Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of campus network security evaluation index system based on extension analytic hierarchy process

Based on the study of factors affecting campus network security,this paper constructs a campus network security evaluation index system including the levels of target,guideline and index.By using the method of extension analytic hierarchy process(EAHP),the weights of the campus network security evaluation index system are calculated and the inaccuracy of the fuzzy subjective judgments and the inconsistency of the judgment matrix in the process of model construction are solved.And according to the campus network topology as well as the node degree,the sample groups of three typical users are selected to conduct the network security risk evaluation.
Observability Analysis of Non-Holonomic Constraints for Land-Vehicle Navigation Systems

Over the past decades, the integration of a MEMS-based (Micro-Electro Mechanical Systems) Inertial Measuring Unit (IMU) with a GNSS receiver-chip has become commonly used navigation techniques by virtue of their advantages such as small sized, light weight, with low power consumption, and have extremely low cost. To provide accurate and reliable positioning solutions with a low-cost GNSS/MEMS INS system, it is valuable to introduce specific auxiliary information that can improve the navigation performance without adding extra hardware costs. The auxiliary information is especially useful during GNSS outage periods or when the vehicle is moving with low dynamics (e.g. no change of attitude and accelerations) which lead to the poor observability of the GNSS/INS navigation system. For LVN applications, Non-Holonomic Constraints (NHC) is one of the most common types of auxiliary information. This paper focuses on studying the contributions of the NHC from the perspective of observability, which provides a deeper insight and shows how the NHC improves the navigation solutions. Considering several typical vehicle dynamics, it is also clear to see the effects of the NHC to the inertial navigation under different situations. Both theoretical analysis and simulation tests have shown that the contributions of the NHC to the estimation of a certain state depend on both the current vehicle dynamic and the relative error magnitude of this state compared to the coupled state under the current vehicle dynamic; both the accelerating and turning motions can enhance the contributions of the NHC to the estimation of both the yaw and the pitch, and such contributions will be stronger with a higher vehicle speed; the NHC has significant effects on controlling the roll in all motion status. Furthermore, the effects of the NHC on the estimation of the biases of both gyroscopes and accelerometers are also analyzed. The outcomes of this paper show that the proposed observability analysis is beneficial to the utilization of NHC or other priori information in low-cost navigation systems.
Thrust Area in Data Science-Big Data and Data Analytics

: The dawn of big data has arisen. Big data may be defined as a term which indicates large sets of data. The challenges that occur due to the big data are its storage, management, capture, curation, sharing, analysis, security, handling, etc. The old or traditional methodologies are not much capable for the above purpose. Hence, it is necessary to know about the upcoming methods regarding the big data and the paper introduces the brief information about it. The concept of big data is becoming popular because of the constant increase need and creation of data. In general, big data is constantly moving data and its size has almost reached till yotta bytes. Certain tools used such a Hadoop, MongoDB, Cassendra and Apache spark to deal with big data are briefly presented in the paper. Also, the terms such as parameters, applications, storage and growth are described.
Computational Gains Using RPVM on a Beowulf Cluster

The Beowulf cluster Becker et al. (1995); Scyld Computing Corporation (1998) is a recent advance in computing technology that harnesses the power of a network of desktop computers using communication software such as PVM Geist et al. (1994) and MPI Message Passing Interface Forum (1997). Whilst the potential of a computing cluster is obvious, expertise in programming is still developing in the statistical community. Recent articles in R-news Li and Rossini (2001) and Yu (2002) entice statistical programmers to consider whether their solutions could be effectively calculated in parallel. Another R package, SNOW Tierney (2002); Rossini et al. (2003) aims to skillfully provide a wrapper interface to these packages, independent of the underlying cluster communication method used in parallel computing. This article concentrates on RPVM and wishes to build upon the contribution of Li and Rossini (2001) by taking an example with obvious orthogonal components and detailing the R code necessary to allocate the computations to each node of the cluster. The statistical technique used to motivate our RPVM application is the geneshaving algorithm Hastie et al. (2000b,a) for which S-PLUS code has been written by Do and Wen (2002) to perform the calculations serially. The first section is a brief description of the Beowulf cluster used to run the R programs discussed in this paper. This is followed by an explanation of the gene-shaving algorithm, identifying the opportunities for parallel computing of bootstrap estimates of the “strength" of a cluster and the rendering of each matrix row orthogonal to the “eigen-gene". The code for spawning child processes is then explained comprehensively and the conclusion compares the speed of RPVM on the Beowulf to serial computing.
Proceedings of the 2016 Workshop on Semantic Spaces at the Intersection of NLP, Physics and Cognitive Science

