Reviewerly/exHarmony huggingface dataset:
Implementing AI Vertical use cases ‐ Scenario 1

This report presents the activities and outcomes related to the five use cases (UCs) identified by EFSA within the Specific Contract No 1 implementing the Framework contract No OC/EFSA/AMU/2021/03. UC1 aims to assess the usability and effectiveness of text summarisation tools applied to the context of Public consultations (PCs) with the objective to use AI-based automatic text summarisation (ATS) techniques to generate a summary for each attachment to the comments received for a PC. UC2 aims at automating the keywords identification step within the systematic review (SR) process, to explore the current capabilities of the existing tools to enhance the keywords identification process to facilitate the subsequent retrieval of potentially relevant studies with the help of machine learning (ML) and artificial intelligence (AI). UC3 assesses the capabilities of deduplication within DistillerSR with the objective to evaluate the effectiveness of different user-defined deduplication settings available to DistillerSR users to correctly identify and remove duplicates while highlighting the limitations of the tool. The objective of UC4 is to develop a tailored manual for the use of Distiller SR within EFSA's SR framework to facilitate the relevance screening step, replacing human-based relevance screening of titles and abstract with an AI-assisted process. UC5 was designed to exploit the capabilities of the DistillerSR Artificial Intelligence SYstem (DAISY) in DistillerSR to build and use classifiers for semi-automated AI-assisted characterisation or classification of studies within the SR process. Analogous to UC4, also the UC5 aimed at creating a manual helping the users to benefit from this recently introduced functionality within DistillerSR.
Fault-tolerant scheduling of graph-based loads on fog/cloud environments with multi-level queues and LSTM-based workload prediction

Workflow scheduling in the fog/cloud environment is a complex challenge, particularly with the increasing number of IoT devices. Many workflow applications have time constraints. The duplication approach is one of the methods that can help tasks to meet their deadline. This paper presents an architecture that uses a multi-criteria scheduling algorithm and multi-level queues. The proposed architecture determines a duplication coefficient for each queue, and tasks are placed in these queues using a triple-tuple approach composed of the tasks' priority, time pressure, and workload prediction of required resources. The proposed architecture uses various methods for calculating desired parameters. CPE is used to calculate the priority of tasks. Workload prediction is performed by Long-Short Term Memory (LSTM) neural network which is known for its forecasting capabilities. To evaluate the effectiveness of our proposed method, we conducted a software simulation study comparing it with four recent algorithms. The simulation results confirmed the superiority of our approach in different areas. The throughput is improved by 48.6%, communication cost was reduced by 56%. Also, make-span, waiting time, and the parallelism degree are improved between 15 to 62%.
Prompt-Based Graph Convolution Adversarial Meta-Learning for Few-Shot Text Classification

Deep learning techniques have demonstrated significant advancements in the task of text classification. Regrettably, the majority of these techniques necessitate a substantial corpus of annotated data to achieve optimal performance. Meta-learning has yielded intriguing outcomes in few-shot learning tasks, showcasing its potential in advancing the field. However, the current meta-learning methodologies are susceptible to overfitting due to the mismatch between a small number of samples and the complexity of the model. To mitigate this concern, we propose a Prompt-based Graph Convolutional Adversarial (PGCA) meta-learning framework, aiming to improve the adaptability of complex models in a few-shot scenario. Firstly, leveraging prompt learning, we generate embedding representations that bridge the downstream tasks. Then, we design a meta-knowledge extractor based on a graph convolutional neural network (GCN) to capture inter-class dependencies through instance-level interactions. We also integrate the adversarial network architecture into a meta-learning framework to extend sample diversity through adversarial training and improve the ability of the model to adapt to new tasks. Specifically, we mitigate the impact of extreme samples by introducing external knowledge to construct a list of class prototype extensions. Finally, we conduct a series of experiments on four public datasets to demonstrate the effectiveness of our proposed method.
Integrated Access to Multidisciplinary Data Through Semantically Interoperable Services in a Metadata-Driven Platform for Solid Earth Science

