bluuebunny/crossref_metadata_2025_split huggingface dataset:
On Constructing Static Evaluation Function using Temporal Difference Learning
Authors: Ping Man Samuel Choi 
Published: October 2018

An Outlier is a data point which is significantly different from the remaining data points. Outlier is also referred as discordant, deviants and abnormalities. Outliers may have a particular interest, such as credit card fraud detection, where outliers indicate fraudulent activity. Thus, outlier detection analysis is an interesting data mining task, referred to as outlier analysis. Detecting outliers efficiently from dataset is an important task in many fields like Credit card Fraud, Medicine, Law enforcement, Earth Sciences etc. Many methods are available to identify outliers in numerical dataset. But there exist limited number of methods are available for categorical and mixed attribute datasets. In the proposed work, a novel outlier detection method is proposed. This proposed method finds anomalies based on each recordâ€™s â€œmulti attribute outlier factor through correlationâ€ score and it has great intuitive appeal. This algorithm utilizes the frequency of each value in categorical part of the dataset and correlation factor of each record with mean record of the entire dataset. This proposed method used Attribute Value Frequency score (AVF score) concept for categorical part. Results of the proposed method are compared with existing methods. The Bank data (Mixed) is used for experiments in this paper which is taken from UCI machine learning repository.
Keyword: Outlier, Mixed Attribute Datasets, Attribute Value Frequency Score
Words Stemming Based on Structural and Semantic Similarity
Authors: Dianati Mohammad Hassan , Sadreddini Mohammad Hadi , Rasekh Amir Hossein , Fakhrahmad Seyed Mostafa , Taghi-Zadeh Hossein 
Published: October 2018

Words Â stemming Â is Â one Â of Â the Â important Â issues Â in Â the field Â of Â natural Â languageÂ processing Â and Â information retrieval. Â There Â are Â different Â methods Â for stemmingÂ which are mostly language-dependent. Therefore, these Â stemmers are onlyÂ applicable Â to Â particular Â languages. Â Because Â of the importance Â of Â this issue, Â in Â thisÂ paper, the proposed method for stemming is aimed to be language-independent. InÂ the Â proposed Â stemmer, Â a Â bilingual Â dictionary Â is Â used and Â all Â of Â the Â words Â in Â theÂ dictionary are firstly clustered. The wordsâ€™ clustering is based on their structural andÂ semantic similarity. Finally, finding the stem of new coming words is performed byÂ making use of the previously formatted clusters. To evaluate the proposed scheme,Â words Â stemming is Â done on both Â Persian Â and Â English Â languages. Â The encouragingÂ results Â indicate Â the Â good Â performance Â of Â the proposed Â method Â compared Â with Â itsÂ counterparts.
Cost Modeling and Risk and Benefit Modeling Approach as a Tools For Decision Making in Adoption Cloud Computing as IT Strategic Business
Authors: Putra Rian Rahmanda , Kinasih Wanda , Sensuse Dana Indra 
Published: October 2018

Cloud computing is an innovation that allows the use of IT as a utility based on-demand. Since cloud computing is a new technology, its led to a variety of risks that required an assessment model to assess the organization's readiness to adopt cloud computing. Moreover, by using cloud computing services means organizations or outsourcing involves a third party. Before adopting cloud computing technology, organizations need to consider some of the effects that arise as a result of cloud computing, namely in terms of costs, risks and benefits. To assist organizations to consider the migration of existing IT systems to the cloud, it can be used the cost approach valuation models and risk and benefit. This paper discusses two models of cost-based assessment of risk and benefit modeling and modeling that can be used as a tool to assist organizations in making decisions related to the migration of existing IT systems to the cloud.
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A new DWT-SVD based robust watermarking scheme for real property rights
Authors: Babakalak Sedigeh Razavi , Balafar Mohammad Ali , Farzan Ali 
Published: October 2018

