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Background: Although some have noted that combining technical and marketing content is precarious, technical communication professionals are increasingly involved in content marketing, which includes the creation of white papers. Literature review: The little existing literature on white papers provides conflicting guidance about managing the combination of technical and marketing content. Both soft-sell and hard-sell marketing approaches have been recommended. One source of such inconsistent guidance may be the lack of agreement about definitions. Research on print advertisements has described hard and soft selling as multidimensional rather than binary aspects of persuasive appeals. Research question: Which dimensions of hard- and soft-sell appeals are predominant in white papers? Research methodology: To complete our descriptive study, we collected a corpus of documents labeled as white papers in TechRepublic, and then selected and trained three raters to complete a series of judgments about dimensions of persuasive appeals in the corpus. We aggregated those ratings, calculating the mean and standard deviation for the dimensions to describe their distribution across the corpus. Results/discussion: Overall, hard-sell dimensions were more prevalent than soft-sell dimensions. However, the soft-sell category of “implicitness” was also dominant. Conclusion: Our results demonstrate the value of treating hard and soft selling as multidimensional, complementary, and combinatory marketing appeals that allow, for example, a single white paper to be both “subjective” (soft sell), and “precise” (hard sell), or both “creative” (soft sell) and “informative” (hard sell). [ABSTRACT FROM AUTHOR]

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This paper studies the low-SNR regime performance of a scalar complex $K$ -user interference channel with the Gaussian noise. The finite bandwidth case is considered, where the low-SNR regime is approached by letting the input power go to zero, while the bandwidth is small and fixed. We show that for all $\delta >0$ , there exists a set of channel coefficients with non-zero measure (probability), in which the wideband slope per user satisfies $ \mathcal {S}_{0}< {\scriptstyle {}^{\scriptstyle 2}}\hspace{-0.224em}/\hspace{-0.112em}{\scriptstyle K}+\delta $. This is quite contrary to the large bandwidth case, where a slope of 1 per user is achievable with probability 1. We also develop an interference alignment scheme for the finite bandwidth case that shows some gain in wideband slope. [ABSTRACT FROM AUTHOR]

The impact of online video advertisement has an evolving and undeniable influence on the success of online video streaming. A successful online video advertisement campaign deployment necessitates: “targeting appropriate marketing audience, determining optimum intervals to insert advertisement, associating the production quality of the content while considering advertisement conceptual features, matching the relevance of advertisement context to the content theme, calculating the applicable number of ads for stitching into the content, and correlating the ratio of advertisement length to total active watch duration”. This paper proposes a novel model for inserting advertisement into online video that considers content and commercial specific properties while optimizing Quality of Experience (QoE) by estimating suitable duration for advertisement, number of splits and content relation. The proposed model has been evaluated in a controlled on-line video test environment so that the success rate of this platform has been compared with the advertisement insertion strategies of technology frontrunners YouTube and Vimeo. In terms of medium and long length online videos, advertisements located within the content provides a better QoE compared to the ones that are located at the beginning of the video. For short length online videos, the general expectation of the audience tends to see the content immediately and any advertisement insertion related delay results in a corresponding customer behavior where 25% tend to quit after 3 seconds and another 25% after 5 seconds. [ABSTRACT FROM AUTHOR]

Robust and accurate control of a flapping-wing aerial vehicle (FWAV) system is a challenging problem due to the existence of backlash-like hysteresis nonlinearity. This paper proposes uncertainty and disturbance estimator (UDE)-based control with output feedback for FWAV systems. The approach enables the acquisition of the approximate plant model with only a partial knowledge of system parameters. For the design of the controller, only the bandwidth information of the unknown plant model is needed, which is available through the UDE filter. The stability analysis of the closed-loop system with the UDE-based controller is presented. It is shown that the proposed control scheme can ensure the boundedness of the control signals. A number of numerical simulations are carried out to demonstrate the satisfactory trajectory tracking performance of the proposed method. [ABSTRACT FROM AUTHOR]

Hybrid ac/dc power systems will rely on power electronics to enable controlled energy exchange between different dc and ac systems. Multiport dc–dc–ac (MP-DC2AC) converters offer the ability to exchange power between two dc systems and an ac system using a single converter structure. Potential benefits include reduced energy conversion stages and a more compact station footprint. To date, limited information is available on modeling, operation, and control of MP-DC2AC converters suitable for HVDC and MVDC applications. This paper helps to fill this gap by presenting a generalized modeling and control framework for two yet unexplored MP-DC2AC modular multilevel converters. Based on derived analytical models of internal power transfer mechanisms, a dynamic controller is proposed that allows independent control of port power flows while keeping capacitor voltages regulated. Results are validated by extensive time-domain simulations. A comprehensive comparative analysis between four different MP-DC2AC topologies is carried out. [ABSTRACT FROM AUTHOR]

This paper explores a dual-channel closed-loop supply chain (CLSC) system consisting of a manufacturer and a retailer. The manufacturer is the CLSC Stackelberg leader. We investigate the effect of exerting marketing effort on the optimal decisions and profits of supply chain members by considering several marketing effort supported models, namely the models when the manufacturer is the investor, the retailer is the investor, and the centralized CLSC system, respectively. We propose a two-part tariff contract for coordinating the CLSC. We analytically reveal that the two-part tariff contract can coordinate the CLSC when the manufacturer is the investor but unable to coordinate supply chain when the retailer is the investor. Finally, we present the numerical analysis to uncover insights on how the consumers’ preference toward the direct channel affects the optimal decisions in the dual-channel CLSC system. [ABSTRACT FROM AUTHOR]

