Miracle-dz/UniFAR-FTUs huggingface dataset:
TTP-free asymmetric fingerprinting protocol based on client side embedding

In this paper, we propose a scheme to employ an asymmetric fingerprinting protocol within a client-side embedding distribution framework. The scheme is based on a novel client-side embedding technique that is able to transmit a binary fingerprint. This enables secure distribution of personalized decryption keys containing the Buyer's fingerprint by means of existing asymmetric protocols, without using a trusted third party. Simulation results show that the fingerprint can be reliably recovered by using non-blind decoding, and it is robust with respect to common attacks. The proposed scheme can be a valid solution to both customer's rights and scalability issues in multimedia content distribution.
Model Checking Security Properties of Control Flow Graphs

A fundamental problem in software-based security is whether local security checks inserted into the code are sufficient to implement a global security property. This article introduces a formalism based on a linear-time temporal logic for specifying global security properties pertaining to the control flow of the program, and illustrates its expressive power with a number of existing properties. We define a minimalistic, security-dedicated program model that only contains procedure call and run-time security checks and propose an automatic method for verifying that an implementation using local security checks satisfies a global security property. We then show how to instantiate the framework to the security architecture of Java 2 based on stack inspection and privileged method calls.
Mobilities of carbon cluster ions: Critical importance of the molecular attractive potential

Mobilities in helium gas for isomers belonging to the major structural families of carbon clusters identified in drift tube studies (chains, monocyclic and bicyclic rings, graphite sheets, and fullerenes and their dimers) have been evaluated by trajectory calculations employing a realistic ion-He interaction potential. For all the species considered, the agreement between the measured and calculated mobilities at room temperature improves by at least a factor of 3 over that obtained with the widely used hard-sphere projection approximation. Furthermore, for a large representative sample of clusters belonging to all the above families, the results of trajectory calculations as a function of temperature over the range of 78–360 K are in a good agreement with the measured mobilities. This shows that the C–He pairwise potential is only weakly dependent on the structure and chemical bonding of a carbon cluster. Thus this study demonstrates the universal suitability of trajectory calculations for the accurate p.
Synthesis, characterisation and antimicrobial screening of some AZO compounds.

In this study, a series of azo compounds were synthesized in excellent yields via the diazotization of different aromatic amines followed by coupling with 2-naphthol. These compounds were characterized by elemental analysis, IR, 1H NMR and 13C NMR spectral techniques. The synthesized compounds have been tested in vitro against a number of microorganisms in order to assess their antimicrobial properties using disk diffusion method. The minimum inhibitory concentrations (MIC) were also determined by the broath microdilution technique. Some of the products exhibited comparable activity with known standard drugs at same concentration.
Prediction of Prosodic Word Boundaries in Chinese TTS Based on Maximum Entropy Markov Model and Transformation Based Learning

Hierarchical prosody structure generation is a key component for a speech synthesis system. As the basic prosodic unit, the prosodic word plays an important role for the naturalness and the intelligibility for the Chinese TTS system. In this paper we proposed an approach for prediction of Chinese prosodic word boundaries in unrestricted Chinese text, which combines Maximum Entropy Markov Model(MEMM) and TBL model. First MEMM is trained to predict the prosodic word boundaries. After that we apply a TBL based error driven learning approach to amend the initial prediction. A comparison is conducted between the new model and HMM for prosodic word boundaries prediction. Experiments show that the combined approach improves overall performance. The precision and recall ratio are improved.
Multi-degree of freedom vibration model for a 3-DOF hybrid robot

In recent years, many applications in precision engineering require a careful isolation of the instrument from the vibration sources by adopting active vibration isolation system to achieve a very low remaining vibration level especially for the very low frequency under 10Hz vibration signals. In this paper, based on our previous research experiences in the systematical modeling and study of parallel robots, a hybrid robot is described and the vibration model is given by using Lagrange's Equations. Then a dynamic analysis by using Kane's method is presented to verify the result. Finally, numerical simulations on the dynamic characteristics and natural frequency are presented.
Complex polarization analysis of particle motion

