Miracle-dz/UniFAR-FTUs huggingface dataset:
Perbandingan Algoritma Backpropagation dan K-Nearest Neighbor (K-NN) untuk Identifikasi Penyakit

Algoritma Backpropagation dan K-Nearest Neighbor merupakan metode yang digunakan untuk klasifikasi yang bersifat suppervised, dimana kedua metode ini membutuhkan target dari data pola yang akan di latih. Untuk memecahkan masalah klasifikasi, berbagai macam metode telah diterapkan. Dibidang soft computing, mulai banyak dikembangkan juga teknik-teknik klasifikasi. Sehingga proses klasifikasi dapat dilakukan dalam waktu yang relatif lebih cepat dengan menggunakan algoritma klasifikasi yang tepat. Pada paper ini obyek penelitian yang digunakan adalah 10 jenis penyakit dengan gejalan awal demam. Terdapat 82 data pasien yang dibagi untuk data training sebanyak 72 pasien dan sisanya digunakan untuk data testing. Fokus penelitian ini adalah pada tingkat akurasi atau succes ratio yang dihasilkan oleh masing-masing metode. Dari hasil pengujian (testing) 10 data pasien diperoleh informasi bahwa hasil identifikasi terbaik untuk algoritma K-NN dengan menggunakan nilai K=3, K=7 dan K-9 dengan akurasi sebesar 100%, sedangkan untuk algoritma backpropagation diperoleh tingkat akurasi untuk identifikasi penyakit sebesar 90% dengan nilai learning rate 0,1 dan nilai toleransi error sebesar 0,001. Dari hasil pengamatan tersebut maka dapat disimpulkan bahwa algoritma K-NN memberikan hasil identifikasi penyakit yang lebih baik dibandingkan algoritma backpropagation.
Symbol timing recovery for generalized minimum shift keying modulations in software radio receiver

A new symbol timing estimator for generalized MSK signals is proposed. It is based on the squaring algorithm and has a feedforward structure. The proposed timing estimator is fully-digital and is suitable for implementation in software radios. The performance in AWGN channel is compared with the modified Cramer-Rao bound and that of the ML algorithm. The proposed timing estimator is found to have a performance close to that of the ML algorithm, but with a lower implementation complexity.
GENERATION OF ENDURANCE TIME ACCELERATION FUNCTIONS USING THE WAVELET TRANSFORM

Endurance Time Acceleration Functions are specially predesigned intensifying excitation functions that their amplitude increases with time. On the other hand, wavelet transform is a mathematical tool that indicates time variations of frequency in a signal. In this paper, an approach is presented for generating endurance time acceleration functions (ETAFs) whose response spectrum is compatible with the European Code regulations (EC8) elastic spectrum. Method applied is a modification of data in time and frequency domain. For this purpose, wavelet transform has been used to decompose a series of random points to several levels such that each level covers a special range of frequency, then every level is divided into the numbers of equal time intervals and each interval of time is multiplied by a variable. Subsequently, the mathematical unconstrained optimization algorithm is used to calculate the variables and minimize error between response and target spectra. The prosed procedure is used in two methods. Then with two methods, two different acceleration functions are produced.
Towards robust scene analysis: A versatile mid-level feature framework

We present a novel set of shape-centered interest points. The interest points are formed at locations of high local symmetry. Our symmetry detection is based on Gradient Vector Flow (GVF) [1] fields which provide a high level of stability against noise. The shape centered interest points allow for a robust scale and orientation estimation. We have shown their usefulness for image encoding and superpixel segmentation and demonstrat that they carry information that is to a certain degree complementary to corner based interest points.
Network covert channels: design, analysis, detection, and elimination

Indirect communication channels have been effectively employed in the communications world to bypass mechanisms that do not permit direct communication between unauthorized parties. Such covert channels emerge as a threat to information-sensitive systems in which leakage to unauthorized parties may be unacceptable (e.g., military systems). In this dissertation, we show that traffic analysis can counter traditional event-based covert channels, which do not employ any additional scheme to obfuscate the channel further. For these channels, we introduce effective noiseless and noisy covert channel detection mechanisms that capture the anomalous traffic patterns. However, because a motivated user can potentially hide the channel further, we introduce a new family of covert channels that do not produce such anomaly. These IP time-replay covert channels transmit covert messages by adjusting packet timings consistent with inter-arrival time sequences that are extracts from recently recorded normal sequences. Under certain assumptions and lowered data rates, these channels generate output sequences that are equal in distribution to normal sequences allowing them to by-pass traffic anomaly detection schemes that are based on distribution analysis. Additionally, we illustrate that these channels can potentially survive channel elimination schemes such as jammers and network data pumps with lowered data rates. Thus, we discuss two types of transformations on packet inter-arrival times to increase the efficacy of existing elimination schemes.
A predistorter design for a memory-less nonlinearity preceded by a dynamic linear system

