Finding Associations and Computing Similarity via Biased Pair Sampling

This version is ***superseded*** by a full version that can be found at http://www.itu.dk/people/pagh/papers/mining-jour.pdf, which contains stronger theoretical results and fixes a mistake in the reporting of experiments. Abstract: Sampling-based methods have previously been proposed for the problem of finding interesting associations in data, even for low-support items. While these methods do not guarantee precise results, they can be vastly more efficient than approaches that rely on exact counting. However, for many similarity measures no such methods have been known. In this paper we show how a wide variety of measures can be supported by a simple biased sampling method. The method also extends to find high-confidence association rules. We demonstrate theoretically that our method is superior to exact methods when the threshold for "interesting similarity/confidence" is above the average pairwise similarity/confidence, and the average support is not too low. Our method is particularly good when transactions contain many items. We confirm in experiments on standard association mining benchmarks that this gives a significant speedup on real data sets (sometimes much larger than the theoretical guarantees). Reductions in computation time of over an order of magnitude, and significant savings in space, are observed.Comment: This is an extended version of a paper that appeared at the IEEE International Conference on Data Mining, 2009. The conference version is (c) 2009 IEE

Industry 4.0 in the Theme Park Sector: Design of a RealTime Monitoring System for Queue Management

The theme park industry is a consolidated sector where different industrial technologies and management procedures are present. However, the Industry 4.0 paradigm aims at disrupting how industrial processes are conceived. In this thesis, we perform a thorough investigation of key relevant features of theme parks and how industry 4.0 could be applied within the theme park sector. Our methodology is as follows. First, we analyse the technology used in the most innovative attractions. Afterwards, we focus on the most recurrent problem within the sector: queue management at attractions. As part of the solution, a system is designed to allow real-time monitoring of waiting times through an IoT infrastructure. Radio Fre- quency Identification and Bluetooth Low Energy are the chosen technologies for people counting. They allow users to be located in the park in addition to counting. This system gives precise waiting times estimates, and park managers can obtain precious data about user behaviour and preferences. Finally, we develop a proof of concept to test the designed solution and detail the benefits of applying industry 4.0 to the theme park sector.Máster en Industria Conectada 4.

Electronic Voting: the Devil is in the Details

Observing electronic voting from an international point of view gives some perspective about its genesis and evolution. An analysis of the voting process through its cultural, ontological, legal and political dimensions explains the difficulty to normalize this process. It appears that international organizations are not capable to properly defend the fundamental rights of the citizens. The approach that was taken when DRE voting computers appeared seems to have reoccured with VVAT voting computers and the european e-poll project.Comment: 9 page

The Visual Social Distancing Problem

One of the main and most effective measures to contain the recent viral outbreak is the maintenance of the so-called Social Distancing (SD). To comply with this constraint, workplaces, public institutions, transports and schools will likely adopt restrictions over the minimum inter-personal distance between people. Given this actual scenario, it is crucial to massively measure the compliance to such physical constraint in our life, in order to figure out the reasons of the possible breaks of such distance limitations, and understand if this implies a possible threat given the scene context. All of this, complying with privacy policies and making the measurement acceptable. To this end, we introduce the Visual Social Distancing (VSD) problem, defined as the automatic estimation of the inter-personal distance from an image, and the characterization of the related people aggregations. VSD is pivotal for a non-invasive analysis to whether people comply with the SD restriction, and to provide statistics about the level of safety of specific areas whenever this constraint is violated. We then discuss how VSD relates with previous literature in Social Signal Processing and indicate which existing Computer Vision methods can be used to manage such problem. We conclude with future challenges related to the effectiveness of VSD systems, ethical implications and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this manuscript and they are listed by alphabetical order. Under submissio

Occupancy Estimation Using Low-Cost Wi-Fi Sniffers

Real-time measurements on the occupancy status of indoor and outdoor spaces can be exploited in many scenarios (HVAC and lighting system control, building energy optimization, allocation and reservation of spaces, etc.). Traditional systems for occupancy estimation rely on environmental sensors (CO2, temperature, humidity) or video cameras. In this paper, we depart from such traditional approaches and propose a novel occupancy estimation system which is based on the capture of Wi-Fi management packets from users' devices. The system, implemented on a low-cost ESP8266 microcontroller, leverages a supervised learning model to adapt to different spaces and transmits occupancy information through the MQTT protocol to a web-based dashboard. Experimental results demonstrate the validity of the proposed solution in four different indoor university spaces.Comment: Submitted to Balkancom 201

System Energy Assessment (SEA), Defining a Standard Measure of EROI for Energy Businesses as Whole Systems

A more objective method for measuring the energy needs of businesses, System Energy Assessment (SEA), identifies the natural boundaries of businesses as self-managing net-energy systems, of controlled and self-managing parts. The method is demonstrated using a model Wind Farm case study, and applied to defining a true physical measure of its energy productivity for society (EROI-S), the global ratio of energy produced to energy cost. The traceable needs of business technology are combined with assignable energy needs for all other operating services. That serves to correct a large natural gap in energy use information. Current methods count traceable energy receipts for technology use. Self-managing services employed by businesses outsource their own energy needs to operate, and leave no records to trace. Those uncounted energy demands are often 80% of the total embodied energy of business end products. The scale of this "dark energy" was discovered from differing global accounts, and corrected so the average energy cost per dollar for businesses would equal the world average energy use per dollar of GDP. Presently the energy needs of paid services that outsource their own energy needs are counted for lack of information to be "0". Our default assumption is to treat them as "average". The result is to assign total energy use and impacts to the demand for energy services, for a "Scope 4" GHG assessment level. Counting only the energy uses of technology understates the energy needs of business services, as if services were more energy efficient than technology. The result confirms a similar finding by Hall et. al. in 1981 [9]. We use exhaustive search for what a business needs to operate as a whole, locating a natural physical boundary for its working parts, to define businesses as physical rather than statistical subjects of science. :measurement, natural systemsComment: 33 pages, 15 figures, accepted as part of pending special issue on EROI organized by Charlie Hall for Sustainability (MDPI