Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Recursive LSAWfP Models are Structured Workflows

Éditeurs: Mathieu Roche, Nabil Gmati, Amel Ben Abda, Marcellin Nkenlifack Workflow languages are a key component of the Business Process Management (BPM) discipline: they are used to model business processes in order to facilitate their automatic management by means of BPM systems. There are numerous workflow languages addressing various issues (expressiveness, formal analysis, etc.). In the last decade, some workflow languages based on context-free grammars (having then formal semantics) and offering new perspectives to process modelling, have emerged: LSAWfP (a Language for the Specification of Administrative Workflow Processes) is one of them. LSAWfP has many advantages over other existing languages, but it is its expressiveness (which has been very little addressed in previous works) that is studied in this paper. Indeed, the work in this paper aims to demonstrate that any non-recursive LSAWfP model is a structured workflow. Knowing that the majority of commercial BPM systems only implement structured workflows, the result of this study establishes that, although LSAWfP is still much more theoretical, it is a language with commercial potential. Les langages de workflow occupent une place importante dans la discipline du Business Process Management (BPM) : ils sont utilisés pour modéliser les processus opérationnels afin de faciliter leur gestion automatique à l'aide de systèmes BPM. Il existe de nombreux langages de workflow qui traitent de divers aspects (expressivité, analyse formelle, etc.). Au cours de la dernière décennie, certains langages de workflow basés sur des grammaires algébriques (ayant alors une sémantique formelle) et offrant de nouvelles perspectives à la modélisation des processus ont vu le jour : LSAWfP (a Language for the Specification of Administrative Workflow Processes) est l'un d'entre eux. LSAWfP présente de nombreux avantages par rapport aux langages existants, mais c'est son expressivité (qui a été très peu abordée dans les travaux antérieurs) qui est étudiée dans le présent document. En effet, les travaux présentés dans ce document visent à démontrer que tout modèle LSAWfP non récursif est un workflow structuré. Sachant que la majorité des systèmes BPM commerciaux ne gèrent que des workflows structurés, le résultat de cette étude montre que, bien que LSAWfP soit encore très théorique, il est un langage au potentiel économique certain.
Degradation effects of the active region in UV-C light-emitting diodes

An extensive analysis of the degradation characteristics of AlGaN-based ultraviolet light-emitting diodes emitting around 265 nm is presented. The optical power of LEDs stressed at a constant dc current of 100 mA (current density = 67 A/cm2 and heatsink temperature = 20 °C) decreased to about 58% of its initial value after 250 h of operation. The origin of this degradation effect has been studied using capacitance-voltage and photocurrent spectroscopy measurements conducted before and after aging. The overall device capacitance decreased, which indicates a reduction of the net charges within the space-charge region of the pn-junction during operation. In parallel, the photocurrent at excitation energies between 3.8 eV and 4.5 eV and the photocurrent induced by band-to-band absorption in the quantum barriers at 5.25 eV increased during operation. The latter effect can be explained by a reduction of the donor concentration in the active region of the device. This effect could be attributed to the compensation of donors by the activation or diffusion of acceptors, such as magnesium dopants or group-III vacancies, in the pn-junction space-charge region. The results are consistent with the observed reduction in optical power since deep level acceptors can also act as non-radiative recombination centers.
Transfer Reinforcement Learning for Dynamic Spectrum Environment

Reinforcement learning (RL) has proven to be an effective approach for achieving intelligence in Cognitive Radio (CR). Through interactions with the environment, RL enables a CR to optimize in an efficient and flexible manner. The vast majority of studies, however, are carried out in a spectrum environment with prefixed user access rules, typically with a constant transition probability and reward distribution. In fact, in a real-world spectrum environment, changes in access rules are common, which has a significant impact on the effectiveness of RL, while few studies have been conducted on this topic. This paper demonstrates how changes in primary user’s (PU) access rules affect RL strategies. To improve the secondary user’s (SU) performance for the dynamic spectrum environment, a transfer Deep Q-Network (DQN) is proposed, this method screens out knowledge from historical experience while avoiding interference from irrelevant information with an experience playback mechanism. Experiments show that this method outperforms traditional RL methods in terms of conflict rate, spectrum utilization rate, and convergence rate in the dynamic spectrum. Given the scarcity of studies on this topic, this study is expected to serve as a benchmark for the future research.
Systemic Treatment for Gynecological Cancer Patients Undergoing Hemodialysis