As the speed of cellular networks has steadily increased so has user consumption of video content. As such, mobile TV deployments over cellular networks have become increasing attractive. Typically mobile TV deployment schemes utilize a subscription-based model. In this model, the advertisements are “baked” into the TV program, i.e., the service provider’s role is typically limited to that of providing a content distribution network. As such, the service provider’s revenue is limited per the contracts negotiated with the content providers. In this paper, we present the design and game theoretic analysis of a non-subscription ad-supported mobile TV system. In our system, we show how to incentivize viewership statistic collection via fair allocation of resources among the TV channels, on a per TV show basis. In addition, we present a real-time auction which enables service providers the ability to directly schedule advertisements/earn revenue based on an accurate set of viewership statistics. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method for detecting primary objects in unconstrained videos in a completely automatic setting. Here, we define the primary object in a video as the object that presents saliently in most of the frames. Unlike previous works considering only local saliency detection or common pattern discovery, the proposed method integrates the local visual/motion saliency extracted from each frame, global appearance consistency throughout the video, and spatiotemporal smoothness constraint on object trajectories. We first identify a temporal coherent salient region throughout the whole video, and then explicitly learn a global appearance model to distinguish the primary object against the background. In order to obtain high-quality saliency estimations from both appearance and motion cues, we propose a novel self-adaptive saliency map fusion method by learning the reliability of saliency maps from labeled data. As a whole, our method can robustly localize and track primary objects in diverse video content, and handle the challenges such as fast object and camera motion, large scale and appearance variation, background clutter, and pose deformation. Moreover, compared with some existing approaches that assume the object is present in all the frames, our approach can naturally handle the case where the object is present only in part of the frames, e.g., the object enters the scene in the middle of the video or leaves the scene before the video ends. We also propose a new video data set containing 51 videos for primary object detection with per-frame ground-truth labeling. Quantitative experiments on several challenging video data sets demonstrate the superiority of our method compared with the recent state of the arts. [ABSTRACT FROM PUBLISHER]

This paper considers two firms that compete on both advertising and pricing. Each firm decides whether to use mass advertising to cover the entire market or adopt targeted advertising to inform only the switching shoppers. We capture this problem through a dynamic game model in which each firm can endogenously choose its advertising strategy. Our results reveal a number of interesting findings. We show that the two firms would not choose mass advertising simultaneously but might adopt targeted advertising at the same time. Targeted advertising can hurt both firms even if it is more cost-efficient than mass advertising: when the marginal cost of advertising is moderate, the two firms may fall into the well-known trap of the prisoners’ dilemma. In another interesting finding, symmetric firms may employ asymmetric advertising strategies in equilibrium: when the advertising cost is sufficiently low, one firm chooses targeted advertising, while the other adopts mass advertising. In addition, while a fixed cost associated with acquiring the targeting capability weakens firms’ incentives to adopt targeted advertising, a large fixed cost might benefit firms in equilibrium. [ABSTRACT FROM AUTHOR]

Mobile platforms are prevalently used in the age of social media. They serve a two-sided market to connect users and app developers. In this system, members simultaneously sell products, publish advertisements (ads), and advertise. This paper aims to explore advertising strategies for the mobile platform incorporating their roles as sellers, ad publishers, and advertisers. Specifically, we develop a game-theoretical model which captures the relationship among the works of the platform owner and app developers in a dynamic setting. Our analysis shows that when the platform displays apps’ ads, the owner may be better off to participate in apps’ advertising under certain conditions, rather than always charging them aggressively as suggested by convention wisdom. Surprisingly, although the negative impacts evoked by multiple apps’ entry deserve much attention, it is unnecessary for app developers to take into account when making advertising decisions. We further find that the coordinating bilateral participation in advertising is a new mechanism to improve the profitability. Unlike the result proposed in the previous literature, the mechanism here will cause free-riding among app developers when they participate in the platform’s advertising. Furthermore, when accompanied with the revenue sharing policy, the relatively low participation rates could absolutely eliminate the system inefficiency. [ABSTRACT FROM AUTHOR]

Multi-armed bandits problem has been widely utilized in economy-related areas. Incentives are explored in the sharing economy to inspire users for better resource allocation. Previous works build a budget-feasible incentive mechanism to learn users’ cost distribution. However, they only consider a special case that all tasks are considered as the same. The general problem asks for finding a solution when the cost for different tasks varies. In this paper, we investigate this problem by considering a system with $k$ k levels of difficulty. We present two incentivizing strategies for offline and online implementation, and formally derive the ratio of utility between them in different scenarios. We propose a regret-minimizing mechanism to decide incentives by dynamically adjusting budget assignment and learning from users’ cost distributions. We further extend the problem to a more generalized k-MAB problem by removing the contextual information of difficulties. CUE-UCB algorithm is proposed to address the online advertisement problem for multi-platforms. Our experiment demonstrates utility improvement about 7 times and time saving of 54% to meet a utility objective compared to the previous works in sharing economy, and up to 175% increment of utility for online advertising. [ABSTRACT FROM AUTHOR]

