title: The K -factor: Individual Differences in Life History Strategy

abstract: Abstract Until recently, variations in life history strategy were studied exclusively at the species level. Although this domain of study has been extended to examine systematic differences in life history strategy among various human ethnic groupings, more recent evolutionary theories of human development and related behavioral genetic work imply substantial within-group individual variation in life history strategy. We constructed a latent variable model identifying a single common factor, denoted as K, which underlies a variety of otherwise disparate life history parameters. This “K-Factor” loaded 0.36 on childhood attachment to the biological father, −0.36 on childhood attachment to any non-biological father figure, 0.38 on adult romantic partner attachment, −0.51 on mating effort, −0.58 on Machiavellianism, and −0.41 on risk propensity. The bivariate correlations of the K-factor with higher-order personality factors were statistically significant, −0.24 with “Big Neuroticism” and −0.67 with “Big Psychoticism”, and approached significance, correlating 0.12, with “Big Extraversion”. The K-factor appears to be an underappreciated individual difference variable of major importance to human development.


title: Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis

abstract: Distance metric learning (DML), which learns a distance metric from labeled "similar" and "dissimilar" data pairs, is widely utilized. Recently, several works investigate orthogonality-promoting regularization (OPR), which encourages the projection vectors in DML to be close to being orthogonal, to achieve three effects: (1) high balancedness -- achieving comparable performance on both frequent and infrequent classes; (2) high compactness -- using a small number of projection vectors to achieve a "good" metric; (3) good generalizability -- alleviating overfitting to training data. While showing promising results, these approaches suffer three problems. First, they involve solving non-convex optimization problems where achieving the global optimal is NP-hard. Second, it lacks a theoretical understanding why OPR can lead to balancedness. Third, the current generalization error analysis of OPR is not directly on the regularizer. In this paper, we address these three issues by (1) seeking convex relaxations of the original nonconvex problems so that the global optimal is guaranteed to be achievable; (2) providing a formal analysis on OPR's capability of promoting balancedness; (3) providing a theoretical analysis that directly reveals the relationship between OPR and generalization performance. Experiments on various datasets demonstrate that our convex methods are more effective in promoting balancedness, compactness, and generalization, and are computationally more efficient, compared with the nonconvex methods.


abstract: Assessing Student Self-Explanations in an Intelligent Tutoring System Vasile Rus (vrus@memphis.edu) Department of Computer Science Institute for Intelligent Systems The University of Memphis Memphis. TN 38152 Arthur C. Graesser (a-graesser@memphis.edu) Philip M. McCarthy (pmmccrth@memphis.edu) Department of Psychology Institute for Intelligent Systems The University of Memphis Memphis. TN 38152 Department of Psychology Institute for Intelligent Systems The University of Memphis Memphis. TN 38152 Danielle S. McNamara (d.mcnamara@mail.psyc.memphis.edu) Mihai C. Lintean (M.Lintean@memphis.edu) Department of Computer Science Institute for Intelligent Systems The University of Memphis Memphis. TN 38152 Department of Psychology Institute for Intelligent Systems The University of Memphis Memphis. TN 38152 generated from a lexico-syntactic computational tool called The Entailer (Rus et al., 2005; Rus, McCarthy, & Graesser, Our corpus of natural language input generated from an ITS is student contributions from users of iSTART (Interactive Strategy Training for Active Reading and Thinking; McNamara, Levinstein, & Boonthum 2004), a web based tutoring system that provides students with self- explanation and reading strategy training. The iSTART student statements were sampled from the final phase of iSTART training. During this stage, a pedagogical agent reads sentences from a textbook aloud and asks the student to type a self-explanation of each sentence. The focus of this study is to distinguish two very similar student self- explanation categories: Topic identification sentences and Paraphrases. This distinction is challenging because the lexicon used for both topic identification and paraphrase tends to largely overlap with the iSTART target sentences. Thus, for the iSTART agent to provide the most appropriate feedback to the student, accurate algorithms are required to successfully interpret the student’s input and make this distinction. This study tests various measures for evaluating student input and formulates an algorithm from a combination of successful indices. The algorithm accurately assesses the student input, distinguishing topic sentence type self explanations from paraphrase-type self explanation. Thus, once implemented, iSTART agents will be able to provide more informative feedback to students. Abstract Research indicates that guided feedback facilitates learning, whether in the classroom or with Intelligent Tutoring Systems (ITS). Improving the accuracy of the evaluation of user input is therefore necessary for providing optimal feedback. This study investigated an automated assessment of students’ input that involved a lexico-syntactic (entailment) approach to textual analysis along with a variety of other textual assessment measures. The corpus consisted of 357 student responses taken from a recent experiment with iSTART, an ITS that provides students with self-explanation and reading strategy training. The results of our study indicated that the entailment approach provided the highest single measure of accuracy for assessing input when compared to the other measures in the study. A set of indices working in conjunction with the entailment approach provided the best overall assessments. Keywords: entailment; intelligent tutoring systems; iSTART; paraphrase; latent semantic analysis. Introduction A major challenge for Intelligent Tutoring Systems (ITSs) that incorporate natural language interaction is to accurately evaluate users’ contributions and to produce accurate feedback. Available research in the learning sciences indicates that guided feedback and explanation is more effective than simply providing an indication of rightness or wrongness of student input (Aleven & Koedinger, 2002; Anderson et al., 1989; Kluger & DeLisi, 1996; McKendree, 1990; Sims-Knight & Upchurch, 2001). And the benefits of feedback specifically in ITS are equally evident (Azevedo & Bernard, 1995). This study addresses the challenge of evaluating users’ textual input in ITS environments. More specifically, we assess entailment evaluations that are Interactive Strategy Training for Active Reading and Thinking (iSTART) iSTART provides young adolescent to college-aged students with tutored self-explanation and reading strategy training via pedagogical agents (McNamara et al., 2004). iSTART is designed to improve students ability to self-explain by teaching them to use reading strategies such as


title: Reverse Engineering State and Strategy Design Patterns using Static Code Analysis

abstract: This paper presents an approach to detect behavioral design patterns from source code using static analysis techniques. It depends on the concept of Code Property Graph and enriching graph with relationships and properties specific to Design Patterns, to simplify the process of Design Pattern detection. This approach used NoSQL graph database (Neo4j) and uses graph traversal language (Gremlin) for doing graph matching. Our approach, converts the tasks of design pattern detection to a graph matching task by representing Design Patterns in form of graph queries and running it on graph database.


title: Aviation Cyber – Physical Systems : Foundations for Future Aircraft and Air Transport Looking at future challenges in aircraft and air transport , this paper proposes a novel cyber – physical system ( CPS ) framework to understand the cyber layer and cyber – physical interactions in aviation , study

abstract: A century of revolutionary growth in aviation has made global travel a reality of daily life. Aircraft and air transport overcame a number of formidable challenges and hostilities in the physical world. Success in this arduous pursuit was not without leveraging advances of the “cyber” layer, i.e., digital computing, data storage and networking, and software, in hardware, infrastructures, humans, and processes, within the airframe, in space, and on the ground. The physical world, however, is evolving continuously in the 21st century, contributing traffic growth and diversity, fossil fuel and ozone layer depletion, demographics and economy dynamics, as some major factors in aviation performance equations. In the next 100 years, apart from breakthrough physical advances, such as aircraft structural and electrical designs, we envision aviation's progress will depend on conquering cyberspace challenges and adversities, while safely and securely transitioning cyber benefits to the physical world. A tight integration of cyberspace with the physical world streamlines this vision. This paper proposes a novel cyber-physical system (CPS) framework to understand the cyber layer and cyber-physical interactions in aviation, study their impacts, and identify valuable research directions. This paper presents CPS challenges and solutions for aircraft, aviation users, airports, and air traffic management.


title: Image Retrieval Based on Local Mesh Vector Co-occurrence Pattern for Medical Diagnosis from MRI Brain Images

abstract: In modern health-care, for evidence-based diagnosis, there is a requirement for an efficient image retrieval approach to retrieve the cases of interest that have similar characteristics from the large image databases. This paper presents a feature extraction approach that aims at extracting texture features present in the medical images using Local Pattern Descriptor (LPD) and Gray-level Co-occurrence Matrix (GLCM). As a main contribution, a novel local pattern named Local Mesh Vector Co-occurrence Pattern (LMVCoP) has been proposed by concatenating the Local Mesh Co-occurrence Pattern (LMCoP) and the Local Vector Co-occurrence Pattern (LVCoP). The fusion of GLCM with the Local Mesh Pattern (LMeP) and the Local Vector Pattern (LVP) produces LMCoP and LVCoP respectively. The LMVCoP method has been investigated on the Open Access Series of Imaging Studies (OASIS): a Magnetic Resonance Imaging (MRI) brain image database. LMVCoP descriptor achieves 87.57% of ARP and 53.21% of ARR which are higher than the existing methods of LTCoP, PVEP, LBDP, LMeP and LVP. The LMVCoP method enhances the retrieval results of LMeP/LVP from 81.36%/83.52% to 87.57% in terms of ARP on OASIS MRI brain database.


