Miracle-dz/UniFAR-FTUs huggingface dataset:
A Performance Comparison of Different Back Propagation Neural Networks Methods in Computer Network Intrusion Detection

This paper demonstrated that neural network (NN) techniques can be used in detecting intruders logging onto a computer network when computer users are profiled accurately. Next, the paper compares the performance of the five neural network methods in intrusion detection. The NN techniques used are the gradient descent back propagation (BP), the gradient descent BP with momentum, the variable learningrate gradient descent BP, the conjugate gradient BP, and the quasi-Newton method.
Decoupling limits of Script N = 4 super Yang-Mills on Bbb R ? S3

We find new decoupling limits of = 4 super Yang-Mills (SYM) on ? S3 with gauge group SU(N). These decoupling limits lead to decoupled theories that are much simpler than the full = 4 SYM but still contain many of its interesting features. The decoupling limits correspond to being in a near-critical region, near a point with zero temperature and critical chemical potentials. The new decoupling limits are found by generalizing the limits of hep-th/0605234 to include not only the chemical potentials for the SU(4) R-symmetry of = 4 SYM but also the chemical potentials corresponding to the SO(4) symmetry. In the decoupled theories it is possible to take a strong coupling limit in a controllable manner since the full effective Hamiltonian is known. For planar = 4 SYM on ? S3 all the decoupled theories correspond to fully integrable spin chains. We study the thermodynamics of the decoupled theories and find the Hagedorn temperature for small and large values of the effective coupling. We find an alternative formulation of the decoupling limits in the microcanonical ensemble. This leads to a characterization of certain regimes of weakly coupled = 4 SYM in which there are string-like states. Finally, we find a similar decoupling limit for pure Yang-Mills theory, which for the planar limit leads to a fully integrable decoupled theory.
Design Exploration of a Spurious Power Suppression Technique (SPST) and Its Applications

This paper presents the design exploration and application of a technique to suppress the spurious power dissipation existed in the data-paths for multimedia VLSI designs. The proposed technique adopts the design concept of separating the arithmetic units into most significant part (MSP) and least significant part (LSP), and then freezing the MSP whenever this part of circuits does not affect the computation result. This paper first explores three implementation approaches of realizing the SPST-based design concept to decide the most efficient one, and then uses this approach to reduce the spurious power of the multi-transform coding design in H.264 systems. The post-layout simulations show that the proposed SPST can save average 27.38% of power dissipation of the multi-transform design
Exploration of a Heterogeneous Concentrated-Sparse On-Chip Interconnect for Energy Efficient Multicore Architecture

We present a novel heterogeneous on-chip interconnect, concentrated-sparse mesh, suitable for high efficiency multicore architectures. The topology is implemented by taking the advantages of both concentrated mesh and sparse mesh networks. The object of the proposed heterogeneous network is to improve performance of the system when running real applications with self-similar, hot-spot and bursty traffic. While regular mesh network has been used widely in on-chip interconnect, concentrated mesh improves average network latency by reducing the number of intermediate network resources. However with high traffic requirements, the limited network bandwidth leads to congestion and performance bottleneck. On the other hand, sparse mesh improves network bandwidth by increasing the number of routers and links, therefore the network can process more hot-spot and bursty traffic than regular and concentrated mesh networks. The weakness of sparse mesh is that, with low traffic injection, the network latency of packets can be higher than other networks. Furthermore the size of the interconnect can become unrealistic for large systems. The proposed heterogeneous interconnect utilizes two networks for processing different traffic. We explore and discuss traffic injection behaviour of several applications. The heterogeneous network is analyzed in details. We investigate a routing algorithm and a mapping algorithm designed for the proposed network. Comparative results are provided by using a full system simulation environment. Results demonstrate that the proposed interconnect improves the average network latency and energy delay product by 15.7% and 44.7%, respectively, compared with regular mesh network.
Shape detection of structural changes in long time-span aerial image samples by new saliency methods

The paper presents a novel, theoretically well-based methodology to find changes in remote sensing image series. The proposed method finds changes in images scanned with a long time-interval difference in very different lighting and surface conditions. The presented method is basically an exploitation of the Harris saliency function and its derivatives for finding featuring points among image samples; then a new local descriptor will be demonstrated by generating local active contours; a graph based shape descriptor will be shown based on the saliency points of the difference and in-layer features; finally, we prove the method’s capabilities for finding structural changes on remote sensing images.
A Thematic Study of Requirements Modeling and Analysis for Self-Adaptive Systems

