title: Cloudlet network design optimization

abstract: Major interest is currently given to the integration of clusters of virtualization servers, also referred to as ‘cloudlets’, into the access network to allow higher performance and reliability in the access to mobile cloud services. We tackle the cloudlet network design problem for mobile access networks. The model is such that virtual machines are associated with mobile users and are allocated to cloudlets. Designing a cloudlet network implies first determining where to install cloudlet facilities among the available sites, then assigning sets of access points such as base-stations to cloudlets, while supporting virtual machine migrations and taking into account partial user mobility information, as well as the satisfaction of service-level agreements. We present link-path formulations supported by heuristics to compute solutions in reasonable time. We qualify the advantage in considering mobility for both users and virtual machines as up to 40% less cloudlet facilities to install and 40% less virtual machine migrations to execute. We compare two migration modes, bulk and live migration, as a function of mobile cloud service requirements, determining that a high preference should be given to bulk migrations for delay-stringent services such as augmented reality support, while for applications with less stringent delay requirements, live migration appears as largely preferable.


title: A Learning-Enabled Neuron Array IC Based Upon Transistor Channel Models of Biological Phenomena

abstract: We present a single-chip array of 100 biologically-based electronic neuron models interconnected to each other and the outside environment through 30,000 synapses. The chip was fabricated in a standard 350 nm CMOS IC process. Our approach used dense circuit models of synaptic behavior, including biological computation and learning, as well as transistor channel models. We use Address-Event Representation (AER) spike communication for inputs and outputs to this IC. We present the IC architecture and infrastructure, including IC chip, configuration tools, and testing platform. We present measurement of small network of neurons, measurement of STDP neuron dynamics, and measurement from a compiled spiking neuron WTA topology, all compiled into this IC.


title: Detection and prevention of possible unauthorized login attempts through stolen credentials from a phishing attack in an online banking system

abstract: With the current technological expansions customers wish to use online banking facilities due to its convenience and worldwide accessibility. The main challenge of going online for a bank is to provide sufficient security for the online customers and their accounts. The dramatic growth of the number of online banking customers has attracted cyber criminals and identity theft is a severe threat to online banking services. Phishing is a famous and easiest method to steal user credential of online customers where the sole intention is to obtain confidential information for the purpose of monetary gain. In such a situation the main purpose of this research paper is to analysis the usage of phishing attacks and the dangers it poses to customers and the bank, then to find out the available methods to detect and prevent unauthorized login attempts, the technologies and security weaknesses of those methods and finally to propose a solution to detect and prevent unauthorized login attempts using behavioral based analysis, IP and device identification technologies.


title: Live cell imaging distinguishes bona fide human iPS cells from partially reprogrammed cells

abstract: Methods for reprogramming human cells are unable to prospectively distinguish bona fide induced pluripotent stem (iPS) cells from partially reprogrammed cells. Using live imaging to monitor cell fate, Chan et al. identify a set of markers that allows identification of rare iPS cells within a heterogeneous cell population.


title: Predicting facebook-users' personality based on status and linguistic features via flexible regression analysis techniques

abstract: The psychological constructs of a user of social media are clearly visible from his/her posts and other activities. But predicting this is a challenging task. This paper explores the use of Linear Regression (LR) and Support Vector Regression (SVR) for predicting the Big Five Personality scores, which provide a quantitative measure of the personality traits of users. A performance comparison is made about the regression models on topics from Facebook users' statuses and topics from Facebook statuses along with features extracted via using Linguistic Inquiry and Word Count (LIWC) tool. Further, we have investigated the effect of number of topics found by Latent Dirichlet Allocation (LDA) on the performance of regression models. We found that SVR with Polynomial and Radial Basis Function kernel, respectively, provides better results in predicting big five personality traits. We found that the mean squared error of LR increases with the number of topics. But this increase is less in the case when we consider additional LIWC features for regression.


title: Unbiased Image Style Transfer

abstract: Recent fast image style transferring methods use feed-forward neural networks to generate an output image of desired style strength from the input pair of a content and a target style image. In the existing methods, the image of intermediate style between the content and the target style is obtained by decoding a linearly interpolated feature in encoded feature space. However, there has been no work on analyzing the effectiveness of this kind of style strength interpolation so far. In this paper, we tackle the missing work on the in-depth analysis of style interpolation and propose a method that is more effective in controlling style strength. We interpret the training task of a style transfer network as a regression learning between the control parameter and output style strength. In this understanding, the existing methods are biased due to the fact that training is performed with one-sided data of full style strength (alpha = 1.0). Thus, this biased learning does not guarantee the generation of a desired intermediate style corresponding to the style control parameter between 0.0 and 1.0. To solve this problem of the biased network, we propose an unbiased learning technique which uses unbiased training data and corresponding unbiased loss for alpha = 0.0 to make the feed-forward networks to generate a zero-style image, i.e., content image when alpha = 0.0. Our experimental results verified that our unbiased learning method achieved the reconstruction of a content image with zero style strength, better regression specification between style control parameter and output style, and more stable style transfer that is insensitive to the weight of style loss without additive complexity in image generating process.


title: Semi-Supervised Multi-View Discrete Hashing for Fast Image Search

abstract: Hashing is an important method for fast neighbor search on large scale dataset in Hamming space. While most research on hash models are focusing on single-view data, recently the multi-view approaches with a majority of unsupervised multi-view hash models have been considered. Despite of existence of millions of unlabeled data samples, it is believed that labeling a handful of data will remarkably improve the searching performance. In this paper, we propose a semi-supervised multi-view hash model. Besides incorporating a portion of label information into the model, the proposed multi-view model differs from existing multi-view hash models in three-fold: 1) a composite discrete hash learning modeling that is able to minimize the loss jointly on multi-view features when using relaxation on learning hashing codes; 2) exploring statistically uncorrelated multi-view features for generating hash codes; and 3) a composite locality preserving modeling for locally compact coding. Extensive experiments have been conducted to show the effectiveness of the proposed semi-supervised multi-view hash model as compared with related multi-view hash models and semi-supervised hash models.


title: A Fully Fabric-Based Bidirectional Soft Robotic Glove for Assistance and Rehabilitation of Hand Impaired Patients

abstract: This letter presents a fully fabric-based bidirectional soft robotic glove designed to assist hand impaired patients in rehabilitation exercises and performing activities of daily living. The glove provides both active finger flexion and extension for hand assistance and rehabilitative training, through its embedded fabric-based actuators that are fabricated by heat press and ultrasonic welding of flexible thermoplastic polyurethane-coated fabrics. Compared to previous developed elastomeric-based actuators, the actuators are able to achieve smaller bend radius and generate sufficient force and torque to assist in both finger flexion and extension at lower air pressure. In this letter, experiments were conducted to characterize the performances of the glove in terms of its kinematic and grip strength assistances on five healthy participants. Additionally, we present a graphical-user interface that allows user to choose the desired rehabilitation exercises and control modes, which include button-controlled-assistive mode, cyclic movement training, intention-driven task-specific training, and bilateral rehabilitation training.


title: Fundamental Storage-Latency Tradeoff in Cache-Aided MIMO Interference Networks

abstract: Caching is an effective technique to improve user perceived experience for content delivery in wireless networks. Wireless caching differs from traditional web caching in that it can exploit the broadcast nature of wireless medium and hence, opportunistically change the network topologies. This paper studies a cache-aided MIMO interference network with three transmitters each equipped with $M$ antennas and three receivers each with $N$ antennas. With caching at both the transmitter and receiver sides, the network is changed to hybrid forms of MIMO broadcast channel, MIMO X channel, and MIMO multicast channels. We analyze the degrees of freedom (DoF) of these new channel models using practical interference management schemes. Based on the collective use of these DoF results, we then obtain an achievable normalized delivery time (NDT) of the network, an information-theoretic metric that evaluates the worst-case delivery time at given cache sizes. The obtained NDT is for arbitrary $M$ , $N$ , and any feasible cache sizes. It is shown to be optimal in certain cases and within a multiplicative gap of 3 from the optimum in other cases. The extension to the network with arbitrary number of transmitters and receivers is also discussed.


title: Diagnosis of Neonatal Transient Tachypnea and Its Differentiation From Respiratory Distress Syndrome Using Lung Ultrasound

abstract: AbstractTransient tachypnea of the newborn (TTN) is one of the most common causes of perinatal dyspnea and is traditionally diagnosed by chest x-ray. This study aimed to explore the diagnostic value of lung ultrasonography (LUS) for TTN as well as differentiate it from respiratory distress syndrome (RDS) by using LUS.From January 2013 to February 2014, 60 infants who were diagnosed with TTN based on medical history, clinical manifestations, arterial blood gas analysis, and chest radiography were recruited to the study group. During the same period, 40 hospitalized neonates with nonlung diseases and 20 patients with RDS were recruited to the control group. In a quiet state, infants were placed in the supine, lateral, or prone position for the examination. Each lung of every infant was divided into 3 regions: the anterior, lateral, and posterior regions as bordered by the anterior axillary and posterior axillary lines. The probe was placed perpendicular to the ribs. Each region of both the lungs was carefully scanned.The common ultrasonographic manifestations of TTN were double-lung point (DLP), interstitial syndromes or white lungs, pleural line abnormalities, and A-line disappearance. A small number of infants (20%) with TTN exhibited pleural effusions, whereas the main ultrasonographic manifestation of RDS was lung consolidation with air bronchograms, which does not occur in TTN. The sensitivity and specificity of DLP for the diagnosis of TTN were 76.7% and 100%, respectively.LUS can accurately and reliably diagnose TTN. The DLP and lung consolidation possess great value in the diagnosis and differential diagnosis of TTN with RDS. Thus, we believe that LUS can be widely used in neonatal intensive care units.


