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Cloud service providers typically provide different types of virtual machines (VMs) to cloud users with various requirements. Thanks to its effectiveness and fairness, auction has been widely applied in this heterogeneous resource allocation. Recently, several strategy-proof combinatorial cloud auction mechanisms have been proposed. However, they fail to protect the bid privacy of users from being inferred from the auction results. In this paper, we design a differentially private combinatorial cloud auction mechanism (DPCA) to address this privacy issue. Technically, we employ the exponential mechanism to compute a clearing unit price vector with a probability proportional to the corresponding revenue. We further improve the mechanism to reduce the running time while maintaining high revenues, by computing a single clearing unit price, or a subgroup of clearing unit prices at a time, resulting in the improved mechanisms DPCA-S and its generalized version DPCA-M, respectively. We theoretically prove that our mechanisms can guarantee differential privacy, approximate truthfulness and high revenue. Extensive experimental results demonstrate that DPCA can generate near-optimal revenues at the price of relatively high time complexity, while the improved mechanisms achieve a tunable trade-off between auction revenue and running time., 12 pages, 6 figures

How to generate more revenues is crucial to cloud providers. Evidences from the Amazon cloud system indicate that ``dynamic pricing'' would be more profitable than ``static pricing.'' The challenges are: How to set the price in real-time so to maximize revenues? How to estimate the price dependent demand so to optimize the pricing decision? We first design a discrete-time based dynamic pricing scheme and formulate a Markov decision process to characterize the evolving dynamics of the price-dependent demand. We formulate a revenue maximization framework to determine the optimal price and theoretically characterize the ``structure'' of the optimal revenue and optimal price. We apply the Q-learning to infer the optimal price from historical transaction data and derive sufficient conditions on the model to guarantee its convergence to the optimal price, but it converges slowly. To speed up the convergence, we incorporate the structure of the optimal revenue that we obtained earlier, leading to the VpQ-learning (Q-learning with value projection) algorithm. We derive sufficient conditions, under which the VpQ-learning algorithm converges to the optimal policy. Experiments on a real-world dataset show that the VpQ-learning algorithm outperforms a variety of baselines, i.e., improves the revenue by as high as 50% over the Q-learning, speedy Q-learning, and adaptive real-time dynamic programming (ARTDP), and by as high as 20% over the fixed pricing scheme.

With the diversity of manipulator grasping methods and the complexity of the unstructured environment, the grasping planning of the target object is very complicated. However, the external factor for the manipulator grasping is primarily the external shape of the target object. Therefore, it is of great significance to establish a grasping system based on target shape analysis. This paper proposes a method for determining the grasping posture of manipulator based on shape analysis and force closure. The irregular or complex objects are reduced to a combination of some basic shapes. The 3D data points of the object are split into blocks and each part is fitted to sphere, cylinder or rectangle by a best-fit algorithm. This allows the grasping posture of the manipulator to be determined quickly. The grasping characteristics of the object are analyzed and the grasping surface is described qualitatively by means of a superellipse. The grasping of objects is simplified according to the stable grasping condition of force closure. The force spiral space for grasping defines the grasping quality of the manipulator. The best grasping posture is obtained by evaluating the indicators. This method eliminates complex training processes, reduces the complexity of robotic grasping and highly universal.

Wireless virtualization has become a key concept in future cellular networks which can provide virtualized wireless networks for different mobile virtual network operators (MVNOs) over the same physical infrastructure. Resource allocation is a main challenging issue in wireless virtualization for which auction approaches have been widely used. However, for most existing auction-based allocation schemes, the objective is to maximize the social welfare due to its simplicity. While in reality, MVNOs are more interested in maximizing their own revenues. However, the revenue-maximization auction problem is much more complex since the price is unknown before calculation. In this paper, we give a first attempt for designing a revenue-optimal auction mechanism for resource allocation in wireless virtualization. Considering the complexity in revenue maximization, deep learning techniques are applied. Specifically, we construct a multi-layer feed-forward neural network based on the analysis of optimal auction design. The neural network adopts users' bids as the input and the allocation rule and conditional payment rule as the output. The training set of this neural network is the users' valuation profiles. The proposed auction mechanism possesses several satisfactory properties, e.g., individual rationality, incentive compatibility and budget constraint. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

The Bitcoin payment system involves two agent types: users that transact with the currency and pay fees and miners in charge of authorizing transactions and securing the system in return for these fees. Two of Bitcoin’s challenges are (i) securing sufficient miner revenues as block rewards decrease, and (ii) alleviating the throughput limitation due to a small maximal block size cap. These issues are strongly related as increasing the maximal block size may decrease revenue due to Bitcoin’s pay-your-bid approach. To decouple them, we analyze the “monopolistic auction” [ 16 ], showing (i) its revenue does not decrease as the maximal block size increases, (ii) it is resilient to an untrusted auctioneer (the miner), and (iii) simplicity for transaction issuers (bidders), as the average gain from strategic bid shading (relative to bidding one’s value) diminishes as the number of bids increases.

