Miracle-dz/UniFAR-FTUs huggingface dataset:
Research of Chinese Segmentation Based on MMSeg and Double Array TRIE

Chinese segmentation system is a difficulty in computer Chinese information handling. A deep discussion on methods of MMSeg and double array TRIE Chinese segmentation matching on the basis of existing technology in Chinese segmentation is made. On this basis of it, some improvements are made in the dictionary construction and segmentation arithmetic, designing a Chinese segmentation system based on MMSeg and double array TRIE. Experiment shows that the improving arithmetic accelerated the speed of Chinese segmentation.
A LITERATURE REVIEW ON MALWARE AND ITS ANALYSIS

Malwares are nasty software’s. It is designed to damage computer systems without the knowledge of the owner using the system and technique advancements are posing big challenges for researchers in both academia and the industry. The purpose of this study is to examine the available literatures on malware analysis and to determine how research has evolved and advanced in terms of quantity, content and publication outlets. Most malware programs are large and complex and one can’t possibly understand every detail. Educating the internet users about malware attack, as well as the implementation and proper application of anti-malware tools, are critical steps in protecting the
Corroboration of Normalized Least Mean Square Based Adaptive Selective Current Harmonic Elimination in Voltage Source Inverter using DSP Processor

A direct Selective current harmonic elimination pulse width modulation technique is proposed for induction motor drive fed from voltage source inverter. The developed adaptive filtering algorithm for the selective current harmonic elimination in a three phase Voltage Source Inverter is a direct method to improve the line current quality of the Voltage Source Inverter base drive at any load condition. The self-adaptive algorithm employed has the capability of managing the time varying nature of load (current). The proposed Normalized Least Mean Squares algorithm based scheme eliminates the selected dominant harmonics in load current using only the knowledge of the frequencies to be eliminated. The algorithm is simulated using Matlab/Simulink tool for a three-phase Voltage Source Inverter to eliminate the fifth and seventh harmonics. The system performance is analyzed based on the simulation results considering total harmonic distortion, magnitude of eliminated harmonics and harmonic spectrum. The corroboration is done in the designed Voltage Source Inverter feeding induction motor using digital signal processor-TMS320L2812.The developed algorithm is transferred to digital signal processor using VisSim TM software. Full Text: PDF DOI: http://dx.doi. org/10.11591/ijpeds.v6. i1.pp178-184
Evaluating ASR Output for Information Retrieval

Within the context of international benchmarks and collection specific projects, much work on spoken document retrieval has been done in recent years. In 2000 the issue of automatic speech recognition for spoken document retrieval was declared 'solved' for the broadcast news domain. Many collections, however, are not in this domain and automatic speech recognition for these collections may contain specific new challenges. This requires a method to evaluate automatic speech recognition optimization schemes for these application areas. Traditional measures such as word error rate and story word error rate are not ideal for this. In this paper, three new metrics are proposed. Their behaviour is investigated on a cultural heritage collection and performance is compared to traditional measurements on TREC broadcast news data.
Now everyone can measure grammar ability through the use of grammar assessment system

This paper describes an initiative to systematically gauge English as Second Language (ESL) learners' grammar proficiency. Grammar Assessment System was developed with the goal to determine learners' grammar proficiency and placing learners in their respective grammar proficiency levels. The novelty of the Grammar Assessment System is in the three notable elements, Grammar Test, Grammar Matrix and Grammar Descriptor. The purpose of the Grammar Test is to measure learnersâ? grammar proficiency levels while the Grammar Matrix is used to categorise and place learners in the precise grammar proficiency levels. Subsequently, the Grammar Descriptors provides descriptions of learners' grammar proficiency. ESL teachers can make use of the information generated by the Grammar Assessment System to systematically plan remedial and enrichment activities for learners. The main objective of this study is to construct a generic grammar proficiency test instrument (that transcends from Year 7 to Year 11) which is valid, reliable and practical that can discriminate and sort students into specific grammar proficiency categories. After developing, piloting and calibrating a prototype grammar proficiency test in a school (where respondents consisted of Year 7 to Year 11 students aged between 13 to 17 years old) the researcher proceeded to develop the "cut-score" to establish the "Bands" of performers. The tested instrument was then administered on Year 10 students in Penang and the data gathered was analysed and benchmarked to set indicators (Exceed Standard, Meet Standard, Below Standard and Academic Warning). These indicators point to the students' grammar proficiency.118 Year 10 students in a sub-urban secondary school participated in this study.The findings of this study however, showed that the majority of the students are in Band 2 (28.6%) (Academic Warning), 31.9% are in Band 3 (Academic Warning) while 26.4% are in Band 4 (Below Standard). Only 7.7% of the Year 10 students are in Band 5 (Meet Standard) whereas 5.5% are in Band 6 (Exceed Standard). The studyâ? s value lies in its capacity to provide practising teachers with a set of indicators to gain accurate information with regards to their studentsâ? grammar proficiency and consequently plan grammar lessons tailored to the needs of the students.
Colored Digital Image Watermarking using the Wavelet Technique

