Reviewerly/exHarmony huggingface dataset:
Twitter User Sentiments Analysis: Health System Cyberattacks Case Study

Social media, such as Twitter, allow people to interact with ongoing events and share their sentiments. Therefore, people use social media to report and express their emotions about events they are experiencing. Furthermore, some officials take advantage of the popularity of social media to keep the public informed, especially during emergent events. Researchers have covered sentiment analysis on Twitter in many fields, such as movie reviews, stocks, politics, health, and sports. However, there is a research gap in studying the public's concerns on social media when a cybersecurity breach occurs and how people's sentiment changes over time. To fill the gap, The researchers selected the cyberattacks against Universal Health Services (UHS) during the late days of September 2020 and collected a large dataset of related tweets over five weeks. Live-streaming tweets and historical ones both were compiled. The focus while gathering tweets was in the context of cyberattacks on UHS using keywords and hashtags such as Universal Health System, UHS cyberattack, UHS Ransome, UHS security breach, and UHS locked. Then, the researchers determined tweets' sentiment classification on this developing event using deep learning of Long Short-Term Memory (LSTM) and Artificial Neural Networks (ANN) and their accuracies. Furthermore, the researchers performed exploratory data analysis for the dataset supplying information about how sentiment has changed over time to compare the sentiment per week since the start of these cyberattacks on UHS. This study is the first to provide an analysis of the public's sentiment toward a significant cybersecurity breach on a healthcare provider dealing with COVID-19 based on a large-scale dataset extracted from social media feeds.
Aspect-Based Sentiment Analysis with Semi-Supervised Approach on Taiwan Social Distancing App User Reviews

Sentiment analysis has a critical role to reveal an opinion in a text-based form. Therefore, we exploit this analysis to discover the sentiment polarity of Taiwan Social Distancing mobile application. This paper proposes a semi-supervised scheme for annotating this mobile application's reviews. The semi-supervised scheme utilized a combination of numeric rating and lexicon-based sentiment. In addition, we also perform the sentiment analysis on an aspect-based level. Based on the experiment, we decide to select three aspects to be analyzed. This paper also evaluates the proposed scheme by implementing bidirectional encoder representations from transformers (BERT) and multilayer perceptron (MLP) as the classification model using the sentiment label of the proposed scheme. The result shows that the annotation of the proposed scheme outperforms the data annotation using counterpart models.
Enhanced-feature pyramid network for semantic segmentation

Semantic segmentation is a complicated topic when they require strictly the object boundary accuracy. For autonomous driving applications, they have to face a long range of objective sizes in the street scenes, so a single field of views is not suitable to extract input features. Feature pyramid network (FPN) is an effective method for computer vision tasks such as object detection and semantic segmentation. The architecture of this approach composes of a bottom-up pathway and a top-down pathway. Based on the structure, we can obtain rich spatial information from the largest layer and extract rich segmentation information from lower-scale features. The traditional FPN efficiently captures different objective sizes by using multiple receptive fields and then predicts the outputs from the concatenated features. The final feature combination is not optimistic when they burden the hardware with huge computation and reduce the semantic information. In this paper, we propose multiple predictions for semantic segmentation. Instead of combining four-feature scales together, the proposed method processes separately three lower scales as the contextual contributor and the largest features as the coarser-information branch. Each contextual feature is concatenated with the coarse branch to generate an individual prediction. By deploying this architecture, a single prediction effectively segments specific objective sizes. Finally, score maps are fused together in order to gather the prominent weights from the different predictions. A series of experiments is implemented to validate the efficiency on various open data sets. We have achieved good results 76.4% <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> IoU at 52 FPS on Cityscapes and 43.6% <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> IoU on Mapillary Vistas.
Using Blockchain for Decentralized Artificial Intelligence with Data Privacy

Artificial Intelligence (AI) solutions require model training, which traditionally needs local datasets to be uploaded to a centralized server. However, centralized learning usually creates issues in terms of data privacy, data control, and data security. To address these issues, decentralized AI, especially federated learning, is proposed to achieve no data sharing or data exchange across servers and decentralized devices. This paper proposes a new decentralized AI paradigm based on blockchain, which goes beyond the decentralized federated learning to require no model pooling or orchestrating on any central server. That is, we propose further decentralization by dismissing the central or-chestrator in federated learning through the usage of blockchain (e.g., Ethereum in this paper), which is inherently a decentralized, distributed, peer-to-peer and autonomous mechanism. We implemented a prototype to demonstrate the idea, in which models are trained on data contributors’ ends. Instead of moving models to a central place for combining (such as in federated learning), models are trained independently on data contributors’ sides, and flow to the decentralized blockchain for updating. In this process, smart contracts on Ethereum provide specifications required for model creation. In addition, data contributors are rewarded Ether coins for their contributions to train the model with their data. This also incentivizes data contributors to provide data for AI training while protecting their data privacy.
FIDGAN: A Generative Adversarial Network with An Inception Distance

