Miracle-dz/UniFAR-FTUs huggingface dataset:
Movement modeling in cellular networks - Markov Movement Model Creator Framework (MMCF)

Nowadays, in the wireless networks the number of users and the transferred packet switched data speed are increasing dramatically. Due to the demands and the market competition the services are becoming more and more complex. The efficient network dimensioning and configuration highly depends on the underlying mathematical model of user distribution and expected data transfer level. In this paper we propose a Markov Movement-Model Creator Framework (MMCF) for setting up a model based on the network parameters and requirements with optimal number of states. Firstly we describe a method that yields an abstract model of the mobile network and the node, and we introduce a simple classifying method that defines the necessary parameters of the exact Markov movement model. The mathematical solutions for determining these parameters are also presented in the paper. Finally we analyze the accuracy, complexity and usability of the proposed MMCF and an analytical comparison is made with other mobility models, the comparison is proved by simulations. The movement model created with the framework helps the network operators in setting up an effective authorization, fraud detection system or solving self-configuration issues. Index Terms-Movement model; Framework; Markov; Accuracy; Complexity; User movements;
Diluted magnetic semiconductor quantum dots: an extreme sensitivity of the hole Zeeman splitting on the aspect ratio of the confining potential

The valence band states confined in infinitely deep quantum dots made of diluted magnetic semiconductors (DMS) are considered theoretically. A complex anisotropic structure of the valence bands in DMSs with cubic symmetry described by the full Luttinger Hamiltonian is taken into account. It is found that the Zeeman splitting is very sensitive to the shape of the confining potential and, in particular, to its orientation relative to the direction of an external magnetic field. This sensitivity has its origin in a mixing of different spin components of a hole wave function which takes place for finite hole wave vectors k. Several consequences of the effect are discussed, including a possibility to control the inter-dot tunneling by an external magnetic field. It is shown also that the polarizations of optical transitions in a single DMS quantum dot depend on details of geometry of its confining potential as well as on the strength of the magnetic field.
Analysis of Factors Influencing Patent Citations

ABSTRACT Recently, the valuation of patented technology has been greatly emphasized, and patent citation has been accepted as a very useful index of this technology. In this study, we performed correlation analyses between the patent citation coun ts and 17 explanatory variables of morphological, technological, and conceptual facto rs with a test dataset of U.S. patents in five subject fields. Seven variables having 5% or more standardized variances(r 2 ) with patent citation counts were identified; number of pages, number of claims, reference-average-citation rate, patent increase/decrea se rate, strength of bibliographic coupling, co-citation counts and document similar ity. The result of the ANOVA test shows that the mean values of these variables vary among most subject fields. 키워드: 특허인용, 인용분석, 특허 피인용횟수, 특허인용 영향요인patent citations, citation analysis, patent citation counts, factors influencing patent citation ****** 이 논문은 박사학위 논문의 일부를 요약 ․정리한 것임. 한국원자력연구원 책임연구원(jbyoo@kaeri.re.kr) ( 제1저자)연세대학교 문헌정보학과 교수(ymchung@yonsei.ac.kr) ( 공동저자)
Predictive PID Control Based on GPC Control of Inverted Pendulum

Having regard to the large application of the inverted pendulum in robotic system, this study is interested in controlling this process with two strategies of controls. The first proposed control is the state feedback with an observer based on the Generalized Predictive Control (GPC) algorithm. In the second proposed control we used the characteristic of predictive control GPC to improve the performance of the classical PID controller. The obtained results have been discussed and compared; the simulation results obtained by the predictive PID control are mentioned.
Bio-Inspired Optimization of Ultra-Wideband Patch Antennas Using Graphics Processing Unit Acceleration

Ultra-wideband (UWB) wireless systems have recently gained considerable attention as effective communications platforms with the properties of low power and high data rates. Applications of UWB such as wireless USB put size constraints on the antenna, however, which can be very difficult to meet using typical narrow band antenna designs. The aim of this thesis is to show how bio-inspired evolutionary optimization algorithms, in particular genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO) can produce novel UWB planar patch antenna designs that meet a size constraint of a 10 mm × 10 mm patch. Each potential antenna design is evaluated with the finite difference time domain (FDTD) technique, which is accurate but time-consuming. Another aspect of this thesis is the modification of FDTD to run on a graphics processing unit (GPU) to obtain nearly a 20× speedup. With the combination of GA, PSO, BBO and GPU-accelerated FDTD, three novel antenna designs are produced that meet the size and bandwidth requirements applicable to UWB wireless USB systems.
The Realization of the IP QoS Based on Network Processor

