Miracle-dz/UniFAR-FTUs huggingface dataset:
Review: Boosting Classifiers For Intrusion Detection

 ABSTRACT— Network and host intrusion detection systems monitor malicious activities and the management station is a technique that generates reports. Security for all networks is becoming a big problem. Hackers and intruders higher number of successful efforts to bring down the company's networks and web services. Intrusion detection system, the availability of an attack and to protect the integrity of the data used for the detection of attacks. In this paper, we use a variety of feature reduction techniques for intrusion detection system (IDS) to compare the performance of classifiers. There are two phases in certain ways, in the first phase will improve decision tree and SVM classifiers for best results and the second phase will boost both the decision tree and SVM classifiers, and detect more than a single class classifier system. Keywords— Intrusion Detection System (IDS), Boosting, decision tree and support vector machine (SVM)
Pareto based optimization of multi-resolution geometry for real time rendering

This paper proposes a view-dependent quality-cost trade-off decision-taking framework for time-critical multi-resolution rendering. The paper extends existing surface to surface error metrics to a view-dependent accuracy heuristic, used in an off-line process for establishing a representative set of elementary accuracy-cost trade-off curves. These so-called Pareto plots are combined at run-time according to the object meshes visibility and importance (e.g. object distance) for proper global view-dependent quality-cost trade-off decision taking at minimum run-time cost. A more realistic viewpoint-dependent quality metric is introduced without sacrificing too much of the simplicity of earlier proposals.
Differential color thresholds and new results on failures of chromaticity and luminance prediction

The experimental determination of differential chromaticity thresholds from metameric matches show discrepancies in colorimetric prediction using the standard observers established by the CIE (Hita et al., J Opt Soc Am A 1986; 3: 1203-09; Jimenez del Barco et al., J Optics 17, 1986; 247-54). In this work we have determined whether the discrepancies of colorimetric prediction are contained in the differential color thresholds. To study the possibility of generalizing prior results to a wider range of experimental conditions we have carried out new experiments determining the differential color thresholds of an achromatic stimulus from metameric matches, using direct vision and a foveal field of 1.5 degrees . The results are expressed in terms of the color-matching functions of CIE-2 degrees standard observer, as well as those proposed by 2 degrees -Stiles and Burch and Nayatani et al. The discussion of the results is made taking into account different causes which may explain the failures of colorimetric prediction detected: rod intrusion, interactions of the responses of cones sensitive to short-wavelengths, errors and matching imprecision. Results show that the failures of colorimetric prediction obtained under our experimental conditions exceed the color differential threshold. These discrepancies increase with the relative difference of the S-cone excitation level for the matched stimuli.
Hierarchical fusion-based cooperative spectrum sensing scheme in cognitive radio networks

Cooperative spectrum sensing scheme has a good effect on the problem of hidden terminal, multipath effect and deep fading. There is also a technique which can make a full use of spectrum resources. In our proposed scheme, clustering is a good idea to avoid the burden of large amount of processing data in fusion center, to reduce the magnitude of cognitive information transmission in the network. The method of location-based is presented to choose cluster heads. The results of each node sensing sent to the cluster heads. Cluster heads make a preliminary decision with the Energy Detection. Then, the preliminary decision is transmitted to the fusion center. The final decision is made in the fusion center by the method of reputation-based. Simulation results show that the scheme can reduce the energy consumption and improve the detection performance effectively.
GA-based input features and learning parameters selection method for decorrelated neural network ensembel model

Using more features than needed as inputs decreases prediction performance and interpretation ability of the learning model. Ensemble learning-based soft measuring model has better generalization performance than that based on single model. Negative correlation learning and random vector functional link networks based decorrelated neural network ensembles (DNNE) can overcome some shortcomings of error back-propagation neural networks (BPNNs) in term of effective and efficient. However, its performance is sensitive to some learning parameters. Thus, genetic algorithm (GA) is used to select input features and leaning parameters of DNNE model jointly. Six benchmark datasets are used to validate the proposed method.
An X-Band low-power and low-phase-noise VCO using bondwire inductor

In this paper a low-power low-phase-noise voltage-controlled-oscillator (VCO) has been designed and, fabricated in 0.25 µm SiGe BiCMOS process. The resonator of the VCO is implemented with on-chip MIM capacitors and a single aluminum bondwire. A tail current filter is realized to suppress flicker noise up-conversion. The mea- sured phase noise is 126.6 dBc/Hz at 1 MHz offset from a 7.8 GHz carrier. The figure of merit (FOM) of the VCO is 192.5 dBc/Hz and the VCO core consumes 4 mA from a 3.3 V power supply. To the best of our knowledge, this is the best FOM and the lowest phase noise for bondwire VCOs in the X-band. This VCO will be used for satellite communica- tions.
Real-time telephone transmission simulation for speech recognizer and dialogue system evaluation and improvement