title: Cloud Computing for parallel Scientific HPC Applications: Feasibility of Running Coupled Atmosphere-Ocean Climate Models on Amazon’s EC2

abstract: Disclosed in the present invention is a process for modifying a polyamide polymer comprising contacting a reactive modifier with diamine/diacid salt and/or an amino-acid of a hydrolyzed lactam or lactam to form a modified polyamide. A second embodiment of the present invention is a process for modifying a polyamide polymer comprising contacting a reactive modifier with diamine then contacting the resulting modified diamine with diacid and/or lactam to form a modified polyamide.


title: Are Small, Isolated Wetlands Expendable?

abstract: What is most evident in the recent debate concerning new wetland regulations drafted by the U.S. Army Corps of Engineers is that small, isolated wetlands will likely continue to be lost. The critical biological question is whether small wetlands are expendable, and the fundamental issue is the lack of biologically rele- vant data on the value of wetlands, especially so-called "isolated" wetlands of small size. We used data from a geographic information system for natural-depression wetlands on the southeastern Atlantic coastal plain (U.S.A.) to examine the frequency distribution of wetland sizes and their nearest-wetland distances. Our re- sults indicate that the majority of natural wetlands are small and that these small wetlands are rich in am- phibian species and serve as an important source of juvenile recruits. Analyses simulating the loss of small wetlands indicate a large increase in the nearest-wetland distance that could impede "rescue" effects at the metapopulation level. We argue that small wetlands are extremely valuable for maintaining biodiversity, that the loss of small wetlands will cause a direct reduction in the connectance among remaining species pop- ulations, and that both existing and recently proposed legislation are inadequate for maintaining the biodi- versity of wetland flora and fauna. Small wetlands are not expendable if our goal is to maintain present lev- els of species biodiversity. At the very least, based on these data, regulations should protect wetlands as small as 0.2 ha until additional data are available to compare diversity directly across a range of wetland sizes. Furthermore, we strongly advocate that wetland legislation focus not only on size but also on local and re- gional wetland distribution in order to protect ecological connectance and the source-sink dynamics of spe- cies populations.


title: Global Context-Aware Attention LSTM Networks for 3D Action Recognition

abstract: Long Short-Term Memory (LSTM) networks have shown superior performance in 3D human action recognition due to their power in modeling the dynamics and dependencies in sequential data. Since not all joints are informative for action analysis and the irrelevant joints often bring a lot of noise, we need to pay more attention to the informative ones. However, original LSTM does not have strong attention capability. Hence we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for 3D action recognition, which is able to selectively focus on the informative joints in the action sequence with the assistance of global contextual information. In order to achieve a reliable attention representation for the action sequence, we further propose a recurrent attention mechanism for our GCA-LSTM network, in which the attention performance is improved iteratively. Experiments show that our end-to-end network can reliably focus on the most informative joints in each frame of the skeleton sequence. Moreover, our network yields state-of-the-art performance on three challenging datasets for 3D action recognition.


Music Emotion Recognition via End-to-End Multimodal Neural Networks


title: Tensor Deep Stacking Networks

abstract: A novel deep architecture, the tensor deep stacking network (T-DSN), is presented. The T-DSN consists of multiple, stacked blocks, where each block contains a bilinear mapping from two hidden layers to the output layer, using a weight tensor to incorporate higher order statistics of the hidden binary (([0,1])) features. A learning algorithm for the T-DSN's weight matrices and tensors is developed and described in which the main parameter estimation burden is shifted to a convex subproblem with a closed-form solution. Using an efficient and scalable parallel implementation for CPU clusters, we train sets of T-DSNs in three popular tasks in increasing order of the data size: handwritten digit recognition using MNIST (60k), isolated state/phone classification and continuous phone recognition using TIMIT (1.1 m), and isolated phone classification using WSJ0 (5.2 m). Experimental results in all three tasks demonstrate the effectiveness of the T-DSN and the associated learning methods in a consistent manner. In particular, a sufficient depth of the T-DSN, a symmetry in the two hidden layers structure in each T-DSN block, our model parameter learning algorithm, and a softmax layer on top of T-DSN are shown to have all contributed to the low error rates observed in the experiments for all three tasks.


title: Reducing Mutants with Mutant Killable Precondition

abstract: Mutation analysis is a method for predicting the quality of test suite accurately. However, it has high computational cost due to the number of mutants that are generated. For example, the ROR (Relational Operator Replacement) mutation operator will generate seven mutants for just one relational operator. Naively applying multiple operators over the entire program can result in a high number of generated mutants. One way to reduce the number of mutants is to omit redundant mutants. In this paper, we propose an approach to reducing mutants by using mutant killable precondition to identify redundant mutants. A mutant killable precondition is a logical expression for killing a mutant. We focus on the conditional expression for control flow statements, such as if and while statements. We describe the mutant killable precondition for conditional expressions that compare numbers, e.g., x > 0. We then discuss mutants that are generated for such conditional expressions, and find the minimal set of mutants. Finally, we show the theoretical and empirical reduction rate of our approach.


Analyzing Uncertainties in Cost : Mitigating Centralization in Multi-Robot Task Allocation ∗


title: Novel Designs for the Audio Mixing Interface Based on Data Visualisation First Principles

abstract: Given the shortcomings of current audio mixing interfaces (AMIs) this study focuses on the development of alternative AMIs based on data visualisation first principles. The elementary perceptual tasks defined by Cleveland informed the design process. Two design ideas were considered for pan: using the elementary perceptual tasks ‘scale’ to display pan on either a single or multiple horizontal lines. Four design ideas were considered for level: ::: using ‘length’, ‘area’, ‘saturation’ or ‘scalable icon’ for visualisation. Each level idea was prototyped with each pan idea, totalling eight novel interfaces. Seven subjects undertook a usability evaluation, replicating a 16 channel reference mix with each interface. Results showed that ‘scalable icons’, especially on multiple horizontal lines appear to show potential.


title: A Balanced-to-Balanced Power Divider With Arbitrary Power Division

abstract: In this paper, a balanced-to-balanced power divider is proposed, for the first time, with arbitrary power division, which can be regarded as the balanced form of a Gysel power divider. The constraint rules are provided for its mixed-mode and single-ended S-parameters. The six-port network is analyzed by simplifying it to two-port networks with other ports matched at the central frequency. Its critical characteristic impedances are then calculated analytically by our derived equations according to the desired differential-mode power division ratio. The maximum achievable power division ratio is 1:4.692 when the characteristic impedances are limited within the realizable range of 20-120 Ω. The impacts of several freely selected design parameters on the operating bandwidth are explored numerically. A prototype is realized by microstrip lines and lumped resistors with the power division ratio of 1:32. The balanced-to-balanced performances of unequal power division, low differential-mode insertion loss, good suppression of common-mode noises, and mode conventions have been demonstrated by the simulated and measured results of the balanced-to-balanced power divider prototype.


title: Inhibition in the amygdala anxiety circuitry

abstract: Inhibitory neurotransmission plays a key role in anxiety disorders, as evidenced by the anxiolytic effect of the benzodiazepine class of γ-aminobutyric acid (GABA) receptor agonists and the recent discovery of anxiety-associated variants in the molecular components of inhibitory synapses. Accordingly, substantial interest has focused on understanding how inhibitory neurons and synapses contribute to the circuitry underlying adaptive and pathological anxiety behaviors. A key element of the anxiety circuitry is the amygdala, which integrates information from cortical and thalamic sensory inputs to generate fear and anxiety-related behavioral outputs. Information processing within the amygdala is heavily dependent on inhibitory control, although the specific mechanisms by which amygdala GABAergic neurons and synapses regulate anxiety-related behaviors are only beginning to be uncovered. Here, we summarize the current state of knowledge and highlight open questions regarding the role of inhibition in the amygdala anxiety circuitry. We discuss the inhibitory neuron subtypes that contribute to the processing of anxiety information in the basolateral and central amygdala, as well as the molecular determinants, such as GABA receptors and synapse organizer proteins, that shape inhibitory synaptic transmission within the anxiety circuitry. Finally, we conclude with an overview of current and future approaches for converting this knowledge into successful treatment strategies for anxiety disorders. Understanding inhibitory neurotransmission in the amygdala is key to developing more effective therapeutics against anxiety. Anxiety disorders are the most prevalent group of psychiatric diseases, yet they are often under-treated due to the limited efficacy and tolerability of current treatment approaches. Dilja Krueger-Burg and colleagues at the Max Planck Institute of Experimental Medicine in Göttingen, Germany, review current knowledge of the inhibitory neuronal circuits and molecular mechanisms that contribute to anxiety behaviors in the amygdala, a key brain region for processing emotions. It is well established that alterations in inhibitory neurotransmission and excessive excitatory neurotransmission underlie anxiety disorders. Using the latest technologies to identify the neuronal populations and synaptic components implicated in the regulation of anxiety-related behaviors should help to develop new targeted treatments with fewer side-effects.


title: Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks

abstract: Designing a new drug is a lengthy and expensive process. As the space of potential molecules is very large (10 23 - 10 60 ), a common technique during drug discovery is to start from a molecule which already has some of the desired properties. An interdisciplinary team of scientists generates hypothesis about the required changes to the prototype. In this work, we develop an algorithmic unsupervised-approach that automatically generates potential drug molecules given a prototype drug. We show that the molecules generated by the system are valid molecules and significantly different from the prototype drug. Out of the compounds generated by the system, we identified 35 FDA-approved drugs. As an example, our system generated Isoniazid - one of the main drugs for Tuberculosis. The system is currently being deployed for use in collaboration with pharmaceutical companies to further analyze the additional generated molecules.