IP network unprecedented develops in the end of the last century and it evolutes to the next generation network. The network service also presents the characteristics of diversification. Thus the traditional real-time services have not already meet people's need. Algorithms proposed in this paper are developed based on network processor IXP2400 and SDK3.1, and observed on advanced router. Measured results show that, ACL processing module realizes filter and classification functions in the QoS model and meet the requirements filter and classification in QoS system.
A Database of Computer Attacks for the Evaluation of Intrusion Detection Systems

Abstract : The 1998 DARPA intrusion detection evaluation created the first standard corpus for evaluating computer intrusion detection systems. This corpus was designed to evaluate both false alarm rates and detection rates of intrusion detection systems using many types of both known and new attacks embedded in a large amount of normal background traffic. The corpus was collected from a simulation network that was used to automatically generate realistic traffic-including attempted attacks. The focus of this thesis is the attacks that were developed for use in the 1998 DARPA intrusion detection evaluation. In all, over 300 attacks were included in the 9 weeks of data collected for the evaluation. These 300 attacks were drawn from 32 different attack types and 7 different attack scenarios. The attack types covered the different classes of computer attacks and included older, well-known attacks, newer attacks that have recently been released to publicly available forums, and some novel attacks developed specifically for this evaluation. The development of a high quality corpus for evaluating intrusion detection systems required not only a variety of attack types, but also required realistic variance in the methods used by the attacker. The attacks included in the 1998 DARPA intrusion detection evaluation were developed to provide a reasonable amount of such variance in attacker methods. Some attacks occur in a single session with all actions occurring in the clear, while others are broken up into several sessions spread out over a long period of time with the attacker taking deliberate steps to minimize the chances of detection by a human administrator or an intrusion detection system. In some attacks, the attacker breaks into a computer system just for fun, while in others the attacker is interested in collecting confidential information or causing damage.
Cloud Computing: A CRM Service Based on a Separate Encryption and Decryption using Blowfish algorithm

Cloud computing is a distributive computation task on the resource pool which consists of massive computers. LMS experiences with Cloud and Managed Cloud Service provider are due to numerous factors. Availability means that the services are available even when quite a number of nodes fail.Cloud services are provided by many IT companies like Google, Amazon, Microsoft, and Salesforce.com. An enterprise usually store data in local storage and then tries to protect the information from other external source. They also provide authentication at certain fixed level. To overcome this limitation, we are presenting some approaches that do not require full dependency on the external security provider. Storing the data in encrypted form is a common method of data privacy security. If a cloud system is responsible for both tasks on storage and encryption/decryption of data, the system administrators may simultaneously hold encrypted data and decryption keys. This allows them to access information without authorization and thus poses a threat to information privacy[1]. A LMS (Learning Management System) service is described in this project using Blowfish algorithm. It promotes more accessibility to LMS service providers to send their training modules and syllabus via Internet at any point of the hour much more efficiently. This gives rise to reduced cost of hardware and software tools, which in return would scale-up the e-learning environment. In the existing system RSA algorithm used. It requires more computation time for large volumes of data. To reduce this computation time we are using Blowfish algorithm. The LMS Service utilizes three cloud systems, including an encryption and decryption system, a storage system, and LMS application system.
Assessment for learning: pushing the boundaries of computer-based assessment

Feedback on assessment tasks has an important part to play in underpinning student learning. Online assessment enables instantaneous feedback to be given so that the student can act on it immediately. However, concern has been expressed that e-assessment tasks (especially multiple-choice questions) can encourage surface-learning. Several projects at the UK Open University are investigating the use of rich interactive e-assessment. One of these projects is using a linguistically based authoring tool to enable sophisticated answer matching for free-text responses of up to a sentence in length. Immediate tailored feedback is provided on incorrect and incomplete responses, and students are able to use this feedback in reattempting the question. Students have been observed attempting the questions and were seen to answer them in different ways, with most students using short phrases but some using full and carefully constructed sentences and some using note form. There was evidence that some students assumed the system to be looking only for keywords. A human-computer marking comparison has demonstrated the computer-based answer matching to be of similar or greater accuracy than that of six course tutors.
Homogeneous multiprocessing and the future of silicon design paradigms