title: Decoder Network over Lightweight Reconstructed Feature for Fast Semantic Style Transfer

abstract: Recently, the community of style transfer is trying to incorporate semantic information into traditional system. This practice achieves better perceptual results by transferring the style between semantically-corresponding regions. Yet, few efforts are invested to address the computation bottleneck of back-propagation. In this paper, we propose a new framework for fast semantic style transfer. Our method decomposes the semantic style transfer problem into feature reconstruction part and feature decoder part. The reconstruction part tactfully solves the optimization problem of content loss and style loss in feature space by particularly reconstructed feature. This significantly reduces the computation of propagating the loss through the whole network. The decoder part transforms the reconstructed feature into the stylized image. Through a careful bridging of the two modules, the proposed approach not only achieves competitive results as backward optimization methods but also is about two orders of magnitude faster.


title: Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks

abstract: Early detection of skin cancer, particularly melanoma, is crucial to enable advanced treatment. Due to the rapid growth of skin cancers, there is a growing need of computerized analysis for skin lesions. These processes including detection, classification, and segmentation. There are three main types of skin lesions in common that are benign nevi, melanoma, and seborrhoeic keratoses which have huge intra-class variations in terms of color, size, place and appearance for each class and high inter-class visual similarities in dermoscopic images. The majority of current research is focusing on melanoma segmentation, but it is also very important to segment the seborrhoeic keratoses and benign nevi lesions as these regions potentially indicate the pre-cancer stage. We propose a multiclass semantic segmentation for these three classes from publicly available ISBI-2017 challenge dataset which consists of 2750 dermoscopic images. We propose an end-to-end solution using fully convolutional networks (FCNs) for multi-class semantic segmentation, which will automatically segment the melanoma, keratoses and benign lesions. To overcome the issue of data deficiency, we propose a transfer learning approach which uses both partial transfer learning and full transfer learning to train FCNs for multi-class semantic segmentation of skin lesions. The results are presented in Dice Similarity Coefficient (Dice) to compare the performance of the deep learning segmentation methods on the dataset with 5-fold cross-validation. The results showed that the two-tier level transfer learning FCN-8s achieved the overall best result with Dice score of 0.785 in a benign category, 0.653 in melanoma segmentation, and 0.557 in seborrhoeic keratoses.


title: MAG: A performance evaluation framework for database systems

abstract: Abstract A database system includes a set of different hardware and software resources with a large number of configuration parameters that affect and control the performance of database systems. Tuning these parameters within their diverse and complex environments requires a lot of expertise and it is a time-consuming, and often a misdirected process. Furthermore, tuning attempts often lack a methodology that has a holistic view of the database system. Therefore, in this paper, we introduce MAG, a layer-by-layer tuning framework that can be used to monitor, analyze, predict, and control database configuration parameters. In particular, the framework comprises three main components: NNMonitor predicts the system performance based on its given parameters; Analyzer identifies and determines the source of the problem and then directs to deal with the problem; and NNGenerator generates the experimental training sets exploited in training NNMonitor. The proposed approach focuses on the root causes of database performance problems. The approach further strives avoiding the repetitive trial-and-error process that is a characteristic of a lot of performance-tuning efforts. We experimentally demonstrate the effectiveness of the proposed framework through an extensive set of evaluations.


title: Biofeedback of Heart Rate Variability and Related Physiology: A Critical Review

abstract: Low heart rate variability (HRV) characterizes several medical and psychological diseases. HRV biofeedback is a newly developed approach that may have some use for treating the array of disorders in which HRV is relatively low. This review critically appraises evidence for the effectiveness of HRV and related biofeedback across 14 studies in improving (1) HRV and baroreflex outcomes and (2) clinical outcomes. Results revealed that HRV biofeedback consistently effectuates acute improvements during biofeedback practice, whereas the presence of short-term and long-term carry-over effects is less clear. Some evidence suggests HRV biofeedback may result in long-term carry-over effects on baroreflex gain, which is an area most promising for future investigations. On the other hand, concerning clinical outcomes, there is ample evidence attesting to efficacy of HRV biofeedback. However, because clinical and physiological outcomes do not improve concurrently in all cases, the mechanism by which HRV biofeedback results in salutary effects in unclear. Considerations for the field in addressing shortcomings of the reviewed studies and advancing understanding of the way in which HRV biofeedback may improve physiological and clinical outcomes are offered in light of the reviewed evidence.


title: Subdivision templates for converting a non-conformal hex-dominant mesh to a conformal hex-dominant mesh without pyramid elements

abstract: This paper presents a computational method for converting a non-conformal hex-dominant mesh to a conformal hex-dominant mesh without help of pyramid elements. During the conversion, the proposed method subdivides a non-conformal element by applying a subdivision template and conformal elements by a conventional subdivision scheme. Although many finite element solvers accept mixed elements, some of them require a mesh to be conformal without a pyramid element. None of the published automated methods could create a conformal hex-dominant mesh without help of pyramid elements, and therefore the applicability of the hex-dominant mesh has been significantly limited. The proposed method takes a non-conformal hex-dominant mesh as an input and converts it to a conformal hex-dominant mesh that consists only of hex, tet, and prism elements. No pyramid element will be introduced. The conversion thus considerably increases the applicability of the hex-dominant mesh in many finite element solvers.


title: Predicting Collaboration Technology Use: Integrating Technology Adoption and Collaboration Research

abstract: The paper presents a model integrating theories from collaboration research (i.e., social presence theory, channel expansion theory, and the task closure model) with a recent theory from technology adoption research (i.e., unified theory of acceptance and use of technology, abbreviated to UTAUT) to explain the adoption and use of collaboration technology. We theorize that collaboration technology characteristics, individual and group characteristics, task characteristics, and situational characteristics are predictors of performance expectancy, effort expectancy, social influence, and facilitating conditions in UTAUT. We further theorize that the UTAUT constructs, in concert with gender, age, and experience, predict intention to use a collaboration technology, which in turn predicts use. We conducted two field studies in Finland among (1) 349 short message service (SMS) users and (2) 447 employees who were potential users of a new collaboration technology in an organization. Our model was supported in both studies. The current work contributes to research by developing and testing a technology-specific model of adoption in the collaboration context.


title: Estimating Result Replicability Using Double Cross-Validation and Bootstrap Methods.

abstract: Statistical significance is often inappropriately equated with evaluating result importance and evaluating result replicability even though these are three somewhat different issues. The prudent researcher must separately assess each of these elements of the "research triumvirate" by using different methods. This paper focuses on two types of empirical methods for estimating research result repliembility: double.cross-validation, and bootstrap procedures. A commonly available statistical computer package, the Statistical Package for the Social Sciences (SPSS-X), is used to carry out the steps required for the double cross-validation procedure, and a recently developed microcomputer program package (developed by C. E. Lunneborg, 1987) is implemented to demonstrate the bootstrap logic. Both methods are applied to a heuristic data set of observed values of three independent variables and one dependent variable for a sample of 25 subjects. It is concluded that although each procedure has some shortcomings, the advantages of using either far outweigh the disadvantages. There are 5 tables of analysis data and a 19-item list of ref=rences. (SLD) ***********************************0**,******************************** Reproductions supplied by EDRS are the best that can be made from the original document. ***********************************************************************


title: Learning from Labeled and Unlabeled Vertices in Networks

abstract: Networks such as social networks, citation networks, protein-protein interaction networks, etc., are prevalent in real world. However, only very few vertices have labels compared to large amounts of unlabeled vertices. For example, in social networks, not every user provides his/her profile information such as the personal interests which are relevant for targeted advertising. Can we leverage the limited user information and friendship network wisely to infer the labels of unlabeled users? In this paper, we propose a semi-supervised learning framework called weighted-vote Geometric Neighbor classifier (wvGN) to infer the likely labels of unlabeled vertices in sparsely labeled networks. wvGN exploits random walks to explore not only local but also global neighborhood information of a vertex. Then the label of the vertex is determined by the accumulated local and global neighborhood information. Specifically, wvGN optimizes a proposed objective function by a search strategy which is based on the gradient and coordinate descent methods. The search strategy iteratively conducts a coarse search and a fine search to escape from local optima. Extensive experiments on various synthetic and real-world data verify the effectiveness of wvGN compared to state-of-the-art approaches.


title: Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine

abstract: Low quality sensor data limits WSN capabilities for providing reliable real-time situation-awareness. Outlier detection is a solution to ensure the quality of sensor data. An effective and efficient outlier detection technique for WSNs not only identifies outliers in a distributed and online manner with high detection accuracy and low false alarm, but also satisfies WSN constraints in terms of communication, computational and memory complexity. In this paper, we take into account the correlation between sensor data attributes and propose two distributed and online outlier detection techniques based on a hyperellipsoidal one-class support vector machine (SVM). We also take advantage of the theory of spatio-temporal correlation to identify outliers and update the ellipsoidal SVM-based model representing the changed normal behavior of sensor data for further outlier identification. Simulation results show that our adaptive ellipsoidal SVM-based outlier detection technique achieves better detection accuracy and lower false alarm as compared to existing SVM-based techniques designed for WSNs.


title: Learning preferences for manipulation tasks from online coactive feedback

abstract: We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots. The preferences we learn are more intricate than simple geometric constraints on trajectories; they are rather governed by the surrounding context of various objects and human interactions in the environment. We propose a coactive online learning framework for teaching preferences in contextually rich environments. The key novelty of our approach lies in the type of feedback expected from the user: the human user does not need to demonstrate optimal trajectories as training data, but merely needs to iteratively provide trajectories that slightly improve over the trajectory currently proposed by the system. We argue that this coactive preference feedback can be more easily elicited than demonstrations of optimal trajectories. Nevertheless, theoretical regret bounds of our algorithm match the asymptotic rates of optimal trajectory algorithms. We implement our algorithm on two high-degree-of-freedom robots, PR2 and Baxter, and present three intuitive mechanisms for providing such incremental feedback. In our experimental evaluation we consider two context rich settings, household chores and grocery store checkout, and show that users are able to train the robot with just a few feedbacks taking only a few minutes.