The ability to use data produced by different sources (social networks, governments, weather sensors etc.) is widely recognized as a key to capitalize the value of data. In the scientific field, such usage may incredibly boost the innovation and foster new discoveries. However, one of the main hurdles is currently represented by the difficulties in achieving the required interoperability to provide integrated access to multi-disciplinary data. The current work presents a metadata-driven approach that uses in a combined way metadata, semantics, and services as key components for providing integrated access to heterogeneous data sources. The integration occurs within a central data integration system, which is driven by a rich metadata catalogue and that can present the data provided by the different data sources in a harmonised way to the end user, by means of RESTful APIs. A real application demonstrating metadata-driven semantic and service interoperability for achieving homogeneous access to multi-disciplinary heterogeneous data sources is illustrated in the case of EPOS, a Research Infrastructure for Solid Earth Science. The advantages in terms of ease of maintenance, of flexibility in plugging different standard without perturbating communities’ long-lasting technical practices, and of ability to track provenance are discussed. Future work for providing open-source implementation of a system built following the proposed approach is also envisaged.
Breaking and Protecting the Crystal: Side-Channel Analysis of Dilithium in Hardware

The lattice-based CRYSTALS-Dilithium signature scheme has been selected for standardization by the NIST. As part of the selection process, a large number of implementations for platforms like x86, ARM Cortex-M4, or – on the hardware side – Xilinx Artix-7 have been presented and discussed by experts. While software implementations have been subject to side-channel analysis with several attacks being published, an analysis of Dilithium hardware implementations and their peculiarities has not taken place. With this work, we aim to fill this gap, presenting an analysis of vulnerable operations and practically showing a successful profiled Simple Power Analysis (SPA) and a Correlation Power Analysis (CPA) on a recent hardware implementation by Beckwith et al. Our SPA attack requires 700 000 profiling traces and targets the first Number-Theoretic Transform (NTT) stage. After finishing profiling, we can identify pairs of coefficients with 1 101 traces. The full CPA attack finds secret coefficients with as low as 66 000 traces. In response, we present specific countermeasures and show that they effectively prevent both attacks.
SHAPEness: A SHACL-Driven Metadata Editor

The Shapes Constraint Language (SHACL) has been recently introduced as a W3C recommendation to define constraints for validating RDF graphs. In this paper a novel SHACL-driven multi-view editor is presented: SHAPEness. It empowers users by offering them a rich interface for assessing and improving the quality of metadata represented as RDF graphs. SHAPEness has been developed and tested in the framework of the European Plate Observing System (EPOS). In this context, the SHAPEness features have proven to be a valuable solution to easily create and maintain valid graphs according to the EPOS data model. The SHACL-driven approach underpinning SHAPEness, makes this tool suitable for a broad range of domains, or use cases, which structure their knowledge by means of SHACL constraints.
Securing Federated GANs: Enabling Synthetic Data Generation for Health Registry Consortiums

In this work, we review the architecture design of existing federated General Adversarial Networks (GAN) solutions and highlight the security and trust-related weaknesses in the existing designs. We then describe how these weaknesses make existing designs unsuitable for the requirements needed for a consortium of health registries working towards generating synthetic data sets for research purposes. Moreover, we propose how these weaknesses can be addressed with our novel architecture solution. Our architecture solution combines several building blocks to generate synthetic data in a decentralised setting. Federated GANs, Consortium blockchains, and Shamir Secret Sharing algorithm are the core building blocks of our proposed architecture solution. Finally, we discuss our proposed solution’s advantages, disadvantages and future research directions.
Proof-of-work based new encoding scheme for information hiding purposes

Steganography techniques often assume that the secret message looks randomly or is encrypted. If encryption is required, it leads to a random-looking message, but key exchange may be problematic and jeopardize covert communication. If encryption is not required, then the question arises of whether other cryptographic solutions that are “cheaper” than encryption can provide the same level of randomness. In this paper, we investigate both questions. First, we propose a proof-of-work-inspired approach to securely transfer the key with the encrypted message, avoiding a previous key exchange. Second, we introduce a scheme that uses T-functions to substitute symmetric encryption algorithms. We implement both proposed solutions, measure the entropy of the resulting messages, and apply the Kolmogorov-Smirnoff tests. The results obtained prove that both schemes are feasible.
When Truth Matters - Addressing Pragmatic Categories in Natural Language Inference (NLI) by Large Language Models (LLMs)

In this paper, we focus on the ability of large language models (LLMs) to accommodate different pragmatic sentence types, such as questions, commands, as well as sentence fragments for natural language inference (NLI).On the commonly used notion of logical inference, nothing can be inferred from a question, a command, or an incomprehensible sentence fragment.We find MNLI, arguably the most important NLI dataset, and hence models fine-tuned on this dataset, insensitive to this fact.Using a symbolic semantic parser, we develop and make publicly available, fine-tuning datasets designed specifically to address this issue, with promising results.We also make a first exploration of ChatGPT's concept of entailment.
An Intelligent Support System to Help Teachers Plan Field Trips

