title: The power of a nod and a glance: Envelope vs. emotional feedback in animated conversational agents

abstract: In this article we describe results froman experiment of user interaction with autonomous , human - like ( humanoid ) conversational agents . We hypothesize that for embodied conversational agents , nonverbal behaviors related to the process of conversation , what we call envelope feedback, is much more important than other feedback , such as emotional expression . We test this hypothesis by having subjects interact with three autonomous agents , all capable of full - duplex multimodal interaction: able to generate and recognize speech , intonation , facial displays , and gesture . Each agent , however , gave a different kind of feedback: ( 1 ) content - related only , ( 2 ) content + envelope feedback , and ( 3 ) content + emotional . Content-related feedback includes answering questions and executing commands; envelope feedback includes behaviors such as gaze , manual beat gesture , and head movements; emotional feedback includes smiles and looks of puzzlement . Subjects' evaluations of the systemwere c...


title: Robot therapy in a care house - its sociopsychological and physiological effects on the residents

abstract: Robot therapy for elderly residents in a care house has been conducted from June, 2005. Two therapeutic seal robots were introduced, and activated for over 9 hours every day to interact with the residents. This paper presents a progress report of this experiment. In order to investigate psychological and social influences of the robots, each subject was interviewed, and their social network was analysed. In addition, their hormones in urine: 17 Ketosteroid sulfate (17-KS-S) and 17-hydroxycorticosteroids (17-OHCS) were obtained and analysed. The results indicate that the density of the social networks was increased through interaction with the seal robots. Furthermore, urinary tests showed that the reactions of the subjects' vital organs to stress were improved after the introduction of the robots


title: Towards Making Systems Forget with Machine Unlearning

abstract: Today's systems produce a rapidly exploding amount of data, and the data further derives more data, forming a complex data propagation network that we call the data's lineage. There are many reasons that users want systems to forget certain data including its lineage. From a privacy perspective, users who become concerned with new privacy risks of a system often want the system to forget their data and lineage. From a security perspective, if an attacker pollutes an anomaly detector by injecting manually crafted data into the training data set, the detector must forget the injected data to regain security. From a usability perspective, a user can remove noise and incorrect entries so that a recommendation engine gives useful recommendations. Therefore, we envision forgetting systems, capable of forgetting certain data and their lineages, completely and quickly. This paper focuses on making learning systems forget, the process of which we call machine unlearning, or simply unlearning. We present a general, efficient unlearning approach by transforming learning algorithms used by a system into a summation form. To forget a training data sample, our approach simply updates a small number of summations -- asymptotically faster than retraining from scratch. Our approach is general, because the summation form is from the statistical query learning in which many machine learning algorithms can be implemented. Our approach also applies to all stages of machine learning, including feature selection and modeling. Our evaluation, on four diverse learning systems and real-world workloads, shows that our approach is general, effective, fast, and easy to use.


title: Protecting endpoint devices in IoT supply chain

abstract: The Internet of Things (IoT), an emerging global network of uniquely identifiable embedded computing devices within the existing Internet infrastructure, is transforming how we live and work by increasing the connectedness of people and things on a scale that was once unimaginable. In addition to increased communication efficiency between connected objects, the IoT also brings new security and privacy challenges. Comprehensive measures that enable IoT device authentication and secure access control need to be established. Existing hardware, software, and network protection methods, however, are designed against fraction of real security issues and lack the capability to trace the provenance and history information of IoT devices. To mitigate this shortcoming, we propose an RFID-enabled solution that aims at protecting endpoint devices in IoT supply chain. We take advantage of the connection between RFID tag and control chip in an IoT device to enable data transfer from tag memory to centralized database for authentication once deployed. Finally, we evaluate the security of our proposed scheme against various attacks.


title: Textbased ontology construction using relational concept analysis

abstract: We present a semi-automated process that constructs an ontology based on a collection of document abstracts for a given domain. The proposed process relies on the formal concept analysis (\fca), an algebraic method for the derivation of a conceptual hierarchy, namely '\textit{concept lattice}', starting from data context, i.e., set of individuals provided with their properties. First, we show how various contexts are extracted and then how concepts of the corresponding lattices are turned into ontological concepts. In order to refine the obtained ontology with transversal relations, the links between individuals that appear in the text are considered by the means of a richer data format. Indeed, Relational Concept Analysis (\rca), a framework that helps \fca in mining relational data is used to model these links and then inferring relations between formal concepts whose semantic is similar to roles between concepts in ontologies. The process describes how the final ontology is mapped to logical formulae which can be expressed in the Description Logics (\dl) language ${\cal FLE}$. To illustrate the process, the construction of a sample ontology on the astronomical field is considered.


title: A Wireless Headstage for Combined Optogenetics and Multichannel Electrophysiological Recording

abstract: This paper presents a wireless headstage with real-time spike detection and data compression for combined optogenetics and multichannel electrophysiological recording. The proposed headstage, which is intended to perform both optical stimulation and electrophysiological recordings simultaneously in freely moving transgenic rodents, is entirely built with commercial off-the-shelf components, and includes 32 recording channels and 32 optical stimulation channels. It can detect, compress and transmit full action potential waveforms over 32 channels in parallel and in real time using an embedded digital signal processor based on a low-power field programmable gate array and a Microblaze microprocessor softcore. Such a processor implements a complete digital spike detector featuring a novel adaptive threshold based on a Sigma-delta control loop, and a wavelet data compression module using a new dynamic coefficient re-quantization technique achieving large compression ratios with higher signal quality. Simultaneous optical stimulation and recording have been performed in-vivo using an optrode featuring 8 microelectrodes and 1 implantable fiber coupled to a 465-nm LED, in the somatosensory cortex and the Hippocampus of a transgenic mouse expressing ChannelRhodospin (Thy1::ChR2-YFP line 4) under anesthetized conditions. Experimental results show that the proposed headstage can trigger neuron activity while collecting, detecting and compressing single cell microvolt amplitude activity from multiple channels in parallel while achieving overall compression ratios above 500. This is the first reported high-channel count wireless optogenetic device providing simultaneous optical stimulation and recording. Measured characteristics show that the proposed headstage can achieve up to 100% of true positive detection rate for signal-to-noise ratio (SNR) down to 15 dB, while achieving up to 97.28% at SNR as low as 5 dB. The implemented prototype features a lifespan of up to 105 minutes, and uses a lightweight (2.8 g) and compact (17 × 18 × 10 mm ::: 3 ::: ) rigid-flex printed circuit board.


title: A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings

abstract: Background ::: Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses.


title: Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

abstract: Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG) and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz) regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT) method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI) technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM) and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals.


title: DetectA: abrupt concept drift detection in non-stationary environments

abstract: Abstract Almost all drift detection mechanisms designed for classification problems work reactively: after receiving the complete data set (input patterns and class labels) they apply a sequence of procedures to identify some change in the class-conditional distribution – a concept drift. However, detecting changes after its occurrence can be in some situations harmful to the process under analysis. This paper proposes a proactive approach for abrupt drift detection, called DetectA (Detect Abrupt Drift). Briefly, this method is composed of three steps: (i) label the patterns from the test set (an unlabelled data block), using an unsupervised method; (ii) compute some statistics from the train and test sets, conditioned to the given class labels for train set; and (iii) compare the training and testing statistics using a multivariate hypothesis test. Based on the results of the hypothesis tests, we attempt to detect the drift on the test set, before the real labels are obtained. A procedure for creating datasets with abrupt drift has been proposed to perform a sensitivity analysis of the DetectA model. The result of the sensitivity analysis suggests that the detector is efficient and suitable for datasets of high-dimensionality, blocks with any proportion of drifts, and datasets with class imbalance. The performance of the DetectA method, with different configurations, was also evaluated on real and artificial datasets, using an MLP as a classifier. The best results were obtained using one of the detection methods, being the proactive manner a top contender regarding improving the underlying base classifier accuracy.


title: Automated analysis of nursing home observations

abstract: Pervasive activity monitoring in a skilled-nursing facility helps capture a continuous audio and video record. The CareMedia project analyzes this video information by automatically tracking people, helping to efficiently label individuals, and characterizing selected activities and actions.


title: Shape from Focus

abstract: The shape from focus method presented here uses different focus levels to obtain a sequence of object images. The sum-modified-Laplacian (SML) operator is developed to provide local measures of the quality of image focus. The operator is applied to the image sequence to determine a set of focus measures at each image point. A depth estimation algorithm interpolates a small number of focus measure values to obtain accurate depth estimates. A fully automated shape from focus system has been implemented using an optical microscope and tested on a variety of industrial samples. Experimental results are presented that demonstrate the accuracy and robustness of the proposed method. These results suggest shape from focus to be an effective approach for a variety of challenging visual inspection tasks. >


title: National Differences in Intelligence, Crime, Income, and Skin Color.

abstract: Abstract National differences in murder, rape, and serious assault were examined in 113 countries in relation to national IQ, income, skin color, birth rate, life expectancy, infant mortality, and HIV/AIDS. Data were collated from the 1993–1996 International Crime Statistics published by INTERPOL. Violent crime was found to be lower in countries with higher IQs, higher life expectancies, lighter skin color, and lower rates of HIV/AIDS, although not with higher national incomes or higher rates of infant mortality. A principal components analysis found the first general factor accounted for 52% of the variance. Moreover, the correlations were significantly higher with skin color, a more biologically influenced variable, than with measures of national income, a more culturally influenced variable. When the 19 sub-Saharan African countries were excluded from analysis the crime/IQ relation held but the crime/skin color relation did not.


