title: Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

abstract: This paper proposed a Soft Filter Pruning (SFP) method to accelerate the inference procedure of deep Convolutional Neural Networks (CNNs). Specifically, the proposed SFP enables the pruned filters to be updated when training the model after pruning. SFP has two advantages over previous works: (1) Larger model capacity. Updating previously pruned filters provides our approach with larger optimization space than fixing the filters to zero. Therefore, the network trained by our method has a larger model capacity to learn from the training data. (2) Less dependence on the pre-trained model. Large capacity enables SFP to train from scratch and prune the model simultaneously. In contrast, previous filter pruning methods should be conducted on the basis of the pre-trained model to guarantee their performance. Empirically, SFP from scratch outperforms the previous filter pruning methods. Moreover, our approach has been demonstrated effective for many advanced CNN architectures. Notably, on ILSCRC-2012, SFP reduces more than 42% FLOPs on ResNet-101 with even 0.2% top-5 accuracy improvement, which has advanced the state-of-the-art. Code is publicly available on GitHub: this https URL


title: Anomaly Machine Component Detection by Deep Generative Model with Unregularized Score

abstract: One of the most common needs in manufacturing plants is rejecting products not coincident with the standards as anomalies. Accurate and automatic anomaly detection improves product reliability and reduces inspection cost. Probabilistic models have been employed to detect test samples with lower likelihoods as anomalies in unsupervised manner. Recently, a probabilistic model called deep generative model (DGM) has been proposed for end-to-end modeling of natural images and already achieved a certain success. However, anomaly detection of machine components with complicated structures is still challenging because they produce a wide variety of normal image patches with low likelihoods. For overcoming this difficulty, we propose unregularized score for the DGM. As its name implies, the unregularized score is the anomaly score of the DGM without the regularization terms. The unregularized score is robust to the inherent complexity of a sample and has a smaller risk of rejecting a sample appearing less frequently but being coincident with the standards.


title: Airline Passenger Name Record Generation using Generative Adversarial Networks

abstract: Passenger Name Records (PNRs) are at the heart of the travel industry. Created when an itinerary is booked, they contain travel and passenger information. It is usual for airlines and other actors in the industry to inter-exchange and access each other's PNR, creating the challenge of using them without infringing data ownership laws. To address this difficulty, we propose a method to generate realistic synthetic PNRs using Generative Adversarial Networks (GANs). Unlike other GAN applications, PNRs consist of categorical and numerical features with missing/NaN values, which makes the use of GANs challenging. We propose a solution based on Cram\'{e}r GANs, categorical feature embedding and a Cross-Net architecture. The method was tested on a real PNR dataset, and evaluated in terms of distribution matching, memorization, and performance of predictive models for two real business problems: client segmentation and passenger nationality prediction. Results show that the generated data matches well with the real PNRs without memorizing them, and that it can be used to train models for real business applications.


title: LineNet: a Zoomable CNN for Crowdsourced High Definition Maps Modeling in Urban Environments

abstract: High Definition (HD) maps play an important role in modern traffic scenes. However, the development of HD maps coverage grows slowly because of the cost limitation. To efficiently model HD maps, we proposed a convolutional neural network with a novel prediction layer and a zoom module, called LineNet. It is designed for state-of-the-art lane detection in an unordered crowdsourced image dataset. And we introduced TTLane, a dataset for efficient lane detection in urban road modeling applications. Combining LineNet and TTLane, we proposed a pipeline to model HD maps with crowdsourced data for the first time. And the maps can be constructed precisely even with inaccurate crowdsourced data.


title: CBAM: Convolutional Block Attention Module

abstract: We propose Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks. Given an intermediate feature map, our module sequentially infers attention maps along two separate dimensions, channel and spatial, then the attention maps are multiplied to the input feature map for adaptive feature refinement. Because CBAM is a lightweight and general module, it can be integrated into any CNN architectures seamlessly with negligible overheads and is end-to-end trainable along with base CNNs. We validate our CBAM through extensive experiments on ImageNet-1K, MS~COCO detection, and VOC~2007 detection datasets. Our experiments show consistent improvements in classification and detection performances with various models, demonstrating the wide applicability of CBAM. The code and models will be publicly available.


title: Implications of the fit between organizational structure and ERP: A structural contingency theory perspective

abstract: Despite the tremendous popularity and great potential, the field of Enterprise Resource Planning (ERP) adoption and implementation is littered with remarkable failures. Though many contributing factors have been cited in the literature, we argue that the integrated nature of ERP systems, which generally requires an organization to adopt standardized business processes reflected in the design of the software, is a key factor contributing to these failures. We submit that the integration and standardization imposed by most ERP systems may not be suitable for all types of organizations and thus the ''fit'' between the characteristics of the adopting organization and the standardized business process designs embedded in the adopted ERP system affects the likelihood of implementation success or failure. In this paper, we use the structural contingency theory to identify a set of dimensions of organizational structure and ERP system characteristics that can be used to gauge the degree of fit, thus providing some insights into successful ERP implementations. Propositions are developed based on analyses regarding the success of ERP implementations in different types of organizations. These propositions also provide directions for future research that might lead to prescriptive guidelines for managers of organizations contemplating implementing ERP systems.


title: Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychol Methods

abstract: Abstract In meta-analysis, the usual way of assessing whether a set of single studies is homogeneous is by means of the Q test. However, the Q test only informs meta-analysts about the presence versus the absence of heterogeneity, but it does not report on the extent of such heterogeneity. Recently, the I(2) index has been proposed to quantify the degree of heterogeneity in a meta-analysis. In this article, the performances of the Q test and the confidence interval around the I(2) index are compared by means of a Monte Carlo simulation. The results show the utility of the I(2) index as a complement to the Q test, although it has the same problems of power with a small number of studies.


title: GazeGAN - Unpaired Adversarial Image Generation for Gaze Estimation

abstract: Recent research has demonstrated the ability to estimate gaze on mobile devices by performing inference on the image from the phone's front-facing camera, and without requiring specialized hardware. While this offers wide potential applications such as in human-computer interaction, medical diagnosis and accessibility (e.g., hands free gaze as input for patients with motor disorders), current methods are limited as they rely on collecting data from real users, which is a tedious and expensive process that is hard to scale across devices. There have been some attempts to synthesize eye region data using 3D models that can simulate various head poses and camera settings, however these lack in realism. ::: In this paper, we improve upon a recently suggested method, and propose a generative adversarial framework to generate a large dataset of high resolution colorful images with high diversity (e.g., in subjects, head pose, camera settings) and realism, while simultaneously preserving the accuracy of gaze labels. The proposed approach operates on extended regions of the eye, and even completes missing parts of the image. Using this rich synthesized dataset, and without using any additional training data from real users, we demonstrate improvements over state-of-the-art for estimating 2D gaze position on mobile devices. We further demonstrate cross-device generalization of model performance, as well as improved robustness to diverse head pose, blur and distance.


title: Evidence-Based Medicine: A Graded Approach to Lower Lid Blepharoplasty

abstract: LEARNING OBJECTIVES After studying this article, the participant should be able to: 1. Define the anatomy of the lower eyelid tarsoligamentous framework and the related periorbital retaining ligaments, and cite their surgical relevance. 2. Perform a systematic functional and aesthetic evaluation of the lower eyelid focusing on the lid-cheek junction, and clinical tests that predict the need for lateral canthal tightening. 3. Enumerate the different approaches to lower eyelid rejuvenation and discuss their merits/limitations. 4. Describe surgical strategies to blend the lid-cheek junction and tighten the lateral canthal retinaculum. SUMMARY Modern lower lid blepharoplasty requires a thorough understanding of periorbital anatomy, age-related changes of the lid-cheek junction, and the variables controlling lower lid tone and position. The surgical strategies are best used in a graded fashion. The patient with isolated lower lid bags may be treated by transconjunctival fat resection alone. Additional mild skin laxity can be improved with skin pinch or skin-only undermining. Skin resurfacing using chemical peeling or laser can further address fine lines. In these patients with an abnormality of the lid-cheek junction, release of the medial orbicularis oculi muscle and variable amounts of the orbicularis retaining ligament is essential. This is combined with orbital fat resection or repositioning through a transconjunctival or transcutaneous skin-muscle flap. The transcutaneous approach most often necessitates lateral canthal tightening to optimize lid margin control. Generally, the degree of laxity dictates whether a canthopexy or a canthoplasty is most appropriate. Lateral canthal procedures can be applied to patients displaying clinical signs predictive of lid malposition and to those presenting with varying degrees of established lid descent.


title: Partial Person Re-identification with Alignment and Hallucination

abstract: Partial person re-identification involves matching pedestrian views where only a part of a body is visible in corresponding images. This reflects practical CCTV surveillance scenario, where full person views are often unavailable. Missing body parts make the comparison very challenging due to significant misalignment and varying scale of the views. We propose Partial Matching Net (PMN) that detects body joints, aligns partial views and hallucinates the missing parts based on the information present in the frame and a learned model of a person. The aligned and reconstructed views are then combined into a joint representation and used for matching images. We evaluate our approach and compare to other methods on three different datasets, demonstrating significant improvements.


