Miracle-dz/UniFAR-FTUs huggingface dataset:
Threat Modeling and Analysis for the Cloud Ecosystem

As the usage of the Cloud proliferates, the need for security evaluation of the Cloud also grows. The process of threat modeling and analysis is advocated to assess potential vulnerabilities that can undermine the Cloud security goals. However, given the plethora of distinct services involved in the Cloud ecosystem and the varied attack surfaces entailed in the Cloud-specific architectures, performing threat analysis for the Cloud is a challenging task. Consequently, contemporary Cloud threat analysis approaches, typically using relational security models (e.g., attack graphs, trees.), primarily focus on specific services/layers of the Cloud. Also, these schemes often fail to include the variants of the identified vulnerabilities in their analysis. Hence, a comprehensive threat analysis approach is required that can (a) model and analyze threats across the multilayer Cloud operational stack, and (b) include variants of the vulnerabilities in the threat analysis procedure. We target achieving a holistic Cloud threat analysis by designing a novel multi-layer Cloud model, using Petri Nets, to comprehensively profile the operational behavior of the services involved in the Cloud operations. We subsequently conduct threat modeling to identify threats within and across the different layers of the Cloud operations. Our proposed threat analysis approach also investigates the variants of the potential vulnerabilities to comprehensively infer the Cloud attack surface.
Experimental Investigation of the Suitability of Orange Peel Oil, Neem Oil and Cotton Seed Oil as Alternate Fuel for Diesel Engines: A Review

In 1979 due to sudden increase in prices of the petroleum products by the supplying countries, attention was diverted to find out substitute indigenous vegetable oils as substitute fuel to diesel oil. Lot of work is going on different types of vegetable oils. The major advantage of vegetable oils as fuel is that they are non exhaustible and renewable. Since 10 years, researchers are studying on the effects of biodiesel on engine performance and emissions. The use of biodiesel leads to the substantial reduction in PM, HC and CO emissions accompanying with the imperceptible power loss, the increase in fuel consumption and the increase in NOx emission on conventional diesel engines with no or fewer modification. And it favors to reduce carbon deposit and wear of the key engine parts. Therefore, the blends of biodiesel with small content in place of petroleum diesel can help in controlling air pollution and easing the pressure on scarce resources without significantly sacrificing engine power and economy. However, many further researches about optimization and modification on engine, low temperature performances of engine, new instrumentation and methodology for measurements, etc., should be performed when petroleum diesel is substituted completely by biodiesel. This review has been taken up for identifying the effect of blending neem oil, orange peel oil and cotton seed oil with diesel on engine performance and emissions of CI
SYNTHESIS AND ANTIPLASMODIAL ACTIVITY OF SOME NOVEL CHALCONE DERIVATIVES

Increased drug resistance in malaria toward many of the existing antimalarials make the condition worse. Hence, indicates the necessity of the novel molecules to overcome the problem. A series of chalcone derivatives (3a-4e) were primed via Claisen-Schmidt condensation of substituted aldehydes with substituted methyl ketones. These derivatives were tested against Plasmodium falciparum clinical isolate for their antiplasmodial activity. Furthermore, in-vitro β-hematin formation assay has been conducted in order to gain insight into the possible mechanism of action. Out of the 10 synthesized compounds, two compounds 4a and 4d exhibited promising antiplasmodial activities (50% inhibitory concentration [IC50] values 7.45±0.65 and 6.01±0.29 μg/ml, respectively). Other compounds (3a, 4b, 4e and 3d) showed moderate inhibition against P. falciparum. Among all the compounds, 4a showed good hemozoin inhibitory activity (IC50 - 19.75 μg/ml) while 3a and 4d showed moderate type inhibition. These molecules may act as templates for medicinal chemistry to discover novel and hybrid molecules with improved characteristics, which may become future candidates for the treatment of malaria. Keywords: Chalcone, Malaria, Plasmodium falciparum, β-hematin.
Researching Less-Resourced Languages – the DigiSami Corpus