title: Converting heterogeneous statistical tables on the web to searchable databases

abstract: Much of the world's quantitative data reside in scattered web tables. For a meaningful role in Big Data analytics, the facts reported in these tables must be brought into a uniform framework. Based on a formalization of header-indexed tables, we proffer an algorithmic solution to end-to-end table processing for a large class of human-readable tables. The proposed algorithms transform header-indexed tables to a category table format that maps easily to a variety of industry-standard data stores for query processing. The algorithms segment table regions based on the unique indexing of the data region by header paths, classify table cells, and factor header category structures of two-dimensional as well as the less common multidimensional tables. Experimental evaluations substantiate the algorithmic approach to processing heterogeneous tables. As demonstrable results, the algorithms generate queryable relational database tables and semantic-web triple stores. Application of our algorithms to 400 web tables randomly selected from diverse sources shows that the algorithmic solution automates end-to-end table processing.


title: Performance Improvement of a Power Conversion Module by Liquid Micro-Jet Impingement Cooling

abstract: Liquid micro-jet array impingement cooling of a power conversion module with 12 power switching devices (six insulated gate bipolar transistors and six diodes) is investigated. The 1200-V/150-A module converts dc input power to variable frequency, variable voltage three-phase ac output to drive a 50HP three-phase induction motor. The silicon devices are attached to a packaging layer [direct bonded copper (DBC)], which in turn is soldered to a metal base plate. DI water micro-jet array impinges on the base plate of the module targeted at the footprint area of the devices. Although the high heat flux cooling capability of liquid impingement is a well-established finding, the impact of its practical implementation in power systems has never been addressed. This paper presents the first one-to-one comparison of liquid micro-jet array impingement cooling (JAIC) with the traditional methods, such as air-cooling over finned heat sink or liquid flow in multi-pass cold plate. Results show that compared to the conventional cooling methods, JAIC can significantly enhance the module output power. If the output power is maintained constant, the device temperature can be reduced drastically by JAIC. Furthermore, jet impingement provides uniform cooling for multiple devices placed over a large area, thereby reducing non-uniformity of temperature among the devices. The reduction in device temperature, both its absolute value and the non-uniformity, implies multi-fold increase in module reliability. The results thus illustrate the importance of efficient thermal management technique for compact and reliable power conversion application


MQTT of IoT Classification in Energy Saving


Deep Embedded Clustering with Data Augmentation


title: BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism

abstract: Neural network frameworks such as PyTorch and TensorFlow are the workhorses of numerous machine learning applications ranging from object recognition to machine translation. While these frameworks are versatile and straightforward to use, the training of and inference in deep neural networks is resource (energy, compute, and memory) intensive. In contrast to recent works focusing on algorithmic enhancements, we introduce BrainSlug, a framework that transparently accelerates neural network workloads by changing the default layer-by-layer processing to a depth-first approach, reducing the amount of data required by the computations and thus improving the performance of the available hardware caches. BrainSlug achieves performance improvements of up to 41.1% on CPUs and 35.7% on GPUs. These optimizations come at zero cost to the user as they do not require hardware changes and only need tiny adjustments to the software.


title: Predicting power consumption of GPUs with fuzzy wavelet neural networks

abstract: We build the power model for GPUs without performance counter or GPU emulator.Our power models can predict power by scanning source code.We extract the static program features by using program slicing.Fuzzy wavelet neural networks is used to adapt various GPU architectures.The power models are validated on various GPU architectures. Prediction and optimization of power consumption have become an essential issue in the field of General-purpose computing on graphic processing units (GPUs) because of the increasing prevalence of GPUs and the constraints of energy consumption. However, previous approaches to build power models need to extract program features related to power consumption from the performance counter or GPU emulators. These approaches are unable to estimate power consumption of applications for software designers owing to the lack of information about detailed GPUs architecture or the supporting of emulators. In this study, we explore a novel method to model GPU power consumption of applications in computing process. By using program slicing, we decompose the source code of applications into slices and extract the power-related static program features. The slicing is used as a basic unit to train a power model based on fuzzy wavelet artificial neural networks. This step allows programmers to investigate the power profile of their applications and identify the code areas with higher energy consumption. To improve prediction accuracy, we further divide the GPUs applications by the branch structure into two categories: sparseness-branch and denseness-branch. The power model is proposed for sparseness-branch programs based on slicing. For denseness-branch programs, probabilistic slicing is utilized to reduce the invalid slices in order to improve accuracy. The models are empirically validated by using typical GPU benchmarks and the results are compared with the measured power. Overall, the average error of our power models is less than 6%.


title: A mixed‐methods systematic review of the effects of mindfulness on nurses

abstract: Aim ::: To review the effects of mindfulness-based interventions on Registered Nurses and nursing students. ::: ::: Background ::: Work-related stress among nurses is estimated to be the biggest occupational health problem after musculoskeletal disorders. ::: ::: Design ::: A mixed-method systematic review incorporating quantitative and qualitative data was conducted. ::: ::: Data sources ::: Studies on the effects of mindfulness-based interventions for nurses and nursing students published between 1980 and 2014 were identified through a systematic search in electronic databases: Medline, Embase, PsycINFO, Cochrane Library and Cinahl. ::: ::: Review methods ::: Data analysis was conducted based on the framework of Thomas and Harden (2004). ::: ::: Results ::: A total of 32 studies, including 17 controlled designs, 11 pre–post designs and four qualitative designs were reviewed. Meta-analysis suggests that mindfulness-based interventions may be effective in significantly reducing state anxiety and depression at posttreatment and state anxiety and trait anxiety at follow-up. Qualitative studies and uncontrolled studies shed light on benefits overlooked in RCTs, including improvements in the well-being of individuals (e.g. inner state of calmness, awareness and enthusiasm) and improved performance at work (better communication with colleagues and patients, higher sensitivity to patients’ experiences, clearer analysis of complex situations and emotional regulation in stressful contexts). ::: ::: Conclusions ::: Mindfulness appeared to improve nurses’ mental health significantly. It could be used in worksite health promotion programmes. Only a few studies have explored the impact of mindfulness on nurses’ professional behaviours and their relationships with patients and colleagues. Future research should further explore the long-term impacts of mindfulness on performance and well-being at work using sound methodological designs.


title: SciHive: Array-Based Query Processing with HiveQL

abstract: The data-intensive scientific discoveries are generating huge amounts of data at an alarming rate. Most of the data are multidimensional and stored in array-based file formats. The processing of such big data becomes an urgent challenge. In this paper, we present SciHive, a scalable and easy-to-use array-based query system. SciHive enables scientists to process raw array datasets in parallel with a SQL-like query language. We implement SciHive as an extension of Hive which is a data warehouse system on Hadoop. SciHive maps the arrays in NetCDF files to a table and executes the queries via MapReduce. Files are loaded dynamically as needed. So SciHive does not need any additional pre-loading or format conversion procedure. In addition, SciHive includes two optimization methods to reduce the generated rows. Experiments with different queries on representative datasets show that the optimizations are very effective in most cases and SciHive is scalable to handle large datasets.


title: A Cocktail Approach for Travel Package Recommendation

abstract: Recent years have witnessed an increased interest in recommender systems. Despite significant progress in this field, there still remain numerous avenues to explore. Indeed, this paper provides a study of exploiting online travel information for personalized travel package recommendation. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. To that end, in this paper, we first analyze the characteristics of the existing travel packages and develop a tourist-area-season topic (TAST) model. This TAST model can represent travel packages and tourists by different topic distributions, where the topic extraction is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes. Then, based on this topic model representation, we propose a cocktail approach to generate the lists for personalized travel package recommendation. Furthermore, we extend the TAST model to the tourist-relation-area-season topic (TRAST) model for capturing the latent relationships among the tourists in each travel group. Finally, we evaluate the TAST model, the TRAST model, and the cocktail recommendation approach on the real-world travel package data. Experimental results show that the TAST model can effectively capture the unique characteristics of the travel data and the cocktail approach is, thus, much more effective than traditional recommendation techniques for travel package recommendation. Also, by considering tourist relationships, the TRAST model can be used as an effective assessment for travel group formation.


title: Modeling Attention in Panoramic Video: A Deep Reinforcement Learning Approach.

abstract: Panoramic video provides immersive and interactive experience by enabling humans to control the field of view (FoV) through head movement (HM). Thus, HM plays a key role in modeling human attention on panoramic video. This paper establishes a database collecting subjects' HM positions on panoramic video sequences. From this database, we find that the HM data are highly consistent across subjects. Furthermore, we find that deep reinforcement learning (DRL) can be applied to predict HM positions, via maximizing the reward of imitating human HM scanpaths through the agent's actions. Based on our findings, we propose a DRL based HM prediction (DHP) approach with offline and online versions, called offline-DHP and online-DHP. In offline-DHP, multiple DRL workflows are run to determine potential HM positions at each panoramic frame. Then, a heat map of the potential HM positions, named the HM map, is generated as the output of offline-DHP. In online-DHP, the next HM position of one subject is estimated given the currently observed HM position, which is achieved by developing a DRL algorithm upon the learned offline-DHP model. Finally, the experimental results validate that our approach is effective in offline and online prediction of HM positions for panoramic video, and that the learned offline-DHP model can improve the performance of online-DHP.