Fueled by the widespread use of the internet, more and more ordinary people have now become merchandise sellers who sell their own possessions, such as antique collections and limited souvenirs, to buyers who are interested in such goods via online auctions. This study examines the decision making related to the bidding strategies used in online auctions by both sellers and buyers. When selling goods for which there is a limited supply, sellers consider whether to sell the single homogenous items in multiple, simultaneous auctions or all the items in a single auction. Moreover, when selling heterogeneous but associated goods, sellers may decide to bundle the items for sale or not with an aim of increasing the potential buyers’ willingness to make a purchase. We investigate the effects that various factors related to the bidding strategies used in online auctions, such as the base price and duration of the auction determined by the seller and the bidding price decided by the buyer, have on the seller’s profit, and the utilities of both parties are considered to derive the equilibrium solutions. This study contributes to the literature by proposing an online auction framework that focuses more on individual sellers selling a limited quantity of items with an aim to establish a favorable online auction for both sellers and buyers and earn more profits for sellers. The results show that the base prices and direct purchase prices should be unestablished to achieve the most attractive characteristics of online auctions, which would encourage more buyers to freely place bids. As a result, the bidding items would have more chances to be eventually obtained by the buyer who places the highest bid, which, thus, maximizes the seller’s profit.

China began connecting farmers directly with supermarkets 10 years ago, when they were at a disadvantage and forced to sell products at low prices, as unstable cooperation among supply chain participants led to inequitable distribution of revenue. Revenue-sharing contracts offer a risk-sharing approach to ensure supply chain coordination and optimize profit for all. Research on short life cycle products with revenue-sharing contracts assume stable prices or investigate the effects of revenue-sharing contracts on supply chain coordination. This study introduced a revenue-sharing contract model into a ‘farmer-supermarket direct-purchase’ supply chain, considering price fluctuation and retail promotional efforts, stochastic market demand, among other factors. Revenue-sharing contracts achieved long-term stability in supply chain coordination, all participants obtained more profits, and the size of revenue-sharing parameter depends on the position and bargaining power of all participants. A case study on Tianhong supermarket and Nanxia farmer cooperative verified these findings, eliciting practical implications for professionals and policymakers.

In this paper, we study a body-to-body network (BBN) framework, which enables wireless body area network (WBAN) users located in close proximity to cooperate and share their network resources to improve the overall network performance. Our main aim is to design a distributed resource allocation mechanism that encourages each participating WBAN user to participate and upload each other’s data collaboratively. We propose an auction-based mechanism that optimizes data uploading for all participating users and the corresponding reimbursement. In the proposed auction mechanism, each user acts as both auctioneer and bidder. Fist, the auctioneer initiates the auction by announcing the amount of resource it wants to share and its price and each bidder submits their bid to each auctioneer based on its demand. We further propose a distributed algorithm for the auction mechanism that jointly solves both the auctioneers’ and the bidders’ optimization problems and determines the optimal amount of resource users should reserve for their own and the portion they should share. Our theoretical analysis demonstrates that the proposed distributed algorithm converges to the solution that maximizes the aggregated benefit of the users. Finally, the simulation results exhibit that the proposed algorithm always improves WBAN user’s individual performance together with overall BBN performance.

The complex and changeable robotic operating environment will often cause the low success rate or failure of the robot grasping. This paper proposes a grasp pose detection method based on the cascaded convolutional neural network, which can be applied to grasp unknown irregular objects under unstructured environment. The grasping feature and the grasp position candidate bounding-boxes of objects are extracted by Mask-RCNN. To guarantee the generalization and improve the detection rate, grasp angle estimation network Y-Net is proposed to accurately obtain the grasp angle. To solve the problem of insufficient accuracy of grasping position, grasping feasibility evaluation network Q-Net is proposed for acquiring the grasp quality distribution. Finally, the optimal grasp posture is obtained for the robotic object grasping task in cluttered scenes. Experiments are validated in Cornell datasets, Jacquard datasets and real environments respectively. The experimental results show that the proposed method can quickly calculate the robot posture for irregular objects with random poses and different shapes. Compared to the previous methods, it has considerable improvement in grasp accuracy and speed. The method can be applied to object grasping scenarios in cluttered scenes and has strong stability and robustness.

When solving multi-objective optimization problems with complex Pareto fronts characteristics, previous work generally ignores the information related to Pareto fronts provided by the population during the evolution, which is detrimental to efficiently tackle them. In order to taking full advantage of information associated with a population evolution, a multi-objective evolutionary optimization method based on online perceiving Pareto front characteristics is proposed in this study. To this end, the information associated with the Pareto front of an optimization problem is first extracted from the population. Following that, the characteristics of the Pareto front in concavity/convexity and continuity are perceived online. For the purpose of each sub-front containing only one characteristic, a Pareto front is divided based on the concavity/convexity and continuity. According to the characteristic of each sub-front, different reference points are selected to refine the distribution of reference vectors. Finally, a multi-objective evolutionary algorithm is designed targeting the characteristics of the Pareto front. The performance of the proposed method is evaluated by comparing it with 8 state-of-the-art optimizers on 31 test problems. Further, the experimental results demonstrate that the proposed method is competitive in handling multi-objective optimization problems with irregular Pareto fronts.

