International Journal of Advanced Robotic Systems Design of a Control System for an Autonomous Vehicle Based on Adaptive-PID Regular Paper

The  autonomous  vehicle  is  a  mobile  robot integrating  multi‐sensor  navigation  and  positioning, intelligent decision making and control  technology. This paper  presents  the  control  system  architecture  of  the autonomous vehicle, called “Intelligent Pioneer”, and the path  tracking  and  stability  of  motion  to  effectively navigate  in unknown  environments  is discussed.  In  this approach,  a  two  degree‐of‐freedom  dynamic  model  is developed to formulate the path‐tracking problem in state space format. For controlling the instantaneous path error, traditional  controllers  have  difficulty  in  guaranteeing performance and stability over a wide range of parameter changes and disturbances. Therefore, a newly developed adaptive‐PID  controller  will  be  used.  By  using  this approach the flexibility of the vehicle control system will be  increased  and  achieving  great  advantages. Throughout,  we  provide  examples  and  results  from Intelligent Pioneer and the autonomous vehicle using this approach  competed  in  the  2010  and  2011  Future Challenge of China. Intelligent Pioneer finished all of the competition programmes and won  first position  in 2010 and third position in 2011.


Random synaptic feedback weights support error backpropagation for deep learning

The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning.


Getting Formal with Dopamine and Reward

Recent neurophysiological studies reveal that neurons in certain brain structures carry specific signals about past and future rewards. Dopamine neurons display a short-latency, phasic reward signal indicating the difference between actual and predicted rewards. The signal is useful for enhancing neuronal processing and learning behavioral reactions. It is distinctly different from dopamine's tonic enabling of numerous behavioral processes. Neurons in the striatum, frontal cortex, and amygdala also process reward information but provide more differentiated information for identifying and anticipating rewards and organizing goal-directed behavior. The different reward signals have complementary functions, and the optimal use of rewards in voluntary behavior would benefit from interactions between the signals. Addictive psychostimulant drugs may exert their action by amplifying the dopamine reward signal.


Deep Big Multilayer Perceptrons for Digit Recognition

The competitive MNIST handwritten digit recognition benchmark has a long history of broken records since 1998. The most recent advancement by others dates back 8 years (error rate 0.4%). Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0.35% error rate on the MNIST handwritten digits benchmark with a single MLP and 0.31% with a committee of seven MLP. All we need to achieve this until 2011 best result are many hidden layers, many neurons per layer, numerous deformed training images to avoid overfitting, and graphics cards to greatly speed up learning.


Feature-based attention influences motion processing gain in macaque visual cortex

Changes in neural responses based on spatial attention have been demonstrated in many areas of visual cortex, indicating that the neural correlate of attention is an enhanced response to stimuli at an attended location and reduced responses to stimuli elsewhere. Here we demonstrate non-spatial, feature-based attentional modulation of visual motion processing, and show that attention increases the gain of direction-selective neurons in visual cortical area MT without narrowing the direction-tuning curves. These findings place important constraints on the neural mechanisms of attention and we propose to unify the effects of spatial location, direction of motion and other features of the attended stimuli in a ‘feature similarity gain model’ of attention.


Gradient-based learning applied to document recognition


Condition monitoring of helicopter drive shafts using quadratic-nonlinearity metric based on cross-bispectrum

Based on cross-bispectrum, quadratic-nonlinearity coupling between two vibration signals is proposed and used to assess health conditions of rotating shafts in an AH-64D helicopter tail rotor drive train. Vibration data are gathered from two bearings supporting the shaft in an experimental helicopter drive train simulating different shaft conditions, namely, baseline, misalignment, imbalance, and combination of misalignment and imbalance. The proposed metric shows better capabilities in distinguishing different shaft settings than the conventional linear coupling based on cross-power spectrum.


Internet security architecture

Fear of security breaches has been a major r eason f or the business world’ s reluctance to embrace the Inter net as a viable means of communication. A widely adopted solution consists of physically separating private networks from the rest of Internet using firewalls. This paper discusses the curr ent cryptographic security measures available for the Internet infrastructur e as an alternative to physical segregation. First the IPsec ar chitecture including security protocols in the Internet Layer and the related key management pr oposals are introduced. The transport layer security protocol and security issues in the netw ork control and management are then presented. The paper is addr essed to r eaders with a basic understanding of common security mechanisms including encryption, authentication and key exchange techniques.


Client attachment, attachment to the therapist and client-therapist attachment match: how do they relate to change in psychodynamic psychotherapy?

OBJECTIVE
We examined the associations between client attachment, client attachment to the therapist, and symptom change, as well as the effects of client-therapist attachment match on outcome. Clients (n = 67) and their therapists (n = 27) completed the ECR to assess attachment.

METHOD
Clients completed also the Client Attachment to Therapist scale three times (early, middle, and late sessions) and the OQ-45 at intake and four times over the course of a year of psychodynamic psychotherapy.

RESULTS
Clients characterized by avoidant attachment and by avoidant attachment to their therapist showed the least improvement. A low-avoidant client-therapist attachment match led to a greater decrease in symptom distress than when a low-avoidant therapist treated a high-avoidant client.

CONCLUSIONS
These findings suggest the importance of considering client-therapist attachment matching and the need to pay attention to the special challenges involved in treating avoidant clients in order to facilitate progress in psychotherapy.


Prioritized multi-task compliance control of redundant manipulators

We propose a new approach for dynamic control of redundant manipulators to deal with multiple prioritized tasks at the same time by utilizing null space projection techniques. The compliance control law is based on a new representation of the dynamics wherein specific null space velocity coordinates are introduced. These allow to efficiently exploit the kinematic redundancy according to the task hierarchy and lead to a dynamics formulation with block-diagonal inertia matrix. The compensation of velocity-dependent coupling terms between the tasks by an additional passive feedback action facilitates a stability analysis for the complete hierarchy based on semi-definite Lyapunov functions. No external forces have to be measured. Finally, the performance of the control approach is evaluated in experiments on a torque-controlled robot. © 2015 Elsevier Ltd. All rights reserved.


The Concept of Flow

What constitutes a good life? Few questions are of more fundamental importance to a positive psychology. Flow research has yielded one answer, providing an understanding of experiences during which individuals are fully involved in the present moment. Viewed through the experiential lens of flow, a good life is one that is characterized by complete absorption in what one does. In this chapter, we describe the flow model of optimal experience and optimal development, explain how flow and related constructs have been measured, discuss recent work in this area, and identify some promising directions for future research.


Corporate social responsibility and bank customer satisfaction A research agenda

Purpose – The purpose of this paper is to explore the relationship between corporate social responsibility (CSR) and customer outcomes. Design/methodology/approach – This paper reviews the literature on CSR effects and satisfaction, noting gaps in the literature. Findings – A series of propositions is put forward to guide future research endeavours. Research limitations/implications – By understanding the likely impact on customer satisfaction of CSR initiatives vis-à-vis customer-centric initiatives, the academic research community can assist managers to understand how to best allocate company resources in situations of low customer satisfaction. Such endeavours are managerially relevant and topical. Researchers seeking to test the propositions put forward in this paper would be able to gain links with, and possibly attract funding from, banks to conduct their research. Such endeavours may assist researchers to redefine the stakeholder view by placing customers at the centre of a network of stakeholders. Practical implications – An understanding of how to best allocate company resources to increase the proportion of satisfied customers will allow bank marketers to reduce customer churn and hence increase market share and profits. Originality/value – Researchers have not previously conducted a comparative analysis of the effects of different CSR initiatives on customer satisfaction, nor considered whether more customer-centric initiatives are likely to be more effective in increasing the proportion of satisfied customers.


Essential Layers, Artifacts, and Dependencies of Enterprise Architecture

After a period where implementation speed was more important than integration, consistency and reduction of complexity, architectural considerations have become a key issue of information management in recent years again. Enterprise architecture is widely accepted as an essential mechanism for ensuring agility and consistency, compliance and efficiency. Although standards like TOGAF and FEAF have developed, however, there is no common agreement on which architecture layers, which artifact types and which dependencies constitute the essence of enterprise architecture. This paper contributes to the identification of essential elements of enterprise architecture by (1) specifying enterprise architecture as a hierarchical, multilevel system comprising aggregation hierarchies, architecture layers and views, (2) discussing enterprise architecture frameworks with regard to essential elements, (3) proposing interfacing requirements of enterprise architecture with other architecture models and (4) matching these findings with current enterprise architecture practice in several large companies.


Agent-based control for decentralised demand side management in the smart grid

Central to the vision of the smart grid is the deployment of smart meters that will allow autonomous software agents, representing the consumers, to optimise their use of devices and heating in the smart home while interacting with the grid. However, without some form of coordination, the population of agents may end up with overly-homogeneous optimised consumption patterns that may generate significant peaks in demand in the grid. These peaks, in turn, reduce the efficiency of the overall system, increase carbon emissions, and may even, in the worst case, cause blackouts. Hence, in this paper, we introduce a novel model of a Decentralised Demand Side Management (DDSM) mechanism that allows agents, by adapting the deferment of their loads based on grid prices, to coordinate in a decentralised manner. Specifically, using average UK consumption profiles for 26M homes, we demonstrate that, through an emergent coordination of the agents, the peak demand of domestic consumers in the grid can be reduced by up to 17% and carbon emissions by up to 6%. We also show that our DDSM mechanism is robust to the increasing electrification of heating in UK homes (i.e., it exhibits a similar efficiency).