Abstract Gynecological cancer poses a serious threat to women’s health. Chemotherapy-based systemic therapy plays a crucial role in the treatment of gynecological cancers. Many systemic therapeutic drugs are metabolized in the kidneys. Therefore, normal renal function is a prerequisite for gynecological tumor patients to complete the full course of systematic treatment and provide a guarantee for achieving an ideal prognosis. Chronic kidney disease often places restrictions on systematic treatment to different extents, such as influencing drug pharmacokinetics, increasing drug toxicity, and the risk of adverse drug reactions. Unfortunately, women undergoing renal replacement have a higher risk of developing gynecological cancers. This article summarizes the current knowledge on systemic treatment drugs for patients with gynecological cancer undergoing dialysis. We discuss the optimal choice of the systematic therapeutic protocol, administration of form and dosage, and window of chemotherapy during hemodialysis sessions to ensure both effectiveness and safety in gynecological cancer patients.
UNIVERSITY SUSTAINABILITY COMPETITIVE ADVANTAGE IN FACING TECHNOLOGICAL CHANGES

The purpose of this study is to analyze sustainability's competitive advantage in the face of technological change mediated by knowledge management and organizational culture. The method used in this study is descriptive verification with model 6 analysis tools from Hayes's theory. The unit of analysis is six private universities that have accreditation A, with 204 respondents consisting of top management, middle management, and low management. The results obtained from the research show that technological change is affecting the university's sustainability competitive advantage, which is mediated by knowledge management and organizational culture. Technological change mediated by knowledge management has a major influence on and contribution to the university's sustainability competitive advantage as compared to organizational culture.
A Cognitive Perspective for Gestalt Therapy

"Abstract: In the past thirty years, a cognitive revolution has occurred in psychology. Cognitive therapists in particular are working to develop a theory of therapy informed by cognitive science. Gestalt therapy, with its theory framed in the language and thinking of a different era, is left out of this contemporary discourse. While cognitive therapists work very differentIy from GestaIt therapists, nameIy with cognitions, Gestalt therapy has many central cognitive features that are not fully described. Further, the basic therapy itself facilitates cognitive framing and cognitive awareness. This paper, drawing on the research and writing of cognitive theorists and therapists, wiIl describe the cognitive concepts of schema, construct and narrative as a first step in taking in the new thinking and integrating it with Gestalt theory. Key words: Cognitive therapy, Gestart therapy, integrative therapy, schema, constructivism, meaning making. "
Change in Electrical/Mechanical Properties of Plasma Polymerized Low Dielectric Constant Films after Etching in CF4/O2 Plasma for Semiconductor Multilevel Interconnects

