Miracle-dz/UniFAR-FTUs huggingface dataset:
An integrated cuckoo search-genetic algorithm for mining frequent itemsets

Abstract Mining frequent itemsets is a contemporary research topic in the data-mining domain. Identifying frequent itemsets has acquired its significance due to recognized objectives in the field of association mining. The frequent itemset mining problem focuses on identifying frequent patterns and correspondence between the itemsets in the transactional database. Frequent itemset mining problems are time consuming when dealing with huge amounts of data and extracting the maximum number of the frequent itemsets from a large search space is a challenging process. Since the transactional database size is enormous, a stochastic method is applied to find optimal solutions. In this article, an integrated approach to extract frequent itemsets is proposed based on the cuckoo search and genetic algorithm. This integrated methodology is performed by merging the reproduction process of cuckoo species with the operators such as crossover and mutation in GA. First, a strategy to diversify the population and to discover maximum frequent patterns in the search process, Second, a neighborhood exploration strategy is applied for recurrent solutions to improve the accuracy. Finally, the proposed integrated approach is validated with respect to the solution quality and convergence rate. The integrated approach is verified using standard data instances. The ICSGA approach efficiently discovers the frequent patterns. The comparison results of the integrated approach are eminently productive than the other approaches in the literature.
Does behaviour match user typologies? An exploratory cluster analysis of behavioural data from a gamified fitness platform

A promising solution to increase user engagement in gamified applications is tailored gamification design. However, current personalisation relies primarily on user types identified through self-reporting rather than actual behaviour. As a novel approach, the present study used an exploratory machine learning analysis to identify seven clusters of users in a gamified fitness application based on their behavioural data (N = 19,576). The clusters were then conceptually compared to common user typologies in gamification, identifying possible relationships between behavioural user clusters and user types motivated by achievement, sociability, and extrinsic incentives. The findings shed light on nuanced behaviour patterns of user types in the fitness context and how knowing these patterns can inform the way in which tailored gamification could be implemented to meet the needs of specific types. Thereby, they contribute to the discussion on utilising behavioural data and user typologies for tailored gamification design.
Heterogeneous Model Ensemble For Automatic Polyp Detection and Tracking In Colonoscopy

Regular colonoscopy screening substantially contributes to the prevention of colon cancer, as a polyp found in early stages can safely be removed. Assisting physicians during screening with automated detection systems can potentially increase the sensitivity of polyp detection. In this work, we present our polyp detection and tracking approach, submitted to the EndoCV2022 challenge. The core of our method is a heterogeneous ensemble of YOLOv5 models, each trained with a different strategy based on external data and varying data augmentation concepts. The output of the ensemble members is merged with the weighted boxes fusion algorithm, and the final output bounding boxes are reduced in size. Our method yields a mean Average Precision (mAP) of 0.44 on our validation test set.
Gamified apps for sustainable consumption: A systematic review

Mobile apps are ubiquitous, affecting our everyday practices because “there is always an app for that”. In this vein, there have been a significant number of apps devised to support people’s lifestyles to make them more sustainable. This study aims to draw an overview of gamified mobile apps for sustainable consumption. Following a systematic process, this study analyzes 67 gamified apps’ sustainability approaches and gamification concepts. It was found that (1) sustainable consumption is generally presented as the efficient use of resources to impact the environment positively, rarely addressing societal impacts or economic gains from shifting consumption practices. Other findings include (2) a lack of diversity in gamification characteristics, given the prevalence of direct communication with the user, the absence of virtual identities, and most apps targeting behavior change without attitude change. A potentially problematic design choice is (3) the presence, in some cases, of external rewards that are often contradictory to the message of sustainable consumption as they lead to more consumption. Nonetheless, based on most apps embedding sustainable consumption activities in the gamification concept and having a large number of users, it is possible to conclude that gamification has the potential to motivate shifts in their users’ lifestyles.
Modeling and Mining Multi-Aspect Graphs With Scalable Streaming Tensor Decomposition

Graphs emerge in almost every real-world application domain, ranging from online social networks all the way to health data and movie viewership patterns. Typically, such real-world graphs are big and dynamic, in the sense that they evolve over time. Furthermore, graphs usually contain multi-aspect information i.e. in a social network, we can have the"means of communication"between nodes, such as who messages whom, who calls whom, and who comments on whose timeline and so on. How can we model and mine useful patterns, such as communities of nodes in that graph, from such multi-aspect graphs? How can we identify dynamic patterns in those graphs, and how can we deal with streaming data, when the volume of data to be processed is very large? In order to answer those questions, in this thesis, we propose novel tensor-based methods for mining static and dynamic multi-aspect graphs. In general, a tensor is a higher-order generalization of a matrix that can represent high-dimensional multi-aspect data such as time-evolving networks, collaboration networks, and spatio-temporal data like Electroencephalography (EEG) brain measurements. The thesis is organized in two synergistic thrusts: First, we focus on static multi-aspect graphs, where the goal is to identify coherent communities and patterns between nodes by leveraging the tensor structure in the data. Second, as our graphs evolve dynamically, we focus on handling such streaming updates in the data without having to re-compute the decomposition, but incrementally update the existing results.
Advantages and challenges of extracting process knowledge through serious games

