Miracle-dz/UniFAR-FTUs huggingface dataset:
Center of Pressure Feedback for Controlling the Walking Stability Bipedal Robots using Fuzzy Logic Controller

This paper presents a sensor-based stability walk for bipedal robots by using force sensitive resistor (FSR) sensor. To perform walk stability on uneven terrain conditions, FSR sensor is used as feedbacks to evaluate the stability of bipedal robot instead of the center of pressure (CoP). In this work, CoP that was generated from four FSR sensors placed on each foot-pad is used to evaluate the walking stability. The robot CoP position provided an indication of walk stability. The CoP position information was further evaluated with a fuzzy logic controller (FLC) to generate appropriate offset angles to be applied to meet a stable situation. Moreover, in this paper designed a FLC through CoP region's stability and stable compliance control are introduced. Finally, the performances of the proposed methods were verified with 18-degrees of freedom (DOF) kid-size bipedal robot.
A New Design of an UWB Circular Fractal Printed Antenna

A Microstrip Antenna (MSA) has been computed and analysed in this study by using CST of Microwave studio as Electromagnetic solver by generating the coefficient of reflection, the Gain, the density of current and the radiation pattern in the frequency range 3.1-10.6 GHz which commercialised by the Federal communication commission (FCC) as an Ultra-wide band (UWB) frequency range. The substrate used to achieve the proposed structure is the FR4-Epoxy with a thickness of 1.6 mm, a constant dielectric of 4.4 and a loss tangent of 0.025. The radiating patch is a circular shape etched with different sizes to create the fractal geometry. The transmission line has been designed by including a tapered section in the part connected to the radiator. The design of the antenna has been verified by using ADS and CST solvers. The fabrication of the antenna has been performed in order to measure the coefficient of reflection and the radiation pattern.
The Factors Affecting Success of Knowledge-Based Systems at the Organizational Level

The success of information systems (IS) has long been studied in various contexts. Yet, few previous studies have examined the success of knowledge-based systems (KBSs) taking into consideration the knowledge-intensive nature of KBSs. This study aims to identify the success factors of KBSs from the knowledge-sharing perspective. Given this purpose, we developed a research model by combining a knowledge-sharing framework with DeLone and McLean's IS success model. The model was tested using data gathered through a field survey of 60 organizations. The results showed that three organizational factors, namely, codifiability of knowledge, absorptive capacity, and relationship with a vendor were essential in interpreting the success of KBSs.
Review of Machine Transliteration Systems

Transliteration is the conversion of a text from one script to another, and thus representing words from one language using the approximate phonetic or spelling equivalents of another language. Machine Transliteration has come out to be an emerging and a very important research area in the field of machine translation. Transliteration systems are very beneficial for removing the language and scriptural barrier. It has gained prime importance as a supporting tool for machine translation and cross-language information retrieval, especially when proper names and technical terms are involved. This paper is intended to give a brief overview on the research work carried out on transliteration for Indian as well as for foreign languages.
Content selection in multi-document summarization

Automatic summarization has advanced greatly in the past few decades. However, there remains a huge gap between the content quality of human and machine summaries. There is also a large disparity between the performance of current systems and that of the best possible automatic systems. In this thesis, we explore how the content quality of machine summaries can be improved. First, we introduce a supervised model to predict the importance of words in the input sets, based on a rich set of features. Our model is superior to prior methods in identifying words used in human summaries (i.e., summary keywords). We show that a modular extractive summarizer using the estimates of word importance can generate summaries comparable to the state-of-the-art systems. Among the features we propose, we highlight global knowledge, which estimate word importance based on information independent of the input. In particular, we explore two kinds of global knowledge: (1) important categories mined from dictionaries, and (2) intrinsic importance of words. We show that global knowledge is very useful in identifying summary keywords that have low frequency in the input. Second, we present a new framework of system combination for multi-document summarization. This is motivated by our observation that different systems generate very different summaries. For each input set, we generate candidate summaries by combining whole sentences produced by different systems. We show that the oracle summary among these candidates is much better than the output from the systems that we have combined. We then introduce a support vector regression model to select among these candidates. The features we employ in this model capture the informativeness of a summary based on the input documents, the outputs of different systems, and global knowledge. Our model achieves considerable improvement over the systems that we have combined while generating summaries up to a certain length. Furthermore, we study what factors could affect the success of system combination. Experiments show that it is important for the systems combined to have a similar performance. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Computer and Information Science First Advisor Ani Nenkova Second Advisor Mitchell P. Marcus This dissertation is available at ScholarlyCommons: http://repository.upenn. edu/edissertations/1765
Evolving agents for personalized information filtering