Maxillary length at 11-14 weeks of gestation in fetuses with trisomy 21.

OBJECTIVE
To determine the value of measuring maxillary length at 11-14 weeks of gestation in screening for trisomy 21.

METHODS
In 970 fetuses ultrasound examination was carried out for measurement of crown-rump length (CRL), nuchal translucency and maxillary length, and to determine if the nasal bone was present or absent, immediately before chorionic villus sampling for karyotyping at 11-14 weeks of gestation. In 60 cases the maxillary length was measured twice by the same operator to calculate the intraobserver variation in measurements.

RESULTS
The median gestation was 12 (range, 11-14) weeks. The maxilla was successfully examined in all cases. The mean difference between paired measurements of maxillary length was -0.012 mm and the 95% limits of agreement were -0.42 (95% CI, -0.47 to -0.37) to 0.40 (95% CI, 0.35 to 0.44) mm. The fetal karyotype was normal in 839 pregnancies and abnormal in 131, including 88 cases of trisomy 21. In the chromosomally normal group the maxillary length increased significantly with CRL from a mean of 4.8 mm at a CRL of 45 mm to 8.3 mm at a CRL of 84 mm. In the trisomy 21 fetuses the maxillary length was significantly shorter than normal by 0.7 mm and in the trisomy 21 fetuses with absent nasal bone the maxilla was shorter than in those with present nasal bone by 0.5 mm. In fetuses with other chromosomal defects there were no significant differences from normal in the maxillary length.

CONCLUSION
At 11-14 weeks of gestation, maxillary length in trisomy 21 fetuses is significantly shorter than in normal fetuses.


From e-commerce to social commerce: A close look at design features

E-commerce is undergoing an evolution through the adoption of Web 2.0 capabilities to enhance customer participation and achieve greater economic value. This new phenomenon is commonly referred to as social commerce, however it has not yet been fully understood. In addition to the lack of a stable and agreed-upon definition, there is little research on social commerce and no significant research dedicated to the design of social commerce platforms. This study offers literature review to explain the concept of social commerce, tracks its nascent state-of-the-art, and discusses relevant design features as they relate to e-commerce and Web 2.0. We propose a new model and a set of principles for guiding social commerce design. We also apply the model and guidelines to two leading social commerce platforms, Amazon and Starbucks on Facebook. The findings indicate that, for any social commerce website, it is critical to achieve a minimum set of social commerce design features. These design features must cover all the layers of the proposed model, including the individual, conversation, community and commerce levels. 2012 Elsevier B.V. All rights reserved.


A spectrogram-based audio fingerprinting system for content-based copy detection

This paper presents a novel audio fingerprinting method that is highly robust to a variety of audio distortions. It is based on an unconventional audio fingerprint generation scheme. The robustness is achieved by generating different versions of the spectrogram matrix of the audio signal by using a threshold based on the average of the spectral values to prune this matrix. We transform each version of this pruned spectrogram matrix into a 2-D binary image. Multiple versions of these 2-D images suppress noise to a varying degree. This varying degree of noise suppression improves likelihood of one of the images matching a reference image. To speed up matching, we convert each image into an n-dimensional vector, and perform a nearest neighbor search based on this n-dimensional vector. We give results with two different feature parameters and their combination. We test this method on TRECVID 2010 content-based copy detection evaluation dataset, and we validate the performance on TRECVID 2009 dataset also. Experimental results show the effectiveness of these features even when the audio is distorted. We compare the proposed method to two state-of-the-art audio copy detection systems, namely NN-based and Shazam systems. Our method by far outperforms Shazam system for all audio transformations (or distortions) in terms of detection performance, number of missed queries and localization accuracy. Compared to NN-based system, our approach reduces minimal Normalized Detection Cost Rate (min NDCR) by 23 % and improves localization accuracy by 24 %.


More than Just Two Sexes: The Neural Correlates of Voice Gender Perception in Gender Dysphoria

Gender dysphoria (also known as "transsexualism") is characterized as a discrepancy between anatomical sex and gender identity. Research points towards neurobiological influences. Due to the sexually dimorphic characteristics of the human voice, voice gender perception provides a biologically relevant function, e.g. in the context of mating selection. There is evidence for a better recognition of voices of the opposite sex and a differentiation of the sexes in its underlying functional cerebral correlates, namely the prefrontal and middle temporal areas. This fMRI study investigated the neural correlates of voice gender perception in 32 male-to-female gender dysphoric individuals (MtFs) compared to 20 non-gender dysphoric men and 19 non-gender dysphoric women. Participants indicated the sex of 240 voice stimuli modified in semitone steps in the direction to the other gender. Compared to men and women, MtFs showed differences in a neural network including the medial prefrontal gyrus, the insula, and the precuneus when responding to male vs. female voices. With increased voice morphing men recruited more prefrontal areas compared to women and MtFs, while MtFs revealed a pattern more similar to women. On a behavioral and neuronal level, our results support the feeling of MtFs reporting they cannot identify with their assigned sex.


Security and compliance challenges in complex IT outsourcing arrangements: A multi-stakeholder perspective

Complex IT outsourcing arrangements promise numerous benefits such as increased cost predictability and reduced costs, higher flexibility and scalability upon demand. Organizations trying to realize these benefits, however, face several security and compliance challenges. In this article, we investigate the pressure to take action with respect to such challenges and discuss avenues toward promising responses. We collected perceptions on security and compliance challenges from multiple stakeholders by means of a series of interviews and an online survey, first, to analyze the current and future relevance of the challenges as well as potential adverse effects on organizational performance and, second, to discuss the nature and scope of potential responses. The survey participants confirmed the current and future relevance of the six challenges auditing clouds, managing heterogeneity of services, coordinating involved parties, managing relationships between clients and vendors, localizing and migrating data and coping with lack of security awareness. Additionally, they perceived these challenges as affecting organizational performance adversely in case they are not properly addressed. Responses in form of organizational measures were considered more promising than technical ones concerning all challenges except localizing and migrating data, for which the opposite was true. Balancing relational and contractual governance as well as employing specific client and vendor capabilities is essential for the success of IT outsourcing arrangements, yet do not seem sufficient to overcome the investigated challenges. Innovations connecting the technical perspective of utility software with the business perspective of application software relevant for security and compliance management, however, nourish the hope that the benefits associated with complex IT outsourcing arrangements can be realized in the foreseeable future whilst addressing the security and compliance challenges. a 2013 Elsevier Ltd. All rights reserved. 61. .fraunhofer.de (D. Bachlechner), stefan.thalmann@uibk.ac.at (S. Thalmann), ronald.maier@ ier Ltd. All rights reserved. c om p u t e r s & s e c u r i t y 4 0 ( 2 0 1 4 ) 3 8e5 9 39


Detection of high variability in gene expression from single-cell RNA-seq profiling

The advancement of the next-generation sequencing technology enables mapping gene expression at the single-cell level, capable of tracking cell heterogeneity and determination of cell subpopulations using single-cell RNA sequencing (scRNA-seq). Unlike the objectives of conventional RNA-seq where differential expression analysis is the integral component, the most important goal of scRNA-seq is to identify highly variable genes across a population of cells, to account for the discrete nature of single-cell gene expression and uniqueness of sequencing library preparation protocol for single-cell sequencing. However, there is lack of generic expression variation model for different scRNA-seq data sets. Hence, the objective of this study is to develop a gene expression variation model (GEVM), utilizing the relationship between coefficient of variation (CV) and average expression level to address the over-dispersion of single-cell data, and its corresponding statistical significance to quantify the variably expressed genes (VEGs). We have built a simulation framework that generated scRNA-seq data with different number of cells, model parameters, and variation levels. We implemented our GEVM and demonstrated the robustness by using a set of simulated scRNA-seq data under different conditions. We evaluated the regression robustness using root-mean-square error (RMSE) and assessed the parameter estimation process by varying initial model parameters that deviated from homogeneous cell population. We also applied the GEVM on real scRNA-seq data to test the performance under distinct cases. In this paper, we proposed a gene expression variation model that can be used to determine significant variably expressed genes. Applying the model to the simulated single-cell data, we observed robust parameter estimation under different conditions with minimal root mean square errors. We also examined the model on two distinct scRNA-seq data sets using different single-cell protocols and determined the VEGs. Obtaining VEGs allowed us to observe possible subpopulations, providing further evidences of cell heterogeneity. With the GEVM, we can easily find out significant variably expressed genes in different scRNA-seq data sets.