With the revolution of information technology and Wide Area Networking, data has become less and less private where the access of media as well as the attempts to change and manipulate the contents of media data have become a common case. For that, we need to use a watermarking technique to protect the copyright of the media as well as for digital right management but without leaving a visual effect. We presented a watermarking technique that deals with images where the used technique to embed a wavelet compressed watermark image within the least significant bit (LSB) of the cover image pixels in a specific pattern which won't be visible after embedding and will cause the cover image to become copyrighted using the embedded watermark image that can be extracted later.
External cultic tradition and internal ethical purity in Matthew 15

In Matthew 15:1–20, Jesus responds to the accusation made by the Pharisees and the scribes that his disciples do not observe the tradition of hand-washing (οὐ γὰρ νίπτονται τὰς χeῖραςὅταν ἄρτον ἐσθίωσιν), because they do not wash their hands before they eat bread. In this story of dispute, two ideas are interwoven, namely the locus of impurity (external or internal) and the manmade tradition of the elders (ἡ παράδοσις τῶν πρeσβυτέρων) versus the Word of God (ὁ λόγος τοῦ Θeοῦ). The Pharisees are depicted as obsessed with external manmade rules to ensure purity, whilst Jesus is concerned with inner purity based on God’s Word. In this article, the story is interpreted on two levels. The first level describes the dispute between Jesus and the Pharisees. The second level explores the tension the Matthean community experienced in their encounter with Pharisean Judaism of their day – the Judaism of the dual Torah. The question is which aspect of the Torah is challenged by Matthew’s Jesus, and what he considers to be the true meaning of the Law. It seems that Matthew uses this story to define and maintain the identity and values of his community over and against that of the Pharisees and their successors. Devices that Matthew uses to define the identity and required morality for his community, are identified. Such devices demonstrate how a community’s values can influence the identity and ethics of a society. In Matteus 15:1–20 reageer Jesus op die aantyging van die Fariseers en skrifgeleerdes dat sy dissipels nie die handewas-tradisie eerbiedig nie. In hierdie verhaal van dispuut word twee idees vervleg: eerstens die van die locus van onreinheid, en tweedens die van mensgemaakte tradisies teenoor die Woord van God. Die Fariseers word voorgestel as mense wat obsessiefis oor mensgemaakte reels wat reinheid verseker, terwyl Jesus op innerlike reinheid gesteld is, wat op die Woord van God gebaseer is. In hierdie artikel word die verhaal op twee vlakke geinterpreteer. Die eerste vlak beskryf die dispuut tussen Jesus en die Fariseers. Die tweede vlak ondersoek die spanning wat die Matteus-gemeenskap in hulle verhouding met die Farisese Judaisme van hulle tyd ervaar het. Die vraag is watter aspek van die Torah deur die Matteaanse Jesus bevraagteken word, en wat Hy as die ware betekenis van die Wet beskou. Dit wil voorkom asof Matteus hierdie verhaal gebruik om die identiteit en waardes van sy gemeenskap teenoor die dominante Farisese Judaisme te definieer en te handhaaf. Middele wat Matteus gebruik om die identiteit en verlangde moraliteit van sy gemeenskap te definieer, word geidentifiseer. Sodanige middele demonstreer hoe ’n gemeenskap se waardes die identiteit en etiek daarvan kan beinvloed.
Framework of a system for the management and assistance in analysis of medical images

Presents the structure of an information system for medical data and images. Thanks to a modular and flexible design, it can be implanted in hospital environments with various kinds of communication infrastructures to high capacity lines. Both its design and combination of small functional modules with a well defined entry/exit interface allows us to implement the functionalities required by the users. A database stores not only the information but also the specialist's reports and notes concerning patients and diagnoses. We have incorporated tools that are not found in conventional systems (image segmentation, advanced visualization, etc.) to assist the physicians in analysis of images and which allow us to incorporate new data into the decision-making process. The user accesses the system with all its information and functionalities via a user-friendly Web interface.
Learning with sparsity for detecting influential nodes in implicit information diffusion networks

