Reviewerly/exHarmony huggingface dataset:
The Research of Chinese Text Proofreading base on Pattern Matching

Chinese automatic proofreading of text is one of the main applications of natural language processing and a difficult problem for natural language understanding. Aiming at the errors caused by local or long-distance syntax or semantic constraints, this paper proposes a text proofreading method based on pattern matching. This method uses the characteristics of Chinese grammatical structure and semantic limitations to construct recognition pattern that integrates grammatical structure and conditional restrictions, and uses the defined pattern to match the sentences to be checked for error checking and error correction. Experiments show that the recall of the method reaches 95%, the accuracy reaches 90%, and the method has certain practicality.
Multi-scale Fusion Medical Image Registration with Segmentation Mask

Image registration plays an indispensable role in medical imaging analysis. CNN-based approaches have been a popular trend in medical image registration. Although it can achieve promising registration performance, they usually capture only partial information about the image. In this paper, we propose a multi-scale fusion medical image registration network with segmentation mask (MSFNet). The network utilizes multi-scale strategy optimization, which can improve the registration performance by avoiding the similarity gradients of most recursive cascade approaches to optimize at coarse resolutions. Meanwhile, we design a Self-Attention-based registration network, which can establish long-distance connections for global images. Extensive evaluations on three public lung X-rays datasets (JRST, Montgomery and Shenzhen) show that MSFNet outperformed other baseline methods on various metrics. Meanwhile, the time and GPU memory consumption are also lower.
The Characteristics, Relationships and Challenges of Metaverse, Web 3.0 and AIGC

With the growing spiritual needs of people, new generations of digital technologies, such as the Internet of Things, computational power network, artificial intelligence, human-computer interactions, blockchain, digital twins, are continuously evolving and innovating. These technologies are changing people's way of life and production, as well as the economic and social forms that support it. The merging and innovation of digital technology and human society, and humans are moving from the information-physical system CPS (Cyber-Physical Systems) to the human-machine-integrated society-level information-physical system CPHS (Cyber-Physical & Human Systems) towards the metaverse. Meanwhile, the continuous iteration and innovation of Internet is also moving from traditional Internet platform models to Web3.0 with digital production, digital consumption, and new virtual spaces in three dimensions as the main economic and social forms based on blockchain technology. In the metaverse and Web3.0, artificial intelligence-generated content (AIGC) plays a powerful and productive tool role, AIGC being not only a production tool for the metaverse and Web3.0, but also a new paradigm for artificial intelligence development. This paper analyzes the origin of the metaverse, the motivations behind the development of Web3.0, and the new model of AIGC, detailed discussions of the technical systems and mutual relations of metaverse and Web3.0, and finally discusses the development and challenges of metaverse and Web3.0 and AIGC.
A Blockchain-Based Cross Domain Authentication Scheme in Edge Computing Environment

At present, a large number of independent Internet of Things (IoT) systems are constructed, resulting in the phenomenon of data islands. In the future, the IoT will urgently require efficient collaboration between independent IoT terminals and systems to meet the increasing and diversified IoT needs. However, in the process of the integration of blockchain and identity authentication mechanism, there are problems such as difficult unification of cross-domain identity trust mapping and low efficiency of authentication service. A cross-domain identity trust authentication scheme for the IoT is proposed by us. We integrate edge computing and blockchain, introduce side chain technology to construct master-slave chain, propose and improve Identity-Based Cryptograph (IBC) mechanism under cross-domain trusted architecture, and design cross-domain trusted authentication architecture. The analysis and evaluation show that this scheme implements the trusted mapping between data on the blockchain, improves the efficiency of cross-domain terminal authentication.
Lightweight and Data-imbalance-aware Defect Detection Approach Based on Federated Learning in Industrial Edge Networks

Accurate and timely defect detection in industrial edge networks is becoming increasingly important in intelligent manufacturing. However, the privacy concern and limited resources of edge devices hinder the applications of existing defect detection models. This paper presents a new defect detection approach based on federated learning (FL), where the transmission of privacy-sensitive data can be avoided and user privacy and data security can be well preserved. We first propose a lightweight defect detection model based on YOLOv5s. We use ShuffleNet as the backbone of YOLOv5s, use the SE attention mechanism and LRELU to improve the learning accuracy. We then develop a clustering-based device selection approach to address the degradation of accuracy caused by imbalanced data distributions of devices in FL. Corroborated by simulations, the accuracy of the proposed defect detection model increases by 26.86% as compared with existing models, and the proposed device selection approach is also superior to the benchmarks in terms of learning accuracy.
Improvement on Knowledge-grounded Dialogue Generation with Continuous Prompt Tuning