Over the last decade, researchers and engineers have developed a vast body of methodologies and technologies in requirements engineering for self-adaptive systems. Although existing studies have explored various aspects of this topic, few of them have categorized and summarized these areas of research in require-ments modeling and analysis. This study aims to investigate the research themes based on the utilized modeling methods and RE activities. We conduct a thematic study in the systematic literature review. The results are derived by synthesizing the extracted data with statistical methods. This paper provides an updated review of the research literature, enabling researchers and practitioners to better understand the research themes in these areas and identify research gaps which need to be further studied.
IntFlow: improving the accuracy of arithmetic error detection using information flow tracking

Integer overflow and underflow, signedness conversion, and other types of arithmetic errors in C/C++ programs are among the most common software flaws that result in exploitable vulnerabilities. Despite significant advances in automating the detection of arithmetic errors, existing tools have not seen widespread adoption mainly due to their increased number of false positives. Developers rely on wrap-around counters, bit shifts, and other language constructs for performance optimizations and code compactness, but those same constructs, along with incorrect assumptions and conditions of undefined behavior, are often the main cause of severe vulnerabilities. Accurate differentiation between legitimate and erroneous uses of arithmetic language intricacies thus remains an open problem. As a step towards addressing this issue, we present IntFlow, an accurate arithmetic error detection tool that combines static information flow tracking and dynamic program analysis. By associating sources of untrusted input with the identified arithmetic errors, IntFlow differentiates between non-critical, possibly developer-intended undefined arithmetic operations, and potentially exploitable arithmetic bugs. IntFlow examines a broad set of integer errors, covering almost all cases of C/C++ undefined behaviors, and achieves high error detection coverage. We evaluated IntFlow using the SPEC benchmarks and a series of real-world applications, and measured its effectiveness in detecting arithmetic error vulnerabilities and reducing false positives. IntFlow successfully detected all real-world vulnerabilities for the tested applications and achieved a reduction of 89% in false positives over standalone static code instrumentation.
Process synchronization in database systems

The problem of process synchronization in database systems is analyzed in a strictly systematic way, on a rather abstract level; the abstraction is chosen such that the essential characteristics of the problem can be distinctly modeled and investigated. Using a small set of concepts, a consistent description of the whole problem is developed; many widely used, but only vaguely defined, notions are defined exactly within this framework. The abstract treatment of the problem immediately leads to practically useful insights with respect to possible solutions, although implementational aspects are not discussed in detail.
Mini-symposium on Liquids (msl2011) List of Poster Presentations Phase Transition in a Quasi-one-dimensional Lattice Triplet Distribution Functions for Hard Spheres Calculated by Monte Carlo Simulation and Integral Equation Theories

We investigate phase behavior of liquids confined to nano pores, using a nearest-neighbor lattice model. The model is characterized by two parameters which represent different substances, and also by the width of the lattice. The phase behavior of our model is obtained from MC simulation. Other results for the case where the nearest neighbor interaction is not isotropic are also given. P2: Y. Kubota, and R. Akiyama (Kyushu University) Triplet Distribution Functions for Hard Spheres Calculated by Monte Carlo Simulation and Integral Equation Theories. Abstract: Nowadays, three-dimensional integral equation theories become important methods to study protein folding, molecular recognition, self-assembly phenomena and other various phenomena in condensed phase. The information given by those methods has multi-body correlation. It, then, is much more fruitful than the information given by the Kirkwood superposition approximation. The difference between the simulation's results and the superposition approximation's results becomes large, when ligand-binding phenomena by a narrow concave recognition site are examined. We can find the test of the three-body correlation for the Born-Green-Yvon 2 theory when the packing fraction is 0.275. However, the test of the HNC approximation for water's packing fraction (0.38) is important because of biological interest. Here, we compare the triplet distribution functions given by 3D-HNC-OZ theory with those given by the superposition approximation and given by the Monte Carlo Simulation. The agreements are qualitatively and semi-quantitatively good. We will compare between the results given by some approximations. Nowadays, three-dimensional integral equation theories become important methods to study protein folding, molecular recognition, self-assembly phenomena and other various phenomena in condensed phase. The information given by those methods has multi-body correlation. It, then, is much more fruitful than the information given by the Kirkwood superposition approximation. The difference between the simulation's results and the superposition approximation's results becomes large, when ligand-binding phenomena by a narrow concave recognition site are examined. We can find the test of the three-body correlation for the Born-Green-Yvon 2 theory when the packing fraction is 0.275. However, the test of the HNC approximation for water's packing fraction (0.38) is important because of biological interest. Here, we compare the triplet distribution functions given by 3D-HNC-OZ theory with those given by the superposition approximation and given by the Monte Carlo Simulation. The agreements are qualitatively and semi-quantitatively good. We will compare between the results given by some approximations. P3: H. Akiyoshi, and K. Koga (Okayama University) Diffusion coefficient of argon in single walled carbon nanotubes
Instantaneous full-motion estimation of arbitrary objects using dual Doppler radar

