Temporal Ensembling for Semi-Supervised Learning

In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions. This ensemble prediction can be expected to be a better predictor for the unknown labels than the output of the network at the most recent training epoch, and can thus be used as a target for training. Using our method, we set new records for two standard semi-supervised learning benchmarks, reducing the (non-augmented) classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from 18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16% by enabling the standard augmentations. We additionally obtain a clear improvement in CIFAR-100 classification accuracy by using random images from the Tiny Images dataset as unlabeled extra inputs during training. Finally, we demonstrate good tolerance to incorrect labels.


Gradient descent GAN optimization is locally stable

Despite the growing prominence of generative adversarial networks (GANs), optimization in GANs is still a poorly understood topic. In this paper, we analyze the “gradient descent” form of GAN optimization, i.e., the natural setting where we simultaneously take small gradient steps in both generator and discriminator parameters. We show that even though GAN optimization does not correspond to a convex-concave game (even for simple parameterizations), under proper conditions, equilibrium points of this optimization procedure are still locally asymptotically stable for the traditional GAN formulation. On the other hand, we show that the recently proposed Wasserstein GAN can have non-convergent limit cycles near equilibrium. Motivated by this stability analysis, we propose an additional regularization term for gradient descent GAN updates, which is able to guarantee local stability for both the WGAN and the traditional GAN, and also shows practical promise in speeding up convergence and addressing mode collapse.


Born Again Neural Networks

Knowledge Distillation (KD) consists of transferring “knowledge” from one machine learning model (the teacher) to another (the student). Commonly, the teacher is a high-capacity model with formidable performance, while the student is more compact. By transferring knowledge, one hopes to benefit from the student’s compactness, without sacrificing too much performance. We study KD from a new perspective: rather than compressing models, we train students parameterized identically to their teachers. Surprisingly, these Born-Again Networks (BANs), outperform their teachers significantly, both on computer vision and language modeling tasks. Our experiments with BANs based on DenseNets demonstrate state-of-the-art performance on the CIFAR-10 (3.5%) and CIFAR-100 (15.5%) datasets, by validation error. Additional experiments explore two distillation objectives: (i) Confidence-Weighted by Teacher Max (CWTM) and (ii) Dark Knowledge with Permuted Predictions (DKPP). Both methods elucidate the essential components of KD, demonstrating the effect of the teacher outputs on both predicted and nonpredicted classes.


Learning Internal Representations

Probably the most important, problem in machine learning is the preliminary biasing of a learner’s hypothesis space so that it is small enough to ensure good generalisation from reasonable training sets, yet large enough that it contains a good solution to the problem being learnt, In this paper a mechanism for automailcall,y learning or biasing the learner’s hypothesis space is introduced. It works by first learning an appropriate [nterna! represenlaiton for a learning en~-ironment and then using that representation to hiss the learner’s hypothesis space for the learning of future tasks drawn from the same environment. Jln internal represent at ion must be learnt by sampling from many s2mtiar tasks, not just a single task as occurs in ordinary machine learning. It is proved that the number of examples m per task required to ensure good generalisation from a representation learner obeys }n = O(a + b/n) where n is the number of tasks being learnt, and a and b are constants. If the tasks are learnt independently (/. e. without a common representation ) then m = 0( a + b). It is argued that for learning environments such as speech and character recognition b >> a and hence representation learning in these environments can potentially yield a drastic reduction in the number of examples required per task. It is also proved that if n = O(b) (with m = O(a+b/n ) ) then the representation learnt will be good for learning novel tasks from the same environment, and that the number of examples required to generalise well on a novel task will be reduced to O(a) (as opposed to {)( a + b) if no represent ation is used). It, is shown that gradient descent can be used to train neural network representations and the results of an experiment are reported in which a neural network representation was learnt for an environment consisting of transJaf ion ail,y in uarlanf Boolean functions. The experiment, provides strong qualitative support for the theoretical results.


Self-Censorship on Facebook

We report results from an exploratory analysis examining “last-minute” self-censorship, or content that is filtered after being written, on Facebook. We collected data from 3.9 million users over 17 days and associate self-censorship behavior with features describing users, their social graph, and the interactions between them. Our results indicate that 71% of users exhibited some level of last-minute self-censorship in the time period, and provide specific evidence supporting the theory that a user’s “perceived audience” lies at the heart of the issue: posts are censored more frequently than comments, with status updates and posts directed at groups censored most frequently of all sharing use cases investigated. Furthermore, we find that: people with more boundaries to regulate censor more; males censor more posts than females and censor even more posts with mostly male friends than do females, but censor no more comments than females; people who exercise more control over their audience censor more content; and, users with more politically and age diverse friends censor less, in general.


On the rate of channel polarization

A bound is given on the rate of channel polarization. As a corollary, an earlier bound on the probability of error for polar coding is improved. Specifically, it is shown that, for any binary-input discrete memoryless channel W with symmetric capacity I(W) and any rate R ≪ I(W), the polar-coding block-error probability under successive cancellation decoding satisfies P<inf>e</inf>(N, R) ≤ 2<sup>−Nβ</sup> for any β ≪ 1/2 when the block-length N is large enough.


Natural Language Generation in Interactive Systems

An informative and comprehensive overview of the state-of-the-art in natural language generation (NLG) for interactive systems, this guide introduces graduate students and new researchers to the field of natural language processing and artificial intelligence, while inspiring them with ideas for future research. Detailing the techniques and challenges of NLG for interactive applications, it focuses on research into systems that model collaborativity and uncertainty, are capable of being scaled incrementally, and can engage with the user effectively. A range of real-world case studies is also included. The book and the accompanying website feature a comprehensive bibliography, and refer the reader to corpora, data, software, and other resources for pursuing research on natural language generation and interactive systems, including dialogue systems, multimodal interfaces, and assistive technologies. It is an ideal resource for students and researchers in computational linguistics, natural language processing, and related fields.


Stretching Agile to fit CMMI Level 3 - the story of creating MSF for CMMI Process Improvement at Microsoft Corporation

Agile practitioners pride themselves on highly productive, responsive, low ceremony, lightweight, tacit knowledge processes with little waste, adaptive planning and frequent iterative delivery of value. It is often assumed that CMMI compliant processes need to be heavyweight, bureaucratic, slow moving, high ceremony and plan driven. Agile developers often skeptically perceive formal process improvement initiatives as management generated inefficiency that gets in the way of productivity. At Microsoft, we’ve adopted the teachings of W. Edwards Deming and stretched our MSF for Agile Software Development method to fit the requirements for CMMI Level 3. The resultant MSF for CMMI Process Improvement is a highly iterative, adaptive planning method, light on documentation, and heavily automated through tooling. It enables management and organization of software engineering through use of agile metrics such as velocity and cumulative flow but with an added dimension of an understanding of variation – adapted from Deming’s teachings. This is the story of how mixing Deming with Agile produced a lightweight CMMI solution for .Net developers everywhere.


Anomaly Detection in Text: The Value of Domain Knowledge

We consider the problem of detecting anomalies from text data. Our hypothesis is that as with classical anomaly detection algorithms, domain-specific features are more important than the linguistic features. We employ the use of first-order logic and demonstrate the effectiveness of useful domain knowledge in two domains. Our results show that the domain-specific features are more predictive and that the relational learning methods exhibit superior performance.


Accuracy of automatic number plate recognition (ANPR) and real world UK number plate problems

This paper considers real world UK number plates and relates these to ANPR. It considers aspects of the relevant legislation and standards when applying them to real world number plates. The varied manufacturing techniques and varied specifications of component parts are also noted. The varied fixing methodologies and fixing locations are discussed as well as the impact on image capture.


Full 3-D Printed Microwave Termination: A Simple and Low-Cost Solution

This paper describes the realization and characterization of microwave 3-D printed loads in rectangular waveguide technology. Several commercial materials were characterized at X-band (8-12 GHz). Their dielectric properties were extracted through the use of a cavity-perturbation method and a transmission/reflection rectangular waveguide method. A lossy carbon-loaded Acrylonitrile Butadiene Styrene (ABS) polymer was selected to realize a matched load between 8 and 12 GHz. Two different types of terminations were realized by fused deposition modeling: a hybrid 3-D printed termination (metallic waveguide + pyramidal polymer absorber + metallic short circuit) and a full 3-D printed termination (self-consistent matched load). Voltage standing wave ratio of less than 1.075 and 1.025 were measured over X-band for the hybrid and full 3-D printed terminations, respectively. Power behavior of the full 3-D printed termination was investigated. A very linear evolution of reflected power as a function of incident power amplitude was observed at 10 GHz up to 11.5 W. These 3-D printed devices appear as a very low cost solution for the realization of microwave matched loads in rectangular waveguide technology.


