title: Data Processing And Calibration Of A Cross-Pattern Stripe Projector

abstract: Dense surface acquisition is one of the most challenging tasks for optical 3-D measurement systems in applications such as inspection of industrial parts, reverse engineering, digitization of virtual reality models and robot guidance. In order to achieve high accuracy we need good hardware equipment, as well as sophisticated data processing methods. Since measurements should be feasible under real world conditions, accuracy, reliability and consistency are a major issue. Based on the experiences with a previous system, a detailed analysis of the performance was carried out, leading to a new hardware setup. On the software side, we improved our calibration procedure and replaced the phase shift technique previously used by a new processing scheme which we call line shift processing. This paper describes our new approach. Results are presented and compared to results derived from the previous system.


title: A Personalized Service for Scheduling Express Delivery Using Courier Trajectories

abstract: With the increasing demand for express delivery, a courier needs to deliver many tasks in one day and it's necessary to deliver punctually as the customers expect. At the same time, they want to schedule the delivery tasks to minimize the total time of a courier's one-day delivery, considering the total travel time. However, most of scheduling researches on express delivery focus on inter-city transportation, and they are not suitable for the express delivery to customers in the “last mile”. To solve the issue above, this paper proposes a personalized service for scheduling express delivery, which not only satisfies all the customers' appointment time but also makes the total time minimized. In this service, personalized and accurate travel time estimation is important to guarantee delivery punctuality when delivering shipments. Therefore, the personalized scheduling service is designed to consist of two basic services: (1) personalized travel time estimation service for any path in express delivery using courier trajectories, (2) an express delivery scheduling service considering multiple factors, including customers' appointments, one-day delivery costs, etc., which is based on the accurate travel time estimation provided by the first service. We evaluate our proposed service based on extensive experiments, using GPS trajectories generated by more than 1000 couriers over a period of two months in Beijing. The results demonstrate the effectiveness and efficiency of our method.


Origami Applications in Structural Engineering: A Look at Temporary Shelters


title: Atraumatic restorative treatment and minimal intervention dentistry

abstract: Too many people worldwide suffer from the consequences of untreated dentine carious lesions. This finding reflects the inability of the currently used traditional mode of treatments to manage such lesions. A change is needed. Dental training institutions should depart from the traditional 'drill and fill' treatments and embrace the holistic oral healthcare approach that is minimal intervention dentistry (MID) and includes within it minimally invasive operative skills. Dental caries is, after all, a preventable disease. The atraumatic restorative treatment (ART) concept is an example of MID. ART consists of a preventive (ART sealant) and a restorative (ART restoration) component. ART sealants using high-viscosity glass-ionomer (HVGIC) have a very high dentine carious lesion preventive effect. The survival rate of these sealants is not significantly different from that of sealants produced with resin. The survival rate of ART/HVGIC restorations matches those of amalgam and resin composite in single- and multiple-surface cavities in primary teeth and in single-surface cavities in permanent teeth. The principles of carious tissue removal within a cavity recommended by the International Caries Consensus Collaboration are in line with those of treating a cavity using ART. Owing to its good performance and the low levels of discomfort/pain and dental anxiety associated with it, ART and/or other evidence-based atraumatic care procedures should be the first treatment for a primary dentine carious lesion. Only if the use of ART is not indicated should other more invasive and less-atraumatic care procedures be used in both primary and permanent dentitions.


title: Projective dynamics: fusing constraint projections for fast simulation

abstract: We present a new method for implicit time integration of physical systems. Our approach builds a bridge between nodal Finite Element methods and Position Based Dynamics, leading to a simple, efficient, robust, yet accurate solver that supports many different types of constraints. We propose specially designed energy potentials that can be solved efficiently using an alternating optimization approach. Inspired by continuum mechanics, we derive a set of continuum-based potentials that can be efficiently incorporated within our solver. We demonstrate the generality and robustness of our approach in many different applications ranging from the simulation of solids, cloths, and shells, to example-based simulation. Comparisons to Newton-based and Position Based Dynamics solvers highlight the benefits of our formulation.


title: Assembled Dual-Band Broadband Quadrifilar Helix Antennas With Compact Power Divider Networks for CNSS Application

abstract: Assembled dual-band quadrifilar helix antennas (QHAs) are proposed for applications in Compass Navigation Satellite System (CNSS) of China. The assembled CNSS antennas proposed are designed specifically to meet the requirements of the system: Duplex communication service on two frequency bands (1.615 GHz and 2.492 GHz); Broad beamwitdh on radiation pattern and broad bandwidth for circularly polarization (CP) applications. In this paper, the structure of assembled CNSS QHAs is demonstrated first, which comprises of emitting and receiving QHAs with novel compact power divider networks. Then the method to control the radiation pattern of QHAs is analyzed. Moreover, the design principle of the compact power divider network to achieve broadband application is presented. In the end, the antenna was fabricated and measured. Experimental results show that the proposed assembled CNSS dual-band QHAs exhibit duplex operation, broad 3 dB beamwidth (>;180 deg), and broad 10 dB return loss bandwidth (emitting antenna 39%; receiving antenna 28%). In addition, the axial ratio is less than 1.5 on two frequency bands of the system (Uplink: 1615.68±7 MHz; Downlink: 2491.75±15 MHz).


title: Leveraging Personal Devices for Stronger Password Authentication from Untrusted Computers

abstract: Internet authentication for popular end-user transactions, such as online banking and e-commerce, continues to be dominated by passwords entered through end-user PCs. Most users continue to prefer (typically untrusted) PCs over smaller personal devices for actual transactions, due to usability features related to keyboard and screen size. However, most such transactions and their underlying protocols are vulnerable to attacks including keylogging, phishing and pharming. We propose Mobile Password Authentication (MP-Auth) to counter such attacks, which cryptographically separates a user's long-term secret input from the client PC, and offers transaction integrity. The PC continues to be used for most of the interaction but has access only to temporary secrets, while the user's long-term secret is input through an independent personal device, e.g., a cellphone which makes it available to the PC only after encryption under the intended far-end recipient's public key. MP-Auth expects users to input passwords only to a personal device, and be vigilant while confirming transactions from the device. To facilitate a comparison to MP-Auth, we also provide a comprehensive survey of web authentication techniques that use an additional factor of authentication; this survey may be of independent interest.


title: Mobile Assisted Language Learning: A Literature Review

abstract: Mobile assisted language learning (MALL) is a subarea of the growing field of mobile learning (mLearning) research which increasingly attracts the attention of scholars. This study provides a syste ...


A REAL-TIME GENERIC TRACKER USING CONVOLUTIONAL NEURAL NETWORKS


title: Physiological Computing: Interfacing with the Human Nervous System

abstract: This chapter describes the physiological computing paradigm where electrophysiological changes from the human nervous system are used to interface with a computer system in real time. Physiological computing systems are categorized into five categories: muscle interfaces, brain-computer interfaces, biofeedback, biocybernetic adaptation and ambulatory monitoring. The differences and similarities of each system are described. The chapter also discusses a number of fundamental issues for the design of physiological computing system, these include: the inference between physiology and behaviour, how the system represents behaviour, the concept of the biocybernetic control loop and ethical issues.


title: Solving two-stage robust optimization problems using a column-and-constraint generation method

abstract: Abstract In this paper, we present a column-and-constraint generation algorithm to solve two-stage robust optimization problems. Compared with existing Benders-style cutting plane methods, the column-and-constraint generation algorithm is a general procedure with a unified approach to deal with optimality and feasibility. A computational study on a two-stage robust location-transportation problem shows that it performs an order of magnitude faster.


title: Anti-diabetic and antioxidant effect of cinnamon in poorly controlled type-2 diabetic Iraqi patients: A randomized, placebo-controlled clinical trial

abstract: AIM ::: To determine the effect of cinnamon on fasting blood glucose, hemoglobin (Hb) A1c, and oxidative stress markers in poorly controlled type 2 diabetes. ::: ::: ::: PATIENTS AND METHODS ::: A total of 25 type 2 diabetic patients of both sexes, aged 49.1 ± 6.0, treated only with hypoglycemic agent sulfonylurea (glibenclamide) were randomly assigned to receive either 1 g of cinnamon or placebo daily for 12 weeks. ::: ::: ::: RESULTS ::: A highly significant (P ≤ 0.001) reduction (10.12%) of fasting blood glucose level after 6 and 12 weeks of treatment 10.12% and 17.4%, respectively, compared to baseline value and to placebo group at corresponding duration. Meanwhile, the value of glycosylated Hb reduced in cinnamon treated group by (2.625%) and (8.25%) after 6 and 12 weeks, respectively, although this reduction was non-significant compared to baseline value. Concerning the oxidative stress markers, the level of serum glutathione showed highly significant (P ≤ 0.001) elevation after 12 weeks as compared to baseline value and placebo group at corresponding duration, malondialdehyde serum level decreased after treatment of diabetic patients with cinnamon resulted in highly significant (P ≤ 0.001) reduction after 6 and 12 weeks compared to placebo group, but when compared to baseline value, there is a (15%) reduction only after 12 weeks of treatment which was considered highly significant (P ≤ 0.001) change, Finally, administration of cinnamon to diabetic patients for 12 weeks resulted in significant (P ≤ 0.05) elevation of superoxide dismutase level. ::: ::: ::: CONCLUSION ::: Intake of 1 g of cinnamon for 12 weeks reduces fasting blood glucose and glycosylated Hb among poorly controlled type 2 diabetes patients, as well as, there is improvement in the oxidative stress markers, indicating the beneficial effect of adjuvant cinnamon as anti-diabetic and antioxidant along with conventional medications to treat poorly controlled type 2 diabetes mellitus.


