title: Aesthetic rhinoplasty plus brow, eyelid and conchal surgery: pitfalls – complications – prevention

abstract: Within the last years aesthetic surgery enjoys greater popularity and acceptance. One of the most frequently asked operations has been the aesthetic rhinoplasty. Hardly any other field of surgery is exposed to such a critical analysis than aesthetic rhinoplasty because the results are so obvious. According to the “International Society of Aesthetic Surgery” (ISAPS) over 980,000 cosmetic rhinoplasties have been performed in 2010. This corresponds to 10.4% of all registered aesthetic procedures worldwide. Complications can not be eliminated in such a large number of nasal operations. Five to 15% of all patients re-consult a doctor for a revision because they are much dissatisfied with their final rhinoplasty result. Findings of the tip followed by functional problems and irregularities of the nasal dorsum are named most frequently. The responsible rhinosurgeon has to take into account all anatomical and physiological details and to consider ethical and psychological aspects in the pre-selection and postoperative care of the patient. Aesthetic surgeons should be acquainted with terms and definitions like body image, dysmorphophobia or Thersites complex. Acronyms, like “SIMON” or “SYLVIA”, support the physician additionally to analyze and assess the patient. The following article describes the most frequent faults, complications and pitfalls after aesthetic rhinoplasty listed by the anatomical structure. Results will be analyzed and strategies and techniques will be suggested to correct the faults and to prevent them in the future. Furthermore psychologic, social and psychiatric aspects will be discussed and handling with aesthetic patients explained.


title: A Gaussian mixture PHD filter for extended target tracking

abstract: In extended target tracking, targets potentially produce more than one measurement per time step. Multiple extended targets are therefore usually hard to track, due to the resulting complex data association. The main contribution of this paper is the implementation of a Probability Hypothesis Density (PHD) filter for tracking of multiple extended targets. A general modification of the PHD filter to handle extended targets has been presented recently by Mahler, and the novelty in this work lies in the realisation of a Gaussian mixture PHD filter for extended targets. Furthermore, we propose a method to easily partition the measurements into a number of subsets, each of which is supposed to contain measurements that all stem from the same source. The method is illustrated in simulation examples, and the advantage of the implemented extended target PHD filter is shown in a comparison with a standard PHD filter.


title: Racism and the physical and mental health status of African Americans: a thirteen year national panel study.

abstract: This paper examined the relationships between the experiences and perceptions of racism and the physical and mental health status of African Americans. The study was based upon thirteen year (1979 to 1992), four wave, national panel data (n = 623) from the National Survey of Black Americans. Personal experiences of racism were found to have both adverse and salubrious immediate and cumulative effects on the physical and mental well-being of African Americans. In 1979-80, reports of poor treatment due to race were inversely related to subjective well-being and positively associated with the number of reported physical health problems. Reports of negative racial encounters over the 13-year period were weakly predictive of poor subjective well-being in 1992. A more general measure of racial beliefs, perceiving that whites want to keep blacks down, was found to be related to poorer physical health in 1979-80, better physical health in 1992, and predicted increased psychological distress, as well as, lower levels of subjective well-being in 1992. In conclusion, the authors suggested future research on possible factors contributing to the relationship between racism and health status among African Americans.


title: A perspective on 16S rRNA operational taxonomic unit clustering using sequence similarity

abstract: The standard pipeline for 16S amplicon analysis starts by clustering sequences within a percent sequence similarity threshold (typically 97%) into ‘Operational Taxonomic Units’ (OTUs). From each OTU, a single sequence is selected as a representative. This representative sequence is annotated, and that annotation is applied to all remaining sequences within that OTU. This perspective paper will discuss the known shortcomings of this standard approach using results obtained from the Human Microbiome Project. In particular, we will show that the traditional approach of using pairwise sequence alignments to compute sequence similarity can result in poorly clustered OTUs. As OTUs are typically annotated based upon a single representative sequence, poorly clustered OTUs can have significant impact on downstream analyses. These results suggest that we need to move beyond simple clustering techniques for 16S analysis.


title: A Very Brief Introduction to Machine Learning With Applications to Communication Systems

abstract: Given the unprecedented availability of data and computing resources, there is widespread renewed interest in applying data-driven machine learning methods to problems for which the development of conventional engineering solutions is challenged by modelling or algorithmic deficiencies. This tutorial-style paper starts by addressing the questions of why and when such techniques can be useful. It then provides a high-level introduction to the basics of supervised and unsupervised learning. For both supervised and unsupervised learning, exemplifying applications to communication networks are discussed by distinguishing tasks carried out at the edge and at the cloud segments of the network at different layers of the protocol stack.


title: Buri: Scaling Big-Memory Computing with Hardware-Based Memory Expansion

abstract: Motivated by the challenges of scaling up memory capacity and fully exploiting the benefits of memory compression, we propose Buri, a hardware-based memory compression scheme, which simultaneously achieves cost efficiency, high performance, and ease of adoption. Buri combines (1) a self-contained, ready-to-adopt hardware compression module, which manages metadata compression and memory allocation/relocation operations; (2) a set of hardware optimization mechanisms, which reduce the area and performance overheads in accommodating the address indirection required by memory compression; and (3) lightweight BIOS/OS extensions used to handle exceptions. Our evaluation with large memory workload traces shows that Buri can increase capacity by 70p, in addition to the compression ratio already provided by database software.


Discovering and linking public omics data sets using the Omics Discovery Index


title: Wide range ZVS operation of dual active bridge DC-DC converters using adaptive modulation and low coupling factor transformers

abstract: One of the central requirements of a Dual Active Bridge (DAB) converter is to operate under Zero Voltage Switching (ZVS) conditions, to reduce switching losses and increase operating efficiency. This paper shows how frequency domain analysis can be used to design and operate a DAB to ensure ZVS across the entire operating range by using transformers with a reduced winding coupling factor and adaptive 3-level bridge modulation, while also including second order effects. The approach is shown to achieve experimentally measured efficiencies between 96.5% and 98.5% over the entire power range for an optimised DAB converter stage (>1 kW).


title: DTR-GAN: dilated temporal relational adversarial network for video summarization

abstract: Video summarization targets the challenge of finding the smallest subset of frames, while still conveying the whole story of a given video. Thus it is of great significance for large-scale video understanding, allowing efficient processing of the large amount of videos that are uploaded every day. In this paper, we introduce a Dilated Temporal Relational Adversarial Network (DTR-GAN) to achieve frame-level video summarization. The dilated temporal relational units in the generator aim to exploit multi-scale temporal context in order to select key frames. To ensure that the model predicts high quality summaries, we present a discriminator that learns to enhance both the information completeness and compactness via a three-player loss. Experiments on the public TVSum dataset demonstrate the effectiveness of the proposed approach.


title: Modeling and Tracking the Driving Environment With a Particle-Based Occupancy Grid

abstract: Modeling and tracking the driving environment is a complex problem due to the heterogeneous nature of the real world. In many situations, modeling the obstacles and the driving surfaces can be achieved by the use of geometrical objects, and tracking becomes the problem of estimating the parameters of these objects. In the more complex cases, the scene can be modeled and tracked as an occupancy grid. This paper presents a novel occupancy grid tracking solution based on particles for tracking the dynamic driving environment. The particles will have a dual nature-they will denote hypotheses, as in the particle filtering algorithms, but they will also be the building blocks of our modeled world. The particles have position and speed, and they can migrate in the grid from cell to cell, depending on their motion model and motion parameters, but they will be also created and destroyed using a weighting-resampling mechanism that is specific to particle filtering algorithms. The tracking algorithm will be centered on particles, instead of cells. An obstacle grid derived from processing a stereovision-generated elevation map is used as measurement information, and the measurement model takes into account the uncertainties of the stereo reconstruction. The resulting system is a flexible real-time tracking solution for dynamic unstructured driving environments.


Intelligent Information and Database Systems


title: Transient fields for an electric dipole in a dissipative medium

abstract: Explicit expressions are presented for the electric and magnetic field components of a step-function excited electric dipole immersed in an infinite homogeneous medium with a specified conductivity and dielectric constant. It is shown that displacement currents influence only the early part of the waveform.


title: A highly sensitive hydrogen sensor with gas selectivity using a PMMA membrane-coated Pd nanoparticle/single-layer graphene hybrid.

abstract: A polymer membrane-coated palladium (Pd) nanoparticle (NP)/single-layer graphene (SLG) hybrid sensor was fabricated for highly sensitive hydrogen gas (H2) sensing with gas selectivity. Pd NPs were deposited on SLG via the galvanic displacement reaction between graphene-buffered copper (Cu) and Pd ion. During the galvanic displacement reaction, graphene was used as a buffer layer, which transports electrons from Cu for Pd to nucleate on the SLG surface. The deposited Pd NPs on the SLG surface were well-distributed with high uniformity and low defects. The Pd NP/SLG hybrid was then coated with polymer membrane layer for the selective filtration of H2. Because of the selective H2 filtration effect of the polymer membrane layer, the sensor had no responses to methane, carbon monoxide, or nitrogen dioxide gas. On the contrary, the PMMA/Pd NP/SLG hybrid sensor exhibited a good response to exposure to 2% H2: on average, 66.37% response within 1.81 min and recovery within 5.52 min. In addition, reliable and repeatable sensing behaviors were obtained when the sensor was exposed to different H2 concentrations ranging from 0.025 to 2%.


