The Web Never Forgets: Persistent Tracking Mechanisms in the Wild

We present the first large-scale studies of three advanced web tracking mechanisms - canvas fingerprinting, evercookies and use of "cookie syncing" in conjunction with evercookies. Canvas fingerprinting, a recently developed form of browser fingerprinting, has not previously been reported in the wild; our results show that over 5% of the top 100,000 websites employ it. We then present the first automated study of evercookies and respawning and the discovery of a new evercookie vector, IndexedDB. Turning to cookie syncing, we present novel techniques for detection and analysing ID flows and we quantify the amplification of privacy-intrusive tracking practices due to cookie syncing.
 Our evaluation of the defensive techniques used by privacy-aware users finds that there exist subtle pitfalls --- such as failing to clear state on multiple browsers at once - in which a single lapse in judgement can shatter privacy defenses. This suggests that even sophisticated users face great difficulties in evading tracking techniques.


Detecting and Defending Against Third-Party Tracking on the Web

While third-party tracking on the web has garnered much attention, its workings remain poorly understood. Our goal is to dissect how mainstream web tracking occurs in the wild. We develop a client-side method for detecting and classifying five kinds of third-party trackers based on how they manipulate browser state. We run our detection system while browsing the web and observe a rich ecosystem, with over 500 unique trackers in our measurements alone. We find that most commercial pages are tracked by multiple parties, trackers vary widely in their coverage with a small number being widely deployed, and many trackers exhibit a combination of tracking behaviors. Based on web search traces taken from AOL data, we estimate that several trackers can each capture more than 20% of a user’s browsing behavior. We further assess the impact of defenses on tracking and find that no existing browser mechanisms prevent tracking by social media sites via widgets while still allowing those widgets to achieve their utility goals, which leads us to develop a new defense. To the best of our knowledge, our work is the most complete study of web tracking to date.


Cookieless Monster: Exploring the Ecosystem of Web-Based Device Fingerprinting

The web has become an essential part of our society and is currently the main medium of information delivery. Billions of users browse the web on a daily basis, and there are single websites that have reached over one billion user accounts. In this environment, the ability to track users and their online habits can be very lucrative for advertising companies, yet very intrusive for the privacy of users. In this paper, we examine how web-based device fingerprinting currently works on the Internet. By analyzing the code of three popular browser-fingerprinting code providers, we reveal the techniques that allow websites to track users without the need of client-side identifiers. Among these techniques, we show how current commercial fingerprinting approaches use questionable practices, such as the circumvention of HTTP proxies to discover a user's real IP address and the installation of intrusive browser plugins. At the same time, we show how fragile the browser ecosystem is against fingerprinting through the use of novel browser-identifying techniques. With so many different vendors involved in browser development, we demonstrate how one can use diversions in the browsers' implementation to distinguish successfully not only the browser-family, but also specific major and minor versions. Browser extensions that help users spoof the user-agent of their browsers are also evaluated. We show that current commercial approaches can bypass the extensions, and, in addition, take advantage of their shortcomings by using them as additional fingerprinting features.


Understanding Malvertising Through Ad-Injecting Browser Extensions

Malvertising is a malicious activity that leverages advertising to distribute various forms of malware. Because advertising is the key revenue generator for numerous Internet companies, large ad networks, such as Google, Yahoo and Microsoft, invest a lot of effort to mitigate malicious ads from their ad networks. This drives adversaries to look for alternative methods to deploy malvertising. In this paper, we show that browser extensions that use ads as their monetization strategy often facilitate the deployment of malvertising. Moreover, while some extensions simply serve ads from ad networks that support malvertising, other extensions maliciously alter the content of visited webpages to force users into installing malware. To measure the extent of these behaviors we developed Expector, a system that automatically inspects and identifies browser extensions that inject ads, and then classifies these ads as malicious or benign based on their landing pages. Using Expector, we automatically inspected over 18,000 Chrome browser extensions. We found 292 extensions that inject ads, and detected 56 extensions that participate in malvertising using 16 different ad networks and with a total user base of 602,417.


Trends and Lessons from Three Years Fighting Malicious Extensions

In this work we expose wide-spread efforts by criminals to abuse the Chrome Web Store as a platform for distributing malicious extensions. A central component of our study is the design and implementation of WebEval, the first system that broadly identifies malicious extensions with a concrete, measurable detection rate of 96.5%. Over the last three years we detected 9,523 malicious extensions: nearly 10% of every extension submitted to the store. Despite a short window of operation—we removed 50% of malware within 25 minutes of creation— a handful of under 100 extensions escaped immediate detection and infected over 50 million Chrome users. Our results highlight that the extension abuse ecosystem is drastically different from malicious binaries: miscreants profit from web traffic and user tracking rather than email spam or banking theft.


Study of Lightning Surge Overvoltages at Substations Due to Direct Lightning Strokes to Phase Conductors

Accurate predictions of lightning surge overvoltages are essential to power equipment insulation design. Recent observations of lightning strokes to ultra-high-voltage designed transmission lines confirmed direct lightning strokes caused by shielding failure and found phenomena unexplainable by conventional shielding theories. However, there are few detailed studies of direct lightning surge overvoltages. This study assumed direct lightning stroke currents based on observational data and performs electromagnetic transient program analysis of the gas-insulated switchgear (GIS) and transformer overvoltages at substations to study the basic characteristics of overvoltages due to direct lightning strokes and evaluate lightning protection design. Consequently, the maximum GIS overvoltages were found during back-flashovers, and the locations of maximum overvoltages from direct lightning strokes and back-flashovers differ. Direct lightning strokes may be more severe than back-flashovers for transformers. This paper also studied the overvoltage generation mechanism and showed the relationship of the maximum voltage to lightning stroke current and transformer capacitance.


A Fast Monte-Carlo Test for Primality


GISMO: a Graphical Interactive Student Monitoring Tool for Course Management Systems

This paper presents GISMO, a graphical interactive student monitoring and tracking system tool that extracts tracking data from an on-line course maintained with a Course Management System, and generates graphical representations that can be explored by course instructors to examine various aspects of distance students. GISMO uses techniques from Information Visualisation to render into an appropriate graphical manner the complex multidimensional student tracking data provided by the Course Management System. GISMO aims to help instructors to become aware of what is happening in their classes and provide a better support to their learners.


Mapping learning and game mechanics for serious games analysis

While there is a consensus on the instructional potential of Serious Games (SGs), there is still a lack of methodologies and tools not only for design but also to support analysis and assessment. Filling this gap is one of the main aims of the Games and Learning Alliance (GALA, www.galanoe.eu) European Network of Excellence on Serious Games (SGs), which has a focus upon pedagogy-driven SGs. This paper relies on the assumption that the fundamental aspect of SG design consists in the translation of learning goals/practices into mechanical element of game-play, serving to an instructional purpose beside that of play and fun. This paper proposes the Learning Mechanics-Game Mechanics (LM-GM) model, which supports SG analysis and design by allowing reflection on the various pedagogical and game elements in a SG. The LM-GM model includes a set of pre-defined game mechanics and pedagogical elements that we have abstracted from literature on game studies and learning theories. Designers and analysts can exploit these mechanics to draw the LM-GM map for a game, so as to identify and highlight its main pedagogical and entertainment features, and their interrelations. The tool may also be useful for teachers to evaluate the effectiveness of a given game and better understand how to implement it in educational settings. A case study is reported to illustrate the framework’s support in determining how game-play and pedagogy intertwine in a SG. Finally, the paper presents the results of two comparative user tests demonstrating the advantages of the proposed model with respect to a similar state-of-the-art framework.


Colyseus: A Distributed Architecture for Online Multiplayer Games

This paper presents the design, implementation, and evaluation of Colyseus, a distributed architecture for interactive multiplayer games. Colyseus takes advantage of a game’s tolerance for weakly consistent state and predictable workload to meet the tight latency constraints of game-play and maintain scalable communication costs. In addition, it provides a rich distributed query interface and effective pre-fetching subsystem to help locate and replicate objects before they are accessed at a node. We have implemented Colyseus and modified Quake II, a popular first person shooter game, to use it. Our measurements of Quake II and our own Colyseus-based game with hundreds of players shows that Colyseus effectively distributes game traffic across the participating nodes, allowing Colyseus to support low-latency game-play for an order of magnitude more players than existing single server designs, with similar per-node bandwidth costs.


AskWorld: Budget-Sensitive Query Evaluation for Knowledge-on-Demand

Recently, several Web-scale knowledge harvesting systems have been built, each of which is competent at extracting information from certain types of data (e.g., unstructured text, structured tables on the web, etc.). In order to determine the response to a new query posed to such systems (e.g., is sugar a healthy food?), it is useful to integrate opinions from multiple systems. If a response is desired within a specific time budget (e.g., in less than 2 seconds), then maybe only a subset of these resources can be queried. In this paper, we address the problem of knowledge integration for on-demand time-budgeted query answering. We propose a new method, AskWorld, which learns a policy that chooses which queries to send to which resources, by accommodating varying budget constraints that are available only at query (test) time. Through extensive experiments on real world datasets, we demonstrate AskWorld’s capability in selecting most informative resources to query within test-time constraints, resulting in improved performance compared to competitive baselines.


