Miracle-dz/UniFAR-FTUs huggingface dataset:
An architectural approach to cyber-physical system design

Cyber-Physical Systems (CPSs) are systems that integrate distributed computation and communication with physical processes. They consist of networked devices that process data between sensors and actuators. They interact with their physical environment in an orchestrated, distributed and useful way. CPSs provide us with new possibilities that augment our world in ways that were previously unreachable. Researchers and policymakers predict that CPSs will play a key role in solving social and economic challenges. These expectations attract the interest of industry and government, which stimulates research and investments in CPS development, applications and related technologies. Subsequently, new technologies and processes become available that take away technological and financial obstacles of CPSs. For example, processors and sensors become ever smaller, cheaper, more powerful and more efficient. But while financial and physical limitations disappear, new ones come into play: the growing complexity of CPSs makes their realization increasingly difficult. Human developers, designers and architects are limited in their ability to manage this complexity. The objective of this research is to improve the design and management of CPSs by making its complexity more manageable. To do this, an Architecture Framework for CPSs is created. This framework aims at supporting the process of designing, testing and maintaining during the life cycle of the system. The framework consists of a selection of stakeholders and concerns and five viewpoints: the scenario viewpoint, the logical viewpoint, the process viewpoint, the deployment viewpoint and the development viewpoint. Application guidelines for using the framework are presented as well. To validate the framework, it is applied to three use cases to evaluate how it supports a selection of eight characteristic concerns: effectiveness, modularity, consistency, reusability, extensibility, testability, understandability and simplicity.
WHAT ABOUT “THE” SCIENTIFIC METHOD? A SURVEY APPLIED TO MIDDLE AND SECONDARY GEOSCIENCE TEACHERS

"The debate over whether there is a single unifying scientific method or a variety of methods, each of which is applied to a different discipline of science, is still a difficult one. Popper idea of refutation was a criticism to the inductive method and claimed the need to submit theories to falsification. His thesis ended up being a demarcation of science and pseudoscience. But the question remains: do all sciences follow the same scientific method? Namely because discoveries in geology have to overcome time and space enormous scales, geologist have been called by Lord Kelvin as “stamp collectors”. Having started as a field science, and even having been denied by Hutton as an experimental science, modelling in geology only took place at the end of the 19th century by the hand of Sir James Hall. The need to mirror scientists’ methods is a demand of inquiry-based teaching, but few geology teachers have correct knowledge about the method used by geologists. In the present study, a survey was undertaken online with the main objective of investigating what is teachers’ knowledge about the (geo)scientific method. Participants were 108 geology middle and secondary teachers in Portugal. The majority of respondents were women (n=79; 73.1%) and the average age was 46 years old. All participants were graduated, but 51 (47.2%) had a master and 5 (4.6%) had a Ph.D. The results showed erroneous conceptions that are commonly reflected in inquiry-based teaching classrooms, namely regarding the scientific method but also about investigative competencies and geology as an experimental science. The majority of the teachers’ said that there only exists one scientific method for all sciences (n=49; 45.4%) and that it has a fundamentally linear nature from observation to conclusion (n=54; 50.0%). The scientific method was claimed as needed to allow the confirmation of hypothesis by many teachers (n=44; 40.7%). Some participants referred Uniformitarianism as a principle that justifies the historical and interpretive reasoning of geologist (n=48; 44.4%), but not so many referred the analogic reasoning (n=28; 25.9%). Teachers also referred to critical and systemic thinking as scientific competencies (n=72; 66.7%) and gave less importance to others like observation and argumentation (n=27; 25.0%). Results analysis corroborate that an inquiry-base teaching methodology requires history of geology and an epistemological reflection to be integrated in teachers’ initial training and professional development. The epistemology behind geology classes has to be taught to eradicate alternative conception about the scientific method. "
Cautious Sexual Attitudes Diminish Intent to Vaccinate Children against HPV in Utah

