Miracle-dz/UniFAR-FTUs huggingface dataset:
Attainment of students’ conception in magnetic fields by using of direct observation and symbolic language ability

This study focuses on description attainment of students’ conception in the magnetic field. The conception was based by using of direct observation and symbolic language ability. The method used is descriptive quantitative research. The subject of study was about 86 students from 3 senior high school at Purworejo. The learning process was done by guided inquiry model. During the learning, students were required to actively investigate the concept of a magnetic field around a straight wire electrical current Data retrieval was performed using an instrument in the form of a multiple choice test reasoned and observation during the learning process. There was four indicator of direct observation ability and four indicators of symbolic language ability to grouping category of students conception. The results of average score showed that students conception about the magnitude more better than the direction of magnetic fields in view of symbolic language. From the observation, we found that students could draw the magnetic fields line not from a text book but their direct observation results. They used various way to get a good accuracy of observation results. Explicit recommendations are presented in the discussion section at the end of this paper.
Practical polar coding method to minimize the embedding impact in steganography

This paper proposes a new practical polar coding methodology for constructing steganographic scheme. We use syndrome coding with binary embedding operation. The approach exploits the form of the syndrome, calculated from cover and secret message. A connection between the syndrome decimal value and the embedding changes position is established and enables defining a new steganographic algorithm. The wet paper codes can also be implemented using this method. Experimental results prove that the scheme minimize the embedding impact with a reduced time complexity compared to the first Polar Coding Steganography (PCS).
Are approximation algorithms for consensus clustering worthwhile?

Consensus clustering has emerged as one of the principal clustering problems in the data mining community. In recent years the theoretical computer science community has generated a number of approximation algorithms for consensus clustering and similar problems. These algorithms run in polynomial time, with performance guaranteed to be at most a certain factor worse than optimal. We investigate the feasibility of the approximation algorithms, in an attempt to link data-mining and theoretical research. On realistic data sets, algorithms with quadratic running times are impractical. Unfortunately these and even worse running times are typical of approximation algorithms. To circumvent this, we sample from the data, run the “slow” algorithms on the sample, and then build a consensus clustering from the seed sample clustering, using a range of techniques. These unsampling techniques are in fact almost as good at creating consensus partitionings as the approximation and data-mining algorithms themselves. We find that one of the latest approximation algorithms is not only fast and effective, but also easy to describe, making it an ideal choice.
PENGEMBANGAN INSTRUMEN PENILAIAN OTENTIK: VALIDITAS TEORITIS DAN KEPRAKTISAN

Abstrak Tujuan penelitian adalah melihat validitas penilaian otentik yang telah dikembangkan baik teoritis maupun empiris. Subyek penelitian ini adalah penilaian otentik yang telah dikembangkan. Penelitian yang telah dilakukan menggunakan desain penelitian R & D ( Research and Development ). Instrumen penilaian yang dikembangkan adalah rubrik penilaian project report dan presentasi. Penilaian yang telah dikambangkan diuji kelayakannya berdasarkan validitas teoritis dan empirisnya. Validitas teoritis diukur berdasarkan perspektif dari pakar, sedangkan kepraktisan dievaluasi berdasarkan pendapat mahasiswa terhadap penerapan penilaian tersebut. Hasil yang ditunjukkan penelitian ini adalah instrumen yang telah dikembangkan layak untuk diterapkan berdasarkan hasil validasi kepada pakar dan respon mahasiswa. Penilaian yang telah dikembangkan berada pada kategori “sangat baik” dan “baik”. Setiap item penilaian memperoleh skor 3 dan 4. Analisis kepraktisan yang digambarkan melalui hasil uji coba terbatas dan respon mahasiswa, menunjukkan bahwa mahasiswa memberikan respon positif terhadap penerapan penilaian tersebut, meskipun ada beberapa bagian dari rubrik penilaian perlu untuk direvisi lebih lanjut. Kata Kunci: penilaian otentik, validitas teoritis, validitas empiris
Internet Dial-Up Traffic Modelling

This paper deals with analysis and modelling of real Internet dial-up traffic based on extensive data, which are described and analysed. When recording the traffic process the time axis is digitalised and analysis and modelling must take this into account. In particular, this is very important for arrival processes. It is also important to distinguish between fast variations within short time intervals and slow variations during the day. The detailed analysis shows that the arrival process is a Poisson process with slow variations.
High speed neural control for robot navigation

This paper addresses the real time control of the Khepera mobile robot navigation in a maze with reflector walls. Boolean neural networks such as RAM and GSN models are applied to drive the vehicle, following a light source, while avoiding obstacles. Both neural networks are implemented with simple logic and arithmetic functions (NOT, AND, OR, Addition, and Comparison), aiming to improve the system speed. The results obtained are compared with two other control strategies: multilayer perceptron and fuzzy logic.
Principles of Optimally Placing Data in Tertiary Storage Libraries