Recognizer performance in telephone-based spoken dialogue systems may be strongly affected by the transmission channel. In order to investigate the impact of different parts of the transmission channel in more detail, a simulation model is presented. It implements all transmission characteristics of modern telephone networks, based on instrumentally measurable values as they are used by network planners. The simulation shows real-time capability and runs on a programmable DSP-based hardware. It can be used for a systematic investigation of recognizer performance as a function of transmission channel degradations, for producing training material with specified transmission characteristics, or for estimating the impact of transmission impairments on dialogue flow and system usability. The impact of transmission channel characteristics on the performance of a speech recognizer integrated in an interactive voice server is analyzed in more detail. It turns out that specific transmission characteristics may lead to a recognition degradation which otherwise would not have been expected from the standard training material. An outlook is given on future extensions of the simulation model, in order to better cover effects of mobile and IP-based telephone systems.
POST: Using Probabilities in Language Processing

We report here on our experiments with POST (Part of Speech Tagger) to address problems of ambiguity and of understanding unknown words. Part of speech tagging, perse, is a well understood problem. Our paper reports experiments in three important areas: handling unknown words, limiting the size of the training set, and returning a set of the most likely tags for each word rather than a single tag. We describe the algorithms that we used and the specific results of our experiments on Wall Street Journal articles and on MUC terrorist messages.
Coloring intersection graphs of geometric figures with a given clique number

The paper discusses the current status of bounds on the chromatic number of the intersection graphs of certain types of geometric figures with given clique number or girth. The families under consideration are boxes in R, intervals in the plane, chords of a circle, and tranlates of a compact convex set. A couple of new results fitting in the picture are proved.
An improvement on impedance control performance of an exoskeleton suit in the presence of uncertainty

In recent years, different exoskeleton devices have been developed to provide the users with the mechanical power required in augmentation and rehabilitation applications. The exoskeleton control is one of the most challenging issues causes widely attention of researches during recent decade. Although different methods of control have been presented, there are several issues which have not been answered yet. This paper tries to focus on one of them entitling the robustness of impedance control in the presence of uncertainty for an exoskeleton and finds a solution for it. For this purpose, a RBF neural network with adaptive learning algorithm is employed to compensate the model uncertainty. Unlike other research works, the introduced controller is merely based on the robot kinematics. The convergence of the closed loop system to the desired impedance in the presence of uncertainty is verified by Lyapanov theorem. In the following, its implementation feasibility is evaluated by a simulation of an exoskeleton leg in the swing phase.
Reactive index replication for distributed search engines

Distributed search engines comprise multiple sites deployed across geographically distant regions, each site being specialized to serve the queries of local users. When a search site cannot accurately compute the results of a query, it must forward the query to other sites. This paper considers the problem of selecting the documents indexed by each site focusing on replication to increase the fraction of queries processed locally. We propose RIP, an algorithm for replicating documents and posting lists that is practical and has two important features. RIP evaluates user interests in an online fashion and uses only local data of a site. Being an online approach simplifies the operational complexity, while locality enables higher performance when processing queries and documents. The decision procedure, on top of being online and local, incorporates document popularity and user queries, which is critical when assuming a replication budget for each site. Having a replication budget reflects the hardware constraints of any given site. We evaluate RIP against the approach of replicating popular documents statically, and show that we achieve significant gains, while having the additional benefit of supporting incremental indexes.
A Qualitative Study of the Pathways Into and Impacts of Family Homelessness

Homelessness has consequences for families, including risk of deterioration in the health of their members, disruption of family dynamics, and separation of parents and children. This study used qualitative interviews to explore pathways into and perceived consequences of homelessness among 18 families living in an emergency family shelter system in Canada. Findings showed that families’ experiences prior to their homelessness were characterized by vulnerability, instability, and isolation. In the emergency shelter system, families faced new challenges in environments that were restrictive, noisy, chaotic, and afforded little privacy. Participants described a further disruption of relationships and described having to change their family practices and routines. Despite the challenges that families encountered, some participants felt optimistic and hopeful about the future. Future research is needed on ways in which shelters can be more hospitable, supportive, and helpful for parents and their families to minimize negative impacts and facilitate timely rehousing of families.
Framework of Intelligent Recommendation System for a Private Tertiary Institution in Nigeria