Based on high-resolution radars a new approach for determining the full 2D-motion state (yaw rate, longitudinal and lateral speed) of an extended rigid object in a single measurement is proposed. The system does not rely on any model assumptions and is independent of the exact position, expansion and orientation of the object. In comparison to related methods it is not based on temporal filtering, e.g. a Kalman Filter. These methods are subject to an initialization phase and depend heavily on compliance of the underlying dynamic model. In contrast to temporal filtering, the proposed approach reduces the time to react to critical situations that occur in many safety and advanced driving assistance applications. This paper analyzes the velocity profile (radial velocity over azimuth angles) of the object received by two Doppler radar sensors. The approach can handle white noise and systematic variations (e.g. micro-Doppler of wheels) in the signal. The proposed system is applied to predict the driving path of traffic participants. Measurement results are presented for a set-up with two 77 GHz automotive radar sensors.
A new chemo-evolutionary population synthesis model for early-type galaxies .1. Theoretical basis

We have developed a new stellar population synthesis model designed to study early-type galaxies. It provides optical and near-infrared colors, and line indices for 25 absorption lines. It can synthesize single age, single metallicity stellar populations, or follow the galaxy through its evolution from an initial gas cloud to the present time. The model incorporates the new isochrones of the Padova group and the latest stellar spectral libraries. We have applied our model to new data for a set of three early-type galaxies to find out whether these can be fitted using single-age old metal-rich stellar populations, as is normal practice when one uses other stellar models of this kind. The model is extensively compared with previous ones in the literature to establish its accuracy as well as the accuracy of this kind of models in general. Using the evolutionary version of the model we find that we cannot fit the most metal-rich elliptical galaxies if we keep the IMF constant and do not allow infall of gas. We do, however, reproduce the results of Arimoto & Yoshii for the evolution of the gas, and produce colors, and, for the first time with this type of models, absorption line strengths. It is in fact possible to fit the data for the elliptical galaxies by varying the IMF with time. Our numerical model is in good broad agreement with the analytical simple model. We prefer, however, to calculate the evolution of the gas numerically instead of using the simple model, since it offers more flexibility, and even improved insight, when comparing with observations. In the present paper we describe the model, and compare a few key observables with new data for three early-type standard galaxies. However, the data, as well as our fits, will be discussed in much more detail in a second paper (by Vazdekis et al.), where some conclusions will be drawn about elliptical galaxies on the basis of this model.
The Persuasive Power of Data Visualization

Data visualization has been used extensively to inform users. However, little research has been done to examine the effects of data visualization in influencing users or in making a message more persuasive. In this study, we present experimental research to fill this gap and present an evidence-based analysis of persuasive visualization. We built on persuasion research from psychology and user interfaces literature in order to explore the persuasive effects of visualization. In this experimental study we define the circumstances under which data visualization can make a message more persuasive, propose hypotheses, and perform quantitative and qualitative analyses on studies conducted to test these hypotheses. We compare visual treatments with data presented through barcharts and linecharts on the one hand, treatments with data presented through tables on the other, and then evaluate their persuasiveness. The findings represent a first step in exploring the effectiveness of persuasive visualization.
High Accuracy User ’ s Distance Estimation by Low Cost Cameras

In this paper, we propose an algorithm using only two commodity webcams without any calibration to detect the distance between users and the display. According to the experimental results, the error is always less than 2cm and the operation range is up to 406cm. Our proposed algorithm can provide users' depth information with high accuracy from calibration free stereo capture image pair for the application, such as interactive 3DTV or user-aware auto-stereoscopic display. Keywordscalibration free; stereo; distance estimation; low cost; high accuracy;
Synthesis of Matsuoka-Based Neuron Oscillator Models in Locomotion Control of Robots

In this paper we present a numerical study of the Matsuoka-based neuron oscillator model. The Matsuoka-based neuron oscillator model is one of the most popular CPG (central pattern generator) models in robot motion control. In this paper, numerical simulation is conducted to analyze the influence of the parameters on the output signals. A mass-spring-damper system is used as an example to analyze the entrainment properties of the neuron oscillator. The main engineering application methods of these CPG-inspired control methods are concluded. The motivation is to present a practical guide to researchers and engineers interested in the CPG-inspired control approaches.
An Open Architecture Numerical Control System Based on Windows CE