Fault Diagnosis in Enterprise Software Systems Using Discrete Monitoring Data


title: Assessing computational learning in K-12

abstract: As computing curricula continue to make their way into K-12 schools, the issue of assessing student learning of computational concepts remains a thorny one. This paper describes the multiple forms of assessments used in a 6-week middle school curriculum with the goal of capturing a holistic view of student learning. A key aspect of this research is the use of instruments developed and shared in prior research. Included among these were several questions used in an Israeli nationwide exam to test middle school student learning of programming in Scratch. This paper reports on the use of the curriculum in two studies conducted in a public US middle school classroom, and compares performances of these students with those reported by the Israeli Ministry of Education in their large-scale study. It also argues for multiple modes of assessment of computational learning in K-12 settings.


title: A Stream-based Query System for Efficiently Detecting Abnormal System Behaviors for Enterprise Security.

abstract: The need for countering Advanced Persistent Threat (APT) attacks has led to the solutions that ubiquitously monitor system activities in each enterprise host, and perform timely abnormal system behavior detection over the stream of monitoring data. However, existing stream-based solutions lack explicit language constructs for expressing anomaly models that capture abnormal system behaviors, thus facing challenges in incorporating expert knowledge to perform timely anomaly detection over the large-scale monitoring data. To address these limitations, we build SAQL, a novel stream-based query system that takes as input, a real-time event feed aggregated from multiple hosts in an enterprise, and provides an anomaly query engine that queries the event feed to identify abnormal behaviors based on the specified anomaly models. SAQL provides a domain-specific query language, Stream-based Anomaly Query Language (SAQL), that uniquely integrates critical primitives for expressing major types of anomaly models. In the demo, we aim to show the complete usage scenario of SAQL by (1) performing an APT attack in a controlled environment, and (2) using SAQL to detect the abnormal behaviors in real time by querying the collected stream of system monitoring data that contains the attack traces. The audience will have the option to perform the APT attack themselves under our guidance, and interact with the system and detect the attack footprints in real time via issuing queries and checking the query results through a command-line UI.


title: Analysis, design, and evaluation of three-phase three-wire isolated ac-dc converter implemented with three single-phase converter modules

abstract: In this paper, a control method for three single-phase isolated ac-dc converter modules employed to implement an isolated three-phase, three-wire ac-dc converter is presented and its detailed design procedure provided. In the described control the input voltages of the Y-connected single-phase modules with floating common Y point are kept within a safe range by equalizing their input admittances. It is shown that for obtaining equal input admittances of the three PFC front stages, the outputs of the PFC voltage controllers should be equal. This is achieved by adjusting the reference currents of the current controllers in the output dc-dc stages. The performance of the presented control with both balanced and unbalanced source voltages, as well as with a phase fault (open or short circuit) is evaluated by Matlab/Simulink simulations and verified experimentally on a 6-kW prototype.


title: Understanding the Effects of Violent Video Games on Violent Crime

abstract: Psychological studies invariably find a positive relationship between violent video game play and aggression. However, these studies cannot account for either aggressive effects of alternative activities video game playing substitutes for or the possible selection of relatively violent people into playing violent video games. That is, they lack external validity. We investigate the relationship between the prevalence of violent video games and violent crimes. Our results are consistent with two opposing effects. First, they support the behavioral effects as in the psychological studies. Second, they suggest a larger voluntary incapacitation effect in which playing either violent or non-violent games decrease crimes. Overall, violent video games lead to decreases in violent crime.


title: Image-Based Visual Servoing With Light Field Cameras

abstract: This paper proposes the first derivation, implementation, and experimental validation of light field image-based visual servoing. Light field image Jacobians are derived based on a compact light field feature representation that is close to the form measured directly by light field cameras. We also enhance feature detection and correspondence by enforcing light field geometry constraints, and directly estimate the image Jacobian without knowledge of point depth. The proposed approach is implemented over a standard visual servoing control loop, and applied to a custom-mirror-based light field camera mounted on a robotic arm. Light field image-based visual servoing is then validated in both simulation and experiment. We show that the proposed method outperforms conventional monocular and stereo image-based visual servoing under field-of-view constraints and occlusions.


title: Datasheets for Datasets

abstract: The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains. To address this gap, we propose datasheets for datasets. In the electronics industry, every component, no matter how simple or complex, is accompanied with a datasheet that describes its operating characteristics, test results, recommended uses, and other information. By analogy, we propose that every dataset be accompanied with a datasheet that documents its motivation, composition, collection process, recommended uses, and so on. Datasheets for datasets will facilitate better communication between dataset creators and dataset consumers, and encourage the machine learning community to prioritize transparency and accountability.


title: Tumour invasion and metastasis initiated by microRNA-10b in breast cancer

abstract: MicroRNAs have been implicated in regulating diverse cellular pathways. Although there is emerging evidence that some microRNAs can function as oncogenes or tumour suppressors, the role of microRNAs in mediating cancer metastasis remains unexplored. Here we show, using a combination of mouse and human cells, that microRNA-10b (miR-10b) is highly expressed in metastatic breast cancer cells and positively regulates cell migration and invasion. Overexpression of miR-10b in otherwise non-metastatic breast tumours initiates robust invasion and metastasis. Expression of miR-10b is induced by the transcription factor Twist, which binds directly to the putative promoter of mir-10b (MIRN10B). The miR-10b induced by Twist proceeds to inhibit translation of the messenger RNA encoding homeobox D10, resulting in increased expression of a well-characterized pro-metastatic gene, RHOC. Significantly, the level of miR-10b expression in primary breast carcinomas correlates with clinical progression. These findings suggest the workings of an undescribed regulatory pathway, in which a pleiotropic transcription factor induces expression of a specific microRNA, which suppresses its direct target and in turn activates another pro-metastatic gene, leading to tumour cell invasion and metastasis.


title: Tensor graph convolutional neural network

abstract: In this paper, we propose a novel tensor graph convolutional neural network (TGCNN) to conduct convolution on factorizable graphs, for which here two types of problems are focused, one is sequential dynamic graphs and the other is cross-attribute graphs. Especially, we propose a graph preserving layer to memorize salient nodes of those factorized subgraphs, i.e. cross graph convolution and graph pooling. For cross graph convolution, a parameterized Kronecker sum operation is proposed to generate a conjunctive adjacency matrix characterizing the relationship between every pair of nodes across two subgraphs. Taking this operation, then general graph convolution may be efficiently performed followed by the composition of small matrices, which thus reduces high memory and computational burden. Encapsuling sequence graphs into a recursive learning, the dynamics of graphs can be efficiently encoded as well as the spatial layout of graphs. To validate the proposed TGCNN, experiments are conducted on skeleton action datasets as well as matrix completion dataset. The experiment results demonstrate that our method can achieve more competitive performance with the state-of-the-art methods.


title: Dynamic Time-Dependent Route Planning in Road Networks with User Preferences

abstract: There has been tremendous progress in algorithmic methods for computing driving directions on road networks. Most of that work focuses on time-independent route planning, where it is assumed that the cost on each arc is constant per query. In practice, the current traffic situation significantly influences the travel time on large parts of the road network, and it changes over the day. One can distinguish between traffic congestion that can be predicted using historical traffic data, and congestion due to unpredictable events, e.g., accidents. In this work, we study the \emph{dynamic and time-dependent} route planning problem, which takes both prediction (based on historical data) and live traffic into account. To this end, we propose a practical algorithm that, while robust to user preferences, is able to integrate global changes of the time-dependent metric~(e.g., due to traffic updates or user restrictions) faster than previous approaches, while allowing subsequent queries that enable interactive applications.


title: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence Stochastic Model Predictive Controller for the Integration of Building Use and Temperature Regulation

abstract: The aim of a modern Building Automation System (BAS) is to enhance interactive control strategies for energy efficiency and user comfort. In this context, we develop a novel control algorithm that uses a stochastic building occupancy model to improve mean energy efficiency while minimizing expected discomfort. We compare by simulation our Stochastic Model Predictive Control (SMPC) strategy to the standard heating control method to empirically demonstrate a 4.3% reduction in energy use and 38.3% reduction in expected discomfort.


title: Rapidly Exploring Application Design Through Speed Dating

abstract: While the user-centered design methods we bring from humancomputer interaction to ubicomp help sketch ideas and refine prototypes, few tools or techniques help explore divergent design concepts, reflect on their merits, and come to a new understanding of design opportunities and ways to address them. We present Speed Dating, a design method for rapidly exploring application concepts and their interactions and contextual dimensions without requiring any technology implementation. Situated between sketching and prototyping, Speed Dating structures comparison of concepts, helping identify and understand contextual risk factors and develop approaches to address them. We illustrate how to use Speed Dating by applying it to our research on the smart home and dual-income families, and highlight our findings from using this method.