This volume contains the Proceedings of the 2016 Workshop on Semantic Spaces at the Intersection of NLP, Physics and Cognitive Science (SLPCS 2016), which was held on the 11th of June at the University of Strathclyde, Glasgow, and was co-located with Quantum Physics and Logic (QPL 2016). Exploiting the common ground provided by the concept of a vector space, the workshop brought together researchers working at the intersection of Natural Language Processing (NLP), cognitive science, and physics, offering them an appropriate forum for presenting their uniquely motivated work and ideas. The interplay between these three disciplines inspired theoretically motivated approaches to the understanding of how word meanings interact with each other in sentences and discourse, how diagrammatic reasoning depicts and simplifies this interaction, how language models are determined by input from the world, and how word and sentence meanings interact logically. This first edition of the workshop consisted of three invited talks from distinguished speakers (Hans Briegel, Peter Gardenfors, Dominic Widdows) and eight presentations of selected contributed papers. Each submission was refereed by at least three members of the Programme Committee, who delivered detailed and insightful comments and suggestions.
An Adaptive Positioning System for Smartphones in Zigbee Networks Using Channel Decomposition and Particle Swarm Optimization

ZigBee wireless sensor networks has the benefit of superior optimization in power consumption and extremely long battery life. In near future, the handheld devices are expected to incorporate support for wireless sensor networks technology, and these devices can become active elements in ZigBee-based applications such as automated buildings and advanced metering systems. Use of these applications is also promising for the expansion of location services into environments that do not have access to Global Navigation Satellite Systems (GNSS). Compared with the ToA (Time of Arrival) and TDoA (Time difference of Arrival) methods, RSSI (Received Signal Strength Indicator) has several advantages. It does not require additional hardware. Owing to the simplicity of RSSI, the most recent studies on localization using wireless sensor networks use RSSI-based algorithms. Previous works RSSI-based location estimation methods discussed by the literature depend on the impractical assumption that signal propagation characteristics are known and independent of both time variations and the environment. That assumption is invalid for the complex structures of indoor areas, which make the signal propagation characteristics for the same node differ significantly based on the orientation, even if the radiation pattern of the node is omnidirectional. Additionally, obtaining knowledge about signal propagation characteristics requires using manual data collection, which is impractical and time consuming. Furthermore, location estimation techniques must consider the large variations and high degree of uncertainty in RSSI observations. In this paper, we developed an adaptive localization system with low power and low cost, IEEE 802.15. 4 ZigBee WSN. The under investigated system does not require manual pre-data collection and operates without performing exhaustive radio surveys. The first contribution in this paper is involving Kalman filtering for RSSI smoothing. Secondly, estimation of multi-link signal propagation characteristics; path loss exponent and Gaussian random variable in real-time by utilizing Particle Swarm Optimization (PSO). The results from the experiments are taken show that using the proposed multi-link channel decomposition estimation technique is more effective than using only one fixed value of PLE and X0. The overall localization accuracy is within two meters in root mean square.
How the mind builds evolutionarily new concepts

Author(s): Barlev, Michael | Advisor(s): Cosmides, Leda | Abstract: The human mind is equipped with a variety of evolved mechanisms, each specialized for representing concepts from an adaptively important domain, such as persons and their mental states, animals and their biology, plants, and physical objects and their mechanical properties. But how does the mind build concepts that were not targets of natural selection, that is, concepts that go beyond or even conflict with the inferences engineered into these evolved mechanisms? Are evolutionarily new concepts built out of nothing (as domain-general learning theories predict), or are they built by initially co-opting evolved concepts? And if evolutionary new concepts initially co-opt evolved ones, do they later revise the evolved concepts, or do they co-exist alongside them? I evaluate these questions using the Christian God concept as a case study.I demonstrate using a novel sentence verification paradigm that, first, the God concept is built by co-opting the evolved person concept, and, second, that in the minds of Christian religious adherents, acquired theological representations of God which conflict with person representations (e.g. infallibility) co-exist alongside and do not revise them. In the experiments reported here, Christian religious adherents were asked to evaluate statements for which core knowledge intuitions about persons and acquired Christian theology about God were consistent (i.e., true according to both [e.g., “God has beliefs that are true”] or false according to both [e.g., “All beliefs God has are false”]) or inconsistent (i.e., true on intuition but false theologically [e.g., “God has beliefs that are false”] or false on intuition but true theologically [e.g., “All beliefs God has are true”]). Exp. 1 demonstrated that participants were less accurate and slower responding to inconsistent versus consistent statements, suggesting that the core knowledge intuitions both co-existed alongside and interfered with the acquired theological representations. Exp. 2 tested the effects of cognitive load on response interference. Exp. 3 ruled out a plausible alternative interpretation of these findings, by demonstrating that response interference is found for God but not for an ordinary entity (a priest). Exp. 4 demonstrated that response interference is invariant with age and with theological experience. Indeed, response interference was found even in Christian religious adherents with a lifetime of theological experience. Finally, Exp. 5-6 expanded on the findings of the previous experiments, which primarily focused on God’s psychology, to God’s physicality. I discuss the implications of these findings to domain-general versus domain-specific theories of learning.
Flow-based Routing Schemes for Minimizing Network Energy Consumption using OpenFlow

