title: Mix and Match: Joint Model for Clothing and Attribute Recognition

abstract: This paper studies clothing and attribute recognition in the fashion domain. Specifically, in this paper, we turn our attention to the compatibility of clothing items and attributes (Fig 1). For example, people do not wear a skirt and a dress at the same time, yet a jacket and a shirt are a preferred combination. We consider such inter-object or inter-attribute compatibility and formulate a Conditional Random Field (CRF) that seeks the most probable combination in the given picture. The model takes into account the location-specific appearance with respect to a human body and the semantic correlation between clothing items and attributes, which we learn using the max-margin framework. Fig 2 illustrates our pipeline. We evaluate our model using two datasets that resemble realistic applica- tion scenarios: on-line social networks and shopping sites. The empirical evaluation indicates that our model effectively improves the recognition performance over various baselines including the state-of-the-art feature designed exclusively for clothing recognition. The results also suggest that our model generalizes well to different fashion-related applications.


title: Averaging Expert Predictions

abstract: We consider algorithms for combining advice from a set of experts. In each trial, the algorithm receives the predictions of the experts and produces its own prediction. A loss function is applied to measure the discrepancy between the predictions and actual observations. The algorithm keeps a weight for each expert. At each trial the weights are first used to help produce the prediction and then updated according to the observed outcome. Our starting point is Vovk's Aggregating Algorithm, in which the weights have a simple form: the weight of an expert decreases exponentially as a function of the loss incurred by the expert. The prediction of the Aggregating Algorithm is typically a non-linear function of the weights and the experts' predictions. We analyze here a simplified algorithm in which the weights are as in the original Aggregating Algorithm, but the prediction is simply the weighted average of the experts' predictions. We show that for a large class of loss functions, even with the simplified prediction rule the additional loss of the algorithm over the loss of the best expert is at most c ln n, where n is the number of experts and c a constant that depends on the loss function. Thus, the bound is of the same form as the known bounds for the Aggregating Algorithm, although the constants here are not quite as good. We use relative entropy to rewrite the bounds in a stronger form and to motivate the update.


title: Data Bits in Karnaugh Map and Increasing Map Capability in Error Correcting

abstract: To provide reliable communication in data transmission, ability of correcting errors is of prime importance. This paper intends to suggest an easy algorithm to detect and correct errors in transmission codes using the well-known Karnaugh map. Referring to past research done and proving new theorems and also using a suggested simple technique taking advantage of the easy concept of Karnaugh map, we offer an algorithm to reduce the number of occupied squares in the map and therefore, reduce substantially the execution time for placing data bits in Karnaugh map. Based on earlier papers, we first propose an algorithm for correction of two simultaneous errors in a code. Then, defining specifications for empty squares of the map, we limit the choices for selection of new squares. In addition, burst errors in sending codes is discussed, and systematically code words for correcting them will be made.


title: Discovering social circles in ego networks

abstract: People's personal social networks are big and cluttered, and currently there is no good way to automatically organize them. Social networking sites allow users to manually categorize their friends into social circles (e.g. 'circles' on Google+, and 'lists' on Facebook and Twitter), however they are laborious to construct and must be updated whenever a user's network grows. In this paper, we study the novel task of automatically identifying users' social circles. We pose this task as a multi-membership node clustering problem on a user's ego-network, a network of connections between her friends. We develop a model for detecting circles that combines network structure as well as user profile information. For each circle we learn its members and the circle-specific user profile similarity metric. Modeling node membership to multiple circles allows us to detect overlapping as well as hierarchically nested circles. Experiments show that our model accurately identifies circles on a diverse set of data from Facebook, Google+, and Twitter, for all of which we obtain hand-labeled ground-truth.


title: Named entity recognition in query

abstract: This paper addresses the problem of Named Entity Recognition in Query (NERQ), which involves detection of the named entity in a given query and classification of the named entity into predefined classes. NERQ is potentially useful in many applications in web search. The paper proposes taking a probabilistic approach to the task using query log data and Latent Dirichlet Allocation. We consider contexts of a named entity (i.e., the remainders of the named entity in queries) as words of a document, and classes of the named entity as topics. The topic model is constructed by a novel and general learning method referred to as WS-LDA (Weakly Supervised Latent Dirichlet Allocation), which employs weakly supervised learning (rather than unsupervised learning) using partially labeled seed entities. Experimental results show that the proposed method based on WS-LDA can accurately perform NERQ, and outperform the baseline methods.


title: Bit-Scalable Deep Hashing With Regularized Similarity Learning for Image Retrieval and Person Re-Identification

abstract: Extracting informative image features and learning effective approximate hashing functions are two crucial steps in image retrieval. Conventional methods often study these two steps separately, e.g., learning hash functions from a predefined hand-crafted feature space. Meanwhile, the bit lengths of output hashing codes are preset in the most previous methods, neglecting the significance level of different bits and restricting their practical flexibility. To address these issues, we propose a supervised learning framework to generate compact and bit-scalable hashing codes directly from raw images. We pose hashing learning as a problem of regularized similarity learning. In particular, we organize the training images into a batch of triplet samples, each sample containing two images with the same label and one with a different label. With these triplet samples, we maximize the margin between the matched pairs and the mismatched pairs in the Hamming space. In addition, a regularization term is introduced to enforce the adjacency consistency, i.e., images of similar appearances should have similar codes. The deep convolutional neural network is utilized to train the model in an end-to-end fashion, where discriminative image features and hash functions are simultaneously optimized. Furthermore, each bit of our hashing codes is unequally weighted, so that we can manipulate the code lengths by truncating the insignificant bits. Our framework outperforms state-of-the-arts on public benchmarks of similar image search and also achieves promising results in the application of person re-identification in surveillance. It is also shown that the generated bit-scalable hashing codes well preserve the discriminative powers with shorter code lengths.


Plate-Laminated Waveguide Monopulse Slot Array Antenna with Full-Corporate-Feed in the E-Band


Room-Temperature Bonding of Wafers with Smooth Au Thin Films in Ambient Air Using a Surface-Activated Bonding Method


title: Throughput-Optimized FPGA Accelerator for Deep Convolutional Neural Networks

abstract: Deep convolutional neural networks (CNNs) have gained great success in various computer vision applications. State-of-the-art CNN models for large-scale applications are computation intensive and memory expensive and, hence, are mainly processed on high-performance processors like server CPUs and GPUs. However, there is an increasing demand of high-accuracy or real-time object detection tasks in large-scale clusters or embedded systems, which requires energy-efficient accelerators because of the green computation requirement or the limited battery restriction. Due to the advantages of energy efficiency and reconfigurability, Field-Programmable Gate Arrays (FPGAs) have been widely explored as CNN accelerators. In this article, we present an in-depth analysis of computation complexity and the memory footprint of each CNN layer type. Then a scalable parallel framework is proposed that exploits four levels of parallelism in hardware acceleration. We further put forward a systematic design space exploration methodology to search for the optimal solution that maximizes accelerator throughput under the FPGA constraints such as on-chip memory, computational resources, external memory bandwidth, and clock frequency. Finally, we demonstrate the methodology by optimizing three representative CNNs (LeNet, AlexNet, and VGG-S) on a Xilinx VC709 board. The average performance of the three accelerators is 424.7, 445.6, and 473.4GOP/s under 100MHz working frequency, which outperforms the CPU and previous work significantly.


title: Advanced Border Intrusion Detection and Surveillance Using Wireless Sensor Network Technology

abstract: Wireless Sensor Network (WSN) has been emerging in the last decade as a powerful tool for connecting physical and digital world. WSN has been used in many applications such habitat monitoring, building monitoring, smart grid and pipeline monitoring. In addition, few researchers have been experimenting with WSN in many mission-critical applications such as military applications. This paper surveys the literature for experimenting work done in border surveillance and intrusion detection using the technology of WSN. The potential benefits of using WSN in border surveillance are huge; however, up to our knowledge very few attempts of solving many critical issues about this application could be found in the literature.


title: Towards the understanding and evolution of monolithic applications as microservices

abstract: Nowadays, software development teams face the challenge of maintenance and evolution of large applications. This is known as modernization and comprises a process of understanding the existing application, and a process of transforming from the as-is state to the to-be state. In this article, we present an approach to modernize legacy applications into microservices. We propose a model-centered process to analyze and visualize the current structure and dependencies between the business layer and the data layer. Furthermore, we propose a modularization into clusters and microservices using four different diagrams. To validate our approach we have analyzed a JEE application with over 74566 LoC. The results show that the understanding is greatly improved through the diagrams proposed, and the modularization proposed constitutes the first step towards the automated modernization of the application.


title: Fully automated real time fatigue detection of drivers through Fuzzy Expert Systems

abstract: A non-intrusive fatigue detection system based on the video analysis of drivers.Eye closure duration measured through eye state information and yawning analyzed through mouth state information.Lips are searched through spatial fuzzy c-means (s-FCM) clustering.Pupils are also detected in the upper part of the face window on the basis of radii, inter-pupil distance and angle.The monitored information of eyes and mouth are further passed to Fuzzy Expert System (FES) that classifies the true state of the driver. This paper presents a non-intrusive fatigue detection system based on the video analysis of drivers. The system relies on multiple visual cues to characterize the level of alertness of the driver. The parameters used for detecting fatigue are: eye closure duration measured through eye state information and yawning analyzed through mouth state information. Initially, the face is located through Viola-Jones face detection method to ensure the presence of driver in video frame. Then, a mouth window is extracted from the face region, in which lips are searched through spatial fuzzy c-means (s-FCM) clustering. Simultaneously, the pupils are also detected in the upper part of the face window on the basis of radii, inter-pupil distance and angle. The monitored information of eyes and mouth are further passed to Fuzzy Expert System (FES) that classifies the true state of the driver. The system has been tested using real data, with different sequences recorded in day and night driving conditions, and with users belonging to different race and gender. The system yielded an average accuracy of 100% on all the videos on which it was tested.


