"Stuff goes into the computer and doesn't come out": a cross-tool study of personal information management

This paper reports a study of Personal Information Management (PIM), which advances research in two ways: (1) rather than focusing on one tool, we collected cross-tool data relating to file, email and web bookmark usage for each participant, and (2) we collected longitudinal data for a subset of the participants. We found that individuals employ a rich variety of strategies both within and across PIM tools, and we present new strategy classifications that reflect this behaviour. We discuss synergies and differences between tools that may be useful in guiding the design of tool integration. Our longitudinal data provides insight into how PIM behaviour evolves over time, and suggests how the supporting nature of PIM discourages reflection by users on their strategies. We discuss how the promotion of some reflection by tools and organizations may benefit users

Optimization Algorithms for Large-Scale Real-World Instances of the Frequency Assignment Problem

Nowadays, mobile communications are experiencing a strong growth, being more and more indispensable. One of the key issues in the design of mobile networks is the Frequency Assignment Problem (FAP). This problem is crucial at present and will remain important in the foreseeable future. Real world instances of FAP typically involve very large networks, which can only be handled by heuristic methods. In the present work, we are interested in optimizing frequency assignments for problems described in a mathematical formalism that incorporates actual interference information, measured directly on the field, as is done in current GSM networks. To achieve this goal, a range of metaheuristics have been designed, adapted, and rigourously compared on two actual GSM networks modeled according to the latter formalism. In order to generate quickly and reliably high quality solutions, all metaheuristics combine their global search capabilities with a local-search method specially tailored for this domain. The experiments and statistical tests show that in general, all metaheuristics are able to improve upon results published in previous studies, but two of the metaheuristics emerge as the best performers: a population-based algorithm (Scatter Search) and a trajectory based (1+1) Evolutionary Algorithm. Finally, the analysis of the frequency plans obtained offers insight about how the interference cost is reduced in the optimal plans.Publicad

An anytime tree search algorithm for the 2018 ROADEF/EURO challenge glass cutting problem

In this article, we present the anytime tree search algorithm we designed for the 2018 ROADEF/EURO challenge glass cutting problem proposed by the French company Saint-Gobain. The resulting program was ranked first among 64 participants. Its key components are: a new search algorithm called Memory Bounded A* (MBA*) with guide functions, a symmetry breaking strategy, and a pseudo-dominance rule. We perform a comprehensive study of these components showing that each of them contributes to the algorithm global performances. In addition, we designed a second tree search algorithm fully based on the pseudo-dominance rule and dedicated to some of the challenge instances with strong precedence constraints. On these instances, it finds the best-known solutions very quickly

DevOps Continuous Integration: Moving Germany’s Federal Employment Agency Test System into Embedded In-Memory Technology

This paper describes the development of a continuous integration database test architecture for a highly important and large software application in the public sector in Germany. We apply action design research and draw from two emerging areas of research – DevOps continuous integration practices and in-memory database development – to define the problem, design, build and implement the solution, analyze challenges encountered, and make adjustments. The result is the transformation of a large test environment originally based on Oracle databases into a flexible and fast embedded in-memory architecture. The main challenges involved overcoming the differences between the SQL specifications supported by the development and production systems and optimizing the test runtime performance. The paper contributes to theory and practice by presenting one of the first studies showing a real-world implementation of a successful database test architecture that enables continuous integration, and identifying technical design principles for database test architectures in general

A new model for the 'Tourism Renaissance: the Case study of the Tuscan village of San Pellegrino in Alpe

In the contemporary age of big data and sharing intelligence, tourism market has constantly changing scenario thanks to the unceasing communication technologies’ revolution. According to this growth perspective, cultural heritage tourism is increasing his offer quality standards, by giving evidence of its social and territorial identity value. In light of this, the research is focused on the study of hospitality places located along the Vie ROMEE maiores (Via Francigena, Via Romea Strata and Via Romea Germanica), the most famous slow tourism product in Italy, that has recently been promoted in such original ways. In particular, the paper describes the touristic system of San Pellegrino in Alpe, a village located on the Tuscan and Emilian Via Francigena route, with the final aim to suggest a touristic renovation model in light of the current Covid-19 changing scenario. Therefore, the research study shows how local stakeholders are able to foster territorial promotion activities, both for residents and for tourists, in order to empower local tourism system and local community awareness. In this context, the new marketing tools for virtual tourism, videomaking and crowdfunding play a strategic role for local tourism development; especially when they are used by stakeholders, such as Film Commissions and small local museums to assure a better quality of life in sustainable tourism destinations