title: The deep learning development for real-time ball and goal detection of barelang-FC

abstract: Studies of object detection have recently attracted increased interest. One of the applications of object detection is robotics. This paper present the real-time object detection integrated to humanoid robot soccer. In order to enhance the vision to detect ball and goal, the You Only Look Once (YOLO) methods is used as deep-learning object detection method. The real-time experiments have been carried out in LINUX OS by using NVIDIA JETSON TX1 controller board. The experimental results show that the proposed method capable to detect and distinguish objects in the different lighting condition, with interference from other objects, also from the different angle of capturing an image.


title: Mometasone furoate in the treatment of vulvar lichen sclerosus: could its formulation influence efficacy, tolerability and adherence to treatment?

abstract: PURPOSE ::: To assess the effectiveness, tolerability, and convenience of the cream formulation of mometasone furoate 0.1% (MMF) in the treatment of active vulvar lichen sclerosus (VLS) and to compare the cream with the ointment formulation. ::: ::: ::: METHODS ::: The following efficacy parameters were assessed in 27 VLS patients treated with MMF cream for 12 weeks (group A): (i) response rate, (ii) percentage of patients achieving an improvement from baseline of ≥75% in subjective and objective scores, and (iii) mean reduction in subjective and objective scores. These efficacy assessments, as well as those regarding safety and adherence, were compared with the assessments recorded among 37 VLS patients treated with MMF ointment (group B). ::: ::: ::: RESULTS ::: 59.3% (group A) and 78.4% (group B) of patients were considered responders; 44.4% and 40.7% of patients in group A and 54.1% and 45.9% in group B achieved an improvement of at least 75% in subjective and objective scores, respectively. MMF ointment obtained a significantly higher improvement in symptom scores in comparison with the cream formulation. ::: ::: ::: CONCLUSIONS ::: MMF in ointment formulation seems to be more effective in treating active VLS in comparison with MMF cream. Both formulations are well tolerated and there is no difference in patient adherence and satisfaction.


title: Learning to Make Better Mistakes: Semantics-aware Visual Food Recognition

abstract: We propose a visual food recognition framework that integrates the inherent semantic relationships among fine-grained classes. Our method learns semantics-aware features by formulating a multi-task loss function on top of a convolutional neural network (CNN) architecture. It then refines the CNN predictions using a random walk based smoothing procedure, which further exploits the rich semantic information. We evaluate our algorithm on a large "food-in-the-wild" benchmark, as well as a challenging dataset of restaurant food dishes with very few training images. The proposed method achieves higher classification accuracy than a baseline which directly fine-tunes a deep learning network on the target dataset. Furthermore, we analyze the consistency of the learned model with the inherent semantic relationships among food categories. Results show that the proposed approach provides more semantically meaningful results than the baseline method, even in cases of mispredictions.


title: Cache coherence requirements for interprocess rendezvous

abstract: Multiprocessors in which a shared bus is used by the processor to communicate with common memory are an emerging class of machines where there is a need to support parallel programming languages. A language construct that is found in a number of parallel programming languages to support synchronization and communication in the interprocess rendezvous. Shared-bus multiprocessor require a protocol to keep the date in their caches coherent. There are two major categories of these protocols: invalidation and write-boadcast. This paper examines the requirements for cache coherence protocols to support efficient interprocessor rendezvous. The approach taken is to examine the memory referencing patterns to the run-time data structures during rendezvous execution. The appropriate coherence protocol is shown to be a function of the processor scheduling strategy used by the run-time system at synchronzation points during the rendezvous. When processes migrate freely as a result of the scheduling strategy, invalidation protocols are found to be more efficient. When migration is restricted by the scheduler, write-broadcast protocols are more efficient.


title: SDALF: Modeling Human Appearance with Symmetry-Driven Accumulation of Local Features

abstract: In video surveillance, person re-identification (re-id) is probably the open challenge, when dealing with a camera network with non-overlapped fields of view. Re-id allows the association of different instances of the same person across different locations and time. A large number of approaches have emerged in the last 5 years, often proposing novel visual features specifically designed to highlight the most discriminant aspects of people, which are invariant to pose, scale and illumination. In this chapter, we follow this line, presenting a strategy with three important key-characteristics that differentiate it with respect to the state of the art: (1) a symmetry-driven method to automatically segment salient body parts, (2) an accumulation of features making the descriptor more robust to appearance variations, and (3) a person re-identification procedure casted as an image retrieval problem, which can be easily embedded into a multi-person tracking scenario, as the observation model.


title: 3D Shape Reconstruction from a Single 2D Image via 2D-3D Self-Consistency

abstract: Aiming at inferring 3D shapes from 2D images, 3D shape reconstruction has drawn huge attention from researchers in computer vision and deep learning communities. However, it is not practical to assume that 2D input images and their associated ground truth 3D shapes are always available during training. In this paper, we propose a framework for semi-supervised 3D reconstruction. This is realized by our introduced 2D-3D self-consistency, which aligns the predicted 3D models and the projected 2D foreground segmentation masks. Moreover, our model not only enables recovering 3D shapes with the corresponding 2D masks, camera pose information can be jointly disentangled and predicted, even such supervision is never available during training. In the experiments, we qualitatively and quantitatively demonstrate the effectiveness of our model, which performs favorably against state-of-the-art approaches in either supervised or semi-supervised settings.


title: Improvement for the design equations for tapered Microstrip-to-Substrate Integrated Waveguide transitions

abstract: This article describes a new design strategy for Microstrip-to-Substrate Integrated Waveguide (SIW) transitions with different geometries. This design strategy focuses in obtaining a good initial point for the length and the width of the transition. Later, these parameters are optimized together in order to calculate a low reflection transition in the desired bandwidth. With this method, different transitions have been designed, in which the impedance variation follows different mathematical expressions, some of them extracted from the classical theory.


title: BeWell+: Multi-dimensional Wellbeing Monitoring with Community-guided User Feedback and Energy Optimization

abstract: Smartphone sensing and persuasive feedback design is enabling a new generation of wellbeing applications capable of automatically monitoring multiple aspects of physical and mental health. In this paper, we present BeWell+ the next generation of the BeWell smartphone health app, which continuously monitors user behavior along three distinct health dimensions, namely sleep, physical activity, and social interaction. BeWell promotes improved behavioral patterns via feedback rendered as an ambient display on the smartphone's wallpaper. With BeWell+, we introduce new wellbeing mechanisms to address challenges identified during the initial deployment of the BeWell app; specifically, (i) community adaptive wellbeing feedback, which automatically generalize to diverse user communities (e.g., elderly, young adults, children) by balancing the need to promote better behavior yet remains realistic to the user's goals; and, (ii) wellbeing adaptive energy allocation, which prioritizes monitoring fidelity and feedback responsiveness on specific health dimensions of wellbeing (e.g., social interaction) where the user needs most help. We evaluate the performance of these mechanisms as part of an initial deployment and user study that includes 27 people using BeWell+ over a 19 day field trial. Our findings show that not only can BeWell+ operate successfully on consumer-grade smartphones, but users understand feedback and respond by taking positive steps towards leading healthier lifestyles.


title: Maximizing system performance by balancing computation loads in LSTM accelerators

abstract: The LSTM is a popular neural network model for modeling or analyzing the time-varying data. The main operation of LSTM is a matrix-vector multiplication and it becomes sparse (spMxV) due to the widely-accepted weight pruning in deep learning. This paper presents a new sparse matrix format, named CBSR, to maximize the inference speed of the LSTM accelerator. In the CBSR format, speed-up is achieved by balancing out the computation loads over PEs. Along with the new format, we present a simple network transformation to completely remove the hardware overhead incurred when using the CBSR format. Also, the detailed analysis on the impact of network size or the number of PEs is performed, which lacks in the prior work. The simulation results show 16∼38% improvement in the system performance compared to the well-known CSC/CSR format. The power analysis is also performed in 65nm CMOS technology to show 9∼22% energy savings.


Combining Word-Level and Character-Level Representations for Relation Classification of Informal Text


title: Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The Adverse Childhood Experiences (ACE) Study.

abstract: Results: More than half of respondents reported at least one, and one-fourth reported $2 categories of childhood exposures. We found a graded relationship between the number of categories of childhood exposure and each of the adult health risk behaviors and diseases that were studied (P , .001). Persons who had experienced four or more categories of childhood exposure, compared to those who had experienced none, had 4to 12-fold increased health risks for alcoholism, drug abuse, depression, and suicide attempt; a 2- to 4-fold increase in smoking, poor self-rated health, $50 sexual intercourse partners, and sexually transmitted disease; and a 1.4- to 1.6-fold increase in physical inactivity and severe obesity. The number of categories of adverse childhood exposures showed a graded relationship to the presence of adult diseases including ischemic heart disease, cancer, chronic lung disease, skeletal fractures, and liver disease. The seven categories of adverse childhood experiences were strongly interrelated and persons with multiple categories of childhood exposure were likely to have multiple health risk factors later in life. Conclusions: We found a strong graded relationship between the breadth of exposure to abuse or household dysfunction during childhood and multiple risk factors for several of the leading causes of death in adults. Medical Subject Headings (MeSH): child abuse, sexual, domestic violence, spouse abuse, children of impaired parents, substance abuse, alcoholism, smoking, obesity, physical activity, depression, suicide, sexual behavior, sexually transmitted diseases, chronic obstructive pulmonary disease, ischemic heart disease. (Am J Prev Med 1998;14:245‐258) © 1998 American Journal of Preventive Medicine