Facial Expression Recognition Using Enhanced Deep 3 D Convolutional Neural Networks


title: Investigating and improving the models of programming concepts held by novice programmers

abstract: The teaching of introductory computer programming seems far from successful, with many first-year students performing more poorly than expected. One possible reason for this is that novices hold ‘non-viable’ mental models (internal explanations of how something works) of key programming concepts which then cause misconceptions and difficulties. An initial study investigated the apparent viability of novices' models of fundamental programming concepts, focusing on value and reference assignment. This revealed that many students appeared to hold ‘non-viable’ mental models of these key concepts and that those students who appeared to hold viable mental models performed significantly better in programming tasks than those with non-viable models. To address this, a teaching model integrating cognitive conflict and program visualisation is proposed. A series of studies found that this teaching model is potentially effective in enhancing engagement with learning materials and may therefore help novice programmer...


title: Modeling and Management of Firewall Policies

abstract: Firewalls are core elements in network security. However, managing firewall rules, especially for enterprise networks, has become complex and error-prone. Firewall filtering rules have to be carefully written and organized in order to correctly implement the security policy. In addition, inserting or modifying a filtering rule requires thorough analysis of the relationship between this rule and other rules in order to determine the proper order of this rule and commit the updates. In this paper we present a set of techniques and algorithms that provide automatic discovery of firewall policy anomalies to reveal rule conflicts and potential problems in legacy firewalls, and anomaly-free policy editing for rule insertion, removal, and modification. This is implemented in a user-friendly tool called ?Firewall Policy Advisor.? The Firewall Policy Advisor significantly simplifies the management of any generic firewall policy written as filtering rules, while minimizing network vulnerability due to firewall rule misconfiguration.


title: A High-Performance Photovoltaic Module-Integrated Converter (MIC) Based on Cascaded Quasi-Z-Source Inverters (qZSI) Using eGaN FETs

abstract: This paper presents a single-phase grid-connected photovoltaic (PV) module-integrated converter (MIC) based on cascaded quasi-Z-source inverters (qZSI). In this system, each qZSI module serves as an MIC and is connected to one PV panel. Due to the cascaded structure and qZSI topology, the proposed MIC features low-voltage gain requirement, single-stage energy conversion, enhanced reliability, and good output power quality. Furthermore, the enhancement mode gallium nitride field-effect transistors (eGaN FETs) are employed in the qZSI module for efficiency improvement at higher switching frequency. It is found that the qZSI is very suitable for the application of eGaN FETs because of the shoot-through capability. Optimized module design is developed based on the derived qZSI ac equivalent model and power loss analytical model to achieve high efficiency and high power density. A design example of qZSI module is presented for a 250-W PV panel with 25-50-V output voltage. The simulation and experimental results prove the validity of the analytical models. The final module prototype design achieves up to 98.06% efficiency with 100-kHz switching frequency.


title: Interpretability of deep learning models: A survey of results

abstract: Deep neural networks have achieved near-human accuracy levels in various types of classification and prediction tasks including images, text, speech, and video data. However, the networks continue to be treated mostly as black-box function approximators, mapping a given input to a classification output. The next step in this human-machine evolutionary process — incorporating these networks into mission critical processes such as medical diagnosis, planning and control — requires a level of trust association with the machine output. Typically, statistical metrics are used to quantify the uncertainty of an output. However, the notion of trust also depends on the visibility that a human has into the working of the machine. In other words, the neural network should provide human-understandable justifications for its output leading to insights about the inner workings. We call such models as interpretable deep networks. Interpretability is not a monolithic notion. In fact, the subjectivity of an interpretation, due to different levels of human understanding, implies that there must be a multitude of dimensions that together constitute interpretability. In addition, the interpretation itself can be provided either in terms of the low-level network parameters, or in terms of input features used by the model. In this paper, we outline some of the dimensions that are useful for model interpretability, and categorize prior work along those dimensions. In the process, we perform a gap analysis of what needs to be done to improve model interpretability.


title: Linear and curvilinear correlations of brain gray matter volume and density with age using voxel-based morphometry with the Akaike information criterion in 291 healthy children

abstract: We examined linear and curvilinear correlations of gray matter volume and density in cortical and subcortical gray matter with age using magnetic resonance images (MRI) in a large number of healthy children. We applied voxel-based morphometry (VBM) and region-of-interest (ROI) analyses with the Akaike information criterion (AIC), which was used to determine the best-fit model by selecting which predictor terms should be included. We collected data on brain structural MRI in 291 healthy children aged 5–18 years. Structural MRI data were segmented and normalized using a custom template by applying the diffeomorphic anatomical registration using exponentiated lie algebra (DARTEL) procedure. Next, we analyzed the correlations of gray matter volume and density with age in VBM with AIC by estimating linear, quadratic, and cubic polynomial functions. Several regions such as the prefrontal cortex, the precentral gyrus, and cerebellum showed significant linear or curvilinear correlations between gray matter volume and age on an increasing trajectory, and between gray matter density and age on a decreasing trajectory in VBM and ROI analyses with AIC. Because the trajectory of gray matter volume and density with age suggests the progress of brain maturation, our results may contribute to clarifying brain maturation in healthy children from the viewpoint of brain structure. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc.


title: A High Efficiency WCDMA RF Power Amplifier With Adaptive, Dual-Mode Buck-Boost Supply and Bias-Current Control

abstract: Radio-frequency (RF) power amplifiers (PAs) are often the most power-consuming blocks in portable wireless systems, and maintaining high PA efficiency at heavy and light loads is therefore critical for extended battery life. This letter presents how a 0.5-mum CMOS dynamically adaptive, dual-mode buck-boost power supply and bias-current control circuit improves PA efficiency. The proposed system is validated with a 1.96-GHz, 25-dBm SiGe heterojunction bipolar transistor (HBT) wideband code division multiple access RF PA, where its supply voltage and bias current are dynamically adjusted from 0.4 to 4.5 V and 25 to 220mA, respectively, over an output power range of -50 to 25dBm. The prototype system is functional for a 1.8-4.2V input supply range, meeting adjacent- and alternate-channel leakage ratio specifications with less than 10% of error vector magnitude across the entire power range. The average efficiency of the dynamically adaptive system (13.67%) was 7times better than the corresponding fixed-supplied, fixed-biased scheme (1.95%), with the same HBT PA


title: An ethological and emotional basis for human-robot interaction

abstract: Abstract This paper presents the role of ethological and emotional models as the basis for an architecture in support of entertainment robotic systems. Specific examples for Sony’s AIBO are presented as well as extensions related to a new humanoid robot, SDR.


title: 3D shape and texture morphing using 2D projection and reconstruction

abstract: We present an algorithm for morphing shape and view-dependent texture that utilizes an unstructured lumigraph representation to (1) gracefully handle topological changes, (2) create plausible appearance transformations, and (3) provide user control independent from the underlying mesh structure. It works by reducing the otherwise complex problem of a 3D shape and material morph into multiple simpler 2D morphs. The shape morph operates on 2D depth maps and then reconstructs a conventional mesh, making it insensitive to the input geometry quality. Our morphing algorithm robustly handles topology changes and outputs complete meshes and lumigraphs for later use. We show how 2D image morphs can be computed from 3D correspondences, while eliminating ambiguities that might result in the projected 2D morphs. Graphical abstractDisplay Omitted HighlightsWe present an algorithm for morphing shape and view-dependent texture.The 3D shape morph is implicit and therefore immune to topological constraints.Complex problem of 3D shape+material morphing is converted to multiple 2D morphs.Morph stages are unstructured lumigraphs with realistic simulation of appearance.


title: Formal Concept Analysis: Mathematical Foundations

abstract: From the Publisher: ::: This is the first textbook on formal concept analysis. It gives a systematic presentation of the mathematical foundations and their relation to applications in computer science, especially in data analysis and knowledge processing. Above all, it presents graphical methods for representing conceptual systems that have proved themselves in communicating knowledge. Theory and graphical representation are thus closely coupled together. The mathematical foundations are treated thoroughly and illuminated by means of numerous examples.


title: The neuroscience of placebo effects: connecting context, learning and health

abstract: Placebo effects are positive effects on health that arise from the response of the brain to the contextual information that accompanies the delivery of a treatment. In this Review, Wager and Atlas examine the neural mechanisms that underlie such effects, focusing on placebo analgesia.


title: Defending Critical Infrastructure

abstract: We apply new bilevel and trilevel optimization models to make critical infrastructure more resilient against terrorist attacks. Each model features an intelligent attacker (terrorists) and a defender (us), information transparency, and sequential actions by attacker and defender. We illustrate with examples of the US Strategic Petroleum Reserve, the US Border Patrol at Yuma, Arizona, and an electrical transmission system. We conclude by reporting insights gained from the modeling experience and many “red-team” exercises. Each exercise gathers open-source data on a real-world infrastructure system, develops an appropriate bilevel or trilevel model, and uses these to identify vulnerabilities in the system or to plan an optimal defense.


title: The role of physiological arousal in time perception: Psychophysiological evidence from an emotion regulation paradigm

abstract: Time perception, crucial for adaptive behavior, has been shown to be altered by emotion. An arousal-dependent mechanism is proposed to account for such an effect. Yet, physiological measure of arousal related with emotional timing is still lacking. We addressed this question using skin conductance response (SCR) in an emotion regulation paradigm. Nineteen participants estimated durations of neutral and negative sounds by comparing them to a previously memorized duration. Instructions were given to attend either to temporal or to emotional stimulus features. Attending to emotion with negative sounds generated longer subjective duration and greater physiological arousal than attending to time. However, a shared-attention condition showed discrepancy between behavioral and physiological results. Supporting the idea of a link between autonomic arousal and subjective duration, our results however suggest that this relation is not as direct as was expected. Results are discussed within recent model linking time perception, emotion and awareness.


