title: Analysis of lysozyme in cheese samples by on-line combination of capillary zone electrophoresis and mass spectrometry.

abstract: Some methodological aspects of an on-line combination of capillary zone electrophoresis with mass spectrometric detection (CZE-QqQ-MS) were studied in this work as well as the possibilities of using this combination for analysis of the high-molecular mass compounds present in multi-component matrices. All experiments using an on-line combination of capillary electrophoresis with mass spectrometric detection were carried out in cationic mode in covalently-coated capillary. The optimised electrolyte system consisted of 100 mmol/L formic acid. Prior to the CZE-QqQ-MS analysis, an extraction of lysozyme from cheese samples using 1 mol/L of acetic acid was performed. The LOD was 3.6 mg lysozyme per kg and the LOQ was 10.9 mg lysozyme per kg. The concentration range of the lysozyme determined in four cheese samples analysed in this work was from 0.5 to 3.3g of lysozyme per kg. The values of the relative standard deviations thus obtained were from 4.6% to 9.3% depending on the cheese sample.


title: Complete and Interpretable Conformance Checking of Business Processes

abstract: This article presents a method for checking the conformance between an event log capturing the actual execution of a business process, and a model capturing its expected or normative execution. Given a process model and an event log, the method returns a set of statements in natural language describing the behavior allowed by the model but not observed in the log and vice versa. The method relies on a unified representation of process models and event logs based on a well-known model of concurrency, namely event structures. Specifically, the problem of conformance checking is approached by converting the event log into an event structure, converting the process model into another event structure, and aligning the two event structures via an error-correcting synchronized product. Each difference detected in the synchronized product is then verbalized as a natural language statement. An empirical evaluation shows that the proposed method can handle real datasets and produces more concise and higher-level difference descriptions than state-of-the-art conformance checking methods. In a survey designed according to the technology acceptance model, practitioners showed a preference towards the proposed method with respect to a state-of-the-art baseline.


title: Drone-vs-Bird detection challenge at IEEE AVSS2017

abstract: Small drones are a rising threat due to their possible misuse for illegal activities, in particular smuggling and terrorism. The project SafeShore, funded by the European Commission under the Horizon 2020 program, has launched the “drone-vs-bird detection challenge” to address one of the many technical issues arising in this context. The goal is to detect a drone appearing at some point in a video where birds may be also present: the algorithm should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds. This paper reports on the challenge proposal, evaluation, and results1.


title: Low-Cost Smart Refrigerator

abstract: This paper presents Smart Home concepts for Internet of Things (IoT) technologies that will make life at home more convenient. In this paper, we first describe the overall design of a low-cost Smart Refrigerator built with Raspberry Pi. Next, we explain two sensors controlling each camera, which are hooked up to our Rasberry Pi board. We further show how the user can use the Graphical User Interface (GUI) to interact with our system. With this Smart Home and Internet of Things technology, a user-friendly graphical user interface, prompt data synchronization among multiple devices, and real-time actual images captured from the refrigerator, our system can easily assist a family to reduce food waste.


title: HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection

abstract: The development of an anomaly-based intrusion detection system (IDS) is a primary research direction in the field of intrusion detection. An IDS learns normal and anomalous behavior by analyzing network traffic and can detect unknown and new attacks. However, the performance of an IDS is highly dependent on feature design, and designing a feature set that can accurately characterize network traffic is still an ongoing research issue. Anomaly-based IDSs also have the problem of a high false alarm rate (FAR), which seriously restricts their practical applications. In this paper, we propose a novel IDS called the hierarchical spatial-temporal features-based intrusion detection system (HAST-IDS), which first learns the low-level spatial features of network traffic using deep convolutional neural networks (CNNs) and then learns high-level temporal features using long short-term memory networks. The entire process of feature learning is completed by the deep neural networks automatically; no feature engineering techniques are required. The automatically learned traffic features effectively reduce the FAR. The standard DARPA1998 and ISCX2012 data sets are used to evaluate the performance of the proposed system. The experimental results show that the HAST-IDS outperforms other published approaches in terms of accuracy, detection rate, and FAR, which successfully demonstrates its effectiveness in both feature learning and FAR reduction.


title: The human gut microbiome in health: establishment and resilience of microbiota over a lifetime

abstract: Summary ::: With technological advances in culture-independent molecular methods, we are uncovering a new facet of our natural history by accounting for the vast diversity of microbial life which colonizes the human body. The human microbiome contributes functional genes and metabolites which affect human physiology and are, therefore, considered an important factor for maintaining health. Much has been described in the past decade based primarily on 16S rRNA gene amplicon sequencing regarding the diversity, structure, stability and dynamics of human microbiota in their various body habitats, most notably within the gastrointestinal tract (GIT). Relatively high levels of variation have been described across different stages of life and geographical locations for the GIT microbiome. These observations may prove helpful for the future contextualization of patterns in other body habitats especially in relation to identifying generalizable trends over human lifetime. Given the large degree of complexity and variability, a key challenge will be how to define baseline healthy microbiomes and how to identify features which reflect deviations therefrom in the future. In this context, metagenomics and functional omics will likely play a central role as they will allow resolution of microbiome-conferred functionalities associated with health. Such information will be vital for formulating therapeutic interventions aimed at managing microbiota-mediated health particularly in the GIT over the course of a human lifetime.


title: An Empirical Study of Language CNN for Image Captioning

abstract: Language models based on recurrent neural networks have dominated recent image caption generation tasks. In this paper, we introduce a language CNN model which is suitable for statistical language modeling tasks and shows competitive performance in image captioning. In contrast to previous models which predict next word based on one previous word and hidden state, our language CNN is fed with all the previous words and can model the long-range dependencies in history words, which are critical for image captioning. The effectiveness of our approach is validated on two datasets: Flickr30K and MS COCO. Our extensive experimental results show that our method outperforms the vanilla recurrent neural network based language models and is competitive with the state-of-the-art methods.


title: A PSO and Pattern Search based Memetic Algorithm for SVMs Parameters Optimization

abstract: Abstract Addressing the issue of SVMs parameters optimization, this study proposes an efficient memetic algorithm based on particle swarm optimization algorithm (PSO) and pattern search (PS). In the proposed memetic algorithm, PSO is responsible for exploration of the search space and the detection of the potential regions with optimum solutions, while pattern search (PS) is used to produce an effective exploitation on the potential regions obtained by PSO. Moreover, a novel probabilistic selection strategy is proposed to select the appropriate individuals among the current population to undergo local refinement, keeping a well balance between exploration and exploitation. Experimental results confirm that the local refinement with PS and our proposed selection strategy are effective, and finally demonstrate the effectiveness and robustness of the proposed PSO-PS based MA for SVMs parameters optimization.


title: Vision in dogs.

abstract: Compared with the visual system in human beings, the canine visual system could be considered inferior in such aspects as degree of binocular overlap, color perception, accommodative range, and visual acuity. However, in other aspects of vision, such as ability to function in dim light, rapidity with which the retina can respond to another image (flicker fusion), field of view, ability to differentiate shades of gray, and perhaps, ability to detect motion, the canine visual system probably surpasses the human visual system. This has made the dog a more efficient predator in certain environmental situations and permits it to exploit an ecological niche inaccessible to humans.


title: An IoT-Aware Architecture for Smart Healthcare Coaching Systems

abstract: In recent decades the aging of the population has led to a change in the health field with special attention to the issue of home care and e-health. The aim is to provide different types of health care services to the patient's home rather than in hospital so improving the quality of life of patients by allowing them to stay in their own home environment. The healthcare sector is undergoing profound transformation thanks to the possibilities offered by the Internet of Things and new technologies, the mobile and wearable. The new model is oriented to the overall health of the patient, stimulated and implemented through a strong pro activities of the patient and realized using mobile devices and multi-channel technology. The idea is to introduce a set of functionalities in order to support the family or the patient, to act independently or to support existing treatments coaching systems. With this aim the paper proposes an architecture, based on a cognitive model, to realize a coaching system for daily living activities which relies on solving a problem as an human so modeling human cognition. The idea is to handle the process of acquiring skills as a transition from use of declarative knowledge of procedures that can be applied quickly and automatically in specific situations.


title: Efficiency improvement of dual mode DC-DC buck converter under light load using PTWS with a zero current detector

abstract: This paper presents a dual-mode DC-DC buck converter using power transistor width scaling (PTWS) with a zero current detector to improve power efficiency under light load. The buck converter is operated in a dual mode, combining the switching frequency modulation (SFM) mode which uses the voltage controlled oscillator (VCO) under light load, with the PWM mode for heavy load. To enhance power efficiency under light load a PTWS scheme is employed, in which the inductor current is detected using a zero current detector (ZCD) and then used to select the gate size of the power switching transistor. The proposed circuit was designed using a Magnachip 0.35μm CMOS process. Simulation results shows that our proposed converter has 81.3% ∼ 93% power efficiency for output load currents of 10mA ∼ 250mA, which is a 5.3% improvement compared to a simple SFM-PWM dual mode and an 8.9% improvement compared to a PWM-only mode under a light load of 10mA, respectively.