1. Introduction Survey paper: Face Detection and Face Recognition


title: Homomorphic encryption in mobile multi cloud computing

abstract: Multi cloud computing has become a new trend for complementing existing cloud computing today. Multi cloud computing is considered safer and more efficient in maintaining data regulation of user(s). The paper discusses the security of mobile multi cloud computing (MMC) and the advantages for mobile user(s), beside that for the data security itself cover with homomorphic encryption which predictable by many researchers as the optimum method for cloud computing environment. The implementation and evaluation of homomorphic encryption in mobile cloud computing are discussed in this paper.


title: Knapp: A Packet Processing Framework for Manycore Accelerators

abstract: High-performance network packet processing benefits greatly from parallel-programming accelerators such as Graphics Processing Units (GPUs). Intel Xeon Phi, a relative newcomer in this market, is a distinguishing platform because its x86-compatible vectorized architecture offers additional optimization opportunities. Its software stack exposes low-level communication primitives, enabling fine-grained control and optimization of offloading processes. Nonetheless, our microbenchmarks show that offloading APIs for Xeon Phi comes in short for combining low latency and high throughput for both I/O and computation. In this work, we exploit Xeon Phi's low-level threading mechanisms to design a new offloading framework, Knapp, and evaluate it using simplified IP routing applications. Knapp lays the ground for full exploitation of Xeon Phi as a packet processing framework.


title: Wavelet based methods on patterned fabric defect detection

abstract: The wavelet transform (WT) has been developed over 20 years and successfully applied in defect detection on plain (unpatterned) fabric. This paper is on the use of the wavelet transform to develop an automated visual inspection method for defect detection on patterned fabric. A method called direct thresholding (DT) based on WT detailed subimages has been developed. The golden image subtraction method (GIS) is also introduced. GIS is an efficient and fast method, which can segment out the defective regions on patterned fabric effectively. In this paper, the method of wavelet preprocessed golden image subtraction (WGIS) has been developed for defect detection on patterned fabric or repetitive patterned texture. This paper also presents a comparison of the three methods. It can be concluded that the WGIS method provides the best detection result. The overall detection success rate is 96.7% with 30 defect-free images and 30 defective patterned images for one common kind of patterned Jacquard fabric.


title: Blockchain Queueing Theory

abstract: Blockchain has many benefits including decentralization, availability, persistency, consistency, anonymity, auditability and accountability, and it also covers a wide spectrum of applications ranging from cryptocurrency, financial services, reputation system, Internet of Things, sharing economy to public and social services. Not only may blockchain be regarded as a by-product of Bitcoin cryptocurrency systems, but also it is a type of distributed ledger technology through using a trustworthy, decentralized log of totally ordered transactions. By summarizing the literature of blockchain, it is found that more papers focus on engineering implementation and realization, while little work has been done on basic theory, for example, mathematical models (Markov processes, queueing theory and game models), performance analysis and optimization of blockchain systems. In this paper, we develop queueing theory of blockchain systems and provide system performance evaluation. To do this, we design a Markovian batch-service queueing system with two different service stages, while the two stages are suitable to well express the mining process in the miners pool and the building of a new blockchain. By using the matrix-geometric solution, we obtain a system stable condition and express three key performance measures: (a) The number of transactions in the queue, (b) the number of transactions in a block, and (c) the transaction-confirmation time. Finally, We use numerical examples to verify computability of our theoretical results. Although our queueing model is simple under exponential or Poisson assumptions, our analytic method will open a series of potentially promising research in queueing theory of blockchain systems.


An exceptional case of bilateral gestational gigantomastia with multiple breast lumps


title: Two-Dimensional Linear Discriminant Analysis

abstract: Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction and dimension reduction. It has been used widely in many applications involving high-dimensional data, such as face recognition and image retrieval. An intrinsic limitation of classical LDA is the so-called singularity problem, that is, it fails when all scatter matrices are singular. A well-known approach to deal with the singularity problem is to apply an intermediate dimension reduction stage using Principal Component Analysis (PCA) before LDA. The algorithm, called PCA+LDA, is used widely in face recognition. However, PCA+LDA has high costs in time and space, due to the need for an eigen-decomposition involving the scatter matrices. ::: ::: In this paper, we propose a novel LDA algorithm, namely 2DLDA, which stands for 2-Dimensional Linear Discriminant Analysis. 2DLDA overcomes the singularity problem implicitly, while achieving efficiency. The key difference between 2DLDA and classical LDA lies in the model for data representation. Classical LDA works with vectorized representations of data, while the 2DLDA algorithm works with data in matrix representation. To further reduce the dimension by 2DLDA, the combination of 2DLDA and classical LDA, namely 2DLDA+LDA, is studied, where LDA is preceded by 2DLDA. The proposed algorithms are applied on face recognition and compared with PCA+LDA. Experiments show that 2DLDA and 2DLDA+LDA achieve competitive recognition accuracy, while being much more efficient.


title: Incremental dialogue system faster than and preferred to its nonincremental counterpart

abstract: Incremental Dialogue System Faster than and Preferred to its Nonincremental Counterpart Gregory Aist 1 (gregory.aist@asu.edu), James Allen 2 (james@cs.rochester.edu), Ellen Campana 2,3,4,5 (ecampana@bcs.rochester.edu), Carlos Gomez Gallo 2 (cgomez@cs.rochester.edu), Scott Stoness 2 (stoness@cs.rochester.edu), Mary Swift 2 (swift@cs.rochester.edu), and Michael K. Tanenhaus 3 (mtan@bcs.rochester.edu) Department of Computer Science and Engineering Arizona State University P.O. Box 878809 Tempe AZ 85287 Department of Computer Science University of Rochester P.O. Box 270226 Rochester NY 14627 Department of Brain and Cognitive Sciences University of Rochester P.O. Box 270268 Rochester, NY 14627 Abstract understanding; Arts, Media, and Engineering Program Arizona State University P.O. Box 878709 Tempe AZ 85287 Department of Psychology Arizona State University P.O. Box 871104 Tempe AZ 85287 brought into context, as determined by hearer eye fixations (Altmann and Kamide 1999). Other actions can also be taken based on partial utterances. Many different sources of knowledge are available for use in understanding. On the speech recognition side, commonly used sources of information include acoustics, phonetics and phonemics, lexical probability, and word order. In dialogue systems, additional sources of information often include syntax and semantics (both general and domain-specific.) There are also however some sources of information that are less frequently programmed. These include such linguistic information as morphology and prosody. Knowledge-based features are also available, such as world knowledge (triangles have three sides), domain knowledge (here there are two sizes of triangles), and task knowledge (the next step is to click on a small triangle.) There is also pragmatic information available from the visual context (there is a small triangle near the flag.) In this paper we discuss some of the progress we have made towards building methods for incremental understanding of spoken language by machines. We first discuss some of our and others’ related work in this area. We then discuss the testbed domain that we have been developing, and show some of the characteristics of human dialogue in the domain. We then discuss the incremental architecture that we have been developing, highlighting its differences from traditional architectures. Finally, we present an experimental evaluation of the performance of the system showing that incremental systems are both faster than and preferred to their nonincremental counterparts. Current dialogue systems generally operate in a pipelined, modular fashion on one complete utterance at a time. Evidence from human language understanding shows that human understanding operates incrementally and makes use of multiple sources of information during the parsing process, including traditionally “later” components such as pragmatics. In this paper we describe a spoken dialogue sys- tem that understands language incrementally, provides visual feedback on possible referents during the course of the user’s utterance, and allows for overlapping speech and actions. We further present findings from an empirical study showing that the resulting dialogue system is faster overall than its nonincremental counterpart. Furthermore, the incremental system is preferred to its nonincremental counterpart – beyond what is accounted for by factors such as speed and accuracy. These results indicate that successful incremental understanding systems will improve both performance and usability. Keywords: natural language systems; incremental processing. dialogue Introduction The standard model of natural language understanding for dialogue systems is pipelined, modular, and operates on complete utterances. By pipelined we mean that only one level of processing operates at a time, in a sequential manner. By modular, we mean that each level of processing depends only on the previous level. By complete utterances we mean that the system operates on one sentence at a time. There is, however, considerable evidence that human language processing is neither pipelined nor modular nor whole-utterance (Marslen-Wilson 1993). Evidence is converging from a variety of sources, including particularly actions taken while speech arrives. For example, natural turn-taking behavior such as backchanneling (uh-huh) and interruption occur while the speaker is still speaking. Eye movements to possible referents also occur while listening: individuals process instructions incrementally, making saccadic eye movements to objects right after hearing relevant words in the instruction (Tanenhaus et al. 1995); verbs appearing earlier in sentences affect which objects are Related Work We have previously shown that incremental parsing can be faster and more accurate than non-incremental parsing (Stoness et al. 2005.) In addition, we have shown that in our testbed domain the relative percentage of language that is of a more interactive style also increases over time (Aist et al. 2005.)


title: A Comparative Study of Staff Removal Algorithms

abstract: This paper presents a quantitative comparison of different algorithms for the removal of stafflines from music images. It contains a survey of previously proposed algorithms and suggests a new skeletonization-based approach. We define three different error metrics, compare the algorithms with respect to these metrics, and measure their robustness with respect to certain image defects. Our test images are computer-generated scores on which we apply various image deformations typically found in real-world data. In addition to modern western music notation, our test set also includes historic music notation such as mensural notation and lute tablature. Our general approach and evaluation methodology is not specific to staff removal but applicable to other segmentation problems as well.


title: Laser treatment of scars.