Model-free control

Model-free control” and the corresponding “intelligent” PID controllers (iPIDs), which already had many successful concrete applications, are presented here for the first time in an unified manner, where the new advances are taken into account. The basics of model-free control is now employing some old functional analysis and some elementary differential algebra. The estimation techniques become quite straightforward via a recent online parameter identification approach. The importance of iPIs and especially of iPs is deduced from the presence of friction. The strange industrial ubiquity of classic PID’s and the great difficulty for tuning them in complex situations is deduced, via an elementary sampling, from their connections with iPIDs. Several numerical simulations are presented which include some infinite-dimensional systems. They demonstrate not only the power of our intelligent controllers but also the great simplicity for tuning them.


A Distributed Test System Architecture for Open-source IoT Software

In this paper, we discuss challenges that are specific to testing of open IoT software systems. The analysis reveals gaps compared to wireless sensor networks as well as embedded software. We propose a testing framework which (a) supports continuous integration techniques, (b) allows for the integration of project contributors to volunteer hardware and software resources to the test system, and (c) can function as a permanent distributed plugtest for network interoperability testing. The focus of this paper lies in open-source IoT development but many aspects are also applicable to closed-source projects.


On Poisson Graphical Models

Undirected graphical models, such as Gaussian graphical models, Ising, and multinomial/categorical graphical models, are widely used in a variety of applications for modeling distributions over a large number of variables. These standard instances, however, are ill-suited to modeling count data, which are increasingly ubiquitous in big-data settings such as genomic sequencing data, user-ratings data, spatial incidence data, climate studies, and site visits. Existing classes of Poisson graphical models, which arise as the joint distributions that correspond to Poisson distributed node-conditional distributions, have a major drawback: they can only model negative conditional dependencies for reasons of normalizability given its infinite domain. In this paper, our objective is to modify the Poisson graphical model distribution so that it can capture a rich dependence structure between count-valued variables. We begin by discussing two strategies for truncating the Poisson distribution and show that only one of these leads to a valid joint distribution. While this model can accommodate a wider range of conditional dependencies, some limitations still remain. To address this, we investigate two additional novel variants of the Poisson distribution and their corresponding joint graphical model distributions. Our three novel approaches provide classes of Poisson-like graphical models that can capture both positive and negative conditional dependencies between count-valued variables. One can learn the graph structure of our models via penalized neighborhood selection, and we demonstrate the performance of our methods by learning simulated networks as well as a network from microRNA-sequencing data.


Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex

IN the hippocampus and neocortex, high-frequency (tetanic) stimu-lation of an afferent pathway leads to long-term potentiation (LTP) of synaptic transmission1–5. In the hippocampus it has recently been shown that long-term depression (LTD) of excitatory transmission can also be induced by certain combinations of synaptic activation6,7. In most hippocampal8 and all neocortical pathways4,9–11 studied so far, the induction of LTP requires the activation of JV-methyl-D-aspartate (NMDA) receptorgated conductances. Here we report that LTD can occur in neurons of slices of the rat visual cortex and that the same tetanic stimulation can induce either LTP or LTD depending on the level of depolarization of the postsynaptic neuron. By applying intracellular current injections or pharmacological disinhibition to modify the depolarizing response of the postsynaptic neuron to tetanic stimulation, we show that the mechanisms of induction of LTD and LTP are both postsynaptic. LTD is obtained if postsynaptic depolarization exceeds a critical level but remains below a threshold related to NMDA receptorgated conductances, whereas LTP is induced if this second threshold is reached.


Identifying adverse drug events from patient social media: A case study for diabetes

Patient social media sites have emerged as major platforms for discussion of treatments and drug side effects, making them a promising source for listening to patients' voices in adverse drug event reporting. However, extracting patient reports from social media continues to be a challenge in health informatics research. In light of the need for more robust extraction methods, the authors developed a novel information extraction framework for identifying adverse drug events from patient social media. They also conducted a case study on a major diabetes patient social media platform to evaluate their framework's performance. Their approach achieves an f-measure of 86 percent in recognizing discussion of medical events and treatments, an f-measure of 69 percent for identifying adverse drug events, and an f-measure of 84 percent in patient report extraction. Their proposed methods significantly outperformed prior work in extracting patient reports of adverse drug events in health social media.


Slip detection and prediction in human walking using only wearable inertial measurement units (IMUs)

Slip and fall is one of the major causes for human injuries for elders and professional workers. Real-time detection and prediction of the foot slip is critical for developing effective assistive and rehabilitation devices to prevent falls and train balance disorder patients. This paper presents a novel real-time slip detection and prediction scheme with wearable inertial measurement units (IMUs). The slip-detection algorithm is built on a new dynamic model for bipedal walking with slips. An extended Kalman filter is designed to reliably predict the foot slip displacement using the wearable IMU measurements and kinematic constraints. The proposed slip detection and prediction scheme has been demonstrated by extensive experiments.


Graphite: Iterative Generative Modeling of Graphs

Graphs are a fundamental abstraction for modeling relational data. However, graphs are discrete and combinatorial in nature, and learning representations suitable for machine learning tasks poses statistical and computational challenges. In this work, we propose Graphite an algorithmic framework for unsupervised learning of representations over nodes in a graph using deep latent variable generative models. Our model is based on variational autoencoders (VAE), and uses graph neural networks for parameterizing both the generative model (i.e., decoder) and inference model (i.e., encoder). The use of graph neural networks directly incorporates inductive biases due to the spatial, local structure of graphs directly in the generative model. We draw novel connections of our framework with approximate inference via kernel embeddings. Empirically, Graphite outperforms competing approaches for the tasks of density estimation, link prediction, and node classification on synthetic and benchmark datasets.


Personalized Dialogue Generation with Diversified Traits

Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues. To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. This large-scale dataset will facilitate not only the study of personalized dialogue generation, but also other researches on sociolinguistics or social science. Secondly, to study how personality traits can be captured and addressed in dialogue generation, we propose persona-aware dialogue generation models within the sequence to sequence learning framework. Explicit personality traits (structured by key-value pairs) are embedded using a trait fusion module. During the decoding process, two techniques, namely persona-aware attention and persona-aware bias, are devised to capture and address trait-related information. Experiments demonstrate that our model is able to address proper traits in different contexts. Case studies also show interesting results for this challenging research problem.


Realizing underwater communication through magnetic induction

The majority of the work on underwater communication has mainly been based on acoustic communication. Acoustic communication faces many known problems, such as high propagation delays, very low data rates, and highly environment-dependent channel behavior. In this article, to address these shortcomings, magnetic induction is introduced as a possible communication paradigm for underwater applications. Accordingly, all research challenges in this regard are explained. Fundamentally different from the conventional underwater communication paradigm, which relies on EM, acoustic, or optical waves, the underwater MI communications rely on the time varying magnetic field to covey information between the transmitting and receiving parties. MI-based underwater communications exhibit several unique and promising features such as negligible signal propagation delay, predictable and constant channel behavior, sufficiently long communication range with high bandwidth, as well as silent and stealth underwater operations. To fully utilize the promising features of underwater MI-based communications, this article introduces the fundamentals of underwater MI communications, including the MI channel models, MI networking protocols design, and MI-based underwater localization.


Integrating Configuration Management with Model-driven Cloud Management based on TOSCA

The paradigm of Cloud computing introduces new approaches to manage IT services going beyond concepts originating in traditional IT service management. The main goal is to automate the whole management of services to reduce costs and to make management tasks less error-prone. Two different service management paradigms are used in practice: configuration management and model-driven Cloud management. The latter one aims to be a holistic management approach for services in the Cloud. However, both management paradigms are originating in different backgrounds, thus model-driven Cloud management does not cover all aspects of configuration management that are key for Cloud services. This paper presents approaches for integrating configuration management with model-driven Cloud management and how they can be realized based on the OASIS Topology and Orchestration Specification for Cloud Applications and Chef, a popular configuration management tool. These approaches enable the creation of holistic and highly portable service models.


The BeiHang Keystroke Dynamics Authentication System

Keystroke Dynamics is an important biometric solution for person authentication. Based upon keystroke dynamics, this paper designs an embedded password protection device, develops an online system, collects two public databases for promoting the research on keystroke authentication, exploits the Gabor filter bank to characterize keystroke dynamics, and provides benchmark results of three popular classification algorithms, one-class support vector machine, Gaussian classifier, and nearest neighbour classifier.


Google Web 1T 5-Grams Made Easy (but not for the computer)

This paper introduces Web1T5-Easy, a simple indexing solution that allows interactive searches of the Web 1T 5-gram database and a derived database of quasi-collocations. The latter is validated against co-occurrence data from the BNC and ukWaC on the automatic identification of non-compositional VPC.


A Face in any Form: New Challenges and Opportunities for Face Recognition Technology

Despite new technologies that make face detection and recognition more sophisticated, long-recognized problems in security, privacy, and accuracy persist. Refining this technology and introducing it into new domains will require solving these problems through focused interdisciplinary efforts among developers, researchers, and policymakers.