title: Simpler sybil-proof mechanisms for multi-level marketing

abstract: Multi-level marketing refers to a marketing approach in which buyers are encouraged to take an active role in promoting the product. This is done by offering them a reward for each successful referral of the product to other prospective buyers. To encourage potential customers to buy early and to give referrals to influential people, these mechanisms also reward indirect referrals --- a direct referral linked to the buyer through other direct referrals. Doing so can make the referral/reward system vulnerable to sybil attacks --- where profit maximizers create several replicas in order to maximize their rewards. In this paper we propose a family of mechanisms for which sybil attacks are not profitable. We do this by modifying any mechanism that satisfies certain natural properties of sensible reward mechanisms to obtain one that is invulnerable to sybil attacks by profit maximizers while preserving its natural properties. Our modified mechanisms are also collusion proof. Finally, we give a concrete example of a natural mechanism that is sybil proof and simple to implement.


title: Nooks: an architecture for reliable device drivers

abstract: With the enormous growth in processor performance over the last decade, it is clear that reliability, rather than performance, is now the greatest challenge for computer systems research. This is particularly true in the context of Internet services that require 24x7 operation and home computers with no professional administration. While operating system products have matured and become more reliable, they are still the source of a significant number of failures. Furthermore, recent studies show that device drivers are frequently responsible for operating system failures. For example, a study at Stanford University found that Linux drivers have 3 to 7 times the bug frequency as the rest of the OS [4]. An analysis of product support calls for Windows 2000 showed that device drivers accounted for 27% of crashes, compared to 2% for the kernel itself [16].


title: Real-time teat detection using haar cascade classifier in smart automatic milking system

abstract: This paper discusses about the teat detection system development using Haar cascade classifier based image processing technique in laboratory conditions. Haar Cascade Classifiers have been used for static object recognition and is first time being implemented on real-time teat-detection for creating a reliable approach in Automatic Milking System's vision system development. The proposed vision system is developed to perform fast and accurate detection of the teats for guiding manipulator to attach milking cups to the teats. The system has evolved through several stages of testing of various image processing techniques using OpenCV. For testing in lab environment, images not included in the training set are used from google image search. The system is also tested in the cow farm on 20 cows with successful detection.


title: Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer's disease.

abstract: BACKGROUND ::: Bapineuzumab, a humanized anti-amyloid-beta monoclonal antibody, is in clinical development for the treatment of Alzheimer's disease. ::: ::: ::: METHODS ::: We conducted two double-blind, randomized, placebo-controlled, phase 3 trials involving patients with mild-to-moderate Alzheimer's disease--one involving 1121 carriers of the apolipoprotein E (APOE) ε4 allele and the other involving 1331 noncarriers. Bapineuzumab or placebo, with doses varying by study, was administered by intravenous infusion every 13 weeks for 78 weeks. The primary outcome measures were scores on the 11-item cognitive subscale of the Alzheimer's Disease Assessment Scale (ADAS-cog11, with scores ranging from 0 to 70 and higher scores indicating greater impairment) and the Disability Assessment for Dementia (DAD, with scores ranging from 0 to 100 and higher scores indicating less impairment). A total of 1090 carriers and 1114 noncarriers were included in the efficacy analysis. Secondary outcome measures included findings on positron-emission tomographic amyloid imaging with the use of Pittsburgh compound B (PIB-PET) and cerebrospinal fluid phosphorylated tau (phospho-tau) concentrations. ::: ::: ::: RESULTS ::: There were no significant between-group differences in the primary outcomes. At week 78, the between-group differences in the change from baseline in the ADAS-cog11 and DAD scores (bapineuzumab group minus placebo group) were -0.2 (P=0.80) and -1.2 (P=0.34), respectively, in the carrier study; the corresponding differences in the noncarrier study were -0.3 (P=0.64) and 2.8 (P=0.07) with the 0.5-mg-per-kilogram dose of bapineuzumab and 0.4 (P=0.62) and 0.9 (P=0.55) with the 1.0-mg-per-kilogram dose. The major safety finding was amyloid-related imaging abnormalities with edema among patients receiving bapineuzumab, which increased with bapineuzumab dose and APOE ε4 allele number and which led to discontinuation of the 2.0-mg-per-kilogram dose. Between-group differences were observed with respect to PIB-PET and cerebrospinal fluid phospho-tau concentrations in APOE ε4 allele carriers but not in noncarriers. ::: ::: ::: CONCLUSIONS ::: Bapineuzumab did not improve clinical outcomes in patients with Alzheimer's disease, despite treatment differences in biomarkers observed in APOE ε4 carriers. (Funded by Janssen Alzheimer Immunotherapy and Pfizer; Bapineuzumab 301 and 302 ClinicalTrials.gov numbers, NCT00575055 and NCT00574132, and EudraCT number, 2009-012748-17.).


title: Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by Averaging Source Word Embeddings

abstract: Creating accurate meta-embeddings from pre-trained source embeddings has received attention lately. Methods based on global and locally-linear transformation and concatenation have shown to produce accurate meta-embeddings. In this paper, we show that the arithmetic mean of two distinct word embedding sets yields a performant meta-embedding that is comparable or better than more complex meta-embedding learning methods. The result seems counter-intuitive given that vector spaces in different source embeddings are not comparable and cannot be simply averaged. We give insight into why averaging can still produce accurate meta-embedding despite the incomparability of the source vector spaces.


title: The quenching of m-methoxycinnamic acid fluorescence

abstract: Abstract Small quantities of diethyl ether quench the fluorescence of trans-m -methoxycinnamic acid dissolved in hexane. Measurements of the rates of trans to cis photoisomerization and the steady state concentrations of trans and cis isomers produced by photolysis of the solution indicate that the non-radiative relaxation promoted by ether occurs principally via a twisted form of the excited state. In the presence of ether, twisting about the ethylenic double bond of m -methoxycinnamic acid appears to be facilitated by disruption of intermolecular hydrogen bonding which exists between the carboxylic groups of pairs of cinnamic acid molecules.


title: Assessing the benefits of computational offloading in mobile-cloud applications

abstract: This paper presents the results of a formative study conducted to determine the effects of computation offloading in mobile applications by comparing “application performance” (chiefly energy consumption and response time). The study examined two general execution scenarios: (1) computation is performed locally on a mobile device, and (2) when it is offloaded entirely to the cloud. The study also carefully considered the underlying network characteristics as an important factor affecting the performance. More specifically, we refactored two mobile applications to offload their computationally intensive functionality to execute in the cloud. We then profiled these applications under different network conditions, and carefully measured “application performance” in each case. The results were not as conclusive as we had expected. On fast networks, offloading is almost always beneficial. However, on slower networks, the offloading cost-benefit analysis is not as clear cut. The characteristics of the data transferred between the mobile device and the cloud may be a deciding factor in determining whether offloading a computation would improve performance.


title: A Compact, Broadband Spiral Antenna With Unidirectional Circularly Polarized Radiation Patterns

abstract: A low-profile, compact spiral antenna with a broadband circularly polarized (CP) response is presented. The antenna is backed by a ground plane and has unidirectional radiation patterns over its entire frequency band of operation. This antenna is a multilayer structure composed of a center-fed modified Archimedean spiral that exploits a novel loading structure, a ring-shaped absorber, and a feed network, which includes a 180° power splitter. The loading structure possesses both inductive and capacitive characteristics, which increase the equivalent electrical length of the antenna while maintaining its maximum dimensions. The Archimedean spiral is integrated into the multilayer dielectric structure along with its differential feed network. An optimized ring-shaped absorber is used on the periphery of the antenna to reduce the ground effects on the antenna performance. The proposed antenna occupies a volume that is 89% smaller than that occupied by a conventional ground-plane-backed Archimedean spiral antenna. At its lowest frequency of operation, the antenna has electrical dimensions of $0.21\lambda_{\rm{ min}}\times 0.21\lambda_{\rm{ min}}\times 0.09\lambda_{\rm{ min}}$ , where $\lambda_{\rm{ min}}$ is the free-space wavelength at the lowest frequency of operation (0.5 GHz). Over the frequency range from 0.5 to 1.4 GHz (2.8:1), the antenna has a VSWR of 2.4:1, and it has a CP radiation pattern with an axial ratio better than 1.2 dB. Within this frequency range, the antenna has minimum and maximum realized gain values of $-5.0$ dBiC and 3.1 dBiC, respectively.


title: The Ostomy-Q: Development and Psychometric Validation of an Instrument to Evaluate Outcomes Associated with Ostomy Appliances.