title: Problematic Smartphone Use, Deep and Surface Approaches to Learning, and Social Media Use in Lectures †

abstract: Several studies have shown that problematic smartphone use (PSU) is related to detrimental outcomes, such as worse psychological well-being, higher cognitive distraction, and poorer academic outcomes. In addition, many studies have shown that PSU is strongly related to social media use. Despite this, the relationships between PSU, as well as the frequency of social media use in lectures, and different approaches to learning have not been previously studied. In our study, we hypothesized that both PSU and the frequency of social media use in lectures are negatively correlated with a deep approach to learning (defined as learning for understanding) and positively correlated with a surface approach to learning (defined as superficial learning). The study participants were 415 Estonian university students aged 19–46 years (78.8% females; age M = 23.37, SD = 4.19); the effective sample comprised 405 participants aged 19–46 years (79.0% females; age M = 23.33, SD = 4.21). In addition to basic socio-demographics, participants were asked about the frequency of their social media use in lectures, and they filled out the Estonian Smartphone Addiction Proneness Scale and the Estonian Revised Study Process Questionnaire. Bivariate correlation analysis showed that PSU and the frequency of social media use in lectures were negatively correlated with a deep approach to learning and positively correlated with a surface approach to learning. Mediation analysis showed that social media use in lectures completely mediates the relationship between PSU and approaches to learning. These results indicate that the frequency of social media use in lectures might explain the relationships between poorer academic outcomes and PSU.


title: On the definition and the computation of the basic reproduction ratio R 0 in models for infectious diseases in heterogeneous populations

abstract: The expected number of secondary cases produced by a typical infected individual during its entire period of infectiousness in a completely susceptible population is mathematically defined as the dominant eigenvalue of a positive linear operator. It is shown that in certain special cases one can easily compute or estimate this eigenvalue. Several examples involving various structuring variables like age, sexual disposition and activity are presented.


title: Microwave Sensors for Breast Cancer Detection

abstract: Breast cancer is the leading cause of death among females, early diagnostic methods with suitable treatments improve the 5-year survival rates significantly. Microwave breast imaging has been reported as the most potential to become the alternative or additional tool to the current gold standard X-ray mammography for detecting breast cancer. The microwave breast image quality is affected by the microwave sensor, sensor array, the number of sensors in the array and the size of the sensor. In fact, microwave sensor array and sensor play an important role in the microwave breast imaging system. Numerous microwave biosensors have been developed for biomedical applications, with particular focus on breast tumor detection. Compared to the conventional medical imaging and biosensor techniques, these microwave sensors not only enable better cancer detection and improve the image resolution, but also provide attractive features such as label-free detection. This paper aims to provide an overview of recent important achievements in microwave sensors for biomedical imaging applications, with particular focus on breast cancer detection. The electric properties of biological tissues at microwave spectrum, microwave imaging approaches, microwave biosensors, current challenges and future works are also discussed in the manuscript.


title: Pathways of sexual desire.

abstract: INTRODUCTION ::: Sexual desire is controlled by brain systems involved in sexual excitation and inhibition. Hypoactive sexual desire disorder (HSDD) may result from hypofunctional excitation, hyperfunctional inhibition, or some mix of the two. ::: ::: ::: AIM ::: This study aimed to identify neurochemical and neuroanatomical systems involved in sexual excitation and inhibition, their role during normal, and hypoactive sexual expressions. ::: ::: ::: METHODS ::: A comprehensive review of the human and animal literature is made, and a theory surrounding the ways that HSDD can be manifested and treated is presented. ::: ::: ::: MAIN OUTCOME MEASURES ::: Drug effects and neural systems derived largely from rat studies that are involved in the stimulation of sexual desire (excitatory system) vs. the stimulation of sexual reward, sedation, and satiety (inhibitory system). ::: ::: ::: RESULTS ::: Brain dopamine systems (incertohypothalamic and mesolimbic) that link the hypothalamus and limbic system appear to form the core of the excitatory system. This system also includes melanocortins, oxytocin, and norepinephrine. Brain opioid, endocannabinoid, and serotonin systems are activated during periods of sexual inhibition, and blunt the ability of excitatory systems to be activated. ::: ::: ::: CONCLUSIONS ::: Drugs that stimulate the activation of hypothalamic dopamine or that blunt endocannabinoid or serotonin release and/or postsynaptic binding may be effective in stimulating sexual desire in animals and humans. The characterization of how those drugs work will help generate a rational approach to drug development in the treatment of HSDD.


title: From semantics to syntax and back again : Argument structure in the third year of life

abstract: An essential part of the human capacity for language is the ability to link conceptual or semantic representations with syntactic representations. On the basis of data from spontaneous production, Tomasello (2000) suggested that young children acquire such links on a verb-by-verb basis, with little in the way of a general understanding of linguistic argument structure. Here, we suggest that a receptive understanding of argument structure—including principles linking syntax and conceptual/semantic structure—appears earlier. In a forced-choice pointing task we have shown that toddlers in the third year of life can map a single scene (involving a novel causative action paired with a novel verb) onto two distinct syntactic frames (transitive and intransitive). This suggests that even before toddlers begin generalizing argument structure in their own speech, they have some representation of conceptual/semantic categories, syntactic categories, and a system that links the two.


title: Joint Computation Offloading and Interference Management in Wireless Cellular Networks with Mobile Edge Computing

abstract: Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with MEC. In this integrated framework, we formulate the computation offloading decision, physical resource block (PRB) allocation, and MEC computation resource allocation as optimization problems. The MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using the graph coloring method. The outcomes of the offloading decision and PRB allocation are then used to distribute the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters.


title: Towards a New Image-Based Spectrogram Segmentation Speech Coder Optimised for Intelligibility

abstract: Speech intelligibility is the very essence of communications. When high noise can degrade a speech signal to the threshold of intelligibility, for example in mobile and military applications, introducing further degradation by a speech coder could prove critical. This paper investigates concepts towards a new speech coder that draws upon the field of image processing in a new multimedia approach. The coder is based on a spectrogram segmentation image processing procedure. The design criterion is for minimal intelligibility loss in high noise, as opposed to the conventional quality criterion, and the bit rate must be reasonable. First phase intelligibility listening test results assessing its potential alongside six standard coders are reported. Experimental results show the robustness of the LD-CELP coder, and the potential of the new coder with particularly good results in car noise conditions below -4.0dB.


title: Activity theory: implications for human-computer interaction

abstract: Recently interest has grown in applying activity theory, the leading theoretical approach in Russian psychology, to issues of human-computer interaction. This chapter analyzes why experts in the field are looking for an alternative to the currently dominant cognitive approach. The basic principles of activity theory are presented and their implications for human-computer interaction are discussed. The chapter concludes with an outline of the potential impact of activity theory on studies and design of computer use in real-life settings.


title: Going Spear Phishing: Exploring Embedded Training and Awareness

abstract: To explore the effectiveness of embedded training, researchers conducted a large-scale experiment that tracked workers' reactions to a series of carefully crafted spear phishing emails and a variety of immediate training and awareness activities. Based on behavioral science findings, the experiment included four different training conditions, each of which used a different type of message framing. The results from three trials showed that framing had no significant effect on the likelihood that a participant would click a subsequent spear phishing email and that many participants either clicked all links or none regardless of whether they received training. The study was unable to determine whether the embedded training materials created framing changes on susceptibility to spear phishing attacks because employees failed to read the training materials.


title: Conceptualizations of the Cataloging Object: A Critique on Current Perceptions of FRBR Group 1 Entities

abstract: Libraries face a double challenge in the digital age: both the describing framework and the describing object are under change. Functional Requirements for Bibliographic Records (FRBR) attempts to generate a coherent theory and yield a new Paradigm of cataloging. This study deploys current conceptualizations of the FRBR Group 1 entities within the FRBR models family with a view to semantic interoperability. FRBR cannot be considered as simple metadata describing a specific resource but more like some kind of knowledge related to the resource. This study reveals that there are different perspectives of what is introduced by FRBR as the cataloging object in the context of various interpretations of the model, namely Resource Description and Access (RDA), FRBRization projects, and FRBROO.


title: Experimental performance evaluation of LoRaWAN: A case study in Bangkok

abstract: The Internet of Things (IoT) is a vision which real-world objects are part of the internet. Every object is uniquely identified, and accessible to the network. There are various types of communication protocol for connect the device to the Internet. One of them is a Low Power Wide Area Network (LPWAN) which is a novel technology use to implement IoT applications. There are many platforms of LPWAN such as NB-IoT, LoRaWAN. In this paper, the experimental performance evaluation of LoRaWAN over a real environment in Bangkok, Thailand is presented. From these experimental results, the communication ranges in both an outdoor and an indoor environment are limited. Hence, the IoT application with LoRaWAN technology can be reliable in limited of communication ranges.


