Miracle-dz/UniFAR-FTUs huggingface dataset:
Perbandingan Simple Logistic Classifier dengan Support Vector Machine dalam Memprediksi Kemenangan Atlet

Abstrak— Prediksi kemenangan atlet adalah hal yang harus dilakukan oleh pelatih ketika memutuskan pemain yang akan diturunkan dalam suatu pertandingan. Banyaknya faktor-faktor yang mempengaruhi kemenangan atlet membuat keputusan tersebut tidak mudah untuk ditentukan. Dalam penelitian ini akan dilakukan perbandingan dari penggunaan metode Simple Logistic Classifier (SLC) dengan Support Vector Machine (SVM) dalam memprediksi kemenangan atlet berdasarkan data kesehatan dan data latihan fisik. Data yang digunakan diambil dari 28 cabang olahraga perorangan. Rata-rata akurasi SLC dan SVM masing-masing diperoleh sebesar 80% dan 88%, sedangkan rata-rata kecepatan pemrosesan metode SLC dan SVM adalah 1,6 detik dan 0,2 detik. Hal ini menunjukkan bahwa penggunaan metode SVM lebih unggul daripada SLC, baik dari segi kecepatan maupun dari nilai akurasi yang dihasilkan. Selain pengujian akurasi, dilakukan pula pengujian terhadap 24 fitur yang digunakan dalam proses klasifikasi. Hasilnya diketahui bahwa pengurangan fitur melalui tahap seleksi mengakibatkan penurunan nilai akurasi. Berdasarkan hal tersebut disimpulkan bahwa semua fitur yang digunakan dalam penelitian ini adalah fitur yang berpengaruh dalam penentuan prediksi kemenangan atlet. Kata Kunci— Prediksi, Simple Logistic Classifier, Sports Data Mining, Support Vector Machine Abstract— A coach must be able to select which athlete has a good prospect of winning a game. There are a lot of aspects which influence the athlete in winning a game, so it's not easy by coach to decide it.This research would compare Simple Logistic Classifier (SLC) and Support Vector Machine (SVM) usage applied to predict winning game of athlete based on health and physical condition record. The data get from 28 sports. The accuracy of SLC and SVM are 80% and 88% meanwhile processing times of SLC and SVM method are 1.6 seconds dan 0.2 seconds.The result shows the SVM usage superior to the SLC both of speed process and the value of accuracy. There were also testing of 24 features used in the classifications process. Based on the test, features selection process can cause decreasing the accuracy value. This result concludes that all features used in this research influence the determination of a victory athletes prediction. Keywords— Prediction, Simple Logistic Classifier, Sports Data Mining, Support Vector Machine
A Hybrid Technique to Classify Trending Topic on Twitter Dataset

— Text mining is in trend of research and development these days. A number of recent contributions in this domain are observed. Different social media text analysis techniques are used to find the trending topic. Both kinds of data mining techniques (i.e. supervised and unsupervised) are used in text mining. The proposed study is focused on the supervised learning approach. In the first step the dataset is prepared, thus different subjects or domain based data is extracted from twitter and according to these subjects the class labels are appended with the data. Further the dataset is split into two sets i.e. the training data and the testing data. In the next step the data is pre-processed where stop words and special characters are removed from the dataset. This dataset is used with the three different classification algorithms KNN (k-nearest neighbor), SVM (support vector machine) and a combination of both SVM and KNN. Hybrid approach is applied to build the classification model and it is implemented on test dataset to discover the twitter subjects. The result of the proposed work is compared with traditional KNN and SVM algorithm. According to the results obtained, the proposed hybrid technique provides more precise and better results as compared to other conventional classifiers.
Credit Risk Analysis in Peer to Peer Lending Data set: Lending Club

This project studies the classification variable ‘default’ in Peer to Peer lending dataset known as Lending Club. The project improved on existing work in terms of accuracy, F-1 measure, precision, recall, and root mean squared error. We explored balancing techniques such as oversampling the minority class, undersampling the majority class, and random forests with balanced bootstraps. We also analyzed and proposed new features that improve the Learner performance.
Resource deployment system implications of migrating the firm into a digital value creation paradigm

ABSTRACT This article uses the intellectual capital-based view of the firm, to draw conclusions about both the change in relative importance of resources and the change in relative influence of transformations in the firm’s resource transformation system when introducing digital technologies. The methodology is a synthesis of research conducted on the implications of digital technologies for the firm’s value creation, through a review of the literature, combined with two case studies. The findings are that when introducing digital technologies, firms will migrate towards a value network logic. There will also be an increasing value creating influence of transformations into and out of the organisational resource category, and there will be an increase in the number of possible revenue streams that can be created. A clear practical implication for managers from this increased complexity is the need to simulate potential new resource deployment systems to verify their effectiveness before implementing them.
Embedding Multimodal Relational Data