An Implementation of Single-View Metrology


title: Optimizing Traffic Signal Settings in Smart Cities

abstract: Traffic signals play a critical role in smart cities for mitigating traffic congestions and reducing the emission in metropolitan areas. This paper proposes a bi-level optimization framework to settle the optimal traffic signal setting problem. The upper-level problem determines the traffic signal settings to minimize the drivers’ average travel time, while the lower-level problem aims for achieving the network equilibrium using the settings calculated at the upper level. Genetic algorithm is employed with the integration of microscopic-traffic-simulation-based dynamic traffic assignment (DTA) to decouple the complex bi-level problem into tractable single-level problems, which are solved sequentially. Case studies on a synthetic traffic network and a real-world traffic subnetwork are conducted to examine the effectiveness of the proposed model and relevant solution methods. Additional strategies are provided for the extension of the proposed model and the acceleration of solution process in large-area traffic network applications.


title: Precise tweet classification and sentiment analysis

abstract: The rise of social media in couple of years has changed the general perspective of networking, socialization, and personalization. Use of data from social networks for different purposes, such as election prediction, sentimental analysis, marketing, communication, business, and education, is increasing day by day. Precise extraction of valuable information from short text messages posted on social media (Twitter) is a collaborative task. In this paper, we analyze tweets to classify data and sentiments from Twitter more precisely. The information from tweets are extracted using keyword based knowledge extraction. Moreover, the extracted knowledge is further enhanced using domain specific seed based enrichment technique. The proposed methodology facilitates the extraction of keywords, entities, synonyms, and parts of speech from tweets which are then used for tweets classification and sentimental analysis. The proposed system is tested on a collection of 40,000 tweets. The proposed methodology has performed better than the existing system in terms of tweets classification and sentiment analysis. By applying the Knowledge Enhancer and Synonym Binder module on the extracted information we have achieved increase in information gain in a range of 0.1% to 55%. The increase in information gain has enabled our proposed system to better summarize the twitter data for user sentiments regarding a keyword from a particular category.


title: Design and Analysis of a 21–29-GHz Ultra-Wideband Receiver Front-End in 0.18-$\mu$ m CMOS Technology

abstract: This paper reports the design and analysis of 21-29-GHz CMOS low-noise amplifier (LNA), balun and mixer in a standard 0.18-μm CMOS process for ultra-wideband automotive radar systems. To verify the proposed LNA, balun, and mixer architectures, a simplified receiver front-end comprising an LNA, a double-balanced Gilbert-cell-based mixer, and two Marchand baluns was implemented. The wideband Marchand baluns can convert the single RF and local oscillator (LO) signals to nearly perfect differential signals over the 21-29-GHz band. The performance of the mixer is improved with the current-bleeding technique and a parallel resonant inductor at the differential outputs of the RF transconductance stage. Over the 21-29-GHz band, the receiver front-end exhibits excellent noise figure of 4.6±0.5 dB, conversion gain of 23.7±1.4 dB, RF port reflection coefficient lower than -8.8 dB, LO-IF isolation lower than -47 dB, LO-RF isolation lower than -55 dB, and RF-IF isolation lower than -35.5 dB. The circuit occupies a chip area of 1.25×1.06 mm2, including the test pads. The dc power dissipation is only 39.2 mW.


title: CLUSTERING TECHNIQUES AND DISCRETE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR MULTI-DOCUMENT SUMMARIZATION

abstract: Multi-document summarization is a process of automatic creation of a compressed version of a given collection of documents that provides useful information to users. In this article we propose a generic multi-document summarization method based on sentence clustering. We introduce five clustering methods, which optimize various aspects of intra-cluster similarity, inter-cluster dissimilarity and their combinations. To solve the clustering problem a modification of discrete particle swarm optimization algorithm has been proposed. The experimental results on open benchmark data sets from DUC2005 and DUC2007 show that our method significantly outperforms the baseline methods for multi-document summarization.


title: Analysis of stray inductance's influence on SiC MOSFET switching performance

abstract: This paper mainly discusses the influence of stray inductance on the switching performance of SiC MOSFET. For package with three leads, stray inductance at the source electrode is shared by the driving path and power path, which can reduce the switching speed and lead to increased power loss. A double pulse test bench is designed to verify the analysis. Based on the test bench, a direct bond copper substrate is designed to eliminate the shared inductance.


title: Learning-Based Procedural Content Generation

abstract: Procedural content generation (PCG) has recently become one of the hottest topics in computational intelligence and AI game research. While some substantial progress has been made in this area, there are still several challenges ranging from content evaluation to personalized content generation. In this paper, we present a novel PCG framework based on machine learning, named learning-based procedure content generation (LBPCG), to tackle a number of challenging problems. By exploring and exploiting information gained in game development and public player test, our framework can generate robust content adaptable to end-user or target players on-line with minimal interruption to their gameplay experience. As the data-driven methodology is emphasized in our framework, we develop learning-based enabling techniques to implement the various models required in our framework. For a proof of concept, we have developed a prototype based on the classic open source first-person shooter game, Quake. Simulation results suggest that our framework is promising in generating quality content.


title: Spike-timing dependent plasticity in a transistor-selected resistive switching memory

abstract: In a neural network, neuron computation is achieved through the summation of input signals fed by synaptic connections. The synaptic activity (weight) is dictated by the synchronous firing of neurons, inducing potentiation/depression of the synaptic connection. This learning function can be supported by the resistive switching memory (RRAM), which changes its resistance depending on the amplitude, the pulse width and the bias polarity of the applied signal. This work shows a new synapse circuit comprising a MOS transistor as a selector and a RRAM as a variable resistance, displaying spike-timing dependent plasticity (STDP) similar to the one originally experienced in biological neural networks. We demonstrate long-term potentiation and long-term depression by simulations with an analytical model of resistive switching. Finally, the experimental demonstration of the new STDP scheme is presented.


title: The power of prediction with social media

abstract: – Social media provide an impressive amount of data about users and their interactions, thereby offering computer and social scientists, economists, and statisticians – among others – new opportunities for research. Arguably, one of the most interesting lines of work is that of predicting future events and developments from social media data. However, current work is fragmented and lacks of widely accepted evaluation approaches. Moreover, since the first techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains. Therefore, better understanding the predictive power and limitations of social media is of utmost importance. , – Different types of forecasting models and their adaptation to the special circumstances of social media are analyzed and the most representative research conducted up to date is surveyed. Presentations of current research on techniques, methods, and empirical studies aimed at the prediction of future or current events from social media data are provided. , – A taxonomy of prediction models is introduced, along with their relative advantages and the particular scenarios where they have been applied to. The main areas of prediction that have attracted research so far are described, and the main contributions made by the papers in this special issue are summarized. Finally, it is argued that statistical models seem to be the most fruitful approach to apply to make predictions from social media data. , – This special issue raises important questions to be addressed in the field of social media-based prediction and forecasting, fills some gaps in current research, and outlines future lines of work.


title: A one-year longitudinal study of English and Japanese vowel production by Japanese adults and children in an English-speaking setting

abstract: Abstract The effect of age of acquisition on first- and second-language vowel production was investigated. Eight English vowels were produced by Native Japanese (NJ) adults and children as well as by age-matched Native English (NE) adults and children. Productions were recorded shortly after the NJ participants' arrival in the USA and then one year later. In agreement with previous investigations [ Aoyama, Flege, Guion, Akahane-Yamada, & Yamada (2004) . Journal of Phonetics, 32, 233–250], children were able to learn faster, leading to higher accuracy than adults in a year's time. Based on the spectral quality and duration comparisons, NJ adults had more accurate production at Time 1 but showed no improvement over time. The NJ children's productions, however, showed significant differences from the NE children's for English “new” vowels /ɪ/, /ɛ/, /ɑ/, /ʌ/ and /ʊ/ at Time 1 but produced all eight vowels in a native-like manner at Time 2. An examination of NJ speakers' productions of Japanese /i/, /a/, /u/ over time revealed significant changes for the NJ Child Group only. Japanese /i/ and /a/ showed changes in production that can be related to second language (L2) learning. The results suggest that L2 vowel production is affected importantly by age of acquisition and that there is a dynamic interaction, whereby the first and second language vowels affect each other.