title: Comparison of naming schema in ICN

abstract: Information-Centric Networking (ICN) treats content as a first-class entity — each content has a unique identity and ICN routers forward traffic based on content identity rather than the locations of the content. This provides benefits like dynamic request routing, caching and mobility support. The choice of naming schema (flat vs. hierarchical) is a fundamental design choice in ICN which determines the functional separation between the network layer and the application layer. With hierarchical names, the network layer is cognizant of the semantics of hierarchical names. Name space management is also part of network layer. ICN architectures using flat names leave these to the application layer. The naming schema affects the performance and scalability of the network in terms of forwarding efficiency, routing table size and name space size. This paper provides both qualitative and quantitative comparison on the two naming schemas using these metrics, noting that they are interdependent. We seek to understand which naming schema would be better for a high-performance, scalable ICN architecture.


title: Underwater Wireless Sensor Networks: A New Challenge for Topology Control–Based Systems

abstract: Underwater wireless sensor networks (UWSNs) will pave the way for a new era of underwater monitoring and actuation applications. The envisioned landscape of UWSN applications will help us learn more about our oceans, as well as about what lies beneath them. They are expected to change the current reality where no more than 5% of the volume of the oceans has been observed by humans. However, to enable large deployments of UWSNs, networking solutions toward efficient and reliable underwater data collection need to be investigated and proposed. In this context, the use of topology control algorithms for a suitable, autonomous, and on-the-fly organization of the UWSN topology might mitigate the undesired effects of underwater wireless communications and consequently improve the performance of networking services and protocols designed for UWSNs. This article presents and discusses the intrinsic properties, potentials, and current research challenges of topology control in underwater sensor networks. We propose to classify topology control algorithms based on the principal methodology used to change the network topology. They can be categorized in three major groups: power control, wireless interface mode management, and mobility assisted–based techniques. Using the proposed classification, we survey the current state of the art and present an in-depth discussion of topology control solutions designed for UWSNs.


title: A life history perspective on skin cancer and the evolution of skin pigmentation

abstract: The ancestral state of human skin pigmentation evolved in response to high ultraviolet radiation (UVR) stress. Some argue that pigmentation evolved to limit folate photolysis, therein limiting neural tube defects. Pigmentation also protects against sunburn which decreases the efficiency of sweating and potentiates skin infection. Pigmentation increases the efficacy of skin as a barrier to infection. Skin cancer has been rejected or minimized as a selective pressure because it is believed to have little or no effect on mortality during reproductive years. This argument ignores evidence of human longevity as a derived life history trait and the adaptive value of investment in offspring and kin, particularly during the post-reproductive lifespan. Opponents argue that lifespan in prehistoric hunter-gatherers was too short to be relevant to the evolution of skin pigmentation. This argument is flawed in that it relies on estimates of longevity at birth rather than adolescence. When appropriate estimates are used, it is clear that human longevity has a deep evolutionary history. We use a life history perspective to demonstrate the value of skin pigmentation as an adaptation to skin cancer with the following points: UVR exposure increases dysregulation of gene expression in skin cells leading to immortal cell lines; cutaneous malignant melanoma (CMM) affects individuals throughout reproductive years; and lifespan was longer than has previously been acknowledged, providing the opportunity for kin selection. This hypothesis is not at odds with the folate or barrier hypotheses. We stress that the evolution of skin pigmentation is complex and is an ongoing process.


title: Multimodal Tactile Sensor

abstract: We have developed a finger-shaped sensor array (BioTac®) that provides simultaneous information about contact forces, microvibrations and thermal fluxes, mimicking the full cutaneous sensory capabilities of the human finger. For many tasks, such as identifying objects or maintaining stable grasp, these sensory modalities are synergistic. For example, information about the material composition of an object can be inferred from the rate of heat transfer from a heated finger to the object, but only if the location and force of contact are well controlled. In this chapter we introduce the three sensing modalities of our sensor and consider how they can be used synergistically. Tactile sensing and signal processing is necessary for human dexterity and is likely to be required in mechatronic systems such as robotic and prosthetic limbs if they are to achieve similar dexterity.


title: Articulated pose estimation with tiny synthetic videos

abstract: We address the task of articulated pose estimation from video sequences. We consider an interactive setting where the initial pose is annotated in the first frame. Our system synthesizes a large number of hypothetical scenes with different poses and camera positions by applying geometric deformations to the first frame. We use these synthetic images to generate a custom labeled training set for the video in question. This training data is then used to learn a regressor (for future frames) that predicts joint locations from image data. Notably, our training set is so accurate that nearest-neighbor (NN) matching on low-resolution pixel features works well. As such, we name our underlying representation “tiny synthetic videos”. We present quantitative results the Friends benchmark dataset that suggests our simple approach matches or exceed state-of-the-art.


title: Dimensionality Reduction for Anomaly Detection in Electrocardiography: A Manifold Approach

abstract: ECG analysis is universal and important in miscellaneous medical applications. However, high computation complexity is a problem which has been shown in several levels of conventional data mining algorithms for ECG analysis. In this paper, we presented a novel manifold approach to visualize and analyze the ECG signal. According to regularity of the data, our algorithm can discover the intrinsic structure and represent the streaming data with a 1-D manifold on a 2-D space. Furthermore, the proposed algorithm can reliably detect the anomaly in ECG streaming data. We evaluated the performance of the algorithm with two different anomalies in wearable applications: for the anomaly from heart disorders such as apnea, arrythmia, our algorithm could achieve up to 90% recognition rate, for the anomaly from the ECG device, our algorithm could detect the outlier with 100%.


title: Reward, Motivation, and Emotion Systems Associated With Early-Stage Intense Romantic Love

abstract: Early-stage romantic love can induce euphoria, is a cross-cultural phenomenon, and is possibly a developed form of a mammalian drive to pursue preferred mates. It has an important influence on social behaviors that have reproductive and genetic consequences. To determine which reward and motivation systems may be involved, we used functional magnetic resonance imaging and studied 10 women and 7 men who were intensely “in love” from 1 to 17 mo. Participants alternately viewed a photograph of their beloved and a photograph of a familiar individual, interspersed with a distraction-attention task. Group activation specific to the beloved under the two control conditions occurred in dopamine-rich areas associated with mammalian reward and motivation, namely the right ventral tegmental area and the right postero-dorsal body and medial caudate nucleus. Activation in the left ventral tegmental area was correlated with facial attractiveness scores. Activation in the right anteromedial caudate was correlated with questionnaire scores that quantified intensity of romantic passion. In the left insula-putamen-globus pallidus, activation correlated with trait affect intensity. The results suggest that romantic love uses subcortical reward and motivation systems to focus on a specific individual, that limbic cortical regions process individual emotion factors, and that there is localization heterogeneity for reward functions in the human brain.


title: When algorithms falter: a case report of a very small melanoma excised due to the dermatoscopic “ugly duckling” sign

abstract: We present a case report of a 3.5 mm diameter superficial spreading melanoma on the upper back of a 27-year-old woman, signed out as Clark level 2, Breslow thickness 0.2 mm with regression to 0.45 mm. The patient, with Fitzpatrick type 1 skin and minimal actinic damage, had presented for a routine skin check with no previous history of skin cancers. At the age of 17 she had received chemotherapy and radiotherapy for Ewing's sarcoma of the right hip with pulmonary metastases. The skin lesion was assessed as dermatoscopically symmetrical and was not predicted as a melanoma by any algorithmic method. The provisional diagnosis of melanoma was made on the basis that this lesion was completely different in dermatoscopic pattern to her other nevi, a dermatoscopic "ugly duckling" lesion. We draw attention to the recently established link between defects in the STAG2 gene and Ewing's sarcoma, glioblastoma and melanoma.


title: Uplift Modeling with Multiple Treatments and General Response Types

abstract: Randomized experiments have been used to assist decision-making in many areas. They help people select the optimal treatment for the test population with certain statistical guarantee. However, subjects can show significant heterogeneity in response to treatments. The problem of customizing treatment assignment based on subject characteristics is known as uplift modeling, differential response analysis, or personalized treatment learning in literature. A key feature for uplift modeling is that the data is unlabeled. It is impossible to know whether the chosen treatment is optimal for an individual subject because response under alternative treatments is unobserved. This presents a challenge to both the training and the evaluation of uplift models. In this paper we describe how to obtain an unbiased estimate of the key performance metric of an uplift model, the expected response. We present a new uplift algorithm which creates a forest of randomized trees. The trees are built with a splitting criterion designed to directly optimize their uplift performance based on the proposed evaluation method. Both the evaluation method and the algorithm apply to arbitrary number of treatments and general response types. Experimental results on synthetic data and industry-provided data show that our algorithm leads to significant performance improvement over other applicable methods.