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Causal Inference plays an important role in decision making in many fields, such as social marketing, healthcare, and public policy. One fundamental problem in causal inference is the treatment effect estimation in observational studies when variables are confounded. Controlling for confounding effects is generally handled by propensity score. But it treats all observed variables as confounders and ignores the adjustment variables, which have no influence on treatment but are predictive of the outcome. Recently, it has been demonstrated that the adjustment variables are effective in reducing the variance of the estimated treatment effect. However, how to automatically separate the confounders and adjustment variables in observational studies is still an open problem, especially in the scenarios of high dimensional variables, which are common in the big data era. In this paper, we first propose a Data-Driven Variable Decomposition (D $^2$ 2 VD) algorithm, which can 1) automatically separate confounders and adjustment variables with a data-driven approach, and 2) simultaneously estimate treatment effect in observational studies with high dimensional variables. Under standard assumptions, we theoretically prove that our D $^2$ 2 VD algorithm can unbiased estimate treatment effect and achieve lower variance than traditional propensity score based methods. Moreover, to address the challenges from high-dimensional variables and nonlinear, we extend our D $^2$ 2 VD to a non-linear version, namely Nonlinear-D $^2$ 2 VD (N-D $^2$ 2 VD) algorithm. To validate the effectiveness of our proposed algorithms, we conduct extensive experiments on both synthetic and real-world datasets. The experimental results demonstrate that our D $^2$ 2 VD and N-D $^2$ 2 VD algorithms can automatically separate the variables precisely, and estimate treatment effect more accurately and with tighter confidence intervals than the state-of-the-art methods. We also demonstrated that the top-ranked features by our algorithm have the best prediction performance on an online advertising dataset. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile technology, nowadays, people spend a large amount of time on mobile devices. The locations and contexts of users are easily accessed by mobile advertising brokers, and the brokers can send customers related location-based advertisements. In this paper, we consider an important location-based advertising problem, namely maximum utility advertisement assignment (MUAA) problem, with the estimation of the interests of customers and the contexts of the vendors, we want to maximize the overall utility of ads by determining the ads sent to each customer subject to the constraints of the capacities of customers, the distance ranges and the budgets of vendors. We prove that the MUAA problem is NP-hard and intractable. Thus, we propose one offline approach, namely the ${\sf reconciliation\ approach}$ reconciliation approach , which has an approximation ratio of $(1-\epsilon)\cdot \theta$ (1 - ε) · θ . In addition, we also address the online scenario, in which customers arrive in a streaming fashion, with one novel online algorithm, namely the ${\sf online\ adaptive\ factor-aware\ approach}$ online adaptive factor - aware approach , which has a competitive ratio (compared to the optimal solution of the offline scenario) of $\frac{\ln (g)+1}{\theta }$ ln (g) + 1 θ , $g>e$ g > e , where $e$ e is the base of the natural logarithm. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches over both real and synthetic datasets. [ABSTRACT FROM AUTHOR]

Online advertising delivers promotional marketing messages to consumers through online media. Advertisers often have the desire to optimize their advertising spending strategies in order to gain the highest return on investment and maximize their key performance indicator. To build accurate advertisement performance predictive models, it is crucial to detect the change-points in the historical data and apply appropriate strategies to address a data pattern shift problem. However, with sparse data, which is common in online advertising and some other applications, online change-point detection is very challenging. We present a novel collaborated online change-point detection method in this paper. Through efficiently leveraging and coordinating with auxiliary time series, we can quickly and accurately identify the change-points in sparse and noisy time series. Simulation studies as well as real data experiments have justified the proposed method’s effectiveness in detecting change-points in sparse time series. Therefore, it can be used to improve the accuracy of predictive models. [ABSTRACT FROM AUTHOR]

In sponsored search advertising, keywords serve as an essential bridge linking advertisers, search users, and search engines. Advertisers have to deal with a series of keyword decisions throughout the entire lifecycle of search advertising campaigns. This paper proposes a multilevel and closed-form computational framework for keyword optimization (MKOF) to support various keyword decisions. Based on this framework, we develop corresponding optimization strategies for keyword targeting, keyword assignment, and keyword grouping at different levels (e.g., market, campaign, and adgroup). With two real-world datasets obtained from past search advertising campaigns, we conduct computational experiments to evaluate our keyword optimization framework and instantiated strategies. Experimental results show that our method can approach the optimal solution in a steady way, and it outperforms two baseline keyword strategies commonly used in practice. The proposed MKOF framework also provides a valid experimental environment to implement and assess various keyword strategies in sponsored search advertising. [ABSTRACT FROM AUTHOR]

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Real-time bidding (RTB) based display advertising has become one of the key technological advances in computational advertising. RTB enables advertisers to buy individual ad impressions via an auction in real-time and facilitates the evaluation and the bidding of individual impressions across multiple advertisers. In RTB, the advertisers face three main challenges when optimizing their bidding strategies, namely (i) estimating the utility (e.g., conversions, clicks) of the ad impression, (ii) forecasting the market value (thus the cost) of the given ad impression, and (iii) deciding the optimal bid for the given auction based on the first two. Previous solutions assume the first two are solved before addressing the bid optimization problem. However, these challenges are strongly correlated and dealing with any individual problem independently may not be globally optimal. In this paper, we propose Bidding Machine, a comprehensive learning to bid framework, which consists of three optimizers dealing with each challenge above, and as a whole, jointly optimizes these three parts. We show that such a joint optimization would largely increase the campaign effectiveness and the profit. From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes. Our extensive offline empirical study and online A/B testing verify the high effectiveness of the proposed bidding machine. [ABSTRACT FROM AUTHOR]

