Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution Oriented Monitoring oriented to Security Properties for Cloud Applications

Internet is changing from an information space to a dynamic computing space. Data distribution and remotely accessible software services, dynamism, and autonomy are prime attributes. Cloud technology offers a powerful and fast growing approach to the provision of infrastructure (platform and software services) avoiding the high costs of owning, operating, and maintaining the computational infrastructures required for this purpose. Nevertheless, cloud technology still raises concerns regarding security, privacy, governance, and compliance of data and software services offered through it. Concerns are due to the difficulty to verify security properties of the different types of applications and services available through cloud technology, the uncertainty of their owners and users about the security of their services, and the applications based on them, once they are deployed and offered through a cloud. This work presents an innovative and novel evolution-oriented, cloud-specific monitoring model (including an architecture and a language) that aim at helping cloud application developers to design and monitor the behavior and functionality of their applications in a cloud environment.
Ontology‐based model‐driven development of a destination management portal: Experience and lessons learned

We present a case study in model‐driven development of an e‐tourism portal that we chose to develop through generation from a domain model encoded as an ontology. We present (1) the requirements of e‐tourism portal, which dictated its high‐level design; (2) the principles behind our implementation strategy, including the use of a domain ontology as a starting model within the context of a model‐driven transformational approach; (3) the ontology development process and the code generation strategy used; and (4) the lessons learned. In particular, we compare our experiences to those reported in the model‐driven engineering (MDE) literature along 3 dimensions, ie, (1) the impact of MDE on the development process, (2) the choice of the modeling approach, and (3) the impact of code generation on design and code quality and testing. Overall, our experiences corroborated some of the theoretical claims and many of the practical experiences with MDE. Key findings include (1) model‐driven development makes maintenance, not development, more efficient; (2) it does require a higher skill level than traditional development; (3) clients and managers need to be educated into what incrementality means in a generative approach; (4) UML is neither necessary nor sufficient to handle the required representational flexibility; (5) it is difficult to build models that are good for both human consumption and code generation; and (6) it is difficult to generate code that is, simultaneously, efficient, pretty, and easy to maintain. We conclude by summarizing the findings of the paper.
Nonvolatile Memory and Computing Using Emerging Ferroelectric Transistors

Ferroelectric FETs (FeFETs) are emerging as a promising nano device candidate for the next-generation energy-efficient embedded nonvolatile memory (NVM). This promise comes from not only the CMOS-scaling compatibility, but also the compact fusion of logic and non-volatility in a single device that provides opportunities for efficient memory access and in-memory computing. This talk investigates circuit opportunities that harness these intriguing FeFET device features, providing insights into new computation paradigms beyond existing solutions.
PLAQUETTES ET SYNDROMES CORONAIRES AIGUS: PHYSIOPATHOLOGIE

214 La Lettre du Pharmacologue Volume 12 n° 10 décembre 1998 es plaquettes sont des cellules anucléées dont la principale fonction est d’assurer l’hémostase primaire au niveau d’une brèche vasculaire, et de rendre secondairement le thrombus plus solide et moins dégradable. Le dysfonctionnement plaquettaire et en particulier le défaut d’agrégation entraînent des complications hémorragiques graves telle la thrombasthénie de Glanzmann, caractérisée par une anomalie du récepteur GPIIb/IIIa. À l’inverse, leur sollicitation extrême en cas de rupture de plaques d’athérome coronaire se complique d’une thrombose coronaire pouvant aboutir à l’accident aigu clinique, parfois fatal. L’objectif de cet article est de faire le point sur le rôle physiopathologique des plaquettes dans la thrombose coronaire en essayant de dégager une base rationnelle aux nouveaux principes thérapeutiques qui prévalent aujourd’hui dans la prise en charge des accidents coronariens aigus.
Study of a Multisection Synchronous Induction Coil Launcher

Synchronous induction coil launcher (SICL) has advantages of friction-free and simple control. It could be widely used in low-speed ejector and high-speed launch. While multistage SICL is hard to design due to complicated inductive coupling. Armature is often subjected to wavelike electromagnetic force (EMF), which is harmful for the sensitive load. Driving coils are difficult to construct for a large power supply. This paper proposed a multisection SICL to smooth the pull EMF, increase the thrust, and reduce the pressure of each coil. A typical two-section SICL is designed and constructed. Motion characteristics and electromagnetic properties of the launcher are analyzed based on finite-element software. The results of simulation and test are in agreement. The single-section and two-section SICL are compared to verify the feasibility of EMF enhancement. Finally, the effect of ignition time on the acceleration smoothness of the launcher is studied.
Automatic Playlist Continuation through a Composition of Collaborative Filters

