Miracle-dz/UniFAR-FTUs huggingface dataset:
Labeling large scale social media data using budget-driven One-class SVM classification

The social media classification problems draw more and more attention in the past few years. With the rapid development of Internet and the popularity of computers, there is astronomical amount of information in the social network (social media platforms). The datasets are generally large scale and are often corrupted by noise. The presence of noise in training set has strong impact on the performance of supervised learning (classification) techniques. A budget-driven One-class SVM approach is presented in this thesis that is suitable for large scale social media data classification. Our approach is based on an existing online One-class SVM learning algorithm, referred as STOCS (Self-Tuning One-Class SVM) algorithm. To justify our choice, we first analyze the noise-resilient ability of STOCS using synthetic data. The experiments suggest that STOCS is more robust against label noise than several other existing approaches. Next, to handle big data classification problem for social media data, we introduce several budget driven features, which allow the algorithm to be trained within limited time and under limited memory requirement. Besides, the resulting algorithm can be easily adapted to changes in dynamic data with minimal computational cost. Compared with two state-of-the-art approaches, Lib-Linear and kNN, our approach is shown to be competitive with lower requirements of memory and time.
Ios Forensic Analysis For Iphone Ipad And Ipod Touch

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An Unsupervised Knowledge Free Algorithm for the Learning of Morphology in Natural Languages - Master's Thesis, May 2002

This thesis describes an unsupervised system to learn natural language morphology, specifically suffix identification from unannotated text. The system is language independent, so that is can learn the morphology of any human language. For English this means identifying “-s”, “-ing”, “-ed”, “-tion” and many other suffixes, in addition to learning which stems they attach to. The system uses no prior knowledge, such as part of speech tags, and learns the morphology by simply reading in a body of unannotated text. The system consists of a generative probabilistic model which is used to evaluate hypotheses, and a directed search and. Read complete abstract on page 2.
Mining High Utility Itemsets from its Concise and Lossless Representations

Mining high utility items from databases using the utility of items is an emerging technology.Recent algorithms have a drawback in the performance level considering memory and time.Novel strategy proposed here is the Miner Algorithm.A vertical data structure is used to store the elements with the utility values. A matrix representation is generated to identify the element co-occurrences and reduce the join operation for the patterns generated. An extensive experimental study with the datasets shows that the resulting algorithm reduces the join operation upto 95% compared with the UP Growth state of the art algorithm.
Verteilte Steuerungen nach IEC 61499

Die zunehmende Modularisierung von Anlagen und die damit einhergehende Dezentralisierung von Steuerungen erfordert neue Konzepte in der Anwendungsentwicklung. Insbesondere die Verteilung von Algorithmen, aber auch die zunehmende Forderung nach wiederverwendbarer Software erfordern Methoden zur Kapselung von Komponenten sowie die klare Definition von Schnittstellen und geeigneten Kommunikationsstrukturen. Die Norm IEC 61499 stellt hier einen Rahmen zur Verfugung, der auf Basis von Funktionsblocken einen einfachen Zugang zur Entwicklung verteilter Systeme erlaubt. In diesem Beitrag werden die wesentlichen Aspekte der Norm beschrieben.
A neural model of adaptive behavior

A neurally plausible model of adaptive behavior is developed in the Boolean domain. Behavior is modeled as the application of specific operators in response to patterns of internal and external features. Behavioral completeness requires that any stimulus (feature pattern) potentially be able to trigger any response (set of operators). Learning completeness requires that any stimulus-response mapping be learnable. Goal based behavior evaluation determines what the appropriate mapping is. The resulting problem is simple enough to be formally approached, but general enough to address a number of interesting issues. Appropriate structures and learning processes at the neuron and assembly level are developed. The problems of learning speed, noise rejection and shared memory are addressed in some detail.
Current Protocols in Bioinformatics

Enzyme Commission numbers that had been assigned to genes in MGI were annotated to GO terms based on the inclusion of EC#s within GO terms from the molecular function ontology. Details of this strategy can be found in Hill et al, Genomics (2001) 74:121-128. Title Gene Ontology Annotation by the MGI Curatorial Staff MGI Accession ID MGI:2152097
Simulations and study of a new scheduling approach for distributed data production