Although most Human papillomavirus (HPV) infections are mild and are cleared by the immune system, some high-risk HPV strains can cause various cancers. Vaccines have been developed that protect against high-risk HPV strains. HPV vaccines have been approved for use by the CDC (Centers for Disease Control and Prevention) and are recommended for everyone aged 11–26. Despite the availability of safe and effective vaccines, uptake is low. HPV vaccine uptake has been extensively studied on a national and international level, but less is known about vaccine acceptance on a state or local level. The state of Utah, in the United States of America, has a relatively low HPV vaccination rate. In this study, we identified factors that impact the intent of Utah parents to vaccinate their children against HPV. A survey was distributed electronically to Utah residents. Survey results were analyzed using confirmatory factor analysis, structural equation modeling, and univariate analysis. Knowledge about HPV and positive vaccine attitudes had the greatest positive effect on intent to vaccinate children against HPV. Cautious sexual attitudes and high religious practice were found to have a negative impact on intent to vaccinate. Effective public health messaging will consider the cultural and religious influences of the target population.
Torus counting and self-joinings of Kleinian groups

Abstract For any integer d ≥ 1 {d\geq 1} , we obtain counting and equidistribution results for tori with small volume for a class of d-dimensional torus packings, invariant under a self-joining Γ ρ < ∏ i = 1 d PSL 2 ⁡ ( ℂ ) {\Gamma_{\rho}<\prod_{i=1}^{d}\operatorname{PSL}_{2}(\mathbb{C})} of a Kleinian group Γ formed by a d-tuple of convex-cocompact representations ρ = ( ρ 1 , … , ρ d ) {\rho=(\rho_{1},\dots,\rho_{d})} . More precisely, if 𝒫 {\mathcal{P}} is a Γ ρ {\Gamma_{\rho}} -admissible d-dimensional torus packing, then for any bounded subset E ⊂ ℂ d {E\subset\mathbb{C}^{d}} with ∂ ⁡ E {\partial E} contained in a proper real algebraic subvariety, we have lim s → 0 ⁡ s δ L 1 ⁢ ( ρ ) ⋅ # ⁢ { T ∈ 𝒫 : Vol ⁡ ( T ) > s , T ∩ E ≠ ∅ } = c 𝒫 ⋅ ω ρ ⁢ ( E ∩ Λ ρ ) . \lim_{s\to 0}{s^{\delta_{L^{1}}({\rho})}}\cdot\#\{T\in\mathcal{P}:% \operatorname{Vol}(T)>s,\,T\cap E\neq\emptyset\}=c_{\mathcal{P}}\cdot\omega_{% \rho}(E\cap\Lambda_{\rho}). Here δ L 1 ⁢ ( ρ ) {\delta_{L^{1}}(\rho)} , 0 < δ L 1 ⁢ ( ρ ) ≤ 2 / d {0<\delta_{L^{1}}(\rho)\leq 2/\! {\sqrt{d}}} , denotes the critical exponent of the self-joining Γ ρ {\Gamma_{\rho}} with respect to the L 1 {L^{1}} -metric on the product ∏ i = 1 d ℍ 3 {\prod_{i=1}^{d}\mathbb{H}^{3}} , Λ ρ ⊂ ( ℂ ∪ { ∞ } ) d {\Lambda_{\rho}\subset(\mathbb{C}\cup\{\infty\})^{d}} is the limit set of Γ ρ {\Gamma_{\rho}} , and ω ρ {\omega_{\rho}} is a locally finite Borel measure on ℂ d ∩ Λ ρ {\mathbb{C}^{d}\cap\Lambda_{\rho}} which can be explicitly described. The class of admissible torus packings we consider arises naturally from the Teichmüller theory of Kleinian groups. Our work extends previous results of [H. Oh and N. Shah, The asymptotic distribution of circles in the orbits of Kleinian groups, Invent. Math. 187 2012, 1, 1–35] on circle packings (i.e., one-dimensional torus packings) to d-torus packings.
GC-Net: Global and Class Attention Blocks for Automated Glaucoma Classification