Multi-channel sales strategies have become common due to the usage of advanced information technologies. Yet, how one trading mechanism can influence the outcome of another, especially in the B2B market, is largely underexplored. This paper investigates the effect of price and quantity information from a pre-sales posted price channel on the performance of the century-old sequential Dutch auctions system. The pre-sales channel is conducted online preceding the auctions. Sellers can control the price and make a proportion of their stock available in the pre-sales. Anything left after the pre-sales is sold via the auctions. Our analysis of nearly 1.5 million flower lots reveals a positive effect where there are higher auction prices and total revenue for lots listed in the pre-sales than for lots that are not. The result holds even for lots with no actual sales in the pre-sales, indicating that buyers pay close attention to the additional information from the pre-sales posted price channel. By teasing out the information effect of pre-sales prices and pre-sales quantity on the auction prices, we evaluate a number of pricing strategies. The results suggest that selling at a high price in the pre-sales is still more beneficial than selling more by discounting prices.

Distinguishing target object under occlusions has become the forefront of research to cope with grasping study in general. In this paper, a novel framework which is able to be utilized for a parallel robotic gripper is proposed. There are two key steps for the proposed method in the process of grasping occluded object: generating template information and grasp detection using the matching algorithm. A neural network, trained by the RGB-D data from the Cornell Grasp Dataset, predicts multiple grasp rectangles on template images. A proposed matching algorithm is utilized to eliminate the influence caused by occluded parts on scene images and generates multiple grasp rectangles for objects under occlusions using the grasp information of matched template images. In order to improve the quality of matching result, the proposed matching algorithm improves the SIFT algorithm and combines it with the improved RANSAC algorithm. In this way, this paper obtains suitable grasp rectangles on scene images and offers a new thought about grasping detection under occlusions. The validation results show the effectiveness and efficiency of this approach.

Pricing decisions of an internet service provider (ISP) are studied in a model based on complementarity between broadband connection and content, congestion externalities on consumer side and oligopolistic externalities on content provider side. When consumers face two-part tariffs from the ISP, the equilibrium is sensitive to usage price level but is invariant to its structure on two sides. With nonlinear pricing, the markup of content providers affects consumer prices while congestion externalities and elasticity of content demand shape the price for providers. For the zero-price rule, a neutrality-of-policy result holds with two-part tariffs but not with nonlinear pricing.

Party competition in Western Europe is increasingly focused on “issue competition”, which is the selective emphasis on issues by parties. The aim of this paper is to contribute methodologically to the increasing number of studies that deal with different aspects of parties’ issue competition and communication. We systematically compare the value and shortcomings of three exploratory text representation approaches to study the issue communication of parties on Twitter. More specifically, we analyze which issues separate the online communication of one party from that of the other parties and how consistent party communication is. Our analysis was performed on two years of Twitter data from six Belgian political parties, comprising of over 56,000 political tweets. The results indicate that our exploratory approach is useful to study how political parties profile themselves on Twitter and which strategies are at play. Second, our method allows to analyze communication of individual politicians which contributes to classical literature on party unity and party discipline. A comparison of our three methods shows a clear trade-off between interpretability and discriminative power, where a combination of all three simultaneously provides the best insights.

A sealed-bid auction is an essential tool for trading commodities in modern society. However, the non-winning bidders in previously proposed sealed-bid auction schemes lack privacy protection. In this paper, we define a sealed-bid auction with strong privacy protection, which can protect the privacy of all non-winning bidders. In order to fulfil the task, we first present a quantum secret sharing protocol and a secure quantum summation protocol and then design their corresponding quantum circuits, where the security of the latter is guaranteed by the former, while the former is information-theoretically secure. We further present a quantum seal-bid auction (QSA) scheme with strong privacy protection based on the designed quantum protocols. In our QSA scheme, each bidder first encodes a private vector by his private bid and public agreement information, and then all bidders jointly compute the summations of their respective private vectors without a trusted third party. Finally, we verify the correctness and the feasibility of the proposed QSA scheme by circuit simulations in IBM Qiskit.

This letter describes a novel grasp quality measure that we developed for evaluating antipodal grasp poses in real-time. To quantify the grasp quality, we compute a set of object movement features from analyzing the interaction between the gripper and the object's projections in the image space. The normalization and weights of the features are tuned to make practical and intuitive grasp quality predictions. To evaluate our grasp quality measure, we conducted a real robot grasping experiment with 1000 robot grasp trials on 10 household objects to examine the relationship between our grasp scores and the actual robot grasping results. The results show that the average grasp success rate increases, and the average amount of undesired object movement decreases as the calculated grasp score increases. We achieved a 100% grasp success rate from 100 grasps of the 10 objects when using our grasp quality measure in planning top quality grasps. In addition, we compared our quality measure with the Q measure and deep learning-based quality measures.