Recently, technological advances have resulted in the wide availability of commercial products offering near-line, robot-based, tertiary storage libraries. Thus, such libraries have become a crucial component of modern largescale storage servers, given the very large storage requirements of modern applications. Although the subject of optimal data placement (ODP) strategies has received considerable attention for other storage devices (such as magnetic and optical disks and disk arrays), the issue of optimal data placement in tertiary libraries has been neglected. The latter issue is more critical since tertiary storage remains three orders of magnitude slower than secondary storage. In this paper, we address this issue by deriving such optimal placement algorithms. First, we study the ODP problem in disk libraries (jukeboxes) and subsequently, in tape libraries. In our studies, we consider different scheduling algorithms, different configurations of disk libraries and different tape library technologies (reflecting different existing commercial products) and show how these impact on the ODP strategy. *Support for this work was provided by the European Community through the ESPRIT Long Term Research Project HERMES no. 9141. Permission to copy without fee all OT part of this material is granted provided that the copies are not made OT distributed JOT direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the VeTy Large Data Base Endowment. To copy otherwise, OT to republish, requires a Jee and/or special permission from the Endowment. Proceedings of the 23rd VLDB Conference Athens, Greece, 1997
An efficient algorithm for web recommendation systems

Different efforts have been made to address the problem of information overload on the Internet. Web recommendation systems based on web usage mining try to mine users' behavior patterns from web access logs, and recommend pages to the online user by matching the user's browsing behavior with the mined historical behavior patterns. In this paper we propose effective and scalable technique to solve the web page recommendation problem. We use distributed learning automata to learn the behavior of previous users' and cluster pages based on learned pattern. One of the challenging problems in recommendation systems is dealing with unvisited or newly added pages. As they would never be recommended, we need to provide an opportunity for these rarely visited or newly added pages to be included in the recommendation set. By considering this problem, and introducing a novel Weighted Association Rule mining algorithm, we present an algorithm for recommendation purpose. We employ the HITS algorithm to extend the recommendation set. We evaluate proposed algorithm under different settings and show how this method can improve the overall quality of web recommendations.
Traffic Sign Recognition Using Dictionary Learning Method

Recent researches have paid more and more attention to traffic sign recognition due to its important role in the intelligence transportation system. In the traditional methods for this task, first the traffic signs are located using the color or shape information of the traffic signs, then a classifier is applied for classification. In this paper, we propose a novel framework using the sparse model for traffic information representation and a classifier using a probability method for classification. Results of experiments using examples from the Caltech 101Object Categories show that the proposed method is efficient for traffic sign recognition.
Speaker recognition system using the improved GMM-based clustering algorithm

According to the sensitivity of initial value in the traditional GMM-based clustering algorithm, this paper proposes the improved GMM-based clustering algorithm which utilizes subtractive clustering to initialize the cluster centroids and uses an approximating K-L divergence as the distance measure. After clustering the universal background model(UBM) is trained for each clustering. In speaker recognition, the algorithm firstly confirms which clustering the aim speaker belongs to and then it uses the value of maximum likelihood probability and the UBM-based testing approach to recognize. According to the results of simulation using Matlab, the improved GMM-based clustering algorithm has the higher clustering accuracy and recognition rate than the traditional GMM-based clustering algorithm.
The Macroeconomy and Long-Term Interest Rates: An Examination of Recent Treasury Yields

From 2001 to 2006, U.S. long-term interest rates have remained steady while the federal funds rate has both declined and increased, as Figure 1 shows. Historically, long term interest rates tend to respond to changes in short term rates, but recently this does not appear to be the case. Former chairman of the Federal Reserve, Alan Greenspan, recently dubbed this occurrence a “conundrum,” because no one can provide a distinct explanation concerning this phenomenon. There are several noteworthy incentives for why long-term yields should have increased from 2004 to 2006, but they have remained constant during this time period. According to current economic theory, the U.S. budget deficit, the Federal Open Market Committee’s (FOMC) recent increases in short term rates, the latest recovery from recession, and the hefty current account deficit should all be contributing to higher long-term rates. Despite these macroeconomic influences, rates have not responded. Therefore, a supplementary force(s) must be creating a substantial impact. For example, this trend may be explained by a decrease in interest rate volatility, the Federal Reserve’s ability to maintain low inflation expectations, or an increase in foreign demand for U.S Treasuries. Is the ten-year Treasury yield truly a conundrum, or have macroeconomic influences caused long-term interest rates to maintain at an appropriate level? [excerpt]
G. Mona Jacqueline 1 and Mrs. Priya Ponnusamy 2

