Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Intrusion Detection System Based on Pattern Recognition

Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. This is a typical problem of the classification, so intrusion detection (ID) can be seen as a pattern recognition problem. In this paper, In this paper, we build the intrusion detection system using Adaboost, a prevailing machine learning algorithm, construction detection classification. In the algorithm, decision RBF neural network are used as weak classifiers. For the training sets is multi-attribute, non-linear and massive, we use pattern recognition method of non-linear data dimension reduction algorithm-Isomap algorithm to feature extraction and to improve the speed and training for the handling of classified speed. In the feature extraction after the feature of the dimension and Adaboost algorithm training rounds, were studied and experimented. Finally,the experiment proved that Isomap and Adaboost combination of testing the effectiveness of the mothod.
ID : 048 17 th ICCRTS “ Operationalizing C 2 Agiligy ” Exploring the Limits of Diversity for C 2 Agility Topic 1 : Concepts , Theory , and Policy Alternative topics : Topic 2 : Approaches and Organizations

C2 agility addresses key issues of cognition, its social context and the very character of knowledge. Consequently, future C2 research might benefit from broadening its theoretical underpinning. Starting out with the question whether diversity can increase agility to gain a richer understanding of the environment and thereby improving decision-making, we explore three different streams of research to analyze how differently they would formulate the research problem:-The " biases and heuristics " tradition in experimental psychology has increased our understanding of the mechanisms behind deviances from earlier expectations of rational decision making as stipulated in economics. -With its roots in anthropology, the " situated-and distributed cognition " research has a radically different view on methodology and knowledge, emphasizing the need for interpretation to grasp the intelligibility of human action. -The " classical organization theory " , developed into a genre of its own, heavily emphasizing the negative consequences of bounded rationality expressed in irrational decisions, routines and rules of thumb heavily outweighing attempts of agility. Although recently converging, these three paradigms generate very different suggestions as to how the problem of the " Limits of Diversity for C2 Agility " should be stated and how this research should be performed. This leads to alternative or complementary recommendations for further research. 3 1. Why diversity? Command and control (C2) agility is a fundamental response to the limits of predictability as a consequence of complexity and dynamics in the operational environment. In an interconnected, interdependent and fast-paced world C2 is no longer nice to have, but increasingly crucial for survival and precision of action. C2 agility is about responsiveness to actual and changeable external conditions, rather than only a matter of speed in action and decision-loops (Alberts, 2011). Unpredictability means a challenge to specialization. In a predictable world, external phenomena can be identified, categorized and acted upon according to pre-designed tasks following pre-set standard operating procedures. In an unpredictable environment they cannot: as outcomes are open-ended, phenomena and their consequences are increasingly hard to identify and action has to be more adaptive to changing conditions due to uncertainty and ambiguity (c.f., Weick, 1979). Furthermore, as modern challenges concern not only physical energy and undisputable facts but increasingly also the matter of understanding the evolving logic of the operational environment through the eyes of a broad range of actors, ambiguity is added to the challenge of uncertainty. Indeed, the very task of classifying actors, and thereby …
Inversely Designed Scramjet Flow-Path

It can be argued that at the heart of functional hypersonic vehicle is its engine. Key to a functionally efficient scramjet engine lies in the design of its flow-path. The flow-path is made up of the following sections: (1) the forebody inlet; (2) the isolator, (3) the combustor, and (4) the nozzle. This chapter focuses on the design of the forebody inlet and the isolator sections of a scramjet engine. In this framework, key to a functionally efficient scramjet engine lies in the design of its flow-path. This flow-path design must consider a complex flow-field physics and the interaction of physical surfaces with this complex flow-field. Many attempts to design efficient scramjet flow-paths have met with some measured degree of success. This research uses a ‘ inverse design ’ approach, which is similar to Darwin ’ s theory of evolution, where an organism adopts to survive in its environment; the scramjet flow-path will be carved/extracted from the operational environment. The objective is to naturally and organically capture, process and direct the flow from the environment; thus preparing it for the combustion process. This approach uses the ideal 2-D oblique shock relations, coupled with Nonweiler ’ s caret waverider theory and streamline marching techniques.
PRELIMINARY RESEARCH PROPOSAL ADVANCED NEURO-IMAGING OF COMMERCIAL MESSAGES

A toy construction kit has an angled profiled member with two arms arranged at a an angle relative to one another and each provided with a plurality of apertures for engaging with the locking pegs of adjoining components, a substantially block-shaped adapter arranged inside the angled profiled member between the arms and fixable to the angled profiled member, the adapter having free external sides provided with connecting elements for connecting further components thereto, and elements for fixing the adapter to the angled profiled member and including a locking peg engages through a respective one of the apertures of the angled profiled member.
Random walker image registration with inverse consistency