Machine-learned models are often perceived as “black boxes”: they are given inputs and hopefully produce desired outputs. There are many circumstances, however, where human-interpretability is crucial to understand (i) why a model outputs a certain prediction on a given instance, (ii) which adjustable features of that instance should be modified, and finally (iii) how to alter a prediction when the mutated instance is input back to the model. In this paper, we present a technique that exploits the feedback loop originated from the internals of any ensemble of decision trees to offer recommendations for transforming a k-labelled predicted instance into a k'-labelled one (for any possible pair of class labels k, k'). Our proposed algorithm perturbs individual feature values of an instance, so as to change the original prediction output by the ensemble on the so-transformed instance. This is also achieved under two constraints: the cost and tolerance of transformation. Finally, we evaluate our approach on four distinct application domains: online advertising, healthcare, spam filtering, and handwritten digit recognition. Experiments confirm that our solution is able to suggest changes to feature values that help interpreting the rationale of model predictions, making it indeed useful in practice especially if implemented efficiently. [ABSTRACT FROM AUTHOR]

Predicting the click-through rate of an advertisement is a critical component of online advertising platforms. In sponsored search, the click-through rate estimates the probability that a displayed advertisement is clicked by a user after she submits a query to the search engine. Commercial search engines typically rely on machine learning models trained with a large number of features to make such predictions. This inevitably requires a lot of engineering efforts to define, compute, and select the appropriate features. In this paper, we propose two novel approaches (one working at character level and the other working at word level) that use deep convolutional neural networks to predict the click-through rate of a query-advertisement pair. Specifically, the proposed architectures consider as input only the textual content appearing in a query-advertisement pair and the page position at which the advertisement appears on the search result page of the query, and produce as output a click-through rate prediction. By comparing the character-level model with the word-level model, we show that language representation can be learnt from scratch at character level when trained on enough data. Through extensive experiments using billions of query-advertisement pairs of a popular commercial search engine, we demonstrate that both approaches significantly outperform a baseline model built on well-selected text features and a state-of-the-art word2vec-based approach. We also show the importance of the position feature in the proposed approaches in improving the prediction accuracy. When combining the predictions of the deep models introduced in this study with the prediction of the model in production of the same commercial search engine, we significantly improve the accuracy and the calibration of the click-through rate prediction of the production system. We also show the potential of leveraging the CTR prediction of the proposed deep learning models for query-ad relevance modeling and query-ad matching tasks in sponsored search. [ABSTRACT FROM AUTHOR]

Vehicular local advertising enables roadside retailers to provide timely and location-aware advertisements to on-road vehicular users to attract more in-store visits. However, the prevalence of the vehicular local advertising has raised increasing transparency and accountability concerns on the spatial keyword query process, such as why a vehicular user receives advertisements from specific spatial entities as well as the timely detection of the advertising misbehavior. In this paper, we develop a transparent and accountable vehicular local advertising system by utilizing the tamper-proof and open nature of the blockchain technology. Considering the large scale of the spatial-keyword database and the expensive computation and storage cost on the blockchain, we introduce two design strategies, digest-and-verify and divide-then-assemble. In specific, a large-scale spatial keyword database is digested and stored on the blockchain. The spatial keyword query is then conducted with modular executions of two off-chain spatial keyword query functions, the results of which are efficiently assembled and verified in an advertising smart contract. By doing so, expensive on-chain computation and storage overheads are significantly reduced at a cost of acceptable off-chain overheads. We define the security requirements of the vehicular local advertising system as Auditing Security and achieve the notion with the security analysis. We also conduct real-world experiments to show the efficiency of the blockchain-based vehicular local advertising system. [ABSTRACT FROM AUTHOR]

In this paper, we study a novel problem of influence maximization in trajectory databases that is very useful in precise location-aware advertising. It finds $k$ best trajectories to be attached with a given advertisement and maximizes the expected influence among a large group of audience. We show that the problem is NP-hard and propose both exact and approximate solutions to find the best set of trajectories. In the exact solution, we devise an expansion-based framework that enumerates trajectory combinations in a best-first manner and propose three types of upper bound estimation techniques to facilitate early termination. In addition, we propose a novel trajectory index to reduce the influence calculation cost. To support large $k$ , we propose a greedy solution with an approximation ratio of (1 − 1/e), whose performance is further optimized by a new proposed cluster-based method. We also propose a threshold method that can support any approximation ratio $\epsilon \in (0,1]$ . In addition, we extend our problem to support the scenario when there are a group of advertisements. In our experiments, we use real datasets to construct user profiles, motion patterns, and trajectory databases. The experimental results verified the efficiency of our proposed methods. [ABSTRACT FROM PUBLISHER]

To support various Internet of Things (IoT) applications, the Bluetooth Low Energy (BLE) standard specifies a wide range of parameter values for the neighbor discovery process (NDP). The parameter values used during neighbor discovery directly affect the performance of the NDP. Therefore, an optimal parameter setting is essential to achieve the best tradeoff between discovery latency and energy consumption. An analytical model can offer a beneficial guideline for such a parameter selection. In this paper, we propose a general model for analyzing the performance of NDP in BLE networks. In the model, the operations of the scanner and the advertiser, which are two main components of NDP, are expressed on the discrete-time axis. Based on the Chinese Remainder Theorem (CRT), the discovery latency and energy consumption of advertiser are derived. The numerical results from our model are almost the same as the simulation results, for any parameter values specified by the standard. When considering that BLE is one of candidate communication technologies for IoT, the proposed model is expected to be very useful in setting the default or initial values of NDP parameters for various IoT applications. [ABSTRACT FROM PUBLISHER]