title: MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

abstract: The prevalence of networked sensors and actuators in many real-world systems such as smart buildings, factories, power plants, and data centers generate substantial amounts of multivariate time series data for these systems. The rich sensor data can be continuously monitored for intrusion events through anomaly detection. However, conventional threshold-based anomaly detection methods are inadequate due to the dynamic complexities of these systems, while supervised machine learning methods are unable to exploit the large amounts of data due to the lack of labeled data. On the other hand, current unsupervised machine learning approaches have not fully exploited the spatial-temporal correlation and other dependencies amongst the multiple variables (sensors/actuators) in the system for detecting anomalies. In this work, we propose an unsupervised multivariate anomaly detection method based on Generative Adversarial Networks (GANs). Instead of treating each data stream independently, our proposed MAD-GAN framework considers the entire variable set concurrently to capture the latent interactions amongst the variables. We also fully exploit both the generator and discriminator produced by the GAN, using a novel anomaly score called DR-score to detect anomalies by discrimination and reconstruction. We have tested our proposed MAD-GAN using two recent datasets collected from real-world CPS: the Secure Water Treatment (SWaT) and the Water Distribution (WADI) datasets. Our experimental results showed that the proposed MAD-GAN is effective in reporting anomalies caused by various cyber-intrusions compared in these complex real-world systems.


title: Binary Codes Embedding for Fast Image Tagging with Incomplete Labels

abstract: Tags have been popularly utilized for better annotating, organizing and searching for desirable images. Image tagging is the problem of automatically assigning tags to images. One major challenge for image tagging is that the existing/training labels associated with image examples might be incomplete and noisy. Valuable prior work has focused on improving the accuracy of the assigned tags, but very limited work tackles the efficiency issue in image tagging, which is a critical problem in many large scale real world applications. This paper proposes a novel Binary Codes Embedding approach for Fast Image Tagging (BCE-FIT) with incomplete labels. In particular, we construct compact binary codes for both image examples and tags such that the observed tags are consistent with the constructed binary codes. We then formulate the problem of learning binary codes as a discrete optimization problem. An efficient iterative method is developed to solve the relaxation problem, followed by a novel binarization method based on orthogonal transformation to obtain the binary codes from the relaxed solution. Experimental results on two large scale datasets demonstrate that the proposed approach can achieve similar accuracy with state-of-the-art methods while using much less time, which is important for large scale applications.


title: Rectangular dielectric resonator antenna array for 28 GHz applications

abstract: In this paper, a Rectangular Dielectric Resonator Antenna (RDRA) with a modified feeding line is designed and investigated at 28 GHz. The modified feed line is designed to excite the DR with relative permittivity of 10 which contributes to a wide bandwidth operation. The proposed single RDRA has been fabricated and mounted on a RT/Duroid 5880 (er = 2.2 and tanδ = 0.0009) substrate. The optimized single element has been applied to array structure to improve the gain and achieve the required gain performance. The radiation pattern, impedance bandwidth and gain are simulated and measured accordingly. The number of elements and element spacing are studied for an optimum performance. The proposed antenna obtains a reflection coefficient response from 27.0 GHz to 29.1 GHz which cover the desired frequency band. This makes the proposed antenna achieve 2.1 GHz impedance bandwidth and gain of 12.1 dB. Thus, it has potential for millimeter wave and 5G applications


title: Prenatal Neurogenesis in Autism Spectrum Disorders

abstract: An ever-increasing body of literature describes compelling evidence that a subset of young children on the autism spectrum show abnormal cerebral growth trajectories. In these cases, normal cerebral size at birth is followed by a period of abnormal growth and starting in late childhood often by regression compared to unaffected controls. Recent work has demonstrated an abnormal increase in the number of neurons of the prefrontal cortex suggesting that cerebral size increase in autism is driven by excess neuronal production. In addition, some affected children display patches of abnormal laminar positioning of cortical projection neurons. As both cortical projection neuron numbers and their correct layering within the developing cortex requires the undisturbed proliferation of neural progenitors, it appears that neural progenitors lie in the center of the autism pathology associated with early brain overgrowth. Consequently, autism spectrum disorders associated with cerebral enlargement should be viewed as birth defects of an early embryonic origin with profound implications for their early diagnosis, preventive strategies, and therapeutic intervention.


title: Restoration of Images Corrupted by Impulse Noise and Mixed Gaussian Impulse Noise using Blind Inpainting

abstract: This article studies the problem of image restoration of observed images corrupted by impulse noise and mixed Gaussian impulse noise. Since the pixels damaged by impulse noise contain no information about the true image, how to find this set correctly is a very important problem. We propose two methods based on blind inpainting and $\ell_0$ minimization that can simultaneously find the damaged pixels and restore the image. By iteratively restoring the image and updating the set of damaged pixels, these methods have better performance than other methods, as shown in the experiments. In addition, we provide convergence analysis for these methods, these algorithms will converge to coordinatewise minimum points. In addition, they will converge to local minimum points (or with probability one) with some modifications in the algorithms.


title: Fast Computation of Orthogonal Fourier-Mellin Moments Using Modified Direct Method

abstract: A novel, modified direct method for Orthogonal Fourier-Mellin Moments (OFMMs) computation, which lacks of any factorial terms, is presented. The proposed algorithm is based on the substitution of the factorial terms by their approximate values, according to Stirling's Approximation formula. In this way, a new set of orthogonal moments, which are approximate versions of the original ones, are extracted. The approximation error introduced by the Stirling's Approximation formula is of low order and thus the resulted moments do not significantly differ from their original values. The resulted formulas are factorial free and therefore the time needed to compute the OFMMs is less than this of the conventional direct method.


title: Wideband Common-Gate CMOS LNA Employing Dual Negative Feedback With Simultaneous Noise, Gain, and Bandwidth Optimization

abstract: This paper presents a wideband common-gate (CG) LNA architecture that overcomes the fundamental tradeoff between power and noise match without compromising its stability. The proposed architecture can achieve the minimum noise figure (NF) over the previously reported feedback amplifiers in a CG configuration. The proposed architecture achieves broadband impedance matching, low noise, large gain, enhanced linearity, and wide bandwidth concurrently by employing an efficient and reliable dual negative-feedback. An amplifier prototype was realized in 0.18-μm CMOS, operates from 1.05 to 3.05 GHz, and dissipates 12.6 mW from 1.8-V supply while occupying a 0.073-mm2 active area. The LNA provides 16.9-dB maximum voltage gain, 2.57-dB minimum NF, better than - 10-dB input matching, and - 0.7-dBm minimum IIP3 across the entire bandwidth.


title: Fully automatic stitching and distortion correction of transmission electron microscope images.

abstract: In electron microscopy, a large field of view is commonly captured by taking several images of a sample region and then by stitching these images together. Non-linear lens distortions induced by the electromagnetic lenses of the microscope render a seamless stitching with linear transformations impossible. This problem is aggravated by large CCD cameras, as they are commonly in use nowadays. We propose a new calibration method based on ridge regression that compensates non-linear lens distortions, while ensuring that the geometry of the image is preserved. Our method estimates the distortion correction from overlapping image areas using automatically extracted correspondence points. Therefore, the estimation of the correction transform does not require any special calibration samples. We evaluate our method on simulated ground truth data as well as on real electron microscopy data. Our experiments demonstrate that the lens calibration robustly corrects large distortions with an average stitching error exceeding 10 pixels to sub-pixel accuracy within two iteration steps.


title: FFDA: A tree based energy aware data aggregation protocol in wireless sensor networks

abstract: In wireless sensor networks (WSN's), data aggregation is used to increase energy efficiency by means of eliminating redundancy and forwarding the collected abstract data of sensor nodes toward the sink. One of the most important challenges in WSN is to keep the remaining energy of nodes high and balanced to achieve longer system lifetime. In this article we propose an energy efficient data aggregation protocol named FFDA (Feed Forward Data Aggregation) for constructing the spanning tree, we also represent a new parameter called EAT (Energy After Transmission). This protocol considers EAT as the main parameter to select a node as root for spanning tree in the beginning of each round of data aggregation. Using this new parameter the remaining energy of nodes remain more balanced thus the first node die is delayed significantly and it also improves the system's lifetime as indicated by simulation results.


title: A FPGA-based Parallel Architecture for Scalable High-Speed Packet Classification

abstract: Multi-field packet classification is a critical function that enables network routers to support a variety of applications such as firewall processing, Quality of Service differentiation, traffic billing, and other value added services. Explosive growth of Internet traffic requires the future packet classifiers be implemented in hardware. However, most of the existing packet classification algorithms need large amount of memory, which inhibits efficient hardware implementations. This paper exploits the modern FPGA technology and presents a partitioning-based parallel architecture for scalable and high-speed packet classification. We propose a coarse-grained independent sets algorithm and then combine it seamlessly with the cross-producting scheme. After partitioning the original rule set into several coarse-grained independent sets and applying the cross-producting scheme for the remaining rules, the memory requirement is dramatically reduced. Our FPGA implementation results show that our architecture can store 10K real-life rules in a single state-of-the-art FPGA while consuming a small amount of on-chip resources. Post place and route results show that the design sustains 90 Gbps throughput for minimum size (40 bytes) packets, which is more than twice the current backbone network link rate.