title: What makes computer games fun? (abstract only)

abstract: One can't deny the effectiveness of video arcade games in reachipg users! Just loop at the number of quarters pushed into the slots, the time spent by people of widely differing abilities, and the number of repeat encounters with the systems. At least part of the success is due to the ease of getting started (the first play of the game gets one comfortable with the procedures), the high degree of visualization of controls and results, and the responsiveness overall. Other factors will be taken up by the panelists.Review of the home computer market shows what can be accomplished by an easy-to-use accounting aid through advertising store demonstrations, and word of mouth. Visicalc has sold over a million dollars! Attendees will have an opportunity to try some of these impressive applications before and after the session.


title: Learning Good Regions to Deblur Images

abstract: The goal of single image deblurring is to recover both a latent clear image and an underlying blur kernel from one input blurred image. Recent methods focus on exploiting natural image priors or additional image observations for deblurring, but pay less attention to the influence of image structure on estimating blur kernels. What is the useful image structure and how can one select good regions for deblurring? We formulate the problem of learning good regions for deblurring within the conditional random field framework. To better compare blur kernels, we develop an effective similarity metric for labeling training samples. The learned model is able to predict good regions from an input blurred image for deblurring without user guidance. Qualitative and quantitative evaluations demonstrate that good regions can be selected by the proposed algorithms for effective single image deblurring.


title: The Artificial Life Roots of Artificial Intelligence

abstract: Behavior-oriented Artificial Intelligence (AI) is a scientific discipline that studies how behavior of agents emerges and becomes intelligent and adaptive. Success of the field is defined in terms of success in building physical agents that are capable of maximizing their own self-preservation in interaction with a dynamically changing environment. The paper addresses this Artificial Life route toward AI and reviews some of the results obtained so far.


title: Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction

abstract: Recidivism prediction scores are used across the USA to determine sentencing and supervision for hundreds of thousands of inmates. One such generator of recidivism prediction scores is Northpointe's Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) score, used in states like California and Florida, which past research has shown to be biased against black inmates according to certain measures of fairness. To counteract this racial bias, we present an adversarially-trained neural network that predicts recidivism and is trained to remove racial bias. When comparing the results of our model to COMPAS, we gain predictive accuracy and get closer to achieving two out of three measures of fairness: parity and equality of odds. Our model can be generalized to any prediction and demographic. This piece of research contributes an example of scientific replication and simplification in a high-stakes real-world application like recidivism prediction.


title: User needs for location-aware mobile services

abstract: Abstract Mobile contexts of use vary a lot, and may even be continuously changing during use. The context is much more than location, but its other elements are still difficult to identify or measure. Location information is becoming an integral part of different mobile devices. Current mobile services can be enhanced with location-aware features, thus providing the user with a smooth transition towards context-aware services. Potential application fields can be found in areas such as travel information, shopping, entertainment, event information and different mobile professions. This paper studies location-aware mobile services from the user's point of view. The paper draws conclusions about key issues related to user needs, based on user interviews, laboratory and field evaluations with users, and expert evaluations of location-aware services. The user needs are presented under five main themes: topical and comprehensive contents, smooth user interaction, personal and user-generated contents, seamless service entities and privacy issues.


title: Low-Complexity Iterative MMSE-PIC Detection for MIMO-GFDM

abstract: Driven by 5G requirements, research on alternatives to the popular cyclic-prefix orthogonal frequency division multiplexing (CP-OFDM) waveform recently arose. In particular, non-orthogonal circularly filtered waveforms such as generalized frequency division multiplexing (GFDM) were proposed due to flexibility and robustness. Applying multiple-input multiple-output (MIMO) techniques for future wireless networks are unquestionable and thereby compulsory for any alternative waveform. Despite advancements in accurate MIMO detection algorithms for GFDM, compared with CP-OFDM their complexity still exhibited a higher order of magnitude, impeding an energy-efficient implementation. In this paper, we propose a low-complexity formulation for iterative minimum mean squared error with parallel interference cancellation (MMSE-PIC) detection for non-orthogonal waveforms with localized inter-carrier interference, where we focus on the application to MIMO-GFDM. The proposal achieves complexity similar to CP-OFDM and we evaluate its performance under realistic channel conditions with imperfect channel state information, where we obtain up to 2-dB gain of GFDM compared with OFDM. We confirm our findings by analyzing the measured extrinsic information transfer charts and show that the proposal achieves the performance of optimal maximum likelihood detection. The results point out the MMSE-PIC algorithm as a viable technique for iterative MIMO receiver implementations for non-orthogonal waveforms.


title: Single-trial EEG Discrimination between Wrist and Finger Movement Imagery and Execution in a Sensorimotor BCI

abstract: Brain-computer interface (BCI) may be used to control a prosthetic or orthotic hand using neural activity from the brain. The core of this sensorimotor BCI lies in the interpretation of the neural information extracted from electroencephalogram (EEG). It is desired to improve on the interpretation of EEG to allow people with neuromuscular disorders to perform daily activities. This paper investigates the possibility of discriminating between the EEG associated with wrist and finger movements. The EEG was recorded from test subjects as they executed and imagined five essential hand movements using both hands. Independent component analysis (ICA) and time-frequency techniques were used to extract spectral features based on event-related (de)synchronisation (ERD/ERS), while the Bhattacharyya distance (BD) was used for feature reduction. Mahalanobis distance (MD) clustering and artificial neural networks (ANN) were used as classifiers and obtained average accuracies of 65 % and 71 % respectively. This shows that EEG discrimination between wrist and finger movements is possible. The research introduces a new combination of motor tasks to BCI research.


title: Idea assessment in open innovation: A state of practice

abstract: Open innovation has become a fruitful approach to increasing the potential of innovation in organisations. Similar to traditional innovation, an open innovation approach can be characterised in three phases; namely idea generation, idea assessment and idea implementation/diffusion. While the academic community has begun to provide initial guidance for improving the various stages of the open innovation process, still little is known about how organisations currently assess ideas once they are collected. The potentially vast quantity of ideas collected through an open innovation approach has limited benefits to an organisation that is not able to categorise and assess ideas. Accordingly, in this study we carry out an exploratory survey among 331 managers to obtain a better understanding of idea assessment in practice. Our findings show, among others, that organisations investing in information systems for idea management have a higher satisfaction with the effectiveness of idea assessment, which is, in turn, associated with higher satisfaction with the innovation process overall.


title: Different Texts, Same Metaphors: Unigrams and Beyond

abstract: Current approaches to supervised learning of metaphor tend to use sophisticated features and restrict their attention to constructions and contexts where these features apply. In this paper, we describe the development of a supervised learning system to classify all content words in a running text as either being used metaphorically or not. We start by examining the performance of a simple unigram baseline that achieves surprisingly good results for some of the datasets. We then show how the recall of the system can be improved over this strong baseline.


title: Geocoding location expressions in Twitter messages: A preference learning method

abstract: Resolving location expressions in text to the correct physical location, also known as geocoding or grounding, is complicated by the fact that so many places around the world share the same name. Correct resolution is made even more difficult when there is little context to determine which place is intended, as in a 140-character Twitter message, or when location cues from different sources conflict, as may be the case among different metadata fields of a Twitter message. We used supervised machine learning to weigh the different fields of the Twitter message and the features of a world gazetteer to create a model that will prefer the correct gazetteer candidate to resolve the extracted expression. We evaluated our model using the F1 measure and compared it to similar algorithms. Our method achieved results higher than state-of-the-art competitors.


title: Reducing the harmful effects of last-level cache polluters with an OS-level, software-only pollute buffer

abstract: It is well recognized that LRU cache-line replacement can be ineffective for applications with large working sets or non-localized memory access patterns. Specifically, in last-level processor caches, LRU can cause cache pollution by inserting non-reuseable elements into the cache while evicting reusable ones. The work presented in this paper addresses last-level cache pollution through a dynamic operating system mechanism, called ROCS, requiring no change to underlying hardware and no change to applications. ROCS employs hardware performance counters on a commodity processor to characterize application cache behavior at run-time. Using this online profiling, cache unfriendly pages are dynamically mapped to a pollute buffer in the cache, eliminating competition between reusable and non-reusable cache lines. The operating system implements the pollute buffer through a page-coloring based technique, by dedicating a small slice of the last-level cache to store non-reusable pages. Measurements show that ROCS, implemented in the Linux 2.6.24 kernel and running on a 2.3 GHz PowerPC 970FX, improves performance of memory-intensive SPEC CPU 2000 and NAS benchmarks by up to 34%, and 16% on average.


