Reviewerly/exHarmony huggingface dataset:
Relieving Popularity Bias in Interactive Recommendation: A Diversity-Novelty-Aware Reinforcement Learning Approach

While personalization increases the utility of item recommendation, it also suffers from the issue of popularity bias. However, previous methods emphasize adopting supervised learning models to relieve popularity bias in the static recommendation, ignoring the dynamic transfer of user preference and amplification effects of the feedback loop in the recommender system (RS). In this paper, we focus on studying this issue in the interactive recommendation. We argue that diversification and novelty are both equally crucial for improving user satisfaction of IRS in the aforementioned setting. To achieve this goal, we propose a D iversity- N ovelty- a ware I nteractive R ecommendation framework (DNaIR) that augments offline reinforcement learning (RL) to increase the exposure rate of long-tail items with high quality. Its main idea is first to aggregate the item similarity, popularity, and quality into the reward model to help the planning of RL policy. It then designs a diversity-aware stochastic action generator to achieve an efficient and lightweight DNaIR algorithm. Extensive experiments are conducted on the three real-world datasets and an authentic RL environment (Virtual-Taobao). The experiments show that our model can better and full use of the long-tail items to improve recommendation satisfaction, especially those low popularity items with high-quality ones, thus achieving state-of-the-art performance.
Augmented Reality and Avatars for Museum Heritage Storytelling

This research has been carried out in the framework of the research agreement to enhance the cultural assets of the Museo d’Arte Orientale (MAO) in Turin through digitization, 3D modelling and experiences in virtual and augmented reality. The aim of the present work is to communicate the evolutionary process in Buddha iconography in the Mathura area through three statues from the permanent collection of the MAO. This aim is connected with the general goal to increase inclusivity and remove spatiotemporal barriers in museums and their collections by meeting the diverse needs of visitors related to age, physical, sensory, cognitive, and cultural factors. The pipeline passes through the digital survey and 3D modelling of the artworks, the philological reconstruction modelling of the lacunae, the creation of an avatar guide, and the elaboration of the tour path in augmented reality (AR). Digital storytelling connected to the avatar enriches the experience by providing information about the context, history, and characters related to the works. One of the values that characterize the entire process is the predominant use of free and open-source software (FOSS) up to the prototype stage.
Cultural Sprawl: The Opportunities of AR for Museum Communication

Digital Culture is progressively spreading to all fields of knowledge. In the field of archaeology, and specifically in the case of museums, it offers a wide range of possible tools and methods that may open new research opportunities. Within the framework of the collaboration between the Fondazione Museo delle Antichità Egizie di Torino, the Department of Control and Computer Engineering, the Department of Architecture and Design, and the VR@POLITO Laboratories, the essay presents the pipeline and the outcomes of a research project aimed at disseminating information associated with archaeological objects. The idea that guided the research is aimed at creating new connections between the museum and the city, contributing to promote cultural tourism by bringing the museum outside the museum. According to the analysis of the state of the art on the opportunities of augmented reality (AR) in cultural institutions and museums, a mobile application has been developed. The application tests an educational and entertaining experience, deploying AR technology to communicate the tangible and intangible aspects associated with some of the objects of the Museo Egizio collection. Through this empirical application, the contribution explores the opportunities of AR technologies to implement the knowledge and dissemination of tangible and intangible aspects of the archaeological objects preserved in the museum and to discover the city’s architectural and cultural heritage.
A Blockchain-Based Solution to Chain (Im)Material Art

This study starts from some preliminary considerations relating to the recent, and tumultuous, changes to the concept of authenticity and ownership of the work of art. Art in the era of its technical reproducibility has seen the very meaning of a work of art change starting from its attribution to the definition of uniqueness and the determination of value. In contemporary works of art, the theme of reproducibility changes radically. Many scholars have analyzed the phenomenon by referring to the reflections of Benjamin and Mc Luan, emphasizing the power of the medium. Immateriality, and the simultaneous need for visibility, have generated the need for new IT tools in digital art for the definition of possession and uniqueness. The procedure that identifies the creation of a Non-Fungible Token (NFT) in fact replaces the art expert and the documents that accompany a work and certify its history, possession, and even its value in some cases. Our research aims to investigate the potential of digital art by a double approach. On the one hand, the approach tries to reconstruct the virtual galaxy to which these digital works belong, and on the other hand, it experiments the techniques for the ad-hoc definition of NFTs without relying on the standard and widely-used platforms. By experimentation, we analyze the strengths and weaknesses derived from the use of blockchain, and we quantify the times and costs for the creation of a customized solution for the creation of NFT through a low-level implementation. The ultimate goal of our research is to offer a synthetic reference framework that highlights the specificities of digital art and the new dissemination and qualification tools.
Castello di Mirafiori: Reconstructive Modelling and WebAR

