Combining CNNs and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System


title: Share a pie?: Privacy-Preserving Knowledge Base Export through Count-min Sketches

abstract: Knowledge base (KB) sharing among parties has been proven to be beneficial in several scenarios. However such sharing can arise considerable privacy concerns depending on the sensitivity of the information stored in each party's KB. In this paper, we focus on the problem of exporting a (part of a) KB of a party towards a receiving one. We introduce a novel solution that enables parties to export data in a privacy-preserving fashion, based on a probabilistic data structure, namely the \emph{count-min sketch}. With this data structure, KBs can be exported in the form of key-value stores and inserted into a set of count-min sketches, where keys can be sensitive and values are counters. Count-min sketches can be tuned to achieve a given key collision probability, which enables a party to deny having certain keys in its own KB, and thus to preserve its privacy. We also introduce a metric, the γ-deniability (novel for count-min sketches), to measure the privacy level obtainable with a count-min sketch. Furthermore, since the value associated to a key can expose to linkage attacks, noise can be added to a count-min sketch to ensure controlled error on retrieved values. Key collisions and noise alter the values contained in the exported KB, and can affect negatively the accuracy of a computation performed on the exported KB. We explore the tradeoff between privacy preservation and computation accuracy by experimental evaluations in two scenarios related to malware detection.


title: Critically appraising qualitative research

abstract: Six key questions will help readers to assess qualitative research ::: ::: #### Summary points ::: ::: Over the past decade, readers of medical journals have gained skills in critically appraising studies to determine whether the results can be trusted and applied to their own practice settings. Criteria have been designed to assess studies that use quantitative methods, and these are now in common use. ::: ::: In this article we offer guidance for readers on how to assess a study that uses qualitative research methods by providing six key questions to ask when reading qualitative research (box 1). However, the thorough assessment of qualitative research is an interpretive act and requires informed reflective thought rather than the simple application of a scoring system. ::: ::: #### Box 1 Key questions to ask when reading qualitative research studies ::: ::: One of the critical decisions in a qualitative study is whom or what to include in the sample—whom to interview, whom to observe, what texts to analyse. An understanding that qualitative research is based in experience and in the construction of meaning, combined with the specific research question, should guide the sampling process. For example, a study of the experience of survivors of domestic violence that examined their reasons for not seeking help from healthcare providers might focus on interviewing a …


title: Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

abstract: Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware.


title: Dielectric permittivity measurements on highly conductive perfluoropolyether microemulsions at frequencies up to 100 MHz

abstract: The impedance analysis technique has been used to measure the complex permittivity and conductivity of highly conductive perfluoropolyether microemulsions. The cell construction details, calibration procedure and data analysis procedure are reported. Examples of dielectric analysis on perfluoropolyether microemulsions are given.


title: WordNet Based Sindhi Text to Speech Synthesis System

abstract: The text-to-speech (TTS) synthesis technology enables machine to convert text into audible speech and used throughout the world to enhance the accessibility of the information. The important component of any TTS synthesis system is the database of sounds. In this study, three types of sound units i.e., phonemes, diphones and syllables are concatenated to produce natural sound for good quality Sindhi text to speech (STTS) system. The object of this paper consists in treating the phonemes, diphones and syllables under the aspect of the lexicon. The methodology used in STTS is to exploit acoustic representations of speech for synthesis, together with linguistic analyses of text. Sindhi is highly homographic language, the text is written without diacritics in real life applications, that creates lexical and morphological ambiguity. The problem of understating non-diacritic words can be solved using semantic knowledge. This paper describes a Sindhi TTS synthesis system that relies on a WordNet to identify the analogical relations between words in the text. The proposed approach is focused on the use of WordNet structures for the task of synthesis. The architecture and novel algorithm for STTS is proposed. The experiments using WordNet that show promising results and the accuracy of our proposed approach is acceptable.


title: Understanding motivation in internet gaming among Singaporean youth: The role of passion

abstract: This study examined the motivation of young people in internet gaming using the dualistic model of passion. Path analysis was used to examine the relationships between the two types of passion: obsessive and harmonious passion, behavioral regulations, and flow. A total of 1074 male secondary school students from Singapore took part in the study. The results of the path analysis showed that external, introjected, and identified regulations positively predicted obsessive passion, while harmonious passion was predicted by identified and intrinsic regulations. Flow in digital gaming was predicted directly by harmonious passion, as well as indirectly through intrinsic regulation. This study supports the proposed dualistic model of passion in explaining young people's motivation in internet gaming.


title: Sensor Discovery on Linked Data

abstract: There has been a drive recently to make sensor data accessible on the Web. However, because of the vast number of sensors collecting data about our environment, finding relevant sensors on the Web is a non-trivial challenge. In this paper, we present an approach to discovering sensors through a standard service interface over Linked Data. This is accomplished with a semantic sensor network middleware that includes a sensor registry on Linked Data and a sensor discovery service that extends the OGC Sensor Web Enablement. With this approach, we are able to access and discover sensors that are positioned near named-locations of interest.


title: Depression During Pregnancy: Review of Epidemiological and Clinical Aspects in Developed and Developing Countries

abstract: Contrary to general belief, gestation is not always characterized by joy and accomplishments. Many women experience sadness or anxiety in these periods of their lives. Gestation and postpartum (puerperium) are periods of woman s life which involve many physical, hormonal, psychic and social insertion changes which can have a direct effect on her mental health (Camacho et al., 2006). The changes caused by the newborn arrival are not limited to psychological and biochemical variables but also involve socioeconomic factors, especially in societies in which women are active in the labor market, contributing to the family income, and pursuing diverse professional and social interests (Maldonado, 1997). The scientific literature indicates that in the gestational-postpartum period is the phase with the highest prevalence of mental disorders of women s life, particularly in the first and third quarters of gestation and during the first 30 days of postpartum (Botega & Dias, 2006). The intensity of these mental health alterations depend and are regulated by interaction of multiple factors, including organic, family, marital, social, cultural aspects and the pregnant woman’s personality (Falcone et al., 2005). Approximately one fifth of pregnant women and women in puerperium present symptoms of depression (Limlomwongse & Liabsuetrakul, 2006). Most of these women are not diagnosed neither adequately treated (Andersson et al, 2003). Depression is the most prevalent mental disorder during pregnancy and the puerperium period (Bennett et al., 2004) and is associated with risk factors such as a psychiatric history, financial hardships, low education level, teenage pregnancy, lack of social support, stressful events and a history of domestic violence. There is evidence that pre-natal depression is not only more common, but it constitutes the main risk factor for postpartum depression. Indeed, in many cases it is the continuation of the depression that started during pregnancy (Alami et al., 2006; Andersson et al., 2006; Da Costa et al., 2000; Heron et al., 2004; Josefsson et al., 2001; Lovisi et al., 2005; Patel et al., 2003; Rich-Edwards et al., 2006; Ryan et al., 2005). Current studies suggest that gestational depression needs to be addressed in a more consistent manner. Although there is a consensus that the factors that affect the relationship


title: Defining the Quality of Life Levels of the Nurses: A Study in Pamukkale University☆

abstract: Abstract Quality of life (QOL) is a multidimensional conception is affected by culture, valve judgements, persons’ positions, purposes, believes, experiences, expectations and perceptions. QOL perceptions can show a change in time. It involves person's physical, mental and social relations in family and out of family, environmental actions as well. Nowadays medicine develops increasingly, not only putting diseases away but also increasing persons’ quality of lives are aimed. Lifelong learning is an eternity process, voluntary and self-motivated pursuit of knowledge for not only personal but professional reasons as well.This activity is creative and constructive process to healthcare professionals for developing QOL. The aim of this study is to determine the QOL of the nurses working in Pamukkale University Research Hospital. The study was realized on 87 nurses the research was done by using random sample method. Using questionnaire short form SF-36 scale developed by Ware.The findings from surveying was analyzed Mann-Whitney U test and Kruskall- Wallis test using SPSS collected data.


title: Commitment before and after: An evaluation and reconceptualization of organizational commitment

abstract: A review of the organizational commitment literature has pointed out several advantages as well as some limitations of the approach advanced by Meyer and Allen (Meyer, P.J. and Allen, J.N. (1997). Commitment in the workplace: Theory, research, and application. Thousand Oaks, CA: Sage.). The advantages include good psychometric properties of the current scales, acceptable discriminant validity of the three dimensions, and research findings that showed the usefulness and acceptable content validity of the three-dimensional approach. Some of the limitations are limited predictive validity, conceptual ambiguity of continuance commitment, and concept redundancy between normative and affective commitment. This paper suggests a conceptualization that builds upon the strengths of the current approaches and minimizes their limitations. The proposed theory contends that organizational commitment is two-dimensional. One dimension is instrumental in nature and the second is affective. In addition, a sharp difference needs to be made between commitment propensity that develops before one's entry into the organization and commitment attitudes that develop after one's entry into the organization. The advantages of the suggested theory and its implications for the understanding of organizational commitment and future research on it are discussed.