Distributed data processing has found its application in many fields of science (High Energy and Nuclear Physics (HENP), astronomy, biology to name only those). We have focused our research on distributed data production, an essential part of computations in HENP. Using our previous experience, we have recently proposed a new scheduling approach for distributed data production which is based on the network flow maximization model. It has a polynomial complexity providing required scalability with respect to the size of computations. Our approach improves the overall data production throughput due to three factors: transfer input files in advance before their processing (allows to decrease I/O latency); balancing of the network traffic (includes splitting the load between several alternative transfer paths); and transfer files sequentially in a coordinated manner (allows to reduce the influence of possible network bottlenecks). In this contribution, we present the results of our new simulations based on the GridSim framework which is one of the commonly used tools in the field of distributed computations. In these simulations we study the behavior of standard scheduling approaches compared to our recently proposed approach in a realistic environment relying on the data from the STAR and ATLAS experiments and considering the influence of the background traffic. The final goal of the research is to integrate the proposed scheduling approach into the real data production framework. In order to achieve this we are constantly moving our simulations towards real use cases, study scalability of the model and the influence of the scheduling parameters on the quality of the solution.
Hubungan Kadar Hepcidin dengan Status Besi pada Inflamasi Akibat Obesitas

Latar belakang. Angka kejadian obesitas yang tinggi cenderung mengalami komplikasi jangka panjang, yaitu penyakit kardiovaskular, diabetes mellitus, dan gangguan homeostasis besi. Tujuan. Menilai hubungan kadar hepcidin dengan status besi akibat inflamasi pada anak obesitas. Metode. Telah dilakukan penelitian dengan desain potong lintang mengenai hubungan kadar hepcidin dengan status besi (feritin, sTfR) pada anak obesitas (IL-6, Hs-CRP). Subjek berasal dari siswa SMP Z di Makassar yang memenuhi kriteria inklusi. Penelitian berlangsung dari September sampai November 2012. Analisis statistik menggunakan uji student t dan Mann Whitney U dengan nilai kemaknaan p=0,05. Hasil. Jumlah subjek yang memenuhi kriteria inklusi 20 anak obes, 20 superobes, dan 35 berat badan normal. Hasil analisis statistik menunjukkan tidak terdapat perbedaan bermakna rerata kadar hepcidin pada obes dengan berat badan normal (p=0,850), tetapi terdapat perbedaan bermakna rerata kadar hepcidin pada superobes dengan berat normal (p=0,012), rerata IL-6 antara obes dengan berat normal (p=0,01), superobes dengan berat normal (p=0,000), rerata hs-CRP antara obes dengan berat normal (p=0,004), superobes dengan berat normal (p=0,011). Tidak terdapat perbedaan bermakna rerata feritin dan sTfR antara superobes dan obes dengan berat normal (p=0,05). Kesimpulan . Pada anak superobes, terjadi peningkatan kadar hepcidin akibat inflamasi tetapi belum menyebabkan gangguan status besi. Sementara itu, pada obes terjadi inflamasi, tetapi belum menyebabkan peningkatan kadar hepcidin
Research and Application of Context Inference Technology in Smart Home

Smart home can provide users with active intelligent services due to its capability of pre-apperceive.This paper builds an ontology model of smart home, uses the inference interface of Jena to implement the basic inference of the context ontology, and filtrates the useless data by adding a context filter between inference layer and knowledge database.Experiment validates Rule-Based Reasoning(RBR) is feasibility and efficiency in smart home.
Nanobiosensors: Potentiality towards Bioanalysis

The biosensor is a device detects an analyte of interest, using a biological component with a physicochemical detector. In another way, a sensor integrates a biological element with a physiochemical transducer to generate an electronic signal, which is proportional to a single analyte detected by a detector. The biologically sensitive elements namely receptors, enzymes, antibodies, nucleic acids, organelles, cell, tissue, microorganisms, and cell which can also be created by biological engineering, which interacts either binding or recognizing with the analyte of interest. The transducer or the detector element (e.g., optical, electrochemical, piezoelectric etc.,) transforms the signal resulting from the interaction of the analyte of interest with the biological element into another signal and then it can be more simply measured and quantified with the help of concerned electronics or signal processing display.
Grammatical feature frequencies of English on Twitter in Finland

Technological change affects the parameters of language use, and as internet access has expanded rapidly in recent decades, communicative encounters resulting from online activity have begun to play an increasing role in daily life. Commercial social media platforms such as Twitter, whose content consists of millions of user messages with global extent, represent an important site of online language use. The use of English online has been subject to much attention in public discourse in mass media as well as in academic scholarship, and while research into online language has addressed a wide range of topical considerations, a recurrent typological interpretation of English as it is used in computer–mediated communication (CMC) is that it differs from traditional language varieties in terms of lexis, grammar, and pragmatic features. Crystal (2006: 18) uses the term Netspeak to refer to “a type of language displaying features that are unique to the Internet. arising out of its character as a medium which is electronic, global and interactive” (cf. Androutsopoulos 2006). 1 At the same time, the global status of English as the world’s lingua franca continues to evolve, with English now serving not only as the principal language of international communication in academia, business, media and diplomacy (Crystal 2003), but increasingly as an important language for online communication in informal and geographically localized communicative contexts, particularly in the European Union (European Commission 2011). Despite widespread recognition of the prevalence of English in global CMC, there have been relatively few efforts to systematically investigate variation in the lexis or grammar of English on social media in international contexts.2 Although studies have investigated the use of particular linguistic features in
A Survey on Sentiment Analysis using Swarm Intelligence