Multicast routing is one in which a source sends data to multiple receivers. Multicast routing uses many types of protocols. In this paper ODMRP protocol is used by slightly modifying it. Instead of using hop count for selecting the route, link quality is used so that high throughput is achieved. Also focus is made on attacks that disrupt routing process by modifying the link value, which are very effective against multicast protocols based on high throughput metrics. Other types of attack such as traffic analysis attack, Sybil attack, eavesdropping, selfish attack etc are not focused. In this paper focus is made on Sybil attack. Sybil attack is defined as an attack, in which a malicious node illegitimately takes on multiple identities by spoofing a legitimate node. Detection of Sybil attack which uses multiple identities can be done by nodes that passively monitor the traffic in the network. One method to detect Sybil attack is Passive Ad hoc Sybil Identity Detection (PASID), where a single node can detect Sybil attacks by recording identities, namely MAC or IP address of other nodes. Overtime the node builds a profile which helps in detecting the Sybil attack.
Shape Invariant Recognition of Segmented Human Faces using Eigenfaces

This paper describes an efficient approach for face recognition as a two step process: 1) segmenting the face region from an image by using an appearance based model, 2) using eigenfaces for person identification for segmented face region. The efficiency lies not only in generation of appearance models which uses the explicit approach for shape and texture but also the combined use of the aforementioned techniques. The result is an algorithm that is robust against facial expressions variances. Moreover it reduces the amount of texture up to 12% of the image texture instead of considering whole face image. Experiments have been performed on Cohn Kanade facial database using ten subjects for training and sever for testing purposes. This achieved a successful face recognition rate up to 92.85% with and without facial expressions. Face recognition using Principal Component Analysis (PCA) is fast and efficient to use, while the extracted appearance model can be further used for facial recognition and tracking under lighting and pose variations. This combination is simple to model and apply in real time. KeywordsActive shape models, active appearance models, principal components analysis, eigenfaces, face
Chapter I Toward High-Level Visual Information Retrieval

Content-based visual information retrieval (CBVIR) as a new generation (with new concepts, techniques, mechanisms, etc.) of visual information retrieval has attracted many interests from the database community. The research starts by using a low-level feature from more than a dozen years ago. The current focus has shifted to capture high-level semantics of visual information. This chapter will convey the research from the feature level to the semantic level by treating the problem of semantic gap under the general framework of CBVIR. This high-level research is the so-called semantic-based visual information retrieval (SBVIR). This chapter first shows some statistics about the research publications on semantic-based retrieval in recent years; it then presents some existing approaches based on multi-level image retrieval and multi-level video retrieval. It also gives an overview of several current centers of attention by summarizing certain results on subjects such as image and video 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.irm-press.com ITB13850 IRM PRESS This chapter appears in the book, Semantic-Based Visual Information Retrieval by Y.J. Zhang © 2007, Idea Group Inc.
srGAP1 regulates lamellipodial dynamics and cell migratory behavior by modulating Rac1 activity

srGAP1 limits Rac1 activity at lamellipodia in a negative feedback manner, allowing concomitant activation of Rac1 and RhoA at lamellipodia. Rho signaling causes membrane ruffling through actomyosin contractility and removes the protrusive structures. Such coordination of Rac and Rho determines migratory behavior through lamellipodial dynamics.
Estimating Importance Features for Fact Mining (With a Case Study in Biography Mining)

We present a transparent model for ranking sentences that incorporates topic relevance as well as an aboutness and importance feature. We describe and compare five methods for estimating the importance feature. The two key features that we use are graph-based ranking and ranking based on reference corpora of sentences known to be important. Independently those features do not improve over the baseline, but combined they do. While our experimental evaluation focuses on informational queries about people, our importance estimation methods are completely general and can be applied to any topic.
Short Term Depression Unmasks the Ghost Frequency

Short Term Plasticity (STP) has been shown to exist extensively in synapses throughout the brain. Its function is more or less clear in the sense that it alters the probability of synaptic transmission at short time scales. However, it is still unclear what effect STP has on the dynamics of neural networks. We show, using a novel dynamic STP model, that Short Term Depression (STD) can affect the phase of frequency coded input such that small networks can perform temporal signal summation and determination with high accuracy. We show that this property of STD can readily solve the problem of the ghost frequency, the perceived pitch of a harmonic complex in absence of the base frequency. Additionally, we demonstrate that this property can explain dynamics in larger networks. By means of two models, one of chopper neurons in the Ventral Cochlear Nucleus and one of a cortical microcircuit with inhibitory Martinotti neurons, it is shown that the dynamics in these microcircuits can reliably be reproduced using STP. Our model of STP gives important insights into the potential roles of STP in self-regulation of cortical activity and long-range afferent input in neuronal microcircuits.
Optimal Spatial Resolution for the Analysis of Human Mobility