title: WiFi-Based Real-Time Calibration-Free Passive Human Motion Detection †

abstract: With the rapid development of WLAN technology, wireless device-free passive human detection becomes a newly-developing technique and holds more potential to worldwide and ubiquitous smart applications. Recently, indoor fine-grained device-free passive human motion detection based on the PHY layer information is rapidly developed. Previous wireless device-free passive human detection systems either rely on deploying specialized systems with dense transmitter-receiver links or elaborate off-line training process, which blocks rapid deployment and weakens system robustness. In the paper, we explore to research a novel fine-grained real-time calibration-free device-free passive human motion via physical layer information, which is independent of indoor scenarios and needs no prior-calibration and normal profile. We investigate sensitivities of amplitude and phase to human motion, and discover that phase feature is more sensitive to human motion, especially to slow human motion. Aiming at lightweight and robust device-free passive human motion detection, we develop two novel and practical schemes: short-term averaged variance ratio (SVR) and long-term averaged variance ratio (LVR). We realize system design with commercial WiFi devices and evaluate it in typical multipath-rich indoor scenarios. As demonstrated in the experiments, our approach can achieve a high detection rate and low false positive rate.


title: Building loyalty to online communities through bond and identity-based attachment to sub-groups

abstract: Researchers and theorists have proposed that feelings of attachment to subgroups within a larger online community or site can increase users' loyalty to the site. They have identified two types of attachment, with distinct causes and consequences. With bond-based attachment, people feel connections to other group members, while with identity-based attachment they feel connections to the group as a whole. In two experiments we show that these feelings of attachment to subgroups increase loyalty to the larger community. Communication with other people in a subgroup but not simple awareness of them increases attachment to the larger community. By varying how the communication is structured, between dyads or with all group members simultaneously, the experiments show that bond- and identity-based attachment have different causes. But the experiments show no evidence that bond and identity attachment have different consequences. We consider both theoretical and methodological reasons why the consequences of bond-based and identity-based attachment are so similar.


title: High-Speed Image Feature Detection Using FPGA Implementation of Fast Algorithm.

abstract: Many of contemporary computer and machine vision applications require finding of corresponding points across multiple images. To that goal, among many features, the most commonly used are corner points. Corners are formed by two or more edges, and mark the boundaries of objects or boundaries between distinctive object parts. This makes corners the feature points that used in a wide range of tasks. Therefore, numerous corner detectors with different properties have been developed. In this paper, we present a complete FPGA architecture implementing corer detection. This architecture is based on the FAST algorithm. The proposed solution is capable of processing the incoming image data with the speed of hundreds of frames per second for a 512×512, 8-bit gray-scale image. The speed is comparable to the results achieved by top-of-the-shelf general purpose processors. However, the use of inexpensive FPGA allows to cut costs, power consumption and to reduce the footprint of a complete system solution. The paper includes also a brief description of the implemented algorithm, resource usage summary, resulting images, as well as block diagrams of the described architecture.


title: The wire-tap channel

abstract: We consider the situation in which digital data is to be reliably transmitted over a discrete, memoryless channel (dmc) that is subjected to a wire-tap at the receiver. We assume that the wire-tapper views the channel output via a second dmc). Encoding by the transmitter and decoding by the receiver are permitted. However, the code books used in these operations are assumed to be known by the wire-tapper. The designer attempts to build the encoder-decoder in such a way as to maximize the transmission rate R, and the equivocation d of the data as seen by the wire-tapper. In this paper, we find the trade-off curve between R and d, assuming essentially perfect (“error-free”) transmission. In particular, if d is equal to Hs, the entropy of the data source, then we consider that the transmission is accomplished in perfect secrecy. Our results imply that there exists a C s > 0, such that reliable transmission at rates up to C s is possible in approximately perfect secrecy.


title: Validity of Consumer-Based Physical Activity Monitors for Specific Activity Types

abstract: ABSTRACTPurposeConsumer-based physical activity (PA) monitors are popular for individual tracking of PA variables. However, current research has not examined how these monitors track energy expenditure (EE) and steps in distinct activities. This study examined the accuracy of the Fitbits One, Zip, a


title: A functional MRI study of deception among offenders with antisocial personality disorders

abstract: Abstract Deceit is a core feature of antisocial personality disorder (ASPD), and the study of deception in ASPD has important implications for identifying the underlying mechanism of ASPD. A great deal of functional neuroimaging literature has described the neural correlates of deception in healthy volunteers, but there have been few imaging studies examining people with ASPD. The neural correlates of lie-telling in ASPD, and which specific brain activities are related to the capacity to lie, are unclear. In this study, 32 offenders who satisfied the Personality Diagnostic Questionaire-4 and PDI-IV (Personality Disorder Interview) criteria for ASPD were divided into three groups based on their capacity for deception, which was evaluated based on the deceitfulness criterion of the PDI-IV ASPD. All offenders underwent functional magnetic resonance imaging (fMRI) while responding to questions in a truthful, inverse, or deceitful manner. We primarily created contrasts in the brain activities between truth-telling and lie-telling, and then computed the Pearson’s correlation coefficients between activities contrasts of individual, i.e. BOLD (blood-oxygen-level-dependent) strength during deception minus that during truth-telling, and the capacity for deception. Our results indicated that the bilateral dorsolateral prefrontal cortex extending to the middle frontal gyrus, the left inferior parietal lobule, and the bilateral anterior cingulate gyrus/medial superior frontal gyrus were associated with deception among people with ASPD. As the capacity for deception increased, the contrasted brain activities of the above regions decreased. This study found that truthful and untruthful communications of ASPD subjects can be differentiated in terms of brain BOLD activities, and more importantly, this study is the first to use fMRI to discover that BOLD activities during deception are correlated with the capacity to lie. The latter finding might challenge the diagnostic accuracy of lie detection and may also caution that greater attention should be given to detecting untruths in individuals who are skilled at lying.


title: Cloud-dew architecture

abstract: Users derive many benefits by storing personal data in cloud computing services; however the drawback of storing data in these services is that the user cannot access his/her own data when an internet connection is not available. To solve this problem in an efficient and elegant way, we propose the cloud-dew architecture. Cloud-dew architecture is an extension of the client-server architecture. In the extension, servers are further classified into cloud servers and dew servers. The dew servers are web servers that reside on user's local computers and have a pluggable structure so that scripts and databases of websites can be installed easily. The cloud-dew architecture not only makes the personal data stored in the cloud continuously accessible by the user, but also enables a new application: web-surfing without an internet connection. An experimental system is presented to demonstrate the ideas of the cloud-dew architecture.


title: Toward Normative Expert Systems : Part I The Pathfinder Project

abstract: Pathfinder is an expert system that assists surgical pathologists with the diagnosis of lymph-node diseases. The program is one of a growing number of normative expert systems that use probability and decision theory to acquire, represent, manipulate, and explain uncertain medical knowledge. In this article, we describe Pathfinder and our research in uncertain-reasoning paradigms that was stimulated by the development of the program. We discuss limitations with early decision-theoretic methods for reasoning under uncertainty and our initial attempts to use non-decision-theoretic methods. Then, we describe experimental and theoretical results that directed us to return to reasoning methods based in probability and decision theory.


title: MACE: high-coverage and robust memory analysis for commodity operating systems

abstract: Memory forensic analysis collects evidence for digital crimes and malware attacks from the memory of a live system. It is increasingly valuable, especially in cloud computing. However, memory analysis on on commodity operating systems (such as Microsoft Windows) faces the following key challenges: (1) a partial knowledge of kernel data structures; (2) difficulty in handling ambiguous pointers; and (3) lack of robustness by relying on soft constraints that can be easily violated by kernel attacks. To address these challenges, we present MACE, a memory analysis system that can extract a more complete view of the kernel data structures for closed-source operating systems and significantly improve the robustness by only leveraging pointer constraints (which are hard to manipulate) and evaluating these constraint globally (to even tolerate certain amount of pointer attacks). We have evaluated MACE on 100 memory images for Windows XP SP3 and Windows 7 SP0. Overall, MACE can construct a kernel object graph from a memory image in just a few minutes, and achieves over 95% recall and over 96% precision. Our experiments on real-world rootkit samples and synthetic attacks further demonstrate that MACE outperforms other external memory analysis tools with respect to wider coverage and better robustness.


title: Alleviating Parental Concerns for Children's Online Privacy: A Value Sensitive Design Investigation

abstract: The objective of this research is to address the acute privacy challenge of protecting children’s online safety by proposing a technological solution to empower parental control over their child’s personal information disclosed online. As a preliminary conceptual investigation, this paper draws on the social, psychological, and legal perspectives of privacy to derive three design principles. We propose that, the technical systems for protecting children’s online privacy (a) should protect children’s personal information online while enabling their access to appropriate online content, (b) should maximally facilitate parental involvement of their children’s online activities, and (c) should comply with legal requirements in terms of notice, choice, access and security. This study reported here is novel to the extent that existing IS research has not systematically examined the privacy issues from the VSD perspective. We believe that, using the groundwork laid down in this study, future research along these directions could contribute significantly to addressing parental concerns for children’s online safety.


title: Time-series prediction using a local linear wavelet neural network

abstract: A local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. A hybrid training algorithm of particle swarm optimization (PSO) with diversity learning and gradient descent method is introduced for training the LLWNN. Simulation results for the prediction of time-series show the feasibility and effectiveness of the proposed method.