The RecSys Challenge 2018 focused on automatic playlist continuation, i.e., the task was to recommend additional music tracks for playlists based on the playlist's title and/or a subset of the tracks that it already contains. The challenge is based on the Spotify Million Playlist Dataset (MPD), containing the tracks and the metadata from one million real-life playlists. This paper describes the automatic playlist continuation solution of team Latte, which is based on a composition of collaborative filters that each capture different aspects of a playlist, where the optimal combination of those collaborative filters is determined using a Tree-structured Parzen Estimator (TPE). The solution obtained the 12th place out of 112 participating teams in the final leaderboard. Team Latte participated in the main track of the challenge of the RecSys Challenge 2018.
Intrinsic Geometric Information Transfer Learning on Multiple Graph-Structured Datasets

Graphs provide a powerful means for representing complex interactions between entities. Recently, deep learning approaches are emerging for representing and modeling graph-structured data, although the conventional deep learning methods (such as convolutional neural networks and recurrent neural networks) have mainly focused on grid-structured inputs (image and audio). Leveraged by the capability of representation learning, deep learning based techniques are reporting promising results for graph applications by detecting structural characteristics of graphs in an automated fashion. In this paper, we attempt to advance deep learning for graph-structured data by incorporating another component, transfer learning. By transferring the intrinsic geometric information learned in the source domain, our approach can help us to construct a model for a new but related task in the target domain without collecting new data and without training a new model from scratch. We thoroughly test our approach with large-scale real corpora and confirm the effectiveness of the proposed transfer learning framework for deep learning on graphs. According to our experiments, transfer learning is most effective when the source and target domains bear a high level of structural similarity in their graph representations.
Fuzzy-Model-Based Output Feedback Controller Design for Discrete-Time Non-Affine Nonlinear Systems via Piecewise Lyapunov Functions

A robust H∞ output-feedback (DOF) control method is proposed for discrete-time non-affine nonlinear systems based on Takagi-Sugeno (T-S) fuzzy models. On the basis of several convexification techniques and piecewise quadratic Lyapunov functions (PQLFs), sufficient conditions for the controller design are derived. A simulation example is presented to show the feasibility of the proposed method.
Sex differences in the effect of cannabinoid type 1 receptor deletion on locus coeruleus‐norepinephrine neurons and corticotropin releasing factor‐mediated responses

Cannabinoids are capable of modulating mood, arousal, cognition and behavior, in part via their effects on the noradrenergic nucleus locus coeruleus (LC). Dysregulation of LC signaling and norepinephrine (NE) efflux in the medial prefrontal cortex (mPFC) can lead to the development of psychiatric disorders, and CB1r deletion results in alterations of α2‐ and β1‐adrenoceptors in the mPFC, suggestive of increased LC activity. To determine how CB1r deletion alters LC signaling, whole‐cell patch‐clamp electrophysiology was conducted in LC‐NE neurons of male and female wild type (WT) and CB1r‐knock out (KO) mice. CB1r deletion caused a significant increase in LC‐NE excitability and input resistance in male but not female mice when compared to WT. CB1r deletion also caused adaptations in several indices of noradrenergic function. CB1r/CB2r‐KO male mice had a significant increase in cortical NE levels and tyrosine hydroxylase and CRF levels in the LC compared to WT males. CB1r/CB2r‐KO female mice showed a significant increase in LC α2‐AR levels compared to WT females. To further probe actions of the endocannabinoid system as an anti‐stress neuromediator, the effect of CB1r deletion on CRF‐induced responses in the LC was investigated. The increase in LC‐NE excitability observed in male and female WT mice following CRF (300 nM) bath application was not observed in CB1r‐KO mice. These results indicate that cellular adaptations following CB1r deletion cause a disruption in LC‐NE signaling in males but not females, suggesting underlying sex differences in compensatory mechanisms in KO mice as well as basal endocannabinoid regulation of LC‐NE activity.
Robustness Estimation of Infrastructure Networks: On the Usage of Degree Centrality

This study aims to clarify a few concepts when assessing the robustness of complex systems with the usage of complex network methods. Many researchers simply use the degree centrality as a proxy for node importance in a network. We show that such assumption can lead to rather overestimated robustness values, neglecting much stronger attacks. Furthermore, we highlight the state-of-the-art in the area of network dismantling, a sub-area of complex networks, emerged in the last 2-3 years. Three case studies on real infrastructure networks, including subway networks, air transportation networks, and power grid networks, are used here to show the consequences of using an inappropriate proxy for robustness. Moreover, we highlight the trade-off between solution quality and computational cost for larger networks. We hope that our study helps to choose an appropriate, recent method from network dismantling instead of simple using degree centrality, when analyzing the robustness of complex networks.
Severe Home Injuries and Disabilities in 2nd İnönü District, İzmir

