Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Low Intensity DoS Attacks using Fuzzy Based Intrusion Detection System

Detecting low intensity denial of service (DoS) attacks in minimum time with less false positives is challenging for network based intrusion detection systems. In this paper we propose a new fuzzy logic based approach, which adapts fuzzy linguistic membership functions instead of modifying the fuzzy rules. The authors use the normal TCP profile, i.e. the `connected-sessions', to guide the fuzzy membership set boundaries. The authors also propose implementation architecture with a hierarchy of fuzzy agents responsible for detecting different types of attacks. This scheme reduces the number of false alarms at lower level of hierarchy. Tests have been conducted and are reported using DARPA98 datasets
Managing decision-making with certainty of threat

Several factors, such as emotion and uncertainty of the outcome, influence decision-making. We assessed decision-making during a risky event (natural hazard, focusing here on two types of volcanic threats) by manipulating the certainty of lethal threat in an information campaign. We hypothesized that the reduction of uncertainty of lethal threat in an information campaign would improve behavior through more suitable choices by reducing the use of emotional choices. In the scenario that occurred in a familiar place, participants who received information with uncertainty of lethal threat presented more emotional and comfortable choices, such as staying at home, rather than detached ones, such as leaving the area. These were either appropriate (for volcanic ash cloud) or inappropriate (for pyroclastic flow). The certainty hypothesis was partially validated, as certainty influenced the quality of choice in the scenarios that took place at home. Furthermore, participants in the volcanic disaster context presented less suitable decisions compared to those in the neutral context, which was discussed in terms of the presence of emotions, such as fear of volcanic eruption. Our results highlight the importance of controlling the comforting emotional aspect of the home environment in any information communication.
Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes

Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian nonparametric probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by imposing a Gaussian process (GP) prior to the latent scalar potential of the magnetic field. By rewriting the GP model in terms of a Hilbert space representation, we circumvent the computational pitfalls associated with GP modeling and provide a computationally efficient and physically justified modeling tool for the ambient magnetic field. The model allows for sequential updating of the estimate and time-dependent changes in the magnetic field. The model is shown to work well in practice in different applications. We demonstrate mapping of the magnetic field both with an inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.
The fictive pass asymmetry : condemnation of harm, but not purity, is mitigated by fictional contexts

Is there a double standard when it comes to the moral acceptability of fiction that encourages the imagination of acts that violate moral norms of harm and moral norms of purity? Observations of ethics, legal proceedings, and public reactions to different types of media seems to suggest so. Over six experiments this phenomenon, coined the fictive pass asymmetry, will be tested. The fictive pass asymmetry hypothesis proposes that fictional contexts including imagination, film, and virtual environments, will mitigate the condemnation of harm code violations more so than purity code violations. In other words, fictional representations of harm are given a "fictive pass" in moral condemnation, but the fictional representation of purity code violations that involve an abnormal use of one's body are denied a pass, and thus evaluated more similarly across real and fictional contexts. Chapters 1 through 3 introduce the fictive pass asymmetry and review the literature that provide its theoretical framework. Chapter 4 presents three experiments that establish initial evidence in support of the fictive pass asymmetry effects. Experiment 1 presented participants (N = 431) with vignettes that described agents committing either sexual acts or violent acts that were described as occurring in real life, being performed in a video game, or watched in a film. Experiments 2 and 3 (N = 360 and N = 321, respectively) systematically improved methodology by expanding upon the fictive contexts and creating manipulations based more strictly on the moral psychology literature. Chapter 5 presents experiment 4 (N = 312) and experiment 5 (N = 352) which deepened the understanding of the fictive pass asymmetry effects by using mediation analyses to demonstrate how the perceived wrongness of fictional purity code violations can be explained by the extent to which they signal poor moral character. Lastly, chapter 6 contains a final experiment (N = 484) and a series of meta-analyses. The final experiment considers fictive pass asymmetry effects in relation to an opposing theoretical framework, validates a number of manipulations, and tests the presumption of desire as an alternate explanation of fictive pass asymmetry effects. Finally, the meta-analyses aggregate the data of these experiments to highlight the robustness of the fictive pass asymmetry effects. Chapter 7, the concluding chapter, reviews the experiments and discusses the results in regards to theories of anger and disgust, moral theories of act and character, as well as the fictive pass asymmetry's implications in media use and regulation.
The PmrA-Regulated pmrC Gene Mediates Phosphoethanolamine Modification of Lipid A and Polymyxin Resistance in Salmonella enterica