Unsupervised Cross-Domain Image Generation


title: Principal Component Analysis by $L_{p}$ -Norm Maximization

abstract: This paper proposes several principal component analysis (PCA) methods based on Lp-norm optimization techniques. In doing so, the objective function is defined using the Lp-norm with an arbitrary p value, and the gradient of the objective function is computed on the basis of the fact that the number of training samples is finite. In the first part, an easier problem of extracting only one feature is dealt with. In this case, principal components are searched for either by a gradient ascent method or by a Lagrangian multiplier method. When more than one feature is needed, features can be extracted one by one greedily, based on the proposed method. Second, a more difficult problem is tackled that simultaneously extracts more than one feature. The proposed methods are shown to find a local optimal solution. In addition, they are easy to implement without significantly increasing computational complexity. Finally, the proposed methods are applied to several datasets with different values of p and their performances are compared with those of conventional PCA methods.


title: Processing of Azimuth-Invariant Bistatic SAR Data Using the Range Doppler Algorithm

abstract: This paper discusses bistatic synthetic aperture radar processing (complex image formation) using the Range Doppler Algorithm. The key step is to use an analytical form of the signal spectrum derived by the method of series reversion. The spectrum is used for secondary range compression (SRC), range cell migration correction, and azimuth compression. The algorithm is able to focus the azimuth-invariant bistatic configuration where the transmitter and receiver platforms are moving in parallel tracks with identical velocities. Moreover, the algorithm is able to handle reasonably high squints and wide apertures because SRC can be performed in the 2-D frequency domain.


title: ABSTRACT Identity, Reference, and Meaning on the Web

abstract: Problems of reference, identity, and meaning are becoming increasingly endemic on the Web. We focus rst on the convergence between Web architecture and classical problems in philosophy, leading to the advent of \philosophical engineering." We survey how the Semantic Web initiative in particular provoked an \identity crisis" for the Web due to its use of URIs for both \things" and web pages and the W3C’s proposed solution. The problem of reference is inspected in relation to both the direct object theory of reference of Russell and the causal theory of reference of Kripke, and the proposed standards of new URN spaces and Published Subjects. Then we progress onto the problem of meaning in light of the Fregean slogan of the priority of truth over reference and the notion of logical interpretation. The popular notion of \social meaning" and the practice of tagging as a possible solution is analyzed in light of the ideas of Lewis on convention. Finally, we conclude that a full notion of meaning, identity, and reference may be possible, but that it is an open problem whether or not practical implementations and standards can be created.


title: A High-Efficiency 24 GHz Rectenna Development Towards Millimeter-Wave Energy Harvesting and Wireless Power Transmission

abstract: This work addresses design and implementation issues of a 24 GHz rectenna, which is developed to demonstrate the feasibility of wireless power harvesting and transmission (WPT) techniques towards millimeter-wave regime. The proposed structure includes a compact circularly polarized substrate integrated waveguide (SIW) cavity-backed antenna array integrated with a self-biased rectifier using commercial Schottky diodes. The antenna and the rectifier are individually designed, optimized, fabricated and measured. Then they are integrated into one circuit in order to validate the studied rectenna architecture. The maximum measured conversion efficiency and DC voltage are respectively equal to 24% and 0.6 V for an input power density of 10 mW/cm2.


title: Light microscopy applications in systems biology: opportunities and challenges

abstract: Biological systems present multiple scales of complexity, ranging from molecules to entire populations. Light microscopy is one of the least invasive techniques used to access information from various biological scales in living cells. The combination of molecular biology and imaging provides a bottom-up tool for direct insight into how molecular processes work on a cellular scale. However, imaging can also be used as a top-down approach to study the behavior of a system without detailed prior knowledge about its underlying molecular mechanisms. In this review, we highlight the recent developments on microscopy-based systems analyses and discuss the complementary opportunities and different challenges with high-content screening and high-throughput imaging. Furthermore, we provide a comprehensive overview of the available platforms that can be used for image analysis, which enable community-driven efforts in the development of image-based systems biology.


title: A Dual-Band Dual-Circular-Polarization Antenna for Ka-Band Satellite Communications

abstract: A novel Ka-band dual-band dual-circularly-polarized antenna array is presented in this letter. A dual-band antenna with left-hand circular polarization for the Ka-band downlink frequencies and right-hand circular polarization for the Ka-band uplink frequencies is realized with compact annular ring slots. By applying the sequential rotation technique, a 2 × 2 subarray with good performance is obtained. This letter describes the design process and presents simulation and measurement results.


title: Object-class segmentation using deep convolutional neural networks

abstract: After successes at image classification, segmentation is the next step towards image understanding for neural networks. We propose a convolutional network architecture that outperforms current methods on the challenging INRIA-Graz02 dataset with regards to accuracy and speed.


title: USING BLUEJ AND BLACKJACK TO TEACH OBJECT- ORIENTED DESIGN CONCEPTS IN CS1 *

abstract: This paper describes a sequence of Java programming assignments for the CS1 course which can be used to reinforce the basic concepts of object-oriented design in a logical, consistent way. By using the BlueJ environment, even programming novices are able to develop, over the course of four assignments, a working implementation of the card game Blackjack. Because the concepts are presented in the context of a fun and familiar application, the assignment increases their level of engagement. In addition, since students are allowed to discover for themselves the advantages of object-oriented design, they develop a better understanding of the material.


title: FiDO: A Community-based Web Browsing Agent and CDN for Challenged Network Environments

abstract: Homes located on tribal lands, particularly in rural areas of the United States, continue to lack access to broadband Internet and cellular connectivity [19]. Inspired by previous observations of community content similarity in tribal networks, we propose FiDO, a community-based Web browsing and content delivery system that takes advantage of user mobility, opportunistic connectivity, and collaborative filtering to provide relevant Web content to members of disconnected households via opportunistic contact with cellular base stations during a daily commute. We evaluate FiDO using trace-driven simulations with network usage data collected from a tribal-operated ISP that serves the Coeur d’Alene Indian Reservation in Western Idaho. By collecting data about household Web preferences and applying a collaborative filtering technique based on the Web usage patterns of the surrounding reservation community, we are able to opportunistically browse the Web on behalf of members of disconnected households, providing an average of 69.4 Web pages (all content from a specific URL, e.g., “http://gis.cdatribe-nsn.gov/LandBuyBack/”) crawled from 73% of their top 10 most visited Web domains (e.g., “cdatribe-nsn.gov” or “cnn.com/”) per day. Moreover, this content is able to be fetched and pushed to users even when the opportunistic data rate is limited to an average of only 0.99 Mbps (σ = 0.24 Mbps) and the daily opportunistic connection time is an average of 45.9 minutes (σ = 2.3 minutes). Additionally, we demonstrate a hybrid “search and browse” approach that allocates a percentage of opportunistic resources to the download of user-specified content. By dedicating only 10% of opportunistic windows of connectivity to the download of social media content, 51% of households were able to receive all of their daily expected social media content in addition to an average of 55.3 Web pages browsed on their behalf from an average of 4 different Web domains. Critically, we demonstrate the feasibility of a collaborative and community-based Web browsing model that extends access to Web content across the last mile(s) using existing infrastructure and rural patterns of mobility.


title: Fisher information distance: a geometrical reading

abstract: This paper is a strongly geometrical approach to the Fisher distance, which is a measure of dissimilarity between two probability distribution functions. The Fisher distance, as well as other divergence measures, are also used in many applications to establish a proper data average. The main purpose is to widen the range of possible interpretations and relations of the Fisher distance and its associated geometry for the prospective applications. It focuses on statistical models of the normal probability distribution functions and takes advantage of the connection with the classical hyperbolic geometry to derive closed forms for the Fisher distance in several cases. Connections with the well-known Kullback-Leibler divergence measure are also devised.


title: Distributed Decision and Control for Cooperative UAVs Using Ad Hoc Communication

abstract: This study develops a novel distributed algorithm for task assignment (TA), coordination, and communication of multiple unmanned aerial vehicles (UAVs) engaging multiple targets and conceives an ad hoc routing algorithm for synchronization of target lists utilizing a distributed computing topology. Assuming limited communication bandwidth and range, coordination of UAV motion is achieved by implementing a simple behavioral flocking algorithm utilizing a tree topology for distributed flight coordination. Distributed TA is implemented by a relaxation process, wherein each node computes a temporary TA based on the union of the TAs of its neighbors in the tree. The computation of the temporary TAs at each node is based on weighted matching in the UAV-target distances graph. A randomized sampling mechanism is used to propagate TAs among different parts of the tree. Thus, changes in the location of the UAVs and targets do not pass through the root of the tree. Simulation experiments show that the combination of the flocking and the TA algorithms yields the best performance.


title: High Prevalence of Non-ST131 CTX-M-15-Producing Escherichia coli in Healthy Cattle in Lebanon

abstract: Extended-Spectrum Beta-Lactamases (ESBL)-producing Escherichia coli have disseminated in both humans and animals worldwide. However, the ESBL epidemiology in these two reservoirs differs markedly, with CTX-M-15 frequently found in humans and CTX-M-1 preferentially found in animals. Our goal was to estimate the prevalence of fecal carriage of ESBL producers in cattle from 31 farms in Lebanon and to characterize the responsible enzymes. This prevalence was high (26/31, 84% of ESBL-positive farms), with a majority of isolates producing CTX-M-15 (27/40, 67.5%). Strikingly, this distribution is reminiscent of the human ESBL epidemiology, even though none of the bovine isolates belonged to the ST131 human clone. This is the first report of ESBL-producing E. coli in animals in Lebanon. Our data rather suggest the spread of CTX-M-15 plasmids in different E. coli backgrounds. Nonetheless, some CTX-M-15-producing E. coli clones found here have already been reported from animal, human, or environmental sources.


