Reviewerly/exHarmony huggingface dataset:
Image Colorization Algorithm Based on Deep Learning

As we know, image colorization is widely used in computer graphics and has become a research hotspot in the field of image processing. Current image colorization technology has the phenomenon of single coloring effect and unreal color, which is too complicated to be implemented and struggled to gain popularity. In this paper, a new method based on a convolution neural network is proposed to study the reasonable coloring of human images and ensures the realism of the coloring effect and the diversity of coloring at the same time. First, this paper selects about 5000 pictures of people and plants from the Imagenet dataset and makes a small dataset containing only people and backgrounds. Secondly, in order to obtain the image segmentation results, this paper improves the U-net network and carries out three times of down sampling and three times of up-sampling. Finally, we add the expanded convolution, and use the sigmoid activation function to replace the ReLU (The Rectified Linear Unit) activation function and put the BN (Batch Normalization) before the activation function. Experimental results show that our proposed image colorization algorithm based on the deep learning scheme can reduce the training time of the network and achieve higher quality segmentation results.
Online Boosting-Based Target Identification among Similar Appearance for Person-Following Robots

It is challenging for a mobile robot to follow a specific target person in a dynamic environment, comprising people wearing similar-colored clothes and having the same or similar height. This study describes a novel framework for a person identification model that identifies a target person by merging multiple features into a single joint feature online. The proposed framework exploits the deep learning output to extract four features for tracking the target person without prior knowledge making it generalizable and more robust. A modified intersection over union between the current frame and the last frame is proposed as a feature to distinguish people, in addition to color, height, and location. To improve the performance of target identification in a dynamic environment, an online boosting method was adapted by continuously updating the features in every frame. Through extensive real-life experiments, the effectiveness of the proposed method was demonstrated by showing experimental results that it outperformed the previous methods.
Deep self‐supervised transformation learning for leukocyte classification

The scarcity of training annotation is one of the major challenges for the application of deep learning technology in medical image analysis. Recently, self-supervised learning provides a powerful solution to alleviate this challenge by extracting useful features from a large number of unlabeled training data. In this article, we propose a simple and effective self-supervised learning method for leukocyte classification by identifying the different transformations of leukocyte images, without requiring a large batch of negative sampling or specialized architectures. Specifically, a convolutional neural network backbone takes different transformations of leukocyte image as input for feature extraction. Then, a pretext task of self-supervised transformation recognition on the extracted feature is conducted by a classifier, which helps the backbone learn useful representations that generalize well across different leukocyte types and datasets. In the experiment, we systematically study the effect of different transformation compositions on useful leukocyte feature extraction. Compared with five typical baselines of self-supervised image classification, experimental results demonstrate that our method performs better in different evaluation protocols including linear evaluation, domain transfer, and finetuning, which proves the effectiveness of the proposed method.
Performance evaluation of 3D median modified Wiener filter in brain T1-weighted magnetic resonance imaging

The purpose of this study aimed to evaluate the noise reduction efficiency of a 3D median modified Wiener filter (MMWF) in brain T1-weighted magnetic resonance (MR) images. A simulation using BrainWeb phantom data and real experimental research based on data of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were performed, and the 2D MMWF was modeled to prove the usefulness of the proposed 3D MMWF. Brain MR images were obtained according to the kernel size and noise level of 2D and 3D MMWF, and the coefficient of variation (COV) and edge preservation index (EPI) were used for the quantitative evaluation of the obtained images. According to the changes in COV with respect to the changes in filter size, simulated T1-weighted images with 3D MMWF had a 2.76 times higher denoising performance than 2D MMWF. Furthermore, the EPI of simulated T1 weighted images with 3D MMWF had a 1.17 times better performance than that of simulated T1-weighted images with 2D MMWF, particularly in noisy images. To confirm the performance of 3D MMWF with clinical T1-weighted images, we obtained a T1-weighted image from ADNI and applied 2D and 3D MMWF with respect to kernel size. According to COV changes with respect to kernel size in both filters, clinical T1-weighted images showed a 1.14 times improvement with 3D MMWF and had a similar tendency as simulated images. We compared 2D and 3D MMWF in terms of tissue preservation and denoising performance in T1-weighted images. Our results indicate that the proposed 3D MMWF has better denoising performance than 2D MMWF for Rician noise and preserved the edges of brain tissues.
Highlight removal for endoscopic images based on accelerated adaptive non-convex RPCA decomposition