As semiconductor chips have been integrated to enhance their performance, a low-dielectric-constant material, SiCOH, with a relative dielectric constant k ≤ 3.5 has been widely used as an intermetal dielectric (IMD) material in multilevel interconnects to reduce the resistance-capacitance delay. Plasma-polymerized tetrakis(trimethylsilyoxy)silane (ppTTMSS) films were created using capacitively coupled plasma-enhanced chemical vapor deposition with deposition plasma powers ranging from 20 to 60 W and then etched in CF4/O2 plasma using reactive ion etching. No significant changes were observed in the Fourier-transform infrared spectroscopy (FTIR) spectra of the ppTTMSS films after etching. The refractive index and dielectric constant were also maintained. As the deposition plasma power increased, the hardness and elastic modulus increased with increasing ppTTMSS film density. The X-ray photoelectron spectroscopy (XPS) spectra analysis showed that the oxygen concentration increased but the carbon concentration decreased after etching owing to the reaction between the plasma and film surface. With an increase in the deposition plasma power, the hardness and elastic modulus increased from 1.06 to 8.56 GPa and from 6.16 to 52.45 GPa. This result satisfies the hardness and elastic modulus exceeding 0.7 and 5.0 GPa, which are required for the chemical–mechanical polishing process in semiconductor multilevel interconnects. Furthermore, all leakage-current densities of the as-deposited and etched ppTTMSS films were measured below 10−6 A/cm2 at 1 MV/cm, which is generally acceptable for IMD materials.
Proactive VNF Scaling and Placement in 5G O-RAN Using ML

5G networks are expected to support various services and applications with more stringent latency, reliability, and bandwidth requirements than previous generations. Open Radio Access Networks (O-RAN) have been proposed to meet these requirements. The O-RAN Alliance assumes O-RAN components to be Virtualized Network Functions (VNFs). Furthermore, O-RAN allows employing Machine Learning (ML) solutions to tackle challenges in resource management. However, intelligently managing resources for O-RAN can be proved challenging. Network providers need to scale resources in response to incoming traffic dynamically. Elastically allocating resources provides higher flexibility, reduces OPerational EXpenditure (OPEX), and increases resource utilization. In this work, we propose and evaluate an elastic VNF orchestration framework for O-RAN. The proposed system consists of a traffic forecasting-based dynamic scaling scheme using ML and a Reinforcement Learning (RL) based VNF placement policy. The models are evaluated based on their predictive capabilities subject to all Service-Level Agreements.
Robust Sampling Consensus

In this thesis, the problem of robust estimation of a multiple view geometry in the computer vision is studied. The main focus is put on random sampling techniques for an estimation of two-view geometries, in particular homography and epipolar geometry, in a presence of outliers. After a thorough analysis of LO-RANSAC, several improvements are proposed to make it more robust to the selection of the inlier/outlier error threshold and to the number of points. A new estimator, faster, more accurate and more robust that the state-of-the-art is the result. The improvements were implemented in the framework of CMP WBS-Demo and extensively tuned and experimentally tested on diverse data, using a newly created testing framework. The LO-RANSAC implementation for homography and epipolar geometry estimation has been separated from the rest of WBS-Demo and is now publicly available. The datasets were made available as well, including new manually annotated ground truth point correspondences.
Hard and Soft Tissue Relapse After Different Genioplasty Procedures: A Scoping Review

The chin is a crucial component of facial aesthetics, and 20% of craniofacial problems require repair of the chin size, shape, and position. Genioplasty is used to treat irregularities in all three planes of the chin. Specific hard and soft tissue relapses following various genioplasty techniques have not been adequately studied in the literature to date. The purpose of this scoping review was to investigate the stability of hard and soft tissue changes achieved by different genioplasty procedures, six months after the procedure. A literature search was performed on PubMed, Web of Science, Embase, Wiley Online, Scopus, Google Scholar, Science Direct, and Cochrane databases from January 1, 2011 to October 31, 2022. Prospective and retrospective cohorts, case-control studies, observational studies, and randomized control trials, with at least 10 patients, which were written in English and evaluated the stability of different genioplasty procedures, with a follow-up period of at least six months were included. The manual and electronic search yielded 523 articles, and after complete screening, seven articles were selected (five with advancement genioplasty and two with reduction genioplasty) that met the eligibility criteria for review. The patients undergoing reduction genioplasty had a mean age of 24.15 years, compared to 20.5 years for augmentation genioplasty. The average follow-up period was 18.64 months for augmentation genioplasty and 10.5 months for reduction genioplasty technique. The relapse was assessed at pogonion, and it was noted that the average surgical advancement at hard tissue pogonion was 7.04 mm with a relapse of 0.69 mm after six months post-treatment. The average vertical movement of the hard tissue pogonion was 1.8 mm with a relapse of 0.74 mm. The average reduction at hard tissue pogonion was 3.2 mm in the vertical direction with a relapse of 0.2 mm and 0.8 mm reduction in soft tissue pogonion with a relapse of 0.3 mm. The soft to hard tissue ratio mentioned in the different studies ranged from 0.89 to 0.97. Both reduction and augmentation genioplasty are stable and reliable for altering the chin position for aesthetic purposes. The recommended mode of fixation is rigid fixation.
Privacy-Preserving Federated Learning Against Label-Flipping Attacks on Non-IID Data