One important property of a registration solution is inverse consistency. While often overlooked, this property is critical in many medical applications, including radiation-therapy treatment planning and unbiased atlas-construction. In this paper, we propose a novel extension to the graph-based random walker image registration (RWIR) algorithm to ensure its inverse consistency. In contrast to many existing inverse-consistent algorithms, where two bi-directional transformations are independently sought and subsequently averaged, we calculate both transformations simultaneously by performing a constrained graph labeling in a common domain onto which both images are mapped, and employ a set of coupled labels so that both transformations are computed within a single optimization step. As our results on synthetic and real problems involving cardiac, thigh and brain images demonstrate, our method not only improved inverse consistency of RWIR, but also statistically significantly improved its accuracy. Comparison to another state-of-the-art symmetric algorithm on various datasets also gave highly encouraging results.
Diversity Management in Intrusion Tolerant Systems Diversity Management in Intrusion Tolerant Systems

One of the key benefits of using intrusion-tolerant systems is the possibility of ensuring correct behavior in the presence of attacks and intrusions. These security gains are directly dependent on the components exhibiting failure diversity. To what extent failure diversity is observed in practical deployment depends on how diverse are the components that constitute the system. In this thesis we present a study with operating systems (OS) vulnerability reports from the NIST National Vulnerability Database. We have analyzed the vulnerabilities of 11 different OS over a period of roughly 15 years, to check how many of these vulnerabilities occur in more than one OS. We found this number to be low for several combinations of OS. Hence, our analysis provides a strong indication that building a system with diverse OS may be a useful technique to improve its intrusion tolerance capabilities. However, even with diversity the attacker eventually will find vulnerabilities in all OS replicas. To mitigate/eliminate this problem we introduce diverse proactive recovery on the replicas. Proactive recovery is a technique that periodically rejuvenates the components of a replicated system. When used in the context of intrusiontolerant systems, in which faulty replicas may be under control of some malicious user, it allows the removal of intrusions from the compromised replicas. We propose that after each recovery a replica starts to run a different software. The selection of the new replica configuration is a non-trivial problem, as we will explain, since we would like to maximize the diversity of the system under the constraint of the available configurations.
Unsupervised discovery of activity correlations using latent topic models

Topic models such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have been successfully used to discover individual activities in a scene. However these methods do not discover group activities which are commonly observed in real life videos of public places. In this paper we address the problem of discovering activities and their associations as a group activity in an unsupervised manner. We propose a method that uses a two layer hierarchical latent structure to correlate individual activities in lower layer with group activity in higher layer. Our model considers each scene to be composed of a mixture of group activities. Each group activity is in turn composed as a mixture of individual activities represented as multinomial distributions. Each individual activity is represented as a distribution over local visual features. We use a Gibbs sampling based algorithm to infer these activities. Our method can summarize not only the individual activities but also the common group activities in a video. We demonstrate the strength of our method by mining activities and the salient correlation amongst them in real life videos of crowded public scenes.
Indonesian music fans group identification using social network analysis in Kaskus forum

The current practices of collecting information about market insight in music industry are through the sampling data collection using surveys, questionnaires and interviews. With the availibility of large-scale online conversation data, we can get faster; cheaper and more accurate data represent the actual phenomena of the market. The aim of this paper is to propose a new approach for helping the music industry parties to utilize network data mining methodology to identify one important insight in understanding music industry market, which is identification of music fans groups or conversations topics. Kaskus, the biggest online forum in Indonesia is a common media for millions Indonesian to discuss about many things, including their music preference. Last.fm is a social network of music enthusiasts. We mine Last.fm conversational topics in Kaskus and model those data using Social Network Analysis methodology based on graph model. We implement community detection algorithms to identify group identification. We show that network interaction pattern among the users in online forum can be used to help music industry parties identify the dominant topics and/or dominant music fans group in Indonesia.
Back To The Source: An Online Approach for Sensor Placement and Source Localization

Source localization, the act of finding the originator of a disease or rumor in a network, has become an important problem in sociology and epidemiology. The localization is done using the infection state and time of infection of a few designated sensor nodes; however, maintaining sensors can be very costly in practice. We propose the first online approach to source localization: We deploy a priori only a small number of sensors (which reveal if they are reached by an infection) and then iteratively choose the best location to place a new sensor in order to localize the source. This approach allows for source localization with a very small number of sensors; moreover, the source can be found while the epidemic is still ongoing. Our method applies to a general network topology and performs well even with random transmission delays.
High-level modelling and automatic generation of dynamicaly reconfigurable systems