This paper addresses two challenges of the consumer semiconductor industry: (1) economical and social forces are increasingly reducing the length of product life cycles, and (2) the continuing exponential growth of the on-chip transistor count is pushing design complexity. In concert these two trends represent a formidable challenge for semiconductor companies that aim to benefit from future technological developments in highly competitive markets. The paper derives a relation between on-chip memory real estate and compute logic, suggesting that homogeneous multiprocessors are an unavoidable consequence of the technology curve. A particular approach to homogeneous multiprocessing is then presented that combines scalability with high computational performance and with high power efficiency. We also present the implementation of a programming paradigm for homogeneous multiprocessors that focuses on reuse of tested and approved functions at the software level. This enables a shift from today's not-so-successful practice of hardware core reuse to the reuse of functions that have very well defined and uniform interfaces. The time frame for large scale commercial application of this type of homogeneous multiprocessor architecture is expected to coincide with the arrival of 0.07 micron technology for consumer products, i.e. 2006 and beyond. The paper concludes with a case study of an MPEG2 decoder and how a few simple guidelines can significantly increase the exposed concurrency of the application.
The performance enhancement of the LF-ASD brain-computer interface : an Energy normalization transform

The brain-computer interface (BCI) has emerged as a potential and radically new mode of communication for users with neuromuscular impairments since it provides a communication channel based on human brain activity as opposed to peripheral nerves and muscles. It is a practical problem to detect user commands among spontaneous Electroencephalograph signals. Low-Frequency Asynchronous Switch Design (LF-ASD) is one of the leading means of addressing this problem. Although the performance of the LF-ASD is encouraging, it is not yet sufficient for real world application. The main goal of this research study is to improve the design of the LF-ASD BCI technique, and then evaluate the performance of this modified design. In this work, the Energy Feature related to Voluntary Movement Related Potential (VMRP) was determined. An energy normalization transform was proposed corresponding to this Energy Feature. A simulation model was set up and E E G data from five able-bodied subjects was applied for offline evaluation. The impact of this normalization transform was evaluated in two studies: the impact of this transform on the low frequency E E G and the impact on the performances of the LF-ASD. By analyzing the experimental results, the characteristics of this normalization filter were determined. In Study 1, it was determined that the proposed normalization transform has two major benefits to the low frequency E E G components (0-4 Hz), which is used by the LF-ASD. First, it can decrease the input scale variance. Consequently, it resulted in more stable feature sets and then a higher successful classification rate. Second, it can increase the separation between the V M R P and idle data. Another side benefit is that the proposed normalization transform can also adjust the input scale automatically. In Study 2, the performances of the LF-ASD with and without this normalization transform were compared. For four out of the five subjects, this transform increased the successful classification rate (True Positive rate with the corresponding False Positive rate at 1%) by 7.7%, 8.3%, 8.5% and 18.9% respectively. By applying an alternative energy normalization transform, the performance increased by 0.4% for the fifth subject. In the future with the parameters of the LF-ASD, especially the codebook in the Feature Classifier, derived from the normalized data, the performance could be further improved. The two studies also showed that, although this transform is non-linear in the broadband (0-64 Hz), it does not distort the features used by the L F A S D . Therefore, it would not hamper the performance of the LF-ASD. The work concluded with the introduction of potential features related to V M R P in the phase spectrum.
Efficient Implementation of Multiplier for Digital FIR Filters

— This paper presents the method for implementing the multiplier for digital Finite Impulse Response (FIR) filter that requires optimized area and low power consumption. Multiplication plays a vital role in most of the high performance systems. The methods include Modified Booth Encoding Algorithm along with carry save adder, shift/add multipliers. These techniques are applied to FIR filters to minimize the area and power consumption. The proposed designs for FIR filters will be designed using Verilog HDL and synthesized, implemented using Xilinx ISE.
Propagation over buildings for paths oblique to the street grid

Prediction of cell shape is important for proposed microcellular systems employing base station antennas at or below the surrounding rooftops. For residential and commercial areas, where the buildings are of nearly uniform height, prediction of cell shape will require knowledge of how the average signal received by street level mobiles depends on the direction of propagation relative to the rows of buildings. This paper presents theoretical results for the signal dependence on direction of propagation, number of intervening rows of buildings and the base station antenna height. Assuming all buildings to be of equal heights, the range dependence of the average signal is found by evaluating multiple forward diffraction past rows of buildings, perpendicular or oblique to the propagation path. These theoretical results are compared with measurements. <<ETX>>
A MULTIMODAL BIOMETRIC DETECTION SYSTEM VIA ROTATED HISTOGRAMS USING HOUGH LINES

