Miracle-dz/UniFAR-FTUs huggingface dataset:
DETEKSI OUTLIER BERBASIS KLASTER PADA SET DATA DENGAN ATRIBUT CAMPURAN NUMERIK DAN KATEGORIKAL

Deteksi oultlier merupakan salah satu bidang penelitian yang penting dalam topik data mining. Penelitian ini sangat bermanfaat untuk mendeteksi perilaku yang tidak normal seperti deteksi penipuan menggunakan kartu kredit, deteksi intrusi jaringan, diagnosa medis, dan lain-lain. Ada banyak metode yang dikembangkan baik dari segi teknik atau obyek yang diteliti. Namun di antara sekian banyak metode tersebut terjadi dikotomi, bahwa metode-metode tersebut hanya fokus pada data dengan atribut yang seragam, yaitu data numerik saja atau data kategorikal saja. Di satu sisi, data di lapangan seringkali merupakan gabungan dari dua nilai atribut seperti ini. Penelitian data mining mengenai set data dengan atribut campuran seperti ini bisa dikatakan masih sangat jarang. Dalam penelitian ini diajukan sebuah metode, yaitu MixCBLOF, untuk mendeteksi adanya outlier pada set data campuran seperti ini. Pendekatan yang dipakai untuk menyelesaikan masalah ini adalah gabungan dari beberapa teknik seperti klasterisasi sub data, deteksi outlier berbasis klaster pada sub data numerik dan kategorikal, dan penggunaan Multi-Atribute Decision Making (MADM) untuk penggabungan derajat akhir dari setiap obyek data. Evaluasi dilakukan pada beberapa set data nyata yang diperoleh dari UCI Machine Learning Repository. Evaluasi dilakukan dengan membandingkan rata-rata coverage untuk top ratio antara jumlah outlier eksak dengan keseluruhan data yang dievaluasi. Dari uji coba yang dilakukan, diperoleh hasil bahwa algoritma MixCBLOF cukup efektif untuk mendeteksi outlier pada set data campuran dengan rata-rata pencapaian coverage sebesar 73,54%. Hasil ini lebih baik jika dibandingkan dengan algoritma CBLOF yang diterapkan pada set data yang sama yang telah didiskritisasi dengan metode equal width, yang hanya menghasilkan rata-rata coverage sebesar 59,48%.
Using SOMbrero for clustering and visualizing graphs

Graphs have attracted a burst of attention in the last years, with applications to social science, biology, computer science. In the present paper, we illustrate how self-organizing maps (SOM) can be used to enlighten the structure of the graph, performing clustering of the graph together with visualization of a simplified graph. In particular, we present the R package SOMbrero which implements a stochastic version of the so-called relational algorithm: the method is able to process any dissimilarity data and several dissimilarities adapted to graphs are described and compared. The use of the package is illustrated on two real-world datasets: one, included in the package itself, is small enough to allow for a full investigation of the influence of the choice of a dissimilarity to measure the proximity between the vertices on the results. The other example comes from an application in biology and is based on a large bipartite graph of chemical reactions with several thousands vertices.
A Detailed Analysis on NSL-KDD Dataset Using Various Machine Learning Techniques for Intrusion Detection

During the last decade the analysis of intrusion detection has become very important, the researcher focuses on various dataset to improve system accuracy and to reduce false positive rate based on DAPRA 98 and later the updated version as KDD cup 99 dataset which shows some statistical issues, it degrades the evaluation of anomaly detection that affects the performance of the security analysis which leads to the replacement of KDD dataset to NSL-KDD dataset. This paper focus on detailed study on NSLKDD dataset that contains only selected record. This selected dataset provide a good analysis on various machine learning techniques for intrusion detection. Keyword: NSL-KDD, Data Mining Technique and KDD Cup 99
Exploring Different Aspects of Social Network Analysis Using Web Mining Techniques

A social network is a set of people connected by a set of social relations. Thanks to the availability of increasing real-world social network data, social networks are receiving increasing attention by scientific community. The purpose of this paper is to explore multiple aspects of Social network Analysis using data mining techniques to the World Wide Web, referred to as Web mining. In particular we will inquire whether there is any other way of matrix representation of social networks other than Adjacency Matrix. Light will be shed on properties of social networks and there will also be a guided tourof different techniques of web mining. The main goal is to provide a roadmap for researchers who are trying to use data mining techniques for discovering different trends in social network data.
Web Document Clustering for Finding Expertise in Research Area