abstract: BACKGROUND ::: Erythematous and hypertrophic scars may occur after surgical procedures. This paper describes a novel technique for their resolution. ::: ::: ::: OBJECTIVE ::: The purpose of this paper is to describe long-term results of treating erythematous, hypertrophic scars with the flashlamp-pumped pulsed dye laser (FLPDL). ::: ::: ::: METHODS ::: Forty-eight patients who presented with erythematous/hypertrophic scars were treated with the FLPDL with or without intralesional triamcinolone. Patients were evaluated by physicians on a 25% increment of objective improvement. ::: ::: ::: RESULTS ::: Seventy-three percent of scars appearing on the face for less than 1 year totally resolved in an average of 2.3 treatments. Twenty percent of scars on the face for greater than 1 year had a total resolution in 4.4 treatments with an average improvement of 88%. Scars on nonfacial areas for less than 1 year had a 25% incidence of total resolution over an average of 2.5 treatments with an average improvement of 81%. Scars in nonfacial distributions over 1 year of age had a 12% total resolution with an average 62% improvement over 2.2 treatments. ::: ::: ::: CONCLUSION ::: The FLPDL is a useful adjuvant in the treatment of erythematous, hypertrophic scars.


title: Unusual Valence State in the Antiperovskites Sr$_3$SnO and Sr$_3$PbO Revealed by X-ray Photoelectron Spectroscopy

abstract: The class of antiperovskite compounds $A_3B$O ($A$ = Ca, Sr, Ba; $B$ = Sn, Pb) has attracted interest as a candidate 3D Dirac system with topological surface states protected by crystal symmetry. A key factor underlying the rich electronic structure of $A_3B$O is the unusual valence state of $B$, i.e., a formal oxidation state of $-4$. Practically, it is not obvious whether anionic $B$ can be stabilized in thin films, due to its unusual chemistry, as well as the polar surface of $A_3B$O, which may render the growth-front surface unstable. We report X-ray photoelectron spectroscopy (XPS) measurements of single-crystalline films of Sr$_3$SnO and Sr$_3$PbO grown by molecular beam epitaxy (MBE). We observe shifts in the core-level binding energies that originate from anionic Sn and Pb, consistent with density functional theory (DFT) calculations. Near the surface, we observe additional signatures of neutral or cationic Sn and Pb, which may point to an electronic or atomic reconstruction with possible impact on putative topological surface states.


title: Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention

abstract: In this paper, we proposed a sentence encoding-based model for recognizing text entailment. In our approach, the encoding of sentence is a two-stage process. Firstly, average pooling was used over word-level bidirectional LSTM (biLSTM) to generate a first-stage sentence representation. Secondly, attention mechanism was employed to replace average pooling on the same sentence for better representations. Instead of using target sentence to attend words in source sentence, we utilized the sentence's first-stage representation to attend words appeared in itself, which is called "Inner-Attention" in our paper . Experiments conducted on Stanford Natural Language Inference (SNLI) Corpus has proved the effectiveness of "Inner-Attention" mechanism. With less number of parameters, our model outperformed the existing best sentence encoding-based approach by a large margin.


title: Benzothiazole: Different Methods of Synthesis and Diverse Biological Activities

abstract: Substituted 1, 3-benzothiazole derivatives are an important class of heterocyclic compounds. In recent years heterocyclic compounds analogues and derivatives have attracted strong interest due to their biological and pharmacological properties. The benzothiazole nucleus containing compounds involved in research aimed at evaluating new products that possess biological activities, such as antimicrobial, anticancer, antifungal, anthelmintic, anti-diabetic, amyloid imagining agents and anticancer agents. The present review focus on the different methods of synthesis of substituted benzothiazoles with potential activities that are now in developing phase.


Cognitive Linguistics, Psychology and Cognitive Science


title: GPS supported city bus tracking & smart ticketing system

abstract: Now-a-days increasing density of vehicles on road is becoming the problem for the traffic control. Ultimately arising obstacle in the managing and tracking of the vehicle. Because of the problem state, it is necessary for every organizations and individuals to track the vehicle. People will monitor and track their vehicles for the safety concerns with the help of our Android app. Public transport and private buses tracked to citizens with traffic and transportation details like location, crowd, etc. The proposed system will be used for the positioning of the bus from remote location. The Smart Card based ticketing module which swaps the card to the smart hand held device for the transaction purpose. The smart ticketing device will also contain the dynamic routes as per the bus depot. The smart device has enhanced with the GSM and GPS technology and made available with required necessary configurations which makes it very efficient than that of the existing system.


title: Approximate graph edit distance computation by means of bipartite graph matching

abstract: In recent years, the use of graph based object representation has gained popularity. Simultaneously, graph edit distance emerged as a powerful and flexible graph matching paradigm that can be used to address different tasks in pattern recognition, machine learning, and data mining. The key advantages of graph edit distance are its high degree of flexibility, which makes it applicable to any type of graph, and the fact that one can integrate domain specific knowledge about object similarity by means of specific edit cost functions. Its computational complexity, however, is exponential in the number of nodes of the involved graphs. Consequently, exact graph edit distance is feasible for graphs of rather small size only. In the present paper we introduce a novel algorithm which allows us to approximately, or suboptimally, compute edit distance in a substantially faster way. The proposed algorithm considers only local, rather than global, edge structure during the optimization process. In experiments on different datasets we demonstrate a substantial speed-up of our proposed method over two reference systems. Moreover, it is emprically verified that the accuracy of the suboptimal distance remains sufficiently accurate for various pattern recognition applications.


title: Comments on “A Closed-Form Solution to Tensor Voting: Theory and Applications”

abstract: We comment on a paper that describes a closed-form formulation to Tensor Voting, a technique to perceptually group clouds of points, usually applied to infer features in images. The authors proved an analytic solution to the technique, a highly relevant contribution considering that the original formulation required numerical integration, a time-consuming task. Their work constitutes the first closed-form expression for the Tensor Voting framework. In this work we first observe that the proposed formulation leads to unexpected results which do not satisfy the constraints for a Tensor Voting output, hence they cannot be interpreted. Given that the closed-form expression is said to be an analytic equivalent solution, unexpected outputs should not be encountered unless there are flaws in the proof. We analyzed the underlying math to find which were the causes of these unexpected results. In this commentary we show that their proposal does not in fact provide a proper analytic solution to Tensor Voting and we indicate the flaws in the proof.


Analysis of EEG Signal Processing Techniques based on Spectrograms


THE SUBNORMAL OFFENDER


title: Discourse indicators for content selection in summarization

abstract: We present analyses aimed at eliciting which specific aspects of discourse provide the strongest indication for text importance. In the context of content selection for single document summarization of news, we examine the benefits of both the graph structure of text provided by discourse relations and the semantic sense of these relations. We find that structure information is the most robust indicator of importance. Semantic sense only provides constraints on content selection but is not indicative of important content by itself. However, sense features complement structure information and lead to improved performance. Further, both types of discourse information prove complementary to non-discourse features. While our results establish the usefulness of discourse features, we also find that lexical overlap provides a simple and cheap alternative to discourse for computing text structure with comparable performance for the task of content selection.


title: Reduced functional segregation between the default mode network and the executive control network in healthy older adults: A longitudinal study

abstract: Abstract The effects of age on functional connectivity (FC) of intrinsic connectivity networks (ICNs) have largely been derived from cross-sectional studies. Far less is known about longitudinal changes in FC and how they relate to ageing-related cognitive decline. We evaluated intra- and inter-network FC in 78 healthy older adults two or three times over a period of 4 years. Using linear mixed modeling we found progressive loss of functional specialization with ageing, evidenced by a decline in intra-network FC within the executive control (ECN) and default mode networks (DMN). In contrast, longitudinal inter-network FC between ECN and DMN showed a u-shaped trajectory whereby functional segregation between these two networks initially increased over time and later decreased as participants aged. The rate of loss in functional segregation between ECN and DMN was associated with ageing-related decline in processing speed. The observed longitudinal FC changes and their associations with processing speed remained after correcting for longitudinal reduction in gray matter volume. These findings help connect ageing-related changes in FC with ageing-related decline in cognitive performance and underscore the value of collecting concurrent longitudinal imaging and behavioral data.


CHAPTER 35 EVALUATION OF YOUTH IN THE JUVENILE JUSTICE SYSTEM


title: Opinion Mining and Sentiment Polarity on Twitter and Correlation between Events and Sentiment

abstract: Twitter, as a social media is a very popular way of expressing opinions and interacting with other people in the online world. When taken in aggregation tweets can provide a reflection of public sentiment towards events. In this paper, we provide a positive or negative sentiment on Twitter posts using a well-known machine learning method for text categorization. In addition, we use manually labeled (positive/negative) tweets to build a trained method to accomplish a task. The task is looking for a correlation between twitter sentiment and events that have occurred. The trained model is based on the Bayesian Logistic Regression (BLR) classification method. We used external lexicons to detect subjective or objective tweets, added Unigram and Bigram features and used TF-IDF (Term Frequency-Inverse Document Frequency) to filter out the features. Using the FIFA World Cup 2014 as our case study, we used Twitter Streaming API and some of the official world cup hashtags to mine, filter and process tweets, in order to analyze the reflection of public sentiment towards unexpected events. The same approach, can be used as a basis for predicting future events.


title: Pitch-angle feedback control of a Biologically Inspired flapping-wing microrobot

abstract: This paper presents the first experimental results on pitch-angle control of a flapping-wing microrobot. First, we describe a control method by which torques can be modulated to change the pitch orientation of the microrobot. The suitability of the proposed method is demonstrated through hardware-in-the-loop experiments, employing a static experimental setup capable of measuring instantaneous torques produced by enforced flapping patterns. Then, using the information learned from the static hardware-in-the-loop experiments, controlled pitch rotation experiments are performed. The pitch-angle is measured using a motion capture system. Compelling results are presented to validate the chosen approach.


