Miracle-dz/UniFAR-FTUs huggingface dataset:
Reactive Software Architectures in IoT: A Literature Review

Reactive architecture is a rapidly growing new trend in the context of the Internet of Things. While design principles around reactive architecture have been identified, i.e., through the use of the reactive manifesto, it is difficult to understand existing research solutions for the reactive system clearly. In this article, we apply the systematic mapping study methodology to identify, classify and evaluate the current state of the art in reactive architecture from the following four perspectives: publishing trends, challenges, architectural diagrams/views, and used quality attributes. Furthermore, we systematically defined a classification framework to categorize research on reactive architecture and applied it rigorously to the 17 selected studies. We synthesized the data obtained and produced a clear view of state of the art. This provides a solid foundation for planning future research and reactive architecture applications.
Multi Temporal Analysis of Floods and Tsunami Effects: Annotation and Quantitative Analysis

This paper addresses the problem of multitemporal analysis of an available TerraSAR-X data time series covering the Sendai region in order to assess flood extent and damages caused by Tohuku-oki tsunami. Over the last decade the use of Earth Observation satellites to support disaster and emergency relief has considerably grown. In order to fully exploit highresolution satellite images, a method based on patches (each image is divided into non-overlapping tiles) is proposed to extract relevant contextual information. The local features of each patch act as a compact content descriptor. Further on, considering the available descriptors, the next step is to cluster the data in order to find similar semantic classes. The SVM classifier implements the concept of query by example using image content. The results include well-defined semantic classes, derived through semiautomatic methods thus developing an effective approach to the multitemporal analysis.
Dual Evolution of Planar Parametric Spline Curves and T – spline Level Sets

By simultaneously considering evolution processes for parametric spline curves and implicitly defined curves, we formulate the framework of dual evolution. This allows us to combine the advantages of both representations. On the one hand, the implicit representation is used to guide the topology of the parametric curve and to formulate additional constraints, such as range constraints. On the other hand, the parametric representation helps to detect and to eliminate unwanted branches of the implicitly defined curves. Moreover, it is required for many applications, e.g., in Computer Aided Design.
Multimodal Face-Pose Estimation With Multitask Manifold Deep Learning

Face-pose estimation aims at estimating the gazing direction with two-dimensional face images. It gives important communicative information and visual saliency. However, it is challenging because of lights, background, face orientations, and appearance visibility. Therefore, a descriptive representation of face images and mapping it to poses are critical. In this paper, we use multimodal data and propose a novel face-pose estimation framework named multitask manifold deep learning (<inline-formula><tex-math notation="LaTeX">$\text{M}^2\text{DL}$</tex-math></inline-formula>). It is based on feature extraction with improved convolutional neural networks (CNNs) and multimodal mapping relationship with multitask learning. In the proposed CNNs, manifold regularized convolutional layers learn the relationship between outputs of neurons in a low-rank space. Besides, in the proposed mapping relationship learning method, different modals of face representations are naturally combined by applying multitask learning with incoherent sparse and low-rank learning with a least-squares loss. Experimental results on three challenging benchmark datasets demonstrate the performance of <inline-formula><tex-math notation="LaTeX">$\text{M}^2\text{DL}$</tex-math></inline-formula>.
Self-determination through explanation: an ethical perspective on the implementation of the transparency requirements for recommender systems set by the Digital Services Act of the European Union

In the contemporary information age, recommender systems (RSs) play a critical role in influencing online behaviour: from social media to e-commerce, from music streaming to news aggregators, individuals are constantly targeted by personalized recommendations suggesting contents that may interest them. Despite such diffusion, the extent to which recommendations influence users’ decisions is still underexplored, given that independent audits on the structure and functioning of RSs deployed on online platforms are usually prevented by proprietary constraints. The nudging potential of RSs can represent a risk for vulnerable people: indeed, judicial cases involving platforms’ responsibility for displaying recommendations that may lead to political radicalization or endangerment of minors have recently caught public attention. The Digital Services Act of the European Union (DSA) is the first supranational regulation that sets specific transparency and auditing requirements for RSs implemented by online platforms with the aim of enhancing users’ self-determination: in particular, it allows users to modify the parameters on which recommendations rely so to let them choose autonomously which kind of content they want to see. This research focuses on whether and how the enforcement of this regulation can mitigate the unfair consequences of the power imbalance between online platforms and users. To this aim, I discuss the harms arising from digital nudging based on RSs and propose explanations as a tool that can reduce the impact of those harms by increasing users’ awareness. Through a comparative analysis of relevant articles of the DSA, the General Data Protection Regulation (GDPR) and the AI Act, I outline how the provisions of the DSA fill some of the gaps left by other relevant European regulations, while leaving the so-called right to explanation substantially unaddressed. As a result of this analysis, I argue that, in order for the implementation of the DSA provisions on recommender systems to be effective, policy-makers should: 1) enhance users’ awareness through clear and easily accessible explanations on how the recommendation process works and how they can be influenced by it; 2) grant users the possibility of intervening directly on the strategies through which RSs target them on the platform’s interface.
Supporting Video Authoring for Communication of Research Results

