Forecast aggregation via recalibration

It is known that the average of many forecasts about a future event tends to outperform the individual assessments. With the goal of further improving forecast performance, this paper develops and compares a number of models for calibrating and aggregating forecasts that exploit the well-known fact that individuals exhibit systematic biases during judgment and elicitation. All of the models recalibrate judgments or mean judgments via a two-parameter calibration function, and differ in terms of whether (1) the calibration function is applied before or after the averaging, (2) averaging is done in probability or log-odds space, and (3) individual differences are captured via hierarchical modeling. Of the non-hierarchical models, the one that first recalibrates the individual judgments and then averages them in log-odds is the best relative to simple averaging, with 26.7 % improvement in Brier score and better performance on 86 % of the individual problems. The hierarchical version of this model does slightly better in terms of mean Brier score (28.2 %) and slightly worse in terms of individual problems (85 %).     

Hide-n-Sense: Preserving Privacy Efficiently in Wireless mHealth

As healthcare in many countries faces an aging population and rising costs, mobile sensing technologies promise a new opportunity. Using mobile health (mHealth) sensing, which uses medical sensors to collect data about the patients, and mobile phones to act as a gateway between sensors and electronic health record systems, caregivers can continuously monitor the patients and deliver better care. Furthermore, individuals can become better engaged in monitoring and managing their own health. Although some work on mHealth sensing has addressed security, achieving strong privacy for low-power sensors remains a challenge. We make three contributions. First, we propose an mHealth sensing protocol that provides strong security and privacy properties at the link layer, with low energy overhead, suitable for low-power sensors. The protocol uses three novel techniques: adaptive security, to dynamically modify transmission overhead; MAC striping, to make forgery difficult even for small-sized Message Authentication Codes; and asymmetric resource requirements, in recognition of the limited resources in tiny mHealth sensors. Second, we demonstrate its feasibility by implementing a prototype on a Chronos wrist device, and evaluating it experimentally. Third, we provide a security, privacy, and energy analysis of our system.     

A method for reverse engineering of material microstructure for heterogeneous CAD

Integration of material composition, microstructure, and mechanical properties with geometry information enables design and analysis of materials and products in a computer-aided design (CAD) environment. In this paper, we propose a method for the construction of material models from microstructure images, that can be integrated into a heterogeneous CAD representation. The method utilizes Radon and wavelet transforms to compute a surfacelet representation. A novel feature recognition method was developed to identify microstructure features, including embedded fibers and grain boundaries, from the surfacelet representation. The models and methods were demonstrated with examples of synthetic and engineered polymer nanocomposites, a metal alloy, and a medical dataset of human feet.     

A metaheuristic-based downlink power allocation for LTE/LTE-A cellular deployments

The explosive growth of cellular networks makes their deployment and maintenance more and more complex, time consuming, and expensive. Self-Organizing Networks have been recognized as a promising way to alleviate this problem by minimizing human intervention in such processes. This paper introduces a novel multiobjective framework, based on evolutionary optimization, aiming at improving network performance and users Quality of Service. By tuning the transmitted power at each cell, average intercell interference levels are minimized. The design of the proposed scheme is feasible for distributed implementations in Long Term Evolution (LTE) and LTE-Advanced networks and its operation is compatible with current specifications. The framework is able to provide effective network-specific optimization and obtained results show that gains in terms of network capacity and cell edge performance are 5 and 10 %, respectively. Energy savings always accompanied such enhancements with reductions up to 35 %.     

Wireless link prediction and triggering using modified Ornstein–Uhlenbeck jump diffusion process

Through time domain observation, typical wireless signal strength values seems to exhibit some forms of mean-reverting and discontinuous “jumps” behaviour. Motivated by this fact, we propose a wireless link prediction and triggering (LPT) technique using a modified mean-reverting Ornstein–Uhlenbeck (OU) jump diffusion process. The proposed technique which we refer as OU-LPT is an integral component of wireless mesh network monitoring system developed by ICT FP7 CARrier grade wireless MEsh Network project. In particular, we demonstrate how this technique can be applied in the context of wireless mesh networks to support link switching or handover in the event of predicted link degradation or failure. The proposed technique has also been implemented and evaluated in a real-time experimental testbed. The results show that OU-LPT technique can significantly enhance the reliability of wireless links by reducing the rate of false triggers compared to a conventional linear prediction technique and therefore offers a new direction on how wireless link prediction, triggering and switching process can be conducted in the future.     

WANTED: QUALIFIED IS PROFESSIONALS A Shrinking Supply Faces a Growing Demand

A survey shows that college students ' interest in IS is declining. Meanwhile, business and industry are facing a growing need for skilled IS workers, with no relief in sight     

Temporarily Expanding the Boundaries of the Self: Motivations for Entering the Story World and Implications for Narrative Effects

A wide variety of motivations for engaging with narratives have been proposed and studied. We propose that underlying these motivations is another, more fundamental motivation. Our premise is that maintenance, defense, and regulation of the personal and social self in daily life are demanding both emotionally and cognitively. Moreover, any individual self is constrained by capability, situation, and social role. Stories and identification with story characters provide a means individuals may use for temporary relief from the task of self‐regulation and from the limitations of individual personal and social identities. Existing supportive research is acknowledged and implications explored, concerning contexts in which story involvement will be particularly attractive and possible impacts on attitudes and acceptance of out‐groups including stigmatized others.     

Performance evaluation of small-batch container delivery systems used in lean manufacturing – Part 2: number of Kanban and workstation starvation

In lean manufacturing, milk run (MR) systems represent route-based, cyclic material handling systems that are used widely to enable frequent and consistent deliveries of containerised parts on an as-needed basis from a central storage area (the ‘supermarket’) to multiple line-side deposit points on the factory floor. In the first part of this two-part paper, a basic, single-tugger MR system is described, and stability conditions as well as the probability of exceeding either the physical capacity of the tugger or the prescribed cycle time are derived. Given the stability conditions, and the distribution of the number of containers requested per MR, in the second part of the paper, the number of Kanban required in the MR system is examined, and analytical approximations are derived both for the number of Kanban required and for predicting workstation starvation. The latter is a key concern when designing a MR system that will support workstations in a manufacturing plant. The performance of the analytic approximation is evaluated by simulating various MR systems. Our results suggest that, in a stable MR system, the number of Kanban and the physical capacity of the tugger have a bigger impact on workstation starvation than the prescribed cycle time.