Two evaluation metrics for GAN models have been proposed in existing studies: Inception score (IS) and Fréchet Inception distance (FID). We propose a new GAN model based on the idea that backpropagating the FID score would guide the GAN to efficiently learn the distribution of real images and generate high-quality images. Based on such an idea, we propose a training loss for the generator to minimize a modified FID loss. Trained with the CIFAR-10 dataset, FIDGAN exhibited an FID of 11.78, which corresponds to a reduced FID compared to an existing model called BigGAN by 20.0%.
Metaverse-based education service adoption and preference study using conjoint analysis

This study analyzed the possibility and detailed properties of the metaverse-based new educational service that can lead the untact and non-face-to-face innovation of university education in the post-corona era. For this purpose, evaluation responses were collected from 120 college students in Korea and analyzed using the conjoint method. As a result of the survey, the intention to use the metaverse-based education service was high at 85%. And, it is considered relatively important in service selection, and the attributes are the personal character display method (27.6%), the educational application area (22.7%), the service usage fee (19.1%), the device type (17.4%), and the educational value (13.2%).) appeared in that order. In particular, college students showed the highest preference for metaverse-based educational services available at a price of less than 5 dollar per month through virtual classrooms while showing themselves as 3D-based avatar-type characters on a web-based PC. The results of this study are meaningful in providing basic data suggesting ways to utilize metaverse to innovate university education.
Proposal of Docker and Kubernetes Direction through the Event Timeline of Kubernetes

Modern developers typically run their workloads through cloud-native environments such as Docker and Kubernetes. Docker is a platform that runs and manages containers. With the birth of Docker, interest in containers and technology has grown. As one of the container orchestration tools that control and manage containers running on multiple hosts, Kubernetes has a very large share and is used by many cloud companies, making it the standard for practical container orchestration tools. Therefore, in this paper, by analyzing the Kubernetes event timeline, we present the future direction of Kubernetes and Docker, which are key tools in the cloud-native environment.
Optimization Framework for Green Networking (Invited Paper)

Reducing energy consumption - and especially carbon emissions - is one of the most important challenges facing humankind. ICT (Information and Communication Technology) is a powerful tool to reduce emissions as it offers alternatives to activities that are costly in energy: video streaming saves energy vs driving to a movie theater, for instance. Still, the carbon footprint of ICT in general and networking in particular have been growing, despite better energy efficiency per transmitted bit, due to the sheer growth in Internet usage and traffic.The information and communication technology (ICT) sector is currently estimated to create 2.7% of all global CO2 emissions [1] and expected to continue to increase. Hence, monitoring and reducing the CO2 emissions from ICT is increasingly important. Networks are responsible for around 13% of ICT energy consumption [2], a third of which in turn is attributable to backbone (core) networks [3]. As such, it is important to offer new mechanisms to reduce the energy footprint of networks.In this paper, we present a framework to include energy considerations (as a proxy for carbon footprint) into the management plane of a network. We apply this framework to optimize the network topology so as to minimize the energy spending while at the same time providing a satisfactory Quality of Experience (QoE) to the end users. We present this framework along high-level considerations, as its deployment and evaluation is left for future work.
Kick-motion Training with DQN in AI Soccer Environment

This paper presents a technique to train a robot to perform kick-motion in AI soccer by using reinforcement learning (RL). In RL, an agent interacts with an environment and learns to choose an action in a state at each step. When training RL algorithms, a problem called the curse of dimensionality (COD) can occur if the dimension of the state is high and the number of training data is low. The COD often causes degraded performance of RL models. In the situation of the robot kicking the ball, as the ball approaches the robot, the robot chooses the action based on the information obtained from the soccer field. In order not to suffer COD, the training data, which are experiences in the case of RL, should be collected evenly from all areas of the soccer field over (theoretically infinite) time. In this paper, we attempt to use the relative coordinate system (RCS) as the state for training kick-motion of robot agent, instead of using the absolute coordinate system (ACS). Using the RCS eliminates the necessity for the agent to know all the (state) information of entire soccer field and reduces the dimension of the state that the agent needs to know to perform kick-motion, and consequently alleviates COD. The training based on the RCS is performed with the widely used Deep Q-network (DQN) and tested in the AI Soccer environment implemented with Webots simulation software.
A Flexible Fall Detection Framework Based on Object Detection and Motion Analysis