Practical Lattice-Based Cryptography: A Signature Scheme for Embedded Systems

Nearly all of the currently used and well-tested signature schemes (e.g. RSA or DSA) are based either on the factoring assumption or the presumed intractability of the discrete logarithm problem. Further algorithmic advances on these problems may lead to the unpleasant situation that a large number of schemes have to be replaced with alternatives. In this work we present such an alternative – a signature scheme whose security is derived from the hardness of lattice problems. It is based on recent theoretical advances in lattice-based cryptography and is highly optimized for practicability and use in embedded systems. The public and secret keys are roughly 12000 and 2000 bits long, while the signature size is approximately 9000 bits for a security level of around 100 bits. The implementation results on reconfigurable hardware (Spartan/Virtex 6) are very promising and show that the scheme is scalable, has low area consumption, and even outperforms some classical schemes.


A new primary power regulation method for contactless power transfer

This paper presents a new power regulation method for contactless power transfer systems that combines current magnitude control of a series tuned resonant converter with energy injection concepts suitable for constant load or single pick-up applications. In the latter, the control of the primary converter operates with asymmetrical control, to regulate the magnitude of the high frequency primary track current according to the load requirement of the secondary power pickup. The feedback control algorithm is developed by monitoring the output voltage of the secondary power pickup. Switching losses and EMI are reduced due to the ZCS (Zero Current Switching) operation of the converter. Simulation and experimental results show that the primary converter has a fast and smooth start-up transient response, and the magnitudes of the primary current and the output voltage of a secondary power pickup are both fully controllable against load variations.


Clustering Web pages based on their structure

Several techniques have been recently proposed to automatically generate Web wrappers, i.e., programs that extract data from HTML pages, and transform them into a more structured format, typically in XML. These techniques automatically induce a wrapper from a set of sample pages that share a common HTML template. An open issue, however, is how to collect suitable classes of sample pages to feed the wrapper inducer. Presently, the pages are chosen manually. In this paper, we tackle the problem of automatically discovering the main classes of pages offered by a site by exploring only a small yet representative portion of it. We propose a model to describe abstract structural features of HTML pages. Based on this model, we have developed an algorithm that accepts the URL of an entry point to a target Web site, visits a limited yet representative number of pages, and produces an accurate clustering of pages based on their structure. We have developed a prototype, which has been used to perform experiments on real-life Web sites. 2004 Elsevier B.V. All rights reserved.


Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks

MOTIVATION
Analysis of co-expressed gene sets typically involves testing for enrichment of different annotations or 'properties' such as biological processes, pathways, transcription factor binding sites, etc., one property at a time. This common approach ignores any known relationships among the properties or the genes themselves. It is believed that known biological relationships among genes and their many properties may be exploited to more accurately reveal commonalities of a gene set. Previous work has sought to achieve this by building biological networks that combine multiple types of gene-gene or gene-property relationships, and performing network analysis to identify other genes and properties most relevant to a given gene set. Most existing network-based approaches for recognizing genes or annotations relevant to a given gene set collapse information about different properties to simplify (homogenize) the networks.

RESULTS
We present a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types that preserve more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only these relevant properties. We then re-rank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. We demonstrate the effectiveness of this algorithm for ranking genes related to Drosophila embryonic development and aggressive responses in the brains of social animals.

AVAILABILITY AND IMPLEMENTATION
DRaWR was implemented as an R package available at veda.cs.illinois.edu/DRaWR.

CONTACT
blatti@illinois.edu

SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.


Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter

There is a widespread intuitive sense that different kinds of information spread differently on-line, but it has been difficult to evaluate this question quantitatively since it requires a setting where many different kinds of information spread in a shared environment. Here we study this issue on Twitter, analyzing the ways in which tokens known as hashtags spread on a network defined by the interactions among Twitter users. We find significant variation in the ways that widely-used hashtags on different topics spread.
 Our results show that this variation is not attributable simply to differences in "stickiness," the probability of adoption based on one or more exposures, but also to a quantity that could be viewed as a kind of "persistence" - the relative extent to which repeated exposures to a hashtag continue to have significant marginal effects. We find that hashtags on politically controversial topics are particularly persistent, with repeated exposures continuing to have unusually large marginal effects on adoption; this provides, to our knowledge, the first large-scale validation of the "complex contagion" principle from sociology, which posits that repeated exposures to an idea are particularly crucial when the idea is in some way controversial or contentious. Among other findings, we discover that hashtags representing the natural analogues of Twitter idioms and neologisms are particularly non-persistent, with the effect of multiple exposures decaying rapidly relative to the first exposure.
 We also study the subgraph structure of the initial adopters for different widely-adopted hashtags, again finding structural differences across topics. We develop simulation-based and generative models to analyze how the adoption dynamics interact with the network structure of the early adopters on which a hashtag spreads.


Meme-tracking and the dynamics of the news cycle

Tracking new topics, ideas, and "memes" across the Web has been an issue of considerable interest. Recent work has developed methods for tracking topic shifts over long time scales, as well as abrupt spikes in the appearance of particular named entities. However, these approaches are less well suited to the identification of content that spreads widely and then fades over time scales on the order of days - the time scale at which we perceive news and events.
 We develop a framework for tracking short, distinctive phrases that travel relatively intact through on-line text; developing scalable algorithms for clustering textual variants of such phrases, we identify a broad class of memes that exhibit wide spread and rich variation on a daily basis. As our principal domain of study, we show how such a meme-tracking approach can provide a coherent representation of the news cycle - the daily rhythms in the news media that have long been the subject of qualitative interpretation but have never been captured accurately enough to permit actual quantitative analysis. We tracked 1.6 million mainstream media sites and blogs over a period of three months with the total of 90 million articles and we find a set of novel and persistent temporal patterns in the news cycle. In particular, we observe a typical lag of 2.5 hours between the peaks of attention to a phrase in the news media and in blogs respectively, with divergent behavior around the overall peak and a "heartbeat"-like pattern in the handoff between news and blogs. We also develop and analyze a mathematical model for the kinds of temporal variation that the system exhibits.


Group formation in large social networks: membership, growth, and evolution

The processes by which communities come together, attract new members, and develop over time is a central research issue in the social sciences - political movements, professional organizations, and religious denominations all provide fundamental examples of such communities. In the digital domain, on-line groups are becoming increasingly prominent due to the growth of community and social networking sites such as MySpace and LiveJournal. However, the challenge of collecting and analyzing large-scale time-resolved data on social groups and communities has left most basic questions about the evolution of such groups largely unresolved: what are the structural features that influence whether individuals will join communities, which communities will grow rapidly, and how do the overlaps among pairs of communities change over time.Here we address these questions using two large sources of data: friendship links and community membership on LiveJournal, and co-authorship and conference publications in DBLP. Both of these datasets provide explicit user-defined communities, where conferences serve as proxies for communities in DBLP. We study how the evolution of these communities relates to properties such as the structure of the underlying social networks. We find that the propensity of individuals to join communities, and of communities to grow rapidly, depends in subtle ways on the underlying network structure. For example, the tendency of an individual to join a community is influenced not just by the number of friends he or she has within the community, but also crucially by how those friends are connected to one another. We use decision-tree techniques to identify the most significant structural determinants of these properties. We also develop a novel methodology for measuring movement of individuals between communities, and show how such movements are closely aligned with changes in the topics of interest within the communities.


INFORMATION AND THE CHANGE IN THE PARADIGM IN ECONOMICS , PART 1 by

The research for which George Akerlof, Mike Spence, and I are being recognized is part of a larger research program which, today, embraces hundred, perhaps thousands, of researchers around the world. In this lecture, I want to set the particular work which was sited within this broader agenda, and that agenda within the broader perspective of the history of economic thought. I hope to show that Information Economics represents a fundamental change in the prevailing paradigm within economics. Problems of information are central to understanding not only market economics but also political economy, and in the last section of this lecture, I explore some of the implications of information imperfections for political processes.


Approximate logic synthesis for error tolerant applications

Error tolerance formally captures the notion that -- for a wide variety of applications including audio, video, graphics, and wireless communications -- a defective chip that produces erroneous values at its outputs may be acceptable, provided the errors are of certain types and their severities are within application-specified thresholds. All previous research on error tolerance has focused on identifying such defective but acceptable chips during post-fabrication testing to improve yield. In this paper, we explore a completely new approach to exploit error tolerance based on the following observation: If certain deviations from the nominal output values are acceptable, then we can exploit this flexibility during circuit design to reduce circuit area and delay as well as to increase yield. The specific metric of error tolerance we focus on is error rate, i.e., how often the circuit produces erroneous outputs. We propose a new logic synthesis approach for the new problem of identifying how to exploit a given error rate threshold to maximally reduce the area of the synthesized circuit. Experiment results show that for an error rate threshold within 1%, our approach provides 9.43% literal reductions on average for all the benchmarks that we target.


