Miracle-dz/UniFAR-FTUs huggingface dataset:
PENGAMANAN RUANG BRANKAS MENGGGUNAKAN KAMERA PENDETEKSI GERAK BERBASIS RASPBERRY PI DENGAN PENYIMPANAN OTOMATIS KE GMAIL DAN DROPBOX

Brankas merupakan lemari atau kotak besi tahan apiyang biasa digunakan untuk menyimpan barang-barang berharga. Brankas juga sudah dilengkapi dengan kunci pengamanan analog ataupun digital.Selain penggunaan kunci pengaman pada brankas, diperlukan juga kamera pengaman sebagai fasilitas keamanan tambahan.Kamera pengaman tersebut dapat memantau segala aktivitas yang terjadi di sekitar brangkas.Kamera pengaman yang diterapkan berupa pendeteksi gerak dan berbasis internet (webcam). Kamera pengaman ruang brankas yang berbasis internet akan mengirimkan hasil foto ke Gmail dan Dropbox, sehingga dapat dipantau dari mana saja dan kapan saja. Kamera dengan sistem deteksi gerak pada Raspberry Pi dapat secara langsung mengambil foto apabila terdapat gerakan pada ruang lingkup cakupan kamera. Sistem deteksi gerak tersebut mempunyai sensitivitas yang dapat diatur oleh penggunanya dengan mengubah nilai threshold pada pengaturan yang sudah diberikan pada program Motion. Semakin besar nilai yang diberikan pada threshold, maka sensitivitas kamera terhadap pendeteksian gerak akan semakin kecil, dan begitu pula sebaliknya. Waktu yang dibutuhkan kamera pendeteksi gerak untuk mengirimkan foto ke Gmail yaitu ±1 detik, sementara itu waktu pengiriman dari Gmail ke Dropbox ±10 menit.Penggunaan kamera pendeteksi gerak untuk brankas ini diharapkan dapat digunakan sebagai deteksi awal jika akan terjadi tindak kejahatan atau gangguan pada brankas, karena hasil foto dapat langsung diterima oleh pengguna melalui Gmail dan Dropbox. Kata kunci: Dropbox, Gmail, Raspberry Pi, Ruang Brankas, Webcam Deteksi Gerak
An efficient evolutionary image segmentation algorithm

In this paper, an efficient evolutionary image segmentation algorithm (EISA) is proposed. The existing evolutionary approach of image segmentation has the advantages over the other approaches such as continuous contour, non-oversegmentation, and non-thresholds, but suffers from long computation time. EISA uses a K-means algorithm to split an image into many homogeneous regions and then merges the split regions automatically using an evolutionary algorithm. The image segmentation problem is formulated as an optimization problem and the objective function is also given. EISA using a novel chromosome encoding method and a novel intelligent genetic algorithm makes the segmentation results robust and the computation time much shorter than the existing evolutionary image segmentation algorithms. Design and analysis of EISA are also presented. Experimental results of natural images with various degrees of noise demonstrate the effectiveness of EISA.
Power Electronics and Ac Drives

An integrated treatment of technological advances in power electronics Add this article to private library Remove from private library Submit corrections to this record View record in the new ADS and ac drives is presented. The topics include: power semiconductor devices, ac machines, phase-controlled converters and cycloconverters, voltage-fed inverter drives, current-fed inverter drives, slip powercontrolled drives, control of induction and synchronous machines, and microcomputer control. Both practical and theoretical aspects of the technology are addressed, and numerical examples are given. Bibtex entry for this abstract Preferred format for this abstract
Project Management With Cpm, Pert and Precedence Diagramming

No wonder you activities are, reading will be always needed. It is not only to fulfil the duties that you need to finish in deadline time. Reading will encourage your mind and thoughts. Of course, reading will greatly develop your experiences about everything. Reading project management with cpm pert and precedence diagramming is also a way as one of the collective books that gives many advantages. The advantages are not only for you, but for the other peoples with those meaningful benefits.
Pattern Synthesis Using Multiple Kernel Learning for Efficient SVM Classification