title: Irreversible fingerprint template using Minutiae Relation Code with Bloom Filter

abstract: Template protected fingerprint authentication techniques have been proposed, which enables to create an irreversible fingerprint template. In this paper, we propose a new irreversible template creation technique using Minutiae Relation Code(MRC) which can describe the minutiae information efficiently, and Bloom Filter which can realize the irreversibility feature. We evaluate the authentication accuracy and security factors such as Shannon Entropy and the number of attack possibilities using FVC2002 and FVC2004 DBs. As a result, our proposed method can achieve 1.8% EER in FVC2002 DB2 with 249 attack possibilities.


title: Determining the Optimal Software Rejuvenation Schedule via Semi-Markov Decision Process

abstract: Software rejuvenation is a preventive and proactive maintenance policy that is particularly ::: useful for counteracting the phenomenon of software aging. In this study we consider an operational ::: software system with multiple degradations and derive the optimal software rejuvenation policy ::: minimizing the expected operation cost per unit time in the steady state, via the dynamic programming ::: approach. Especially, we show analytically that the control-limit type of software rejuvenation policy is ::: optimal. A numerical example is presented to make a decision table and to perform the sensitivity ::: analysis of cost parameters.


title: Extracting Patterns of Urban Activity from Geotagged Social Data

abstract: Data generated on location-based social networks provide rich information on the whereabouts of urban dwellers. Specifically, such data reveal who spends time where, when, and on what type of activity (e.g., shopping at a mall, or dining at a restaurant). That information can, in turn, be used to describe city regions in terms of activity that takes place therein. For example, the data might reveal that citizens visit one region mainly for shopping in the morning, while another for dining in the evening. Furthermore, once such a description is available, one can ask more elaborate questions: What are the features that distinguish one region from another -- is it simply the type of venues they host or is it the visitors they attract? What regions are similar across cities? ::: In this paper, we attempt to answer these questions using publicly shared Foursquare data. In contrast with previous work, our method makes use of a probabilistic model with minimal assumptions about the data and thus relieves us from having to make arbitrary decisions in our analysis (e.g., regarding the granularity of discovered regions or the importance of different features). We perform an empirical comparison with previous work and discuss insights obtained through our findings.


title: High gain circularly polarized slot antenna based on microstrip-ridge gap waveguide technology

abstract: This paper presents a novel circular polarized slot antenna design using microstrip-ridge gap waveguide technology, it possesses the easy manufacturing since it realizes the gap waveguide with PCB process. The antenna is composed of two substrates, by using the ring slot with a shorted strip on the upper substrate and the EBG textures located on the lower substrate, the circular polarized characteristic of the antenna is obtained, the simulated results with CST simulation software show the antenna has the impedance bandwidth of 13.8% (72.1GHz-82.8GHz, |S11|<-10dB), and the axial ratio is from 75.2GHz-79.9GHz (AR<3dB) with 6% circular polarization bandwidth. And also the microstrip slot antenna is designed and compared with the antenna in this paper. The compared results give the strong evidence that the microstrip-ridge gap waveguide circular polarized slot antenna has wider bandwidth and higher gain.


title: PCKGen: A Phase Change Memory based cryptographic key generator

abstract: Physical Unclonable Function (PUF) is widely known as an effective countermeasure to withstand non-invasive computational attacks as well as invasive tempering attacks on trusted computing systems. However, vast majority of the PUFs reported to-date are defined by static Challenge-Response Pairs (CRPs) with inferior security. In this paper, we propose a novel design of dynamically reconfigurable PUF based on Phase Change Memory (PCM) technology to yield refreshed cryptographic keys whenever the need arises to achieve enhanced security. A dedicated circuit framework is also introduced to reinforce the diversity of the CRP sets and improve the stability of the proposed PUF. Extensive simulation results show that our proposed work promises a clean delineation from the security bottlenecks faced by the state-of-the-art PUF designs.


title: The Relationship between Learning Style, Test Anxiety and Academic Achievement.

abstract: This study aimed to investigate the relationship between social studies pre-service teachers' (SSPTs) learning style, test anxiety and academic achievement. A total of 315 SSPTs participated in the study. Data were collected using Turkish versions of Grasha-Reichmann learning style scale (GRLSS) and test anxiety scale (TAS) by Spielberger. According to the findings, SSPTs' academic achievements had negative and low-level relationship with the TAS' worry sub-dimension while they had no significant relationship with the emotionality sub-dimension. No statistically significant relationship was observed between test anxiety and independent, participant and avoidant learning styles, which are among GRLSS sub-dimensions. The findings revealed that competitive and cooperative learning styles had positive, low-level and significant relationship with the TAS' emotionality sub-dimension, and the same relationship was observed between the competitive learning style and the worry sub-dimension. The relationship of gender and class level with learning styles and test anxiety was also investigated in this study.


title: Expansion of Human-Phone Interface By Sensing Structure-Borne Sound Propagation

abstract: ForcePhone is a novel system that enables commodity phones to recognize the force applied to their touch screen and body. Researchers have shown the usefulness and importance of this expressive input interface (especially for the one-hand operation), but this advanced function has not yet been realized and deployed in most state-of-the-art smartphones. Instead of employing or augmenting specialized/proprietary sensors, ForcePhone uses only the phone's built-in sensors to measure the applied force via a physical property called structure-borne sound propagation. ForcePhone has been implemented and evaluated on both iOS and Android phones. Multiple demo applications, such as getting the option menu by hard-pressing a button or surfing the previous webpage by squeezing the phone, have been implemented and tested successfully. The estimated force is shown highly correlated to the real applied force and the estimation error is less than 54g when the phone is stationary. Users can easily control the applied force at two different levels with a 97% accuracy. Moreover, ForcePhone can detect the squeeze of the phone body with a higher than 90% accuracy. Most participants in our usability study were able to master the ForcePhone-based apps and find them very useful.


title: Design of Braun Multiplier with Kogge Stone Adder & It’s Implementation on FPGA

abstract: Multiplication is the basic building block for several DSP processors, Image processing and many other. Over the years the computational complexities of algorithms used in Digital Signal Processors (DSPs) have gradually increased. This requires a parallel array multiplier to achieve high execution speed or to meet the performance demands. A typical implementation of such an array multiplier is Braun design. Braun multiplier is a type of parallel array multiplier. The architecture of Braun multiplier mainly consists of some Carry Save Adders, array of AND gates and one Ripple Carry Adder. In this research work, a new design of Braun Multiplier is proposed and this proposed design of multiplier uses a very fast parallel prefix adder ( Kogge Stone Adder) in place of Ripple Carry Adder. The architecture of standard Braun Multiplier is modified in this work for reducing the delay due to Ripple Carry Adder and performing faster multiplication of two binary numbers. This research also presents a comparative study of FPGA implementation on Spartan2 and Spartartan2E for new multiplier design and standard braun multiplier. The RTL design of proposed new Braun Multiplier and standard braun multiplier is done using Verilog HDL. The simulation is performed using ModelSim. The Xilinx ISE design tool is used for FPGA implementation. Comparative result shows the modified design is effective when compared in terms of delay with the standard design.


title: An open source SDR-based NOMA system for 5G networks

abstract: With the rapid advent of various new applications and services, the anticipated use of bandwidth and frequency resources is beyond expectation in future mobile networks. To maximize spectral efficiency, novel radio access techniques need to multiplex users in the most suitable combinations of frequency and radio resources. Non-orthogonal multiple access (NOMA) is one of the candidate radio access techniques for improving spectral efficiency in the 5G mobile network through multiplexing users in the power domain, which has never been explored in past and current communications systems. While the concept of NOMA was proposed several years ago, the performance of NOMA has only been verified in theory but not in practice. In this article, we first introduce the state of the art in open source SDR. Due to the high flexibility and reconfigurability of open source SDR, we choose general-purpose-processor-based SDR to implement our NOMA system, which is based on an open source LTE program. Over-the-air experiments are carried out on the designed NOMA system for the purpose of performance evaluation, and to demonstrate its potential in future 5G mobile networks.


