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Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Now showing 1 - 7 of 7
  • Conference Object
    Off-the Electronics in Rescue Robotics
    (Assoc Computing Machinery, 2018) Sadeghi, Majid Mohammad; Kececi, Emin Faruk
    The design and manufacturing methods of rescue robots with different locomotion principles are explained in the literature in detail. However, the design and realization of electronic circuits of a rescue robot still pose a great challenge, especially for the academics with mechanical background, who know how to design and build the mechanics of the robot but do not know how to make the robot work and make the right choices for the electronic parts, such as selecting a microcontroller or drivers. This research reports the methodology of building an electronic system for a mobile robot with off-the-shelf products.
  • Conference Object
    Citation - WoS: 7
    Citation - Scopus: 8
    Evaluation of Dominant and Non-Dominant Hand Movements for Volleyball Action Modelling
    (Assoc Computing Machinery, 2019-10-14) Haider, Fasih; Salim, Fahim A.; Tasdemir, Sena Busra Yengec; Naghashi, Vahid; Tengiz, Izem; Cengiz, Kubra; Luz, Saturnino; Yengec Tasdemir, Sena Busra
    In this paper, we assess the use of Inertial Measurement Units (IMU) in recognising different volleyball-specific actions. Analysis of the results suggests that all sensors in the IMU (i.e. magnetometer, accelerometer, barometer and gyroscope) contribute unique information in the classification of volleyball-specific actions. We demonstrate that while the accelerometer feature set provides the best Unweighted Average Recall (UAR) overall, "decision fusion" of the accelerometer with the magnetometer improves UAR slightly from 85.86% to 86.9%. Interestingly, it is also demonstrated that the non-dominant hand provides better UAR than the dominant hand. These results are even more marked with "decision fusion".
  • Conference Object
    Email Clustering & Generating Email Templates Based on Their Topics
    (Assoc Computing Machinery, 2021-05-27) Coskun, Fatih; Gezer, Cengiz; Gungor, V. Cagri
    Email templates have a significant impact on users in terms of productivity. Using an email template that is produced successfully is going to transfer the main information with a considerable impression. While the previous studies were focused on the email generation by text-differences in the content of the emails, generated templates based on email topics can provide better productivity for the companies. This article proposes a system, in which user emails are clustered according to the topics of the emails, and introduces an email template generation system that utilizes the sample emails belonging to the formed email clusters. For this purpose, the Enron email dataset has been used and the performance of different text preprocessing and topic modeling algorithms, such as DMM, GPU-DMM, GPU-PDMM, LF-DMM, LDA, LF-LDA, BTM, WNTM, PTM, SATM, have been investigated and compared to determine the most efficient one. After obtaining the email topics, the system shows the examples of the emails representing the selected topics and enables the authorized users to create templates that generalize these topics.
  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 4
    Data Mining Techniques in Direct Marketing on Imbalanced Data Using Tomek Link Combined With Random Under-Sampling
    (Assoc Computing Machinery, 2021-05-27) Yilmaz, Umit; Gezer, Cengiz; Aydin, Zafer; Gungor, V. CaGri; Yllmaz, Ümit; Aydln, Zafer
    Determining the potential customers is very important in direct marketing. Data mining techniques are one of the most important methods for companies to determine potential customers. However, since the number of potential customers is very low compared to the number of non-potential customers, there is a class imbalance problem that significantly affects the performance of data mining techniques. In this paper, different combinations of basic and advanced resampling techniques such as Synthetic Minority Over-sampling Technique (SMOTE), Tomek Link, RUS, and ROS were evaluated to improve the performance of customer classification. Different feature selection techniques are used in order the decrease the number of non-informative features from the data such as Information Gain, Gain Ratio, Chi-squared, and Relief. Classification performance was compared and utilized using several data mining techniques, such as LightGBM, XGBoost, Gradient Boost, Random Forest, AdaBoost, ANN, Logistic Regression, Decision Trees, SVC, Bagging Classifier based on ROC AUC and sensitivity metrics. A combination of Tomek Link and Random Under-Sampling as a resampling technique and Chi-squared method as feature selection algorithm showed superior performance among the other combinations. Detailed performance evaluations demonstrated that with the proposed approach, LightGBM, which is a gradient boosting algorithm based on decision tree, gave the best results among the other classifiers with 0.947 sensitivity and 0.896 ROC AUC value.