Design and control of underactuated tendon-driven mechanisms

Many robotic hands or prosthetic hands have been developed in the last several decades, and many use tendon-driven mechanisms for their transmissions. Robotic hands are now built with underactuated mechanisms, which have fewer actuators than degrees of freedom, to reduce mechanical complexity or to realize a biomimetic motion such as flexion of an index finger. The design is heuristic and it is useful to develop design methods for the underactuated mechanisms. This paper classifies mechanisms driven by tendons into three classes, and proposes a design method for them. The two classes are related to underactuated tendon-driven mechanisms, and these have been used without distinction so far. An index finger robot, which has four active tendons and two passive tendons, is developed and controlled with the proposed method.


Semi-Supervised Learning with the Deep Rendering Mixture Model

Semi-supervised learning algorithms reduce the high cost of acquiring labeled training data by using both labeled and unlabeled data during learning. Deep Convolutional Networks (DCNs) have achieved great success in supervised tasks and as such have been widely employed in the semi-supervised learning. In this paper we leverage the recently developed Deep Rendering Mixture Model (DRMM), a probabilistic generative model that models latent nuisance variation, and whose inference algorithm yields DCNs. We develop an EM algorithm for the DRMM to learn from both labeled and unlabeled data. Guided by the theory of the DRMM, we introduce a novel nonnegativity constraint and a variational inference term. We report state-of-the-art performance on MNIST and SVHN and competitive results on CIFAR10. We also probe deeper into how a DRMM trained in a semi-supervised setting represents latent nuisance variation using synthetically rendered images. Taken together, our work provides a unified framework for supervised, unsupervised, and semisupervised learning.


Visual analytics of spatial interaction patterns for pandemic decision support

This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. Population mobility, i.e. the movement and contact of individuals across geographic space, is one of the essential factors that determine the course of a pandemic disease spread. This research views both individual-based daily activities and a pandemic spread as spatial interaction problems, where locations interact with each other via the visitors that they share or the virus that is transmitted from one place to another. The research proposes a general visual analytic approach to synthesize very large spatial interaction data and discover interesting (and unknown) patterns. The proposed approach involves a suite of visual and computational techniques, including (1) a new graph partitioning method to segment a very large interaction graph into a moderate number of spatially contiguous subgraphs (regions); (2) a reorderable matrix, with regions 'optimally' ordered on the diagonal, to effectively present a holistic view of major spatial interaction patterns; and (3) a modified flow map, interactively linked to the reorderable matrix, to enable pattern interpretation in a geographical context. The implemented system is able to visualize both people's daily movements and a disease spread over space in a similar way. The discovered spatial interaction patterns provide valuable insight for designing effective pandemic mitigation strategies and supporting decision-making in time-critical situations.


On Bayesian Upper Confidence Bounds for Bandit Problems

Stochastic bandit problems have been analyzed from two different perspectives: a frequentist view, where the parameter is a deterministic unknown quantity, and a Bayesian approach, where the parameter is drawn from a prior distribution. We show in this paper that methods derived from this second perspective prove optimal when evaluated using the frequentist cumulated regret as a measure of performance. We give a general formulation for a class of Bayesian index policies that rely on quantiles of the posterior distribution. For binary bandits, we prove that the corresponding algorithm, termed BayesUCB, satisfies finite-time regret bounds that imply its asymptotic optimality. More generally, Bayes-UCB appears as an unifying framework for several variants of the UCB algorithm addressing different bandit problems (parametric multi-armed bandits, Gaussian bandits with unknown mean and variance, linear bandits). But the generality of the Bayesian approach makes it possible to address more challenging models. In particular, we show how to handle linear bandits with sparsity constraints by resorting to Gibbs sampling.


Psychometric Properties of Physical Activity and Leisure Motivation Scale in Farsi: an International Collaborative Project on Motivation for Physical Activity and Leisure.

BACKGROUND
Given the importance of regular physical activity, it is crucial to evaluate the factors favoring participation in physical activity. We aimed to report the psychometric analysis of the Farsi version of the Physical Activity and Leisure Motivation Scale (PALMS).

METHODS
The Farsi version of PALMS was completed by 406 healthy adult individuals to test its factor structure and concurrent validity and reliability.

RESULTS
Conducting the exploratory factor analysis revealed nine factors that accounted for 64.6% of the variances. The PALMS reliability was supported with a high internal consistency of 0.91 and a high test-retest reliability of 0.97 (95% CI: 0.97-0.98). The association between the PALMS and its previous version Recreational Exercise Motivation Measure scores was strongly significant (r= 0.86, P < 0.001).

CONCLUSION
We have shown that the Farsi version of the PALMS appears to be a valuable instrument to measure motivation for physical activity and leisure.


An extended online purchase intention model for middle-aged online users

This article focuses on examining the determinants and mediators of the purchase intention of nononline purchasers between ages 31 and 60 who mostly have strong purchasing power. It propose anew online purchase intention model by integrating the technology acceptance model with additional determinants and adding habitual online usage as a new mediator. Based on a sample of more than 300 middle-aged non-online purchasers, beyond some situationally-specific predictor variables, online purchasing attitude and habitual online usage are key mediators. Personal awareness of security only affects habitual online usage, t indicating a concern of middle-aged users. Habitual online usage is a


Maximum Complex Task Assignment: Towards Tasks Correlation in Spatial Crowdsourcing

Spatial crowdsourcing has gained emerging interest from both research communities and industries. Most of current spatial crowdsourcing frameworks assume independent and atomic tasks. However, there could be some cases that one needs to crowdsource a spatial complex task which consists of some spatial sub-tasks (i.e., tasks related to a specific location). The spatial complex task's assignment requires assignments of all of its sub-tasks. The currently available frameworks are inapplicable to such kind of tasks. In this paper, we introduce a novel approach to crowdsource spatial complex tasks. We first formally define the Maximum Complex Task Assignment (MCTA) problem and propose alternative solutions. Subsequently, we perform various experiments using both real and synthetic datasets to investigate and verify the usability of our proposed approach.


A survey on policy search algorithms for learning robot controllers in a handful of trials

Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word “big-data”, we refer to this challenge as “micro-data reinforcement learning”. We show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based policy search), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots (e.g., humanoids), designing generic priors, and optimizing the computing time.


Visual Analysis of TED Talk Topic Trends

TED Talks are short, powerful talks given by some of the world's brightest minds - scientists, philanthropists, businessmen, artists, and many others. Funded by members and advertising, these talks are free to access by the public on the TED website and TED YouTube channel, and many videos have become viral phenomena. In this research project, we perform a visual analysis of TED Talk videos and playlists to gain a good understanding of the trends and relationships between TED Talk topics.


Fine-grained Concept Linking using Neural Networks in Healthcare

To unlock the wealth of the healthcare data, we often need to link the real-world text snippets to the referred medical concepts described by the canonical descriptions. However, existing healthcare concept linking methods, such as dictionary-based and simple machine learning methods, are not effective due to the word discrepancy between the text snippet and the canonical concept description, and the overlapping concept meaning among the fine-grained concepts. To address these challenges, we propose a Neural Concept Linking (NCL) approach for accurate concept linking using systematically integrated neural networks. We call the novel neural network architecture as the COMposite AttentIonal encode-Decode neural network (COM-AID). COM-AID performs an encode-decode process that encodes a concept into a vector and decodes the vector into a text snippet with the help of two devised contexts. On the one hand, it injects the textual context into the neural network through the attention mechanism, so that the word discrepancy can be overcome from the semantic perspective. On the other hand, it incorporates the structural context into the neural network through the attention mechanism, so that minor concept meaning differences can be enlarged and effectively differentiated. Empirical studies on two real-world datasets confirm that the NCL produces accurate concept linking results and significantly outperforms state-of-the-art techniques.


Structural XML Classification in Concept Drifting Data Streams

Classification of large, static collections of XML data has been intensively studied in the last several years. Recently however, the data processing paradigm is shifting from static to streaming data, where documents have to be processed online using limited memory and class definitions can change with time in an event called concept drift. As most existing XML classifiers are capable of processing only static data, there is a need to develop new approaches dedicated for streaming environments. In this paper, we propose a new classification algorithm for XML data streams called XSC. The algorithm uses incrementally mined frequent subtrees and a tree-subtree similarity measure to classify new documents in an associative manner. The proposed approach is experimentally evaluated against eight state-of-the-art stream classifiers on real and synthetic data. The results show that XSC performs significantly better than competitive algorithms in terms of accuracy and memory usage.


Bitter to Better - How to Make Bitcoin a Better Currency

Bitcoin is a distributed digital currency which has attracted a substantial number of users. We perform an in-depth investigation to understand what made Bitcoin so successful, while decades of research on cryptographic e-cash has not lead to a large-scale deployment. We ask also how Bitcoin could become a good candidate for a long-lived stable currency. In doing so, we identify several issues and attacks of Bitcoin, and propose suitable techniques to address them.


SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies

Bit coin has emerged as the most successful cryptographic currency in history. Within two years of its quiet launch in 2009, Bit coin grew to comprise billions of dollars of economic value despite only cursory analysis of the system's design. Since then a growing literature has identified hidden-but-important properties of the system, discovered attacks, proposed promising alternatives, and singled out difficult future challenges. Meanwhile a large and vibrant open-source community has proposed and deployed numerous modifications and extensions. We provide the first systematic exposition Bit coin and the many related crypto currencies or 'altcoins.' Drawing from a scattered body of knowledge, we identify three key components of Bit coin's design that can be decoupled. This enables a more insightful analysis of Bit coin's properties and future stability. We map the design space for numerous proposed modifications, providing comparative analyses for alternative consensus mechanisms, currency allocation mechanisms, computational puzzles, and key management tools. We survey anonymity issues in Bit coin and provide an evaluation framework for analyzing a variety of privacy-enhancing proposals. Finally we provide new insights on what we term disinter mediation protocols, which absolve the need for trusted intermediaries in an interesting set of applications. We identify three general disinter mediation strategies and provide a detailed comparison.


Majority Is Not Enough: Bitcoin Mining Is Vulnerable


Information propagation in the Bitcoin network

Bitcoin is a digital currency that unlike traditional currencies does not rely on a centralized authority. Instead Bitcoin relies on a network of volunteers that collectively implement a replicated ledger and verify transactions. In this paper we analyze how Bitcoin uses a multi-hop broadcast to propagate transactions and blocks through the network to update the ledger replicas. We then use the gathered information to verify the conjecture that the propagation delay in the network is the primary cause for blockchain forks. Blockchain forks should be avoided as they are symptomatic for inconsistencies among the replicas in the network. We then show what can be achieved by pushing the current protocol to its limit with unilateral changes to the client's behavior.


Double-spending fast payments in bitcoin

Bitcoin is a decentralized payment system that relies on Proof-of-Work (PoW) to verify payments. Nowadays, Bitcoin is increasingly used in a number of fast payment scenarios, where the time between the exchange of currency and goods is short (in the order of few seconds). While the Bitcoin payment verification scheme is designed to prevent double-spending, our results show that the system requires tens of minutes to verify a transaction and is therefore inappropriate for fast payments. An example of this use of Bitcoin was recently reported in the media: Bitcoins were used as a form of \emph{fast} payment in a local fast-food restaurant. Until now, the security of fast Bitcoin payments has not been studied. In this paper, we analyze the security of using Bitcoin for fast payments. We show that, unless appropriate detection techniques are integrated in the current Bitcoin implementation, double-spending attacks on fast payments succeed with overwhelming probability and can be mounted at low cost. We further show that the measures recommended by Bitcoin developers for the use of Bitcoin in fast payments are not always effective in detecting double-spending; we show that if those recommendations are integrated in future Bitcoin implementations, double-spending attacks on Bitcoin will still be possible. Finally, we propose and implement a modification to the existing Bitcoin implementation that ensures the detection of double-spending attacks against fast payments.


Disruptive Innovation : Removing the Innovators ’ Dilemma

The objectives of this research are to co-create understanding and knowledge on the phenomenon of disruptive innovation in order to provide pragmatic clarity on the term’s meaning, impact and implications. This will address the academic audience’s gap in knowledge and provide help to practitioners wanting to understand how disruptive innovation can be fostered as part of a major competitive strategy. This paper reports on the first eighteen months of a three year academic and industrial investigation. It presents a new pragmatic definition drawn from the literature and an overview of the conceptual framework for disruptive innovation that was co-created via the collaborative efforts of academia and industry. The barriers to disruptive innovation are presented and a best practice case study of how one company is overcoming these barriers is described. The remainder of the research, which is supported by a European Commission co-sponsored project called Disrupt-it, will focus on developing and validating tools to help overcome these barriers. Thomond, P., Herzberg, T. and Lettice, F. (2003). "Disruptive Innovation: Removing the Innovators’ Dilemma". Knowledge into Practice British Academy of Management Annual Conference, Harrogate, UK, September 2003. 2 1.0. Introduction and Background. In his ground breaking book “The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail”, Clayton Christensen first coined the phrase ‘disruptive technologies’. He showed that time and again almost all the organisations that have ‘died’ or been displaced from their industries because of a new paradigm of customer offering could see the disruption coming but did nothing until it was too late (Christensen, 1997). They assess the new approaches or technologies and frame them as either deficient or as an unlikely threat much to the managers’ regret and the organisation’s demise (Christensen 2002). In the early 1990s, major airlines such as British Airways decided that the opportunities afforded by a low-cost, point-to point no frills strategy such as that introduced by the newly formed Ryanair was an unlikely threat. By the mid-1990’s other newcomers such as easyJet had embraced Ryanair’s foresight and before long, the ‘low cost’ approach had captured a large segment of the market. Low-cost no frills proved a hit with European travellers but not with the established airlines who had either ignored the threat or failed to capitalise on the approach. Today DVD technology and Charles Schwab are seen to be having a similar impact upon the VHS industry and Merrill Lynch respectively, however, disruption is not just a recent phenomenon it has firm foundations as a trend in the past that will also inevitably occur in the future. Examples of past disruptive innovations would include the introduction of the telegraph and its impact upon businesses like Pony Express and the transistor’s impact upon the companies that produced cathode ray tubes. Future predictions include the impact of Light Emitting Diode’ (L.E.D.) technology and its potential to completely disrupt the traditional light bulb sector and its supporting industries. More optimistically, Christensen (2002) further shows that the process of disruptive innovation has been one of the fundamental causal mechanisms through which access to life improving products and services has been increased and the basis on which long term organisational survival could be ensured (Christensen, 1997). In spite of the proclaimed importance of disruptive innovation and the ever increasing interest from both the business and academic press alike, there still appears to be a disparity between rhetoric and reality. To date, the multifaceted and interrelated issues of disruptive innovation have not been investigated in depth. The phenomenon with examples has been described by a number of authors (Christensen, 1997, Moore, 1995 Gilbert and Bower, 2002) and practitioner orientated writers have begun to offer strategies for responding to disruptive Thomond, P., Herzberg, T. and Lettice, F. (2003). "Disruptive Innovation: Removing the Innovators’ Dilemma". Knowledge into Practice British Academy of Management Annual Conference, Harrogate, UK, September 2003. 3 change (Charitou and Markides, 2003, Rigby and Corbett, 2002, Rafi and Kampas, 2002). However, a deep integrated understanding of the entire subject is missing. In particular, there is an industrial need and academic gap knowledge in the pragmatic comprehension of how organisations can understand and foster disruptive innovation as part of a major competitive strategy. The objectives of this research are to co-create understanding and knowledge on the phenomenon of disruptive innovation in order to provide pragmatic clarity on the term’s meaning, impact and implications. This will address the academic audience’s gap in knowledge and provide help to practitioners wanting to understand how disruptive innovation can be fostered as part of a major competitive strategy. The current paper reports on the first eighteen months of a three year academic and industrial investigation. It presents a new pragmatic definition drawn from the literature and an overview of the conceptual framework for disruptive innovation that was co-created via the collaborative efforts of academia and industry. The barriers to disruptive innovation are presented and a best practice case study of how one company is overcoming these barriers is described. The research contributes to “Disrupt-it”, a €3million project for the Information Society Technologies Commission under the 5th Framework Program of the European Union, which will focus on developing and validating tools to help organisations foster disruptive innovation. 2.0 Understanding the Phenomenon of Disruptive Innovation. ‘Disruptive Innovation’, ‘Disruptive Technologies’ and ‘Disruptive Business Strategies’ are emerging and increasingly prominent business terms that are used to describe a form of revolutionary change. They are receiving ever more academic and industrial attention, yet these terms are still poorly defined and not well understood. A key objective of this research is to improve the understanding of disruptive innovation by drawing together multiple perspectives on the topic, as shown in Figure 1, into a more holistic and comprehensive definition. Much of the past investigation into discontinuous and disruptive innovation has been path dependent upon the researchers’ investigative history. For example, Hamel’s strategy background leads him to see disruptive innovation through the lens of the ‘business model’; whereas Christensen’s technologically orientated past leads to a focus on ‘disruptive technologies’. What many researchers share is the view that firms need to periodically engage in the process of revolutionary change for long-term survival and this is not a new Thomond, P., Herzberg, T. and Lettice, F. (2003). "Disruptive Innovation: Removing the Innovators’ Dilemma". Knowledge into Practice British Academy of Management Annual Conference, Harrogate, UK, September 2003. 4 phenomenon (Christensen, 1997; Christensen and Rosenbloom, 1995; Hamel, 2000; Schumpeter, 1975, Tushman and Anderson, 1986; Tushman and Nadler, 1986, Gilbert and Bower, 2002; Rigby and Corbett, 2002; Charitou and Markides, 2003; Foster and Kaplan, 2001; Thomond and Lettice, 2002). Disruptive innovation has also been defined as “a technology, product or process that creeps up from below an existing business and threatens to displace it. Typically, the disrupter offers lower performance and less functionality... The product or process is good enough for a meaningful number of customers – indeed some don’t buy the older version’s higher functionality and welcome the disruption’s simplicity. And gradually, the new product or process improves to the point where it displaces the incumbent.” (Rafi and Kampas p 8, 2002). This definition borrows heavily from the work of Christensen (1997), which in turn has some of its origins in the findings of Dosi (1982). For example, each of the cases of disruptive innovation mentioned thus far represents a new paradigm of customer offering. Dosi (1982) claims that these can be represented as discontinuities in trajectories of progress as defined within earlier paradigms where a technological paradigm is a pattern of solutions for selected technological problems. In fact, new paradigms redefine the future meaning of progress and a new class of problems becomes the target of normal incremental innovation (Dosi, 1982). Therefore, disruptive innovations appear to typify a particular type of ‘discontinuous innovation’ (a term which has received much more academic attention). The same characteristics are found, except that disruptive innovations first establish their commercial footing in new or simple market niches by enabling customers to do things that only specialists could do before (e.g. low cost European airlines are opening up air travel to thousands that did not fly before) and that these new offerings, through a period of exploitation, migrate upmarket and eventually redefine the paradigms and value propositions on which the existing industry is based (Christensen, 1997, 2002; Moore, 1995; Business Model Charitou & Markides, 2003 Hamel, 2000