title: ORB-SLAM: a Versatile and Accurate Monocular SLAM System

abstract: The gold standard method for tridimensional reconstruction and camera localization from a set of images is well known to be Bundle Adjustment (BA). Although BA was regarded for years as a costly method restricted to the offline domain, several real time algorithms based on BA flourished in the last decade. However those algorithms were limited to perform SLAM in small scenes or only Visual Odometry. In this work we built on excellent algorithms of the last years to design from scratch a Monocular SLAM system that operates in real time, in small and large, indoor and outdoor environments, with the capability of wide baseline loop closing and relocalization, and including full automatic initialization. Our survival of the fittest approach to select the points and keyframes of the reconstruction generates a compact and trackable map that only grows if the scene content changes, enhancing lifelong operation. We present an exhaustive evaluation in 27 sequences from the most popular datasets achieving unprecedented performance with a typical localization accuracy from 0.2% to 1% of the trajectory dimension in scenes from a desk to several city blocks. We make public a ROS implementation.


title: The Creation and Exploration of Revit Family Based on BIM Technology

abstract: With the popularization and spread of the multi-dimensional parameterized models based on BIM technology, a technological revolution has been raised in the construction engineering. Parametric modeling is the core and basis of the BIM technology, the modelers are expected to be proficient and accurate in creating project model. This paper provides a detailed description of the creation process of the European lunette model which takes the practical engineering for instance, and introduces briefly the building model of a dome in family template of Autodesk Revit. Moreover, the paper provides some suggestions for the application of BIM technology through the exploration of the modeling examples of typical components in European architecture, and puts forward some reasonable proposals for the modeling of Autodesk Revit.


title: Real time Sign Language Recognition using PCA

abstract: The Sign Language is a method of communication for deaf-dumb people. This paper presents the Sign Language Recognition system capable of recognizing 26 gestures from the Indian Sign Language by using MATLAB. The proposed system having four modules such as: pre-processing and hand segmentation, feature extraction, sign recognition and sign to text and voice conversion. Segmentation is done by using image processing. Different features are extracted such as Eigen values and Eigen vectors which are used in recognition. The Principle Component Analysis (PCA) algorithm was used for gesture recognition and recognized gesture is converted into text and voice format. The proposed system helps to minimize communication barrier between deaf-dumb people and normal people.


title: Targeted immunotherapy of cancer with CAR T cells: achievements and challenges

abstract: The adoptive transfer of chimeric antigen receptor (CAR)-expressing T cells is a relatively new but promising approach in the field of cancer immunotherapy. This therapeutic strategy is based on the genetic reprogramming of T cells with an artificial immune receptor that redirects them against targets on malignant cells and enables their destruction by exerting T cell effector functions. There has been an explosion of interest in the use of CAR T cells as an immunotherapy for cancer. In the pre-clinical setting, there has been a considerable focus upon optimizing the structural and signaling potency of the CAR while advances in bio-processing technology now mean that the clinical testing of these gene-modified T cells has become a reality. This review will summarize the concept of CAR-based immunotherapy and recent clinical trial activity and will further discuss some of the likely future challenges facing CAR-modified T cell therapies.


Recognizing Bird Species in Audio Files Using Transfer Learning


title: Visual working memory as visual attention sustained internally over time

abstract: Abstract Visual working memory and visual attention are intimately related, such that working memory encoding and maintenance reflects actively sustained attention to a limited number of visual objects and events important for ongoing cognition and action. Although attention is typically considered to operate over perceptual input, a recent taxonomy proposes to additionally consider how attention can be directed to internal perceptual representations in the absence of sensory input, as well as other internal memories, choices, and thoughts ( Chun, Golomb, & Turk-Browne, 2011 ). Such internal attention enables prolonged binding of features into integrated objects, along with enhancement of relevant sensory mechanisms. These processes are all limited in capacity, although different types of working memory and attention, such as spatial vs. object processing, operate independently with separate capacity. Overall, the success of maintenance depends on the ability to inhibit both external (perceptual) and internal (cognitive) distraction. Working memory is the interface by which attentional mechanisms select and actively maintain relevant perceptual information from the external world as internal representations within the mind.


title: Control-flow recovery from partial failure reports

abstract: Debugging is difficult. When software fails in production, debugging is even harder, as failure reports usually provide only an incomplete picture of the failing execution. We present a system that answers control-flow queries posed by developers as formal languages, indicating whether the query expresses control flow that is possible or impossible for a given failure report. We consider three separate approaches that trade off precision, expressiveness for failure constraints, and scalability. We also introduce a new subclass of regular languages, the unreliable trace languages, which are particularly suited to answering control-flow queries in polynomial time. Our system answers queries remarkably efficiently when we encode failure constraints and user queries entirely as unreliable trace languages.


title: ICON: Inferring Temporal Constraints from Natural Language API Descriptions

abstract: Temporal constraints of an Application Programming Interface (API) are the allowed sequences of method invocations in the API governing the secure and robust operation of client software using the API. These constraints are typically described informally in natural language API documents, and therefore are not amenable to existing constraint-checking tools. Manually identifying and writing formal temporal constraints from API documents can be prohibitively time-consuming and error-prone. To address this issue, we propose ICON: an approach based on Machine Learning (ML) and Natural Language Processing (NLP) for identifying and inferring formal temporal constraints. To evaluate our approach, we use ICON to infer and formalize temporal constraints from the Amazon S3 REST API, the PayPal Payment REST API, and the java.io package in the JDK API. Our results indicate that ICON can effectively identify temporal constraint sentences (from over 4000 human annotated API sentences) with the average 79.0% precision and 60.0% recall. Furthermore, our evaluation demonstrates that ICON achieves an accuracy of 70% in inferring 77 formal temporal constraints from these APIs.


title: Brain structure abnormalities in adolescent girls with conduct disorder

abstract: BACKGROUND ::: Conduct disorder (CD) in female adolescents is associated with a range of negative outcomes, including teenage pregnancy and antisocial personality disorder. Although recent studies have documented changes in brain structure and function in male adolescents with CD, there have been no neuroimaging studies of female adolescents with CD. Our primary objective was to investigate whether female adolescents with CD show changes in grey matter volume. Our secondary aim was to assess for sex differences in the relationship between CD and brain structure. ::: ::: ::: METHODS ::: Female adolescents with CD (n = 22) and healthy control participants matched in age, performance IQ and handedness (n = 20) underwent structural magnetic resonance imaging. Group comparisons of grey matter volume were performed using voxel-based morphometry. We also tested for sex differences using archive data obtained from male CD and control participants. ::: ::: ::: RESULTS ::: Female adolescents with CD showed reduced bilateral anterior insula and right striatal grey matter volumes compared with healthy controls. Aggressive CD symptoms were negatively correlated with right dorsolateral prefrontal cortex volume, whereas callous-unemotional traits were positively correlated with bilateral orbitofrontal cortex volume. The sex differences analyses revealed a main effect of diagnosis on right amygdala volume (reflecting reduced amygdala volume in the combined CD group relative to controls) and sex-by-diagnosis interactions in bilateral anterior insula. ::: ::: ::: CONCLUSIONS ::: We observed structural abnormalities in brain regions involved in emotion processing, reward and empathy in female adolescents with CD, which broadly overlap with those reported in previous studies of CD in male adolescents.


THE INSUFFICIENCY OF EXISTING MOMENTUM SCHEMES FOR S TOCHASTIC O PTIMIZATION


title: VLAD3: Encoding Dynamics of Deep Features for Action Recognition

abstract: Previous approaches to action recognition with deep features tend to process video frames only within a small temporal region, and do not model long-range dynamic information explicitly. However, such information is important for the accurate recognition of actions, especially for the discrimination of complex activities that share sub-actions, and when dealing with untrimmed videos. Here, we propose a representation, VLAD for Deep Dynamics (VLAD3), that accounts for different levels of video dynamics. It captures short-term dynamics with deep convolutional neural network features, relying on linear dynamic systems (LDS) to model medium-range dynamics. To account for long-range inhomogeneous dynamics, a VLAD descriptor is derived for the LDS and pooled over the whole video, to arrive at the final VLAD3 representation. An extensive evaluation was performed on Olympic Sports, UCF101 and THUMOS15, where the use of the VLAD3 representation leads to stateof-the-art results.


title: Stopping time condition for practical IPv6 Cryptographically Generated Addresses

abstract: Cryptographically Generated Addresses (CGA) are employed as an authentication mechanism in IPv6 network to realize the proof of address ownership without relying on any trust authority. The security parameter (Sec) indicates the security level of the CGA address. For Sec value greater than zero, there is no guarantee to stop the brute-force search after certain time. The address generator tries different values of Modifier until (16×Sec)-leftmost-bit of the second hash (Hash2) computes to zero. This paper proposes some modifications to the standard CGA “RFC 3972” in order to limit the time that CGA generation may takes. The modified CGA generation algorithm takes the upper bound of CGA running time as an input and the Sec value is determined as an output of the brute-force computations. The modified CGA keeps track of the best founded Hash2 value during the running time. The paper also proposes to reduce the granularity of the security level from “16” to “8”, to increase the chance to have better Sec value within the time limit. We called the modified CGA as Time-Based CGA (TB-CGA). The implementation and evaluation of TB-CGA are done in this paper.


title: Self-Monitoring and Age

abstract: Two adult samples were surveyed to investigate the relation between individuals' levels of self-monitoring and age. A negative relation was predicted as older individuals were seen as most likely to exhibit the low self-monitoring tendency of behaving in accordance with one's own attitudes and feelings, whereas younger individuals appeared most likely to exhibit the high self-monitoring tendency of behaving according to social cues. A significant negative correlation between age and selfmonitoring was found in both samples. The self-monitoring construct is discussed in relation to other social-cognitive life-span differences and to the idea of critical periods throughout the life span.