title: Automatic Nuclei Segmentation in H&E Stained Breast Cancer Histopathology Images

abstract: The introduction of fast digital slide scanners that provide whole slide images has led to a revival of interest in image analysis applications in pathology. Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images. We therefore developed an automated nuclei segmentation method that works with hematoxylin and eosin (H&E) stained breast cancer histopathology images, which represent regions of whole digital slides. The procedure can be divided into four main steps: 1) pre-processing with color unmixing and morphological operators, 2) marker-controlled watershed segmentation at multiple scales and with different markers, 3) post-processing for rejection of false regions and 4) merging of the results from multiple scales. The procedure was developed on a set of 21 breast cancer cases (subset A) and tested on a separate validation set of 18 cases (subset B). The evaluation was done in terms of both detection accuracy (sensitivity and positive predictive value) and segmentation accuracy (Dice coefficient). The mean estimated sensitivity for subset A was 0.875 (±0.092) and for subset B 0.853 (±0.077). The mean estimated positive predictive value was 0.904 (±0.075) and 0.886 (±0.069) for subsets A and B, respectively. For both subsets, the distribution of the Dice coefficients had a high peak around 0.9, with the vast majority of segmentations having values larger than 0.8.


title: Avoiding, Finding and Fixing Spreadsheet Errors - A Survey of Automated Approaches for Spreadsheet QA

abstract: Spreadsheet programs can be found everywhere in organizations and they are used for a variety of purposes, including financial calculations, planning, data aggregation and decision making tasks. A number of research surveys have however shown that such programs are particularly prone to errors. Some reasons for the error-proneness of spreadsheets are that spreadsheets are developed by end users and that standard software quality assurance processes are mostly not applied. Correspondingly, during the last two decades, researchers have proposed a number of techniques and automated tools aimed at supporting the end user in the development of error-free spreadsheets. In this paper, we provide a review of the research literature and develop a classification of automated spreadsheet quality assurance (QA) approaches, which range from spreadsheet visualization, static analysis and quality reports, over testing and support to model-based spreadsheet development. Based on this review, we outline possible opportunities for future work in the area of automated spreadsheet QA.


title: miRBase: annotating high confidence microRNAs using deep sequencing data

abstract: We describe an update of the miRBase database (http://www.mirbase.org/), the primary microRNA sequence repository. The latest miRBase release (v20, June 2013) contains 24 521 microRNA loci from 206 species, processed to produce 30 424 mature microRNA products. The rate of deposition of novel microRNAs and the number of researchers involved in their discovery continue to increase, driven largely by small RNA deep sequencing experiments. In the face of these increases, and a range of microRNA annotation methods and criteria, maintaining the quality of the microRNA sequence data set is a significant challenge. Here, we describe recent developments of the miRBase database to address this issue. In particular, we describe the collation and use of deep sequencing data sets to assign levels of confidence to miRBase entries. We now provide a high confidence subset of miRBase entries, based on the pattern of mapped reads. The high confidence microRNA data set is available alongside the complete microRNA collection at http://www.mirbase.org/. We also describe embedding microRNA-specific Wikipedia pages on the miRBase website to encourage the microRNA community to contribute and share textual and functional information.


title: Performance evaluation of Ka-Band satellite communication system in rain fading channel at tropical area

abstract: Generally, commercial satellites use C-band and Ku-band, but because of the rapid growth of satellite services and congestion of lower frequencies bands, more and more commercial applications are being promoted for Ka-Band. In order to use Ka-band for satellite communication in tropical areas, there are several issues that have to be considered. One of the disadvantages of Ka-band is it is susceptible to be attenuated by rain and the earth's atmospheric conditions such as gas, clouds, and ice particles. This paper is aimed to evaluate the performance of Ka-band wireless communication system in tropical area by comparing several channel coding schemes.


Technical Report Variational Circle Packing Based on Power Diagram


title: Handover Control in Wireless Systems via Asynchronous Multiuser Deep Reinforcement Learning

abstract: In this paper, we propose a two-layer framework to learn the optimal handover (HO) controllers in possibly large-scale wireless systems supporting mobile Internet-of-Things users or traditional cellular users, where the user mobility patterns could be heterogeneous. In particular, our proposed framework first partitions the user equipments (UEs) with different mobility patterns into clusters, where the mobility patterns are similar in the same cluster. Then, within each cluster, an asynchronous multiuser deep reinforcement learning (RL) scheme is developed to control the HO processes across the UEs in each cluster, in the goal of lowering the HO rate while ensuring certain system throughput. In this scheme, we use a deep neural network (DNN) as an HO controller learned by each UE via RL in a collaborative fashion. Moreover, we use supervised learning in initializing the DNN controller before the execution of RL to exploit what we already know with traditional HO schemes and to mitigate the negative effects of random exploration at the initial stage. Furthermore, we show that the adopted global-parameter-based asynchronous framework enables us to train faster with more UEs, which could nicely address the scalability issue to support large systems. Finally, simulation results demonstrate that the proposed framework can achieve better performance than the state-of-art online schemes, in terms of HO rates.


title: Technology-dependency among patients discharged from a children's hospital: a retrospective cohort study

abstract: BackgroundAdvances in medical technology may be increasing the population of children who are technology-dependent (TD). We assessed the proportion of children discharged from a children's hospital who are judged to be TD, and determined the most common devices and number of prescription medications at the time of discharge.MethodsChart review of 100 randomly selected patients from all services discharged from a children's hospital during the year 2000. Data were reviewed independently by 4 investigators who classified the cases as TD if the failure or withdrawal of the technology would likely have adverse health consequences sufficient to require hospitalization. Only those cases where 3 or 4 raters agreed were classified as TD.ResultsAmong the 100 randomly sampled patients, the median age was 7 years (range: 1 day to 24 years old), 52% were male, 86% primarily spoke English, and 54% were privately insured. The median length of stay was 3 days (range: 1 to 103 days). No diagnosis accounted for more than 5% of cases. 41% were deemed to be technology dependent, with 20% dependent upon devices, 32% dependent upon medications, and 11% dependent upon both devices and medications. Devices at the time of discharge included gastrostomy and jejeunostomy tubes (10%), central venous catheters (7%), and tracheotomies (1%). The median number of prescription medications was 2 (range: 0–13), with 12% of cases having 5 or more medications. Home care services were planned for 7% of cases.ConclusionTechnology-dependency is common among children discharged from a children's hospital.


title: Systematic Planning for ICT Integration in Topic Learning

abstract: Integrating Information and Communication Technology (ICT) into teaching and learning is a growing area that has attracted many educators’ efforts in recent years. Based on the scope of content covered, ICT integration can happen in three different areas: curriculum, topic, and lesson. This paper elaborates upon the concept of ICT integration, and presents a systematic planning model for guiding ICT integration in the topic area. A sample of an ICT integration plan is described in this paper to demonstrate how this model can be applied in practice.


title: How Does Facial Feedback Modulate Emotional Experience

abstract: Contracting muscles involved in facial expressions (e.g. smiling or frowning) can make emotions more intense, even when unaware one is modifying expression [e.g. Strack, F., Martin, L., & Stepper, S. (1988). Inhibiting and facilitating conditions of the human smile: A non-obtrusive test of the facial feedback hypothesis. Journal of Personality and Social Psychology, 54(5), 768–777]. However, it is unresolved whether and how inhibiting facial expressions might weaken emotional experience. In the present study, 142 participants watched positive and negative video clips while either inhibiting their facial expressions or not. When hypothesis awareness and effects of distraction were experimentally controlled, inhibiting facial expressions weakened some emotional experiences. These findings provide new insight into ways that inhibition of facial expression can affect emotional experience: the link is not dependent on experimental demand, lay theories about connections between expression and experience, or the distraction involved in inhibiting one’s expressions.


title: SMI 2012: Full GPU accelerated convex hull computation

abstract: We present a hybrid algorithm to compute the convex hull of points in three or higher dimensional spaces. Our formulation uses a GPU-based interior point filter to cull away many of the points that do not lie on the boundary. The convex hull of remaining points is computed on a CPU. The GPU-based filter proceeds in an incremental manner and computes a pseudo-hull that is contained inside the convex hull of the original points. The pseudo-hull computation involves only localized operations and maps well to GPU architectures. Furthermore, the underlying approach extends to high dimensional point sets and deforming points. In practice, our culling filter can reduce the number of candidate points by two orders of magnitude. We have implemented the hybrid algorithm on commodity GPUs, and evaluated its performance on several large point sets. In practice, the GPU-based filtering algorithm can cull up to 85M interior points per second on an NVIDIA GeForce GTX 580 and the hybrid algorithm improves the overall performance of convex hull computation by 10-27 times (for static point sets) and 22-46 times (for deforming point sets).


title: Benchmarking Image Segmentation Algorithms

abstract: We present a thorough quantitative evaluation of four image segmentation algorithms on images from the Berkeley Segmentation Database. The algorithms are evaluated using an efficient algorithm for computing precision and recall with regard to human ground-truth boundaries. We test each segmentation method over a representative set of input parameters, and present tuning curves that fully characterize algorithm performance over the complete image database. We complement the evaluation on the BSD with segmentation results on synthetic images. The results reported here provide a useful benchmark for current and future research efforts in image segmentation.


title: Health risks in areas close to urban solid waste landfill sites.

