Reviewerly/exHarmony huggingface dataset:
Assessing Ensemble Learning Techniques in Bug Prediction

The application of ensemble learning techniques is continuously increasing, since they have proven to be superior over traditional machine learning techniques in various domains. These algorithms could be employed for bug prediction purposes as well. Existing studies investigated the performance of ensemble learning techniques only for PROMISE and the NASA MDP public datasets; however, it is important to evaluate the ensemble learning techniques on additional public datasets in order to test the generalizability of the techniques. We investigated the performance of the two most widely-used ensemble learning techniques AdaBoost and Bagging on the Unified Bug Dataset, which encapsulates 3 class level public bug datasets in a uniformed format with a common set of software product metrics used as predictors. Additionally, we investigated the effect of using 3 different resampling techniques on the dataset. Finally, we studied the performance of using Decision Tree and Naïve Bayes as the weak learners in the ensemble learning. We also fine tuned the parameters of the weak learners to have the best possible end results.
Comparison on Machine Learning Algorithm to fast detection of Malicious Web pages

The advance growth of technology today, has simply brought to the increasing of the Internet and online activities such as in business, management, education and other areas. However, this has exposed to the malicious activities and online threat to the users which will disrupt the method of every system and performance. Accurate and fast detection on such threats in a timely manner is vital. Furthermore, the scale of the web pages (i.e huge data) and with heterogeneous nature of the web itself has complicate the detection process, and unable to detect accurately. Defining features in detecting malicious web pages is a vital condition in order to generate accurate detection result. These may lead to misdetection of malicious web as it only focus on certain criteria of feature selection. Furthermore, previous approached have used blacklist technique which a conventional method and have shown promising result in detecting malicious webpages. Therefore, implementing the principle of the machine learning, which is training the classification algorithm will be perform to improve the detection accuracy. Output will be evaluated using correctly classified instances and incorrectly classified instances. The WEKA (Waikato Environment for Knowledge Analysis) will be used for testing and generating the comparison output. Selected dataset from well-known resources will be used based on identified features in order to verify the web pages form legitimates ones. Compared to several decision tree method, Random Forest has shown promising and higher sensitivity result towards malicious data which is 98.3% compared to other classification algorithm
Research on Fall Detection Algorithm Based on CNN and LSTM

At present, the use of sensors for fall detection mainly uses manual methods to extract characteristic signals, which limits the accuracy of the algorithm. In order to improve the accuracy of fall recognition, a fall detection algorithm based on fusion convolutional neural network (CNN) and long short-term memory network (LSTM) is designed. The algorithm uses the efficient feature extraction capabilities of CNN to automatically extract the feature values of the data, and uses them as the input information of the LSTM network, and then uses the ability of LSTM to describe the long-term dependence to obtain the overall time series, and finally uses the Soft-max classifier to perform the fall and Identification of daily activities. In order to verify the feasibility of the algorithm, designed a comparative experiment. By comparing with the latest fall detection algorithm, the fusion algorithm has a fall detection accuracy rate of 98.8%, and the accuracy of human activity recognition and loss accuracy are 98.7% and 0.18%, respectively. The experimental results show that the algorithm proposed in this paper has higher recognition accuracy and lower loss accuracy, can effectively identify daily activities and fall behavior, and provide timely assistance for the elderly.
May I Show You the Route? Developing a Service Robot Application in a Library Using Design Science Research

The present study demonstrates the ongoing development of a service robot application in a campus library through the application of Design Science Research (DSR) combined with user-centered design (UCD). In particular, we involved librarians and co-created an application solution addressing two use cases: giving directions to a desired subject area/book and providing recommendations for an event/book. Additionally, we involved potential end users to evaluate the robot artifact through an onsite usability test (N = 14) already within the design cycle. This process resulted in a successful application of the robot artifact for the identified use cases and shows feasibility of combining DSR with UCD.
Human Action recognition using STIP Evaluation techniques

Abstract Human action recognition plays a big role in human-to-human interaction and interpersonal relations. As a result, it provides information regarding the identity of someone, their temperament, and condition, it’s tough to extract. Recognition of actions within the video isn’t a matter for human sensory system. The identification of the actions of the person by the system wants someone special mechanisms. The identification of the actions by the systems are going to be useful in computer vision method. This method is divided into low level action recognition process and high level recognition process. Recognizing the actions victimization the one feature values extracted goes under low level action recognition method. These method is simple to implement and that they don’t seem to be reliable all the time. The high level action recognition method needs some special hardware’s to discover the actions within the video. The experimental results proved that the proposed methodology achieved better performance in terms of accuracy, sensitivity and specificity.
Diversifying Emotional Experience by Layered Interfaces in Affective Interactive Installations