—With the growing amount of trafﬁc on the Internet, network devices are consuming increasing energy as their transmission capacity increases. Therefore, the reduction of network power consumption is an important issue. Software-deﬁned networking is receiving signiﬁcant attention as a means to manage increased network trafﬁc efﬁciently. We consider energy efﬁcient routing schemes with trafﬁc aggregation for each ﬂow that maintain acceptable performance using OpenFlow. The proposed schemes aggregate trafﬁc to build a network using the minimum active devices necessary. Through simulation evaluation, we show that the proposed schemes can reduce power consumption, in comparison with the conventional routing protocol.
The decision-making role of the patient in localised prostate cancer treatment

Our objective was to review the current literature on patient participation and decision-making in the treatment selection process for localised prostate cancer, and to evaluate capacity for improvement. Methods: 42 articles from our literature search were deemed eligible and relevant for review. We reviewed studies on all facets of the treatment decision-making process with most number of articles (16) on treatment preferences. Results: The majority of the patients prefer an active or collaborative role in decision-making. Patients are seeking information from a myriad of sources but the recommendation from their treating physician is often the most influential on the final decision. Radical prostatectomy is more likely to be selected in patients who view a cure for cancer as being of the utmost importance and radiation therapy is preferred in patients who are concerned about treatment side effects. Conclusion: Currently no ideal tool exists to assist patients in making informed treatment decisions that also takes into account patients’ values and preferences. We encourage collaborative partnership in a multidisciplinary setting to optimise this process and individualised risk-based decision-making tools may provide a better pathway to assist patients reach decisions.
Research on filter processing of LIDAR data

The aim of the filter processing to LIDAR dataset is to divide the dataset into ground points and non-ground points. So, the filtering of LIDAR dataset is a crucial step to obtain the DEM with high precision. Over the last few years, some algorithms have been developed to filter LIDAR data. This paper studies three filtering algorithms that are used in common at present, brings about some improvement to the MLS filtering algorithm which is one of the three filtering algorithms and gives two datasets as experimental data in order to compare and analyze the filtering results.
Frequency mining closed path algorithm based in the modern logistic management system

With widely use in supply chain management applications, the Radio Frequency IDentification (RFID) systems collected massive path data sets for moving objects. Because of the complexity of path data, mining frequency path from the path data sets was difficult task with high cost. To deal with this problem, an algorithm called Mining Closed Path (MCP) to mine closed path in path data sets was proposed. Based on the characteristics of path data that a path was an ordered list stages and a stage had two parts location and duration, MCP divided a path into a locations sequence and a duration sequence. Then closed path mining was conducted in these sequences. This strategy could decrease the cost and improve the speed for mining frequency path. The theory and experiments showed that the performance of MCP was superior, and it was effective when mining closed path with massive path data sets.
Breadth Fixed Gossip: a Route Discovery Mechanism in Ad Hoc Network

The lack of information from a root node to neighbors becomes a particular constraint in a message delivery. While the transmission range of node needs to be considered also for ensuring a delivered packet message to them. Therefore, a route discovery mechanism is a good start to make a reliable path in an unknown environment. The objective of this study is to explore the implementation of Breadth First Search, fixed radius model, and gossip algorithms (Breadth Fixed Gossip) on the ad hoc network. Simulations were conducted to obtain the value of three metrics i.e. saved retransmission, transmission failure, and hop counts. These results were compared with Depth First Search and Gossip performance. It indicates that saved retransmission metric Breadth Fixed Gossip has a 58 times better performance, reduces transmission failure by 20%, and requires 50% more hops at transmission range below 15 than Depth First Search and Gossip.
Sonar-Based Autonomous Navigation and Mapping of Indoor Environments Using Micro-Aerial Vehicles

The purpose of this project is to add autonomous navigation and mapping to a micro aerial vehicle (MAV) with the aid of additional sonar sensors that are mounted to the MAV’s chassis. In this paper, an Extended Kalman Filter Simultaneous Localization and Mapping (EKF-SLAM) system is implemented on a MAV with four sonar sensors. Some simulations are developed to test the various modules and processes created for the project, and the (cid:28)nal system is tested on an AR.Drone 2.0 quadcopter with an added sonar sensor package.
Routing through Efficient Channel Assignment in Cognitive Radio Networks