'Brains are Survival Engines, not Truth Detectors': Machine-Oriented Ontology and the Horror of Being Human in Blindsight


title: Path-based depth-first search for strong and biconnected components

abstract: Depth-first search, as developed by Tarjan and coauthors, is a fundamental technique of efficient algorithm design for graphs [23]. This note presents depth-first search algorithms for two basic problems, strong and biconnected components. Previous algorithms either compute auxiliary quantities based on the depth-first search tree (e.g., LOWPOINT values) or require two passes. We present one-pass algorithms that only maintain a representation of the depth-first search path. This gives a simplified view of depth-first search without sacrificing efficiency. In greater detail, most depth-first search algorithms (e.g., [23,10,11]) compute so-called LOWPOINT values that are defined in terms of the depth-first search tree. Because of the success of this method LOWPOINT values have become almost synonymous with depth-first search. LOWPOINT values are regarded as crucial in the strong and biconnected component algorithms, e.g., [14, pp. 94, 514]. Tarjan’s LOWPOINT method for strong components is presented in texts [1, 7,14,16,17,21]. The strong component algorithm of Kosaraju and Sharir [22] is often viewed as conceptu-


title: Local feature extraction and matching on range images: 2.5D SIFT

abstract: This paper presents an algorithm that extracts robust feature descriptors from 2.5D range images, in order to provide accurate point-based correspondences between compared range surfaces. The algorithm is inspired by the two-dimensional (2D) Scale Invariant Feature Transform (SIFT) in which descriptors comprising the local distribution function of the image gradient orientations, are extracted at each sampling keypoint location over a local measurement aperture. We adapt this concept into the 2.5D domain by concatenating the histogram of the range surface topology types, derived using the bounded [-1,1] shape index, and the histogram of the range gradient orientations to form a feature descriptor. These histograms are sampled within a measurement window centred over each mathematically derived keypoint location. Furthermore, the local slant and tilt at each keypoint location are estimated by extracting range surface normals, allowing the three-dimensional (3D) pose of each keypoint to be recovered and used to adapt the descriptor sampling window to provide a more reliable match under out-of-plane viewpoint rotation.


title: A classification of data quality assessment methods.

abstract: Data quality (DQ) assessment can be significantly enhanced with the use of the right DQ assessment methods, which provide automated solutions to assess DQ. The range of DQ assessment methods is very broad: from data profiling and semantic profiling to data matching and data validation. This paper gives an overview of current methods for DQ assessment and classifies the DQ assessment methods into an existing taxonomy of DQ problems. Specific examples of the placement of each DQ method in the taxonomy are provided and illustrate why the method is relevant to the particular taxonomy position. The gaps in the taxonomy, where no current DQ methods exist, show where new methods are required and can guide future research and DQ tool development.


title: Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification

abstract: Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is neovascularisation, the growth of abnormal new vessels. This paper describes an automated method for the detection of new vessels in retinal images. Two vessel segmentation approaches are applied, using the standard line operator and a novel modified line operator. The latter is designed to reduce false responses to non-vessel edges. Both generated binary vessel maps hold vital information which must be processed separately. This is achieved with a dual classification system. Local morphology features are measured from each binary vessel map to produce two separate feature sets. Independent classification is performed for each feature set using a support vector machine (SVM) classifier. The system then combines these individual classification outcomes to produce a final decision. Sensitivity and specificity results using a dataset of 60 images are 0.862 and 0.944 respectively on a per patch basis and 1.00 and 0.90 respectively on a per image basis.


title: Camera-based observation of football games for analyzing multi-agent activities

abstract: This paper describes a camera-based observation system for football games that is used for the automatic analysis of football games and reasoning about multi-agent activity. The observation system runs on video streams produced by cameras set up for TV broadcasting. The observation system achieves reliability and accuracy through various mechanisms for adaptation, probabilistic estimation, and exploiting domain constraints. It represents motions compactly and segments them into classified ball actions.


title: Tangible user interface for chemistry education: comparative evaluation and re-design

abstract: Augmented Chemistry (AC) is an application that utilizes a tangible user interface (TUI) for organic chemistry education. The empirical evaluation described in this paper compares learning effectiveness and user acceptance of AC versus the more traditional ball-and-stick model (BSM). Learning effectiveness results were almost the same for both learning environments. User preference and rankings, using NASA-TLX and SUMI, showed more differences and it was therefore decided to focus mainly on improving these aspects in a re-design of the AC system. For enhanced interaction, keyboard-free system configuration, and internal/external database (DB) access, a graphical user interface (GUI) has been incorporated into the TUI. Three-dimensional (3D) rendering has also been improved using shadows and related effects, thereby enhancing depth perception. The re-designed AC system was then compared to the old system by means of a small qualitative user study. This user study showed an improvement in subjective opinions a out the system's ease of use and ease of learning.


title: The cost of interrupted work: more speed and stress

abstract: We performed an empirical study to investigate whether the context of interruptions makes a difference. We found that context does not make a difference but surprisingly, people completed interrupted tasks in less time with no difference in quality. Our data suggests that people compensate for interruptions by working faster, but this comes at a price: experiencing more stress, higher frustration, time pressure and effort. Individual differences exist in the management of interruptions: personality measures of openness to experience and need for personal structure predict disruption costs of interruptions. We discuss implications for how system design can support interrupted work.


title: Facebook and Online Privacy: Attitudes, Behaviors, and Unintended Consequences

abstract: This article investigates Facebook users' awareness of privacy issues and perceived benefits and risks of utilizing Facebook. Research found that Facebook is deeply integrated in users' daily lives through specific routines and rituals. Users claimed to understand privacy issues, yet reported uploading large amounts of personal information. Risks to privacy invasion were ascribed more to others than to the self. However, users reporting privacy invasion were more likely to change privacy settings than those merely hearing about others' privacy invasions. Results suggest that this lax attitude may be based on a combination of high gratification, usage patterns, and a psychological mechanism similar to third-person effect. Safer use of social network services would thus require changes in user attitude.


title: Delayed Impact of Fair Machine Learning

abstract: Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect. ::: We study how static fairness criteria interact with temporal indicators of well-being, such as long-term improvement, stagnation, and decline in a variable of interest. We demonstrate that even in a one-step feedback model, common fairness criteria in general do not promote improvement over time, and may in fact cause harm in cases where an unconstrained objective would not. ::: We completely characterize the delayed impact of three standard criteria, contrasting the regimes in which these exhibit qualitatively different behavior. In addition, we find that a natural form of measurement error broadens the regime in which fairness criteria perform favorably. ::: Our results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria, suggesting a range of new challenges and trade-offs.


title: Radar micro-Doppler feature extraction using the spectrogram and the cepstrogram

abstract: The radar micro-Doppler signature of a target is determined by parts of the target moving or rotating in addition to the main body motion. The relative motion of parts is characteristic for different classes of targets, e.g. the flapping motion of a bird's wings vs. the spinning of propeller blades. In the present study, the micro-Doppler signature is exploited to discriminate birds and small unmanned aerial vehicles (UAVs). Emphasis is on micro-Doppler features that can be extracted from spectrograms and cepstrograms, enabling the human eye or indeed automatic classification algorithms to make a quick distinction between man-made objects and bio-life. In addition, in case of man-made objects, it is desired to further characterize the type of mini-UAV to aid the threat assessment. Also this characterization is done on the basis of micro-Doppler features.


title: Segmentation of Brain Tumour and Its Area Calculation in Brain MR Images using K-Mean Clustering and Fuzzy C- Mean Algorithm

abstract: This paper deals with the implementation of Simple Algorithm for detection of range and shape of tumour in brain MR images. Tumour is an uncontrolled growth of tissues in any part of the body. Tumours are of different types and they have different Characteristics and different treatment. As it is known, brain tumour is inherently serious and life-threatening because of its character in the limited space of the intracranial cavity (space formed inside the skull). Most Research in developed countries show that the number of people who have brain tumours were died due to the fact of inaccurate detection. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection & diagnosis of brain tumour. However this method of detection resists the accurate determination of stage & size of tumour. To avoid that, this project uses computer aided method for segmentation (detection) of brain tumour based on the combination of two algorithms. This method allows the segmentation of tumour tissue with accuracy and reproducibility comparable to manual segmentation. In addition, it also reduces the time for analysis. At the end of the process the tumour is extracted from the MR image and its exact position and the shape also determined. The stage of the tumour is displayed based on the amount of area calculated from the cluster. This paper deals with the concept for automatic brain tumour segmentation. Normally the anatomy of the Brain can be viewed by the MRI scan or CT scan. In this paper the MRI scanned image is taken for the entire process. The MRI scan is more comfortable than CT scan for diagnosis. It is not affect the human body. Because it doesn't use any radiation. It is based on the magnetic field and radio waves (1). There are different types of algorithm were developed for brain tumour detection. But they may have some drawback in detection and extraction. In this paper, two algorithms are used for segmentation. So it gives the accurate result for tumour segmentation (2). Tumour is due to the uncontrolled growth of the tissues in any part of the body. The tumour may be primary or secondary. If it is an origin, then it is known as primary. If the part of the tumour is spread to another place and grown as its own then it is known as secondary. Normally brain tumour affects CSF (Cerebral Spinal Fluid). It causes for strokes. The physician gives the treatment for the strokes rather than the treatment for tumour. So detection of tumour is important for that treatment. The lifetime of the person who affected by the brain tumour will increase if it is detected at current stage. That will increase the lifetime about 1 to 2 years. Normally tumour cells are of two types. They are Mass and Malignant. The detection of the malignant tumour is somewhat difficult to mass tumour. For the accurate detection of the malignant tumour that needs a 3-D representation of brain and 3-D analyzer tool. Image segmentation plays