ABSTRACT The PmrA/PmrB regulatory system of Salmonella enterica controls the modification of lipid A with aminoarabinose and phosphoethanolamine. The aminoarabinose modification is required for resistance to the antibiotic polymyxin B, as mutations of the PmrA-activated pbg operon or ugd gene result in strains that lack aminoarabinose in their lipid A molecules and are more susceptible to polymyxin B. Additional PmrA-regulated genes appear to participate in polymyxin B resistance, as pbgP and ugd mutants are not as sensitive to polymyxin B as a pmrA mutant. Moreover, the role that the phosphoethanolamine modification of lipid A plays in the resistance to polymyxin B has remained unknown. Here we address both of these questions by establishing that the PmrA-activated pmrC gene encodes an inner membrane protein that is required for the incorporation of phosphoethanolamine into lipid A and for polymyxin B resistance. The PmrC protein consists of an N-terminal region with five transmembrane domains followed by a large periplasmic region harboring the putative enzymatic domain. A pbgP pmrC double mutant resembled a pmrA mutant both in its lipid A profile and in its susceptibility to polymyxin B, indicating that the PmrA-dependent modification of lipid A with aminoarabinose and phosphoethanolamine is responsible for PmrA-regulated polymyxin B resistance.
Disentangling the relation between intentions, planning, and behaviour: A moderated mediation analysis

Action planning is assumed to mediate between intentions and health behaviours. Moreover, intentions are assumed to moderate the planning-behaviour relation, because people with high intentions are more likely to enact their plans. The present studies extend these suppositions by integrating both assumptions to a novel and parsimonious model of moderated mediation: the mediation effect is hypothesised to be stronger in individuals who report higher intention levels. In two longitudinal studies on physical activity (N = 124) and interdental hygiene (N = 209), intentions and action planning were assessed at baseline, and behaviour was measured four (Study 1), and respectively, three (Study 2) months later. The moderated mediation hypothesis was tested with continuously measured intentions using regression analyses with non-parametric bootstrapping. Results from both studies suggest that levels of intentions moderate the mediation process: The strength of the mediated effect increased along with levels of intentions. Planning mediates the intention-behaviour relation, if individuals hold sufficient levels of intentions. Implications for theory advancement and intervention development are discussed.
Visualization of Data Mining Conclusions and Futures

Trying to apply a visualization to data without prepro-cessing can often lead to incomprehensible results because of cluttered and confused displays. A solution is to use data mining to extract just the interesting results and to visualize them. There are two basic ways to carry out the mining: supervised and unsupervised. For the supervised case, the user has a particular goal in mind, while in the second case the user is performing a global search for interesting patterns. MineSet provides two classifiers for goal directed learning. A classifier is a model for predicting one attribute of a set of data given the values for several other attributes. Prior to inducing a classifier a user must select one of the attributes as a label (the goal). The classifier is constructed based on a training set where the label values are known to be accurate. Once built, the model may be applied to new data where the label is unknown. The Evidence Visualizer displays the classifier structure produced by running an evidence inducer on the serv-er. Figure 4 shows the Evidence Visualizer being used to determine data attributes correlate with earning more than $50,000 in salary. Each attribute is a row of pie charts. Each pie chart represents an interval or value of the attribute. The attributes are listed in order of usefulness for predicting the label (salary in this case). The height of the pies show the number records having a particular value. Interaction with the scene allows the user to ask questions like 'what is the probability that someone earns over $50,000 given that their education is High School graduate and their occupation is professional specialty? '. The Tree Visualizer is used to display the decision tree structure produced by a decision tree inducer. The attribute shown at the root of the tree is the one determined to be the most important. After the root split, branches are further split based on other attributes until further splits are deemed to be insignificant statistically. At this point leaf nodes are close to containing only one label value. In figure 5 one can see that odor is the most important attribute for determining whether or not a mushroom is poisonous. It completely determines the edibility, except when there is no odor, in which case stalk shape is used to discriminate further. A common commercial application for this is determining loan approvals based on …
Human assisted robotic team campaigns for aquatic monitoring

Large‐scale environmental sensing, e.g., understanding microbial processes in an aquatic ecosystem, requires coordination across a multidisciplinary team of experts working closely with a robotic sensing and sampling system. We describe a human‐robot team that conducted an aquatic sampling campaign in Lake Fulmor, San Jacinto Mountains Reserve, California during three consecutive site visits (May 9–11, June 19–22, and August 28–31, 2006). The goal of the campaign was to study the behavior of phytoplankton in the lake and their relationship to the underlying physical, chemical, and biological parameters. Phytoplankton form the largest source of oxygen and the foundation of the food web in most aquatic ecosystems. The reported campaign consisted of three system deployments spanning four months. The robotic system consisted of two subsystems—NAMOS (networked aquatic microbial observing systems) comprised of a robotic boat and static buoys, and NIMS‐RD (rapidly deployable networked infomechanical systems) comprised of an infrastructure‐supported tethered robotic system capable of high‐resolution sampling in a two‐dimensional cross section (vertical plane) of the lake. The multidisciplinary human team consisted of 25 investigators from robotics, computer science, engineering, biology, and statistics.We describe the lake profiling campaign requirements, the robotic systems assisted by a human team to perform high fidelity sampling, and the sensing devices used during the campaign to observe several environmental parameters. We discuss measures taken to ensure system robustness and quality of the collected data. Finally, we present an analysis of the data collected by iteratively adapting our experiment design to the observations in the sampled environment. We conclude with the plans for future deployments. © 2007 Wiley Periodicals, Inc.
Joint Estimation of Angle , Doppler and Polarization Parameters for Narrowband MIMO Multipath Channels Using Polarization Sensitive Antenna Arrays

