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This repository behaves as a support material for the paper entitled "Move Cultural Heritage Knowledge Graphsin Everyone’s Pocket" submitted (and under evaluation) as a **Application report** for the *Cultural Heritage and Semantic Web* special issue at the Semantic Web journal. It provides a Permanent URI for the Virtual Assistant Generator (virtual_assistant_generator.zip) that, at its actual version v 1.0, enables the automatic generation of Alexa skills to reply to natural language questions by querying SPARQL endpoints. The operating mechanism of the generator is detailed in Section 5 of the article. To demonstrate the operating mechanisms of the generator, we create the following Alexa skills as use cases, documented in Section 7 of the paper: - Wikidata_usecase.zip contains the two versions of the Wikidata Alexa skill to show the possibility to customize the linking mode performed by the generator. While in the dictionary-based WikiSkill we exploit a dictionary to perform the entity and relation linking, in the API-driven WikiSkill, we perform the linking phase by relying on the Wikidata APIs. For each skill, we report 1) the Alexa skill components (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator (when applicable); 3) the replies corresponding to the evaluation of the Wikidata skills on the QALD-7 training set; - DBpedia_usecase.zip contains the DBpedia Alexa skills. They are used to demonstrate the different generator configuration options: users can manually create the generator configuration file or auto-generate them, and use it as-is or manually refine it before generating the Alexa skill. For each DBpedia skill, we report 1) the Alexa skills components (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator; 3) its evaluation on the QALD-9 training set; - usesco_usecase.zip contains 1) the UNESCO Thesaurus Alexa skills component (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator; - wordnet_personal_assistant.zip contains 1) the WordNet Alexa skills component (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator; - warsampo_usecase.zip contains 1) the WarSampo Alexa skills component (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator; - dbtune_usecase.zip contains 1) the DBTune classical Alexa skills component (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator; - hungarian_museum_usecase.zip contains 1) the Hungarian museum Alexa skills component (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator; - mmm_usecase.zip contains 1) the Mapping Manuscript Migrations (MMM) Alexa skills component (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator; - natural_features_usecase.zip contains 1) the Natural Features (part of the Scotland's official statistics) Alexa skills component (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator; The content of this repository is also provided on GitHub at https://github.com/mariaangelapellegrino/virtual_assistant_generator. The entire project is released under the MIT license., This repository behaves as a support material for the paper entitled "Move Cultural Heritage Knowledge Graphsin Everyone's Pocket" submitted (and under evaluation) as a Application report for the Cultural Heritage and Semantic Web special issue at the Semantic Web journal. The content of this repository is also provided on GitHub at https://github.com/mariaangelapellegrino/virtual_assistant_generator.

Simulation models are becoming an increasingly popular tool for the analysis and optimization of complex real systems in different fields. Finding an optimal system design requires performing a large sweep over the parameter space in an organized way. Hence, the model optimization process is extremely demanding from a computational point of view, as it requires careful, time-consuming, complex orchestration of coordinated executions. In this paper, we present the design of SOF (Simulation Optimization and exploration Framework in the cloud), a framework which exploits the computing power of a cloud computational environment in order to carry out effective and efficient simulation optimization strategies. SOF offers several attractive features. Firstly, SOF requires "zero configuration", as it does not require any additional software installed on the remote node; only standard Apache Hadoop and SSH access are sufficient. Secondly, SOF is transparent to the user, since the user is totally unaware that the system operates on a distributed environment. Finally, SOF is highly customizable and programmable, since it enables the running of different simulation optimization scenarios using diverse programming languages - provided that the hosting platform supports them - and different simulation toolkits, as developed by the modeler. The tool has been fully developed and is available on a public repository.11SOF GitHub public repository, https://github.com/isislab-unisa/sof. under the terms of the open source Apache License. It has been tested and validated on several private platforms, such as a dedicated cluster of workstations, as well as on public platforms, including the Hortonworks Data Platform and Amazon Web Services Elastic MapReduce solution.

In order to increase transparency, Public Administrations (PAs) have their own portals to publish Open Data, resulting in more openness, reduced corruption and improved services. Open Data (OD) initiatives are achieving less citizens' engagement than expected. Hence, the need to find other ways and services to obtain more engagement and extract value from OD. This paper introduces an architectural model to design software platforms with the objective to increase public value through collective participation of citizens, public administrations and key stakeholders. The architectural model originates from the Data-Information-Knowledge hierarchy, where greater value is at the top of the pyramid, in information and knowledge gathered from data. Thus, the architectural model adds the collaborative and presentation layers to the classical data layer; citizens, public administrations, and stakeholders form groups of interests to understand, reason and interpret Open Data to gather information and generate knowledge that will be communicated to the general audience over Internet, thus, increasing public value. The paper describes three platform instances: the ROUTE-TO-PA ecosystem, the data.world, and DKAN, which functionalities can be mapped onto the architectural model proposed in the paper. Furthermore, the paper describes HETOR, a concreate experience of exploitation of the architectural model and the ROUTE-TO-PA ecosystem with groups of students and associations of citizens, who collaborated together to ultimately generate new knowledge for Cultural Heritage to be communicated over Internet through blog posts.