Knowledge bases (KB) are an essential part of many computational systems with applications in variety of domains, such as search, structured data management, recommendations, question answering, and information retrieval. However, KBs often suffer from incompleteness, noise in their entries, and inefficient inference. Due to these deficiencies, learning the relational knowledge representation has been a focus of active research [1, 2, 32, 9, 19, 26, 3]. These approaches represent relational triples, consisting of a subject entity, relation, and an object entity, by estimating fixed, low-dimensional representations for each entity and relation from observations, thus encode the uncertainty and infer missing facts accurately and efficiently. The subject and the object entities come from a fixed, enumerable set of entities that appear in the knowledge base.
Systematic Review and Meta-Analysis: An overview with the help of R Software

When a clinician wants to take a decision on a treatment, procedure or diagnosis with respect to a disease or condition, outcome from a single research may not be right or helpful. Systematic review [ 1 ] provides the clinician with the strongest evidence about the available treatment or procedure related to a particular disease by including the evidences from the research studies which are similar in nature with respect to the scope, objective and the inclusion criteria of the study. Systematic reviews are different from the normal review or literature review as the later will not include all the relevant studies with respect to the particular problem and hence the conclusion drawn from it are not valid and generalizable. Meta-Analysis [ 2 ] is a statistical tool helps us to combine the results from the studies under consideration and arrive at an aggregate value for the parameters under study such as odds ratio, survival rate etc. This paper provides an overview of systematic review and meta-analysis with the use of an illustrative example. Meta-analysis is carried out using the r statistical software package.
A novel solution in the simultaneous deep optimization of RGB-D camera calibration parameters using metaheuristic algorithms

This article presents a novel method for estimating 19 parameters of RGB and depth camera calibration simultaneously. The proposed algorithm is based on applying metaheuristic methods for deep optimization and estimating all parameters of intrinsic, extrinsic, and lens distortions of cameras. This paper compares four metaheuristic algorithms, i.e. a genetic algorithm, particle swarm optimization, the colonial competitive algorithm, and the shuffled frog leaping algorithm, with a numerical algorithm called singular value decomposition. The proposed method does not need the initial estimation for optimization and it can avoid being trapped in local minima. By using nondirect estimation, we achieve middle computing matrices such as the homography matrix, which is used in the pinhole camera model. Both versions of Kinect sensors are used for the experimental evaluation. The mean square of the reprojection error criteria is defined as the objective function in the proposed algorithm. The experimental results show that the proposed method is more efficient and accurate than traditional numerical solutions.
Preprocessing Techniques to Support Event Detection Data Fusion on Social Media Data

This thesis focuses on collection and preprocessing of streaming social media feeds for metadata as well as the visual and textual information. Today, news media has been the main source of immediate news events, large and small. However, the information conveyed on these news sources is delayed due to the lack of proximity and general knowledge of the event. Such news have started relying on social media sources for initial knowledge of these events. Previous works focused on captured textual data from social media as a data source to detect events. This preprocessing framework postures to facilitate the data fusion of images and text for event detection. Results from the preprocessing techniques explained in this work show the textual and visual data collected are able to be proceeded into a workable format for further processing. Moreover, the textual and visual data collected are transformed into bag-of-words vectors for future data fusion and event detection.
CUSTOMER PORTFOLIO OF A CONSUMER GOODS BASED VIRTUAL STORE: IDENTIFYING CUSTOMER SEGMENTS WITH CLUSTER ANALYSIS

In the last decade, analyzing and identifying customers became an irreplaceable need for companies. This research concentrates on discovering a company’s customer segments using different machine learning algorithms, benchmarking different algorithms and its parameters to conclude the best results. Improvements in the technology provided several approaches to dive in and gain insights from a mass amount of data. Machine learning algorithms which is one of the most popular approaches was chosen to convey this empirical study. A dataset with mix categorical and numeric variables is analyzed with one of the conventional machine learning algorithms, namely Hierarchical Agglomerative Clustering Algorithm with Gower’s distance. Kernel Principal Component Analysis is used for preprocessing due to the existence of categorical variables. K-prototypes Algorithm is chosen as benchmark algorithm that fits the qualities of the dataset with mixed categorical and numeric features. Benchmarking provides verification in respect to the accuracy of the results by evaluating the final clusters. Also, examining different parameters and comparing their effects on analysis results indicates the importance and vitality of them for machine learning algorithms, which need to be enlightened to do more accurate analyses. The results showed that both K-prototypes and HAC yield similar results proving that clusters mostly divided appropriately. However, there are a few significant points that are different at both algorithms’ results, which should be examined in further study.
Developing a Specialized Service Quality Model for Universities