A novel subspace based joint angle of arrival (AOA), angles of departure (AOD), Doppler shifts and polarization states parameter estimation scheme for polarized two-dimensional (2D) double directional MIMO multipath channels is proposed in this paper. A narrowband system with non-polarized uniform linear array at the transmitter and cross-polarized antenna array at the receiver is considered. The proposed algorithm perform a simple transformation on the estimated channel state information (CSI) matrix in such a manner that multidimensional ESPRIT can be utilized to exploit the translational invariance in the angle, Doppler and polarization dimensions. Simulation results show that the proposed algorithm can accurately estimate the AOA, AOD, Doppler shifts and polarization angles of the multipath channel even for closely spaced scattering sources. Key–Words: Polarization, MIMO, Multidimensional ESPRIT, Multipath Parameter Estimation
Endurance Training Intensity Does Not Mediate Interference to Maximal Lower-Body Strength Gain during Short-Term Concurrent Training

We determined the effect of concurrent training incorporating either high-intensity interval training (HIT) or moderate-intensity continuous training (MICT) on maximal strength, counter-movement jump (CMJ) performance, and body composition adaptations, compared with single-mode resistance training (RT). Twenty-three recreationally-active males (mean ± SD: age, 29.6 ± 5.5 y; V˙O2peak, 44 ± 11 mL kg−1·min−1) underwent 8 weeks (3 sessions·wk−1) of either: (1) HIT combined with RT (HIT+RT group, n = 8), (2) work-matched MICT combined with RT (MICT+RT group, n = 7), or (3) RT performed alone (RT group, n = 8). Measures of aerobic capacity, maximal (1-RM) strength, CMJ performance and body composition (DXA) were obtained before (PRE), mid-way (MID), and after (POST) training. Maximal (one-repetition maximum [1-RM]) leg press strength was improved from PRE to POST for RT (mean change ± 90% confidence interval; 38.5 ± 8.5%; effect size [ES] ± 90% confidence interval; 1.26 ± 0.24; P < 0.001), HIT+RT (28.7 ± 5.3%; ES, 1.17 ± 0.19; P < 0.001), and MICT+RT (27.5 ± 4.6%, ES, 0.81 ± 0.12; P < 0.001); however, the magnitude of this change was greater for RT vs. both HIT+RT (7.4 ± 8.7%; ES, 0.40 ± 0.40) and MICT+RT (8.2 ± 9.9%; ES, 0.60 ± 0.45). There were no substantial between-group differences in 1-RM bench press strength gain. RT induced greater changes in peak CMJ force vs. HIT+RT (6.8 ± 4.5%; ES, 0.41 ± 0.28) and MICT+RT (9.9 ± 11.2%; ES, 0.54 ± 0.65), and greater improvements in maximal CMJ rate of force development (RFD) vs. HIT+RT (24.1 ± 26.1%; ES, 0.72 ± 0.88). Lower-body lean mass was similarly increased for RT (4.1 ± 2.0%; ES; 0.33 ± 0.16; P = 0.023) and MICT+RT (3.6 ± 2.4%; ES; 0.45 ± 0.30; P = 0.052); however, this change was attenuated for HIT+RT (1.8 ± 1.6%; ES; 0.13 ± 0.12; P = 0.069). We conclude that concurrent training incorporating either HIT or work-matched MICT similarly attenuates improvements in maximal lower-body strength and indices of CMJ performance compared with RT performed alone. This suggests endurance training intensity is not a critical mediator of interference to maximal strength gain during short-term concurrent training.
Supervised learning Methods for Bangla Web Document Categorization

This paper explores the use of machine learning approaches, or more specifically, four supervised learning Methods, namely Decision Tree(C 4.5), K-Nearest Neighbour (KNN), Na\"ive Bays (NB), and Support Vector Machine (SVM) for categorization of Bangla web documents. This is a task of automatically sorting a set of documents into categories from a predefined set. Whereas a wide range of methods have been applied to English text categorization, relatively few studies have been conducted on Bangla language text categorization. Hence, we attempt to analyze the efficiency of those four methods for categorization of Bangla documents. In order to validate, Bangla corpus from various websites has been developed and used as examples for the experiment. For Bangla, empirical results support that all four methods produce satisfactory performance with SVM attaining good result in terms of high dimensional and relatively noisy document feature vectors.
Sentiment analysis method based on the focus sentence and context for Chinese comment text