title: The expansion of Google Scholar versus Web of Science: a longitudinal study

abstract: Web of Science (WoS) and Google Scholar (GS) are prominent citation services with distinct indexing mechanisms. Comprehensive knowledge about the growth patterns of these two citation services is lacking. We analyzed the development of citation counts in WoS and GS for two classic articles and 56 articles from diverse research fields, making a distinction between retroactive growth (i.e., the relative difference between citation counts up to mid-2005 measured in mid-2005 and citation counts up to mid-2005 measured in April 2013) and actual growth (i.e., the relative difference between citation counts up to mid-2005 measured in April 2013 and citation counts up to April 2013 measured in April 2013). One of the classic articles was used for a citation-by-citation analysis. Results showed that GS has substantially grown in a retroactive manner (median of 170 % across articles), especially for articles that initially had low citations counts in GS as compared to WoS. Retroactive growth of WoS was small, with a median of 2 % across articles. Actual growth percentages were moderately higher for GS than for WoS (medians of 54 vs. 41 %). The citation-by-citation analysis showed that the percentage of citations being unique in WoS was lower for more recent citations (6.8 % for citations from 1995 and later vs. 41 % for citations from before 1995), whereas the opposite was noted for GS (57 vs. 33 %). It is concluded that, since its inception, GS has shown substantial expansion, and that the majority of recent works indexed in WoS are now also retrievable via GS. A discussion is provided on quantity versus quality of citations, threats for WoS, weaknesses of GS, and implications for literature research and research evaluation.


title: An Architecture to Support Autonomic Slice Networking

abstract: Network slices combine resource virtualization with the isolation level required by future 5G applications. In addition, the use of monitoring and data analytics help to maintain the required network performance, while reducing total cost of ownership. In this paper, an architecture to enable autonomic slice networking is presented. Extended nodes make local decisions close to network devices, whereas centralized domain systems collate and export metered data transparently to customer controllers, all of them leveraging customizable and isolated data analytics processes. Discovered knowledge can be applied for both proactive and reactive network slice reconfiguration, triggered either by service providers or customers, thanks to the interaction with state-of-the-art software-defined networking controllers and planning tools. The architecture is experimentally demonstrated by means of a complex use case for a multidomain multilayer multiprotocol label switching (MPLS)-over-optical network. In particular, the use case consists of the following observe–analyze–act loops: 1) proactive network slice rerouting after bit error rate (BER) degradation detection in a lightpath supporting a virtual link (vlink); 2) reactive core network restoration after optical link failure; and 3) reactive network slice rerouting after the degraded lightpath is restored. The proposed architecture is experimentally validated on a distributed testbed connecting premises in UPC (Spain) and CNIT (Italy).


title: OFDM over mm-Wave OAM Channels in a Multipath Environment with Intersymbol Interference

abstract: This paper reports an experimental investigation of using orthogonal frequency division multiplexing (OFDM) on orbital-angular momentum (OAM) channels when there is intersymbol interference (ISI) caused by multipath effects. The impulse response measurement indicates that due to the divergence of OAM beams, channels of larger OAM number are more affected by the ISI caused the reflections from surrounding objects. Channel performance of three different OAM numbers l = 0, +1 and +3 are studied. When a single QPSK channel is used, the more channel performance degradation in term of BER and EVM is observed for higher OAM channels. OFDM with 4 sub-carriers is used on the OAM channels to help reduce the ISI and improve the channel performance to some extent.


title: Oxidation of odor compound indole in aqueous solution with ferrate (VI): Kinetics, pathway, and the variation of assimilable organic carbon

abstract: Abstract Indole is a typical fecal odor compound and has negative impact on water quality at low concentrations. Here, the oxidation of indole by ferrate [Fe(VI)] was investigated, in terms of the reaction kinetics, the oxidation products, and the variation of assimilable organic carbon (AOC). The oxidation of indole was pH-dependent (6.0–9.5), and the pH-dependent profile could be well explained by the acid-base equilibrium of Fe(VI). The apparent second-order rate constants (k app ) for indole oxidation with Fe(VI) ranged from 692 ± 26 M −1 s −1 at pH 6.0 to 3.08 ± 0.15 M −1 s −1 at pH 9.5. The results of experiments conducted under real source water background showed that indole could be effectively removed by Fe(VI) from actual polluted waters. After analyzing the reaction pathway, it was found that the initial steps in the oxidation of indole were the hydroxylation of indole to 2,3-dihydroxyindole, followed by the cleavage of N-heterocyclic ring. Formanilide was a major oxidation product in this process, and it is less reactive with Fe(VI) than that of indole. High concentrations of Fe(VI) could effectively oxidize indole in the water and has little impact on the microbiological stability of the treated water. Based on the results, Fe(VI) can be an effective agent for the control of indole in water treatment.


title: Mathematical Formulation of Multi-Layer Networks

abstract: A network representation is useful for describing the structure of a large variety of complex systems. However, most real and engineered systems have multiple subsystems and layers of connectivity, and the data produced by such systems are very rich. Achieving a deep understanding of such systems necessitates generalizing ‘‘traditional’’ network theory, and the newfound deluge of data now makes it possible to test increasingly general frameworks for the study of networks. In particular, although adjacency matrices are useful to describe traditional single-layer networks, such a representation is insufficient for the analysis and description of multiplex and time-dependent networks. One must therefore develop a more general mathematical framework to cope with the challenges posed by multilayer complex systems. In this paper, we introduce a tensorial framework to study multilayer networks, and we discuss the generalization of several important network descriptors and dynamical processes—including degree centrality, clustering coefficients, eigenvector centrality, modularity, von Neumann entropy, and diffusion—for this framework. We examine the impact of different choices in constructing these generalizations, and we illustrate how to obtain known results for the special cases of single-layer and multiplex networks. Our tensorial approach will be helpful for tackling pressing problems in multilayer complex systems, such as inferring who is influencing whom (and by which media) in multichannel social networks and developing routing techniques for multimodal transportation systems.


title: Measurement of Tire Tread Depth with Image Triangulation

abstract: Tires bear the grip and friction force between a vehicle and the ground. Worn tires will result in slippage, a longer braking distance, and even flat tires, which could eventually result in a driving accident. Therefore, governments, including Taiwan, have formulated standards stipulating the appropriate tire pressure and depth of tire treads. The tire tread depth used to be determined using manual contact measurement. This study created a non-contact measurement prototype system which applying machine vision to measure tread depth that uses image triangulation to reduce the manpower expense and improve the convenience of measurement. Based on the Epipolar plane, this system obtains the distance between the tire treads and camera lens, consequently, the depth image of the tire tread was constructed. Along with the normal direction of groove of the tire's main tread on the depth images, obtaining the correct depth data using the majority decision voting strategy. Consequently, the tread depth was measured. In addition, this system produced an application (APP) which allows a user to obtain and record the tire tread depth on their Android smart phone.


Priority-Based Genetic Algorithm for Conflict-Free Automated Guided Vehicle Routing


title: Optimal number of response categories in rating scales: reliability, validity, discriminating power, and respondent preferences

abstract: Using a self-administered questionnaire, 149 respondents rated service elements associated with a recently visited store or restaurant on scales that differed only in the number of response categories (ranging from 2 to 11) and on a 101-point scale presented in a different format. On several indices of reliability, validity, and discriminating power, the two-point, three-point, and four-point scales performed relatively poorly, and indices were significantly higher for scales with more response categories, up to about 7. Internal consistency did not differ significantly between scales, but test-retest reliability tended to decrease for scales with more than 10 response categories. Respondent preferences were highest for the 10-point scale, closely followed by the seven-point and nine-point scales. Implications for research and practice are discussed.


title: Crosstalk cancellation for hybrid fiber twisted-pair systems

abstract: Broadband access over the existing telephone network can be provided by the deployment of hybrid fiber twisted-pair (HFTP) systems. For HFTP the twisted-pair cable has to be modelled as a vector channel with distributed transceivers on the subscriber side. The upstream transmission capacity can be increased by FEXT cancellation based on a 2 dimensional decision feedback equalizer (2-D-DFE). The achievable gain depends on the usable upstream bandwidth as well as on the background noise and can be more than 100 % for the transmission of 27.6 Mbit/s with a 16-QAM. About 15 feedback taps are required per FEXT canceller, resulting in 19000 gates for a gate array implementation in 0.35 /spl mu/m technology.


title: Software Defined Networking for Improved Wireless Sensor Network Management: A Survey

abstract: Wireless sensor networks (WSNs) are becoming increasingly popular with the advent of the Internet of things (IoT). Various real-world applications of WSNs such as in smart grids, smart farming and smart health would require a potential deployment of thousands or maybe hundreds of thousands of sensor nodes/actuators. To ensure proper working order and network efficiency of such a network of sensor nodes, an effective WSN management system has to be integrated. However, the inherent challenges of WSNs such as sensor/actuator heterogeneity, application dependency and resource constraints have led to challenges in implementing effective traditional WSN management. This difficulty in management increases as the WSN becomes larger. Software Defined Networking (SDN) provides a promising solution in flexible management WSNs by allowing the separation of the control logic from the sensor nodes/actuators. The advantage with this SDN-based management in WSNs is that it enables centralized control of the entire WSN making it simpler to deploy network-wide management protocols and applications on demand. This paper highlights some of the recent work on traditional WSN management in brief and reviews SDN-based management techniques for WSNs in greater detail while drawing attention to the advantages that SDN brings to traditional WSN management. This paper also investigates open research challenges in coming up with mechanisms for flexible and easier SDN-based WSN configuration and management.