Abstract Support Vector Machines (SVMs) have gained prominence because of their high generalization ability for a wide range of applications. However, the size of the training data that it requires to achieve a commendable performance becomes extremely large with increasing dimensionality using RBF and polynomial kernels. Synthesizing new training patterns curbs this effect. In this paper, we propose a novel multiple kernel learning approach to generate a synthetic training set which is larger than the original training set. This method is evaluated on seven of the benchmark datasets and experimental studies showed that SVM classifier trained with synthetic patterns has demonstrated superior performance over the traditional SVM classifier.
A new tree data structure to extract frequent pattern from transactional database

Frequent Pattern mining has attracted wide attention in both research and application area recently. In this paper, we propose a novel tree data structure to extract all frequent patterns from transactional database with single database scan. It is also supports Interactive and Incremental mining without rescan the original database. Experimental results show that our new tree data structure is efficient for frequent pattern mining, interactive, and incremental mining with single database scan. It extracts frequent patterns faster than most current one like CP-Tree.
Anonymization by Data Relocation Using Sub-clustering for Privacy Preserving Data Mining

As there are new techniques growing to reveal the hidden information on data, the threat towards those data also increases. Therefore, privacy preservation in data mining is an emerging research area which develops various algorithms to anonymize the data provided for data mining. The existing methodology handles the tradeoff between utility and privacy of data in a more expensive way in terms of execution time. In this paper, a simple Anonymization technique using subclustering is specified which achieves maximum privacy and also utility with minimum execution time. The methodology is explained with algorithm and the results are compared with the baseline method.
Switched-current wave analog filters

A novel approach to the realization of switched-current (SI) filters is presented. This approach relies on wave filter functions, which the authors have proved to be particularly suitable for switched-current implementations owing to the simplicity of SI realization for summation, scaling, and time delay operations. Filter design using this concept requires a limited library of modular building blocks (adaptors and delay elements). The total size, power consumption, and dynamic range of these building blocks can be made nearly independent of the filter function coefficient values, which enables the practical realization of wave SI filters over a wide range of bandwidths. <<ETX>>
Detecting buffer overflows using testcase synthesis and code instrumentation

The research presented in this thesis aims to improve existing approaches to dynamic buffer overflow detection by developing a system that utilizes code instrumentation and adaptive test case synthesis to find buffer overflows and corresponding failure-inducing inputs automatically. An evaluation of seven modern dynamic buffer overflow detection tools determined that C Range Error Detector (CRED) is capable of providing fine-grained buffer access information necessary for the creation of this system. CRED was also selected because of its ability to provide comprehensive error reports and compile complex programs with reasonable performance overhead. CRED was extended to provide appropriate code instrumentation for the adaptive testing system, which also includes a test case synthesizer that uses data perturbation techniques on legal inputs to produce new test cases, and an analytical module that evaluates the effectiveness of these test cases. Using information provided by code instrumentation in further test case generation creates a feedback loop that enables a focused exploration of the input space and faster buffer overflow detection. Applying the adaptive testing system to jabberd, a Jabber Instant Messaging server, demonstrates its effectiveness in finding buffer overflows and its advantages over existing dynamic testing systems. Adaptive test case synthesis using CRED to provide buffer access information for feedback discovered 6 buffer overflows in jabberd using only 53 messages, while dynamic testing using random messages generated from a protocol description found only 4 overflows after sending 10,000 messages. Thesis Supervisor: Richard Lippmann Title: Senior Scientist, MIT Lincoln Laboratory
Content analysis coding schemes for online asynchronous discussion