  • Article
    Citation - WoS: 20
    Citation - Scopus: 33
    Cold Start Latency in Serverless Computing: A Systematic Review, Taxonomy, and Future Directions
    (Assoc Computing Machinery, 2024-11-11) Golec, Muhammed; Walia, Guneet kaur; Kumar, Mohit; Cuadrado, Felix; Gill, Sukhpal singh; Uhlig, Steve
    Recently, academics and the corporate sector have paid attention to serverless computing, which enables dynamic scalability and an economic model. In serverless computing, users only pay for the time they actually use resources, enabling zero scaling to optimise cost and resource utilisation. However, this approach also introduces the serverless cold start problem. Researchers have developed various solutions to address the cold start problem, yet it remains an unresolved research area. In this article, we propose a systematic literature review on cold start latency in serverless computing. Furthermore, we create a detailed taxonomy of approaches to cold start latency, which we use to investigate existing techniques for reducing the cold start time and frequency. We have classified the current studies on cold start latency into several categories such as caching and application-level optimisation-based solutions, as well as Artificial Intelligence/Machine Learning-based solutions. Moreover, we have analyzed the impact of cold start latency on quality of service, explored current cold start latency mitigation methods, datasets, and implementation platforms, and classified them into categories based on their common characteristics and features. Finally, we outline the open challenges and highlight the possible future directions.
  • Conference Object
    Citation - WoS: 4
    Citation - Scopus: 7
    A Searching and Automatic Video Tagging Tool for Events of Interest During Volleyball Training Sessions
    (Assoc Computing Machinery, 2019-10-14) Salim, Fahim A.; Postma, Dees B. W.; van Delden, Robby; Reidsma, Dennis; van Beijnum, Bert-Jan; Haider, Fasih; Cengiz, Kubra
    Quick and easy access to performance data during matches and training sessions is important for both players and coaches. While there are many video tagging systems available, these systems require manual effort. This paper proposes a system architecture that automatically supplements video recording by detecting events of interests in volleyball matches and training sessions to provide tailored and interactive multi modal feedback.
  • Article
    Citation - WoS: 9
    Citation - Scopus: 13
    A Methodology for Comparing the Reliability of GPU-Based and CPU-Based HPCS
    (Assoc Computing Machinery, 2020-02-06) Cini, Nevin; Yalcin, Gulay
    Today, GPUs are widely used as coprocessors/accelerators in High-Performance Heterogeneous Computing due to their many advantages. However, many researches emphasize that GPUs are not as reliable as desired yet. Despite the fact that GPUs are more vulnerable to hardware errors than CPUs, the use of GPUs in HPCs is increasing more and more. Moreover, due to native reliability problems of GPUs, combining a great number of GPUs with CPUs can significantly increase HPCs' failure rates. For this reason, analyzing the reliability characteristics of GPU-based HPCs has become a very important issue. Therefore, in this study we evaluate the reliability of GPU-based HPCs. For this purpose, we first examined field data analysis studies for GPU-based and CPU-based HPCs and identified factors that could increase systems failure/error rates. We then compared GPU-based HPCs with CPU-based HPCs in terms of reliability with the help of these factors in order to point out reliability challenges of GPU-based HPCs. Our primary goal is to present a study that can guide the researchers in this field by indicating the current state of GPU-based heterogeneous HPCs and requirements for the future, in terms of reliability. Our second goal is to offer a methodology to compare the reliability of GPU-based HPCs and CPU-based HPCs. To the best of our knowledge, this is the first survey study to compare the reliability of GPU-based and CPU-based HPCs in a systematic manner.