Captology: A Critical Review

This critical review of B.J. Fogg’s book Persuasive Technology regards captology as an eclectic and formative work. It summarises two other reviewers’ work and identifies several new strengths. It scrutinises Fogg’s functional triad computers functioning as tools, media and social actors and some categorical changes are recommended. It investigates further Johnson’s concerns about specific ethical omissions, nominating a new term, compusuasion, for the resultant but unintended, exogenous behaviour/attitude change effects of captological design. The review commences to more carefully define what constitutes persuasion and draws attention to the distinction between persuasion techniques in general and the behavioural changes that result from advocacy and education. The reviewer concludes that a fundamental ethic be that the designer’s intent be exposed at the commencement of the user’s engagement with the program and proffers the idea of persuasion resulting in a new conviction, induced by others, as a helpful definition of


Identifying the Scan and Attack Infrastructures Behind Amplification DDoS Attacks

Amplification DDoS attacks have gained popularity and become a serious threat to Internet participants. However, little is known about where these attacks originate, and revealing the attack sources is a non-trivial problem due to the spoofed nature of the traffic.
 In this paper, we present novel techniques to uncover the infrastructures behind amplification DDoS attacks. We follow a two-step approach to tackle this challenge: First, we develop a methodology to impose a fingerprint on scanners that perform the reconnaissance for amplification attacks that allows us to link subsequent attacks back to the scanner. Our methodology attributes over 58% of attacks to a scanner with a confidence of over 99.9%. Second, we use Time-to-Live-based trilateration techniques to map scanners to the actual infrastructures launching the attacks. Using this technique, we identify 34 networks as being the source for amplification attacks at 98\% certainty.


Finger Sleeve : A Wearable Navigation Device

In this paper we present a novel wearable navigation system along with implicit HCI (iHCI) model, where interaction with technology is dissolved into a day-today activity. In this type of HCI model a computer takes the input and tries to output an action that is a proactive anticipation of next action of a user. Usually, in urban areas people use voice assisted navigation systems or navigation guidelines displayed on a mobile phone. Some navigation systems are already installed on car dashboard, which needs explicit attention in order to make driving decisions. A navigation system using haptic perception to guide a user throughout a journey is the key contribution of this paper. It does not ask for explicit user attention and demonstrates the indolent form of technological interaction. This wearable device is an index finger sleeve, which consists of vibrator modules, Bluetooth communication module and Microcontroller Unit (MCU). A working prototype has been built and tested.


Learning to Detect Rooftops in Aerial Images

In this paper, we examine the use of machine learning to improve the robustness of systems for image analysis on the task of roof detection. We review the problem of analyzing aerial photographs, and describe an existing vision system that attempts to automate the identiication of buildings in aerial images. After this, we brieey review several well-known learning algorithms that represent a wide variety of inductive biases. We report three experiments designed to illuminate facets of applying machine learning methods to the image analysis task; one experiment focuses on within-image learning, another deals with the cost of diierent errors, and a third addresses between-image learning. Experimental results demonstrate that machine-learned classiiers meet or exceed the accuracy of handcrafted solutions and that useful generalization occurs when training and testing on data derived from diierent images.


SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification

Sentiment in social media is increasingly considered as an important resource for customer segmentation, market understanding, and tackling other socio-economic issues. However, sentiment in social media is difficult to measure since user-generated content is usually short and informal. Although many traditional sentiment analysis methods have been proposed, identifying slang sentiment words remains untackled. One of the reasons is that slang sentiment words are not available in existing dictionaries or sentiment lexicons. To this end, we propose to build the first sentiment dictionary of slang words to aid sentiment analysis of social media content. It is laborious and time-consuming to collect and label the sentiment polarity of a comprehensive list of slang words. We present an approach to leverage web resources to construct an extensive Slang Sentiment word Dictionary (SlangSD) that is easy to maintain and extend. SlangSD is publicly available1 for research purposes. We empirically show the advantages of using SlangSD, the newly-built slang sentiment word dictionary for sentiment classification, and provide examples demonstrating its ease of use with an existing sentiment system.


Vehicle control system for automatic valet parking with infrastructure sensors

This paper presents vehicle control system for automatic valet parking with infrastructure sensors. First, we describe the automatic valet parking service system. In the service, vehicle moves autonomously based on sensing data generated by infrastructure sensors. Second, we implement vehicle control system for automatic valet parking. We design hardware and software components focusing on minimizing the add-on devices and maximizing data integration. Finally, we do a feasibility test for verification of the system.


Comparisons of centerline extraction methods for liver blood vessels in ImageJ and 3D slicer

In this paper, we introduce the centerline extraction processes of the liver vessel systems by using the plug-ins in ImageJ and 3D Slicer. The Skeletonize (2D/3D) plug-in in ImageJ is easy to use, and only based on the volumetric data. It performs fast and require no user interactions in terms of seed points selection. The VMTK toolkit in 3D slicer is more powerful. It uses the surface model and can provide more smooth and consistent results. Due to its complexities, it requires more computational resources and user specified seed points. Some possible improvements for these two methods are discussed.


Implantable Myoelectric Sensors (IMESs) for Intramuscular Electromyogram Recording

We have developed a multichannel electrogmyography sensor system capable of receiving and processing signals from up to 32 implanted myoelectric sensors (IMES). The appeal of implanted sensors for myoelectric control is that electromyography (EMG) signals can be measured at their source providing relatively cross-talk-free signals that can be treated as independent control sites. An external telemetry controller receives telemetry sent over a transcutaneous magnetic link by the implanted electrodes. The same link provides power and commands to the implanted electrodes. Wireless telemetry of EMG signals from sensors implanted in the residual musculature eliminates the problems associated with percutaneous wires, such as infection, breakage, and marsupialization. Each implantable sensor consists of a custom-designed application-specified integrated circuit that is packaged into a biocompatible RF BION capsule from the Alfred E. Mann Foundation. Implants are designed for permanent long-term implantation with no servicing requirements. We have a fully operational system. The system has been tested in animals. Implants have been chronically implanted in the legs of three cats and are still completely operational four months after implantation.


Comparative Metagenomics Reveals Host Specific Metavirulomes and Horizontal Gene Transfer Elements in the Chicken Cecum Microbiome

BACKGROUND
The complex microbiome of the ceca of chickens plays an important role in nutrient utilization, growth and well-being of these animals. Since we have a very limited understanding of the capabilities of most species present in the cecum, we investigated the role of the microbiome by comparative analyses of both the microbial community structure and functional gene content using random sample pyrosequencing. The overall goal of this study was to characterize the chicken cecal microbiome using a pathogen-free chicken and one that had been challenged with Campylobacter jejuni.

METHODOLOGY/PRINCIPAL FINDINGS
Comparative metagenomic pyrosequencing was used to generate 55,364,266 bases of random sampled pyrosequence data from two chicken cecal samples. SSU rDNA gene tags and environmental gene tags (EGTs) were identified using SEED subsystems-based annotations. The distribution of phylotypes and EGTs detected within each cecal sample were primarily from the Firmicutes, Bacteroidetes and Proteobacteria, consistent with previous SSU rDNA libraries of the chicken cecum. Carbohydrate metabolism and virulence genes are major components of the EGT content of both of these microbiomes. A comparison of the twelve major pathways in the SEED Virulence Subsystem (metavirulome) represented in the chicken cecum, mouse cecum and human fecal microbiomes showed that the metavirulomes differed between these microbiomes and the metavirulomes clustered by host environment. The chicken cecum microbiomes had the broadest range of EGTs within the SEED Conjugative Transposon Subsystem, however the mouse cecum microbiomes showed a greater abundance of EGTs in this subsystem. Gene assemblies (32 contigs) from one microbiome sample were predominately from the Bacteroidetes, and seven of these showed sequence similarity to transposases, whereas the remaining sequences were most similar to those from catabolic gene families.

CONCLUSION/SIGNIFICANCE
This analysis has demonstrated that mobile DNA elements are a major functional component of cecal microbiomes, thus contributing to horizontal gene transfer and functional microbiome evolution. Moreover, the metavirulomes of these microbiomes appear to associate by host environment. These data have implications for defining core and variable microbiome content in a host species. Furthermore, this suggests that the evolution of host specific metavirulomes is a contributing factor in disease resistance to zoonotic pathogens.


A Survey and Analysis of Various Health-Related Knowledge Mining Techniques in Social Media

Smart extraction of knowledge from social media has received the recent interest of the Biomedical and Health Informatics community for the simultaneous improvement of healthcare outcomes and lessen the expenses making use of consumergenerated reviews. Social media provides chances for patients and doctors to share their views and experiences without any obtrusion through online communities that might generate information, which is much beyond what is known by the domain experts. Nonetheless, for conventional public health surveillance systems, it is difficult to detect and then monitor the concerns related to health and the changes seen in attitudes of the public towards health-related problems. To solve this problem, several studies have shown the usage of information in social media for the discovery of biomedical and healthrelated information. Several disease-specific knowledge exchanges are now available on Face book and other portals of online social networking. These kind of new sources of information, support, and engagement have gone to become significant for patients who are suffering with the disease, and still the quality and the content of the knowledge contributed in these digital areas are not properly comprehended. The existing research methodologies are discussed with their merits and demerits, so that the further research works can be concentrated more. The experimental tests conducted were on all the research works in mat lab simulation environment and it is compared against each other to find the better approach under various performance measures such as Accuracy, Precision and Recall.