Increased use of digital devices and data repositories has enabled a digital revolution in data collection and language research, and has also led to important activities supporting speech and language technology research for less-resourced languages. This paper describes the DigiSami project and its research results, focussing on spoken corpus collection and speech technology for the Fenno-Ugric language North Sami. The paper also discusses multifaceted questions on ethics and privacy related to data collection for less-resourced languages and indigenous communities.
The Politics of Flat Taxes

We study the tax systems that arise in a once-and-for-all majority voting equilibrium embedded within a macroeconomic model of inequality. We find that majority voting delivers (i) a small set of outcomes, (ii) zero labor income taxation, and (iii) nearly zero transfers. We find that majority voting, contrary to the literature developed in models without idiosyncratic risk, is quite powerful at restricting outcomes; however, it also delivers predictions inconsistent with observed tax systems.
Efficient Framework to Mitigate DDoS Attacks and Response System 1

A Distributed Denial-of-Service (DDoS) attack is one in which a multitude of compromised systems attack a single target, thereby causing denial of service for users of the targeted system. The flood of incoming messages to the target system essentially forces it to shut down, thereby denying service to the system to legitimate users . The nature of the threats posed by DDoS attacks on large networks, such as the Internet, demands effective detection and response methods. These methods must be deployed not only at the edge but also at the core of the network. This paper presents methods to identify DDoS attacks by computing entropy and frequency-sorted distributions of selected packet attributes. The DDoS attacks show anomalies in the characteristics of the selected packet attributes. The detection accuracy and performance are analyzed using live traffic traces from a variety of network environments ranging from points in the core of the Internet to those inside an edge network. The results indicate that these methods can be effective against current attacks and suggest directions for improving detection of more stealthy attacks. We also describe our detection-response prototype and how the detectors can be extended to make effective response decisions.
Data-Driven Learning Control for Stochastic Nonlinear Systems: Multiple Communication Constraints and Limited Storage

This paper proposes a data-driven learning control method for stochastic nonlinear systems under random communication conditions, including data dropouts, communication delays, and packet transmission disordering. A renewal mechanism is added to the buffer to regulate the arrived packets, and a recognition mechanism is introduced to the controller for the selection of suitable update packets. Both intermittent and successive update schemes are proposed based on the conventional P-type iterative learning control algorithm, and are shown to converge to the desired input with probability one. The convergence and effectiveness of the proposed algorithms are verified by means of illustrative simulations.
Combining high resolution color and depth images for dense 3D reconstruction

In this paper, we present an effective method to reconstruct a dense 3D model of an object or a scene by combining high resolution color and depth images. Conventionally, multiple views of color images can be used for reconstructing a 3D model of the captured scene. Although the conventional method can give the accuracy of the textured-regions of an object, it lacks density and leaves many holes in the texture-less regions. However, a depth camera is capable to capture 3D distance information even in homogenous regions. Still, it gives low resolution and is unable to provide an accurate result of a detailed object. We thus propose a combined method of utilizing both high resolution color and depth images to obtain a high-quality, accurate and dense 3D model. Compared to the conventional methods, our proposed method produces a much denser and more all-over 3D results.
Towards Universal Social Protection. Latin American Pathways and Policy Tools

This book reflects on the public policies, programmes and regulatory frameworks that are taking a rights-based approach to expanding social protection coverage and benefits in Latin America, with a view to achieving universal coverage. The book’s discussion of the policy tools and programmes pursued in the region aims to provide the reader with technical and programmatic insights for assembling and coordinating public policies within consistent and sustainable social protection systems. The combination of normative orientations and stock of technical knowledge, together with advances regarding the rights-based approach to social protection within a life cycle framework, afford the reader not only a tool box of specific social protection instruments, but also an in-depth examination of related political economy aspects. The book has three parts. The first describes the context in which the shifts in social protection policies have occurred. The second examines social protection tools from a life cycle perspective and looks at issues that involve significant geographical or spatial considerations. The last part addresses cross-cutting themes that are crucial for tackling the pathway towards universal social protection.
Systems of Cahn-Hilliard Equations