Purpose: The aim of this study was to determine severe home injuries resulting from a disability in Turkey. Methods: This cross-sectional study was conducted in 2. İnönü district in İzmir. There were 1,765 households, and a sample of 326 households was randomly selected. Data on 1,332 people were collected via face-to-face interviews. The chi-square test, Fisher’s exact test, and logistic regression analysis were used. Results: There were a total of 145 home injuries (10.9%). There were 22 severe home injuries (1.7%) such as falling from high levels, electrical shock, fire burn, and poisoning. Most severe home injuries were due to falling from high (45%) and took place in slum settlements (77%). Nine of these injuries resulted in a disability (7 per 1000). When we evaluated all home injuries, children under the age of five years old (28.8%), women (14.5%), and those with moderate–good income (12.8%) had significantly more injuries. However when special attention was paid to severe home injuries and disabilities, only children under the age of five years old had more severe injuries (5.4%) and disabilities (2.7%). The risk of severe home injuries is four times higher (Odds Ratio-OR: 4.27; 95% CI: 1.6-11.3) and that of disabilities is nearly six times higher (OR: 5.57; 95% CI: 1.3–22.9) in children under the age of five. Conclusion: Children under the age of five years old had severe home injuries and disabilities. Routine home visits and training of mothers who have children under the age of five by primary health care workers should be organized to prevent severe home injuries.
System Modeling and Switching Control Strategy of Wireless Power Transfer System

This paper proposed a fast and robust control strategy for a wireless power transfer system using primary side control. First, a sinusoidal reference trajectory signal was generated by analyzing the properties of the vector fields associated with all possible configurations of the primary power converter. Then, a switching controller was designed based on the switching behavior of the converter and the trajectory reference signal for the switched differential equations, capturing the dynamics of the primary side converter. Finally, a set of points around the reference trajectory was defined to establish the global asymptotic stability. The proposed control strategy has higher dynamic responding speed to large variations and perturbations of the load in comparison to other suggested controllers. The simulation and experimental results show that the primary current overshoot was efficiently suppressed, and the system response performance was improved.
Postmortem T2*- Weighted MRI Imaging of Cortical Iron Reflects Severity of Alzheimer’s Disease

The value of iron-based MRI changes for the diagnosis and staging of Alzheimer’s disease (AD) depends on an association between cortical iron accumulation and AD pathology. Therefore, this study determined the cortical distribution pattern of MRI contrast changes in cortical regions selected based on the known distribution pattern of tau pathology and investigated whether MRI contrast changes reflect the underlying AD pathology in the different lobes. T2*-weighted MRI was performed on postmortem cortical tissue of controls, late-onset AD (LOAD), and early-onset AD (EOAD) followed by histology and correlation analyses. Combining ex vivo high-resolution MRI and histopathology revealed that: 1) LOAD and EOAD have a different distribution pattern of AD pathological hallmarks and MRI contrast changes over the cortex, with EOAD showing more severe MRI changes; 2) per lobe, severity of AD pathological hallmarks correlates with iron accumulation, and hence with MRI. Therefore, iron-sensitive MRI sequences allow detection of the cortical distribution pattern of AD pathology ex vivo.
Cu-In Fine-Pitch-Interconnects with Enhanced Shear Strength

The heterogeneous 3D integration of temperature sensitive components requires a further development of joining processes at low temperatures. A viable approach is based on In as the low-melting component for eutectic/solid-liquid-interdiffusion (SLID) bonding. In our present study, we investigated the effect of different fluxes and processing conditions on the interconnect quality. The shear strength of the Cu-In fine-pitch interconnects was determined by shearing single-stack samples. Nearly defect-free joining zones and shear strengths up to 120 MPa indicate a high potential of the Cu-In SLID bonding technology.
High-Average and Guaranteed Performance for Wireless Networks-on-Chip Architectures

Network on Chip (NoC) is the underlying communication platform for multi-core embedded systems. Wireless NoCs (WNoC) employ wired and wireless structures simultaneously to facilitate communication scenarios. In this paper, we propose an arbitration mechanism to guarantee the performance parameters for real-time traffic flows transferred in the wireless plane, while preserving high average performance for all the traffic flows in the wired or wireless sections of a wireless NoC. Different scenarios have been carefully selected and clear suggestions are provided using analytic performance models to effectively use a wireless NoC for real time application category. We have provided different examples to illustrate the effectiveness of the proposal.
Resource Allocation in Energy-Harvesting Sensor Networks

We consider an energy-harvesting sensor network, possibly a subnetwork of a larger system, with a central unit that acts as information sink, and also recharges periodically the sensors. At each stage, a system supervisor allows the central unit to manage a certain amount of energy, which is to be apportioned amongst the sensors for their sensing tasks. How should energy be apportioned? And how successful can the network be? We relate this sensing and optimization task to log-optimal portfolio theory, and find that the underlying model can give rise to two quite different regimes. One is characterized by unbounded information accumulation and a nontrivial optimal apportionment strategy, and the other by information stagnation and a best strategy of winner-take-all.
Small Shielded Bluetooth Module Equipped with Slot Antenna on the Surface

