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Even though the various features of satirical language have been studied in computational linguistics, most of the research works have relied on the performance of the single machine learning algorithm. However, the implicit traits embedded in the language demand more certain, precise and accurate combination powers of an individual algorithm. In this study, we analyzed the performance of emotion-based...
By analyzing the disadvantages of the traditional KNN using lazy learning that directly classify the data based on the K neighboring classes using the majority voting method, a new Sigmoid weighted classification algorithm WKS (Weighted KNN Based On Sigmoid) was proposed. WKS provides a new method for learning and training, since each training data di ∊ D contributes to the correct classification...
We investigate the differentiation of cold and heat syndromes in Traditional Chinese Medicine with a special concern on the issue of data imbalance. Data imbalance occurs frequently in syndrome differentiation. In this study, we use a neural network classifier, fastText, to differentiate cold and heat syndromes, which have skewed distributions in the medical records and in the population. We investigate...
One of the important problems in social media platforms like Twitter is the large number of social bots or sybil accounts which are controlled by automated agents, generally used for malicious activities. These include directing more visitors to certain websites which can be considered as spam, influence a community on a specific topic, spread misinformation, recruit people to illegal organizations,...
VGG 16 and Inception-v3 networks were trained using a texture dataset of muddied and clean cows. A new dataset with 600 images that is similar to the actual texture dataset was introduced and used to train the networks. The method used to train the networks was transfer learning. ImageNet weights were trained using the similar dataset, then the newly trained weights were trained again using the actual...
This paper describes a sophomore-level ECE course that bridges the gap between the maker movement and typical engineering design courses. This course incorporates the practical skills of fabrication, assembly, and hardware integration found in makerspaces but deepens the perspective from one of getting it to work to designing it to work well and efficiently. This paper discusses mastery-based education...
This paper presents a neural-network-based approach for the detection of misplaced and missing regions in images. The main objective of this project is to develop an intelligent system that can identify a misplaced or missing region of a tested image. The system can be used to detect misplaced and missing components of printed circuit boards during the manufacturing process. Jigsaw puzzle pieces can...
Voice applications often require the ability to make user-friendly responses by judging the user or user-type from an extremely short utterance, such as a single word. However, it is assumed that performance becomes degraded as the utterance length decreases. In this paper, we examine the performance of speaker identification for extremely short utterances of less than two seconds and then study the...
Undergraduates perform independent research or use research, regardless of their discipline. For most students, this work is part of a formalized experience. However, formalized training in research methods, biases, and disposition is rare; students acquire these skills through mentoring, modeling, and making mistakes, and in the context and content of the discipline. These methods are not ideal....
Empirical-based models for security technologies in the commercial and military domain, including those that focus on protection, detection, and broader risk analysis, leverage data captured from sensors on network-connected devices including gateways, routers, and host nodes. Lacking, however, are datasets that contain specific state observations and actions from the evaluator (red/blue teammer)...
Many machine learning tasks have been shown solvable with impressive levels of success given large amounts of training data and computational power. For the problems which lack data sufficient to achieve high performance, methods for transfer learning can be applied. These refer to performing the new task while having prior knowledge of the nature of the data, gained by first performing a different...
Searching sounds by text labels is often difficult, as text labels cannot always provide sufficient information for the sound content. Previously we proposed an unsupervised system called IMISOUND for sound search by vocal imitation. In this paper, we further propose a Convolutional Semi-Siamese Network (CSN) called IMINET. IMINET uses two towers of Convolutional Neural Networks (CNN) to extract features...
A set of methods for estimating the parameters of the production process is proposed. This set includes expert assessments, Pareto analysis and elements of neural networks based artificial intelligence. The set forms up the methodology for assessing the state of the production process and forecasting its implementation. The sequence of application of individual methods is substantiated. Algorithms...
ConCIENCIA Estadística is a digital educational material proposed as support for the development of the homonymous workshop, organized in the framework of the Project for Improvement of Training in Exact and Natural Sciences in the Secondary School (Facultad de Ciencias Exactas y Naturales, Universidad Nacional de La Pampa). This material is described in this paper, offering technical details of its...
Clinical research registries need to be driven by data quality to improve the outcome of clinical trials and to provide the possibility to facilitate new research initiatives. The International Niemann-Pick Disease Registry (INPDR) is one such example of a clinical research registry. Unlike other registries where data quality is largely based around best effort manual data entry, the INPDR registry...
Research on activity recognition provides a wide range of ubiquitous computing applications. Once activities are recognized, computers can use this information to provide people with suitable services. In the past decade, many classification algorithms have been applied to activity recognition. However, most of them were based on the use of inertial measurement sensors, such as tri-axial accelerometers...
The advance of deep learning has made huge changes in computer vision and produced various off-the-shelf trained models. Particularly, Convolutional Neural Network (CNN) has been widely used to build image classification model which allow researchers transfer the pre-trained learning model for other classifications. We propose a transfer learning method to detect breast cancer using histopathology...
This paper introduces a new lifelong learning solution where a single model is trained for a sequence of tasks. The main challenge that vision systems face in this context is catastrophic forgetting: as they tend to adapt to the most recently seen task, they lose performance on the tasks that were learned previously. Our method aims at preserving the knowledge of the previous tasks while learning...
This paper presents a serious game-based system for dance notation training named LabanDance. The system tracks the full body motion via Microsoft Kinect of the user while performing the movement according to the dancing score. The system provides two training modes which a practice mode and an examination mode. The practice mode is designed for users with less knowledge in the notation where movement...
Industrial Control System (ICS) is used to monitor and control critical infrastructures. Programmable logic controllers (PLCs) are major components of ICS, which are used to form automation system. It is important to protect PLCs from any attacks and undesired incidents. However, it is not easy to apply traditional tools and techniques to PLCs for security protection and forensics because of its unique...
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