Private higher education has become increasingly important in China which has the largest numbers of students enrolled in private universities in the world. The Chinese government, in an attempt to regulate the sector, issued legislation in 2016 which required private universities to classify and register themselves as either for-profit or not-for-profit. However, implementation of the policy is slow and the majority of private universities so far have not re-registered their legal status. This study reports on an empirical investigation of the factors that have affected implementation of the policy through interviews with stakeholders involved in Chinese private HE, and an examination of policy documents. It fills a gap in the English language study of the classification management as for-profit or not-for-profit of private universities. As such, it provides insight into practice for policymakers and academics in the field of governance of private universities.

Companies adopt pricing strategies to maximize their profits. However, in light of an ongoing pandemic, these strategies have been put under the scanner across industries. Companies need to modify their pricing strategies to adapt to these unprecedented times. The study focuses on these pricing strategies, their necessity during the pandemic, and how they can be effectively implemented without any long-term implications for various companies and businesses. The study focuses on various aspects of pricing and the suitability of pricing strategies during the pandemic. The study suggests pricing strategies which companies may follow. Pricing strategies of several industries which are in crisis are also discussed. The study will help companies to understand the economic situation and formulate pricing strategies accordingly. Such pricing strategies will allow companies to satisfy their customers, sustain in the competition, and achieve business excellence.

Current accounting practice expenses many investments in intangible assets to the income statement, confusing earnings from current revenues with investments to gain future revenues. This has led to increasing calls to book those investments to the balance sheet. Drawing on the relevant research, this paper proposes solutions for the accounting for intangible assets that contrast with balance sheet recognition, and compares them to current practice and the IFRS standards that dictate practice. Key is the recognition that an accounting solution comes from a double-entry system which produces an income statement as well as a balance sheet, and that has features that both enable and limit the information that can be conveyed about the value in intangible assets. In this system, asset recognition in the balance sheet must consider the effect on measurement in the income statement, for the income statement conveys value added to investment on the balance sheet. A determining feature is uncertainty about investment outcome and how that affects the income statement, so our solutions center on accounting under uncertainty. Two other accounting features are added: There has to be an investment expenditure for balance sheet recognition and that expenditure must be separately identifiable from transactions. These features rather than the tangible-intangible asset dichotomy lead to the prescribed solutions.

In this paper, a quantum sealed-bid protocol based on semi-quantum bidders is proposed. The protocol uses Bell states to encrypt message and realizes the process that bidders can directly transmit bidding information to the auction center safely. Its essence is a semi-quantum secure direct communication protocol using Bell states. Unlike most similar protocols, our scheme eliminates the trusted third-party Trent and sets the auction center Charlie as completely honest. Considering that the auction involves human activities, too many quantum servers are not only costly, but also unrealistic. Therefore, we set the bidders as semi-quantum users and implement the Vickrey auction. In addition, the security analysis shows that our scheme has high security and is completely feasible.

The mining of private vehicle trajectories can be used to establish the regular travel behavior of private vehicles and provide novel solutions to address problems related to urban traffic. With the help of automotive sensors in private vehicles, we can obtain a considerable amount of trajectory data on connected private vehicles. We herein assessed the regular travel behavior from connected private vehicle trajectory data. First, we explored the travel features of private vehicles through an empirical evaluation of move-and-stay frequency, time, duration, and distance. Second, we acquired the fitting distribution of private vehicle move-and-stay features based on the sum of the squared errors. We then proposed a move-and-stay feature based detection (MSFD) method, i.e., a semi-supervised model with incomplete data labels, to identify regular travelers from a massive private vehicle trajectory dataset. Extensive experiments were conducted on the private vehicle trajectory dataset collected from more than 68,069 private vehicles in Shenzhen, China, from June 1, 2016 to August 31, 2016. We compared the proposed MSFD with the existing baselines. The quantitative experimental results demonstrated that the MSFD outperformed these baselines.

With more users outsourcing their applications to the cloud, resource pricing becomes an important issue for IaaS cloud management. Jointly considering her own bidding budget and the price of cloud resources, each user is self-motivated to purchase cloud resources according to her resource demand which maximizes her own utility. Meanwhile, the cloud service provider (CSP) regulates the price of cloud resources with a certain profitability objective achieved. With an elaborate resource pricing strategy, the goals from users and the CSP are balanced and respectively satisfied to some extent. This article provides an insight into the market-oriented cloud pricing strategy. In specific, we propose an auction market in the IaaS cloud, where multiple users with heterogeneous bidding budgets and QoS requirements subscribe cloud resources according to their resource demands. The resource pricing and demand allocation scheme targeting revenue maximization also satisfies essential properties including budget feasibility, incentive compatibility and envy-freeness. To attack the NP-hardness and non-convexity of revenue maximization problem, we design a price-incentive resource auction mechanism namely RARM, which preserves an ( $1+\alpha $ ) approximation ratio on revenue maximization. Finally, we evaluate our RARM mechanism based on the real-world dataset to certify the efficacy of our proposed approach.