The social web data has increased tremendously in the recent years in form of comments, blogs, reviews and tweets. The nature of this data is highly un-structured and high- dimensional, making text classification a tedious task. Sentiment analysis, which is a text classification technique is applied on this data to gauge user opinion on several pertinent issues. As a natural language processing task, sentiment analysis automatically mines attitudes or views of users on specific issues. It is a multi-step process where selecting and extracting features is a vital step that controls performance of sentiment classifier. The statistical techniques of feature selection like document frequency thresholding produce sub optimal feature subset due to the Non Polynomial (NP) hard nature of the problem. Swarm intelligence algorithms are extensively used in optimization problems. Optimization techniques could be applied to feature selection problem to produce Optimum feature set. Swarm Intelligence algorithms are used in feature subset selection for reducing feature subset dimensionality and computational complexity thereby increasing the classification accuracy. In this paper we study the state-of-art of the various swarm intelligence algorithms which are presently used for feature subset selection within the sentiment analysis framework. The study shows that swarm optimization brings significant accuracy gains. There are only few swarm algorithms which have been applied in this area and there are many other algorithms which can be explored, this study provides an insight into the various algorithms which can be expounded for improved sentiment analysis.
An adaptive bias configuration strategy for range extension in LTE-advanced heterogeneous networks

The coexistence of macro eNodeBs and the additional low power nodes (LPNs) may lead to much higher interference in LTE-Advanced Heterogeneous Networks (HetNets). In Macro Pico scenario, Range Extension (RE) is a promising scheme to utilize the LPN resources more effectively and improve the cell edge performance. However, it may introduce more interference if the bias value is not properly set. In this paper, an adaptive bias configuration strategy for RE is proposed. In this strategy, the bias value is set adaptively according to the environment variability, which contains three stages including bias initialization, system performance feedback and dynamic bias value setting. The proposed scheme aims to make the RE scheme adaptive and improve the cell edge users' performance through cell load balance. Simulation results show that the proposed scheme can improve the cell edge performance compared with the static bias setting strategies, while maintaining the overall cell performance.
Motivation to E-Learn: A Quantitative Design Technique

absTracT One of e-learning challenges is to promote effectiveness in order to fully get expected benefits. Achieving effectiveness will contribute to its establishment as a credible way to support educational endeavours. To address this complex and multidisciplinary challenge, development teams need proper design techniques to build effective learning experiences. The literature does not show solid quantitative approaches to support learning-centered design, where student needs and their immediate and broader contexts are taken into account. This work explores a variable called " motivation-toe learn , " a key component to design technology-supported learning experiences. Our goal is to identify what motivation-related variables are critical for student engagement in learning online. This will be the basis for a specific, bottom-up and quantitative design technique. To this end, we further explored the importance of a set of motivation to -e-learn variables building on previous results in real instructional settings. From this activity, an exploratory two-factor structure emerged which explains 96% of motivation to e-learn construct. We discuss our results, together with their implications for learning-support design and future work. Our contribution is a step towards quantitatively understanding and cost-effectively improving the link among learning-design process, supporting systems and students into an effective and harmonious whole.
Knowledge discovery in computer network data: a security perspective

From a security perspective, computer network data is analyzed largely for two purposes: to detect known structures, and to identify previously unknown structures. As an example of the former, it is considered standard procedure to filter network traffic for previously identified viruses in order to prevent infection and to reduce virus spread. As an example of the latter, security researchers may want to search datasets in order to identify and discover previously unknown relationships or structures in the data, such as intrusions into a network by an external hacker. However, among other limitations, traditional methods of network data analysis are insufficient when processing large volumes of network traffic, do not allow for the discovery of local structures, do not visualize high-dimensional data in meaningful ways, and do not allow user input during search iterations. We present the development, analysis, and testing of a new framework for the analysis of network traffic data. In particular, and among others, the framework addresses the following questions: How is network traffic represented in high-dimensional space? (normalized graph Laplacians); How can we extract features from the data? (flow-based feature extraction); How to embed high-dimensional representations in low dimensions? (Laplacian Eigenmaps); How to intuitively visualize low-dimensional structures? (Fiedler Space projections); How to address scaleability concerns? (proximity computation, partitioning, and eigenpair computation approximations); How can the user be involved in the search? (iterative denoising applied to network data); How might this framework be used on empirical network data? (application to computer network intrusion data, backscatter data, and computer network application data). As such, this work presents theoretical as well as practical contributions, and these results are discussed within the context of traditional methods and techniques. Thus, due to its theoretical and applied benefits of visualizing and classifying a variety of unsupervised, heterogeneous, high-dimensional computer network traffic datasets, we feel that Iterative Denoising can be a unifying technology for protection, detection, and response groups coordinating around a network security monitoring system.
Estimating Reliability Bounds on Industrial Plants