To protect the elderly against harmful falling events, automatic fall detection solutions have been developed using wearable devices, ambient sensors, and video cameras. Vision-based fall detection is a non-invasive and low-cost solution for practical fall detection systems, as video cameras have already been deployed ubiquitously for surveillance purposes. There has been growing interest in leveraging the latest advances in artificial intelligence for image processing and computer vision to detect human falls from surveillance videos. This paper proposes a Motion-based Multi-Eye Fall Detection framework (MMEFD) based on detecting human objects and tracking objects' motion in the temporal domain. Unlike other vision-based methods that require predefined thresholds to detect a fall, the proposed framework localizes and tracks a person in videos via object detection and motion analysis over a time window with an appropriate size. As fall events may look different from different view angles, a multi-view fall dataset is used to train a classifier to detect falls. The proposed framework is flexible for different use cases as it could incorporate different object detection methods and analyze videos captured from different angles. It has produced good detection results on several other datasets that outperform two traditional fall detection methods.
Text Preprocessing Approaches in CNN for Disaster Reports Dataset

This study aims to compare the performance of the text-preprocessing methods namely automatic and semi-automatic preprocessing techniques in the CNN algorithm to carry out learning on disaster report dataset. The experimental results on the disaster dataset with a total of 200 records with the automatic text preprocessing technique produce an average accuracy of 0.81 and 1 with training data of 80:20 and 90:10. While in the optimize model that is semi-automatic text preprocessing approach (which is the author's proposed approach), the average accuracy obtained are 0.95 and 1 for dataset 80:20 and 90:10. The experimental results conclude that cleaning the dataset with the semi-automatic text preprocessing model can improve accuracy compared to the previous model. The proposed model will get convergence with 80:20 training data at epoch 20, batch size 5 and random state 34, while for dataset 90:10 the best convergence value at epoch 20–30.
Traffic Behavior-based Device Type Classification

Network operators play a fundamental role of providing network connectivity which fulfill service requirements of user devices. In the evolved 5G and 6G, the Internet of Things (IoT) is used for various purposes and the number of network connected devices is increasing. Typical types of connected devices in a network are not only IoT but also mobile phones and PCs, which may vary each access point or cell. For this reason, better network design and management requires awareness of connected device types based on traffic, service types, and surroundings. Possible approaches for device type classification can be categorized in two classes: analysis based on system logs and management databases; and analysis based on traffic contents and behavior in the network. However, data which network operators can collect is limited to traffic behavior because of privacy law and immature standardization of connected device-related data. Therefore, a new approach to classify device types in the network, whose connected devices are not limited to IoT, based on traffic behavior is necessary. This paper proposes a method to classify device types, such as hubs and cameras by analyzing only traffic behavior, without using traffic contents, in two stages. In the first stage, the proposed method classifies general device types, such as IoT and not IoT, by analyzing packet header statistics using machine learning. Then, in the second stage, the proposed method classifies connected devices that are classified as IoT in the first stage into IoT device types, by analyzing time series traffic behaviors using deep learning. We demonstrate that the proposed method classifies device type by analyzing real traffic dataset and outperforms the existing IoT-only device classification methods in terms of the number of types and the accuracy. The proposed method is suitable for automated network design, management, and control where a number of various devices are connected.
Shallow- and Deep- fake Image Manipulation Localization Using Deep Learning

Forged image localization is an important research task, as such images may have a tremendous impact of various aspects of society. Images can be manipulated using image editing tools (known as “shallowfakes”) or, recently, artificial intelligence techniques (“deepfakes”). While there are many existing works that are designed for manipulated areas localization on either shallow- or deep-fake images, there is no single solution that works for both cases. In this paper, we propose the first solution that can perform the localization task on both shallow- and deep-fake images, with high inference accuracy. The dataset and code are available at: https://github.com/zjbthomas/ShallowDeepFakesLocalization.
Blockchain-enabled Efficient and Secure Federated Learning in IoT and Edge Computing Networks