Approaching the Limit of Predictability in Human Mobility

In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records. By measuring the uncertainties of movements using entropy, considering both the frequencies and temporal correlations of individual trajectories, we find that the theoretical maximum predictability is as high as 88%. To verify whether such a theoretical limit can be approached, we implement a series of Markov chain (MC) based models to predict the actual locations visited by each user. Results show that MC models can produce a prediction accuracy of 87% for stationary trajectories and 95% for non-stationary trajectories. Our findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy.


Simple Customization of Recursive Neural Networks for Semantic Relation Classification

In this paper, we present a recursive neural network (RNN) model that works on a syntactic tree. Our model differs from previous RNN models in that the model allows for an explicit weighting of important phrases for the target task. We also propose to average parameters in training. Our experimental results on semantic relation classification show that both phrase categories and task-specific weighting significantly improve the prediction accuracy of the model. We also show that averaging the model parameters is effective in stabilizing the learning and improves generalization capacity. The proposed model marks scores competitive with state-of-the-art RNN-based models.


Effects of communication styles on acceptance of recommendations in intercultural collaboration

The objective of this study is to investigate the impact of culture and communication style (explicit versus implicit) on people’s reactions on recommendations in intercultural collaboration. The experimental results from three intercultural collaboration teams were studied: Chinese-American, Chinese-German, and Chinese-Korean. The results indicate that Chinese participants showed more positive evaluations (i.e., higher trust, higher satisfaction, and more future collaboration intention) on the implicit advisor than American and German participants. Compared with Chinese participants, Korean participants accepted explicit recommendations more often and showed more positive evaluations on the explicit advisor. The results also show that when Chinese express recommendations in an explicit way, their recommendations were accepted more often and were more positively evaluated by cross-cultural partners.


R2O, an extensible and semantically based database-to-ontology mapping language

We present R2O, an extensible and declarative language to describe mappings between relational DB schemas and ontologies implemented in RDF(S) or OWL. R2O provides an extensible set of primitives with welldefined semantics. This language has been conceived expressive enough to cope with complex mapping cases arisen from situations of low similarity between the ontology and the DB models.


Low-Dimensional Representation of Spectral Envelope Without Deterioration for Full-Band Speech Analysis/Synthesis System

A speech coding for a full-band speech analysis/synthesis system is described. In this work, full-band speech is defined as speech with a sampling frequency above 40 kHz, whose Nyquist frequency covers the audible frequency range. In prior works, speech coding has generally focused on the narrowband speech with a sampling frequency below 16 kHz. On the other hand, statistical parametric speech synthesis currently uses the full-band speech, and low-dimensional representation of speech parameters is being used. The purpose of this study is to achieve speech coding without deterioration for full-band speech. We focus on a high-quality speech analysis/synthesis system and mel-cepstral analysis using frequency warping. In the frequency warping function, we directly use three auditory scales. We carried out a subjective evaluation using the WORLD vocoder and found that the optimum number of dimensions was around 50. The kind of frequency warping did not significantly affect the sound quality in the dimensions.


Bayesian Regularization and Pruning Using a Laplace Prior

Standard techniques for improved generalization from neural networks include weight decay and pruning. Weight decay has a Bayesian interpretation with the decay function corresponding to a prior over weights. The method of transformation groups and maximum entropy suggests a Laplace rather than a gaussian prior. After training, the weights then arrange themselves into two classes: (1) those with a common sensitivity to the data error and (2) those failing to achieve this sensitivity and that therefore vanish. Since the critical value is determined adaptively during training, pruningin the sense of setting weights to exact zerosbecomes an automatic consequence of regularization alone. The count of free parameters is also reduced automatically as weights are pruned. A comparison is made with results of MacKay using the evidence framework and a gaussian regularizer.


An MRF Model for Binarization of Natural Scene Text

Inspired by the success of MRF models for solving object segmentation problems, we formulate the binarization problem in this framework. We represent the pixels in a document image as random variables in an MRF, and introduce a new energy (or cost) function on these variables. Each variable takes a foreground or background label, and the quality of the binarization (or labelling) is determined by the value of the energy function. We minimize the energy function, i.e. find the optimal binarization, using an iterative graph cut scheme. Our model is robust to variations in foreground and background colours as we use a Gaussian Mixture Model in the energy function. In addition, our algorithm is efficient to compute, and adapts to a variety of document images. We show results on word images from the challenging ICDAR 2003 dataset, and compare our performance with previously reported methods. Our approach shows significant improvement in pixel level accuracy as well as OCR accuracy.


A New Gradient Based Character Segmentation Method for Video Text Recognition

The current OCR cannot segment words and characters from video images due to complex background as well as low resolution of video images. To have better accuracy, this paper presents a new gradient based method for words and character segmentation from text line of any orientation in video frames for recognition. We propose a Max-Min clustering concept to obtain text cluster from the normalized absolute gradient feature matrix of the video text line image. Union of the text cluster with the output of Canny operation of the input video text line is proposed to restore missing text candidates. Then a run length algorithm is applied on the text candidate image for identifying word gaps. We propose a new idea for segmenting characters from the restored word image based on the fact that the text height difference at the character boundary column is smaller than that of the other columns of the word image. We have conducted experiments on a large dataset at two levels (word and character level) in terms of recall, precision and f-measure. Our experimental setup involves 3527 characters of English and Chinese, and this dataset is selected from TRECVID database of 2005 and 2006.


Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images

In this paper we describe a new technique for general purpose interactive segmentation of N-dimensional images . The user marks certain pixels as “object” or “background” to provide hard constraints for segmentation. Additional soft constraints incorporate both boundary and region information. Graph cuts are used to find the globally optimal segmentation of the N-dimensional image. The obtained solution gives the best balance of boundary and region properties among all segmentations satisfying the constraints . The topology of our segmentation is unrestricted and both “object” and “background” segments may consist of several isolated parts. Some experimental results are present ed in the context of photo/video editing and medical image segmentation. We also demonstrate an interesting Gestalt example. A fast implementation of our segmentation method is possible via a new max-flow algorithm in [2].


Corporate Governance and Corporate Social Responsibility Synergies and Interrelationships

Manuscript Type: Empirical Research Questions/Issue: This paper seeks to explore the interrelationships between corporate governance (CG) and corporate social responsibility (CSR): first, theoretically, by reviewing the literature and surveying various postulations on offer; second, empirically, by investigating the conception and interpretation of this relationship in the context of a sample of firms operating in Lebanon. Accordingly, the paper seeks to highlight the increasing cross-connects or interfaces between CG and CSR, capitalizing on fresh insights from a developing country perspective. Research Findings/Results: A qualitative interpretive research methodology was adopted, drawing on in-depth interviews with the top managers of eight corporations operating in Lebanon, with the findings suggesting that the majority of managers conceive of CG as a necessary pillar for sustainable CSR. These findings are significant and interesting, implying that recent preoccupation with CG in developing countries is starting to be counterbalanced by some interest/attention to CSR, with growing appreciation of their interdependencies and the need to move beyond CG conformance toward voluntary CSR performance. Theoretical Implications: This study makes two important contributions. First, it suggests that there is a salient two-way relationship and increasing overlap between CG and CSR. While much previous literature has researched CG and CSR independently, this paper makes the case for considering them jointly and systematically. Second, the paper outlines a number of theoretical propositions that can serve as the basis for future research on the topic, particularly in developing countries, given that the data and theoretical propositions are both derived from and tailored to developing country contexts. Practical Implications: This study can potentially alert managers to the increasing overlap between the CG and CSR agendas and the need to exert diligent systematic efforts on both fronts. CG and CSR share more in common than previously assumed, and this needs to be accounted for by practitioners. The research can also alert policy makers in developing countries to the need to increase the vigilance and capacity of the regulatory and judicial systems in the context of CG reform and to increase institutional pressures, particularly of the coercive and normative variety to enhance CSR adoption.


Cultural adaptation and validation of the Health Literacy Questionnaire (HLQ): robust nine-dimension Danish language confirmatory factor model

Health literacy is an important construct in population health and healthcare requiring rigorous measurement. The Health Literacy Questionnaire (HLQ), with nine scales, measures a broad perception of health literacy. This study aimed to adapt the HLQ to the Danish setting, and to examine the factor structure, homogeneity, reliability and discriminant validity. The HLQ was adapted using forward-backward translation, consensus conference and cognitive interviews (n = 15). Psychometric properties were examined based on data collected by face-to-face interview (n = 481). Tests included difficulty level, composite scale reliability and confirmatory factor analysis (CFA). Cognitive testing revealed that only minor re-wording was required. The easiest scale to respond to positively was 'Social support for health', and the hardest were 'Navigating the healthcare system' and 'Appraisal of health information'. CFA of the individual scales showed acceptably high loadings (range 0.49-0.93). CFA fit statistics after including correlated residuals were good for seven scales, acceptable for one. Composite reliability and Cronbach's α were >0.8 for all but one scale. A nine-factor CFA model was fitted to items with no cross-loadings or correlated residuals allowed. Given this restricted model, the fit was satisfactory. The HLQ appears robust for its intended application of assessing health literacy in a range of settings. Further work is required to demonstrate sensitivity to measure changes.