Content-based advertisement detection.


title: Cloud computing: Software as a service

abstract: At present, SaaS(Software as a Service) is a new mantra for many software industries and individuals. How software can be offered as a service? What are the requirements and issues of SaaS? What is architectural difference between SaaS, and other cloud computing models? What are the implementation challenges for SaaS? These are the few questions one need to answer while implementing SaaS. Offering software as a service actually minimizes the software piracy. It also makes software update and distribution process easy. Centrally maintaining a software in secure environment of CSP (Cloud Service Provider) frees the user from providing security to it. SaaS is an upper layer of cloud computing, so Cloud users do not require very high end computational power at their end. CSP provides it with SaaS. This helps in cost cutting at users' end, by reducing the amount of buying extra hardware for carrying their work. This paper is a brief review of SaaS, explaining its concept and requirements. SaaS, being a popular cloud computing model and emerging trend in technology usage, is exploited by many software developers and industries to deploy their software. This paper also discuss on how to implement a private SaaS using minimal resources at our hand implementation of private SaaS is useful for any institute in increasing its work efficiency while reducing the cost involved.


title: Vision on Wheels: Looking at Driver, Vehicle, and Surround for On-Road Maneuver Analysis

abstract: Automotive systems provide a unique opportunity for mobile vision technologies to improve road safety by understanding and monitoring the driver. In this work, we propose a real-time framework for early detection of driver maneuvers. The implications of this study would allow for better behavior prediction, and therefore the development of more efficient advanced driver assistance and warning systems. Cues are extracted from an array of sensors observing the driver (head, hand, and foot), the environment (lane and surrounding vehicles), and the ego-vehicle state (speed, steering angle, etc.). Evaluation is performed on a real-world dataset with overtaking maneuvers, showing promising results. In order to gain better insight into the processes that characterize driver behavior, temporally discriminative cues are studied and visualized.


title: BANA: body area network authentication exploiting channel characteristics

abstract: Wireless body area network (BAN) is a promising technology for real-time monitoring of physiological signals to support medical applications. In order to ensure the trustworthy and reliable gathering of patient's critical health information, it is essential to provide node authentication service in a BAN, which prevents an attacker from impersonation and false data/command injection. Although quite fundamental, the authentication in BAN still remains a challenging issue. On one hand, traditional authentication solutions depend on prior trust among nodes whose establishment would require either key pre-distribution or non-intuitive participation by inexperienced users, while they are vulnerable to key compromise. On the other hand, most existing non-cryptographic authentication schemes require advanced hardware capabilities or significant modifications to the system software, which are impractical for BANs. In this paper, for the first time, we propose a lightweight body area network authentication scheme (BANA) that does not depend on prior-trust among the nodes and can be efficiently realized on commercial off-the-shelf low-end sensor devices. This is achieved by exploiting physical layer characteristics unique to a BAN, namely, the distinct received signal strength (RSS) variation behaviors between an on-body communication channel and an off-body channel. Our main finding is that the latter is more unpredictable over time, especially under various body motion scenarios. This unique channel characteristic naturally arises from the multi-path environment surrounding a BAN, and cannot be easily forged by attackers. We then adopt clustering analysis to differentiate the signals from an attacker and a legitimate node. The effectiveness of BANA is validated through extensive real-world experiments under various scenarios. It is shown that BANA can accurately identify multiple attackers with minimal amount of overhead.


title: Max-min convolutional neural networks for image classification

abstract: Convolutional neural networks (CNN) are widely used in computer vision, especially in image classification. However, the way in which information and invariance properties are encoded through in deep CNN architectures is still an open question. In this paper, we propose to modify the standard convolutional block of CNN in order to transfer more information layer after layer while keeping some invariance within the network. Our main idea is to exploit both positive and negative high scores obtained in the convolution maps. This behavior is obtained by modifying the traditional activation function step before pooling. We are doubling the maps with specific activations functions, called MaxMin strategy, in order to achieve our pipeline. Extensive experiments on two classical datasets, MNIST and CIFAR-10, show that our deep MaxMin convolutional net outperforms standard CNN.


title: DPLL( T): Fast Decision Procedures

abstract: The logic of equality with uninterpreted functions (EUF) and its extensions have been widely applied to processor verification, by means of a large variety of progressively more sophisticated (lazy or eager) translations into propositional SAT. Here we propose a new approach, namely a general DPLL(X) engine, whose parameter X can be instantiated with a specialized solver Solver T for a given theory T, thus producing a system DPLL(T). We describe this DPLL(T) scheme, the interface between DPLL(X) and Solver T , the architecture of DPLL(X), and our solver for EUF, which includes incremental and backtrackable congruence closure algorithms for dealing with the built-in equality and the integer successor and predecessor symbols. Experiments with a first implementation indicate that our technique already outperforms the previous methods on most benchmarks, and scales up very well.


title: Software designers, are you biased?

abstract: Methods of representing and capturing design rationale have been studied in past years. Many meta-models, methods and techniques have been proposed. Are these software engineering methods sufficient to help designers make logical and appropriate design decisions? Studies have shown that people make biased decisions, software designers may also be subjected to such cognitive biases. In this paper, I give an overview of how cognitive biases and reasoning failures may lead to unsound design decisions. I conjecture that in order to improve the overall quality of software design, we as a community need to improve our understanding and teaching of software design reasoning.


title: Speeding up distributed request-response workflows

abstract: We found that interactive services at Bing have highly variable datacenter-side processing latencies because their processing consists of many sequential stages, parallelization across 10s-1000s of servers and aggregation of responses across the network. To improve the tail latency of such services, we use a few building blocks: reissuing laggards elsewhere in the cluster, new policies to return incomplete results and speeding up laggards by giving them more resources. Combining these building blocks to reduce the overall latency is non-trivial because for the same amount of resource (e.g., number of reissues), different stages improve their latency by different amounts. We present Kwiken, a framework that takes an end-to-end view of latency improvements and costs. It decomposes the problem of minimizing latency over a general processing DAG into a manageable optimization over individual stages. Through simulations with production traces, we show sizable gains; the 99th percentile of latency improves by over 50% when just 0.1% of the responses are allowed to have partial results and by over 40% for 25% of the services when just 5% extra resources are used for reissues.


title: Spraying analysis for a coconut climbing robot

abstract: Due to the recent epidemic outbreak of the coconut black-headed caterpillars in the southern provinces of Thailand, this research aims to develop a tree-specific pest control equipment using robotic and computer simulation technology. The climbing device allows the spraying head to reach the foliage of the coconut regardless of the height of the tree. The spraying should access the area of the foliage that is infested with the coconut black-headed caterpillars. This research proposes the new technique of modeling spray deposition on to the coconut leaves using image processing and a probabilistic model. By spraying the substance directly to the foliage from an optimum position, the proposed method shows that it can largely reduce the amount of chemical pesticide. The coconut climbing robot was developed as a wheel robot with a series elastic actuator. There are two motors on the robot, the drive motor drives the robot up and down the tree while the other motor is attached to a spring mechanism which keeps the holding force between robot and the tree. The robot can climb up and down the coconut tree at an average speed of 0.3 m/s and can carry a sprayer with an additional payload of 2 kg.


title: Development of Ontology for Smart Hospital and Implementation using UML and RDF

abstract: Patient’s information is an important component of the patient privacy in any health care system that is based on the overall quality of each patient in the health care system. The main commitment for any health care system is to improve the quality of the patient and privacy of patient’s information. There are many organizational units or departments in the hospital, from which, it is necessary for them that there should be good coordination in each others. Even the available health care automation software also does not provide such coordination among them. These softwares are limited up to the hospital works but do not provide the interconnectivity with other hospitals and blood banks etc. Thus, these hospitals cannot share information in-spite of the good facilities and services. Today, there is a need of such computer environment where treatment to patients can be given on the basis of his/her previous medical history at the time of emergency at any time, on any place and anywhere. Pervasive and ubiquitous environment and Semantic Web can bring the boon in this field. For this it is needed to develop the ubiquitous health care computing environment using the Semantic Web with traditional hospital environment. This paper is based on the ubiquitous and pervasive computing environment and semantic web, in which these problems has been tried to remove so that hospital may be smart hospital in the near future. This paper is the effort to develop the knowledge-base ontological description for smart hospital.


title: Personal data vaults: a locus of control for personal data streams

abstract: The increasing ubiquity of the mobile phone is creating many opportunities for personal context sensing, and will result in massive databases of individuals' sensitive information incorporating locations, movements, images, text annotations, and even health data. In existing system architectures, users upload their raw (unprocessed or filtered) data streams directly to content-service providers and have little control over their data once they "opt-in". We present Personal Data Vaults (PDVs), a privacy architecture in which individuals retain ownership of their data. Data are routinely filtered before being shared with content-service providers, and users or data custodian services can participate in making controlled data-sharing decisions. Introducing a PDV gives users flexible and granular access control over data. To reduce the burden on users and improve usability, we explore three mechanisms for managing data policies: Granular ACL, Trace-audit and Rule Recommender. We have implemented a proof-of-concept PDV and evaluated it using real data traces collected from two personal participatory sensing applications.


