Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-View Co-Modeling and Co-Simulation Framework for Heterogeneous Embedded Systems

The design of embedded platforms for connected objects (smartphones and beyond) is made harder because such systems must satisfy stringent constraint beyond functional correctness and accuracy. Namely, extra-functional requirements such as performance, power consumption and thermal dissipation are equally important, while there are important trade-offs between them. In the recent years so-called virtual platform-based design has been proposed, so that rather abstract models of functional and extra-functional views could be applied, together with application models seen as task graphs with performance and memory requirements , to simulate global system dynamics before hardware was actually built. While a truly based Model Driven Engineering approach, tools and methods from this methodological area were seldom used in the process , which mainly consist of SystemC multiform simulation in practice. But, increasingly, dedicated domain-specific simulation tools, appear in the landscape, and the way to combine them properly for co-simulation becomes a true issue. It is our belief that joint multi-view modeling is key as the co-modeling approach to occur prior to co-simulation. This is all the more true as the various views/aspects here meant to be co-simulated are not distinct components of a system, as one generally assume, but distinct facets of a same component. For instance in our case a SystemC simu-lator may be found to extract abstract execution traces (or uns) from a generator program, traces which will be further annotated and decorated in the other distinct extra functional view ranges (performance, power, temperature), whose results may in turn impact the forthcoming rest of the trace. We provide a multiview modeling framework, and then show as an example how to connect it with a real (co)-simulation tool such as Synopsys Platform Architect MCO.
A Credibility-based Defense SSDF Attacks Scheme for the Expulsion of Malicious Users in Cognitive Radio

Cognitive radio (CR) can improve the utilization of the spectrum by making use of licensed spectrum in an opportunistic manner. However, the security aspects of cognitive radio networks have garnered little attention. In this paper, we identify a threat to cognitive radio networks, which we call the spectrum sensing data falsification (SSDF) attack. SSDF attack can hugely degrade the achievable detection accuracy. To counter this threat, we proposed a new method to confront the SSDF attacks by excluding malicious users is cognitive radio. In detail, the proposed scheme defense the SSDF attacks by calculating and updating the credit value of the Secondary Users (SUs), malicious users are excluded to avoid the attacks affect in cooperative spectrum sensing. Simulations results show that both the detection probability and the false alarm probability are significantly improved compared to the case when all users are by default trusted to be normal users.
Effect Preservation in Transaction Processing in Rule Triggering Systems

Rules provide an expressive means for implementing database behavior: They cope with changes and their ramifications. Rules are commonly used for integrity enforcement, i.e., for repairing database actions in a way that integrity constraints are kept. Yet, Rule Triggering Systems fall short in enforcing effect preservation, i.e., guaranteeing that repairing events do not undo each other, and in particular, do not undo the original triggering event. A method for enforcement of effect preservation on updates in general rule triggering systems is suggested. The method derives transactions from rules, and then splits the work between compile time and run time. At compile time, a data structure is constructed, that analyzes the execution sequences of a transaction and computes minimal conditions for effect preservation. The transaction code is augmented with instructions that navigate along the data structure and test the computed minimal conditions. This method produces minimal effect preserving transactions, and under certain conditions, provides meaningful improvement over the quadratic overhead of pure run time procedures. For transactions without loops, the run time overhead is linear in the size of the transaction, and for general transactions, the run time overhead depends linearly on the length of the execution sequence and the number of loop repetitions. The method is currently being implemented within a traditional database system.
Ruchi: Rating Individual Food Items in Restaurant Reviews

Restaurant recommendation systems are capable of recommending restaurants based on various aspects such as location, facilities and price range. There exists some research that implements restaurant recommendation systems, as well as some famous online recommendation systems such as Yelp. However, automatically rating individual food items of a restaurant based on online customer reviews is an area that has not received much attention. This paper presents Ruchi, a system capable of rating individual food items in restaurants. Ruchi makes use of Named Entity Recognition (NER) techniques to identify food names in restaurant reviews. Typed dependency technique is used to identify opinions associated with different food names in a single sentence, thus it was possible to carry out entity-level sentiment analysis to rate individual food items instead of sentence-level sentiment analysis as done by previous research.
Biophysically inspired model for functionalized nanocarrier adhesion to cell surface: roles of protein expression and mechanical factors

