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Besides extremely high throughput, Wi-Fi 7 is also aimed at providing users a more deterministic behavior, characterized by shorter average latency and smaller jitters. A key mechanism to achieve this is multi-link operation, which brings simultaneous multi-band communication to client stations as well. In this paper, traffic steering policies are briefly reviewed and grouped into general classes, each one with its advantages and limitations. A basic mechanism for supporting dynamic steering is then described, which is simple enough to allow implementation in real Wi-Fi chipsets but highly flexible at the same time. Its operation can be driven by the host on a per-packet basis, and this permits to optimize spectrum usage depending on the requirements of applications and the traffic pattern they generate., Comment: preprint, 4 pages, 2024

Executable QR codes, also known as eQR codes or just sQRy, are a special kind of QR codes that embed programs conceived to run on mobile devices like smartphones. Since the program is directly encoded in binary form within the QR code, it can be executed even when the reading device is not provided with Internet access. The applications of this technology are manifold, and range from smart user guides to advisory systems. The first programming language made available for eQR is QRtree, which enables the implementation of decision trees aimed, for example, at guiding the user in operating/maintaining a complex machinery or for reaching a specific location. In this work, an additional language is proposed, we term QRind, which was specifically devised for Industry. It permits to integrate distinct computational blocks into the QR code, e.g., machine learning models to enable predictive maintenance and algorithms to ease machinery usage. QRind permits the Industry 4.0/5.0 paradigms to be implemented, in part, also in those cases where Internet is unavailable., Comment: preprint, 4 pages, 2024

One of the main goals of wireless sensor networks is to permit the involved nodes to communicate with low energy budgets, as they are typically battery-powered. When such networks are employed in industrial scenarios, constraints about latency may have a significant role, too. The TSCH mechanism, and more in general TDMA schemes, rely on traffic scheduling, and consequently they can feature low power consumption and more predictable latency. Some recent proposals like PRIL-M enable further consistent energy savings, but unfortunately they cause at the same time a dramatic increase in latency. This work presents an extension of PRIL-M, we named PRIL-ML, that achieves a significantly shorter latency in exchange for a slight increase in power consumption. Its operating principles are first illustrated, then some approximate equations are provided for assessing analytically the improvements it achieves, starting from simulation results obtained for both standard TSCH and the original PRIL-M technique., Comment: preprint, 4 pages, 2024

The radio spectrum is characterized by a noticeable variability, which impairs performance and determinism of every wireless communication technology. To counteract this aspect, mechanisms like Minstrel are customarily employed in real Wi-Fi devices, and the adoption of machine learning for optimization is envisaged in next-generation Wi-Fi 8. All these approaches require communication quality to be monitored at runtime. In this paper, the effectiveness of simple techniques based on moving averages to estimate wireless link quality is analyzed, to assess their advantages and weaknesses. Results can be used, e.g., as a baseline when studying how artificial intelligence can be employed to mitigate unpredictability of wireless networks by providing reliable estimates about current spectrum conditions., Comment: preprint, 8 pages, 2024

Multi-Link Operation (MLO) in Wi-Fi 7 is expected to tangibly boost throughput while lowering transmission latency at the same time. This is very relevant in industrial scenarios and makes MLO suitable, e.g., to support seamless device mobility. Benefits depend on the ability of multi-link devices to select at run-time the best link, among the available ones, in order to maximize both communication performance and reliability. In this paper an experimental platform is proposed, with the aim of leveraging commercial hardware and open source software, and easing prototyping and evaluation of MLO techniques. The platform has been employed to analyze the transmission quality of two pairs of non-overlapping channels, and in particular to assess whether or not adequate diversity is provided, so that those channels can be exploited to improve reliability. Results point out that correlation between different links is, in most cases, limited, which makes MLO a valuable approach., Comment: preprint, 8 pages, 2024