— With the increasing competition among higher education institutions, more attention is on how to increase student retention rates and the number of degree completion. Also, there is a growing interest and concern in many countries about the problem of school failure and the determination of its main contributing factors. Many tertiary institutions have implemented innovative information systems and services to support tertiary academic management processes. So far, there are limited numbers of research; which examine the influence of family factors on first year academic performance in tertiary institutions using machine learning algorithms. This research focus on designing a predictive/recommender system framework to serve as a basis for predicting the performance of students at the end of their first year in tertiary institution based on their family background factors and previous academic achievements using machine learning algorithms.
A graph-oriented object model for database end-user interfaces

The current database research trend 1s towards systems whrch can deal with advanced data apphcatrons that go beyond the standard “enterprise” or “office” database apphcatron This trend 1s reflected m the research on extension architectures [5,18,21] and obJect-oriented databases [2,3,12,21] Along wrth thus trend, the need for better and easier-to-use database end-user Interfaces has been stressed [18,21] Therefore, we propose a graph-based data model, which shares many features wrth exrstmg data models, but which better facrhtates the rigorous study of graphical database end-user interfaces Graphs have been an integral part of the database design process ever since the mtroductron of semantic data models [11,12] Therr usage m data mampulatron languages, however, IS far more sparse To deal wrth data mampulatron, typically, schemes m semantrc data models are transformed mto a conceptual data model such as the relatronal model [19] The required database language features then become those of the conceptual model CbJectoriented data models, on the other hand, often offer computational complete, non-graphical data languages, usually m the style of object-oriented programmmg languages such as Smalltalk Due to their expressrveness, however, these language do not lend themselves easily as high-level data languages [2,21] The first graphical database end-user interfaces were developed for the relational model (for example Zloof’s Query-By-Example (QBE) [22])
Guided Monte Carlo Tree Search for Planning in Learned Environments

Monte Carlo tree search (MCTS) is a sampling and simulation based technique for searching in large search spaces containing both decision nodes and probabilistic events. This technique has recently become popular due to its successful application to games, e.g. Poker Van den Broeck et al. (2009) and Go Coulom (2006); Chaslot et al. (2006); Gelly and Silver (2012)). Such games have known rules and the alternation between self-moves and non-deterministic events or opponent moves can be used to prune uninteresting branches. In this paper we study a real-world setting where the processes in the domain have a high degree of uncertainty and the need for longer-term planning implies a sequence of (planning) decisions without any intermediate feedback. Fortunately, unlike the combinatorial complexity in strategic games, many real-world environments can be approximated by ecient algorithms on a short term. This paper proposes an MCTS variant using a new type of prior information based on estimating the eects of part of the world and explores its application to the problem of hospital planning, where machine learning algorithms can be used to predict the length of stay of patients for each of the dierent stages of their recovery.
Estimation and Comparison of Receiver Operating Characteristic Curves

The receiver operating characteristic (ROC) curve displays the capacity of a marker or diagnostic test to discriminate between two groups of subjects, cases versus controls. We present a comprehensive suite of Stata commands for performing ROC analysis. Nonparametric, semiparametric, and parametric estimators are calculated. Comparisons between curves are based on the area or partial area under the ROC curve. Alternatively, pointwise comparisons between ROC curves or inverse ROC curves can be made. We describe options to adjust these analyses for covariates and to perform ROC regression in a companion article. We use a unified framework by representing the ROC curve as the distribution of the marker in cases where we have standardized it to the control reference distribution.
The Economics of Small Business Finance: The Roles of Private Equity and Debt Markets in the Financial Growth Cycle

We examine the economics of financing small business in private equity and debt markets. Firms are viewed through a financial growth cycle paradigm in which different capital structures are optimal at different points in the cycle. We show the sources of small business finance, and how capital structure varies with firm size and age. The interconnectedness of small firm finance is discussed along with the impact of the macroeconomic environment. We also analyze a number of research and policy issues, review the literature, and suggest topics for future research.
TERRASAR-X DATA FOR IMPROVING GEOMETRIC ACCURACY OF OPTICAL HIGH AND VERY HIGH RESOLUTION SATELLITE DATA

Most high resolution optical images from space need ground control information (GCP) before an orthorectification can be performed. The very high geometric accuracy of geocoded data of the TerraSAR-X satellite has been shown in several investigations. This precision has been reached fully automatically without any GCP and is due to good sensor calibration, high accuracy of satellite position and the low dependency on the satellites attitude solution. Therefore TerraSAR-X data can be used as “ground control” to improve the exterior orientation and thereby the geometric accuracy of orthorectified optical satellite data. The technique used is the measurement of identical points in the images, either by manual measurements or through local image matching using adapted mutual information (MI) and to estimate improvements for the exterior orientation or Rational Polynomial Coefficients (RPCs). To be able to use this intensity based method, the radar data have to be filtered before starting the matching procedure. Through adjustment calculations falsely matched points are eliminated and an optimal improvement for the attitude angles is found. The optical data are orthorectified using these improvements and the available DEM. The results are very promising and compared using conventional ground control information from maps or GPS measurements.
Towards the evolutionary emergence of increasingly complex advantageous behaviours

