Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Matrix Factorization Techniques for Recommender Systems

The article reviews the book “Information Rules: A Strategic Guide to the Network Economy,” by Carl Shapiro and Hal R. Varian.

Information Rules: A Strategic Guide to the Network Economy

Research in closed-loop supply chains (CLSCs) has grown rapidly over the last 10 years. The authors offer a critical review of influential CLSC research that takes a business economics perspective. Much of the research was inspired by practice-driven thoughtpieces, and this helped to keep research focused on relevant issues. However, CLSC research has several assumptions, such as perfect substitution between new and remanufactured products that risk becoming institutionalized. There is a strong need to carefully examine current industrial practice so that research remains focused on relevant problems. Deeper understanding of consumer perceptions of remanufactured products, product diffusion, and valuation of returned products are needed for the field to continue to add insights into developing sustainable economies.

Production and Operations Management

Mobile crowdsensing is a novel paradigm to collect sensing data and extract useful information about regions of interest. It widely employs incentive mechanisms to recruit a number of mobile users to fulfill coverage requirement in the interested regions. In practice, sensing service providers face a pressing optimization problem: How to maximize the valuation of the covered interested regions under a limited budget? However, the relation between two important factors, i.e., Coverage Maximization and Budget Feasibility , has not been fully studied in existing incentive mechanisms for mobile crowdsensing. Furthermore, the existing approaches on coverage maximization in sensor networks can work, when mobile users are rational and selfish. In this paper, we present the first in-depth study on the coverage problem for incentive-compatible mobile crowdsensing, and propose BEACON, which is a B udget f EA sible and strategy-proof incentive mechanism for weighted CO verage maximizatio N in mobile crowdsensing. BEACON employs a novel monotonic and computationally tractable approximation algorithm for sensing task allocation, and adopts a newly designed proportional share rule based compensation determination scheme to guarantee strategy-proofness and budget feasibility. Our theoretical analysis shows that BEACON can achieve strategy-proofness, budget feasibility, and a constant-factor approximation. We deploy a noise map crowdsensing system to capture the noise level in a selected campus, and evaluate the system performance of BEACON on the collected sensory data. Our evaluation results demonstrate the efficacy of BEACON.

https://ieeexplore.ieee.org/ielaam/7755/8000754/7756413-aam.pdf

A Budget Feasible Incentive Mechanism for Weighted Coverage Maximization in Mobile Crowdsensing

With the rapid growth of smart devices, mobile crowdsensing is becoming an important paradigm to acquire information from physical environments. Considering that the sensing data collected by mobile users are normally noisy and imprecise, one of the pressing problems in mobile crowdsensing is to evaluate the data quality in real time and to steer users to acquire data with high quality. However, it is challenging to estimate the data quality without the availability of ground truth data. In this paper, we observe that sensing context has a significant impact on data quality, which motivates us to propose a context-aware data quality estimation scheme. With historical sensing data, we train a context-quality classifier, which captures the relation between context information and data quality, to estimate data quality in an online manner. We apply such a context-aware data quality estimation scheme to guide user recruitment in mobile crowdsensing. We model the process of user recruitment as a stochastic submodular maximization problem, and design a random adaptive greedy algorithm to guarantee a constant approximation ratio. We evaluate our algorithm on a real-world temperature data set. The evaluation results show that our algorithm outperforms other existing techniques, in terms of prediction accuracy.

Context-aware data quality estimation in mobile crowdsensing

As a significant business paradigm, data trading has attracted increasing attention. However, the study of data acquisition in data markets is still in its infancy. Mobile crowdsensing has been recognized as an efficient and scalable way to acquire large-scale data. Designing a practical data acquisition scheme for crowd-sensed data markets has to consider three major challenges: crowd-sensed data trading format determination, profit maximization with polynomial computational complexity, and payment minimization in strategic environments. In this paper, we jointly consider these design challenges, and propose VENUS, which is the first profit-driVEN data acqUiSition framework for crowd-sensed data markets. Specifically, VENUS consists of two complementary mechanisms: VENUS-PRO for profit maximization and VENUS-PAY for payment minimization. Given the expected payment for each of the data acquisition points, VENUS-PRO greedily selects the most “cost-efficient” data acquisition points to achieve a sub-optimal profit. To determine the minimum payment for each data acquisition point, we further design VENUS-PAY, which is a data procurement auction in Bayesian setting. Our theoretical analysis shows that VENUS-PAY can achieve both strategy-proofness and optimal expected payment. We evaluate VENUS on a public sensory data set, collected by Intel Research, Berkeley Laboratory. Our evaluation results show that VENUS-PRO approaches the optimal profit, and VENUS-PAY outperforms the canonical second-price reverse auction, in terms of total payment.

Trading Data in the Crowd: Profit-Driven Data Acquisition for Mobile Crowdsensing

Bandwidth reservation has been recognized as a value-added service to the cloud provider in recent years We consider an open market of cloud bandwidth reservation, in which cloud providers offer bandwidth reservation services to cloud tenants, especially online streaming service providers, who have strict requirements on the amount of bandwidth to guarantee their quality of services In this paper, we model the open market as a double-sided auction, and propose the first family of ST rategy-proof double A uctions for multi-cloud, multi-tenant bandwidth R eservation (STAR) STAR contains two auction mechanisms The first one, STAR-Grouping, divides the tenants into groups by a bid-independent way, and carefully matches the cloud providers with the tenant groups to form good trades The second one, STAR-Padding, greedily matches the cloud providers with the tenants, and fills the partially reserved cloud provider(s) with a novel virtual padding tenant who can be a component of the auctioneer Our analysis shows that both of the two auction mechanisms achieve strategy-proofness and ex-post budget balance Our evaluation results show that they achieve good performance in terms of social welfare, cloud bandwidth utilization, and tenant satisfaction ratio

/pdf/star-strategy-proof-double-auctions-for-multi-cloud-multi-1furr808ux.pdf

STAR: Strategy-Proof Double Auctions for Multi-Cloud, Multi-Tenant Bandwidth Reservation

Although data has become an important kind of commercial goods, there are few appropriate online platforms to facilitate the trading of mobile crowd-sensed data so far. In this paper, we present the first architecture of mobile crowd-sensed data market, and conduct an in-depth study of the design problem of online data pricing. To build a practical mobile crowd-sensed data market, we have to consider three major design challenges: data uncertainty, economic-robustness (arbitrage-freeness in particular), revenue maximization. By jointly considering the design challenges, we propose a novel online query-bAsed cRowd-sensEd daTa pricing mEchanism, namely ARETE, to determine the trading price of crowd-sensed data. Our theoretical analysis shows that ARETE guarantees both arbitrage-freeness and a constant competitive ratio in terms of revenue maximization. We have evaluated ARETE on a real-world sensory data set collected by Intel Berkeley lab. Evaluation results show that ARETE outperforms the state-of-the-art pricing mechanisms, and achieves around 90% of the optimal revenue.

Zhenzhe Zheng

Papers

A Budget Feasible Incentive Mechanism for Weighted Coverage Maximization in Mobile Crowdsensing

Context-aware data quality estimation in mobile crowdsensing

Trading Data in the Crowd: Profit-Driven Data Acquisition for Mobile Crowdsensing

STAR: Strategy-Proof Double Auctions for Multi-Cloud, Multi-Tenant Bandwidth Reservation

An Online Pricing Mechanism for Mobile Crowdsensing Data Markets