abstract: OBJECTIVE ::: To evaluate the association between living close to solid waste landfill sites and occurrences of cancer and congenital malformations among populations in their vicinity. ::: ::: ::: METHODS ::: Deaths among people living in the municipality of São Paulo, Southeastern Brazil, between 1998 and 2002 were selected and geocoded, according to selected causes. Over the period evaluated, there were 351 deaths due to liver cancer, 160 due to bladder cancer and 224 due to leukemia, among adults, 25 due to childhood leukemia and 299 due to congenital malformation, in areas close to landfill sites. Buffer zones of radius 2 km around the 15 sites delimited the areas exposed. Standardized mortality ratios for each outcome were analyzed in Bayesian spatial models. ::: ::: ::: RESULTS ::: In a general manner, the highest values for the standardized mortality ratios were found in more central areas of the municipality, while the landfill sites were located in more peripheral areas. The standardized mortality ratios did not indicate any excess risk for people living in areas close to solid waste landfill sites in the municipality of São Paulo. For landfill sites in operation, there was a greater risk of bladder and liver cancer, and death due to congenital malformation, but without statistical significance. ::: ::: ::: CONCLUSIONS ::: No increase in the risk of cancer or congenital malformations was found in areas in the vicinity of urban waste dumps in the municipality of São Paulo. The weak associations and the imprecision of the estimates obtained did not allow any causal relationship to be established.


title: Learning to Extract Action Descriptions From Narrative Text

abstract: This paper focuses on the mapping of natural language sentences in written stories to a structured knowledge representation. This process yields an exponential explosion of instance combinations since each sentence may contain a set of ambiguous terms, each one giving place to a set of instance candidates. The selection of the best combination of instances is a structured classification problem that yields a high-demanding combinatorial optimization problem which, in this paper, is approached by a novel and efficient formulation of a genetic algorithm, which is able to exploit the conditional independence among variables, while improving the parallel scalability. The automatic rating of the resulting set of instance combinations, i.e., possible text interpretations, demands an exhaustive exploitation of the state-of-the-art resources in natural language processing to feed the system with pieces of evidence to be fused by the proposed framework. In this sense, a mapping framework able to reason with uncertainty, to integrate supervision and evidence from external sources, was adopted. To improve the generalization capacity while learning from a limited amount of annotated data, a new constrained learning algorithm for Bayesian networks is introduced. This algorithm bounds the search space through a set of constraints which encode information on mutually exclusive values. The mapping of natural language utterances to a structured knowledge representation is important in the context of game construction, e.g., in an RPG setting, as it alleviates the manual knowledge acquisition bottleneck. The effectiveness of the proposed algorithm is evaluated on a set of three stories, yielding nine experiments. Our mapping framework yields performance gains in predicting the most likely structured representations of sentences when compared with a baseline algorithm.


title: Hesitation Disfluencies in Spontaneous Speech: The Meaning of um

abstract: Human speech is peppered with ums and uhs, among other signs of hesitation in the planning process. But are these so-called fillers (or filled pauses) intentionally uttered by speakers, or are they side-effects of difficulties in the planning process? And how do listeners respond to them? In the present paper, we review evidence concerning the production and comprehension of fillers such as um and uh, in an attempt to determine whether they can be said to be ‘words’ with ‘meanings’ that are understood by listeners. We conclude that, whereas listeners are highly sensitive to hesitation disfluencies in speech, there is little evidence to suggest that they are intentionally produced, or should be considered to be words in the conventional sense.


title: Data-Driven Monitoring of Cyber-Physical Systems Leveraging on Big Data and the Internet-of-Things for Diagnosis and Control

abstract: The majority of projects dealing with monitoring and diagnosis of Cyber Physical Systems (CPSs) relies on models created by human experts. But these models are rarely available, are hard to verify and to maintain and are often incomplete. Data-driven approaches are a promising alternative: They leverage on the large amount of data which is collected nowadays in CPSs, this data is then used to learn the necessary models automatically. For this, several challenges have to be tackled, such as real-time data acquisition and storage solutions, data analysis and machine learning algorithms, task specific human-machine-interfaces (HMI) and feedback/control mechanisms. In this paper, we propose a cognitive reference architecture which addresses these challenges. This reference architecture should both ease the reuse of algorithms and support scientific discussions by providing a comparison schema. Use cases from different industries are outlined and support the correctness of the architecture.


title: Estimation of personal NO2 exposure in a cohort of pregnant women.

abstract: Abstract There is a growing concern about the possible adverse effects of exposure to air pollution on health during pregnancy. Therefore, a priority of the INMA (environment and childhood) study was to estimate personal exposure to traffic-related air pollution. In the cohort from Valencia ( n = 855), ambient levels of NO 2 were measured at 93 sampling sites spread over the study area during four different sampling periods of 7 days each. Multiple regression models were used to map ambient NO 2 over the area. Geographical data and predictions from kriging obtained by the “let one out” procedure were used as predictors. Individual exposure was assigned as 1) the estimated ambient NO 2 level at the home address and 2) the average of estimated ambient NO 2 levels at home and work addresses, weighted by the time spent in each environment. Estimations were temporally customised using the NO 2 levels registered daily by the regional Air Pollution Monitoring Network. The entire pregnancy and each trimester were taken as exposure windows. The model for the mean levels of NO 2 during the sampling periods explained 81% of the variation in NO 2 levels. Relative percent differences between the two models of personal exposure assignment were less than 9% for more than 90% of the participants; however the rest of them showed marked differences. Personal exposure estimates were slightly higher in the second model. In both cases, exposure during the whole pregnancy was strongly correlated with exposure in the second trimester. Considering periods shorter than the entire pregnancy will provide us the opportunity to identify specific windows of susceptibility.


title: Automatic Classification of Change Requests for Improved IT Service Quality

abstract: Faulty changes to the IT infrastructure can lead to critical system and application outages, and therefore cause serious economical losses. In this paper, we describe a change planning support tool that aims at assisting the change requesters in leveraging aggregated information associated with the change, like past failure reasons or best implementation practices. The thus gained knowledge can be used in the subsequent planning and implementation steps of the change. Optimal matching of change requests with the aggregated information is achieved through the classification of the change request into about 200 fine-grained activities. We propose to automatically classify the incoming change requests using various information retrieval and machine learning techniques. The cost of building the classifiers is reduced by employing active learning techniques or by leveraging labeled features. Historical tickets from two customers were used to empirically assess and compare the accuracy of the different classification approaches (Lucene index, multinomial logistic regression, and generalized expectation criteria).


title: Improving Big Data Visual Analytics with Interactive Virtual Reality

abstract: For decades, the growth and volume of digital data collection has made it challenging to digest large volumes of information and extract underlying structure. Coined ‘Big Data’, massive amounts of information has quite often been gathered inconsistently (e.g from many sources, of various forms, at different rates, etc.). These factors impede the practices of not only processing data, but also analyzing and displaying it in an efficient manner to the user. Many efforts have been completed in the data mining and visual analytics community to create effective ways to further improve analysis and achieve the knowledge desired for better understanding. Our approach for improved big data visual analytics is two-fold, focusing on both visualization and interaction. Given geo-tagged information, we are exploring the benefits of visualizing datasets in the original geospatial domain by utilizing a virtual reality platform. After running proven analytics on the data, we intend to represent the information in a more realistic 3D setting, where analysts can achieve an enhanced situational awareness and rely on familiar perceptions to draw in-depth conclusions on the dataset. In addition, developing a human-computer interface that responds to natural user actions and inputs creates a more intuitive environment. Tasks can be performed to manipulate the dataset and allow users to dive deeper upon request, adhering to desired demands and intentions. Due to the volume and popularity of social media, we developed a 3D tool visualizing Twitter on MIT's campus for analysis. Utilizing emerging technologies of today to create a fully immersive tool that promotes visualization and interaction can help ease the process of understanding and representing big data.


title: Vascular biology of atherosclerosis: overview and state of the art

abstract: Our current understanding of the vascular biology of atherogenesis and its clinical manifestations suggests a pathophysiology that is much more complex than mere lipid storage. Recent advances support the current view of atherosclerosis as an inflammatory process that initiates and promotes lesion development to the point of acute thrombotic complications and clinical events. Inflammatory cells localize in early-stage atherosclerotic lesions, and recent basic research has established a causal relation between inflammatory mediators or cytokines, and the steps involved in progressing from local inflammation through plaque formation. Inhibition of the action of certain specific proinflammatory cytokines, such as CD40 ligand, interferes with atherogenesis in mice. Increased circulating levels of inflammatory markers indicate increased cardiovascular risk. Thus, the time has come to embrace inflammation as a common pathway for atherogenic risk factors and for providing new opportunities for therapeutic intervention.


title: Exploit Bounding Box Annotations for Multi-Label Object Recognition

abstract: Convolutional neural networks (CNNs) have shown great performance as general feature representations for object recognition applications. However, for multi-label images that contain multiple objects from different categories, scales and locations, global CNN features are not optimal. In this paper, we incorporate local information to enhance the feature discriminative power. In particular, we first extract object proposals from each image. With each image treated as a bag and object proposals extracted from it treated as instances, we transform the multi-label recognition problem into a multi-class multi-instance learning problem. Then, in addition to extracting the typical CNN feature representation from each proposal, we propose to make use of ground-truth bounding box annotations (strong labels) to add another level of local information by using nearest-neighbor relationships of local regions to form a multi-view pipeline. The proposed multi-view multiinstance framework utilizes both weak and strong labels effectively, and more importantly it has the generalization ability to even boost the performance of unseen categories by partial strong labels from other categories. Our framework is extensively compared with state-of-the-art handcrafted feature based methods and CNN based methods on two multi-label benchmark datasets. The experimental results validate the discriminative power and the generalization ability of the proposed framework. With strong labels, our framework is able to achieve state-of-the-art results in both datasets.