The project is born from a research agreement between the Department of Architecture and Design of the Politecnico di Torino and the Municipality of Turin to realise an augmented reality simulation of Castello di Mirafiori and cultural accompaniment for citizens. The building and the gardens are documented by archival and bibliographical sources that allow to digitally reconstruct them in the period of greatest splendour and hypothesise their location. The project included the realisation of an installation incorporating augmented reality features to make the results of documentary research and digital reconstruction accessible to a broad audience. The software was developed that interacts with an information panel installed at the castle ruins. The result is a web app made with the free and open-source software AR.js, which allows users to access AR directly from a web browser without downloading applications. It uses as the experience repository a web page of MuseoTorino, the virtual museum of the city. Moreover, the creation of physical reproduction to scale through digital fabrication processes, as a function of inclusive fruition, with which to associate the AR experience, foreshadows future research developments.
Blockchain‐based <scp>SLA</scp> management for <scp>6G</scp> networks

Abstract In this letter, we propose the use of a blockchain‐based solution for inter‐provider agreements in 6G networks, utilizing smart contracts and Chainlink for monitoring and assessing Service Level Agreements (SLAs), including penalty calculations. We evaluate the proposed solution by deploying it on two public blockchains, Ethereum and Polygon, and find that Polygon provides a cost‐effective option for creating inter‐provider agreements. This is particularly important for use cases involving numerous transactions. Additionally, Chainlink enables secure and transparent access to off‐chain SLA data, and we observe lower transaction latency in Polygon. This suggests that utilizing blockchain‐based solutions such as smart contracts can aid the process of creating inter‐provider agreements for leasing and selling resources in 6G networks, with Chainlink oracle providing support for their SLA management. However, factors such as transaction cost and latency need to be considered.
AR for Virtual Restoration

Among the possibilities offered by Augmented Reality is its potential use to give new life to works of art in museums, through the implementation of integration or completion in an operation that can be called virtual restoration. Without modifying the works of art in their current state of preservation in order to maintain their evocative power and, in particular, without invasive interventions on the signs left by time, through digital visualisation tools it is possible to provide users with a new way of re-reading objects and telling stories around them. The contribution aims to present the results of virtual restoration operations carried out on some statuary works in the collection of the Museum of the Archaeological Park of Ostia Antica in Rome, where innovative technologies are compared with the immense Heritage of the Museum, animating the exhibition itinerary by offering new points of view of the works through the creation of an interactive AR application.
A Multi-Stage Adaptive Copy-Paste Data Augmentation Algorithm Based on Model Training Preferences

Datasets play an important role in the field of object detection. However, the production of the dataset is influenced by objective environment and human subjectivity, resulting in class imbalanced or even long-tailed distribution in the datasets. At present, the optimization methods based on data augmentation still rely on subjective parameter adjustments, which is tedious. In this paper, we propose a multi-stage adaptive Copy-Paste augmentation (MSACP) algorithm. This algorithm divides model training into multiple training stages, each stage forming unique training preferences for that stage. Based on these training preferences, the class information of the training set is adaptively adjusted, which not only alleviates the problem of class imbalance in training, but also expands different sample sizes for categories with insufficient information at different training stages. Experimental verification of the traffic sign dataset Tsinghua–Tencent 100K (TT100K) was carried out and showed that the proposed method not only can improve the class imbalance in the dataset, but can also improve the detection performance of models. By using MSACP to transplant the trained optimal weights to an embedded platform, and combining YOLOv3-tiny, the model’s accuracy in detecting traffic signs in autonomous driving scenarios was improved, verifying the effectiveness of the MSACP algorithm in practical applications.
Asymmetric bidirectional quantum 1⇔2 qubit teleportation scheme via six-qubit Bell-cluster state

Abstract Using a six-qubit Bell-cluster state, we proposed an asymmetric bidirectional quantum teleportation scheme. Two participants Anne and Benson are both the sender and receiver. Only applying Bell-state measurement, single- and two-qubit unitary operations as well as classical communication, Anne can transmit an arbitrary two-qubit unknown state to Benson, meanwhile Benson can also send an arbitrary single-qubit unknown state to Anne. Analysis shows that our scheme is feasible with the present experiment technologies.
Neural nets for sustainability conversations: modeling discussion disciplines and their impacts