We consider a digital product seller who needs to determine the number of items to sell and its price over an infinite horizon. The seller indeed owns unlimited supply of the digital products like music or software. Each buyer is risk-neutral and needs one unit of the product. The number of buyers in each period and their private valuations are random. The seller conducts an auction to allocate the items in each period. The buyers who fail to gain one item in the previous periods will rebid in the subsequent auctions. Regarding the formulated dynamic program, we prove that the optimal allocation solution is a variant of the base-stock policy. Based on the known Revenue Equivalence Principle, we also prove that the generalized second-price auction and the first-price auction will result in the same expected revenue for the seller. Finally, with mild technical modifications, the results of the infinite-horizon case can be extended to the finite-horizon case even if the demand is time-varying stochastic and independent.

Auction theory is of central importance in the study of markets. Unfortunately, we do not know equilibrium bidding strategies for most auction games. For realistic markets with multiple items and value interdependencies, the Bayes Nash equilibria (BNEs) often turn out to be intractable systems of partial differential equations. Previous numerical techniques have relied either on calculating pointwise best responses in strategy space or iteratively solving restricted subgames. We present a learning method that represents strategies as neural networks and applies policy iteration on the basis of gradient dynamics in self-play to provably learn local equilibria. Our empirical results show that these approximated BNEs coincide with the global equilibria whenever available. The method follows the simultaneous gradient of the game and uses a smoothing technique to circumvent discontinuities in the ex post utility functions of auction games. Discontinuities arise at the bid value where an infinite small change would make the difference between winning and not winning. Convergence to local BNEs can be explained by the fact that bidders in most auction models are symmetric, which leads to potential games for which gradient dynamics converge. Auction games present an interesting challenge for multi-agent learning. Finding the Bayes Nash equilibria for optimum bidding strategies is intractable for numerical approaches. In a new, deep learning approach, strategies are represented as neural networks, and policy iteration based on gradient dynamics in self-play enables learning of local equilibria.

With the decline of advertising revenue, local newspapers must shift their revenue sources from primarily advertising to deriving a larger share from subscription fees. Although existing studies on...

Bayesian Epistemology bases its analysis of the objects under study on a prior, a (jointly known) probability distribution, which is in turn the subject matter in statistical learning, and that of machine learning at least implicitly. Our interests are in data related to bidder behavior in a game theoretical setting where the learner and the owners of data to be learned are affected by each other’s strategies of data reporting and data utilization. More specifically, the learner takes actions according to the probability governing the data distributions. The owners of data may reveal false probability distributions of their private data for their better utilities. We focus on the Myerson auction for such an issue of learning and optimization of the seller who wants to gain information on bidders’ value distributions to achieve the maximum revenue. We study bidders’ best responses against each other. We make a special effort to understand the possibility of truthful reporting of agent value distributions. For cases this is not possible, we study the dynamic properties of bidder value distribution reporting and the convergence toward a local Nash equilibria.

The pricing mechanism of cloud-computing resources is an essential issue for both cloud customers and service providers, especially from the point of multi-provider competition. Although various mechanisms for resource provision are proposed, few studies have focused on multi-attribute resource provision with the objective of improving benefits of both cloud customers and service providers. To address the issue, we propose a price bidding mechanism for multi-attribute cloud-computing resource provision from the perspective of a non-cooperative game, in which the information of each player (customers and providers) is incomplete to others and each player wishes to maximize his/her own benefit. More specifically, considering the fairness pricing competition, we propose a novel and incentive resource provision model referring to the Quality-of-Service (QoS) and the bidding price. Then, combining with the resource provision model, the problem of price bidding is formulated as a game to find a proper price for each cloud provider. We demonstrate the existence of Nash equilibrium solution set for the formulated game model by assuming that the quantity function of provided resources from every provider is continuous. To find a Nash equilibrium solution, we propose an Equilibrium Solution Iterative (ESI) algorithm, which is proved to converge to a Nash equilibrium. Finally, a Near-equalization Price Bidding (NPB) algorithm is proposed to modify the obtained Nash equilibrium solution. Extensive simulated experiments results and the comparison experiments with the state-of-the-art and benchmark solutions validate and show the feasibility of the proposed method.