Abstract This paper aims to improve users’ experience in affective interactive installations through the diversification of interfaces. With logically organized hierarchical experience, diverse interfaces with emotion data as inputs enhance users’ emotional interaction to be more natural and immersive. By using facial affect detection technology, an installation with diverse input interfaces was tested with an organic formal setting. Mechanical flowers and support structure based on the organic form were deployed as its physical output for a multitude of sensorial dimensions. With actions of the mechanical flowers, such as blooming, closing, rotating, glowing and blinking, a layered experiential sequence was created and the atmosphere of the installation was evaluated to be more engaging. In this way, the layered complexity of information was transferred to users’ immersive emotional experience. We believe that the practices in this work can contribute to deeper emotional engagement with users and add new layers of emotional interactivity.
Object Detection Based Handwriting Localization

We present an object detection based approach to localize handwritten regions from documents, which initially aims to enhance the anonymization during the data transmission. The concatenated fusion of original and preprocessed images containing both printed texts and handwritten notes or signatures are fed into the convolutional neural network, where the bounding boxes are learned to detect the handwriting. Afterwards, the handwritten regions can be processed (e.g. replaced with redacted signatures) to conceal the personally identifiable information (PII). This processing pipeline based on the deep learning network Cascade R-CNN works at 10 fps on a GPU during the inference, which ensures the enhanced anonymization with minimal computational overheads. Furthermore, the impressive generalizability has been empirically showcased: the trained model based on the English-dominant dataset works well on the fictitious unseen invoices, even in Chinese. The proposed approach is also expected to facilitate other tasks such as handwriting recognition and signature verification.
Design and Implementation of Morphed Multi-Rotor Vehicles with Real-Time Obstacle Detection and Sensing System

Multirotor unmanned aerial vehicles (MUAVs) are becoming more prominent for diverse real-world applications due to their inherent hovering ability, swift manoeuvring and vertical take-off landing capabilities. Nonetheless, to be entirely applicable for various obstacle prone environments, the conventional MUAVs may not be able to change their configuration depending on the available space and perform designated missions. It necessitates the morphing phenomenon of MUAVS, wherein it can alter their geometric structure autonomously. This article presents the development of a morphed MUAV based on a simple rotary actuation mechanism capable of driving each arm’s smoothly and satisfying the necessary reduction in workspace volume to navigate in the obstacle prone regions. The mathematical modelling for the folding mechanism was formulated, and corresponding kinematic analysis was performed to understand the synchronous motion characteristics of the arms during the folding of arms. Experiments were conducted by precisely actuating the servo motors based on the proximity ultrasonic sensor data to avoid the obstacle for achieving effective morphing of MUAV. The flight tests were conducted to estimate the endurance and attain a change in morphology of MUAV from “X-Configuration” to “H-Configuration” with the four arms actuated synchronously without time delay.
Word Sense Disambiguation based on Sequence Topic Model using sense dependency

Word sense disambiguation is a challenging task that aims to distinguish the correct sense for a target word. Unlike typical methods which only use the context information, we present the basic idea that incorporates the global sense distribution and contextual sense dependency to determine the correct sense. In this paper, we leverage the generative process of the sequence topic model to model the sense distribution from the whole global corpus directly. Since contextual sense dependency is more likely to happen between successive words, we hypothesize that the sense assignment of the target word depends on the sense of the previous word. Hence, the sense dependency could be modeled by the probabilistic topic model with hidden Markov chain assumption. Furthermore, the information in the sense inventory of WordNet is used as prior knowledge for obtaining the non-uniform sense distribution over words. It is notable that the prior knowledge contributes to parameter learning and inference during the Gibbs Sampling procedure, hence we call the proposed method a knowledge-based unsupervised approach. We evaluate the proposed method on Senseval-2, Senseval-3, SemEval-2007, SemEval-2013, and SemEval-2015 English All-Word WSD datasets and the experimental results show that although the performance of the proposed method is restricted by the sparse problem, it still achieves comparable performance compared with the state-of-the-art knowledge-based approaches.
Text and Synthetic Data for Domain Adaptation in End-to-End Speech Recognition