Highlights always occur in endoscopic images due to their special imaging environment. It not only increases the difficulty of observation and diagnosis from surgeons, but also influences the performance of Mixed/Augmented Reality techniques in surgery navigation.In this paper, we propose a novel accelerated adaptive non-convex robust principal component analysis method (AANC-RPCA) to remove highlights in endoscopic images. We first detect absolute highlight pixels of the enhanced endoscopic images with adaptive threshold segmentation. The quasi-convex function is proposed to approximate a new non-convex objective function. With detected highlight pixels and quasi-convex function, it introduces gradient to shrink sparse matrix and obtains a faster speed of convergence. Then we divide the image into multiple blocks and perform the parallel computation to enhance the efficiency. Finally, we design a weighted template that decays outward with dilation and linear filtering to reconstruct the endoscopic images. Our approach is almost independent of hyper-parameters and can achieve adaptive decomposition.It has been verified on multiple types of endoscopic images through experiments and clinical blind tests. The results demonstrate that our method can obtain the best performance for the recovered images with more details in a shorter time (about 3-5 times).Coupled with the user study, both the quantitative and qualitative results indicate that our approach has the potential to be highly useful in endoscopy images. Compared with the existing highlight removal approaches, our method obtains the SOTA results and has the potential to be applied in the various medical processing processes.
Performance Analysis of Cloud-based Deep Learning Models on Images Recovered without Channel Correction in OFDM System

Channel correction plays an important role in performance of wireless communication systems. In conventional systems, channel estimation is one of the blocks at receiver side to compute channel impulse response. Various algorithms have been proposed to make them efficient and improve their performance. However, precise channel estimation incurs additional computational cost and increases complexity of the overall system. In this study, we have considered the otherwise to bypass channel estimation of an Orthogonal Frequency Division Multiplexing (OFDM) based image communication system designed to enable cloud-based deep learning (DL) computation. The simulations present performance analysis of OFDM system with and without channel correction in-terms of bit error rate (BER), and two image quality measures. Recovered image quality difference between the two systems significantly increases with higher E <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</inf> /N <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> . For inferencing analysis, in higher E <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</inf> /N <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> regions, model performance on images recovered with correction is same as on the original images while lags behind by 6% on images without correction. In lower E <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</inf> /N <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> regions, the model accuracy reduces by 10% on average for both systems. In addition, the model accuracy shows overlapping pattern in that region and for 3 dB, it has performed better on images recovered without correction.
Sentiment analysis on E-Marketplace User Opinions Using Lexicon-Based and Naïve Bayes Model

E-marketplaces experienced a surge in visits from 2018 to the present. The largest e-marketplaces in Indonesia (Tokopedia, Shopee, and Lazada) currently use operator services or e-services using Instagram and Twitter as customer service centers. Customer service centers have caused these three E-Marketplaces to receive massive user opinions on social media. Therefore, it is necessary to research a model that can understand the emotions in E-Marketplace users' opinions using sentiment analysis. This study uses a lexicon-based method to understand textual data in a dataset containing user opinions, then classify it using the Naive Bayes Model. This study examines, compares several models, and then evaluates them. This research uses the Lexicon-Based Model and Naive Bayes model. Thus, this research contributes to recommending the best model for Sentiment analysis on E-Marketplace User Opinion. The results of the study found that the combination of the TextBlob lexicon method and the Multinomial Naïve Bayes classification method outperformed the other three models.
Implementation of Lightweight Cryptography Core PRESENT and DM-PRESENT on FPGA

In this paper, two lightweight cryptography methods were introduced and developed on hardware. The PRESENT lightweight block cipher, and the DM-PRESENT lightweight hash function were implemented on Intel FPGA. The PRESENT core with 64-bit block data and 80-bit data key consumes 2,945 logic element, 1,824 registers, and 273,408 memory bits. Meanwhile, the DM-PRESENT core with 64-bit input and 80-bit key consumes 2,336 logic element, 1,380 registers, and 273,408 memory bits. The PRESENT core with 128-bit key and DM-PRESENT based on this core were also implemented. These cores were simulated for functional verification and embedded in NIOS II for implementation possibility on hardware. They consumed less logic resources and power consumption compared with conventional cryptography methods.
A kinematics principle for iterated revision

