Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Trust and Behavioral Analysis to Detect Security Threats in Open Environments

Abstract : Open computing environments are under a deluge of network attacks from complex threats. These threats are distributed, decentralized , dynamic, and operate over multiple timescales. Trusted Computing environments provide a means to manage cryptographic identity and authentication operations in the form of static assertions, but were not developed to provide complete end-to-end security for heterogeneous environments such as the NATO Architecture Framework (NAF). There is a gap in the contextual understanding of trust that reaches beyond identity to the behavior of that identity. The challenge in deriving trust, and ultimately risk, from network behavior is that it is inherently subjective compared to identity. Trust is defined as the "assured reliance on the character, ability, strength, or truth of someone or something". When we trust a person there is the notion of identity. Structural identity alone cannot be used to define measure of an entity's trust; behavior must be taken into account. Trust then becomes a layered concept. In assessing the trustworthiness of an entity, a cyber defense strategy should take into account various signals regarding identity and behavior that promote attestation of a digital "self and non-self". We describe a model and approach through which a detection capability can derive trust, and rate the trustworthiness of hosts, based on aggregated network behaviors. This approach is rooted in the context of a global/enterprise identity management and cryptographic key management (IdM/CKM) which serves as a bridge between the sensor network and the user/administrator/ISP. It offers a mechanism for aggregated behavioral analysis of network flow data. Our unique view into network behaviors can be used to provide a basis for language to define the various behaviors that threats exhibit over time. We conclude that a more formal model of trust is needed that couples identity with behavior along with the identity of the user of a computer.
Acute Stress Responses Are Early Molecular Events of Retinal Degeneration in Abca4−/−Rdh8−/− Mice After Light Exposure

Purpose Mice lacking ATP-binding cassette transporter 4 (ABCA4) and retinol dehydrogenase 8 (RDH8) mimic features of human Stargardt disease and age-related macular degeneration. RNA-sequencing of whole eyes was done to study early gene expression changes in Abca4−/−Rdh8−/− mice. Methods Abca4−/−Rdh8−/− mice at 4 weeks of age were exposed to intense light. Total RNA was extracted from whole eyes and used to generate RNA libraries that were paired-end sequenced on the Illumina HiSeq 2500 device. Differentially expressed genes were annotated using Gene set enrichment analysis (GSEA). Selected genes in enriched pathways exhibiting differential expression were validated using quantitative qRT-PCR and ELISA. Results Transcriptome analysis of the whole eye identified 200 genes that were differentially expressed 24 hours after light exposure compared to no light in Abca4−/−Rdh8−/− mice. Expression of several visual cycle and photoreceptor genes were decreased, indicative of photoreceptor/RPE cell death. Gene categories of early stress response genes, inflammatory cytokines, immune factors, and JAK STAT components were upregulated. Lipocalin 2 (Lcn2) was the most upregulated early stress response gene identified. Protein LCN2 was produced by RPE cells and the neural retina after intense light exposure as well as in cultured RPE cells from mice and humans incubated with lipopolysaccharide or photoreceptor outer segments. Conclusions Identification of important mediators involved in the crosstalk between the acute stress response and immune activation in RPE cells and the neural retina, such as LCN2, provide novel molecular targets for reducing cellular stress during retinal degeneration.
Finding Interaction Partners Using Attitude-Based Decision Strategies

In a multi-agent system, a single agent may not be capable of completing complex tasks. Therefore agents are required to form a team to fulfill the task requirements. In this paper an agent model is introduced that facilitates cooperation among agents. The experimental results demonstrate that the model is significantly useful in achieving cooperation under various environmental constraints. It also allows agents to adjust their teammate selection strategy according to environmental constraints.
Les budgets énergie transport: un concept, une pratique, des résultats

La depense d'energie des menages pour leurs deplacements quotidiens varie dans des proportions considerables avec le statut social et la localisation residentielle. Les resultats presentes dans cet article, obtenus par reexploitation de sept enquetes sur des agglomerations francaises de taille variee, permettent d'esquisser les grands axes d'une politique de maitrise de l'energie dans les transports qui ne serait pas uniquement technologique et qui rejoindrait a bien des egards des preoccupations en matiere de securite, d'environnement et d'equite sociale
Image-Based Rendering Using Unstructured Image Set

This paper proposes a new, simple and effective image based rendering (IBR) technique for rendering new viewpoint images from large unstructured image set. Unlike most of the existing image based rendering systems, the proposed system is compatible with the standard model based rendering systems and able to synthesize the target scene with other artificial CG objects or scenes.
Fast approximate computation of non-uniform DFTs for biological sequence analysis