Applications of the Finite-Difference Frequency-Domain Mode Solution Method to Photonic Crystal Structures


title: Efficient, Safe, and Probably Approximately Complete Learning of Action Models

abstract: In this paper we explore the theoretical boundaries of planning in a setting where no model of the agent's actions is given. Instead of an action model, a set of successfully executed plans are given and the task is to generate a plan that is safe, i.e., guaranteed to achieve the goal without failing. To this end, we show how to learn a conservative model of the world in which actions are guaranteed to be applicable. This conservative model is then given to an off-the-shelf classical planner, resulting in a plan that is guaranteed to achieve the goal. However, this reduction from a model-free planning to a model-based planning is not complete: in some cases a plan will not be found even when such exists. We analyze the relation between the number of observed plans and the likelihood that our conservative approach will indeed fail to solve a solvable problem. Our analysis show that the number of trajectories needed scales gracefully.


title: Content-based spam filtering

abstract: The growth of email users has resulted in the dramatic increasing of the spam emails. Helpfully, there are different approaches able to automatically detect and remove most of these messages, and the best-known ones are based on Bayesian decision theory and Support Vector Machines. However, there are several forms of Naive Bayes filters, something the anti-spam literature does not always acknowledge. In this paper, we discuss seven different versions of Naive Bayes classifiers, and compare them with the well-known Linear Support Vector Machine on six non-encoded datasets. Moreover, we propose a new measurement in order to evaluate the quality of anti-spam classifiers. In this way, we investigate the benefits of using Matthews correlation coefficient as the measure of performance.


title: Exploiting Self-Similar Traffic Analysis in network resource control: the IP over WDM networks case

abstract: This article focuses on the control plan of IP over WDM networks. Analytical and measurement studies have shown that self-similar traffic can have detrimental impact on network performance in terms of queuing delay and packet loss ratio. Nevertheless, the ubiquity of scale-invariant burstiness observed across diverse network contexts can be exploited to make more efficient resource-control decisions. This paper shows that an appropriately truncated self-similar process keeps the self-similarity behaviour. Hence, we propose to apply prediction methods on the truncated process instead of the original one. This proposition provides (i) dynamic communication abilities between IP and the optical layers (ii) cost reduction related to traffic measurements and (iii) optimization of the network resources utilization


title: Frequency Scanning Radiation by Decoupling Spoof Surface Plasmon Polaritons via Phase Gradient Metasurface

abstract: On the basis of generalized Snell’s law of reflection, we propose to achieve frequency scanning radiation by decoupling spoof surface plasmon polaritons (SSPPs) from a corrugated metallic strip (CMS). Phase gradient metasurface (PGM) is utilized to modulate the dispersion behavior of the CMS. Since the phase gradient of the PGM is contrary to the wave vector of SSPPs, the dispersion curve of SSPPs can be translated into the fast wave zone, leading to frequency scanning radiation. As an example, we demonstrate a frequency scanning antenna operating at 8.8–10.7 GHz. A prototype was designed, fabricated, and measured. The measured results show that the prototype can realize continuous beam scanning from 4.8° to 37.2°, with realized gain varying from 8 to 13.1 dB. Owing to surface confinement of SSPPs, the proposed method can be readily extended to the design of conformal frequency scanning antennas on curved faces.


title: Development of an OPC Client-Server Framework for Monitoring and Control Systems

abstract: In this article, the current technological state of OPC (Openness, Productivity, and Collaboration; formerly "OLE for Process Control") standards and the problem statement of these OPC standards are discussed. The development of an OPC clientserver framework for monitoring and control systems is introduced by using the new OPC Unified Architecture (UA) specifications, Service Oriented Architecture (SOA), web services, XML, etc. The developed framework in turn minimizes the efforts of developers in learning new techniques and allows system architects and designers to perform dependency analysis on the development of monitoring and control applications. The potential areas of the proposed framework and the redundancy strategies to increase the efficiency and reliability of the system are also represented according to the initial results from the system that was developed by the Visual Studio 2008 and OPC UA SDK.


ℓ1 regularization in infinite dimensional feature spaces


title: When Can I Expect an Email Response? A Study of Rhythms in Email Usage

abstract: A study of email responsiveness was conducted to understand how the timing of email responses conveys important information. Interviews and observations explored users' perceptions of how they responded to email and formed expectations of others' responses to them. We identified ways in which users maintain and cultivate a responsiveness image for projecting expectations about their email response. We also discuss other contextual cues people use to discover email responsiveness, which include using other tools such as the calendar and phone, accounting for the amount of work time overlap available, and establishing a pacing between email correspondents. These cues help users develop a sense of when to expect a response and when breakdown has occurred, requiring further action.


title: A Benchmark Data Set and Evaluation of Deep Learning Architectures for Ball Detection in the RoboCup SPL

abstract: This paper presents a benchmark data set for evaluating ball detection algorithms in the RoboCup Soccer Standard Platform League. We created a labelled data set of images with and without ball derived from vision log files recorded by multiple NAO robots in various lighting conditions. The data set contains 5209 labelled ball image regions and 10924 non-ball regions. Non-ball image regions all contain features that had been classified as a potential ball candidate by an existing ball detector. The data set was used to train and evaluate 252 different Deep Convolutional Neural Network (CNN) architectures for ball detection. In order to control computational requirements, this evaluation focused on networks with 2–5 layers that could feasibly run in the vision and cognition cycle of a NAO robot using two cameras at full frame rate (2 × 30 Hz). The results show that the classification performance of the networks is quite insensitive to the details of the network design including input image size, number of layers and number of outputs at each layer. In an effort to reduce the computational requirements of CNNs we evaluated XNOR-Net architectures which quantize the weights and activations of a neural network to binary values. We examined XNOR-Nets corresponding to the real-valued CNNs we had already tested in order to quantify the effect on classification metrics. The results indicate that ball classification performance degrades by 12% on average when changing from real-valued CNN to corresponding XNOR-Net.


title: Feature covariance matrix-based dynamic hand gesture recognition

abstract: Over the past 2 decades, vision-based dynamic hand gesture recognition (HGR) has made significant progresses and been widely adopted in many practical applications. Although the advent of RGB-D cameras and deep learning-based methods provides more feasible solutions for HGR, it is still very challenging to satisfy the requirements of both high efficiency and accuracy for real-world HGR systems. In this paper, we propose a novel method using the feature covariance matrix for effective and efficient dynamic HGR. We extract a set of local feature vectors that represent local motion patterns to construct the feature covariance matrix efficiently, which also provides a compact representation of a dynamic hand gesture. By tracking hand keypoints in three successive frames and calculating their motion features, our method can be extended to both 2D dynamic HGR and 3D dynamic HGR. To evaluate the effectiveness of the proposed framework, we perform extensive experiments on three publicly available datasets (one 2D dataset and two 3D datasets). The experimental results demonstrate the effectiveness of our proposed method.


title: Malware detection using machine learning

abstract: We propose a versatile framework in which one can employ different machine learning algorithms to successfully distinguish between malware files and clean files, while aiming to minimise the number of false positives. In this paper we present the ideas behind our framework by working firstly with cascade one-sided perceptrons and secondly with cascade kernelized one-sided perceptrons. After having been successfully tested on medium-size datasets of malware and clean files, the ideas behind this framework were submitted to a scaling-up process that enable us to work with very large datasets of malware and clean files.


title: Aparapi-UCores: A high level programming framework for unconventional cores

abstract: Combining several types of devices and architectures is at the heart of heterogeneous computing's power efficiency advantage, but the strength of heterogeneous systems is also their Achilles heel, i.e. the diversity of the devices and ecosystems needed to maintain them present major technological challenges. Some of the biggest challenges are in the realm of system programing. We believe that for heterogeneous systems computing to become a mainstream system design choice, high level and standard system design flows need to be adopted in order to achieve transparency when dealing with diverse devices and architectures. In this paper we present an open source high level framework and design flow that allows working with any type of device that supports OpenCL. In addition we test our design flow and framework on an N-body simulation across multiple device types and show how such high level framework and heterogeneous system design can deliver a more power efficient solution when compared to a single general purpose device and dual CPU+GPU device type approach.


title: Supervised Matrix Factorization Hashing for Cross-Modal Retrieval

abstract: The target of cross-modal hashing is to embed heterogeneous multimedia data into a common low-dimensional Hamming space, which plays a pivotal part in multimedia retrieval due to the emergence of big multimodal data. Recently, matrix factorization has achieved great success in cross-modal hashing. However, how to effectively use label information and local geometric structure is still a challenging problem for these approaches. To address this issue, we propose a cross-modal hashing method based on collective matrix factorization, which considers both the label consistency across different modalities and the local geometric consistency in each modality. These two elements are formulated as a graph Laplacian term in the objective function, leading to a substantial improvement on the discriminative power of latent semantic features obtained by collective matrix factorization. Moreover, the proposed method learns unified hash codes for different modalities of an instance to facilitate cross-modal search, and the objective function is solved using an iterative strategy. The experimental results on two benchmark data sets show the effectiveness of the proposed method and its superiority over state-of-the-art cross-modal hashing methods.


title: First Experimental Demonstration of Probabilistic Enumerative Sphere Shaping in Optical Fiber Communications

abstract: We transmit probabilistic enumerative sphere shaped dual-polarization 64-QAM at 350Gbit/s/channel over 1610km SSMF using a short blocklength of 200. A reach increase of 15% over constant composition distribution matching with identical blocklength is demonstrated.