A Survey on Software Estimation Techniques in Traditional and Agile Development Models

Software projects mostly exceeds budget, delivered late and does not meet with the customer’s satisfaction for years. In the past, many traditional development models like waterfall, spiral, iterative, and prototyping methods are used to build the software systems. In recent years, agile models are widely used in developing the software products. The major reasons are – simplicity, incorporating the requirement changes at any time, light-weight approach and delivering the working product early and in short duration. Whatever the development model used, it still remains a challenge for software engineer’s to accurately estimate the size, effort and the time required for developing the software system. This survey focuses on the existing estimation models used in traditional as well in agile software development.


Coarse-to-fine volumetric segmentation of teeth in Cone-Beam CT

We consider the problem of localizing and segmenting individual teeth inside 3D Cone-Beam Computed Tomography (CBCT) images. To handle large image sizes we approach this task with a coarse-to-fine framework, where the whole volume is first analyzed as a 33-class semantic segmentation (adults have up to 32 teeth) in coarse resolution, followed by binary semantic segmentation of the cropped region of interest in original resolution. To improve the performance of the challenging 33-class segmentation, we first train the Coarse step model on a large weakly labeled dataset, then fine-tune it on a smaller precisely labeled dataset. The Fine step model is trained with precise labels only. Experiments using our inhouse dataset show significant improvement for both weaklysupervised pretraining and for the addition of the Fine step. Empirically, this framework yields precise teeth masks with low localization errors sufficient for many real-world applications.


Experience with SCRAM, a SCenario Requirements Analysis Method

A method of scenario based requirements engineering is described that uses a combination of early prototypes, scenario scripts and design rationale to elicit and validate user requirements. Experience in using the method on an EU project, Multimedia Broker, is reported. Quantitative data on requirements sessions is analysed to assess user participation and quality of requirements captured. The method worked well but there were problems in the use of design rationale and control of turn taking in RE sessions. Lessons learned in using the method are summarised and future improvements for the method are discussed.


Constrained Arc-Eager Dependency Parsing

Arc-eager dependency parsers process sentences in a single left-to-right pass over the input and have linear time complexity with greedy decoding or beam search. We show how such parsers can be constrained to respect two different types of conditions on the output dependency graph: span constraints, which require certain spans to correspond to subtrees of the graph, and arc constraints, which require certain arcs to be present in the graph. The constraints are incorporated into the arc-eager transition system as a set of preconditions for each transition and preserve the linear time complexity of the parser.


Show Me You Care: Trait Empathy, Linguistic Style, and Mimicry on Facebook

Linguistic mimicry, the adoption of another’s language patterns, is a subconscious behavior with pro-social benefits. However, some professions advocate its conscious use in empathic communication. This involves mutual mimicry; effective communicators mimic their interlocutors, who also mimic them back. Since mimicry has often been studied in face-to-face contexts, we ask whether individuals with empathic dispositions have unique communication styles and/or elicit mimicry in mediated communication on Facebook. Participants completed Davis’s Interpersonal Reactivity Index and provided access to Facebook activity. We confirm that dispositional empathy is correlated to the use of particular stylistic features. In addition, we identify four empathy profiles and find correlations to writing style. When a linguistic feature is used, this often “triggers” use by friends. However, the presence of particular features, rather than participant disposition, best predicts mimicry. This suggests that machine-human communications could be enhanced based on recently used features, without extensive user profiling.


Survey Research in HCI


SemLens: visual analysis of semantic data with scatter plots and semantic lenses

Querying the Semantic Web and analyzing the query results are often complex tasks that can be greatly facilitated by visual interfaces. A major challenge in the design of these interfaces is to provide intuitive and efficient interaction support without limiting too much the analytical degrees of freedom. This paper introduces SemLens, a visual tool that combines scatter plots and semantic lenses to overcome this challenge and to allow for a simple yet powerful analysis of RDF data. The scatter plots provide a global overview on an object collection and support the visual discovery of correlations and patterns in the data. The semantic lenses add dimensions for local analysis of subsets of the objects. A demo accessing DBpedia data is used for illustration.


Pointer Networks

We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. Such problems cannot be trivially addressed by existent approaches such as sequence-to-sequence [1] and Neural Turing Machines [2], because the number of target classes in each step of the output depends on the length of the input, which is variable. Problems such as sorting variable sized sequences, and various combinatorial optimization problems belong to this class. Our model solves the problem of variable size output dictionaries using a recently proposed mechanism of neural attention. It differs from the previous attention attempts in that, instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, it uses attention as a pointer to select a member of the input sequence as the output. We call this architecture a Pointer Net (Ptr-Net). We show Ptr-Nets can be used to learn approximate solutions to three challenging geometric problems – finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem – using training examples alone. Ptr-Nets not only improve over sequence-to-sequence with input attention, but also allow us to generalize to variable size output dictionaries. We show that the learnt models generalize beyond the maximum lengths they were trained on. We hope our results on these tasks will encourage a broader exploration of neural learning for discrete problems.


Interactions between the Midbrain Superior Colliculus and the Basal Ganglia

An important component of the architecture of cortico-basal ganglia connections is the parallel, re-entrant looped projections that originate and return to specific regions of the cerebral cortex. However, such loops are unlikely to have been the first evolutionary example of a closed-loop architecture involving the basal ganglia. A phylogenetically older, series of subcortical loops can be shown to link the basal ganglia with many brainstem sensorimotor structures. While the characteristics of individual components of potential subcortical re-entrant loops have been documented, the full extent to which they represent functionally segregated parallel projecting channels remains to be determined. However, for one midbrain structure, the superior colliculus (SC), anatomical evidence for closed-loop connectivity with the basal ganglia is robust, and can serve as an example against which the loop hypothesis can be evaluated for other subcortical structures. Examination of ascending projections from the SC to the thalamus suggests there may be multiple functionally segregated systems. The SC also provides afferent signals to the other principal input nuclei of the basal ganglia, the dopaminergic neurones in substantia nigra and to the subthalamic nucleus. Recent electrophysiological investigations show that the afferent signals originating in the SC carry important information concerning the onset of biologically significant events to each of the basal ganglia input nuclei. Such signals are widely regarded as crucial for the proposed functions of selection and reinforcement learning with which the basal ganglia have so often been associated.


Internet of Things: A Comprehensive Study of Security Issues and Defense Mechanisms

The Internet of Things (IoT) is an evolving global trend in Web-based information architecture aiding in the exchange of services and goods over a network without necessitating human-to-human or human-to-computer interaction. It has the potential to revolutionize physical world interaction of individuals and the organizations. The application of IoT can be recognized significantly in many areas such as in healthcare, resource management, learning, knowledge processing, and many more. The practical realization of IoT is met with a plethora of security and privacy challenges that need to be tackled for IoT’s successful deployment on a commercially viable large scale. This paper analyzes the security issues related to IoT networks through an analysis of the existing empirical researches to get an insight on the security requirements of the IoT networks. The findings of the study revealed that security threats are one of the biggest and ever-growing challenges for IoT, and it is essential to substantially mitigate them for the success of this platform.


Deep People Counting with Faster R-CNN and Correlation Tracking

Crowd counting is a key problem for many computer vision tasks while most existing methods try to count people based on regression with hand-crafted features. Recently, the fast development of deep learning has resulted in many promising detectors of generic object classes. In this paper, to effective leverage the discriminability of convolutional neural networks, we propose a method to people counting based on Faster R-CNN[9] head-shoulder detection and correlation tracking. Firstly, we train a Faster R-CNN head-shoulder detector with Zeiler model to detect people with multiple poses and views. Next, we employ kernelized correlation filter(KCF)[7] to track the people and obtain the trajectory. Considering the results of the detection and tracking, we fuse the two bounding box to obtain a continuous and stable trajectory. Extensive experiments and comparison show the promise of the proposed approach.


Design and Experimentation of WPT Charger for Electric City Car

Wireless power transfer systems (WPTSs) with inductive coupling are advantageously used to charge the battery pack of electric vehicles. They basically consist of coil coupling, power supply circuitry connected to the transmitting side of the coil coupling, and power-conditioning circuitry connected to the receiving side of the coil coupling. This paper presents the design and implementation of a wireless power transfer (WPT) battery charger for an electric city car. A short overview on the working principles of a series-series resonant WPTS is given before describing in detail the design procedure of the power circuitry needed for its operation, i.e., an alternating-current-direct-current converter cascaded by a high-frequency inverter in the transmitting section and a diode rectifier cascaded by a chopper in the receiving section. The coil coupling with spiral coils is designed with the help of a finite-element-method code. A prototype of the WPT battery charger is built up according to the design results, and experiments that validate the design procedure are carried out.


An Introduction to Logistic Regression Analysis and Reporting

The purpose of this article is to provide researchers, editors, and readers with a set of guidelines for what to expect in an article using logistic regression techniques. Tables, figures, and charts that should be included to comprehensively assess the results and assumptions to be verified are discussed. This article demonstrates the preferred pattern for the application of logistic methods with an illustration of logistic regression applied to a data set in testing a research hypothesis. Recommendations are also offered for appropriate reporting formats of logistic regression results and the minimum observation-to-predictor ratio. The authors evaluated the use and interpretation of logistic regression presented in 8 articles published in The Journal of Educational Research between 1990 and 2000. They found that all 8 studies met or exceeded recommended criteria.