Sarcasm Detection: Beyond Machine Learning Algorithms


title: Investigation of Drilling Conditions of Printed Circuit Board Based on Data Mining Method from Tool Catalog Data-Base

abstract: Data-mining methods using hierarchical and non-hierarchical clustering are proposed that will help engineers determine appropriate drilling conditions. We have constructed a system that uses clustering techniques and tool catalog data to support the determination of drilling conditions for printed wiring boards (PWBs). Variable cluster analysis and the K-means method were used together to identify tool shape parameters that have a linear relationship with the drilling conditions listed in the catalogs. The response surface method and significant tool shape parameters obtained by clustering were used to derive drilling condition decision equations, which were used to determine the indicative drilling conditions for PWBs. Comparison of the conditions recommended by toolmakers demonstrated that our proposed system can be used to determine the drilling condition for PWBs. We carried out the drilling experiments in accordance with the catalog conditions and mining conditions, and estimated the board temperature around a drilled hole, the drilling forces, and the roughness of the drilled hole wall.


title: A Practical Guide to Applying Echo State Networks

abstract: Reservoir computing has emerged in the last decade as an alternative to gradient descent methods for training recurrent neural networks. Echo State Network (ESN) is one of the key reservoir computing “flavors”. While being practical, conceptually simple, and easy to implement, ESNs require some experience and insight to achieve the hailed good performance in many tasks. Here we present practical techniques and recommendations for successfully applying ESNs, as well as some more advanced application-specific modifications.


title: On limits of wireless communications in a fading environment when using multiple antennas

abstract: This paper is motivated by the need for fundamental understanding of ultimate limits of bandwidth efficient delivery of higher bit-rates in digital wireless communications and to also begin to look into how these limits might be approached. We examine exploitation of multi-element array (MEA) technology, that is processing the spatial dimension (not just the time dimension) to improve wireless capacities in certain applications. Specifically, we present some basic information theory results that promise great advantages of using MEAs in wireless LANs and building to building wireless communication links. We explore the important case when the channel characteristic is not available at the transmitter but the receiver knows (tracks) the characteristic which is subject to Rayleigh fading. Fixing the overall transmitted power, we express the capacity offered by MEA technology and we see how the capacity scales with increasing SNR for a large but practical number, n, of antenna elements at both transmitter and receiver. ::: ::: We investigate the case of independent Rayleigh faded paths between antenna elements and find that with high probability extraordinary capacity is available. Compared to the baseline n = 1 case, which by Shannon‘s classical formula scales as one more bit/cycle for every 3 dB of signal-to-noise ratio (SNR) increase, remarkably with MEAs, the scaling is almost like n more bits/cycle for each 3 dB increase in SNR. To illustrate how great this capacity is, even for small n, take the cases n = 2, 4 and 16 at an average received SNR of 21 dB. For over 99% of the channels the capacity is about 7, 19 and 88 bits/cycle respectively, while if n = 1 there is only about 1.2 bit/cycle at the 99% level. For say a symbol rate equal to the channel bandwith, since it is the bits/symbol/dimension that is relevant for signal constellations, these higher capacities are not unreasonable. The 19 bits/cycle for n = 4 amounts to 4.75 bits/symbol/dimension while 88 bits/cycle for n = 16 amounts to 5.5 bits/symbol/dimension. Standard approaches such as selection and optimum combining are seen to be deficient when compared to what will ultimately be possible. New codecs need to be invented to realize a hefty portion of the great capacity promised.


title: Tentpole technique for bone regeneration in vertically deficient alveolar ridges: A prospective study.

abstract: Purpose ::: The purpose of the study was to qualitatively and quantitatively assess bone regeneration potential of tentpole technique using beta tricalcium phosphate bone graft in vertically deficient alveolar ridges. ::: ::: ::: Materials and Method ::: This prospective study comprised of 20 patients with vertically deficient alveolar ridges, wherein thirty one implants were placed. Tenting of the soft tissue matrix was done using titanium screws and beta tricalcium phosphate synthetic bone graft was filled in the vertical defect. Clinical and radiographic assessment was done at 1, 3 and 6 months. Preoperative biochemical analysis of Osteopontin and RANKL was done and then reassesed at 3 months postoperative. Bone core collected at 6 postoperative months, while removal of screw, was analysed histologically. ::: ::: ::: Results ::: A significant gain in bone height of 2.87 ± 0.79 mm was seen at 6 months postoperative. All implants osseointegrated. Loosening of screw was seen in two patients.There was no postoperative wound dehiscence. Histologic analysis revealed new bone formation .There was no significant change in the levels of RANKL and Osteopontin at 3 months postoperative. ::: ::: ::: Conclusion ::: This is a safe and effective technique for bone regeneration in vertically deficient alveolar ridges and has minimal complications. The regenerated bone has new and viable bone content and supports implant material. More studies with long term follow up are needed to assess the stability of bone after long term loading.


title: Toward evolving robust, deliberate motion planning with HyperNEAT

abstract: Previous works have used a novel hybrid network architecture to create deliberative behaviours to solve increasingly challenging tasks in two-dimensional and three-dimensional artificial worlds. At the foundation of each is a static hand-designed neural network for robust and deliberative motion planning. This paper presents results from replacing the hand-designed motion-planning subnetwork with HyperNEAT. Simulations are run on the original two-dimensional world with, and without, relative position inputs and a multievaluation fitness function, thus assessing the relative performance of each strategy. The focus of this work is on solutions adaptable to general environments; following evolution, each strategy's performance is evaluated on 10,000 world configurations. The results demonstrate that although HyperNEAT was not able to achieve as robust results as a hand-design approach, the best strategy was comparable, with just a 3–4% drop in performance. Relative position inputs and the multievaluation fitness function were both significant in achieving superior general performance, compared to those simulations without.


title: Clustering by passing messages between data points

abstract: Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such "exemplars" can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initial choice is close to a good solution. We devised a method called "affinity propagation," which takes as input measures of similarity between pairs of data points. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We used affinity propagation to cluster images of faces, detect genes in microarray data, identify representative sentences in this manuscript, and identify cities that are efficiently accessed by airline travel. Affinity propagation found clusters with much lower error than other methods, and it did so in less than one-hundredth the amount of time.


title: Staging Urban Interactions with Media Façades

abstract: Using media facades as a subcategory of urban computing, this paper contributes to the understanding of spatial interaction, sense-making, and social mediation as part of identifying key characteristics of interaction with media facades. Our research addresses in particular the open-ended but framed nature of interaction, which in conjunction with varying interpretations enables individual sense-making. Moreover, we contribute to the understanding of flexible social interaction by addressing urban interaction in relation to distributed attention, shared focus, dialogue and collective action. Finally we address challenges for interaction designers encountered in a complex spatial setting calling for a need to take into account multiple viewing and action positions. Our research-through-design approach has included a real-life design intervention in terms of the design, implementation, and reflective evaluation of a 180 m2 (1937 square feet) interactive media facade in operation 24/7 for more than 50 days.


title: A Cloud Gaming System Based on User-Level Virtualization and Its Resource Scheduling

abstract: Many believe the future of gaming lies in the cloud, namely Cloud Gaming, which renders an interactive gaming application in the cloud and streams the scenes as a video sequence to the player over Internet. This paper proposes GCloud , a GPU/CPU hybrid cluster for cloud gaming based on the user-level virtualization technology. Specially, we present a performance model to analyze the server-capacity and games’ resource-consumptions, which categorizes games into two types: CPU-critical and memory-io-critical . Consequently, several scheduling strategies have been proposed to improve the resource-utilization and compared with others. Simulation tests show that both of the First-Fit-like and the Best-Fit-like strategies outperform the other(s); especially they are near optimal in the batch processing mode. Other test results indicate that GCloud is efficient: An off-the-shelf PC can support five high-end video-games run at the same time. In addition, the average per-frame processing delay is 8∼19 ms under different image-resolutions, which outperforms other similar solutions.


title: Sensor Guardian: prevent privacy inference on Android sensors

abstract: Privacy inference attacks based on sensor data is an emerging and severe threat on smart devices, in which malicious applications leverage data from innocuous sensors to infer sensitive information of user, e.g., utilizing accelerometers to infer user’s keystroke. In this paper, we present Sensor Guardian, a privacy protection system that mitigates this threat on Android by hooking and controlling applications’ access to sensors. Sensor Guardian inserts hooks into applications by statically instrumenting their APK (short for Android Package Kit) files and enforces control policies in these hooks at runtime. Our evaluation shows that Sensor Guardian can effectively and efficiently mitigate the privacy inference threat on Android sensors, with negligible overhead during both static instrumentation and runtime control.


title: Outlier Detection for Multidimensional Time Series Using Deep Neural Networks

abstract: Due to the continued digitization of industrial and societal processes, including the deployment of networked sensors, we are witnessing a rapid proliferation of time-ordered observations, known as time series. For example, the behavior of drivers can be captured by GPS or accelerometer as a time series of speeds, directions, and accelerations. We propose a framework for outlier detection in time series that, for example, can be used for identifying dangerous driving behavior and hazardous road locations. Specifically, we first propose a method that generates statistical features to enrich the feature space of raw time series. Next, we utilize an autoencoder to reconstruct the enriched time series. The autoencoder performs dimensionality reduction to capture, using a small feature space, the most representative features of the enriched time series. As a result, the reconstructed time series only capture representative features, whereas outliers often have non-representative features. Therefore, deviations of the enriched time series from the reconstructed time series can be taken as indicators of outliers. We propose and study autoencoders based on convolutional neural networks and long-short term memory neural networks. In addition, we show that embedding of contextual information into the framework has the potential to further improve the accuracy of identifying outliers. We report on empirical studies with multiple time series data sets, which offers insight into the design properties of the proposed framework, indicating that it is effective at detecting outliers.


title: Trust-Based Collaborative Privacy Management in Online Social Networks

abstract: Online social networks have now become the most popular platforms for people to share information with others. Along with this, there is a serious threat to individuals’ privacy. One privacy risk comes from the sharing of co-owned data, i.e., when a user shares a data item that involves multiple users, some users’ privacy may be compromised, since different users generally have different opinions on who can access the data. How to design a collaborative management mechanism to deal with such a privacy issue has recently attracted much attention. In this paper, we propose a trust-based mechanism to realize collaborative privacy management. Basically, a user decides whether or not to post a data item based on the aggregated opinion of all involved users. The trust values between users are used to weight users’ opinions, and the values are updated according to users’ privacy loss. Moreover, the user can make a tradeoff between data sharing and privacy preserving by tuning the parameter of the proposed mechanism. We formulate the selecting of the parameter as a multi-armed bandit problem and apply the upper confidence bound policy to solve the problem. Simulation results demonstrate that the trust-based mechanism can encourage the user to be considerate of others’ privacy, and the proposed bandit approach can bring the user a high payoff.