Predicting the behavior of a wireless link in terms of, e.g., the frame delivery ratio, is a critical task for optimizing the performance of wireless industrial communication systems. This is because industrial applications are typically characterized by stringent dependability and end-to-end latency requirements, which are adversely affected by channel quality degradation. In this work, we studied two neural network models for Wi-Fi link quality prediction in dense indoor environments. Experimental results show that their accuracy outperforms conventional methods based on exponential moving averages, due to their ability to capture complex patterns about communications, including the effects of shadowing and multipath propagation, which are particularly pronounced in industrial scenarios. This highlights the potential of neural networks for predicting spectrum behavior in challenging operating conditions, and suggests that they can be exploited to improve determinism and dependability of wireless communications, fostering their adoption in the industry., Comment: preprint, 6 pages, 2024

The Industrial Internet of Things (IIoT) paradigm has emerged as a transformative force, revolutionizing industrial processes by integrating advanced wireless technologies into traditional procedures to enhance their efficiency. The importance of this paradigm shift has produced a massive, yet heterogeneous, proliferation of scientific contributions. However, these works lack a standardized and cohesive characterization of the IIoT framework coming from different entities, like the 3rd Generation Partnership Project (3GPP) or the 5G Alliance for Connected Industries and Automation (5G-ACIA), resulting in divergent perspectives and potentially hindering interoperability. To bridge this gap, this article offers a unified characterization of (i) the main IIoT application domains, (ii) their respective requirements, (iii) the principal technological gaps existing in the current literature, and, most importantly, (iv) we propose a systematic approach for assessing and addressing the identified research challenges. Therefore, this article serves as a roadmap for future research endeavors, promoting a unified vision of the IIoT paradigm and fostering collaborative efforts to advance the field.

Novelty detection is a critical task in various engineering fields. Numerous approaches to novelty detection rely on supervised or semi-supervised learning, which requires labelled datasets for training. However, acquiring labelled data, when feasible, can be expensive and time-consuming. For these reasons, unsupervised learning is a powerful alternative that allows performing novelty detection without needing labelled samples. In this study, numerous unsupervised machine learning algorithms for novelty detection are compared, highlighting their strengths and weaknesses in the context of vibration sensing. The proposed framework uses a continuous metric, unlike most traditional methods that merely flag anomalous samples without quantifying the degree of anomaly. Moreover, a new dataset is gathered from an actuator vibrating at specific frequencies to benchmark the algorithms and evaluate the framework. Novel conditions are introduced by altering the input wave signal. Our findings offer valuable insights into the adaptability and robustness of unsupervised learning techniques for real-world novelty detection applications., Comment: To be published in the 8th International Conference on System Reliability and Safety. Sicily, Italy - November 20-22, 2024. 15 pages, 7 figures, 4 tables

In the space sector, due to environmental conditions and restricted accessibility, robust fault detection methods are imperative for ensuring mission success and safeguarding valuable assets. This work proposes a novel approach leveraging Physics-Informed Real NVP neural networks, renowned for their ability to model complex and high-dimensional distributions, augmented with a self-supervised task based on sensors' data permutation. It focuses on enhancing fault detection within the satellite multivariate time series. The experiments involve various configurations, including pre-training with self-supervision, multi-task learning, and standalone self-supervised training. Results indicate significant performance improvements across all settings. In particular, employing only the self-supervised loss yields the best overall results, suggesting its efficacy in guiding the network to extract relevant features for fault detection. This study presents a promising direction for improving fault detection in space systems and warrants further exploration in other datasets and applications., Comment: SPAICE: AI in and for Space, 2024