Efficient Neural Architecture Search with Network Morphism


title: Locally adaptive discounting in multi sensor occupancy grid fusion

abstract: Obstacle fusion algorithms usually perform obstacle association and gating in order to improve the obstacle position if it was detected by multiple sensors. However, this strategy is not common in multi sensor occupancy grid fusion. Thus, the quality of the fused grid, in terms of obstacle position accuracy, largely depends on the sensor with the lowest accuracy. In this paper an efficient method to associate obstacles across sensor grids is proposed. Imprecise sensors are discounted locally in cells where a more accurate sensor, that detected the same obstacle, derived free space. Furthermore, fixed discount factors to optimize false negative and false positive rates are used. Because of its generic formulation with the covariance of each sensor grid, the method is scalable to any sensor setup. The quantitative evaluation with a highly precise navigation map shows an increased obstacle position accuracy compared to standard evidential occupancy grid fusion.


title: Theoretical Framework for Plastic Waste Management in Ghana through Extended Producer Responsibility: Case of Sachet Water Waste

abstract: Currently, use and disposal of plastic by consumers through waste management activities in Ghana not only creates environmental problems, but also reinforces the notion of a wasteful society. The magnitude of this problem has led to increasing pressure from the public for efficient and practical measures to solve the waste problem. This paper analyses the impact of plastic use and disposal in Ghana. It emphasizes the need for commitment to proper management of the impacts of plastic waste and effective environmental management in the country. Sustainable Solid Waste Management (SSWM) is a critical problem for developing countries with regards to climate change and greenhouse gas emission, and also the general wellbeing of the populace. Key themes of this paper are producer responsibility and management of products at end of life. The paper proposes two theatrical recovery models that can be used to address the issue of sachet waste in Ghana.


title: Branching embedding: A heuristic dimensionality reduction algorithm based on hierarchical clustering

abstract: This paper proposes a new dimensionality reduction algorithm named branching embedding (BE). It converts a dendrogram to a two-dimensional scatter plot, and visualizes the inherent structures of the original high-dimensional data. Since the conversion part is not computationally demanding, the BE algorithm would be beneficial for the case where hierarchical clustering is already performed. Numerical experiments revealed that the outputs of the algorithm moderately preserve the original hierarchical structures.


title: Towards warp-scheduler friendly STT-RAM/SRAM hybrid GPGPU register file design

abstract: Modern Graphics Processing Units (GPUs) widely adopt large SRAM based register file (RF) to enable fast context-switch. A large SRAM RF may consume 20% to 40% GPU power, which has become one of the major design challenges for GPUs. Recent studies mitigate the issue through hybrid RF designs that architect a large STT-RAM (Spin Transfer Torque Magnetic memory) RF and a small SRAM buffer. However, the long STT-RAM write latency throttles the data exchange between STT-RAM and SRAM, which deprecates warp scheduler with frequent context switches, e.g., round robin scheduler. In this paper, we propose HC-RF, a warp-scheduler friendly hybrid RF design using novel SRAM/STT-RAM hybrid cell (HC) structure. HC-RF exploits cell level integration to improve the effective bandwidth between STT-RAM and SRAM. By enabling silent data transfer from SRAM to STT-RAM without blocking RF banks, HC-RF supports concurrent context-switching and decouples its dependency on warp scheduler. Our experimental results show that, on average, HC-RF achieves 50% performance improvement and 44% energy consumption reduction over the coarse-grained hybrid design when adopting LRR(Loose Round Robin) warp scheduler.


title: Learning Content Similarity for Music Recommendation

abstract: Many tasks in music information retrieval, such as recommendation, and playlist generation for online radio, fall naturally into the query-by-example setting, wherein a user queries the system by providing a song, and the system responds with a list of relevant or similar song recommendations. Such applications ultimately depend on the notion of similarity between items to produce high-quality results. Current state-of-the-art systems employ collaborative filter methods to represent musical items, effectively comparing items in terms of their constituent users. While collaborative filter techniques perform well when historical data is available for each item, their reliance on historical data impedes performance on novel or unpopular items. To combat this problem, practitioners rely on content-based similarity, which naturally extends to novel items, but is typically outperformed by collaborative filter methods. In this paper, we propose a method for optimizing content-based similarity by learning from a sample of collaborative filter data. The optimized content-based similarity metric can then be applied to answer queries on novel and unpopular items, while still maintaining high recommendation accuracy. The proposed system yields accurate and efficient representations of audio content, and experimental results show significant improvements in accuracy over competing content-based recommendation techniques.


title: A taxonomy and survey of grid resource management systems for distributed computing

abstract: The resource management system is the central component of distributed network computing systems. There have been many projects focused on network computing that have designed and implemented resource management systems with a variety of architectures and services. In this paper, an abstract model and a comprehensive taxonomy for describing resource management architectures is developed. The taxonomy is used to identify approaches followed in the implementation of existing resource management systems for very large-scale network computing systems known as Grids. The taxonomy and the survey results are used to identify architectural approaches and issues that have not been fully explored in the research. Copyright © 2001 John Wiley & Sons, Ltd.


Asymmetric multiprocessing for simultaneous multithreading processors


title: Antagonism also Flows through Retweets: The Impact of Out-of-Context Quotes in Opinion Polarization Analysis

abstract: In this paper, we study the implications of the commonplace assumption that most social media studies make with respect to the nature of message shares (such as retweets) as a predominantly positive interaction. By analyzing two large longitudinal Brazilian Twitter datasets containing 5 years of conversations on two polarizing topics - Politics and Sports - we empirically demonstrate that groups holding antagonistic views can actually retweet each other more often than they retweet other groups. We show that assuming retweets as endorsement interactions can lead to misleading conclusions with respect to the level of antagonism among social communities, and that this apparent paradox is explained in part by the use of retweets to quote the original content creator out of the message's original temporal context, for humor and criticism purposes. As a consequence, messages diffused on online media can have their polarity reversed over time, what poses challenges for social and computer scientists aiming to classify and track opinion groups on online media. On the other hand, we found that the time users take to retweet a message after it has been originally posted can be a useful signal to infer antagonism in social platforms, and that surges of out-of-context retweets correlate with sentiment drifts triggered by real-world events. We also discuss how such evidences can be embedded in sentiment analysis models.


title: Phish Phinder: A Game Design Approach to Enhance User Confidence in Mitigating Phishing Attacks

abstract: Phishing is an especially challenging cyber security threat as it does not attack computer systems, but targets the user who works on that system by relying on the vulnerability of their decision-making ability. Phishing attacks can be used to gather sensitive information from victims and can have devastating impact if they are successful in deceiving the user. Several anti-phishing tools have been designed and implemented but they have been unable to solve the problem adequately. This failure is often due to security experts overlooking the human element and ignoring their fallibility in making trust decisions online. In this paper, we present Phish Phinder, a serious game designed to enhance the user's confidence in mitigating phishing attacks by providing them with both conceptual and procedural knowledge about phishing. The user is trained through a series of gamified challenges, designed to educate them about important phishing related concepts, through an interactive user interface. Key elements of the game interface were identified through an empirical study with the aim of enhancing user interaction with the game. We also adopted several persuasive design principles while designing Phish Phinder to enhance phishing avoidance behaviour among users.


title: High performance imaging using large camera arrays

abstract: The advent of inexpensive digital image sensors and the ability to create photographs that combine information from a number of sensed images are changing the way we think about photography. In this paper, we describe a unique array of 100 custom video cameras that we have built, and we summarize our experiences using this array in a range of imaging applications. Our goal was to explore the capabilities of a system that would be inexpensive to produce in the future. With this in mind, we used simple cameras, lenses, and mountings, and we assumed that processing large numbers of images would eventually be easy and cheap. The applications we have explored include approximating a conventional single center of projection video camera with high performance along one or more axes, such as resolution, dynamic range, frame rate, and/or large aperture, and using multiple cameras to approximate a video camera with a large synthetic aperture. This permits us to capture a video light field, to which we can apply spatiotemporal view interpolation algorithms in order to digitally simulate time dilation and camera motion. It also permits us to create video sequences using custom non-uniform synthetic apertures.


title: Applied Sociology and Prisoner Reentry a Primer for More Successful Reintegration

abstract: Prisoner reentry has assumed increased attention recently as hundreds of thousands of former inmates return to their communities through mandatory release, parole, or placement on probation: Most of the large influx of offenders is due to the enormous financial commitment of incarceration exacerbated by the strain of diminishing state revenues and decades of punitive sentencing policy. This potential mass transfer of people from institutional settings requires a concerted effort from those involved in applied sociology. While psychology has established a strong foothold in the field of correctional rehabilitation, sociology has retreated since the early 1970s following the publication of Robert Martinson's research on rehabilitation programs in prisons. Sociology began as an applied science of society. More than ever before, sociological practice must embrace and re-engage communities in facilitating reentry and reducing recidivism among those released or under community supervision. This article offers d...


title: Interactive multiple anisotropic scattering in clouds

abstract: We propose an algorithm for the real time realistic simulation of multiple anisotropic scattering of light in a volume. Contrary to previous real-time methods we account for all kinds of light paths through the medium and preserve their anisotropic behavior. Our approach consists of estimating the energy transport from the illuminated cloud surface to the rendered cloud pixel for each separate order of multiple scattering. We represent the distribution of light paths reaching a given viewed cloud pixel with the mean and standard deviation of their entry points on the lit surface, which we call the collector area. At rendering time for each pixel we determine the collector area on the lit cloud surface for different sets of scattering orders, then we infer the associated light transport. The fast computation of the collector area and light transport is possible thanks to a preliminary analysis of multiple scattering in plane-parallel slabs and does not require slicing or marching through the volume. Rendering is done efficiently in a shader on the GPU, relying on a cloud surface mesh augmented with a Hypertexture to enrich the shape and silhouette. We demonstrate our model with the interactive rendering of detailed animated cumulus and cloudy sky at 2--10 frames per second.


title: Partition-edit-count: Naïve extensional reasoning in conditional probability judgment

abstract: The authors provide evidence that people typically evaluate conditional probabilities by subjectively partitioning the sample space into n interchangeable events, editing out events that can be eliminated on the basis of conditioning information, counting remaining events, then reporting probabilities as a ratio of the number of focal to total events. Participants' responses to conditional probability problems were influenced by irrelevant information (Study 1), small variations in problem wording (Study 2), and grouping of events (Study 3), as predicted by the partition-edit-count model. Informal protocol analysis also supports the authors' interpretation. A 4th study extends this account from situations where events are treated as interchangeable (chance and ignorance) to situations where participants have information they can use to distinguish among events (uncertainty).