Agent-based transportation planning compared with scheduling heuristics

Here we consider the problem of dynamically assigning vehicles to transportation orders that have di¤erent time windows and should be handled in real time. We introduce a new agent-based system for the planning and scheduling of these transportation networks. Intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. We use simulation to compare the on-time delivery percentage and the vehicle utilization of an agent-based planning system to a traditional system based on OR heuristics (look-ahead rules, serial scheduling). Numerical experiments show that a properly designed multi-agent system may perform as good as or even better than traditional methods

A New Model for the ‘Tourism Renaissance’: the Case Study of the Tuscan Village of San Pellegrino in Alpe

In the contemporary age of big data and sharing intelligence, tourism market has constantly changing scenario thanks to the unceasing communication technologies’ revolution. According to this growth perspective, cultural heritage tourism is increasing his offer quality standards, by giving evidence of its social and territorial identity value. In light of this, the research is focused on the study of hospitality places located along the Vie ROMEE maiores (Via Francigena, Via Romea Strata and Via Romea Germanica), the most famous slow tourism product in Italy, that has recently been promoted in such original ways. In particular, the paper describes the touristic system of San Pellegrino in Alpe, a village located on the Tuscan and Emilian Via Francigena route, with the final aim to suggest a touristic renovation model in light of the current Covid-19 changing scenario. Therefore, the research study shows how local stakeholders are able to foster territorial promotion activities, both for residents and for tourists, in order to empower local tourism system and local community awareness. In this context, the new marketing tools for virtual tourism, videomaking and crowdfunding play a strategic role for local tourism development; especially when they are used by stakeholders, such as Film Commissions and small local museums to assure a better quality of life in sustainable tourism destinations

Reactive approach for automating exploration and exploitation in ant colony optimization

Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. Exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is an alternative technique to maintain the dynamism of the mechanics. However, ACO-based reactive search technique has three (3) problems. First, the memory model to record previous search regions did not completely transfer the neighborhood structures to the next iteration which leads to arbitrary restart and premature local search. Secondly, the exploration indicator is not robust due to the difference of magnitudes in distance matrices for the current population. Thirdly, the parameter control techniques that utilize exploration indicators in their feedback process do not consider the problem of indicator robustness. A reactive ant colony optimization (RACO) algorithm has been proposed to overcome the limitations of the reactive search. RACO consists of three main components. The first component is a reactive max-min ant system algorithm for recording the neighborhood structures. The second component is a statistical machine learning mechanism named ACOustic to produce a robust exploration indicator. The third component is the ACO-based adaptive parameter selection algorithm to solve the parameterization problem which relies on quality, exploration and unified criteria in assigning rewards to promising parameters. The performance of RACO is evaluated on traveling salesman and quadratic assignment problems and compared with eight metaheuristics techniques in terms of success rate, Wilcoxon signed-rank, Chi-square and relative percentage deviation. Experimental results showed that the performance of RACO is superior than the eight (8) metaheuristics techniques which confirmed that RACO can be used as a new direction for solving optimization problems. RACO can be used in providing a dynamic exploration and exploitation mechanism, setting a parameter value which allows an efficient search, describing the amount of exploration an ACO algorithm performs and detecting stagnation situations

Designing behaviourally informed policies for land stewardship: A new paradigm

This paper argues the case for a new approach to the stewardship of land resources that uses behavioural science theory to support the design and application of policies that facilitate changes in behaviour by those who develop policy and the farmers who implement it. Current approaches have: focused on legally-based expert system; and have been devised by national and international bureaucracies with little or no knowledge of how land owners and managers are motivated, and how they think, behave and operate as stewards of their natural resources. A review of current approaches from the social scientific literature is provided, with a particular focus on principles from social psychology. This is followed by an examination of how these principles can be applied to influence behaviour related to land restoration and soil conservation. Examples of the problems with traditional approaches and the evolution of new approaches with full engagement of farmers as the delivery agents are provided from within the European Union, Iceland and Scotland. In the light of these examples and emerging thinking in other parts of the world, the paper sets out the basis for a new approach based on behavioural science theory and application, reinforcing the arguments already made in the literature for a social license for farming