abstract: Using an ostomy appliance can affect many aspects of a person's health-related quality of life (HRQL). A 2-part, descrip- tive study was designed to develop and validate an instrument to assess quality-of-life outcomes related to ostomy ap- pliance use. Study inclusion/exclusion criteria stipulated participants should be 18 to 85 years of age, have an ileostomy or colostomy, used an appliance for a minimum of 3 months without assistance, and able to complete an online survey. All participants provided sociodemographic and clinical information. In phase 1, a literature search was conducted and existing instruments used to measure HRQL in persons with an ostomy were assessed. Subsequently, the Ostomy-Q, a 23-item, Likert-response type questionnaire, divided into 4 domains (Discreetness, Comfort, Confidence, and Social Life), was developed based on published evidence and existing ostomy-related HRQL tools. Seven (7) participants re- cruited from a manufacturer user panel took part in exploratory/cognitive qualitative interviews to refine the new quality- of-life questionnaire. In phase 2, the instrument was tested to assess item variability and conceptual structure, item-total correlation, internal consistency, test-retest reliability, sensitivity, and minimal important difference (MID) in an online validation study among 200 participants from the manufacturer's user panel (equally divided by gender, 125 [62.5%] >50 years old, 128 [64%] with an ileostomy). This exercise also included completion of the Stoma Quality of Life Question- naire and 2 domains from the Ostomy Adjustment Inventory-23 to assess convergent validity. Eighty-two (82) participants recompleted these study instruments 2 weeks later to assess test-retest reliability. Sociodemographic and clinical data were assessed using descriptive statistics; Cronbach's alpha was used for internal consistency (minimum 0.70), principle component analysis for item variability/conceptual structure, and item-total correlation; intraclass correlation coefficient was used for test-retest reliability; and standard error of measurement was applied to MID. All domains demonstrated good internal consistency (between 0.69 and 0.78). All scales showed stability, with a minimum intraclass correlation coefficient of 0.743 (P <.001). The Ostomy-Q showed good convergent validity with other instruments to which it was compared (P <.01). In this study, the Ostomy-Q was found to be a reliable and valid outcome measure that can enhance understanding of the impact of ostomy appliances on users. Some items for social relationships and discreetness may need more exploring in the future with other patient groups.


title: Traffic monitoring and accident detection at intersections

abstract: We have developed an algorithm, referred to as spatio-temporal Markov random field, for traffic images at intersections. This algorithm models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the x-y image axes as well as the time axes. Our algorithm is sufficiently robust to segment and track occluded vehicles at a high success rate of 93%-96%. This success has led to the development of an extendable robust event recognition system based on the hidden Markov model (HMM). The system learns various event behavior patterns of each vehicle in the HMM chains and then, using the output from the tracking system, identifies current event chains. The current system can recognize bumping, passing, and jamming. However, by including other event patterns in the training set, the system can be extended to recognize those other events, e.g., illegal U-turns or reckless driving. We have implemented this system, evaluated it using the tracking results, and demonstrated its effectiveness.


title: Point-of-Interest Recommendations: Learning Potential Check-ins from Friends

abstract: The emergence of Location-based Social Network (LBSN) services provides a wonderful opportunity to build personalized Point-of-Interest (POI) recommender systems. Although a personalized POI recommender system can significantly facilitate users' outdoor activities, it faces many challenging problems, such as the hardness to model user's POI decision making process and the difficulty to address data sparsity and user/location cold-start problem. To cope with these challenges, we define three types of friends (i.e., social friends, location friends, and neighboring friends) in LBSN, and develop a two-step framework to leverage the information of friends to improve POI recommendation accuracy and address cold-start problem. Specifically, we first propose to learn a set of potential locations that each individual's friends have checked-in before and this individual is most interested in. Then we incorporate three types of check-ins (i.e., observed check-ins, potential check-ins and other unobserved check-ins) into matrix factorization model using two different loss functions (i.e., the square error based loss and the ranking error based loss). To evaluate the proposed model, we conduct extensive experiments with many state-of-the-art baseline methods and evaluation metrics on two real-world data sets. The experimental results demonstrate the effectiveness of our methods.


title: The Constant Inapproximability of the Parameterized Dominating Set Problem

abstract: We prove that there is no fpt-algorithm that can approximate the dominating set problem with any constant ratio, unless FPT= W[1]. Our hardness reduction is built on the second author's recent W[1]-hardness proof of the biclique problem. This yields, among other things, a proof without the PCP machinery that the classical dominating set problem has no polynomial time constant approximation under the exponential time hypothesis.


title: A new optical tracking system for virtual and augmented reality applications

abstract: A new stereo vision tracker setup for virtual and augmented reality applications is presented in this paper. The performance, robustness and accuracy of the system are achieved under real-time constraints. The method is based on blobs extraction, two-dimensional prediction, the epipolar constraint and three-dimensional reconstruction. Experimental results using a stereo rig setup (equipped with IR capabilities) and retroreflective targets are presented to demonstrate the capabilities of our optical tracking system. The system tracks up to 25 independent targets at 30 Hz.


title: Architecture Technical Debt: Understanding Causes and a Qualitative Model

abstract: A known problem in large software companies is to balance the prioritization of short-term with long-term responsiveness. Specifically, architecture violations (Architecture Technical Debt) taken to deliver fast might hinder future feature development, which would hinder agility. We conducted a multiple-case embedded case study in 7 sites at 5 large companies in order to shed light on the current causes for the accumulation of Architectural Technical Debt that causes effort. We provide a taxonomy of the factors and their influence in the accumulation of debt, and we provide a qualitative model of how the debt is accumulated and recovered over time.


title: FILA: Fine-grained indoor localization

abstract: Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current systems. How to eliminate such effect so as to enhance the indoor localization performance is a big challenge. In this work, we analyze this effect across the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers in OFDM systems and propose a novel approach called FILA, which leverages the channel state information (CSI) to alleviate multipath effect at the receiver. We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve the localization accuracy compared with the corresponding RSSI approach.


title: Pet therapy: an effective strategy to care for the elderly? An experimental study in a nursing home.

abstract: BACKGROUND AND AIM ::: There has recently been a growing interest towards patients' affective and emotional needs, especially in relational therapies, which are considered vital as to increase the understanding of those needs and patients' well-being. In particular, we paid attention to those patients who are forced to spend the last phase of their existence in residential facilities, namely elderly people in nursing homes, who often feel marginalized, useless, depressed, unstimulated or unable to communicate. The aim of this study is to verify the effectiveness of pet therapy in improving well-being in the elderly living in a nursing home. ::: ::: ::: METHODS ::: This is a longitudinal study with before and after intervention variables measurement in two groups of patients of a nursing home for elderly people. One group followed an AAI intervention (experimental group) the other one did not (control group). As to perform an assessment of well-being we measured the following dimensions in patients: anxiety (HAM-A), depression (GDS), apathy (AES), loneliness (UCLA), and quality of life (QUALID). Both groups filled the questionnaires as to measure the target variables (time 0). Once finished the scheduled meetings (time 1), all the participants, including the control group, filled the same questionnaires. ::: ::: ::: RESULTS ::: In accordance with scientific evidence the results confirmed a significant reduction of the measured variables. Especially for the quality of life, which showed a greater reduction than the other. ::: ::: ::: CONCLUSIONS ::: The implementation and success of the Pet Therapy could have a great emotional and social impact, bringing relief to patients and their family members, but also to health professionals.


title: Analysis of road accidents in India using data mining classification algorithms

abstract: Classification is a model finding process which is used for segmenting the data into different classes based on some constraints. This work analyzes the road accidents in India data set using classification algorithms namely linear regression, logistic regression, decision tree, SVM, Naive Bayes, KNN, Random Forest and gradient boosting algorithm. Performance measures used are accuracy, error rate and execution time. This analysis is done in R data mining tool. The performance of KNN is better than other algorithms.


title: Finding SHA-2 Characteristics: Searching through a Minefield of Contradictions

abstract: In this paper, we analyze the collision resistance of SHA-2 and provide the first results since the beginning of the NIST SHA-3 competition. We extend the previously best known semi-free-start collisions on SHA-256 from 24 to 32 (out of 64) steps and show a collision attack for 27 steps. All our attacks are practical and verified by colliding message pairs. We present the first automated tool for finding complex differential characteristics in SHA-2 and show that the techniques on SHA-1 cannot directly be applied to SHA-2. Due to the more complex structure of SHA-2 several new problems arise. Most importantly, a large amount of contradicting conditions occur which render most differential characteristics impossible. We show how to overcome these difficulties by including the search for conforming message pairs in the search for differential characteristics.