Cardioprotective and Metabolomic Profiling of Selected Medicinal Plants against Oxidative Stress


title: Summarization beyond sentence extraction: A probabilistic approach to sentence compression

abstract: When humans produce summaries of documents, they do not simply extract sentences and concatenate them. Rather, they create new sentences that are grammatical, that cohere with one another, and that capture the most salient pieces of information in the original document. Given that large collections of text/abstract pairs are available online, it is now possible to envision algorithms that are trained to mimic this process. In this paper, we focus on sentence compression, a simpler version of this larger challenge. We aim to achieve two goals simultaneously: our compressions should be grammatical, and they should retain the most important pieces of information. These two goals can conflict. We devise both a noisy-channel and a decision-tree approach to the problem, and we evaluate results against manual compressions and a simple baseline.


title: Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition

abstract: Epilepsy is one of the most common neurological disorders characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is an invaluable measurement for the purpose of assessing brain activities, containing information relating to the different physiological states of the brain. It is a very effective tool for understanding the complex dynamical behavior of the brain. This paper presents the application of empirical mode decomposition (EMD) for analysis of EEG signals. The EMD decomposes a EEG signal into a finite set of bandlimited signals termed intrinsic mode functions (IMFs). The Hilbert transformation of IMFs provides analytic signal representation of IMFs. The area measured from the trace of the analytic IMFs, which have circular form in the complex plane, has been used as a feature in order to discriminate normal EEG signals from the epileptic seizure EEG signals. It has been shown that the area measure of the IMFs has given good discrimination performance. Simulation results illustrate the effectiveness of the proposed method.


title: Camera Based Localization for Autonomous UAV Formation Flight

abstract: This work considers the task of accurate in-air localization for multiple unmanned or autonomous aerial vehicles flying in close formation. The paper describes our experimental setup using two small UAVs and the details of the localization algorithm. The algorithm was implemented on two low-cost, electric powered, remote control aircraft with wing spans of approximately 2 meters. Our control software, running on an onboard x86 CPU, uses LQG control (an LQR controller coupled with an EKF state estimator) and a linearized state space model to control both aircraft to fly synchronized circles. In addition to its control system, the lead aircraft is outfitted with a known pattern of high-intensity LED lights. The trailing aircraft captures images of these LEDs with a camera and uses the Orthogonal Iteration computer vision algorithm to determine the relative position and orientation of the trailing aircraft with respect to the lead aircraft at 25Hz. The entire process is carried-out in real-time with both vehicles flying autonomously. We note that the camera based system is used for localization, but not yet for closed-loop control. Although, an absolute quantification of the error for the in-air localization system is difficult as we do not have ground truth positioning data during flight testing, our simulation results analysis and indoor measurements suggest that we can achieve localization accuracy on the order of 10 cm (5% wingspan) when the UAVs are separated by a distance of about 10 meters (5 spans).


title: A Fast Lightweight Time-Series Store for IoT Data

abstract: With the advent of the Internet-of-Things (IoT), handling large volumes of time-series data has become a growing concern. Data, generated from millions of Internet-connected sensors, will drive new IoT applications and services. A key requirement is the ability to aggregate, preprocess, index, store and analyze data with minimal latency so that timeto-insight can be reduced. In the future, we expect real-time data collection and analysis to be performed both on small devices (e.g., in hubs and appliances) as well in server-based infrastructure. The ability to localize sensitive data to the home, and thus preserve privacy, is a key driver for smalldevice deployment. In this paper, we present an efficient architecture for timeseries data management that provides a high data ingestion rate, while still being sufficiently lightweight that it can be deployed in embedded environments or small virtual machines. Our solution strives to minimize overhead and explores what can be done without complex indexing schemes that typically, for performance reasons, must be held in main memory. We combine a simple in-memory hierarchical index, log-structured store and in-flight sort, with a high-performance data pipeline architecture that is optimized for multicore platforms. We show that our solution is able to handle streaming insertions at over 4 million records per second (on a single x86 server) while still retaining SQL query performance better than or comparable to existing RDBMS.


title: Computer-Aided Diagnosis Sensor and System of Breast Sonography: A Clinical Study

abstract: Early detection of breast cancer is critical in determining the best possible treatment approach. Ultrasound imaging has become an important modality in breast tumor detection and classification owing to its superiority to mammography in its ability to detect focal abnormalities in dense breast tissue. This paper discusses novel Fourier-based shape feature extraction techniques that provide enhanced classification accuracy for breast tumors in the computer-aided B-mode ultrasound diagnosis system. To verify the effectiveness of the proposed technique, experiments were performed using 4,107 ultrasound images containing 2,508 malignancy cases. Experimental results showed that the breast tumor classification accuracy has specificity of 95.8 %, sensitivity of 94.1 %, precision and recall of 95.7 %, and accuracy of 94.9 %. Copyright © 2014 IFSA Publishing, S. L.


title: Weakly Supervised Multimodal Kernel for Categorizing Aerial Photographs

abstract: Accurately distinguishing aerial photographs from different categories is a promising technique in computer vision. It can facilitate a series of applications, such as video surveillance and vehicle navigation. In this paper, a new image kernel is proposed for effectively recognizing aerial photographs. The key is to encode high-level semantic cues into local image patches in a weakly supervised way, and integrate multimodal visual features using a newly developed hashing algorithm. The flowchart can be elaborated as follows. Given an aerial photo, we first extract a number of graphlets to describe its topological structure. For each graphlet, we utilize color and texture to capture its appearance, and a weakly supervised algorithm to capture its semantics. Thereafter, aerial photo categorization can be naturally formulated as graphlet-to-graphlet matching. As the number of graphlets from each aerial photo is huge, to accelerate matching, we present a hashing algorithm to seamlessly fuze the multiple visual features into binary codes. Finally, an image kernel is calculated by fast matching the binary codes corresponding to each graphlet. And a multi-class SVM is learned for aerial photo categorization. We demonstrate the advantage of our proposed model by comparing it with state-of-the-art image descriptors. Moreover, an in-depth study of the descriptiveness of the hash-based graphlet is presented.


title: Penalized PET Reconstruction Using Deep Learning Prior and Local Linear Fitting

abstract: Motivated by the great potential of deep learning in medical imaging, we propose an iterative positron emission tomography reconstruction framework using a deep learning-based prior. We utilized the denoising convolutional neural network (DnCNN) method and trained the network using full-dose images as the ground truth and low dose images reconstructed from downsampled data by Poisson thinning as input. Since most published deep networks are trained at a predetermined noise level, the noise level disparity of training and testing data is a major problem for their applicability as a generalized prior. In particular, the noise level significantly changes in each iteration, which can potentially degrade the overall performance of iterative reconstruction. Due to insufficient existing studies, we conducted simulations and evaluated the degradation of performance at various noise conditions. Our findings indicated that DnCNN produces additional bias induced by the disparity of noise levels. To address this issue, we propose a local linear fitting function incorporated with the DnCNN prior to improve the image quality by preventing unwanted bias. We demonstrate that the resultant method is robust against noise level disparities despite the network being trained at a predetermined noise level. By means of bias and standard deviation studies via both simulations and clinical experiments, we show that the proposed method outperforms conventional methods based on total variation and non-local means penalties. We thereby confirm that the proposed method improves the reconstruction result both quantitatively and qualitatively.


title: Computational ghost imaging using deep learning

abstract: Abstract Computational ghost imaging (CGI) is a single-pixel imaging technique that exploits the correlation between known random patterns and the measured intensity of light transmitted (or reflected) by an object. Although CGI can obtain two- or three-dimensional images with a single or a few bucket detectors, the quality of the reconstructed images is reduced by noise due to the reconstruction of images from random patterns. In this study, we improve the quality of CGI images using deep learning. A deep neural network is used to automatically learn the features of noise-contaminated CGI images. After training, the network is able to predict low-noise images from new noise-contaminated CGI images.


title: Deep Learning for Information Retrieval

abstract: Recent years have observed a significant progress in information retrieval and natural language processing with deep learning technologies being successfully applied into almost all of their major tasks. The key to the success of deep learning is its capability of accurately learning distributed representations (vector representations or structured arrangement of them) of natural language expressions such as sentences, and effectively utilizing the representations in the tasks. This tutorial aims at summarizing and introducing the results of recent research on deep learning for information retrieval, in order to stimulate and foster more significant research and development work on the topic in the future. The tutorial mainly consists of three parts. In the first part, we introduce the fundamental techniques of deep learning for natural language processing and information retrieval, such as word embedding, recurrent neural networks, and convolutional neural networks. In the second part, we explain how deep learning, particularly representation learning techniques, can be utilized in fundamental NLP and IR problems, including matching, translation, classification, and structured prediction. In the third part, we describe how deep learning can be used in specific application tasks in details. The tasks are search, question answering (from either documents, database, or knowledge base), and image retrieval.