A Pattern Decomposition (PD) Algorithm for Finding All Frequent Patterns in Large Datasets


Machine Learning Techniques for Short-Term Rain Forecasting System


title: Lexicon-Based Methods for Sentiment Analysis

abstract: We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.


L.: Novel dataset for fine-grained image categorization


title: A new mapping rule for RNA secondary structures with its applications

abstract: According to the characterization of RNA secondary structures, the RNA secondary structures are transformed into elementary sequences, namely characteristic sequences of RNA secondary structures, by representing A, U, G, C in A-U/ G-C pairs, as A′, U′, G′, C′. Based on the representation, three recurrences for mapping RNA secondary structures into 1-D graph, 2-D graph and 3-D graph are given, respectively. Furthermore, a frequency-based method for RNA secondary structures is given in terms of 1-D graph.


title: Understanding Twitter data with TweetXplorer

abstract: In the era of big data it is increasingly difficult for an analyst to extract meaningful knowledge from a sea of information. We present TweetXplorer, a system for analysts with little information about an event to gain knowledge through the use of effective visualization techniques. Using tweets collected during Hurricane Sandy as an example, we will lead the reader through a workflow that exhibits the functionality of the system.


title: Microplastics in the Terrestrial Ecosystem: Implications forLumbricus terrestris(Oligochaeta, Lumbricidae)

abstract: Plastic debris is widespread in the environment, but information on the effects of microplastics on terrestrial fauna is completely lacking. Here, we studied the survival and fitness of the earthworm Lumbricus terrestris (Oligochaeta, Lumbricidae) exposed to microplastics (Polyethylene, <150 μm) in litter at concentrations of 7, 28, 45, and 60% dry weight, percentages that, after bioturbation, translate to 0.2 to 1.2% in bulk soil. Mortality after 60 days was higher at 28, 45, and 60% of microplastics in the litter than at 7% w/w and in the control (0%). Growth rate was significantly reduced at 28, 45, and 60% w/w microplastics, compared to the 7% and control treatments. Due to the digestion of ingested organic matter, microplastic was concentrated in cast, especially at the lowest dose (i.e., 7% in litter) because that dose had the highest proportion of digestible organic matter. Whereas 50 percent of the microplastics had a size of <50 μm in the original litter, 90 percent of the microplastics in the casts was <50 μm in all treatments, which suggests size-selective egestion by the earthworms. These concentration-transport and size-selection mechanisms may have important implications for fate and risk of microplastic in terrestrial ecosystems.


title: Lagrange multiplier tests for testing non-linearities in time series models

abstract: Lagrange multiplier tests of linear autoregressive moving average time series models against bilinear and exponential autoregressive alternatives are derived. A simulation experiment is performed to check the validity of the asymptotic null distributions of the test statistics and to investigate the power properties of the tests. Two recent time domain tests are also included in the simulations and compared with the Lagrange multiplier tests of the paper.


The Relationship Between Academic Procrastination and Parenting Styles Among Jordanian Undergraduate University Students


title: Automatic Performance Diagnosis and Tuning in Oracle

abstract: Performance tuning in modern database systems requires a lot of expertise, is very time consuming and often misdirected. Tuning attempts often lack a methodology that has a holistic view of the database. The absence of historical diagnostic information to investigate performance issues at first occurrence exacerbates the whole tuning process often requiring that problems be reproduced before they can be correctly diagnosed. In this paper we describe how Oracle overcomes these challenges and provides a way to perform automatic performance diagnosis and tuning. We define a new measure called ‘Database Time’ that provides a common currency to gauge the performance impact of any resource or activity in the database. We explain how the Automatic Database Diagnostic Monitor (ADDM) automatically diagnoses the bottlenecks affecting the total database throughput and provides actionable recommendations to alleviate them. We also describe the types of performance measurements that are required to perform an ADDM analysis. Finally we show how ADDM plays a central role within Oracle 10g’s manageability framework to self-manage a database and provide a comprehensive tuning solution.


title: A Neural Network Model for Low-Resource Universal Dependency Parsing

abstract: Accurate dependency parsing requires large treebanks, which are only available for a few languages. We propose a method that takes advantage of shared structure across languages to build a mature parser using less training data. We propose a model for learning a shared “universal” parser that operates over an interlingual continuous representation of language, along with language-specific mapping components. Compared with supervised learning, our methods give a consistent 8-10% improvement across several treebanks in low-resource simulations.


title: Trolls just want to have fun

abstract: Abstract In two online studies (total N = 1215), respondents completed personality inventories and a survey of their Internet commenting styles. Overall, strong positive associations emerged among online commenting frequency, trolling enjoyment, and troll identity, pointing to a common construct underlying the measures. Both studies revealed similar patterns of relations between trolling and the Dark Tetrad of personality: trolling correlated positively with sadism, psychopathy, and Machiavellianism, using both enjoyment ratings and identity scores. Of all personality measures, sadism showed the most robust associations with trolling and, importantly, the relationship was specific to trolling behavior. Enjoyment of other online activities, such as chatting and debating, was unrelated to sadism. Thus cyber-trolling appears to be an Internet manifestation of everyday sadism.


title: Vehicular Communications for 5G Cooperative Small-Cell Networks

abstract: The cooperative transmission is an effective approach for vehicular communications to improve the wireless transmission capacity and reliability in the fifth generation (5G) small cell networks. Based on distances between the vehicle and cooperative small cell BSs, the cooperative probability and the coverage probability have been derived for 5G cooperative small cell networks where small cell base stations (BSs) follow Poisson point process distributions. Furthermore, the vehicular handoff rate and the vehicular overhead ratio have been proposed to evaluate the vehicular mobility performance in 5G cooperative small cell networks. To balance the vehicular communication capacity and the vehicular handoff ratio, an optimal vehicular overhead ratio can be achieved by adjusting the cooperative threshold of 5G cooperative small cell networks.


title: Multimodal human discourse: gesture and speech

abstract: Gesture and speech combine to form a rich basis for human conversational interaction. To exploit these modalities in HCI, we need to understand the interplay between them and the way in which they support communication. We propose a framework for the gesture research done to date, and present our work on the cross-modal cues for discourse segmentation in free-form gesticulation accompanying speech in natural conversation as a new paradigm for such multimodal interaction. The basis for this integration is the psycholinguistic concept of the coequal generation of gesture and speech from the same semantic intent. We present a detailed case study of a gesture and speech elicitation experiment in which a subject describes her living space to an interlocutor. We perform two independent sets of analyses on the video and audio data: video and audio analysis to extract segmentation cues, and expert transcription of the speech and gesture data by microanalyzing the videotape using a frame-accurate videoplayer to correlate the speech with the gestural entities. We compare the results of both analyses to identify the cues accessible in the gestural and audio data that correlate well with the expert psycholinguistic analysis. We show that "handedness" and the kind of symmetry in two-handed gestures provide effective supersegmental discourse cues.


title: Personalization and user verification in wearable systems using biometric walking patterns

abstract: In this article, a novel technique for user's authentication and verification using gait as a biometric unobtrusive pattern is proposed. The method is based on a two stages pipeline. First, a general activity recognition classifier is personalized for an specific user using a small sample of her/his walking pattern. As a result, the system is much more selective with respect to the new walking pattern. A second stage verifies whether the user is an authorized one or not. This stage is defined as a one-class classification problem. In order to solve this problem, a four-layer architecture is built around the geometric concept of convex hull. This architecture allows to improve robustness to outliers, modeling non-convex shapes, and to take into account temporal coherence information. Two different scenarios are proposed as validation with two different wearable systems. First, a custom high-performance wearable system is built and used in a free environment. A second dataset is acquired from an Android-based commercial device in a `wild' scenario with rough terrains, adversarial conditions, crowded places and obstacles. Results on both systems and datasets are very promising, reducing the verification error rates by an order of magnitude with respect to the state-of-the-art technologies.


title: The TREC-8 Question Answering Track Report

abstract: A method for preparing silica-containing olefin polymerization catalysts, and the process performable therewith, the preparation involving adding an alkali metal silicate to an acid under defined conditions of addition to produce a hydrogel, recovering the gel in the substantially dry condition by employment of an oxygenated organic compound and impregnating the gel with a chromium compound.


title: Emerging event detection in social networks with location sensitivity

abstract: With the increasing number of real-world events that are originated and discussed over social networks, event detection is becoming a compelling research issue. However, the traditional approaches to event detection on large text streams are not designed to deal with a large number of short and noisy messages. This paper proposes an approach for the early detection of emerging hotspot events in social networks with location sensitivity. We consider the message-mentioned locations for identifying the locations of events. In our approach, we identify strong correlations between user locations and event locations in detecting the emerging events. We evaluate our approach based on a real-world Twitter dataset. Our experiments show that the proposed approach can effectively detect emerging events with respect to user locations that have different granularities.