Comp 322: Fundamentals of Parallel Programming Module 2: Concurrency


title: How Images Inspire Poems: Generating Classical Chinese Poetry from Images with Memory Networks

abstract: With the recent advances of neural models and natural language processing, automatic generation of classical Chinese poetry has drawn significant attention due to its artistic and cultural value. Previous works mainly focus on generating poetry given keywords or other text information, while visual inspirations for poetry have been rarely explored. Generating poetry from images is much more challenging than generating poetry from text, since images contain very rich visual information which cannot be described completely using several keywords, and a good poem should convey the image accurately. In this paper, we propose a memory based neural model which exploits images to generate poems. Specifically, an Encoder-Decoder model with a topic memory network is proposed to generate classical Chinese poetry from images. To the best of our knowledge, this is the first work attempting to generate classical Chinese poetry from images with neural networks. A comprehensive experimental investigation with both human evaluation and quantitative analysis demonstrates that the proposed model can generate poems which convey images accurately.


title: Several EDCA parameter sets for improving channel access in IEEE 802.11ax networks

abstract: To improve efficiency of wide channel usage in dense scenarios, Wi-Fi community is currently developing a new amendment - 802.11ax - which introduces OFDMA in Wi-Fi. To provide backward compatibility, OFDMA works on top of CSMA/CA, native for Wi-Fi. Although OFDMA transmission is much more efficient than traditional one, the straightforward usage of 802.11ax functionality leads to the low ratio of OFDMA uplink transmissions. This problem can be solved by changing channel access parameters, which can be done with a legacy mechanism. However, in a heterogeneous network with legacy and 802.11ax stations, this mechanism leads to severe unfairness. In the paper, we describe our recent proposal, which solves the problem, and evaluate its performance with an analytical model.


title: Comprehensive formal verification of an OS microkernel

abstract: We present an in-depth coverage of the comprehensive machine-checked formal verification of seL4, a general-purpose operating system microkernel. We discuss the kernel design we used to make its verification tractable. We then describe the functional correctness proof of the kernel's C implementation and we cover further steps that transform this result into a comprehensive formal verification of the kernel: a formally verified IPC fastpath, a proof that the binary code of the kernel correctly implements the C semantics, a proof of correct access-control enforcement, a proof of information-flow noninterference, a sound worst-case execution time analysis of the binary, and an automatic initialiser for user-level systems that connects kernel-level access-control enforcement with reasoning about system behaviour. We summarise these results and show how they integrate to form a coherent overall analysis, backed by machine-checked, end-to-end theorems. The seL4 microkernel is currently not just the only general-purpose operating system kernel that is fully formally verified to this degree. It is also the only example of formal proof of this scale that is kept current as the requirements, design and implementation of the system evolve over almost a decade. We report on our experience in maintaining this evolving formally verified code base.


title: High-Order Attention Models for Visual Question Answering

abstract: The quest for algorithms that enable cognitive abilities is an important part of machine learning. A common trait in many recently investigated cognitive-like tasks is that they take into account different data modalities, such as visual and textual input. In this paper we propose a novel and generally applicable form of attention mechanism that learns high-order correlations between various data modalities. We show that high-order correlations effectively direct the appropriate attention to the relevant elements in the different data modalities that are required to solve the joint task. We demonstrate the effectiveness of our high-order attention mechanism on the task of visual question answering (VQA), where we achieve state-of-the-art performance on the standard VQA dataset.


title: A 0.7–2.5 GHz, 61% EIRP System Efficiency, Four-Element MIMO TX System Exploiting Integrated Power-Relaxed Power Amplifiers and an Analog Spatial De-Interleaver

abstract: This paper describes a four-element multiple-input multiple-output (MIMO) transmitter (TX) system that features an analog spatial de-interleaver to simplify the baseband-input complexity and increase the spatial matching of the sub-TXs. The MIMO diversity gain and power-combining gain are jointly exploited to relax the output power of the four power amplifiers and eliminate their output matching networks, leading to a compact implementation of the entire TX system. The MIMO effectiveness is improved by introducing an radio-frequency to baseband dc feedback technique that enhances the matching among the sub-TXs against process variation. In the verification, the TX system is co-designed with a compact antenna array-on-PCB that generates a null zone in the propagation pattern, and the electric-field polarization angle, to achieve diversity propagation. All techniques together improve the signal-to-noise ratio or data rate by generating multiple data streams according to the signal power arriving at the receiver over different fading channels. The four-element TX chip fabricated in 65-nm CMOS occupies a die area of 1.44 mm2. It covers an RF range of 0.7–2.5 GHz, and shows an equivalent isotropically radiated power of 23.9 dBm. When transmitting a 20-MHz 64-QAM orthogonal frequency division multiplexing signal at 2.3 GHz, the average system efficiency is 61% and error-vector magnitude is −26 dB.


title: Bloom Filter for Fixed-Size Beacon in VANET

abstract: Most work in vehicular ad hoc network focuses on an efficient mechanism to deliver data. These mechanisms need a beacon to exchange all necessary information between neighbors. Although a beacon is a small packet that is periodically broadcast to maintain accuracy, too much information can cause a bulky beacon. This leads to a contention problem due to limited resources in wireless networks. Many approaches are proposed to avoid the problem by reducing the frequency of beacon broadcasting. However, bulky beacons still exist. In this paper, we propose a solution using a Bloom filter to create fixed-size beacons. A single Bloom filter can replace all variable size data structures. Our solution reduces the complexity of a connected dominating set algorithm from O(n^5) to O(n). The evaluation indicates that our proposed solution can significantly reduce the beacon overhead without decreasing the protocol performance.


title: Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems

abstract: Product review websites provide an incredible lens into the wide variety of opinions and experiences of different people, and play a critical role in helping users discover products that match their personal needs and preferences. To help address questions that can't easily be answered by reading others' reviews, some review websites also allow users to pose questions to the community via a question-answering (QA) system. As one would expect, just as opinions diverge among different reviewers, answers to such questions may also be subjective, opinionated, and divergent. This means that answering such questions automatically is quite different from traditional QA tasks, where it is assumed that a single `correct' answer is available. While recent work introduced the idea of question-answering using product reviews, it did not account for two aspects that we consider in this paper: (1) Questions have multiple, often divergent, answers, and this full spectrum of answers should somehow be used to train the system; and (2) What makes a `good' answer depends on the asker and the answerer, and these factors should be incorporated in order for the system to be more personalized. Here we build a new QA dataset with 800 thousand questions---and over 3.1 million answers---and show that explicitly accounting for personalization and ambiguity leads both to quantitatively better answers, but also a more nuanced view of the range of supporting, but subjective, opinions.


title: A critical review of real-time map-matching algorithms: Current issues and future directions

abstract: Abstract Current navigation systems/services allow drivers to keep track of their precise whereabouts and provide optimal routes to reach specified locations. A reliable map-matching algorithm is an indispensable and integral part of any land-based navigation system/service. This paper reviews existing map-matching algorithms with the aim of highlighting their qualities as well as unfolding their unresolved issues as a means to provide directions for future studies in this field. Existing map-matching algorithms are compared and contrasted with respect to positioning sensors, map qualities, assumptions and accuracies. The results of these comparisons provide interesting insights into the workings of existing algorithms and the issues they must address for improving their performance. Example findings are: (a) not all map-matching algorithms pay sufficient attention to topology of networks, directionality of roads or turn-restrictions; (b) most map-matching algorithms make an unbalanced trade-off between performance and accuracy; and (c) weight-based map-matching algorithms balance simplicity and accuracy and advanced map-matching algorithms provide high accuracy but with low performance. Based on the findings, suggestions are made to improve existing algorithms.


title: Interleaved Group Convolutions

abstract: In this paper, we present a simple and modularized neural network architecture, named interleaved group convolutional neural networks (IGCNets). The main point lies in a novel building block, a pair of two successive interleaved group convolutions: primary group convolution and secondary group convolution. The two group convolutions are complementary: (i) the convolution on each partition in primary group convolution is a spatial convolution, while on each partition in secondary group convolution, the convolution is a point-wise convolution; (ii) the channels in the same secondary partition come from different primary partitions. We discuss one representative advantage: Wider than a regular convolution with the number of parameters and the computation complexity preserved. We also show that regular convolutions, group convolution with summation fusion, and the Xception block are special cases of interleaved group convolutions. Empirical results over standard benchmarks, CIFAR-10, CIFAR-100, SVHN and ImageNet demonstrate that our networks are more efficient in using parameters and computation complexity with similar or higher accuracy.


title: A hybrid data analytic approach to predict college graduation status and its determinative factors

abstract: Purpose ::: ::: ::: ::: ::: The prediction of graduation rates of college students has become increasingly important to colleges and universities across the USA and the world. Graduation rates, also referred to as completion rates, directly impact university rankings and represent a measurement of institutional performance and student success. In recent years, there has been a concerted effort by federal and state governments to increase the transparency and accountability of institutions, making “graduation rates” an important and challenging university goal. In line with this, the main purpose of this paper is to propose a hybrid data analytic approach which can be flexibly implemented not only in the USA but also at various colleges across the world which would help predict the graduation status of undergraduate students due to its generic nature. It is also aimed at providing a means of determining and ranking the critical factors of graduation status. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: This study focuses on developing a novel hybrid data analytic approach to predict the degree completion of undergraduate students at a four-year public university in the USA. Via the deployment of the proposed methodology, the data were analyzed using three popular data mining classifications methods (i.e. decision trees, artificial neural networks, and support vector machines) to develop predictive degree completion models. Finally, a sensitivity analysis is performed to identify the relative importance of each predictor factor driving the graduation. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The sensitivity analysis of the most critical factors in predicting graduation rates is determined to be fall-term grade-point average, housing status (on campus or commuter), and which high school the student attended. The least influential factors of graduation status are ethnicity, whether or not a student had work study, and whether or not a student applied for financial aid. All three data analytic models yielded high accuracies ranging from 71.56 to 77.61 percent, which validates the proposed model. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This study presents uniqueness in that it presents an unbiased means of determining the driving factors of college graduation status with a flexible and powerful hybrid methodology to be implemented at other similar decision-making settings.