Video summaries of scientific publications have gained more and more popularity over the last years, requiring many researchers to familiarize themselves with the tools and techniques of video production which can be an overwhelming task. This paper introduces a video structuring framework embedded into the authoring tool Pub2Vid. The tool supports users with the creation of their video outline and script, providing real video examples and recommendations based on the analysis of 40 publication summarization videos which were rated in a user study with 68 participants. Following a four-tier evaluation methodology, the application’s usability is assessed and improved via amateur and expert interviews, two rounds of usability tests and two case studies. It is shown that the tool and its recommendations are particularly useful for beginners due to the simple design and intuitive components as well as suggestions based on real video examples.
Designing an Assistance Tool for Analyzing and Modeling Trainer Activity in Professional Training Through Simulation

Human audience analysis is crucial in numerous domains to understand people’s behavior based on their knowledge and environment. In this paper we focus on simulation-based training which has become a popular teaching approach that requires a trainer able to manage a lot of data at the same time. We present tools that are currently being developed to help trainers from two different fields: training for practical teaching gestures and training in civil defense. In this sense, three technological blocks are built to collect and analyze data about trainers’ and learners’ gestures, gaze, speech and movements. The paper also discusses the future work planned for this project, including the integration of the framework into the Noldus system and its use in civil security training. Overall, the article highlights the potential of technology to improve simulation-based training and provides a roadmap for future development.
Spatial Dynamic Models for Fishery Management and Waterborne Disease Control

As the human population continues to grow, there is a need for better management of our natural resources in order for our planet to be able to produce enough to sustain us. One important resource we must consider is marine fish populations. We use the tool of optimal control to investigate harvesting strategies for maximizing yield of a fish population in a heterogeneous, finite domain. We determine whether these solutions include no-take marine reserves as part of the optimal solution. The fishery stock is modeled using a nonlinear, parabolic partial differential equation with logistic growth, movement by diffusion and advection, and with Robin boundary conditions. The objective for the problem is to find the harvest rate that maximizes the discounted yield. Optimal harvesting strategies are found numerically. Infectious diseases are another area of concern for the human population. Recently, questions have been raised as to the importance of spatial features on disease spread and how movement patterns affect management strategies. The role of spatial arrangements in a metapopulation on the spread and management strategies of a cholera epidemic is investigated. We consider how the movement of individuals and water affects the optimal vaccination strategy. For each metapopulation, the model has an Susceptible-InfectedRecovered (SIR) system of differential equations coupled with an equation modeling the concentration of Vibrio cholerae in an aquatic reservoir. The model is used to compare spatial arrangements and varying scenarios to draw conclusions on how to effectively manage outbreaks. The work is motivated by the recent cholera outbreak in Haiti.
A ResNet-BiLSTM Multi-lead ECG Classification Method Embedded with Attention Mechanism

Computer-assisted electrocardiogram analysis has important clinical significance for the prevention and treatment of cardiovascular diseases. A multi-lead electrocardiogram (ECG) classification method based on the residual network and bidirectional long short-term memory neural network was proposed. In order to extract more effective ECG features, the Squeeze-and-Excitation (SE) attention mechanism was embedded into the depth model. Finally, the effectiveness of the proposed method was verified on the Chinese cardiovascular disease database (CCDD) and the internationally recognized MIT-BIH-AR database. The accuracy, sensitivity and specificity of normal and abnormal heartbeats classification on the MIT-BIH-AR database that contains 48 recordings were 99.52%, 99.46% and 99.54%, respectively. The accuracy, sensitivity and specificity of the classification of normal and abnormal ECG recordings on the CCDD that contains more than 150000 recordings were 84.44%, 79.27% and 88.45%, respectively. The overall experimental results show that the classification performance of the proposed method is good on both small-scale and large-scale data sets.
Microscopic and metatranscriptomic analyses revealed unique cross-domain symbiosis between Candidatus Patescibacteria/candidate phyla radiation (CPR) and methanogenic archaea in anaerobic ecosystems