title: Integrating extra knowledge into word embedding models for biomedical NLP tasks

abstract: Word embedding in the NLP area has attracted increasing attention in recent years. The continuous bag-of-words model (CBOW) and the continuous Skip-gram model (Skip-gram) have been developed to learn distributed representations of words from a large amount of unlabeled text data. In this paper, we explore the idea of integrating extra knowledge to the CBOW and Skip-gram models and applying the new models to biomedical NLP tasks. The main idea is to construct a weighted graph from knowledge bases (KBs) to represent structured relationships among words/concepts. In particular, we propose a GCBOW model and a GSkip-gram model respectively by integrating such a graph into the original CBOW model and Skip-gram model via graph regularization. Our experiments on four general domain standard datasets show encouraging improvements with the new models. Further evaluations on two biomedical NLP tasks (biomedical similarity/relatedness task and biomedical Information Retrieval (IR) task) show that our methods have better performance than baselines.


title: Thinking in circuits: toward neurobiological explanation in cognitive neuroscience

abstract: Cognitive theory has decomposed human mental abilities into cognitive (sub) systems, and cognitive neuroscience succeeded in disclosing a host of relationships between cognitive systems and specific structures of the human brain. However, an explanation of why specific functions are located in specific brain loci had still been missing, along with a neurobiological model that makes concrete the neuronal circuits that carry thoughts and meaning. Brain theory, in particular the Hebb-inspired neurocybernetic proposals by Braitenberg, now offers an avenue toward explaining brain–mind relationships and to spell out cognition in terms of neuron circuits in a neuromechanistic sense. Central to this endeavor is the theoretical construct of an elementary functional neuronal unit above the level of individual neurons and below that of whole brain areas and systems: the distributed neuronal assembly (DNA) or thought circuit (TC). It is shown that DNA/TC theory of cognition offers an integrated explanatory perspective on brain mechanisms of perception, action, language, attention, memory, decision and conceptual thought. We argue that DNAs carry all of these functions and that their inner structure (e.g., core and halo subcomponents), and their functional activation dynamics (e.g., ignition and reverberation processes) answer crucial localist questions, such as why memory and decisions draw on prefrontal areas although memory formation is normally driven by information in the senses and in the motor system. We suggest that the ability of building DNAs/TCs spread out over different cortical areas is the key mechanism for a range of specifically human sensorimotor, linguistic and conceptual capacities and that the cell assembly mechanism of overlap reduction is crucial for differentiating a vocabulary of actions, symbols and concepts.


title: Big Data Analytics: A Literature Review Paper

abstract: In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain valuable insights from such varied and rapidly changing data, ranging from daily transactions to customer interactions and social network data. Such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper aims to analyze some of the different analytics methods and tools which can be applied to big data, as well as the opportunities provided by the application of big data analytics in various decision domains.


title: Compact, high power capacitor charger

abstract: We are developing compact, high-power chargers for rapid charging of energy storage capacitors. The main application is presently rapid charging of the capacitors inside of compact Marx generators for reprated operation. Compact Marx generators produce output pulses with amplitudes above 300 kV with ns or subns rise-times. A typical application is the generation of high power microwaves. Initially all energy storage capacitors in a Marx generator are charged in parallel. During the so-called erection cycle, the capacitors are connected in series. The charging voltage in the parallel configuration is around 40-50 kV. The input voltage of our charger is in the range of several hundred volts. Rapid charging of the capacitors in the parallel configuration will enable a high pulse repetition-rate of the compact Marx generator. The high power charger uses state-of-the-art IGBTs (isolated gate bipolar transistors) in an H-bridge topology and a compact, high frequency transformer. The IGBTs and the associated controls are packaged for minimum weight and maximum power density. The packaging and device selection makes use of burst mode operation (thermal inertia) of the charger. The present charger is considerably smaller than the one presented in Giesselmann, M et al., (2001).


title: Analysis and design of current starved ring VCO

abstract: This paper focuses on and analysis and design of current starved voltage controlled ring oscillator. The analysis includes effect of delay time, phase noise, layout area, technology etc. on the frequency of oscillation at various power supplies and control voltages. The simulation results shows that the circuit has higher tuning range and low power consumption suitable for various application domains. Added benefit of this VCO is to maintain a constant amplitude level and oscillation. It is also proved here that the frequency of oscillation is inverse of the supply voltage and therefore consuming less power.


title: The Multiscalar Architecture

abstract: The centerpiece of this thesis is a new processing paradigm for exploiting instruction level parallelism. This paradigm, called the multiscalar paradigm, splits the program into many smaller tasks, and exploits fine-grain parallelism by executing multiple, possibly (control and/or data) dependent tasks in parallel using multiple processing elements. Splitting the instruction stream at statically determined boundaries allows the compiler to pass substantial information about the tasks to the hardware. The processing paradigm can be viewed as extensions of the superscalar and multiprocessing paradigms, and shares a number of properties of the sequential processing model and the dataflow processing model. ::: The multiscalar paradigm is easily realizable, and we describe an implementation of the multiscalar paradigm, called the multiscalar processor. The central idea here is to connect multiple sequential processors, in a decoupled and decentralized manner, to achieve overall multiple issue. The multiscalar processor supports speculative execution, allows arbitrary dynamic code motion (facilitated by an efficient hardware memory disambiguation mechanism), exploits communication localities, and does all of these with hardware that is fairly straightforward to build. Other desirable aspects of the implementation include decentralization of the critical resources, absence of wide associative searches, and absence of wide interconnection/data paths. ::: The multiscalar processor design has novel features such as the Address Resolution Buffer (ARB), which is a powerful hardware mechanism for enforcing memory dependencies. The ARB allows arbitrary dynamic motion of memory reference instructions by providing an efficient way of detecting memory hazards, and instructing the multiscalar hardware to restart portions of the dynamic instruction stream when hazards are violated. ::: We also present the results of a detailed simulation study showing the performance improvements offered by the multiscalar processor. These preliminary results are very encouraging. The multiscalar paradigm seems to be an ideal candidate to reach our goal of 10+ instructions per cycle, and has tremendous potential to be the paradigm of choice for a circa 2000 (and beyond) ILP processor.


title: Intelligent Biometric Group Hand Tracking (IBGHT) database for visual hand tracking research and development

abstract: With the increase of innovations in vision-based hand gesture interaction system, new techniques and algorithms are being developed by researchers. However, less attention has been paid on the scope of dismantling hand tracking problems. There is also limited publicly available database developed as benchmark data to standardize the research on hand tracking area. For this purpose, we develop a versatile hand gesture tracking database. This database consists of 60 video sequences containing a total of 15,554 RGB color images. The tracking sequences are captured in different situations ranging from an easy indoor scene to extremely high challenging outdoor scenes. Complete with annotated ground truth data, this database is made available on the web for the sake of assisting other researchers in the related fields to test and evaluate their algorithms based on standard benchmark data.


title: Stack4Things as a fog computing platform for Smart City applications

abstract: Fog computing envisions computation logic to be moved at the edge of the Internet where data needs to be quickly elaborated, decisions made, and actions performed. Delegating to the Cloud the whole burden of applications could not be efficient indeed, for example in case of workload bursts. This is especially true in the context of IoT and Smart City where thousands of smart objects, vehicles, mobiles, people interact to provide innovative services. We thus designed Stack4Things as an OpenStack-based framework spanning the Infrastructure-as-a-Service and Platform-as-a-Service layers. It enables developers and users to manage an IoT infrastructure, remotely controlling nodes as well as virtualizing their functions and creating network overlays among them, implementing a provisioning model for Cyber-Physical Systems. Moreover, it provides mechanisms to scatter the application logic on top of the involved smart objects and to choose with fine granularity which specific tasks to delegate to centralized Cloud infrastructure. In this paper, we show the core Stack4Things mechanisms implementing a Fog computing approach towards a run-time “rewireable” Smart City paradigm. We demonstrate its effectiveness in a smart mobility scenario where vehicles interact with City-level smart objects to provide end users with highly responsive geolocalised services.


title: Predicting and analyzing water quality using Machine Learning: A comprehensive model

abstract: The deteriorating quality of natural water resources like lakes, streams and estuaries, is one of the direst and most worrisome issues faced by humanity. The effects of un-clean water are far-reaching, impacting every aspect of life. Therefore, management of water resources is very crucial in order to optimize the quality of water. The effects of water contamination can be tackled efficiently if data is analyzed and water quality is predicted beforehand. This issue has been addressed in many previous researches, however, more work needs to be done in terms of effectiveness, reliability, accuracy as well as usability of the current water quality management methodologies. The goal of this study is to develop a water quality prediction model with the help of water quality factors using Artificial Neural Network (ANN) and time-series analysis. This research uses the water quality historical data of the year of 2014, with 6-minutes time interval. Data is obtained from the United States Geological Survey (USGS) online resource called National Water Information System (NWIS). For this paper, the data includes the measurements of 4 parameters which affect and influence water quality. For the purpose of evaluating the performance of model, the performance evaluation measures used are Mean-Squared Error (MSE), Root Mean-Squared Error (RMSE) and Regression Analysis. Previous works about Water Quality prediction have also been analyzed and future improvements have been proposed in this paper.


title: Comparison of different attributes in modeling a FSM based vending machine in 2 different styles

abstract: Vending Machine as we all know is a machine which can vend different products which is more like an automated process with no requirement of man handling which we normally see in fast moving cities because of fast paced life. This paper compares different aspects like area, timing constraint, speed, power dissipation of a vending machine with 2 different design styles algorithm while installation. FSM based algorithm has been utilized to simulate model, synthesize the machine on the stratix III family of FPGA provided with quartus design tool which is logic device design software from Altera.