We propose an algorithm for detecting patterns exhibited by anomalous clusters in high dimensional discrete data. Unlike most anomaly detection (AD) methods, which detect individual anomalies, our proposed method detects groups ( clusters) of anomalies; i.e., sets of points which collectively exhibit abnormal patterns. In many applications, this can lead to a better understanding of the nature of the atypical behavior and to identifying the sources of the anomalies. Moreover, we consider the case where the atypical patterns exhibit on only a small (salient) subset of the very high dimensional feature space. Individual AD techniques and techniques that detect anomalies using all the features typically fail to detect such anomalies, but our method can detect such instances collectively, discover the shared anomalous patterns exhibited by them, and identify the subsets of salient features. In this paper, we focus on detecting anomalous topics in a batch of text documents, developing our algorithm based on topic models. Results of our experiments show that our method can accurately detect anomalous topics and salient features (words) under each such topic in a synthetic data set and two real-world text corpora and achieves better performance compared to both standard group AD and individual AD techniques. All required code to reproduce our experiments is available from https://github.com/hsoleimani/ATD. [ABSTRACT FROM PUBLISHER]

In vehicular ad hoc network (VANET) safety applications, the source vehicle that detects an accident can generate a warning message and propagate it to the following vehicles to notify other drivers before they reach the potential danger zone on the road. Recent studies have shown that sparse vehicle traffic leads to network fragmentation, which poses a crucial research challenge for safety applications. In this paper, we analyze and quantify the improvement in VANET connectivity when a limited number of roadside units (RSUs) are deployed and to investigate the routing performance for broadcast-based safety applications in this enhanced VANET environment. Our results show that, even with a small number of RSUs, the performance in terms of the probability of network connectivity, the rehealing delay, the number of rehealing hops, and the message penetration time can be significantly improved in highway VANET scenarios. [ABSTRACT FROM PUBLISHER]

Interruption-based advertising has gained prominence in the online channel. Yet, little attention has been paid to deriving design principles and conceptualizations for online interruption-based advertising. This paper examines three novel design factors related to this phenomenon, namely, exposure timing, advertising intent, and brand image. Exposure timing pertains to the time by which the advertisement (ad) is launched within a website. Advertising intent refers to the explicitness of ad content in portraying the desire to induce purchase behavior. Brand image relates to consumers' overall perceptions of the advertised brand. In a laboratory experiment, participants were exposed to pop-up ads that were operationalized based on these three design considerations. Results reveal three two-way interactions among the study constructs. Online interruption-based ads shown in the predecisional shopping phase are more effective when their contents are designed with implicit advertising intent compared to explicit intent. Brand image is found to moderate the effects of advertising intent on consumer's purchase intention. Participants' responses also show that ads promoting weak brands with less favorable image tend to enjoy higher purchase intention when shown in the predecisional phase compared with the postdecisional phase. Theoretical and practical implications together with suggestions for future research are discussed. [ABSTRACT FROM AUTHOR]

Recently, spatial crowdsourcing has been drawing increasing attention with its great potential in collecting geographical knowledge. The system throughput (number of assigned tasks) and workers’ travel distance are two of many important factors in spatial crowdsourcing, and the improvement to one of them usually means the sacrifice of the other. However, most existing works resolve the trade-off between these two factors by simply targeting tasks within a bounding circle of each worker. In this paper, we compromise between the throughput and the distance by formulating these two factors as score terms in the objective function. This flexible formulation has the advantages of abandoning distant tasks and minimizing workers’ travel distance for reachable tasks. Aside from that, we study the multi-campaign scenario of spatial crowdsourcing, which is not uncommon in practical applications while not yet discussed in existing works. The worker diversity of the campaigns is considered to be another goal and formulated as another score term in the objective function. Subsequently, the problem of multi-campaign oriented spatial crowdsourcing is to maximize the objective function comprised by the aforementioned score terms. We prove that the problem is NP-hard, thus, we propose several approximation solutions. Extensive experiments have been conducted to confirm the effectiveness and the efficiency of the devised solutions. [ABSTRACT FROM AUTHOR]

Advertising options have been recently studied as a special type of guaranteed contracts in online advertising, which are an alternative sales mechanism to real-time auctions. An advertising option is a contract which gives its buyer a right but not obligation to enter into transactions to purchase page views or link clicks at one or multiple pre-specified prices in a specific future period. Different from typical guaranteed contracts, the option buyer pays a lower upfront fee but can have greater flexibility and more control of advertising. Many studies on advertising options so far have been restricted to the situations where the option payoff is determined by the underlying spot market price at a specific time point and the price evolution over time is assumed to be continuous. The former leads to a biased calculation of option payoff and the latter is invalid empirically for many online advertising slots. This paper addresses these two limitations by proposing a new advertising option pricing framework. First, the option payoff is calculated based on an average price over a specific future period. Therefore, the option becomes path-dependent. The average price is measured by the power mean, which contains several existing option payoff functions as its special cases. Second, jump-diffusion stochastic models are used to describe the movement of the underlying spot market price, which incorporate several important statistical properties including jumps and spikes, non-normality, and absence of autocorrelations. A general option pricing algorithm is obtained based on Monte Carlo simulation. In addition, an explicit pricing formula is derived for the case when the option payoff is based on the geometric mean. This pricing formula is also a generalized version of several other option pricing models discussed in related studies [1], [2], [3], [4], [5], [6]. [ABSTRACT FROM AUTHOR]