Neural sequence-to-sequence systems deliver state-of-the-art performance for automatic speech recognition (ASR). When training such systems, one often faces the situation where sufficient amounts of training data for the language in question are available, however, with only small amounts of data for the domain in question. This problem is even bigger for end-to-end speech recognition systems that only accept transcribed speech as training data, which is harder and more expensive to obtain than text data. To alleviate this problem we supplement an end-to-end ASR system with a Text-Encoder which injects text-only input directly into the decoder. In addition, we compare the performance of using text-only input with synthetic speech. Furthermore, we prove for a specific domain that using a very small amount of transcribed speech and a sufficient amount of text-only data from the target domain outperforms adapting with a large amount of domain transcribed speech. Finally, we improve with the Text-Encoder learning new words, e.g., named entities, with no need for any context.
Digital fashion - An optimal solution for fashion industry during Covid-19 pandemic

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Nhut Tran Minh, Ha Ngo Ngan; Digital fashion - An optimal solution for fashion industry during Covid-19 pandemic. AIP Conf. Proc. 20 September 2021; 2406 (1): 020039. https://doi.org/10.1063/5.0066478 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
A method of Mission-driven knowledge reasoning for military information service

In order to efficiently provide accurate information services for commanders at all levels and enhance our army's information service capabilities, this paper presents a method of Mission-driven knowledge reasoning for military information service. The military information service ontology model is established with the core concepts of mission, information service activities and information resources. The semantic relationship and timing relationship between concepts in the model are defined. These can help us extract accurate information from massive information resources and provide a theoretical basis for mission-driven reasoning of knowledge in the field of military information services. Take blocking the river bank breach as an example, the information service model and the knowledge reasoning method of the case are tested. The experimental results show that the constructed ontology model and knowledge reasoning method is validity.
Using Robust Regression to Find Font Usage Trends

Fonts have had trends throughout their history, not only in when they were invented but also in their usage and popularity. In this paper, we attempt to specifically find the trends in font usage using robust regression on a large collection of text images. We utilize movie posters as the source of fonts for this task because movie posters can represent time periods by using their release date. In addition, movie posters are documents that are carefully designed and represent a wide range of fonts. To understand the relationship between the fonts of movie posters and time, we use a regression Convolutional Neural Network (CNN) to estimate the release year of a movie using an isolated title text image. Due to the difficulty of the task, we propose to use of a hybrid training regimen that uses a combination of Mean Squared Error (MSE) and Tukey’s biweight loss. Furthermore, we perform a thorough analysis on the trends of fonts through time.
DeepSCT: Deep Learning Based Self Correcting Object Tracking Mechanism

This paper presents a novel mechanism, DeepSCT, to handle the long-term object tracking problem in Computer Vision. The paper builds around the premise that the classical tracking algorithms can handle short-term tracking problems efficiently; however, they fail in the case of long-term tracking due to several environmental disturbances like occlusion and out-of-frame going targets. The relatively newer Deep Learning based trackers have higher efficacy but suffer from working in real-time on low-end hardware. We try to fuse the two methods in a unique way such that the resulting algorithm has higher efficiency and accuracy simultaneously. We present a modular mechanism, which can accommodate improvements in its sub-blocks. The algorithm was tested on the VisDrone-SOT2019 dataset for a person tracking task. We quantitatively and qualitatively show that DeepSCT significantly improved classical algorithms' performance in short-term and long-term tracking problems.
Ontology for Knowledge Graphs of Telecommunication Network Monitoring Systems

When Knowledge Graphs (KG) are used in practice, in particular for complex objects modeling, Semantic web recommends building KGs using existing common and domain ontologies as the base ontologies for modeling and extend them when developing new applications. In this work, detailed ontology for telecommunication networks (TN) monitoring system based on KG is discussed. The proposed ontology uses common ontologies and TN domain ontologies like Telecommunication Services Domain Ontology (TSDO) and TOUCAN Ontology (ToCo) as basic ontologies. To build the required ontology TSDO is extended on application level, in particular, new sub-ontologies for subdomains tasks solving are added. The case study of creation an ontology for TN monitoring system of a Cable TV operator is introduced. The developed ontology is described using Web Ontology Language (OWL).
Design and Implementation of a Sign Language Translation System for Deaf People