Glaucoma is an irreversible vision loss, which develops gradually without obvious symptoms. It is hard to detect in early stages and diagnostic procedure is a time-consuming work. Therefore, early screening and treatment are essential to protect vision and maintain quality of life. In previous work of glaucoma classification, convolutional neural network (CNN) has been used in lots of researches and got a good performance. However, the convolution operator only focuses on local information in feature extraction and context information will be lost to a large extent. Attention block pays more attention to global information, which has full coverage of the whole feature extraction. In this paper, a novel CNN model embedded with two attention blocks is proposed. Global attention block (GAB) has advantages on extracting global attention maps and focusing on context information for fundus images. We also put forward class attention block (CAB) to focus on the characteristics of each disease category and reduce the impact of data set imbalance. By combining the above modules and CNN backbone, our GC-Net is constructed for glaucoma classification task, which can be trained in an end-to-end manner. We verify our model through two public dataset experiments and both of them show that our global and classes attention network (GC-Net) produces the best performance compared with the baseline CNN models and other existing state-of-the-art deep learning models.
Conversion d'Identité de la Voix Chantée par Sélection et Concaténation d'Unités Spectrales

Cet article pr´esente un algorithme de s´election d’unit´es spectrales pour la conversion de l’identit´e de la voix chant´ee `a partir de bases de donn´ees non parall`eles. Les algorithmes de conversion bas´es sur des unit´es de parole pr´esentent des avantages importants pour la conversion de l’identit´e vocale : la conversion vocale par s´election d’unit´es permet la pr´eservation des caract´eristiques originales de la voix cible, en utilisant des unit ´ es r ´ eelles; et la segmentation en unit ´ es linguistiques permet d’apprendre la conversion `a partir d’enregistrements de la voix cible non n´ecessairement align´es avec ceux de la voix source. La contribution principale de cet article est de r´ealiser la s´election des unit´es spectrales de la voix cible en fonction de plusieurs facteurs : acoustique, linguistique (phon`emes) et musicaux (hauteur, intensit´e et dur´ee). Pour ce faire, la s´election de la s´equence d’unit´es d’enveloppe spectrale est ´etablie comme un probl`eme d’optimisation `a partir d’une fonction de coˆut multiple qui comprend la distorsion spectrale des chanteurs source et cible ainsi que les diff´erences de hauteur, d’intensit´e et de dur´ee des unit´es spectrales correspondantes. L’objectif est de guider la s´election vers des enveloppes spectrales du chanteur cible partageant un contexte musical similaire avec celles du chanteur source. Il est montr´e lors d’une exp´erience perceptive que l’algorithme propos´e am´eliore le naturel de la conversion et la similarit´e avec la voix cible.
Getting the unknown from the known in bacteria, and the role of text mining

Recent advances in high-throughput technologies have resulted in a ood of large-scale biological data sets. These are a challenge to the data mining community for two reasons primarily: rst, most of the data sets contain a lot of noise and can thus not be considered as "facts", and second, knowledge about the underlying biology is needed to make sense of them. In this talk, I will focus on the available data relevant to the eukaryotic cell cycle. I will try to illustrate how careful reanalysis of the data can drastically improve the signal-to-noise ratio. how integration of heterogeneous data sets can be used in an explorative fashion to make new biological hypotheses, and nally how these can sometimes be proven through comparison of data from multiple organ-
High transcriptional error rates 2 vary as a function of gene expression level

18 Errors in gene transcription can be costly, and organisms have evolved to prevent their 19 occurrence or mitigate their costs. The simplest interpretation of the drift barrier hypothesis 20 suggests that species with larger population sizes would have lower transcriptional error rates. 21 However, Escherichia coli seems to have a higher transcriptional error rate than species with 22 lower effective population sizes, e.g. Saccharomyces cerevisiae . This could be explained if 23 selection in E. coli were strong enough to maintain adaptations that mitigate the consequences 24 of transcriptional errors through robustness, on a gene by gene basis, obviating the need for 25 low transcriptional error rates and associated costs of global proofreading. Here we note that if 26 selection is powerful enough to evolve local robustness, selection should also be powerful 27 enough to locally reduce error rates. We therefore predict that transcriptional error rates will 28 be lower in highly abundant proteins on which selection is strongest. However, we only expect 29 this result when error rates are high enough to significantly impact fitness. As expected, we find 30 such a relationship between expression and transcriptional error rate for non C → U errors in E. 31 coli (especially G → A), but not in S. cerevisiae . We do not find this pattern for C → U changes in E. 32 coli , presumably because most deamination events occurred during sample preparation, but do 33 for C → U changes in S. cerevisiae , supporting the interpretation that C → U error rates estimated 34 with an improved protocol, and which occur at rates comparable to E. coli non C → U errors, are 35 biological. 36
SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack

Previous studies have revealed that artificial intelligence (AI) systems are vulnerable to adversarial attacks. Among them, model extraction attacks fool the target model by generating adversarial examples on a substitute model. The core of such an attack is training a substitute model as similar to the target model as possible, where the simulation process can be categorized in a data-dependent and data-free manner. Compared with the data-dependent method, the data-free one has been proven to be more practical in the real world since it trains the substitute model with synthesized data. However, the distribution of these fake data lacks diversity and cannot detect the decision boundary of the target model well, resulting in the dissatisfactory simulation effect. Besides, these data-free techniques need a vast number of queries to train the substitute model, increasing the time and computing consumption and the risk of exposure. To solve the aforementioned problems, in this paper, we propose a novel data-free model extraction method named SCME (Self-Contrastive Model Extraction), which considers both the inter- and intra-class diversity in synthesizing fake data. In addition, SCME introduces the Mixup operation to augment the fake data, which can explore the target model's decision boundary effectively and improve the simulating capacity. Extensive experiments show that the proposed method can yield diversified fake data. Moreover, our method has shown superiority in many different attack settings under the query-limited scenario, especially for untargeted attacks, the SCME outperforms SOTA methods by 11.43\% on average for five baseline datasets.
Stimulation of NCAM1-14.3.3.ζδ-derived Peptide Interaction Fuels Angiogenesis and Osteogenesis in Ageing

The skeletal structure and bone marrow endothelium collectively form a critical functional unit essential for bone development, health, and aging. At the core of osteogenesis and bone formation lies the dynamic process of angiogenesis. In this study, we reveal a potent new endogenous anabolic NCAM1-14.3. 3. ζδ-derived-Peptide interaction, which stimulates bone angiogenesis and osteogenesis during homeostasis, aging, and age-related bone diseases. Employing high-resolution imaging and inducible cell-specific mouse genetics, our results elucidate the pivotal role of the NCAM1-14.3. 3. ζδ-derived-Peptide interaction in driving the expansion of Clec14a+ angiogenic endothelial cells. Notably, Clec14a+ endothelial cells express key osteogenic factors. The NCAM1-14.3. 3. ζδ-derived-Peptide interaction in osteoblasts drives osteoblast differentiation, ultimately contributing to the genesis of new bone. Moreover, the NCAM1-14.3. 3. ζδ-derived-Peptide interaction leads to a reduction in bone resorption. In age-associated vascular and bone loss diseases, stimulating the NCAM1-14.3. 3. ζδ-derived-Peptide interaction not only promotes angiogenesis but also reverses bone loss. Consequently, harnessing the endogenous anabolic potential of the NCAM1-14.3. 3. ζδ-derived-Peptide interaction emerges as a promising therapeutic modality for managing age-related bone diseases.
Composition and cation order variations in a sector-zoned blueschist pyroxene

Pyroxene crystals in aknost monomineralic veins cutting a blueschist rock from California show sector zoning. Portions of each crystal which grew at faces cutting the c axis are sodic augite (e.9. Jd,,AcroAugur) whereas omphacite (e.g. JdrrAc,rAugaT) formed at faces parallel to the c axis (probably at {100}, {010}, {1 l0}). Examination by transmission electron microscopy reveals that the omphacite has a primitive lattice with antiphase domains on a scale of less than -50A and, in a few areas, exsolution lamellae approximately parallel to (100) of a disordered pyroxene. The sodic augite contains fine lamellae of amphibole (? ) approximately parallel to (010). I suggest hat the activities of ions in the solutions from which crystallization occurred dictated a pyroxene composition between omphacite and augite but that this lay within a broad, low-temperature miscibility gap and two pyroxenes grew simultaneously. The sectoral arrangement developed during rapid crystal growth; local charge balance etrects in each new layer of material controlled the composition distribution. Short-range ordering occurred during growth in directions normal to the pyroxene chains (giving omphacite) but was restricted during growth parallel to the chains (giving sodic augite).
Potential Assessment of Eucalyptus Grown for Biorrefinery Processes