Generic and Scalable Framework for Automated Time-series Anomaly Detection

This paper introduces a generic and scalable framework for automated anomaly detection on large scale time-series data. Early detection of anomalies plays a key role in maintaining consistency of person's data and protects corporations against malicious attackers. Current state of the art anomaly detection approaches suffer from scalability, use-case restrictions, difficulty of use and a large number of false positives. Our system at Yahoo, EGADS, uses a collection of anomaly detection and forecasting models with an anomaly filtering layer for accurate and scalable anomaly detection on time-series. We compare our approach against other anomaly detection systems on real and synthetic data with varying time-series characteristics. We found that our framework allows for 50-60% improvement in precision and recall for a variety of use-cases. Both the data and the framework are being open-sourced. The open-sourcing of the data, in particular, represents the first of its kind effort to establish the standard benchmark for anomaly detection.


Kinematics and design of a portable and wearable exoskeleton for hand rehabilitation

We present the kinematic design and actuation mechanics of a wearable exoskeleton for hand rehabilitation of post-stroke. Our design method is focused on achieving maximum safety, comfort and reliability in the interaction, and allowing different users to wear the device with no manual regulations. In particular, we propose a kinematic and actuation solution for the index finger flexion/extension, which leaves full movement freedom on the abduction-adduction plane. This paper presents a detailed kineto-static analysis of the system and a first prototype of the device.


The Growing Relationship Between China and Sub-Saharan Africa : Macroeconomic , Trade , Investment , and Aid Links

China’s economic ascendance over the past two decades has generated ripple effects in the world economy. Its search for natural resources to satisfy the demands of industrialization has led it to Sub-Saharan Africa. Trade between China and Africa in 2006 totaled more than $50 billion, with Chinese companies importing oil from Angola and Sudan, timber from Central Africa, and copper from Zambia. Demand from China has contributed to an upward swing in prices, particularly for oil and metals from Africa, and has given a boost to real GDP in Sub-Saharan Africa. Chinese aid and investment in infrastructure are bringing desperately needed capital to the continent. At the same time, however, strong Chinese demand for oil is contributing to an increase in the import bill for many oil-importing SubSaharan African countries, and its exports of low-cost textiles, while benefiting African consumers, is threatening to displace local production. China poses a challenge to good governance and macroeconomic management in Africa because of the potential Dutch disease implications of commodity booms. China presents both an opportunity for Africa to reduce its marginalization from the global economy and a challenge for it to effectively harness the influx of resources to promote poverty-reducing economic development at home. JEL codes: F01, F35, F41, N55, N57, Q33, Q43


A Guide to Differential Privacy Theory in Social Network Analysis

Privacy of social network data is a growing concern which threatens to limit access to this valuable data source. Analysis of the graph structure of social networks can provide valuable information for revenue generation and social science research, but unfortunately, ensuring this analysis does not violate individual privacy is difficult. Simply anonymizing graphs or even releasing only aggregate results of analysis may not provide sufficient protection. Differential privacy is an alternative privacy model, popular in data-mining over tabular data, which uses noise to obscure individuals' contributions to aggregate results and offers a very strong mathematical guarantee that individuals' presence in the data-set is hidden. Analyses that were previously vulnerable to identification of individuals and extraction of private data may be safely released under differential-privacy guarantees. We review two existing standards for adapting differential privacy to network data and analyse the feasibility of several common social-network analysis techniques under these standards. Additionally, we propose out-link privacy, a novel standard for differential privacy over network data, and introduce two powerful out-link private algorithms for common network analysis techniques that were infeasible to privatize under previous differential privacy standards.


A ripple voltage sensing MPPT circuit for ultra-low power microsystems

We propose a maximum power point tracking (MPPT) circuit for micro-scale sensor systems that measures ripple voltages in a switched capacitor energy harvester. Compared to conventional current mirror type MPPT circuits, this design incurs no voltage drop and does not require high bandwidth amplifiers. Using correlated double sampling, high accuracy is achieved with a power overhead of 5%, even at low harvested currents of 1.4uA based on measured results in 180nm CMOS.


Student See Versus Student Do: A Comparative Study of Two Online Tutorials

This study examines the impact on student performance after interactive and non-interactive tutorials using a 2×2 treatment-control design. In an undergraduate management course, a control group watched a video tutorial while the treatment group received the same content using a dynamic tutorial. Both groups received the same quiz questions. Using effect size to determine magnitude of change, it was found that those in the treatment condition performed better than those in the control condition. Students were able to take the quiz up to two times. When examining for change in performance from attempt one to attempt two, the treatment group showed a greater magnitude of change. Students who consistently performed lowest on the quizzes outperformed all students in learning gains.


A programmable and virtualized network & IT infrastructure for the internet of things: How can NFV & SDN help for facing the upcoming challenges

The Internet of Things (IoT) revolution has major impacts on the network & Information Technology (IT) infrastructure. As IT in the past decade, network virtualization is simultaneously on its way with for instance Network Functions Virtualization (NFV) and Software-Defined Networking (SDN). NFV and SDN are approaches enhancing the infrastructure agility thus facilitating the design, delivery and operation of network services in a dynamic and scalable manner. IoT will push the infrastructure to its limit with numerous and diverse requirements to fulfill, we therefore believe that the agility brought by the combination of NFV and SDN is essential to face the IoT revolution. In this article, we first highlight some IoT challenges that the network & IT infrastructure will face. The NFV and SDN benefits are presented from a network operator point of view. Following a description of the IoT ecosystem and a recall of some of the IoT stakeholders expectations, a new multi-layered IoT architecture involving SDN and NFV and based upon network & IT resources is put forward. Finally, the article illustrates how the proposed architecture is able to cope with the identified IoT challenges.


Cable-suspended planar parallel robots with redundant cables: controllers with positive cable tensions

Cable-suspended robots are structurally similar to parallel actuated robots but with the fundamental difference that cables can only pull the end-effector but not push it. From a scientific point of view, this feature makes feedback control of cable-suspended robots lot more challenging than their counterpart parallel-actuated robots. In the case with redundant cables, feedback control laws can be designed to make all tensions positive while attaining desired control performance. This paper describes these approaches and their effectiveness is demostrated through simulations of a three degree-offreedom cable suspended robots with four, five, and six cables.


Abnormal Event Detection in Videos Using Hybrid Spatio-Temporal Autoencoder

The LSTM Encoder-Decoder framework is used to learn representation of video sequences and applied for detect abnormal event in complex environment. However, it generally fails to account for the global context of the learned representation with a fixed dimension representation and the learned representation is crucial for decoder phase. Based on the LSTM Encoder-Decoder and the Convolutional Autoencoder, we explore a hybrid autoencoder architecture, which not only extracts better spatio-temporal context, but also improves the extrapolate capability of the corresponding decoder with the shortcut connection. The experimental results demonstrate that our approach outperforms lots of state-of-the-art methods on benchmark datasets.


Hierarchical classification of Web content

This paper explores the use of hierarchical structure for classifying a large, heterogeneous collection of web content. The hierarchical structure is initially used to train different second-level classifiers. In the hierarchical case, a model is learned to distinguish a second-level category from other categories within the same top level. In the flat non-hierarchical case, a model distinguishes a second-level category from all other second-level categories. Scoring rules can further take advantage of the hierarchy by considering only second-level categories that exceed a threshold at the top level.
We use support vector machine (SVM) classifiers, which have been shown to be efficient and effective for classification, but not previously explored in the context of hierarchical classification. We found small advantages in accuracy for hierarchical models over flat models. For the hierarchical approach, we found the same accuracy using a sequential Boolean decision rule and a multiplicative decision rule. Since the sequential approach is much more efficient, requiring only 14%-16% of the comparisons used in the other approaches, we find it to be a good choice for classifying text into large hierarchical structures.


A Case for Hardware Protection of Guest VMs from Compromised Hypervisors in Cloud Computing

Cloud computing, enabled by virtualization technologies, is becoming a mainstream computing model. Many companies are starting to utilize the infrastructure-as-a-service (IaaS) cloud computing model, leasing guest virtual machines (VMs) from the infrastructure providers for economic reasons: to reduce their operating costs and to increase the flexibility of their own infrastructures. Yet, many companies may be hesitant to move to cloud computing due to security concerns. An integral part of any virtualization technology is the all-powerful hyper visor. A hyper visor is a system management software layer which can access all resources of the platform. Much research has been done on using hyper visors to monitor guest VMs for malicious code and on hardening hyper visors to make them more secure. There is, however, another threat which has not been addressed by researchers -- that of compromised or malicious hyper visors that can extract sensitive or confidential data from guest VMs. Consequently, we propose that a new research direction needs to be undertaken to tackle this threat. We further propose that new hardware mechanisms in the multi core microprocessors are a viable way of providing protections for the guest VMs from the hyper visor, while still allowing the hyper visor to flexibly manage the resources of the physical platform.