Sign language is a common language for deaf and mute people to communicate information. In order to make deaf people watch video without barrier, video plays and sign language translation solves the problem of deaf people obtaining video information. Subtitle processing and Chinese word segmentation were adopted to find corresponding words in sign language dictionary, transcoding was sent to unity through socket mechanism, realizing the process of sign language translation driven by video subtitle. Deaf people can watch video with Chinese subtitles and see clear, smooth and natural sign language translation animation when using it. The sign language translation animation library is based on the unified, standardized and common grammar sign language. Users can easily get the information in video and gradually standardize the use of sign language. Movie sign language translation system is of special significance to the hearing impaired people to improve their quality of life and has produced practical value for social progress.
The Impact of the Quantum Data Plane Overhead on the Throughput

Currently, although a standard distinction between quantum data plane and quantum control plane is still missing, preliminary works specify that classical control messages operating at the granularity of individual qubits and entangled pairs are, in terms of functionalities, closer to classical packet headers than control plane messages. Thus, they have been considered as part of the quantum data plane, by contributing to its overall overhead. As a consequence, the very concept of throughput needs to be re-defined and studied within the Quantum Internet. The aim of this treatise is to shed the light on this crucial aspect. Specifically, we conduct a theoretical analysis to understand the factors determining the overhead in the quantum data plane and their reflection on the throughput. The analysis is crucial and preliminary for designing any effective quantum communication protocol. Specifically, we derive closed-form expressions of the throughput in different scenarios, and the nonlinear relationship between throughput, entanglement throughput and classical bit rate is disclosed. Finally, we validate the theoretical analysis through numerical results conducted on the IBM Q-Experience platform.
Designing for Driver's Emotional Transitions and Rituals

Emotions are a topic of increasing interest in vehicle design and research as they have a substantive impact on people's behaviour, affecting driving performance and being a source of safety issues particularly on long journeys. However, emotions do not usually occur distinctly and individually and frequently transition and transform between states. It can be challenging to obtain information about the exact emotions drivers experience, especially when subtle. We present design-led research focusing on identifying scenarios that contain normally unarticulated emotions and mental reminders that drivers use to make a journey safer and develop concepts for in-vehicle interactions that assist with these rituals. As results of the research, we designed and user tested in-vehicle interactions for two emotional transition scenarios - pre-journey preparation (‘Ready... Steady … Relax’) and checking the progress of a journey (‘Driving Whisper’).
Combating Word-level Adversarial Text with Robust Adversarial Training

NLP models perform well on many tasks, but they are also easy to be fooled by adversarial examples. A small perturbation can change the output of the deep neural network model. This kind of perturbation is hard to be perceived by humans, especially adversarial examples generated by word-level adversarial attack. Character-level adversarial attack can be defended by grammar detection and word recognition. The existing word-level textual adversarial attacks are based on synonym replacement, so adversarial texts usually have correct grammar and semantics. The defense of word-level adversarial attack is more challenging. In this paper, we propose a framework which is called Robust Adversarial Training (RAT) to defend against word-level adversarial attacks. RAT enhances the model by combining adversarial training and data perturbation during training. Our experiments on two datasets show that the model based on our framework can effectively defend against word-level adversarial attacks. Compared with the existing defense methods, the model trained under RAT has a higher defense success rate on 1000 adversarial examples. In addition, the accuracy of our model on the standard testing set is also better than the existing defense methods, and the accuracy is very close to or even higher than that of the standard model.
Data Collection: Use and Transformation in Predictive Maintenance Models

Operational Reliability of the equipment for the operation of industrial plants is an indicator of great importance in industrial processes, which must be measured as continuously as possible with a minimum of interruptions. To avoid the occurrence of failures, the appropriate time to intervene the equipment must be established. The prediction of this indicator is based on data collected from the equipment condition and its proper analysis. However, failure data are not always available. The key issue is how to obtain and process good quality fault data to obtain high precision results. This document studies the use of Industry 4.0 and big data principles in the identification of potential faults that leadinglead to a functional failure in a multistage injection pump, which is a critical equipment for the disposal of wastewater in the hydrocarbon industry. The values of torque, amperage and rpm of the equipment, as well as temperature, and pressure and flow of the water were generated using the specialized simulation software Simscape™ from the MATLAB tool kit. Various fault types were defined for the pump and some models were built using machine learning with the simulation data to detect the presence of faults and identify their type. The model with the highest precision was selected and corresponds to a tree classification algorithm with an accuracy of 95%.