Periodicity is emerging as a useful method for characterising the structure within biological sequences such as DNA. For sequence data, integer periods are usually of most interest, which poses the problem of fast computation if conventional Fourier-based analyses are applied. An existing complex polynomial re-evaluation algorithm is adapted, and a fast approximation rule applicable to any discrete Fourier transform-based analysis is proposed, where the frequencies to be evaluated are not uniformly spaced. Experiments evaluating binary signals on an integer-period frequency scale show that magnitude spectrum approximations differing from the exact magnitude spectrum by less than 1-3- can be obtained with a reduction in complexity of 3-10 times.
RADIOLYTIC CORROSION OF GRAIN BOUNDARIES ONTO THE UO2 TRISO PARTICLE SURFACE

This work is dealing with the understanding of the corrosion mechanisms at solid/solution interface and taking into account for the 4 He 2+ ions irradiation effects on these mechanisms. These corrosion and 4 He 2+ ions radiolysis phenomena append at solid/solution interface and will be studied at a μmetric scale by the Raman spectroscopy. Moreover, a 4 He 2+ ions irradiation appends onto a low volume and let us to control the irradiated area (solution, solid or interface). For the solid, th e chemical species induced by 4 He 2+ ions radiolysis of water are such reactive and are involved in classical corrosion mechanisms of UO 2 . Moreover, we want to study the impact of the 4 He 2+ ions radiolysis of water laye rs physisorbed into the surface onto corrosion mechanisms. That is the reason w hy we want to use a local irradiation, allowed by the 4 He 2+ ions ion beam provided by the ARRO NAX cyclotron (E = 64.7 MeV). In this work an experimental apparatus will be perf ormed in order to characterize solid/solution interface at μmetri c scale by Raman spectroscopy under 4 He 2+ ions irradiation provided by the cyclotron ARRONAX facility. Th e leaching experiments under ir radiation will be performed for a short time in order to study the parameters during the fast instant release step. The grain boundaries effect will be studied by the compar ison between one TRISO particles set (solids with grain boundaries) and one TRISO particles set previously washed by one acid solution (solid without grain bounda ries). The role of H 2 will be studied by the comparison between experiments under Ar or Ar/H 2 atmosphere. The dose rate range will be between 0 and 100 Gy/min by using the alpha ion beam which let us control the dose set down into the sample. For all these experiments, measur ements will be performed by the in situ Raman spectroscopy during the irradiation in order to follow the formation/consumption of the secondary phases formed onto the solid. The SEM will be perf ormed in order to characterize the grain boundaries and the secondary phases formed by the leaching/irradiation experiments. The μGC is used to measure the P H2 into the irradiation cell to follow the production/consumption of this gaseous species formed by the water radiolysis and consumed by the leaching process.
FROM COLORED PETRI NETS TO MARKOV DECISION PROCESSES

 The present work is motivated by the need to consider stochastic behavior when planning production mix in a manufacturing system. Because methods that are suitable for planning are not always appropriate for modeling the systems and viceversa, two methods are combined here: systems are modeled using colored Petri nets and then converted into Markov decision processes. A special type of Petri nets, named factored Petri nets, is proposed, so as to represent probabilities efficiently. A conversion method from factored Petri nets into Markov decision processes is described. We show that plans can be produced within seconds using state-of-art algorithms for Markov decision processes. Keywords Markov decision processes. Colored Petri nets. Factored Markov decision processes.
An Early Fire Detection Method Based on Smoke Texture Analysis and Discrimination

Texture is an important property of fire smoke, which is a significant signal for early fire detection. This paper describes a method of analyzing the texture of fire smoke combining two innovative texture analysis tools, Wavelet Analysis and Gray Level Cooccurrence Matrices (GLCM). Tree-Structured Wavelet transform is used to represent the textural images and GLCM are used to compute the different scales of the wavelet transform and to extract the features of fire-smoke texture. The smoke texture and the non-smoke texture are classified by neural network classifier. The discrimination performance is related to the quantity of input vectors.
Toward Delay Tolerant Network Anonymity : Threshold Pivot Scheme