We propose a data predistorter design scheme for a memory-less nonlinearity which is preceded by a linear system with memory. This system configuration is often found in telecommunications. The predistorter technique is useful since the compensation of the nonlinearity of high-power amplifiers allows the efficient use of the power resource and bandwidth, while maintaining the prescribed signal spectral distribution. We use either a modified indirect learning architecture or a stochastic gradient method for training the predistorters. As a predistorter structure, we use a Volterra series model or a time-delayed neural network. We apply our approach to the compensation of various nonlinear systems including TWT-type nonlinearities. The results show that our approach is very effective in compensating the memory-less nonlinearity preceded by a linear system with memory. We show the results for nonlinear systems with a TWT-type nonlinearity.
A Method for Long Extrapolation of Audio Signals

A new method for a long extrapolation of audio signals is presented. The method is fast and can extrapolate several thousand samples of noisy audio signals. All the information for the extrapolation is taken from the signal itself, so that no additional knowledge about the signal content is required. The basic theory is presented and tested by simulation. The major problem for optimal extrapolation is the determination of the impulse response of the signal. Different methods to solve the impulse response are described. The requirements for the signals that can be extrapolated are studied. The ability of the method to extrapolate audio signals with different types of amplitude envelopes is studied and simulated. The fact that signals with exponential or polynomial amplitude envelopes can be extrapolated is proven. A mathematical measurement tool for the quality of the extrapolation is presented. The effect of noise on the extrapolation is also studied. The method is tested with real music signals.
Light parsing as finite state filtering

For a number of language processing tasks, such as informati on retrieval and information extraction tasks, pertinent information can be e xtracted from text without doing a full parse of the individual sentences. The most c mmon restriction of the parser is to adopt a non-recursive model of the languag e treated, which allows an implementation of the parser using efficient finite st ate tools at the cost of missing some coverage. These light parsers allow the succ e sive introduction of symbols into the input string wherever specified regular e xpr ssions of words and/or part-of-speech tags match. Recent advances in finite state expression compilation make writing mark up transducers simpler, leading to quicker implementations of layered finite state parsers. The resulting parse rs ar easier to create and maintain. In this article, we describe a light parsing metho d using recently created finite state operators. Two applications of this parser are d escribed: grouping adjacent syntactically related units, and extracting non-ad jacent n-ary grammatical relations. A system for evaluating the parser over a large co rpus is described.
TOWARDS THE DEVELOPMENT OF AN AUTOMATIC FEEDBACK SYSTEM FOR PROGRAMMING ASSIGNMENT

Programming problems and assignment are considered essential elements of software engineering and computer scienceeducation. We propose a framework with which student programming assignments can receive automatic feedback on thesemantics of their program codes. The proposed system increases the interest to study and understand the concept of theprogramming subjects. The objective is to assist teachers to promote programming as a subject and increase, increasestudent’s performance while improving the quality of content delivered in computer programing courses.Keywords: Programming, students, framework, feedback, understanding and assignments.
A historical perspective on developing foundations for privacy-friendly client cloud computing: The Paradigm Shift from "Inconsistency Denial" to "Practical Semantic Integration(TM)"

Arguably, the original paradigm for computation was Logic Programming broadly conceived as “deducing computational steps from existing information. ” The idea has a long development that went through many twists in which important questions turned out to have surprising answers, including the following:  How much of concurrent computation is reducible to deduction?  Are the laws of thought consistent?  Is “rapid recovery” a more viable policy than “inconsistency denial”?  How can massive concurrency and strong paraconsistency provide practical semantic integration of diverse sources of information? A historical perspective on the above questions is highly pertinent to the current quest to develop foundations for client cloud computing.
Standard Cell Behavior Analysis and Waveform Set Model for Statistical Static Timing Analysis

As we are moving toward nanometre technology, the variability in the circuit parameters and operating environment (Process, Voltage and Temperature (PVT)) are increasing, causing uncertainty in the circuit performance. Statistical Static Timing Analysis (SSTA) is a category of methodologies to analyse the variations in delay due to PVT variations. This thesis work is a part of the MODERN project, which is involved in developing a new SSTA methodology. In this thesis, the variation of the delay in 45nm standard cells is analysed. In industry practice, the Monte Carlo method is often used to estimate the statistical moments. This method needs a large number of simulation iterations and these simulations are parameter distribution dependent. A fast statistical moment estimation method is proposed in this work. The proposed methodology is at least 100 x faster than the Monte Carlo method and simulations are independent of the parameter distribution. In the SSTA methodology of the MODERN project, the signal waveforms with their variations are preserved at each pin of the standard cell. The concept of a "set of waveforms" as a representation of a variable electrical signal is also developed in this thesis work. Possible methods to represent the set of waveforms and their integration with the timing analysis methodology are analysed. The pseudo circuit based representation turns out to be the most compact model. A methodology for the analysis of the accuracy and efficiency of the pseudo circuit model is proposed.
A cognitive model for problem solving in computer science