NONPARAMETRIC APPROACHES FOR TRAINING DEEP GENERATIVE NETWORKS


title: A delayed proximal gradient method with linear convergence rate

abstract: This paper presents a new incremental gradient algorithm for minimizing the average of a large number of smooth component functions based on delayed partial gradients. Even with a constant step size, which can be chosen independently of the maximum delay bound and the number of objective function components, the expected objective value is guaranteed to converge linearly to within some ball around the optimum. We derive an explicit expression that quantifies how the convergence rate depends on objective function properties and algorithm parameters such as step-size and the maximum delay. An associated upper bound on the asymptotic error reveals the trade-off between convergence speed and residual error. Numerical examples confirm the validity of our results.


title: InfraStructs: fabricating information inside physical objects for imaging in the terahertz region

abstract: We introduce InfraStructs, material-based tags that embed information inside digitally fabricated objects for imaging in the Terahertz region. Terahertz imaging can safely penetrate many common materials, opening up new possibilities for encoding hidden information as part of the fabrication process. We outline the design, fabrication, imaging, and data processing steps to fabricate information inside physical objects. Prototype tag designs are presented for location encoding, pose estimation, object identification, data storage, and authentication. We provide detailed analysis of the constraints and performance considerations for designing InfraStruct tags. Future application scenarios range from production line inventory, to customized game accessories, to mobile robotics.


An equity lens for scaling: a critical juncture for exploring computer science


title: Mobile Cultural Heritage: The Case Study of Locri

abstract: The goal of this project is to study the use of mobile technologies equipped with global positioning systems as an information aid for archaeological visits. In this study we will focus in the study of the technologies used to implement these systems. To this end we analyze an archaeological site where this systems have been tested. In this experiment we have applied state of the art technologies in virtual and augmented reality to implement a system that allows users to access the site using their mobile devices. We conclude that the use of this kind of technologies is an effective tool to promote the archeo-geographical value of the site.


Robust Eye Center Localization through Face Alignment and Invariant Isocentric Patterns


Effects of Lactobacillus johnsonii and Lactobacillus reuteri on gut barrier function and heat shock proteins in intestinal porcine epithelial cells


title: Electroencephalography (EEG) Based Mobile Robot Control through an Adaptive Brain Robot Interface

abstract: This project mentioned a couple of brain controlled automaton supported Brain–computer interfaces (BCI). BCIs square measure systems that may by pass standard channels of communication (i.e., muscles and thoughts) to produce direct communication and management between the human brain and physical devices by translating totally different patterns of brain activity into commands in real time. With these commands a mobile automaton may be controlled. The intention of the project work is to develop a automaton that may assist the disabled folks in their standard of living to try and do some work freelance on others. Brain signals are detected by the brain wave device and it'll convert the info into packets and transmit through Bluetooth medium. Level instrument unit (LAU) can receive the brain wave information and it'll extract and method the signal victimization Mat lab platform. Then the management commands are transmitted to the robotic ARM module to method. With this whole system, we will choose AN object and place it consequently through the designed brain signals.


title: Trust in automation: Designing for appropriate reliance

abstract: Automation is often problematic because people fail to rely upon it appropriately. Because people respond to technology socially, trust influences reliance on automation. In particular, trust guides reliance when complexity and unanticipated situations make a complete understanding of the automation impractical. This review considers trust from the organizational, sociological, interpersonal, psychological, and neurological perspectives. It considers how the context, automation characteristics, and cognitive processes affect the appropriateness of trust. The context in which the automation is used influences automation performance and provides a goal-oriented perspective to assess automation characteristics along a dimension of attributional abstraction. These characteristics can influence trust through analytic, analogical, and affective processes. The challenges of extrapolating the concept of trust in people to trust in automation are discussed. A conceptual model integrates research regarding trust in automation and describes the dynamics of trust, the role of context, and the influence of display characteristics. Actual or potential applications of this research include improved designs of systems that require people to manage imperfect automation.


title: The modular organization of human anatomical brain networks: Accounting for the cost of wiring

abstract: Brain networks are expected to be modular. However, existing techniques for estimating a network’s modules make it difficult to assess the influence of organizational principles such as wiring cost reduction on the detected modules. Here we present a modification of an existing module detection algorithm that allowed us to focus on connections that are unexpected under a cost-reduction wiring rule and to identify modules from among these connections. We applied this technique to anatomical brain networks and showed that the modules we detected differ from those detected using the standard technique. We demonstrated that these novel modules are spatially distributed, exhibit unique functional fingerprints, and overlap considerably with rich clubs, giving rise to an alternative and complementary interpretation of the functional roles of specific brain regions. Finally, we demonstrated that, using the modified module detection approach, we can detect modules in a developmental dataset that track normative pa...


title: Evaluating Improvised Hip Hop Lyrics - Challenges and Observations

abstract: We investigate novel challenges involved in comparing model performance on the task of improvising responses to hip hop lyrics and discuss observations regarding inter-evaluator agreement on judging improvisation quality. We believe the analysis serves as a first step toward designing robust evaluation strategies for improvisation tasks, a relatively neglected area to date. Unlike most natural language processing tasks, improvisation tasks suffer from a high degree of subjectivity, making it difficult to design discriminative evaluation strategies to drive model development. We propose a simple strategy with fluency and rhyming as the criteria for evaluating the quality of generated responses, which we apply to both our inversion transduction grammar based Freestyle hip hop challenge-response improvisation system, as well as various contrastive systems. We report inter-evaluator agreement for both English and French hip hop lyrics, and analyze correlation with challenge length. We also compare the extent of agreement in evaluating fluency with that of rhyming, and quantify the difference in agreement with and without precise definitions of evaluation criteria.


title: Female sexual arousal: Genital anatomy and orgasm in intercourse

abstract: In men and women sexual arousal culminates in orgasm, with female orgasm solely from sexual intercourse often regarded as a unique feature of human sexuality. However, orgasm from sexual intercourse occurs more reliably in men than in women, likely reflecting the different types of physical stimulation men and women require for orgasm. In men, orgasms are under strong selective pressure as orgasms are coupled with ejaculation and thus contribute to male reproductive success. By contrast, women's orgasms in intercourse are highly variable and are under little selective pressure as they are not a reproductive necessity. The proximal mechanisms producing variability in women's orgasms are little understood. In 1924 Marie Bonaparte proposed that a shorter distance between a woman's clitoris and her urethral meatus (CUMD) increased her likelihood of experiencing orgasm in intercourse. She based this on her published data that were never statistically analyzed. In 1940 Landis and colleagues published similar data suggesting the same relationship, but these data too were never fully analyzed. We analyzed raw data from these two studies and found that both demonstrate a strong inverse relationship between CUMD and orgasm during intercourse. Unresolved is whether this increased likelihood of orgasm with shorter CUMD reflects increased penile–clitoral contact during sexual intercourse or increased penile stimulation of internal aspects of the clitoris. CUMD likely reflects prenatal androgen exposure, with higher androgen levels producing larger distances. Thus these results suggest that women exposed to lower levels of prenatal androgens are more likely to experience orgasm during sexual intercourse.


title: Transfer learning from multiple source domains via consensus regularization

abstract: Recent years have witnessed an increased interest in transfer learning. Despite the vast amount of research performed in this field, there are remaining challenges in applying the knowledge learnt from multiple source domains to a target domain. First, data from multiple source domains can be semantically related, but have different distributions. It is not clear how to exploit the distribution differences among multiple source domains to boost the learning performance in a target domain. Second, many real-world applications demand this transfer learning to be performed in a distributed manner. To meet these challenges, we propose a consensus regularization framework for transfer learning from multiple source domains to a target domain. In this framework, a local classifier is trained by considering both local data available in a source domain and the prediction consensus with the classifiers from other source domains. In addition, the training algorithm can be implemented in a distributed manner, in which all the source-domains are treated as slave nodes and the target domain is used as the master node. To combine the training results from multiple source domains, it only needs share some statistical data rather than the full contents of their labeled data. This can modestly relieve the privacy concerns and avoid the need to upload all data to a central location. Finally, our experimental results show the effectiveness of our consensus regularization learning.


Combining fast_align with Hierarchical Sub-sentential Alignment for Better Word Alignments.


title: On Rendering Synthetic Images for Training an Object Detector

abstract: We propose a novel approach to generating synthetic training data.We introduce detector-specific similarity measures between real and synthetic images.We show that the choice of the similarity measure relies on the features used by the detector.We have tested our approach on different object detection tasks. We propose a novel approach to synthesizing images that are effective for training object detectors. Starting from a small set of real images, our algorithm estimates the rendering parameters required to synthesize similar images given a coarse 3D model of the target object. These parameters can then be reused to generate an unlimited number of training images of the object of interest in arbitrary 3D poses, which can then be used to increase classification performances. A key insight of our approach is that the synthetically generated images should be similar to real images, not in terms of image quality, but rather in terms of features used during the detector training. We show in the context of drone, plane, and car detection that using such synthetically generated images yields significantly better performances than simply perturbing real images or even synthesizing images in such way that they look very realistic, as is often done when only limited amounts of training data are available.


title: Chlorhexidine and Alcohol Versus Povidone-Iodine for Antisepsis in Gynecological Surgery

abstract: Abstract Background: Surgical site infections (SSIs) cause severe morbidity and are associated with tremendous health costs. Skin antisepsis (cleansing) with chlorhexidine and alcohol solutions has demonstrated superiority to povidone-iodine in a variety of surgical interventions. Our objective was to determine if chlorhexidine and alcohol antisepsis protocol reduces the rate of SSIs in elective gynecological laparotomies compared with povidone-iodine antisepsis. Methods: This retrospective study was carried out at the Department of Gynecology in a tertiary medical center in Tel Aviv. Patients undergoing elective gynecological laparotomies during two periods of time and who were treated with two different antisepsis protocols were included. The protocols for antisepsis were povidone-iodine 10% scrub followed by 10% povidone-iodine in 65% alcohol (n = 145) and chlorhexidine 2% followed by 70% alcohol (n = 111). The rate of SSIs as defined by the Centers for Disease Control and Prevention (CDC), and the ris...