In Cognitive Radio Networks (CRNs) there is much dynamicity due to the activities of primary users which results in instability of routes. Therefore, an efficient routing protocol based on good channel assignment strategy is required in CRNs. A good channel selection strategy makes route stable by selecting channels having larger capacity and greater availability time. Therefore, the focus of this chapter is joint channel assignment and routing in CRNs, which provides a comprehensive survey on routing and channel assignment in CRNs. First, the importance of joint channel assignment and routing for successful communication in cognitive radio networks is discussed. Then classification and challenges related to channel assignment and routing are discussed in detail. In order to establish reliable routes in CRNs, some factors are discussed that further enhance the communication in CRNs. Finally, guidelines for the development of efficient routing protocols are discussed.
When the Future Finally Arrives: Web 2.0 Becomes Web 3.0

As technology grows and changes, some people like to sit back and see where the ride takes them. Others prefer to keep an eye on where the ride is going—they have learned from the past that if one fails to keep an eye on the future, he or she could end up with a broken Betamax player and a stack of video tapes that no longer play on anyone’s machine. In education, knowing what the future holds is especially critical when dealing with technology. Educators are no longer dealing with making technology decisions for themselves; they now have to make decisions for entire classes or even entire departments or schools. Knowing what is coming around the bend in technology could make the difference between creating a cutting-edge classroom or an odd collection of useless gadgets in the corner of a bland, normal classroom. “Web 2.0” is one of the many technology buzzwords that has gained the attention of the academic world recently. Many educators are beginning to grasp the concept of Web 2.0—but what comes ABSTRACT
Testing for Changes in Trend in Water Quality Data

Time series of water quality variables typically possess many of several characteristics which complicate analysis. Of interest to researchers is often the trend over time of the water quality variable. However, sometimes water quality variable levels appear to increase or decrease monotonically for a period of time then switch direction after some intervention affects the factors which have a causal relationship with the level of the variable. Naturally, when analyzed for trend as a whole, these time series usually do not provide significant results. The problem of testing for a change in trend is addressed, and a method for performing this test based on a test of equivalence of two modified Kendall’s Tau nonparametric correlation coefficients (neither necessarily equal to zero) is presented. The test is made valid for use with serially correlated data by use of a new bootstrap method titled the effective sample size bootstrap. Further issues involved in applying this test to water quality variables are also addressed.
Software Reliability Model Evolved By Genetic Programming

In order to solve the inconsistency of software reliability model and to cast off traditional models’ multi-subjective assumption,this essay adopts genetic programming(GP) evolved algorithm to establish software reliability model based on the software mean time between failure time series.It creates evolution modeling and evaluates its performances index by testing a software from armored force of engineering university.The result certifies the feasibility and the availability of data fitting and forecasting of GP algorithm used on the software reliability modeling,which therefore can be more availably used on software system testing and then secures the availability of data.
Cheminement et difficultés analytiques en méthodologie de la théorisation enracinée : expérience de deux doctorantes

Les difficultes analytiques de la methodologie de la theorisation enracinee (MTE) sont rarement abordees dans les ecrits scientifiques. Comment mettre en oeuvre la MTE? Quelles sont les difficultes analytiques de la MTE rencontrees par les chercheurs novices? Cet article aborde des moments critiques vecus par les deux auteures au cours de l’analyse de leurs donnees de recherche doctorale suivant les principes de la MTE. L’objectif est de mettre en lumiere des difficultes qui surviennent pour une majorite de chercheurs novices dans la realisation d’une recherche qualitative employant un devis MTE ainsi que d’offrir des conseils notamment en lien avec la gestion de la panoplie de donnees, la saturation des donnees et la demarche iterative. Des exemples precis tires du projet de doctorat des deux auteures sont partages. Il en ressort que l’encadrement du directeur de these est primordial dans le cheminement analytique. Ceci rappelle que le mentorat est un element important dans l’apprentissage de la MTE.
Moving Object Detecting and Tracking Based on Statistical Background Model and Filter Estimation

Motion object detecting and tracking is widely used in the areas of smart traffic monitoring,vehicle navigation.A new method based on statistical background model and α-β-γ filter model is proposed in this paper.In this method,Firstly,a Gauss model is used as the background model,the moving objects are extracted from the image sequence of the scenes,Finally,the motion parameters of detected region are estimated by the α-β-γ filter,and the motion trajectories of objects are also tracked.It can be seen from the experiment that the moving objects can be detected and tracked exactly,it is proved that the method adopted is very feasible.