Design and Analysis of a 3-Arm Spiral Antenna

A novel 3-arm spiral antenna structure is presented in this paper. This antenna similar to traditional two-arm or four-arm spiral antennas exhibits wideband radiation characteristic and circular polarization. Advantages offered by the new design are two fold. Unlike the traditional spiral antennas the three-arm spiral can be fed by an unbalanced transmission line, such as a coaxial line or coplanar waveguide, and therefore an external balun is not needed at the feed point. Also by proper choice of arms' dimensions the antenna can be directly matched to any practical transmission line characteristic impedance and therefore external matching networks are not required. This is accomplished by feeding the antenna at the outer radius by a coplanar waveguide (CPW) transmission line and tapering it towards the center. The antenna can also be fed from the center using a coaxial or CPW line perpendicular to the plane of the spiral antenna. A full-wave numerical simulation tool is used to optimize the geometry of the proposed 3-arm spiral to achieve a compact size, wide bandwidth operation, and low axial ratio. The antenna is also designed over a ground plane to achieve a unidirectional radiation and center loading is examined that improves the axial ratio. Simulated results like return loss, radiation pattern, gain, and axial ratio are compared with those obtained from measurements and good agreements are shown. Because of its unique feed structure and compact size, application of the proposed 3-arm spiral antenna for wideband array applications is demonstrated


Network game traffic modelling

A significant share of today's Internet traffic is generated by network gaming. This kind of traffic is interesting in regard to it's market potential as well as to it's real time requirements on the network. For the consideration of game traffic in network dimensioning, traffic models are required that allow to generate a characteristic load for analytical or simulative performance evaluation of networks. In this paper we evaluate the fast action multiplayer game ,,Counter Strike" from a 36 hour LAN party measurement and present traffic models for client and server. The paper concludes with remarks on the use of game traffic models in simulations and on QoS metrics for an adequate evaluation of simulation results.


ROBHAZ-rescue: rough-terrain negotiable teleoperated mobile robot for rescue mission

This paper presents design and integration of the ROBHAZ-DT3, which is a newly developed mobile robot system with chained double-track mechanisms. It is designed to carry out military and civilian missions in various hazardous environments. A passive adaptation mechanism equipped between the front and rear body enables the ROBHAZ-DT3 to have good adaptability to uneven terrains including stairs. The passive adaptation mechanism reduces energy consumption when moving on uneven terrain as well as its simplicity in design and remote control, since no actuator is necessary for adaptation. Based on this novel mobile platform, a rescue version of the ROBHAZ-DT3 with appropriate sensors and a semi-autonomous mapping and localization algorithm is developed to participate in the RoboCup2004 US-Open: Urban Search and Rescue Competition. From the various experiments in the realistic rescue arena, we can verify that the ROBHAZ-DT3 is reliable in travelling rugged terrain and the proposed mapping and localization algorithm are effective in the unstructured environment with uneven ground.


Detection of buried objects in FLIR imaging using mathematical morphology and SVM

In this paper we describe a method for detecting buried objects of interest using a forward looking infrared camera (FLIR) installed on a moving vehicle. Infrared (IR) detection of buried targets is based on the thermal gradient between the object and the surrounding soil. The processing of FILR images consists in a spot-finding procedure that includes edge detection, opening and closing. Each spot is then described using texture features such as histogram of gradients (HOG) and local binary patterns (LBP) and assigned a target confidence using a support vector machine (SVM) classifier. Next, each spot together with its confidence is projected and summed in the UTM space. To validate our approach, we present results obtained on 6 one mile long runs recorded with a long wave IR (LWIR) camera installed on a moving vehicle.


Smart home and smart city solutions enabled by 5G, IoT, AAI and CoT services

In a nearby future 5G technologies will connect the world from the largest megacities to the smallest internet of things in an always online fashion. Such a connected hierarchy must combine the smart cities, the smart homes, and the internet of things into one large coherent infrastructure. This paper suggest a four layer model which join and interfaces these elements by deploying technologies such as 5G, internet of things, cloud of things, and distributed artificial intelligence. Many advantages and service possibilities are offered by this new infrastructure such as interconnected internet of things, smart homes with artificial intelligence, and a platform for new combined smart home and smart city services based on big-data.


A Simple Steganography Algorithm Based on Lossless Compression Technique in WSN

Image security has wide applications in data transferring from source to destination system. However, cryptography is a data secrecy technique between sender and receiver, the steganography increases the level of security and acts a protective layer to the hidden information within the source image. In this paper, a compression scheme based image security algorithm for wireless sensor network is proposed to hide a secret color image within the source color image. The main contribution of this paper is to propose a compression scheme which is based on level matrix and integer matrix, and increases the compression level significantly. The performance of the proposed system is evaluated in terms of peak signal to noise ratio (PSNR), mean square error (MSE), number of pixels change rate (NPCR) and unified average changing intensity (UACI). The proposed method achieves 42.65% PSNR, 27.16% MSE, 99.9% NPCR and 30.99% UACI.


Application of Tikhonov Regularization to Super-Resolution Reconstruction of Brain MRI Images

This paper presents an image super-resolution method that enhances spatial resolution of MRI images in the slice-select direction. The algorithm employs Tikhonov regularization, using a standard model of general imaging process and then reformulating the reconstruction as a regularized minimization problem. Our experimental result shows improvements in both signal-to-noise ratio and visual quality.


Chapter 18 Building Intelligent Tutoring Systems : An Overview

This chapter addresses the challenge of building or authoring an Intelligent Tutoring System (ITS), along with the problems that have arisen and been dealt with, and the solutions that have been tested. We begin by clarifying what building an ITS entails, and then position today's systems in the overall historical context of ITS research. The chapter concludes with a series of open questions and an introduction to the other chapters in this part of the book.


A segmental CRF approach to large vocabulary continuous speech recognition

This paper proposes a segmental conditional random field framework for large vocabulary continuous speech recognition. Fundamental to this approach is the use of acoustic detectors as the basic input, and the automatic construction of a versatile set of segment-level features. The detector streams operate at multiple time scales (frame, phone, multi-phone, syllable or word) and are combined at the word level in the CRF training and decoding processes. A key aspect of our approach is that features are defined at the word level, and are naturally geared to explain long span phenomena such as formant trajectories, duration, and syllable stress patterns. Generalization to unseen words is possible through the use of decomposable consistency features [1], [2], and our framework allows for the joint or separate discriminative training of the acoustic and language models. An initial evaluation of this framework with voice search data from the Bing Mobile (BM) application results in a 2% absolute improvement over an HMM baseline.


Low power in-memory computing based on dual-mode SOT-MRAM

In this paper, we propose a novel Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) array design that could simultaneously work as non-volatile memory and implement a reconfigurable in-memory logic (AND, OR) without add-on logic circuits to memory chip as in traditional logic-in-memory designs. The computed logic output could be simply read out like a normal MRAM bit-cell using the shared memory peripheral circuits. Such intrinsic in-memory logic could be used to process data within memory to greatly reduce power-hungry and long distance data communication in conventional Von-Neumann computing systems. We further employ in-memory data encryption using Advanced Encryption Standard (AES) algorithm as a case study to demonstrate the efficiency of the proposed design. The device to architecture co-simulation results show that the proposed design can achieve 70.15% and 80.87% lower energy consumption compared to CMOS-ASIC and CMOL-AES implementations, respectively. It offers almost similar energy consumption as recent DW-AES implementation, but with 60.65% less area overhead.


Evolving boxes for fast vehicle detection

We perform fast vehicle detection from traffic surveillance cameras. A novel deep learning framework, namely Evolving Boxes, is developed that proposes and refines the object boxes under different feature representations. Specifically, our framework is embedded with a light-weight proposal network to generate initial anchor boxes as well as to early discard unlikely regions; a fine-turning network produces detailed features for these candidate boxes. We show intriguingly that by applying different feature fusion techniques, the initial boxes can be refined for both localization and recognition. We evaluate our network on the recent DETRAC benchmark and obtain a significant improvement over the state-of-the-art Faster RCNN by 9.5% mAP. Further, our network achieves 9–13 FPS detection speed on a moderate commercial GPU.


DETRAC: A New Benchmark and Protocol for Multi-Object Tracking

In recent years, most effective multi-object tracking (MOT) methods are based on the tracking-by-detection framework. Existing performance evaluations of MOT methods usually separate the target association step from the object detection step by using the same object detection results for comparisons. In this work, we perform a comprehensive quantitative study on the effect of object detection accuracy to the overall MOT performance. This is based on a new large-scale DETection and tRACking (DETRAC) benchmark dataset. The DETRAC benchmark dataset consists of 100 challenging video sequences captured from real-world traffic scenes (over 140 thousand frames and 1.2 million labeled bounding boxes of objects) for both object detection and MOT. We evaluate complete MOT systems constructed from combinations of state-of-the-art target association methods and object detection schemes. Our analysis shows the complex effects of object detection accuracy on MOT performance. Based on these observations, we propose new evaluation tools and metrics for MOT systems that consider both object detection and target association for comprehensive analysis.