title: Music Genre Classification

abstract: Music classification is an interesting problem with many applications, from Drinkify (a program that generates cocktails to match the music) to Pandora to dynamically generating images that complement the music. However, music genre classification has been a challenging task in the field of music information retrieval (MIR). Music genres are hard to systematically and consistently describe due to their inherent subjective nature.


title: Models for Measuring E-Learning Success in Universities: A Literature Review

abstract: It is obvious that in the Internet era the higher education institutions (HEIs) must innovate the services they offer by integrating ICT (Information and Communication Technology) in the learning process. According to the theoreticians and practitioners insights in the matter, the e-learning systems offer many advantages and compensate the weaknesses of the traditional learning methods. In consequence, it emerged the need for developing a model that measures the success of the e-learning systems. This paper presents results of the research conducted in order to develop a comprehensive model for measuring e-learning system success in universi-ties.


title: Towards Coherent Multi-Document Summarization

abstract: This paper presents G-FLOW, a novel system for coherent extractive multi-document summarization (MDS). 1 Where previous work on MDS considered sentence selection and ordering separately, G-FLOW introduces a joint model for selection and ordering that balances coherence and salience. G-FLOW’s core representation is a graph that approximates the discourse relations across sentences based on indicators including discourse cues, deverbal nouns, co-reference, and more. This graph enables G-FLOW to estimate the coherence of a candidate summary. We evaluate G-FLOW on Mechanical Turk, and find that it generates dramatically better summaries than an extractive summarizer based on a pipeline of state-of-the-art sentence selection and reordering components, underscoring the value of our joint model.


Locomotion modes of an hybrid wheel-legged robot


title: Anatomy of Forehead, Glabellar, Nasal and Orbital Muscles, and Their Correlation with Distinctive Patterns of Skin Lines on the Upper Third of the Face: Reviewing Concepts

abstract: BACKGROUND ::: The purpose of this study is to establish a relationship between the skin lines on the upper third of the face in cadavers, which represent the muscle activity in life and the skin lines achieved by voluntary contraction of the forehead, glabellar, and orbital muscles in patients. ::: ::: ::: METHODS ::: Anatomical dissection of fresh cadavers was performed in 20 fresh cadavers, 11 females and 9 males, with ages ranging from 53 to 77 years. Subcutaneous dissection identified the muscle shape and the continuity of the fibers of the eyebrow elevator and depress muscles. Subgaleal dissection identified the cutaneous insertions of the muscles. They were correlated with skin lines on the upper third of the face of the cadavers that represent the muscle activity in life. Voluntary contraction was performed by 20 voluntary patients, 13 females and 7 males, with ages ranging from 35 to 62 years. Distinct patterns of skin lines on the forehead, glabellar and orbital areas, and eyebrow displacement were identified. ::: ::: ::: RESULTS ::: The frontalis exhibited four anatomical shapes with four different patterns of horizontal parallel lines on the forehead skin. The corrugator supercilii showed three shapes of muscles creating six patterns of vertical glabellar lines, three symmetrical and three asymmetrical. The orbicularis oculi and procerus had single patterns. The skin lines exhibited in voluntary contraction of the upper third of the face in patients showed the same patterns of the skin lines achieved in cadavers. ::: ::: ::: CONCLUSIONS ::: Skin lines in cadavers, which are the expression of the muscle activity in life, were similar to those achieved in the voluntary contraction of patients, allowing us to assert that the muscle patterns of patients were similar to those identified in cadavers. ::: ::: ::: NO LEVEL ASSIGNED ::: This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors http://www.springer.com/00266 .


title: Popularity prediction of images and videos on Instagram

abstract: We live in a world surrounded by numerous social media platforms, applications and websites which produce various texts, images and videos (posts) daily. People share their moments with their friends and families via these tools to keep in touch. This extensiveness of social media has led to an expansion of information in various forms. It is difficult to imagine someone totally unfamiliar with these concepts and not having posted any content on a platform. All users, ranging from individuals to large companies, want to get the most of their audiences' attention. Nevertheless, the problem is that not all these posts are admired and noticed by their audience. Therefore, it would be important to know what characteristics a post should have to become the most popular. Studying this enormous data will develop a knowledge from which we can understand the best way to publish our posts. To this end, we gathered images and videos from Instagram accounts and we used some image/video context features to predict the number of likes a post obtains as a meaning of popularity through some regression and classification methods. By the experiments with 10-fold cross-validation, we get the results of Popularity Score prediction with 0.002 in RMSE and Popularity Class prediction with 90.77% accuracy. As we know, this study is the first exploring of Iranian Instagram users for popularity prediction.


title: Ambiguities in Visual Tracking of Articulated Objects Using Two- and Three-Dimensional Models

abstract: Three-dimensional (3D) kinematic models are widely-used in video-based figure tracking. We show that these models can suffer from singularities when motion is directed along the viewing axis of a single camera. The single camera case is important because it arises in many interesting applications, such as motion capture from movie footage, video surveillance, and vision-based user-interfaces.We describe a novel two-dimensional scaled prismatic model (SPM) for figure registration. In contrast to 3D kinematic models, the SPM has fewer singularity problems and does not require detailed knowledge of the 3D kinematics. We fully characterize the singularities in the SPM and demonstrate tracking through singularities using synthetic and real examples.We demonstrate the application of our model to motion capture from movies. Fred Astaire is tracked in a clip from the film “Shall We Dance”. We also present the use of monocular hand tracking in a 3D user-interface. These results demonstrate the benefits of the SPM ...


title: Differentiation of pupillary signals using statistical and functional analysis

abstract: Emotion recognition nowadays plays vital role in human-computer reaction. Different approaches and methods were applied to recognize accurately individual's emotions. However, pupil diameter (PD) is one of the well-known and reliable approaches in identifying emotions. It is controlled by Automatic Nervous System (ANS) and is easy to detect. The goal of this paper is to identify the difference in PD due to individual's positive and negative emotional states. The paper introduces an experiment conducted to measure dilation in pupil diameter using eye-tracking system during positive and negative emotions. Thirty participants were involved in this experiment and the initial results suggested differences in pupil dilation during negative and positive stimulation at the last seconds of a played sound. It shows more sustained, stable and longer dilation in high arousal negative stimuli. It also discussed functional analysis that is proven to be a useful tool in expressing pupil dilation differences.


title: Enterprise Systems: State-of-the-Art and Future Trends

abstract: Rapid advances in industrial information integration methods have spurred tremendous growth in the use of enterprise systems. Consequently, a variety of techniques have been used for probing enterprise systems. These techniques include business process management, workflow management, Enterprise Application Integration (EAI), Service-Oriented Architecture (SOA), grid computing, and others. Many applications require a combination of these techniques, which is giving rise to the emergence of enterprise systems. Development of the techniques has originated from different disciplines and has the potential to significantly improve the performance of enterprise systems. However, the lack of powerful tools still poses a major hindrance to exploiting the full potential of enterprise systems. In particular, formal methods and systems methods are crucial for modeling complex enterprise systems, which poses unique challenges. In this paper, we briefly survey the state of the art in the area of enterprise systems as they relate to industrial informatics.


title: A Study of Smoothing Algorithms for Item Categorization on e-Commerce Sites

abstract: One central issue in a long-tail online marketplace such as eBay is to automatically put user self-input items into a catalog in real time. This task is extremely challenging when the inventory scales up, the items become ephemeral, and the user input remains noisy. Indeed, catalog learning has emerged as a key technical property for other major online ecommerce applications including search and recommendation. We formulate the item cataloging task as a Bayesian classification problem, which shall scale well in very large data set and have good online prediction performance. The inherent data sparseness issue, especially for those tail categories, is key to the overall model performance. We address the data sparseness issue by adapting statistically sound smoothing methods well studied in language modeling tasks. However, there are data characteristics specific to the ecommerce domain, including short yet focused item description, very large and hierarchical catalog taxonomy, and highly skewed distribution over types of items. We investigate these domain-specific regularities empirically, and report practically significant results with real-world true-scale data.


title: A Survey of Skyline Query Processing

abstract: Living in the Information Age allows almost everyone have access to a large amount of information and options to choose from in order to fulfill their needs. In many cases, the amount of information available and the rate of change may hide the optimal and truly desired solution. This reveals the need of a mechanism that will highlight the best options to choose among every possible scenario. Based on this the skyline query was proposed which is a decision support mechanism, that retrieves the valuefor- money options of a dataset by identifying the objects that present the optimal combination of the characteristics of the dataset. This paper surveys the state-of-the-art techniques for skyline query processing, the numerous variations of the initial algorithm that were proposed to solve similar problems and the application-specific approaches that were developed to provide a solution efficiently in each case. Aditionally in each section a taxonomy is outlined along with the key aspects of each algorithm and its relation to previous studies.


title: Transconductance with capacitances feedback compensation for multistage amplifiers

abstract: A new performance-boosting frequency compensation technique is presented, named Transconductance with Capacitances Feedback Compensation (TCFC). A transconductance stage and two capacitors introduce negative feedback to a three-stage amplifier, which significantly improves the performance such as gain-bandwidth product, slew rate, stability and sensitivity. An optimized TCFC amplifier has been implemented, and fabricated in a 0.35-/spl mu/m CMOS process. The TCFC amplifier driving a 150-pF load capacitor achieved 2.9-MHz gain-bandwidth product dissipating only 45-/spl mu/W power with a 1.5 V supply, which shows a significant improvement in MHz/spl middot/pF/mA performance.