In this paper, a new robust digital image watermarking algorithm which was based on singular value decomposition (SVD) and discrete wavelet transform (DWT) was proposed and simulated for protecting real property rights. A gray scale logo image, rather than a randomly generated Gaussian noise type watermark, was used as a watermark. Its embedding algorithm hid a watermark LL sub-band blocks in the lowâ€“low (LL) and high-high (HH) sub-bands of a target non-overlapping block of the host image by modifying singular values on SVD version of these blocks. A semi-blind watermark extraction was designed to estimate the original coefficients.
Experimental results showed that the proposed scheme made significant improvements in terms of both transparency and robustness and was superior to the existing methods which were considered in this paper.
Predicting Academic Performance with Applying Data Mining Techniques (Generalizing the results of two Different Case Studies)
Authors: Oskouei Rozita Jamili , Askari Mohsen 
Published: October 2018

Several research works are attempted to predict students academic performance andÂ assess Â the Â evaluating students knowledge Â or Â detecting Â studentsâ€™ Â weakness andÂ probability of failure in final semester examinations. However, several factors affectÂ the performance of students in different countries or even in different states of oneÂ country. Therefore, understanding these factors and analyzing the effects of each oneÂ of those factors in each country, is necessary for improving instructorsâ€™ decisions inÂ selecting Â the best teaching method for helping weak students or Â increasingÂ performance Â of Â other Â students. This study is motivated Â to Â study Â the Â studentsâ€™Â academic performance in high Â school Â and Â bachelor Â degree Â studies Â in Â Iran andÂ comparing these analysis results with the similar studyâ€™s results in India.
An Improved Multi-Cycle Deadlock Detection and Resolution Algorithm for Distributed Systems
Authors: Askari Mohsen , Oskouei Rozita Jamili 
Published: October 2018

Distributed systems exhibit a high degree of resource and data sharing creating aÂ state in which deadlocks might make their appearance. Since deadlock detection andÂ resolution Â is one of the important concerns in distributed systems which lead toÂ minimizing available resources, therefore instigating Â the Â system Â throughputÂ decrease. Â Our Â proposed algorithm detects and resolves Â the Â multi-cycle Â deadlocks,Â whether the initiator is involved in the deadlock cycle directly or indirectly. Also theÂ chance Â of Â phantom Â deadlock Â detection is minimized. This Â algorithm Â not Â only Â canÂ manage the simultaneous execution of it but also detects the multi-cycle deadlocksÂ in Â the Â distributed Â systems. Our Â algorithm introduces a modified probe and victimÂ message Â structure. Moreover, Â no Â extra Â storage Â required Â to Â store Â prob message Â inÂ each node which is known as memory overhead in the distributed systems.
Learning from Imbalanced Multi-label Data Sets by Using Ensemble Strategies
Authors: Javidi Mohammad Masoud , Shamsezat Fatemeh 
Published: October 2018

Multi-label classification is an extension of conventional classification in which a single instance can be associated with multiple labels. Problems of this type are ubiquitous in everyday life. Such as, a movie can be categorized as action, crime, and thriller. Most algorithms on multi-label classification learning are designed for balanced data and donâ€™t work well on imbalanced data. On the other hand, in real applications, most datasets are imbalanced. Therefore, we focused to improve multi-label classification performance on imbalanced datasets. In this paper, a state-of-the-art multi-label classification algorithm, which called IBLR_ML, is employed. This algorithm is produced from combination of k-nearest neighbor and logistic regression algorithms. Logistic regression part of this algorithm is combined with two ensemble learning algorithms, Bagging and Boosting. My approach is called IB-ELR. In this paper, for the first time, the ensemble bagging method whit stable learning as the base learner and imbalanced data sets as the training data is examined. Finally, to evaluate the proposed methods; they are implemented in JAVA language. Experimental results show the effectiveness of proposed methods.
Keywords: Multi-label classification, Imbalanced data set, Ensemble learning, Stable algorithm, Logistic regression, Bagging, Boosting
Network Attacks Detection by Hierarchical Neural Network
Authors: Javidi Mohammad Masoud , Nattaj Mohammad Hassan 
Published: October 2018