Knowledge of particle motion polarization aids in identifying phases on three-component seismograms. The scheme of Montalbetti and Kanasewich (1970) is extended to analytic three-component seismograms, where the imaginary part of the signal is the Hilbert transform of the real part. This scheme has only one free parameter, the length of the time window over which the polarization parameters are estimated, so it can be applied in a routine way to three-component data. The azimuth and dip of the direction of maximum polarization and the degree of elliptical polarization as a function of time for the seismograms are obtained. Polarization analysis of strong motion data from the 1971 San Fernando earthquake aids in the discrimination between wave types, which is important for the understanding of the complicated earthquake-induced shaking observed in basins. Most arrivals are incident on the receivers in the direction of the back-azimuth to the epicenter, which suggests that despite the complicated motions, two-dimensional finite difference methods are sufficient to understand the effect on seismic waves of the Los Angeles and San Fernando basins (Vidale and Helmberger, 1986b).
Hydrothermal Vents and Methane Seeps: Rethinking the Sphere of Influence

Although initially viewed as oases within a barren deep ocean, hydrothermal vent and methane seep communities are now recognized to interact with surrounding ecosystems on the sea floor and in the water column, and to affect global geochemical cycles. The importance of understanding these interactions is growing as the potential rises for disturbance from oil and gas extraction, seabed mining and bottom trawling. Here we synthesize current knowledge of the nature, extent and time and space scales of vent and seep interactions with background systems. We document an expanded footprint beyond the site of local venting or seepage with respect to elemental cycling and energy flux, habitat use, trophic interactions, and connectivity. Heat and energy are released, global biogeochemical and elemental cycles are modified, and particulates are transported widely in plumes. Hard and biotic substrates produced at vents and seeps are used by “benthic background” fauna for attachment substrata, shelter, and access to food via grazing or through position in the current, while particulates and fluid fluxes modify planktonic microbial communities. Chemosynthetic production provides nutrition to a host of benthic and planktonic heterotrophic background species through multiple horizontal and vertical transfer pathways assisted by flow, gamete release, animal movements, and succession, but these pathways remain poorly known. Shared species, genera and families indicate that ecological and evolutionary connectivity exists among vents, seeps, organic falls and background communities in the deep sea; the genetic linkages with inactive vents and seeps and background assemblages however, are practically unstudied. The waning of venting or seepage activity generates major transitions in space and time that create links to surrounding ecosystems, often with identifiable ecotones or successional stages. The nature of all these interactions is dependent on water depth, as well as regional oceanography and biodiversity. Many ecosystem services are associated with the interactions and transitions between chemosynthetic and background ecosystems, for example carbon cycling and sequestration, fisheries production, and a host of non-market and cultural services. The quantification of the sphere of influence of vents and seeps could be beneficial to better management of deep-sea environments in the face of growing industrialization.
Archaeal Production of Polyhydroxyalkanoate (PHA) Co- and Terpolyesters from Biodiesel Industry-Derived By-Products

The archaeon Haloferax mediterranei was selected for production of PHA co- and terpolyesters using inexpensive crude glycerol phase (CGP) from biodiesel production as carbon source. CGP was assessed by comparison with the application of pure glycerol. Applying pure glycerol, a copolyester with a molar fraction of 3-hydroxybutyrate (3HB) of 0.90 mol/mol and 3-hydroxyvalerate (3HV) of 0.10 mol/mol, was produced at a volumetric productivity of 0.12 g/Lh and an intracellular PHA content of 75.4 wt. -% in the sum of biomass protein plus PHA. Application of CGP resulted in the same polyester composition and volumetric productivity, indicating the feasibility of applying CGP as feedstock. Analysis of molar mass distribution revealed a weight average molar mass M w of 150 kDa and polydispersity P i of 2.1 for pure glycerol and 253 kDa and 2.7 for CGP, respectively; melting temperatures ranged between 130 and 140°C in both setups. Supplying γ-butyrolactone as 4-hydroxybutyrate (4HB) precursor resulted in a poly[(R)-3-hydroxybutyrate-co-(R)-3-hydroxyvalerate-co-4-hydroxybutyrate] (PHBHV4HB) terpolyester containing 3HV (0.12 mol/mol) and 4HB (0.05 mol/mol) in the poly[(R)-3-hydroxybutyrate] (PHB) matrix; in addition, this process runs without sterilization of the bioreactor. The terpolyester displayed reduced melting (melting endotherms at 122 and 137°C) and glass transition temperature (2.5°C), increased molar mass (391 kDa), and a polydispersity similar to the copolyesters.
PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites

The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s). Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner in order to efficiently identify protein substrates by in silico approaches. To address this problem, we present PROSPER, an integrated feature-based server for in silico identification of protease substrates and their cleavage sites for twenty-four different proteases. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database) with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to performance improvement in terms of identifying known cleavage sites in substrates for these enzymes. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate sequence using machine learning techniques. It is freely available at http://lightning.med.monash.edu. au/PROSPER/.
Hypoxia and P. gingivalis Synergistically Induce HIF-1 and NF-κB Activation in PDL Cells and Periodontal Diseases

Periodontitis is characterized by deep periodontal pockets favoring the proliferation of anaerobic bacteria like Porphyromonas gingivalis (P. gingivalis), a periodontal pathogen frequently observed in patients suffering from periodontal inflammation. Therefore, the aim of the present study was to investigate the signaling pathways activated by lipopolysaccharide (LPS) of P. gingivalis (LPS-PG) and hypoxia in periodontal ligament (PDL) cells. The relevant transcription factors nuclear factor-kappa B (NF-κB) and hypoxia inducible factor-1 (HIF-1) were determined. In addition, we analyzed the expression of interleukin- (IL-) 1β, matrix metalloproteinase-1 (MMP-1), and vascular endothelial growth factor (VEGF) in PDL cells on mRNA and protein level. This was accomplished by immunohistochemistry of healthy and inflamed periodontal tissues. We detected time-dependent additive effects of LPS-PG and hypoxia on NF-κB and HIF-1α activation in PDL cells followed by an upregulation of IL-1β, MMP-1, and VEGF expression. Immunohistochemistry performed on tissue samples of gingivitis and periodontitis displayed an increase of NF-κB, HIF-1, and VEGF immunoreactivity in accordance with disease progression validating the importance of the in vitro results. To conclude, the present study underlines the significance of NF-κB and HIF-1α and their target genes VEGF, IL-1β, and MMP-1 in P. gingivalis and hypoxia induced periodontal inflammatory processes.
Use Your Illusion: secure authentication usable anywhere

In this paper, we propose and evaluate Use Your Illusion, a novel mechanism for user authentication that is secure and usable regardless of the size of the device on which it is used. Our system relies on the human ability to recognize a degraded version of a previously seen image. We illustrate how distorted images can be used to maintain the usability of graphical password schemes while making them more resilient to social engineering or observation attacks. Because it is difficult to mentally "revert" a degraded image, without knowledge of the original image, our scheme provides a strong line of defense against impostor access, while preserving the desirable memorability properties of graphical password schemes. Using low-fidelity tests to aid in the design, we implement prototypes of Use Your Illusion as i) an Ajax-based web service and ii) on Nokia N70 cellular phones. We conduct a between-subjects usability study of the cellular phone prototype with a total of 99 participants in two experiments. We demonstrate that, regardless of their age or gender, users are very skilled at recognizing degraded versions of self-chosen images, even on small displays and after time periods of one month. Our results indicate that graphical passwords with distorted images can achieve equivalent error rates to those using traditional images, but only when the original image is known.
A model-driven approach to formal refactoring

Applying refactorings to object-oriented systems usually affects source code and its associated models, involving complex maintenance efforts to keep those artifacts up to date. Most projects abandon design information in the form of models early in the life cycle, as their sustentation becomes extremely expensive. We propose a formal approach to consistently refactor systems in a model-driven manner. The refactoring applied to the model is linked to a sequence of behavior-preserving transformations that automatically refactor the underlying source code, based on structural properties from the model that must be implemented by the program. As a consequence, sound program refactoring can be accomplished without developer intervention, based only on the applied model transformations. Also, the refactored source code is consistent with the refactored model. Model information can be additionally used to improve refactoring automation, as more powerful transformations can be mechanized.
Optimum Feature Selection and Extraction for Fault Diagnosis and Prognosis

Fault diagnosis and failure prognosis of critical dynamic systems, such as aircraft and industrial processes, rely on degradation or fatigue models and measurements typically acquired on-line in real-time. Such sensor data must be pre-processed in order to remove artifacts and improve the signal-to-noise ratio. Furthermore, they must be processed appropriately so that useful information in compact form can be extracted and used to detect incipient failures and predict the remaining useful life of failing components. We present a methodology to select an optimum feature vector from a list of candidate features, prioritize and rank them to meet set performance objectives. The enabling technologies include genetic programming tools, data fusion and model-based approaches for feature selection and extraction. We will suggest a multi-core processing environment for the efficient and expedient implementation of these technologies. Performance metrics are defined to assess the efficacy of the methodology. Typical examples from aircraft systems are used to demonstrate the proposed techniques.
A unified evaluation framework for coarse grained reconfigurable array architectures