title: Why Google stores billions of lines of code in a single repository

abstract: Google's monolithic repository provides a common source of truth for tens of thousands of developers around the world.


title: Efficient Sparse Pose Adjustment for 2D mapping

abstract: Pose graphs have become a popular representation for solving the simultaneous localization and mapping (SLAM) problem. A pose graph is a set of robot poses connected by nonlinear constraints obtained from observations of features common to nearby poses. Optimizing large pose graphs has been a bottleneck for mobile robots, since the computation time of direct nonlinear optimization can grow cubically with the size of the graph. In this paper, we propose an efficient method for constructing and solving the linear subproblem, which is the bottleneck of these direct methods. We compare our method, called Sparse Pose Adjustment (SPA), with competing indirect methods, and show that it outperforms them in terms of convergence speed and accuracy. We demonstrate its effectiveness on a large set of indoor real-world maps, and a very large simulated dataset. Open-source implementations in C++, and the datasets, are publicly available.


title: Artificial Intelligence in the Rising Wave of Deep Learning: The Historical Path and Future Outlook [Perspectives]

abstract: Artificial intelligence (AI) is a branch of computer science and a technology aimed at developing the theories, methods, algorithms, and applications for simulating and extending human intelligence. Modern AI enables going from an old world-where people give computers rules to solve problems-to a new world-where people give computers problems directly and the machines learn how to solve them on their own using a set of algorithms. An algorithm is a self-contained sequence of instructions and actions to be performed by a computational machine. Starting from an initial state and initial input, the instructions describe computational steps, which, when executed, proceed through a finite number of well-defined successive states, eventually producing an output and terminating at a final ending state. AI algorithms are a rich set of algorithms used to perform AI tasks, notably those pertaining to perception and cognition that involve learning from data and experiences simulating human intelligence.


title: Overfitting Avoidance as Bias

abstract: Strategies for increasing predictive accuracy through selective pruning have been widely adopted by researchers in decision tree induction. It is easy to get the impression from research reports that there are statistical reasons for believing that these overfitting avoidance strategies do increase accuracy and that, as a research community, we are making progress toward developing powerful, general methods for guarding against overfitting in inducing decision trees. In fact, any overfitting avoidance strategy amounts to a form of bias and, as such, may degrade performance instead of improving it. If pruning methods have often proven successful in empirical tests, this is due, not to the methods, but to the choice of test problems. As examples in this article illustrate, overfitting avoidance strategies are not better or worse, but only more or less appropriate to specific application domains. We are not—and cannot be—making progress toward methods both powerful and general.


title: Emotion and lying in a non-native language

abstract: Bilingual speakers frequently report experiencing greater emotional resonance in their first language compared to their second. In Experiment 1, Turkish university students who had learned English as a foreign language had reduced skin conductance responses (SCRs) when listening to emotional phrases in English compared to Turkish, an effect which was most pronounced for childhood reprimands. A second type of emotional language, reading out loud true and false statements, was studied in Experiment 2. Larger SCRs were elicited by lies compared to true statements, and larger SCRs were evoked by English statements compared to Turkish statements. In contrast, ratings of how strongly participants felt they were lying showed that Turkish lies were more strongly felt than English lies. Results suggest that two factors influence the electrodermal activity elicited when bilingual speakers lie in their two languages: arousal due to emotions associated with lying, and arousal due to anxiety about managing speech production in non-native language. Anxiety and emotionality when speaking a non-naive language need to be better understood to inform practices ranging from bilingual psychotherapy to police interrogation of suspects and witnesses.


title: Development and simulation on V-REP of an algorithm for the RoboCup@Work BNT

abstract: Recently the RoboCup@Work league emerged in the world's largest robotics competition, intended for competitors wishing to compete in the field of mobile robotics for manipulation tasks in industrial environments. This competition consists of several tasks with one reflected in this work (Basic Navigation Test). This project involves the simulation in Virtual Robot Experimentation Platform (V-REP) of the behavior of a KUKA youBot. The goal is to verify that the robots can navigate in their environment, in a standalone mode, in a robust and secure way. To achieve the proposed objectives, it was necessary to create a program in Lua and test it in simulation. This involved the study of robot kinematics and mechanics, Simultaneous Localization And Mapping (SLAM) and perception from sensors. In this work is introduced an algorithm developed for a KUKA youBot platform to perform the SLAM while reaching for the goal position, which works according to the requirements of this competition BNT. This algorithm also minimizes the errors in the built map and in the path travelled by the robot.


title: Health and well-being benefits of spending time in forests: systematic review

abstract: Numerous studies have reported that spending time in nature is associated with the improvement of various health outcomes and well-being. This review evaluated the physical and psychological benefits of a specific type of exposure to nature, forest therapy. A literature search was carried out using MEDLINE, PubMed, ScienceDirect, EMBASE, and ProQuest databases and manual searches from inception up to December 2016. Key words: “Forest” or “Shinrin -Yoku” or “Forest bath” AND “Health” or “Wellbeing”. The methodological quality of each randomized controlled trials (RCTs) was assessed according to the Cochrane risk of bias (ROB) tool. Six RCTs met the inclusion criteria. Participants’ ages ranged from 20 to 79 years. Sample size ranged from 18 to 99. Populations studied varied from young healthy university students to elderly people with chronic disease. Studies reported the positive impact of forest therapy on hypertension (n = 2), cardiac and pulmonary function (n = 1), immune function (n = 2), inflammation (n = 3), oxidative stress (n = 1), stress (n = 1), stress hormone (n = 1), anxiety (n = 1), depression (n = 2), and emotional response (n = 3). The quality of all studies included in this review had a high ROB. Forest therapy may play an important role in health promotion and disease prevention. However, the lack of high-quality studies limits the strength of results, rendering the evidence insufficient to establish clinical practice guidelines for its use. More robust RCTs are warranted.


title: A body-conforming tactile jacket to enrich movie viewing

abstract: Adding haptic stimulation to movies is a promising step in creating more emotionally immersive experiences. To explore the potential of this concept, we have created a wearable tactile jacket that is used to deliver movie-specific tactile stimuli to the viewer's body that are specifically targeted to influence the viewer's emotions. Immersion was evaluated in a user test using questionnaires and physiological measurements. The findings show promising effects of the haptic stimuli that need to be substantiated in further more refined user tests.


title: Open Learning Platform Based on Personal and Social Analytics for Individualized Learning Support

abstract: With the help of several emerging computing paradigms, such as Ubiquitous Computing, Social Computing, and Mobile Computing, OLP (Open Learning Process) has become an important research issue in the online learning environment. It becomes possible to share the learning process related information in and cross systems to meet the needs of all stakeholders, such as teachers, learners and managers. In this study, we focus on the OLP in the social learning environment based on the personal and social analytics, which can provide users with the process-oriented learning support. We present a data processing framework to collect, analyze, and organize the learning-related data in a user-centric way. Three important issues regarding to the unified data management, socialized learning analytics, and sharable open learning process are discussed towards the process-oriented learning innovation. Based on these, an open learning platform is proposed and designed to provide users with the individualized support in the collaborative learning process.


title: Large scale biomedical texts classification: a kNN and an ESA-based approaches

abstract: With the large and increasing volume of textual data, automated methods for identifying significant topics to classify textual documents have received a growing interest. While many efforts have been made in this direction, it still remains a real challenge. Moreover, the issue is even more complex as full texts are not always freely available. Then, using only partial information to annotate these documents is promising but remains a very ambitious issue. MethodsWe propose two classification methods: a k-nearest neighbours (kNN)-based approach and an explicit semantic analysis (ESA)-based approach. Although the kNN-based approach is widely used in text classification, it needs to be improved to perform well in this specific classification problem which deals with partial information. Compared to existing kNN-based methods, our method uses classical Machine Learning (ML) algorithms for ranking the labels. Additional features are also investigated in order to improve the classifiers' performance. In addition, the combination of several learning algorithms with various techniques for fixing the number of relevant topics is performed. On the other hand, ESA seems promising for this classification task as it yielded interesting results in related issues, such as semantic relatedness computation between texts and text classification. Unlike existing works, which use ESA for enriching the bag-of-words approach with additional knowledge-based features, our ESA-based method builds a standalone classifier. Furthermore, we investigate if the results of this method could be useful as a complementary feature of our kNN-based approach.ResultsExperimental evaluations performed on large standard annotated datasets, provided by the BioASQ organizers, show that the kNN-based method with the Random Forest learning algorithm achieves good performances compared with the current state-of-the-art methods, reaching a competitive f-measure of 0.55% while the ESA-based approach surprisingly yielded reserved results.ConclusionsWe have proposed simple classification methods suitable to annotate textual documents using only partial information. They are therefore adequate for large multi-label classification and particularly in the biomedical domain. Thus, our work contributes to the extraction of relevant information from unstructured documents in order to facilitate their automated processing. Consequently, it could be used for various purposes, including document indexing, information retrieval, etc.