Semantic-Based Text Document Clustering Using Cognitive Semantic Learning and Graph Theory

Semantic-based text document clustering aims to group documents into a set of topic clusters. We propose a new approach for semantically clustering of text documents based on cognitive science and graph theory. We apply a computational cognitive model of semantic association for human semantic memory, known as Incremental Construction of an Associative Network (ICAN). The vector-based model of Latent Semantic Analysis (LSA), has been a leading computational cognitive model for semantic learning and topic modeling, but it has well-known limitations including not considering the original word-order and doing semantic reduction with neither a linguistic nor cognitive basis. These issues have been overcome by the ICAN model. ICAN model is used to generate document-level semantic-graphs. Cognitive and graph-based topic and context identification methods are used for semantic reduction of ICAN graphs. A corpus-graph is generated from ICAN graphs, and then a community-detection graph algorithm is applied for the final step of document clustering. Experiments are conducted on three commonly used datasets. Using the purity and entropy criteria for clustering quality, our results show a notable outperformance over the LSA-based approach.


RevoMaker: Enabling Multi-directional and Functionally-embedded 3D printing using a Rotational Cuboidal Platform

In recent years, 3D printing has gained significant attention from the maker community, academia, and industry to support low-cost and iterative prototyping of designs. Current unidirectional extrusion systems require printing sacrificial material to support printed features such as overhangs. Furthermore, integrating functions such as sensing and actuation into these parts requires additional steps and processes to create "functional enclosures", since design functionality cannot be easily embedded into prototype printing. All of these factors result in relatively high design iteration times. We present "RevoMaker", a self-contained 3D printer that creates direct out-of-the-printer functional prototypes, using less build material and with substantially less reliance on support structures. By modifying a standard low-cost FDM printer with a revolving cuboidal platform and printing partitioned geometries around cuboidal facets, we achieve a multidirectional additive prototyping process to reduce the print and support material use. Our optimization framework considers various orientations and sizes for the cuboidal base. The mechanical, electronic, and sensory components are preassembled on the flattened laser-cut facets and enclosed inside the cuboid when closed. We demonstrate RevoMaker directly printing a variety of customized and fully-functional product prototypes, such as computer mice and toys, thus illustrating the new affordances of 3D printing for functional product design.


Emotional intelligence and social and academic adaptation to school.

In a sample of 127 Spanish adolescents, the ability to understand and manage emotions, assessed by a performance measure of emotional intelligence (the MSCEIT), correlated positively with teacher ratings of academic achievement and adaptation for both males and females. Among girls, these emotional abilities also correlated positively with peer friendship nominations. After controlling for IQ and the Big Five personality traits, the ability to understand and manage emotions remained significantly associated with teacher ratings of academic adaptation among boys and peer friendship nominations among girls. Self-perceived emotional intelligence was unrelated to these criteria. These findings provide partial support for hypotheses that emotional abilities are associated with indicators of social and academic adaptation to school.


Personalization as a service: the architecture and a case study

Cloud computing has become a hot topic in the IT industry. Great efforts have been made to establish cloud computing platforms for enterprise users, mostly small businesses. However, there are few researches about the impact of cloud computing over individual users. In this paper we focus on how to provide personalized services for individual users in the cloud environment. We argue that a personalized cloud service shall compose of two parts. The client side program records user activities on personal de-vices such as PC. Besides that, the user model is also computed on the client side to avoid server overhead. The cloud side program fetches the user model periodically and adjusts its results accordingly. We build a personalized cloud data search engine prototype to prove our idea.


A Densely Connected End-to-End Neural Network for Multiscale and Multiscene SAR Ship Detection

Synthetic aperture radar (SAR) images have been widely used for ship monitoring. The traditional methods of SAR ship detection are difficult to detect small scale ships and avoid the interference of inshore complex background. Deep learning detection methods have shown great performance on various object detection tasks recently but using deep learning methods for SAR ship detection does not show an excellent performance it should have. One of the important reasons is that there is no effective model to handle the detection of multiscale ships in multiresolution SAR images. Another important reason is it is difficult to handle multiscene SAR ship detection including offshore and inshore, especially it cannot effectively distinguish between inshore complex background and ships. In this paper, we propose a densely connected multiscale neural network based on faster-RCNN framework to solve multiscale and multiscene SAR ship detection. Instead of using a single feature map to generate proposals, we densely connect one feature map to every other feature maps from top to down and generate proposals from each fused feature map. In addition, we propose a training strategy to reduce the weight of easy examples in the loss function, so that the training process more focus on the hard examples to reduce false alarm. Experiments on expanded public SAR ship detection dataset, verify the proposed method can achieve an excellent performance on multiscale SAR ship detection in multiscene.


Cancer genes and the pathways they control

The revolution in cancer research can be summed up in a single sentence: cancer is, in essence, a genetic disease. In the last decade, many important genes responsible for the genesis of various cancers have been discovered, their mutations precisely identified, and the pathways through which they act characterized. The purposes of this review are to highlight examples of progress in these areas, indicate where knowledge is scarce and point out fertile grounds for future investigation.


A Semi-supervised Approach to Measuring User Privacy in Online Social Networks

During our digital social life, we share terabytes of information that can potentially reveal private facts and personality traits to unexpected strangers. Despite the research efforts aiming at providing efficient solutions for the anonymization of huge databases (including networked data), in online social networks the most powerful privacy protection is in the hands of the users. However, most users are not aware of the risks derived by the indiscriminate disclosure of their personal data. With the aim of fostering their awareness on private data leakage risk, some measures have been proposed that quantify the privacy risk of each user. However, these measures do not capture the objective risk of users since they assume that all user’s direct social connections are close (thus trustworthy) friends. Since this assumption is too strong, in this paper we propose an alternative approach: each user decides which friends are allowed to see each profile item/post and our privacy score is defined accordingly. We show that it can be easily computed with minimal user intervention by leveraging an active learning approach. Finally, we validate our measure on a set of real Facebook users.


Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset

In modern cities, more and more vehicles, such as taxis, have been equipped with GPS devices for localization and navigation. Gathering and analyzing these large-scale real-world digital traces have provided us an unprecedented opportunity to understand the city dynamics and reveal the hidden social and economic “realities”. One innovative pervasive application is to provide correct driving strategies to taxi drivers according to time and location. In this paper, we aim to discover both efficient and inefficient passenger-finding strategies from a large-scale taxi GPS dataset, which was collected from 5350 taxis for one year in a large city of China. By representing the passenger-finding strategies in a Time-Location-Strategy feature triplet and constructing a train/test dataset containing both top- and ordinary-performance taxi features, we adopt a powerful feature selection tool, L1-Norm SVM, to select the most salient feature patterns determining the taxi performance. We find that the selected patterns can well interpret the empirical study results derived from raw data analysis and even reveal interesting hidden “facts”. Moreover, the taxi performance predictor built on the selected features can achieve a prediction accuracy of 85.3% on a new test dataset, and it also outperforms the one based on all the features, which implies that the selected features are indeed the right indicators of the passenger-finding strategies.


Texture segmentation for seam carving

This paper analyzes the relationships between texture characteristics of an image and seam-carving (SC) algorithm for image retargeting (resizing). SC is a smart image content aware algorithm. The algorithm, changes the aspect ratio of an image while keeping the "important" objects untouched. Based on a subjective test, we combine between an image's characteristics and a saliency map which provides best results. Furthermore, in order to determine the significance of these results, we use principal component analysis (PCA) statistical method, which identifies those relationships in the subjective test's results. Finally, we present a novel saliency map for SC algorithm. This saliency map is based on a new classification model, which uses statistical connections between neighboring pixels and significantly reduces the level of distortion along the process, providing optimal preservation of important objects within the image.


Interpreting image databases by region classification

This paper addresses automatic interpretation of images of outdoor scenes. The method allows instances of objects from a number of generic classes to be identi ed: vegetation, buildings, vehicles, roads, etc., thereby enabling image databases to be queried on scene content. The feature set is based, in part, on psychophysical principles and includes measures of colour, texture and shape. Using a large database of ground-truth labelled images, a neural network is trained as a pattern classi er. The method is demonstrated on a large test set to provide highly accurate image interpretations, with over 90% of the image area labelled correctly. Scene Understanding Object Recognition Classi cation Image Segmentation Neural Networks


Health-related quality of life in breast cancer patients: A bibliographic review of the literature from 1974 to 2007

BACKGROUND
Quality of life in patients with breast cancer is an important outcome. This paper presents an extensive overview on the topic ranging from descriptive findings to clinical trials.

METHODS
This was a bibliographic review of the literature covering all full publications that appeared in English language biomedical journals between 1974 and 2007. The search strategy included a combination of key words 'quality of life' and 'breast cancer' or 'breast carcinoma' in titles. A total of 971 citations were identified and after exclusion of duplicates, the abstracts of 606 citations were reviewed. Of these, meetings abstracts, editorials, brief commentaries, letters, errata and dissertation abstracts and papers that appeared online and were indexed ahead of publication were also excluded. The remaining 477 papers were examined. The major findings are summarized and presented under several headings: instruments used, validation studies, measurement issues, surgical treatment, systemic therapies, quality of life as predictor of survival, psychological distress, supportive care, symptoms and sexual functioning.