Inheritance and population genetics of four marker traits in rose clover


title: Flexible Fabric Sensor Toward a Humanoid Robot's Skin: Fabrication, Characterization, and Perceptions

abstract: We have developed a fabric sensor knitted of tension-sensitive electro-conductive yarns. Each yarn has an elastic core, around which is wound two other separate, tension-sensitive electro-conductive threads, making this sensor inherently flexible and stretchable and allowing it to conform to any complicated surface on a robot, acting as a robotic skin. The pile-shaped surface of the sensor enhances its ability to detect tangential traction, while also enabling it to sense a normal load. Our aim is to use this sensor in applications involving relative sliding between its surface and a touched object, such as contact recognition, slip detection, and surface identification through a sliding motion. We carefully analyzed the static and dynamic characteristics of this sensor while varying the load and stretching force to fully understand its response and determine its degree of flexibility and stretchability. We found that a discrete wavelet transformation may be used to indicate stick/slip states while the sensor is sliding over surfaces. This method was then used to detect slippage events acting on the sensor's surface, and to decode textures in a classification test using an artificial neural network. Because of its flexibility and sensitivity, this sensor can be used widely as a robotic skin in humanoid robots.


title: A New Rectenna Using Beamwidth-Enhanced Antenna Array for RF Power Harvesting Applications

abstract: This letter presents a new rectenna using beamwidth-enhanced antenna array for RF power harvesting applications. Based on an example of a 1 × 4 patch antenna array, the beamwidth-enhancement method is demonstrated. By maximizing the power transmission efficiency between the 1 × 4 antenna array and two auxiliary antennas, the optimal excitation distribution that leads to an enhanced beamwidth can be determined. In comparison to the same antenna array fed by the uniform amplitude and phase, the beamwidth of the designed antenna array at the H-plane is about two times wider. A high-efficiency rectifier is designed to convert the harvested RF power into dc power and is then combined with the 1 × 4 antenna array to form the proposed beamwidth-enhanced rectenna. Measurement results show that the rectenna's efficiency is more than 50% when the wave incident angle is between −38° and 35° at the H-plane under the power density of 1276 μW/cm2.


Translation Prediction with Source Dependency-Based Context Representation.


title: Situated Cellular Agents and Immune System Modelling

abstract: The Immune System (IS) constitutes the defence mechanism of higher level organisms to micro organismic threats: it is a real distributed system providing mechanisms of adaptation to unknown threats through the cooperation of heterogenous entities, and learning capabilities. This paper describes how the Situated Cellular Agents (SCA) model was applied to model the IS. After a brief description of the composing parts and internal mechanisms of the IS, the SCA model will be introduced and exploited to represent them.


title: A review and analysis of churn prediction methods for customer retention in telecom industries

abstract: Customer churn prediction has gathered greater interest in business especially in telecommunications industries. Many authors have presented different versions of the churn prediction models greatly based on the data mining concepts employing the machine learning and meta-heuristic algorithms. This aim of this paper is to study some of the most important churn prediction techniques developed over the recent years. The primary objective is on the churn in telecom industries to accurately estimate the customer survival and customer hazard functions to gain the complete knowledge of churn over the customer tenure. Another objective is the identification of the customers who are at the blink of churn and approximating the time they will churn. This paper focuses on analyzing the churn prediction techniques to identify the churn behavior and validate the reasons for customer churn. This paper summarizes the churn prediction techniques in order to have a deeper understanding of the customer churn and it shows that most accurate churn prediction is given by the hybrid models rather than single algorithms so that telecom industries become aware of the needs of high risk customers and enhance their services to overturn the churn decision.


title: ParaDrop: Enabling Lightweight Multi-tenancy at the Network’s Extreme Edge

abstract: We introduce ParaDrop, a specific edge computing platform that provides computing and storage resources at the "extreme" edge of the network allowing third-party developers to flexibly create new types of services. This extreme edge of the network is the WiFi Access Point (AP) or the wireless gateway through which all end-device traffic (personal devices, sensors, etc.) passes through. ParaDrop's focus on WiFi APs also stems from the fact that the WiFi AP has unique contextual knowledge of its end-devices (e.g., proximity, channel characteristics) that are lost as we get deeper into the network. While different variations and implementations of edge computing platforms have been created over the last decade, ParaDrop focuses on specific design issues around how to structure an architecture, a programming interface, and orchestration framework through which such edge computing services can be dynamically created, installed, and revoked. ParaDrop consists of the following three main components: a flexible hosting substrate in the WiFi APs that supports multi-tenancy, a cloud-based backend through which such computations are orchestrated across many ParaDrop APs, and an API through which third-party developers can deploy and manage their computing functions across such different ParaDrop APs. We have implemented and deployed the entire ParaDrop framework and, in this paper, describe its overall architecture and our initial experiences using it as an edge computing platform.


title: Full-resolution interactive CPU volume rendering with coherent BVH traversal

abstract: We present an efficient method for volume rendering by raycasting on the CPU. We employ coherent packet traversal of an implicit bounding volume hierarchy, heuristically pruned using preintegrated transfer functions, to exploit empty or homogeneous space. We also detail SIMD optimizations for volumetric integration, trilinear interpolation, and gradient lighting. The resulting system performs well on low-end and laptop hardware, and can outperform out-of-core GPU methods by orders of magnitude when rendering large volumes without level-of-detail (LOD) on a workstation. We show that, while slower than GPU methods for low-resolution volumes, an optimized CPU renderer does not require LOD to achieve interactive performance on large data sets.


title: Developed Distributed Energy-Efficient Clustering (DDEEC) for heterogeneous wireless sensor networks

abstract: Typically, a wireless sensor network contains an important number of inexpensive power constrained sensors, which collect data from the environment and transmit them towards the base station in a cooperative way. Saving energy and therefore, extending the wireless sensor networks lifetime, imposes a great challenge. Clustering techniques are largely used for these purposes. In this paper, we propose and evaluate a clustering technique called a Developed Distributed Energy-Efficient Clustering scheme for heterogeneous wireless sensor networks. This technique is based on changing dynamically and with more efficiency the cluster head election probability. Simulation results show that our protocol performs better than the Stable Election Protocol (SEP) by about 30% and than the Distributed Energy-Efficient Clustering (DEEC) by about 15% in terms of network lifetime and first node dies.


title: The Function of Fish Cytokines

abstract: What is known about the biological activity of fish cytokines is reviewed. Most of the functional studies performed to date have been in teleost fish, and have focused on the induced effects of cytokine recombinant proteins, or have used loss- and gain-of-function experiments in zebrafish. Such studies begin to tell us about the role of these molecules in the regulation of fish immune responses and whether they are similar or divergent to the well-characterised functions of mammalian cytokines. This knowledge will aid our ability to determine and modulate the pathways leading to protective immunity, to improve fish health in aquaculture.


title: Effect of the Temperature and Concentration of NaOH on the Formation of Porous TiO2

abstract: The influence of the temperature and concentration of NaOH on the formation of porous TiO 2 was investigated. The porous TiO 2 was obtained only after a 24-h immersion in 5 M NaOH at 353 K. The X-ray diffraction pattern indicated that the porous layer consisted of a mixture of anatase and rutile types of TiO 2 . During immersion in the NaOH solution, the anodic and cathodic reactions proceed at the same time on the titanium. From the cathodic polarization measurements, the cathodic reaction proceeded mainly the hydrogen evolution reaction. Therefore, a part of the adsorbed hydrogen penetrated into the titanium. The penetration of hydrogen confirmed with a glow discharge optical emission spectroscopy. The hydrogen evolution reaction continues during immersion in the 5 M NaOH solution at 353 K. However, the hydrogen evolution did not proceed in the low concentration of NaOH solution or at below 283 K. In addition, although the hydrogen evolution continues during immersion in 5 M NaOH solution at 303 or 333 K. As a result, the content of hydrogen in the titanium is insufficient to form the porous TiO 2 layer so that the porous layer is not produced on the titanium.


title: Mobile payment in mobile e-commerce

abstract: The main problem of Chinese mobile e-commerce at present is its insufficient modes of payment. Therefore, the research on brand-new and high-efficiency form of mobile payment is very important for the development of Chinese mobile e-commerce. There are two forms of mobile payment in China at present, namely mobile fee account payment and bank card mobile payment. The paper brings forward a new form of mobile payment by referring to the advantages of the existing two forms of mobile payment and aiming at their disadvantages at the same time.


title: Design of Dual Band Wearable Antenna Using Metamaterials

abstract: This paper presents two types of dual band (2.4 and 5.8 GHz) wearable planar dipole antennas, one printed on a conventional substrate and the other on a two-dimensional metamaterial surface (Electromagnetic Bandgap (EBG) structure). The operation of both antennas is investigated and compared under different bending conditions (in E and H-planes) around human arm and leg of different radii. A dual band, Electromagnetic Band Gap (EBG) structure on a wearable substrate is used as a high impedance surface to control the Specific Absorption Rate (SAR) as well as to improve the antenna gain up to 4.45 dBi. The EBG inspired antenna has reduced the SAR effects on human body to a safe level (< 2W/Kg). I.e. the SAR is reduced by 83.3% for lower band and 92.8% for higher band as compared to the conventional antenna. The proposed antenna can be used for wearable applications with least health hazard to human body in Industrial, Scientific and Medical (ISM) band (2.4 GHz, 5.2 GHz) applications. The antennas on human body are simulated and analyzed in CST Microwave Studio (CST MWS).