Intrusion detection is an emerging area of research in the computer security and net-works with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behavior or anomalous. However, these single classifier systems fail to provide the best possible attack detection rate with low false alarm rate. In this paper,we propose to use a hybrid intelligent approach using a combination of classifiers in order to make the decision intelligently, so that the overall performance of the resul-tant model is enhanced. The general procedure in this is to follow the supervised or un-supervised data filtering with classifier or cluster first on the whole training dataset and then the output are applied to another classifier to classify the data. In this re- search, we applied Neural Network with Supervised and Unsupervised Learning in order to implement the intrusion detection system. Moreover, in this project, we used the method of Parallelization with real time application of the system processors to detect the systems intrusions.Using this method enhanced the speed of the intrusion detection. In order to train and test the neural network, NSLKDD database was used. Creating some different intrusion detection systems, each of which considered as a single agent, we precisely proceeded with the signature-based intrusion detection of the network.In the proposed design, the attacks have been classified into 4 groups and each group is detected by an Agent equipped with intrusion detection system (IDS).These agents act independently and report the intrusion or non-intrusion in the system; the results achieved by the agents will be studied in the Final Analyst and at last the analyst reports that whether there has been an intrusion in the system or not.
Keywords:
Intrusion Detection, Multi-layer Perceptron, False Positives, Signature- based intrusion detection, Decision tree, Nave Bayes Classifier
Cloud-Based Retrieval Information System Using Concept for Multi-Format Data
Authors: Alghamdi Ibrahim , Ali F. Alshehri Dhafer , Alghamdi Abdullah , Kerim Bedine , Budiarto Rahmat 
Published: October 2018

The need of effective and efficient method to retrieving non-Web-enabled and Web-enabled information entities is essential, due to the fact of inaccuracy of the existing search engines that still use traditional term-based indexing for text documents and annotation text for images, audio and video files. Previous works showed that incorporating the knowledge in the form of concepts into an information retrieval system may increase the effectiveness of the retrieving method. Unfortunately, most of the works that implemented the concept-based information retrieval system still focused on one information format. This paper proposes a multi-format (text, image, video and, audio) concept-based information retrieval method for Cloud environment. The proposed method is implemented in a laboratory-scale heterogeneous cloud environment using Eucalyptus middleware.Â 755 multi-format information is experimented and the performance of the proposed method is measured.
Creating a Business Value while Transforming Data Assets using Machine Learning
Authors: Dimitrovska Ivana , Malinovski Toni 
Published: October 2018

Machine learning enables computers to learn from large amounts of data without specific programming. Besides its commercial application, companies are starting to recognize machine learning importance and possibilities in order to transform their data assets into business value. This study explores integration of machine learning into business core processes, while enabling predictive analytics that can increase business values and provide competitive advantage. It proposes machine learning algorithm based on regression analysis for a business solution in large enterprise company in Macedonia, while predicting real-value outcome from a given array of business inputs. The results show that most of the machine learning predictive values for the desired process output deviated from 0 to 15% of actual employees' decision. Hence, it verifies the appropriateness of the chosen approach, with predictive accuracy that can be meaningful in practice. As a machine learning case study in business context, it contains valuable information that can help companies understand the significance of machine learningÂ for enterprise computing. It also points out some potential pitfalls of machine learning misuse.
Swarm Intelligent in Bio-Inspired Perspective: A Summary
Authors: Latifah Nyayu Husni , Silvia Ade , Prihatini Ekawati , Nurmaini Siti , Yani Irsyadi 
Published: October 2018

This paper summarizes the research performed in the field of swarm intelligent in recent years. The classification of swarm intelligence based on behavior is introduced. The principles of each behaviors, i.e. foraging, aggregating, gathering, preying, echolocation, growth, mating, clustering, climbing, brooding, herding, and jumping are described. 3 algorithms commonly used in swarm intelligent are discussed. At the end of summary, the applications of the SI algorithms are presented.
HALKLA İLİŞKİLER MÜFREDATINDA KÜRESELLEŞMEYE İLİŞKİN BİR DEĞERLENDİRME: AMERİKA VE İNGİLTERE ÖRNEĞİ
Authors: Şardagı EMİNE , Öztürk Mesude Canan 
Published: October 2018