Dynamic Partial Reconfiguration (DPR) has been introduced in recent years as a method to increase the flexibility of FPGA designs. However, using DPR for building complex systems remains a daunting task. Recently, approaches based on MDE and UML MARTE standard have emerged which aim to simplify the design of complex SoCs. Moreover, with the recent standardization of the IP-XACT specification, there is an increasing interest to use it in MDE methodologies to ease system integration and to enable design flow automation. In this paper we propose an MARTE/MDE approach which exploits the capabilities of IP-XACT to model and automatically generate DPR SoC designs. In particular, our goal is the creation of the structural top level description of the system and to include DPR support in the used IP cores. The generated IP-XACT descriptions are transformed to obtain the files required as inputs by the EDK flow and then synthesized to generate the netlists used by the DPR flow. The methodology is demonstrated using two CODEC cores (CAVLC and VLC) into a MicroBlaze based DPR SoC.
Separation of duty in role-based environments

The separation of duty is a principle that has a long history in computer security research. Many computing systems provide rudimentary support for this principle, but often the support is inconsistent with the way the principle is applied in non-computing environments. Furthermore, there appears to be no single accepted meaning of the term. We examine the ways in which separation of duty has been used, adding the notion of history-based separation of duty. We assess ways in which computing systems may support separation of duty. We discuss the mechanisms we are implementing to support separation of duty and roles in Adage, a general-purpose authorization language and toolkit.
Kinect-Based Systems For Maritime Operation Simulators?

Maritime operations involve complex tasks that must be taught through practical training. Using modern technologies offshore simulators can serve as realistic environment for training. The degree of immersiveness can be increased by replacing conventional input devices (keyboards, joysticks) with natural user interface (NUI) devices. This paper surveys different NUI technologies and advocates that vision-based systems, such as Microsoft Kinect, can provide gesture recognition with high fidelity. The unobtrusiveness of vision-based technologies is highly important to avoid user resistance and preserve realism of the simulation.
Monoclonal Antibodies for Non-Hodgkin's Lymphoma: State of the Art and Perspectives

Monoclonal antibodies have been the most successful therapeutics ever brought to cancer treatment by immune technologies. The use of monoclonal antibodies in B-cell Non-Hodgkin's lymphomas (NHL) represents the greatest example of these advances, as the introduction of the anti-CD20 antibody rituximab has had a dramatic impact on how we treat this group of diseases today. Despite this success, several questions about how to optimize the use of monoclonal antibodies in NHL remain open. The best administration schedules, as well as the optimal duration of rituximab treatment, have yet to be determined. A deeper knowledge of the mechanisms underlying resistance to rituximab is also necessary in order to improve the activity of this and of similar therapeutics. Finally, new antibodies and biological agents are entering the scene and their advantages over rituximab will have to be assessed. We will discuss these issues and present an overview of the most significant clinical studies with monoclonal antibodies for NHL treatment carried out to date.
NaviVew: Virtual Slope Visualization of Blind Area at an Intersection

�NaviView” represents our concept of visual assistance for drivers at the wheel. Here, we propose a new visual assistance system that can visualize the blind area behind other large vehicles under real driving conditions. Our system visualizes the blind area as a virtual slope and reduces collision accidents while turning over opposite lanes at an intersection.
Video Saliency Detection via Dynamic Consistent Spatio-Temporal Attention Modelling

Human vision system actively seeks salient regions and movements in video sequences to reduce the search effort. Modeling computational visual saliency map provides im-portant information for semantic understanding in many real world applications. In this paper, we propose a novel video saliency detection model for detecting the attended regions that correspond to both interesting objects and dominant motions in video sequences. In spatial saliency map, we in-herit the classical bottom-up spatial saliency map. In tem-poral saliency map, a novel optical flow model is proposed based on the dynamic consistency of motion. The spatial and the temporal saliency maps are constructed and further fused together to create a novel attention model. The pro-posed attention model is evaluated on three video datasets. Empirical validations demonstrate the salient regions de-tected by our dynamic consistent saliency map highlight the interesting objects effectively and efficiency. More im-portantly, the automatically video attended regions detected by proposed attention model are consistent with the ground truth saliency maps of eye movement data.
Risk-Association of DNA Methyltransferases Polymorphisms with Gastric Cancer in the Southern Chinese Population