The unique challenges posed by the space environment, characterized by extreme conditions and limited accessibility, raise the need for robust and reliable techniques to identify and prevent satellite faults. Fault detection methods in the space sector are required to ensure mission success and to protect valuable assets. In this context, this paper proposes an Artificial Intelligence (AI) based fault detection methodology and evaluates its performance on ADAPT (Advanced Diagnostics and Prognostics Testbed), an Electrical Power System (EPS) dataset, crafted in laboratory by NASA. Our study focuses on the application of a physics-informed (PI) real-valued non-volume preserving (Real NVP) model for fault detection in space systems. The efficacy of this method is systematically compared against other AI approaches such as Gated Recurrent Unit (GRU) and Autoencoder-based techniques. Results show that our physics-informed approach outperforms existing methods of fault detection, demonstrating its suitability for addressing the unique challenges of satellite EPS sub-system faults. Furthermore, we unveil the competitive advantage of physics-informed loss in AI models to address specific space needs, namely robustness, reliability, and power constraints, crucial for space exploration and satellite missions., Comment: C. Cena, U. Albertin, M. Martini, S. Bucci and M. Chiaberge, "Physics-Informed Real NVP for Satellite Power System Fault Detection," 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Boston, MA, USA, 2024, pp. 679-684, doi: 10.1109/AIM55361.2024.10636990

The next generation of Wi-Fi is meant to achieve ultra-high reliability for wireless communication. Several approaches are available to this extent, some of which are being considered for inclusion in standards specifications, including coordination of access points to reduce interference. In this paper, we propose a centralized architecture based on digital twins, called WiTwin, with the aim of supporting wireless stations in selecting the optimal association according to a set of parameters. Unlike prior works, we assume that Wi-Fi 7 features like multi-link operation (MLO) are available. Moreover, one of the main goals of this architecture is to preserve communication quality in the presence of mobility, by helping stations to perform reassociation at the right time and in the best way., Comment: preprint, 4 pages

Embedding a programming language in a QR code is a new and extremely promising opportunity, as it makes devices and objects smarter without necessarily requiring an Internet connection. In this paper, all the steps needed to translate a program written in a high-level programming language to its binary representation encoded in a QR code, and the opposite process that, starting from the QR code, executes it by means of a virtual machine, have been carefully detailed. The proposed programming language was named QRscript, and can be easily extended so as to integrate new features. One of the main design goals was to produce a very compact target binary code. In particular, in this work we propose a specific sub-language (a dialect) that is aimed at encoding decision trees. Besides industrial scenarios, this is useful in many other application fields. The reported example, related to the configuration of an industrial networked device, highlights the potential of the proposed technology, and permits to better understand all the translation steps., Comment: preprint, 8 pages

This specification document specifies the syntax and semantics of QRtree, which is a specific dialect of QRscript particularly suited to represent decision trees without chance nodes. The term dialect identifies one of the possible sub-languages that can be encoded inside of an eQR code via QRscript. This specification will describe an intermediate representation of QRtree, made through a language derived by the three-address code. It will then define the transformation rules from the intermediate representation to a binary code. The latter is a binary representation called eQRtreebytecode. These rules can also be applied inversely to transform the eQRtreeBytecode into the intermediate representation. This specification document will pay particular attention to the creation of a compact eQRtreebytecode, as the maximum number of bits that can be stored in a QR code is, at the time of writing, equal to 2953 bytes (in the case of QR code version 40 with a "low" error correction level)., Comment: Specification document, 32 pages

This specification document specifies the syntax and semantics of QRscript. The current document only shows the part related to the QRscript header, i.e., the first part of the binary code that must be inserted into the QR code. A QR code containing an executable code is called an executable QR code (eQR code). QRscript supports different dialects, i.e., sublanguages with implementation characteristics specific to the application field. The specifications of the individual dialects will be described in separate documents., Comment: Specification document, 13 pages

The ability to predict the behavior of a wireless channel in terms of the frame delivery ratio is quite valuable, and permits, e.g., to optimize the operating parameters of a wireless network at runtime, or to proactively react to the degradation of the channel quality, in order to meet the stringent requirements about dependability and end-to-end latency that typically characterize industrial applications. In this work, prediction models based on the exponential moving average (EMA) are investigated in depth, which are proven to outperform other simple statistical methods and whose performance is nearly as good as artificial neural networks, but with dramatically lower computational requirements. Regarding the innovation and motivation of this work, a new model that we called EMA linear combination (ELC), is introduced, explained, and evaluated experimentally. Its prediction accuracy, tested on some databases acquired from a real setup based on Wi-Fi devices, showed that ELC brings tangible improvements over EMA in any experimental conditions, the only drawback being a slight increase in computational complexity., Comment: preprint, 6 pages