title: A statistical machine learning approach for ticket mining in IT service delivery

abstract: Ticketing is a fundamental management process of IT service delivery. Customers typically express their requests in the form of tickets related to problems or configuration changes of existing systems. Tickets contain a wealth of information which, when connected with other sources of information such as asset and configuration information, monitoring information, can yield new insights that would otherwise be impossible to gain from one isolated source. Linking these various sources of information requires a common key shared by these data sources. The key is the server names. Unfortunately, due to historical as well as practical reasons, the server names are not always present in the tickets as a standalone field. Rather, they are embedded in unstructured text fields such as abstract and descriptions. Thus, automatically identifying server names in tickets is a crucial step in linking various information sources. In this paper, we present a statistical machine learning method called Conditional Random Field (CRF) that can automatically identify server names in tickets with high accuracy and robustness. We then illustrate how such linkages can be leveraged to create new business insights.


title: Frequency and beam reconfigurable antenna using photoconducting switches

abstract: A design for an optically reconfigurable printed dipole antenna is presented. A wideband coplanar waveguide (CPW) to coplanar stripline (CPS) transition is used to feed the balanced printed dipole. Two silicon photo switches are placed on small gaps in both dipole arms equidistant from the centre feed. Light from two infrared laser diodes channelled through fiber optic cables is applied to the switches. With the gaps in the dipole bridged, the antenna resonates at a lower frequency. Measured return loss results that compare well to the simulated values are also presented, showing a frequency shift of nearly 40%. The change in bore-sight gain along with radiation patterns are also presented. Activating each switch individually results in a near 50/spl deg/ shift in beam nulls.


title: Architectural mechanisms for dynamic changes of behavior selection strategies in behavior-based systems

abstract: Behavior selection is typically a "built-in" feature of behavior-based architectures and hence, not amenable to change. There are, however, circumstances where changing behavior selection strategies is useful and can lead to better performance. In this paper, we demonstrate that such dynamic changes of behavior selection mechanisms are beneficial in several circumstances. We first categorize existing behavior selection mechanisms along three dimensions and then discuss seven possible circumstances where dynamically switching among them can be beneficial. Using the agent architecture framework activation, priority, observer, and component (APOC), we show how instances of all (nonempty) categories can be captured and how additional architectural mechanisms can be added to allow for dynamic switching among them. In particular, we propose a generic architecture for dynamic behavior selection, which can integrate existing behavior selection mechanisms in a unified way. Based on this generic architecture, we then verify that dynamic behavior selection is beneficial in the seven cases by defining architectures for simulated and robotic agents and performing experiments with them. The quantitative and qualitative analyzes of the results obtained from extensive simulation studies and experimental runs with robots verify the utility of the proposed mechanisms.


title: SLA-Based Resource Allocation for Software as a Service Provider (SaaS) in Cloud Computing Environments

abstract: Cloud computing has been considered as a solution for solving enterprise application distribution and configuration challenges in the traditional software sales model. Migrating from traditional software to Cloud enables on-going revenue for software providers. However, in order to deliver hosted services to customers, SaaS companies have to either maintain their own hardware or rent it from infrastructure providers. This requirement means that SaaS providers will incur extra costs. In order to minimize the cost of resources, it is also important to satisfy a minimum service level to customers. Therefore, this paper proposes resource allocation algorithms for SaaS providers who want to minimize infrastructure cost and SLA violations. Our proposed algorithms are designed in a way to ensure that Saas providers are able to manage the dynamic change of customers, mapping customer requests to infrastructure level parameters and handling heterogeneity of Virtual Machines. We take into account the customers' Quality of Service parameters such as response time, and infrastructure level parameters such as service initiation time. This paper also presents an extensive evaluation study to analyze and demonstrate that our proposed algorithms minimize the SaaS provider's cost and the number of SLA violations in a dynamic resource sharing Cloud environment.


title: Fast detection of multiple textureless 3-D objects

abstract: We propose a fast edge-based approach for detection and approximate pose estimation of multiple textureless objects in a single image. The objects are trained from a set of edge maps, each showing one object in one pose. To each scanning window in the input image, the nearest neighbor is found among these training templates by a two-level cascade. The first cascade level, based on a novel edge-based sparse image descriptor and fast search by index table, prunes the majority of background windows. The second level verifies the surviving detection hypotheses by oriented chamfer matching, improved by selecting discriminative edges and by compensating a bias towards simple objects. The method outperforms the state-of-the-art approach by Damen et al. (2012). The processing is near real-time, ranging from 2 to 4 frames per second for the training set size ~104.


title: Sentiment Analysis in Czech Social Media Using Supervised Machine Learning

abstract: This article provides an in-depth research of machine learning methods for sentiment analysis of Czech social media. Whereas in English, Chinese, or Spanish this field has a long history and evaluation datasets for various domains are widely available, in case of Czech language there has not yet been any systematical research conducted. We tackle this issue and establish a common ground for further research by providing a large humanannotated Czech social media corpus. Furthermore, we evaluate state-of-the-art supervised machine learning methods for sentiment analysis. We explore different pre-processing techniques and employ various features and classifiers. Moreover, in addition to our newly created social media dataset, we also report results on other widely popular domains, such as movie and product reviews. We believe that this article will not only extend the current sentiment analysis research to another family of languages, but will also encourage competition which potentially leads to the production of high-end commercial solutions.


title: Glasses with haptic feedback of gaze gestures

abstract: We introduce eyeglasses that present haptic feedback when using gaze gestures for input. The glasses utilize vibrotactile actuators to provide gentle stimulation to three locations on the user's head. We describe two initial user studies that were conducted to evaluate the easiness of recognizing feedback locations and participants' preferences for combining the feedback with gaze gestures. The results showed that feedback from a single actuator was the easiest to recognize and also preferred when used with gaze gestures. We conclude by presenting future use scenarios that could benefit from gaze gestures and haptic feedback.


title: ZipG: A Memory-efficient Graph Store for Interactive Queries

abstract: We present ZipG, a distributed memory-efficient graph store for serving interactive graph queries. ZipG achieves memory efficiency by storing the input graph data using a compressed representation. What differentiates ZipG from other graph stores is its ability to execute a wide range of graph queries directly on this compressed representation. ZipG can thus execute a larger fraction of queries in main memory, achieving query interactivity. ZipG exposes a minimal API that is functionally rich enough to implement published functionalities from several industrial graph stores. We demonstrate this by implementing and evaluating graph queries from Facebook TAO, LinkBench, Graph Search and several other workloads on top of ZipG. On a single server with 244GB memory, ZipG executes tens of thousands of queries from these workloads for raw graph data over half a TB; this leads to an order of magnitude (sometimes as much as 23×) higher throughput than Neo4j and Titan. We get similar gains in distributed settings compared to Titan.


Frictional Behaviour of Bare Foot and Foot Wearing Socks Sliding Against Marble Flooring Tiles


title: Human Pose Estimation using Deep Consensus Voting

abstract: In this paper we consider the problem of human pose estimation from a single still image. We propose a novel approach where each location in the image votes for the position of each keypoint using a convolutional neural net. The voting scheme allows us to utilize information from the whole image, rather than rely on a sparse set of keypoint locations. Using dense, multi-target votes, not only produces good keypoint predictions, but also enables us to compute image-dependent joint keypoint probabilities by looking at consensus voting. This differs from most previous methods where joint probabilities are learned from relative keypoint locations and are independent of the image. We finally combine the keypoints votes and joint probabilities in order to identify the optimal pose configuration. We show our competitive performance on the MPII Human Pose and Leeds Sports Pose datasets.


title: Guiding and confining light in void nanostructure.

abstract: We present a novel waveguide geometry for enhancing and confining light in a nanometer-wide low-index material. Light enhancement and confinement is caused by large discontinuity of the electric field at high-index-contrast interfaces. We show that by use of such a structure the field can be confined in a 50-nm-wide low-index region with a normalized intensity of 20 microm(-2). This intensity is approximately 20 times higher than what can be achieved in SiO2 with conventional rectangular waveguides.


title: Towards a holistic perspective of customer relationship management (CRM) implementation: a case study of the housing and development board, Singapore

abstract: Organizations have increasingly recognized the importance of managing customer relationships, and many organizations are turning to customer relationship management (CRM) to better serve customers and facilitate closer relationships with them. This paper examines the implementation of CRM at the Housing and Development Board (HDB) in Singapore. The CRM architecture (comprising operational CRM, collaborative CRM and analytical CRM) deployed at HDB reflects a holistic approach to CRM implementation that integrates three key perspectives of CRM, namely, the business, technology and customer perspectives. Drawing from the case study, we present a holistic framework for CRM that binds information technologies with business processes for the delivery of high service quality.


