Reviewerly/exHarmony huggingface dataset:
Multi-target segmentation of pancreas and pancreatic tumor based on fusion of attention mechanism

Existing neural network segmentation schemes perform well in the task of segmenting images of organs with large areas and clear morphology, such as the liver and lungs. However, it is difficult to segment organs with variable morphology and small target area, such as pancreas and tumors. In order to achieve accurate segmentation of pancreas and its cysts, MDAG-Net (Multi-dimensional Attention Gate Network) is proposed in this paper. Combining three attention mechanisms: spatial, channel and multi-dimensional feature map input, MDAG(Multi-dimensional Attention Gate) obtains the global distribution of semantic information in spatial and channel dimensions, filters redundant information in shallow feature maps, realizes feature response, and recalibrates convolution kernel parameters. In addition, the WML(Weighted cross entropy and MIoU loss function) loss can adaptively assign the weight of category loss and count the classification error of global pixels, which can increase the error attention of the target area and improving the segmentation accuracy of the network. The algorithm is experimented on the Task07_Pancreas dataset, compared with U-Net under the same conditions, the Dice coefficient, Precision, Recall rate and MIoU (Mean Intersection over Union) of MDAG-Net are improved by 5.3%, 1.5%, 12.7% and 7.6% respectively. The results show that MDAG-Net can accurately segment the region of pancreas and its cyst in CT(Computed Tomography) images, which proves that MDAG has better segmentation efficiency for such small target regions.
Structural health monitoring data cleaning based on Bayesian robust tensor learning

Data cleaning is necessary to obtain high-quality monitoring data, which consists of two parts: anomaly identification and repair. A high-quality dataset is always used to repair data anomalies in current research, which may be difficult to obtain in practice because of the large number of data anomalies existing in real data. Besides, outliers, baseline shifts, and noise in dataset can lead to wrong missing restoration results. Therefore, the anomaly identification and location are needed to remove outliers, baseline shifts, and noise to regard the repair of these anomalies as missing data restoration. However, the identification of multiple data anomalies is time consuming and challenging. To this end, we propose a Bayesian robust tensor learning model to reconstruct the monitoring data tensor by extracting potential data features of the spatiotemporal tensor. A mixture of Laplace distribution with generalized inverse Gaussian distribution is used to model noise to enhance robustness. The tensor formed from the data containing anomalies can be input directly into the model without the identification and location of the data anomalies in advance. All data anomalies are repaired during the tensor reconstruction process. In addition, outliers, baseline shifts, and noise do not interfere with the missing restoration results owing to the robustness of the model. Furthermore, different cleaning strategies are proposed for dynamic and static response data. The data performance is verified using the real monitoring data of a concrete box girder bridge. The results show that the proposed method exhibits good data-cleaning performance when dealing with data containing a large number of data anomalies. In addition, as the abnormal rate increases, the proposed method still exhibits good data-cleaning performance.
Vietnamese hate and offensive detection using PhoBERT-CNN and social media streaming data

Society needs to develop a system to detect hate and offense to build a healthy and safe environment. However, current research in this field still faces four major shortcomings, including deficient pre-processing techniques, indifference to data imbalance issues, modest performance models, and lacking practical applications. This paper focused on developing an intelligent system capable of addressing these shortcomings. Firstly, we proposed an efficient pre-processing technique to clean comments collected from Vietnamese social media. Secondly, a novel hate speech detection (HSD) model, which is the combination of a pre-trained PhoBERT model and a Text-CNN model, was proposed for solving tasks in Vietnamese. Thirdly, EDA techniques are applied to deal with imbalanced data to improve the performance of classification models. Besides, various experiments were conducted as baselines to compare and investigate the proposed model's performance against state-of-the-art methods. The experiment results show that the proposed PhoBERT-CNN model outperforms SOTA methods and achieves an F1-score of 67.46% and 98.45% on two benchmark datasets, ViHSD and HSD-VLSP, respectively. Finally, we also built a streaming HSD application to demonstrate the practicality of our proposed system.
A Blockchain Implementation for Configurable Multi-Factor Challenge-Set Self-Sovereign Identity Authentication

Multi-factor challenge-set self-sovereign identity authentication (MFFSIA) is an important part for establishing trust between systems, devices, organizations and humans for the emerging machine-to-everything (M2X) economy. Most systems for identity authentication (IA) are single sign-on (SSO), or have fixed challenge sets of limited degree. Additionally, IA systems are controlled by governments, or corporations that are closely affiliated with government entities. The available systems for self-sovereign IA do not offer the necessary flexible configurability of challenge sets. Based on research publications about a formal MFSSIA protocol, this paper presents a blockchain employing implementation for a running case assuming different smart-contract blockchain systems must be connected for sensitive data exchange. The prototype offers a marketplace for challenge-set creation and the challenge/response-lifecycle management employs decentralized knowledge graphs (DKG) together with oracles for response evaluations.
Sentiment Analysis using deep learning for use in recommendation systems of various public media applications