According to industry representatives, computer science education needs to emphasize the processes involved in solving computing problems rather than their solutions. Most of the current assessment tools used by universities and computer science departments analyze student answers to problems rather than investigating the processes involved in solving them. Approaching assessment from this perspective would reveal potential errors leading to incorrect solutions. This dissertation proposes a model describing how people solve computational problems by storing, retrieving, and manipulating information and knowledge. It describes how metacognition interacts with schemata representing conceptual and procedural knowledge, as well as with the external sources of information that might be needed to arrive at a solution. Metacognition includes higher-order, executive processes responsible for controlling and monitoring schemata, which in turn represent the algorithmic knowledge needed for organizing and adapting concepts to a specific domain. The model illustrates how metacognitive processes interact with the knowledge represented by schemata as well as the information from external sources. This research investigates the differences in the way computer science novices use their metacognition and schemata to solve a computer programming problem. After J. Parham and L. Gugerty reached an 85% reliability for six metacognitive processes and six domain-specific schemata for writing a computer program, the resulting vocabulary provided the foundation for supporting the existence of and the interaction between metacognition, schemata, and external sources of information in computer programming. Overall, the participants in this research used their schemata 6% more than their metacognition and their metacognitive processes to control and monitor their schemata used to write a computer program. This research has potential implications in computer science education and software development through its understanding of the cognitive behavior used to solve computational problems. ii
Link Network Search Analysis of Literary Books: The Achebe's

Measures of impact of academic research has considerable attention in the cyber metrics world today that it application becomes very relevant in the evaluation of connectivity of books in the field of literary discipline. This paper considered seven literary books- Things fall apart; No Longer At Ease, Arrow of God; Man of the People; Anthills’ of the Savannah; The Trouble with Nigeria and There Was a Country connectivity on the web links using Web Analyst and Pajek Software to draw the network links diagrams based on adaptation of Kamada-Kawai Algorithms and Frutchmann-Reign Gold. The finding confirmed that Achebe literary books have seen much impact and connectivity over the years. Key words: Graphs-F-R, K-K, Networks, Literary, Achebe, Connectivity, Pajek
A neural-net based fuzzy connection admission controller for wireless ATM

A neural-net based fuzzy connection admission controller for a wireless ATM network is designed. It is a QoS-guaranteed controller, where QoSs of both the air interface and ATM network are taken into consideration. Hence, for the neural fuzzy admission decision controller, we further design a fuzzy capacity requirement estimator, fuzzy air interface performance estimator, and a fuzzy system status estimator for reflecting the equivalent capacity required by the new call request, air interface performance, and the ATM network status, respectively. The simulation result shows that the neural fuzzy connection admission controller can gain high bandwidth utilization for wireless ATM networks.
Concept Hierarchy in Data Mining: Specificat ion, Generat ion and Implement at ion

Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. As one of the most important background knowledge, concept hierarchy plays a fundamentally important role in data mining. It is the purpose of this thesis to study some aspects of concept hierarchy such as the automatic generation and encoding technique in the context of data mining. After the discussion on the basic terminology and categorization, automatic generation of concept hierarchies is studied for both nominal and numerical hierarchies. One algorithm is designed for determining the partial order on a given set of nominal attributes. The resulting partial order is a useful guide for users to finalize the concept hierarchy for their particular data mining tasks. Based on hierarchical and partitioning clustering methods, two algorithms are proposed for the automatic generation of numerical hierarchies. The quality and performance comparisons indicates that the proposed algorithms can correctly capture the distribution nature of the concerned numerical data and generate reasonable concept hierarchies. The applicability of the algorithms is also discussed and some useful guides are given for the selection of the algorithms. As an important technique for efficient implementation, encoding of concept hierarchy is investigated. An encoding method is presented and its properties are studies. The superior advantages of this method are shown by comparing the storage requirement and performance with some other techniques. Finally, the applications of concept hierarchies in processing typical data mining tasks are discussed.
Exploratory study of multi-criteria recommendation algorithms over technology enhanced learning datasets