title: Flavor Pairing in Medieval European Cuisine: A Study in Cooking with Dirty Data

abstract: An important part of cooking with computers is using statistical methods to create new, flavorful ingredient combinations. The flavor pairing hypothesis states that culinary ingredients with common chemical flavor components combine well to produce pleasant dishes. It has been recently shown that this design principle is a basis for modern Western cuisine and is reversed for Asian cuisine. ::: Such data-driven analysis compares the chemistry of ingredients to ingredient sets found in recipes. However, analytics-based generation of novel flavor profiles can only be as good as the underlying chemical and recipe data. Incomplete, inaccurate, and irrelevant data may degrade flavor pairing inferences. Chemical data on flavor compounds is incomplete due to the nature of the experiments that must be conducted to obtain it. Recipe data may have issues due to text parsing errors, imprecision in textual descriptions of ingredients, and the fact that the same ingredient may be known by different names in different recipes. Moreover, the process of matching ingredients in chemical data and recipe data may be fraught with mistakes. Much of the `dirtiness' of the data cannot be cleansed even with manual curation. ::: In this work, we collect a new data set of recipes from Medieval Europe before the Columbian Exchange and investigate the flavor pairing hypothesis historically. To investigate the role of data incompleteness and error as part of this hypothesis testing, we use two separate chemical compound data sets with different levels of cleanliness. Notably, the different data sets give conflicting conclusions about the flavor pairing hypothesis in Medieval Europe. As a contribution towards social science, we obtain inferences about the evolution of culinary arts when many new ingredients are suddenly made available.


title: High-Performance Neural Networks for Visual Object Classification

abstract: We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve the best published results on benchmarks for object classification (NORB, CIFAR10) and handwritten digit recognition (MNIST), with error rates of 2.53%, 19.51%, 0.35%, respectively. Deep nets trained by simple back-propagation perform better than more shallow ones. Learning is surprisingly rapid. NORB is completely trained within five epochs. Test error rates on MNIST drop to 2.42%, 0.97% and 0.48% after 1, 3 and 17 epochs, respectively.


title: Herzberg's motivation-hygiene theory and job satisfaction in the malaysian retail sector: the mediating effect of love of money

abstract: This paper examines what motivates employees in the retail industry, and examines their level of job satisfaction, using Herzberg's hygiene factors and motivators. In this study, convenience sampling was used to select sales personnel from women's clothing stores in Bandar Sunway shopping mall in the state of Selangor. The results show that hygiene factors were the dominant motivators of sales personnel job satisfaction. Working conditions were the most significant in motivating sales personnel. Recognition was second, followed by company policy and salary. There is a need to delve more deeply into why salespeople place such a high importance on money. Further analysis was performed to assess how much the love of money mediates the relationship between salary and job satisfaction. Based on the general test for mediation, the love of money could explain the relationship between salary and job satisfaction. The main implication of this study is that sales personnel who value money highly are satisfied with their salary and job when they receive a raise.


abstract: This is the companion paper of "Mining Software Repair Models for Reasoning on the Search Space of Automated Program Fixing", accepted for publication in Empirical Software Engineering (Springer) on Sep. 11, 2013.


title: Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data size

abstract: CT is commonly used in orthopedic procedures. MRI is used along with CT to identify muscle structures and diagnose osteonecrosis due to its superior soft tissue contrast. However, MRI has poor contrast for bone structures. Clearly, it would be helpful if a corresponding CT were available, as bone boundaries are more clearly seen and CT has a standardized (i.e., Hounsfield) unit. Therefore, we aim at MR-to-CT synthesis. While the CycleGAN was successfully applied to unpaired CT and MR images of the head, these images do not have as much variation of intensity pairs as do images in the pelvic region due to the presence of joints and muscles. In this paper, we extended the CycleGAN approach by adding the gradient consistency loss to improve the accuracy at the boundaries. We conducted two experiments. To evaluate image synthesis, we investigated dependency of image synthesis accuracy on (1) the number of training data and (2) incorporation of the gradient consistency loss. To demonstrate the applicability of our method, we also investigated segmentation accuracy on synthesized images.


title: Does impulsivity relate to perceived dependence on and actual use of the mobile phone?

abstract: Several authors have studied the risks arising from the growth in mobile phone use (e.g. large debts incurred by young people, banned or dangerous use of cellular phones). The aim of this study is to analyse whether impulsivity, which has often been related to various forms of addictive behaviours, is associated with massive use of and dependence on the mobile phone. In this study, 108 female undergraduate psychology students were screened using a questionnaire evaluating actual use of and perceived dependence on the mobile phone, and with the French adaptation of the UPPS Impulsive Behavior Scale. This scale identifies four distinct components associated with impulsive behaviour: Urgency, lack of Premeditation, lack of Perseverance, and Sensation Seeking. The results showed that a relationship can be established between the use of and perceived dependence on the cellular phone and two facets of impulsivity: Urgency and lack of Perseverance.


Media richness theory and new electronic communication media: a study of voice mail and electronic mail


title: Does Driver Suicide Occur in Iran?

abstract: Background: Iran has a relatively low suicide rate and the highest road death rate in the world. Objectives: To explore the possibility that driver suicide occurs in Iran. ::: Methods: Examination of the relevant suicide and road traffic literature. Results: Suicide in Iran includes self-immolation, hanging and other violent methods. It is stigmatized and suicidal intent and actions ::: may be denied. The choice of suicide method depends on availability and culture. Driver suicide is reported in many countries ::: and represents a small percentage of the total number of suicides. It is a method which may hide the fact of suicide and suggest ::: accident. We could find no report of driver suicide from Iran. Conclusions: There is great interest in reducing the road toll in Iran. Driver suicide may be occurring but not being reported. If ::: driver suicide has a role in the road deaths of Iran, identification and quantification would be helpful.


title: Public health, GIS, and spatial analytic tools.

abstract: We review literature that uses spatial analytic tools in contexts where Geographic Information Systems (GIS) is the organizing system for health data or where the methods discussed will likely be incorporated in GIS-based analyses in the future. We conclude the review with the point of view that this literature is moving toward the development and use of systems of analysis that integrate the information geo-coding and data base functions of GISystems with the geo-information processing functions of GIScience. The rapidity of this projected development will depend on the perceived needs of the public health community for spatial analysis methods to provide decision support. Recent advances in the analysis of disease maps have been influenced by and benefited from the adoption of new practices for georeferencing health data and new ways of linking such data geographically to potential sources of environmental exposures, the locations of health resources and the geodemographic characteristics of populations. This review focuses on these advances.


title: VIRTUAL ENVIRONMENT AS AIDS IN PERSUADING USERS IN LEARNING PROCESS

abstract: With the advent of computing technologies, a plethora of computer applications were developed to solve simple to complex human and organization problems. Computer applications cover variety of fields and have been known to be powerful tools in changing person‘s attitudes, behaviours, feelings and actions. This paper investigates the use of virtual environment technology as aids in persuading users in learning process. It explains the concept of persuasive and virtual environment technologies and the integration of both technologies in persuading learners to learn. It elaborates on the design of persuasion and explains the benefits of integrating persuasive and virtual environment technologies in the learning process. Finally, it gives an overview of a proposed approach in utilizing virtual environment as aids in persuasive design for learning.


title: Unit Tests for Stochastic Optimization

abstract: Optimization by stochastic gradient descent is an important component of many large-scale machine learning algorithms. A wide variety of such optimization algorithms have been devised; however, it is unclear whether these algorithms are robust and widely applicable across many different optimization landscapes. In this paper we develop a collection of unit tests for stochastic optimization. Each unit test rapidly evaluates an optimization algorithm on a small-scale, isolated, and well-understood difficulty, rather than in real-world scenarios where many such issues are entangled. Passing these unit tests is not sufficient, but absolutely necessary for any algorithms with claims to generality or robustness. We give initial quantitative and qualitative results on numerous established algorithms. The testing framework is open-source, extensible, and easy to apply to new algorithms.


title: Metaphors and diaphors in science communication: Mapping the case of ‘stem-cell research

abstract: “Stem cell research” has become a subject of political discussion in recent years because of its social and ethical implications. The intellectual research program, however, has a history of several decades. Therapeutic applications and patents on the basis of stem cell research became available during the 1990s. Currently, the main applications of stem cell research are found in marrow transplantation (e.g., for the treatment of leukemia). In this study, the various meanings of the term stem cell are examined in these different contexts of research, applications, and policy debates. Translation mechanisms between contexts are specified, and a quantitative indicator for the degree of codification is proposed.