In probabilistic belief revision, the kinematics principle is a well-known and powerful principle which ensures that changing the probabilities of facts does not change unnecessarily conditional probabilities. A related principle, the principle of conditional preservation, has also been one of the main guidelines for the axioms of iterated belief revision in the seminal paper by Darwiche and Pearl. However, to date, a fully elaborated kinematics principle for iterated revision has not been presented. We aim to fill this gap in this paper by proposing a qualitative kinematics principle for iterated revision of epistemic states represented by total preorders. As new information, we allow sets of conditional beliefs, going far beyond the current state of the art of belief revision. We introduce a qualitative conditioning operator for total preorders which is compatible with conditioning for Spohn's ranking functions as far as possible, and transfer the technique of c-revisions to total preorders to provide a proof of concept for our kinematics principle at least for special revision scenarios.
Mobile-assisted peer feedback on EFL pronunciation: Outcome effects, interactional processes, and shaping factors

This article reports on a study examining mobile-assisted peer feedback on EFL pronunciation. The study focused on 10 Chinese EFL students majoring in music. They worked as mixed-proficiency dyads in a peer feedback task mediated by a social networking app (WeChat). The higher-proficiency students acted as feedback givers, and the lower-proficiency students as feedback receivers. Data sources included (a) comprehensibility scores in the pretest, posttest and delayed posttest, (b) recorded peer feedback sessions, and (c) interview transcripts. It was found that the feedback task contributed to the pronunciation improvement for both feedback givers and receivers. 13 interactional moves were identified in the peer feedback sessions. Based on the distribution of interactional moves and the participants’ self-positioning, it was found that four dyads displayed clear interactional patterns (collaborative, expert/novice, or dominant/dominant) but one dyad showed a mixed pattern. Four factors were found to shape the interactional processes. The first two factors were task -related (i.e. role assignment) and person -related (i.e. prior music learning and teaching experience), while the remaining two were context -related (i.e. significant others and online resources). The article also discusses pedagogical implications for maximizing the learning potentials of mobile-assisted peer feedback.
The benefits and challenges of applying Blockchain technology into Big Data: A literature review

Blockchain and big data are two technologies with great potential and great influence in the field of information technology. Big data research applied in many fields of society. However, big data has features as large size, temporal events, complex structure, and incompleteness. Therefore, big data exists many challenges that need to be researched such as data security, data integrity, anti-fraud, data quality, data management, data analysis, and data mining. Blockchain technology has the characteristics of distribution, immutability, transparency, and security. Therefore, integrating blockchain technology into big data is a promising solution to overcome these challenges. However, blockchain technology is not really mature yet. Researchers need to identify the problem and have a suitable approach for applying blockchain technology to big data. In this article, we survey and present a complete picture of the integrated base. At the same time, cloud services for big data, application range, and blockchain big data projects also presented. From there, the researchers were able to clearly identify the development challenges and future directions for this exciting topic.
Deep Learning-Based QoE Prediction for Streaming Services in Mobile Networks

Video streaming accounts for the most of the global Internet traffic and providing a high user Quality of Experience (QoE) is considered an essential target for mobile network operators (MNOs). QoE strongly depends on network Quality of Service (QoS) parameters. In this work, we use real-world network traces obtained from a major cellular operator in Turkey to establish a mapping from network side parameters to the user QoE. To this end, we use a model-aided deep learning method for first predicting channel path loss, and then, employ this prediction for predicting video streaming MOS. The experimental results demonstrate that the proposed model-aided deep learning model can guarantee higher prediction accuracy compared to predictions only relying on mathematical models. We also demonstrate that even though a trained model cannot be directly transferred from one geographical area to another, they significantly reduce the volume of required training when used for prediction in a new area.
Semantic Communication for Capacity-aware Remote Collaboration

The global spread of coronavirus has sparked a considerable interest in technologies that facilitate seamless communication between users which are physically or spatially distant. Using current remote collaboration systems that utilize 3D sensing with LiDAR and depth cameras, point cloud streaming, and MR/VR devices, distant users can communicate with each other as if they did in person. However, these systems may violate users' privacy since they can share information of their entire personal space with other users. In addition, although various point cloud compression methods have been proposed, remote transmission of 3D scenes still requires significant bandwidth. This paper proposes a 3D spatial data sharing system based on the paradigm of “semantic communication”, i.e., controlling communication in the units of semantic objects. Our system understands the semantics of the scene and leverages point cloud streaming, thereby enabling users to assert fine-grained control over their privacy. Further, the system adaptively controls the size of the data frame based on network capacity and scene context. The experimental results show that the network delay can be reduced by 96%. We have also tested our system in a commercial 4G network, showing that 3-D spatial sharing with point clouds over severe networks is possible.
Image Restoration under Semantic Communications