The availability of massive network and mobility data from diverse domains has fostered the analysis of human behaviors and interactions. This data availability leads to challenges in the knowledge discovery community. Several different analyses have been performed on the traces of human trajectories, such as understanding the real borders of human mobility or mining social interactions derived from mobility and vice versa. However, the data quality of the digital traces of human mobility has a dramatic impact over the knowledge that it is possible to mine, and this issue has not been thoroughly tackled so far in literature. In this paper, we mine and analyze with complex network techniques a large dataset of human trajectories, a GPS dataset from more than 150k vehicles in Italy. We build a multi resolution grid and we map the trajectories with several complex networks, by connecting the different areas of our region of interest. Then we analyze the structural properties of these networks and the quality of the borders it is possible to infer from them. The result is a significant advancement in our understanding of the data transformation process that is needed to connect mobility with social network analysis and mining.
Virus-like particle (VLP)-based vaccines for pandemic influenza

The influenza pandemic of 2009 demonstrated the inability of the established global capacity for egg-based vaccine production technology to provide sufficient vaccine for the population in a timely fashion. Several alternative technologies for developing influenza vaccines have been proposed, among which non-replicating virus-like particles (VLPs) represent an attractive option because of their safety and immunogenic characteristics. VLP vaccines against pandemic influenza have been developed in tobacco plant cells and in Sf9 insect cells infected with baculovirus that expresses protein genes from pandemic influenza strains. These technologies allow rapid and large-scale production of vaccines (3–12 weeks). The 2009 influenza outbreak provided an opportunity for clinical testing of a pandemic influenza VLP vaccine in the midst of the outbreak at its epicenter in Mexico. An influenza A(H1N1)2009 VLP pandemic vaccine (produced in insect cells) was tested in a phase II clinical trial involving 4,563 healthy adults. Results showed that the vaccine is safe and immunogenic despite high preexisting anti-A(H1N1)2009 antibody titers present in the population. The safety and immunogenicity profile presented by this pandemic VLP vaccine during the outbreak in Mexico suggests that VLP technology is a suitable alternative to current influenza vaccine technologies for producing pandemic and seasonal vaccines.
DACSA: A Decoupled Architecture for Cloud Security Analysis

Monitoring virtual machine execution from the hypervisor provides new opportunities for evaluating cloud security. Unfortunately, traditional hypervisor based monitoring techniques tightly couple monitoring with internal VM operations and as a result 1) impose unacceptably high overhead to both guest and host environments and 2) do not scale. Towards addressing this problem, we present DACSA, a decoupled "Out-of-VM" cloud analysis architecture for cyber testing. DACSA leverages guest VMs that act as sensors to capture security centric information for analysis. Guest VMs and host environments incur minimal impact. We measure DACSA's impact to VMs at 0-6% and host impact at 0-3% which is only incurred during state acquisition. As a result, DACSA can enable production environments as a testbed for security analysis.
Integrated on-line robot-camera calibration and object pose estimation

We present a novel on-line approach for extrinsic robot-camera calibration, a process often referred to as hand-eye calibration, that uses object pose estimates from a real-time model-based tracking approach. While off-line calibration has seen much progress recently due to the incorporation of bundle adjustment techniques, on-line calibration still remains a largely open problem. Since we update the calibration in each frame, the improvements can be incorporated immediately in the pose estimation itself to facilitate object tracking. Our method does not require the camera to observe the robot or to have markers at certain fixed locations on the robot. To comply with a limited computational budget, it maintains a fixed size configuration set of samples. This set is updated each frame in order to maximize an observability criterion. We show that a set of size 20 is sufficient in real-world scenarios with static and actuated cameras. With this set size, only 100 microseconds are required to update the calibration in each frame, and we typically achieve accurate robot-camera calibration in 10 to 20 seconds. Together, these characteristics enable the incorporation of calibration in normal task execution.
Challenges and Direction toward Secure Communication in the SCADA System

In the past few years the security issues in the supervisory control and data acquisition (SCADA) system have been investigated, and many security mechanisms have been proposed from research communities. The international standard organizations also have published several standard documents for secured SCADA systems. In this paper we explain the technological challenges for the SCADA security and overview the approaches which address these challenges. Then we focus on the security protocol which has been proposed in the SCADA cyber security initiatives and implementation issues when the both security function and the communication function are implemented on the embedded system devices in future power grid including the SCADA network.