Federated learning (FL) has attracted widespread attention in the Internet of Things domain recently. With FL, multiple distributed devices can cooperatively train a global model by transmitting model updates without disclosing the original data. However, the distributed nature of FL makes it vulnerable to data poisoning attacks. In practice, malicious clients can launch the label-flipping attack (LFA) by simply tampering with the labels of local data, thus causing the global model to misclassify the samples of a selected class as the target class. Although some defense mechanisms have been proposed, they rely on specific assumptions about data distribution, and their performance degrades significantly when the data on clients are non-IID. Besides, most existing methods require clients to upload model updates in plaintext so that the server can identify and remove the malicious updates. But, direct transmission of model updates may still reveal private information. Considering these issues, we develop a label-flipping-robust and privacy-preserving FL (LFR-PPFL) algorithm, which is applicable to both independent and identically distributed (IID) and non-IID data. We first propose a detection method based on temporal analysis on cosine similarity to distinguish malicious clients from benign clients. Then, we propose a privacy-preserving computation protocol based on homomorphic encryption to implement this detection method and perform federated aggregation while protecting the privacy of clients. Besides, a detailed theoretical analysis is given to demonstrate the privacy guarantee of the proposed protocol. Experimental results on real-world data sets show that the proposed algorithm can effectively defend against LFAs under various data distributions.
Determinants of voters behaviour and voting intention: The mediating role of social media

Abstract The dominant media ideology argues that the successful application of social media engagement in commercial marketing has the potential to influence customers buying decisions by X percentage. This can equally play a pivotal role in voters` voting decisions. Political marketing research scholars have proposed the need to study the role of social media in the voting decision. However, scholarly responses to this call have received limited attention with interesting debates and opposing arguments. The diverse opinion in the field complicates our understanding, making it difficult to draw a viable conclusion on its relevance and proper application. The authors, therefore, empirically examined the role of social media in the relationship between determinants of voters` behaviour and voting intention in Ghana. This is to help overcome the limited empirical understanding to stir up its relevance and proper application in the political space. The authors collected data from 600 Ghanaian voters, which were analyzed with the help of the Structural Equation Model (SEM) Smart PLS. We found a positive and significant relationship between determinants of voters` behaviour dimensions and voting intention. The study further established that social media engagement plays a vital role in the relationship between the determinants of voters` behaviour and voting intention. We, therefore, recommend that since the information displayed on social media platforms about political parties and their leaders has the potential to influence voters voting decisions, the media actors, political leaders, and political consultants should pay particular attention to media engagement and the kind of information placed in the media about the political leaders.
On-Chip Unsupervised Learning Using STDP in a Spiking Neural Network

In this article, we propose an energy-efficient Ge-based device that enables on-chip unsupervised learning using Spike-Timing-Dependent-Plasticity (STDP) in a Spiking Neural Network (SNN). A Ferromagnetic Domain Wall (FM-DW) based device, which has decoupled read and write paths, is used as a synapse in this work. The proposed device comprises a dual pocket Fully-Depleted Silicon-on-Insulator (FD-SOI) MOSFET with dual asymmetric gates. Using a well-calibrated 2D device simulation model, we show that a pair of such devices can generate a current, which depends exponentially on the temporal correlation of spiking events in the pre- and post-neuronal layer. This current is fed to the FM-DW synapse, which in turn modulates the conductance of the synapse in accordance with the STDP learning rule. The proposed implementation requires 2-3× fewer transistors and offers a lower latency compared to existing literature. We further demonstrate the application of the proposed device at the system-level to train an SNN to recognize handwritten digits in the MNIST dataset and obtained a classification accuracy of 84%.
Preventing Loneliness and Reducing Dropout: Results from the COMPLETE Intervention Study in Upper Secondary Schools in Norway