Several systems require full identification of a user, as any misclassification may deteriorate the performance of the entire system. Such systems must grant access only to the genuine user. For this reason, single biometrics becomes insufficient for authentication and identification. Consequently, the need for implementing highly integrated systems is necessary to promote security of such systems. At the same time, multi-biometric attracts much attention. The current study put forward a pioneering multimodal biometric detection approach using the principle of detecting lines through Hough Transform (HT). The images were converted in to histograms using histogram plot function. However, these histograms images were rotated by 30 degrees and HT functions were applied on the rotated histograms to detect the query biometric features. The new technique was tested on face, iris, palm and fingerprint. The final plot accomplished detection of whole biometric features with an average detection time of 4.506 seconds per individual. The new technique can be used to detect the aforementioned biometric traits using the same feature extraction algorithm at limited time, since each biometric trait’s dimensions was drastically reduced. The new system outperformed many methods in the literature reported using conventional detection methods. Hence, the modified algorithm is applicable in multi-biometrics detection prior to recognition especially where little computation and fast performance is highly demanded.
Keyword spotting based on recurrent neural network

In this paper, a recognition method is proposed which has been applied to KWS (keyword spotting) and based on a recurrent neural network. The proposed recurrent neural network has memory within a several time steps' interval, and its classification ability is more powerful than the HMM (hidden Markov models). It can process a time sequence signal and shows some interesting properties in performing time warping. The RTRL (real time recurrent learning) algorithm is used to train the neural network. Two numerical examples are presented to demonstrate the merit of the neural network. Finally, we look at the defects of the scheme, and discuss strategies for combining this approach with HMM in order to decide the time position of KWS by in a more precise manner.
Collective Classification in Network Data

Many real-world applications produce networked data such as the world-wide web (hypertext documents connected via hyperlinks), social networks (for example, people connected by friendship links), communication networks (computers connected via communication links) and biological networks (for example, protein interaction networks). A recent focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such networks. In this article, we provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data.
LOW ERROR AND REAL-TIME STEREO VISON ALGORITHM FOR 3D VISUAL INSPECTION

To visually assess a structure such as corrosion, cracks as well as other irregularities, visual inspection is typically used. From the 3D information of the stereo camera, we can detect their exact shape and location. To inspect this defect by the robot which surveys around the structure, we need a small-size, low error, and real-time stereo vision module which produces 3-D spatial information from a pair of 2-D images. In this paper, we will present a fast and small stereo vision chip which has a low error rate. By adding the smoothness cost model between image scan lines, we can obtain a high quality disparity image with a small computational complexity. The architecture has the form of linear systolic array using a simple processing element (PE)s that are connected with neighboring PEs. The chip is embedded in the robot’s vision board, which can output 3D depth image of 320 by 240 size and 128 levels at 30 frames/s.
Web-based education: characteristics, problems, and some solutions

Since the web first began, it has provided numerous significant educational opportunities and the web-based education phenomenon is has spread throughout the world. The potential of the medium to enhance students' learning experiences is being increasingly recognised and utilised, and many educational organisations are emerging and providing full-fledged curricula. However, the web has brought concerns as well as opportunities. In this article, we will address those concerns by considering the following questions: Can web-based education be effective? Can it provide the same or better quality as conventional face-to-face instruction? If not, what are the reasons and the alternatives? In the first part of this article, we examine the characteristics, advantages, and problems of web-based education. In part two, we propose solutions for designing environments that will help to achieve efficient and effective web-based education.
Combining Prediction Models in a Linear Way: Results of NumericSimulation

Background: Using standard expressions for logistic regression and proportional hazard models and data from published outcome studies might allow generating prediction models and risk stratification tools in a more streamline fashion. However it might require combining the models, adding or removing predictors. The feasibility of this approach has been examined here. Methods: The outcome of this simulation study is mortality. The simulation exercise was based on the imaginary population of 20,000 subjects whose mortality was completely determined by five variables in the specified logistic regression model. In the first simulation exercise using “full model”, we evaluated the option of combining the results of two separate studies (studies A and B) each based on subset of the population. In the second simulation exercise studies A and B were based on limited number of predictors. Each simulation was repeated 50 times. Results: Both simulation exercises demonstrated the robustness of the model and feasibility of adding or removing predictors to/from the model. We also compared the results of linear model to the more complex exponential model using all five predictors. In subjects with lower risk indicator the outcome of linear model is similar to the outcome of the logistic regression model and to the true outcome rate, however it underestimates the risk in the high-risk groups. On the other hand, logistic regression model is accurate compared to actual outcomes. This confirms our hypothesis that dropping or adding variables should not distort the prediction in any noticeable way. Conclusions: Simple linear combination of prediction models, adding or removing predictors do not cause distortion of the model and predictions remain robust. Prediction of linear model is similar to exponential model, except the former underestimate the outcome in the high risk groups.