The phase separation of alloys with two or more components is studied, with emphasis on more than two components. Particular attention is given to differences between multicomponent and binary alloys.Specific topics of the paper include equilibrium theory, aspects of the dynamics, and numerical simulations. In the equilibrium theory, it is found that there is an enriched equilibrium structure that allows for multiple coexisting phases and the presence of triple points in the solution. Dynamic results include the characterization of the spinodal region and of the concentration variations that lead to spinodal decomposition. Unlike the binary theory, not all composition variations lead to separation, and the compositions are not restricted to the convex hull of the equilibrium concentrations. Linear analysis is used to predict that a pseudo-binary will initially result from spinodal decomposition. Numerical simulations of the dynamics for a ternary alloy verify this initially, but more than two phases often.
Applying Multiradio Access Technologies for Reliability Enhancement in Vehicle-to-Everything Communication

The design of the 5G cellular network should take account of the emerging services with divergent quality of service requirements. For instance, a vehicle-to-everything (V2X) communication is required to facilitate the local data exchange and therefore improve the automation level in automated driving applications. In this paper, we inspect the performance of two different air interfaces (i.e., LTE-Uu and PC5) which are proposed by the third generation partnership project to enable the V2X communication. With these two air interfaces, the V2X communication can be realized by transmitting data packets either over the network infrastructure or directly among traffic participants. In addition, the ultra-high reliability requirement in some V2X communication scenarios cannot be fulfilled with any single transmission technology (i.e., either LTE-Uu or PC5). Therefore, we discuss how to efficiently apply multi-radio access technologies (multi-RAT) to improve the communication reliability. In order to exploit the multi-RAT in an efficient manner, both the independent and the coordinated transmission schemes are designed and inspected. Subsequently, the conventional uplink is also extended to the case where a base station can receive data packets through both the LTE-Uu and PC5 interfaces. Moreover, different multicast-broadcast single-frequency network area mapping approaches are also proposed to improve the communication reliability in the LTE downlink. Last but not least, a system level simulator is implemented in this paper. The simulation results do not only provide us insights on the performances of different technologies but also validate the effectiveness of the proposed multi-RAT scheme.
Analysis of Small Oscillations in Complex Electric Power Systems

In this article the mathematical model of complex regulated electric system in matrix form is developed. This mathematical model makes it possible to study the steady-state stability of a complex electrical system by determining the eigenvalues of the dynamics matrix. The model of an electrical system that reflects transient processes for small deviations is convenient, both algorithmically and computationally, in particular, in cases of their joint solution with steady-state equations—the equations of nodal voltages. The obtained results in the form of the eigenvalues of the matrix spectrum are qualitatively the same as the results of classical studies, which is a consequence of the adequacy of the proposed model and the correct reflection of the dynamic processes occurring in a real electrical system. In addition, the equations obtained are of independent importance for the analysis of various modes, including transient, electrical systems of any complexity.
Altered expression of 3β-HSD, CYP17 and 17β-HSD in the foetal porcine gonads in response to anti-androgen flutamide exposure.