Semantic communication has emerged as the break-through beyond the Shannon theorem by transmitting and receiving semantic information instead of data bits or symbols regardless of its content. This paper proposes a two-stage reconstruction process to boost the system's performance. In the first phase, the image information is first decoded from the noisy received data by exploiting the channel knowledge. The decoded image is enhanced by a post-filter and image statistics. Different metrics are exploited to evaluate the image restoration quality of our considered model. Numerical results are obtained using natural images that verify the superior improvements of the proposed two-stage reconstruction process over the traditional decoded data. Moreover, the different metrics assessing the system performance based on their criteria can be conflicted with each other.
Quantum Key Distribution: A Security Solution for 5G-based IoT Networks

This paper aims to take a new look at a novel security solution for 5G-based Internet of things (IoT) networks using quantum key distribution (QKD). Quantum keys are created and sent from a key distribution server to IoT gateways thanks to radio-over-fiber (RoF) systems. More specifically, the raw key is encoded into the intensity of radio-frequency subcarrier before being sent to the gNB by optical fiber. It is then wirelessly forwarded to IoT gateways through radio frequency links. The major findings of the study reveal that the proposed system is an effective approach in order to distribute the quantum key with low quantum bit error rate and high secret key rate for 5G-based IoT networks.
Integrating AR and IoT Services into mHealth Applications for Promoting Wellbeing

By blending real world information with data from digital environments using modern visualizations empowered with augmented reality features, more comprehensive and attractive software solutions can be created. Augmented Realty and Internet of Things are among the state-of-the-art technologies that contribute to the adoption of new, disrupting solutions in the fields of health and urban planning. IoT body sensors can record real-time measurements and offer instant access to users' vital signs while in parallel, environmental IoT sensors record real-time parameters for weather and pollution. In this work we are introducing a prototype application as part of a Nature-based Solution in an urban environment that offers advanced interactivity features with the users and motivates them to increase their physical activity. AR objects are placed at certain locations in the park and present user-specific or location-related information. Modern gamification techniques are also utilized, such as leaderboards and badges with content generated from the users' health data. Furthermore, location-related information is visualized including weather forecasts, environmental data and or briefing and events regarding the intervention in the urban area. The evaluation results of the system in real-world scenarios highlight positive user engagement remarks for promoting mental health and wellbeing in urban environments.
A Dataset Auditing Method for Collaboratively Trained Machine Learning Models

Dataset auditing for machine learning (ML) models is a method to evaluate if a given dataset is used in training a model. In a Federated Learning setting where multiple institutions collaboratively train a model with their decentralized private datasets, dataset auditing can facilitate the enforcement of regulations, which provide rules for preserving privacy, but also allow users to revoke authorizations and remove their data from collaboratively trained models. This paper first proposes a set of requirements for a practical dataset auditing method, and then present a novel dataset auditing method called Ensembled Membership Auditing ( EMA ). Its key idea is to leverage previously proposed Membership Inference Attack methods and to aggregate data-wise membership scores using statistic testing to audit a dataset for a ML model. We have experimentally evaluated the proposed approach with benchmark datasets, as well as 4 X-ray datasets (CBIS-DDSM, COVIDx, Child-XRay, and CXR-NIH) and 3 dermatology datasets (DERM7pt, HAM10000, and PAD-UFES-20). Our results show that EMA meet the requirements substantially better than the previous state-of-the-art method. Our code is at:https://github.com/Hazelsuko07/EMA.
Temporal Analysis of Transaction Ego Networks with Different Labels on Ethereum

Due to the widespread use of smart contracts, Ethereum has become the second-largest blockchain platform after Bitcoin. Many different types of Ethereum accounts (ICO, Mining, Gambling, etc.) also have quite active trading activities on Ethereum. Studying the transaction records of these specific Ethereum accounts is very important for understanding their particular transaction characteristics, and further labeling the pseudonymous accounts. However, traditional methods are generally based on static and global transaction networks to conduct research, ignoring useful information about dynamic changes. Our work chooses six kinds of important account labels, and builds ego networks for each kind of Ethereum account. We focus on the interaction between the target node and neighbor nodes with temporal analysis. Experiments show that there is a significant difference between various types of accounts in terms of several network features, helping us better understand their transaction patterns. To the best of our knowledge, this is the first work to analyze the dynamic characteristics of Ethereum labeled accounts from the perspective of transaction ego networks.