Describes how techniques from artificial life can be used to evolve a population of personalized information filtering agents. The technique of artificial evolution and the technique of learning from feedback are combined to develop a semi-automated information filtering system which dynamically adapts to the changing interests of the user. Results of a set of experiments are presented in which a small population of information filtering agents was evolved to make a personalized selection of news articles from the USENET newsgroups. The results show that the artificial evolution component of the system is responsible for improving the recall rate of the selected set of articles, while learning from feedback component improves the precision rate. <<ETX>>
Comparative Study between the Proposed GA Based ISODAT Clustering and the Conventional Clustering Methods

A method of GA: Genetic Algorithm based ISODATA clustering is proposed.GA clustering is now widely available. One of the problems for GA clustering is a poor clustering performance due to the assumption that clusters are represented as convex functions. Well known ISODATA clustering has parameters of threshold for merge and split. The parameters have to be determined without any assumption (convex functions). In order to determine the parameters, GA is utilized. Through comparatives studies between with and without parameter estimation with GA utilizing well known UCI Repository data clustering performance evaluation, it is found that the proposed method is superior to the original ISODATA and also the other conventional clustering methods.
RELATIONSHIPS BETWEEN METALINGUISTIC AND SPELLING DEVELOPMENT ACROSS LANGUAGES: EVIDENCE FROM ENGLISH AND HEBREW

Metalinguistic awareness is transferable between oral and written forms of language, and between different languages. Recent research has established a connection between monolingual children's grammatical awareness and their morphological spelling knowledge. Studies of bilingual children have shown that phonological awareness and alphabetic knowledge transfer across languages, even if the languages are dissimilar and are written with different scripts. This study investigates transfer of grammatical awareness and morphological spelling knowledge across dissimilar languages and scripts. In spoken language, children learn not only surface-level language 'facts' specific to that language (e.g. vocabulary) but also deeper-level grammatical principles (e.g. morphological and syntactic relationships), which govern other languages. Similarly, literacy requires surface-level knowledge of a specific script (e.g. letters and their sound values), and knowledge of the principle underlying that script (e.g. that alphabets represent phonology and morphology), which governs other scripts of the same type. I propose that transfer across languages occurs at the level of grammatical awareness but not at the level of vocabulary. The hypothesis was tested in Englishspeaking children (6-11 years) learning Hebrew as a second language. In Study 1, Hebrew learners were given oral measures of vocabulary and grammatical awareness, and measures of morphological spelling knowledge. Grammatical awareness and morphological spelling knowledge were significantly correlated across languages, but vocabulary was not. In Study 2, awareness of conceptually similar aspects of English and Hebrew morphology was measured in oral language and spelling. These were significantly correlated across languages. In both studies, Hebrew learners with high levels of Hebrew scored significantly higher than English-speaking monolinguals on grammatical and spelling measures. I conclude that grammatical awareness and morphological spelling knowledge are transferable across languages and scripts, and that learning a second language can benefit specific aspects of metalinguistic and spelling development.
Classification of CT-brain slices based on local histograms

Neurosurgical intervention is a very complicated process. Modern operating procedures based on data such as CT, MRI, etc. Automated analysis of these data is an important task for researchers. Some modern methods of brain-slice segmentation use additional data to process these images. Classification can be used to obtain this information. To classify the CT images of the brain, we suggest using local histogram and features extracted from them. The paper shows the process of feature extraction and classification CT-slices of the brain. The process of feature extraction is specialized for axial cross-section of the brain. The work can be applied to medical neurosurgical systems.
Distribution System Secondary Circuit Parameter Estimation for Model Calibration

To analyze and coordinate the operation of distribution systems with rapidly increasing amounts of PV, more accurate distribution system models are required, especially for the distribution system secondary (low-voltage) circuits down to the point of common coupling for distributed PV. There is a growing need for automated procedures to calibrate the distribution system secondary circuit models that are typically either not modeled at all or are modeled with a lower level of detail than the better modeled medium-voltage systems. This report presents an accurate, flexible, and computationally efficient method to use measurement data to estimate secondary circuit series impedance parameters in existing utility feeder models. The parameter estimation method assumes well-modeled primary circuit models, known secondary circuit topologies, and AMI active power, and reactive power measurements at all the loads in the secondary circuit. The method also requires AMI voltage measurement at most of the loads in the secondary circuit but can handle loads that do not have voltage measurements. No existing secondary circuit model information is needed, except for topology. The method is based on the well-known linearized voltage drop approximation and linear regression. The performance of the method is demonstrated on a three-phase test circuit with ten different secondary circuit topologies and on the Georgia Tech campus distribution system with AMI data. The developed method can be utilized to improve existing utility feeder models for more accurate analysis and operation with ubiquitous distributed PV interconnected on the low-voltage circuits.
New Way for Encryption Data Using Hourglass