title: Accelerated Optimization in the PDE Framework: Formulations for the Active Contour Case

abstract: Following the seminal work of Nesterov, accelerated optimization methods have been used to powerfully boost the performance of first-order, gradient-based parameter estimation in scenarios where second-order optimization strategies are either inapplicable or impractical. Not only does accelerated gradient descent converge considerably faster than traditional gradient descent, but it also performs a more robust local search of the parameter space by initially overshooting and then oscillating back as it settles into a final configuration, thereby selecting only local minimizers with a basis of attraction large enough to contain the initial overshoot. This behavior has made accelerated and stochastic gradient search methods particularly popular within the machine learning community. In their recent PNAS 2016 paper, Wibisono, Wilson, and Jordan demonstrate how a broad class of accelerated schemes can be cast in a variational framework formulated around the Bregman divergence, leading to continuum limit ODE's. We show how their formulation may be further extended to infinite dimension manifolds (starting here with the geometric space of curves and surfaces) by substituting the Bregman divergence with inner products on the tangent space and explicitly introducing a distributed mass model which evolves in conjunction with the object of interest during the optimization process. The co-evolving mass model, which is introduced purely for the sake of endowing the optimization with helpful dynamics, also links the resulting class of accelerated PDE based optimization schemes to fluid dynamical formulations of optimal mass transport.


title: Design of area and power efficient full adder in 180nm

abstract: This paper presents a high drivability of full adder with less area and power consumption. This GDI based full adder is implemented by using both gate diffusion input (GDI) technique and pass transistor logic that leads to be a reduced area and power. To reducing the static power, ultralow power diode (ULPD) is used. The leakage current of this diode lies within the range of pA. The comparison has been done between existing systems like CMOS, CPL and hybrid full adders with proposed full adder. All full adders are designed with gpdk 0.18 um in Cadence Virtuoso schematic, and simulations are done in a Spectre Simulator.


title: Edge detection based multi-material interface extraction on industrial CT volumes

abstract: X-ray CT scanning is recently becoming a popular technology for industrial applications as well as medical ones. Since the geometrical accuracy is often important for industrial applications, more precise methods for processing CT volumes are required. This paper proposes a method for extracting the multi-material interfaces on CT volumes obtained by industrial CT scanners. Instead of extracting isosurfaces we detect edge-points, at which the norm of the volume gradient takes a local maximum in the gradient direction, and then interpolate the points as the material interfaces represented by the zero-level of compounded implicit functions. In order to achieve a robust material-identification, multilabel graph-cut is utilized in our method. Using edge-points, we can reduce the inaccuracy caused by CT scanning artifacts.


title: Defeating SQL Injection

abstract: The best strategy for combating SQL injection, which has emerged as the most widespread website security risk, calls for integrating defensive coding practices with both vulnerability detection and runtime attack prevention methods.


title: Threshold-Optimal DSA/ECDSA Signatures and an Application to Bitcoin Wallet Security

abstract: While threshold signature schemes have been presented before, there has never been an optimal threshold signature algorithm for DSA. The properties of DSA make it quite challenging to build a threshold version. In this paper, we present a threshold DSA scheme that is efficient and optimal. We also present a compelling application to use our scheme: securing Bitcoin wallets. Bitcoin thefts are on the rise, and threshold DSA is necessary to secure Bitcoin wallets. Our scheme is the first general threshold DSA scheme that does not require an honest majority and is useful for securing Bitcoin wallets.


title: The development of nurse-led bowel dysfunction clinics in Saudi Arabia: against all odds

abstract: Around the world, nurse-led clinics are commonly introduced within health-care settings to reduce waiting times, improve patient satisfaction, and reduce health-care costs. The importance of improving or maintaining a high standard of patient-centred care must not be forgotten. The Ministry of Health (MOH) in Saudi Arabia has yet to call on nurses to provide nurse-led clinics to meet national health-care requirements. Furthermore, advanced nursing practice is yet to be defined, legislated or regulated. Despite this, patient needs are driving the advancement of nursing practice, including the development of nurse-led clinics. This article will describe the development of a nurse-led bowel dysfunction clinic in a tertiary referral centre in Saudi Arabia and will discuss the benefits.


title: Computer-based technology and student engagement: a critical review of the literature

abstract: Computer-based technology has infiltrated many aspects of life and industry, yet there is little understanding of how it can be used to promote student engagement, a concept receiving strong attention in higher education due to its association with a number of positive academic outcomes. The purpose of this article is to present a critical review of the literature from the past 5 years related to how web-conferencing software, blogs, wikis, social networking sites (Facebook and Twitter), and digital games influence student engagement. We prefaced the findings with a substantive overview of student engagement definitions and indicators, which revealed three types of engagement (behavioral, emotional, and cognitive) that informed how we classified articles. Our findings suggest that digital games provide the most far-reaching influence across different types of student engagement, followed by web-conferencing and Facebook. Findings regarding wikis, blogs, and Twitter are less conclusive and significantly limited in number of studies conducted within the past 5 years. Overall, the findings provide preliminary support that computer-based technology influences student engagement, however, additional research is needed to confirm and build on these findings. We conclude the article by providing a list of recommendations for practice, with the intent of increasing understanding of how computer-based technology may be purposefully implemented to achieve the greatest gains in student engagement.


Depression: Clinical, Experimental, and Theoretical Aspects


title: DeepLaser: Practical Fault Attack on Deep Neural Networks

abstract: As deep learning systems are widely adopted in safety- and security-critical applications, such as autonomous vehicles, banking systems, etc., malicious faults and attacks become a tremendous concern, which potentially could lead to catastrophic consequences. In this paper, we initiate the first study of leveraging physical fault injection attacks on Deep Neural Networks (DNNs), by using laser injection technique on embedded systems. In particular, our exploratory study targets four widely used activation functions in DNNs development, that are the general main building block of DNNs that creates non-linear behaviors -- ReLu, softmax, sigmoid, and tanh. Our results show that by targeting these functions, it is possible to achieve a misclassification by injecting faults into the hidden layer of the network. Such result can have practical implications for real-world applications, where faults can be introduced by simpler means (such as altering the supply voltage).


title: 3D Cell Nuclear Morphology: Microscopy Imaging Dataset and Voxel-Based Morphometry Classification Results

abstract: Cell deformation is regulated by complex underlying biological mechanisms associated with spatial and temporal morphological changes in the nucleus that are related to cell differentiation, development, proliferation, and disease. Thus, quantitative analysis of changes in size and shape of nuclear structures in 3D microscopic images is important not only for investigating nuclear organization, but also for detecting and treating pathological conditions such as cancer. While many efforts have been made to develop cell and nuclear shape characteristics in 2D or pseudo-3D, several studies have suggested that 3D morphometric measures provide better results for nuclear shape description and discrimination. A few methods have been proposed to classify cell and nuclear morphological phenotypes in 3D, however, there is a lack of publicly available 3D data for the evaluation and comparison of such algorithms. This limitation becomes of great importance when the ability to evaluate different approaches on benchmark data is needed for better dissemination of the current state of the art methods for bioimage analysis. To address this problem, we present a dataset containing two different cell collections, including original 3D microscopic images of cell nuclei and nucleoli. In addition, we perform a baseline evaluation of a number of popular classification algorithms using 2D and 3D voxel-based morphometric measures. To account for batch effects, while enabling calculations of AU-ROC and AUPR performance metrics, we propose a specific cross-validation scheme that we compare with commonly used k-fold cross-validation. Original and derived imaging data are made publicly available on the project webpage: http://www.socr.umich.edu/projects/3d-cell-morphometry/data.html.


title: Integrated model of visual processing

abstract: Cortical processing of visual information requires that information be exchanged between neurons coding for distant regions in the visual field. It is argued that feedback connections are the best candidates for such rapid long-distance interconnections. In the integrated model, information arriving in the cortex from the magnocellular layers of the lateral geniculate nucleus is first sent and processed in the parietal cortex that is very rapidly activated by a visual stimulus. Results from this first-pass computation are then sent back by feedback connections to areas V1 and V2 that act as 'active black-boards' for the rest of the visual cortical areas: information retroinjected from the parietal cortex is used to guide further processing of parvocellular and koniocellular information in the inferotemporal cortex. © 2001 Elsevier Science B.V. All rights reserved.


title: Change-Link: a digital forensic tool for visualizing changes to directory trees

abstract: We present Change-Link, a customizable data exploration tool which empowers the user to see visual representations of directories that have changed over time within a computer operating system that supports the Microsoft Volume Shadow Copy Service (VSS). Change-Link displays change information in a split-screen interface comprising an overview of directory change for the entire dataset and a detail view of change for individual directories. Input to Change-Link is an evidence hard drive containing an active file system and previous versions of the directory structure that were archived by the VSS. This approach to browsing change within a directory structure helps a digital forensic examiner understand how a particular computer was used to support criminal activity. Because data that have changed are often the most important, identifying directories that have changed over time directs attention towards data of higher importance. By examining the most important data, digital forensic examiners are better able to keep pace with the data explosion that is making current digital forensic examinations unmanageable. Our contributions include the development of a segmented box and whisker glyph for representing change over time for individual directories, an approach for aggregating VSS data for digital forensic examinations, and a data visualization tool for exploring digital forensic data.