title: Language Independent Named Entity Recognition in Indian Languages

abstract: This paper reports about the development of a Named Entity Recognition (NER) system for South and South East Asian languages, particularly for Bengali, Hindi, Telugu, Oriya and Urdu as part of the IJCNLP-08 NER Shared Task 1 . We have


title: Production of Type VI Collagen by Human Macrophages: A New Dimension in Macrophage Functional Heterogeneity

abstract: Macrophages derived from human blood monocytes perform many tasks related to tissue injury and repair. The main effect of macrophages on the extracellular matrix is considered to be destructive in nature, because macrophages secrete metalloproteinases and ingest foreign material as part of the remodeling process that occurs in wound healing and other pathological conditions. However, macrophages also contribute to the extracellular matrix and hence to tissue stabilization both indirectly, by inducing other cells to proliferate and to release matrix components, and directly, by secreting components of the extracellular matrix such as fibronectin and type VIII collagen, as we have recently shown. We now report that monocytes and macrophages express virtually all known collagen and collagen-related mRNAs. Furthermore, macrophages secrete type VI collagen protein abundantly, depending upon their mode of activation, stage of differentiation, and cell density. The primary function of type VI collagen secreted by macrophages appears to be modulation of cell-cell and cell-matrix interactions. We suggest that the production of type VI collagen is a marker for a nondestructive, matrix-conserving macrophage phenotype that could profoundly influence physiological and pathophysiological conditions in vivo.


title: A design methodology of chip-to-chip wireless power transmission system

abstract: A design methodology to transmit power using a chip-to-chip wireless interface is proposed. The proposed power transmission system is based on magnetic coupling, and the power transmission of 5mW/mm2 was verified. The transmission efficiency trade-off with the transmitted power is also discussed.


title: Federation and Revenue Sharing in Cloud Computing Environment

abstract: In Cloud computing, resources such as CPU, RAM, and disk etc. are provided as a service via the Internet. Elasticity is a key feature of Cloud Computing. It aims to enabling a Cloud Service Provider (CSP) to negotiate the possibility to borrow external resources from other CSPs when its own facilities are not able to satisfy a client request. Reciprocally, a CSP may sell some of its unused resources to another CSP in case of under load. Today, Cloud Federation is a key approach considered for Cloud elasticity. In the context of the Easi-Clouds European ITEA 2 research project, we aim to develop Pricing-as-a-Service (PraaS) suited to the Federated Cloud environment. The aim of this paper is twofold. First, we provide a state of the art of the pricing and revenue sharing models in federated environment. We discuss the specifications of Cloud Federation as well as its different drivers and barriers. Three types of pricing strategies (on-demand, spot and reserved) are presented. In the second part, we present the problem of revenue sharing in the federation with some properties we are willing to fulfill. We propose and evaluate our revenue sharing model suited to the Federated environment with numerical analysis via simulation. We compare our approach to the proportional share and the Shapley value method. Finally, we provide and analyze the results of our simulations with our conclusion and perspectives.


title: Assistance strategy for stair ascent with a robotic hip exoskeleton

abstract: In this paper, we propose a novel assistance strategy for stair ascent walking using our robotic hip exoskeleton. Our strategy exploits foot contact event estimated by an inertial measurement unit (IMU) and can detect user intention as well as reflect user preference. In our strategy, a gait cycle is divided into 4 phases and the transitions between the phases are based on events that are unavoidable and can be detected reliably using the sensors available for our exoskeleton. The proposed strategy presents criteria for the initiation and termination of assistance by recognizing user intention, as well as methods to adjust the timing and the amount of assistance corresponding to user preference. When each step starts, the duration of assistance is determined and the torque profile for the whole step is planned ahead. We demonstrate the validity of the proposed strategy for stair ascent and examine its effects on hip joint angle trajectory through experimental results.


title: Negotiated Materialization: Design Approaches Integrating Wood Heterogeneity Through Advanced Robotic Fabrication

abstract: Whilst robots are predictable, repetitive, predefined and constant, natural materials present unpredictable complexity. Over the past few centuries, materials have been standardized to fit industrial processes, in an attempt to defy this unpredictability. Thanks to new advances in sensing technologies and computational design, today we have the opportunity to reintegrate the intrinsic properties of natural materials in their full complexity. What is the potential of a synthesis between the particularity of each specific material element—specific properties and parameters—informing the fabrication process? Digital and Robotic Fabrication are based on the use of flexible machines that open the possibility to mass-customize the production process. Combined with sensors and computational analysis, they allow to work with “soft systems”, both adaptable and continuously evolving, whose dynamism is constantly fed by a flow of information. How can the designer integrate this uncertainty and complexity in the design process? In this paper the authors specifically discuss the management of structural and material tolerance inherent to large scale construction and anisotropic materials, such as wood. A series of projects developed and built at the Institute for Advanced Architecture of Catalonia and the Bartlett School of Architecture are used as case studies to investigate tolerance management in Digital Fabrication with different kinds of wood.


title: A survey on technique for prediction of disease in medical data

abstract: In today's era data mining plays important role for prediction of diseases in medical field. With the growing research on disease predicting system, it has become important to discover hidden patterns and relationships from medical databases. In classical clinical diagnosis, it requires lots of tests which could complicate the disease prediction. Hence the data mining techniques can help medical expertise to take the decision about the disease using computer aided decision support system. In this paper comprehensive survey on various data mining techniques used for disease prediction is presented.


title: Identifying the sentiment styles of YouTube's vloggers

abstract: Vlogs provide a rich public source of data in a novel setting. This paper examined the continuous sentiment styles employed in 27,333 vlogs using a dynamic intra-textual approach to sentiment analysis. Using unsupervised clustering, we identified seven distinct continuous sentiment trajectories characterized by fluctuations of sentiment throughout a vlog's narrative time. We provide a taxonomy of these seven continuous sentiment styles and found that vlogs whose sentiment builds up towards a positive ending are the most prevalent in our sample. Gender was associated with preferences for different continuous sentiment trajectories. This paper discusses the findings with respect to previous work and concludes with an outlook towards possible uses of the corpus, method and findings of this paper for related areas of research.


title: Towards Blending Semantics with an Education Based Ontology Using Protege 5.2.0 A Revisit

abstract: In today's world of globalization, there is a serious need of incorporating semantics in Education Domain which is very significant with an ultimate goal of providing an efficient, adaptive and personalized learning environment. An attempt towards this goal has been made to develop an Education based Ontology with some capability to describe a semantic web based sharable knowledge. So as a contribution, this paper presents a revisit towards amalgamating Semantics in Education. In this direction, an effort has been made to construct an Education based Ontology using Protege 5.2.0, where a hierarchy of classes and subclasses have been defined along with their properties, relations, and instances. Finally, at the end of this paper an implementation is also presented involving query retrieval using DLquery illustrations.


title: Role of Deep LSTM Neural Networks and Wi-Fi Networks in Support of Occupancy Prediction in Smart Buildings

abstract: Knowing how many people occupy a building, and where they are located, is a key component of smart building services. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy. However, relatively simple sensor technology and control algorithms limit the effectiveness of smart building services. In this paper we propose to replace sensor technology with time series models that can predict the number of occupants at a given location and time. We use Wi-Fi data sets readily available in abundance for smart building services and train Auto Regression Integrating Moving Average (ARIMA) models and Long Short-Term Memory (LSTM) time series models. As a use case scenario of smart building services, these models allow forecasting of the number of people at a given time and location in 15, 30 and 60 minutes time intervals at building as well as Access Point (AP) level. For LSTM, we build our models in two ways: a separate model for every time scale, and a combined model for the three time scales. Our experiments show that LSTM combined model reduced the computational resources with respect to the number of neurons by 74.48 % for the AP level, and by 67.13 % for the building level. Further, the root mean square error (RMSE) was reduced by 88.2% - 93.4% for LSTM in comparison to ARIMA for the building levels models and by 80.9% - 87% for the AP level models.


title: Deep Learning for Lip Reading using Audio-Visual Information for Urdu Language

abstract: Human lip-reading is a challenging task. It requires not only knowledge of underlying language but also visual clues to predict spoken words. Experts need certain level of experience and understanding of visual expressions learning to decode spoken words. Now-a-days, with the help of deep learning it is possible to translate lip sequences into meaningful words. The speech recognition in the noisy environments can be increased with the visual information [1]. To demonstrate this, in this project, we have tried to train two different deep-learning models for lip-reading: first one for video sequences using spatiotemporal convolution neural network, Bi-gated recurrent neural network and Connectionist Temporal Classification Loss, and second for audio that inputs the MFCC features to a layer of LSTM cells and output the sequence. We have also collected a small audio-visual dataset to train and test our model. Our target is to integrate our both models to improve the speech recognition in the noisy environment


title: Conspiracy and Information Flow in the Take-Grant Protection Model

abstract: The Take Grant Protection Model is a theoretic model of access control that captures the notion of information flow throughout the modelled system. This paper analyzes the problem of sharing information in the context of paths along which information can flow, and presents the number of actors necessary and sufficient to share information, in this model. The results are applied to information flow in a network to reduce the size of the set of actors who could have participated in the theft.


title: Topological Landscapes: A Terrain Metaphor for Scientific Data

abstract: Scientific visualization and illustration tools are designed to help people understand the structure and complexity of scientific data with images that are as informative and intuitive as possible. In this context the use of metaphors plays an important role since they make complex information easily accessible by using commonly known concepts. In this paper we propose a new metaphor, called "topological landscapes," which facilitates understanding the topological structure of scalar functions. The basic idea is to construct a terrain with the same topology as a given dataset and to display the terrain as an easily understood representation of the actual input data. In this projection from an n-dimensional scalar function to a two-dimensional (2D) model we preserve function values of critical points, the persistence (function span) of topological features, and one possible additional metric property (in our examples volume). By displaying this topologically equivalent landscape together with the original data we harness the natural human proficiency in understanding terrain topography and make complex topological information easily accessible.