In this study, a method for a robot to recall multiple grasping methods for a given object is proposed. The aim of this study was for robots to learn grasping methods for new objects by observing the grasping activities of humans in daily life without special instructions. For this setting, only one grasping motion was observed for an object at a time, and it was never known whether other grasping methods were possible for the object, although supervised learning generally requires all possible answers for each training input. The proposed method gives a solution for that learning situations by employing a convolutional neural network with automatic clustering of the observed grasping method. In the proposed method, the grasping methods are clustered during the process of learning of the grasping position. The method first recalls grasping positions and the network estimates the multi-channel heatmap such that each channel heatmap indicates one grasping position, then checks the graspability for each estimated position. Finally, the method recalls the hand shapes based on the estimated grasping position and the object’s shape. This paper describes the results of recalling multiple grasping methods and demonstrates the effectiveness of the proposed method.

Community detection for Location-based Social Networks (LBSNs) has been received great attention mainly in the field of large-scale Wireless Communication Networks. In this paper, we present a Differential Privacy Latent Trajectory cOmmunity Discovering (DP-LTOD) scheme, which obfuscates original trajectory sequences into differential privacy-guaranteed trajectory sequences for trajectory privacy-preserving, and discovers latent trajectory communities through clustering the uploaded trajectory sequences. Different with traditional trajectory privacy-preserving methods, we first partition original trajectory sequence into different segments. Then, the suitable locations and segments are selected to constitute obfuscated trajectory sequence. Specifically, we formulate the trajectory obfuscation problem to select an optimal trajectory sequence which has the smallest difference with original trajectory sequence. In order to prevent privacy leakage, we add Laplace noise and exponential noise to the outputs during the stages of location obfuscation matrix generation and trajectory sequence function generation, respectively. Through formal privacy analysis, we prove that DP-LTOD scheme can guarantee $\epsilon$ e -differential private. Moreover, we develop a trajectory clustering algorithm to classify the trajectories into different kinds of clusters according to semantic distance and geographical distance. Extensive experiments on two real-world datasets illustrate that our DP-LTOD scheme can not only discover latent trajectory communities, but also protect user privacy from leaking.

The aim of the study was to evaluate whether electronic revenue collection systems improves Municipal revenues. The study specifically aimed to determine the effective sources of revenues; to compare the revenue collection before and after the introduction of electronic revenues collection system; and to analyze the challenges encountered with the use of electronic revenue collection system. A descriptive research design was adopted in conducting the study. The study sample was 79 respondents whereby simple random sampling was employed. Data were collected using questionnaires to provide quantitative data. Data was analyzed using descriptive statistics and Pearson correlation coefficient. The findings revealed that the major sources of revenues to Municipal Council are Guest house/hotel levy, Land rent fees, Markets fees, Intoxicating liquor licenses, Abattoirs charges, Revenue from agricultural and forestry products and Meat inspection charges. The study finding revealed that electronic revenue collection system improves Municipal revenue collection in the financial year 2015/2016 compare to that of 2014/2015 revenues. The major challenges encountered the e-payment is access to internet and lack of civic education to revenues payers on the use of online payments. The study concludes that online revenue collection system gives accurate revenue computations to a very great extent and the revenue payers is satisfied with the electronic revenues collection service which assured no queuew is experienced on revenue due dates. The study recommends to the Municipal officials that it‘s high time for Moshi Municipal Council to put much efforts to encourage revenue payers to adopt electronic revenue collection system as the findings shows it has reduced queuing and increased revenue. Also civic education should be provided to other revenue payers who did not registered in order to be aware and help the Municipal to improve their revenues collection status.

The article reveals how Hungarian parties expand their members’ participatory repertoires in intra-party democracy with the help of information and communication technology. By answering the questi...

Nonprofit organizations increasingly rely on earned revenue to sustain their mission-driven activities. Previous research examining the effects of earned revenue on other income streams tends to study earned revenue in the aggregate. Using panel data from 12,372 organizations from 2010 to 2015, this analysis applies a framework of revenue embeddedness to link earned revenue activities to mission and analyze the effects of earned revenue activities on donations. Earned revenue activities offering new products or services to existing donors appear to complement individual donations. These findings have theoretical and practical applications related to how nonprofits pursue earned revenue.

Are tourists sensitive to price? A field experiment was conducted to draw conclusions regarding the effect of three different price strategies on the revenues of a small business selling to tourists. Price elasticity determinations during each phase of the pricing experiment were also computed. This research was conducted in a tourist-based monopolistic competition market structure. Results from the pricing strategies examined indicated little to no difference in sales revenues regardless of pricing strategy employed. This finding is relevant for small businesses operating in a tourist market who often compete on price.

Social networks display homophily and clustering, and are usually sparse. I develop and estimate a structural model of strategic network formation with heterogeneous players and latent community structure, whose equilibrium networks are sparse and exhibit homophily and clustering. Each player belongs to a community unobserved by the econometrician. Players' payoffs vary by community and depend on the composition of direct links and common neighbors, allowing preferences to have a bias for similar people. Players meet sequentially and decide whether to form bilateral links, after receiving a random matching shock. The probability of meeting people in different communities is smaller than the probability of meeting people in the same community, and it decreases with the size of the network. The model converges to an exponential family random graph, with weak dependence among links. As a consequence the equilibrium networks are sparse and the sufficient statistics of the network are asymptotically normal. The posterior distribution of structural parameters and unobserved heterogeneity is estimated with school friendship network data from Add Health, using a Bayesian exchange algorithm. The estimates detect high levels of racial homophily, and heterogeneity in both costs of links and payoffs from common friends. The posterior predictions show that the model is able to replicate the homophily levels and the aggregate clustering of the observed network, in contrast with standard exponential family network models.