Surface Reconstruction from Unorganized Sparse Cross Sections


title: Modularity-Maximizing Network Communities via Mathematical Programming

abstract: In many networks, it is of great interest to identify communities, unusually densely knit groups of individuals. Such communities often shed light on the function of the networks or underlying properties of the individuals. Recently, Newman suggested modularity as a natural measure of the quality of a network partitioning into communities. Since then, various algorithms have been proposed for (approximately) maximizing the modularity of the partitioning determined. In this paper, we introduce the technique of rounding mathematical programs to the problem of modularity maximization, presenting two novel algorithms. More specifically, the algorithms round solutions to linear and vector programs. Importantly, the linear programing algorithm comes with an a posteriori approximation guarantee: by comparing the solution quality to the fractional solution of the linear program, a bound on the available “room for improvement” can be obtained. The vector programming algorithm provides a similar bound for the best partition into two communities. We evaluate both algorithms using experiments on several standard test cases for network partitioning algorithms, and find that they perform comparably or better than past algorithms, while being more efficient than exhaustive techniques.


title: Psychological adjustment and sleep quality in a randomized trial of the effects of a Tibetan yoga intervention in patients with lymphoma

abstract: BACKGROUND ::: Research suggests that stress-reduction programs tailored to the cancer setting help patients cope with the effects of treatment and improve their quality of life. Yoga, an ancient Eastern science, incorporates stress-reduction techniques that include regulated breathing, visual imagery, and meditation as well as various postures. The authors examined the effects of the Tibetan yoga (TY) practices of Tsa lung and Trul khor, which incorporate controlled breathing and visualization, mindfulness techniques, and low-impact postures in patients with lymphoma. ::: ::: ::: METHODS ::: Thirty-nine patients with lymphoma who were undergoing treatment or who had concluded treatment within the past 12 months were assigned to a TY group or to a wait-list control group. Patients in the TY group participated in 7 weekly yoga sessions, and patients in the wait-list control group were free to participate in the TY program after the 3-month follow-up assessment. ::: ::: ::: RESULTS ::: Eighty nine percent of TY participants completed at least 2-3 three yoga sessions, and 58% completed at least 5 sessions. Patients in the TY group reported significantly lower sleep disturbance scores during follow-up compared with patients in the wait-list control group (5.8 vs. 8.1; P < 0.004). This included better subjective sleep quality (P < 0.02), faster sleep latency (P < 0.01), longer sleep duration (P < 0.03), and less use of sleep medications (P < 0.02). There were no significant differences between groups in terms of intrusion or avoidance, state anxiety, depression, or fatigue. ::: ::: ::: CONCLUSIONS ::: The participation rates suggested that a TY program is feasible for patients with cancer and that such a program significantly improves sleep-related outcomes. However, there were no significant differences between groups for the other outcomes.


title: SimPL A Simple Software Production Line for End User Development

abstract: Model-driven engineering (MDE) and product line engineering (PLE) will influence the future of software development and increase the automation of application development. End user development (EUD) is an emerging paradigm where end users create and adapt systems themselves. To leverage MDE and PLE for EUD in small domains, we introduce a simple software production line that allows domain engineers to easily set up product line member instantiation environments for end users. The main contribution of our work is SimPL-the production line tools. SimPL consists of a graphical editor to model the concrete products and a code generator to produce the corresponding software, both based on a single product line specification. In this paper, we describe the concepts and processes incorporated in SimPL and a case study of successful employment.


title: Near-optimal hybrid analog and digital precoding for downlink mmWave massive MIMO systems

abstract: Millimeter wave (mmWave) massive MIMO can achieve orders of magnitude increase in spectral and energy efficiency, and it usually exploits the hybrid analog and digital precoding to overcome the serious signal attenuation induced by mmWave frequencies. However, most of hybrid precoding schemes focus on the full-array structure, which involves a high complexity. In this paper, we propose a near-optimal iterative hybrid precoding scheme based on the more realistic subarray structure with low complexity. We first decompose the complicated capacity optimization problem into a series of ones easier to be handled by considering each antenna array one by one. Then we optimize the achievable capacity of each antenna array from the first one to the last one by utilizing the idea of successive interference cancelation (SIC), which is realized in an iterative procedure that is easy to be parallelized. It is shown that the proposed hybrid precoding scheme can achieve better performance than other recently proposed hybrid precoding schemes, while it also enjoys an acceptable computational complexity.


title: Deep Network Flow for Multi-object Tracking

abstract: Data association problems are an important component of many computer vision applications, with multi-object tracking being one of the most prominent examples. A typical approach to data association involves finding a graph matching or network flow that minimizes a sum of pairwise association costs, which are often either hand-crafted or learned as linear functions of fixed features. In this work, we demonstrate that it is possible to learn features for network-flow-based data association via backpropagation, by expressing the optimum of a smoothed network flow problem as a differentiable function of the pairwise association costs. We apply this approach to multi-object tracking with a network flow formulation. Our experiments demonstrate that we are able to successfully learn all cost functions for the association problem in an end-to-end fashion, which outperform hand-crafted costs in all settings. The integration and combination of various sources of inputs becomes easy and the cost functions can be learned entirely from data, alleviating tedious hand-designing of costs.


title: Underwater image dehazing using modified dark channel prior

abstract: In an underwater environment, light always scatters and absorbs as it travels from the object to the camera, seriously affecting image quality. As a result, underwater images have poorer contrast, like a haze covering them. In this paper, a modified method for images restoration based on the dark channel prior (DCP) is presented. Firstly, the ambient light is estimated according to the difference between the blue and red channel. Then, attenuations of three RGB channels are obtained separately. Finally, a color collection is used to compensate the remaining color distortion. The experiments carried out at last demonstrate the good performance of this method for improving the visibility of underwater images.


title: Ordering Systems: Coordinative Practices and Artifacts in Architectural Design and Planning

abstract: In their cooperative effort, architects depend critically on elaborate coordinative practices and artifacts. The article presents, on the basis of an in-depth study of architectural work, an analysis of these practices and artifacts and shows that they are multilaterally interrelated and form complexes of interrelated practices and artifacts which we have dubbed 'ordering systems'. In doing so, the article outlines an approach to investigating and conceiving of such practices.


title: Toward a framework for levels of robot autonomy in human-robot interaction

abstract: Autonomy is a critical construct related to human-robot interaction (HRI) and varies widely across robot platforms. Levels of robot autonomy (LORA), ranging from teleoperation to fully autonomous systems, influence the way in which humans and robots interact with one another. Thus, there is a need to understand HRI by identifying variables that influence---and are influenced by---robot autonomy. Our overarching goal is to develop a framework for LORA in HRI. To reach this goal, our framework draws links between HRI and human-automation interaction, a field with a long history of studying and understanding human-related variables. The construct of autonomy is reviewed and redefined within the context of HRI. Additionally, this framework proposes a process for determining a robot's autonomy level by categorizing autonomy along a 10-point taxonomy. The framework is intended to be treated as a guideline for determining autonomy, categorizing the LORA along a qualitative taxonomy and considering HRI variables (e.g., acceptance, situation awareness, reliability) that may be influenced by the LORA.


title: XBee wireless sensor networks for Heart Rate Monitoring in sport training

abstract: Heart Rate Monitors (HRMs) have become widely used since the last two decades. It used as training aid for various types of sports. And the development of new HRMs has evolved rapidly. Thus, in order to determine the exercise intensity of training session or race, HRMs are mainly used. Compared to the other indication of exercise intensity, Heart rate is easy to monitor, compatible to use in most situation and relatively cheap. Thus, it is most beneficial if wireless sensor network can be implementing into the system. Other than monitored their condition by themselves, this system may allowed a number of athletes being monitored simultaneously. Arduino-Nano board was used to interface with nRF24AP1 and XBee. The wireless sensor network consists of a microcontroller on Arduino-Nano board, nRF24AP1 and the XBee wireless communication module which is based on the IEEE 802.15.4. This system will involve peer to peer communication of ANT+ and mesh networking among the XBee.


title: Deep transfer network for face recognition using 3D synthesized face

abstract: Face recognition has experienced a flurry of advances with deep learning. However, training a model requires a lot of data. In order to meet this condition, some researchers use the 3D rendering technique to synthesize fake face images to expand the training data. Experimental results have demonstrated that this method is an effective way. There exist, however, dataset bias between the real 2D real face images and 3D synthesized face images. In this paper, we use Deep Transfer Network(DTN) to reduce dataset bias. First, we utilize the 3DMM face model to synthesize face images with various poses and natural expression. We choose the Inception-Resnet-V1 as our benchmark model. Then, we optimize our DTN based on maximum mean discrepancy(MMD) of the shared feature extraction layers and the discrimination layers. Our experiments demonstrate that the model jointly trained using synthesized images and real images is more robust than using either dataset (2D real faces or 3D synthesized faces). Furthermore, the performance obtained by our approach is comparable to the-state-of-the-art results to the systems trained on millions of real images.


Gamification to develop coding skills


title: A design rule for inset-fed rectangular microstrip patch antenna

abstract: In our paper, an inset-fed microstrip patch antenna has been designed and the dependency of resonant frequency on the notch gap and the feed line geometry has been studied. Our study suggests that a narrower notch resulted in better impedance matching. A design rule has also been formulated and presented the performance of the proposed design.