This study examines the impact of interventions aimed at improving psychosocial health on students’ perception of a caring school climate, their feelings of loneliness, and school completion in Norwegian upper secondary education. Two intervention conditions were tested: a universal single-tier intervention focused on improving the psychosocial school climate: the Dream School Program, and a multi-tier intervention combining the Dream School Program with a targeted measure, the Mental Health Support Team. The direct and indirect effects of these interventions on school completion were analyzed using structural equation models (SEM), with data from 1508 students (mean age at Time 1: 17.02 (SD = 0.92); 60.7% girls; 72.1% Norwegian-born). The results indicated that loneliness levels did not differ significantly between the intervention conditions. However, students in the multi-tier intervention group reported a significantly higher perception of a caring school climate compared to those in the single-tier intervention group. The multi-tier intervention group had a lower rate of school completion compared to the control group and the single-tier intervention group. The SEM analysis revealed that the multi-tier intervention reduced loneliness in the second year of upper secondary school by promoting a caring school climate in the first school year. In contrast, the single-tier intervention was associated with increased loneliness due to a decrease in the perception of a caring school climate. The implications of these findings are discussed.
Variable selection in model-based clustering for high-dimensional data

This thesis deals with variable selection for clustering. This problem has become all the more challenging since the recent increase in high-dimensional data where the number of variables can largely exceeds the number of observations (DNA analysis, functional data clustering.). We propose a variable selection procedure for clustering suited to high-dimensional contexts. We consider clustering based on finite Gaussian mixture models in order to recast both the variable selection and the choice of the number of clusters into a global model selection problem. We use the variable selection property of l1-regularization to build a data-driven model collection in a efficient way. Our procedure differs from classical procedures using l1-regularization as regards the estimation of the mixture parameters: in each model of the collection, rather than considering the Lasso estimator, we calculate the maximum likelihood estimator. Then, we select one of these maximum likelihood estimators by a non-asymptotic penalized criterion. From a theoretical viewpoint, we establish a model selection theorem for maximum likelihood estimators in a density estimation framework with a random model collection. We apply it in our context to determine a convenient penalty shape for our criterion. From a practical viewpoint, we carry out simulations to validate our procedure, for instance in the functional data clustering framework. The basic idea of our procedure, which consists in variable selection by l1-regularization but estimation by maximum likelihood estimators, comes from theoretical results we establish in the first part of this thesis: we provide l1-oracle inequalities for the Lasso in the regression framework, which are valid with no assumption at all contrary to the usual l0-oracle inequalities in the literature, thus suggesting a gap between l1-regularization and l0-regularization.
Identification Through Heterogeneity

We analyze set identification in Bayesian vector autoregressions (VARs). Because set identification can be challenging, we propose to include micro data on heterogeneous entities to sharpen inference. First, we provide conditions when imposing a simple ranking of impulse-responses sharpens inference in bivariate and trivariate VARs. Importantly; we show that this set reduction also applies to variables not subject to ranking restrictions. Second, we develop two types of inference to address recent criticism: (1) an efficient fully Bayesian algorithm based on an agnostic prior that directly samples from the admissible set and (2) a prior-robust Bayesian algorithm to sample the posterior bounds of the identified set. Third, we apply our methodology to U.S. data to identify productivity news and defense spending shocks. We find that under both algorithms, the bounds of the identified sets shrink substantially under heterogeneity restrictions relative to standard sign restrictions.