By removing wire harness, Wi-Fi is becoming increasingly pervasive in every aspect of our lives, in both the consumer and industrial worlds. Besides flexibility, the recent high efficiency and extremely high throughput versions managed to close the performance gap with Ethernet. However, it still lags behind Ethernet for what concerns dependability. To this aim, the ultra high reliability study group has been recently formed. This paper reports on some preliminary ideas and proposals about the ways seamless redundancy can be exploited to make Wi-Fi more reliable, yet retaining a good degree of backward compatibility with existing network infrastructures., Comment: preprint, 4 pages

New generation electrified and self-driving vehicles require much higher performance and flexibility for onboard digital communications than Controller Area Networks may offer. For this reason, automotive Ethernet is often regarded as the next de facto standard technology in these contexts, and by extension for networked embedded systems as well. However, an abrupt and drastic move from CAN to Ethernet is likely to cause further cost increases, which can be hardly tolerated by buyers. This paper analyzes the third generation of CAN, termed CAN XL, and studies how interoperability can be ensured with Ethernet. Likely, composite CAN XL-Ethernet networks are the key for getting the best of both worlds, not only in the automotive domain but also for sensing and control in scenarios like building automation, wired sensor networks, and low-cost networked embedded systems with real-time constraints., Comment: preprint, 8 pages

The ability to reliably predict the future quality of a wireless channel, as seen by the media access control layer, is a key enabler to improve performance of future industrial networks that do not rely on wires. Knowing in advance how much channel behavior may change can speed up procedures for adaptively selecting the best channel, making the network more deterministic, reliable, and less energy-hungry, possibly improving device roaming capabilities at the same time. To this aim, popular approaches based on moving averages and regression were compared, using multiple key performance indicators, on data captured from a real Wi-Fi setup. Moreover, a simple technique based on a linear combination of outcomes from different techniques was presented and analyzed, to further reduce the prediction error, and some considerations about lower bounds on achievable errors have been reported. We found that the best model is the exponential moving average, which managed to predict the frame delivery ratio with a 2.10\% average error and, at the same time, has lower computational complexity and memory consumption than the other models we analyzed., Comment: preprint, 8 pages

In this paper, we report on three experiments we have carried out in the context of the EMPATHY project, with the aim of helping users make better configuration choices in a smart home environment, and discuss our results. We found that there are psychological traits, such as Need for Cognition, which influence the way individuals tend to use recommendations, that there are non obvious relationships between the perceived usefulness of recommendations in different domains and individuals' ability to exploit suggestions on configuration choices, and that detailed, easy-to-understand security explanations are more persuasive than simple security warnings, when it comes to make decisions on the applicability of rules which might cause privacy and security risks.

Agglomerative hierarchical clustering based on Ordered Weighted Averaging (OWA) operators not only generalises the single, complete, and average linkages, but also includes intercluster distances based on a few nearest or farthest neighbours, trimmed and winsorised means of pairwise point similarities, amongst many others. We explore the relationships between the famous Lance-Williams update formula and the extended OWA-based linkages with weights generated via infinite coefficient sequences. Furthermore, we provide some conditions for the weight generators to guarantee the resulting dendrograms to be free from unaesthetic inversions.

Minimum spanning trees (MSTs) provide a convenient representation of datasets in numerous pattern recognition activities. Moreover, they are relatively fast to compute. In this paper, we quantify the extent to which they are meaningful in low-dimensional partitional data clustering tasks. By identifying the upper bounds for the agreement between the best (oracle) algorithm and the expert labels from a large battery of benchmark data, we discover that MST methods can be very competitive. Next, we review, study, extend, and generalise a few existing, state-of-the-art MST-based partitioning schemes. This leads to some new noteworthy approaches. Overall, the Genie and the information-theoretic methods often outperform the non-MST algorithms such as K-means, Gaussian mixtures, spectral clustering, Birch, density-based, and classical hierarchical agglomerative procedures. Nevertheless, we identify that there is still some room for improvement, and thus the development of novel algorithms is encouraged.