To verify the parasitic lifestyle of Candidatus Patescibacteria in the enrichment cultures derived from a methanogenic bioreactor, we applied multifaceted approaches combining cultivation, microscopy, metatranscriptomic, and protein structure prediction analyses. Cultivation experiments with the addition of exogenous methanogenic archaea with acetate, amino acids, and nucleoside monophosphates and 16S rRNA gene sequencing confirmed the increase in the relative abundance of Ca. Patescibacteria and methanogens. The predominant Ca. Patescibacteria were Ca. Yanofskybacteria and 32-520 lineages (to which belongs to class Ca. Paceibacteria) and positive linear relationships (r2 ≥ 0.70) between the relative abundance of Ca. Yanofskybacteria and Methanothrix, suggesting that the tendency of the growth rate is similar to that of the host. By fluorescence in situ hybridization (FISH) observations, the FISH signals of Methanothrix and Methanospirillum cells with Ca. Yanofskybacteria and with 32-520 lineages, respectively, were significantly lower than those of the methanogens without Ca. Patescibacteria, suggesting their parasitic interaction. The TEM and SEM observations also support parasitism in that the cell walls and plugs of these methanogens associated with submicron cells were often deformed. In particular, some Methanothrix-like filamentous cells were dented where the submicron cells were attached. Metatranscriptomic and protein structure prediction analyses identified highly expressed secreted genes from the genomes of Ca. Yanofskybacteria and 32-520, and these genes contain adhesion-related domains to the host cells. Considering the results through the combination of microscopic observations, gene expression, and computational protein modeling, we propose that the interactions between Ca. Yanofskybacteria and 32-520 belonging to class Ca. Paceibacteria and methanogenic archaea are parasitism.
Perceived Algorithmic Fairness using Organizational Justice Theory: An Empirical Case Study on Algorithmic Hiring

Growing concerns about the fairness of algorithmic decision-making systems have prompted a proliferation of mathematical formulations aimed at remedying algorithmic bias. Yet, integrating mathematical fairness alone into algorithms is insufficient to ensure their acceptance, trust, and support by humans. It is also essential to understand what humans perceive as fair. In this study, we, therefore, conduct an empirical user study into crowdworkers’ algorithmic fairness perceptions, focusing on algorithmic hiring. We build on perspectives from organizational justice theory, which categorizes fairness into distributive, procedural, and interactional components. By doing so, we find that algorithmic fairness perceptions are higher when crowdworkers are provided not only with information about the algorithmic outcome but also about the decision-making process. Remarkably, we observe this effect even when the decision-making process can be considered unfair, when gender, a sensitive attribute, is used as a main feature. By showing realistic trade-offs between fairness criteria, we moreover find a preference for equalizing false negatives over equalizing selection rates amongst groups. Our findings highlight the importance of considering all components of algorithmic fairness, rather than solely treating it as an outcome distribution problem. Importantly, our study contributes to the literature on the connection between mathematical– and perceived algorithmic fairness, and highlights the potential benefits of leveraging organizational justice theory to enhance the evaluation of perceived algorithmic fairness.
A General Framework to Interpret Hydrogen-Deuterium Exchange Native Mass Spectrometry of G-Quadruplex DNA

G-quadruplexes (G4s) are secondary structures formed by guanine-rich oligonucleotides involved in various biological processes. However, characterizing G4s is challenging because of their structural polymorphism. Here, we establish how hydrogen-deuterium exchange native mass spectrometry (HDX/MS) can help to characterize G4 structures and dynamics in solution. We correlated the time range of G4 exchange to the number of guanines involved in inner and outer tetrads. We also established relationships between exchange rates, number of tetrads, bound cations, and stability. The use of HDX/native MS allows for the determination of tetrads formed and assessment of G4 stability at a constant temperature. A key finding is that stable G4s exchange through local fluctuations (EX2 exchange), whereas less stable G4s also undergo exchange through partial or complete unfolding (EX1 exchange). Deconvolution of the bimodal isotope distributions resulting from EX1 exchange provides valuable insight into the kinetics of folding and unfolding processes, and allows one to detect and characterize transiently unfolded intermediates, even if scarcely populated. HDX/native MS thus represents a powerful tool for a more comprehensive exploration of the folding landscapes of G4s. TOC graphic
The 2006 International Arab Conference on Information Technology (ACIT'2006)

A single Digital Library (DL) contains enormous research knowledge which can be used for several application domains e.g. e-learning, e-research etc. Due to enormously increasing digital contents and number of facilitating services, DLs face challenges of seeking wide-scale deployment solutions in Technological Spaces (TS). Such solutions necessitate: (i) understanding the process models of DLs at both macro and micro levels, and (ii) identifying the suitable candidates from TS for their successful deployment. This paper is an effort to cover the first part of the above challenges and captures the socio-technical aspects in DL processes by modelling its processes both in Riva and Role Activity Diagrams (RAD). A first-cut Riva based architecture of DLs provides a macro view of inter-communicating and evolving complex processes. This has been further elaborated to develop a micro view by using RADs applied to the Scientific Publishing Process, as an example. This macro-micro process modelling combination helps to understand, identify and reduce the technical implications that may arise at later stages of DL system development, deployment and evolution. Finally, this paper is a step forward towards identifying DL processes and the role of Riva and RADs towards their enactment.
Short proofs of the coloring principle