Carbon Nanotubes Field Effect Transistor : A Review


title: A Cognitive-Radio-Based Method for Improving Availability in Body Sensor Networks

abstract: One of the main threats to body sensor networks (BSNs) is Denial of Service attacks that disrupt communications used to transmit patients' health data. The application of cognitive radio (CR) technology into BSNs can mitigate such a threat and improve network availability, by allowing network nodes to cooperatively agree on a new radio channel whenever the quality of the channel being in use decreases. However, the cooperative spectrum sensing mechanisms used by CRs should also be protected to prevent an attacker from predicting the new channel of operation. In this work, we present a lightweight and robust mechanism that appropriately secures the channel selection process while minimizing resources consumption, thus being suited for resource constrained devices such as body sensor nodes. The proposed method has been analyzed in terms of energy consumption and transmission overhead and it has been shown that it outperforms existing cryptographic approaches.


title: Universal trench edge termination design

abstract: The minimum edge termination length for various trench structure is theoretically determined to obtain universal curve for efficient design under considering trade-off relation with trench fabrication difficulty, cost and limitation specially for thin wafer IGBTs and diodes. This study unveils for the first time clear relationship between trench depth and termination length. We present practical design for each trench depth based on design curve considering trench filler material.


title: A low-power wide-linear-range transconductance amplifier

abstract: The linear range of approximately ±75mV of traditional subthreshold transconductance amplifiers istoo small for certain applications—for example, for filtersin electronic cochleas, where it is desirable to handle loudsounds without distortion and to have a large dynamic range.We describe a transconductance amplifier designed for low-power(< 1 µW) subthreshold operation with a wideinput linear range. We obtain wide linear range by widening thetanh, or decreasing the ratio of transconductance to bias current,by a combination of four techniques. First, the well terminalsof the input differential-pair transistors are used as the amplifierinputs. Then, feedback techniques known as source degeneration(a common technique) and gate degeneration (a new technique)provide further improvements. Finally, a novel bump-linearizationtechnique extends the linear range even further. We present signal-flowdiagrams for speedy analysis of such circuit techniques. Ourtransconductance reduction is achieved in a compact 13-transistorcircuit without degrading other characteristics such as dc-inputoperating range. In a standard 2 µm process,we were able to obtain a linear range of ±1.7V.Using our wide-linear-range amplifier and a capacitor, we constructa follower–integrator with an experimental dynamic rangeof 65 dB. We show that, if the amplifier's noise is predominantlythermal, then an increase in its linear range increases thefollower–integrator'sdynamic range. If the amplifier's noise is predominantly 1/f,then an increase in its linear range has no effect on thefollower–integrator'sdynamic range. To preserve follower–integrator bandwidth,power consumption increases proportionately with an increasein the amplifier's linear range. We also present data for changesin the subthreshold exponential parameter with current leveland with gate-to-bulk voltage that should be of interest to alllow-power designers. We have described the use of our amplifierin a silicon cochlea [1, 2].


title: CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach

abstract: With the fast-growing demand of location-based services in indoor environments, indoor positioning based on fingerprinting has attracted significant interest due to its high accuracy. In this paper, we present a novel deep-learning-based indoor fingerprinting system using channel state information (CSI), which is termed DeepFi. Based on three hypotheses on CSI, the DeepFi system architecture includes an offline training phase and an online localization phase. In the offline training phase, deep learning is utilized to train all the weights of a deep network as fingerprints. Moreover, a greedy learning algorithm is used to train the weights layer by layer to reduce complexity. In the online localization phase, we use a probabilistic method based on the radial basis function to obtain the estimated location. Experimental results are presented to confirm that DeepFi can effectively reduce location error, compared with three existing methods in two representative indoor environments.


title: Multi-Objective Optimisation of PID and H ∞ Fin/Rudder Roll Controllers

abstract: Active control of ship roll is necessary for operability of an important number of ships. As such it has been strongly developed in the past twenty years. A way of improving performances is to use and control rudders as well as fins. A MIMO control law synthesis methodology is presented in this paper, which is based on multi-objective optimisation. The optimisation is realised with a genetic algorithm. It is applied to a PID and a H∞ controller, both MIMO. Simulation results with various speeds are given.


Objectification of the content of consciousness


A Survey Paper on Security in Cloud Computing: A Bibliographic Analysis


title: Reinforced Concrete Beams with Small Opening in Bending and Torsion

abstract: A method of analysis is presented for predicting the strength of rein forced concrete beams that are subjected to combined bending and torsion and contain a small transverse opening. The analysis is based on the familiar skew-bending model. Strength equations are derived for failure Modes I, 2, and 3 by assuming a suitable pattern of rein forcement around the opening. Test results of 10 beams containing a circular opening are also presented and discussed. The theoretical predictions are in good agreement with the test results.


title: Optimized reversible BCD adder using new reversible logic gates

abstract: Reversible logic has received great attention in the recent years due to their ability to reduce the power dissipation which is the main requirement in low power digital design. It has wide applications advanced computing, low power CMOS design, Optical information processing, DNA computing, bio information, quantum computation and nanotechnology. This paper presents an optimized reversible BCD adder using a new reversible gate. A comparative result is presented which shows that the proposed design is more optimized in terms of number of gates, number of garbage outputs and quantum cost than the existing designs.


title: AIBO's first words. The social learning of language and meaning

abstract: This paper explores the hypothesis that language communication in its very first stage is bootstrapped in a social learning process under the strong influence of culture. A concrete framework for social learning has been developed based on the notion of a language game. Autonomous robots have been programmed to behave according to this framework. We show experiments that demonstrate why there has to be a causal role of language on category acquisition; partly by showing that it leads effectively to the bootstrapping of communication and partly by showing that other forms of learning do not generate categories usable in communication or make information assumptions which cannot be satisfied.


title: A Review of Network Traffic Analysis and Prediction Techniques

abstract: Analysis and prediction of network traffic has applications in wide comprehensive set of areas and has newly attracted significant number of studies. Different kinds of experiments are conducted and summarized to identify various problems in existing computer network applications. Network traffic analysis and prediction is a proactive approach to ensure secure, reliable and qualitative network communication. Various techniques are proposed and experimented for analyzing network traffic including neural network based techniques to data mining techniques. Similarly, various Linear and non-linear models are proposed for network traffic prediction. Several interesting combinations of network analysis and prediction techniques are implemented to attain efficient and effective results. ::: This paper presents a survey on various such network analysis and traffic prediction techniques. The uniqueness and rules of previous studies are investigated. Moreover, various accomplished areas of analysis and prediction of network traffic have been summed.


title: CryptoRec: Privacy-preserving Recommendation as a Service

abstract: Recommender systems rely on large datasets of historical data and entail serious privacy risks. A server offering Recommendation as a Service to a client might leak more information than necessary regarding its recommendation model and dataset. At the same time, the disclosure of the client's preferences to the server is also a matter of concern. Devising privacy-preserving protocols using general cryptographic primitives (e.g., secure multi-party computation or homomorphic encryption), is a typical approach to overcome privacy concerns, but in conjunction with state-of-the-art recommender systems often yields far-from-practical solutions. ::: In this paper, we tackle this problem from the direction of constructing crypto-friendly machine learning algorithms. In particular, we propose CryptoRec, a secure two-party computation protocol for Recommendation as a Service, which encompasses a novel recommender system. This model possesses two interesting properties: (1) It models user-item interactions in an item-only latent feature space in which personalized user representations are automatically captured by an aggregation of pre-learned item features. This means that a server with a pre-trained model can provide recommendations for a client whose data is not in its training set. Nevertheless, re-training the model with the client's data still improves accuracy. (2) It only uses addition and multiplication operations, making the model straightforwardly compatible with homomorphic encryption schemes. ::: We demonstrate the efficiency and accuracy of CryptoRec on three real-world datasets. CryptoRec allows a server with thousands of items to privately answer a prediction query within a few seconds on a single PC, while its prediction accuracy is still competitive with state-of-the-art recommender systems computing over clear data.


title: A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI

abstract: Automated cardiac segmentation from magnetic resonance imaging datasets is an essential step in the timely diagnosis and management of cardiac pathologies. We propose to tackle the problem of automated left and right ventricle segmentation through the application of a deep fully convolutional neural network architecture. Our model is efficiently trained end-to-end in a single learning stage from whole-image inputs and ground truths to make inference at every pixel. To our knowledge, this is the first application of a fully convolutional neural network architecture for pixel-wise labeling in cardiac magnetic resonance imaging. Numerical experiments demonstrate that our model is robust to outperform previous fully automated methods across multiple evaluation measures on a range of cardiac datasets. Moreover, our model is fast and can leverage commodity compute resources such as the graphics processing unit to enable state-of-the-art cardiac segmentation at massive scales. The models and code are available at this https URL


title: Improving performance, perceived usability, and aesthetics with culturally adaptive user interfaces

abstract: When we investigate the usability and aesthetics of user interfaces, we rarely take into account that what users perceive as beautiful and usable strongly depends on their cultural background. In this paper, we argue that it is not feasible to design one interface that appeals to all users of an increasingly global audience. Instead, we propose to design culturally adaptive systems, which automatically generate personalized interfaces that correspond to cultural preferences. In an evaluation of one such system, we demonstrate that a majority of international participants preferred their personalized versions over a nonadapted interface of the same Website. Results show that users were 22p faster using the culturally adapted interface, needed fewer clicks, and made fewer errors, in line with subjective results demonstrating that they found the adapted version significantly easier to use. Our findings show that interfaces that adapt to cultural preferences can immensely increase the user experience.