Finding new terminology in very large corpora

Most technical and scientific terms are comprised of complex, multi-word noun phrases but certainly not all noun phrases are technical or scientific terms. The distinction of specific terminology from common non-specific noun phrases can be based on the observation that terms reveal a much lesser degree of distributional variation than non-specific noun phrases. We formalize the limited paradigmatic modifiability of terms and, subsequently, test the corresponding algorithm on bigram, trigram and quadgram noun phrases extracted from a 104-million-word biomedical text corpus. Using an already existing and community-wide curated biomedical terminology as an evaluation gold standard, we show that our algorithm significantly outperforms standard term identification measures and, therefore, qualifies as a high-performant building block for any terminology identification system. We also provide empirical evidence that the superiority of our approach, beyond a 10-million-word threshold, is essentially domain- and corpus-size-independent.


Review of axial flux induction motor for automotive applications

Hybrid and electric vehicles have been the focus of many academic and industrial studies to reduce transport pollution; they are now established products. In hybrid and electric vehicles, the drive motor should have high torque density, high power density, high efficiency, strong physical structure and variable speed range. An axial flux induction motor is an interesting solution, where the motor is a double sided axial flux machine. This can significantly increase torque density. In this paper a review of the axial flux motor for automotive applications, and the different possible topologies for the axial field motor, are presented.


Generalized vulnerability extrapolation using abstract syntax trees

The discovery of vulnerabilities in source code is a key for securing computer systems. While specific types of security flaws can be identified automatically, in the general case the process of finding vulnerabilities cannot be automated and vulnerabilities are mainly discovered by manual analysis. In this paper, we propose a method for assisting a security analyst during auditing of source code. Our method proceeds by extracting abstract syntax trees from the code and determining structural patterns in these trees, such that each function in the code can be described as a mixture of these patterns. This representation enables us to decompose a known vulnerability and extrapolate it to a code base, such that functions potentially suffering from the same flaw can be suggested to the analyst. We evaluate our method on the source code of four popular open-source projects: LibTIFF, FFmpeg, Pidgin and Asterisk. For three of these projects, we are able to identify zero-day vulnerabilities by inspecting only a small fraction of the code bases.


A Combinatorial Noise Model for Quantum Computer Simulation

Quantum computers (QCs) have many potential hardware implementations ranging from solid-state silicon-based structures to electron-spin qubits on liquid helium. However, all QCs must contend with gate infidelity and qubit state decoherence over time. Quantum error correcting codes (QECCs) have been developed to protect program qubit states from such noise. Previously, Monte Carlo noise simulators have been developed to model the effectiveness of QECCs in combating decoherence. The downside to this random sampling approach is that it may take days or weeks to produce enough samples for an accurate measurement. We present an alternative noise modeling approach that performs combinatorial analysis rather than random sampling. This model tracks the progression of the most likely error states of the quantum program through its course of execution. This approach has the potential for enormous speedups versus the previous Monte Carlo methodology. We have found speedups with the combinatorial model on the order of 100X-1,000X over the Monte Carlo approach when analyzing applications utilizing the [[7,1,3]] QECC. The combinatorial noise model has significant memory requirements, and we analyze its scaling properties relative to the size of the quantum program. Due to its speedup, this noise model is a valuable alternative to traditional Monte Carlo simulation.


Random Walk with Restart on Large Graphs Using Block Elimination

Given a large graph, how can we calculate the relevance between nodes fast and accurately? Random walk with restart (RWR) provides a good measure for this purpose and has been applied to diverse data mining applications including ranking, community detection, link prediction, and anomaly detection. Since calculating RWR from scratch takes a long time, various preprocessing methods, most of which are related to inverting adjacency matrices, have been proposed to speed up the calculation. However, these methods do not scale to large graphs because they usually produce large dense matrices that do not fit into memory. In addition, the existing methods are inappropriate when graphs dynamically change because the expensive preprocessing task needs to be computed repeatedly.
 In this article, we propose Bear, a fast, scalable, and accurate method for computing RWR on large graphs. Bear has two versions: a preprocessing method BearS for static graphs and an incremental update method BearD for dynamic graphs. BearS consists of the preprocessing step and the query step. In the preprocessing step, BearS reorders the adjacency matrix of a given graph so that it contains a large and easy-to-invert submatrix, and precomputes several matrices including the Schur complement of the submatrix. In the query step, BearS quickly computes the RWR scores for a given query node using a block elimination approach with the matrices computed in the preprocessing step. For dynamic graphs, BearD efficiently updates the changed parts in the preprocessed matrices of BearS based on the observation that only small parts of the preprocessed matrices change when few edges are inserted or deleted. Through extensive experiments, we show that BearS significantly outperforms other state-of-the-art methods in terms of preprocessing and query speed, space efficiency, and accuracy. We also show that BearD quickly updates the preprocessed matrices and immediately computes queries when the graph changes.


An energy-efficient unequal clustering mechanism for wireless sensor networks

Clustering provides an effective way for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques, selecting cluster heads with more residual energy and rotating cluster heads periodically, to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, they rarely consider the hot spots problem in multihop wireless sensor networks. When cluster heads cooperate with each other to forward their data to the base station, the cluster heads closer to the base station are burdened with heavy relay traffic and tend to die early, leaving areas of the network uncovered and causing network partition. To address the problem, we propose an energy-efficient unequal clustering (EEUC) mechanism for periodical data gathering in wireless sensor networks. It partitions the nodes into clusters of unequal size, and clusters closer to the base station have smaller sizes than those farther away from the base station. Thus cluster heads closer to the base station can preserve some energy for the inter-cluster data forwarding. We also propose an energy-aware multihop routing protocol for the inter-cluster communication. Simulation results show that our unequal clustering mechanism balances the energy consumption well among all sensor nodes and achieves an obvious improvement on the network lifetime


Art-Bots: Toward Chat-Based Conversational Experiences in Museums


Learning Bound for Parameter Transfer Learning

We consider a transfer-learning problem by using the parameter transfer approach, where a suitable parameter of feature mapping is learned through one task and applied to another objective task. Then, we introduce the notion of the local stability and parameter transfer learnability of parametric feature mapping, and thereby derive a learning bound for parameter transfer algorithms. As an application of parameter transfer learning, we discuss the performance of sparse coding in selftaught learning. Although self-taught learning algorithms with plentiful unlabeled data often show excellent empirical performance, their theoretical analysis has not been studied. In this paper, we also provide the first theoretical learning bound for self-taught learning.


Analyzing Posture and Affect in Task-Oriented Tutoring

Intelligent tutoring systems research aims to produce systems that meet or exceed the effectiveness of one on one expert human tutoring. Theory and empirical study suggest that affective states of the learner must be addressed to achieve this goal. While many affective measures can be utilized, posture offers the advantages of non intrusiveness and ease of interpretation. This paper presents an accurate posture estimation algorithm applied to a computer mediated tutoring corpus of depth recordings. Analyses of posture and session level student reports of engagement and cognitive load identified significant patterns. The results indicate that disengagement and frustration may coincide with closer postural positions and more movement, while focused attention and less frustration occur with more distant, stable postural positions. It is hoped that this work will lead to intelligent tutoring systems that recognize a greater breadth of affective expression through channels of posture and gesture.


Contributions of the prefrontal cortex to the neural basis of human decision making

The neural basis of decision making has been an elusive concept largely due to the many subprocesses associated with it. Recent efforts involving neuroimaging, neuropsychological studies, and animal work indicate that the prefrontal cortex plays a central role in several of these subprocesses. The frontal lobes are involved in tasks ranging from making binary choices to making multi-attribute decisions that require explicit deliberation and integration of diverse sources of information. In categorizing different aspects of decision making, a division of the prefrontal cortex into three primary regions is proposed. (1) The orbitofrontal and ventromedial areas are most relevant to deciding based on reward values and contribute affective information regarding decision attributes and options. (2) Dorsolateral prefrontal cortex is critical in making decisions that call for the consideration of multiple sources of information, and may recruit separable areas when making well defined versus poorly defined decisions. (3) The anterior and ventral cingulate cortex appear especially relevant in sorting among conflicting options, as well as signaling outcome-relevant information. This topic is broadly relevant to cognitive neuroscience as a discipline, as it generally comprises several aspects of cognition and may involve numerous brain regions depending on the situation. The review concludes with a summary of how these regions may interact in deciding and possible future research directions for the field.