Digitalization promises huge improvements in various domains, such as production, health care, or mobility, through the integration of big data and artificial intelligence (AI). However, AI often builds on labelled data but labeling data can be complex or expensive, depending on both the properties of the data and access to people with domain knowledge. In particular, an underexplored field is capturing process knowledge, i.e., knowledge about the relationships among process steps. In this work, we propose and evaluate a game-based approach for capturing process knowledge. Taking the cooking domain as an example, we developed a prototype, in which players act as chef and cook dishes following their own recipes while each action is logged. The captured data is then compared to ground-truth models of common recipes. While the quantitative evaluation shows a decrease in motivation as well as fewer logged steps, qualitative feedback from participants identifies possible improvements of the concept. In summary, games can be a suitable approach for extracting experts’ process knowledge, when certain user requirements are considered.
Virtual reality in emotion regulation: A scoping review

Academic research investigating virtual reality (VR) is growing rapidly; as a result of VR becoming more easily accessible, it has become a more viable tool for helping individuals regulate their emotions. This research aims to provide an overview of the field and the contexts in which VR has been implemented to facilitate emotion regulation. Results indicate that although VR has been used to facilitate ER for over two decades, empirical research in the field has remained somewhat static until 2018. Furthermore, the contexts in which it has been employed have remained narrow with more than half of all included publications utilising VR to administer exposure therapy. While the recent increase of published works in the field, combined with more varied uses of VR, indicate a broadening of the field this work highlights several gaps in the extant literature, identifying a series of potential avenues for future research.
Should We Perform an Immediate Coronary Angiogram in All Patients After€Cardiac Arrest?

METHODS From May 2011 to May 2015, we collected data for all patients admitted in hospital after OHCA in Paris and its suburbs (France). Risk of in-hospital death was retrospectively calculated using the validated Cardiac Arrest Hospital Prognosis score, which includes age, setting, initial rhythm, durations from collapse to basic life support and from basic life support to return of spontaneous circulation, pH, and epinephrine dose. Independent predictors of survival at discharge (including immediate CAG) were assessed in multivariate logistic regression in each of the 3 pre-defined subgroups of Cardiac Arrest Hospital Prognosis score: low risk (<150 points), medium risk (150 to 200 points), and high risk (>200 points) for in-hospital death.
Endoscopic computer vision challenges 2.0

Accurate detection of artefacts is a core challenge in a wide-range of endoscopic applications addressing multiple different disease areas. The importance of precise detection of these artefacts is essential for high-quality endoscopic video acquisition crucial for realising reliable computer assisted endoscopy tools for improved patient care. In particular, colonoscopy requires colon preparation and cleaning to obtain improved adenoma detection rate. Computer aided systems can help to guide both expert and trainee endoscopists to obtain consistent high quality surveillance and detect, localize and segment widely known cancer precursor lesion, “polyps”. While deep learning has been successfully applied in the medical imaging, generalization is still an open problem. Generalizability issue of deep learning models need to be clearly defined and tackled to build more reliable technology for clinical translation. Inspired by the enthusiasm of participants on our previous challenges, this year we put forward a 2.0 version of two sub-challenges (Endoscopy artefact detection) EAD 2.0 and (Polyp generalization) PolypGen 2.0. Both the sub-challenges consists of multi-center and diverse population datasets with tasks for both detection and segmentation but focus on assessing generalizability of algorithms. In this challenge, we aim to add more sequence/video data and multimodality data from different centers. The participants is aimed to be evaluated on both standard (some already present at leaderboard) and generalization metrics presented in our previous challenges. However, unlike previous challenges, in 2.0 we aimed to benchmark methods on larger test-set comprising of mostly video sequences as in the real-world clinical scenario.
Temporal Context Framework for Endoscopy Artefact Segmentation and Detection

Endoscopic video processing could facilitate pre-operative planning, intra-operative image guidance and generation of post-operative analysis of the surgical procedure. However, most of the current methods are still based on a single frame of image analysis, which makes the results of the previous frame images independent of each other and causes vibration. In this paper, we propose an temporal context framework for endoscopy artefact segmentation and detection. The framework extends the general segmentation and detection model to the form based on temporal input, and we add a Temporal Context Transformer(TCT) after the encoder of the model to improve the model’s ability to construct temporal context features. the experiments of the EndoCV 2022 challenge dataset that this framework can improve the robustness of the model.
Proposed criteria to determine whether a territory is free of a given animal disease