The efficiency of a coarse grained reconfigurable array architecture in terms of performance and hardware cost is hard to be determined. The large number of parameters that define an architecture instance and the mapping complexity makes the evaluation extremely difficult to accomplish without tool assistance. This paper investigates the four factors that are directly related with the efficiency of these architectures namely; the area, the clock frequency, the scheduling efficiency and performance. A unified exploration framework has been build for estimating the values of the 4 aforementioned factors for different architecture alternatives. The exploration framework consists of two parts: a) an existing retargetable compiler from which the mapping efficiency is estimated and b) from the parametric realization of the coarse grained reconfigurable array in hardware description language (VHDL). The latter is used for the estimation of the area and clock frequency of each architecture instance with the realization of the system in the 0. 13¼m process of ASIC technology. Also, the experiments refer to different architecture instances in terms of the processing elements. interconnection network, the register files. size, their number of input output ports, and finally the available bandwidth. Totally 72 architecture scenarios have been studied revealing how each characteristic influences performance and area for efficiently make design decisions.
Novel Disease Targets Are the Life-blood of the Biotech and Pharmaceutical Industries. However, of Equal Fragment Screening: Biochemical Assays versus Nmr

and pharmaceutical industries. However, of equal importance is the discovery, for such biological targets, of novel and efficient starting points for medicinal chemistry optimisation. Thus there is a focus within the industry on the development of effective techniques to identify chemical matter such as, for example, highthroughput screening (HTS), high-content screening (HCS) and virtual screening. In the last decade, the screening of low molecular weight molecules has emerged as a new drug discovery paradigm for lead generation when allied to structure-guided medicinal chemistry optimisation of weakly active chemical starting points (1). A key challenge in the successful prosecution of Fragment-Based Drug Discovery (FBDD) is to identify weakly active fragments in the first place. This has led to the development of a range of fragment screening methods that, for the most part, are biophysical in nature – such as NMR, high-throughput crystallography and surface-plasmon resonance. The advantages of fragment-based drug discovery over traditional approaches include speed, a reduction in programme attrition and the ability to access novel chemical matter. By starting from fragments with high ligand efficiency and using an iterative structure-based approach from the outset, rapid, incremental step increases in ligand potency can be achieved. In particular, the concept of ligand efficiency (2) can be used to guide the medicinal chemistry optimisation of fragment compounds (3). Furthermore, utilising small highly soluble starting points, desired druglike physicochemical/ADMET properties can be designedin at an early stage. Finally, a relatively small number of fragments is required to provide excellent diversity coverage of chemistry space, and hence give rise to novel starting points. However, a limitation to fragment-based drug discovery is that only soluble protein targets for which structural information can be obtained can be effectively progressed. This is because, in order to fast-track the medicinal chemistry optimisation, information obtained from structural biology (see Figure 1, page 20) on how
Anonymous Unemployed 2.0: The Needs of Forgotten Italian Workers

The unemployment rate of economically developed countries up to a few years ago is increasing and there are many intervention projects to manage this increase. Despite this, more and more human resources belong to the Job-less generation, including workers in protected categories, young people and workers forced to retrain, who fall into a spiral of silence, from which they can hardly get out, becoming “invisible”. For this reason, the research aims to introduce a new perspective regarding listening to marginalized workers, observing them in natural contexts where it is easier to express their opinions. To date, the digital revolution and the organization of users on the Net, has allowed the creation of virtual groups that have become real movements of revolt or self-support. The study aims to investigate, through the virtual narratives of invisible workers, what are the real needs of a Job-less Human Resources in the current working situation and how it is possible to intervene to manage this emergency, starting from the assumption that it is necessary to promote a dialogue between workers and companies. The perspective used is the diatextual analysis, thanks to which the virtual experiences of 2.0 workers are considered in the light of the dense interweaving of text and context of enunciation. The methodology used is qualitative: a critical analysis of the discourse is applied, where the socio-epistemic rhetoric is highlighted, and it has been useful for understanding the requirements and needs of workers in the era of professional flexibility, on which they can take an intervention based on the person as a human resource, in order to face unemployment and not to forget them.