Researchers often need to find expertise in their chosen area of research. Finding expertise is very useful as relevant research papers can be studied and the experts could be identified. Therefore finding expertise in the chosen area of research has always attracted interest among academic community. These days research institutions and individual researchers make their publications and research findings available on web. With the exclusive growth of World Wide Web search engine users are overwhelmed by the huge volume of results returned in response to a simple query, which is far too large to get the desired knowledge. Therefore one of the methods of finding the expertise is by way of efficiently and accurately clustering the web documents, which enhances the integrity of web search engine. Data mining techniques matured making it possible to automate the web document clustering. In this paper, we present mutually exclusive Maximal Frequent Item set discovery based K- Means clustering approach. It has been implemented in JAVA. The common text processing approach is to convert the downloaded web documents into vectors. It is being done by extracting document features and it generates the document-feature data set. For a set of documents, the feature set is composed of all terms appearing in any one of the documents. We call this a document-feature data set. If document m contains feature n, then the corresponding value, in row n and column m of the table, is set to one. Otherwise, it is zero. Then, Apriori algorithm is applied to these document feature data set. The mutually exclusive frequent sets generated by Apriori algorithm are taken as initial points of K-Means algorithm. The output of the K- Means clustering algorithm will be the sets of highly related documents appearing together with same features. This approach enables the clustering of the web documents. It enables researchers to find the documents related to their desired area clustered and displayed together during the web search. It will significantly help them in terms of saving the time and getting all the relevant papers together in a cluster.
SYNCHRONIC AND DIACHRONIC ASPECTS OF ENGLISH AUXILIARIES

Auxiliaries are one of the most intriguing and complex areas in English grammar, and much effort has been devoted to clarifying their synchronic and diachronic aspects within various theoretical frameworks. In this book, Anthony Warner (henceforth, W) attempts to provide an analysis of a range of properties of auxiliaries in Present-day English (henceforth, PE) and their historical development from a new perspective. Although W does not deny the possibility of formalizing his ideas within other theoretical frameworks, he presents a lexical account of auxiliaries conducted within Head-driven Phrase Structure Grammar (henceforth, HPSG). Furthermore, he applies the theory of cognitive categorization by Rosch and her associates (Rosch (1978, 1988)) to account for the historical development of English auxiliaries. In this review article, I will present W's main proposals and discuss some problems as well as possible alternatives. Among others, the following three points will emerge from the discussion. First, W's lexical account of auxiliaries in PE is apparently descriptive and far from explanatory; moreover, serious problems arise with regard to his analysis of be. Second, W's analysis of the historical change of English auxiliaries in terms of Rosch's theory of cognitive categorization will be more plausible if triggering factors for the change are taken into
Kinetic Monte Carlo study of accelerated optimization problem search using Bose-Einstein condensates

In a previous work [ArXiv:0909.2530] we proposed a method for accelerating optimization problem search using Bose-Einstein condensation (BEC). The system encodes an optimization problem into an Ising model and cools it down by the process of BEC to find its ground state spin configuration which corresponds to the solution of the problem. The system uses the final state stimulation (FSS) property of bosonic particles, an effect originating from the quantum indistinguishability of bosons, to provide speedups over the classical case. The speedup is typically ∝ N, where N is the number of bosons in the system per site. In this article we firstly review the proposed system, and give a more detailed numerical study of the equilibration time with the boson number and the number of sites M in the Ising model. We find that the equilibration time scales as τ ∼ exp(M)/N in agreement with previous arguments based on simulated annealing. A detailed description of the kinetic Monte Carlo method used for the study of the proposed system is also discussed.
Active Disks: Programming Model, Algorithms and Evaluation

Several application and technology trends indicate that it might be both profitable and feasible to move computation closer to the data that it processes. In this paper, we evaluate Active Disk architectures which integrate significant processing power and memory into a disk drive and allow application-specific code to be downloaded and executed on the data that is being read from (written to) disk. The key idea is to offload bulk of the processing to the disk-resident processors and to use the host processor primarily for coordination, scheduling and combination of results from individual disks. To program Active Disks, we propose a stream-based programming model which allows disklets to be executed efficiently and safely. Simulation results for a suite of six algorithms from three application domains (commercial data warehouses, image processing and satellite data processing) indicate that for these algorithms, Active Disks outperform conventional-disk architectures.
Lattice: Multivariate Data Visualization with R