Abstract We live in the age polarization, where conversations on matters of sustainability more often produce acrimony or stalemate than productive action. Better understanding conversation features and their impacts may lead to better innovation, solution-design, and ongoing collaboration. We describe a study to test alternate machine learning models for classifying six “discussion disciplines”, which are conversation features associated with rhetorical intent. The model providing the best outcome used the Bi-directional Encoder Representations from Transformers (BERT) layered with a Residual Network (ResNet). The training data were 1135 utterances from Maine aquaculture town hall-like meetings and similar conversations, which had been hand-coded for the discussion disciplines. In addition, we generated 300 phrases corresponding to three conversation outcomes: Intent-to-Act, Options-Generation, and Relationship-Building. We then used the trained model and information retrieval to classify a large corpus of 591 open-source transcripts, containing over 21,000 utterances. A binary logistic regression analysis showed that two discussion disciplines, “ Inclusion ” and “ Courtesy ,” had positive, statistically significant, impacts on Intent-to-act: a 10 percentage point increase in the share of the Inclusion or Courtesy yielded a 45% or 34% increase, respectively, in the likelihood of Intent-to-Act . This study shows the applicability of neural networks in modeling conversations and identifying the dialog acts that can provide measurable and predictable impact on conversation outcomes. Conversational intelligence can support a variety of human interactions, such as town halls, policy-deliberations, private–public partnerships, and sustainability teamwork.
Experimentation of a Web Database for Augmented Reality Apps: The Case Study of Ruled Geometries

The growing field of Augmented Reality Apps is an indication of greater interest, by private markets and public institutions, towards immersive visualization and those tools that can enhance cultural content through user involvement. An often underrated feature of this digitalization lies in the smart language with which the notions are shown, in quick synthesis based on schemes, slides, lists, images, and comparisons. But the continuous input of data in an app can cause various problems, both on the hardware and software front: from the obvious weight gain, not negligible for pocket devices, to the continuous updating of scientific knowledge, that risks leaving behind all those theoretical contents that are not controlled or renewed. To this is added the problem of the constant maintenance of mobile devices for what concerns software, operating systems, security protocols, and systems for decoding existing data, especially for AR Apps that use audio and video devices. With this in mind, and in order to optimize the performance of mobile tools, a digital archive in the form of an open-source site is useful, providing centralized and pre-cataloged data specifically for use in the AR App. In this way, qualified users can contribute to the knowledge presented through a client–server upload system on the site, properly filtered by semi-automatic security checks. The experimentation of the contribution investigates the advantages and possibilities of such a structured system, setting the case study on the theory of ruled geometries, and the architectures corresponding to them.
Educative performance transitions in engineering students through the COVID-19 pandemic

Higher education is a multivariable system by nature; thus, it is a complex task to maintain consistent academic success for students. This is a key factor to understand the positive and negative effects generated by the COVID-19 lockdown, particularly during the current stage of the “New Normal” period. The research presented herein considers a set of variables corresponding to students and faculty as causal factors to track, analyze, and assess the impact on the academic performance of engineering students in an urban Mexican university in both periods: online teaching during lockdown, and returning to face-to-face learning during the “New Normal.” Through a hybrid survey, looking for representative learning styles, academic personality traits, and technology competencies, academic performance in both periods has been recorded along with each student's learning preference. The suggested analysis model sought correlations in the stated causal factors to find valuable behavioral patterns. The outcomes show that good students in both models have attained a high level of adaptation and feel competitive in them. On the contrary, students with lower adaptation have shown poor academic performance in both models, but they perceived the online model as the less effective learning environment. Particularly, personality traits appoint on a notable impact on performance. In addition, learning styles are not significant. Still, it has been suggested this situation could be due to a greater diversity of teaching approaches established by the faculty to take care of student performance.
Hierarchical Edge-Preserving Dense Matching by Exploiting Reliably Matched Line Segments

Image dense matching plays a crucial role in the reconstruction of three-dimensional models of buildings. However, large variations in target heights and serious occlusion lead to obvious mismatches in areas with discontinuous depths, such as building edges. To solve this problem, the present study mines the geometric and semantic information of line segments to produce a constraint for the dense matching process. First, a disparity consistency-based line segment matching method is proposed. This method correctly matches line segments on building structures in discontinuous areas based on the assumption that, within the corresponding local areas formed by two corresponding line pairs, the disparity obtained by the coarse-level matching of the hierarchical dense matching is similar to that derived from the local homography estimated from the corresponding line pairs. Second, an adaptive guide parameter is designed to constrain the cost propagation between pixels in the neighborhood of line segments. This improves the rationality of cost aggregation paths in discontinuous areas, thereby enhancing the matching accuracy near building edges. Experimental results using satellite and aerial images show that the proposed method efficiently obtains reliable line segment matches at building edges with a matching precision exceeding 97%. Under the constraint of the matched line segments, the proposed dense matching method generates building edges that are visually clearer, and achieves higher accuracy around edges, than without the line segment constraint.
Locksynth: Deriving Synchronization Code for Concurrent Data Structures with ASP