We investigated whether the limited access to androgens during late prenatal period alters expression of steroidogenic enzymes involved in androgen production: 3β-hydroxysteroid dehydrogenase/Δ5-Δ4 isomerase (3β-HSD), cytochrome P450 17α-hydroxylase/17,20-lyase (CYP17) and 17β-hydroxysteroid dehydrogenase type 1 (17β-HSD1) or type 3 (17β-HSD3) in the foetal porcine gonads. Pregnant gilts were injected with anti-androgen flutamide (for seven days, 50 mg/day/kg bw) or corn oil (control) starting at 83 (GD90) or 101 (GD108) gestational day. To assess 3β-HSD, CYP17 and 17β-HSD1 or 17β-HSD3 expression, real-time PCR and immunohistochemistry were performed. In testes from flutamide-treated foetuses, increased 3β-HSD and CYP17 mRNA expression was observed in the GD90 group, while decreased 3β-HSD and 17β-HSD3 mRNA expression and increased CYP17 mRNA expression were found in the GD108 group. CYP17 and 17β-HSD3 were localized in Leydig cells. Following flutamide administration, the intensity of CYP17 immunostaining was higher in both treated groups, while 17β-HSD3 intensity was lower in the GD108 group. In ovaries from flutamide-treated foetuses in the GD90 group, mRNA level for 3β-HSD was elevated, but it was diminished for CYP17 and 17β-HSD1. In the GD108 group, flutamide treatment led to lower mRNA level for 3β-HSD but higher for CYP17. 3β-HSD was found in granulosa cells, while CYP17 was localized within egg nests and oocytes of forming follicles. Following flutamide treatment, the intensity of 3β-HSD and CYP17 immunostaining was higher in the GD90 and GD108 groups, respectively. Immunohistochemical staining for 3β-HSD was restricted to the ovary. Concluding, diminished androgen action in the porcine foetal gonads during late gestation induces changes in steroidogenic enzymes expression, which may led to changes in gonadal function. However, it seems that androgens exert diverse biological effects depending on the gestational period.
DualSentiNet: Dual Prediction of Word and Document Sentiments Using Shared Word Embedding

With the popularization of social networking services, numerous words are newly emerging every day in personalized document sources. Slang terms, abbreviations, newly coined words, and non-grammatical words or expressions belong here, and people are more likely to use these words with a certain sentimental tendency compared to other standard words. Thus, it becomes important to find their meanings or sentiments to analyze the sentiment of user-generated texts. This paper proposes a novel sentiment analysis model, termed DualSentiNet, which predicts the sentiments of newly emerged words and documents at the same time. Our model is composed of three parts: (i) a word-level sentiment regression network, (ii) a document-level sentiment classification network, and (iii) a shared word embedding layer. DualSentiNet makes a word embedding layer shared by two different networks, thereby learning richer information about both word-level and document-level sentiments through two-way back-propagation. Consequently, it improves the performance of sentiment prediction by preventing word vectors from being overfitted. Experimental results show that DualSentiNet significantly outperforms competitors in terms of both document sentiment classification accuracy and the word sentiment regression RMSE. In addition, DualSentiNet produces better word embedding by reflecting both word and document sentiments.
Facile fabrication of microfluidic surface-enhanced Raman scattering devices via lift-up lithography

We describe a facile and low-cost approach for a flexibly integrated surface-enhanced Raman scattering (SERS) substrate in microfluidic chips. Briefly, a SERS substrate was fabricated by the electrostatic assembling of gold nanoparticles, and shaped into designed patterns by subsequent lift-up soft lithography. The SERS micro-pattern could be further integrated within microfluidic channels conveniently. The resulting microfluidic SERS chip allowed ultrasensitive in situ SERS monitoring from the transparent glass window. With its advantages in simplicity, functionality and cost-effectiveness, this method could be readily expanded into optical microfluidic fabrication for biochemical applications.
Saliency detection by aggregating complementary background template with foreground information

This paper proposes an unsupervised bottom-up saliency detection approach by exploiting novel background template and foreground information. First, a discriminative feature vector is extracted from each super-pixel to cover regional color, contrast and texture information. Then we apply it to get a background based saliency map based on a background template. In order to get more accurate saliency map, we select highly confident compact foreground seeds to compute a foreground based saliency map. After fusing the two saliency maps, the integrated map is refined to achieve the final result. Experimental results show that the proposed algorithm generates high-quality saliency maps against the state-off-the-art saliency detection methods on four publicly available datasets.
Achieving massive connectivity with non-orthogonal pilots and dirty-RFs