In this paper, a land grid array-type ultra-small shielded module that has a 2.4 GHz slot antenna is presented. Slots are patterned on both an interposer and a shield layer. The slots are connected at the side of the interposer and compose the slot antenna. A large part of the slot antenna is formed on the shield deposited on the surface of the module. The slot on the interposer is short and occupy a small area, therefore the slot antenna allows the area of the interposer to be reduced. In addition to that the slot antenna reduces the keep-out zone in which no wires can be laid out on the board. It is because the area of the slot on the interposer is small. A prototype of the module with a Bluetooth Low Energy chip was fabricated. The volume of the prototype is 5.25 x 9.0 x 1.0 mm3. The keep-out zone of the module is 9.0 mm2. The module has received Japanese modular approval certification because it contains all the elements for wireless communication except for a power supply.
Fast path space filtering by jittered spatial hashing

Restricting path tracing to a small number of paths per pixel for performance reasons rarely achieves a satisfactory image quality for scenes of interest. However, path space filtering may dramatically improve the visual quality by sharing information across vertices of paths classified as "nearby". While thus contributions can be filtered in path space and beyond the first intersection, searching "nearby" paths is more expensive than filtering in screen space. We greatly improve over this performance penalty by storing and looking up the required information in a hash map using hash keys constructed from jittered and quantized information, such that only a single query may replace costly neighborhood searches.
Resilient Control and Safety for Cyber-Physical Systems

Many Cyber-Physical Systems (CPSs) are comprising a multitude of computing entities that can collectively exhibit an emergent behavior. A compelling example of such systems is the drone swarm, which are beginning to see increasing application in battlefield surveillance and reconnaissance. The emergent behavior they exhibit is that of flight formation. A particularly interesting flight configuration is V-formation, especially for long-range missions. V-formation is emblematic of migratory birds such as Canada geese, where a bird flying in the upwash region of the bird in front of it can enjoy significant energy savings. In addition, the V-formation offers a clear view benefit, as no bird's field of vision is obstructed by another bird in the formation. Hence, it is important to quantify the resiliency of the control algorithms underlying this class of CPSs to various kinds of attacks. This question provides the motivation for the investigation put forth in this abstract and detailed in [4].
Tree Detection With Low-Cost Three-Dimensional Sensors for Autonomous Navigation in Orchards

This letter deals with autonomous farming and with the autonomous navigation of an agricultural robot in orchards. These letter are typical semistructured environments where the dense canopy prevents from using GPS signal and embedded sensors are often preferred to localize the vehicle. To move safely in such environments, it is necessary to provide the robot the ability of detecting and localizing trees. This letter focuses on this problem. It presents a low cost but an efficient vision-based system allowing to detect accurately, quickly, and robustly the trees. It is made of four stereo cameras that provide a point cloud characterizing the environment. The key idea is to find the tree trunks by detecting their shadows, which are materialized by concavities in the obtained point cloud. In this way, branches and leaves are not taken into account, improving the detection robustness and, therefore, the navigation strategy. The method has been implemented using robot operating system (ROS) and validated using data sequences taken in several different orchards. The obtained results definitely validate the approach and its performances show that the processing time (around 1 ms) is sufficiently short for the data to be used at the control level. A comparison with other approaches from the literature is also provided.
Deep Multi-Task Learning with Shared Memory

Neural network based models have achieved impressive results on various specific tasks. However, in previous works, most models are learned separately based on single-task supervised objectives, which often suffer from insufficient training data. In this paper, we propose two deep architectures which can be trained jointly on multiple related tasks. More specifically, we augment neural model with an external memory, which is shared by several tasks. Experiments on two groups of text classification tasks show that our proposed architectures can improve the performance of a task with the help of other related tasks.
Robust Image Segmentation in Low Depth Of Field Images

In photography, low depth of field (DOF) is an important technique to emphasize the object of interest (OOI) within an image. Thus, low DOF images are widely used in the application area of macro, portrait or sports photography. When viewing a low DOF image, the viewer implicitly concentrates on the regions that are sharper regions of the image and thus segments the image into regions of interest and non regions of interest which has a major impact on the perception of the image. Thus, a robust algorithm for the fully automatic detection of the OOI in low DOF images provides valuable information for subsequent image processing and image retrieval. In this paper we propose a robust and parameterless algorithm for the fully automatic segmentation of low DOF images. We compare our method with three similar methods and show the superior robustness even though our algorithm does not require any parameters to be set by hand. The experiments are conducted on a real world data set with high and low DOF images.