In order to achieve selective targeting of affinity–ligand coated nanoparticles to the target tissue, it is essential to understand the key mechanisms that govern their capture by the target cell. Next-generation pharmacokinetic (PK) models that systematically account for proteomic and mechanical factors can accelerate the design, validation and translation of targeted nanocarriers (NCs) in the clinic. Towards this objective, we have developed a computational model to delineate the roles played by target protein expression and mechanical factors of the target cell membrane in determining the avidity of functionalized NCs to live cells. Model results show quantitative agreement with in vivo experiments when specific and non-specific contributions to NC binding are taken into account. The specific contributions are accounted for through extensive simulations of multivalent receptor–ligand interactions, membrane mechanics and entropic factors such as membrane undulations and receptor translation. The computed NC avidity is strongly dependent on ligand density, receptor expression, bending mechanics of the target cell membrane, as well as entropic factors associated with the membrane and the receptor motion. Our computational model can predict the in vivo targeting levels of the intracellular adhesion molecule-1 (ICAM1)-coated NCs targeted to the lung, heart, kidney, liver and spleen of mouse, when the contributions due to endothelial capture are accounted for. The effect of other cells (such as monocytes, etc.) do not improve the model predictions at steady state. We demonstrate the predictive utility of our model by predicting partitioning coefficients of functionalized NCs in mice and human tissues and report the statistical accuracy of our model predictions under different scenarios.
Urinary C-Peptide of Insulin as a Non-Invasive Marker of Nutritional Status: Some Practicalities

Nutritional status is a critical element of many aspects of animal ecology, but has proven difficult to measure non-invasively in studies of free-ranging animals. Urinary C-peptide of insulin (UCP), a small polypeptide cleaved in an equimolar ratio from proinsulin when the body converts it to insulin, offers great promise in this regard, and recent studies of several non-human primate species have utilized it with encouraging results. Despite this, there are a number of unresolved issues related to the collection, processing, storage and transport of samples. These include: contamination of samples on collection (most commonly by dirt or faeces), short-term storage before returning to a field station, differences in processing and long-term storage methods (blotting onto filter paper, freezing, lyophilizing), and for frozen samples, transportation while keeping samples frozen. Such issues have been investigated for urine samples in particular with respect to their effects on steroid hormone metabolites, but there has been little investigation of their effects on UCP measurement. We collected samples from captive macaques, and undertook a series of experiments where we systematically manipulated samples and tested the effects on subsequent UCP measurements. We show that contamination of urine samples by faeces led to a decrease in UCP levels by >90%, but that contamination with dirt did not have substantial effects. Short-term storage (up to 12 hours) of samples on ice did not affect UCP levels significantly, but medium-term storage (up to 78 hours) did. Freezing and lyophilization for long-term storage did not affect UCP levels, but blotting onto filter paper did. A transportation simulation showed that transporting frozen samples packed in ice and insulated should be acceptable, but only if it can be completed within a period of a few days and if freeze-thaw can be avoided. We use our data to make practical recommendations for fieldworkers.
Iris recognition based on contourlet transform and dominant energy features

Iris recognition a new biometric technology has great advantages such as variability, stability and security. In this paper we propose a new feature extraction method for iris recognition based on contourlet transform. Contourlet transform captures the intrinsic geometrical structures of iris image. It decomposes the iris image into a set of directional subbands with texture details captured in different orientations at various scales. Discriminant analysis is used to determine the optimal threshold for the selection of dominant directional energy components. Only dominant directional energy components are employed as elements of the input feature vector. These input feature vectors are compared with the template feature vectors. Experimental results show that the proposed method reduce processing time and increase the classification accuracy and outperforms the wavelet based method. Index Terms-- Biometrics, Contourlet transform, Directional filter bank, Iris recognition, Laplacian pyramid, Thresholding.
GPU-based simulation of cold air flow for environmental planning

Simulating the effects of different land-use types regarding flow resistance and cold air production is important for controlling air quality around urban areas. In this paper we present a mathematical model and a simulation method for this problem. This model describes the cold air flow to be composed of two variables. The first is the velocity field which depends on flow resistance and the flow gradient. The second variable is a height field of the cold air which depends on cold air production and advection. To accelerate the simulation and its visualization, it is adapted to run on a GPU(Graphical Processing Unit). Implementing the simulation on fragment shaders makes it possible to combine the height field of the landscape with a color-coded volume rendering of the associated cold-air height. In two passes we compute the cold air height for each time step and render the result to a texture. In a third pass, we render the height field of the landscape using this texure as multi-layered opacity map.
Wave Propagation Characteristics of Piezoelectric Fg-Cntrc Plates

The problem of the dynamic response of a structure is fundamentally caused by the propagation of elastic waves in the structure, so the study of wave propagation characteristics is of great importance for analyzing mechanical behavior of the structure. This paper investigated the wave propagation characteristics of infinite piezoelectric carbon nanotube reinforced composite (FG-CNTRC) sandwich plates. Based on the third-order shear deformation theory, the dynamic kinetic equations for sandwich panels were developed by using the Hamiltonian principle. According to the displacement form function of the infinity plate, the dispersion characteristic equation for piezoelectric FG-CNTRC sandwich plates is derived. By the solution, the dispersion relations are obtained. On this basis, the effects of parameter variations on wave propagation characteristics are discussed.
Anti-HDGF Targets Cancer and Cancer Stromal Stem Cells Resistant to Chemotherapy