Halkla ilişkilere yönelik ilgi son
yirmi yıl içinde giderek artmıştır. Bu durum halkla ilişkiler eğitiminin önemli
hale gelmesine sebep olmuştur. Uygulama alanlarının gelişmesi farklı bir ülkede
ya da kültürde halkla ilişkiler çalışması yapıldığında küreselleşmenin etkilerinin
düşünülmesini gerekli kılmıştır. Ülkelerin kültürel, siyasal, ekonomik vb.
yapısının farkında olmak buna yönelik verilen eğitim ve bilinçle oluşmaktadır.
Bu bilincin oluşturulması için halkla ilişkiler programı içinde küreselleşmeye
yönelik eğitim verilmesi önem taşımaktadır. Bu
çalışmada, halkla ilişkiler eğitimi veren üniversitelerin ders içeriklerinde
küreselleşme ve uluslararasılaşmaya yönelik eğitimlerin varlığının incelenmesi
amaçlanmıştır. Böylece, halkla ilişkiler eğitiminde küreselleşme kavramı için
benzerlikler ve farklılıklar tespit edilmeye çalışılmıştır. Çalışmada, halkla
ilişkiler eğitimi veren üniversiteleri belirlemek için iki liste seçilmiş ve
küreselleşme ile ilgili derslerin içeriği incelenmiştir. Eğitimin içeriği,
içerik analizi yöntemi ile belirlenen başlıklar altında değerlendirilmiştir.
Literatürde eğitimde küreselleşme kavramının önemi sıkça vurgulanmaktadır.
Ancak bu çalışmadan elde edilen sonuçlar üniversitelerin bu konuya yeterince
önem vermediğini göstermektedir. Bulgular, programların çoğunda halkla
ilişkilerin uluslararası ve küresel boyutuyla ilgili çok az ders verildiğini ortaya
koymuştur. Bununla birlikte verilen uluslararası derslerin büyük bir bölümü
seçmeli ders olarak verilmektedir. Bu durum bu anlamda bir anlayışın henüz
yerleşemediğinin göstergesi sayılabilir. Ders içeriğinin konusu kültür ve
kültürlerarası iletişim üzerine odaklanmaktadır.
TWO C-GLYCOSIDE FLAVONES FROM CORN (ZEA MAYS) SILK
Authors: Ren Shuncheng 
Published: October 2018

Background: Corn silk, a traditional Chinese herbal medicine in China, has been used to remedy nephritis, hepatitis,
measles, hyperglycemia, tumor, etc. In this study, flavonoid monomers in corn silk were isolated and identified. This
study could serve as a basis for determining the curative effects of these monomers on human diseases.
Method: Corn silk flavonoids were extracted using 80% (v/v) ethanol and isolated by repeated polyamide column and
Toyopearl HW-40 column chromatography. Corn silk flavonoids were identified by UV spectroscopy, liquid
chromatography/mass spectroscopy, and nuclear magnetic resonance spectroscopy.
Results: Two C-glycoside flavones, namely, ax-4″-OH-3′-methoxymaysin and a 3′-methoxymaysin isomer, were
isolated from corn silk and identified.
Conclusion: The two C-glycoside flavones were successfully isolated from corn silk and identified. This study is the
first to report 3′-methoxymaysin in corn silk.
NEUTRALIDAD Y CONFLICTOS DE INTERESES DE LAS PLATAFORMAS DE FINANCIACIÓN PARTICIPATIVA
Authors: Sánchez Ruiz Mercedes 
Published: October 2018