title: CNN-MERP: An FPGA-Based Memory-Efficient Reconfigurable Processor for Forward and Backward Propagation of Convolutional Neural Networks

abstract: Large-scale deep convolutional neural networks (CNNs) are widely used in machine learning applications. While CNNs involve huge complexity, VLSI (ASIC and FPGA) chips that deliver high-density integration of computational resources are regarded as a promising platform for CNN's implementation. At massive parallelism of computational units, however, the external memory bandwidth, which is constrained by the pin count of the VLSI chip, becomes the system bottleneck. Moreover, VLSI solutions are usually regarded as a lack of the flexibility to be reconfigured for the various parameters of CNNs. This paper presents CNN-MERP to address these issues. CNN-MERP incorporates an efficient memory hierarchy that significantly reduces the bandwidth requirements from multiple optimizations including on/off-chip data allocation, data flow optimization and data reuse. The proposed 2-level reconfigurability is utilized to enable fast and efficient reconfiguration, which is based on the control logic and the multiboot feature of FPGA. As a result, an external memory bandwidth requirement of 1.94MB/GFlop is achieved, which is 55% lower than prior arts. Under limited DRAM bandwidth, a system throughput of 1244GFlop/s is achieved at the Vertex UltraScale platform, which is 5.48 times higher than the state-of-the-art FPGA implementations.


title: Design, Implementation, and Performance of a Load Balancer for SIP Server Clusters

abstract: This paper introduces several novel load-balancing algorithms for distributing Session Initiation Protocol (SIP) requests to a cluster of SIP servers. Our load balancer improves both throughput and response time versus a single node while exposing a single interface to external clients. We present the design, implementation, and evaluation of our system using a cluster of Intel ×86 machines running Linux. We compare our algorithms to several well-known approaches and present scalability results for up to 10 nodes. Our best algorithm, Transaction Least-Work-Left (TLWL), achieves its performance by integrating several features: knowledge of the SIP protocol, dynamic estimates of back-end server load, distinguishing transactions from calls, recognizing variability in call length, and exploiting differences in processing costs for different SIP transactions. By combining these features, our algorithm provides finer-grained load balancing than standard approaches, resulting in throughput improvements of up to 24% and response-time improvements of up to two orders of magnitude. We present a detailed analysis of occupancy to show how our algorithms significantly reduce response time.


title: On Compressing Deep Models by Low Rank and Sparse Decomposition

abstract: Deep compression refers to removing the redundancy of parameters and feature maps for deep learning models. Low-rank approximation and pruning for sparse structures play a vital role in many compression works. However, weight filters tend to be both low-rank and sparse. Neglecting either part of these structure information in previous methods results in iteratively retraining, compromising accuracy, and low compression rates. Here we propose a unified framework integrating the low-rank and sparse decomposition of weight matrices with the feature map reconstructions. Our model includes methods like pruning connections as special cases, and is optimized by a fast SVD-free algorithm. It has been theoretically proven that, with a small sample, due to its generalizability, our model can well reconstruct the feature maps on both training and test data, which results in less compromising accuracy prior to the subsequent retraining. With such a warm start to retrain, the compression method always possesses several merits: (a) higher compression rates, (b) little loss of accuracy, and (c) fewer rounds to compress deep models. The experimental results on several popular models such as AlexNet, VGG-16, and GoogLeNet show that our model can significantly reduce the parameters for both convolutional and fully-connected layers. As a result, our model reduces the size of VGG-16 by 15×, better than other recent compression methods that use a single strategy.


title: Provision of Load Frequency Control by PHEVs, Controllable Loads, and a Cogeneration Unit

abstract: This paper presents a method for tracking a secondary frequency control (Load Frequency Control) signal by groups of plug-in hybrid electric vehicles (PHEVs), controllable thermal household appliances under a duty-cycle coordination scheme, and a decentralized combined-heat-and-power generation unit. The distribution of the control action on the participating units is performed by an aggregator utilizing a Model Predictive Control strategy which allows the inclusion of unit and grid constraints. In addition to the individual dynamic behavior, the varying availability of the units during the day is taken into account. The proposed methodology, easily extendable to larger networks, is evaluated on a four-bus system corresponding to a medium-voltage distribution grid and illustrates a possible operation mode of an aggregator in the power system.


title: Broadband High-Gain Beam-Scanning Antenna Array for Millimeter-Wave Applications

abstract: A novel method of achieving low profile, broadband microstrip array antennas with high antenna gain is proposed for millimeter-wave applications. The element employs a novel third-order vertically coupled resonant structure that a U-slot resonator in the ground is used to couple with the feeding resonator and the radiating patch, simultaneously. This proposed structure can significantly improve the bandwidth and frequency selectivity without increasing the thickness of the antenna. Then, to achieve the subarray, a new wideband power divider with loaded resonators is employed, which can be used to further improve the bandwidth. To demonstrate the working schemes of broadside radiation and scanned beam, two $4 \times 4$ array antennas are implemented on the same board. Measured results agree well with the simulations, showing a wide bandwidth from 22 to 32 GHz (FBW = 37%) with the gain of around 19 dBi. The beam-scanning array can realize a scanning angle of 25° over a broadband. In addition, due to the filtering features are integrated in the design, the proposed antenna could also reduce the complexity and potential cost of the frontends.


title: Categorizing Obstacles in e-Government: Formal and Informal

abstract: E-Government implementation and adoption is influenced by several factors having either an enhancing or an aggravating effect on e-government implementation and use. This paper aims at shedding light on obstacles hindering mainly e-government implementation from two perspectives: the supply- and the demand-side of e-government services. The contribution to research is seen in summarized insights into what obstacles in e-government were identified in prior research and the suggestion of a classification of obstacles into the two categories of formal and informal obstacles. Literature was reviewed following a conceptual model encompassing a merger and extension of existing approaches. A process of identifying obstacles and improving services in the form of a loop is discussed before possible future research lines will be pointed to.


title: Effectiveness and cost of implementing an active surveillance screening policy for Acinetobacter baumannii: a Monte Carlo simulation model.

abstract: BACKGROUND ::: Acinetobacter baumannii infections are common and associated with high mortality and costs. Early identification of asymptomatic carriers can reduce patient-to-patient transmission, but the sensitivity of A baumannii surveillance tools is poor, and thus active surveillance is not routine practice. This study examined whether an active surveillance screening policy can reduce the transmission, mortality, and costs associated with A baumannii. ::: ::: ::: METHODS ::: A simulation model was developed to determine the impact of active screening on patient outcomes. Model parameters included A baumannii prevalence, screening sensitivity and specificity, probability of transmission, progression from colonization to infection, mortality, and cost of screening, contact precautions, and infection. A scenario analysis was performed to evaluate the robustness of the results when varying the sensitivity of the screening test and the prevalence rate of A baumannii. ::: ::: ::: RESULTS ::: Assuming a screening sensitivity of 55%, active screening reduced A baumannii transmissions, infections, and deaths by 48%. As the screening sensitivity approached 90%, the reduction in transmissions, infections, and deaths reached 78%. For all scenarios tested, active surveillance was cost saving (19%-53% reduction in mean hospital cost per patient) except at a carrier prevalence of ≤2% and screening test sensitivity of ≤55%. ::: ::: ::: CONCLUSIONS ::: In institutions where A baumannii is endemic or during epidemics, implementing a surveillance program is cost-saving and can greatly reduce transmissions and deaths. Methodologies to improve the sensitivity of surveillance testing will help optimize the clinical impact of active screening programs on preventing the spread of A baumannii in health care facilities.


title: The effects of trust assurances on consumers' initial online trust: A two-stage decision-making process perspective

abstract: Abstract This research examines the effects of type and displaying phase of trust assurances on consumers’ initial trust in an online retailer. We propose a two-stage formation process of initial online trust. The main effects of trust assurances on initial trust and the interaction between type and displaying phase of trust assurances are explored. We conducted a laboratory experiment to test our hypotheses. The results demonstrate that there exist positive effects of displaying trust assurances on initial trust. In particular, general trust assurances perform better when displayed in the information-searching stage, while specific trust assurances lead to higher initial trust when displayed in the choice stage. In the context of providing trust assurances, consumers’ knowledge of trust assurances exerts a direct and positive effect on initial trust. High knowledge level of trust assurances helps to generate the advantage of displaying assurances in the right stage. These findings have several important implications for research and practice.


title: Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models

abstract: Summary ::: Motivated by a real life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyse synthetic graphs to protect privacy of individual relationships captured by the social network while maintaining the validity of statistical results. A case-study using a version of the Enron e-mail corpus data set demonstrates the application and usefulness of the proposed techniques in solving the challenging problem of maintaining privacy and supporting open access to network data to ensure reproducibility of existing studies and discovering new scientific insights that can be obtained by analysing such data. We use a simple yet effective randomized response mechanism to generate synthetic networks under e-edge differential privacy and then use likelihood-based inference for missing data and Markov chain Monte Carlo techniques to fit exponential family random-graph models to the generated synthetic networks.


title: The evolution of personality variation in humans and other animals.

abstract: A comprehensive evolutionary framework for understanding the maintenance of heritable behavioral variation in humans is yet to be developed. Some evolutionary psychologists have argued that heritable variation will not be found in important, fitness-relevant characteristics because of the winnowing effect of natural selection. This article propounds the opposite view. Heritable variation is ubiquitous in all species, and there are a number of frameworks for understanding its persistence. The author argues that each of the Big Five dimensions of human personality can be seen as the result of a trade-off between different fitness costs and benefits. As there is no unconditionally optimal value of these trade-offs, it is to be expected that genetic diversity will be retained in the population.