Design and Comparative Study of Three Photovoltaic Battery Charge Control Algorithms in MATLAB/SIMULINK Environment


title: Development of joint torque sensor applied to compensate crosstalk error

abstract: For force control of robot and collision detection with humans, robots that has joint torque sensors have been developed. However, existing torque sensors cannot measure correct torque because of crosstalk error. In order to solve this problem, we proposed a novel torque sensor that can measure the pure torque without crosstalk. The hexaform of the proposed sensor with truss structure increase deformation of the sensor and restoration, and the Wheatstone bridge circuit of strain gauge removes crosstalk error. Sensor performance is verified with FEM analysis.


title: A Gait Analysis Approach to Track Parkinson's Disease Evolution Using Principal Component Analysis

abstract: A research work is reproducible when all research artifacts such as as text, data, figure and code are available for independent researchers reproduce the results. In this paper, we present a reproducible gait analysis to track Parkinson's Disease evolution by monitoring walking abnormalities. Weapplied Principal Component Analysis into gait data to detect user's abnormalities that may indicate the progression of Parkinson's Disease. We validated our approach with a public database of foot sensor data, which includes vertical ground reaction force records of subjects with healthy gait and Parkinson's Disease patients. We used the euclidean distance asdata classifier. We reached a classification accuracy of 81.00% with leave-one-out cross-validation, which demonstrates the feasibility of our approach for tracking PD's symptoms based on user gait. All relevant data to reproduce our results are available in a public web page.


title: Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection

abstract: In an effort to assist factcheckers in the process of factchecking, we tackle the claim detection task, one of the necessary stages prior to determining the veracity of a claim. It consists of identifying the set of sentences, out of a long text, deemed capable of being factchecked. This paper is a collaborative work between Full Fact, an independent factchecking charity, and academic partners. Leveraging the expertise of professional factcheckers, we develop an annotation schema and a benchmark for automated claim detection that is more consistent across time, topics and annotators than previous approaches. Our annotation schema has been used to crowdsource the annotation of a dataset with sentences from UK political TV shows. We introduce an approach based on universal sentence representations to perform the classification, achieving an F1 score of 0.83, with over 5% relative improvement over the state-of-the-art methods ClaimBuster and ClaimRank. The system was deployed in production and received positive user feedback.


title: Grounded theory in software engineering research: a critical review and guidelines

abstract: Grounded Theory (GT) has proved an extremely useful research approach in several fields including medical sociology, nursing, education and management theory. However, GT is a complex method based on an inductive paradigm that is fundamentally different from the traditional hypothetico-deductive research model. As there are at least three variants of GT, some ostensibly GT research suffers from method slurring, where researchers adopt an arbitrary subset of GT practices that are not recognizable as GT. In this paper, we describe the variants of GT and identify the core set of GT practices. We then analyze the use of grounded theory in software engineering. We carefully and systematically selected 98 articles that mention GT, of which 52 explicitly claim to use GT, with the other 46 using GT techniques only. Only 16 articles provide detailed accounts of their research procedures. We offer guidelines to improve the quality of both conducting and reporting GT studies. The latter is an important extension since current GT guidelines in software engineering do not cover the reporting process, despite good reporting being necessary for evaluating a study and informing subsequent research.


title: Big Data: Understanding Big Data

abstract: Steve Jobs, one of the greatest visionaries of our time was quoted in 1996 saying "a lot of times, people do not know what they want until you show it to them" [38] indicating he advocated products to be developed based on human intuition rather than research. With the advancements of mobile devices, social networks and the Internet of Things, enormous amounts of complex data, both structured and unstructured are being captured in hope to allow organizations to make better business decisions as data is now vital for an organizations success. These enormous amounts of data are referred to as Big Data, which enables a competitive advantage over rivals when processed and analyzed appropriately. However Big Data Analytics has a few concerns including Management of Data-lifecycle, Privacy & Security, and Data Representation. This paper reviews the fundamental concept of Big Data, the Data Storage domain, the MapReduce programming paradigm used in processing these large datasets, and focuses on two case studies showing the effectiveness of Big Data Analytics and presents how it could be of greater good in the future if handled appropriately.


title: Emotional Conception How Embodied Emotion Concepts Guide Perception and Facial Action

abstract: This study assessed embodied simulation via electromyography (EMG) as participants first encoded emotionally ambiguous faces with emotion concepts (i.e., “angry,” “happy”) and later passively viewed the faces without the concepts. Memory for the faces was also measured. At initial encoding, participants displayed more smiling-related EMG activity in response to faces paired with “happy” than in response to faces paired with “angry.” Later, in the absence of concepts, participants remembered happiness-encoded faces as happier than anger-encoded faces. Further, during passive reexposure to the ambiguous faces, participants' EMG indicated spontaneous emotion-specific mimicry, which in turn predicted memory bias. No specific EMG activity was observed when participants encoded or viewed faces with non-emotion-related valenced concepts, or when participants encoded or viewed Chinese ideographs. From an embodiment perspective, emotion simulation is a measure of what is currently perceived. Thus, these findings p...


title: Non-verbal memory measured by Rey–Osterrieth Complex Figure B: normative data

abstract: One of the major problems that clinical neuropsychology has had in memory clinics is to apply ecological, easily administrable and sensitive tests that can make the diagnosis of dementia both precocious and reliable. Often the choice of the best neuropsychological test is hard because of a number of variables that can influence a subject’s performance. In this regard, tests originally devised to investigate cognitive functions in healthy adults are not often appropriate to analyze cognitive performance in old subjects with low education because of their intrinsically complex nature. In the present paper, we present normative values for the Rey–Osterrieth Complex Figure B Test (ROCF-B) a simple test that explores constructional praxis and visuospatial memory. We collected normative data of copy, immediate and delayed recall of the ROCF-B in a group of 346 normal Italian subjects above 40 years. A multiple regression analysis was performed to evaluate the potential effect of age, sex, and education on the three tasks administered to the subjects. Age and education had a significant effect on copying, immediate recall, and delayed recall as well as on the rate of forgetting. Correction grids and equivalent scores with cut-off values relative to each task are available. The availability of normative values can make the ROCF-B a valid instrument to assess non-verbal memory in adults and in the elderly for whom the commonly used ROCF-A is too demanding.


title: Realistic 3D Facial Wrinkles Simulation Based on Tessellation

abstract: Realistic wrinkles are important for enhancing the realism of 3D facial expression. In this paper, we proposed a combined method to simulate realistic wrinkles generated in facial expressions. By taking physiological mechanism of facial wrinkles into account, we used the muscle model for facial animation to extract the wrinkle region, and then an improved wrinkle model was applied to generate wrinkles on wrinkle region. In the simulation, we subdivided face model using DirectX 11 Tessellation technique. The experiment results of our combined method demonstrated that it can generate realistic wrinkles on a 3D virtual face.


title: Author ' s personal copy How motivational orientation influences the evaluation and choice of hedonic and pragmatic interactive products : The role of regulatory focus

abstract: The perceived quality of interactive products can be roughly divided into instrumental, task-related, pragmatic attributes (e.g., usefulness, usability) and non-instrumental, self-referential, hedonic attributes (e.g., novelty, beauty). Recent studies suggest that the weighting of both aspects in forming an overall evaluation of an interactive product heavily depends on features of the actual situation, such as whether an individual has to perform a specific task or not. The present paper extends these findings by assuming that a match between an individual's motivational orientation and particular product attributes (i.e., pragmatic, hedonic) moderates the perceived value of interactive products. Specifically, it shows how differences in regulatory foci (promotion or prevention focus), that is, differences in the way goal-directed behavior is regulated, influence product evaluation and choice. Participants were either set in a prevention focus (concern for safety and the avoidance of negative outcomes) or promotion focus (concern for personal growth and the attainment of positive outcomes). Subsequently, they were asked to evaluate and choose between a primarily pragmatic and a primarily hedonic mp3-player. The results revealed the expected effect of the activated regulatory focus on evaluation and choice. Individuals in a promotion focus rated the hedonic player as more appealing and chose it more frequently compared to individuals in a prevention focus. Reverse results, albeit not as strong, were found for the evaluation and choice of the pragmatic player. Our findings support the idea that product appeal and choice is strongly context-dependent. It further extends previous findings by showing that not only major differences in the situation, such as providing a specific task or not, impact product appreciation but that more subtle, motivational orientations can have similar effects.


title: Thermal-Visible Video Fusion for Moving Target Tracking and Pedestrian Classification

abstract: The paper presents a fusion-tracker and pedestrian classifier for color and thermal cameras. The tracker builds a background model as a multi-modal distribution of colors and temperatures. It is constructed as a particle filter that makes a number of informed reversible transformations to sample the model probability space in order to maximize posterior probability of the scene model. Observation likelihoods of moving objects account their 3D locations with respect to the camera and occlusions by other tracked objects as well as static obstacles. After capturing the coordinates and dimensions of moving objects we apply a pedestrian classifier based on periodic gait analysis. To separate humans from other moving objects, such as cars, we detect, in human gait, a symmetrical double helical pattern, that can then be analyzed using the Frieze Group theory. The results of tracking on color and thermal sequences demonstrate that our algorithm is robust to illumination noise and performs well in the outdoor environments.