The creation of Wireless Personal Area Networks (WPANs) offers the Consumer Electronics industry a mechanism to truly unwire consumer products, leading to portability and ease of installation as never seen before. WPAN's can offer data-rates exceeding those that are required to convey high quality broadcast video, thus users can easily connect to high quality video for multimedia presentations in education, libraries, advertising, or have a wireless connection at home. There have been many WPAN proposals, but this paper concentrates on ECMA-368 as this standard has the largest industrial and implementers' forum backing. With the aim to define and create cost effective consumer electronic equipment this paper discusses the technology behind ECMA-368 physical layer, the design freedom availabilities, the required processing, buffer memory requirements and implementation considerations while concentrating on supporting all the offered data-rates. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a tomographical system suitable for imaging absorbers in strongly scattering media using a single light source-detector pair. Their reflective alignment would make it possible to implement both source and detector into a compact scan-head. Suitable probes would be soft tissue, some strongly scattering biological probes, or translucent technical materials, for example. An intensity-modulation technique is utilized to take ad- vantage of the amplitude and delay alterations between the source and the detector. The propagation of the photons is modeled by the Helmholtz equation and then appropriate inverse calculations of several measurements reveal the distribution of the absorption co-efficient inside the investigated volume. Therefore a reconstruction algorithm employing a Born approximation of higher order and a Tikhonov-regularization scheme were used to solve the deduced ill-posed and underdetermined system of equations. In this paper, we present results of simulations as well as of measurements. [ABSTRACT FROM AUTHOR]

Predicting the video popularity is an essential part of fast growing online media services. It is beneficial to an array of domains, from targeted advertising, personalized recommendation, to traffic load optimization. However, popularity prediction is a challenge problem due to the uncertainty of information cascade. In this paper, we treat the popularity of online videos as time series over the given periods and propose a novel time series model for popularity prediction. The proposed model is based on the correlation between early and future popularity series. Instead of inferring the precise view counts for a video, this paper focuses on accurately identifying the most popular videos based on the predicted popularity, because it is of the most interest to service providers. Experimental result on real world data have demonstrated that the proposed model outperforms several existing popularity prediction models. [ABSTRACT FROM PUBLISHER]

Display advertising is the most important revenue source for publishers in the online publishing industry. The ad pricing standards are shifting to a new model in which ads are paid only if they are viewed. Consequently, an important problem for publishers is to predict the probability that an ad at a given page depth will be shown on a user's screen for a certain dwell time. This paper proposes deep learning models based on Long Short-Term Memory (LSTM) to predict the viewability of any page depth for any given dwell time. The main novelty of our best model consists in the combination of bi-directional LSTM networks, encoder-decoder structure, and residual connections. The experimental results over a dataset collected from a large online publisher demonstrate that the proposed LSTM-based sequential neural networks outperform the comparison methods in terms of prediction performance. [ABSTRACT FROM AUTHOR]

In this paper, we model and formulate the search-based advertising auction problem with multiple slots, choice behaviors of advertisers, and the popular generalized second-price mechanism. A Lagrangian-based method is then proposed for tackling this problem. We present an extension to the subgradient algorithm based on Lagrangian relaxation coupled with the column generation method in order to improve the dual multipliers and accelerate its convergence. Simulation results show that the proposed algorithm is efficient and it shows significant improvement compared to the greedy algorithm. [ABSTRACT FROM AUTHOR]

One of the goals of third generation partnership project (3GPP) is to deploy device to device (D2D) proximity services in both commercial and public safety settings. Furthermore, as the mobile market is nearing saturation, service providers (SPs) are searching for additional sources of revenue. Commercial D2D can make use of a SP's unused/underutilized spectrum, yet there are few feasible economic models for deploying commercial D2D services. In this paper, we present Localized Advertisements over D2D specTrum Reuse coAlition fraMework, which is a feasible economic model for deploying a localized advertisement coalition-based D2D service that exploits spectrum leasing and reuse. D2D Ad stations, i.e., user equipments, interested in providing the D2D service form a coalition in order to share spectrum allocated by the SP. Using game theory we design an incentive compatible resource determination mechanism and auction. In the auction localized advertisers compete for resources. We show that our auction approximates the profit performance characteristics of the vickrey clarke groves auction, yet runs in P-time. In addition, we show that with proper planning, the D2D Ad stations can be deployed in a manner such that they can fully exploit the spectrum in the subsector formed by the desired cell size. Our proposed framework enables advertisers to indirectly fund the D2D Advertisement service, while also permitting the SP the ability to profit from leasing their unused spectrum to commercial D2D services. [ABSTRACT FROM AUTHOR]