Human-Machine Collaboration for Medical Image Segmentation


title: TeleHuman: effects of 3d perspective on gaze and pose estimation with a life-size cylindrical telepresence pod

abstract: In this paper, we present TeleHuman, a cylindrical 3D display portal for life-size human telepresence. The TeleHuman 3D videoconferencing system supports 360 degree motion parallax as the viewer moves around the cylinder and optionally, stereoscopic 3D display of the remote person. We evaluated the effect of perspective cues on the conveyance of nonverbal cues in two experiments using a one-way telecommunication version of the system. The first experiment focused on how well the system preserves gaze and hand pointing cues. The second experiment evaluated how well the system conveys 3D body postural information. We compared 3 perspective conditions: a conventional 2D view, a 2D view with 360 degree motion parallax, and a stereoscopic view with 360 degree motion parallax. Results suggest the combined presence of motion parallax and stereoscopic cues significantly improved the accuracy with which participants were able to assess gaze and hand pointing cues, and to instruct others on 3D body poses. The inclusion of motion parallax and stereoscopic cues also led to significant increases in the sense of social presence and telepresence reported by participants.


Experienced Grey Wolf Optimizer through Reinforcement Learning and Neural Networks


Augmentation for Time Series Classification using Convolutional Neural Networks


title: Single image density map estimation based on multi-column CNN and boosting

abstract: In this paper, we explore the task to estimate the density map of objects from single image with unknown perspective map. We follow the recent progress in object counting through density map estimation. Object density map estimation is usually suffered from scale variance of objects caused by unknown perspective. In addition, the background and irrelevant objects in the image lead to artifacts in the resulting density maps, which build up the error when heat maps are needed by aggregating density maps. We propose a multi-CNN network with gradient boosting (MCNN-boost) to address these two problems at once. The receptive fields with different sizes in each column of CNNs make the model adaptive to variance in sizes caused by perspective, and the gradient boosting strategy will effectively remove the artifacts caused by the background and irrelevant objects. Thanks to the simple architecture of the network, MCNN-boost is efficient to train and highly adaptive to different data sets with different scenes. We run experiments on one shopping mall data sets with heavy perspective distortion and complex background to show the effectiveness of the proposed algorithm.


title: Thermal Conduction in one dimensional $\Phi^4$ chains with colliding particles

abstract: Studying thermal conduction in low-dimensional non-integrable systems is necessary for understanding the microscopic origin of macroscopic irreversible behavior and Fourier's law of heat conduction. Two distinct types of low-dimensional thermal conduction models have been proposed in the literature -- ones that display normal thermal conduction (like the $\Phi^4$ chain) and others that show anomalous thermal conduction (like the Fermi-Pasta-Ulam chain). However, in both these models nothing prevents two nearby particles from crossing each other. In this manuscript, we introduce a modification in the Hamiltonian of the traditional $\Phi^4$ chain (henceforth called $\Phi^{4C}$ model) through soft-sphere potential that prevents two particles from crossing. The proposed model is then subjected to thermal conduction by keeping its two ends at different temperatures using Nose-Hoover thermostats. Equations of motion, derived from the Hamiltonian, are solved using $4^{th}$ order Runge-Kutta method for 1 billion time-steps, where each time-step is of 0.0005 time units. Averages have been computed using the last 750 million time-steps. Our results indicate that the boundary effects due to contact with the thermostats is minimized in the $\Phi^{4C}$ model as compared to the traditional $\Phi^4$ chain, ensuring a smoother temperature distribution across the chain. Further, the rate of convergence of heat flux is much faster in the $\Phi^{4C}$ chain vis-a-vis the traditional $\Phi^4$ chain. However, the absolute value of heat flux is much smaller in the $\Phi^{4C}$ chain. These results suggest that collision plays an important role in quickly ensuring that a steady-state is reached and diminishing the magnitude of heat flux. Interestingly enough, collision does not significantly alter the diffusive properties of the chain, irrespective of the temperature gradient within the chain.


title: A study of injection locking and pulling in oscillators

abstract: Injection locking characteristics of oscillators are derived and a graphical analysis is presented that describes injection pulling in time and frequency domains. An identity obtained from phase and envelope equations is used to express the requisite oscillator nonlinearity and interpret phase noise reduction. The behavior of phase-locked oscillators under injection pulling is also formulated.


title: Activities, Context and Ubiquitous Computing

abstract: Context and context-awareness provides computing environments with the ability to usefully adapt the services or information they provide. It is the ability to implicitly sense and automatically derive the user needs that separates context-aware applications from traditionally designed applications, and this makes them more attentive, responsive, and aware of their user's identity, and their user's environment. This paper argues that context-aware applications capable of supporting complex, cognitive activities can be built from a model of context called Activity-Centric context. A conceptual model of Activity-Centric context is presented. The model is illustrated via a detailed example.


title: Compressing Networks with Super Nodes

abstract: Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns. To facilitate community detection in large networks, we recast the network as a smaller network of ‘super nodes’, where each super node comprises one or more nodes of the original network. We can then use this super node representation as the input into standard community detection algorithms. To define the seeds, or centers, of our super nodes, we apply the ‘CoreHD’ ranking, a technique applied in network dismantling and decycling problems. We test our approach through the analysis of two common methods for community detection: modularity maximization with the Louvain algorithm and maximum likelihood optimization for fitting a stochastic block model. Our results highlight that applying community detection to the compressed network of super nodes is significantly faster while successfully producing partitions that are more aligned with the local network connectivity and more stable across multiple (stochastic) runs within and between community detection algorithms, yet still overlap well with the results obtained using the full network.


title: Beyond the stomach: an updated view of Helicobacter pylori pathogenesis, diagnosis, and treatment.

abstract: Helicobacter pylori (H. pylori) is an extremely common, yet underappreciated, pathogen that is able to alter host physiology and subvert the host immune response, allowing it to persist for the life of the host. H. pylori is the primary cause of peptic ulcers and gastric cancer. In the United States, the annual cost associated with peptic ulcer disease is estimated to be $6 billion and gastric cancer kills over 700000 people per year globally. The prevalence of H. pylori infection remains high (> 50%) in much of the world, although the infection rates are dropping in some developed nations. The drop in H. pylori prevalence could be a double-edged sword, reducing the incidence of gastric diseases while increasing the risk of allergies and esophageal diseases. The list of diseases potentially caused by H. pylori continues to grow; however, mechanistic explanations of how H. pylori could contribute to extragastric diseases lag far behind clinical studies. A number of host factors and H. pylori virulence factors act in concert to determine which individuals are at the highest risk of disease. These include bacterial cytotoxins and polymorphisms in host genes responsible for directing the immune response. This review discusses the latest advances in H. pylori pathogenesis, diagnosis, and treatment. Up-to-date information on correlations between H. pylori and extragastric diseases is also provided.


title: Cloudlet load balancing in wireless metropolitan area networks

abstract: With advances in wireless communication technology, more and more people depend heavily on portable mobile devices for businesses, entertainments and social interactions. Although such portable mobile devices can offer various promising applications, their computing resources remain limited due to their portable size. This however can be overcome by remotely executing computation-intensive tasks on clusters of near by computers known as cloudlets. As increasing numbers of people access the Internet via mobile devices, it is reasonable to envision in the near future that cloudlet services will be available for the public through easily accessible public wireless metropolitan area networks (WMANs). However, the outdated notion of treating cloudlets as isolated data-centers-in-a-box must be discarded as there are clear benefits to connecting multiple cloudlets together to form a network. In this paper we investigate how to balance the workload between multiple cloudlets in a network to optimize mobile application performance. We first introduce a system model to capture the response times of offloaded tasks, and formulate a novel optimization problem, that is to find an optimal redirection of tasks between cloudlets such that the maximum of the average response times of tasks at cloudlets is minimized. We then propose a fast, scalable algorithm for the problem. We finally evaluate the performance of the proposed algorithm through experimental simulations. The experimental results demonstrate the significant potential of the proposed algorithm in reducing the response times of tasks.


title: 2008. Networks and natural language processing

abstract: Over the last few years, a number of areas of natural language processing have begun applying graph-based techniques. These include, among others, text summarization, syntactic parsing, word-sense disambiguation, ontology construction, sentiment and subjectivity analysis, and text clustering. In this paper, we present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work.


title: Generality in artificial intelligence

abstract: My 1971 Turing Award Lecture was entitled "Generality in Artificial Intelligence." The topic turned out to have been overambitious in that I discovered I was unable to put my thoughts on the subject in a satisfactory written form at that time. It would have been better to have reviewed my previous work rather than attempt something new, but such was not my custom at that time. I am grateful to ACM for the opportunity to try again. Unfortunately for our science, although perhaps fortunately for this project, the problem of generality in artificial intelligence (AI) is almost as unsolved as ever, although we now have many ideas not available in 1971. This paper relies heavily on such ideas, but it is far from a full 1987 survey of approaches for achieving generality. Ideas are therefore discussed at a length proportional to my familiarity with them rather than according to some objective criterion. It was obvious in 1971 and even in 1958 that AI programs suffered from a lack of generality. It is still obvious; there are many more details. The first gross symptom is that a small addition to the idea of a program often involves a complete rewrite beginning with the data structures. Some progress has been made in modularizing data structures, but small modifications of the search strategies are even less likely to be accomplished without rewriting. Another symptom is no one knows how to make a general database of commonsense knowledge that could be used by any program that needed the knowledge. Along with other information, such a database would contain what a robot would need to know about the effects of moving objects around, what a person can be expected to know about his family, and the facts about buying and selling. This does not depend on whether the knowledge is to be expressed in a logical language or in some other formalism. When we take the logic approach to AI, lack of generality shows up in that the axioms we devise to express commonsense knowledge are too restricted in their applicability for a general commonsense database. In my opinion, getting a language for expressing general commonsense knowledge for inclusion in a general database is the key problem of generality in AI. Here are some ideas for achieving generality proposed both before and after 1971. I repeat my disclaimer of comprehensiveness.