Provable algorithms for machine learning problems


Adaptive Block-based Light Field Coding


Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization.


title: CL-PRE: a certificateless proxy re-encryption scheme for secure data sharing with public cloud

abstract: We propose CL-PRE, a certificateless proxy re-encryption scheme for secure data sharing with public cloud, which leverages maximal cloud resources to reduce the computing and communication cost for data owner. Towards running proxy in public cloud environment, we further propose multi-proxy CL-PRE and randomized CL-PRE, which enhance the security and robustness of CL-PRE. We implement all CL-PRE schemes and evaluate their security and performance.


title: FPGA based real-time lane detection and tracking implementation

abstract: In this article, we present a system for robust lane detection and tracking. The proposed algorithm has two main parts. The first one is for lane detection using the Sobel operator with an adaptive threshold and the Hough transform, and the second part deals with lane tracking using the Kalman filter. To meet real time requirements, we use gradient directions of edge stage to simplify CORDIC algorithm. The obtained results show that the proposed algorithm can reliably detect and track lanes in different illumination condition.


Root Previous Hash Block Header i Smart Contract Root Account RootNonce Timestamp Transaction Root Previous Hash Block Header i-1 Smart Contract Root Account RootNonce Timestamp Transaction Root Previous Hash Block Header i + 1 Smart Contract Root


title: Center-surround divergence of feature statistics for salient object detection

abstract: In this paper, we introduce a new method to detect salient objects in images. The approach is based on the standard structure of cognitive visual attention models, but realizes the computation of saliency in each feature dimension in an information-theoretic way. The method allows a consistent computation of all feature channels and a well-founded fusion of these channels to a saliency map. Our framework enables the computation of arbitrarily scaled features and local center-surround pairs in an efficient manner. We show that our approach outperforms eight state-of-the-art saliency detectors in terms of precision and recall.


title: Annotating Named Entities in Twitter Data with Crowdsourcing

abstract: We describe our experience using both Amazon Mechanical Turk (MTurk) and Crowd-Flower to collect simple named entity annotations for Twitter status updates. Unlike most genres that have traditionally been the focus of named entity experiments, Twitter is far more informal and abbreviated. The collected annotations and annotation techniques will provide a first step towards the full study of named entity recognition in domains like Facebook and Twitter. We also briefly describe how to use MTurk to collect judgements on the quality of "word clouds."


title: Quantifying Interactive User Experience on Thin Clients

abstract: We describe an approach to quantifying the impact of network latency on interactive response and show that the adequacy of thin-client computing is highly variable and depend on both the application and available network quality. If near ideal network conditions (low latency and high bandwidth) can be guaranteed, thin clients offer a good computing experience. As network quality degrades, interactive performance suffers. It is latency - not bandwidth -that is the greater challenge. Tightly coupled tasks such as graphics editing suffer more than loosely coupled tasks such as Web browsing. The combination of worst anticipated network quality and most tightly coupled tasks determine whether a thin-client approach is satisfactory for an organization.


Organic Agriculture's Contribution to Sustainability


title: Should Persons Detained During Public Health Crises Receive Compensation

abstract: One of the ways in which public health officials control outbreaks of epidemic disease is by attempting to control the situations in which the infectious agent can spread. This may include isolation of infected persons, quarantine of persons who may be infected and detention of persons who are present in or have entered premises where infected persons are being treated. Most who have analysed such measures think that the restrictions in liberty they entail and the detriments in welfare they impose can be justified and this paper proceeds from the assumption that detention measures are justifiable in some circumstances. Such measures are often implemented without any compensation being given to the persons who are detained. This raises the question: What do we owe to those whose liberty is justifiably restricted (e.g. through isolation, quarantine or detention) as a public health measure during a public health emergency? More specifically, do we owe them compensation for any losses they experience? The paper falls in four main sections. The first section provides examples of the current regulatory state of affairs from the US, Canada and WHO. The second section lays out the liberal, welfarist and pragmatic arguments for providing compensation. The third section discusses the arguments against compensation and the fourth and final section provides the conclusion. It is argued that the arguments for providing compensation clearly outweigh the counterarguments and that the default public policy therefore should be that compensation is provided.


title: Manufacturing of the Iseult/INUMAC Whole Body 11.7 T MRI Magnet

abstract: As part the Iseult/Inumac project, a French-German initiative focused on very high magnetic-field molecular imaging, the Whole Body 11.7 T MRI Magnet currently under development is the world's largest to-date. It is an actively shielded magnet system, manufactured from NbTi superconductor, with a homogeneous field level of 11.75 T within a 90 cm warm bore. It will operate at a current of 1483 A, in nonpersistent mode, in a bath of superfluid LHe at 1.8 K. The stored energy is 338 MJ and the inductance 308 H. The cryostat has external dimensions of 5 m in diameter and 5.2 m in length, the total weight of the magnet is 132 tons. The magnet is serviced by a separate cryogenic and electrical facility forming an integral part of the installation. It is currently being manufactured at Alstom Belfort under the supervision of CEA Saclay. Several reduced scale prototypes, each addressing a specific set of design and manufacturing risks, have been tested. Full-scale serial production of the 170 double pancakes that form the main coil has been finished by Alstom. The project plan includes finishing the cold mass and cryostat assembly in May 2014. Full tests and commissioning of the magnet at 1.8 K will be performed at the Neurospin center upon completion of assembly. The paper reviews the manufacturing status of the 11.7 T magnet and its dedicated equipment.


title: Breaking boundaries: the uses & gratifications of grindr

abstract: "People-nearby applications" (PNAs) are a form of ubiquitous computing that connect users based on their physical location data. One example is Grindr, a popular PNA that facilitates connections among gay and bisexual men. Adopting a uses and gratifications approach, we conducted two studies. In study one, 63 users reported motivations for Grindr use through open-ended descriptions. In study two, those descriptions were coded into 26 items that were completed by 525 Grindr users. Factor analysis revealed six uses and gratifications: social inclusion, sex, friendship, entertainment, romantic relationships, and location-based search. Two additional analyses examine (1) the effects of geographic location (e.g., urban vs. suburban/rural) on men's use of Grindr and (2) how Grindr use is related to self-disclosure of information. Results highlight how the mixed-mode nature of PNA technology may change the boundaries of online and offline space, and how gay and bisexual men navigate physical environments.


title: ViTBAT: Video tracking and behavior annotation tool

abstract: Reliable and repeatable evaluation of low-level (tracking) and high-level (behavior analysis) vision tasks require annotation of ground-truth information in videos. Depending on the scenarios, ground-truth annotation may be required for individual targets and/or groups of targets. Unlike the existing tools that generally allow an explicit annotation for individual targets only, we propose a tool that enables an explicit annotation both for individual targets and groups of targets for the tracking and behavior recognition tasks together with effective visualization features. Whether for individuals or groups, the tool allows labeling of their states and behaviors manually or semi-automatically through a simple and friendly user interface in a time-efficient manner. Based on a subjective assessment, the proposed tool is found to be more effective than the well-known ViPER tool on a series of defined criteria. A dedicated website makes the tool publicly available for the community.


title: Impact Of Content Features For Automatic Online Abuse Detection

abstract: Online communities have gained considerable importance in recent years due to the increasing number of people connected to the Internet. Moderating user content in online communities is mainly performed manually, and reducing the workload through automatic methods is of great financial interest for community maintainers. Often, the industry uses basic approaches such as bad words filtering and regular expression matching to assist the moderators. In this article, we consider the task of automatically determining if a message is abusive. This task is complex since messages are written in a non-standardized way, including spelling errors, abbreviations, community-specific codes... First, we evaluate the system that we propose using standard features of online messages. Then, we evaluate the impact of the addition of pre-processing strategies, as well as original specific features developed for the community of an online in-browser strategy game. We finally propose to analyze the usefulness of this wide range of features using feature selection. This work can lead to two possible applications: 1) automatically flag potentially abusive messages to draw the moderator's attention on a narrow subset of messages ; and 2) fully automate the moderation process by deciding whether a message is abusive without any human intervention.


Development of Usability Questionnaires for Electronic Mobile Products and Decision Making Methods


title: A Systematic Approach for Cross-source Point Cloud Registration by Preserving Macro and Micro Structures

abstract: We propose a systematic approach for registering cross-source point clouds. The compelling need for cross-source point cloud registration is motivated by the rapid development of a variety of 3D sensing techniques, but many existing registration methods face critical challenges as a result of the large variations in cross-source point clouds. This paper therefore illustrates a novel registration method which successfully aligns two cross-source point clouds in the presence of significant missing data, large variations in point density, scale difference and so on. The robustness of the method is attributed to the extraction of macro and micro structures. Our work has three main contributions: (1) a systematic pipeline to deal with cross-source point cloud registration; (2) a graph construction method to maintain macro and micro structures; (3) a new graph matching method is proposed which considers the global geometric constraint to robustly register these variable graphs. Compared to most of the related methods, the experiments show that the proposed method successfully registers in cross-source datasets, while other methods have difficulty achieving satisfactory results. The proposed method also shows great ability in same-source datasets.


title: A search and summary application for traffic events detection based on Twitter data

abstract: As a form of social media, Twitter records real life events in our cities as they happen. Huge numbers of tweets under the heading of transportation or metro are published every day. This paper presents an application for Traffic Events Detection and Summary (TEDS) based on mining representative terms from the tweets posted when anomalies occur. The proposed ensemble application contains an efficient TEDS search engine with multiple indexing, ranking, and scoring schemes. Spatio-temporal analysis and a novel wavelet analysis model are applied for traffic event detection. This application could benefit both drivers and transportation authorities. Users can search transportation status and analyze traffic events in specific locations of interest. Utilizing the proposed signal processing technology, we demonstrate the system's effectiveness by examining traffic and metro travel in the Washington D.C. area. As the collaboration between a citizen's life and social media becomes ever greater, this could have a significant impact on the prediction of traffic flow, travel selection, and other city computing functions.