Emerging 5G wireless networks need to support sufficient connectivity for a large number of machine-type communication devices, which may have poor radio-frequency (RF) circuit quality due to cost- and energy-efficient design. In this paper, the connectivity is analyzed for a training-based large-scale antenna system employing both non-orthogonal pilots and dirty-RFs on both the transmitter and receiver sides. By considering the effects of imperfect hardware and interference caused by non-orthogonal pilots, the performance of the linear minimum mean-square error (MMSE) channel estimator is derived, and the corresponding average rate of the maximum ratio combiner (MRC) is obtained. From these results, the connectivity maximization problem is formulated and the closed-form solutions for the optimal training length and the optimal number of simultaneously served users are provided. Asymptotic analysis further reveals that allowing only orthogonal pilots limits both the connectivity even when a large number of antenna is employed. However, by allowing non-orthogonal pilots, both the connectivity and the energy efficiency can be improved significantly, even when dirty-RFs are taken into account.
Early postoperative oral feeding impacts positively in patients undergoing colonic resection: results of a pilot study

Background and aims: Early oral feeding after colorectal resections is one of the many factors that contributes to enhance recovery after surgery, mainly impacting on postoperative ileus. The aim of this study was to evaluate the impact of early postoperative oral feeding in patients undergoing elective colorectal resection. Methods: Patients were randomly assigned to either a conventional postoperative dietary regimen or a free diet on the first postoperative day. Results: Altogether 29 patients were enrolled. Clinical characteristics were similar in both groups. Good nutritional status was seen in 86% of patient in the traditional care (TRAD) group versus 46% in the early fed (EF) group (p<0.05). There was no difference between groups in terms of procedures. Median hospital stay was 5.0 days in the TRAD group versus 3.0 days in the EF group (p<0.05). Complication rates and acceptance of diet were similar in both groups. Diarrhea occurred more frequently in the TRAD group (OR=1.86; IC95%:1.08-3.20). Conclusion: Early oral intake is well tolerated, leads to significant shorter hospital stay and does not increase complications.
A Multilingual Approach to Question Classification

In this paper we present the Konstanz Resource of Questions (KRoQ), the ﬁrst dependency-parsed, parallel multilingual corpus of information-seeking and non-information-seeking questions. In creating the corpus, we employ a linguistically motivated rule-based system that uses linguistic cues from one language to help classify and annotate questions across other languages. Our current corpus includes German, French, Spanish and Koine Greek. Based on the linguistically motivated heuristics we identify, a two-step scoring mechanism assigns intra-and inter-language scores to each question. Based on these scores, each question is classiﬁed as being either information seeking or non-information seeking. An evaluation shows that this mechanism correctly classiﬁes questions in 79% of the cases. We release our corpus as a basis for further work in the area of question classiﬁcation. It can be utilized as training and testing data for machine-learning algorithms, as corpus-data for theoretical linguistic questions or as a resource for further rule-based approaches to question identiﬁcation.
Arap-Tweet: A Large Multi-Dialect Twitter Corpus for Gender, Age and Language Variety Identification

In this paper, we present Arap-Tweet, which is a large-scale and multi-dialectal corpus of Tweets from 11 regions and 16 countries in the Arab world representing the major Arabic dialectal varieties. To build this corpus, we collected data from Twitter and we provided a team of experienced annotators with annotation guidelines that they used to annotate the corpus for age categories, gender, and dialectal variety. During the data collection effort, we based our search on distinctive keywords that are specific to the different Arabic dialects and we also validated the location using Twitter API. In this paper, we report on the corpus data collection and annotation efforts. We also present some issues that we encountered during these phases. Then, we present the results of the evaluation performed to ensure the consistency of the annotation. The provided corpus will enrich the limited set of available language resources for Arabic and will be an invaluable enabler for developing author profiling tools and NLP tools for Arabic.