Web portal services have become an important medium to deliver digital content and service, such as news, advertisements, and so on, to Web users in a timely fashion. To attract more users to various content modules on the Web portal, it is necessary to design a recommender system that can effectively achieve online content optimization by automatically estimating content items' attractiveness and relevance to users' interests. User interaction plays a vital role in building effective content optimization, as both implicit user feedbacks and explicit user ratings on the recommended items form the basis for designing and learning recommendation models. However, user actions on real-world Web portal services are likely to represent many implicit signals about users' interests and content attractiveness, which need more accurate interpretation to be fully leveraged in the recommendation models. To address this challenge, we investigate a couple of critical aspects of the online learning framework for personalized content optimization on Web portal services, and, in this paper, we propose deeper user action interpretation to enhance those critical aspects. In particular, we first propose an approach to leverage historical user activity to build behavior-driven user segmentation; then, we introduce an approach for interpreting users' actions from the factors of both user engagement and position bias to achieve unbiased estimation of content attractiveness. Our experiments on the large-scale data from a commercial Web recommender system demonstrate that recommendation models with our user action interpretation can reach significant improvement in terms of online content optimization over the baseline method. The effectiveness of our user action interpretation is also proved by the online test results on real user traffic. [ABSTRACT FROM PUBLISHER]

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This paper proposes definitions of implementation and adoption delays, arising from firm and client behaviors, in the context of market share dynamics. Information delay refers to the existence of lags in the information used for the advertising policy, while adoption delay refers to the existence of a lag in the effect of the advertising policy. These are natural lags in the flow of information and have not been considered in several models proposed in the literature. In this paper, these delays are introduced into recently proposed extensions of the Vidale-Wolfe-Deal and Lanchester models of market share dynamics subjected to affine advertising control policies. Conditions for stability of the equilibrium market share are derived. In addition, it is shown that Hopf bifurcations leading to oscillatory behavior exist for certain parameter values, and corresponding conditions for these are given. The main results are: the equilibrium market shares of the extended Vidale-Wolfe-Deal and Lanchester models are both robust to implementation delays, but, in the case of adoption delays, for both models, numerical results show that there is a critical value such that if the sum of the adoption delays exceeds this value, there is an onset of oscillations of market shares, through a Hopf bifurcation.

The skyline of a data point set is made up of the best points in the set, and is very important for multi-criteria decision making. In these years, the skyline problem attracts more and more attention, and many variants of the traditional skyline emerge in the database field. One recent and important variant is group-based skyline, which aims to find the best groups of points in a given set. In this paper, we bring forward an efficient approach, called minimum dominance search (MDS), to solve the g-skyline problem, a latest group-based skyline problem. MDS consists of two steps: In the first step, we construct a novel g-skyline support structure, i.e., minimum dominance graph (MDG), which proves to be a minimum g-skyline support structure. In the second step, we search for g-skyline groups based on the MDG through two searching algorithms, and a skyline-combination based optimization strategy is employed to improve these two algorithms. We conduct comprehensive experiments on both synthetic and real-world data sets, and show that our algorithms are orders of magnitude faster than the state-of-the-art in most cases. [ABSTRACT FROM AUTHOR]

Online display advertising has becomes a billion-dollar industry, and it keeps growing. Advertisers attempt to send marketing messages to attract potential customers via graphic banner ads on publishers’ webpages. Advertisers are charged for each view of a page that delivers their display ads. However, recent studies have discovered that more than half of the ads are never shown on users’ screens due to insufficient scrolling. Thus, advertisers waste a great amount of money on these ads that do not bring any return on investment. Given this situation, the Interactive Advertising Bureau calls for a shift toward charging by viewable impression, i.e., charge for ads that are viewed by users. With this new pricing model, it is helpful to predict the viewability of an ad. This paper proposes two probabilistic latent class models (PLC) that predict the viewability of any given scroll depth for a user-page pair. Using a real-life dataset from a large publisher, the experiments demonstrate that our models outperform comparison systems. [ABSTRACT FROM PUBLISHER]

Budget optimization is one of the primary decision-making issues faced by advertisers in search auctions. A quality budget optimization strategy can significantly improve the effectiveness of search advertising campaigns, thus helping advertisers to succeed in the fierce competition of online marketing. This paper investigates budget optimization problems in search advertisements and proposes a novel hierarchical budget optimization framework (BOF), with consideration of the entire life cycle of advertising campaigns. Then, we formulated our BOF framework, made some mathematical analysis on some desirable properties, and presented an effective solution algorithm. Moreover, we established a simple but illustrative instantiation of our BOF framework which can help advertisers to allocate and adjust the budget of search advertising campaigns. Our BOF framework provides an open testbed environment for various strategies of budget allocation and adjustment across search advertising markets. With field reports and logs from real-world search advertising campaigns, we designed some experiments to evaluate the effectiveness of our BOF framework and instantiated strategies. Experimental results are quite promising, where our BOF framework and instantiated strategies perform better than two baseline budget strategies commonly used in practical advertising campaigns. [ABSTRACT FROM PUBLISHER]

In marketing, it is well known that social needs play an important role in the purchase of conspicuous products such as high-end luxury fashion labels. In this paper, we analytically study the optimal advertising and pricing decisions for luxury fashion brands in a market that consists of two consumer groups with contrasting social needs for fashion products, namely, the leader group (LG) and the follower group (FG). We consider a situation where the LG consumers have the desire to distinguish themselves from the FG consumers, whereas the FG consumers would like to assimilate themselves with the LG consumers. We first develop an original optimization model for this problem. We then explore the optimal solution scheme by separating the problem into tactic-based subproblems and conduct an extensive sensitivity analysis. Our analysis reveals that the optimal strategies follow different scenarios, and it can be optimal for a brand of conspicuous product to do the following: 1) Advertise to only one group while sell to the whole market; 2) advertise and sell to the FG only; and 3) advertise and sell to the LG only. Important insights are also reported. [ABSTRACT FROM PUBLISHER]