Abstract Given the important role of universities in economic, social, cultural, and political dimensions, it is vital to ensure optimal quality of their performance in order to prevent loss of human and material capital and to maximize their competitiveness. This study develops a model for assessing perceived quality of universities and explains the outcomes of perceived quality among students. Results show that quality of lecturers, prestige, connection with outside world, extra-curriculum activities, research potential, responsiveness, facilities, design and ambiance, and diversity are considered components of perceived quality. Students with higher perceived university quality will make greater efforts to grow the brand.
A third road to the past? Historical scholarship in the age of big data

ABSTRACT Is a third passage to the past possible, beyond Elton's and Fogel's two roads of narrative history and scientific/quantitative history? One that would combine narrative history's focus on the event, on individuals and their actions, at a particular time and place, to scientific/quantitative history's emphasis on explicit behavioral models based on social-science theories? That is the question this article addresses. It illustrates a computer-assisted methodology for the study of narrative—quantitative narrative analysis (QNA)—that does just that. Based on the 5 Ws + H of narrative—Who, What, When, Where, Why, and How—QNA quantifies events without losing the event itself, without losing people behind numbers, diachronic time behind synchronic statistical coefficients. When used in conjunction with dynamic and interactive data visualization tools (and new natural language processing tools), QNA may provide a third unforeseen road to the past.
Target tracking, moving target detection, stabilisation and enhancement of airborne video

The ability to automatically detect and track moving targets whilst stabilizing and enhancing the incoming video would be highly beneficial in a range of aerial reconnaissance scenarios. We have implemented a number of image-processing algorithms on our ADEPT hardware to perform these and other useful tasks in real-time. Much of this functionality is currently being migrated onto a smaller PC104 form-factor implementation that would be ideal for UAV applications. In this paper, we show results from both software and hardware implementations of our current suite of algorithms using synthetic and real airborne video. We then investigate an image processing architecture that integrates mosaic formation, stabilisation and enhancement functionality using micro-mosaics, an architecture which yields benefits for all the processes.
Can Distributed Word Embeddings be an alternative to costly linguistic features: A Study on Parsing Hindi

Word Embeddings have shown to be useful in wide range of NLP tasks. We explore the methods of using the embeddings in Dependency Parsing of Hindi, a MoR-FWO (morphologically rich, relatively freer word order) language and show that they not only help improve the quality of parsing, but can even act as a cheap alternative to the traditional features which are costly to acquire. We demonstrate that if we use distributed representation of lexical items instead of features produced by costly tools such as Morphological Analyzer, we get competitive results. This implies that only mono-lingual corpus will suffice to produce good accuracy in case of resource poor languages for which these tools are unavailable. We also explored the importance of these representations for domain adaptation.
Cross Miniapp Request Forgery: Root Causes, Attacks, and Vulnerability Detection

A miniapp is a full-fledged app that is executed inside a mobile super app such as WeChat or SnapChat. Being mini by nature, it often has to communicate with other miniapps to accomplish complicated tasks. However, unlike a web app that uses network domains (i.e., IP addresses) to navigate between different web apps, a miniapp uses a unique global appId assigned by the super app to navigate between miniapps. Unfortunately, any missing checks of the sender's appId in a receiver miniapp can lead to a new type of attacks we name it cross-miniapp request forgery (CMRF). In addition to demystifying the root cause of this attack (i.e., the essence of the vulnerability), this paper also seeks to measure the popularity of this vulnerability among miniapps by developing CmrfScanner, which is able to statically detect the CMRF-vulnerability based on the abstract syntax tree of miniapp code to determine whether there are any missing checks of the appIds. We have tested CmrfScanner with 2,571,490 WeChat miniapps and 148,512 Baidu miniapps, and identified 52,394 (2.04%) WeChat miniapps and 494 (0.33%) Baidu miniapps that involve cross-communication. Among them, CmrfScanner further identified that 50,281 (95.97%) of WeChat miniapps, and 493 (99.80%) of Baidu miniapps lack the appID checks of the sender's mini-apps, indicating that a large amount of miniapp developers are not aware of this attack. We also estimated the impact of this vulnerability and found 55.05% of the lack of validation WeChat miniapps (7.09% of such Baidu miniapps) can have direct security consequences such as privileged data access, information leakage, promotion abuse, and even shopping for free. We hope that our findings can raise awareness among miniapp developers, and future miniapps will not be subject to CMRF attacks.
The Data Processor System of EUSO Balloon: in flight performance.