title: Mental Maze Solving

abstract: We sought to determine how a visual maze is mentally solved. Human subjects (N = 13) viewed mazes with orthogonal, unbranched paths; each subject solved 200-600 mazes in any specific experiment below. There were four to six openings at the perimeter of the maze, of which four were labeled: one was the entry point and the remainder were potential exits marked by Arabic numerals. Starting at the entry point, in some mazes the path exited, whereas in others it terminated within the maze. Subjects were required to type the number corresponding to the true exit (if the path exited) or type zero (if the path did not exit). In all cases, the only required hand movement was a key press, and thus the hand never physically traveled through the maze. Response times (RT) were recorded and analyzed using a multiple linear regression model. RT increased as a function of key parameters of the maze, namely the length of the main path, the number of turns in the path, the direct distance from entry to termination, and the presence of an exit. The dependence of RT on the number of turns was present even when the path length was fixed in a separate experiment (N = 10 subjects). In a different experiment, subjects solved large and small mazes (N = 3 subjects). The former was the same as the latter but was scaled up by 1.77 times. Thus both kinds of mazes contained the same number of squares but each square subtended 1.77° of visual angle (DVA) in the large maze, as compared to 1 DVA in the small one. We found that the average RT was practically the same in both cases. A multiple regression analysis revealed that the processing coefficients related to maze distance (i.e., path length and direct distance) were reduced by approximately one-half when solving large mazes, as compared to solving small mazes. This means that the efficiency in processing distance-related information almost doubled for scaled-up mazes. In contrast, the processing coefficients for number of turns and exit status were practically the same in the two cases. Finally, the eye movements of three subjects were recorded during maze solution. They consisted of sequences of saccades and fixations. The number of fixations in a trial increased as a linear function of the path length and number of turns. With respect to the fixations themselves, eyes tended to fixate on the main path and to follow it along its course, such that fixations occurring later in time were positioned at progressively longer distances from the entry point. Furthermore, the time the eyes spent at each fixation point increased as a linear function of the length and number of turns in the path segment between the current and the upcoming fixation points. These findings suggest that the maze segment from the current fixation spot to the next is being processed during the fixation time (FT), and that a significant aspect of this processing relates to the length and turns in that segment. We interpreted these relations to mean that the maze was mentally traversed. We then estimated the distance and endpoint of the path mentally traversed within a specific FT; we also hypothesized that the next portion of the main path would be traversed during the ensuing FT, and so on for the whole path. A prediction of this hypothesis is that the upcoming saccade would land the eyes at or near the locus on the path where the mental traversing ended, so that "the eyes would pick up where the mental traversal left off." In this way, a portion of the path would be traversed during a fixation and successive such portions would be strung together closely along the main path to complete the processing of the whole path. We tested this prediction by analyzing the relations between the path distance of mental traverse and the distance along the path between the current and the next fixation spot. Indeed, we found that these distances were practically the same and that the endpoint of the hypothesized mental path traversing was very close to the point where the eye landed by the saccade to initiate a new mental traversing. This forward progression of fixation points along the maze path, coupled with the ongoing analysis of the path between successive fixation points, would constitute an algorithm for the routine solution of a maze.


title: A deep pyramid Deformable Part Model for face detection

abstract: We present a face detection algorithm based on Deformable Part Models and deep pyramidal features. The proposed method called DP2MFD is able to detect faces of various sizes and poses in unconstrained conditions. It reduces the gap in training and testing of DPM on deep features by adding a normalization layer to the deep convolutional neural network (CNN). Extensive experiments on four publicly available unconstrained face detection datasets show that our method is able to capture the meaningful structure of faces and performs significantly better than many competitive face detection algorithms.


Text Advertising Blindness: The New Banner Blindness?


title: Differential Evolution with an Evolution Path: A DEEP Evolutionary Algorithm

abstract: Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC’13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs.


title: Health Behavior, Psychosocial Theories of

abstract: A number of psychosocial theories have been developed to predict, explain, and change health behaviors. In this article, six widely used theories are discussed in turn and then compared. They include four continuum theories (the health belief model; protection motivation theory; the theory of planned behavior; and social cognitive theory), one stage theory (the transtheoretical model), and one theory that has both stage and continuum versions (the health action process approach). Common criticisms of these theories are briefly addressed.


title: Self-organization on Riemannian manifolds

abstract: We consider an aggregation model that consists of an active transport equation for the macroscopic population density, where the velocity has a nonlocal functional dependence on the density, modelled via an interaction potential. We set up the model on general Riemannian manifolds and provide a framework for constructing interaction potentials which lead to equilibria that are constant on their supports. We consider such potentials for two specific cases (the two-dimensional sphere and the two-dimensional hyperbolic space) and investigate analytically and numerically the long-time behaviour and equilibrium solutions of the aggregation model on these manifolds. Equilibria obtained numerically with other interaction potentials and an application of the model to aggregation on the rotation group \begin{document}$ SO(3) $\end{document} are also presented.


title: Structural Architectures for a Deployable Wideband UHF Antenna

abstract: This paper explores concepts for a wideband deployable antenna suitable for small satellites. The general approach taken was to closely couple antenna theory and structural mechanics, to produce a deployable antenna that is considered efficient in both fields. Approaches that can be deployed using stored elastic strain energy have been favored over those that require powered actuators or environmental effects to enforce deployment. Two types of concepts were developed: thin shell structure and pantograph. These concepts cover four antenna topologies: crossed log periodic, conical log spiral, helical and quadrifilar helix. Of these, the conical log spiral antenna and the accompanying deployment concepts are determined to be the most promising approaches that warrant further study.


title: Image compression with auto-encoder algorithm using deep neural network (DNN)

abstract: An image compression is necessary for the image processing applications such as data storing, image classification, image recognition etc. Then, several research articles have been proposed to reserve for these topics. However, the image compression with auto encoder has been found for a small number of the improvements. Therefore, this paper presents a detailed study to demonstrate the image compression algorithm using the deep neural network (DNN). The proposed algorithm consists of 1) compressing image with auto encoder, and 2) decoding image. The proposed compressing image with auto encoder algorithm uses non-recurrent three-layer neural networks (NRTNNs) which use an extended Kalman filter (EKF) to update the weights of the networks. To evaluate the proposed algorithm performances, the Matlab program is used for implementations of the overall testing algorithm. From our simulation results, it shows that the proposed image compression algorithm is able to reduce the image dimensionality and is able to recall the compressed image with low loss.


title: Does mindfulness training improve cognitive abilities? A systematic review of neuropsychological findings.

abstract: Abstract Mindfulness meditation practices (MMPs) are a subgroup of meditation practices which are receiving growing attention. The present paper reviews current evidence about the effects of MMPs on objective measures of cognitive functions. Five databases were searched. Twenty three studies providing measures of attention, memory, executive functions and further miscellaneous measures of cognition were included. Fifteen were controlled or randomized controlled studies and 8 were case–control studies. Overall, reviewed studies suggested that early phases of mindfulness training, which are more concerned with the development of focused attention, could be associated with significant improvements in selective and executive attention whereas the following phases, which are characterized by an open monitoring of internal and external stimuli, could be mainly associated with improved unfocused sustained attention abilities. Additionally, MMPs could enhance working memory capacity and some executive functions. However, many of the included studies show methodological limitations and negative results have been reported as well, plausibly reflecting differences in study design, study duration and patients' populations. Accordingly, even though findings here reviewed provided preliminary evidence suggesting that MMPs could enhance cognitive functions, available evidence should be considered with caution and further high quality studies investigating more standardized mindfulness meditation programs are needed.


title: The problems and solutions of network update in SDN: A survey

abstract: Network is dynamic and requires update in the operation. However, many confusions and problems can be caused by careless schedule in the update process. Although the problem is noticed for long in traditional networks, software defined networking (SDN) brings new opportunities and solutions to this problem by the separation of control and data plane, as well as the centralized control. This paper makes a survey on the problems caused by network update, including forwarding loop, forwarding black hole, link congestion, network policy violation, etc., as well as the state-of-the-art solutions to these problems in the SDN paradigm.


title: Ultra-Processed Food Products and Obesity in Brazilian Households (2008–2009)

abstract: Background ::: Production and consumption of industrially processed food and drink products have risen in parallel with the global increase in overweight and obesity and related chronic non-communicable diseases. The objective of this study was to analyze the relationship between household availability of processed and ultra-processed products and the prevalence of excess weight (overweight plus obesity) and obesity in Brazil. ::: ::: ::: Methods ::: The study was based on data from the 2008–2009 Household Budget Survey involving a probabilistic sample of 55,970 Brazilian households. The units of study were household aggregates (strata), geographically and socioeconomically homogeneous. Multiple linear regression models were used to assess the relationship between the availability of processed and ultra-processed products and the average of Body Mass Index (BMI) and the percentage of individuals with excess weight and obesity in the strata, controlling for potential confounders (socio-demographic characteristics, percentage of expenditure on eating out of home, and dietary energy other than that provided by processed and ultra-processed products). Predictive values for prevalence of excess weight and obesity were estimated according to quartiles of the household availability of dietary energy from processed and ultra-processed products. ::: ::: ::: Results ::: The mean contribution of processed and ultra-processed products to total dietary energy availability ranged from 15.4% (lower quartile) to 39.4% (upper quartile). Adjusted linear regression coefficients indicated that household availability of ultra-processed products was positively associated with both the average BMI and the prevalence of excess weight and obesity, whereas processed products were not associated with these outcomes. In addition, people in the upper quartile of household consumption of ultra-processed products, compared with those in the lower quartile, were 37% more likely to be obese. ::: ::: ::: Conclusion ::: Greater household availability of ultra-processed food products in Brazil is positively and independently associated with higher prevalence of excess weight and obesity in all age groups in this cross-sectional study.


title: Vocal Melody Extraction in the Presence of Pitched Accompaniment in Polyphonic Music

abstract: Melody extraction algorithms for single-channel polyphonic music typically rely on the salience of the lead melodic instrument, considered here to be the singing voice. However the simultaneous presence of one or more pitched instruments in the polyphony can cause such a predominant-F0 tracker to switch between tracking the pitch of the voice and that of an instrument of comparable strength, resulting in reduced voice-pitch detection accuracy. We propose a system that, in addition to biasing the salience measure in favor of singing voice characteristics, acknowledges that the voice may not dominate the polyphony at all instants and therefore tracks an additional pitch to better deal with the potential presence of locally dominant pitched accompaniment. A feature based on the temporal instability of voice harmonics is used to finally identify the voice pitch. The proposed system is evaluated on test data that is representative of polyphonic music with strong pitched accompaniment. Results show that the proposed system is indeed able to recover melodic information lost to its single-pitch tracking counterpart, and also outperforms another state-of-the-art melody extraction system designed for polyphonic music.