The diffusion of information and spreading influence are ubiquitous in social networks. How to model and extract useful information from diffusion networks especially in social media domain is still an open research area that requires significant attention. Many real applications pose new challenges in modeling information diffusion process. In particular, the first challenge comes from the fact that the underlying network structure over which the propagation spreads is unknown or unobserved. It is often the case that one can only observe that when nodes got infected by which contagion but without the knowledge about who infecting whom. The second challenge comes from the simultaneous transmissions of multiple correlated contagions through an implicit network. The third one comes from strong temporal effect in the diffusion process which needs to be carefully modeled. In my thesis, we address two fundamental tasks, forecasting and influential-node detection, in an implicit diffusion network by a unified approach. In particular, we first proposed a sparse linear influence model (SLIM) which takes a nice form of a convex optimization problem. We further extended SLIM to multi-task sparse linear influence model (MSLIM), which could model diffusion networks with multiple correlated contagions. MSLIM, as a richer model than SLIM, not only improves prediction accuracy, but also allows to select influential nodes on a finer grid, i.e., select different sets of influential nodes for different contagions. For SLIM and MSLIM, we developed both deterministic and stochastic optimization algorithms for solving the corresponding problems and showed the fast theoretical convergence guarantees. Another contribution of the thesis is the development of a general purpose system, called Slow Intelligent System (SIS), which is able to continuously learn and improve performance over time. We proposed the component-based SIS and developed the software with applications to face recognition task. Furthermore, we utilized the idea of the SIS to systematize the information diffusion process modeling and influential node detection and proposed SIS-based SLIM/MSLIM approaches, which further improve the flexibility and scalability of learning from implicit diffusion networks. We demonstrated the superiority of the proposed approaches on several real datasets from social media domains.
Research of Automatic Marking on Access Skill Assessment Based on XML

In this paper a model of automatic marking on access skill operation based on XML is proposed after analyzing the existing logically formalized automatic marking technology. And the implementation principles and processes are introduced with focuses on two critical technical links: XML formalized expression and XML formalized automatic marking. Define the structure of table’s information and Get the information of table in access. Introduce the implementation of automatic marking. Practice proves that it is practical and feasible to automatically score skill operation on access database based on XML
A tool for hemodynamic data analysis

Nearly 2% of all live births are very premature (gestational age less than 32 weeks), and 50% of all new cases of cerebral palsy occur in survivors of premature birth (gestational age less than 37 weeks). Because of their underdeveloped vascular structure, premature infants are especially vulnerable to brain injury caused by unregulated and erratic changes in blood pressure. A challenge in the prevention of serious brain injury in premature infants is the inability to identify impending or recent hemodynamic events that might lead to injury of the newborn’s brain. If events that indicate a propensity to experience brain injury can be identified, then such events can be monitored clinically, and steps can be taken to prevent them from occurring. We designed and implemented a software tool, HemDAT, that can be used to test hemodynamics related hypotheses and to facilitate the discovery of interesting relationships among hemodynamic signals. HemDAT uses signal processing and statistical knowledge to provide clinical researchers a tool that can help develop a better understanding of how brain injury occurs in premature newborns. HemDAT is capable of processing and navigating large data sets of blood pressure and cerebral blood flow. Large data sets are important because the events that cause brain injury are believed to be short-lived, possibly infrequent, and unpredictable. Additionally, since this is a relatively unexplored area in human infants, HemDAT provides flexibility in performing repeated analyses with different parameters modifiable by the user. HemDAT also provides convenient visualizations of results and does not demand signal processing or statistical expertise from the user. Thesis Supervisor: John V. Guttag Title: Professor, Computer Science and Engineering
Array signal processing for a wireless MEM sensor network

We first review the high-level signal processing architecture of a wireless MEM sensor system for source detection, signal enhancement, localization, and identification. A blind beamformer using only the measured data of randomly distributed sensors to form a sample correlation matrix is proposed. The maximum power collection criterion is used to obtain array weights from the dominant eigenvector of the sample correlation matrix. An effective blind beamforming estimation of the time delays of the dominant source is demonstrated. Source localization based on a novel least-squares method for time delay estimation is also given. Array system performance based on analysis, simulation, and measured acoustical/seismic sensor data is presented. Applications of such a system to multimedia, intrusion detection, and surveillance are briefly discussed.
Business information systems : technology, development and management

Preface Guided tour Plan of the book About the authors Acknowledgements PART ONE: INTRODUCTION TO BUSINESS INFORMATION SYSTEMS 1. Basic concepts - understanding information 2. Basic concepts - an introduction to information systems 3. Hardware 4. Software 5. Networks, telecommunications and the Internet 6. Enterprise and functional BIS PART TWO: BUSINESS INFORMATION SYSTEMS DEVELOPMENT 7. An introduction to acquiring and developing BIS 8. Initiating systems development 9. BIS project management 10. Systems analysis 11. Systems design 12. System build, implementation and maintenance: change management PART THREE: BUSINESS INFORMATION SYSTEMS MANAGEMENT 13. Information systems strategy 14. Information systems management 15. Managing information security 16. End-user computing - providing end-user services 17. Ethical, legal and moral constraints on information systems Glossary Index
Chess games study & prediction through the use of Web-Scraping and regression analysis