The Antecedents of Customer Loyalty : An Empirical Investigation in Life Insurance Context

The present paper seeks to offer the most decipherable and widely applicable antecedents of customer loyalty. It explores the extant literature on customer loyalty and brings out seven variables which are responsible for formation of customer loyalty. Further, the relative importance of these variables has been ascertained through Multiple Regression Analysis which revealed that service quality and commitment are the strongest predictors of customer loyalty in the Indian life insurance industry. The paper also attempts to assess the loyalty status of life insurance customers in India and draw a comparison between public and private sector life insurance companies in order to provide significant insights to the life insurance companies that may assist them in devising better loyalty practices. The findings suggest that Indian customers do care about the public sector status of a financial service provider as it entails a sense of security and stability and thus creates a difference between customer loyalty of public sector life insurer and that of private sector life insurer. The paper holds significant implications for academicians interested in dynamics of customer loyalty as well as the marketers of life insurance services who are concerned with customer relationships.


The Interplay of Cross-Situational Word Learning and Sentence-Level Constraints

A variety of mechanisms contribute to word learning. Learners can track co-occurring words and referents across situations in a bottom-up manner (cross-situational word learning, CSWL). Equally, they can exploit sentential contexts, relying on top-down information such as verb-argument relations and world knowledge, offering immediate constraints on meaning (word learning based on sentence-level constraints, SLCL). When combined, CSWL and SLCL potentially modulate each other's influence, revealing how word learners deal with multiple mechanisms simultaneously: Do they use all mechanisms? Prefer one? Is their strategy context dependent? Three experiments conducted with adult learners reveal that learners prioritize SLCL over CSWL. CSWL is applied in addition to SLCL only if SLCL is not perfectly disambiguating, thereby complementing or competing with it. These studies demonstrate the importance of investigating word-learning mechanisms simultaneously, revealing important characteristics of their interaction in more naturalistic learning environments.


Anisotropic diffusion-based detail-preserving smoothing for image restoration

It is important in image restoration to remove noise while preserving meaningful details such as edges and fine features. The existing edge-preserving smoothing methods may inevitably take fine detail as noise or vice versa. In this paper, we propose a new edge-preserving smoothing technique based on a modified anisotropic diffusion. The proposed method can simultaneously preserve edges and fine details while filtering out noise in the diffusion process. Since the fine detail in the neighborhood of a small image window generally have a gray-level variance larger than that of the noisy background, the proposed diffusion model incorporates both local gradient and gray-level variance to preserve edges and fine details while effectively removing noise. Experimental results have shown that the proposed anisotropic diffusion scheme can effectively smooth noisy background, yet well preserve edge and fine details in the restored image. The proposed method has the best restoration result compared with other edge-preserving methods.


Interacting Multiple Model Filter-Based Sensor Fusion of GPS With In-Vehicle Sensors for Real-Time Vehicle Positioning

Vehicle position estimation for intelligent vehicles requires not only highly accurate position information but reliable and continuous information provision as well. A low-cost Global Positioning System (GPS) receiver has widely been used for conventional automotive applications, but it does not guarantee accuracy, reliability, or continuity of position data when GPS errors occur. To mitigate GPS errors, numerous Bayesian filters based on sensor fusion algorithms have been studied. The estimation performance of Bayesian filters primarily relies on the choice of process model. For this reason, the change in vehicle dynamics with driving conditions should be addressed in the process model of the Bayesian filters. This paper presents a positioning algorithm based on an interacting multiple model (IMM) filter that integrates low-cost GPS and in-vehicle sensors to adapt the vehicle model to various driving conditions. The model set of the IMM filter is composed of a kinematic vehicle model and a dynamic vehicle model. The algorithm developed in this paper is verified via intensive simulation and evaluated through experimentation with a real-time embedded system. Experimental results show that the performance of the positioning system is accurate and reliable under a wide range of driving conditions.


High-Integrity IMM-EKF-Based Road Vehicle Navigation With Low-Cost GPS/SBAS/INS

User requirements for the performance of Global Navigation Satellite System (GNSS)-based road applications have been significantly increasing in recent years. Safety systems based on vehicle localization, electronic fee-collection systems, and traveler information services are just a few examples of interesting applications requiring onboard equipment (OBE) capable of offering a high available accurate position, even in unfriendly environments with low satellite visibility such as built-up areas or tunnels and at low cost. In addition to that, users and service providers demand from the OBEs not only accurate continuous positioning but integrity information of the reliability of this position as well. Specifically, in life-critical applications, high-integrity monitored positioning is absolutely required. This paper presents a solution based on the fusion of GNSS and inertial sensors (a Global Positioning System/satellite-based augmentation system/inertial navigation system integrated system) running an extended Kalman filter combined with an interactive multimodel method (IMM-EKF). The solution developed in this paper supplies continuous positioning in marketable conditions and a meaningful trust level of the given solution. A set of tests performed in controlled and real scenarios proves the suitability of the proposed IMM-EKF implementation as compared with low-cost GNSS-based solutions, dead reckoning systems, single-model EKF, and other filtering approaches of the current literature.


Lane-Level Integrity Provision for Navigation and Map Matching With GNSS, Dead Reckoning, and Enhanced Maps

Lane-level positioning and map matching are some of the biggest challenges for navigation systems. Additionally, in safety applications or in those with critical performance requirements (such as satellite-based electronic fee collection), integrity becomes a key word for the navigation community. In this scenario, it is clear that a navigation system that can operate at the lane level while providing integrity parameters that are capable of monitoring the quality of the solution can bring important benefits to these applications. This paper presents a pioneering novel solution to the problem of combined positioning and map matching with integrity provision at the lane level. The system under consideration hybridizes measurements from a global navigation satellite system (GNSS) receiver, an odometer, and a gyroscope, along with the road information stored in enhanced digital maps, by means of a multiple-hypothesis particle-filter-based algorithm. A set of experiments in real environments in France and Germany shows the very good results obtained in terms of positioning, map matching, and integrity consistency, proving the feasibility of our proposal.


IMM object tracking for high dynamic driving maneuvers

Classical object tracking approaches use a Kalman-filter with a single dynamic model which is therefore optimised to a single driving maneuver. In contrast the interacting multiple model (IMM) filter allows for several parallel models which are combined to a weighted estimate. Choosing models for different driving modes, such as constant speed, acceleration and strong acceleration changes, the object state estimation can be optimised for highly dynamic driving maneuvers. The paper describes the analysis of Stop&Go situations and the systematic parametrisation of the IMM method based on these statistics. The evaluation of the IMM approach is presented based on real sensor measurements of laser scanners, a radar and a video image processing unit.


Overview of the Lucy Project: Dynamic Stabilization of a Biped Powered by Pneumatic Artificial Muscles

This paper gives an overview of the Lucy project. What is special is that the biped is not actuated with the classical electrical drives, but with pleated pneumatic artificial muscles. In an antagonistic setup of such muscles both the torque and the compliance are controllable. From human walking there is evidence that joint compliance plays an important role in energy-efficient walking and running. To be able to walk at different walking speeds and step lengths, a trajectory generator and joint trajectory tracking controller are combined. The first generates dynamically stable trajectories based on the objective locomotion parameters which can be changed from step to step. The joint trajectory tracking unit controls the pressure inside the muscles so the desired motion is followed. It is based on a computed torque model and takes the torque– angle relation of the antagonistic muscle setup into account. With this strategy the robot is able to walk at a speed up to 0.15 m/s. A compliance controller is developed to reduce the energy consumption by combining active trajectory control with the exploitation of the natural dynamics. A mathematical formulation was developed to find an optimal compliance setting depending on the desired trajectory and physical properties of the system. This strategy is experimentally evaluated on a single pendulum structure and not implemented on the real robot because the walking speed of the robot is currently too slow. At the end a discussion is given about the pros and cons of building a pneumatic biped, and the control architecture used. © Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2008


DeepTag: inferring diagnoses from veterinary clinical notes

Large scale veterinary clinical records can become a powerful resource for patient care and research. However, clinicians lack the time and resource to annotate patient records with standard medical diagnostic codes and most veterinary visits are captured in free-text notes. The lack of standard coding makes it challenging to use the clinical data to improve patient care. It is also a major impediment to cross-species translational research, which relies on the ability to accurately identify patient cohorts with specific diagnostic criteria in humans and animals. In order to reduce the coding burden for veterinary clinical practice and aid translational research, we have developed a deep learning algorithm, DeepTag, which automatically infers diagnostic codes from veterinary free-text notes. DeepTag is trained on a newly curated dataset of 112,558 veterinary notes manually annotated by experts. DeepTag extends multitask LSTM with an improved hierarchical objective that captures the semantic structures between diseases. To foster human-machine collaboration, DeepTag also learns to abstain in examples when it is uncertain and defers them to human experts, resulting in improved performance. DeepTag accurately infers disease codes from free-text even in challenging cross-hospital settings where the text comes from different clinical settings than the ones used for training. It enables automated disease annotation across a broad range of clinical diagnoses with minimal preprocessing. The technical framework in this work can be applied in other medical domains that currently lack medical coding resources.