In an equilibrium framework, we explore how players communicate in games with multiple Nash equilibria when messages that make sense are not ignored. Communication is about strategies and not about private information. It begins with the choice of a language, followed by a message that is allowed to be vague. We focus on equilibria where the sender is believed whenever possible, and develop a theory of credible communication. We show that credible communication is sensitive to changes in the timing of communication. Sufficient conditions for communication leading to efficient play are provided.

Multifingered robotic hands offer stable grasping for a wide variety of objects, yet grasp planning with these hands is more challenging due to the high dimensionality of the search space. In this article, we propose a method for grasping unknown objects from cluttered scenes using a noisy point cloud as an input. Our approach is based on a shape complementarity metric. A fast algorithm for finding a small set of potential grasps is proposed followed by a local shape completion method to infer the occluded parts of the object. Finally, we propose an optimization-based refinement of the hand poses and finger configurations to achieve a power grasp of the target object. The proposed approach is validated extensively both on a simulated and a real world environment. We demonstrate that the proposed grasp planning algorithm produces stable grasps even in heavily dense clutter. Finally, our experiments indicate improved grasp success rate over algorithms that employ precision grasping in the same scene.

Ex-post split-award auctions are a frequently used form of combinatorial auction mechanism in practice. The procurement quantity is split into several shares and suppliers can submit bids on separate shares as well as on the entire quantity. Markets with diseconomies of scale are wide-spread, but strategically challenging. In a game-theoretical equilibrium analysis, Kokott et al. (2019) have recently shown that in contrast to single-object auctions, there is no strategic equivalence between first-price sealed-bid (FPSB) and Dutch combinatorial auctions. The FPSB auctions are characterized by efficient and inefficient equilibria while the Dutch auctions only possess efficient equilibria. We report the results of extensive laboratory experiments and show that the theory explains the bid data surprisingly well. Importantly, a compound Dutch auction format weakly outperforms the wide-spread combinatorial first-price sealed-bid auction in efficiency and total procurement costs. The results provide guidance for procurement managers in the field.

This paper investigates the impact of confirmation bias on competitive information spread in the cyber-social network that comprises individuals in a social network and competitive information sources in cyber layer. We formulate the problem as a zero-sum game, which admits a unique Nash equilibrium in pure strategies. We characterize the dependence of pure Nash equilibrium on the public's innate opinions, the social network topology, as well as the parameters of confirmation bias. We uncover that confirmation bias moves the equilibrium towards the center only when the innate opinions are not neutral, and this move does not occur for the competitive information sources simultaneously. Numerical examples in the context of well-known Krackhardt's advice network are provided to demonstrate the correctness of theoretical results., Comment: submitted

To perform object grasping in dense clutter, we propose a novel algorithm for grasp detection. To obtain grasp candidates, we developed instance segmentation and view-based experience transfer as part of the algorithm. Subsequently, we established an algorithm for collision avoidance and stability analysis to determine the optimal grasp for robot grasping. The strategy for the view-based experience transfer was to first find the object view and then transfer the grasp experience onto the clutter scenario. This strategy has two advantages over existing learning-based methods for finding grasp candidates. (1) our approach can effectively exclude the influence of noise or occlusion on images and precisely detect grasps that are well aligned on each target object. (2) our approach can efficiently find out optimal grasps on each target object and has the flexibility of adjusting and redefining the grasp experience based on the type of target object. We evaluated our approach using some open-source datasets and with a real-world robot experiment, which involved a six-axis robot arm with a two-jaw parallel gripper and a Kinect V2 RGB-D camera. The experimental results show that our proposed approach can be generalized to objects with complex shape, and is able to grasp on dense clutter scenarios where different types of objects are in a bin. To demonstrate our grasping pipeline, a video is provided at https://youtu.be/gQ3SO6vtTpA .

We consider a location-then-price game where two traditional retailers compete with a location-irrelevant online retailer. We characterize the existing equilibria, and we show that in any possible equilibrium there is direct competition between the traditional retailers. Furthermore, the traditional retailers locate at neither a maximal nor minimal distance. In equilibrium, the price of the online retailer might be higher or lower than the price of the traditional retailers, depending on the relative competitiveness of the online retailer and the traditional retailers.

Local governments forecast revenues and expenditures as part of their budget process. Despite growing evidence of biased revenue forecasts, there is limited research on expenditure forecast bias. T...