La prediccion del resultado de partidas de ajedrez usando distintos tipos de Analisis de Regresion. Regresion Logistica tomando la diferencia de los Rating Elo de los jugadores involucrados y Regresion basada en distancias haciendo uso de variables explicativas no-cuantitativas. A partir de la obtencion de los resultados, una comparacion y analisis entre ellos fue llevado a cabo. Para la obtencion de la data se hizo uso de tecnicas de Web Scraping y funciones de Regular Expressions (deteccion de patrones en 'strings') y asi tenerla en un formato con el cual se pudiese trabajar. El desarrollo del proyecto (tanto la parte de Web Scraping como la parte del analisis de regresion) se hizo con el Software R.
Learning with Technology: A Constructivist Perspective

This book addresses how to use very specific types of technology and focuses on how technology can be used as a thinking tool to foster meaningful learning. The book approaches learning from a constructivist view and relates it to using technology to engage meaningful learning.Within each chapter, the book provides different activities and implementation strategies in the "Technique" sections and follow-up questions in the "Things to Think About" sections. Very current uses of technology such as video theater, cybermentoring, creating homepages, and hypermedia are discussed throughout the book.
Watching the sky : new realizations, new meanings, and surprizing aspects in university level astronomy

Learning astronomy is challenging at all levels due to the highly specialized form of communication used to share knowledge. When taking astronomy courses at different levels at university, learners are exposed to a variety of representations that are intended to help them learn about the structure and complexity of the Universe. However, not much is known about the reflective awareness that these representations evoke. Using a simulation video that provides a vivid virtual journey through our Milky Way galaxy, the nature of this awareness is captured and categorised for an array of learners (benchmark by results obtained for experts). The results illustrate how the number and nature of new things grounded in dimensionality, scale, time and perspective reflective awareness can too easily be taken for granted by both teachers and learners.
Automated generation of high-quality training data for appearance-based object models

Methods for automated person detection and person tracking are essential core components in modern security and surveillance systems. Most state-of-the-art person detectors follow a statistical approach, where prototypical appearances of persons are learned from training samples with known class labels. Selecting appropriate learning samples has a significant impact on the quality of the generated person detectors. For example, training a classifier on a rigid body model using training samples with strong pose variations is in general not effective, irrespective of the classifiers capabilities. Generation of high-quality training data is, apart from performance issues, a very time consuming process, comprising a significant amount of manual work. Furthermore, due to inevitable limitations of freely available training data, corresponding classifiers are not always transferable to a given sensor and are only applicable in a well-defined narrow variety of scenes and camera setups. Semi-supervised learning methods are a commonly used alternative to supervised training, in general requiring only few labeled samples. However, as a drawback semi-supervised methods always include a generative component, which is known to be difficult to learn. Therefore, automated processes for generating training data sets for supervised methods are needed. Such approaches could either help to better adjust classifiers to respective hardware, or serve as a complement to existing data sets. Towards this end, this paper provides some insights into the quality requirements of automatically generated training data for supervised learning methods. Assuming a static camera, labels are generated based on motion detection by background subtraction with respect to weak constraints on the enclosing bounding box of the motion blobs. Since this labeling method consists of standard components, we illustrate the effectiveness by adapting a person detector to cameras of a sensor network. While varying the training data and keeping the detection framework identical, we derive statements about the sample quality.
End-user support for access to heterogeneous linked data

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PENDEKATAN POSITIONAL TEXT GRAPH UNTUK PEMILIHAN KALIMAT REPRESENTATIF CLUSTER PADA PERINGKASAN MULTI-DOKUMEN

Coverage and saliency are major problems in Automatic Text Summarization. Sentence clusteringapproaches are methods able to provide good coverage on all topics, but the point to be considered is theselection of important sentence that can represent the cluster’s topic. The salient sentences selected asconstituent to the final summary should have information density so that can convey important informationcontained in the cluster. Information density from the sentence can be mined by extracting the sentenceinformation density (SID) feature that built from positional text graph approach of every sentence in the cluster.This paper proposed a cluster representative sentence selection strategy that used the positional text graphapproach in multi-document summarization. There are three concepts that used in this paper: (1) sentenceclustering based on similarity based histogram clustering, (2) cluster ordering based on cluster importance and(3) representative sentence selection based on sentence information density feature score. The candidatesummary sentence is a sentence that has greatest sentence information density feature score of a cluster. Trialsconducted on task 2 DUC 2004 dataset. ROUGE-1 measurement was used as performance metric to comparethe use of SID feature with other method namely Local Importance and Global Importance (LIGI). Test resultshowed that the use of SID feature was successfully outperform LIGI method based on ROUGE-1 values wherethe greatest average value of ROUGE-1 that achieved by SID features is 0.3915.