Image-based Airborne LiDAR Point Cloud Encoding for 3 D Building Model Retrieval

With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority over related methods.


THE ROLE OF DIGITAL AND SOCIAL MEDIA MARKETING IN CONSUMER BEHAVIOR

This article reviews recently published research about consumers in digital and social media marketing settings. Five themes are identified: (i) consumer digital culture, (ii) responses to digital advertising, (iii) effects of digital environments on consumer behavior, (iv) mobile environments, and (v) online word of mouth (WOM). Collectively these articles shed light from many different angles on how consumers experience, influence, and are influenced by the digital environments in which they are situated as part of their daily lives. Much is still to be understood, and existing knowledge tends to be disproportionately focused on WOM, which is only part of the digital consumer experience. Several directions for future research are advanced to encourage researchers to consider a broader range of phenomena.


A Spreadsheet Algebra for a Direct Data Manipulation Query Interface

A spreadsheet-like "direct manipulation" interface is more intuitive for many non-technical database users compared to traditional alternatives, such as visual query builders. The construction of such a direct manipulation interfacemay appear straightforward, but there are some significant challenges. First, individual direct manipulation operations cannot be too complex, so expressive power has to be achieved through composing (long) sequences of small operations. Second, all intermediate results are visible to the user, so grouping and ordering are material after every small step. Third, users often find the need to modify previously specified queries. Since manipulations are specified one step at a time, there is no actual queryexpression to modify. Suitable means must be provided to address this need. Fourth, the order in which manipulations are performed by the user should not affect the results obtained, to avoid user confusion. We address the aforementioned challenges by designing a new spreadsheet algebra that: i) operates on recursively grouped multi-sets, ii) contains a selectively designed set of operators capable of expressing at least all single-block SQL queries and can be intuitively implemented in a spreadsheet, iii) enables query modification by the notion of modifiable query state, and iv) requires no ordering in unary data manipulation operators since they are all designed to commute. We built a prototype implementation of the spreadsheet algebra and show, through user studies with non-technical subjects, that the resultant query interface is easier to use than a standard commercial visual query builder.


OBJECTIVE SELF-AWARENESS THEORY : RECENT PROGRESS AND ENDURING PROBLEMS By :

Objective self-awareness theory has undergone fundamental changes in the 3 decades since Duval and Wicklund's (1972) original formulation. We review new evidence that bears on the basic tenets of the theory. Many of the assumptions of self-awareness theory require revision, particularly how expectancies influence approach and avoidance of self-standard discrepancies; the nature of standards, especially when they are changed; and the role of causal attribution in directing discrepancy reduction. However, several unresolved conceptual issues remain; future theoretical and empirical directions are discussed. Article: The human dilemma is that which arises out of a man's capacity to experience himself as both subject and object at the same time. Both are necessary--for the science of psychology, for therapy, and for gratifying living. (May, 1967, p. 8) Although psychological perspectives on the self have a long history (e.g., Cooley, 1902; James, 1890; Mead, 1934), experimental research on the self has emerged only within the last 40 years. One of the earliest "self theories" was objective self-awareness (OSA) theory (Duval & Wicklund, 1972). OSA theory was concerned with the self-reflexive quality of the consciousness. Just as people can apprehend the existence of environmental stimuli, they can be aware of their own existence: "When attention is directed inward and the individual's consciousness is focused on himself, he is the object of his own consciousness--hence 'objective' self awareness" (Duval & Wicklund, 1972, p. 2). This is contrasted with "subjective self-awareness" that results when attention is directed away from the self and the person "experiences himself as the source of perception and action" (Duval & Wicklund, 1972, p. 3). By this, Duval and Wicklund (1972,chap. 3) meant consciousness of one's existence on an organismic level, in which such existence is undifferentiated as a separate and distinct object in the world. OSA theory has stimulated a lot of research and informed basic issues in social psychology, such as emotion (Scheier & Carver, 1977), attribution (Duval & Wicklund, 1973), attitude--behavior consistency (Gibbons, 1983), self-standard comparison (Duval & Lalwani, 1999), prosocial behavior (Froming, Nasby, & McManus, 1998), deindividuation (Diener, 1979), stereotyping (Macrae, Bodenhausen, & Milne, 1998), self-assessment (Silvia & Gendolla, in press), terror management (Arndt, Greenberg, Simon, Pyszczynski, & Solomon, 1998; Silvia, 2001), and group dynamics (Duval, 1976; Mullen, 1983). Self-focused attention is also fundamental to a host of clinical and health phenomena (Hull, 1981; Ingram, 1990; Pyszczynski, Hamilton, Greenberg, & Becker, 1991; Wells & Matthews, 1994). The study of self-focused attention continues to be a dynamic and active research area. A lot of research relevant to basic theoretical issues has been conducted since the last maj or review (Gibbons, 1990). Recent research has made progress in understanding links between self-awareness and causal attribution, the effects of expectancies on self-standard discrepancy reduction, and the nature of standards--the dynamics of selfawareness are now viewed quite differently. We review these recent developments[1] and hope that a conceptual integration of new findings will further stimulate research on self-focused attention. However, there is still much conceptual work left to be done, and many basic issues remain murky and controversial. We discuss these unresolved issues and sketch the beginnings of some possible solutions. Original Theory The original statement of OSA theory (Duval & Wicklund, 1972) employed only a few constructs, relations, and processes. The theory assumed that the orientation of conscious attention was the essence of selfevaluation. Focusing attention on the self brought about objective self-awareness, which initiated an automatic comparison of the self against standards. The self was defined very broadly as the person's knowledge of the person. A standard was "defined as a mental representation of correct behavior, attitudes, and traits ... All of the standards of correctness taken together define what a 'correct' person is" (Duval & Wicklund, 1972, pp. 3, 4). This simple system consisting of self, standards, and attentional focus was assumed to operate according to gestalt consistency principles (Heider, 1960). If a discrepancy was found between self and standards, negative affect was said to arise. This aversive state then motivated the restoration of consistency. Two behavioral routes were proposed. People could either actively change their actions, attitudes, or traits to be more congruent with the representations of the standard or could avoid the self-focusing stimuli and circumstances. Avoidance effectively terminates the comparison process and hence all self-evaluation. Early research found solid support for these basic ideas (Carver, 1975; Gibbons & Wicklund, 1976; Wicklund & Duval, 1971). Duval and Wicklund (1972) also assumed that objective self-awareness would generally be an aversive state--the probability that at least one self-standard discrepancy exists is quite high. This was the first assumption to be revised. Later work found that self-awareness can be a positive state when people are congruent with their standards (Greenberg & Musham, 1981; Ickes, Wicklund, & Ferris, 1973). New Developments OSA theory has grown considerably from the original statement (Duval & Wicklund, 1972). Our review focuses primarily on core theoretical developments since the last review (Gibbons, 1990). Other interesting aspects, such as interpersonal processes and interoceptive accuracy, have not changed significantly since previous reviews (Gibbons, 1990; Silvia & Gendolla, in press). We will also overlook the many clinical consequences of self-awareness; these have been exhaustively reviewed elsewhere (Pyszczynski et al., 1991; Wells & Matthews, 1994). Expectancies and Avoiding Self-Awareness Reducing a discrepancy or avoiding self-focus are equally effective ways of reducing the negative affect resulting from a discrepancy. When do people do one or the other? The original theory was not very specific about when approach versus avoidance would occur. Duval and Wicklund (1972) did, however, speculate that two factors should be relevant. The first was whether people felt they could effectively reduce the discrepancy; the second was whether the discrepancy was small or large. In their translation of OSA theory into a "test--operate--test--exit" (TOTE) feedback system, Carver, Blaney, and Scheier (1979a, 1979b) suggested that expectancies regarding outcome favorability determine approach versus avoidance behavior. When a self-standard discrepancy is recognized, people implicitly appraise their likelihood of reducing the discrepancy (cf. Bandura, 1977; Lazarus, 1966). If eventual discrepancy reduction is perceived as likely, people will try to achieve the standard. When expectations regarding improvement are unfavorable, however, people will try to avoid self-focus. Later research and theory (Duval, Duval, & Mulilis, 1992) refined Duval and Wicklund's (1972) speculations and the notion of outcome favorability. Expectancies are not simply and dichotomously favorable or unfavorable--they connote a person's rate of progress in discrepancy reduction relative to the magnitude of the discrepancy. More specifically, people will try to reduce a discrepancy to the extent they believe that their rate of progress is sufficient relative to the magnitude of the problem. Those who believe their rate of progress to be insufficient will avoid. To test this hypothesis, participants were told they were either highly (90%) or mildly (10%) discrepant from an experimental standard (Duval et al., 1992, Study 1). Participants were then given the opportunity to engage in a remedial task that was guaranteed by the experimenter to totally eliminate their deficiency provided that they worked on the task for 2 hr and 10 min. However, the rate at which working on the task would reduce the discrepancy was varied. In the low rate of progress conditions individuals were shown a performance curve indicating no progress until the last 20 min of working on the remedial task. During the last 20 min discrepancy was reduced to zero. In the constant rate of progress condition, participants were shown a performance curve in which progress toward discrepancy reduction began immediately and continued throughout such efforts with 30% of the deficiency being reduced in the first 30 min of activity and totally eliminated after 2 hr and 10 min. Results indicated that persons who believed the discrepancy to be mild and progress to be constant worked on the remedial task; those who perceived the problem to be mild but the rate of progress to be low avoided this activity. However, participants who thought that the discrepancy was substantial and the rate of progress only constant avoided working on the remedial task relative to those in the mild discrepancy and constant rate of progress condition. These results were conceptually replicated in a second experiment (Duval et al., 1992) using participants' time to complete the total 2 hr and 10 min of remedial work as the dependent measure. This pattern suggests that the rate of progress was sufficient relative to the magnitude of the discrepancy in the mild discrepancy and constant rate of progress condition; this in turn promoted approaching the problem. In the high discrepancy and constant rate of progress condition and the high and mild discrepancy and low rate of progress conditions, rate of progress was insufficient and promoted avoiding the problem. In a third experiment (Duval et al., 1992), people were again led to believe that they were either highly or mildly discrepant from a standard on an intellectual dimension and then given the opportunity to reduce that deficiency by working on a remedial task. A