DNA hypomethylation and/or hypermethylation are presumed to be early events in carcinogenesis, and one or more DNA methyltransferases (DNMTs) have been suggested to play roles in carcinogenesis of gastric cancer (GC). However, there have been no systematic studies regarding the association between DNMT gene polymorphisms and GC risk. Here, we examined the associations of 16 single nucleotide polymorphisms (SNPs) from DNMT1 (rs2114724, rs2228611, rs2228612, rs8101866, rs16999593), DNMT2 (rs11695471, rs11254413), DNMT3A (rs1550117, rs11887120, rs13420827, rs13428812, rs6733301), DNMT3B (rs2424908, rs2424913, rs6087990) and DNMT3L (rs113593938) with GC in the Southern Chinese population. We assessed the associations of these 16 SNPs with GC in a case-control study that consisted of 242 GC cases and 294 controls, using the Sequenom MALDI-TOF-MS platform. Association analyses based on the χ2 test and binary logistic regression were performed to determine the odds ratio (OR) and 95% confidence interval (95%CI) for each SNP. We found that rs16999593 in DNMT1, rs11254413 in DNMT2 and rs13420827 in DNMT3A were significantly associated with GC susceptibility (OR 1.45, 0.15, 0.66, respectively; 95% CI 1.00–2.11, p = 0.047; 0.08–0.27, p < 0.01; 0.45–0.97, p = 0.034, respectively, overdominant model). These results suggested that DNMT1, DNMT2 and DNMT3A may play important roles in GC carcinogenesis. However, further studies are required to elucidate the mechanism.
Neurobiology of Disease P 2 X 7 Receptor Blockade Prevents ATP Excitotoxicity in Oligodendrocytes and Ameliorates Experimental Autoimmune Encephalomyelitis

Carlos Matute,1 Iratxe Torre,1 Fernando Pérez-Cerdá,1 Alberto Pérez-Samartı́n,1 Elena Alberdi,1 Estibaliz Etxebarria,1 Amaia M. Arranz,1 Rivka Ravid,2 Alfredo Rodrı́guez-Antigüedad,3 Marı́aVictoria Sánchez-Gómez,1 and Marı́a Domercq1 1Departamento de Neurociencias, Universidad del Paı́s Vasco, 48940 Leioa, Spain, 2Netherland Brain Bank, 1105 AZ Amsterdam ZO, The Netherlands, and 3Servicio de Neurologı́a, Hospital de Basurto, 48008 Bilbao, Spain
The Evolution of Story: How Time and Modality Affect Visual and Verbal Narratives

A majority of Americans distrust the news media due to concerns over comprehensiveness, accuracy, and fairness. Since many interactions between journalists and their subjects last only minutes and can be published within seconds, if not live, research is needed to explore how journalists' understandings of their subjects' narratives evolve over time and how much time is necessary to avoid surface-level coverage. Also, since people are now exposed to more image-based rather than text-based messages, additional research is necessary to explore how the verbal narratives spoken by subjects compare to their nonverbal narratives as captured by news photographers in visual form. Through a longitudinal, interview-based approach, a photojournalist working on a 30-plus-day picture story was interviewed weekly for six weeks over the course of his project to track perceptions of how his subjects' verbal narratives changed. At the conclusion of the project, the photojournalist's subjects were also interviewed to explore how their verbal and nonverbal narratives compared. Informed by literature in role theory, narrative, and visual journalism, the findings explore how news media narratives can be more nuanced and how people shape their visual and verbal narratives consciously and unconsciously. Additional findings suggest that comprehensiveness, accuracy, and fairness are intimately related to interaction duration and that visual narratives can highlight role conformity and conflict in ways not possible through verbal narratives alone.
Fast and SNP-tolerant detection of complex variants and splicing in short reads

Motivation: Next-generation sequencing captures sequence differences in reads relative to a reference genome or transcriptome, including splicing events and complex variants involving multiple mismatches and long indels. We present computational methods for fast detection of complex variants and splicing in short reads, based on a successively constrained search process of merging and filtering position lists from a genomic index. Our methods are implemented in GSNAP (Genomic Short-read Nucleotide Alignment Program), which can align both single- and paired-end reads as short as 14 nt and of arbitrarily long length. It can detect short- and long-distance splicing, including interchromosomal splicing, in individual reads, using probabilistic models or a database of known splice sites. Our program also permits SNP-tolerant alignment to a reference space of all possible combinations of major and minor alleles, and can align reads from bisulfite-treated DNA for the study of methylation state. Results: In comparison testing, GSNAP has speeds comparable to existing programs, especially in reads of ≥70 nt and is fastest in detecting complex variants with four or more mismatches or insertions of 1–9 nt and deletions of 1–30 nt. Although SNP tolerance does not increase alignment yield substantially, it affects alignment results in 7–8% of transcriptional reads, typically by revealing alternate genomic mappings for a read. Simulations of bisulfite-converted DNA show a decrease in identifying genomic positions uniquely in 6% of 36 nt reads and 3% of 70 nt reads. Availability: Source code in C and utility programs in Perl are freely available for download as part of the GMAP package at http://share.gene.com/gmap. Contact: twu@gene.com