Last-minute bidding is a prevalent strategy in which bidders submit their bids at the very end of an online auction; mobile push notifications can remind these bidders of upcoming deadlines and thus might determine bidding success. Using field data, this study demonstrates that last-minute bidders use mobile push notifications purposefully and strategically as well as more frequently than bidders with different bidding patterns. Among last-minute bidders, chances of winning the auction increase with their use of push notifications. In detail, mobile push notifications are particularly effective when last-minute bidders place less serious bids in search of a bargain, yet they lose effectiveness if the bids are placed closer to the auctions' end. They prove to be equally useful tools for last-minute bidders with varying experience levels and for bids placed through both mobile and stationary devices. Managers of online (auction) platforms therefore should design and market their mobile push notifications as customizable, helpful features.

The main contributions of the paper are summarized as follows. We take an analytical approach in the sense that the quality of service and the price of service as well as the revenue, cost, and profit of a cloud service provider (CSP) can all be quantitatively available based on well established analytical models. We argue that the satisfaction of a customer includes two aspects, i.e., satisfaction on the price of service and satisfaction on the quality of service. We are able to derive a closed-form expression of the expected customer satisfaction of a CSP. We develop a non-cooperative game formulation for a competitive cloud computing market with competing CSPs. We discuss the market stability mechanism which creates interaction among the CSPs, give the best response of a CSP based on the other CSPs' strategies, mention the existence of the Nash equilibrium, and develop an algorithm to find the Nash equilibrium.

Due to the uncertainty of the value of big data, it is difficult to directly give a reasonable price for big data. Auction is an effective method of distributing goods to the bidder with the highest valuation. Hence, the use of auction strategy can not only guarantee the interests of data sellers, but also conform to market principles. However, existing data auction mechanisms are centralized. It is hard to build trust among sellers, buyers and auctioneers. An open and anonymous online environment may cause entities involved in data auctions to collude to manipulate the results of data auctions. This will cause the price of auction data to fail to reach a fair and truthful level. Therefore, the first anti-collusion data auction mechanism based on smart contract is proposed. Through a well-designed anti-collusion data auction algorithm, mutual distrust and rational buyers and sellers safely participate in the data auction without a trusted third party. The data auction mechanism designed in the smart contract can effectively prevent collusion and realize the fairness and truthfulness of data auction. The webpack in the Truffle Boxes is used to implement the data auction mechanism, and the anti-collusion property of the mechanism has been verified. The source code of the smart contract has been uploaded to GitHub.

We consider a distributed IoT network, where each node wants to minimize its own age of information and there is a cost to make any transmission. A collision model is considered, where any transmission is successful from a node to a common monitor if no other node transmits in the same slot. Nodes cannot coordinate their transmission, and can learn about the network only via binary collision information. Under this distributed competition model, the objective of this paper is to find a distributed transmission strategy for each node that converges to an equilibrium that only depends on the past observations seen by each node and does not require network information, e.g., the number of other nodes, or their strategies. A simple update strategy is shown to converge to an equilibrium for any number of nodes that are unknown to the update strategy. The equilibrium achieved is in fact a Nash equilibrium for a suitable utility function, that captures all the right tradeoffs for each node.

The purpose of this article is to consider the advantages of private labels, the process of their creation and promotion, as well as the classification of private labels. The article discusses the benefits of private labels for their owners and consumers. The classification of private labels is presented and the definition of their types and varieties is given. The process of creating and promoting private labels, as well as the features of their creation in retail trade networks, is considered. It has been established that successful private labels are beneficial for all participants in the distribution channel: manufacturers who receive large orders for the production of goods and their guaranteed sale; retailers whose private labels provide competitive advantages, customer loyalty and high profits. Meanwhile, end consumers have the opportunity to buy goods of proper quality at reduced prices, as well as the confidence that they will not be deceived.

The present study elaborates on the effect of visual and verbal metaphors in online advertising effectiveness. It suggests that the advertising copy execution (hard-sell vs. soft-sell) moderates th...

We analyze repeated games in which players have private information about their levels of patience and in which they would like to maintain the privacy of this information vis-a-vis third parties. We show that privacy protection in the form of shielding players’ actions from outside observers is harmful, as it limits and sometimes eliminates the possibility of attaining Pareto-optimal payoffs.

With the privatization of the internet in 1995, capital managed to contain the digital common, to classify it as private property, and to generalize and extend the digital common of the private int...

Although the non-profit marketization literature argues that governments’ performance-based accountability demands can hinder non-profit organizational performance, rational planning literature suggests a more positive yet indirect impact. To unravel the mechanisms at play for those steering their organization, we investigate (1) if performance-based accountability can alter how non-profit directors’ perceive organizational performance and (2) if such relationship is mediated by rational planning inspired practices. Results from structural equation modelling on two-wave survey data among 297 non-profit directors in Flemish non-profits confirm that management tool use and performance measurement are central in explaining how performance-based accountability impacts subjective performance.