title: Mechanism and control of robotic arm using rotational counterweights

abstract: This paper proposes an actuation mechanism of robotic arms using rotational counterweights. The robotic arm is driven by gravitational and inertia forces of the counterweight mounted on links. The joints of the robotic arms are able to rotate freely though this robot does not have any actuators on joints. Thereby, the robot has high joint flexibility. Additionally, the counterweight system reduces the moment applied to base link caused by gravity effect since the posture of the arm keeps a gravitational equilibrium state statically. Therefore, it is expected that the robotic arm is utilized in narrow and high altitude environment or on small vehicles. This paper also proposes a control method of the new robotic arm. The control method realizes precise position control of joint angle. The usefulness of the method is demonstrated by numerical simulations and experiments using a prototype.


title: Exploration and practice of inter-bank application based on blockchain

abstract: Blockchain is considered as the important technological innovation behind Bitcoin system. It facilitates the transaction payment process by creating a decentralized, general ledger to improve regulatory capacity and remove unnecessary intermediaries. At present, the blockchain technology has been employed in the financial industry for a wide range of experimental application and exploration. In this paper, we firstly analyze the principle architecture and the technical characteristics of blockchain. Then its current research achievements and application scenarios are introduced. Finally, the application of blockchain in China Foreign Exchange Trade System is designed and explored. Combining with the credit matching trading system X-Swap, an inter-bank application based on the blockchain technology is implemented.


title: Present and Future Trends in Filters and Multiplexers

abstract: In the last 10 or 15 years, exciting new developments have occurred in the field of filters and multiplexers. The two words that best explain this phenomenon are “topology” and “technology.” Thus, this paper will cover new connections, new materials, and new processes. This paper summarizes what is new, describes the new devices, and avoids simply surveying the entire field. It is not possible to cover everything, and regrettably, some new and possibly exciting areas are omitted (such as filters fabricated using liquid crystal polymers, active element inclusion, extreme power devices used in accelerators), and others, $\ldots$ , because something has to be left for the next paper! The paper does cover bandstop filters, intrinsically switched film bulk acoustic resonator, and barium–strontium–titanate filters, brand new derivations of lossy filter synthesis and dual-band filter synthesis, multimode networks, new work on optimal multiplexer (multiport network) configurations, and substrate integrated waveguide filters. In each of these areas significant new work has been recently performed and is presented in a summary format suitable for those thinking that the area of filters and multiplexers has become mature (stagnant) with nothing interesting to learn. Nothing could be further from the truth!


title: Speeding up packet I/O in virtual machines

abstract: Most of the work on VM network performance has focused so far on bulk TCP traffic, which covers classical applications of virtualization. Completely new ``paravirtualized devices'' (Xenfront, VIRTIO, vmxnet) have been designed and implemented to improve network throughput. We expect virtualization to become widely used also for different workloads: packet switching devices and middleboxes, Software Defined Networks, etc.. These applications involve very high packet rates that are problematic not only for the hypervisor (which emulates network interfaces) but also for the host itself (which switches packets between guests and physical NICs). In this paper we provide three main results. First, we demonstrate how rates of millions of packets per second can be achieved even within VMs, with limited but targeted modifications on device drivers, hypervisors and the host's virtual switch. Secondly, we show that emulation of conventional NICs (e.g., Intel e1000) is perfectly capable of achieving such packet rates, without requiring completely different device models. Finally, we provide sets of modifications suitable for different use cases (acting only on the guest, or only on the host, or on both) which can improve the network throughput of a VM by 20 times or more. These results are important because they enable a new set of applications within virtual machines. In particular, we achieve guest-to-guest UDP speeds of over 1~Mpps with short frames (and 6~Gbit/s with 1500-byte frames) using a conventional e1000 device, and socket-based sender/receivers. This matches the speed of the OS on bare metal. Furthermore, we reach over 5~Mpps when guests use the netmap API. Our work requires only small changes to device drivers (about 100 lines, both for FreeBSD and Linux version of e1000), similarly small modifications to the hypervisor (we have a QEMU prototype available) and the use of the VALE switch as a network backend. Relevant changes are being incorporated and/or distributed as external patches for FreeBSD, QEMU and Linux.


title: Semantic Change Detection with Hypermaps

abstract: Change detection is the study of detecting changes between two different images of a scene taken at different times. By the detected change areas, however, a human cannot understand how different the two images. Therefore, a semantic understanding is required in the change detection research such as disaster investigation. The paper proposes the concept of semantic change detection, which involves intuitively inserting semantic meaning into detected change areas. We mainly focus on the novel semantic segmentation in addition to a conventional change detection approach. In order to solve this problem and obtain a high-level of performance, we propose an improvement to the hypercolumns representation, hereafter known as hypermaps, which effectively uses convolutional maps obtained from convolutional neural networks (CNNs). We also employ multi-scale feature representation captured by different image patches. We applied our method to the TSUNAMI Panoramic Change Detection dataset, and re-annotated the changed areas of the dataset via semantic classes. The results show that our multi-scale hypermaps provided outstanding performance on the re-annotated TSUNAMI dataset.


title: Agent-Based Intelligent Interface for Wheelchair Movement Control

abstract: People who suffer from any kind of motor difficulty face serious complications to autonomously move in their daily lives. However, a growing number research projects which propose different powered wheelchairs control systems are arising. Despite of the interest of the research community in the area, there is no platform that allows an easy integration of various control methods that make use of heterogeneous sensors and computationally demanding algorithms. In this work, an architecture based on virtual organizations of agents is proposed that makes use of a flexible and scalable communication protocol that allows the deployment of embedded agents in computationally limited devices. In order to validate the proper functioning of the proposed system, it has been integrated into a conventional wheelchair and a set of alternative control interfaces have been developed and deployed, including a portable electroencephalography system, a voice interface or as specifically designed smartphone application. A set of tests were conducted to test both the platform adequacy and the accuracy and ease of use of the proposed control systems yielding positive results that can be useful in further wheelchair interfaces design and implementation.


title: NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations

abstract: This paper introduces Non-Autonomous Input-Output Stable Network (NAIS-Net), a very deep architecture where each stacked processing block is derived from a time-invariant non-autonomous dynamical system. Non-autonomy is implemented by skip connections from the block input to each of the unrolled processing stages and allows stability to be enforced so that blocks can be unrolled adaptively to a pattern-dependent processing depth. NAIS-Net induces non-trivial, Lipschitz input-output maps, even for an infinite unroll length. We prove that the network is globally asymptotically stable so that for every initial condition there is exactly one input-dependent equilibrium assuming tanh units, and multiple stable equilibria for ReL units. An efficient implementation that enforces the stability under derived conditions for both fully-connected and convolutional layers is also presented. Experimental results show how NAIS-Net exhibits stability in practice, yielding a significant reduction in generalization gap compared to ResNets.


Using Model Checking to Identify Customers Purchasing Behaviour in an E-Commerce


title: Estimation of Graphlet Statistics

abstract: Graphlets are induced subgraphs of a large network and are important for understanding and modeling complex networks. Despite their practical importance, graphlets have been severely limited to applications and domains with relatively small graphs. Most previous work has focused on exact algorithms, however, it is often too expensive to compute graphlets exactly in massive networks with billions of edges, and finding an approximate count is usually sufficient for many applications. In this work, we propose an unbiased graphlet estimation framework that is (a) fast with significant speedups compared to the state-of-the-art, (b) parallel with nearly linear-speedups, (c) accurate with <1% relative error, (d) scalable and space-efficient for massive networks with billions of edges, and (e) flexible for a variety of real-world settings, as well as estimating macro and micro-level graphlet statistics (e.g., counts) of both connected and disconnected graphlets. In addition, an adaptive approach is introduced that finds the smallest sample size required to obtain estimates within a given user-defined error bound. On 300 networks from 20 domains, we obtain <1% relative error for all graphlets. This is significantly more accurate than existing methods while using less data. Moreover, it takes a few seconds on billion edge graphs (as opposed to days/weeks). These are by far the largest graphlet computations to date.


title: Wireless Sensor Network for Wearable Physiological Monitoring

abstract: Wearable physiological monitoring system consists of an array of sensors embedded into the fabric of the wearer to continuously monitor the physiological parameters and transmit wireless to a remote monitoring station. At the remote monitoring station the data is correlated to study the overall health status of the wearer. In the conventional wearable physiological monitoring system, the sensors are integrated at specific locations on the vest and are interconnected to the wearable data acquisition hardware by wires woven into the fabric. The drawbacks associated with these systems are the cables woven in the fabric pickup noise such as power line interference and signals from nearby radiating sources and thereby corrupting the physiological signals. Also repositioning the sensors in the fabric is difficult once integrated. The problems can be overcome by the use of physiological sensors with miniaturized electronics to condition, process, digitize and wireless transmission integrated into the single module. These sensors are strategically placed at various locations on the vest. Number of sensors integrated into the fabric form a network (Personal Area Network) and interacts with the human system to acquire and transmit the physiological data to a wearable data acquisition system. The wearable data acquisition hardware collects the data from various sensors and transmits the processed data to the remote monitoring station. The paper discusses wireless sensor network and its application to wearable physiological monitoring and its applications. Also the problems associated with conventional wearable physiological monitoring are discussed.