Purpose – Researchers commonly utilize coding‐based analysis of classroom asynchronous discussion contributions as part of studies of online learning and instruction. However, this analysis is inconsistent from study to study with over 50 coding schemes and procedures applied in the last eight years. The aim of this article is to provide a basis for more consistent use of coding schemes and to facilitate comparison of studies utilizing different coding schemes.Design/methodology/approach – The paper identifies coding schemes presented in the research literature, classifies these schemes, and presents a list of synthesis codes reflecting the content of the many different schemes for each classification.Findings – Based on the initial and follow‐up literature review, 56 different coding schemes were identified as having been employed within the last eight years. Initial sorting indicated that schemes primarily focused on identifying critical thinking, describing social interactions, or characterizing online.
Performance optimization on low-cost cellular array processors

A massively parallel architecture is composed of a high number of processing elements (PE), but seldom a 1:1 mapping between the PEs and the data set can be achieved. To overcome this problem, a processor virtualization mechanism is needed. This work provides a theoretical study on performance optimization in the specific virtualization process used when a small amount of memory is associated to each PE. Moreover, this paper presents an algorithm for performance optimization and for the determination of the maximum processing speed on low-cost cellular array processors implementing the above mentioned visualization mechanism. These considerations are then used to improve the execution of morphological image processing tasks on the first hardware prototype of the special-purpose cellular array processor PAPRICA. <<ETX>>
Authentication by Keystroke Timing: Some Preliminary Results

Abstract : The growing use of computers to store sensitive, private, and classified information makes it increasingly important to be able to determine with a very high degree of confidence the identity of an individual seeking access to the computer. This report summarizes preliminary efforts to establish whether an individual can be identified by the statistical characteristics of his or her typing. Can people be identified by the way they type? To investigate this question, an experiment was carried out at Rand, in which seven professional typists were each given a paragraph of prose to type, and the times between successive keystrokes were recorded. This procedure was repeated 4 months later with the same typists and the same paragraph of prose. By examining the probability distributions of the times each typist required to type certain pairs of successively typed letters (digraphs), the authors found that of the large number of digraphs represented in most ordinary paragraphs, there were five which, considered together, could serve as a basis for distinguishing among the subjects. The implications of this finding are that touch typists appear to have a typing "signature," and that this method of distinguishing subjects might provide the basis for a computer authentication system.
Common focus point technology

Dit proefschrift is goedgekeurd door de promotor: 'Alles komt voort uit de chaos', wordt er in de Griekse Anthologie gezegd. En in-derdaad, alles komt voort uit de chaos. Buiten de wiskunde, die enkel met dode getallen en loze formules te maken heeft en derhalve volmaakt logisch kan zijn, is de wetenschap niet meer dan een kinderspel in de schemering, het willen vangen van vogelschaduwen en het willen tegenhouden van de schaduw van gras dat waait in de wind. Preface In January 1991 I finished my MSc project, which was supervised by professor Fokkema and professor Van Den Berg, at the department of applied Earth Sciences. During the MSc project professor Fokkema showed me the good things of seismic research. At the end of the academic year, August 1991, professor Fokkema introduced me to the group of professor Berkhout where I could begin a PhD study within the DELPHI project. Research in the Geophysical context was something I started to like a lot and working in a team was also an important reason for starting my PhD project in the group of professor Berkhout. The subject of my PhD project was initially set up to investigate the combination of elastic wavefield decomposition and weathered layer influences. In the first year it became clear that correct estimation of the propagation properties of the weathered layer is very important for the decomposition and all the further processing steps. Therefore the subject of my research changed to weathered layer estimation, using the areal shot record technology. An important side-off project of these first years was an optimization technique I used to construct the decomposition operators. This technique is also very successful for the optimization of extrapolation operators. During my work with areal shot records the areal shot record designed for one point in the sub-surface turned out to be a very useful intermediate step for imaging and velocity analysis. So finally the subject changed for the last time to Common Focus Point technology the main subject of this thesis. The last 2 years I have been working intensively on this subject and the results can be found back in this thesis. Note that although it seems that I have been doing a lot of other things besides the main subject of this thesis, all the things I have done before helped me to understand the main geophysical problems and gave me a broad overview. …
Rethinking neural networks : quantum fields and biological data