Abstract Field trips enrich learning programs with out-of-school activities that can bring gains in students’ academic content knowledge and personal growth. However, they are a source of anxiety for teachers because of the bureaucracy, pedagogy, etc., risks they imply. To address this issue, we propose FieldTripOrganizer, a field trip planner based on the mixed-initiative approach aimed at increasing teachers’ autonomy and motivation in designing educational tours. The key aspects of our application are (i) the simultaneous provision of information filtering and automated scheduling support while the user designs the field trip, and (ii) the visual annotation of places and activities to show whether they can be included in the itinerary without violating its time constraints. Different from current tour planners, these functions enable the user to be in full control of the design process, delegating the system to manage difficult and burdensome tasks such as consistency checks and itinerary optimization. We evaluated FieldTripOrganizer in the use case of organizing a science field trip. In a preliminary user study involving 18 science teachers, our application turned up to be superior to a baseline tour planner in both usability and user experience. Moreover, the teachers declared that it was helpful, motivated them, and reduced their anxiety during the design of the field trips.
Cross-lingual extreme summarization of scholarly documents

Abstract The number of scientific publications nowadays is rapidly increasing, causing information overload for researchers and making it hard for scholars to keep up to date with current trends and lines of work. Recent work has tried to address this problem by developing methods for automated summarization in the scholarly domain, but concentrated so far only on monolingual settings, primarily English. In this paper, we consequently explore how state-of-the-art neural abstract summarization models based on a multilingual encoder–decoder architecture can be used to enable cross-lingual extreme summaries of scholarly texts. To this end, we compile a new abstractive cross-lingual summarization dataset for the scholarly domain in four different languages, which enables us to train and evaluate models that process English papers and generate summaries in German, Italian, Chinese and Japanese. We present our new X-SCITLDR dataset for multilingual summarization and thoroughly benchmark different models based on a state-of-the-art multilingual pre-trained model, including a two-stage pipeline approach that independently summarizes and translates, as well as a direct cross-lingual model. We additionally explore the benefits of intermediate-stage training using English monolingual summarization and machine translation as intermediate tasks and analyze performance in zero- and few-shot scenarios. Finally, we investigate how to make our approach more efficient on the basis of knowledge distillation methods, which make it possible to shrink the size of our models, so as to reduce the computational complexity of the summarization inference.
Design, implementation and evaluation of a network-oriented service with environmental adaptability based on core/periphery structure

Abstract Many new network-oriented services have been developed in recent years, and they are expected to be virtualized in multi-access edge computing (MEC) environments, which are being standardized along with fifth generation (5 G). Because many new network-oriented services have been developed to meet various user requests, and service-oriented development, wherein service functions are divided and combined, is expected to facilitate the development of flexible services at low costs. A core/periphery structure is an information processing system in biological systems consisting of core units, that is densely connected and provide efficient process, and peripheral units that can accommodate a variety of inputs and outputs. In this paper, we introduce a core/periphery structure into the service design since the service based on this structure can adapt to various inputs and outputs with only modifying peripheral functions. We expect that development cost is reduced by designing services based on core/periphery structure, because the entire service is not modified against environmental changes. Besides, we also consider the balance between the penalty and the reduction of development costs since dividing functions and placing them in different devices creates extra communication paths and degrades service responsiveness. We designed and implemented two service scenarios for our shopping service with a remote robot based on a core/periphery structure. Using the implemented services, we show that the design using the core/periphery structure is effective in terms of implementation cost and overhead for information sharing among remote robots. Furthermore, we measured the penalty through experiments on actual devices and showed that it is tolerable.
Bagging or boosting? Empirical evidence from financial statement fraud detection

Abstract Ensemble learning, specifically bagging and boosting, has been widely used in the financial field for detecting financial fraud, but their relative performance still lacks consensus. This study compares the performance of five ensemble learning models based on bagging and boosting, using data from Chinese A‐share listed companies from 2012 to 2022, including the COVID‐19 pandemic period. Results show that bagging outperforms boosting in various evaluation indicators, with profitability and asset quality positively affecting financial fraud. This study reveals the mechanism by which ensemble learning affects financial fraud detection and expands related research in the financial field.
Asymmetrical Influences of Service Robots’ Perceived Performance on Overall Customer Satisfaction: An Empirical Investigation Leveraging Online Reviews