Purpose: Approximately one third of the patients with advanced non–small cell lung carcinoma (NSCLC) will initially respond to platinum-based chemotherapy, but virtually all tumors will progress (acquired resistance). The remainder will progress during initial treatment (primary resistance). In this study, we test whether the treatment can be improved by inhibiting hepatoma-derived growth factor (HDGF). Experimental Design: Thirteen primary NSCLC heterotransplant models were used to test four treatment regimens, including platinum-based chemotherapy with and without bevacizumab (VEGF-neutralizing antibody) or HDGF-H3 (HDGF-neutralizing antibody) and chemotherapy with bevacizumab and HDGF-H3. Expression of stem cell–related genes was measured using quantitative reverse transcription PCR (qRT-PCR) and immunohistochemistry. Results: Among 13 primary NSCLC heterotransplant models, three (23%) responded to chemotherapy but all relapsed within 20 days. The residual tumors after response to the chemotherapy exhibited an increased expression in 51 (61%) of 84 genes related with stem cell proliferation and maintenance, particularly those in Notch and Wnt pathways, suggesting enrichment for stem cell populations in the residual tumors. Interestingly, tumors from two of three models treated with HDGF-H3, bevacizumab, and chemotherapy combination did not relapse during 6 months of posttreatment observation. Importantly, this treatment combination substantially downregulated expression levels in 57 (68%) of 84 stem cell–related genes, including 34 (67%) of 51 genes upregulated after the chemotherapy. Conclusion: These data support the hypothesis that cancer stem cells (CSC) are a mechanism for chemotherapy resistance and suggest HDGF may be a target for repressing CSCs to prevent relapse of NSCLC sensitive to chemotherapy. Clin Cancer Res; 19(13); 3567–76. ©2013 AACR.
Low-Latency Sliding-Window Aggregation in Worst-Case Constant Time

Sliding-window aggregation is a widely-used approach for extracting insights from the most recent portion of a data stream. The aggregations of interest can usually be cast as binary operators that are associative, but they are not necessarily commutative nor invertible. Non-invertible operators, however, are difficult to support efficiently. The best published algorithms require O(log n) aggregation steps per window operation, where n is the sliding-window size at that point. For a FIFO window, this can be improved to O(1) on average by using two aggregation stacks. This paper presents DABA, a novel algorithm for aggregating FIFO sliding windows that significantly improves upon these time bounds. DABA requires only O(1) aggregation steps per operation in the worst case (not just on average). As such, DABA asymptotically improves the performance of sliding-window aggregation without restricting the operator to be invertible. Our experimental results demonstrate that these theoretical improvements hold in practice. DABA is a substantial improvement over the state of the art in terms of both latency and throughput.
The Influence of Stress Gradient on the Pull-in Phenomena of Microelectromechanical Switches

The electromechanical behaviour of cantilever beam MEM switches incorporating linear residual stress gradient is studied in this work. Using Finite-difference method (FDM), behavioural models that predict the electrostatic deflection and pull-in voltage of such structures have been established. The model accounts for the effect of residual stress gradient on the mechanical behaviour of the cantilever MEM switches, and results have been validated via comparison with experimental data. The completed model offers an efficient means of design, analysis and optimization of cantilever-based electrostatically actuated MEMS devices. It can also be utilized for material property measurement and analysis.
Analytic Continuation by the Fast Fourier Transform

The ill-posed problem of analytic continuation is regularized by a prescribed bound. A simple computer algorithm is given that is based on the fast Fourier transform. The algorithm computes m complex values and a positive error bound with time complexity $O(m\log m)$. As a function of the data errors and the prescribed bound, the numerical error is shown to be consistent with that prescribed by the three-circles principle of Hadamard.
Object Detection Using Keygraphs

We propose a new framework for object detection based on a generalization of the keypoint correspondence framework. This framework is based on replacing keypoints by keygraphs, i.e. isomorph directed graphs whose vertices are keypoints, in order to explore relative and structural information. Unlike similar works in the literature, we deal directly with graphs in the entire pipeline: we search for graph correspondences instead of searching for individual point correspondences and then building graph correspondences from them afterwards. We also estimate the pose from graph correspondences instead of falling back to point correspondences through a voting table. The contributions of this paper are the proposed framework and an implementation that properly handles its inherent issues of loss of locality and combinatorial explosion, showing its viability for real-time applications. In particular, we introduce the novel concept of keytuples to solve a running time issue. The accuracy of the implementation is shown by results of over 800 experiments with a well-known database of images. The speed is illustrated by real-time tracking with two different cameras in ordinary hardware.
Reducing Harmonic Instability and Resonance Problems in PMSG-Based Wind Farms