title: Metacognitions in proneness towards hallucinations and delusions

abstract: Abstract The objectives of the present study were to examine the degree of co-existence of hallucinations and delusions in the nonclinical population. In addition, we wished to investigate the role of metacognitions in hallucinations and delusions. Finally, we explored the relative roles of positive and negative metacognitive beliefs in proneness to hallucinations and delusions. Three hundred and thirty-one nonclinical participants completed instruments assessing: hallucination-proneness (Launay–Slade Hallucinations Scale; LSHS), delusion-proneness (21-item version of the Peters et al. Delusions Inventory; PDI-21) and metacognitive beliefs (Meta-Cognitions Questionnaire; MCQ). Participants were successively grouped according to their scores on the LSHS and the PDI-21. Results revealed that hallucination-proneness was positively and significantly associated with delusion-proneness. Furthermore, hallucination-prone and delusion-prone participants scored significantly higher on some sub-scales of the MCQ compared to non-prone participants. Finally, multiple regression analysis revealed that positive and negative beliefs were good predictors of proneness towards hallucinations and delusions.


title: Regularized Minimax Conditional Entropy for Crowdsourcing

abstract: There is a rapidly increasing interest in crowdsourcing for data labeling. By crowdsourcing, a large number of labels can be often quickly gathered at low cost. However, the labels provided by the crowdsourcing workers are usually not of high quality. In this paper, we propose a minimax conditional entropy principle to infer ground truth from noisy crowdsourced labels. Under this principle, we derive a unique probabilistic labeling model jointly parameterized by worker ability and item difficulty. We also propose an objective measurement principle, and show that our method is the only method which satisfies this objective measurement principle. We validate our method through a variety of real crowdsourcing datasets with binary, multiclass or ordinal labels.


title: G-CSF for stem cell therapy in acute myocardial infarction: friend or foe?

abstract: Stem cell-based therapy has emerged as a potential therapeutic option for patients with acute myocardial infarction. The ability of granulocyte colony-stimulating factor (G-CSF) to mobilize endogenous stem cells as well as to protect cardiomyocytes at risk via paracrine effects has attracted considerable attention. In the past decade, a number of clinical trials were carried out to study the efficacy of G-CSF in cardiac repair. These trials showed variable outcomes in terms of improved cardiac contractile function and suppressed left ventricular negative remodelling. Critical examinations of these results have raised doubts concerning the effectiveness of G-CSF in modulating functional recovery. However, these cumulative clinical experiences are helpful in the understanding of mechanisms and roles of signalling pathways in regulating homing and engraftment of bone marrow stem cells to the infarcted heart. In this review, we discuss some of the observations that may have influenced the clinical outcomes. Improving strategies that target the critical aspects of G-CSF-driven cardiac therapy may provide a better platform to augment clinical benefits in future trials.


title: The Influence of Culture on Consumer Impulsive Buying Behavior

abstract: Impulse buying generates over $4 billion in annual sales volume in the United States. With the growth of e-commerce and television shopping channels, consumers have easy access to impulse purchasing opportunities, but little is known about this sudden, compelling, hedonically complex purchasing behavior in non-Western cultures. Yet cultural factors moderate many aspects of consumer's impulsive buying behavior, including self-identity, normative influences, the suppression of emotion, and the postponement of instant gratification. From a multi-country survey of consumers in Australia, United States, Hong Kong, Singapore, and Malaysia, our analyses show that both regional level factors (individualism–collectivism) and individual cultural difference factors (independent –interdependent self-concept) systematically influence impulsive purchasing behavior.


title: Learning from Message Pairs for Automatic Email Answering ?

abstract: We consider the problem of learning a mapping from question to answer messages. The training data for this problem consist of pairs of messages that have been received and sent in the past. We formulate the problem setting, discuss appropriate performance metrics, develop a solution and describe two baseline methods for comparison. We present a case study based on emails received and answered by the service center of a large online store.


title: An analysis of review content and reviewer variables that contribute to review helpfulness

abstract: Abstract Review helpfulness is attracting increasing attention of practitioners and academics. It helps in reducing risks and uncertainty faced by users in online shopping. This study examines uninvestigated variables by looking at not only the review characteristics but also important indicators of reviewers. Several significant review content and two reviewer variables are proposed and an effective review helpfulness prediction model is built using stochastic gradient boosting learning method. This study derived a mechanism to extract novel review content variables from review text. Six popular machine learning models and three real-life Amazon review data sets are used for analysis. Our results are robust to several product categories and along three Amazon review data sets. The results show that review content variables deliver the best performance as compared to the reviewer and state-of-the-art baseline as a standalone model. This study finds that reviewer helpfulness per day and syllables in review text strongly relates to review helpfulness. Moreover, the number of space, aux verb, drives words in review text and productivity score of a reviewer are also effective predictors of review helpfulness. The findings will help customers to write better reviews, help retailers to manage their websites intelligently and aid customers in their product purchasing decisions.


title: Efficient power harvesting from the mobile phone audio jack for mHealth peripherals

abstract: A tremendous opportunity currently exists to enable practical and portable mHealth tools by taking advantage of the ubiquity of mobile phones across the world. To this end, we describe the design of a circuit to efficiently harvest energy from the standard audio jack of a mobile phone to power mHealth peripherals, lowering cost, reducing size, and improving the practicality of portable medical devices. This design not only obviates the need to charge or change an extra battery but also is universally compatible with all phones. The audio output channels of several popular phones were characterized in order to determine the range of design parameters. Using these data, different power harvesting topologies are presented, simulated, and compared. A PCB implementation was used to measure and confirm the performance. Compared with prior art, which have achieved 21% and 47% efficiency, this design is able to achieve greater than 77% efficiency by using a tunable impedance matching network.


title: Latent fault detection in large scale services

abstract: Unexpected machine failures, with their resulting service outages and data loss, pose challenges to datacenter management. Existing failure detection techniques rely on domain knowledge, precious (often unavailable) training data, textual console logs, or intrusive service modifications.


title: Exemplar similarity and rule application

abstract: We report four experiments examining effects of instance similarity on the application of simple explicit rules. We found effects of similarity to illustrative exemplars in error patterns and reaction times. These effects arose even though participants were given perfectly predictive rules, the similarity manipulation depended entirely on rule-irrelevant features, and attention to exemplar similarity was detrimental to task performance. Comparison of results across studies suggests that the effects are mandatory, non-strategic and not subject to conscious control, and as a result, should be pervasive throughout categorization.


title: Learning Structured Embeddings of Knowledge Bases

abstract: Many Knowledge Bases (KBs) are now readily available and encompass colossal quantities of information thanks to either a long-term funding effort (e.g. WordNet, OpenCyc) or a collaborative process (e.g. Freebase, DBpedia). However, each of them is based on a different rigid symbolic framework which makes it hard to use their data in other systems. It is unfortunate because such rich structured knowledge might lead to a huge leap forward in many other areas of AI like natural language processing (word-sense disambiguation, natural language understanding, ...), vision (scene classification, image semantic annotation, ...) or collaborative filtering. In this paper, we present a learning process based on an innovative neural network architecture designed to embed any of these symbolic representations into a more flexible continuous vector space in which the original knowledge is kept and enhanced. These learnt embeddings would allow data from any KB to be easily used in recent machine learning methods for prediction and information retrieval. We illustrate our method on WordNet and Freebase and also present a way to adapt it to knowledge extraction from raw text.


title: Online inference for time-varying temporal dependency discovery from time series

abstract: Large-scale time series data are prevalent across diverse application domains including system management, biomedical informatics, social networks, finance, etc. Temporal dependency discovery performs an essential part to identify the hidden interactions among the observed time series and helps to gain more insight into the behavior of the applications. However, the time-varying sparsity of the interactions among time series often poses a big challenge to temporal dependency discovery in practice. This paper formulates the temporal dependency problem with a novel Bayesian model allowing for both the sparsity and evolution of the hidden interactions among the observed time series. Taking advantage of the Bayesian modeling, an online inference method is proposed for time-varying temporal dependency discovery. Extensive empirical studies on both the synthetic and real application time series data are conducted to demonstrate the effectiveness and the efficiency of the proposed method.


title: Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization

abstract: Web usage mining, possibly used in conjunction with standard approaches to personalization such as collaborative filtering, can help address some of the shortcomings of these techniques, including reliance on subjective user ratings, lack of scalability, and poor performance in the face of high-dimensional and sparse data. However, the discovery of patterns from usage data by itself is not sufficient for performing the personalization tasks. The critical step is the effective derivation of good quality and useful (i.e., actionable) “aggregate usage profiles” from these patterns. In this paper we present and experimentally evaluate two techniques, based on clustering of user transactions and clustering of pageviews, in order to discover overlapping aggregate profiles that can be effectively used by recommender systems for real-time Web personalization. We evaluate these techniques both in terms of the quality of the individual profiles generated, as well as in the context of providing recommendations as an integrated part of a personalization engine. In particular, our results indicate that using the generated aggregate profiles, we can achieve effective personalization at early stages of users' visits to a site, based only on anonymous clickstream data and without the benefit of explicit input by these users or deeper knowledge about them.