Nowadays, there are many encryption algorithms to protect information. Data confidentiality is one of the most important functions of encryption algorithms, it means when the transferring data between different systems is vague for unauthorized systems or people. Moreover Encryption algorithms must maintain data integrity and provide availability for information. New encryption methods cause the attackers cannot simply access to the information and do not allow discovering the relationship between information and the encrypted one. Therefore availability can be difficult for them. Existing complexities make their longevity and effectiveness increase (Mandal, 2012). In This Article, It has been tried to present an encryption method which has the characteristic of encryption algorithms and also has some unique complexities which are not easily detectable and efficient.
Open Source Intelligent Network Intrusion Detection System Analyzer

Today computers are part of networked; distributed systems that may span multiple buildings sometimes located thousands of miles apart. The network of such a system is a pathway for communication between the computers in the distributed system. The network is also a pathway for intrusion. Intrusion detection is an important technology in business sector as well as an active area of research. It is an important tool for information security. A Network Intrusion Detection System is used to monitor networks for attacks or intrusions and report these intrusions to the administrator in order to take evasive action. This system is designed to detect and combat some common attacks on network systems. It follows the signature based IDs methodology for ascertaining attacks. A signature based IDS will monitor packets on the network and compare them against a database of signatures or attributes from known malicious threats. In this system the attack log displays the list of attacks to the administrator for evasive action. This system works as an alert device in the event of attacks directed towards an entire network. This paper discuss about various architecture of network intrusion detection system & their limitations and advantages. Snort is used as NIDS & its output is used by Open Source Intelligent Network Intrusion Detection System Analyzer for generating various reports for analysis.
Design of Flight Control System Using Gain Schedule Fractional PID Controller

Abstract: The goal of this paper is to control the trajectory of the flight path of six degree of freedom flying body model using Fractional PID controller (FPID) and Gain Schedule Fractional PID controller. FPID and gain schedule FPID controllers gains with non linear flying body simulation are tuned by Simulink design optimization. Gain Schedule FPID controller is able to compensate for constraints that represent physical limits of actuators in pitch angle. The gain schedule FPID for the six degree of freedom flying body is designed in two phases. The first phase is boost phase where the thrust force is maximized. The second phase is sustain phase where the thrust force is minimized. The results of gain schedule FPID controller are compared with the results of FPID controller.
Policy regularized model predictive control framework for robust legged locomotion

A novel Policy Regularized Model Predictive Control (PR-MPC) framework is developed to allow general robust legged locomotion with the MIT Cheetah quadruped robot. The full system is approximated by a simple control model that retains the key nonlinearities characteristic to legged contact dynamics while reducing the complexity of the continuous dynamics. Nominal footstep locations and feedforward forces for controlling the robot’s center of mass are designed from simple physics-based heuristics for steady state legged movement. By regularizing the predictive optimization with these policies, we can exploit the known dynamics of the system to bias the controller towards the steady state gait while remaining free to explore the cost space during transient behaviors and disturbances. The nonlinear optimization makes use of direct collocation on the simplified dynamics to pose the problem with a highly sparse structure for fast computation. A generalized approach to the controller design is independent from specific gait pattern and reference policy and allows stabilization of aperiodic locomotion. Simulation results show dynamic capabilities in a variety of gaits including trotting, bounding, and galloping, all without changing the set of algorithm parameters between experiments. Robustness to sensor and input noise, large push disturbances, and unstructured terrain demonstrate the ability of the predictive controller to adapt to uncertainty. Thesis Supervisor: Sangbae Kim Title: Associate Professor of Mechanical Engineering Thesis Supervisor: Russ Tedrake Title: Professor of Electrical Engineering and Computer Science
Efficient Employment of Large Format Sensor Data Transfer Architectures

Due to the increasing quantity of data collected by Air Force intelligence, surveillance and reconnaissance (ISR) assets and the focus on timely access to the data collected by these systems, operational data transfer network architectures have become a critical component of their employment in the intelligence production process. Efficient utilization of the provided long-haul communications component of the ISR system improves the value of the single asset to the warfighter and enables connectivity of additional assets via the data transfer network architecture. This research effort focused on the creation and implementation of a structured test design methodology based on the principles of Design of Experiments to propose recommendations for optimization of one such operational architecture while avoiding the common pitfalls of inadequate and inefficient test design and implementation. Factors that could influence the performance of the data transfer network architecture were researched and evaluated to recommend the factors of interest that most greatly affect the efficiency of the operational architecture. To support this evaluation, an emulated network testbed was utilized to develop a representative model of system efficiency. The results of this model indicate that increased aggressiveness for data transfer leads to decreased efficiency in the attempt to utilize available network resources, especially in realm of operations under study that represent non-traditional bandwidth delay product (BDP) networks where network delay is the dominating factor in the determination of BDP. The analysis documented a baseline model of system performance that will be used to guide ongoing maintenance, v sustainment and enhancement efforts for the current data transfer capability and provides insight into the recommended test design process for use in development and deployment of future capabilities. The ability to model system performance through the use of a structured and straight-forward process allows for the inclusion of the test design and analysis process in software design and development, as well as, system deployment and operations improvements.
Difficulties in Lexical Stress Versus Difficulties in Segmental Phonology Among Adolescents With Dyslexia