This paper introduces a promising application over vehicular ad hoc networks (VANETs), where advertisers use VANETs to disseminate their commercial ads via car-to-car communication, targeting a large number of potential customers inside cars. However, due to the noncooperative behavior of selfish or even malicious nodes in real-world scenarios, such a vehicular advertisement system cannot be realized unless proper incentives and security mechanisms are in place. This paper presents signature-seeking drive (SSD), which is a secure incentive framework that stimulates cooperative dissemination of advertising messages among vehicular users in a secure way. Unlike existing incentive systems, SSD does not rely on tamper-proof hardware or game-theoretic approaches but leverages a public key infrastructure to provide secure incentives for cooperative nodes. With a set of ad dissemination designs proposed, we demonstrate that our SSD is robust in both incentive and security perspectives. [ABSTRACT FROM PUBLISHER]

In this paper, we study the problem of mining the informative structure of a news Web site that consists of thousands of hyperlinked documents. We define the informative structure of a news Web site as a set of index pages (or referred to as TOC, i.e., table of contents, pages) and a set of article pages linked by these TOC pages. Based on the Hyperlink Induced Topics Search (HITS) algorithm, we propose an entropy-based analysis (LAMIS) mechanism for analyzing the entropy of anchor texts and links to eliminate the redundancy of the hyperlinked structure so that the complex structure of a Web site can be distilled. However, to increase the value and the accessibility of pages, most of the content sites tend to publish their pages with intrasite redundant information, such as navigation panels, advertisements, copy announcements, etc. To further eliminate such redundancy, we propose another mechanism, called Info-Discoverer, which applies the distilled structure to identify sets of article pages. Info-Discoverer also employs the entropy information to analyze the information measures of article sets and to extract informative content blocks from these sets. Our result is useful for search engines, information agents, and crawlers to index, extract, and navigate significant information from a Web site. Experiments on several real news Web sites show that the precision and the recall of our approaches are much superior to those obtained by conventional methods in mining the informative structures of news Web sites. On the average, the augmented LAMIS leads to prominent performance improvement and increases the precision by a factor ranging from 122 to 257 percent when the desired recall falls between 0.5 and 1. In comparison with manual heuristics, the precision and the recall of Info-Discoverer are greater than 0.956. [ABSTRACT FROM AUTHOR]

The location selection (ls) problem, which aims to mine the optimal location from a set of candidates to place a new facility such that a score (i.e., benefit or influence on some given objects) can be maximized, has drawn significant research attention in recent years. State-of-the-art ls techniques assume each object is static and can only be influenced by a single facility. However, in reality, objects (e.g., people, vehicles) are mobile and are influenced by multiple facilities, which prevents classical ls solutions from selecting accurate results. In this paper, we introduce a generalized ls problem called Prime-ls which takes mobility and probability factors into consideration to address the aforementioned limitations. Specifically, given a set of candidate locations, Prime-ls aims to mine the optimal location which can influence the most number of moving objects. Also, to address the problem we propose an efficient algorithm called Pinocchio that leverages two pruning rules based on a novel distance measure. These rules enable us to prune many inferior candidate locations prior to influence computation, paving the way to efficient and accurate solution. Furthermore, we extend Pinocchio (Pinocchio-vo) by incorporating two optimization strategies during candidate validation phase, which further reduce unnecessary computations. Experimental study over two real-world datasets demonstrates superiority of our framework in comparison to state-of-the-art ls techniques. [ABSTRACT FROM PUBLISHER]

Mobile advertising in vehicular networks is of great interest with which timely information can be fast spread into the network. Given a limited budget for hiring seed vehicles, how to achieve the maximum advertising coverage within a given period of time is NP-hard. In this paper, we propose an innovative scheme, POST, for mobile advertising in vehicular networks. The POST design is based on two key observations we have found by analyzing three large-scale vehicular traces. First, vehicles demonstrate dynamic sociality in the network; second, such vehicular sociality has strong temporal correlations. With the knowledge, POST uses Markov chains to infer future vehicular sociality and adopts two greedy heuristics to select the most “centric” vehicles as seeds for mobile advertising. Extensive simulations based on three real data sets of taxi and bus traces have been carried out. The results show that POST can greatly improve the coverage and the intensity of advertising. For all the three involved data sets, it achieves an average gain of 64 percent comparing with the state-of-art schemes. [ABSTRACT FROM PUBLISHER]

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In this paper, we propose a novel best-effort broadcasting service (BEBS) scheme in cellular networks, which can be used for location-based advertising and real-time multimedia broadcasting. Unlike the conventional broadcasting service that aims to assure reliable data delivery to every user, the main objective of the proposed BEBS is to maximize the effective capacity at the cost of reliability. To this end, we introduce the notion of target reliability and formulate the problem of maximizing the capacity of BEBS under any given reliability constraint. By deriving an analytical model, we investigate the effect of target reliability on the achievable capacity. We further prove several key properties of BEBS with respect to the optimal value of target reliability and the maximum gain of the achievable capacity. To realize the maximum achievable capacity of BEBS, we propose two feedback control schemes that adjust the target reliability or modulation and coding scheme based on the estimate of relative gain of BEBS. We validate our analytical model by comparison with simulation. In addition, the simulation results confirm that the proposed schemes for BEBS significantly outperform conventional reliable broadcasting under various network configurations. [ABSTRACT FROM PUBLISHER]