R is rapidly growing in popularity as the environment of choice for data analysis and graphics both in academia and industry. Lattice brings the proven design of Trellis graphics (originally developed for S by William S. Cleveland and colleagues at Bell Labs) to R, considerably expanding its capabilities in the process. Lattice is a powerful and elegant high level data visualization system that is sufficient for most everyday graphics needs, yet flexible enough to be easily extended to handle demands of cutting edge research. Written by the author of the lattice system, this book describes it in considerable depth, beginning with the essentials and systematically delving into specific low levels details as necessary. No prior experience with lattice is required to read the book, although basic familiarity with R is assumed. The book contains close to150 figures produced with lattice. Many of the examples emphasize principles of good graphical design; almost all use real data sets that are publicly available in various R packages. All code and figures in the book are also available online, along with supplementary material covering more advanced topics.
Information Extraction From Images

Text detection and optical character recognition (OCR) in images is a new challenging area in the fields of computer vision (CV), Artificial Intelligence (AI) and Machine learning (ML). Knowledge integration in recognition of optical characters (KROC) is merger of CV, AI and ML fields. Its main function is to increase the accuracy rate of existing OCR tools in the domain of character recognition, spell checking and knowledge integration. KROC take images or image based PDF files to fetch data content and uses set of templates to separate one character from the other. For many images or PDF files KROC is more accurate, efficient and optimistic method.
The Business Case for Tele-emergency.

Abstract Background: Tele-emergency is an expanding telehealth service that provides real-time audio/visual consultation delivered by an emergency medicine team to a remote, often rural, emergency department (ED). Financial analyses of tele-emergency in the literature are limited. This article expands the tele-emergency literature to describe the business case for tele-emergency. “Business case” is defined as a reasoned argument, supported by objective data and/or qualitative judgment, to implement or continue a service or product. Materials and Methods: To evaluate tele-emergency financing from the perspective of a critical access hospital (CAH), 10 financial analysis categories were defined. Telephone interviews, site visits, and financial data from the eEmergency program of Avera Health (Sioux Falls, SD) were used to populate the categories. Avera Health information was augmented with national data where available. Three financial scenarios were then analyzed for CAH profit/loss associated with tele-em.
Selecting Relays for Faster Paths in Embedded Internet Delay Space1

Application relays can be used to reduce latency for end-to-end communication. We propose a novel mechanism called TIVER to discover the latency-reducing relays in a distributed and proactive way. We boil down the relay discovery problem into TIV detours (the edges that are part of relay paths) discovery in the delay space, since the relay path causes the triangle inequality violation (TIV) phenomenon. Network embedding techniques are then introduced to model the delay space and provide a heuristic method to predict TIV detours with low measurement overhead. Subsequently, TIVER mechanism is designed for any given destination to allow nodes to quickly choose appropriate latencyreducing relays. The simulation results show the effects and efficiency of TIVER.
Analyzing Two-Dimensional Gel Images

mainly come from computer scientists. The existingmethodologies forgel analysis are mostly in the form of computer algorithms that in many cases have been implemented in software packages developed specificallyfor analyzing twodimensional gel images. Early examples ofautomated gelanalysistechniques can be found in Appel et al. (1991) and references therein. Figure I shows a typical image of a two-dimensional gel. Just by glancing at it, the reader can imagine how hard a task it is for any automated algorithm to accurately identify hundreds of protein spots among the various kinds of noise, and also to compare and match proteins over several gels when presented with multiple copies of gels 300
An Optimized; Grid Independent; Narrow Band Data Structure for High Resolution Level Sets

Level sets have recently proven successful in many areas of computer graphics including water simulations[Enright et al. 2002] and geometric modeling[Museth et al. 2002]. However, current implementations of these level set methods are limited by factors such as computational efficiency, storage requirements and the restriction to a domain enforced by the convex boundaries of an underlying cartesian computational grid. Here we present a novel very memory efficient narrow band data structure, dubbed the Sparse Grid, that enables the representation of grid independent high resolution level sets. The key features our new data structure are
Discovery of Abnormal Flight Patterns in Flight Track Data