The generation of complex entities with advantageous behaviours beyond our manual design capability requires long-term incremental evolution with continuing emergence. In this paper, we argue that artificial selection models, such as traditional genetic algorithms, are fundamentally inadequate for this goal. Existing natural selection systems are evaluated, revealing both significant achievements and pitfalls. Thus, some requirements for the perpetuation of evolutionary emergence are established. An (artificial) environment containing simple virtual autonomous organisms with neural controllers has been created to satisfy these requirements and to aid in the development of an accompanying theory of evolutionary emergence. Resulting behaviours are reported alongside their neural correlates. In a particular example, the collective behaviour of one species provides a selective force which is overcome by another species, demonstrating the incremental evolutionary emergence of advantageous behaviours via naturally arising coevolution. While the results fall short of the ultimate goal, experience with the system has provided some useful lessons for the perpetuation of emergence towards increasingly complex advantageous behaviours.
Insulin improves alveolar-capillary membrane gas conductance in type 2 diabetes.

OBJECTIVE In type 1 diabetes, lung diffusing capacity for carbon monoxide (DL(CO)) may be impaired, and insulin has been shown to be beneficial in cases in which near-normal metabolic control is achieved. An influence of insulin, per se, on the alveolar-capillary membrane conductance is unexplored. We aimed at testing this possibility. RESEARCH DESIGN AND METHODS We studied 19 life-long nonsmoking, asymptomatic patients with type 2 diabetes and normal cardiac function, whose GHb averaged 6.2 +/- 0.3% with diet and hypoglycemic drugs. DL(CO) and its subcomponents (alveolar capillary membrane conductance [D(M)] and pulmonary capillary blood volume available for gas exchange [Vc]), vital capacity (VC), forced expiratory volume 1 s (FEV(1)), cardiac output (CO), ejection fraction (EF), pulmonary wedge pressure (WPP), and pulmonary arteriolar resistance (PAR) were determined before and within 60 min after infusion of 50 ml saline + 10 IU of regular insulin or after saline alone on 2 consecutive days (random block design). Glycemia was kept at baseline levels during experiments by dextrose infusion. RESULTS Percent of normal predicted DL(CO) averaged 84.2 +/- 7.9% and in 14 patients was <100%. Insulin infusion, not saline alone, improved (P < 0.01) DL(CO) (12%) and D(M) (14%) and raised DL(CO) to 98% of the normal predicted value. There were no variations in VC, FEV(1,) CO, EF, WPP, or PAR, suggesting that the influences of the hormone on gas transfer were not mediated by changes in spirometry, volumes, and hemodynamics of the lung. CONCLUSIONS Several cases of type 2 diabetes present with increased impedance to gas transfer across the alveolar-capillary membrane, and hypoglycemic drugs do not prevent this inconvenience. Insulin, independently of the metabolic effects, acutely improves gas exchange, possibly through a facilitation of the alveolar-capillary interface conductance.
The Poisson Gamma Belief Network

To infer a multilayer representation of high-dimensional count vectors, we propose the Poisson gamma belief network (PGBN) that factorizes each of its layers into the product of a connection weight matrix and the nonnegative real hidden units of the next layer. The PGBN's hidden layers are jointly trained with an upward-downward Gibbs sampler, each iteration of which upward samples Dirichlet distributed connection weight vectors starting from the first layer (bottom data layer), and then downward samples gamma distributed hidden units starting from the top hidden layer. The gamma-negative binomial process combined with a layer-wise training strategy allows the PGBN to infer the width of each layer given a fixed budget on the width of the first layer. The PGBN with a single hidden layer reduces to Poisson factor analysis. Example results on text analysis illustrate interesting relationships between the width of the first layer and the inferred network structure, and demonstrate that the PGBN, whose hidden units are imposed with correlated gamma priors, can add more layers to increase its performance gains over Poisson factor analysis, given the same limit on the width of the first layer.
Subject matter categorization of tags applied to digital images from art museums

In recent years, cultural heritage institutions have increasingly used social tagging. To better understand the nature of these tags, we analyzed tags assigned to a collection of 100 images of art (provided by the steve.museum project) using subject matter categorization. Our results show that the majority of tags describe the people and objects in the image and are generic in nature. This contradicts prior subject matter analyses of queries, tags, and index terms of other image collections, suggesting that the nature of social tags largely depends on the type of collection and on user needs. This insight may help cultural heritage institutions improve their management and use of tags.