APPLICATION OF AUGMENTED REALITY GIS IN ARCHITECTURE


title: An Attribute-Based Access Control Model for Secure Big Data Processing in Hadoop Ecosystem

abstract: Apache Hadoop is a predominant software framework for distributed compute and storage with capability to handle huge amounts of data, usually referred to as Big Data. This data collected from different enterprises and government agencies often includes private and sensitive information, which needs to be secured from unauthorized access. This paper proposes extensions to the current authorization capabilities offered by Hadoop core and other ecosystem projects, specifically Apache Ranger and Apache Sentry. We present a fine-grained attribute-based access control model, referred as HeABAC, catering to the security and privacy needs of multi-tenant Hadoop ecosystem. The paper reviews the current multi-layered access control model used primarily in Hadoop core (2.x), Apache Ranger (version 0.6) and Sentry (version 1.7.0), as well as a previously proposed RBAC extension (OT-RBAC). It then presents a formal attribute-based access control model for Hadoop ecosystem, including the novel concept of cross Hadoop services trust. It further highlights different trust scenarios, presents an implementation approach for HeABAC using Apache Ranger and, discusses the administration requirements of HeABAC operational model. Some comprehensive, real-world use cases are also discussed to reflect the application and enforcement of the proposed HeABAC model in Hadoop ecosystem.


title: Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison

abstract: Brain computer interfaces (BCIs) have been attracting a great interest in recent years. The common spatial patterns (CSP) technique is a well-established approach to the spatial filtering of the electroencephalogram (EEG) data in BCI applications. Even though CSP was originally proposed from a heuristic viewpoint, it can be also built on very strong foundations using information theory. This paper reviews the relationship between CSP and several information-theoretic approaches, including the Kullback–Leibler divergence, the Beta divergence and the Alpha-Beta log-det (AB-LD)divergence. We also revise other approaches based on the idea of selecting those features that are maximally informative about the class labels. The performance of all the methods will be also compared via experiments.


title: WarpNet: Weakly Supervised Matching for Single-View Reconstruction

abstract: We present an approach to matching images of objects in fine-grained datasets without using part annotations, with an application to the challenging problem of weakly supervised single-view reconstruction. This is in contrast to prior works that require part annotations, since matching objects across class and pose variations is challenging with appearance features alone. We overcome this challenge through a novel deep learning architecture, WarpNet, that aligns an object in one image with a different object in another. We exploit the structure of the fine-grained dataset to create artificial data for training this network in an unsupervised-discriminative learning approach. The output of the network acts as a spatial prior that allows generalization at test time to match real images across variations in appearance, viewpoint and articulation. On the CUB-200-2011 dataset of bird categories, we improve the AP over an appearance-only network by 13.6%. We further demonstrate that our WarpNet matches, together with the structure of fine-grained datasets, allow single-view reconstructions with quality comparable to using annotated point correspondences.


title: Supply Chain Simulation Methods

abstract: One of the most costly and difficult parts of a business to manage is the supply chain. Effectively managing a complex, global supply chain has a positive impact on a company's financial performance. By using simulation, managers are able to create a model of their supply chain systems and test various levels of input that can emulate real-life inconsistencies. This paper mentions different types of simulation for supply chain management and describes main characteristics of them. Each simulation type should be applied, depends on the type of managerial question to be answered by the model. This paper explain how different types can answer different questions in SCM or may have better results in some special conditions.


title: Bio-inspired Tensegrity Soft Modular Robots

abstract: In this paper, we introduce a design principle to develop novel soft modular robots based on tensegrity structures and inspired by the cytoskeleton of living cells. We describe a novel strategy to realize tensegrity structures using planar manufacturing techniques, such as 3D printing. We use this strategy to develop icosahedron tensegrity structures with programmable variable stiffness that can deform in a three-dimensional space. We also describe a tendon-driven contraction mechanism to actively control the deformation of the tensegrity mod-ules. Finally, we validate the approach in a modular locomotory worm as a proof of concept.


Design and Simulation of Low Noise Amplifier


title: High throughput heavy hitter aggregation for modern SIMD processors

abstract: Heavy hitters are data items that occur at high frequency in a data set. They are among the most important items for an organization to summarize and understand during analytical processing. In data sets with sufficient skew, the number of heavy hitters can be relatively small. We take advantage of this small footprint to compute aggregate functions for the heavy hitters in fast cache memory in a single pass. We design cache-resident, shared-nothing structures that hold only the most frequent elements. Our algorithm works in three phases. It first samples and picks heavy hitter candidates. It then builds a hash table and computes the exact aggregates of these elements. Finally, a validation step identifies the true heavy hitters from among the candidates. We identify trade-offs between the hash table configuration and performance. Configurations consist of the probing algorithm and the table capacity that determines how many candidates can be aggregated. The probing algorithm can be perfect hashing, cuckoo hashing and bucketized hashing to explore trade-offs between size and speed. We optimize performance by the use of SIMD instructions, utilized in novel ways beyond single vectorized operations, to minimize cache accesses and the instruction footprint.


title: The role of the basal ganglia in habit formation

abstract: Many organisms, especially humans, are characterized by their capacity for intentional, goal-directed actions. However, similar behaviours often proceed automatically, as habitual responses to antecedent stimuli. How are goal-directed actions transformed into habitual responses? Recent work combining modern behavioural assays and neurobiological analysis of the basal ganglia has begun to yield insights into the neural basis of habit formation.


title: Adaptive Load Balancing: A Study in Multi-Agent Learning

abstract: We study the process of multi-agent reinforcement learning in the context of load balancing in a distributed system, without use of either central coordination or explicit communication. We first define a precise framework in which to study adaptive load balancing, important features of which are its stochastic nature and the purely local information available to individual agents. Given this framework, we show illuminating results on the interplay between basic adaptive behavior parameters and their effect on system efficiency. We then investigate the properties of adaptive load balancing in heterogeneous populations, and address the issue of exploration vs. exploitation in that context. Finally, we show that naive use of communication may not improve, and might even harm system efficiency.


title: Short-range FMCW monopulse radar for hand-gesture sensing

abstract: Intelligent driver assistance systems have become important in the automotive industry. One key element of such systems is a smart user interface that tracks and recognizes drivers' hand gestures. Hand gesture sensing using traditional computer vision techniques is challenging because of wide variations in lighting conditions, e.g. inside a car. A short-range radar device can provide additional information, including the location and instantaneous radial velocity of moving objects. We describe a novel end-to-end (hardware, interface, and software) short-range FMCW radar-based system designed to effectively sense dynamic hand gestures. We provide an effective method for selecting the parameters of the FMCW waveform and for jointly calibrating the radar system with a depth sensor. Finally, we demonstrate that our system guarantees reliable and robust performance.


title: Neurocomputational approaches to modelling multisensory integration in the brain: A review

abstract: The Brain's ability to integrate information from different modalities (multisensory integration) is fundamental for accurate sensory experience and efficient interaction with the environment: it enhances detection of external stimuli, disambiguates conflict situations, speeds up responsiveness, facilitates processes of memory retrieval and object recognition. Multisensory integration operates at several brain levels: in subcortical structures (especially the Superior Colliculus), in higher-level associative cortices (e.g.,?posterior parietal regions), and even in early cortical areas (such as primary cortices) traditionally considered to be purely unisensory. Because of complex non-linear mechanisms of brain integrative phenomena, a key tool for their understanding is represented by neurocomputational models. This review examines different modelling principles and architectures, distinguishing the models on the basis of their aims: (i) Bayesian models based on probabilities and realizing optimal estimator of external cues; (ii) biologically inspired models of multisensory integration in the Superior Colliculus and in the Cortex, both at level of single neuron and network of neurons, with emphasis on physiological mechanisms and architectural schemes; among the latter, some models exhibit synaptic plasticity and reproduce development of integrative capabilities via Hebbian-learning rules or self-organizing maps; (iii) models of semantic memory that implement object meaning as a fusion between sensory-motor features (embodied cognition). This overview paves the way to future challenges, such as reconciling neurophysiological and Bayesian models into a unifying theory, and stimulates upcoming research in both theoretical and applicative domains.