Dyslexic difficulties in lexical stress were compared to difficulties in segmental phonology. Twenty-nine adolescents with dyslexia and 29 typically developing adolescents, matched on age and nonverbal ability, were assessed on reading, spelling, phonological and stress awareness, rapid naming, and short-term memory. Group differences in stress assignment were larger than in segmental phonology in reading and spelling pseudowords but not words, indicating a fragility of explicit processes that manipulate stress representations. Despite impaired stress performance in dyslexia at the group level, individual variability failed to reveal evidence for a stress-specific deficit or for a distinct stress-impaired subgroup.
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN SISWA PENERIMA BEASISWA DENGAN METODE SIMPLE ADDITIVE WEIGHTING BERBASIS JAVA DESKTOP APPLICATION

Sebagai salah satu sekolah kejuruan yang berada di bawah naungan Dinas Pendidikan Kabupaten Klaten, Sekolah Menengah Kejuruan Negeri (SMKN) 1 Pedan mendapatkan kuota penerima beasiswa dari pemerintah bagi siswa yang kurang mampu maupun siswa berprestasi. Untuk menentukan siswa penerima beasiswa, pihak SMK N 1 Pedan masih menggunakan cara manual yaitu dengan membandingkan satu persatu siswa calon penerima beasiswa. Hal tersebut menyebabkan proses seleksi memakan banyak waktu dan cenderung terjadi kesalahan karena jumlah data siswa pendaftar beasiswa yang banyak. Penelitian ini bertujuan untuk membangun sebuah sistem pendukung keputusan untuk membantu pihak sekolah dalam menyeleksi siswa yang layak menerima beasiswa berdasarkan kriteria yang berlaku di SMK N 1 Pedan. Metode yang diterapkan pada sistem adalah metode Simple Additive Weighting (SAW). Sedangkan bahasa pemrograman yang dipakai adalah Java. Dalam membangun sistem pendukung keputusan seleksi beasiswa, digunakan metode pengembangan sistem model waterfall yang memiliki 5 tahapan. Tahapan tersebut meliputi tahap analisis kebutuhan, desain sistem, implementasi, pengujian, dan perawatan sistem. Inti dari metode waterfall adalah pengerjaan dari suatu sistem dilakukan secara berurutan atau secara linear. Pengujian sistem dilakukan dengan tiga tahapan, yaitu pengujian kinerja sistem, pengujian black box, dan uji pengguna. Penelitian ini menghasilkan sebuah Sistem Pendukung Keputusan Seleksi Siswa Penerima Beasiswa dengan menereapkan metode SAW di dalamnya. Setelah dilakukan pengujian black box pada sistem didapatkan bahwa fungsi-fungsi di dalam SPK Seleksi Beasiswa telah berjalan dengan baik dan sesuai dengan perancangan yang telah dibuat sebelumnya. Dari hasil pengujian kinerja sistem menyatakan bahwa sistem mampu memberikan perankingan rekomendasi siswa penerima beasiswa dengan baik sesuai dengan kriteria yang digunakan. Hasil dari uji pengguna menyatakan, pengguna dari SPK Seleksi Beasiswa puas dengan kinerja dan fungsionalitas dari sistem ini. Kesimpulan dari penelitian ini adalah sistem ini dibangun untuk membantu pihak SMK N 1 Pedan untuk menyeleksi siswa penerima beasiswa dan membantu untuk memanajemen data siswa penerima beasiswa. Metode yang diterapkan dalam sistem mampu memberikan solusi terhadap permasalahan seleksi siswa penerima beasiswa dengan model perankingan yang sesuai dengan kriteria yang berlaku serta bobot dari kriteria tersebut.
A Bilingual Approach for Arabic Paraphrases Acquisition : Preliminary Experiments

This paper presents preliminary experiments on a bi lingual approach for Arabic paraphrase acquisition; a research which is motivated by the importance of paraphrasing for overcoming sparseness of data and its importance for many NLP applications such as Question Answering (QA) and In formation Retrieval (IR). The proposed approach develops an unsupervised bilingua algorithm to acquire Arabic paraphrases at the phrase level which is rather mor e challenging than the elementary word-level paraphrasing and is less efficiently han dled by current Arabic paraphrasing systems. Preliminary results show that our approach manages to get term variations – orthographic, lexical and syntactic – for ~ 70% of 4000 randomly selected phrases.