Multi-Objective Resource Allocation for Secure Communication in Cognitive Radio Networks with Wireless Information and Power Transfer

In this paper, we study resource allocation for multiuser multiple-input single-output (MISO) secondary communication systems with multiple system design objectives. We consider cognitive radio (CR) networks where the secondary receivers are able to harvest energy from the radio frequency when they are idle. The secondary system provides simultaneous wireless power and secure information transfer to the secondary receivers. We propose a multi-objective optimization framework for the design of a Pareto optimal resource allocation algorithm based on the weighted Tchebycheff approach. In particular, the algorithm design incorporates three important system design objectives: total transmit power minimization, energy harvesting efficiency maximization, and interference power leakage-totransmit power ratio minimization. The proposed framework takes into account a quality of service (QoS) requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information (CSI) of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The proposed framework includes total harvested power maximization and interference power leakage minimization as special cases. The adopted multiobjective optimization problem is non-convex and is recast as a convex optimization problem via semidefinite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP relaxed problem. Besides, two suboptimal resource allocation schemes for the case when the solution of the dual problem is unavailable for constructing the optimal solution are proposed. Numerical results not only demonstrate the close-to-optimal performance of the proposed suboptimal schemes, but also unveil an interesting trade-off between the considered conflicting system design objectives.


Latent Discriminant Analysis with Representative Feature Discovery

Linear Discriminant Analysis (LDA) is a well-known method for dimension reduction and classification with focus on discriminative feature selection. However, how to discover discriminative as well as representative features in LDA model has not been explored. In this paper, we propose a latent Fisher discriminant model with representative feature discovery in an semi-supervised manner. Specifically, our model leverages advantages of both discriminative and generative models by generalizing LDA with data-driven prior over the latent variables. Thus, our method combines multi-class, latent variables and dimension reduction in an unified Bayesian framework. We test our method on MUSK and Corel datasets and yield competitive results compared to baselines. We also demonstrate its capacity on the challenging TRECVID MED11 dataset for semantic keyframe extraction and conduct a human-factors ranking-based experimental evaluation, which clearly demonstrates our proposed method consistently extracts more semantically meaningful keyframes than chal-


Approximate Orthogonal Sparse Embedding for Dimensionality Reduction

Locally linear embedding (LLE) is one of the most well-known manifold learning methods. As the representative linear extension of LLE, orthogonal neighborhood preserving projection (ONPP) has attracted widespread attention in the field of dimensionality reduction. In this paper, a unified sparse learning framework is proposed by introducing the sparsity or L1 -norm learning, which further extends the LLE-based methods to sparse cases. Theoretical connections between the ONPP and the proposed sparse linear embedding are discovered. The optimal sparse embeddings derived from the proposed framework can be computed by iterating the modified elastic net and singular value decomposition. We also show that the proposed model can be viewed as a general model for sparse linear and nonlinear (kernel) subspace learning. Based on this general model, sparse kernel embedding is also proposed for nonlinear sparse feature extraction. Extensive experiments on five databases demonstrate that the proposed sparse learning framework performs better than the existing subspace learning algorithm, particularly in the cases of small sample sizes.


Piccolo: An Ultra-Lightweight Blockcipher

We propose a new 64-bit blockcipher Piccolo supporting 80 and 128-bit keys. Adopting several novel design and implementation techniques, Piccolo achieves both high security and notably compact implementation in hardware. We show that Piccolo offers a sufficient security level against known analyses including recent related-key differential attacks and meet-in-the-middle attacks. In our smallest implementation, the hardware requirements for the 80 and the 128-bit key mode are only 683 and 758 gate equivalents, respectively. Moreover, Piccolo requires only 60 additional gate equivalents to support the decryption function due to its involution structure. Furthermore, its efficiency on the energy consumption which is evaluated by energy per bit is also remarkable. Thus, Piccolo is one of the competitive ultra-lightweight blockciphers which are suitable for extremely constrained environments such as RFID tags and sensor nodes.


A Post-failure Analysis of Mobile Payment Platforms

Despite on-going efforts over the past decade, mobile payments are yet to take off successfully. Repeated failures show that mobile payment platforms are complex to launch. The objective of this paper is to unveil factors explaining the failure of past mobile payment platforms. The use of a multi-level framework aims at enriching the variety of issues investigated. In order to explore the reasons of failure, we selected four cases from different countries happening at different times. Our results show that these cases share many of the same failure factors: lack of collaboration between the stakeholders, no technology standard, and low value-added for consumers and merchants compared to existing payment solutions. Even though the list is not exhaustive, the uncovered factors in our study seem to be necessary (but not sufficient) for the further developments of mobile payment platforms.


Multi-dimensional pattern discovery in financial time series using sax-ga with extended robustness

This paper proposes a new Multi-Dimensional SAX-GA approach to pattern discovery using genetic algorithms (GA). The approach is capable of discovering patterns in multi-dimensional financial time series. First, the several dimensions of data are converted to a Symbolic Aggregate approXimation (SAX) representation, which is, then, feed to a GA optimization kernel. The GA searches for profitable patterns occurring simultaneously in the multi-dimensional time series. Based on the patterns found, the GA produces more robust investment strategies, since the simultaneity of patterns on different dimensions of the data, reinforces the strength of the trading decisions implemented. The proposed approach was tested using stocks from S&P500 index, and is compared to previous reference works of SAX-GA and to the Buy & Hold (B&H) classic investment strategy.


A survey on human activity recognition from videos

Understanding the activities of human from videos is demanding task in Computer Vision. Identifying the actions being accomplished by the human in the video sequence automatically and tagging their actions is the prime functionality of intelligent video systems. The goal of activity recognition is to identify the actions and objectives of one or more objects from a series of examination on the action of object and their environmental condition. The major applications of Human Activity Recognition varies from Content-based Video Analytics, Robotics, Human-Computer Interaction, Human fall detection, Ambient Intelligence, Visual Surveillance, Video Indexing etc. This paper collectively summarizes and deciphers the various methodologies, challenges and issues of Human Activity Recognition systems. Variants of Human Activity Recognition systems such as Human Object Interactions and Human-Human Interactions are also explored. Various benchmarking datasets and their properties are being explored. The Experimental Evaluation of various papers are analyzed efficiently with the various performance metrics like Precision, Recall, and Accuracy.


Towards Agile Implementation of Test Maturity Model Integration (TMMI) Level 2 using Scrum Practices

The software industry has invested the substantial effort to improve the quality of its products like ISO, CMMI and TMMI. Although applying of TMMI maturity criteria has a positive impact on product quality, test engineering productivity, and cycle-time effort. But it requires heavy software development processes and large investments in terms of cost and time that medium/small companies do not deal with it. At the same time, agile methods deal with changing requirements and continuous delivery of valuable software by short time iterations. So that the aim of this paper is to improve the testing process by applying a detailed mapping between TMMI and Scrum practices and verifying this mapping with a study providing empirical evidence of the obtained results. The research has been evaluated on a sample of 2 large-sized TMMI certified companies. In conclusion, the experimental results show the effectiveness of using this integrated approach compared to other existing approaches. Keywords—Agile software development; Scrum; TMMI; Software Testing; CMMI


An Experimental Study of Genetic Crossover Operators for the Job Shop Scheduling Problem

The aim of this paper is to show the influence of genetic crossover operators on the performance of a genetic algorithm (GA). The GA is applied to the job shop scheduling problem (JSSP). To achieve this aim an experimental study of a set of crossover operators is presented. The experimental study is based on a decision support system (DSS). To compare the abilities of different crossover operators, the DSS was designed giving all the operators the same opportunities. The genetic crossover operators are tested on a set of standard instances taken from the literature. The makespan is the measure used to evaluate the genetic crossover operators. The main conclusion is that there is a crossover operator having the best average performance on a specific set of solved instances. The DSS developed can be utilized in a common industrial or construction environment. Key-Words: Genetic algorithms, crossover operators, scheduling, heuristics, JSSP.


Text mining in healthcare. Applications and opportunities.

Healthcare information systems collect massive amounts of textual and numeric information about patients, visits, prescriptions, physician notes and more. The information encapsulated within electronic clinical records could lead to improved healthcare quality, promotion of clinical and research initiatives, fewer medical errors and lower costs. However, the documents that comprise the health record vary in complexity, length and use of technical vocabulary. This makes knowledge discovery complex. Commercial text mining tools provide a unique opportunity to extract critical information from textual data archives. In this paper, we share our experience of a collaborative research project to develop predictive models by text mining electronic clinical records. We provide an overview of the text mining process, examples of existing studies, experiences of our collaborative project and future opportunities.