title: Period Coded Phase Shifting Strategy for Real–time 3-D Structured Light Illumination

abstract: Phase shifting structured light illumination for range sensing involves projecting a set of grating patterns where accuracy is determined, in part, by the number of stripes. However, high pattern frequencies introduce ambiguities during phase unwrapping. This paper proposes a process for embedding a period cue into the projected pattern set without reducing the signal-to-noise ratio. As a result, each period of the high frequency signal can be identified. The proposed method can unwrap high frequency phase and achieve high measurement precision without increasing the pattern number. Therefore, the proposed method can significantly benefit real-time applications. The method is verified by theoretical and experimental analysis using prototype system built to achieve 120 fps at 640 × 480 resolution.


title: Female Pattern Hair Loss: a clinical and pathophysiological review*

abstract: Female Pattern Hair Loss or female androgenetic alopecia is the main cause of hair loss in adult women and has a major impact on patients' quality of life. It evolves from the progressive miniaturization of follicles that lead to a subsequent decrease of the hair density, leading to a non-scarring diffuse alopecia, with characteristic clinical, dermoscopic and histological patterns. In spite of the high frequency of the disease and the relevance of its psychological impact, its pathogenesis is not yet fully understood, being influenced by genetic, hormonal and environmental factors. In addition, response to treatment is variable. In this article, authors discuss the main clinical, epidemiological and pathophysiological aspects of female pattern hair loss.


title: Studying the values of hard-to-reach populations: content analysis of tweets by the 21st century homeless

abstract: This paper describes a content analysis of a corpus of 5,313 tweets from 32 individuals collected during a three-week period in March/April 2011. The corpus comprised two study groups: Group H -- Twitter users who self-identified as homeless or formerly homeless in their Twitter profiles, and Group NH -- a random, stratified sample of Twitter users who did not self-identify as homeless and who shared similar Twitter characteristics with those in Group H. The study uses the Meta-Inventory of Human Values for Informal Communication (MIHV-IC) to study value expression in tweets. Two rounds of inter-coder reliability testing demonstrated the challenges of reliably detecting human values in tweets. Analysis of categories with substantial inter-coder agreement indicated significant differences between the two groups for helpfulness and wealth. This approach provides a promising opportunity for detecting the values of hard-to-reach populations such as the 21st century homeless.


title: A Dexterous and Self-adaptive Humanoid Robot Hand: Gesture-Changeable Under-Actuated Hand

abstract: A novel concept called gesture-changeable under-actuated (GCUA) function is proposed to improve the dexterities of traditional under-actuated hands and reduce the control difficulties of dexterous hands. Based on the GCUA function, a new humanoid robot hand, GCUA Hand is designed and manufactured. The GCUA Hand can grasp different objects self-adaptively and change its initial gesture dexterously before contacting objects. The hand has 5 fingers and 15 DOFs, each finger is based on screw-nut transmission, flexible drawstring constraint and belt-pulley under-actuated mechanism to realize GCUA function. The analyses on grasping static forces and grasping stabilities are put forward. The analyses and Experimental results show that the GCUA function is very nice and valid. The hands with the GCUA function can meet the requirements of grasping and operating with lower control and cost, which is the middle road between traditional under-actuated hands and dexterous hands.


title: A mobile phone application enabling visually impaired users to find and read product barcodes

abstract: While there are many barcode readers available for identifying products in a supermarket or at home on mobile phones (e.g., Red Laser iPhone app), such readers are inaccessible to blind or visually impaired persons because of their reliance on visual feedback from the user to center the barcode in the camera's field of view. We describe a mobile phone application that guides a visually impaired user to the barcode on a package in real-time using the phone's built-in video camera. Once the barcode is located by the system, the user is prompted with audio signals to bring the camera closer to the barcode until it can be resolved by the camera, which is then decoded and the corresponding product information read aloud using text-to-speech. Experiments with a blind volunteer demonstrate proof of concept of our system, which allowed the volunteer to locate barcodes which were then translated to product information that was announced to the user. We successfully tested a series of common products, as well as user-generated barcodes labeling household items that may not come with barcodes.


title: Compact Broadband Microstrip-Line-Fed Sleeve Monopole Antenna for DTV Application and Ground Plane Effect

abstract: This letter presents a compact broadband microstrip-line-fed sleeve monopole antenna for application in the DTV system. The design of meandering the monopole into a compact structure is applied for size reduction. By properly selecting the length and spacing of the sleeve, the broadband operation for the proposed design can be achieved, and the obtained impedance bandwidth covers the whole DTV (470862 MHz) band. Most importantly, the matching condition over a wide frequency range can be performed well even when a small ground-plane length is used; meanwhile, a small variation in the impedance bandwidth is observed for the ground-plane length varied in a great range.


title: OPG Treatment Prevents Bone Loss During Lactation But Does Not Affect Milk Production or Maternal Calcium Metabolism.

abstract: Lactation is associated with increased bone turnover and rapid bone loss, which liberates skeletal calcium used for milk production. Previous studies suggested that an increase in the skeletal expression of receptor activator of nuclear factor kappa-light-chain-enhancer of activated B cells ligand (RANKL) coupled with a decrease in osteoprotegerin (OPG) levels likely triggered bone loss during lactation. In this study, we treated lactating mice with recombinant OPG to determine whether bone loss during lactation was dependent on RANKL signaling and whether resorption of the maternal skeleton was required to support milk production. OPG treatment lowered bone resorption rates and completely prevented bone loss during lactation but, surprisingly, did not decrease osteoclast numbers. In contrast, OPG was quite effective at lowering osteoblast numbers and inhibiting bone formation in lactating mice. Furthermore, treatment with OPG during lactation prevented the usual anabolic response associated with reversal of lactational bone loss after weaning. Preventing bone loss had no appreciable effect on milk production, milk calcium levels, or maternal calcium homeostasis when mice were on a standard diet. However, when dietary calcium was restricted, treatment with OPG caused maternal hypocalcemia, maternal death, and decreased milk production. These studies demonstrate that RANKL signaling is a requirement for bone loss during lactation, and suggest that osteoclast activity may be required to increase osteoblast numbers during lactation in preparation for the recovery of bone mass after weaning. These data also demonstrate that maternal bone loss is not absolutely required to supply calcium for milk production unless dietary calcium intake is inadequate.


title: A Secure and Efficient ID-Based Aggregate Signature Scheme for Wireless Sensor Networks

abstract: Affording secure and efficient big data aggregation methods is very attractive in the field of wireless sensor networks (WSNs) research. In real settings, the WSNs have been broadly applied, such as target tracking and environment remote monitoring. However, data can be easily compromised by a vast of attacks, such as data interception and data tampering, etc. In this paper, we mainly focus on data integrity protection, give an identity-based aggregate signature (IBAS) scheme with a designated verifier for WSNs. According to the advantage of aggregate signatures, our scheme not only can keep data integrity, but also can reduce bandwidth and storage cost for WSNs. Furthermore, the security of our IBAS scheme is rigorously presented based on the computational Diffie–Hellman assumption in random oracle model.


title: Multi-Type Itemset Embedding for Learning Behavior Success

abstract: Contextual behavior modeling uses data from multiple contexts to discover patterns for predictive analysis. However, existing behavior prediction models often face difficulties when scaling for massive datasets. In this work, we formulate a behavior as a set of context items of different types (such as decision makers, operators, goals and resources), consider an observable itemset as a behavior success, and propose a novel scalable method, "multi-type itemset embedding", to learn the context items' representations preserving the success structures. Unlike most of existing embedding methods that learn pair-wise proximity from connection between a behavior and one of its items, our method learns item embeddings collectively from interaction among all multi-type items of a behavior, based on which we develop a novel framework, LearnSuc, for (1) predicting the success rate of any set of items and (2) finding complementary items which maximize the probability of success when incorporated into an itemset. Extensive experiments demonstrate both effectiveness and efficency of the proposed framework.


title: Project-Based Learning with an Online Peer Assessment System in a Photonics Instruction for Enhancing LED Design Skills.

