Attention allocation for decision making queues

We consider the optimal servicing of a queue with sigmoid server performance. There are various systems with sigmoid server performance, including systems involving human decision making, visual perception, human–machine communication and advertising response. Tasks arrive at the server according to a Poisson process. Each task has a deadline that is incorporated as a latency penalty. We investigate the trade-off between the reward obtained by processing the current task and the penalty incurred due to the tasks waiting in the queue. We study this optimization problem in a Markov decision process (MDP) framework. We characterize the properties of the optimal policy for the MDP and show that the optimal policy may drop some tasks; that is, may not process a task at all. We determine an approximate solution to the MDP using the certainty-equivalent receding horizon optimization framework and derive performance bounds on the proposed receding horizon policy. We also suggest guidelines for the design of such queues.     

Anonymity preserving sequential pattern mining

The increasing availability of personal data of a sequential nature, such as time-stamped transaction or location data, enables increasingly sophisticated sequential pattern mining techniques. However, privacy is at risk if it is possible to reconstruct the identity of individuals from sequential data. Therefore, it is important to develop privacy-preserving techniques that support publishing of really anonymous data, without altering the analysis results significantly. In this paper we propose to apply the Privacy-by-design paradigm for designing a technological framework to counter the threats of undesirable, unlawful effects of privacy violation on sequence data, without obstructing the knowledge discovery opportunities of data mining technologies. First, we introduce a k-anonymity framework for sequence data, by defining the sequence linking attack model and its associated countermeasure, a k-anonymity notion for sequence datasets, which provides a formal protection against the attack. Second, we instantiate this framework and provide a specific method for constructing the k-anonymous version of a sequence dataset, which preserves the results of sequential pattern mining, together with several basic statistics and other analytical properties of the original data, including the clustering structure. A comprehensive experimental study on realistic datasets of process-logs, web-logs and GPS tracks is carried out, which empirically shows how, in our proposed method, the protection of privacy meets analytical utility.     

Recommending multimedia visiting paths in cultural heritage applications

The valorization and promotion of worldwide Cultural Heritage by the adoption of Information and Communication Technologies represent nowadays some of the most important research issues with a large variety of potential applications. This challenge is particularly perceived in the Italian scenario, where the artistic patrimony is one of the most diverse and rich of the world, able to attract millions of visitors every year to monuments, archaeological sites and museums. In this paper, we present a general recommendation framework able to uniformly manage heterogeneous multimedia data coming from several web repositories and to provide context-aware recommendation techniques supporting intelligent multimedia services for the users—i.e. dynamic visiting paths for a given environment. Specific applications of our system within the cultural heritage domain are proposed by means of real case studies in the mobile environment related both to an outdoor and indoor scenario, together with some results on user’s satisfaction and system accuracy.     

Distributed estimation via iterative projections with application to power network monitoring

This work presents a distributed method for control centers to monitor the operating condition of a power network, i.e., to estimate the network state, and to ultimately determine the occurrence of threatening situations. State estimation has been recognized to be a fundamental task for network control centers to operate safely and reliably a power grid. We consider (static) state estimation problems, in which the state vector consists of the voltage magnitude and angle at all network buses. We consider the state to be linearly related to network measurements, which include power flows, current injections, and voltage phasors at some buses. We admit the presence of several cooperating control centers, and we design two distributed methods for them to compute the minimum variance estimate of the state, given the network measurements. The two distributed methods rely on different modes of cooperation among control centers: in the first method an incremental mode of cooperation is used, whereas, in the second method, a diffusive interaction is implemented. Our procedures, which require each control center to know only the measurements and the structure of a subpart of the whole network, are computationally efficient and scalable with respect to the network dimension, provided that the number of control centers also increases with the network cardinality. Additionally, a finite-memory approximation of our diffusive algorithm is proposed, and its accuracy is characterized. Finally, our estimation methods are exploited to develop a distributed algorithm to detect corrupted network measurements.     

3D floor plan recovery from overlapping spherical images

Computational Visual Media - We present a novel approach to automatically recover, from a small set of partially overlapping spherical images, an indoor structure representation in terms of a 3D...     

Parametric probabilistic transition systems for system design and analysis

We develop a model of parametric probabilistic transition Systems (PPTSs), where probabilities associated with transitions may be parameters. We show how to find instances of the parameters that satisfy a given property and instances that either maximize or minimize the probability of reaching a certain state. As an application, we model a probabilistic non-repudiation protocol with a PPTS. The theory we develop allows us to find instances that maximize the probability that the protocol ends in a fair state (no participant has an advantage over the others). A preliminary version of this paper was presented at SEFM’04 [LMT04]. 05 April 2006     

Low-frequency suppression of auditory nerve responses to characteristic frequency tones

The effects of low-frequency (50, 100, 200 and 400 Hz) 'suppressor' tones on responses to moderate-level characteristic frequency (CF) tones were measured in chinchilla auditory nerve fibers. Two-tone interactions were evident at suppressor intensities of 70-100 dB SPL. In this range, the average response rate decreased as a function of increasing suppressor level and the instantaneous response rate was modulated periodically. At suppression threshold, the phase of suppression typically coincided with basilar membrane displacement toward scala tympani, regardless of CF. At higher suppressor levels, two suppression maxima coexisted, synchronous with peak basilar membrane displacement toward scala tympani and scala vestibuli. Modulation and rate-suppression thresholds did not vary as a function of spontaneous activity and were only minimally correlated with fiber sensitivity. Except for fibers with CF < 1 kHz, modulation and rate-suppression thresholds were lower than rate and phase-locking thresholds for the suppressor tones presented alone. In the case of high-CF fibers with low spontaneous activity, excitation thresholds could exceed suppression thresholds by more than 30 dB. The strength of modulation decreased systematically with increasing suppressor frequency. For a given suppressor frequency, modulation was strongest in high-CF fibers and weakest in low-CF fibers. The present findings strongly support the notion that low-frequency suppression in auditory nerve fibers largely reflects an underlying basilar membrane phenomenon closely related to compressive non-linearity.     

Optimal strategies in the average consensus problem

Let a set of communicating agents compute the average of their initial positions, where every agent is restricted to communicate to a given small number of other agents (average consensus problem). We prove that the optimal topology of communication is given by a de Bruijn graph. Consensus is then reached in finitely many steps. This solution is valid when the number of agents is an exact power of the out-degree of the communication graph. We introduce an algebraic tool, the shifted Kronecker product, and a more general family of strategies, also based on a de Bruijn communication graph. Those strategies are compared to Cayley strategies in terms of the speed of convergence. We also show that quantized communication between the agents still allows finite convergence, to a consensus, which is not in general the average of the initial positions.