Sentiment Analysis is a method of analyzing text and extracting opinions from it. It’s also known as emotion or opinion extraction, and it’s part of the machine learning as well as data mining categories. There are numerous ways to convey one’s sentiments. It can be articulated in a variety of ways, such as through feelings, making judgments, or expressing one’s vision or insight. Sentiment investigation is the act of detecting, recognizing, and categorizing a user’s emotion or view for any service, such as movies, product issues, events, or any other attribute that can be good, negative, or neutral. This analysis is based on social communication channels such as websites that included ratings, forum conversations, blogs, micro blogs, Twitter, and other social media platforms. The important goal of suggested system is to improvise accuracy and to generate recommendation system using deep learning algorithms.
DareFightingICE Competition: A Fighting Game Sound Design and AI Competition

This paper presents a new competition-at the 2022 IEEE Conference on Games (CoG)- called DareFightingICE Competition. The competition has two tracks: a sound design track and an AI track. The game platform for this competition is also called DareFightingICE, a fighting game platform. DareFightingICE is a sound-design-enhanced version of FightingICE, used earlier in a competition at CoG until 2021 to promote artificial intelligence (AI) research in fighting games. In the sound design track, participants compete for the best sound design, given the default sound design of DareFightingICE as a sample, where we define a sound design as a set of sound effects combined with the source code that implements their timing-control algorithm. Participants of the AI track are asked to develop their AI algorithm that controls a character given only sound as the input (blind AI) to fight against their opponent; a sample deep-learning blind AI will be provided by us. Our means to maximize the synergy between the two tracks are also described. This competition serves to come up with effective sound designs for visually impaired players, a group in the gaming community which has been mostly ignored. To the best of our knowledge, DareFightingICE Competition is the first of its kind within and outside of CoG.
Graph Analysis of the Ethereum Blockchain Data: A Survey of Datasets, Methods, and Future Work

Ethereum, currently the most actively-used and the second-largest blockchain platform, consists of a heterogeneous ecosystem, cohabited by human users, smart contracts (autonomous agents), ether (native cryptocurrency), tokens (digital assets), dApps (decentralized applications), and DeFi (decentralized finance). These key actors in the Ethereum interact with each other via transactions and contract calls. Given the highly connected structure, graph-based modeling is an optimal tool to analyze the data stored in Ethereum blockchain. Recently, several research works performed graph analysis on the publicly available Ethereum blockchain data to reveal insights into its transactions and for important downstream tasks, e.g., cryptocurrency price prediction, address clustering, phishing scams and counterfeit tokens detection. In this work, we conduct an in-depth survey of the existing literature. We categorize them based on publication years, venues, core ranking, and authors' affiliations, data usage and graphs construction, graph mining and machine learning techniques employed, and the new insights derived by them. We conclude by discussing our recommendations on the future work. Our article will be useful to data scientists, researchers, financial analysts, and blockchain enthusiasts.
Blockchain-related identity and access management challenges: (de)centralized digital identities regulation

The decentralization of digital identity management brought notable advantages to the user, together with complete ownership of his identity. In this paper is summarized the state of the art of the Identity and Access Management in the blockchain environment and the related regulatory or guidelines with a proposal, based on literature and on the previous experiences.
A Generalized Model for Identifying Fake Digital Images through the Application of Deep Learning

Digital photography usage plays a major role in today’s daily communication. Digital picture modification and tampering have become a lot easier because of enhanced technology in photo editing. The public’s perception, political views, and police investigations are all influenced by the manipulation of photographs. Forgery or tampering of digital photograph can be done by numerous techniques such as image splicing, copy-move, JPEG compression, light inconsistencies, retouching, etc. Every manipulation technique will leave some traces after forgery and with the help of those traces detection of forgery can be done by forensics. Digital forensics faces difficulty in identifying the authenticity of such digital images. Though manipulation of a single image is done by applying numerous techniques, traces are erased by using post-processing. Many approaches are proposed for image forgery detection by using any one of the tampering techniques (like copy-move, splicing, etc.). In the real–world scenario a universal model is recommended for detecting multiple manipulation operations on a single image.This paper aims to help detect forgery more effectively in digital images even with multiple tampering techniques used to forge one single image using deep learning techniques. Deep learning techniques take a large set of original and tampered images for forgery detection. This model is a self-learned model which differentiates the two classes of authentic and forgery images by extracting hidden features. The experiment results of the proposed approach will illustrate the model’s accuracy and efficacy.
Improving IoT Data Security and Integrity Using Lightweight Blockchain Dynamic Table

