: Competence in lexical semantics

abstract: We investigate from the competence standpoint two recent models of lexical semantics, algebraic conceptual representations and continuous vector models.


title: Nutch : A Flexible and Scalable Open-Source Web Search Engine

abstract: is an open-source Web search engine that can be used at global, local, and even personal scale. Its initial design goal was to enable a transparent alternative for global Web search in the public interest — one of its signature features is the ability to "explain" its result rankings. Recent work has emphasized how it can also be used for intranets; by local communities with richer data models, such as the Creative Commons metadata-enabled search for licensed content; on a personal scale to index a user's files, email, and web-surfing history; and we also report on several other research projects built on Nutch. In this paper, we present how the architecture of the Nutch system enables it to be more flexible and scalable than other comparable systems today.


Detection and Isolation of Zombie Attack under Cloud Environment


title: New object detection features in the OpenCV library

abstract: In this work the object detection problem is considered. A short description of implementations of the object detection system with a discriminatively trained part based model and a gradient boosting trees algorithm (as part of OpenCV library) is given. Application of the gradient boosting trees learner to the object detection problem (in terms of the pedestrian detection problem) is explored.


An Empirical Research on the Evaluation System of Cross-border E-commerce


title: Neodymium-doped alkaline-earth oxide catalysts for propane oxidative dehydrogenation. Part II. Catalytic properties

abstract: Abstract Two series of alkaline-earth oxides of Mg, Ca, and Sr, doped with 5 mol% Nd 2 O 3 and synthesised by two different procedures: (i) evaporation of nitrate aqueous solutions followed by nitrate decomposition and (ii) sol–gel technique, have been studied for propane oxidative dehydrogenation reaction. Solid solutions of both Nd in alkaline-earth oxides and of alkaline-earth elements in Nd 2 O 3 were shown to be formed as a result of both preparation procedures. It was observed that Nd increased strongly propane conversion, while propene and ethene selectivity generally followed the catalyst basicity measured by CO 2 -TPD. The sol–gel preparation led to materials exhibiting enhanced alkene selectivity, higher basicity and higher content of structural defects. The selectivity was observed to increase with propane conversion at increased contact time at difference with what was usually reported for redox-type catalysts. The relationship between catalyst preparation method, solid solution formation, lattice structural defects, surface basicity, and catalytic performance has been discussed.


title: Forward error correction for high-speed I/O

abstract: Modern state-of-the-art high-speed (Gb/s) I/O links today rely exclusively upon an equalization-based transceiver to achieve a bit error-rate (BER) of 10-15. This paper explores the potential of applying forward error-correction (FEC) in such links to reduce power and BER. The FEC coding gain can be employed to lower the power consumed in the analog components (e.g., transmit driver, clock recovery unit (CRU)) since these do not scale with process technology. A BER improvement of six orders-of-magnitude and ten orders-of-magnitude is demonstrated for a 20" FR4 channel operating at 10 Gb/s with a LE and a DFE, respectively, using a BCH code. Savings in the encoder-decoder power overhead of up to 50% is demonstrated for a (63, 36, 11) BCH code using a novel gated decoder architecture.


title: A detailed systematic anatomical study of monocephalic conjoined symmetric twin lambs

abstract: A case of conjoined twins with monocephalus, thoracopagus, partial abdominopagus, tetrabrachius and tetrascelus in lambs complicated with several defects of skeletal, cardiovascular, gastrointestinal, and urogenital systems is reported. The twins were dead and delivered by cesarean section. This case report highlights the detailed anatomical study of defects in different systems due to an abnormal birth defect.