Service scholars seem to have empirically overlooked the impact of service robots in the overall customer evaluation of tourism services. This study addresses this gap by leveraging three-factor theory and electronic Word-Of-Mouth data to assess human-robot interaction’ influence on customer satisfaction. Text analytics are deployed alongside a penalty-reward contrast technique on almost 70,000 online reviews spanning 44 hotels worldwide that incorporated service robots into their operations. Customer satisfaction with hospitality services is significantly increased by positive service robots’ performance, while no significant effect is associated with negative service robots’ performance. The traveler type does not moderate the relationship between service robots’ performance and customer satisfaction. These findings, confirmed through Propensity Score Matching, reveal that service robots constitute an “excitement factor” in hospitality service offerings, thus providing a strong incentive for their integration into the workforce. Policymakers are urged to proactively facilitate the transition to a more automated service economy.
Optimal decision-making in the water, land and food nexus using artificial intelligence and extreme machine learning

Abstract The development of decision-making systems based on artificial intelligence can lead to achieving optimal solutions water-land-food nexus. In this paper, an extreme learning machine model was developed with the objective function of wheat production maximization. The constraints defined for this problem are divided into three categories: technical parameters of production in agriculture, climatic stress on water resources and land limits. The water, land and food nexus was simulated using 23 experimental farms in Henan province during the 2021–2022 cultivation year. Root-mean-square error was used as an error criterion, and Pearson's coefficient was incorporated into the decision-making system as a correlation index of variables. Harvest index, length of the growth period, cultivation costs and irrigation water were the criteria to evaluate the impact of the sustainable model. The harvest index and the length of the growth period showed the highest and lowest correlation with the production rate, respectively. Furthermore, the optimal management of irrigation water and cost had the most significant impact on increasing crop production. The method proposed in this paper can be a virtual cropping model by changing the area under cultivation of a crop in the different farms of a study area, which increases yield production.
A Qualitative Case Study on Deconstructing Presence for Young Adults and Older Adults

Abstract In this paper, we present the results of an exploratory qualitative case study on presence experienced by groups of young adults and older adults during their use of an interactive virtual reality application mixing realistic and fantasy elements. In contrast to most previous studies, we do not focus on a set of predetermined factors but instead adopt an open-ended qualitative approach to identify emerging factors from the users' experiences. We then analyze these factors against the place illusion/plausibility illusion (PI/PSI) framework of Skarbez, Neyret, et al. (2017) to investigate whether PI and PSI, as well as their contributing factors, can be separated. According to our findings, a user can experience PI and PSI independently from each other; however, they often appeared intermixed when investigated on the scope of the whole experience. Breaks in presence, as well as breaks in plausibility, could mostly, but not entirely, be attributed to immersion and coherence factors, respectively. An interesting finding is that both participant groups turned out to have two subgroups interpreting their experience with a particular frame of reference of differing expectations. These frames of reference affected not only PSI, as expected, but PI as well, suggesting that coherence could be a contributing factor to both PI and PSI. Our contribution adds to the relatively small body of research investigating the separation of PI and PSI. Our exploratory findings can be utilized as directions for designing future confirmatory studies.
Systems for Non-Reflexive Consequence

Abstract Substructural logics and their application to logical and semantic paradoxes have been extensively studied. In the paper, we study theories of naïve consequence and truth based on a non-reflexive logic. We start by investigating the semantics and the proof-theory of a system based on schematic rules for object-linguistic consequence. We then develop a fully compositional theory of truth and consequence in our non-reflexive framework.
Social prioritisation in scene viewing and the effects of a spatial memory load

When free-viewing scenes, participants tend to preferentially fixate social elements (e.g., people). In the present study, we tested whether this bias would be disrupted by increasing the demands of a secondary dual-task: holding a set of (one or six) spatial locations in memory, presented either simultaneously or sequentially. Following a retention interval, participants judged whether a test location was present in the to-be-remembered stimuli. During the retention interval participants free-viewed scenes containing a social element (a person) and a non-social element (an object) that served as regions of interest. In order to assess the impact of physical salience, the non-social element was presented in both an unaltered baseline version, and in a version where its salience was artificially increased. The results showed that the preference to look at social elements decreased when the demands of the spatial memory task were increased from one to six locations, regardless of presentation mode (simultaneous or sequential). The high-load condition also resulted in more central fixations and reduced exploration of the scene. The results indicate that the social prioritisation effect, and scene viewing more generally, can be affected by a concurrent memory load.