Unlike conventional power systems where harmonic resonances are coming from passive inductive and capacitive elements, large-scale power electronic systems like wind farms present a more complex system, where the fast dynamics of the power electronic converters may present an inductive or capacitive behavior. Therefore, the interactions between the fast controllers of the power converters and the passive elements may lead to harmonic instability and new resonances at various frequencies. This paper presents an optimum design technique for the wind turbine inner controllers in a PMSG-based wind farm in order to reduce the number of resonances and to mitigate harmonic instability. In this approach, a PMSG-based wind farm is modeled as a multi-input multi-output (MIMO) dynamic system by modeling the high-bandwidth control loops of the power converters. Resonance frequencies and oscillatory modes of the wind farm are identified based on the MIMO matrix. Afterward, a multiobjective optimization procedure based on genetic algorithm is proposed to put the oscillatory modes of the wind farm in suitable locations in order to minimize the number of the resonances and to guarantee a stable operation of the wind farm. A 400-MW wind farm is studied in the PSCAD/EMTDC software environment to confirm the validity of the proposed optimum design technique.
Comparison of SSD Models for Fertile Egg Image Classification

Breeding laying hens and eggs products are prevalent in many parts of Thailand. Egg produced from these farms are important economic product. One important process in industrial incubation is the culling out of eggs that are unlikely to hatch before setting them into an incubator. In this paper we propose a novel method for real-time egg image classification using Single Shot Detector (SSD) with Transfer Learning from MobileNetV2 and RestNet101 model built from Microsoft COCO dataset. Images of eggs on its side are collected, cleaned, augmented, and labeled. The results of predictions using our two models are similar with mAP@. 50IoU at 0.99, however SSD MobileNetV2 is faster to train. A preliminary test of models on images fed from a webcam show good performance with images with one egg, but still need improvement when there are 4 eggs in one image.
Distributed Algorithms for Content Caching in Mobile Backhaul Networks

The growing popularity of mobile multimedia content and the increase of wireless access bitrates are straining backhaul capacity in mobile networks. A cost-effective solution to reduce the strain, enabled by emerging all-IP 4G and 5G mobile backhaul architectures, could be in-network caching of popular content during times of peak demand. In this paper we formulate the problem of content caching in a mobile backhaul as a binary integer programming problem, and we propose a 2- approximation algorithm for the problem. The 2-approximation requires full information about the network topology and the link costs, as well as about the content demands at the different caches, we thus propose two distributed algorithms that are based on limited information on the content demands. We show that the distributed algorithms terminate in a finite number of steps, and we provide analytical results on their approximation ratios. We use simulations to evaluate the proposed algorithms in terms of the achieved approximation ratio and computational complexity on realistic mobile backhaul topologies.
Inhibitory Effect of TNF-α on Malaria Pre-Erythrocytic Stage Development: Influence of Host Hepatocyte/Parasite Combinations

Background The liver stages of malaria parasites are inhibited by cytokines such as interferon-γ or Interleukin (IL)-6. Binding of these cytokines to their receptors at the surface of the infected hepatocytes leads to the production of nitric oxide (NO) and radical oxygen intermediates (ROI), which kill hepatic parasites. However, conflicting results were obtained with TNF-α possibly because of differences in the models used. We have reassessed the role of TNF-α in the different cellular systems used to study the Plasmodium pre-erythrocytic stages. Methods and Findings Human or mouse TNF-α were tested against human and rodent malaria parasites grown in vitro in human or rodent primary hepatocytes, or in hepatoma cell lines. Our data demonstrated that TNF-α treatment prevents the development of malaria pre-erythrocytic stages. This inhibitory effect however varies with the infecting parasite species and with the nature and origin of the cytokine and hepatocytes. Inhibition was only observed for all parasite species tested when hepatocytes were pre-incubated 24 or 48 hrs before infection and activity was directed only against early hepatic parasite. We further showed that TNF-α inhibition was mediated by a soluble factor present in the supernatant of TNF-α stimulated hepatocytes but it was not related to NO or ROI. Treatment TNF-α prevents the development of human and rodent malaria pre-erythrocytic stages through the activity of a mediator that remains to be identified. Conclusions Treatment TNF-α prevents the development of human and rodent malaria pre-erythrocytic stages through the activity of a mediator that remains to be identified. However, the nature of the cytokine-host cell-parasite combination must be carefully considered for extrapolation to the human infection.