Uno de los requisitos que debe cumplir una sociedad de capital para obtener (o mantener) su autorización como plataforma de financiación participativa es el siguiente: “Disponer de un reglamento interno de conducta que contemple, enparticular, los posibles conflictos de intereses y los términos de la participación de los administradores, directivos, empleados y apoderados en las solicitudes de financiación que se instrumenten a través de la plataforma”1. Asimismo, entre las obligaciones de información general que se imponen legalmente a las plataformas de financiación participativa se encuentra la de incluir en su página web información sobre “las medidas adoptadas para evitar los conflictos de interés”.
Modelling Complex Well Behaviour in the Field Using a Transient Multiphase Flow Simulator
Authors: Karatayev Kanat , Bissakayev Beibit , Saada Tamer , Madeley Benjamin , Brancolini Alberto , Sirenova Gulzat 
Published: October 2018

Abstract
The objective of the paper is to present the application of a transient multiphase flow simulator for the purpose of modelling and improving understanding of complex well behaviour which is not possible with a steady-state solution. The outcome was optimizing well production performance of problematic wells in terms of slugging flow and water loading at high network pressure in a gas condensate and oil field.
Continuous reservoir pressure decline, increasing gas-oil-ratio (GOR) and water cut have led several wells in the field to exhibit slugging flow. Different intervention trials in the subject wells failed to bring the well online in continuous flow. The methodology employed was to construct the well model in a transient multiphase flow simulator using the available data; well completion schematic, compositional PVT fluid properties, and well test data. The fluid was characterised using the PVT package. The well model was built, and its performance was matched to dynamic natural slugging flow, shut-in conditions, clean-up operations, and artificial lifting case where nitrogen was injected via coiled tubing to lighten the wellbore fluid density.
The modelling results of the well performance analysis have explored crossflow between reservoir zones and water loading phenomena on long sub-horizontal oil producers, at high operating well head pressures, as the cause of flow instability. The multi-stage completion and multiple reservoir intervals, with differing reservoir properties, were captured during construction of the well model. The static fluid gradient survey analysis verified the simulator outcomes, and this in turn proved its applicability for the complex fluids. The well model provided deep understanding of fluid flow in the wellbore for flowing and static conditions. The model was used to evaluate well intervention scenarios to establish a stable flow regime. These studies highlighted the possibility to achieve optimal operating well head pressures to avoid aggravating water loading and stable production process. Various multiphase stable flow optimization methods were examined along with an economic assessment. The dynamic multiphase flow simulator has been found useful in reproducing complex flow behaviour observed in problematic wells and improve stable production.
The approach of using a transient multiphase flow simulator on wells with water loading issues and also with crossflowing intervals is vital, and this first time application has proved beneficial for the Karachaganak gas condensate and oil field.
New Application of Old Data by Utilizing Long Duration Pressure Transient Tests and Permanent Downhole Gauges as High Value Assets for Improving Full Field Simulation Model with Additional Interference
Authors: Levchenko Pavel , Iskakov Elrad , Manakhayev Ruslan 
Published: October 2018

Abstract
Accurate fracture characterization has a huge impact on production forecasts and evaluation of projects in Naturally Fractured Carbonate fields. Pulse/Interference Tests can give valuable information about fracture network by providing additional constraint for spatial fracture distribution between wells, which can be honored with application of geologically consistent trends (Levchenko et.al, 2017). However, operational execution of the Pulse/Interference tests is very challenging. Therefore, usually limited data are available from the planned surveillance jobs leaving large sections of the reservoir without information from Pulse/Interference tests, which could be used for calibration of the Fracture Model.
Lack of Interference data can be resolved by examining already available measurements, which are generally used for other purposes. For example, the primary purpose of Pressure Transient Tests (PTTs) is obtaining kh and skin values, while Permanent Downhole Gauges (PDHG) are mostly utilized for recording reservoir pressure, which is used for conventional History Matching process. As it was found from this study, historical data from both PTTs and PDHGs could be a source of additional high value information of Interference tests occurring in the field, which nobody was aware of.
Properly designed Pulse/Interference Tests are very difficult to execute in the field with high production deliverability requirements. However, examining historical data can reveal a lot of good quality Interference Tests, which were recorded and stored for decades, but not used for Fracture Model characterization. Additional information from Interference tests were applied to calibrate a full-field simulation model, significantly improving quality of the history match in comparison with previous models, and improved confidence in the production forecasts.