The National Airspace System (NAS) is an ever changing and complex engineering system. As the Next Generation Air Transportation System (NextGen) is developed, there will be an increased emphasis on safety and operational and environmental efficiency. Current operations in the NAS are monitored using a variety of data sources, including data from flight recorders, radar track data, weather data, and other massive data collection systems. Although numerous technologies exist to monitor the frequency of known but undesirable behaviors in the NAS, there are currently few methods that can analyze the large repositories to discover new and previously unknown events in the NAS. Having a tool to discover events that have implications for safety or incidents of operational importance, increases the awareness of such scenarios in the community and helps to broaden the overall safety of the NAS, whereas only monitoring the frequency of known events can only provide mitigations for already established problems. This paper discusses a novel approach for discovering operationally significant events in the NAS that are currently not monitored and have potential safety and/or efficiency implications using radar-track data. This paper will discuss the discovery algorithm and describe in detail some flights of interest with comments from subject matter experts who are familiar with the operations in the airspace that was studied.
The Challenge of Complex Dynamics for Theoretical Models of Cardiac Activity

Electrical stimulation of excitable cardiac tissue results in a variety of rhythms similar to those clinically recorded. Simplified momlinear equations and ionic models give complementary descriptions of the dynamics in cardiac preparations. Nonlinear midels often capture the essential dynamical features of the experimental observations but do not properly describe the underlying ionic mechanisms. Ionic midels are descriptive, complex, and despite their general sucess in reproducing the cardiac action potential, seldom undergo extensive testing against experimentally determined rhythms. We review work from our group devoted to the modeling of cardiac rhythms, including nonlinear models and the Shrier-Clay ionic model of electrical activity in embryonic chick heart cell aggregates. Since sustained stimulation may have long lasting consequences on the intrinsic excitability of the tissue, we also discuss recent advances in modeling the stimulation history-dependent aspects of pacemaking.
Research on GUI-based Automation Test Technology Driven by Separated Definition Data

GUI-based software is often developed a complicated test script with existing traditional automation software testing tools. The software automated test development technology consequently carried out some study of existing automated testing with current automated testing tools to simplify the script. The paper proposed a novel test automation technology with separated definition data, which replaced by script development or modification. The definition data are depicted for script driven, page description and test data separately. The test automation technology provided completed function and interface between this method and actual test tool. The test engineers is not need to write the any test script, but modify the definition files for test developing to some extent. The automation functional test technology has support the typical test tool and proved effective in the specific cases.
Analysis of Motion Patterns for Recognition of Human Activities

Automatic recognition of human activity is one of the most important and challenging open areas of research in context-aware and physical training applications. The activity profiling systems normally use wearable sensors to record motion patterns over extended periods of time. The performance of these systems depends on the activity set, training data quality, extracted features and learning algorithms. In this paper, we describe the development of efficient activity recognition techniques using wearable motion sensors. An extensive evaluation is provided on three well-known classifiers with light-weight time series features to distinguish among thirty three different fitness activities. The effects of the segmentation, sensor placement as well as different sampling rates on the classification performance are discussed. The experimental results conducted with 17 subjects show an improvement of the classification accuracy compared with the previous work. In addition, the statistical analysis of the investigated models quantifies relative effects of the accelerometer axes reductions on the classification performance.
Eliciting Personal Network Data in Web Surveys through Participant-generated Sociograms

The article presents a method to elicit personal network data in Internet surveys, exploiting the renowned appeal of network visualizations to reduce respondent burden and risk of dropout. It is a participant-generated computer-based sociogram, an interactive graphical interface enabling participants to draw their own personal networks with simple and intuitive tools. In a study of users of websites on eating disorders, we have embedded the sociogram within a two-step approach aiming to first elicit the broad ego network of an individual and then to extract subsets of issue-specific support ties. We find this to be a promising tool to facilitate survey experience and adaptable to a wider range of network studies.
Design of Microstrip Wideband/Dual Band Antenna for Wireless Applications

This paper presents a Microstrip Antenna for Dual band/Wide Band frequency for cover a large bandwidth of 300 MHz and 2GHzforthe resonance frequency of 5.8 GHz and 8.1 GHz respectively. and its wide application like WLAN, Medical Application, radar imaging technology, Satellite communications etc.The gain and directivity of the proposed antenna are presented at different frequencies of covered band . for HFSS is used to design and simulation of antenna.
Review on Data Hiding Schemes into Multimedia Data

In this period of internet, Multimedia data security is crucial issue because there are many cases of illegal production and redistribution through the Internet. Data hiding and encryption algorithms can be used for Security and protection of multimedia data. Video encryption is new area of research for researchers. Data hiding in encrypted videos is important to conquer the aim of content annotation, copy right protection, access control and/or tapering detection. This survey summarizes the latest research results on video encryption with a special focus on applicability and on the most widely-deployed video format H.264 including advanced video Coding.