title: Eight-Port Orthogonally Dual-Polarized Antenna Array for 5G Smartphone Applications

abstract: A dual-polarized hybrid eight-antenna array operating in the 2.6-GHz band (2550-2650 MHz) for 5G communication multi-input multi-output (MIMO) operation in the smartphone is presented. The proposed hybrid antenna array elements are symmetrically placed along the long edges of the smartphone, and they are composed of two different four-antenna array types (C-shaped coupled-fed and L-shaped monopole slot) that exhibit orthogonal polarization. Therefore, coupling between the two antenna array types can be reduced, and the MIMO system performances are enhanced. A prototype of the proposed eight-antenna array is manufactured and measured. A good impedance matching (10 dB return loss or better), desirable cross-polarization discrimination (better than 15 dB), and an acceptable isolation (better than 12.5 dB) are obtained. Envelope correlation coefficient and channel capacity are also calculated to evaluate the MIMO performances of the proposed antenna array.


title: ShuttleDB: Database-Aware Elasticity in the Cloud

abstract: Motivated by the growing popularity of database-as-aservice clouds, this paper presents ShuttleDB, a holistic approach enabling flexible, automated elasticity of database tenants in the cloud. We first propose a database-aware live migration and replication method designed to work with off-the-shelf databases without any database engine modifications. We then combine these database-aware techniques with VM-level mechanisms to implement a flexible elasticity approach that can achieve efficient scale up, scale out, or scale back for diverse tenants with fluctuating workloads. Our experimental evaluation of the ShuttleDB prototype shows that by applying migration and replication techniques at the tenant level, automated elasticity can be achieved both intraand inter-datacenter in a database agnostic way. We further show that ShuttleDB can reduce the time and data transfer needed for elasticity by 80% or more compared to tenant-oblivious approaches.


The application of multiwavelet filter banks to image processing


title: Automated workpiece localization for robotic welding

abstract: Precise knowledge of a workpiece's position is essential to robotic manufacturing. This often requires design and use of special purpose fixtures and programming between manufacturing operations. Our goal is to remove these requirements and automate the discovery of position (localization) of the workpiece. For quick setup and localization we have augmented the end-effector of the robot with a laser projector and a laser displacement sensor. The laser projector guides the worker in initial fixture and workpiece placement and the laser displacement sensor acquires a 3D point cloud of the workspace. The point cloud collected after scanning the workspace is processed to provide a sparse outline of the workpiece. This outline is then compared to the computer aided design (CAD) data of the workpiece to estimate a transformation between the actual and planned position. This estimation is made by using an iterative closest point algorithm. Multiple searches are run using different seed points to improve the chance of finding the best fit while maintaining low run times. In this paper, we have used flat weldments as test cases. In our experiments we were able to localize and weld workpieces with significant time savings against current practices in manual welding.


Digital Nations – Smart Cities, Innovation, and Sustainability


title: Full body motion adaption based on task-space distance meshes

abstract: This paper presents a novel robot pose measure for human movement imitation based entirely on the Euclidean distance information between any two links of a robot and any link and object in the robot's environment in a Cartesian task space. A Hidden Markov Model is used to encode the spatio-temporal information of multiple demonstrations. In combination with Gaussian Mixture Regression for extracting the important task properties, feasible full-body motion adaption can be achieved. The method is suited for use with a humanoid robot by considering additional constraints like balance control and collision avoidance. In order to tackle modeling errors occurring due to the human movement demonstration and the robotic reproduction, a manipulability based weighting scheme is proposed. Complexity reduction of the otherwise redundant pose measure is performed based upon a mechanical analogy of an interconnected spring system. Experiments are conducted using a HRP-4 robot and display the applicability of the presented methods for robotic full-body motion imitation tasks.


title: Unsupervised classification of music genre using hidden Markov model

abstract: Music genre classification can be of great utility to musical database management. Most current classification methods are supervised and tend to be based on contrived taxonomies. However, due to the ambiguities and inconsistencies in the chosen taxonomies, these methods are not applicable for a much larger database. We proposed an unsupervised clustering method, based on a given measure of similarity which can be provided by hidden Markov models. In addition, in order to better characterize music content, a novel segmentation scheme is proposed, based on music intrinsic rhythmic structure analysis and features are extracted based on these segments. The performance of this feature segmentation scheme performs better than the traditional fixed-length method, according to experimental results. Our preliminary results also suggest that the proposed method is comparable to the supervised classification method.