title: A Bit Level Representation for Time Series Data Mining with Shape Based Similarity

abstract: Clipping is the process of transforming a real valued series into a sequence of bits representing whether each data is above or below the average. In this paper, we argue that clipping is a useful and flexible transformation for the exploratory analysis of large time dependent data sets. We demonstrate how time series stored as bits can be very efficiently compressed and manipulated and that, under some assumptions, the discriminatory power with clipped series is asymptotically equivalent to that achieved with the raw data. Unlike other transformations, clipped series can be compared directly to the raw data series. We show that this means we can form a tight lower bounding metric for Euclidean and Dynamic Time Warping distance and hence efficiently query by content. Clipped data can be used in conjunction with a host of algorithms and statistical tests that naturally follow from the binary nature of the data. A series of experiments illustrate how clipped series can be used in increasingly complex ways to achieve better results than other popular representations. The usefulness of the proposed representation is demonstrated by the fact that the results with clipped data are consistently better than those achieved with a Wavelet or Discrete Fourier Transformation at the same compression ratio for both clustering and query by content. The flexibility of the representation is shown by the fact that we can take advantage of a variable Run Length Encoding of clipped series to define an approximation of the Kolmogorov complexity and hence perform Kolmogorov based clustering.


title: PID position control for 2-DOF robotic finger

abstract: This paper presents the evaluation of motor performance for different loads, PID tuning and evaluation of the new PID values for a two degree of freedom (DOF) robotic finger. The prototype 2-DOF finger which has been developed in a separate study has two joints and two links, and is made of prospective plastic material. The control system comprises of two DC-micromotors, magnetic encoders, a motion controller unit and a PC. Initially, an experiment to observe the effect of different loads to the motor is implemented. The observation is then used for the PID controller tuning to improve the position control performance in terms of the overshoot, rise time, peak time, settling time and the error. Next, the evaluation of the position control with the new PID parameters is implemented to the robotic finger for continuous motions of the motor at three different positions. Motion manager software provided by the Faulhaber Company is used to obtain the encoder measurement for the motor position and to execute command to run the motor. From the experimental data, it can be observed that with the new controller parameters the performance of motor has shown improvement for both robot joints. This proves that the efficiency of motor control is an important element for developing robotic finger for performing task at high precision standard.


title: 2.5D and 3D technology challenges and test vehicle demonstrations

abstract: Three-dimensional (3D) chip integration with through-silicon-vias (TSV's) can enable system benefits of enhanced performance, power efficiency, and cost reduction leveraging micro-architecture designs such as 2.5D silicon packages and 3D die stacks. 2.5D silicon packages and 3D die stacks structures integrated in modules each have unique technical challenges but each can provide system benefits including lower latency and higher bandwidth compared to traditional packaging solutions. Additional system benefits using 2.5D or 3D integration can include product miniaturization or increased function in the same size product. Leveraging proper design and micro-architecture for a system application, 3D technology can aide chip manufacturability for lower costs, sub-component heterogeneous integration, modular design and sub-component design reuse, which can reduce development expense and decrease time to market. 2.5D and 3D technology can reduce interconnection length between circuits leading to lower power consumption and lower latency as well as increase the number of interconnections which supports increased bandwidth to traditional 2D off chip interconnection. Appropriate design ground rules, clocking, and electrical models should match well defined technology attributes such as TSV's and silicon to silicon interconnection electrical parametrics. In addition a wafer test methodology for known good die (KGD) and high yield assembly integration approach are important to obtain integrated 2.5D and 3D modules. For complex 3D integration, proper consideration for module or integrated die stacked with TSV's and Si to Si interconnection may require redundancy and an integral repair methodology. 2.5D and 3D technology challenges may include an increase in the power delivery and cooling requirements to meet the increased circuit density and power density of these structures. For small, low power applications such as mobile devices, 2.5D and 3D technology can provide substantial benefit through both performance benefit and power savings and lead to longer battery life for the same function. For some high performance and high power applications, the 2.5D approach simplifies heterogeneous die integration without requiring leading to increases power density and heat removal cooling density. Whereas some high performance and high power applications using 3D technology may require extensive planning for power delivery with localized power regulation and specialized cooling approaches to avoid excessive in die stack temperatures while taking advantage of performance gains that these short links between heterogeneous die can provide. 3D die stacks using multi-core processors and wide I/O DRAM, eDRAM, SRAM or cache stacks can provide high bandwidth, performance improvements with lower latency. In addition to the power delivery and thermal challenges of 2.5D and 3D described above, there are 3D fabrication and industry compatibility challenges. Technology challenges include wafer integration and finishing with TSV's, test for known-good-die (KGD), assembly and module integration. Infrastructure compatibility and use of newly evolving industry standards such as Semi-3D standards for wafer handling and JEDEC standards for wide I/O memory to name two examples. Standards for wafer shipping are underway and other 3D compatibility standards are being defined over time. This research paper describes key challenges to enable systems using 2.5D and 3D technology. The paper also highlights progress and results for 2.5D and 3D hardware demonstrations and gives an outlook on future demonstrations.


title: Office procedure as practical action: models of work and system design

abstract: The design of office technology relies upon underlying conceptions of human organization and action. The goal of building office information systems requires a representation of office work and its relevant objects. The concern of this paper is that although system designers recognize the centrality of procedural tasks in the office, they tend to ignore the actual work involved in accomplishing those tasks. A perspicuous instance of work in an accounting office is used to recommend a new line of research into the practical problems of office work, and to suggest preliminary implications of that research for office systems design.


title: Migraine pathophysiology: Anatomy of the trigeminovascular pathway and associated neurological symptoms, cortical spreading depression, sensitization, and modulation of pain

abstract: Scientific evidence supports the notion that migraine pathophysiology involves inherited alteration of brain excitability, intracranial arterial dilatation, recurrent activation, and sensitization of the trigeminovascular pathway, and consequential structural and functional changes in genetically susceptible individuals. Evidence of altered brain excitability emerged from clinical and preclinical investigation of sensory auras, ictal and interictal hypersensitivity to visual, auditory, and olfactory stimulation, and reduced activation of descending inhibitory pain pathways. Data supporting the activation and sensitization of the trigeminovascular system include the progressive development of cephalic and whole-body cutaneous allodynia during a migraine attack. In addition, structural and functional alterations include the presence of subcortical white mater lesions, thickening of cortical areas involved in processing sensory information, and cortical neuroplastic changes induced by cortical spreading depression. Here, we review recent anatomical data on the trigeminovascular pathway and its activation by cortical spreading depression, a novel understanding of the neural substrate of migraine-type photophobia, and modulation of the trigeminovascular pathway by the brainstem, hypothalamus and cortex.


title: A modified Covariance Matrix Adaptation Evolution Strategy with adaptive penalty function and restart for constrained optimization

abstract: In the last decades, a number of novel meta-heuristics and hybrid algorithms have been proposed to solve a great variety of optimization problems. Among these, constrained optimization problems are considered of particular interest in applications from many different domains. The presence of multiple constraints can make optimization problems particularly hard to solve, thus imposing the use of specific techniques to handle fitness landscapes which generally show complex properties. In this paper, we introduce a modified Covariance Matrix Adaptation Evolution Strategy (CMA-ES) specifically designed for solving constrained optimization problems. The proposed method makes use of the restart mechanism typical of most modern variants of CMA-ES, and handles constraints by means of an adaptive penalty function. This novel CMA-ES scheme presents competitive results on a broad set of benchmark functions and engineering problems, outperforming most state-of-the-art algorithms as for both efficiency and constraint handling.


title: NativeGuard: protecting android applications from third-party native libraries

abstract: Android applications often include third-party libraries written in native code. However, current native components are not well managed by Android's security architecture. We present NativeGuard, a security framework that isolates native libraries from other components in Android applications. Leveraging the process-based protection in Android, NativeGuard isolates native libraries of an Android application into a second application where unnecessary privileges are eliminated. NativeGuard requires neither modifications to Android nor access to the source code of an application. It addresses multiple technical issues to support various interfaces that Android provides to the native world. Experimental results demonstrate that our framework works well with a set of real-world applications, and incurs only modest overhead on benchmark programs.


title: Next Generation Cloud Computing: New Trends and Research Directions

abstract: The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems. Distributed cloud infrastructure will make use of the network edge in the future.Two tier applications will be replaced by new multi-tier cloud architectures.Next generation cloud computing impacts both societal and scientific avenues.A new marketplace will need to be developed for resources at the network edge.Security and sustainability are key to architecting future cloud systems.


title: High-Risk Sex Offenders May Not Be High Risk Forever

abstract: This study examined the extent to which sexual offenders present an enduring risk for sexual recidivism over a 20-year follow-up period. Using an aggregated sample of 7,740 sexual offenders from 21 samples, the yearly recidivism rates were calculated using survival analysis. Overall, the risk of sexual recidivism was highest during the first few years after release, and decreased substantially the longer individuals remained sex offense–free in the community. This pattern was particularly strong for the high-risk sexual offenders (defined by Static-99R scores). Whereas the 5-year sexual recidivism rate for high-risk sex offenders was 22% from the time of release, this rate decreased to 4.2% for the offenders in the same static risk category who remained offense-free in the community for 10 years. The recidivism rates of the low-risk offenders were consistently low (1%-5%) for all time periods. The results suggest that offense history is a valid, but time-dependent, indicator of the propensity to sexually ...