Query: Modeling direct and indirect influence across heterogeneous social networks

Relevant passages:
Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter <=SEP=> 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 <=SEP=> 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.

Users of the world , unite ! The challenges and opportunities of Social Media <=SEP=> As of January 2009, the online social networking application Facebook registered more than 175 million active users. To put that number in perspective, this is only slightly less than the population of Brazil (190 million) and over twice the population of Germany (80 million)! At the same time, every minute, 10 hours of content were uploaded to the video sharing platform YouTube. And, the image hosting site Flickr provided access to over 3 billion photographs, making the world-famous Louvre Museum’s collection of 300,000 objects seem tiny in comparison. According to Forrester Research, 75% of Internet surfers used ‘‘Social Media’’ in the second quarter of 2008 by joining social networks, reading blogs, or contributing reviews to shopping sites; this represents a significant rise from 56% in 2007. The growth is not limited to teenagers, either; members of Generation X, now 35—44 years old, increasingly populate the ranks of joiners, spectators, and critics. It is therefore reasonable to say that Social Media represent a revolutionary new trend that should be of interest to companies operating in online space–—or any space, for that matter. Yet, not overly many firms seem to act comfortably in a world where consumers can speak so freely Business Horizons (2010) 53, 59—68

Group formation in large social networks: membership, growth, and evolution <=SEP=> 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.

Latent Dirichlet Allocation <=SEP=> We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hofmann's aspect model , also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is generated as a mixture of topics, where the continuous-valued mixture proportions are distributed as a latent Dirichlet random variable. Inference and learning are carried out efficiently via variational algorithms. We present empirical results on applications of this model to problems in text modeling, collaborative filtering, and text classification.

INFORMATION AND THE CHANGE IN THE PARADIGM IN ECONOMICS , PART 1 by <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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.

Challenges during the transition to Agile Methodologies : A Holistic Overview <=SEP=> 

Ensemble Algorithms in Reinforcement Learning <=SEP=> This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chosen actions or action probabilities of different RL algorithms. We designed and implemented four different ensemble methods combining the following five different RL algorithms: Q-learning, Sarsa, actor-critic (AC), QV-learning, and AC learning automaton. The intuitively designed ensemble methods, namely, majority voting (MV), rank voting, Boltzmann multiplication (BM), and Boltzmann addition, combine the policies derived from the value functions of the different RL algorithms, in contrast to previous work where ensemble methods have been used in RL for representing and learning a single value function. We show experiments on five maze problems of varying complexity; the first problem is simple, but the other four maze tasks are of a dynamic or partially observable nature. The results indicate that the BM and MV ensembles significantly outperform the single RL algorithms.

Design and Analysis of a 3-Arm Spiral Antenna <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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.