Research on Delay and Disruption Tolerant Networks (DTNs) challenges the traditional assumption of end-toend connectivity, extending networked communication to e.g. intermittently connected devices, ad-hoc mobile environments, first-responder disaster scenarios, etc. In such environments, ensuring the security and privacy of both content, networks, and participants is often vital. In this work, we consider DTN anonymity and privacy. The disconnected nature of DTNs presents a unique difficulty for traditional anonymity approaches, namely limited knowledge of other nodes and paths in the dynamic, mobile network. We develop a particular solution, the Threshold Pivot Scheme (TPS), to provide source anonymity and sender-receiver unlinkability in DTNs. Our scheme, based on secret sharing primitives, permits a user-selectable level of anonymity, an important feature for DTN environments that must balance security and usability. Through simulation and analytical analysis, we evaluate the performance and overhead of TPS and find that it addresses the constraints of DTNs while providing a suitably high-level of anonymity.
Chapter 1 Introduction Consider a Linear Partial Diierential Equation

A striking feature of mankind is the urge to understand the surrounding environment and to improve life-conditions. Understanding is the domain of science, improving the quality of life is the domain of technology. Modern science and technology are characterized by i n tense interaction. Indeed technology often provides the actual tools for scientiic investigation. Science provides us with models which ooer a predictive capability. These predictions may involve the whims of nature e.g. the weather, epidemics or the innuence of human actions e.g. economics, pollution and environmental policy. We also construct scientiic models of our tools with the aim of arriving at optimal designs. Aircraft design and the development of semiconductor devices are just two o f numerous examples where designing by trial and error and manufacturing specimens of each design soon becomes prohibitively expensive. If a model is able to predict the performance of a speciic design, the design can be adapted to the necessary requirements at the drawing-board stage, long before a product is committed to manufacture and testing. Mathematics is the language in which scientiic models are described. Often this results in a set of partial diierential equations which h a ve to be solved. The discipline of numerical mathematics facilitates an approximation to the solution of a problem using limited computer resources. This is often achieved by subdivision of space into small ctitious cells. In each cell, variables of physical importance, such as uxes, are assumed to be constant. For each cell equations may be derived which express physical conservation laws. A common example of such a l a w is that the sum of ingoing and outcoming mass across the boundaries of a cell should be zero. After this so-called discretization process one is left with a nite number of algebraic equations in a nite number of unknowns. However, though such problems are nite, they may b e v ery large, mainly because the accuracy of the solution requires a huge number of cells. Furthermore, as a result of enormous advances in computer hardware, larger and 2 Chapter 1 larger computational problems are being attempted. In spite of the said progress, it is imperative that the numerical solution method is highly eecient. A basic problem with many n umerical methods is that the number of arithmetic operations involved is more than proportional to the number of unknowns. As a consequence, the computational load soon becomes …
Fast and Adaptive Mesh Access Control in Low-Power and Lossy Networks

Low-power and lossy networks (LLNs) comprised of thousands of embedded networking devices connected to the larger Internet architecture can be used in a variety of applications, leading to the emerging concept of the Internet of Things (IoT). Access control and key management in such LLN is a big challenge since authentication, authorization, and key management process involves several handshakes of large size packets over low bandwidth and high latency links through a bottlenecked LLN border router (LBR). This may result in significant wasted bandwidth and slow authentication completion time. The goal of this work is to propose a “fast and adaptive node authentication mechanism for low-power lossy networks,” called FINALLY, that solves these problems and significantly improves the efficiency and speed of mesh authentication process. We evaluate FINALLY through simulations and show that it allows devices to authenticate quickly without repeated authentication failures, reduces unnecessarily wasted transmissions, and improves the overall completion time of the authentication process.
MfWMA: A Novel Web Mining Architecture for Expert Discovery

Identification of expert to domain knowledge in any field of interest is essential for consulting in industry, academia and scientific community. The objective of this study is to address the expert-finding task in contemporary communities. We proposed Multifaceted Web Mining Architecture (MfWMA) and implemented a tool with data extracted from Growbag, dblpXML and web authors home page resource to identify personnel with specific expertise. We mined two thousand and five hundred author's personal web pages with the underlying criteria of a dozen of key parameters; while parsing on each page in pursuit of 8 thousand topics. This study corroborate this quantification in terms of a measure of expertise. The prototype provides its users to distinguish the level of expertise in a particular area; thus resulting in the capability to mark people with broader expertise. Through this extension to the web enabling technique, we have demonstrated that the proposed architecture presents a novel web mining approximation with realistic results.
Adaptive frequency technique for eliminating power line interference in ECG signal

In this research, the new system which can eliminate the power line interference (PLI) from the electrocardiogram (ECG) signal is proposed. The system consists of a band pass filter, an automatic gain control and an adaptive filter. With the proposed system, the varied frequency and varied amplitude of the power line interference can be significantly eliminated from the ECG signal. In addition, the simulation results illustrates that the performance of PLI cancellation of the proposed system is improved comparing to the method of [4].
A Logical Framework for Active Rules Institut F Ur Informatik | Report 78 *