With the predictability of oil shortage, there is a strong demand for renewable and sustainable raw materials. In this scenario, lignocellulosic material stands out as a potential solution. With the use of bio-refinery, they can be processed into high value added products through chemical, biochemical and thermochemical processes. A great source of lignocellulosic biomass today is the wood of Eucalyptus, which has high levels of production and productivity in Brazil, reaching numbers between 6080 m3ha1year-1. In order to achieve a more efficient utilization of biomass in a biorefinery concept, it is necessary first to perform chemical analyses to define the operating conditions of the conversion processes, since heterogeneity and high chemical complexity is an inherent property of the biomass. In this context, this study aimed to chemically characterize and investigate the potential of three species of Eucalyptus (E. urophylla, E. saligna and E. dunnii) grown in Rio Grande do Sul state for biorefinery purposes. Results of higher heating value for E. urophylla, E. saligna and E.dunnii were similar (19.10, 19.10 and 19.15 MJ/ kg respectively). However, cellulose content results for E. saligna (47.5%±2.40) were highlighted, being slightly higher than those for E. urophylla (42.75%±1.34) and E. dunnii (43.10%±1.13). Hemicelluloses content for E. urophylla (23.25%±0.78) was slightly superior to the others (20.35%±2.05 for E. saligna and 18.80%±2.40 for E. dunnii). Finally, it was concluded that the analysed species of Eucalyptus have high potential for biorefinery in thermochemical processes. The species E. urophylla has the greatest potential for biorefinery processes using hemicelluloses, and the species E. saligna has greater potential for biorefinery processes utilizing cellulose.
An Anomaly Detection Fabric for Clouds Based on Collaborative VM Communities

The vast attack surface of clouds presents a challenge in deploying scalable and effective defenses. Traditional security mechanisms, which work from inside the VM fail to provide strong protection as attackers can bypass them easily. The only available option is to provide security from the layer below the VM i.e., the hypervisor. Previous works that attempt to secure VMs from "outside" either incur substantial space or compute overheads making them slow and impractical or require modifications to the OS or the application codebase. To address these issues, we propose an anomaly detection fabric for clouds based on system call monitoring, which compresses the stream of system calls at their source making the system scalable and near real-time. Our system requires no modifications to the guest OS or the application making it ideal for the data center setting. Additionally, for robust and early detection of threats, we leverage the notion of VM/container communities that share information about attacks in their early stages to provide immunity to the entire deployment. We make certain aspects of the system flexible so that vendors can tune metrics to offer customized protection to clients based on their workload types. Detailed evaluation on a prototype implementation on KVM substantiates our claims.
REPENT: Analyzing the Nature of Identifier Renamings

Source code lexicon plays a paramount role in software quality: poor lexicon can lead to poor comprehensibility and even increase software fault-proneness. For this reason, renaming a program entity, i.e., altering the entity identifier, is an important activity during software evolution. Developers rename when they feel that the name of an entity is not (anymore) consistent with its functionality, or when such a name may be misleading. A survey that we performed with 71 developers suggests that 39 percent perform renaming from a few times per week to almost every day and that 92 percent of the participants consider that renaming is not straightforward. However, despite the cost that is associated with renaming, renamings are seldom if ever documented-for example, less than 1 percent of the renamings in the five programs that we studied. This explains why participants largely agree on the usefulness of automatically documenting renamings. In this paper we propose REanaming Program ENTities (REPENT), an approach to automatically document-detect and classify-identifier renamings in source code. REPENT detects renamings based on a combination of source code differencing and data flow analyses. Using a set of natural language tools, REPENT classifies renamings into the different dimensions of a taxonomy that we defined. Using the documented renamings, developers will be able to, for example, look up methods that are part of the public API (as they impact client applications), or look for inconsistencies between the name and the implementation of an entity that underwent a high risk renaming (e.g., towards the opposite meaning). We evaluate the accuracy and completeness of REPENT on the evolution history of five open-source Java programs. The study indicates a precision of 88 percent and a recall of 92 percent. In addition, we report an exploratory study investigating and discussing how identifiers are renamed in the five programs, according to our taxonomy.