With the increasing popularization and extensive application of Internet, more and more netizens begin to post their views, opinions and comments on the Internet. The sentiment analysis on these comment texts will become a focus for future research. According to the characteristics of Chinese comment text, a method based on the text sentiment analysis of the focus sentence and context is put forward in this paper, and it proves a certain validity by experiments.
Learning from the Past for the Future: How to Make Adult Education Sustainable

Abstract When compared to education with children, adolescents and students, adult education has particular opportunities and challenges, depending on stronger biographical influences: adults have developed their own learning style and learning competence and apart from more formal learning opportunities facilitated through digital learning and/or learning in schools/universities, there are many opportunities for informal learning, often influenced by experiences of education as a child. In this regard, biographical learning offers opportunities for transformational, organic, remembrance and experiences. The study, therefore, focuses on such kinds of adult education as a core element, particularly of Protestant adult education. This paper presents a multidimensional concept of Protestant adult education, which integrates existing concepts into a memory oriented educational framework. Therefore, the theoretical considerations explain the phenomenological background of this particular concept. In a second step, this concept will be contextualized within the historical context of Protestant adult education. The summary emphasizes the particular aspect of educational coping with past, current and future experiences and situations.
Speculative lock reordering: optimistic out-of-order execution of critical sections

We present a new method - speculative lock reordering (SLR) - that enables multiple threads to concurrently and speculatively execute within a critical section. Its novel feature is to exploit that there is no a priori execution order between separate invocations of a critical section that speculatively executed threads must respect. In contrast to previously proposed speculative synchronization schemes, we show that since an execution order can be selected that removes as many data dependences as possible, SLR can expose more concurrency. Additionally, it is shown that SLR can be implemented in a chip-multiprocessor by only modest extensions to already published thread-level data dependence speculation systems.
A Scalable Index Architecture for Supporting Multi-Dimensional Range Queries in Peer-to-Peer Networks

Distributed hash table based peer-to-peer systems are emerging as new paradigms for building large-scale distributed applications, due to their scalability, fault-tolerance and self-organization. However, most existing DHTs are designed for exact-key searching, and the support of multi-dimensional range queries in peer-to-peer networks is still a challenging problem. In this paper, we propose a distributed index architecture called Dak to support range queries on multi-dimensional data. Based on efficient space mapping and query routing mechanisms, Dak can provide a scalable platform to support any number of indexes with different dimensionalities. Significantly, this architecture does not need to generate or maintain any search trees. Instead, it exploits the embedded trees in the underlying distributed hash tables to refine and deliver queries. To deal with skewed data distribution, we also provide load-balancing mechanisms to ensure that no node in the system is unduly loaded
Building a Parallel Spatio-Temporal Data-Text Corpus for Summary Generation

We describe a corpus of naturally occurring road ice weather forecasts and the associated weather prediction data they are based upon. We also show how observations from an analysis of this corpus have been applied to build a prototype Natural Language Generation (NLG) system for producing road ice forecasts. While this corpus occurs in a narrow domain, it has much wider applicability due to the nature of its spatial descriptions, whose primary communicative goal is to describe the interaction between meteorological parameters and geographic features.
The predicted metallicity distribution of stars in dwarf spheroidal galaxies

We predict the metallicity distribution of stars for 6 Dwarf Spheroidal (dSph) galaxies of the Local Group by means of a chemical evolution model which is able to reproduce several observed abundance ratios and the present day total mass and gas content of these galaxies. The model adopts up to date nucleosynthesis and takes into account the role played by supernovae of different types (II, Ia). Each galaxy model is specified by the prescriptions of the star formation rate and by the galactic wind efficiency chosen to reproduce the main features of these galaxies.To search for other articles by the author(s) go to: http://adsabs.harvard. edu/abstract_service. html
Customer Loyalty and Supplier Strategies for Quality Competition

We analyze a model of consumer response to variation in product or service quality. The model, developed in [4], provides closed-form expressions that characterize short-term and long-term measures of customer loyalty to a supplier. In this paper, we develop sensitivity analyses that offer a rich characterization of how factors - such as the underlying quality levels of the suppliers, the customer's ability to distinguish between good and bad suppliers, and the customer's prior beliefs regarding the suppliers affect both short-term and long-term loyalty. We also use the expressions to develop simple normative models for suppliers that wish to develop effective quality strategies.