title: BIM-Based Integrated Framework for Detailed Cost Estimation and Schedule Planning of Construction Projects

abstract: With the increasing popularity of Building Information Modeling (BIM), several research studies have focused on its application in the construction industry, and advancements such as developments of BIM-based tools for construction scheduling and cost estimating have been carried out. Nevertheless, these tools are each developed only for one particular aspect of construction management, such as scheduling or cost estimating. As such, most are still limited to the product element level, rather than the construction operation level. This paper thus presents a BIM-based integrated framework for detailed cost estimation and schedule planning of construction projects. In the proposed framework, a BIM product model developed in Autodesk Revit, is integrated with a construction process model retrieved from RS Means, with the objective of generating detailed cost estimation and a workface construction schedule simultaneously. In this paper, the literature pertaining to BIM, cost estimating and scheduling is surveyed; then, the proposed framework is presented, and a simple building project is used as a case study to facilitate understanding of and verify the applicability of the integrated framework. Finally, findings from the implementation of the proposed framework are summarized.


title: 1.4-Mb/s White Organic LED Transmission System Using Discrete Multitone Modulation

abstract: In this letter, we experimentally demonstrate the discrete multitone (DMT) modulation scheme for organic light emitting diode-based visible light communications (OLED-VLC). DMT is based upon quadrature modulation of orthogonally spaced frequencies, meaning the scheme is extremely spectrally efficient. With this technique, we set a new world record data rate of 1.4 Mb/s, which is far in excess of the previous record of 550 kb/s. We achieve this using a 32-level quadrature amplitude modulation (32-QAM) in conjunction with 1024 subcarriers. We show that it is possible to recover signals up to 1.4 Mb/s with an OLED of bandwidth less than 100 kHz, which is an increase of over 14 times.


title: Aether: A Scalable Approach to Logging

abstract: The shift to multi-core hardware brings new challenges to database systems, as the software parallelism determines performance. Even though database systems traditionally accommodate simultaneous requests, a multitude of synchronization barriers serialize execution. Write-ahead logging is a fundamental, omnipresent component in ARIES-style concurrency and recovery, and one of the most important yet-to-be addressed potential bottlenecks, especially in OLTP workloads making frequent small changes to data. ::: ::: In this paper, we identify four logging-related impediments to database system scalability. Each issue challenges different level in the software architecture: (a) the high volume of small-sized I/O requests may saturate the disk, (b) transactions hold locks while waiting for the log flush, (c) extensive context switching overwhelms the OS scheduler with threads executing log I/Os, and (d) contention appears as transactions serialize accesses to in-memory log data structures. We demonstrate these problems and address them with techniques that, when combined, comprise a holistic, scalable approach to logging. Our solution achieves a 20%-69% speedup over a modern database system when running log-intensive workloads, such as the TPC-B and TATP benchmarks. Moreover, it achieves log insert throughput over 1.8GB/s for small log records on a single socket server, an order of magnitude higher than the traditional way of accessing the log using a single mutex.


title: Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation

abstract: Generating long and topic-coherent reports to describe medical images poses challenges to bridging visual patterns with informative human linguistic descriptions. We propose a novel Hybrid Retrieval-Generation Reinforced Agent (HRGR-Agent) which reconciles traditional retrieval-based approaches populated with human prior knowledge, and modern learning-based approaches to achieve structured, robust and diverse report generation. HRGR-Agent employs a retrieval policy module to generate a sequence of actions to decide between retrieving appropriate template sentences from an off-the-shelf template database, and automatic generating sentences by a generation module. Thus multiple sentences are sequentially generated via hierarchical decision-making. Our HRGR-Agent is updated via reinforcement learning, guided by sentence-level and word-level rewards. Experiments show that HRGR-Agent achieves the state-of-the-art results on two medical report datasets, generating a well balanced structured and complicated sentences with robust coverage of heterogeneous medical report contents. In addition to automatic evaluations, we demonstrate that our model achieves the highest detection accuracy of medical terminologies, and best human evaluation performance.


title: Mirror that Talks: A Self-Motivating Personal Vision Assistant

abstract: This work presents a self-motivating personal vision assistant that serves as a self-reflecting and motivating system for people such as those with low self-esteem, lonely or depressed individuals, single mothers, divorced or widowed, who constantly needs to be motivated to remain positive and focused. The system is similar to the rest of the personal digital assistants in the market but with the added functionality of reading the mood of the user and motivating them based on their present states of mind such as happy, sad, bored etc. The system uses facial recognition to determine the users' mood and a database of user's history or past events to adequately motivate the users. Further extensions include creating and adding imagery of beloved ones that have impacts on the lives of the users, such as missed loved ones like mothers, spouse, etc as added stimulation.


title: Design flows and collateral for the ASAP7 7nm FinFET predictive process design kit

abstract: Educators and researchers exploring integrated circuit design methods need models and design flows for advanced integrated circuit processes. As commercial processes have become highly proprietary, predictive technology models fill the gap. This work describes a design flow for ASAP7, the first 7 nm FinFET PDK, including schematic and layout entry, library characterization, synthesis, placement and routing, parasitic extraction, and HSPICE simulation.


title: Integration of Cognitive Radio Technology with unmanned aerial vehicles

abstract: Unmanned aerial vehicles have gained much popularity for applications which do not require human operator or are too dangerous for human operators. They operate on IEEE L-Band, IEEE S-Band and ISM band. However, with recent advances in technology, new wireless devices have been developed which also operate on these bands. Therefore, these bands have become overcrowded and unmanned aerial vehicles may face the problem of spectrum scarcity. Moreover, there are specific challenges associated with aeronautical communication links. Cognitive radio has emerged as a promising solution for solving the challenges caused by scarce spectrum. We focus on the integration of unmanned aerial vehicles with cognitive radio technology. Our main objective is to highlight and discuss some of the issues, challenges, and future research challenges associated with the integration of unmanned aerial vehicles and cognitive radio technology which occur as a result of the intrinsic characteristics of unmanned aerial vehicles or cognitive radio technology.


title: GLOBAL OPTIMAL PATH PLANNING OF AN AUTONOMOUS VEHICLE FOR OVERTAKING A MOVING OBSTACLE

abstract: In this paper, the global optimal path planning of an autonomous vehicle for overtaking a moving obstacle is proposed. In this study, the autonomous vehicle overtakes a moving vehicle by performing a double lane-change maneuver after detecting it in a proper distance ahead. The optimal path of vehicle for performing the lane-change maneuver is generated by a path planning program in which the sum of lateral deviation of the vehicle from a reference path and the rate of steering angle become minimum while the lateral acceleration of vehicle does not exceed a safe limit value. A nonlinear optimal control theory with the lateral vehicle dynamics equations and inequality constraint of lateral acceleration are used to generate the path. The indirect approach for solving the optimal control problem is used by applying the calculus of variation and the Pontryagin's Minimum Principle to obtain first-order necessary conditions for optimality. The optimal path is generated as a global optimal solution and can be used as the benchmark of the path generated by the local motion planning of autonomous vehicles. A full nonlinear vehicle model in CarSim software is used for path following simulation by importing path data from the MATLAB code. The simulation results show that the generated path for the autonomous vehicle satisfies all vehicle dynamics constraints and hence is a suitable overtaking path for the following vehicle.


title: Educational Living Labs: A Novel Internet-of-Things Based Approach to Teaching and Research

abstract: This paper explores some a novel approaches to harnessing the Internet-of-Things (IoT) as a teaching and research vehicle in education. For teaching we argue that the Internet-of-Things provides a highly motivating topic to capture students' imaginations, and a perfect platform for teaching computer science. In addition, we explain the potential for entire campuses or buildings to be constructed from Internet-of-Things technologies and the potential for this infrastructure to act as a teaching platform. This proposition is perfectly captured by the axiom "The college building (or campus) is the lab". This philosophy is part of a wider movement that started in the EU, called Living Labs. In achieving these aims, our work seeks to combine a number of concepts, first we utilise the Internet-of-Things, second we incorporate Living Labs ideas, third we harness the iCampus vision, forth we use the 'Smart Box' concept and finally we implement the Pervasive-interactive-Programming (PiP) paradigm. We contend this approach can be used in various mixes to produce highly motivating and effective educational environments. We illustrate this work by describing the application of these ideas to a real-world venture, the Harlow UTC (in the UK). The main focus of this paper concerns the use of PiP, together with the Internet-of-Things, to teach elementary programming skills. In in support of this we present results of an evaluation of PiP with 18 participants (students and staff) of varied age and gender. The main conclusions of these evaluations were that PiP enabled students and staff, with diverse backgrounds, to quickly master the programming skills involved. The paper concludes by describing our future plans for this work.