Scientific Computing (SC) is a multidisciplinary field that uses the computational approach to understand and study complex artificial and natural systems belonging many scientific sectors. Optimization via Simulation (OvS) is a fast developing area in SC field. OvS combines classical optimization algorithms and stochastic simulations to face problems with unknown and/or dynamic data distribution. We present Heterogeneous Simulation Optimization (HSO), an architecture that enable to distribute the OvS process on an Heterogeneous Computing systems. HSO is designed according to two levels of heterogeneity: hardware heterogeneity, that is the ability to exploit the computational power of several general-purpose CPUs and/or hardware accelerators such as Graphics Processing Units (GPUs); programming languages heterogeneity, that is the capability to develop the OvS methodology combining different programming languages such as C++, C, Clojure, Erlang, Go, Haskel, Java, Node.js, Objective-C, PHP, Python, Scala and many others. The proposed HSO architecture has been fully developed and is available on a public GitHub repository. We have validated and tested the scalability of HSO developing two different use cases that show both the levels of heterogeneity, and showing how to exploit Optimal Computing Budget Allocation (OCBA) algorithm and a Genetic Algorithm in a OvS process.

Agent-based simulations are becoming widespread among scientists from different areas, who use them to model increasingly complex problems. To cope with the growing computational complexity, parallel and distributed implementations have been developed for a wide range of platforms. However, it is difficult to have simulations that are portable to different platforms while still achieving high performance. We present OpenABL, a domain-specific language for portable, high-performance, parallel agent modeling. It comprises an easy-to-program language that relies on high-level abstractions for programmability and explicitly exploits agent parallelism to deliver high performance. A source-to-source compiler translates the input code to a high-level intermediate representation exposing parallelism, locality and synchronization, and, thanks to an architecture based on pluggable backends, generates target code for multi-core CPUs, GPUs, large clusters and cloud systems. OpenABL has been evaluated on six applications from various fields such as ecology, animation, and social sciences. The generated code scales to large clusters and performs similarly to hand-written target-specific code, while requiring significantly fewer lines of codes.

Open Data are valuable initiatives in favour of transparency. Public administrations are increasing the availability of datasets for citizens, associations, innovators and other stakeholders, by releasing their data with open licenses. Open initiatives are achieving less success than expected, mainly due to the lack of engagement. There is a growing demand for approaches to actively engage citizens in exploiting Open Data. This paper introduces SPOD, a Social Platform for Open Data, which aims to engage citizens, local associations and organizations in forming communities of interests, stimulating the interpretation of Open Data and exploiting their use in Data-driven discussions, something not well-supported on traditional social networks. Social collaboration is the key aspect to increase the public value, where citizens participate in the discussions, co-create knowledge and data. The paper describes the engagement of four communities of citizens, which contributed to the public value by discussing topics in the context of Cultural Heritage, generating information from existing and co-created open datasets, by using SPOD.

Work partitioning is a key challenge with ap- plications in many scientific and technological fields. The problem is very well studied with a rich literature on both distributed and parallel computing architectures. In this paper we deal with the work partitioning problem for parallel and distributed agent-based simulations which aims at (i) balancing the overall load distribution, (ii) minimizing, at the same time, the communication overhead due to agents' inter-dependencies. We introduce a classification taxonomy of work partitioning strategies and present a space-based work partitioning ap- proach, based on a Quad-tree data structure, which enables to: identify a good space partitioning (even when the distribution of agents on the fields is non-uniform) with a limited impact in terms of communication. Being a multi-objective problem, the results are difficult to compare and it is hard to foresee what can be the impact of one solution. For this reason we evaluate our strategy in a real context using a well-known behavior (the boids flocking model), on a distributed agent based simulation framework (D-MASON). The results show that our proposal provides a sensible impact on the performances of the system and scales in terms of the number of logical processors.

This paper presents the DatalEt-Ecosystem Provider (DEEP), an extensible, and scalable Edge-centric architecture to visualize Open Data, retrieved in real time from institutional open data portals. The aim is to engage citizens and stakeholders through reusable, portable and interactive visualizations, named datalets. The DEEP architecture exploits the increasing computing power and capacity of end-users devices, moving the computation to process and visualize data, from the central server, directly to the client-side ensuring data trustiness, privacy, scalability and dynamic data loading. DEEP and its datalets have been fully exploited, in the ROUTE-TO-PA, HORIZON 2020 funded project, by five public administrations across Europe as pilot projects. The project engages and involves citizens in creating, sharing and commenting existing visualizations of Open Data. DEEP is open source, its source code is fully available on GitHub, thus every single component can be reused by other projects.

The aim of our research is to study how to increase Public Value through the collective participation, involving Public Administrations, stakeholders and citizens together. The Public Value for citizens is in the available and gained Knowledge. The paper models this concept by introducing a variant of the classic Data-Information-Knowledge pyramid, considering everything published as open and public. The paper introduces a social and iterative process designed for user appropriation, that includes the Knowledge and Data Co-Creation with the aim to generate public Open Knowledge. Users with process and technology appropriation can creatively follow the process in different ways. The paper concludes by introducing a brief preliminary scenario that exploits the process, platform and technology in the context of Cultural Heritage.

This paper introduces the DatalEt-Ecosystem Provider (DEEP), an open source, extensible, modular and pluggable architecture providing datalets, which are web-component visualisations of datasets content. DEEP enables the shar-ing and collaboration around data visualisations, supporting for instance communities in public deliberation around Open Data. Users can create, reconfigure, reuse and share inter-active visualisations in any web-page and other systems.