Deep Visuo-Tactile Learning: Estimation of Material Properties from Images

Estimation of materials properties, such as softness or roughness from visual perception is an essential factor in deciding our way of interaction with our environment in e.g., object manipulation tasks or walking. In this research, we propose a method for deep visuo-tactile learning in which we train a encoder-decoder network with an intermediate layer in an unsupervised manner with images as input and tactile sequences as output. Materials properties are then represented in the intermediate layer as a continuous feature space and are estimated from image information. Unlike past studies utilizing tactile sensors focusing on classification for object recognition or recognizing material properties, our method does not require manually designing class labels or annotation, does not cause unknown objects to be classified into known discrete classes, and can be used without a tactile sensor after training. To collect data for training, we have attached a uSkin tactile sensor and a camera to the end-effector of a Sawyer robot to stroke surfaces of 21 different material surfaces. Our results after training show that features are indeed expressed continuously, and that our method is able to handle unknown objects in its feature space.


Dynamic modeling and inverse dynamic control of mobile robot

This paper presents a dynamic modeling and control studies of mobile robot. The robot is mainly built all around three wheels and platform. A mathematical model of unicycle mobile robot is determined by kinematic and dynamic model. The dynamic modelling equations are based on Lagrangian formulation. The motion control strategy is based on the inverse of dynamic control. This leads to accurate tracking of trajectory. The goal to reach in this paper is to improve the performances of inverse dynamic control of mobile robot. The validity of the proposed controller is demonstrated by the simulation of two wheels mobile robots case.


A Method for Building Personalized Ontology Summaries

In the context of ontology engineering, the ontology understanding is the basis for its further development and reuse. One intuitive effective approach to support ontology understanding is the process of ontology summarization which highlights the most important concepts of an ontology. Ontology summarization identifies an excerpt from an ontology that contains the most relevant concepts and produces an abridged ontology. In this article, we present a method for summarizing ontologies that represent a data source schema or describe a knowledge domain. We propose an algorithm to produce a personalized ontology summary based on user-defined parameters, e.g. summary size. The relevance of a concept is determined through user indication or centrality measures, commonly used to determine the relative importance of a vertex within a graph. The algorithm searches for the best ontology summary, i.e., the one containing the most relevant ontology concepts respecting all the original relationships between concepts and the parameters set by the user. Experiments were done comparing our generated ontology summaries against golden standard summaries as well as summaries produced by methods available in related work. We achieved in average more than 62.5% of similarity with golden standard summaries.


Systematic Review of Clustering High-Dimensional and Large Datasets

Technological advancement has enabled us to store and process huge amount of data in relatively short spans of time. The nature of data is rapidly changing, particularly its dimensionality is more commonly multi- and high-dimensional. There is an immediate need to expand our focus to include analysis of high-dimensional and large datasets. Data analysis is becoming a mammoth task, due to incremental increase in data volume and complexity in terms of heterogony of data. It is due to this dynamic computing environment that the existing techniques either need to be modified or discarded to handle new data in multiple high-dimensions. Data clustering is a tool that is used in many disciplines, including data mining, so that meaningful knowledge can be extracted from seemingly unstructured data. The aim of this article is to understand the problem of clustering and various approaches addressing this problem. This article discusses the process of clustering from both microviews (data treating) and macroviews (overall clustering process). Different distance and similarity measures, which form the cornerstone of effective data clustering, are also identified. Further, an in-depth analysis of different clustering approaches focused on data mining, dealing with large-scale datasets is given. These approaches are comprehensively compared to bring out a clear differentiation among them. This article also surveys the problem of high-dimensional data and the existing approaches, that makes it more relevant. It also explores the latest trends in cluster analysis, and the real-life applications of this concept. This survey is exhaustive as it tries to cover all the aspects of clustering in the field of data mining.


Scalable Distributed Stream Processing

Many stream-based applications are naturally distributed. Applications are often embedded in an environment with numerous connected computing devices with heterogeneous capabilities. As data travels from its point of origin (e.g., sensors) downstream to applications, it passes through many computing devices, each of which is a potential target of computation. Furthermore, to cope with time-varying load spikes and changing demand, many servers would be brought to bear on the problem. In both cases, distributed computation is the norm. Abstract


CLOUDS: A Decision Tree Classifier for Large Datasets

Classification for very large datasets has many practical applications in data mining. Techniques such as discretization and dataset sampling can be used to scale up decision tree classifiers to large datasets. Unfortunately, both of these techniques can cause a significant loss in accuracy. We present a novel decision tree classifier called CLOUDS, which samples the splitting points for numeric attributes followed by an estimation step to narrow the search space of the best split. CLOUDS reduces computation and I/O complexity substantially compared to state of the art classifters, while maintaining the quality of the generated trees in terms of accuracy and tree size. We provide experimental results with a number of real and synthetic data~ets.


What are the Gaps in Mobile Patient Portal? Mining Users Feedback Using Topic Modeling

Patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. In this article, we extend the existing literature by discovering design gaps for patient portals from a systematic analysis of negative users’ feedback from the actual use of patient portals. Specifically, we adopt topic modeling approach, LDA algorithm, to discover design gaps from online low rating user reviews of a common mobile patient portal, EPIC’s mychart. To validate the extracted gaps, we compared the results of LDA analysis with that of human analysis. Overall, the results revealed opportunities to improve collaboration and to enhance the design of portals intended for patientcentered care.


Text-to-3 D Scene Generation using Semantic Parsing and Spatial Knowledge with Rule Based System

Scene Generation plays an important role in digital media to represent a news or a specific domain to the viewers. It’s not easy to produce a scene from a text. Text may not completely express the whole situation in digital media. Most of the people are not conscious about the news until it's not visualized to them. Text to 3D scene generation is a process where people do not need to read a news. The 3D Scene will represent the situation. It will help people to conscious about their life. In this paper, we introduce a rule-based framework where scene generated from text using semantic parsing and spatial knowledge. Semantic parsing has identified the templates, objects, and constraints and spatial knowledge have built the relation between object and template. Our rule based framework has identified the uncountable noun and some spatial relations to generate 3D scenes.


A deadlock prevention method for railway networks using monitors for colored Petri nets

The real-time traffic control of railway networks authorizes movements of the trains and imposes safety constraints. The paper deals with the real time traffic control focusing on deadlock prevention problem. Colored Petri nets are used to model the dynamics of the railway network system: places represent tracks and stations, tokens are trains. The prevention policy is expressed by a set of linear inequality constraints, called colored Generalized Mutual Exclusion Constraints that are enforced by adding appropriate monitor places. Using digraph tools, deadlock situations are characterized and a strategy is established to define off-line a set of Generalized Mutual Exclusion Constraints that prevent deadlock. An example shows in detail the design of the proposed control logic.


User Preference Modeling and Exploitation in IoT Scenarios

Recommender Systems are commonly used in web applications to support users in finding items of their interest. We here propose to use Recommender Systems in Internet of Things scenarios to support human decision making in the physical world. For instance, users' choices (visit actions) for points of interests (POIs) while they explore a sensor enabled city can be tracked and used to generate recommendations for not yet visited POIs. In this PhD research, we propose a novel learning approach for generating an explainable human behaviour model and relevant recommendations in sensor enabled areas. Moreover, we propose techniques for simulating user behaviour and analyse the collective dynamics of a population of users.


The Race Between Man and Machine : Implications of Technology for Growth , Factor Shares and Employment

The advent of automation and the simultaneous decline in the labor share and employment among advanced economies raises concerns that labor will be marginalized and made redundant by new technologies. This paper examines this proposition in a task-based framework wherein not only are tasks previously performed by labor automated, but also more complex versions of existing tasks, in which labor has a comparative advantage, can be created. We fully characterize the structure of equilibrium in this model, establishing how the allocation of factors to tasks and factor prices are determined by the available technology and the endogenous choices of firms between capital and labor. We then demonstrate that although automation tends to reduce employment and the share of labor in national income, the creation of more complex tasks has the opposite effect. Our full model endogenizes the direction of research and development towards automation and the creation of new complex tasks. We show that, under reasonable conditions, there exists a stable balanced growth path in which the two types of innovations go hand-in-hand. Consequently, an increase in automation reduces the wage to rental rate ratio, and thus discourages further automation, encourages greater creation of more labor-intensive tasks, and restores the share of labor in national income and the employment to population ratio back towards their initial values. Though the economy is self-correcting, the equilibrium allocation of research effort is not optimal: to the extent that wages reflect quasi-rents for workers, firms will engage in too much automation. Finally, we extend the model to include workers of different skills. We find that inequality increases during transitions, but the self-correcting forces also serve to limit the increase in inequality over longer periods. Still in Progress. Comments Welcome. ∗We thank Philippe Aghion, David Autor, Erik Brynjolfsson, Chad Jones, and seminar participants at the AEA 2015, the MIT Initiative on the Digital Economy, and the NBER 2015 Economic Growth Group for their valuable comments.


Stock Market Forecasting Using Machine Learning Algorithms

Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global stock markets and various financial products to predict the next-day stock trend with the aid of SVM. Numerical results indicate a prediction accuracy of 74.4% in NASDAQ, 76% in S&P500 and 77.6% in DJIA. The same algorithm is also applied with different regression algorithms to trace the actual increment in the markets. Finally, a simple trading model is established to study the performance of the proposed prediction algorithm against other benchmarks.