This text describes the general principles underlying the concept of disease-free territory and the required statistical basis for the corresponding epidemiological surveillance operations. Among the essential points, it is emphasised that “disease-free” status should be given only under conditions substantiating the absence of infection (or infestation) and not simply on basis of a known low level of infection (or infestation). This manuscript also raises concerns about possible confusion that has arisen between the inevitable requirement to set a threshold on the level of detection of epidemiological surveillance tools, for economic reasons, and the acceptance of a level of infection (or infestation) that is known, but occurring below the accepted threshold, when according the official status of “territory-free from a given disease”. In such a situation, it would be preferable to accord the status of “territory where the disease is in the process of eradication”. free territory / infectious disease / prevalence rate threshold Résumé – Proposition de critères pour déclarer un territoire indemne d’une maladie animale. Ce texte rappelle les principes généraux sur lesquels devrait être fondée la notion de territoire indemne d'une maladie, ainsi que les bases statistiques nécessaires aux opérations d'épidémiosurveillance correspondantes. Parmi les éléments essentiels, les auteurs insistent sur la nécessité de ne conférer l'appellation « indemne de » que lorsqu’il n’est pas possible de prouver l’infection (ou l’infestation) et non pas lors de la présence certaine d'un faible taux d'infection (ou d’infestation). Ils attirent l'attention sur la confusion qui a dû s'installer entre l'inévitable exigence de fixer une limite au niveau de détectabilité des outils d'épidémiosurveillance, pour des raisons économiques, et l'acceptation d'un taux d'infection (ou d’infestation) connu, mais situé au-dessous de ce niveau, pour l'attribution officielle de la qualification « indemne de ». Pour une telle situation (prévalence faible, située au-dessous d'un seuil défini en fonction des caractéristiques épidémiologiques des maladies), l'appellation « territoire où la maladie est en voie d'éradication » est préférable. territoire indemne / maladie infectieuse / prévalence limite acceptable Vet. Res. 32 (2001) 545–563 545 © INRA, EDP Sciences, 2001 * Correspondence and reprints Tel. : (33) 1 49 77 13 31; fax: (33) 1 49 77 13 36; e-mail: b.dufour@afssa.fr B. Dufour et al. 546
3D particle tracking velocimetry and statistical analysis of turbulent pipe flow

Air pollution and industrial mixing processes are just two examples of processes in which dispersion of particles in turbulent flows is of importance. Particle dispersion can be described by means of stochastic models. For most practical flow geometries, knowledge of Lagrangian flow statistics is necessary to determine the coefficients present in such models. Because numerical methods are either too inaccurate or limited to low Reynolds numbers, this thesis looks at the possibility of determining these model coefficients by means of experiments. An inhomogeneous turbulent flow is generated in a pipe geometry and the Lagrangian experimental technique 3D particle tracking velocimetry (3D PTV) is used to determine Lagrangian velocity statistics. An experimental set-up for 3D PTV measurements in turbulent pipe flow is designed. Important design parameters and the final design are discussed. 3D PTV experiments are carried out at two moderate Reynolds numbers. Special care has to be taken to ensure correct analysis of the measured particle tracks. Two possible particle sampling methods to avoid biased statistics are presented. To our knowledge, no experimentally determined Lagrangian statistics are available in literature for turbulent pipe flow. Furthermore, the only numerical Lagrangian results are those generated by the DNS code of Veenman as presented in [56, 62]. Eulerian as well as Lagrangian 3D PTV results are compared with results from literature and all compared quantities show good agreement. A stochastic model for particle velocity is presented for the case of turbulent pipe flow, regarding the particle acceleration as a Markov process. In this way, the model neglects viscosity effects and is therefore not able to resolve the small scale effects of low Reynolds number flow. It is, however, asymptotically exact for infinitely high Reynolds numbers. The model coefficients, being the Kolmogorov constant and damping coefficients, are determined from 3D PTV and DNS results. The model results are in agreement with the available experimental and DNS results at the moderate Reynolds numbers presented in this thesis. For Lagrangian results, this agreement is restricted to times much larger than the Kolmogorov time as a result of the Markovian assumption. As most practical examples of turbulent flows, as well as the validity of the presented model, are restricted to high Reynolds numbers, the real challenge is to obtain model coefficients at high Reynolds numbers. Upper Reynolds numbers limits for the experimental method are discussed and suggestions on how to raise these limits for the current geometry are given.
Non-fragile mixed H ∞ / l 2 − l ∞ synchronisation control for complex networks with Markov jumping-switching topology under unreliable communication links