Approximation alogorithms for the maximum acyclic subgraph problem


title: Template-based Synthetic Handwriting Generation for the Training of Recognition Systems

abstract: In this paper we present a method for synthesizing English handwritten textlines from ASCII transcriptions. The method is based on templates of characters and the Delta LogNormal model of handwriting generation. To generate a textline, rst a static image of the textline is built by concatenating perturbed versions of the character templates. Then strokes and corresponding virtual targets are extracted and randomly perturbed, and nally the textline is drawn using overlapping strokes and delta-lognormal velocity proles in accordance with the Delta LogNormal theory. The generated textlines are used as training data for a hidden Markov model based o-line handwritten textline recognizer. First results show that adding such generated textlines to the natural training set may be benecial.


title: A Distributed Anomaly Detection System for In-Vehicle Network Using HTM

abstract: With the development of 5G and Internet of Vehicles technology, the possibility of remote wireless attack on an in-vehicle network has been proven by security researchers. Anomaly detection technology can effectively alleviate the security threat, as the first line of security defense. Based on this, this paper proposes a distributed anomaly detection system using hierarchical temporal memory (HTM) to enhance the security of a vehicular controller area network bus. The HTM model can predict the flow data in real time, which depends on the state of the previous learning. In addition, we improved the abnormal score mechanism to evaluate the prediction. We manually synthesized field modification and replay attack in data field. Compared with recurrent neural networks and hidden Markov model detection models, the results show that the distributed anomaly detection system based on HTM networks achieves better performance in the area under receiver operating characteristic curve score, precision, and recall.


title: Performance Evaluation of a Natural Language Processing approach applied in White Collar crime investigation

abstract: In today world we are confronted with increasing amounts of information every day coming from a large variety of sources. People and co-operations are producing data on a large scale, and since the rise of the internet, e-mail and social media the amount of produced data has grown exponentially. From a law enforcement perspective we have to deal with these huge amounts of data when a criminal investigation is launched against an individual or company. Relevant questions need to be answered like who committed the crime, who were involved, what happened and on what time, who were communicating and about what? Not only the amount of available data to investigate has increased enormously, but also the complexity of this data has increased. When these communication patterns need to be combined with for instance a seized financial administration or corporate document shares a complex investigation problem arises. Recently, criminal investigators face a huge challenge when evidence of a crime needs to be found in the Big Data environment where they have to deal with large and complex datasets especially in financial and fraud investigations. To tackle this problem, a financial and fraud investigation unit of a European country has developed a new tool named LES that uses Natural Language Processing (NLP) techniques to help criminal investigators handle large amounts of textual information in a more efficient and faster way. In this paper, we present briefly this tool and we focus on the evaluation its performance in terms of the requirements of forensic investigation: speed, smarter and easier for investigators. In order to evaluate this LES tool, we use different performance metrics. We also show experimental results of our evaluation with large and complex datasets from real-world application.


title: Analysis and design of an analog control loop for digital input class D amplifiers

abstract: Analog input Class D Amplifiers are widely used in battery powered systems such as mobile phones to achieve high efficiency but it suffers from a complex DAC to convert the digital audio signal into an analog one. To increase the playback time, this paper presents digital input class D amplifiers using digital modulation. The proposed Class D amplifier is also controlled using an analog loop to achieve a good power supply immunity and low harmonic distortion. Usual AC analysis of this loop cannot be done due to its switching behavior. Very long transient simulation was the only solution to predict the dynamics performances of the control. To overcome this issue, the presented work includes a modeling method in order to study faster the control performances. The proposed modeling is then used to increase the audio quality reproduction of our digital input Class D amplifier. The complete audio path is implemented in CMOS 130nm process and characterized in order to validate the architecture, the modeling method and the integrated design.


Epi-convergence of lossy likelihoods


title: Opinion Full-body illusions and minimal phenomenal selfhood

abstract: We highlight the latest research on body perception and self-consciousness, but argue that despite these achievements, central aspects have remained unexplored, namely, global aspects of bodily self-consciousness. Researchers investigated central representations of body parts and actions involving these, but neglected the global and unitary character of self-consciousness, the ‘I’ of experience and behaviour. We ask, what are the minimally sufficient conditions for the appearance of a phenomenal self, that is, the fundamental conscious experience of being someone? What are necessary conditions for self-consciousness in any type of system? We offer conceptual clarifications, discuss recent empirical evidence from neurology and cognitive science and argue that these findings offer a new entry point for the systematic study of global and more fundamental aspects of self-consciousness.


title: Solving (Weighted) . . . Satisfiability Testing

abstract: Recently, Fu and Malik described an unweighted Partial MaxSAT solver based on successive calls to a SAT solver. At the k th iteration the SAT solver tries to certify that there exist an assignment that satisfies all but k clauses. Later Marques-Silva and Planes implemented and extended these ideas. In this paper we present and implement two Partial MaxSAT solvers and the weighted variant of one of them. Both are based on Fu and Malik ideas. We prove the correctness of our algorithm and compare our solver with other (Weighted) MaxSAT and (Weighted) Partial MaxSAT solvers.


title: Social Robots as Embedded Reinforcers of Social Behavior in Children with Autism

abstract: In this study we examined the social behaviors of 4- to 12-year-old children with autism spectrum disorders (ASD; N = 24) during three tradic interactions with an adult confederate and an interaction partner, where the interaction partner varied randomly among (1) another adult human, (2) a touchscreen computer game, and (3) a social dinosaur robot. Children spoke more in general, and directed more speech to the adult confederate, when the interaction partner was a robot, as compared to a human or computer game interaction partner. Children spoke as much to the robot as to the adult interaction partner. This study provides the largest demonstration of social human-robot interaction in children with autism to date. Our findings suggest that social robots may be developed into useful tools for social skills and communication therapies, specifically by embedding social interaction into intrinsic reinforcers and motivators.


title: Enhanced Passive RF-DC Converter Circuit Efficiency for Low RF Energy Harvesting

abstract: For radio frequency energy transmission, the conversion efficiency of the receiver is decisive not only for reducing sending power, but also for enabling energy transmission over long and variable distances. In this contribution, we present a passive RF-DC converter for energy harvesting at ultra-low input power at 868 MHz. The novel converter consists of a reactive matching circuit and a combined voltage multiplier and rectifier. The stored energy in the input inductor and capacitance, during the negative wave, is conveyed to the output capacitance during the positive one. Although Dickson and Villard topologies have principally comparable efficiency for multi-stage voltage multipliers, the Dickson topology reaches a better efficiency within the novel ultra-low input power converter concept. At the output stage, a low-pass filter is introduced to reduce ripple at high frequencies in order to realize a stable DC signal. The proposed rectifier enables harvesting energy at even a low input power from −40 dBm for a resistive load of 50 kΩ. It realizes a significant improvement in comparison with state of the art solutions.


title: Two-stage fully-differential inverter-based self-biased CMOS amplifier with high efficiency

abstract: This paper describes a novel two-stage fully-differential CMOS amplifier comprising two self-biased inverter stages, with optimum compensation and high efficiency. Although it relies on a class A topology, it is shown through simulations, that it achieves the highest efficiency of its class and comparable to the best class AB amplifiers. Due to the self-biasing, a low variability in the DC gain over process, temperature, and supply is achieved. A detailed circuit analysis, a design methodology for optimization and the most relevant simulation results are presented, together with a final comparison among state-of-the-art amplifiers.


title: Emotion Recognition and Depression Diagnosis by Acoustic and Visual Features: A Multimodal Approach

abstract: There is an enormous number of potential applications of the system which is capable to recognize human emotions. Such opportunity can be useful in various applications, e.g., improvement of Spoken Dialogue Systems (SDSs) or monitoring agents in call-centers. Depression is another aspect of human beings which is closely related to emotions. The system, that can automatically diagnose patient's depression can be helpful to physicians in order to support their decisions and avoid critical mistakes. Therefore, the Affect and Depression Recognition Sub-Challenges (ASC and DSC correspondingly) of the second combined open Audio/Visual Emotion and Depression recognition Challenge (AVEC 2014) is focused on estimating emotions and depression. This study presents the results of multimodal affect and depression recognition based on four different segmentation methods, using support vector regression. Furthermore, a speaker identification procedure has been introduced in order to build the speaker-specific emotion/depression recognition systems.