Results of previous studies have indicated that the performance of recommendation algorithms depends on the characteristics of the application context. The same algorithms have shown to be performing in totally different ways when a new or evolved data set is considered, thus leading to a need for continuous monitoring of how they operate in a realistic setting. In this paper we investigate such a real life implementation of a multi-criteria recommender system and try to identify the needed adjustments that need to take place in order for it to better match the requirements of its operational environment. More specifically, we examine the case of a multi-attribute collaborative filtering algorithm that has been supporting the recommendation service within a Web portal for organic and sustainable education. Our study particularly explores the experimental performance of the already implemented algorithm, as well as an alternative one, using data from the intended application, a simulated expansion of it, and from similar portals. The results of this study indicate the importance of the frequent experimental investigation of a recommender system's various design options, and the need for the exploration of adaptive implementations in real life recommender systems.
Assessment of a laser-powered multiwavelength near-infrared spectrometer

Near infrared spectroscopy is a non-invasive technique for measuring relative blood volume and oxygen saturation in tissue. The authors have designed and built a research NIR-spectrometer which offers the flexibility to study changes in blood oxygen saturation (SO/sub 2/) and in blood volume (BV) during skeletal muscle pacing. The instrument consists of five 1 watt solid state lasers (780, 800, 830, 850 and 980 nm) fired sequentially at 5 /spl mu/s pulses for a 1 ms cycle, and a 5 mm/sup 2/ photodiode receiver. Features of the spectrometer include, rapid realtime data acquisition (1000 samples/s), receiver protection against ambient light, large dynamic optical power output adjustable for each wavelength, and portability. In vitro photon scattering experiments and linear response to blood oxygen saturation changes for differential absorption (780-850 nm) provide an accurate measure of changes in SO/sub 2/, while the 800 nm signal can be used as a measure of blood volume change independently of SO/sub 2/ (/spl plusmn/2%-SO/sub 2/ error). In addition, the 980 nm signal level is explored as an index of mean pathlength which may provide crucial information for determining absolute SO/sub 2/.
An Energy Aware Congestion Controlled Cross Layered Approach in MANETS

In the proposed Energy Aware Congestion Controlled Cross Layered protocol, a cross layered approach is handled using adhoc on demand distance vector routing protocol for attaining a congestion controlled network. At the Medium Access Control layer, the nodes which possess signal to noise interference ratio requirements are only selected in the network and Shannon's capacity is found for those selected nodes. Hence at the Medium Access Control layer, only energy constrained nodes are selected. These selected nodes are moved to transport layer using the proposed cross layered approach. At the transport layer, if any congestion is found it can be detected using congestion threshold rate which is calculated using packet loss rate. Based on the congestion threshold rate, a congestion fewer routes can be found out that improves the throughput. Simulation results using NS2 shows improvement in the packet delivery ratio.
Investigation of silicon auditory models and generalization of linear discriminant analysis for improved speech recognition

Biologically motivated feature extraction algorithms have been found to provide significantly robust performance in speech recognition systems, in the presence of channel and noise degradation, when compared to the standard features such as mel-cepstrum coefficients. However, auditory feature extraction is computationally expensive, and makes these features useless for real-time speech recognition systems. In this thesis, I investigate the use of low power techniques and custom analog VLSI for auditory feature extraction. I first investigated the basilar-membrane model and the hair-cell model chips that were designed by Liu (Liu, 1992). I performed speech recognition experiments to evaluate how well these chips would perform as a front-end to a speech recognizer. Based on the experience gained by these experiments, I propose an alternate architecture that goes beyond the basilar-membrane model, and, using which, auditory features can be computed in real time. These chips have been designed and tested, and consume only a few milliwatts of power as compared to general purpose digital machines that consume several Watts. I have also investigated Linear Discriminant Analysis (LDA) for dimension reduction of auditory features. Researchers have used Fisher-Rao linear discriminant analysis (LDA) to reduce the feature dimension. They model the low-dimensional features obtained from LDA as the outputs of a Markov process with hidden states (HMM). I present a unified framework for the problem of dimension reduction and HMM parameter estimation by modeling the original features with reduced-rank HMM. This re-formulation also leads to a generalization of LDA that is consistent with the heteroscedastic state models used in HMM, and give better performance when tested on a digit recognition task.
A Comparative Study of IS-IS and IGRP Protocols for Real- Time Application Based on OPNET

Developing of Internet networks and increasing demand of real-time applications, such as voice, videoconferencing and routing algorithms play important roles in the real network in recent years. Depending on the traffic-demand, choosing configuration of the routing protocols can demonstrate the best performance in network operation. Interior Gateway Routing Protocol (IGRP) are distance vector protocol and Intermediate Systems-Intermediate Systems (IS-IS) are link state protocol. Also, both are main interior gateway routing protocols planned to work in complex network topologies. In this paper, we use OPNET software to design and simulate the network and compare the performance of the IS-IS, IGRP and combination of IS-IS, IGRP routing protocols in terms of convergence duration time, throughput, packet delay variation, packet end-to-end delay and traffic sent and propose which configuration is better than another and suitable for IP networks in real-time application.