This paper presents an open-architecture Computerized Numerical Control (CNC) system based on Windows CE, which is a multi-task embedded real-time operating system. The CNC system adopts a master-slave architecture, And the Master applies with Windows CE, which charges weak real-time tasks such as NC code's editor, interpreter and simulator function block, fault diagnosis, I/O monitoring and control etc, while the slave adopts a motion control card based on Digital Signal Processor (DSP) and Field Programmable Gate Array (FPGA), which charges hard realtime tasks including interpolation, electromotor control etc. The research realizes the construction of hardware and software of CNC system and solves such core issues as NC code interpreter and simulation, Rate Monotonic Scheduling (RMS) task schedule algorithm and task scheduling of CNC system. The experimental results have proven the development of CNC system based on Windows CE is a feasible, novel and fast technology course in the field of CNC.
Community detection in political Twitter networks using Nonnegative Matrix Factorization methods

Community detection is a fundamental task in social network analysis. In this paper, first we develop an endorsement filtered user connectivity network by utilizing Heider's structural balance theory and certain Twitter triad patterns. Next, we develop three Nonnegative Matrix Factorization frameworks to investigate the contributions of different types of user connectivity and content information in community detection. We show that user content and endorsement filtered connectivity information are complementary to each other in clustering politically motivated users into pure political communities. Word usage is the strongest indicator of users' political orientation among all content categories. Incorporating user-word matrix and word similarity regularizer provides the missing link in connectivity-only methods which suffer from detection of artificially large number of clusters for Twitter networks.
Diagnostic and Prognostic Significance of Brief Limited Intermittent Psychotic Symptoms (BLIPS) in Individuals at Ultra High Risk

Background: Brief Limited Intermittent Psychotic Symptoms (BLIPS) are key inclusion criteria to define individuals at ultra high risk for psychosis (UHR). Their diagnostic and prognostic significance is unclear. Objectives: To address the baseline diagnostic relationship between BLIPS and the ICD-10 categories and examine the longitudinal prognostic impact of clinical and sociodemographic factors. Methods: Prospective long-term study in UHR individuals meeting BLIPS criteria. Sociodemographic and clinical data, including ICD-10 diagnoses, were automatically drawn from electronic health records and analyzed using Kaplan–Meier failure function (1-survival), Cox regression models, bootstrapping methods, and Receiver Operating Characteristics (ROC) curve. Results: Eighty BLIPS were included. At baseline, two-thirds (68%) of BLIPS met the diagnostic criteria for ICD-10 Acute and Transient Psychotic Disorder (ATPD), most featuring schizophrenic symptoms. The remaining individuals met ICD-10 diagnostic criteria for unspecified nonorganic psychosis (15%), mental and behavioral disorders due to use of cannabinoids (11%), and mania with psychotic symptoms (6%). The overall 5-year risk of psychosis was 0.54. Recurrent episodes of BLIPS were relatively rare (11%) but associated with a higher risk of psychosis (hazard ratio [HR] 3.98) than mono-episodic BLIPS at the univariate analysis. Multivariate analysis revealed that seriously disorganizing or dangerous features increased greatly (HR = 4.39) the risk of psychosis (0.89 at 5-year). Bootstrapping confirmed the robustness of this predictor (area under the ROC = 0.74). Conclusions: BLIPS are most likely to fulfill the ATPD criteria, mainly acute schizophrenic subtypes. About half of BLIPS cases develops a psychotic disorder during follow-up. Recurrent BLIPS are relatively rare but tend to develop into psychosis. BLIPS with seriously disorganizing or dangerous features have an extreme high risk of psychosis.
International Journal of Advanced Robotic Systems Two-stage Hidden Markov Model in Gesture Recognition for Human Robot Interaction Regular Paper

Hidden Markov Model (HMM) is very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications including gesture representation. Most research in this field, however, uses only HMM for recognizing simple gestures, while HMM can definitely be applied for whole gesture meaning recognition. This is very effectively applicable in Human‐ Robot Interaction (HRI). In this paper, we introduce an approach for HRI in which not only the human can naturally control the robot by hand gesture, but also the robot can recognize what kind of task it is executing. The main idea behind this method is the 2‐stages Hidden Markov Model. The 1st HMM is to recognize the prime command‐like gestures. Based on the sequence of prime gestures that are recognized from the 1st stage and which represent the whole action, the 2nd HMM plays a role in task recognition. Another contribution of this paper is that we use the output Mixed Gaussian distribution in HMM to improve the recognition rate. In the experiment, we also complete a comparison of the different number of hidden states and mixture components to obtain the optimal one, and compare to other methods to evaluate this performance.
On infinite words avoiding a finite set of squares

Building an infinite square-free word by appending one letter at a time while simultaneously avoiding the creation of squares is most likely to fail. When the alphabet has two letters this approach is impossible. When the alphabet has three or more letters, one will most probably create a word in which the addition of any letter invariably creates a square. When one restricts the set of undesired squares to a finite one, this can be possible. We study the constraints on the alphabet and the set of squares which permit this approach to work.