Contents: K.H. Pribram, Foreword. J.C. Eccles, Keynote: Evolution of Complexity of the Brain with the Emergence of Consciousness. M. Stadler, P. Kruse, Viewpoint: Neurodynamics and Synergetics. Part I:The Dendritic Microprocess. D. Berger, K.H. Pribram, From Stochastic Resonance to Gabor Functions: An Analysis of the Probability Density Function of Interspike Intervals Recorded from Visual Cortical Neurons. I.N. Bankman, Automated Recognition of Action Potentials on Extracellular Recordings. A.R. Bulsara, A.J. Maren, Coupled Neural-Dendritic Processes: Cooperative Stochastic Effects and the Analysis of Spike Trains. Part II:Quantum Neurodynamics. M. Jibu, K. Yasue, The Basics of Quantum Brain Dynamics. R.L. Dawes, Advances in the Theory of Quantum Neurodynamics. B. MacLennan, Information Processing in the Dendritic Net. B. MacLennan, Field Computation in the Brain. W. Schempp, Analog VLSI Network Models: Cortical Linking Neural Net Models and Quantum Holographic Neural Technology. P.J. Werbos, Quantum Theory and Neural Systems: Alternative Approaches and a New Design. Part III:Nanoneurology. S. Hameroff, J.E. Dayhoff, R. Lahoz-Beltra, S. Rasmussen, E.M. Insinna, D. Koruga, Nanoneurology and the Cytoskeleton: Quantum Signaling and Protein Conformational Dynamics as Cognitive Substrate. G. Rein, Modulation of Neurotransmitter Function by Quantum Fields. J.E. Dayhoff, S. Hameroff, C.E. Swenberg, R. Lahoz-Beltra, The Neuronal Cytoskeleton: A Complex System that Subserves Neural Learning. H. Szu, B. Telfer, G. Rogers, D. Gobovic, C. Hsu, M. Zaghloul, Spatiotemporal Chaos Information Processing in Neural Networks -- Electronic Implementation. Part IV:Perceptual Processing. B.J. Richmond, T.J. Gawne, T.W. Kjaer, J.A. Hertz, Neuronal Encoding of Information Related to Visual Perception, Memory, and Motivation. B. Bridgeman, Efferent Programming of the Striate Cortex. W.J. Freeman, The Emergence of Chaotic Dynamics as a Basis for Comprehending Intentionality in Experimental Subjects. H. Szu, J. Kim, I. Kim, The Formation of Live Neural Networks on Electronic Chips. K.H. Pribram, Afterword.
A GENERAL APPROACH BASED ON AUTOCORRELATION TO DETERMINE INPUT VARIABLES OF NEURAL NETWORKS FOR TIME SERIES FORECASTING

Input selection is probably one of the most critical decision issues in neural network designing, because it has a great impact on forecasting performance. Among the many applications of artificial neural networks to finance, time series forecasting is perhaps one of the most challenging issues. Considering the features of neural networks, we propose a general approach called Autocorrelation Criterion (AC) to determine the inputs variables for a neural network. The purpose is to seek optimal lag periods, which are more predictive and less correlated. AC is a data-driven approach in that there is no prior assumptiona bout the models for time series under study. So it has extensive applications and avoids a lengthy experimentation and tinkering in input selection. We apply the approach to the determination of input variables for foreign exchange rate forecasting and conductcomparisons between AC and information-based in-sample model selection criterion. The experiment results show that AC outperforms information-based in-sample model selection criterion.
H/sub /spl infin// control design for the active rack isolation system

The active rack isolation system is a rack-level vibration isolation system aboard the International Space Station. A stringent isolation performance target, in conjunction with constraints imposed on the control design by model uncertainty and system noise make for a challenging control problem. In this paper the key control design constraints are introduced. H/sub /spl infin// methods are used to design multivariable controllers that achieve robust vibration attenuation.
Text Emotion Classification Research Based on Improved Latent Semantic Analysis Algorithm