Mapping Verbs in Different Languages to Knowledge Base Relations using Web Text as Interlingua


title: Studying the effectiveness of android application permissions requests

abstract: Popular platforms including Android and Facebook have adopted a permissions-based model. Under this model applications (apps) are required to declare specific access to user information required for functionality. We conducted two user studies on Amazon's Mechanical Turk to test the efficacy of these permissions requests on the Android platform. We found permissions were ineffective, even with the addition of an additional text warning. Conversely, we found that an app's download count had a strong effect on app installations. In order to determine if it was a failure of our text-based warning, we ran a second experiment with a previously proven visual indicator.


title: Implementation of health-care monitoring system using Raspberry Pi

abstract: Devotion towards own body is one of the important factor considered in this era. The equipments which provide results at run time and also accuracy maintained are provided by the electronic engineers. With the help of new technology of Raspberry Pi, health care system can be monitored. In this type of technology same area network is shared by multiple users which helps in monitoring. Wireless communication is done through Wi-Fi which provides flexibility and extendibility. In this paper basic parameters like body temperature is monitored and is transferred on webpage to make it locally visible for users.


title: Multiple human skeleton recognition in RGB and depth images with graph theory, anatomic refinement of point clouds and machine learning

abstract: Computer visual recognition of multiple human poses infers technological benefit in a variety of systems, including security surveillance, medical therapeutics, sports analytics and many more. For this goal the set of detected body parts on color or depth images must be aligned to reconstruct the skeletons of the humans. Here, an algorithm is introduced that models the body part point clouds using principal component analysis to obtain anatomically correct positions of joints, and that assembles the redundant and/or incomplete number of candidate joints with graph theoretical methods using Suurballe's k-shortest disjoint paths algorithm to build the skeletons. The computations were applied to MOCAP database motions rendered in Blender to produce idealized classified point clouds, and to real human depth images classified with decision forests similar to Shotton et al. For MOCAP data, in 68 images showing 3 persons all 204 skeletons were correctly aligned using 4.285 of 4.682 joints with no false assignment. For 33 real human images each showing 3 people, 71 skeletons were correctly detected with 1 false detection and 17 misses, which is promising with respect to non-perfect body part classification in real world.


title: Interaction design gone wild: striving for wild theory

abstract: Interaction designers, with less technical expertise and modest resources, can conjure, create, and deploy a diversity of prototypes in all manner of places in the everyday world. The outcome of conducting in the-wild studies like these can be most revealing, demonstrating different results from those arising out of lab studies. If a system that is designed based on theories is shown to improve a particular behavior, to what extent can it be said to be due to a specific phenomena identified by a theory. Several theories in the behavioral sciences, philosophy, and ecological psychology that explain how people behave and act in the real world are beginning to find traction in interaction design. An interaction design, embodiment, which is concerned with the social and physical context of the body in structuring cognition and how the world is experienced. Other promising theories that are starting to make their mark in interaction design include proxemics and mindfulness.


title: The role of physical embodiment in human-robot interaction

abstract: Autonomous robots are agents with physical bodies that share our environment. In this work, we test the hypothesis that physical embodiment has a measurable effect on performance and perception of social interactions. Support of this hypothesis would suggest fundamental differences between virtual agents and robots from a social standpoint and have significant implications for human-robot interaction. We measure task performance and perception of a robot's social abilities in a structured but open-ended task based on the Towers of Hanoi puzzle. Our experiment compares aspects of embodiment by evaluating: (1) the difference between a physical robot and a simulated one; (2) the effect of physical presence through a co-located robot versus a remote tele-present robot. We present data from a pilot study with 12 subjects showing interesting differences in perception of remote physical robot's and simulated agent's attention to the task, and task enjoyment.


title: Analyza: Exploring Data with Conversation

abstract: We describe Analyza, a system that helps lay users explore data. Analyza has been used within two large real world systems. The first is a question-and-answer feature in a spreadsheet product. The second provides convenient access to a revenue/inventory database for a large sales force. Both user bases consist of users who do not necessarily have coding skills, demonstrating Analyza's ability to democratize access to data. We discuss the key design decisions in implementing this system. For instance, how to mix structured and natural language modalities, how to use conversation to disambiguate and simplify querying, how to rely on the ``semantics' of the data to compensate for the lack of syntactic structure, and how to efficiently curate the data.


title: Potential Functions based Sampling Heuristic For Optimal Path Planning

abstract: Rapidly-exploring Random Tree star (RRT*) is a recently proposed extension of Rapidly-exploring Random Tree (RRT) algorithm that provides a collision-free, asymptotically optimal path regardless of obstacles geometry in a given environment. However, one of the limitation in the RRT* algorithm is slow convergence to optimal path solution. As a result it consumes high memory as well as time due to the large number of iterations utilised in achieving optimal path solution. To overcome these limitations, we propose the potential function based-RRT* that incorporates the artificial potential field algorithm in RRT*. The proposed algorithm allows a considerable decrease in the number of iterations and thus leads to more efficient memory utilization and an accelerated convergence rate. In order to illustrate the usefulness of the proposed algorithm in terms of space execution and convergence rate, this paper presents rigorous simulation based comparisons between the proposed techniques and RRT* under different environmental conditions. Moreover, both algorithms are also tested and compared under non-holonomic differential constraints.


You Are How You Move: Linking Multiple User Identities From Massive Mobility Traces


title: Effects of a Multicomponent Life-Style Intervention on Weight, Glycemic Control, Depressive Symptoms, and Renal Function in Low-Income, Minority Patients With Type 2 Diabetes: Results of the Community Approach to Lifestyle Modification for Diabetes Randomized Controlled Trial

abstract: ABSTRACTObjectiveFew interventions have combined life-style and psychosocial approaches in the context of Type 2 diabetes management. The purpose of this study was to determine the effect of a multicomponent behavioral intervention on weight, glycemic control, renal function, and depressive symptoms


title: Behavioral markers and recognizability of the smile of enjoyment.

abstract: Ekman and Friesen (1982) predicted that smiles that express enjoyment would be marked by smoother zygomatic major actions of more consistent duration than the zygomatic major actions of nonenjoyment smiles. Study 1 measured the duration and smoothness of smiles shown by female subjects in response to positive emotion films while alone and in a social interaction. Enjoyment smiles in both situations were of more consistent duration and smoother than nonenjoyment smiles. In Study 2 observers who were shown videotapes of enjoyment and nonenjoyment smiles were able to accurately identify enjoyment smiles at rates greater than chance; moreover, accuracy was positively related to increased salience of orbicularis oculi action. In Study 3, another group of observers were asked to record their impressions of the smiling women shown in Study 2. These women were seen as more positive when they showed enjoyment compared with nonenjoyment smiles. These results provide further evidence that enjoyment smiles are entities distinct from smiles in general.


title: A refined weighted K-Nearest Neighbors algorithm for text categorization

abstract: Text categorization is one important task of text mining, for automated classification of large numbers of documents. Many useful supervised learning methods have been introduced to the field of text classification. Among these useful methods, K-Nearest Neighbor (KNN) algorithm is a widely used method and one of the best text classifiers for its simplicity and efficiency. For text categorization, one document is often represented as a vector composed of a series of selected words called as feature items and this method is called the vector space model. KNN is one of the algorithms based on the vector space model. However, traditional KNN algorithm holds that the weight of each feature item in various categories is identical. Obviously, this is not reasonable. For each feature item may have different importance and distribution in different categories. Considering this disadvantage of traditional KNN algorithm, we put forward a refined weighted KNN algorithm based on the idea of variance. Experimental results show that the refined weighted KNN makes a significant improvement on the performance of traditional KNN classifier.