RESULTS
Instruments-Several valid instruments were used to measure quality of life in breast cancer patients. The European Organization for Research and Treatment of Cancer Core Cancer Quality of Life Questionnaire (EORTC QLQ-C30) and its breast cancer specific complementary measure (EORTC QLQ-BR23) and the Functional Assessment Chronic Illness Therapy General questionnaire (FACIT-G) and its breast cancer module (FACIT-B) were found to be the most common and well developed instruments to measure quality of life in breast cancer patients. Surgery-different surgical procedures led to relatively similar results in terms of quality of life assessments, although mastectomy patients compared to conserving surgery patients usually reported a lower body image and sexual functioning. Systemic therapies-almost all studies indicated that breast cancer patients receiving chemotherapy might experience several side-effects and symptoms that negatively affect their quality of life. Adjuvant hormonal therapies also were found to have similar negative impact on quality of life, although in general they were associated with improved survival. Quality of life as predictor of survival-similar to known medical factors, quality of life data in metastatic breast cancer patients was found to be prognostic and predictive of survival time. Psychological distress-anxiety and depression were found to be common among breast cancer patients even years after the disease diagnosis and treatment. Psychological factors also were found to predict subsequent quality of life or even overall survival in breast cancer patients. Supportive care-clinical treatments to control emesis, or interventions such as counseling, providing social support and exercise could improve quality of life. Symptoms-Pain, fatigue, arm morbidity and postmenopausal symptoms were among the most common symptoms reported by breast cancer patients. As recommended, recognition and management of these symptoms is an important issue since such symptoms impair health-related quality of life. Sexual functioning-breast cancer patients especially younger patients suffer from poor sexual functioning that negatively affect quality of life.

CONCLUSION
There was quite an extensive body of the literature on quality of life in breast cancer patients. These papers have made a considerable contribution to improving breast cancer care, although their exact benefit was hard to define. However, quality of life data provided scientific evidence for clinical decision-making and conveyed helpful information concerning breast cancer patients' experiences during the course of the disease diagnosis, treatment, disease-free survival time, and recurrences; otherwise finding patient-centered solutions for evidence-based selection of optimal treatments, psychosocial interventions, patient-physician communications, allocation of resources, and indicating research priorities were impossible. It seems that more qualitative research is needed for a better understanding of the topic. In addition, issues related to the disease, its treatment side effects and symptoms, and sexual functioning should receive more attention when studying quality of life in breast cancer patients.


Anonymity and Online Commenting: The Broken Windows Effect and the End of Drive-by Commenting

In this study we ask how regulations about commenter identity affect the quantity and quality of discussion on commenting fora. In December 2013, the Huffington Post changed the rules for its comment forums to require participants to authenticate their accounts through Facebook. This enabled a large-scale 'before and after' analysis. We collected over 42m comments on 55,000 HuffPo articles published in the period January 2013 to June 2014 and analysed them to determine how changes in identity disclosure impacted on discussions in the publication's comment pages. We first report our main results on the quantity of online commenting, where we find both a reduction and a shift in its distribution from politicised to blander topics. We then discuss the quality of discussion. Here we focus on the subset of 18.9m commenters who were active both before and after the change, in order to disentangle the effects of the worst offenders withdrawing and the remaining commenters modifying their tone. We find a 'broken windows' effect, whereby comment quality improves even when we exclude interaction with trolls and spammers.


Windows NT pagefile.sys Virtual Memory Analysis

As hard disk encryption, RAM disks, persistent data avoidance technology and memory resident malware become morewidespread, memory analysis becomes more important. In order to provide more virtual memory than is actually physicalpresent on a system, an operating system may transfer frames of memory to a pagefile on persistent storage. Current memoryanalysis software does not incorporate such pagefiles and thus misses important information. We therefore present a detailedanalysis of Windows NT paging. We use dynamic gray-box analysis, in which we place known data into virtual memory andexamine where it is mapped to, in either the physical memory or the pagefile, and cross-reference these findings with theWindows NT Research Kernel source code. We demonstrate how to decode the non-present page table entries, and accuratelyreconstruct the complete virtual memory space, including non-present memory pages on Windows NT systems using 32-bit,PAE or IA32e paging. Our analysis approach can be used to analyze other operating systems as well.


Automated detection of euro pallet loads by interpreting PMD camera depth images

In this study, a novel approach for the detection of parcel loading positions on a pallet is presented. This approach was realized as a substantial change in comparison with traditional system design of contour detection in de-palletizing processes. Complex 3D-vision systems, costly laser scanners or throughput decreasing local sensor solutions integrated in grippers are substituted by a lowcost photonic mixing device (PMD) camera. By combining PMD technology and a predetermined model of loading situations, stored during assembling the pallet, this approach can compensate for the drawbacks of each respective system. An essential part of the approach are computer-graphics methods specific to the given problem to both detect the deviation between the nominal and the actual loading position and if necessary an automated correction of the packaging scheme. From an economic point of view, this approach can decrease the costs of mandatory contour checking in automated de-palletizing processes.


A Measurement-Driven Anti-Jamming System for 802.11 Networks

Dense, unmanaged IEEE 802.11 deployments tempt saboteurs into launching jamming attacks by injecting malicious interference. Nowadays, jammers can be portable devices that transmit intermittently at low power in order to conserve energy. In this paper, we first conduct extensive experiments on an indoor 802.11 network to assess the ability of two physical-layer functions, rate adaptation and power control, in mitigating jamming. In the presence of a jammer, we find that: 1) the use of popular rate adaptation algorithms can significantly degrade network performance; and 2) appropriate tuning of the carrier sensing threshold allows a transmitter to send packets even when being jammed and enables a receiver to capture the desired signal. Based on our findings, we build ARES, an Anti-jamming REinforcement System, which tunes the parameters of rate adaptation and power control to improve the performance in the presence of jammers. ARES ensures that operations under benign conditions are unaffected. To demonstrate the effectiveness and generality of ARES, we evaluate it in three wireless test-beds: 1) an 802.11n WLAN with MIMO nodes; 2) an 802.11a/g mesh network with mobile jammers; and 3) an 802.11a WLAN with TCP traffic. We observe that ARES improves the network throughput across all testbeds by up to 150%.


Building Hierarchical Representations for Oracle Character and Sketch Recognition

In this paper, we study oracle character recognition and general sketch recognition. First, a data set of oracle characters, which are the oldest hieroglyphs in China yet remain a part of modern Chinese characters, is collected for analysis. Second, typical visual representations in shape- and sketch-related works are evaluated. We analyze the problems suffered when addressing these representations and determine several representation design criteria. Based on the analysis, we propose a novel hierarchical representation that combines a Gabor-related low-level representation and a sparse-encoder-related mid-level representation. Extensive experiments show the effectiveness of the proposed representation in both oracle character recognition and general sketch recognition. The proposed representation is also complementary to convolutional neural network (CNN)-based models. We introduce a solution to combine the proposed representation with CNN-based models, and achieve better performances over both approaches. This solution has beaten humans at recognizing general sketches.


Reverse engineering digital circuits using functional analysis

Integrated circuits (ICs) are now designed and fabricated in a globalized multi-vendor environment making them vulnerable to malicious design changes, the insertion of hardware trojans/malware and intellectual property (IP) theft. Algorithmic reverse engineering of digital circuits can mitigate these concerns by enabling analysts to detect malicious hardware, verify the integrity of ICs and detect IP violations.
 In this paper, we present a set of algorithms for the reverse engineering of digital circuits starting from an unstructured netlist and resulting in a high-level netlist with components such as register files, counters, adders and subtracters. Our techniques require no manual intervention and experiments show that they determine the functionality of more than 51% and up to 93% of the gates in each of the practical test circuits that we examine.


A possible mechanism of horseback riding on dynamic trunk alignment

The study aimed to clarify the regularity of the motions of horse's back, rider's pelvis and spine associated with improvement of rider's dynamic trunk alignment. The study used a crossover design, with exercise using the horseback riding simulator (simulator hereafter) as the control condition. The experiments were conducted at Tokyo University of Agriculture Bio-therapy Center. The sample consisted of 20 healthy volunteers age 20-23 years. Participants performed 15-min sessions of horseback riding with a Hokkaido Pony and exercise using the simulator in experiments separated by ≥2 weeks. Surface electromyography (EMG) after horseback riding revealed decreased activity in the erector spinae. Exploratory data analysis of acceleration and angular velocity inferred associations between acceleration (Rider's neck/longitudinal axis [Y hereafter]) and angular velocity (Horse saddle/Y) as well as angular velocity (Rider's pelvis/Y) and angular velocity (Horse saddle/Y). Acceleration (Rider's neck/Y) tended to be associated with angular velocity (Rider's pelvis/Y). Surface EMG following exercise revealed decreased activity in the rectus abdominis and erector spinae after the simulator exercise. Horseback riding improved the rider's dynamic trunk alignment with a clear underlying mechanism, which was not observed with the simulator.


Orthogonal least squares methods and their application to non-linear system identification

This article maybe used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. Identification algorithms based on the well-known linear least squares methods of gaussian elimination, Cholesky decomposition, classical Gram-Schmidt, modified Gram-Schmidt, Householder transformation, Givens method, and singular value decomposition are reviewed. The classical Gram-Schmidt, modified Gram-Schmidt,and Householder transformation algorithms are then extended to combine structure determination, or which terms to include in the model, and parameter estimation in a very simple and efficient manner for a class of multivariable discrete-time non-linear stochastic systems which are linear in the parameters.