The emotion classification of text is an important research direction of text mining. Application on emotion text classification, latent semantic analysis algorithm has advantage of small occupied space, applicable to a large scale of text classifications. Compared with the traditional vector space model, latent semantic analysis algorithms reduce the search space for text classification by means of singular value decomposition for term and document matrix. Moreover, latent semantic analysis algorithms solve the problem of words with multiple meanings by analyzing the term at the semantic level. Using an improved latent semantic analysis algorithm to classify the test set by their emotion. The new cluster centroid is the average vector for each emotion category, and access to emotions classification for training dataset by calculating similarity of the average vector and test textual. The experimental results show that the improved latent semantic analysis algorithm have high precision and recall rate as same as the original algorithm, the efficiency of text emotion classification improved 4 percentage points. Keywords-Latent Semantic Analysis; Vector Space Model; Text Emotion Classification;
An Introduction to R for Quantitative Economics

This book has been published in the “Springer Briefs in Economics” series of Springer, which has quite a broad scope. In its preface the author states that the book“. gives an introduction to R to build up graphing, simulating and computing skills to enable one to see theoretical and statistical models in economics in a unified way. [. ] The book addresses the specific needs of economists, and helps them move up the R learning curve. Students of economics or economists keen to learn how to use R would find this book very useful. ”
The Effectiveness of Data Shuffling for Privacy-Preserving Data Mining Applications

Abstract Preserving the confidentiality of sensitive data, while permitting knowledge discovery, is an important goal in privacy-preserving data mining. This paper investigates the effectiveness of data shuffling for classification tree and regression analysis. We compare the effectiveness of data shuffling to the tree based data perturbation method which was developed specifically for the purpose of data mining. Results suggest that data shuffling provides the higher levels of data security and more effectively preserves data mining knowledge than tree based data perturbation method.
Reliability of Formant Measurements from Lossy Compressed Audio

Lossy audio compression algorithms, such as those u sed in mp3s and VoIP services like Skype, achieve their high levels of compression through de structively modifying the original waveform. Although lightly compressed audio is indistinguisha ble from uncompressed audio to the human ear, it is unclear how strong an effect compression might have upon the accuracy of acoustic measurements made for the purpose of phonetic study . This study examined formant measurements from a series of sociolinguistic recor dings of both male and female speakers, and their reliability when these recordings were co mpressed. If acoustic analysis of compressed audio is suffici ently reliable, it could provide practical benefits to researchers. For instance, some collect ions of linguistically interesting recordings may not be available in a lossless format. In addit ion, the much smaller file size of compressed audio (potentially 10-30 times smaller than uncompr essed audio) could simplify the management of large corpora, and make it more feasi ble to share them with other researchers, especially over the internet. Finally, VoIP service s could offer a potentially useful tool for gathering linguistic recordings from remote locatio ns. In this study, recordings originally encoded as 24- bit, 44Hz wav files were re-encoded as mp3s, at three levels of compression (64, 128, and 320 kbps (CBR)) using the encoder built into Sound Forge. Additionally, the originals were trans mitted and re-recorded over Skype, to test the compression algorithms used internally by this program. F0 though F4 were measured using Praat (Boersma and Weenink 2009) at the temporal mi dpoint of approximately 100 vowel utterances for two speakers, and these measurements were repeated at the same timestamps for each version of the recording. The results for each compressed version were then compared with the original measurements. Results suggest that even high levels of mp3 compre ssion have a minimal effect on the accuracy of formant measurements (a result similar to Van Son 2005). For the speakers examined, F1 and F2 for each vowel type differed fr om the original recording by an average of 3Hz and 9Hz, respectively, on average. For many lin guistic purposes, this is an acceptable margin-of-error. Recordings transmitted over Skype differed from their originals to a significantly greater degree, and it does not appear at this time to be a suitable tool for gathering recordings where accurate acoustic analysis is required.