title: Extending quality metrics to full luminance range images

abstract: Many quality metrics take as input gamma corrected images and assume that pixel code values are scaled perceptually uniform. Although this is a valid assumption for darker displays operating in the luminance range typical for CRT displays (from 0.1 to 80 cd/m2), it is no longer true for much brighter LCD displays (typically up to 500 cd/m2), plasma displays (small regions up to 1000 cd/m2) and HDR displays (up to 3000 cd/m2). The distortions that are barely visible on dark displays become clearly noticeable when shown on much brighter displays. To estimate quality of images shown on bright displays, we propose a straightforward extension to the popular quality metrics, such as PSNR and SSIM, that makes them capable of handling all luminance levels visible to the human eye without altering their results for typical CRT display luminance levels. Such extended quality metrics can be used to estimate quality of high dynamic range (HDR) images as well as account for display brightness.© (2008) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Energy Storage Sharing in Smart Grid: A Modified Auction-Based Approach

abstract: This paper studies the solution of joint energy storage (ES) ownership sharing between multiple shared facility controllers (SFCs) and those dwelling in a residential community. The main objective is to enable the residential units (RUs) to decide on the fraction of their ES capacity that they want to share with the SFCs of the community in order to assist them in storing electricity, e.g., for fulfilling the demand of various shared facilities. To this end, a modified auction-based mechanism is designed that captures the interaction between the SFCs and the RUs so as to determine the auction price and the allocation of ES shared by the RUs that governs the proposed joint ES ownership. The fraction of the capacity of the storage that each RU decides to put into the market to share with the SFCs and the auction price are determined by a noncooperative Stackelberg game formulated between the RUs and the auctioneer. It is shown that the proposed auction possesses the incentive compatibility and the individual rationality properties, which are leveraged via the unique Stackelberg equilibrium solution of the game. Numerical experiments are provided to confirm the effectiveness of the proposed scheme.


title: The Web of Topics: Discovering the Topology of Topic Evolution in a Corpus

abstract: In this paper we study how to discover the evolution of topics over time in a time-stamped document collection. Our approach is uniquely designed to capture the rich topology of topic evolution inherent in the corpus. Instead of characterizing the evolving topics at fixed time points, we conceptually define a topic as a quantized unit of evolutionary change in content and discover topics with the time of their appearance in the corpus. Discovered topics are then connected to form a topic evolution graph using a measure derived from the underlying document network. Our approach allows inhomogeneous distribution of topics over time and does not impose any topological restriction in topic evolution graphs. We evaluate our algorithm on the ACM corpus. The topic evolution graphs obtained from the ACM corpus provide an effective and concrete summary of the corpus with remarkably rich topology that are congruent to our background knowledge. In a finer resolution, the graphs reveal concrete information about the corpus that were previously unknown to us, suggesting the utility of our approach as a navigational tool for the corpus.


title: A Semantic Foundation for Role-Related Concepts in Enterprise Modelling

abstract: In this paper, we provide a semantic foundation for role-related concepts in enterprise modelling. We use a conceptual modelling framework to provide a well-founded underpinning for these concepts. We review a number of enterprise modelling approaches in light of the concepts described. This allows us to understand the various approaches, to contrast them and to identify problems in the definition and/or usage of these concepts.


title: Annotation order matters: Recurrent Image Annotator for arbitrary length image tagging

abstract: Automatic image annotation has been an important research topic in facilitating large scale image management and retrieval. Existing methods focus on learning image-tag correlation or correlation between tags to improve annotation accuracy. However, most of these methods evaluate their performance using top-k retrieval performance, where k is fixed. Although such setting gives convenience for comparing different methods, it is not the natural way that humans annotate images. The number of annotated tags should depend on image contents. Inspired by the recent progress in machine translation and image captioning, we propose a novel Recurrent Image Annotator (RIA) model that forms image annotation task as a sequence generation problem so that RIA can natively predict the proper length of tags according to image contents. We evaluate the proposed model on various image annotation datasets. In addition to comparing our model with existing methods using the conventional top-k evaluation measures, we also provide our model as a high quality baseline for the arbitrary length image tagging task. Moreover, the results of our experiments show that the order of tags in training phase has a great impact on the final annotation performance.


Title Nazi race theory and belief in an “ Aryan race ” : a profound failureof interdisciplinary communication


title: A framework for Arabic sentiment analysis using supervised classification

abstract: Sentiment analysis aims to determine the polarity that is embedded in people comments and reviews. Sentiment analysis is important for companies and organisations which are interested in evaluating their products or services. The current paper deals with sentiment analysis in Arabic reviews. Three classifiers were applied on an in-house developed dataset of tweets/comments. In particular, the Naive Bayes, SVM and K-nearest neighbour classifiers were employed. This paper also addresses the effects of term weighting schemes on the accuracy of the results. The binary model, term frequency and term frequency inverse document frequency were used to assign weights to the tokens of tweets/comments. The results show that alternating between the three weighting schemes slightly affects the accuracies. The results also clarify that the classifiers were able to remove false examples (high precision) but were not that successful in identifying all correct examples (low recall).


title: The coarse wrinkle layer of Palaeozoic ammonoids: new evidence from the Early Carboniferous of Morocco

abstract: The wrinkle layer is a dorsal shell structure occurring in a number of ammonoids, but its function is still debated. Here, we describe, from Moroccan material of the Early Carboniferous species Maxigoniatites saourensis (Pareyn, 1961), the most conspicuous wrinkle layer known within the Ammonoidea. This additional shell layer occurs in the ventrolateral portion of the adult body chamber and forms continuous lamellae, which range about two millimetres into the lumen of the body chamber. Possible functions are discussed and the most likely interpretation for the structure is ‘fabricational noise’, which is related to the coarsening of the shell ornament of the terminal body chamber.


title: Security Issues in Wireless Mesh Networks

abstract: Wireless mesh network (WMN) is a new wireless networking paradigm. Unlike traditional wireless networks, WMNs do not rely on any fixed infrastructure. Instead, hosts rely on each other to keep the network connected. Wireless Internet service providers are choosing WMNs to offer Internet connectivity, as it allows a fast, easy and inexpensive network deployment. One main challenge in design of these networks is their vulnerability to security attacks. In this paper, we investigate the principal security issues for WMNs. We study the threats a WMN faces and the security goals to be achieved. We identify the new challenges and opportunities posed by this new networking environment and explore approaches to secure its communication.


title: Schema induction and analogical transfer

abstract: An analysis of the process of analogical thinking predicts that analogies will be noticed on the basis of semantic retrieval cues and that the induction of a general schema from concrete analogs will facilitate analogical transfer. These predictions were tested in experiments in which subjects first read one or more stories illustrating problems and their solutions and then attempted to solve a disparate but analogous transfer problem. The studies in Part I attempted to foster the abstraction of a problem schema from a single story analog by means of summarization instructions, a verbal statement of the underlying principle, or a diagrammatic representation of it. None of these devices achieved a notable degree of sucess. In contrast, the experiments in Part II demonstrated that if two prior analogs were given, subjects often derived a problem schema as an incidental product of describing the similarities of the analogs. The quality of the induced schema was highly predictive of subsequent transfer performance. Furthermore, the verbal statements and diagrams that had failed to facilitate transfer from one analog proved highly beneficial when paired with two. The function of examples in learning was discussed in light of the present study.


title: Framing Bias: Media in the Distribution of Power

abstract: This article proposes integrating the insights generated by framing, priming, and agenda-setting research through a systematic effort to conceptualize and understand their larger implications for political power and democracy. The organizing concept is bias, that curiously undertheorized staple of public discourse about the media. After showing how agenda setting, framing and priming fit together as tools of power, the article connects them to explicit definitions of news slant and the related but distinct phenomenon of bias. The article suggests improved measures of slant and bias. Properly defined and measured, slant and bias provide insight into how the media influence the distribution of power: who gets what, when, and how. Content analysis should be informed by explicit theory linking patterns of framing in the media text to predictable priming and agenda-setting effects on audiences. When unmoored by such underlying theory, measures and conclusions of media bias are suspect. doi:10.1111/j.1460-2466.2006.00336.x This article proposes integrating the insights generated by framing, priming, and agenda-setting research through a new, systematic effort to conceptualize and understand their implications for political power. The organizing concept is bias, that curiously undertheorized staple of public discourse about the media. With all the heat and attention it incites among activists and ordinary citizens, bias is yet to be defined clearly, let alone received much serious empirical attention (Niven, 2002). The term seems to take on three major meanings. Sometimes, it is applied to news that purportedly distorts or falsifies reality (distortion bias), sometimes to news that favors one side rather than providing equivalent treatment to both sides in a political conflict (content bias), and sometimes to the motivations and mindsets of journalists who allegedly produce the biased content (decision-making bias). This essay argues that we can make bias a robust, rigorous, theory-driven, and productive research concept by abandoning the first use while deploying new, more precisely delineated variants of the second and third. Depending on specific research objectives, the distinctions among these three concepts can be crucial (Scheufele, 2000). The present article suggests that parsimonious


The knowledge chain model: activities for competitiveness


title: The Intersecting Roles of Consumer and Producer: A Critical Perspective on Co-production, Co-creation and Prosumption

abstract: The terms ‘co-creation’, ‘co-production’, and ‘prosumption’ refer to situations in which consumers collaborate with companies or with other consumers to produce things of value. These situations sometimes appear to blur the traditional roles of ‘producer’ and ‘consumer’. Building on Marx’s distinction between ‘use value’ and ‘exchange value’, we argue that, when consumers perform tasks normally handled by the company, this does not necessarily represent a fundamental change in exchange roles or economic organization. We then argue that, when individuals who are traditionally defined as ‘consumers’ produce exchange value for companies, this does represent a fundamental change.