An empirical study on dependence clusters for effort-aware fault-proneness prediction

A dependence cluster is a set of mutually inter-dependent program elements. Prior studies have found that large dependence clusters are prevalent in software systems. It has been suggested that dependence clusters have potentially harmful effects on software quality. However, little empirical evidence has been provided to support this claim. The study presented in this paper investigates the relationship between dependence clusters and software quality at the function-level with a focus on effort-aware fault-proneness prediction. The investigation first analyzes whether or not larger dependence clusters tend to be more fault-prone. Second, it investigates whether the proportion of faulty functions inside dependence clusters is significantly different from the proportion of faulty functions outside dependence clusters. Third, it examines whether or not functions inside dependence clusters playing a more important role than others are more fault-prone. Finally, based on two groups of functions (i.e., functions inside and outside dependence clusters), the investigation considers a segmented fault-proneness prediction model. Our experimental results, based on five well-known open-source systems, show that (1) larger dependence clusters tend to be more fault-prone; (2) the proportion of faulty functions inside dependence clusters is significantly larger than the proportion of faulty functions outside dependence clusters; (3) functions inside dependence clusters that play more important roles are more fault-prone; (4) our segmented prediction model can significantly improve the effectiveness of effort-aware fault-proneness prediction in both ranking and classification scenarios. These findings help us better understand how dependence clusters influence software quality.


Reinforcement Learning in Continuous State and Action Spaces

Many traditional reinforcement-learning algorithms have been designed for problems with small finite state and action spaces. Learn ing in such discrete problems can been difficult, due to noise and delayed reinfor cements. However, many real-world problems have continuous state or action sp aces, which can make learning a good decision policy even more involved. In this c apter we discuss how to automatically find good decision policies in continuous d omains. Because analytically computing a good policy from a continuous model c an be infeasible, in this chapter we mainly focus on methods that explicitly up date a representation of a value function, a policy or both. We discuss conside rations in choosing an appropriate representation for these functions and disc uss gradient-based and gradient-free ways to update the parameters. We show how to a pply these methods to reinforcement-learning problems and discuss many speci fic algorithms. Amongst others, we cover gradient-based temporal-difference lear ning, evolutionary strategies, policy-gradient algorithms and (natural) actor-cri ti methods. We discuss the advantages of different approaches and compare the perform ance of a state-of-theart actor-critic method and a state-of-the-art evolutiona ry strategy empirically.


Decision Support based on Bio-PEPA Modeling and Decision Tree Induction: A New Approach, Applied to a Tuberculosis Case Study

The problem of selecting determinant features generating appropriate model structure is a challenge in epidemiological modelling. Disease spread is highly complex, and experts develop their understanding of its dynamic over years. There is an increasing variety and volume of epidemiological data which adds to the potential confusion. The authors propose here to make use of that data to better understand disease systems. Decision tree techniques have been extensively used to extract pertinent information and improve decision making. In this paper, the authors propose an innovative structured approach combining decision tree induction with Bio-PEPA computational modelling, and illustrate the approach through application to tuberculosis. By using decision tree induction, the enhanced Bio-PEPA model shows considerable improvement over the initial model with regard to the simulated results matching observed data. The key finding is that the developer expresses a realistic predictive model using relevant features, thus considering this approach as decision support, empowers the epidemiologist in his policy decision making. KEywoRDS Bio-PEPA Modelling, Data Mining, Decision Support, Decision Tree Induction, Epidemiology, Modelling and Simulation, Optimisation, Refinement, Tuberculosis


On Optimizing VLC Networks for Downlink Multi-User Transmission: A Survey

The evolving explosion in high data rate services and applications will soon require the use of untapped, abundant unregulated spectrum of the visible light for communications to adequately meet the demands of the fifth-generation (5G) mobile technologies. Radio-frequency (RF) networks are proving to be scarce to cover the escalation in data rate services. Visible light communication (VLC) has emerged as a great potential solution, either in replacement of, or complement to, existing RF networks, to support the projected traffic demands. Despite of the prolific advantages of VLC networks, VLC faces many challenges that must be resolved in the near future to achieve a full standardization and to be integrated to future wireless systems. Here, we review the new, emerging research in the field of VLC networks and lay out the challenges, technological solutions, and future work predictions. Specifically, we first review the VLC channel capacity derivation, discuss the performance metrics and the associated variables; the optimization of VLC networks are also discussed, including resources and power allocation techniques, user-to-access point (AP) association and APs-toclustered-users-association, APs coordination techniques, nonorthogonal multiple access (NOMA) VLC networks, simultaneous energy harvesting and information transmission using the visible light, and the security issue in VLC networks. Finally, we propose several open research problems to optimize the various VLC networks by maximizing either the sum rate, fairness, energy efficiency, secrecy rate, or harvested energy.


Comparison of CoAP and MQTT Performance Over Capillary Radios

The IoT protocols used in the application layer, namely the Constraint Application Protocol (CoAP) and Message Queue Telemetry Transport (MQTT) have dependencies to the transport layer. The choice of transport, Transmission Control Protocol (TCP) or the User Datagram Protocol(UDP), on the other hand, has an impact on the Internet of Things (IoT) application level performance, especially over a wireless medium. The motivation of this work is to look at the impact of the protocol stack on performance over two different wireless medium realizations, namely Bluetooth Low Energy and Wi-Fi. The use case studied is infrequent small reports sent from the sensor device to a central cloud storage over a last mile radio access link. We find that while CoAP/UDP based transport performs consistently better both in terms of latency and power consumption over both links, MQTT/TCP may also work when the use requirements allow for longerlatency providing better reliability. All in all, the full connectivity stack needs to be considered when designing an IoT deployment.


Design of a compact high power phased array for 5G FD-MIMO system at 29 GHz

This paper presents a new design concept of a beam steerable high gain phased array antenna based on WR28 waveguide at 29 GHz frequency for fifth generation (5G) full dimension multiple input multiple output (FD-MIMO) system. The 8×8 planar phased array is fed by a three dimensional beamformer to obtain volumetric beam scanning ranging from −60 to +60 degrees both in azimuth and elevation direction. Beamforming network (BFN) is designed using 16 set of 8×8 Butler matrix beamformer to get 64 beam states, which control the horizontal and vertical angle. This is a new concept to design waveguide based high power three-dimensional beamformer for volumetric multibeam in Ka band for 5G application. The maximum gain of phased array is 28.5 dBi that covers 28.9 GHz to 29.4 GHz frequency band.


Development and evaluation of low cost game-based balance rehabilitation tool using the microsoft kinect sensor

The use of the commercial video games as rehabilitation tools, such as the Nintendo WiiFit, has recently gained much interest in the physical therapy arena. Motion tracking controllers such as the Nintendo Wiimote are not sensitive enough to accurately measure performance in all components of balance. Additionally, users can figure out how to "cheat" inaccurate trackers by performing minimal movement (e.g. wrist twisting a Wiimote instead of a full arm swing). Physical rehabilitation requires accurate and appropriate tracking and feedback of performance. To this end, we are developing applications that leverage recent advances in commercial video game technology to provide full-body control of animated virtual characters. A key component of our approach is the use of newly available low cost depth sensing camera technology that provides markerless full-body tracking on a conventional PC. The aim of this research was to develop and assess an interactive game-based rehabilitation tool for balance training of adults with neurological injury.