title: Semantic Documents Relatedness using Concept Graph Representation

abstract: We deal with the problem of document representation for the task of measuring semantic relatedness between documents. A document is represented as a compact concept graph where nodes represent concepts extracted from the document through references to entities in a knowledge base such as DBpedia. Edges represent the semantic and structural relationships among the concepts. Several methods are presented to measure the strength of those relationships. Concepts are weighted through the concept graph using closeness centrality measure which reflects their relevance to the aspects of the document. A novel similarity measure between two concept graphs is presented. The similarity measure first represents concepts as continuous vectors by means of neural networks. Second, the continuous vectors are used to accumulate pairwise similarity between pairs of concepts while considering their assigned weights. We evaluate our method on a standard benchmark for document similarity. Our method outperforms state-of-the-art methods including ESA (Explicit Semantic Annotation) while our concept graphs are much smaller than the concept vectors generated by ESA. Moreover, we show that by combining our concept graph with ESA, we obtain an even further improvement.


title: Offline arabic handwritten text recognition: A Survey

abstract: Research in offline Arabic handwriting recognition has increased considerably in the past few years. This is evident from the numerous research results published recently in major journals and conferences in the area of handwriting recognition. Features and classifications techniques utilized in recent research work have diversified noticeably compared to the past. Moreover, more efforts have been diverted, in last few years, to construct different databases for Arabic handwriting recognition. This article provides a comprehensive survey of recent developments in Arabic handwriting recognition. The article starts with a summary of the characteristics of Arabic text, followed by a general model for an Arabic text recognition system. Then the used databases for Arabic text recognition are discussed. Research works on preprocessing phase, like text representation, baseline detection, line, word, character, and subcharacter segmentation algorithms, are presented. Different feature extraction techniques used in Arabic handwriting recognition are identified and discussed. Different classification approaches, like HMM, ANN, SVM, k-NN, syntactical methods, etc., are discussed in the context of Arabic handwriting recognition. Works on Arabic lexicon construction and spell checking are presented in the postprocessing phase. Several summary tables of published research work are provided for used Arabic text databases and reported results on Arabic character, word, numerals, and text recognition. These tables summarize the features, classifiers, data, and reported recognition accuracy for each technique. Finally, we discuss some future research directions in Arabic handwriting recognition.


title: Development of multi-D.O.F. tracked vehicle to traverse weak slope and climb up rough slope

abstract: During a volcanic activity, it is very dangerous to approach a restricted area. For this reason, robotic remote observation system would be quite useful, and it is particularly urgent for a country with a high degree of volcanic activity, such as Japan. In response to this need, our research group developed a novel multi-D.O.F. tracked vehicle, called ELF, which can conduct observation in a restricted volcanic area. The robot essentially consists of six tracks, and it has eleven actuators that change its configuration. These actuators enable the robot to assume various configurations, which increase its ability to traverse weak and rough terrains in the area around a volcano. In this research, we propose one configuration of the robot, in which the surface of the contact plane at the bottom of the track is horizontal, which is advantageous for traversing a weak slope. The feasibility of this design was verified in a field experiment on Mt. Kushigata, on the island of Izu-Oshima, and in a simulated volcanic field that was filled with pumice stones.


title: CMOS compatible many-core noc architectures with multi-channel millimeter-wave wireless links

abstract: Traditional many-core designs based on the Network-on-Chip (NoC) paradigm suffer from high latency and power dissipation as the system size scales up due to their inherent multi-hop communication. NoC performance can be significantly enhanced by introducing long-range, low power, and high-bandwidth single-hop wireless links between far apart cores. This paper presents a design methodology and performance evaluation for a hierarchical small-world NoC with CMOS compatible on-chip millimeter (mm)-wave wireless long-range communication links. The proposed wireless NoC offers significantly higher bandwidth and lower energy dissipation compared to its conventional non-hierarchical wired counterpart in presence of both uniform and non-uniform traffic patterns. The performance improvement is achieved through efficient data routing and optimum placement of wireless hubs. Multiple wireless shortcuts operating simultaneously provide an energy efficient solution for design of many-core communication infrastructures.


title: Designing for Cohabitation: Naturecultures, Hybrids, and Decentering the Human in Design

abstract: Recent research in urban informatics has presented the city as both a complex technological center and a diverse cultural, social, and political entity. However, there has been little research into the changing role that nature plays in urban space, particularly when it comes to understanding how animals have adapted to life in technological and networked cities. In the wake of urbanization, new kinds of cohabitation, including increased interactions between humans and animals, has resulted in new challenges for those working in urban informatics. We leverage key concepts in the Anthropocene-naturecultures, hybrids, and decentering the human in design-to unpack the entanglements of animal-human-computer interaction in two design cases: The Big Cat Behavioral Tracking Initiative and The Phenology Clock. We contribute to urban informatics and HCI research by reflecting on ways in which design can promote new forms of cohabitation and support a broader conception of the city that sees animals as an essential part of the urban landscape.


title: The Digital Sin City: An Empirical Study of Craigslist's Impact on Prostitution Trends

abstract: Do solicitation sites lead to an increase in prostitution trends in United States? We shed light on this issue by examining the impact that Craigslist (via its Erotic Service section) has on prosti...


title: Interactive spacetime control for animation

abstract: This paper describes new techniques to design physically based, goal directed motion of synthetic creatures. More specifically, it concentrates on developing an interactive framework for specifying constraints and objectives for the motion, and for guiding the numericrd solution of the optimization problem thus defined. The ability to define, modify and guide constrained spacetime problems is provided through an interactive user interface. Innovations that are introduced include, (1) the subdivision of spacetime into discrete pieces, or Spacetime Windows, over which subproblems can be formulated and solved, (2) the use of cubic B-spline approximation techniques to define a C2 function for the creature’s time dependent degrees of freedom, (3) the use of both symbolic and numerical processes to construct and solve the constrained optimization problem, and (4) the ability to specify inequality and conditional constraints. Creatures, in the context of this work, consist of rigid links connected by joints defining a set of generalized degrees of freedom. Hybrid symbolic and numeric techniques to solve the resulting complex constrained optimization problems are made possible by the special structure of physically based models of such creatures, and by the recent development of symbolic algebraic languages. A graphical user interface process handles communication between the user and two other processes; one devoted to symbolic differentiation and manipulation of the constraints and objectives, and one that performs the iterative numerical solution of the optimization problem. The user interface itself provides both high and low level definition of, interaction with, and inspection of, the optimization process and the resulting animation. Implementation issues and experiments with the Spacetime Windows system are discussed,


title: A Novel AHRS Inertial Sensor-Based Algorithm for Wheelchair Propulsion Performance Analysis

abstract: With the increasing rise of professionalism in sport, athletes, teams, and coaches are looking to technology to monitor performance in both games and training in order to find a competitive advantage. The use of inertial sensors has been proposed as a cost effective and adaptable measurement device for monitoring wheelchair kinematics; however, the outcomes are dependent on the reliability of the processing algorithms. Though there are a variety of algorithms that have been proposed to monitor wheelchair propulsion in court sports, they all have limitations. Through experimental testing, we have shown the Attitude and Heading Reference System (AHRS)-based algorithm to be a suitable and reliable candidate algorithm for estimating velocity, distance, and approximating trajectory. The proposed algorithm is computationally inexpensive, agnostic of wheel camber, not sensitive to sensor placement, and can be embedded for real-time implementations. The research is conducted under Griffith University Ethics (GU Ref No: 2016/294).


title: Using Reinforcement Learning for Autonomic Resource Allocation in Clouds: Towards a Fully Automated Workflow

abstract: Dynamic and appropriate resource dimensioning isa crucial issue in cloud computing. As applications go more andmore 24/7, online policies must be sought to balance performancewith the cost of allocated virtual machines. Most industrialapproaches to date use ad hoc manual policies, such as thresholdbasedones. Providing good thresholds proved to be tricky andhard to automatize to fit every application requirement. Researchis being done to apply automatic decision-making approaches,such as reinforcement learning. Yet, they face a lot of problemsto go to the field: having good policies in the early phasesof learning, time for the learning to converge to an optimalpolicy and coping with changes in the application performancebehavior over time. In this paper, we propose to deal with theseproblems using appropriate initialization for the early stages aswell as convergence speedups applied throughout the learningphases and we present our first experimental results for these.We also introduce a performance model change detection onwhich we are currently working to complete the learning processmanagement. Even though some of these proposals were knownin the reinforcement learning field, the key contribution of thispaper is to integrate them in a real cloud controller and toprogram them as an automated workflow.


title: Universal Multi-Factor Authentication Using Graphical Passwords

abstract: In this paper, we present a series of methods to authenticate a user with a graphical password. To that end, we employ the user?s personal handheld device as the password decoder and the second factor of authentication. In our methods, a service provider challenges the user with an image password. To determine the appropriate click points and their order, the user needs some hint information transmitted only to her handheld device. We show that our method can overcome threats such as key-loggers, weak password, and shoulder surfing. With the increasing popularity of handheld devices such as cell phones, our approach can be leveraged by many organizations without forcing the user to memorize different passwords or carrying around different tokens.