Federated learning (FL) has proven to be a promising solution to enable on-device machine learning over massive data generated by Internet of Things (IoT) devices at the network edge. However, the wide vulnerability space of the IoT network increases the risk of model poisoning attacks carried out by malicious or compromised IoT devices against FL model training. This paper proposes to exploit the use of blockchain technology to perform optimized monitoring of the behavior of IoT devices and select only reliable ones to provide model updates to the global FL model while preserving network performance. We formulate our worker device monitoring problem as an optimization problem and solve it to produce the optimal number of monitoring miners in the blockchain network in order to reduce the latency, bandwidth and energy consumption in the overall IoT network. Our results show that the optimal monitoring solution was able to reduce by 75% the total delay incurred by the IoT devices during training.
Guiding Interactive Film With Emotion-Profiling Chatbots

In interactive films, users have chances to influence the storyline at fixed points in time. To grant more user autonomy, this work offers a new model for interaction, driven by chatbots representing story characters. Whenever they desire, users may interact with the film characters in open-ended conversation. Through generative language models, the chatbots can respond according to the story context for dynamic entertainment. This unique interface is augmented by emotion recognition to profile the users’ character preferences, and guide them to suitable storylines. A user study on a prototype film confirmed the subjective value of this system, and its potential to drive story progression with minimal authorial scripting. An objective analysis of path selection algorithms further showed the benefits of assessing long-term storyline quality.
Cyber Security in Fog Computing Using Blockchain: A Mini Literature Review

Due to its many uses and ability to save and restore data, whether for businesses or individuals, cloud computing has become one of the mainstays in the technology world over the past few years. However, the limitations on speeds and sizes as well as the evolving of the Internet of Things made it challenging to rely on the cloud due to the extremely slow data processing. Therefore, to store and interpret the massive amount of data rapidly, programmers switched towards a new technology which is fog computing. This mini survey paper will outline fog computing as an integral part of cloud and edge computing for improving security and data protection. A systematic literature review of selected research papers that include findings and algorithms used to enhance security-related problems such as availability, latency, and intrusion detection will be discussed. This mini review paper finds out a state-of-the-art on the cybersecurity issues in fog computing using blockchain.
Byzantine-Resilient Federated Learning With Differential Privacy Using Online Mirror Descent

Federated learning is a privacy-preserving machine learning paradigm to protect the data of clients against privacy breaches. Federated learning algorithms are further reinforced with differential privacy to provide added privacy. Yet, many existing federated learning algorithms are not robust against Byzantine clients. Specifically, in the online federated learning environments, such as in real-time sensing and dynamic systems where data varies with time, coping with Byzantine clients poses a serious challenge. Byzantine clients disrupt convergence by poisoning the local models of non-faulty clients. Hence, it is important to develop an algorithm that is robust against Byzantine clients with the guarantee of convergence to the sequence of global models over time. Thus, this work proposes a robust algorithm based on online mirror descent to guarantee optimal convergence. The regret bound obtained is compared with the Federated Averaging algorithm. The regret bound shows that the proposed algorithm performs well even in the presence of Byzantine clients.
Illegal 3D Content Distribution Tracking System based on DNN Forensic Watermarking

With the development of the metaverse industry and the expansion of the 3D content market required to build the metaverse ecosystem, artificial intelligence technology that can create usable 3D content is developing. On the other hand, there is currently no legal definition of copyright for 3D content created based on artificial intelligence, and the scope of copyright application is ambiguous. In the current situation where copyright disputes and infringements on 3D contents are expected, this paper proposes a Deep Neural Network (DNN) forensic watermarking-based illegal 3D content distribution tracking system to protect illegal copying and distribution of 3D content. In this paper, we present our design result for the illegal 3D content distribution tracking system with detailed architecture, components, and message flows.
Performance Evaluation of Training ResNet Receiver Under Mobility for 5G NR Wireless Communication Systems

This paper investigates the bit error rate (BER) performances of a 5G NR-based uplink wireless communication system having a Residual Network-based (ResNet) receiver. The performances are analyzed in the clustered delay line (CDL) channel model under various training scenarios based on the speed of a transmitter. Computer simulation results confirm the robustness of a ResNet Receiver model trained under the highest transmitter speed scenario to recover the signals from a transmitter with various speeds. Two different channel encoders are also presented to demonstrate the consistency of the robustness.