Software radio on smartphones: feasible?

In this work-in-progress paper, we explore the expected impacts, the reality, and the technical issues of making MAC/PHY protocols downloadable softwares (like apps) for smartphones. The notion of software radio on a small number of fixed stations has been investigated for decades, but moving its platform to billions of smartphones and even making it downloadable as an app has never been attempted. We firmly believe that such attempt could open new fronts for the software radio research. Beyond considering its possible impacts and ramifications, we demonstrate through measurements that today's smartphone hardware is already capable enough to support the real-time execution of down-loadable MAC/PHY software. It is certainly true for low-speed technologies such as ZigBee, and is close to providing real-time operation of higher-speed technologies like Wi-Fi. In our study, we also find that different application processor architectures lead to different bottlenecks in the MAC/PHY processing chain. This implies that the design of protocols that might be executed in software on general-purpose platforms should factor in the processor architecture like multiple cores and SIMD instruction sets, as it will facilitate the softwarization of wireless communication protocols in the future.


IEEE 802.15.7 visible light communication: modulation schemes and dimming support

Visible light communication refers to shortrange optical wireless communication using visible light spectrum from 380 to 780 nm. Enabled by recent advances in LED technology, IEEE 802.15.7 supports high-data-rate visible light communication up to 96 Mb/s by fast modulation of optical light sources which may be dimmed during their operation. IEEE 802.15.7 provides dimming adaptable mechanisms for flicker-free high-data-rate visible light communication.


Indoor optical wireless communication: potential and state-of-the-art

In recent years, interest in optical wireless (OW) as a promising complementary technology for RF technology has gained new momentum fueled by significant deployments in solid state lighting technology. This article aims at reviewing and summarizing recent advancements in OW communication, with the main focus on indoor deployment scenarios. This includes a discussion of challenges, potential applications, state of the art, and prospects. Related issues covered in this article are duplex transmission, multiple access, MAC protocols, and link capacity improvements.


Fundamental analysis for visible-light communication system using LED lights

White LED offers advantageous properties such as high brightness, reliability, lower power consumption and long lifetime. White LEDs are expected to serve in the next generation of lamps. An indoor visible-light communication system utilizing white LED lights has been proposed from our laboratory. In the proposed system, these devices are used not only for illuminating rooms but also for an optical wireless communication system. Generally, plural lights are installed in our room. So, their optical path difference must be considered. In this paper, we discuss about the influence of interference and reflection. Based on numerical analyses, we show that the system is expected to be the indoor communication of the next generation.


GNU Radio 802 . 15 . 4 En-and Decoding

The IEEE wireless standard 802.15.4 gets widespread attention because of its adoption in sensor networks, home automation, and other networked systems. The goal of the project is to implement an enand decoding block for the IEEE 802.15.4 protocol in GNU Radio, an open source solution for software defined radios. This report will give an insight into the working of GNU Radio and some of its hardware components. Additionally, it gives details about the implementation of the enand decoding blocks. At the end, we will verify the implementation by sending and receiving messages to and from an actual IEEE 802.15.4 radio chip, the CC2420 from ChipCon, and give a small bandwidth comparison of the two solutions.


Generating 3D Faces Using Convolutional Mesh Autoencoders

Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as character generation and animation. Traditional models learn a latent representation of a face using linear subspaces or higher-order tensor generalizations. Due to this linearity, they can not capture extreme deformations and nonlinear expressions. To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface. We introduce mesh sampling operations that enable a hierarchical mesh representation that captures non-linear variations in shape and expression at multiple scales within the model. In a variational setting, our model samples diverse realistic 3D faces from a multivariate Gaussian distribution. Our training data consists of 20,466 meshes of extreme expressions captured over 12 different subjects. Despite limited training data, our trained model outperforms state-of-the-art face models with 50% lower reconstruction error, while using 75% fewer parameters. We also show that, replacing the expression space of an existing state-ofthe-art face model with our autoencoder, achieves a lower reconstruction error. Our data, model and code are available at http://github.com/ anuragranj/coma.


Analysing the Impact of a DDoS Attack Announcement on Victim Stock Prices

DDoS attacks are increasingly used by 'hackers' and 'hacktivists' for various purposes. A number of on-line tools are available to launch an attack of significant intensity. These attacks lead to a variety of losses at the victim's end. We analyse the impact of Distributed Denial-of-Service (DDoS) attack announcements over a period of 5 years on the stock prices of the victim firms. We propose a method for event studies that does not assume the cumulative abnormal returns to be normally distributed, instead we use the empirical distribution for testing purposes. In most cases we find no significant impact on the stock returns but in cases where a DDoS attack creates an interruption in the services provided to the customer, we find a significant negative impact.


56-Gb/s direct modulation in InGaAlAs BH-DFB lasers at 55°C

Direct modulation at 56 Gb/s of 1.3-μm InGaAlAs-MQW DFB laser, incorporating a ridge-shaped buried heterostructure (RS-BH), operating at 55°C, is experimentally demonstrated for the first time.


Large gain linear series-fed microstrip antenna arrays at Ka and C bands

In this paper, the design of a series-fed microstrip antenna array is discussed and its design challenge of limited gain is explained. It discusses that the phase between the patches of the array plays a significant role in overcoming the challenge. A 23 element series fed linear array at Ka-band gives gain of 19dBi with beam in broadside direction and has SLL better than -15dB. A scaled down version at C-band has been designed and fabricated. A 7-element series fed antenna array yielded measured gain of 15.1 dBi at 5.79 GHz. This design can be extended for planar antenna arrays to obtain higher gain.


A Simple Edit-Time Metaobject Protocol

We present a simple edit-time metaobject protocol (ETMOP) which runs as part of a code editor and enables metadata annotations to customize the rendering and editing of code. The protocol is layered, so that simple render/edit customizations are easy to implement, while more substantial customizations are still manageable. Experiments with a prototype implementation of the protocol as an Eclipse plug-in show that the ETMOP is flexible enough to allow easy customization of the rendering and editing of a number of annotations being used in current practice. The flexibility and performance of the prototype suggest that the ETMOP approach is viable and warrants further study.


Congestion Control in Linux TCP

The TCP protocol is used by the majority of the network applications on the Internet. TCP performance is strongly influenced by its congestion control algorithms that limit the amount of transmitted traffic based on the estimated network capacity and utilization. Because the freely available Linux operating system has gained popularity especially in the network servers, its TCP implementation affects many of the network interactions carried out today. We describe the fundamentals of the Linux TCP design, concentrating on the congestion control algorithms. The Linux TCP implementation supports SACK, TCP timestamps, Explicit Congestion Notification, and techniques to undo congestion window adjustments after incorrect congestion notifications. In addition to features specified by IETF, Linux has implementation details beyond the specifications aimed to further improve its performance. We discuss these, and finally show the performance effects of Quick acknowledgements, Rate-halving, and the algorithms for correcting incorrect congestion window adjustments by comparing the performance of Linux TCP implementing these features to the performance achieved with an implementation that does not use the algorithms in question.


A Review of Smart Homes—Past, Present, and Future

A smart home is an application of ubiquitous computing in which the home environment is monitored by ambient intelligence to provide context-aware services and facilitate remote home control. This paper presents an overview of previous smart home research as well as the associated technologies. A brief discussion on the building blocks of smart homes and their interrelationships is presented. It describes collective information about sensors, multimedia devices, communication protocols, and systems, which are widely used in smart home implementation. Special algorithms from different fields and their significance are explained according to their scope of use in smart homes. This paper also presents a concrete guideline for future researchers to follow in developing a practical and sustainable smart home.


A SMUGGLING APPROACH TO THE PASSIVE IN ENGLISH

I propose a theory of the passive that combines aspects of the principles and parameters analysis (no specific rules, no downward movement) and Chomsky’s (1957) Syntactic Structures analysis (the arguments in the passive are generated in the same positions as they are in the active).


Correction of deep bite in adults using the Invisalign system.

719 D overbite is commonly treated by molar extrusion, incisor intrusion, or both. Careful diagnostic assessment is needed to determine the best treatment approach. Patients with structural deep bites may require orthognathic surgery for full correction. The amount of incisor exposure in smiling is an important consideration in deep-bite cases. When the maxillary incisor display is correct, maxillary incisor intrusion is unnecessary; treatment should involve intrusion and leveling of the mandibular anterior teeth to avoid flattening of the smile arc. Any maxillary incisor intrusion or mandibular leveling should be carefully monitored during treatment to maintain proper incisor exposure while effectively correcting the deep bite. After the development of thermoformed appliances that could be used for minor tooth movements, the Invisalign* system was introduced for treatment of moderate malocclusions. In 2004, Wheeler reported that Invisalign could be used in patients with anterior and posterior crossbites, deep bites, anterior open bites or shallow overbites, and periodontal problems. The present article describes the use of Invisalign appliances for the correction of deep bite in adult patients with normal skeletal patterns. Case 1