We present a state-oriented extension to Datalog called Statelog which comprises two kinds of rules within a simple, yet exible uniied framework, i.e. (i) passive query rules used to express queries and static integrity constraints, and (ii) active transition rules for deening state-changing operations. We show that Statelog is powerful enough to capture many typical active rule applications (including complex updates, integrity enforcement and incremental view maintenance strategies) while retaining a declarative, set-oriented, and deterministic semantics thus reconciling active and de-ductive databases. Using the Statelog framework, formal results on termination, ex-pressiveness and complexity of active rules are established which carry over to related state-oriented languages like XY-stratiied Datalog. We prove that it is undecidable for general Statelog programs whether a program terminates on all databases. We then develop syntactic criteria which guarantee program termination and investigate the impact of these restrictions on expressive power. The class of-monotone programs extends previous results on Statelog and is of practical importance since it includes self-triggering active rules and expresses all Fixpoint transformations. In addition, our syntactic conditions yield an alternative static analysis technique based on the logical notion of monotony whereas previous approaches use execution and triggering graphs.
Predictive Collaborative Filtering with Side Information

Recommender systems have been widely studied in the literature as they have real world impacts in many E-commerce platforms and social networks. Most previous systems are based on the user-item recommendation matrix, which contains users' history recommendation activities on items. In this paper, we propose a novel predictive collaborative filtering approach that exploits both the partially observed user-item recommendation matrix and the item-based side information to produce top-N recommender systems. The proposed approach automatically identifies the most interesting items for each user from his or her non-recommended item pool by aggregating over his or her recommended items via a low-rank coefficient matrix. Moreover, it also simultaneously builds linear regression models from the item-based side information such as item reviews to predict the item recommendation scores for the users. The proposed approach is formulated as a rank constrained joint minimization problem with integrated least squares losses, for which an efficient analytical solution can be derived. To evaluate the proposed learning technique, empirical evaluations on five recommendation tasks are conducted. The experimental results demonstrate the efficacy of the proposed approach comparing to the competing methods.
Stereotyped and specific gene expression programs in human innate immune responses to bacteria

The innate immune response is crucial for defense against microbial pathogens. To investigate the molecular choreography of this response, we carried out a systematic examination of the gene expression program in human peripheral blood mononuclear cells responding to bacteria and bacterial products. We found a remarkably stereotyped program of gene expression induced by bacterial lipopolysaccharide and diverse killed bacteria. An intricately choreographed expression program devoted to communication between cells was a prominent feature of the response. Other features suggested a molecular program for commitment of antigen-presenting cells to antigens captured in the context of bacterial infection. Despite the striking similarities, there were qualitative and quantitative differences in the responses to different bacteria. Modulation of this host-response program by bacterial virulence mechanisms was an important source of variation in the response to different bacteria.
Health promotion from the perspective of social cognitive theory

Abstract This article examines health promotion and disease prevention from the perspective of social cognitive theory. The areas of overlap with some of the most widely applied psychosocial models of health are identified. The models of health promotion and disease prevention have undergone several generational changes. We have shifted from trying to scare people into health, to rewarding them into health, to equipping them with self-regulatory skills to manage their health habits, to shoring up their habit changes with dependable social supports. These transformations have evolved a multifaceted approach that addresses the reciprocal interplay between self-regulatory and environmental determinants of health behavior. Social cognitive theory addresses the socio structural determinants of health as well as the personal determinants. A comprehensive approach to health promotion requires changing the practices of social systems that have widespread detrimental effects on health rather than solely changing t.
Recommendation based on weighted social trusts and item relationships

Recommender systems have been well studied to overcome the information overload problem in both academia and industry during the past decade. Collaborative filtering is the most popular approach in recommender systems, among which Probabilistic Matrix Factorization (PMF) has proved very effective. In social networks, users linked together tend to have similar interests and some social-based methods emerged based on this assumption. However these methods treat different friends equally and actually different people may have different influence on a person. Besides social trusts, item relationships also provide rich information especially when trust information is not sufficient. The items liked by the same user may have similar characteristics and whether an item is preferred is influenced by its similar items. But most of these methods ignore the role of item relationships. In this paper, we propose a novel recommendation method which incorporates social trusts and item relationships into PMF. We compute user influence degrees and item similarities to weight user-user and item-item relationships when learning user and item latent factors. Experimental results on Epinions dataset demonstrate that our method has shown a significant improvement over existing approaches especially for the users who have made few ratings.