: This study is concerned with the non-fragile mixed H ∞ / l 2 − l ∞ synchronisation control problem for discrete-time complex networks with Markov jumping-switching topology under unreliable communication links. The network topology under consideration is assumed to be governed by a Markov chain with time-varying transition probabilities (TPs). The variation of TPs is subject to a kind of slow switching signals; that is, the average dwell time (ADT) switching. The focus is on the design of non-fragile mixed mode-dependent/-independent controllers such that the underlying network reaches stochastic mean-square synchronisation with a mixed H ∞ / l 2 − l ∞ performance level for an admissible switching signal with ADT. By using a new mixed H ∞ / l 2 − l ∞ performance index, combined with the switched control method, the solutions to the considered problem are formulated. Finally, simulation results demonstrate the effectiveness of our proposed approach.
CMAR_A_266807 10311..10319

1Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 2Department of Oncology, Dajiangdong Hospital of Hangzhou First People’s Hospital Group, Hangzhou, Zhejiang, People’s Republic of China; 3Department of Thyroid and Breast Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, People’s Republic of China
MOBILE AUGMENTED REALITY GAMES AS AN ENGAGING TOOL FOR CULTURAL HERITAGE DISSEMINATION: A CASE STUDY

In recent years, plenty of novel educational tools using mobile augmented reality (mobile AR) have been developed, altering the way students interact with a cultural heritage environment. Nevertheless, the relevant literature still lacks studies exploring the usability, effectiveness and acceptance of the technology by users, and there is still need for in-depth research to better understand the way students interact with mobile AR interfaces in this context. This study explores the use of mobile-AR technology in an open-air archaeological site and the results of this study are aimed at increasing the current literature on the digital encahcement of open spaces of cultural interest. This study also seeks to provide cultural heritage administrators and teachers with some key elements and factors that should be taken into account when deciding on the methods used to guide students to archaeological sites to develop new and more effective digital approach strategies. The paper describes the design, development and evaluation of the pilot application "Oracle of Delphi app", an educational augmented reality application for the Delphi outdoor archaeological site. It is a location-aware portable game aimed at secondary school students and aims to involve them in a playful exploration of the area through which they will learn about the most important monuments of the sacred site of Delphi and discover their history. This study is the only attempt so far to create an educational game using mobileAR for a guided tour of the archaeological site of Delphi which is then tested and evaluated by the students.
DDDT_A_319786 3255..3276

1Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, People’s Republic of China; 2Institute of Clinical Pharmacy, Central South University, Changsha, Hunan, 410011, People’s Republic of China; 3Department of Dermatology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, People’s Republic of China; 4Department of Integrated Traditional Chinese & Western Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, People’s Republic of China; 5School of Pharmacy, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, People’s Republic of China Background: Huai Hua San (HHS), a famous Traditional Chinese Medicine (TCM) formula, has been widely applied in treating ulcerative colitis (UC). However, the interaction of bioactives from HHS with the targets involved in UC has not been elucidated yet. Aim: A network pharmacology-based approach combined with molecular docking and in vitro validation was performed to determine the bioactives, key targets, and potential pharmacological mechanism of HHS against UC. Materials and Methods: Bioactives and potential targets of HHS, as well as UC-related targets, were retrieved from public databases. Crucial bioactive ingredients, potential targets, and signaling pathways were acquired through bioinformatics analysis, including protein–protein interaction (PPI), as well as the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Subsequently, molecular docking was carried out to predict the combination of active compounds with core targets. Lastly, in vitro experiments were conducted to further verify the findings. Results: A total of 28 bioactive ingredients of HHS and 421 HHS-UC-related targets were screened. Bioinformatics analysis revealed that quercetin, luteolin, and nobiletin may be potential candidate agents. JUN, TP53, and ESR1 could become potential therapeutic targets. PI3K-AKT signaling pathway might play an important role in HHS against UC. Moreover, molecular docking suggested that quercetin, luteolin, and nobiletin combined well with JUN, TP53, and ESR1, respectively. Cell experiments showed that the most important ingredient of HHS, quercetin, could inhibit the levels of inflammatory factors and phosphorylated c-Jun, as well as PI3K-Akt signaling pathway in LPS-induced RAW264.7 cells, which further confirmed the prediction by network pharmacology strategy and molecular docking. Conclusion: Our results comprehensively illustrated the bioactives, potential targets, and molecular mechanism of HHS against UC. It also provided a promising strategy to uncover the scientific basis and therapeutic mechanism of TCM formulae in treating diseases.
Supplement to “Accurate and Efficient P -value Calculation via Gaussian Approximation: a Novel Monte-Carlo Method”

In Section 1, we provide the proofs of the main theorems and technical lemmas. In Section 2, we justify Condition (A.3) that is required for Theorem 2 and 3. In Section 3, we perform simulations to assess the accuracy of our main theorems. The formulae of asymptotical critical values and additional tables for the simulation results of uniform, gamma and Bernoulli distributions are given in Section 4.