title: Smart wheelchairs : A literature review

abstract: Abstract— Several studies have shown that both children andadults benefit substantially from access to a means of indepen-dent mobility. While the needs of many individuals with disabil-ities can be satisfied with traditional manual or poweredwheelchairs, a segment of the disabled community finds it diffi-cult or impossible to use wheelchairs independently. To accom-modate this population, researchers have used technologiesoriginally developed for mobile robots to create “smart wheel-chairs.” Smart wheelchairs have been the subject of researchsince the early 1980s and have been developed on four conti-nents. This article presents a summary of the current state of theart and directions for future research. Key words: artificial intelligence, independent mobility, infra-red range finder, laser range finder, machine vision, powerwheelchairs, robotics, sonar, subsumption, voice control. INTRODUCTION Several studies have shown that both children andadults benefit substantially from access to a means ofindependent mobility, including power wheelchairs, man-ual wheelchairs, scooters, a nd walkers [1–2]. Independentmobility increases vocational and educational opportuni-ties, reduces dependence on caregivers and family mem-bers, and promotes feelings of self-reliance. For youngchildren, independent mobility serves as the foundationfor much early learning [1]. Nonambulatory children lackaccess to the wealth of stimuli afforded self-ambulatingchildren. This lack of exploration and control often pro-duces a cycle of deprivation and reduced motivation thatleads to learned helplessness [3].For adults, independent mobility is an importantaspect of self-esteem and plays a pivotal role in “aging inplace.” For example, if older people find it increasinglydifficult to walk or wheel themselves to the commode,they may do so less often or they may drink less fluid toreduce the frequency of urination. If they become unableto walk or wheel themselves to the commode and help isnot routinely available in the home when needed, a moveto a more enabling environment (e.g., assisted living) maybe necessary. Mobility limitati ons are the leading cause offunctional limitations among adults, with an estimatedprevalence of 40 per 1,000 persons age 18 to 44 and 188per 1,000 at age 85 and older [4]. Mobility difficulties arealso strong predictors of activities of daily living (ADL)and instrumental ADL disabi lities because of the need to


title: Multi-camera object detection for robotics

abstract: Robust object detection is a critical skill for robotic applications in complex environments like homes and offices. In this paper we propose a method for using multiple cameras to simultaneously view an object from multiple angles and at high resolutions. We show that our probabilistic method for combining the camera views, which can be used with many choices of single-image object detector, can significantly improve accuracy for detecting objects from many viewpoints. We also present our own single-image object detection method that uses large synthetic datasets for training. Using a distributed, parallel learning algorithm, we train from very large datasets (up to 100 million image patches). The resulting object detector achieves high performance on its own, but also benefits substantially from using multiple camera views. Our experimental results validate our system in realistic conditions and demonstrates significant performance gains over using standard single-image classifiers, raising accuracy from 0.86 area-under-curve to 0.97.


title: Acquiring comparative commonsense knowledge from the web

abstract: Applications are increasingly expected to make smart decisions based on what humans consider basic commonsense. An often overlooked but essential form of commonsense involves comparisons, e.g. the fact that bears are typically more dangerous than dogs, that tables are heavier than chairs, or that ice is colder than water. In this paper, we first rely on open information extraction methods to obtain large amounts of comparisons from the Web. We then develop a joint optimization model for cleaning and disambiguating this knowledge with respect to WordNet. This model relies on integer linear programming and semantic coherence scores. Experiments show that our model outperforms strong baselines and allows us to obtain a large knowledge base of disambiguated commonsense assertions.


title: Automatic mutation test input data generation via ant colony

abstract: Fault-based testing is often advocated to overcome limitations ofother testing approaches; however it is also recognized as beingexpensive. On the other hand, evolutionary algorithms have beenproved suitable for reducing the cost of data generation in the contextof coverage based testing. In this paper, we propose a newevolutionary approach based on ant colony optimization for automatictest input data generation in the context of mutation testingto reduce the cost of such a test strategy. In our approach the antcolony optimization algorithm is enhanced by a probability densityestimation technique. We compare our proposal with otherevolutionary algorithms, e.g., Genetic Algorithm. Our preliminaryresults on JAVA testbeds show that our approach performed significantlybetter than other alternatives.


title: DECODA: a call-centre human-human spoken conversation corpus

abstract: The goal of the DECODA project is to reduce the development cost of Speech Analytics systems by reducing the need for manual annotation. This project aims to propose robust speech data mining tools in the framework of call-center monitoring and evaluation, by means of weakly supervised methods. The applicative framework of the project is the call-center of the RATP (Paris public transport authority). This project tackles two very important open issues in the development of speech mining methods from spontaneous speech recorded in call-centers : robustness (how to extract relevant information from very noisy and spontaneous speech messages) and weak supervision (how to reduce the annotation effort needed to train and adapt recognition and classification models). This paper describes the DECODA corpus collected at the RATP during the project. We present the different annotation levels performed on the corpus, the methods used to obtain them, as well as some evaluation of the quality of the annotations produced.


title: Clustering Evolving Networks

abstract: Roughly speaking, clustering evolving networks aims at detecting structurally dense subgroups in networks that evolve over time. This implies that the subgroups we seek for also evolve, which results in many additional tasks compared to clustering static networks. We discuss these additional tasks and difficulties resulting thereof and present an overview on current approaches to solve these problems. We focus on clustering approaches in online scenarios, i.e., approaches that incrementally use structural information from previous time steps in order to incorporate temporal smoothness or to achieve low running time. Moreover, we describe a collection of real world networks and generators for synthetic data that are often used for evaluation.


title: Cyberbullying: A Review of the Literature

abstract: The article is a literature review on cyberbullying from 2007-2013. Topics covered in the review have been categorized starting with definition of cyberbullying; roles of persons involved and statistics of who is being targeted; reasons for cyberbullying; differences between traditional bullying and cyberbullying; and gender comparisons related to cyberbullying. This introduction to cyberbullying will provide a foundation for developing a cyberbullying intervention/prevention program.


title: Multimodal freight transportation planning: A literature review

abstract: Multimodal transportation offers an advanced platform for more efficient, reliable, flexible, and sustainable freight transportation. Planning such a complicated system provides interesting areas in Operations Research. This paper presents a structured overview of the multimodal transportation literature from 2005 onward. We focus on the traditional strategic, tactical, and operational levels of planning, where we present the relevant models and their developed solution techniques. We conclude our review paper with an outlook to future research directions.


title: Development of a Brief Ataxia Rating Scale (BARS) Based on a Modified Form of the ICARS

abstract: To develop a brief ataxia rating scale (BARS) for use by movement disorder specialists and general neurologists. Current ataxia rating scales are cumbersome and not designed for clinical practice. We first modified the International Cooperative Ataxia Rating Scale (ICARS) by adding seven ataxia tests (modified ICARS, or MICARS), and observed only minimally increased scores. We then used the statistics package R to find a five-test subset in MICARS that would correlate best with the total MICARS score. This was accomplished first without constraints and then with the clinical constraint requiring one test each of Gait, Kinetic Function-Arm, Kinetic Function-Leg, Speech, and Eye Movements. We validated these clinical constraints by factor analysis. We then validated the results in a second cohort of patients; evaluated inter-rater reliability in a third cohort; and used the same data set to compare BARS with the Scale for the Assessment and Rating of Ataxia (SARA). Correlation of ICARS with the seven additional tests that when added to ICARS form MICARS was 0.88. There were 31,481 five-test subtests (48% of possible combinations) that had a correlation with total MICARS score of ≥0.90. The strongest correlation of an unconstrained five-test subset was 0.963. The clinically constrained subtest validated by factor analysis, BARS, had a correlation with MICARS-minus-BARS of 0.952. Cronbach alpha for BARS and SARA was 0.90 and 0.92 respectively; and inter-rater reliability (intraclass correlation coefficient) was 0.91 and 0.93 respectively. BARS is valid, reliable, and sufficiently fast and accurate for clinical purposes. © 2009 Movement Disorder Society


title: Learning an Extension of the Class of Functional Dependencies with Queries

abstract: Data dependencies are a useful tool to design relational databases. In particular, functional and multivalued dependencies are used to obtain relation schemes that satisfy the 4th normal form, a property that is considered good enough for most applications. It is known that the class of sets of functional dependencies is learnable in the exact model of learning with queries. Also a subclass of multivalued dependencies, the class of consequent-restricted multivalued dependencies has been shown to be learnable in this model. Here, we present an algorithm that learns a generalization of both classes. We also show an algorithm that learns a non-trivial subclass of 2-quasi Horn formulas, closely related to the classes mentioned above.


title: Closed-Loop Global Motion Planning for Reactive, Collision-Free Execution of Learned Tasks

abstract: We present a robot motion planning approach for performing a learned task while reacting to the movement of obstacles and task-relevant objects. We employ a closed-loop, sampling-based motion planner operating multiple times a second that senses obstacles and task-relevant objects and generates collision-free motion plans based on a learned-task model. The task model is learned from expert demonstrations prior to task execution and is represented as a hidden Markov model. During task execution, our motion planner quickly searches in the Cartesian product of the task model and a probabilistic roadmap for a collision-free plan with features most similar to the demonstrations given the current locations of the task-relevant objects. We accelerate replanning using a fast bidirectional search and by biasing the sampling distribution using information from the learned-task model. We show the efficacy of our approach with the Baxter robot performing two tasks.


title: Improving extractive dialogue summarization by utilizing human feedback

abstract: Automatic summarization systems usually are trained and evaluated in a particular domain with fixed data sets. When such a system is to be applied to slightly different input, labor- and cost-intensive annotations have to be created to retrain the system. We deal with this problem by providing users with a GUI which allows them to correct automatically produced imperfect summaries. The corrected summary in turn is added to the pool of training data. The performance of the system is expected to improve as it adapts to the new domain.


title: Bridging belief function theory to modern machine learning

abstract: Machine learning is a quickly evolving field which now looks really different from what it was 15 years ago, when classification and clustering were major issues. This document proposes several trends to explore the new questions of modern machine learning, with the strong afterthought that the belief function framework has a major role to play.


title: Social Media, Big Data, and Public Health Informatics: Ruminating Behavior of Depression Revealed through Twitter

abstract: Undiagnosed and untreated depressive disorders have become a serious public health issue and it is prevalent among people of all ages, gender and race. Social media sites, such as Twitter, have become a major venue for people to express/disclose their thoughts and feelings. The tweets from these micro-blogging sites could be used to screen for and potentially detect depression. To date, studies in this area have focused on developing and validating the terms and vocabulary used by users with depression, or evaluating tweets related to depression by using terms that are synonymous with depression. This approach has not produced reliable findings. In this study, we depart from this approach and instead, base our analysis on research on depressive disorders, which indicates the critical significance of repetitive thoughts and ruminating behavior of people with depression. The current study and findings hold important implications for research on depression, social media, and public health informatics.