Time slotted channel hopping provides reliable and deterministic communication in IEEE 802.15.4 mesh networks. Although slotted access is able to lower energy consumption drastically by reducing the duty cycle of the radio module, it usually leads to significant idle listening experienced by receivers, which makes it a sub-optimal solution when ultra low-power wireless is sought for. In this paper a listening suspension mechanism is described, which operates at the MAC layer and is part of a more general approach aimed at cutting down energy consumption by proactively reducing idle listening. Links can be temporarily disabled, that convey slow-rate data streams whose characteristics, e.g., the generation period, are either known in advance to some extent or can be inferred by traffic inspection., Comment: preprint, 8 pages

The use of white and black listing techniques in Wireless Sensor Networks (WSN), and in particular those which are based on the Time Slotted Channel Hopping (TSCH) operating mode of IEEE 802.15.4, permits to improve reliability and latency by performing transmissions on the best channels. Techniques that operate on a per-link basis are deemed quite effective, but proper operation requires that the two end points involved in the communication agree on the channels to be used for transmission. On the contrary, communication in the network can be prevented, eventually leading, in the worst cases, to the disconnection of part of the nodes. This paper presents CONSIP, a technique aimed to ensure strict consistency in the information exchanged between the nodes and used to drive communication, by preventing a prior the aforementioned problem from occurring. Results show a slight increase in energy consumption, due to the use of a backup cell, whereas communication latency does not worsen. The effectiveness of CONSIP was assessed by means of an experimental campaign, and the only drawback we found is that the backup cell, which is required to be reserved per link, may limit the number of nodes in dense networks., Comment: preprint, 8 pages

Seamless redundancy layered atop Wi-Fi has been shown able to tangibly increase communication quality, hence offering industry-grade reliability. However, it also implies much higher network traffic, which is often unbearable as the wireless spectrum is a shared and scarce resource. To deal with this drawback the Wi-Red proposal includes suitable duplication avoidance mechanisms, which reduce spectrum consumption by preventing transmission on air of inessential frame duplicates. In this paper, the ability of such mechanisms to save wireless bandwidth is experimentally evaluated. To this purpose, specific post-analysis techniques have been defined, which permit to carry out such an assessment on a simple testbed that relies on plain redundancy and do not require any changes to the adapters' firmware. As results show, spectrum consumption decreases noticeably without communication quality is impaired. Further saving can be obtained if a slight worsening is tolerated for latencies., Comment: preprint, 13 pages

Reliability and determinism of Wi-Fi can be tangibly improved by means of seamless redundancy, to the point of making this technology suitable for industrial environments. As pointed out in recent papers, the most benefits can be achieved when no phenomena can simultaneously affect transmissions on all channels of a redundant link. In this paper, several aspects are analyzed which, if not properly counteracted, may worsen seamless redundancy effectiveness. Effects they cause on communication have been experimentally evaluated in real testbeds, which rely on commercial Wi-Fi devices. Then, practical guidelines are provided, which aim at preventing joint interference through a careful system design. Results show that measured communication quality can be made as good as expected in theory., Comment: preprint, 13 pages

In distributed control systems where devices are connected through Wi-Fi, direct access to low-level MAC operations may help applications to meet their timing constraints. In particular, the ability to timely control single transmission attempts on air, by means of software programs running at the user space level, eases the implementation of mechanisms aimed at improving communication timeliness and reliability. Relevant examples are deterministic traffic scheduling, seamless channel redundancy, rate adaptation algorithms, and so on. In this paper, a novel architecture is defined, we call SDMAC, which in its current embodiment relies on conventional Linux PCs equipped with commercial Wi-Fi adapters. Preliminary SDMAC implementation on a real testbed and its experimental evaluation showed that integrating this paradigm in existing protocol stacks constitutes a viable option, whose performance suits a wide range of applications characterized by soft real-time requirements., Comment: preprint, 11 pages

