Many aspects of the design of efficient crowdsourcing processes, such as defining workers bonuses, fair prices and time limits of the tasks, involve knowledge of the likely duration of the task at hand. In this work we introduce a new timesensitive Bayesian aggregation method that simultaneously estimates a tasks duration and obtains reliable aggregations of crowdsourced judgments. Our method, called BCCTime, uses latent variables to represent the uncertainty about the workers completion time, the tasks duration and the workers accuracy. To relate the quality of a judgment to the time a worker spends on a task, our model assumes that each task is completed within a latent time window within which all workers with a propensity to genuinely attempt the labelling task (i.e., no spammers) are expected to submit their judgments. In contrast, workers with a lower propensity to valid labelling, such as spammers, bots or lazy labellers, are assumed to perform tasks considerably faster or slower than the time required by normal workers. Specifically, we use efficient message-passing Bayesian inference to learn approximate posterior probabilities of (i) the confusion matrix of each worker, (ii) the propensity to valid labelling of each worker, (iii) the unbiased duration of each task and (iv) the true label of each task. Using two real- world public datasets for entity linking tasks, we show that BCCTime produces up to 11% more accurate classifications and up to 100% more informative estimates of a tasks duration compared to stateoftheart methods.

We introduce the concept of continuous transportation task to the context of multi-agent systems. A continuous transportation task is one in which a multi-agent team visits a number of fixed locations, picks up objects, and delivers them to a final destination. The goal is to maximize the rate of transportation while the objects are replenished over time. Examples of problems that need continuous transportation are foraging, area sweeping, and first/last mile problem. Previous approaches typically neglect the interference and are highly dependent on communications among agents. Some also incorporate an additional reconnaissance agent to gather information. In this paper, we present a hybrid of centralized and distributed approaches that minimize the interference and communications in the multi-agent team without the need for a reconnaissance agent. We contribute two partitioning-transportation algorithms inspired by existing algorithms, and contribute one novel online partitioning-transportation algorithm with information gathering in the multi-agent team. Our algorithms have been implemented and tested extensively in the simulation. The results presented in this paper demonstrate the effectiveness of our algorithms that outperform the existing algorithms, even without any communications between the agents and without the presence of a reconnaissance agent.

This work addresses the issue of large covariance matrix estimation in high-dimensional statistical analysis. Recently, improved iterative algorithms with positive-definite guarantee have been developed. However, these algorithms cannot be directly extended to use a nonconvex penalty for sparsity inducing. Generally, a nonconvex penalty has the capability of ameliorating the bias problem of the popular convex lasso penalty, and thus is more advantageous. In this work, we propose a class of positive-definite covariance estimators using generalized nonconvex penalties. We develop a first-order algorithm based on the alternating direction method framework to solve the nonconvex optimization problem efficiently. The convergence of this algorithm has been proved. Further, the statistical properties of the new estimators have been analyzed for generalized nonconvex penalties. Moreover, extension of this algorithm to covariance estimation from sketched measurements has been considered. The performances of the new estimators have been demonstrated by both a simulation study and a gene clustering example for tumor tissues. Code for the proposed estimators is available at https://github.com/FWen/Nonconvex-PDLCE.git.

This paper concerns the mechanism design for online scheduling in a strategic setting. In this setting, each job is owned by a self-interested agent who may misreport the release time, deadline, length, and value of her job, while we need to determine not only the schedule of the jobs, but also the payment of each agent. We focus on the design of incentive compatible (IC) mechanisms, and study the maximization of social welfare (i.e., the aggregated value of completed jobs) by competitive analysis. We first derive two lower bounds on the competitive ratio of any deterministic IC mechanism to characterize the landscape of our research. We then propose a deterministic IC mechanism and show that such a simple mechanism works very well for both the preemption-restart model and the preemption-resume model. We show the mechanism can achieve the optimal competitive ratio of 5 for equal-length jobs and a near optimal competitive ratio (within a constant factor) for unequal-length jobs.

Estimating the gradient of a given function (with or without noise) is often an important part of problems in reinforcement learning, optimization and manifold learning. In reinforcement learning, policy-gradient methods are used to obtain an unbiased estimator for the gradient. The policy parameters are then updated with increments proportional to the estimated gradient [27]. The objective is to learn a locally optimum policy. REINFORCE and PGPE methods (policy gradients with parameter-based exploration) are popular instances of this approach (See [35] for details and comparisons, [13] for a survey on policy gradient methods in the context of actor-critic algorithms).

Unstructured data refers to information that does not have a predefined data model or is not organized in a pre-defined manner. Loosely speaking, unstructured data refers to text data that is generated by humans. In after-sales service businesses, there are two main sources of unstructured data: customer complaints, which generally describe symptoms, and technician comments, which outline diagnostics and treatment information. A legitimate customer complaint can eventually be tracked to a failure or a claim. However, there is a delay between the time of a customer complaint and the time of a failure or a claim. A proactive strategy aimed at analyzing customer complaints for symptoms can help service providers detect reliability problems in advance and initiate corrective actions such as recalls. This paper introduces essential text mining concepts in the context of reliability analysis and a method to detect emerging reliability issues. The application of the method is illustrated using a case study.

Many people spend their childhood peering up into the vast expanse of the sky, dreaming of growing up to become an astronaut. But these dreams could be dashed as the idea of people venturing into space will one day become a distant memory, according to a report published today. Robots will eventually have enough capabilities to replace humans and other animals on space missions, experts have said. Robots will eventually have enough capabilities to replace humans and other animals on space missions, experts have said. Russia is planning to send robots to the ISS to do jobs that are too dangerous for astronauts.

The 2011 triumph of IBM's Watson supercomputer in US game show, Jeopardy, was the moment it became a real-world commercial venture within the enterprise services giant. The question-answering system, named in honour of IBM's first CEO, Thomas J Watson, defeated two former winners of the show, Brad Rutter and Ken Jennings, to clinch a 1 million prize, using onboard (rather than cloud-based) data. IBM began offering Watson as a cloud service in 2015, and since then the company has found itself at the centre of a range of new, speculative ventures. As we will explore, some of these blur the lines between classical computing, AI, and machine learning, and may point towards a networked future for humanoid robots. Duncan Anderson, IBM's European CTO of the Watson Program, picks up the story: We started to think about how we could make the Watson technology more consumable and less resource intensive.

A few well-publicized recent papers have tempered the hype surrounding deep learning. The papers identify both that images can be subtly altered to induce misclassification and that seemingly random garbage images can easily be generated which receive high confidence classifications. A wave of press has sensationalized the message. Several blog posts, a YouTube video, and others have amplified and occasionally distorted the results, professing the gullibility of deep networks. Given the hoopla, it's appropriate to examine these findings.

The decision has provided a solid legal foundation on which a rules-based approach for resolving disputes in the South China Sea can be built,