Aesthetics and Entropy III . Aesthetic measures 2

1 Consultant, Maplewood, MN USA 4 * Correspondence: sahyun@infionline.net; 1-(651)-927-9686 5 6 Abstract: We examined a series of real-world, pictorial photographs with varying 7 characteristics, along with their modification by noise addition and unsharp masking. As 8 response metrics we used three different versions of the aesthetic measure originally 9 proposed by Birkhoff. The first aesthetic measure, which has been used in other studies, 10 and which we used in our previous work as well, showed a preference for the least 11 complex of the images. It provided no justification for noise addition, but did reveal 12 enhancement on unsharp masking. Optimum level of unsharp masking varied with the 13 image, but was predictable from the individual image’s GIF compressibility. We expect this 14 result to be useful for guiding the processing of pictorial photographic imagery. The 15 second aesthetic measure, that of informational aesthetics based on entropy alone failed 16 to provide useful discrimination among the images or the conditions of their modification. 17 A third measure, derived from the concepts of entropy maximization, as well as the 18 hypothesized preference of observers for “simpler”, i.e., more compressible, images, 19 yielded qualitatively the same results as the more traditional version of the measure. 20 Differences among the photographs and the conditions of their modification were more 21 clearly defined with this metric, however. 22


A Syllable-based Technique for Word Embeddings of Korean Words

Word embedding has become a fundamental component to many NLP tasks such as named entity recognition and machine translation. However, popular models that learn such embeddings are unaware of the morphology of words, so it is not directly applicable to highly agglutinative languages such as Korean. We propose a syllable-based learning model for Korean using a convolutional neural network, in which word representation is composed of trained syllable vectors. Our model successfully produces morphologically meaningful representation of Korean words compared to the original Skip-gram embeddings. The results also show that it is quite robust to the Out-of-Vocabulary problem.


Anti-phishing based on automated individual white-list

In phishing and pharming, users could be easily tricked into submitting their username/passwords into fraudulent web sites whose appearances look similar as the genuine ones. The traditional blacklist approach for anti-phishing is partially effective due to its partial list of global phishing sites. In this paper, we present a novel anti-phishing approach named Automated Individual White-List (AIWL). AIWL automatically tries to maintain a white-list of user's all familiar Login User Interfaces (LUIs) of web sites. Once a user tries to submit his/her confidential information to an LUI that is not in the white-list, AIWL will alert the user to the possible attack. Next, AIWL can efficiently defend against pharming attacks, because AIWL will alert the user when the legitimate IP is maliciously changed; the legitimate IP addresses, as one of the contents of LUI, are recorded in the white-list and our experiment shows that popular web sites' IP addresses are basically stable. Furthermore, we use Naïve Bayesian classifier to automatically maintain the white-list in AIWL. Finally, we conclude through experiments that AIWL is an efficient automated tool specializing in detecting phishing and pharming.


Perpetual environmentally powered sensor networks

Environmental energy is an attractive power source for low power wireless sensor networks. We present Prometheus, a system that intelligently manages energy transfer for perpetual operation without human intervention or servicing. Combining positive attributes of different energy storage elements and leveraging the intelligence of the microprocessor, we introduce an efficient multi-stage energy transfer system that reduces the common limitations of single energy storage systems to achieve near perpetual operation. We present our design choices, tradeoffs, circuit evaluations, performance analysis, and models. We discuss the relationships between system components and identify optimal hardware choices to meet an application's needs. Finally we present our implementation of a real system that uses solar energy to power Berkeley's Telos Mote. Our analysis predicts the system will operate for 43 years under 1% load, 4 years under 10% load, and 1 year under 100% load. Our implementation uses a two stage storage system consisting of supercapacitors (primary buffer) and a lithium rechargeable battery (secondary buffer). The mote has full knowledge of power levels and intelligently manages energy transfer to maximize lifetime.


AmbiMax: Autonomous Energy Harvesting Platform for Multi-Supply Wireless Sensor Nodes

AmbiMax is an energy harvesting circuit and a supercapacitor based energy storage system for wireless sensor nodes (WSN). Previous WSNs attempt to harvest energy from various sources, and some also use supercapacitors instead of batteries to address the battery aging problem. However, they either waste much available energy due to impedance mismatch, or they require active digital control that incurs overhead, or they work with only one specific type of source. AmbiMax addresses these problems by first performing maximum power point tracking (MPPT) autonomously, and then charges supercapacitors at maximum efficiency. Furthermore, AmbiMax is modular and enables composition of multiple energy harvesting sources including solar, wind, thermal, and vibration, each with a different optimal size. Experimental results on a real WSN platform, Eco, show that AmbiMax successfully manages multiple power sources simultaneously and autonomously at several times the efficiency of the current state-of-the-art for WSNs


Integrated photovoltaic maximum power point tracking converter

A low-power low-cost highly efficient maximum power point tracker (MPPT) to be integrated into a photovoltaic (PV) panel is proposed. This can result in a 25% energy enhancement compared to a standard photovoltaic panel, while performing functions like battery voltage regulation and matching of the PV array with the load. Instead of using an externally connected MPPT, it is proposed to use an integrated MPPT converter as part of the PV panel. It is proposed that this integrated MPPT uses a simple controller in order to be cost effective. Furthermore, the converter has to be very efficient, in order to transfer more energy to the load than a directly coupled system. This is achieved by using a simple soft-switched topology. A much higher conversion efficiency at lower cost will then result, making the MPPT an affordable solution for small PV energy systems.


Hardware design experiences in ZebraNet

The enormous potential for wireless sensor networks to make a positive impact on our society has spawned a great deal of research on the topic, and this research is now producing environment-ready systems. Current technology limits coupled with widely-varying application requirements lead to a diversity of hardware platforms for different portions of the design space. In addition, the unique energy and reliability constraints of a system that must function for months at a time without human intervention mean that demands on sensor network hardware are different from the demands on standard integrated circuits. This paper describes our experiences designing sensor nodes and low level software to control them.
 In the ZebraNet system we use GPS technology to record fine-grained position data in order to track long term animal migrations [14]. The ZebraNet hardware is composed of a 16-bit TI microcontroller, 4 Mbits of off-chip flash memory, a 900 MHz radio, and a low-power GPS chip. In this paper, we discuss our techniques for devising efficient power supplies for sensor networks, methods of managing the energy consumption of the nodes, and methods of managing the peripheral devices including the radio, flash, and sensors. We conclude by evaluating the design of the ZebraNet nodes and discussing how it can be improved. Our lessons learned in developing this hardware can be useful both in designing future sensor nodes and in using them in real systems.


Adaptive Computer-Based Training Increases on the Job Performance of X-Ray Screeners

Due to severe terrorist attacks in recent years, aviation security issues have moved into the focus of politicians as well as the general public. Effective screening of passenger bags using state-of-the-art X-ray screening systems is essential to prevent terrorist attacks. The performance of the screening process depends critically on the security personnel, because they decide whether bags are OK or whether they might contain a prohibited item. Screening X-ray images of passenger bags for dangerous and prohibited items effectively and efficiently is a demanding object recognition task. Effectiveness of computer-based training (CBT) on X-ray detection performance was assessed using computer-based tests and on the job performance measures using threat image projection (TIP). It was found that adaptive CBT is a powerful tool to increase detection performance and efficiency of screeners in X-ray image interpretation. Moreover, the results of training could be generalized to the real life situation as shown in the increased detection performance in TIP not only for trained items, but also for new (untrained) items. These results illustrate that CBT is a very useful tool to increase airport security from a human factors perspective.


Rotation-discriminating template matching based on Fourier coefficients of radial projections with robustness to scaling and partial occlusion

We consider brightness/contrast-invariant and rotation-discriminating template matching that searches an image to analyze A for a query image Q. We propose to use the complex coefficients of the discrete Fourier transform of the radial projections to compute new rotation-invariant local features. These coefficients can be efficiently obtained via FFT. We classify templates in “stable” and “unstable” ones and argue that any local feature-based template matching may fail to find unstable templates. We extract several stable sub-templates of Q and find them in A by comparing the features. The matchings of the sub-templates are combined using the Hough transform. As the features of A are computed only once, the algorithm can find quickly many different sub-templates in A, and it is suitable for: finding many query images in A; multi-scale searching and partial occlusion-robust template matching.


Haswell: The Fourth-Generation Intel Core Processor

Haswell, Intel's fourth-generation core processor architecture, delivers a range of client parts, a converged core for the client and server, and technologies used across many products. It uses an optimized version of Intel 22-nm process technology. Haswell provides enhancements in power-performance efficiency, power management, form factor and cost, core and uncore microarchitecture, and the core's instruction set.


Working model of Self-driving car using Convolutional Neural Network, Raspberry Pi and Arduino

The evolution of Artificial Intelligence has served as the catalyst in the field of technology. We can now develop things which was once just an imagination. One of such creation is the birth of self-driving car. Days have come where one can do their work or even sleep in the car and without even touching the steering wheel, accelerator you will still be able to reach your target destination safely. This paper proposes a working model of self-driving car which is capable of driving from one location to the other or to say on different types of tracks such as curved tracks, straight tracks and straight followed by curved tracks. A camera module is mounted over the top of the car along with Raspberry Pi sends the images from real world to the Convolutional Neural Network which then predicts one of the following directions. i.e. right, left, forward or stop which is then followed by sending a signal from the Arduino to the controller of the remote controlled car and as a result of it the car moves in the desired direction without any human intervention.