Over the past few years, the Internet of Things (IoT) is one of the most significant technologies ever used, as everything is connected to the Internet. Integrating IoT technologies with the cloud improves the performance, activity, and innovation of such a system. However, one of the major problems which cannot be ignored in such integration is the security of the data that are transferred between the client (IoT) and the server (cloud). Solving that problem leads to the use the of IoT technologies in more critical applications and fields. This paper proposes a new security framework by combining blockchain technology with the AES algorithm. Blockchain technology is used and modified to protect data integrity and generate unique device identification within minimal power consumption and best performance. The AES algorithm is used to improve the data confidentiality when being transmitted to the server. The outcomes demonstrated that the proposed solution improves the security system of the IoT healthcare data and proved its efficiency and power consumption compared to other methods.
IoT Data Storage on a Blockchain Using Smart Contracts and IPFS

Since the creation of the cryptocurrency Bitcoin, the interest in blockchain technology has increased, entering areas such as loT (Internet of Things) and data sharing. The main objective of this paper is to develop a system that allows the storage of data from loT services in a decentralized network with a blockchain managing transactions through a smart contract. The project was carried out using the blockchain Ethereum, IPFS (InterPlanetary File System) for storage, Solidity language for contract development, NodeJS for coding the simulation of loT devices, web interface and back-end of the solution. The MQTT protocol was used to transport data from the devices. Our main objective was achieved, as the tests carried out show use cases in which this solution has an advantage over the direct storage in the Ethereum blockchain.
Multiscale 3D-Shift Graph Convolution Network for Emotion Recognition From Human Actions

Emotion recognition from body gestures is challenging since similar emotions can be expressed by arbitrary spatial configurations of joints, which results in relying on modeling spatial-temporal patterns from a more global level. However, most recent powerful graph convolution networks (GCNs) separate the spatial and temporal modeling into isolated processes, where GCN models spatial interactions using partially fixed adjacent matrices and 1D convolution captures temporal dynamics, which is insufficient for emotion recognition. In this work, we propose the 3D-Shift GCN, which enables interactions of joints within a spatial-temporal volume for global feature extraction. Besides, we further develop a multiscale architecture, the MS-Shift GCN, to fuse features captured under different temporal ranges for modeling richer dynamics. After conducting evaluation on two regular action recognition benchmarks and two gesture based emotion recognition datasets, the results show that the proposed method outperforms several state-of-the-art methods.
NaNG-ST: A natural neighborhood graph-based self-training method for semi-supervised classification

The self-training method has been favored by scholars in semi-supervised classification. One of the greatest challenges in self-training methods is finding high-confidence unlabeled samples at each iteration. While multiple variations of self-training methods are developed, the strategy of finding high-confidence unlabeled samples heavlies on parameters and only utilizes labeled data alone, which makes the self-training method limited by the distribution of labeled data. To solve the above issues, a novel natural neighborhood graph-based self-training method (NaNG-ST) is proposed. In NaNG-ST, a parameter-free natural neighborhood graph (NaNG) is first constructed. The NaNG roughly reveals the real data distribution by exploiting unlabeled and labeled data. Based on NaNG, homogeneous and heterogeneous edges are defined to divide unlabeled samples into three cases. After that, homogeneous and heterogeneous edges can use the revealed distribution rather than labeled data alone to help NaNG-ST fast and effectively find confident unlabeled samples without any parameters. Besides, they also help NaNG-ST not to be limited by the distribution of initial labeled data. When a few initial labeled data can not roughly represent the distribution of the entire data, the NaNG helps NaNG-ST restore the real data distribution better. Intensive experiments on real-world data sets prove that NaNG-ST outperforms 7 popular semi-supervised self-taught approaches in terms of classification accuracy, mean F-measure and required running time.
Deep Learning for Real-Time Malaria Parasite Detection and Counting Using YOLO-mp