Defect-Based Testing.


title: Facial Performance Capture with Deep Neural Networks.

abstract: We present a deep learning technique for facial performance capture, i.e., the transfer of video footage into a motion sequence of a 3D mesh representing an actor's face. Specifically, we build on a conventional capture pipeline based on computer vision and multi-view video, and use its results to train a deep neural network to produce similar output from a monocular video sequence. Once trained, our network produces high-quality results for unseen inputs with greatly reduced effort compared to the conventional system. ::: In practice, we have found that approximately 10 minutes worth of high-quality data is sufficient for training a network that can then automatically process as much footage from video to 3D as needed. This yields major savings in the development of modern narrative-driven video games involving digital doubles of actors and potentially hours of animated dialogue per character.


title: Android Rooting: Methods, Detection, and Evasion

abstract: Android rooting enables device owners to freely customize their own devices and run useful apps that require root privileges. While useful, rooting weakens the security of Android devices and opens the door for malware to obtain privileged access easily. Thus, several rooting prevention mechanisms have been introduced by vendors, and sensitive or high-value mobile apps perform rooting detection to mitigate potential security exposures on rooted devices. However, there is a lack of understanding whether existing rooting prevention and detection methods are effective. To fill this knowledge gap, we studied existing Android rooting methods and performed manual and dynamic analysis on 182 selected apps, in order to identify current rooting detection methods and evaluate their effectiveness. Our results suggest that these methods are ineffective. We conclude that reliable methods for detecting rooting must come from integrity-protected kernels or trusted execution environments, which are difficult to bypass.


title: Rate-constrained coder control and comparison of video coding standards

abstract: A unified approach to the coder control of video coding standards such as MPEG-2, H.263, MPEG-4, and the draft video coding standard H.264/AVC (advanced video coding) is presented. The performance of the various standards is compared by means of PSNR and subjective testing results. The results indicate that H.264/AVC compliant encoders typically achieve essentially the same reproduction quality as encoders that are compliant with the previous standards while typically requiring 60% or less of the bit rate.


title: Bottom-up saliency is a discriminant process

abstract: A bottom-up visual saliency detector is proposed, following a decision-theoretic formulation of saliency, previously developed for top-down processing (object recognition) [5]. The saliency of a given location of the visual field is defined as the power of a Gabor-like feature set to discriminate between the visual appearance of 1) a neighborhood centered at that location (the center) and 2) a neighborhood that surrounds it (the surround). Discrimination is defined in an information-theoretic sense and the optimal saliency detector derived for a class of stimuli that complies with known statistical properties of natural images, so as to achieve a computationally efficient solution. The resulting saliency detector is shown to replicate the fundamental properties of the psychophysics of pre-attentive vision, including stimulus pop-out, inability to detect feature conjunctions, asymmetries with respect to feature presence vs. absence, and compliance with Weber's law. It is also shown that the detector produces better predictions of human eye fixations than two previously proposed bottom-up saliency detectors.


title: Improvement of BM3D Algorithm Based on Wavelet and Directed Diffusion

abstract: Block-matching and 3D filtering (BM3D) has showngreat power for image denoising. By decomposing the BM3Drestored image into three orthogonal subbands, we find thatthe subband of the high-frequency component, of which thewavelet coefficients are less than a given threshold, has madesome true textures of the original image lost and, the PSNR ofthe restored image is pulled down. Therefore, in this paper, wepropose a modified BM3D algorithm to solve these problems. We diffuse the subband of the high-frequency component, ofwhich the wavelet coefficients are less than the threshold of theBM3D restored image to the corresponding subband of thenoisy image to get a new estimate by using an improveddirected diffusion equation model. The Laplace operator isreplaced by an anisotropic diffusion operator and two differentcoefficients are added in the two diffusion terms. By replacingthe corresponding subband of the BM3D restored image withthe new estimate, we obtain the improved denoising result. Experimental results demonstrate that the proposed methodachieves better performance than original BM3D algorithm interms of both removing noise and persevering the edges anddetails of the image.


title: Word Sense Disambiguation using a Bidirectional LSTM

abstract: In this paper we present a clean, yet effective, model for word sense disambiguation. Our approach leverage a bidirectional long short-term memory network which is shared between all words. This enables the model to share statistical strength and to scale well with vocabulary size. The model is trained end-to-end, directly from the raw text to sense labels, and makes effective use of word order. We evaluate our approach on two standard datasets, using identical hyperparameter settings, which are in turn tuned on a third set of held out data. We employ no external resources (e.g. knowledge graphs, part-of-speech tagging, etc), language specific features, or hand crafted rules, but still achieve statistically equivalent results to the best state-of-the-art systems, that employ no such limitations.


title: Template Matching Techniques in Computer Vision: Theory and Practice

abstract: The detection and recognition of objects in images is a key research topic in the computer vision community. Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems. This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications. Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms. Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection. It is also of interest to graduate students undertaking studies in these areas.


title: Hyperspectral Remote Sensing Data Analysis and Future Challenges

abstract: Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely on sophisticated and complex data analysis methods. The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement process such as noise and atmospheric effects. This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. In all topics, we describe the state-of-the-art, provide illustrative examples, and point to future challenges and research directions.


title: A 1 GHz ADPLL With a 1.25 ps Minimum-Resolution Sub-Exponent TDC in 0.18 $\mu$ m CMOS

abstract: An all-digital PLL for wireline applications is designed with a sub-exponent TDC which adaptively scales its resolution according to input time difference. By cascading 2× time amplifiers, the TDC efficiently generates the exponent-only information for fractional time difference. To improve linearity in a wide input range, a replica-based self-calibration scheme is applied to the time amplifier. The TDC, implemented in a 0.18 μm CMOS, shows the minimum resolution of 1.25 ps with a total conversion range of 2.5 ns, the maximum operating frequency of 250 MHz, and power consumption of 1.8 mW at 60 MHz. The measured rms jitter of PLL was 5.03 ps at 960 MHz.


title: An Experiment of Influences of Facebook Posts in Other Users

abstract: People usually present similar behavior of their friends due to the social influence that is produced by group pressure. This factor is also present in online social networks such as Facebook. In this paper, we make a study on social influence produced by the contents published on Facebook. First, we have executed a systematic literature review of the previous works to understand better this field of research. Then, by executing the Asch experiment, we have illustrated how such social influence can change the behavior of users in Facebook. Through this experiment, we could identify how the social influence type called “conformity” is present in online social network platforms and how this influence can make changes in the behavior of people.


title: Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations

abstract: This article considers the application of variational Bayesian methods to joint recursive estimation of the dynamic state and the time-varying measurement noise parameters in linear state space models. The proposed adaptive Kalman filtering method is based on forming a separable variational approximation to the joint posterior distribution of states and noise parameters on each time step separately. The result is a recursive algorithm, where on each step the state is estimated with Kalman filter and the sufficient statistics of the noise variances are estimated with a fixed-point iteration. The performance of the algorithm is demonstrated with simulated data.


title: Human behavior prediction for smart homes using deep learning

abstract: There is a growing interest in smart homes and predicting behaviors of inhabitants is a key element for the success of smart home services. In this paper, we propose two algorithms, DBN-ANN and DBN-R, based on the deep learning framework for predicting various activities in a home. We also address drawbacks of contrastive divergence, a widely used learning method for restricted Boltzmann machines, and propose an efficient online learning algorithm based on bootstrapping. From experiments using home activity datasets, we show that our proposed prediction algorithms outperform existing methods, such as a nonlinear SVM and k-means, in terms of prediction accuracy of newly activated sensors. In particular, DBN-R shows an accuracy of 43.9% (51.8%) for predicting newly activated sensors based on MIT home dataset 1 (dataset 2), while previous work based on the n-gram algorithm has shown an accuracy of 39% (43%) on the same dataset.


title: Batch Size Matters: A Diffusion Approximation Framework on Nonconvex Stochastic Gradient Descent

abstract: In this paper, we study the stochastic gradient descent method in analyzing nonconvex statistical optimization problems from a diffusion approximation point of view. Using the theory of large deviation of random dynamical system, we prove in the small stepsize regime and the presence of omnidirectional noise the following: starting from a local minimizer (resp.~saddle point) the SGD iteration escapes in a number of iteration that is exponentially (resp.~linearly) dependent on the inverse stepsize. We take the deep neural network as an example to study this phenomenon. Based on a new analysis of the mixing rate of multidimensional Ornstein-Uhlenbeck processes, our theory substantiate a very recent empirical results by \citet{keskar2016large}, suggesting that large batch sizes in training deep learning for synchronous optimization leads to poor generalization error.