abstract: This study proposed a novel instructional approach, a two-stage LED simulation of Project-based learning (PBL) course with online peer assessment (OPA), and explored how to apply OPA to the different structured problems in a PBL course to enhance students’ professional skills in LED design as well as meta-cognitive thinking. The participants of the study, 73 junior students were divided into two groups, OPA group (with OPA) and Traditional group (without OPA). The evaluation results were listed as follows. (1) OPA group performed better than Traditional group in concept clarification. (2) For the enhancement of LED design skills in well-structured problem solving, OPA group performed better than Traditional group. (3) For the enhancement of LED design skills in ill-structured problem solving, there was no significant difference between the performances of these two groups. (4) For students’ perception about the effect of OPA applied in PBL, OPA group could benefit from inquiry learning and reflective thinking. Most students agreed that the two-stage LED simulation of PBL course was challenging and interesting and they learned useful things from the course.


title: Big Data Opportunities and Challenges: Discussions from Data Analytics Perspectives [Discussion Forum]

abstract: "Big Data" as a term has been among the biggest trends of the last three years, leading to an upsurge of research, as well as industry and government applications. Data is deemed a powerful raw material that can impact multidisciplinary research endeavors as well as government and business performance. The goal of this discussion paper is to share the data analytics opinions and perspectives of the authors relating to the new opportunities and challenges brought forth by the big data movement. The authors bring together diverse perspectives, coming from different geographical locations with different core research expertise and different affiliations and work experiences. The aim of this paper is to evoke discussion rather than to provide a comprehensive survey of big data research.


title: Microwave engineering using microstrip circuits

abstract: Transmission line theory two-port parameters microstrip transmission lines - basic theory microstrip transmission lines - further considerations discontinuities the Smith chart and its uses hybrid-line couplers parallel-coupled lines and directional couplers filters miscellaneous components active circuit characterization microstrip circuits and subsystems microstrip experiments.


title: Design and application of Rogowski coil current sensor without integrator for fault detection in induction motors

abstract: This paper presents the study, design and application of a Rogowski coil current sensor without the integrator circuit that is typically used. This sensor is used as current probe for fault detection in induction motors by means of motor current signal analysis (MCSA).


title: The Use of Pro-Ana Blogs for Online Social Support

abstract: The aim of this research was to document the content on pro-ana blogs and to uncover how the unique socio-technical features of blogs (interactivity, self-disclosure, masspersonal communication) facilitate social support among members. A final sample of 48 pro-ana blogs provided 624 individual units for coding. Results indicate that prevalent forms of social support were emotional support, esteem support, and informational support. A new category, reciprocal self-disclosure, was also revealed to be quite frequent. Blogs are spaces where social support is sought and communicated among members of the pro-ana network. Interpretation of blog communication and implications for treatment and research are discussed.


title: Spatial frequency masking and Weber's Law

abstract: The threshold masking effect of one simultaneously presented grating upon another was studied as a function of mask contrast and frequency. The masking function typically obeys Weber's Law with method-of-adjustment psychophysics. and typically does not with forced-choice. This apparent discrepancy was studied in some detail. We suggest that thresholds can be set with at least two different criteria. depending upon experimental conditions. When the mask is unfamiliar, it functions as noise and detection occurs at a constant signai,/noise ratio. which yields Weber's Law. When the mask is highly familiar, its masking effect is less and obeys a power law. This power-law masking appears to represent an inherent non-linearity of the visual system. niques in the study of psychophysi~l thresholds, it is surprising that masking has only recently been applied to the study of spatial-frequency channels. In principle. the paradigm is extremely simple: the sub- ject is presented with a superthreshold masking grat- ing, and is asked to detect the presence of a superim- posed test grating which is at or near threshold. Although this problem was studied as early as 1974 by Pantle (see Table l), thorough parametric studies have not been published until recently (Legge and Foley, 1980). Possibly investigators have doubted their data because of a substantial discrepancy between the results reported by different groups. In many sensory modalities. experiments using masking or increment threshold tasks find that threshold ele- vation obeys the well-known Weber's Law: that is. increment threshold is proportional to background or mask intensity. Of the studies surveyed. about half find that Weber's Law holds. while the other half find substantial and systematic deviations; typically threshold increases as some power of the masking contrast, with an exponent of about 0.65. Although the various studies differ in detail. they generally appear to be equivalent in principle. and it is by no means clear why this discrepancy occurs.


title: States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning

abstract: Reinforcement learning (RL) uses sequential experience with situations (“states”) and outcomes to assess actions. Whereas model-free RL uses this experience directly, in the form of a reward prediction error (RPE), model-based RL uses it indirectly, building a model of the state transition and outcome structure of the environment, and evaluating actions by searching this model. A state prediction error (SPE) plays a central role, reporting discrepancies between the current model and the observed state transitions. Using functional magnetic resonance imaging in humans solving a probabilistic Markov decision task, we found the neural signature of an SPE in the intraparietal sulcus and lateral prefrontal cortex, in addition to the previously well-characterized RPE in the ventral striatum. This finding supports the existence of two unique forms of learning signal in humans, which may form the basis of distinct computational strategies for guiding behavior.


title: Design of HERIC inverter for PV systems by using hardware in the loop (HIL) concept

abstract: In PV systems grid-connected, we always have to work with power electronics modules. For safety, any design of power electronics converter should be simulated carefully in order to prevent from serious damages from high voltage, high current which can cause fire, explosion. By using hardware-in-the-loop (HIL) simulation, we could make sure the converter would operate the right way, which is not always achieve on the real hardware design when the first attempt, so that we can reduce ricks and furthermore reduce product development time and cost. After success on simulation, it will be not much complex to implement the algorithm to the real hardware. This paper presents the process for designing the HERIC inverter by using PSIM software which can be simulate and generate the project code for implement to the floating point DSP: TMS320F28335. The HERIC (Highly Ef??cient and Reliable Inverter Concept) inverter is used in the design because of its advantages: high efficiency and safety by reducing the leakage ground current when the inverter is connected to the national grid without using a transformer. The algorithm was executed by the chip TMS320F28335 high performance up to 150MHz with a math-optimized core. The process produced in this paper can be applied for any design idea about a high power, high efficiency power electronics converter.


title: Oxidation Resistance of Graphene-Coated Cu and Cu/Ni Alloy

abstract: The ability to protect refined metals from reactive environments is vital to many industrial and academic applications. Current solutions, however, typically introduce several negative effects, including increased thickness and changes in the metal physical properties. In this paper, we demonstrate for the first time the ability of graphene films grown by chemical vapor deposition to protect the surface of the metallic growth substrates of Cu and Cu/Ni alloy from air oxidation. In particular, graphene prevents the formation of any oxide on the protected metal surfaces, thus allowing pure metal surfaces only one atom away from reactive environments. SEM, Raman spectroscopy, and XPS studies show that the metal surface is well protected from oxidation even after heating at 200 °C in air for up to 4 h. Our work further shows that graphene provides effective resistance against hydrogen peroxide. This protection method offers significant advantages and can be used on any metal that catalyzes graphene growth.


title: Fault Analysis and Condition Monitoring of the Wind Turbine Gearbox

abstract: Data mining algorithms and statistical methods are applied to analyze the jerk data obtained from monitoring the gearbox of a wind turbine. Two types of analyses are performed-failure component identification and monitoring vibration excitement. In failure component identification, the failed stages of the gearbox are identified in time-domain analysis and frequency-domain analysis. In the time domain, correlation coefficient and clustering analysis are applied. The fast Fourier transformation with time windows is utilized to analyze the frequency data. To monitor the vibration excitement of the gearbox in its high-speed stage, data mining algorithms and statistical quality control theory are combined to develop a monitoring model. The capability of the monitoring model to detect changes in the gearbox vibration excitement is validated by the collected data.


A Functional Dynamic Boltzmann Machine


title: Jump process for the trend estimation of time series

abstract: A jump process approach is proposed for the trend estimation of time series. The proposed jump process estimator can locally minimize two important features of a trend, the smoothness and fidelity, and explicitly balance the fundamental tradeoff between them. A weighted average form of the jump process estimator is derived. The connection of the proposed approach to the Hanning filter, Gaussian kernel regression, the heat equation and the Wiener process is discussed. It is found that the weight function of the jump process approaches the Gaussian kernel, as the smoothing parameter increases. The proposed method is validated through numerical applications to both real data analysis and simulation study, and a comparison with the Henderson filter.


title: Separating Overlapped Fingerprints

abstract: Fingerprint images generally either contain only a single fingerprint or a set of non-overlapped fingerprints (e.g., slap fingerprints). However, there are situations where more than one fingerprint overlap on each other. Such situations are frequently encountered when latent fingerprints are lifted from crime scenes or residue fingerprints are left on fingerprint sensors. Overlapped fingerprints constitute a serious challenge to existing fingerprint recognition techniques, since these techniques are designed under the assumption that fingerprints have been properly segmented. In this paper, a novel algorithm is proposed to separate overlapped fingerprints into component or individual fingerprints. We first use local Fourier transform to estimate an initial overlapped orientation field, which contains at most two candidate orientations at each location. Then relaxation labeling technique is employed to label each candidate orientation as one of two classes. Based on the labeling result, we separate the initial overlapped orientation field into two orientation fields. Finally, the two fingerprints are obtained by enhancing the overlapped fingerprint using Gabor filters tuned to these two component separated orientation fields, respectively. Experimental results indicate that the algorithm leads to a good separation of overlapped fingerprints.


Automatic Mashup Creation by Considering both Vertical and Horizontal Mashabilities