Malaria in the rural and remote regions of tropical countries remain a major public health challenge. Early diagnosis and prompt effective treatment are the basis for the management of malaria and for reducing malaria mortality and morbidity worldwide and the key to malaria elimination. While Rapid Diagnostic Test (RDT) remains the current mainstay testing malaria infections, it is usually used in conjunction with clinical findings and lab tests of blood films through Microscopy- the gold standard of malaria diagnosis. Recent reports suggest that the accuracy of RDTs could be compromised due to parasite antigen gene deletion(s), and the lack of expertise and high turnover time makes microscopy impractical to be used in rural and remote areas which impede the diagnosis and treatment of the disease. Delay in receiving treatment for uncomplicated malaria is reported to increase the risk of developing severe malaria and mortality. Thus, the need to develop advanced, faster, and smarter tools for malaria diagnosis is paramount, specially to reinforce the gold standard method, i.e., malaria microscopy which is a full-proof tool given the limitations be addressed. Deep learning-based methods have proven to provide human expert level performance on object detection/classification on image data. Such methods can be utilized for automation of repetitive task in assessing large number of microscope images of blood samples. In this paper, we propose a novel approach to improve the performance of deep learning models through consistent labelling of ground truth bounding box for the task of pathogen detection on microscope images of thick blood smears. Recommendations are made on the reliability and repeatability testing of the trained models. A custom deep learning architecture (YOLO-mp) is developed based on the design criteria of optimizing accuracy and speed of detection with minimal resources. The custom three-layered YOLO-mp-3l and four-layered YOLO-mp-4l models achieved the best mAP scores of 93.99 (@IoU=0.5) and 94.07 (@IoU=0.5), respectively outperforming standard YOLOv4 (mAP 92.56 @IoU=0.5) for detection of malaria pathogen on a public dataset of thick blood smear microscope images captured using phone camera. YOLO-mp-3l (BFLOPs =21.8, model size =24.5Mb) and YOLO-mp-4l (BFLOPs=24.477, model size =25.4Mb) outperformed standard YOLOv4 (BFLOPs=127.232, model size = 244Mb) in terms of computation and memory requirements proving them suitable to run on low resource devices.
Arachne: Search-Based Repair of Deep Neural Networks

The rapid and widespread adoption of Deep Neural Networks (DNNs) has called for ways to test their behaviour, and many testing approaches have successfully revealed misbehaviour of DNNs. However, it is relatively unclear what one can do to correct such behaviour after revelation, as retraining involves costly data collection and does not guarantee to fix the underlying issue. This paper introduces Arachne, a novel program repair technique for DNNs, which directly repairs DNNs using their input-output pairs as a specification. Arachne localises neural weights on which it can generate effective patches and uses Differential Evolution to optimise the localised weights and correct the misbehaviour. An empirical study using different benchmarks shows that Arachne can fix specific misclassifications of a DNN without reducing general accuracy significantly. On average, patches generated by Arachne generalise to 61.3% of unseen misbehaviour, whereas those by a state-of-the-art DNN repair technique generalise only to 10.2% and sometimes to none while taking tens of times more than Arachne. We also show that Arachne can address fairness issues by debiasing a gender classification model. Finally, we successfully apply Arachne to a text sentiment model to show that it generalises beyond Convolutional Neural Networks.
Digital skills critical for education: Video analysis of students' technology use in Norwegian secondary English classrooms

Abstract Background Globally, digital skills are a crucial aspect of education that schools should develop systematically. Research on digital skills tends to be measured using self‐reports, performance tests or interventions. There is less knowledge about student and teacher uptake of technology in school, making it important to investigate the actual use of technology and digital skills in authentic classroom settings. Objectives This study contributes unique baseline data concerning students' use of technology and digital skills across mandatory English courses in real classroom settings in secondary schools in Norway over time. Methods The study adapted a national framework for digital skills into an observation protocol. With it, this study analysed videos from 60 naturally occurring English lessons in 13 English classes at seven lower secondary schools over two school years (grades 9 and 10), following 186 students (aged 13–15) and 10 teachers. Results and Conclusions Students used digital skills critical for education in half of the video‐recorded English lessons, with more digital skill use at some schools over time. The main finding across classrooms and school years regards students' use of basic, not advanced, digital skills. Takeaways Although teachers provide opportunities for students to use digital skills in school, more advanced skills are needed. This work calls for continued use of video recordings to provide systematic comparisons of potential shifts in students' digital skills in real English secondary classroom settings over time.
Multiscale diffractive U-Net: a robust all-optical deep learning framework modeled with sampling and skip connections

As an all-optical learning framework, diffractive deep neural networks (D 2 NNs) have great potential in running speed, data throughput, and energy consumption. The depth of networks and the misalignment of layers are two problems to limit its further development. In this work, a robust all-optical network framework (multiscale diffractive U-Net, MDUNet) based on multi-scale features fusion has been proposed. The depth expansion and alignment robustness of the network can be significantly improved by introducing sampling and skip connections. Compared with common all-optical learning frameworks, MDUNet achieves the highest accuracy of 98.81% and 89.11% on MNIST and Fashion-MNIST respectively. The testing accuracy of MNIST and Fashion-MNIST can be further improved to 99.06% and 89.86% respectively by using the ensemble learning method to construct the optoelectronic hybrid neural network.