title: A Novel Dummy-Based KNN Query Anonymization Method in Mobile Services

abstract: Due to the advances of mobile devices with GPS (Global Positioning System), a user's privacy threat is increased in location based services (LBSs).So, various Location Privacy-Preserving Mechanisms (LPPMs) have been proposed in the literature to address the privacy risks derived from the exposure of user locations through the use of LBSs. However, these methods obfuscate the locations disclosed to the LBS provider using a variety of strategies, most of which come at a cost of resource consumption. Therefore, we propose a privacy-protected KNN query anonymization method based on Bayesian estimation for Location-based services. Unlike previous dummy-based approaches, in our method, the request to the LBS server doesn't contain the genuine user location, so we can't calculate whether meet the threshold condition of two location directly, but must to decision making by transition probability. In addition, our method just requires the server returns the results the client needs. Further, we propose an effective search algorithm to improve the server processing. So it can reduce bandwidth usages and efficiently support K-nearest neighbor queries without revealing the private information of the query issuer. An empirical study shows that our proposal is effective in terms of offering location privacy, and efficient in terms of computation and communication costs. Keyword: Location privacy protection, Location-based services(LBSs),K-nearest neighbor query, Bayesian estimation


title: Serious Games for Cyber Security Education

abstract: Phishing is an online identity theft that aims to steal sensitive information such as username, passwords and online banking details from its victims. Phishing education needs to be considered as a means to combat this threat. This book focuses on a design and development of a mobile game prototype as an educational tool helping computer users to protect themselves against phishing attacks. The elements of a game design framework for avoiding phishing attacks were used to address the game design issues. The mobile game design aimed to enhance the user's avoidance behaviour through motivation to protect themselves against phishing threats. A think-aloud study was conducted, along with a pre- and post-test, to assess the game design framework through the developed mobile game prototype. The study results showed a significant improvement of participants' phishing avoidance behaviour in their post-test assessment. Furthermore, the study findings suggest that participants' threat perception, safeguard effectiveness, self-efficacy, perceived severity and perceived susceptibility elements positively impact threat avoidance behaviour, whereas safeguard cost had a negative impact on it.


Breaking KeeLoq in a Flash: On Extracting Keys at Lightning Speed


title: Combined spectroscopic analysis for identification of mural paintings materials

abstract: In the present paper, an analytical characterization of mural painting samples coming from a 14th century Romanian Monastery has been carried. Investigations were performed through non-destructive spectroscopic techniques: X-Ray Fluorescence (XRF) using a handheld energy-dispersive XRF analyzer and Fourier Transform Infrared Spectroscopy (FT-IR) in Attenuated Total Reflectance (ATR) geometry. Results obtained via XRF, corroborated with FTIR data, permitted a complete material characterization in terms of elemental composition and molecular structure. A series of mineral pigments were identified — green earth, azurite, cinnabar, lead white, zinc white, ochre, emerald green; as well as the presence in some of the analyzed areas of organic binders such as vegetable wax or linseed oil. Previous restoration interventions were also evidenced as identification of one pigment respect to another, or via the presence of barium sulfate that we assume as coming from a filler, barium sulfate being often used in the chemical formulation of modern painting materials.


title: Automatic Detection and Classification of Traffic Signs

abstract: This paper proposes algorithms for the automatic detection of traffic signs from photo or video images and their classification to provide a driver alert system. Several examples taken from Portuguese roads are used to demonstrate the effectiveness of the proposed system. Traffic signs are detected by analyzing color information, notably red and blue, contained on the images. The detected signs are then classified according to their shape characteristics, as triangular, squared and circular shapes. Combining color and shape information, traffic signs are classified into one of the following classes: danger, information, obligation or prohibition. Both the detection and classification algorithms include innovative components to improve the overall system performance.


title: On the use of visualization for supporting software reuse

abstract: Reuse is present in the daily routine of software developers, yet mostly in an ad-hoc or pragmatic way. Reuse practices allow for reducing the time and effort spent on software development. However, organizations struggle in beginning and coping with a reuse program. The APPRAiSER environment, proposed in this work, aims at providing reuse awareness according to each stakeholder’s needs for performing reuse-related tasks, by providing appropriate software visualization mechanisms. The long-term goal is to help introducing, instigating, establishing and monitoring software reuse initiatives, by decreasing the effort and time spent by stakeholders in performing reuse tasks.


title: Distant Supervision for Relation Extraction with an Incomplete Knowledge Base

abstract: Distant supervision, heuristically labeling a corpus using a knowledge base, has emerged as a popular choice for training relation extractors. In this paper, we show that a significant number of “negative“ examples generated by the labeling process are false negatives because the knowledge base is incomplete. Therefore the heuristic for generating negative examples has a seriousflaw. Building on a state-of-the-art distantly-supervised extraction algorithm, we proposed an algorithm that learns from only positive and unlabeled labels at the pair-of-entity level. Experimental results demonstrate its advantage over existing algorithms.


title: Regulation of Muscle Fiber Type and Running Endurance by PPARδ

abstract: Endurance exercise training can promote an adaptive muscle fiber transformation and an increase of mitochondrial biogenesis by triggering scripted changes in gene expression. However, no transcription factor has yet been identified that can direct this process. We describe the engineering of a mouse capable of continuous running of up to twice the distance of a wild-type littermate. This was achieved by targeted expression of an activated form of peroxisome proliferator-activated receptor delta (PPARdelta) in skeletal muscle, which induces a switch to form increased numbers of type I muscle fibers. Treatment of wild-type mice with PPARdelta agonist elicits a similar type I fiber gene expression profile in muscle. Moreover, these genetically generated fibers confer resistance to obesity with improved metabolic profiles, even in the absence of exercise. These results demonstrate that complex physiologic properties such as fatigue, endurance, and running capacity can be molecularly analyzed and manipulated.


title: Genetic algorithm based feature selection and MOE Fuzzy classification algorithm on Pima Indians Diabetes dataset

abstract: Diabetes Mellitus is a dreadful disease characterized by increased levels of glucose in the blood, termed as the condition of hyperglycemia. As this disease is prominent among the tropical countries like India, an intense research is being carried out to deliver a machine learning model that could learn from previous patient records in order to deliver smart diagnosis. This research work aims to improve the accuracy of existing diagnostic methods for the prediction of Type 2 Diabetes with machine learning algorithms. The proposed algorithm selects the essential features from the Pima Indians Diabetes Dataset with Goldberg's Genetic algorithm in the pre-processing stage and a Multi Objective Evolutionary Fuzzy Classifier is applied on the dataset. This algorithm works on the principle of maximum classifier rate and minimum rules. As a result of feature selection with GA the number of features is reduced to 4 from 8 and the classifier rate is improved to 83.0435 % with NSGA II in training rate of 70% and 30% testing.


title: A Low-Profile Dual-Polarized Microstrip Antenna Array for Dual-Mode OAM Applications

abstract: A dual-polarized dual-mode orbital angular momentum (OAM) microstrip antenna array is presented. The OAM radio beams are generated by employing a four-element antenna array with specific excitation. Each antenna element is an aperture-coupled microstrip patch antenna with a low profile of $0.1\lambda _{{\rm{0}}}$ . The quasi-cross-shaped aperture is excited by a U-shaped and an M-shaped microstrip feedline, which leads to two orthogonal polarizations. By adopting two types of phase-shifting schemes, the antenna array can be used to produce dual-mode OAM waves ( ${\rm{l}} = \pm 1$ ). The measured results show that the proposed antenna array has achieved good impedance matching over the band of 5.4–5.6 GHz, and a port-to-port isolation better than 25 dB. Moreover, the rotational phase fronts indicate waves properly bearing OAM with different states.


title: Double-Switch Single-Transformer Cell Voltage Equalizer Using a Half-Bridge Inverter and a Voltage Multiplier for Series-Connected Supercapacitors

abstract: Cell voltage equalizers are necessary to ensure years of operation for series-connected supercapacitors (SCs) by eliminating cell voltage imbalance. Conventional equalizers require numerous switches and/or magnetic components; hence, the circuit complexity and size tend to increase. In this paper, a double-switch single-transformer equalizer, which is based on the combination of a half-bridge inverter and a voltage multiplier, is proposed. The operation of the proposed equalizer is analyzed in detail, and a dc equivalent circuit is mathematically derived. Experimental equalization tests were performed for eight SCs connected in series under a voltage-imbalanced condition. The energy of the series-connected SCs was preferentially redistributed to SCs with low voltages, and eventually, the voltage imbalance was eliminated, demonstrating the equalization performance. Simulation-based equalizations using the derived dc equivalent circuit were also performed under the same condition as the experiments. The experimental and simulation results were in good agreement, indicating that the derived dc equivalent circuit can adequately express real equalization profiles.


Fast Constrained Non-negative Matrix Factorization for Whole-Brain Calcium Imaging Data


title: Adscape: Harvesting and Analyzing Online Display Ads

abstract: Over the past decade, advertising has emerged as the primary source of revenue for many web sites and apps. In this paper we report a first-of-its-kind study that seeks to broadly understand the features, mechanisms and dynamics of display advertising on the web - i.e., the Adscape. Our study takes the perspective of users who are the targets of display ads shown on web sites. We develop a scalable crawling capability that enables us to gather the details of display ads including creatives and landing pages. Our crawling strategy is focused on maximizing the number of unique ads harvested. Of critical importance to our study is the recognition that a user's profile (i.e., browser profile and cookies) can have a significant impact on which ads are shown. We deploy our crawler over a variety of websites and profiles and this yields over 175K distinct display ads. We find that while targeting is widely used, there remain many instances in which delivered ads do not depend on user profile; further, ads vary more over user profiles than over websites. We also assess the population of advertisers seen and identify over 3.7K distinct entities from a variety of business segments. Finally, we find that when targeting is used, the specific types of ads delivered generally correspond with the details of user profiles, and also on users' patterns of visit.