Reliability analysis is particularly relevant for industrial plants where plant failures can lead to large financial losses. Existing reliability analysis approaches mostly rely on heavyweight simulations that are computationally expensive and require extensive modeling effort. On the other hand, there is an industrial need for quickly evaluating plant reliability for developing new services and business models. In this paper, we extend and apply the reliability bound approach using linear programming to address this need. The reliability bound approach is based on a system model in the form of a graph, an event vector, and estimates for component reliabilities. Based on this model, lower and upper reliability bounds are calculated by solving a linear programming problem. The advantage of this approach is the ubiquity of solvers for linear programming. Furthermore, the approach is guaranteed to produce the narrowest bound with respect to the reliability data. We demonstrate the applicability of the approach to a subsystem of an industrial plant as a test case. Future work consists applying the method to whole plants and comparing the results with simulation-based approaches. Moreover, the approach is planned to be extended to system attributes such as buffers and multiple failure states.
PERHITUNGAN DENYUT JANTUNG BERDASARKAN SINYAL EKG BERBASIS FPGA

EKG merupakan suatu sinyal yang dihasilkan dari aktivitas kelistrikan dari jatung manusia yang memiliki informasi medis mengenai kondisi kesehatan manusia yang bersangkutan. Dari sinyal EKG ini, maka kita dapat mengetahui kondisi kesehatan jantung seseorang berdasarkan heart rate. Pada penelitian ini akan direalisasikan suatu core FPGA yang dapat mendeteksi dan menghitung jumlah denyut jantung seseorang per menit berdasarkan sinyal EKG. Sinyal ECG analog yang sebelumnya dikonversi menjadi format digital oleh ADC dikirim secara serial ke perangkat FPGA. Pada FPGA, data tersebut dibaca dan diolah menggunakan algoritma Pan-Tomkins untuk menentukan QRS dari sinyal EKG. Pada algoritma Pan-Tomkins terdapat bagian yang dihilangkan yaitu blok derivative dan integral, dengan ini diharapkan penggunaan memori FPGA dapat direduksi tanpa harus mengurangi performansinya. Pengujian dilakukan pada level simulasi dan implementasi. Pada level simulasi digunakan ModelSim untuk melihat data keluaran modul. Dari sample sinyal ECG yang digunakan sebagai masukan, sistem dapat mendeteksi QRS dengan baik dengan tingkat akurasi 100 % untuk sinyal ECG normal dan dapat menghitung jumlah heart rate dengan baik. Pengujian level implementasi digunakan perangkat FPGA jenis Altera Cyclone 3. Pada pengujian ini, QRS sinyal ECG belum dapat dideteksi dengan baik sehingga jumlah heart rate yang dihitung masih mengalami kesalahan.
Energy-Balanced Unequal Clustering Routing Protocol for Wireless Sensor Networks

A distributed energy-balanced unequal clustering routing protocol(DEBUC) is proposed and evaluated in this paper,which adopts an unequal clustering mechanism in combination with an inter-cluster multihop routing.Through a time based competitive clustering algorithm,DEBUC partitions all nodes into clusters of unequal size,in which the clusters closer to the base station have smaller size.The cluster heads of these clusters can preserve some more energy for the inter-cluster relay traffic,and the "hot-spots" problem can be avoided.For inter-cluster communication,DEBUC adopts an energy-aware multihop routing system to reduce and balance the energy consumption of the cluster heads.Simulation results demonstrate that the protocol can efficiently decrease the dead speed of the nodes,balance the energy dissipation of all nodes,and prolong the network lifetime.
What Is Sound

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6 Measuring Social Presence in Team-Based Digital Games

: Increasingly, digital games offer sophisticated multiplayer experiences with teams of players able to play against “the computer” in raids or against each other in a variety of team matches. Social presence is therefore an important part of the gaming experience. However, existing measures of social presence in games lack the sophistication to deal with these more diverse gaming situations. This paper describes the development of a new questionnaire to measure the complex nature of social presence in digital games. The resulting questionnaire consists of 39 items with two main modules addressing competitive and collaborative components of social play.