The adoption of wireless communications and, in particular, Wi-Fi, at the lowest level of the factory automation hierarchy has not increased as fast as expected so far, mainly because of serious issues concerning determinism. Actually, besides the random access scheme, disturbance and interference prevent reliable communication over the air and, as a matter of fact, make wireless networks unable to support distributed real-time control applications properly. Several papers recently appeared in the literature suggest that diversity could be leveraged to overcome this limitation effectively. In this paper a reference architecture is introduced, which describes how seamless link-level redundancy can be applied to Wi-Fi. The framework is general enough to serve as a basis for future protocol enhancements, and also includes two optimizations aimed at improving the quality of wireless communication by avoiding unnecessary replicated transmissions. Some relevant solutions have been analyzed by means of a thorough simulation campaign, in order to highlight their benefits when compared to conventional Wi-Fi. Results show that both packet losses and network latencies improve noticeably., Comment: preprint, 13 pages (Winner of the "2017 Best Paper Award for the IEEE Transactions on Industrial Informatics")

Seamless redundancy can be profitably exploited to improve predictability of wireless networks in general and, in particular, IEEE 802.11. According to this approach, packets are transmitted by senders on two (or more) channels at the same time and duplicate copies are discarded by receivers. As long as the behavior of physical channels is uncorrelated, communication quality improves noticeably, in terms of both transmission latencies and percentage of dropped frames. In this paper, communication over redundant links has been analyzed by means of a thorough experimental campaign, based on measurements carried out on real devices. Results confirm that, under typical operating conditions, the assumption of independence among channels in properly designed systems is verified reasonably well. Indeed, in our experiments, measured link quality indices did not differ more than 10% from what we expected from theory. This grants for redundant solutions tangible advantages over conventional Wi-Fi networks., Comment: preprint, 10 pages

Lifetime of motes in wireless sensor networks can be enlarged by decreasing the energy spent for communication. Approaches like time slotted channel hopping pursue this goal by performing frame exchanges according to a predefined schedule, which helps reducing the duty cycle. Unfortunately, whenever the receiving radio interface is active but nobody in the network is transmitting, idle listening occurs. If the traffic pattern is known in advance, as in the relevant case of periodic sensing, proactive reduction of idle listening (PRIL) noticeably lowers energy waste by disabling receivers when no frames are expected for them. Optimal PRIL operation demands that, at any time, the transmitter and receiver sides of a link have a coherent view of its state (either enabled or disabled). However, this is not ensured in the presence of acknowledgment frame losses. This paper presents and analyzes some strategies to cope with such events. An extensive experimental campaign has been carried out through discrete event simulation to determine what consequences above errors may have from both a functional and performance viewpoint. Results show that, although no strategy is optimal in all circumstances, different solutions can be profitably adopted depending on the specific operating conditions., Comment: preprint, 11 pages

A social robot is an autonomous robot that interact with people by engaging in social emotive behaviors, skills, capacities, and rules attached to its collaborative role. In order to achieve these goals we believe that modeling the interaction with the user and adapt the robot behavior to the user herself are fundamental for its social role. This paper presents our first attempt to integrate user modeling features in social and affective robots. We propose a cloud-based architecture for modeling the user-robot interaction in order to reuse the approach with different kind of social robots., Comment: paper presented at the cAESAR workshop 2020, IUI 2020 conference. arXiv admin note: substantial text overlap with arXiv:2108.02513

