November 19 ~ 20, 2022, Zurich, Switzerland
Nezhurina Marianna, Kuban State Technological University, Krasnodar, Russia
In the current paper we present a new Data Augmentation technique – Random Zeroing. Thistechnique is easy to implement, it can be usedalongside with the existing data augmentation techniques. We trained Convolutional Neural Networks on three datasets CIFAR10, MNIST and Fashion MNIST and compared validation accuracies of networks trained with different variations of Random Zeroing techniques.
Data Augmentation, Computer Vision, Convolutional Neural Networks, Random Erasing.
Lowri Williams, Eirini Anthi, Amir Javed, Pete Burnap, School of Computer Science & Informatics, Cardiff University, Cardiff, UK
The performance of emotive text classification using affective hierarchical schemes (e.g., WordNet-Affect) is often evaluated using the same traditional measures used to evaluate the performance of when a finite set of isolated classes are used. However, applying such measures means the full characteristics and structure of the emotive hierarchical scheme are not considered. Thus, the overall performance of emotive text classification using emotion hierarchical schemes is often inaccurately reported and may lead to ineffective information retrieval and decision making. This paper provides a comparative investigation of how four extended evaluation metrics which consider the characteristics of the hierarchical scheme can be applied and subsequently improve the performance of the classification of emotive texts. This study investigates the classification performance of three widely used classifiers, Naive Bayes, J48 Decision Tree, and SVM, following the application of the aforementioned methods. The results demonstrated that all methods improved the performance of all classifiers. However, the most notable improvement was recorded when a depth-based method was applied to both the testing and validation data, where the precision, recall, and F1-score were significantly improved by around 70 percentage points for each classifier.
Sentiment Analysis, Emotion Classification, Supervised Machine Learning, Hierarchical Classification, Natural Language Processing.
Shehab Eldeen Ayman1, Walid Hussein2, Omar H. Karam3, 1Department of Software Engineering, Faculty of Informatics and Computer Science, The British University in Egypt, Cairo, Egypt, 2Department of Computer Science, Faculty of Informatics and Computer Science, The British University in Egypt, Cairo, Egypt, 3Department of Information Systems, Faculty of Informatics and Computer Science, The British University in Egypt, Cairo, Egypt
Many real-time object recognition systems operate on two-dimensional images, degrading the influence of the involved objects third-dimensional (i.e., depth) information. The depth information of a captured scene provides a thorough understanding of an object in full-dimensional space. During the last decade, several region proposal techniques have been integrated into object detection. scenes’objects are then localized and classified but only in a two-dimensional space.Such techniques exist under the umbrella of two-dimensional object detection models such as YOLO and SSD. However, these techniques have the issue of being uncertain that an objects boundaries are properly specified intothescene. This paper proposes a unique region proposal and object detection strategy based on retrieving depth information forlocalization and segmentation of the scenes’ objects in real-time manner. The obtained results on different datasets show superior accuracy in comparison to the commonly implemented techniques with regards to not only detection but also apixel-by-pixel accurate localization of objects.
Real time object detection, region proposal, computer vision, RGBD object detection, two stage object detection.
Pablo Josué Francia Del Busto, Rodson Vladimir Ayme Tambra and Juan Antonio Flores Moroco, Department of Software Engineering, Universidad Peruana de Ciencias Aplicadas, Lima, Perú
Serverless models are one of the latest architecture models provided by Cloud vendors such as AWS and Microsoft, we are exploring serverless applications to develop a progressive web application that will help future developers and project leaders to better manage their projects. As seen in the investigationdone, we see Latin America being below the global average incompleting projects on time or within the planned budget. In this work we will explain the agile life cycle of the web application developed, focusing on the Scrum aspects of the tool design, the serverless architecture of the app and its following development, while also applying an analysis of the current projects to measurethe level of effectiveness that a proper Scrum method can haveon finishing a project correctly on time and within budget Journals.
Progressive app, Web application, FaaS, Serverless, Scrum, Agile.