We prove that propositional translations of the Kneser-Lov´asz theorem have polynomial size extended Frege proofs and quasi-polynomial size Frege proofs for all ﬁxed values of k . We present a new counting-based combinatorial proof of the Kneser-Lov´asz theorem based on the Hilton-Milner theorem; this avoids the topological arguments of prior proofs for all but ﬁnitely many base cases. We introduce new “truncated Tucker lemma” principles, which are miniaturizations of the octahedral Tucker lemma. The truncated Tucker lemma implies the =
Short Proofs of the Kneser-Lovász Coloring Principle 1

We prove that propositional translations of the Kneser-Lovász theorem have polynomial size extended Frege proofs and quasi-polynomial size Frege proofs for all fixed values of k. We present a new counting-based combinatorial proof of the Kneser-Lovász theorem based on the Hilton-Milner theorem; this avoids the topological arguments of prior proofs for all but finitely many base cases. We introduce new “truncated Tucker lemma” principles, which are miniaturizations of the octahedral Tucker lemma. The truncated Tucker lemma implies the Email addresses: jaisenberg@math.ucsd.edu (James Aisenberg), bonet@cs.upc. edu (Maria Luisa Bonet), sbuss@ucsd.edu (Sam Buss), acraciun@ieat.ro (Adrian Crăciun), gabrielistrate@acm.org (Gabriel Istrate) This is an expanded version of a paper [3] which appeared in ICALP 2015. Supported in part by NSF grants DMS-1101228 and CCF-1213151. Part of this work was carried out while visiting St. Petersburg with support from the Skolkovo Institute of Technology. Supported in part by grant TIN2013-48031-C4-1. Supported in part by Simons Foundation award 306202. Supported in part by IDEI grant PN-II-ID-PCE-2011-3-0981“Structure and computational difficulty in combinatorial optimization: an interdisciplinary approach”. Preprint submitted to Elsevier January 20, 2017 Kneser-Lovász theorem. We show that the k = 1 case of the truncated Tucker lemma has polynomial size extended Frege proofs.
BERT-based classification of fungi protein sequences with multiple GO labels

Due to the increase of reported fungi-related diseases, it has come to many health organizations concern that there may be possible highly contagious fungi that may cause yet another pandemic. Though the likelihood of such is low, research is needed to grasp the understanding of unknown fungi. Identifying and figuring out the traits of unknown fungi through in vitro and in vivo experiments take time and resources. In silico methods yield faster results with a slight drop in accuracy. Modern in silico approaches utilizing deep learning, allow for faster and more accurate classifications. In this study, we perform the classification of one or more gene ontologies of fungi protein sequences. We collected open-source protein sequences from UniProt and applied an algorithm to label the sequences with their gene ontologies. We use ProtBERT with additional layers to give classification results to all the different gene ontologies. Experimental results reveal that when classifying with the top 5 most frequent gene ontologies, the model was able to yield 0.7915 for F1-score, 0.7073 for MCC, and 0.8865 for AuROC. With the top 10 most frequent gene ontologies it yielded 0.6490 for F1-score, 0.6836 for MCC, and 0.7653 for AuROC.
Multiple Time-sensitive Inferences Scheduling on Energy-harvesting IoT Devices

Energy-harvesting intelligent IoT devices are being attracted to provide sustainable development and overcome unstable communication. However, executing multiple latency-constrained deep neural network (DNN) inferences suffer from scheduling and energy management challenges. The inference task is usually performed non-preemptively in the accelerator to maintain data consistency between the MCU and accelerator, resulting in longer blocking time for other applications. The energy-hungry inference task also significantly affects power failure in energy-harvesting devices, impacting their ability to meet timely response requirements. We propose a framework, E-TIGHT, that combines the preemption point and early exit to trade off the accuracy and responsiveness of the inferences with admission control. Finally, we implement our approach on a real platform, compare it with state-of-the-art work, and show that our proposed method significantly increases the performance of the meet and progress ratios.
Application of finite element technology in distance education: a case study

Distance education has become a popular form of learning, but it often suffers from a lack of digital teaching materials, which can negatively impact student engagement and comprehension. This paper proposes the use of Finite Element Technology (FET) to generate digital resources, such as Mises stress nephograms, for integration into distance education. We conducted a case study to evaluate the effectiveness of this approach, and our results indicate that FET can significantly enhance the quality of distance education. Mises stress nephograms and other digital resources generated by FET provide students with visual and intuitive means to comprehend complex concepts. The use of FET and other frontier technologies has facilitated innovation in distance education and offers new possibilities for enhancing student outcomes. This paper demonstrates the potential of FET-generated resources to improve the quality of distance education and encourages further exploration of these technologies.