Preference-based Search using Example-Critiquing with Suggestions

We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine example-critiquing, a technique for enabling users to incrementally construct preference models by critiquing example options that are presented to them. We present novel techniques for improving the example-critiquing technology by adding suggestions to its displayed options. Such suggestions are calculated based on an analysis of users current preference model and their potential hidden preferences. We evaluate the performance of our model-based suggestion techniques with both synthetic and real users. Results show that such suggestions are highly attractive to users and can stimulate them to express more preferences to improve the chance of identifying their most preferred item by up to 78%

Neural progenitor cell implants modulate vascular endothelial growth factor and brain-derived neurotrophic factor expression in rat axotomized neurons

Axotomy of central neurons leads to functional and structural alterations which largely revert when neural progenitor cells (NPCs) are implanted in the lesion site. The new microenvironment created by NPCs in the host tissue might modulate in the damaged neurons the expression of a high variety of molecules with relevant roles in the repair mechanisms, including neurotrophic factors. In the present work, we aimed to analyze changes in neurotrophic factor expression in axotomized neurons induced by NPC implants. For this purpose, we performed immunofluorescence followed by confocal microscopy analysis for the detection of vascular endothelial growth factor (VEGF), brain-derived neurotrophic factor (BDNF), neurotrophin-3 (NT-3) and nerve growth factor (NGF) on brainstem sections from rats with axotomy of abducens internuclear neurons that received NPC implants (implanted group) or vehicle injections (axotomized group) in the lesion site. Control abducens internuclear neurons were strongly immunoreactive to VEGF and BDNF but showed a weak staining for NT-3 and NGF. Comparisons between groups revealed that lesioned neurons from animals that received NPC implants showed a significant increase in VEGF content with respect to animals receiving vehicle injections. However, the immunoreactivity for BDNF, which was increased in the axotomized group as compared to control, was not modified in the implanted group. The modifications induced by NPC implants on VEGF and BDNF content were specific for the population of axotomized abducens internuclear neurons since the neighboring abducens motoneurons were not affected. Similar levels of NT-3 and NGF immunolabeling were obtained in injured neurons from axotomized and implanted animals. Among all the analyzed neurotrophic factors, only VEGF was expressed by the implanted cells in the lesion site. Our results point to a role of NPC implants in the modulation of neurotrophic factor expression by lesioned central neurons, which might contribute to the restorative effects of these implants

DUCT: An upper confidence bound approach to distributed constraint optimization problems

The Upper Confidence Bounds (UCB) algorithm is a well-known near-optimal strategy for the stochastic multi-armed bandit problem. Its extensions to trees, such as the Upper Confidence Tree (UCT) algorithm, have resulted in good solutions to the problem of Go. This paper introduces DUCT, a distributed algorithm inspired by UCT, for solving Distributed Constraint Optimization Problems (DCOP). Bounds on the solution quality are provided, and experiments show that, compared to existing DCOP approaches, DUCT is able to solve very large problems much more efficiently, or to find significantly higher quality solutions. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved

Agile decision agent for service-oriented e-commerce systems

In a service-oriented e-commerce environment, it is a crucial task to help consumers choose desired products efficiently from a huge amount of dynamically configured product candidates. Decision agents can be designed to provide interactive decision aids for end-users by eliciting their preferences and then recommending matching products. In reality the users' preferences may keep changing along with the dynamic decision environment and may not be fully satisfied. As a result, the decision agent is required to be agile; it should allow decision making with an incomplete user's preference model and should afford users to add, retract or revise their various preferences with little effort. In this paper we propose the general design of an agile decision agent to meet this need. We model users' preferences with the soft constraint technique and elicit them by the example critiquing interaction paradig