Abstract We present Locksynth , a tool that automatically derives synchronization needed for destructive updates to concurrent data structures that involve a constant number of shared heap memory write operations. Locksynth serves as the implementation of our prior work on deriving abstract synchronization code. Designing concurrent data structures involves inferring correct synchronization code starting with a prior understanding of the sequential data structure’s operations. Further, an understanding of shared memory model and the synchronization primitives is also required. The reasoning involved transforming a sequential data structure into its concurrent version can be performed using Answer Set Programming, and we mechanized our approach in previous work. The reasoning involves deduction and abduction that can be succinctly modeled in ASP. We assume that the abstract sequential code of the data structure’s operations is provided, alongside axioms that describe concurrent behavior. This information is used to automatically derive concurrent code for that data structure, such as dictionary operations for linked lists and binary search trees that involve a constant number of destructive update operations. We also are able to infer the correct set of locks (but not code synthesis) for external height-balanced binary search trees that involve left/right tree rotations. Locksynth performs the analyses required to infer correct sets of locks and as a final step, also derives the C++ synchronization code for the synthesized data structures. We also provide a performance analysis of the C++ code synthesized by Locksynth with the hand-crafted versions available from the Synchrobench microbenchmark suite. To the best of our knowledge, our tool is the first to employ ASP as a backend reasoner to perform concurrent data structure synthesis.
Decentralized Communication-assisted Sensing based on Federated Learning Framework for IIoT

In Industry 4.0, with the increasing scale of data generated in IIoT, it is necessary for federated learning (FL) algorithms to process and analyze these data in real time, thereby quickly generating high-quality models for edge computing/intelligence. But there are still challenges on current FL frameworks in IIoT, such as difficult client management, prolonged communication delays, and compromised learning effectiveness induced by attacks. To address these challenges, we propose a new FL framework that integrates a digital identity module for user perception and authentication, a decentralized blockchain module for trustworthy FL, and an adaptive model sparsification algorithm for communication-assisted sensing FL. Our FL framework aims to conduct some sense tasks on image classification and sentiment analysis. The effectiveness of our proposed framework for IIoT is demonstrated through technical explanations and experimental results.
FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment

In the quest to optimize user experience, network, and service, providers continually seek to deliver high-quality content tailored to individual preferences. However, predicting user perception of quality remains a challenging task, given the subjective nature of human perception and the plethora of technical attributes that contribute to the overall viewing experience. Thus, we introduce a Fuzzy Logic-bAsed ModEl for Video Quality Assessment (FLAME-VQA), leveraging the LIVE-YT-HFR database containing 480 video sequences and subjective ratings of their quality from 85 test subjects. The proposed model addresses the challenges of assessing user perception by capturing the intricacies of individual preferences and video attributes using fuzzy logic. It operates with four input parameters: video frame rate, compression rate, and spatio-temporal information. The Spearman Rank–Order Correlation Coefficient (SROCC) and Pearson Correlation Coefficient (PCC) show a high correlation between the output and the ground truth. For the training, test, and complete dataset, SROCC equals 0.8977, 0.8455, and 0.8961, respectively, while PCC equals 0.9096, 0.8632, and 0.9086, respectively. The model outperforms comparative models tested on the same dataset.
Trends of Artificial Intelligence and Blockchain in New Public Management

Artificial Intelligence and Blockchain are technologies that are attracting more and more attention from different actors in various fields, including the New Public Management thanks to its unique features, such as decentralization, reliability, predictability, network distribution, profiling and security. Therefore, a study on the state of the new public management is considered necessary. The aim of the present study is to provide an overview of the state of the art related to the application of artificial intelligence and blockchain in the new public management to serve as a reference for future research in this field. For this purpose, a systematic literature review was conducted through the PRISMA protocol combined with the Tree of Science (ToS). In total, 2100 articles published between 2016 and 2023 in the Web of Science, Google Scholar, IEEE Xplore and Scopus databases participated in the study, where the Tree of Science (ToS) algorithm was subsequently applied to identify the most relevant literature on the topic. As a result, 81 papers were analyzed. The results indicate as research trends on the topic: supply chain, technology, digital business and technology adoption.