Sameir A. Aziez, Electromechanical Engineering Dept., University of Technology, Dr. Asst. Prof. Saad M, khaleefah, Al- hikma college university, BAGHDAD – IRAQ, Bassam H. Abed, Electrical Engineering Dept., University of Technology, Iraq, Thamir R. Saeed, Electrical Engineering Dept., University of Technology, Iraq, Shaymaa A. Mohammed, Electrical Engineering Dept., University of Technology, Iraq, Ghufran. M. Hatema, Najaf Technical College, Al-Najaf Al-Ashraf, Iraq
The observation of dynamic systems is essential in many fields. Many algorithms are used to estimate this dynamic system. Short-Time Fourier transform (STFT) is one of these algorithms. This paper presents an optimized STFT to extract the Doppler properties of that system. The improvement reached 7-11% against the unoptimized process, and the processing speed was also affected by 35%.
Short-time Fourier transform, optimization, Doppler, Dynamic system.
Wei Liu1, Fang Wei Li2, Hai Bo Zhang3, Bo Li4, 1School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China, 2Chongqing Key Laboratory of Public Big Data Security Technology, Chongqing, China, 3School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China, 4Chongqing Key Lab of Mobile Communications Technology，Chongqing Universityof Post and Communications
This paper studies a wireless power communication network(WPCN) based on orthogonal frequency division multiplexing (OFDM) with time reversal(TR). In this paper, the " Harvest Then Transmit " protocol is adopted, and the transmission time block is divided into three stages, the first stage is for power transmission, the second stage is for TR detection, and the third stage is for informationtransmission. The energy limited access point (AP) and the terminal node obtain energy fromthe radiofrequency signal sent by the power beacon (PB) to assist the terminal data transmission. The energy limited AP and the terminal node obtain energy from the radio frequency signal sent by the PB to assist the terminal data transmission. In the TR phase and the wireless information transmission (WIT) phase, the terminal transmits the TR detection signal to the AP using the collected energy, and the AP uses the collected energy to transmit independent signals to a plurality of terminals through OFDM. In order tomaximize the sum rate of WPCN, the energy collection time and AP power allocation are jointly optimized. Under the energy causal constraint, the subcarrier allocation, power allocation and time allocation of the whole process are studied, and because of the binary variables involved in the subcarrier allocation, the problem belongs to the mixed integer non-convex programming problem. the problem is transformed into a quasiconvex problem, and then binary search is used to obtain the optimal solution. The simulation results verify the ef ectiveness of this scheme. The results showthat the proposed scheme significantly improves the sum rate of the terminal compared to the reference scheme.
Wireless Powered Communication Network, Ttime Reversal, OFDM.
José Manuel Brotons Martínez, Economic and Financial Department, Miguel Hernández University, Elche, Spain
Since water is an essential element for agriculture, it is crucial to measure its productivity. In this regard, regions with a scarcity of water coexist with others that have an abundance of it, and whose cost is practically non-existent. So, to make the results comparable, we need to obtain a correct measurement, which will require setting a market price for water in areas where no price has yet been set. Therefore, the aim of this paper is to propose new productivity indicators based on fuzzy logic, whereby experts’ opinions about the possible price of the use of water as well as the annual variability of agricultural prices can be added. Therefore, the fuzzy willingness to pay (FWTP) and fuzzy willingness to accept (FWTA) methodology will be applied to create an artificial water market. The use of fuzzy logic will allow the uncertainty inherent in the experts’ answers to be collected. Ordered Weighted Averaging (OWA) operators and their different extensions will allow different aggregations based on the sentiment or interests reflected by the experts. These same aggregators, applied to the prices of the products at origin, will make it possible to create new indicators of the economic productivity of water. Finally, through an empirical application for a pepper crop in south-eastern Spain we can visualize the importance of the different indicators and their influence on the final results.
Water Economic Productivity, OWA, Fuzzy Willingness to Pay, Fuzzy Willingness to Accept.