A LSTM-based method for stock returns prediction: A case study of China stock market

The presented paper modeled and predicted China stock returns using LSTM. The historical data of China stock market were transformed into 30-days-long sequences with 10 learning features and 3-day earning rate labeling. The model was fitted by training on 900000 sequences and tested using the other 311361 sequences. Compared with random prediction method, our LSTM model improved the accuracy of stock returns prediction from 14.3% to 27.2%. The efforts demonstrated the power of LSTM in stock market prediction in China, which is mechanical yet much more unpredictable.


Learning to trade via direct reinforcement

We present methods for optimizing portfolios, asset allocations, and trading systems based on direct reinforcement (DR). In this approach, investment decision-making is viewed as a stochastic control problem, and strategies are discovered directly. We present an adaptive algorithm called recurrent reinforcement learning (RRL) for discovering investment policies. The need to build forecasting models is eliminated, and better trading performance is obtained. The direct reinforcement approach differs from dynamic programming and reinforcement algorithms such as TD-learning and Q-learning, which attempt to estimate a value function for the control problem. We find that the RRL direct reinforcement framework enables a simpler problem representation, avoids Bellman's curse of dimensionality and offers compelling advantages in efficiency. We demonstrate how direct reinforcement can be used to optimize risk-adjusted investment returns (including the differential Sharpe ratio), while accounting for the effects of transaction costs. In extensive simulation work using real financial data, we find that our approach based on RRL produces better trading strategies than systems utilizing Q-learning (a value function method). Real-world applications include an intra-daily currency trader and a monthly asset allocation system for the S&P 500 Stock Index and T-Bills.


Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning

Since most real-world applications of classification learning involve continuous-valued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuous-valued attribute into multiple intervals. We briefly present theoretical evidence for the appropriateness of this heuristic for use in the binary discretization algorithm used in ID3, C4 , CART, and other learning algorithms. The results serve to justify extending the algorithm to derive multiple intervals. We formally derive a criterion based on the minimum description length principle for deciding the partitioning of intervals. We demonstrate via empirical evaluation on several real-world data sets that better decision trees are obtained using the new multi-interval algorithm. Introduction Classification learning algorithms typically use heuristics to guide their search through the large space of possible relations between combinations of attribute' values and classes. One such heuristic uses the notion of selecting attributes locally minimizing the information entropy of the classes in a data set (d. the ID3 algorithm (13) and its extensions, e.g. GID3 (2), GID3* (5), and C4 (15), CART (1), CN2 (3) and others). See (11; 5; 6) for a general discussion of the attribute selection problem. The attributes in a learning problem may be nominal (categorical), or they may be continuous (numerical). The term continuous" is used in the literature to refer to attributes taking on numerical values (integer or real); or in general an attribute with a linearly ordered range of values. The above mentioned attribute selection process assumes that all attributes are nominal. Continuous-valued attributes are discretized prior to selection , typically by paritioning the range of the attribute into subranges. In general, a discretization is simply a logical condition , in terms of one or more attributes, that serves to partition the data into at least two subsets. In this paper, we focus only on the discretization of continuous-valued attributes. We first present a result about the information entropy minimization heuristic for binary discretization (two-interval splits). This gives us: . a better understanding of the heuristic and its behavior 1022 Machine Learning :;. . formal evidence that supports the usage of the heuristic ;;' in this context , and . a gain in computational effciency that results in speeding . up the evaluation process for continuous-valued attribute discretization. We then proceed to extend the algorithm to divide the range of a continuous-valued attribute into multiple intervals rather than just two. We first motivate the need for such a capability, then we present the multiple interval generalization, and finally we present the empirical evaluation results confirming that the new capability does indeed result in producing better decision trees. Binary Discretization A continuous-valued attribute is typically discretized during decision tree generation by partitioning its range into two intervals. A threshold value for the continuous-valued attribute is determined, and the test ::


A Multi-task Deep Network for Person Re-identification

Person re-identification (ReID) focuses on identifying people across different scenes in video surveillance, which is usually formulated as a binary classification task or a ranking task in current person ReID approaches. In this paper, we take both tasks into account and propose a multi-task deep network (MTDnet) that makes use of their own advantages and jointly optimize the two tasks simultaneously for person ReID. To the best of our knowledge, we are the first to integrate both tasks in one network to solve the person ReID. We show that our proposed architecture significantly boosts the performance. Furthermore, deep architecture in general requires a sufficient dataset for training, which is usually not met in person ReID. To cope with this situation, we further extend the MTDnet and propose a cross-domain architecture that is capable of using an auxiliary set to assist training on small target sets. In the experiments, our approach outperforms most of existing person ReID algorithms on representative datasets including CUHK03, CUHK01, VIPeR, iLIDS and PRID2011, which clearly demonstrates the effectiveness of the proposed approach.


T-S Fuzzy Path Controller Design for the Omnidirectional Mobile Robot

This paper focuses on the design of T-S fuzzy path controller for improving the high-speed tracking accuracy of a four-wheel omnidirectional mobile robot (ODMR). Usually, when an ODMR moves in high speed, the adverse effects induced by Coriolis force significantly deteriorate the tracking performances. In this paper, a T-S fuzzy control designed with both the state feedback and path tracking error is proposed to meet the design specifications. Moreover, a new approach of kinematics inversion considering the nonlinear term of coordinate transform is proposed to simplify the analysis of ODMR motion. Finally, an example is provided to compare the present T-S fuzzy path controller with the well-tuned PID controller. The simulation results indicate that the proposed approach provides much better tracking performances as the speed of ODMR increases


Beam-steerable patch antenna array using parasitic coupling and reactive loading

A parasitically-coupled and reactively-loaded patch antenna array is demonstrated to achieve beam steering in an analog manner. The strong parasitic coupling between closely-spaced patch antennas is used to form a phased array with a single driven element. The phase shift between the array elements is adjustable by changing the reactive loading on the parasitic patch antenna. A prototype of this antenna array is demonstrated at 2.3 GHz.


Food Image Recognition via Superpixel Based Low-Level and Mid-Level Distance Coding for Smart Home Applications

Food image recognition is a key enabler for many smart home applications such as smart kitchen and smart personal nutrition log. In order to improve living experience and life quality, smart home systems collect valuable insights of users’ preferences, nutrition intake and health conditions via accurate and robust food image recognition. In addition, efficiency is also a major concern since many smart home applications are deployed on mobile devices where high-end GPUs are not available. In this paper, we investigate compact and efficient food image recognition methods, namely low-level and mid-level approaches. Considering the real application scenario where only limited and noisy data are available, we first proposed a superpixel based Linear Distance Coding (LDC) framework where distinctive low-level food image features are extracted to improve performance. On a challenging small food image dataset where only 12 training images are available per category, our framework has shown superior performance in both accuracy and robustness. In addition, to better model deformable food part distribution, we extend LDC’s feature-to-class distance idea and propose a mid-level superpixel food parts-to-class distance mining framework. The proposed framework show superior performance on a benchmark food image datasets compared to other low-level and mid-level approaches in the literature.


Vulnerability , risk and adaptation : A conceptual framework

The purpose of this paper is to present a tentative conceptual framework for studies of vulnerability and adaptation to climate variability and change, generally applicable to a wide range of contexts, systems and hazards. Social vulnerability is distinguished from biophysical vulnerability, which is broadly equivalent to the natural hazards concept of risk. The IPCC definition of vulnerability is discussed within this context, which helps us to reconcile apparently contradictory definitions of vulnerability. A concise typology of physically defined hazards is presented; the relationship between the vulnerability and adaptive capacity of a human system depends critically on the nature of the hazard faced. Adaptation by a system may be inhibited by process originating outside the system; it is therefore important to consider " external " obstacles to adaptation, and links across scales, when assessing adaptive capacity.


Boosting Neural Machine Translation

Training efficiency is one of the main problems for Neural Machine Translation (NMT). Deep networks need for very large data as well as many training iterations to achieve state-of-the-art performance. This results in very high computation cost, slowing down research and industrialisation. In this paper, we propose to alleviate this problem with several training methods based on data boosting and bootstrap with no modifications to the neural network. It imitates the learning process of humans, which typically spend more time when learning “difficult” concepts than easier ones. We experiment on an English-French translation task showing accuracy improvements of up to 1.63 BLEU while saving 20% of training time.


Markov Chain Monte Carlo with People

Many formal models of cognition implicitly use subjective probability distributions to capture the assumptions of human learners. Most applications of these models determine these distributions indirectly. We propose a method for directly determining the assumptions of human learners by sampling from subjective probability distributions. Using a correspondence between a model of human choice and Markov chain Monte Carlo (MCMC), we describe a method for sampling from the distributions over objects that people associate with different categories. In our task, subjects choose whether to accept or reject a proposed change to an object. The task is constructed so that these decisions follow an MCMC acceptance rule, defining a Markov chain for which the stationary distribution is the category distribution. We test this procedure for both artificial categories acquired in the laboratory, and natural categories acquired from experience.