The time needed to apply a hierarchical clustering algorithm is most often dominated by the number of computations of a pairwise dissimilarity measure. Such a constraint, for larger data sets, puts at a disadvantage the use of all the classical linkage criteria but the single linkage one. However, it is known that the single linkage clustering algorithm is very sensitive to outliers, produces highly skewed dendrograms, and therefore usually does not reflect the true underlying data structure -- unless the clusters are well-separated. To overcome its limitations, we propose a new hierarchical clustering linkage criterion called Genie. Namely, our algorithm links two clusters in such a way that a chosen economic inequity measure (e.g., the Gini- or Bonferroni-index) of the cluster sizes does not drastically increase above a given threshold. The presented benchmarks indicate a high practical usefulness of the introduced method: it most often outperforms the Ward or average linkage in terms of the clustering quality while retaining the single linkage's speed. The Genie algorithm is easily parallelizable and thus may be run on multiple threads to speed up its execution even further. Its memory overhead is small: there is no need to precompute the complete distance matrix to perform the computations in order to obtain a desired clustering. It can be applied on arbitrary spaces equipped with a dissimilarity measure, e.g., on real vectors, DNA or protein sequences, images, rankings, informetric data, etc. A reference implementation of the algorithm has been included in the open source 'genie' package for R. See also https://genieclust.gagolewski.com for a new implementation (genieclust) -- available for both R and Python.

Internal cluster validity measures (such as the Calinski-Harabasz, Dunn, or Davies-Bouldin indices) are frequently used for selecting the appropriate number of partitions a dataset should be split into. In this paper we consider what happens if we treat such indices as objective functions in unsupervised learning activities. Is the optimal grouping with regards to, say, the Silhouette index really meaningful? It turns out that many cluster (in)validity indices promote clusterings that match expert knowledge quite poorly. We also introduce a new, well-performing variant of the Dunn index that is built upon OWA operators and the near-neighbour graph so that subspaces of higher density, regardless of their shapes, can be separated from each other better.

Personality traits such as Need for Cognition, Locus of Control, Mindset and Self-efficacy could impact the perception, acceptance and appreciation of recommendations provided to support configuration tasks in the End User Development (EUD) context. In this paper we describe the user model services we have developed to measure such traits. These services can be accessed by users through a simple web interface and can be queried by EUD applications by means of REST API.

When suggesting Points of Interest (PoIs) to people with autism spectrum disorders, we must take into account that they have idiosyncratic sensory aversions to noise, brightness and other features that influence the way they perceive places. Therefore, recommender systems must deal with these aspects. However, the retrieval of sensory data about PoIs is a real challenge because most geographical information servers fail to provide this data. Moreover, ad-hoc crowdsourcing campaigns do not guarantee to cover large geographical areas and lack sustainability. Thus, we investigate the extraction of sensory data about places from the consumer feedback collected by location-based services, on which people spontaneously post reviews from all over the world. Specifically, we propose a model for the extraction of sensory data from the reviews about PoIs, and its integration in recommender systems to predict item ratings by considering both user preferences and compatibility information. We tested our approach with autistic and neurotypical people by integrating it into diverse recommendation algorithms. For the test, we used a dataset built in a crowdsourcing campaign and another one extracted from TripAdvisor reviews. The results show that the algorithms obtain the highest accuracy and ranking capability when using TripAdvisor data. Moreover, by jointly using these two datasets, the algorithms further improve their performance. These results encourage the use of consumer feedback as a reliable source of information about places in the development of inclusive recommender systems.

This paper presents our first attempt to integrate user modeling features in social and affective robots. We propose a cloud-based architecture for modeling the user-robot interaction in order to re-use the approach with different kind of social robots., Comment: 2 pages, paper presented at I-RIM 2020, Seconda Conferenza di Robotica e Macchine Intelligenti 2020

The MURaM (Max Planck University of Chicago Radiative MHD) code is a solar atmosphere radiative MHD model that has been broadly applied to solar phenomena ranging from quiet to active sun, including eruptive events such as flares and coronal mass ejections. The treatment of physics is sufficiently realistic to allow for the synthesis of emission from visible light to extreme UV and X-rays, which is critical for a detailed comparison with available and future multi-wavelength observations. This component relies critically on the radiation transport solver (RTS) of MURaM; the most computationally intensive component of the code. The benefits of accelerating RTS are multiple fold: A faster RTS allows for the regular use of the more expensive multi-band radiation transport needed for comparison with observations, and this will pave the way for the acceleration of ongoing improvements in RTS that are critical for simulations of the solar chromosphere. We present challenges and strategies to accelerate a multi-physics, multi-band MURaM using a directive-based programming model, OpenACC in order to maintain a single source code across CPUs and GPUs. Results for a $288^3$ test problem show that MURaM with the optimized RTS routine achieves 1.73x speedup using a single NVIDIA V100 GPU over a fully subscribed 40-core Intel Skylake CPU node and with respect to the number of simulation points (in millions) per second, a single NVIDIA V100 GPU is equivalent to 69 Skylake cores. We also measure parallel performance on up to 96 GPUs and present weak and strong scaling results.