The EUSO-Balloon experiment is a pathfinder mission for JEM-EUSO which has as its main objective an end-to-end test of all the key technologies and instrumentation of JEM-EUSO detectors. The instrument is a telescope of smaller dimension with respect to the one designed for the ISS, it is mounted in an unpressurized gondola of a stratospheric balloon. It was launched during the CNES flight campaign in August 2014 from the Timmins (Ontario) base. The flight lasted about five hours and the payload reached a float altitude of about 38 km. In this paper we will present the Data Processor (DP) of EUSO-Balloon. The DP is the component of the electronics system which performs the data handling and, through the interface with the telemetry system, allows the controlling and the monitoring of the instrument from ground. We will describe the main components of the system and their performance during the flight.
Comparison of methods of data mining techniques for the predictive accuracy

This paper is based on the work of Yeh, Lien (2009). In the paper, authors used the payment data set from the important bank in Taiwan. To build a model, the whole sample was divided in two subsets - training and testing sets - so each model could be trained on the first one and then be evaluated on the second. Our motivation was to see whether the same result could be obtained if we repeatedly apply the models to the different data sets. To do so, Monte Carlo simulation was implemented to generate these sets.
EVALUATION OF THE ACCURACY OF GAIT EVENTS DETECTED USING THREE DIFFERENT METHODS DURING RUNNING

Different methods have been proposed for determining initial contact and toe off with inertial measurement unit (IMU) attached to different anatomical positions. However, the accuracy has not yet been compared. This study aimed to evaluate the accuracy of three commonly used methods (S-, M-, and L-method) in detecting gait events at two running speed conditions (slow and fast). Obvious differences of detected initial contact and toe off and estimated stance duration among the three detection methods using IMU were found at the two running speed conditions. It was shown that initial contact detected using the S-method and toe off detected by the M-method were the best estimates of the gait events. Combined use of both methods is recommended for determining stance duration during overground running.
Wireless Network SNR Enhancement Using Mobile Relay Stations

Wireless Network SNR Enhancement Using Mobile Relay Stations Rostom Ohannessian Master of Applied Science (M.A.Sc.) Graduate Department of Electrical and Computer Engineering University of Toronto 2010 With the proliferation of wireless technologies, wireless Internet access in public places will become a necessity in the near future. In outdoor areas, where the base stations are sparsely distributed, mobile users at the edge of the network communicate with the base station at a very low rate and thus waste network resources. To solve this problem, one of the previously taken approaches was the use of relay stations to improve the throughput of the network. In this work, we take this approach to the next level by updating the positions of the relays according to the particular distribution of the users at certain time instants. By comparing the proposed scheme to fixed relay placement strategies, we show that the former has 15-60% performance improvement over the latter, in terms of the average SNR of the network.
Black and White Image Recording Using Holographic Memory Effect

In this paper black and white image recording using holographic memory effect is presented. different black and white images are use as objects for digital hologram construction. Each black and white image is treated separately during the digital hologram construction, but all of them are recorded on one holographic plate. Given hologram represents combination of all images. Digital hologram is constructed using composition of asherical waves. For the purpose of visualization, hologram is quantified on 256 levels (greyscale hologram). Hologram is reconstructed and each reconstructed image is compared with original 2D binary image. Given results showed very good reconstruction, which means that different black and white images can be recorded on one holographic plate, and later reconstructed without significant information loss.
An Automatic Forecasting Method for Time Series

An automatic forecasting method is proposed concerning automation problem in the field of linear time series forecasting. The method is on the basis of econometric theory and overcomes the difficulty to mine and forecast automatically with econometric models. The proposed algorithm is divided into 4 stages, i.e. preprocessing, unit root testing and stationary processing, modeling, and ultimately forecasting. Future values and trends would be estimated and forecasted precisely through the 4 stages of the algorithm according to input data without manual intervention. Experimental comparisons were made between the proposed algorithm and the 2 data driven forecasting algorithms, i.e. moving average method and Holt exponential smoothing method. It was demonstrated with the experimental results that automatic forecasting is feasible utilizing the proposed algorithm and higher accuracy can be acquired than these 2 data driven-based methods.
NEXUS OF KNOWLEDGE TRANSFER, GREEN INNOVATION AND ENVIRONMENTAL PERFORMANCE: IMPACT OF ENVIRONMENTAL MANAGEMENT ACCOUNTING

The present study is motivated to investigate the importance of ecological innovations and knowledge management in influencing environmental performance (EPR). The focus of the present study lies in identifying the contribution of environmental management accounting (EMA) in driving the firm’s knowledge transfer (KTR), green innovation (GIN), and EPR in Indonesian small and medium enterprises sector. The current collected the data from 223 respondents from different SMEs of Indonesia. The results of PLS-SEM confirm that all variables have a positive and significant impact on the EPR of Indonesia SMEs. Moreover, the outcomes of the PLS-SEM confirm that KTR, GIN, and EPR have significantly and positively impacted by EMA system. The outcomes of partial least square structural equation modelling also indicate that KTR and GIN have also positively and significantly impact on the EPR. The results further recommended that the firms can improve their EPR by implementing good EMA system.