title: Humans and Bots in Internet Chat: Measurement, Analysis, and Automated Classification

abstract: The abuse of chat services by automated programs, known as chat bots, poses a serious threat to Internet users. Chat bots target popular chat networks to distribute spam and malware. In this paper, we first conduct a series of measurements on a large0 commercial chat network. Our measurements capture a total of 16 different types of chat bots ranging from simple to advanced. Moreover, we observe that human behavior is more complex than bot behavior. Based on the measurement study, we propose a classification system to accurately distinguish chat bots from human users. The proposed classification system consists of two components: 1) an entropy-based classifier; and 2) a Bayesian-based classifier. The two classifiers complement each other in chat bot detection. The entropy-based classifier is more accurate to detect unknown chat bots, whereas the Bayesian-based classifier is faster to detect known chat bots. Our experimental evaluation shows that the proposed classification system is highly effective in differentiating bots from humans.


title: Glamour: A History

abstract: Glamour is one of the most tantalizing and bewitching aspects of contemporary culture - but also one of the most elusive. ::: ::: The aura of celebrity, the style of the fashion world, the vanity of the rich and beautiful, and the publicity-driven rites of cafe society are all imbued with its irresistible magnetism. But what exactly is glamour? Where does it come from? How old is it? And can anyone quite capture its magic? ::: ::: Stephen Gundle answers all these questions and more in this first ever history of the phenomenon, from Paris in the tumultuous final decades of the eighteenth century through to Hollywood, New York, and Monte Carlo in the twentieth and twenty-first centuries, from Napoleon to Marlene Dietrich and Marilyn Monroe, from Beau Brummell to Gianni Versace. ::: ::: Throughout, the book captures the excitement and sex appeal of glamour while exposing its mechanisms and exploring its sleazy and sometimes tragic underside. As Gundle shows, while glamour is exciting and magnetic, its promise is ultimately an illusion that can only ever be partially fulfilled.


title: Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization

abstract: Artificial autonomous agents and robots interacting in complex environments are required to continually acquire and fine-tune knowledge over sustained periods of time. The ability to learn from continuous streams of information is referred to as lifelong learning and represents a long-standing challenge for neural network models due to catastrophic forgetting in which novel sensory experience interferes with existing representations and leads to abrupt decreases in the performance on previously acquired knowledge. Computational models of lifelong learning typically alleviate catastrophic forgetting in experimental scenarios with given datasets of static images and limited complexity, thereby differing significantly from the conditions artificial agents are exposed to. In more natural settings, sequential information may become progressively available over time and access to previous experience may be restricted. Therefore, specialized neural network mechanisms are required that adapt to novel sequential experience while preventing disruptive interference with existing representations. In this paper, we propose a dual-memory self-organizing architecture for lifelong learning scenarios. The architecture comprises two growing recurrent networks with the complementary tasks of learning object instances (episodic memory) and categories (semantic memory). Both growing networks can expand in response to novel sensory experience: the episodic memory learns fine-grained spatiotemporal representations of object instances in an unsupervised fashion while the semantic memory uses task-relevant signals to regulate structural plasticity levels and develop more compact representations from episodic experience. For the consolidation of knowledge in the absence of external sensory input, the episodic memory periodically replays trajectories of neural reactivations. We evaluate the proposed model on the CORe50 benchmark dataset for continuous object recognition, showing that we significantly outperform current methods of lifelong learning in three different incremental learning scenarios.


title: Measuring the Evolution of a Scientific Field through Citation Frames

abstract: Citations have long been used to characterize the state of a scientific field and to identify influential works. However, writers use citations for different purposes, and this varied purpose influ...


title: A 94% efficiency near-constant frequency self-oscillating class-D audio amplifier with voltage control resistor

abstract: A self-oscillating constant frequency class-D audio amplifier with voltage control resistor (VCR) technique is proposed for system-on-a-chip (SoC) applications. The VCR control circuit is used to ensure stable switching frequency over a wide range of the modulation index. The proposed method also improves the THD performance at high output power condition. The proposed self-oscillating class-D amplifier can have extremely good performance since the carrier distortion is avoided and the intrinsic harmonic can be attenuated by the second-order loop filter. The VCR control can dynamically adjust the switching frequency according to distinct modulation index. In addition, stable frequency operation can reduce the switching frequency at low output amplitude to improve power efficiency. Experimental results demonstrate that the proposed class-D amplifier can delivers 1.4W to the 8Ω load with a 5V supply. The power efficiency is achieved over 95% and the output THD can be smaller than 0.001%.


title: Accuracy of smartphone apps for heart rate measurement

abstract: Background Smartphone manufacturers offer mobile health monitoring technology to their customers, including apps using the built-in camera for heart rate assessment. This study aimed to test the diagnostic accuracy of such heart rate measuring apps in clinical practice. Methods The feasibility and accuracy of measuring heart rate was tested on four commercially available apps using both iPhone 4 and iPhone 5. 'Instant Heart Rate' (IHR) and 'Heart Fitness' (HF) work with contact photoplethysmography (contact of fingertip to built-in camera), while 'Whats My Heart Rate' (WMH) and 'Cardiio Version' (CAR) work with non-contact photoplethysmography. The measurements were compared to electrocardiogram and pulse oximetry-derived heart rate. Results Heart rate measurement using app-based photoplethysmography was performed on 108 randomly selected patients. The electrocardiogram-derived heart rate correlated well with pulse oximetry ( r = 0.92), IHR ( r = 0.83) and HF ( r = 0.96), but somewhat less with WMH ( r = 0.62) and CAR ( r = 0.60). The accuracy of app-measured heart rate as compared to electrocardiogram, reported as mean absolute error (in bpm ± standard error) was 2 ± 0.35 (pulse oximetry), 4.5 ± 1.1 (IHR), 2 ± 0.5 (HF), 7.1 ± 1.4 (WMH) and 8.1 ± 1.4 (CAR). Conclusions We found substantial performance differences between the four studied heart rate measuring apps. The two contact photoplethysmography-based apps had higher feasibility and better accuracy for heart rate measurement than the two non-contact photoplethysmography-based apps.


title: What's my age?: Predicting Twitter User's Age using Influential Friend Network and DBpedia

abstract: Social media is a rich source of user behavior and opinions. Twitter senses nearly 500 million tweets per day from 328 million users.An appropriate machine learning pipeline over this information enables up-to-date and cost-effective data collection for a wide variety of domains such as; social science, public health, the wisdom of the crowd, etc. In many of the domains, users demographic information is key to the identification of segments of the populations being studied. For instance, Which age groups are observed to abuse which drugs?, Which ethnicities are most affected by depression per location?. Twitter in its current state does not require users to provide any demographic information. We propose to create a machine learning system coupled with the DBpedia graph that predicts the most probable age of the Twitter user. In our process to build an age prediction model using social media text and user meta-data, we explore the existing state of the art approaches. Detailing our data collection, feature engineering cycle, model selection and evaluation pipeline, we will exhibit the efficacy of our approach by comparing with the"predict mean"age estimator baseline.


Intelligent automation entering the business world


title: Representative Discovery of Structure Cues for Weakly-Supervised Image Segmentation

abstract: Weakly-supervised image segmentation is a challenging problem with multidisciplinary applications in multimedia content analysis and beyond. It aims to segment an image by leveraging its image-level semantics (i.e., tags). This paper presents a weakly-supervised image segmentation algorithm that learns the distribution of spatially structural superpixel sets from image-level labels. More specifically, we first extract graphlets from a given image, which are small-sized graphs consisting of superpixels and encapsulating their spatial structure. Then, an efficient manifold embedding algorithm is proposed to transfer labels from training images into graphlets. It is further observed that there are numerous redundant graphlets that are not discriminative to semantic categories, which are abandoned by a graphlet selection scheme as they make no contribution to the subsequent segmentation. Thereafter, we use a Gaussian mixture model (GMM) to learn the distribution of the selected post-embedding graphlets (i.e., vectors output from the graphlet embedding). Finally, we propose an image segmentation algorithm, termed representative graphlet cut, which leverages the learned GMM prior to measure the structure homogeneity of a test image. Experimental results show that the proposed approach outperforms state-of-the-art weakly-supervised image segmentation methods, on five popular segmentation data sets. Besides, our approach performs competitively to the fully-supervised segmentation models.


title: Ant Colony Optimization for task allocation in Multi-Agent Systems

abstract: Task allocation is a key issue of agent cooperation mechanism in Multi-Agent Systems. The important features of an agent system such as the latency of the network infrastructure, dynamic topology, and node heterogeneity impose new challenges on the task allocation in Multi-Agent environments. Based on the traditional parallel computing task allocation method and Ant Colony Optimization (ACO), a novel task allocation method named Collection Path Ant Colony Optimization (CPACO) is proposed to achieve global optimization and reduce processing time. The existing problems of ACO are analyzed; CPACO overcomes such problems by modifying the heuristic function and the update strategy in the Ant-Cycle Model and establishing a three-dimensional path pheromone storage space. The experimental results show that CPACO consumed only 10.3% of the time taken by the Global Search Algorithm and exhibited better performance than the Forward Optimal Heuristic Algorithm.