Recommendation using textual opinions

Many web sites collect reviews of products and services and use them provide rankings of their quality. However, such rankings are not personalized. We investigate how the information in the reviews written by a particular user can be used to personalize the ranking she is shown. We propose a new technique, topic profile collaborative filtering, where we build user profiles from users' review texts and use these profiles to filter other review texts with the eyes of this user. We verify on data from an actual review site that review texts and topic profiles indeed correlate with ratings, and show that topic profile collaborative filtering provides both a better mean average error when predicting ratings and a better approximation of user preference orders

IconoNET: a tool for automated bandwidth allocation planning

Communication networks are expected to offer a wide range of services to an increasingly large number of users, with a diverse range of quality of service. This calls for efficient control and management of these networks. In this paper, we address the problem of quality-of-service routing, more specifically the planning of bandwidth allocation to communication demands. Shortest path routing is the traditional technique applied to this problem. However, this can lead to poor network utilization and even congestion. We show how an abstraction technique combined with systematic search algorithms and heuristics derived from Artificial Intelligence make it possible to solve this problem more efficiently and in much tighter networks, in terms of bandwidth usage

Privacy preserving and cost optimal mobile crowdsensing using smart contracts on blockchain

The popularity and applicability of mobile crowdsensing applications are continuously increasing due to the widespread of mobile devices and their sensing and processing capabilities. However, we need to offer appropriate incentives to the mobile users who contribute their resources and preserve their privacy. Blockchain technologies enable semi-anonymous multi-party interactions and can be utilized in crowdsensing applications to maintain the privacy of the mobile users while ensuring first-rate crowdsensed data. In this work, we propose to use blockchain technologies and smart contracts to orchestrate the interactions between mobile crowdsensing providers and mobile users for the case of spatial crowdsensing, where mobile users need to be at specific locations to perform the tasks. Smart contracts, by operating as processes that are executed on the blockchain, are used to preserve users’ privacy and make payments. Furthermore, for the assignment of the crowdsensing tasks to the mobile users, we design a truthful, cost-optimal auction that minimizes the payments from the crowdsensing providers to the mobile users. Extensive experimental results show that the proposed privacy preserving auction outperforms state-of-the-art proposals regarding cost by ten times for high numbers of mobile users and tasks. © 2018 IEEE.Peer reviewe

Putting ridesharing to the test: efficient and scalable solutions and the power of dynamic vehicle relocation

We study the optimization of large-scale, real-time ridesharing systems and propose a modular design methodology, Component Algorithms for Ridesharing (CAR). We evaluate a diverse set of CARs (14 in total), focusing on the key algorithmic components of ridesharing. We take a multi-objective approach, evaluating 10 metrics related to global efficiency, complexity, passenger, and platform incentives, in settings designed to closely resemble reality in every aspect, focusing on vehicles of capacity two. To the best of our knowledge, this is the largest and most comprehensive evaluation to date. We (i) identify CARs that perform well on global, passenger, or platform metrics, (ii) demonstrate that lightweight relocation schemes can significantly improve the Quality of Service by up to 50 % , and (iii) highlight a practical, scalable, on-device CAR that works well across all metrics

Relational network-service clustering analysis with set evidences

Network administrators are faced with a large amount of network data that they need to sift through to analyze user behaviors and detect anomalies. Through a network monitoring tool, we obtained TCP and UDP connection records together with additional information of the associated users and software in an enterprise network. Instead of using traditional payload inspection techniques, we propose a method that clusters such network traffic data by using relations between entities so that it can be analyzed for frequent behaviors and anomalies. Relational methods like Markov Logic Networks is able to avoid the feature extraction stage and directly handle multi-relation situations. We extend the common pairwise representation in relational models by adopting set evidence to build a better objective for the network service clustering problem. The automatic clustering process helps the administrator filter out normal traffic in shorter time and get an abstract overview of opening transport layer ports in the whole network, which is beneficial for assessing network security risks. Experimental results on synthetic and real datasets suggest that our method is able to discover underlying services and anomalies (malware or abused ports) with good interpretations. © 2010 ACM