The suggestion of Points of Interest to people with Autism Spectrum Disorder (ASD) challenges recommender systems research because these users' perception of places is influenced by idiosyncratic sensory aversions which can mine their experience by causing stress and anxiety. Therefore, managing individual preferences is not enough to provide these people with suitable recommendations. In order to address this issue, we propose a Top-N recommendation model that combines the user's idiosyncratic aversions with her/his preferences in a personalized way to suggest the most compatible and likable Points of Interest for her/him. We are interested in finding a user-specific balance of compatibility and interest within a recommendation model that integrates heterogeneous evaluation criteria to appropriately take these aspects into account. We tested our model on both ASD and "neurotypical" people. The evaluation results show that, on both groups, our model outperforms in accuracy and ranking capability the recommender systems based on item compatibility, on user preferences, or which integrate these two aspects by means of a uniform evaluation model.

The Hirsch's $h$-index is perhaps the most popular citation-based measure of the scientific excellence. In 2013 G. Ionescu and B. Chopard proposed an agent-based model for this index to describe a publications and citations generation process in an abstract scientific community. With such an approach one can simulate a single scientist's activity, and by extension investigate the whole community of researchers. Even though this approach predicts quite well the $h$-index from bibliometric data, only a solution based on simulations was given. In this paper, we complete their results with exact, analytic formulas. What is more, due to our exact solution we are able to simplify the Ionescu-Chopard model which allows us to obtain a compact formula for $h$-index. Moreover, a simulation study designed to compare both, approximated and exact, solutions is included. The last part of this paper presents evaluation of the obtained results on a real-word data set.

This paper focuses on the recommendation of events in the Social Web, and addresses the problem of finding if, and to which extent, certain features, which are peculiar to events, are relevant in predicting the users' interests and should thereby be taken into account in recommendation. We consider in particular three "additional" features that are usually shown to users within social networking environments: reachability from the user location, the reputation of the event in the community, and the participation of the user's friends. Our study is aimed at evaluating whether adding this information to the description of the event type and topic, and including in the user profile the information on the relevance of these factors, can improve our capability to predict the user's interest. We approached the problem by carrying out two surveys with users, who were asked to express %with a score their interest in a number of events. We then trained, by means of linear regression, a scoring function defined as a linear combination of the different factors, whose goal was to predict the user scores. We repeated this experiment under different hypotheses on the additional factors, in order to assess their relevance by comparing the predictive capabilities of the resulting functions. The compared results of our experiments show that additional factors, if properly weighted, can improve the prediction accuracy with an error reduction of 4.1%. The best results were obtained by combining content-based factors and additional factors in a proportion of approximately 10:4., Comment: 21 pages. Accepted for publication on Interacting with Computers

Analysis of the deep structure of certain Tagalog sentences reveals buried agents. In Tagalog, verbs are inflected for the case role of the subject Noun Phrase (NP). However, Tagalog contains many sentences which, on the surface, do not appear to adhere to this rule, because they are missing the agent. Among sentences which deviate from the rule are those with locative subjects ("Linindol any Manila," Manila was earthquaked) and those with attack predicates, ("Simipon any doktor," The doctor was attacked by cold). The absence of the agent in the surface structure is due to its re-lexicalization in the verb base. This supports Postals' notion that in Philippine linguistics there are two subjects, the initial (underlying) and the cyclic. (PMJ)