title: Communities of Practice and Social Learning Systems: the Career of a Concept

abstract: The concept of community of practice was not born in the systems theory tradition. It has its roots in attempts to develop accounts of the social nature of human learning inspired by anthropology and social theory (Lave, 1988; Bourdieu, 1977; Giddens, 1984; Foucault, 1980; Vygotsky, 1978). But the concept of community of practice is well aligned with the perspective of systems traditions. A community of practice itself can be viewed as a simple social system. And a complex social system can be viewed as constituted by interrelated communities of practice. In this essay I first explore the systemic nature of the concept at these two levels. Then I use this foundation to look at the applications of the concept, some of its main critiques, and its potential for developing a social discipline of learning.


title: KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics

abstract: Modern advanced analytics applications make use of machine learning techniques and contain multiple steps of domain-specific and general-purpose processing with high resource requirements. We present KeystoneML, a system that captures and optimizes the end-to-end large-scale machine learning applications for high-throughput training in a distributed environment with a high-level API. This approach offers increased ease of use and higher performance over existing systems for large scale learning. We demonstrate the effectiveness of KeystoneML in achieving high quality statistical accuracy and scalable training using real world datasets in several domains.


title: Conjugative Plasmid Transfer in Gram-Positive Bacteria

abstract: Conjugative transfer of bacterial plasmids is the most efficient way of horizontal gene spread, and it is therefore considered one of the major reasons for the increase in the number of bacteria exhibiting multiple-antibiotic resistance. Thus, conjugation and spread of antibiotic resistance represents a severe problem in antibiotic treatment, especially of immunosuppressed patients and in intensive care units. While conjugation in gram-negative bacteria has been studied in great detail over the last decades, the transfer mechanisms of antibiotic resistance plasmids in gram-positive bacteria remained obscure. In the last few years, the entire nucleotide sequences of several large conjugative plasmids from gram-positive bacteria have been determined. Sequence analyses and data bank comparisons of their putative transfer (tra) regions have revealed significant similarities to tra regions of plasmids from gram-negative bacteria with regard to the respective DNA relaxases and their targets, the origins of transfer (oriT), and putative nucleoside triphosphatases NTP-ases with homologies to type IV secretion systems. In contrast, a single gene encoding a septal DNA translocator protein is involved in plasmid transfer between micelle-forming streptomycetes. Based on these clues, we propose the existence of two fundamentally different plasmid-mediated conjugative mechanisms in gram-positive microorganisms, namely, the mechanism taking place in unicellular gram-positive bacteria, which is functionally similar to that in gram-negative bacteria, and a second type that occurs in multicellular gram-positive bacteria, which seems to be characterized by double-stranded DNA transfer.


title: Readiness-to-Change Cluster Profiles Among Adults with Mental Illness Who were Homeless Participating in a Life Skills Intervention

abstract: This longitudinal study examined differences in intervention outcomes based on readiness-to-change cluster profiles among 73 adults with a mental illness at risk for homelessness participating in a manualized life skills intervention. Intervention topics included money management, food management, safe community participation, and room- and self-care. Life skill knowledge and readiness-to-change, measured using the University of Rhode Island Change Assessment, was examined at baseline, post-intervention, and 3–6 months later. Two scoring patterns emerged for readiness-to-change at each time point: Pre-Engaged and Engaged. Participants who were Engaged at the time of assessment scored significantly better than Pre-Engaged on post-intervention life skill testing, however group identification changed over time. Baseline readiness-to-change did not predict future performance or attrition, and therefore may not provide accurate indication of client investment for future learning or participation. Further investigation is needed to determine what factors contribute to Engaged membership.


title: A throwable miniature robotic system

abstract: Before the soldiers or police take actions, a fast and real-time detection in dangerous places, such as indoor, passageway and underpass, can both guarantee the soldiers' safety and increase the battle's accuracy, so it is significative and necessary to design a kind of scout robot which is able to arrive in the target region to acquire the message fast by the ways of thrown, shot or airdrop. This kind of robot should be small, easy to hide and have the ability of anti-impact and semi-autonomous. This paper mainly proposes a design method of the throwable miniature scout robot, analyses the anti-pact mechanism and autonomous strategy, and shows that the anti-impact mechanism is useful to attenuate the impact and the semi-autonomous control strategy can be fit for the robot's application through several experiments at last.


title: A novel alpha-cut representation for type-2 fuzzy sets

abstract: Alpha-cuts and the extension principle form a methodology for extending mathematical concepts from crisp sets to fuzzy sets. They have been applied to many operations, and have also been extended to interval valued fuzzy sets. Recently, some researchers defined new representations of type-2 fuzzy sets, namely, the alpha-plane representation and the zSlice representation. In this paper we investigate an alternative representation utilising alpha-cuts and the extension principle for interval valued fuzzy sets and type-2 fuzzy sets.


title: Cognitive Load Theory vs. Constructivist Approaches: Which Best Leads to Efficient, Deep Learning?

abstract: Computer-assisted learning, in the form of simulation-based training, is heavily focused upon by the military. Because computer-based learning offers highly portable, reusable, and cost-efficient training options, the military has dedicated significant resources to the investigation of instructional strategies that improve learning efficiency within this environment. In order to identify efficient instructional strategies, this paper investigates the two major learning theories that dominate the recent literature on optimizing knowledge acquisition: cognitive load theory (CLT) and constructivism. According to CLT, instructional guidance that promotes efficient learning is most beneficial. Constructivist approaches, in contrast, emphasize the importance of developing a conceptual understanding of the learning material. Supporters of these theories have debated the merits and shortcomings of both positions. However, in the absence of consensus, instructional designers lack a well-defined model for training complex skills in a rapid, efficient manner. The current study investigates the relative utility of CLT and constructivist-based approaches for teaching complex skills using a military command and control task. Findings suggest that the acquisition of procedural, declarative, and conceptual knowledge, as well as decision-making skills, did not differ as a function of the type of instruction used. However, integrated knowledge was slightly better retained by the group provided with CLT-based instruction. These results are contrary to our expectation that constructivist approaches, which focus on the development and integration of information, would yield better performance in an applied problem-based environment. Thus, while contemporary researchers continue to defend the use of constructivist strategies for teaching, our research supports earlier findings that question the utility, efficiency, and impact of these strategies in applied domains.


title: Investigation and Classification of Short-Circuit Failure Modes Based on Three-Dimensional Safe Operating Area for High-Power IGBT Modules

abstract: Insulated-gate bipolar transistor (IGBT) short-circuit failure modes have been under research for many years, successfully paving the way for device short-circuit ruggedness improvement. The aim of this paper is to classify and discuss recent contributions about IGBT short-circuit failure modes, in order to establish the current state of the art and trends in this area. First, the concept of 3-D safe operating area is proposed as the IGBT's operational boundary to divide the device short-circuit failure modes into short-circuit $V_{{\rm{DC}}}/ V_{{\rm{rated}}}\hbox{--} I_{{\rm{SC}}}$ SOA limiting and short-circuit endurance time limiting groups. Then, the discussion is centered on currently reported IGBT short-circuit failure modes in terms of their relationships with the device 3-D short-circuit safe operating area (3D-SCSOA) characteristics. In addition, further investigation on the interaction of 3D-SCSOA characteristics is implemented to motivate advanced contributions in future dependence research of device short-circuit failure modes on temperature. Consequently, a comprehensive and thoughtful review of where the development of short-circuit failure mode research works of IGBT stands and is heading is provided.


title: Control of Electroosmotic Flow by Polymer Coating: Effects of the Electrical Double Layer

abstract: We report on the molecular dynamics simulations of electroosmotic flow control by polymer coating. We show that polymer coating modulates the flow by rendering drag to fluids and by changing the ion distribution and ion-surface interactions in the electrical double layer. Because of the latter two effects, the polymer coating can even enhance the flow under certain conditions. Identifying the effects of these processes is crucial for the rational design of polymer coating for electroosmotic flow control.


title: A Wideband Dual-Polarized Inductor-End Slot Antenna With Stable Beamwidth

abstract: This letter presents a wideband cross-slot antenna (CSA) with inductor loadings for dual polarization. The effects of the ground size on the impedance are investigated. To achieve a compact size, a very small ground having the same hypotenuse length as the slot is used. The inductor loadings are used at both ends of the slot to reduce the size further. Simulated results show that the loaded inductors contribute to a more constant impedance to the frequency response at 1- λ mode, and the impedance moves toward the lower frequency with inductors. Based on the constant impedance of the inductor-end slot at 1- λ mode, a dual-polarized CSA using two orthogonal slot radiators is designed. Measured results show that the proposed dual-polarized antenna has wide impedance bandwidths of 56.2% from 1.56 to 2.78 GHz and 60.6% from 1.46 to 2.73 GHz for Ports 1 and 2, respectively, with a high isolation of more than 26 dB over the operating bandwidth. The antenna also has a stable broadside gain of 8.05 ± 0.45 dBi and stable half-power beamwidths of 60.5° ± 1.8° in the E -plane and 76.1° ± 1.5° in the H -plane for Port 1.


title: Neural Autoregressive Flows

abstract: Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF), and to accelerate state-of-the-art WaveNet-based speech synthesis to 20x faster than real-time, via Inverse Autoregressive Flows (IAF). We unify and generalize these approaches, replacing the (conditionally) affine univariate transformations of MAF/IAF with a more general class of invertible univariate transformations expressed as monotonic neural networks. We demonstrate that the proposed neural autoregressive flows (NAF) are universal approximators for continuous probability distributions, and their greater expressivity allows them to better capture multimodal target distributions. Experimentally, NAF yields state-of-the-art performance on a suite of density estimation tasks and outperforms IAF in variational autoencoders trained on binarized MNIST.


title: Urban Green Space and Its Impact on Human Health

abstract: Background: Over half of the world’s population now lives in urban areas, and this proportion is expected to increase. While there have been numerous reviews of empirical studies on the link between nature and human health, very few have focused on the urban context, and most have examined almost exclusively cross-sectional research. This review is a first step toward assessing the possibility of causal relationships between nature and health in urban settings. Methods: Through systematic review of published literature, we explored the association between urban green space and human health. Results: We found consistent negative association between urban green space exposure and mortality, heart rate, and violence, and positive association with attention, mood, and physical activity. Results were mixed, or no association was found, in studies of urban green space exposure and general health, weight status, depression, and stress (via cortisol concentration). The number of studies was too low to generalize about birth outcomes, blood pressure, heart rate variability, cancer, diabetes, or respiratory symptoms. Conclusions: More studies using rigorous study design are needed to make generalizations, and meta-analyses, of these and other health outcomes possible. These findings may assist urban managers, organizations, and communities in their efforts to increase new or preserve existing green space.


title: A security evaluation of popular Internet of Things protocols for manufacturers

abstract: The Internet of Things (IoT) continues to increase in popularity as more “smart” devices are released and sold every year. Three protocols in particular, Zigbee, Z-wave, and Bluetooth Low Energy (BLE) are used for network communication on a significant number of IoT devices. However, devices utilizing each of these three protocols have been compromised due to either implementation failures by the manufacturer or security shortcomings in the protocol itself. This paper identifies the security features and shortcomings of each protocol citing employed attacks for reference. Additionally, it will serve to help manufacturers make two decisions: First, should they invest in creating their own protocol, and second, if they decide against this, which protocol should they use and how should they implement it to ensure their product is as secure as it can be. These answers are made with respect to the specific factors manufacturers in the IoT space face such as the reversed CIA model with availability usually being the most important of the three and the ease of use versus security tradeoff that manufacturers have to consider. This paper finishes with a section aimed at future research for IoT communication protocols.


title: Aligning supply chain strategies with product uncertainties

abstract: Today's supply chain managers have been bombarded with a wide variety of the socalled leading-edge supply chain strategies. New terminologies and initiatives are being developed constantly. However not all these initiatives or strategies are appropriate for all firms. Companies need to first understand the uncertainties faced by the demand and supply of its products and then try to match these uncertainties with the right supply chain strategies. Based on an analysis of the uncertainties of supply and demand faced by the firm, this article develops a framework that can assist managers in developing the right supply chain strategy for their products.


title: Interleaved active clamp flyback inverter using a synchronous rectifier for a photovoltaic AC module system

abstract: An interleaved active clamp flyback inverter using a synchronous rectifier for a photovoltaic AC module system is proposed. In a conventional single flyback inverter for the photovoltaic AC module system, this inverter has drawbacks of a conduction losses of each switch, transformer copper and high ripple current of capacitor. To overcome these problems, two flyback converter modules with interleaved pulse-width modulation (PWM) are connected in parallel at the input and output sides reducing the ripple current on the input and output capacitors and decrease the current stress on the transformer windings. Thus, the transformer copper losses and the conduction losses on the switches are reduced. Also, reliability and life of capacitor are increased due to reducing the ripple current of input and output capacitors. In order to verify these, theoretical analysis and simulation are performed.


title: A Survey on Smart use of BBM and its Influence on Academic Achievement in SMK Health PGRI Denpasar

abstract: Black Berry Messenger chat facility is very popular in the process of communicating through text messages, pictures and videos, so that the process of communication in the interaction between users can be carried out actively in virtual world. In this study, it will be examined as to whether the Black Berry Messenger has a significant influence on academic achievement in SMK Health PGRI Denpasar. This study will show the effect of the BBM in the improvement of academic achievement, using quantitative methods by distributing questionnaires from a random sample of the student population of SMK health PGRI Denpasar. The correlation analysis uses the correlation coefficient Kendall's tau which managed to deliver the results that the BBM is very influential with the increase of academic achievement.


title: High tech, high touch: The effect of employee skills and customer heterogeneity on customer satisfaction with enterprise system support services

abstract: Although firms have invested significant resources in implementing enterprise software systems (ESS) to modernize and integrate their business process infrastructure, customer satisfaction with ESS has remained an understudied phenomenon. In this exploratory research study, we investigate customer satisfaction for support services of ESS and focus on employee skills and customer heterogeneity. We analyze archival customer satisfaction data from 170 real-world customer service encounters of a leading ESS vendor. Our analysis indicates that the technical and behavioral skills of customer support representatives play a major role in influencing overall customer satisfaction with ESS support services. We find that the effect of technical skills on customer satisfaction is moderated by behavioral skills. We also find that the technical skills of the support personnel are valued more by repeat customers than by new customers. We discuss the implications of these findings for managing customer heterogeneity in ESS support services and for the allocation and training of ESS support personnel.


title: A graphical query language supporting recursion

abstract: We define a language G for querying data represented as a labeled graph G . By considering G as a relation, this graphical query language can be viewed as a relational query language, and its expressive power can be compared to that of other relational query languages. We do not propose G as an alternative to general purpose relational query languages, but rather as a complementary language in which recursive queries are simple to formulate. The user is aided in this formulation by means of a graphical interface. The provision of regular expressions in G allows recursive queries more general than transitive closure to be posed, although the language is not as powerful as those based on function-free Horn clauses. However, we hope to be able to exploit well-known graph algorithms in evaluating recursive queries efficiently, a topic which has received widespread attention recently.


title: A Sequence Labeling Based Approach for Character Segmentation of Historical Documents

abstract: As an important prerequisite step of historical document image analysis, character segmentation is fundamental but challenging. In this paper, we propose a novel approach for the handwritten character segmentation of historical documents by treating it as a sequence labeling problem. In more detail, the proposed model first segments document image into lines, then each column in the line image is given a label to indicate it is a segmentation position or not. The segmentation labeling is achieved by a neural model, which combines a CNN for feature extraction, a LSTM for sequence modeling and a CRF for sequence labeling. The performance of our methods has been evaluated on a 300-page dataset including 96,479 characters. The experimental results demonstrate that the proposed methods achieve superior or highly competitive performance compared with other methods.


TheMultiple Meanings of a Flowchart


title: Face attribute prediction with convolutional neural networks

abstract: In the background of big data and better computer hardware, convolutional neural network (CNN) has also made great progress. Since AlexNet model won the ILSVRC-2012 competition, CNN has been widely applied to computer version tasks including prediction task of face attributes. Different with single attribute prediction, multi-attribute task is subject to different attributes and is difficult to achieve particularly good performance. With the aim of building a CNN network with good performance and simple framework, ATNet is born as a based network with 4 convolution layers. Based on strong correlation, attributes have a positive effect on optimization of network weights, and conversely will be a negative effect. Therefore, 40 attributes of CelebA dataset are grouped to 3 groups. Because the correlation between groups is also different, groups are trained by branching from different depth. Meanwhile, 1×1 convolution layer is used to reduce dimension and limit the size of networks. Finally, we use a network named ATNet_GT with the above skills and get a good performance on CelebA dataset. The average accuracy of 40 attributes is 90.18%, and the standard deviation of accuracies is 7.25%.


title: Multi-Objective Deep Reinforcement Learning

abstract: We propose Deep Optimistic Linear Support Learning (DOL) to solve high-dimensional multi-objective decision problems where the relative importances of the objectives are not known a priori. Using features from the high-dimensional inputs, DOL computes the convex coverage set containing all potential optimal solutions of the convex combinations of the objectives. To our knowledge, this is the first time that deep reinforcement learning has succeeded in learning multi-objective policies. In addition, we provide a testbed with two experiments to be used as a benchmark for deep multi-objective reinforcement learning.


title: An adaptive ontology mapping approach with neural network based constraint satisfaction

abstract: Ontology mapping seeks to find semantic correspondences between similar elements of different ontologies. It is a key challenge to achieve semantic interoperability in building the Semantic Web. This paper proposes a new generic and adaptive ontology mapping approach, called the PRIOR+, based on propagation theory, information retrieval techniques and artificial intelligence. The approach consists of three major modules, i.e., the IR-based similarity generator, the adaptive similarity filter and weighted similarity aggregator, and the neural network based constraint satisfaction solver. The approach first measures both linguistic and structural similarity of ontologies in a vector space model, and then aggregates them using an adaptive method based on their harmonies, which is defined as an estimator of performance of similarity. Finally to improve mapping accuracy the interactive activation and competition neural network is activated, if necessary, to search for a solution that can satisfy ontology constraints. The experimental results show that harmony is a good estimator of f-measure; the harmony based adaptive aggregation outperforms other aggregation methods; neural network approach significantly boosts the performance in most cases. Our approach is competitive with top-ranked systems on benchmark tests at OAEI campaign 2007, and performs the best on real cases in OAEI benchmark tests.