In this paper, a two-stage knowledge-grounded dialogue generation framework is proposed, which addresses the problem of generic and uninformative dialogue generation. An integrated system of continuous prompt tuning and response selection is introduced, which effectively filters out invalid results and improves the quality of the generated answers. The number of training parameters is significantly reduced by our training method, making it more efficient compared to existing models. The effectiveness of our approach is demonstrated on a recently proposed dialogue dataset using various metrics.
New approach to discover meaningful terms to specify cause of death from narratives verbal autopsy using TF-IDF and the LDA topic model

Due to a lack of coroners in some remote areas of the world, epidemiological researchers have created a database for collecting causes of death, called a verbal autopsy. The unstructured verbal autopsy (VA) narratives that are collected in this database are full of hidden knowledge about mortality. However, they are under-exploited due to inadequate processing mechanisms, or some of the computational techniques used are inappropriate for the data format. In this paper, we propose an unsupervised approach that is essentially based on a new algorithm for preprocessing such data. This is not only to address the challenge of topic extraction with the Latent Dirichlet Allocation (LDA) topic model in the context of data scarcity, but also to improve the exploitation of topics (causes of death). Experiments with the Population Health Metrics Research Consortium (PHMRC) data have demonstrated the validity of the approach and have led to the identification of reliable causes of death as well as the discovery of new ones.
Sugeno/ Mamdani Interval Type II Fuzzy Logic Controller Based DSTATCOM with Controlled Load

The controlling method of DSTATCOM device attracts many researchers because it affects the performance of the mentioned device. Recently, interval type II fuzzy logic controller has been used in many aspects of electrical engineering for controlling power electronics devices. MATLAB R2022b provides the researchers with two different types of interval type II fuzzy logic, Mamdani and Sugeno types and the ability to convert among them easily. In this paper, Mamdani and Sugeno interval type II fuzzy logic controllers with two different control algorithms were examined by means of MATLAB R2022b Simulink environment along with the tuning process. The Mamdani interval type II fuzzy logic controller with centroid defuzzification method gives the best performance of DSTATCOM device at controlled rectifier loads with both examined controlled algorithms UVT and SRF.
Smart Assistive Wearable Device for Alzheimer's Patients

Alzheimer's Disease (AD) is a progressive brain disease that affects patients and their relatives emotionally. Patients experience an ongoing increase in memory loss, thus forgetting the names of their close people. The device is implemented using Raspberry Pi 4 and Raspberry Pi camera. Raspberry Pi was programmed using Convolutional Neural Network (CNN) for face detection, the ResNet-50 model for feature extraction, and Support Vector Machine (SVM) for classification, integrated with an audio output. This project aims to develop a device for AD patients in the first stage and earlier of the second stage by implementing a face recognition (FR) system that will help them recognize their families and friends.
Bitrate Analysis in 5G Networks for Video Streaming Services Using L-Moment Ratio Diagrams

Now a days, the expansion of wireless technologies and the development of new services have arisen new network requirements that completely change the way that network providers supply the resources to these new services. In this context, this work includes a novel analysis based on the L-moments statistical theory oriented to the understanding of the 5G networks, specially in the situation when video streaming applications are used. Our analysis involves computing and representing the L-moment ratio diagram of the bitrate of both uplink and downlink channel for three specific services, i.e. the download of a large file and Netflix and Amazon Prime video streaming. Obtained results show clear differences in the statistical behaviour for all services when uplink and downlink, mainly due the different objectives and characteristics of these links. Results also show significant differences between the Netflix and Amazon Prime video streaming services. However, interesting and surprising similarities have also been detected, specially between the Amazon Prime video streaming service and the download of a large file. Conclusions obtained in this work could be useful input to novel network management and resource allocation algorithms in the next generation networks, being this idea one of our future research lines.
Towards the Electrical Self-Sufficiency of a University Campus: Techno-Economic Case Studies

A university Campus is a potentially optimal playground to approach the self-sufficiency of the electrical consumption by using photovoltaic (PV) generation and appropriate storage with a profitable economic investment. This work presents the case study of Politecnico di Torino, in which PV generation with nominal power of 1 MW is already operating with ≈100% self-consumption and ≈10% self-sufficiency. However, the simulation results demonstrate that the self-sufficiency can be significantly improved (up to 70%) by installing multi-megawatt PV generation and two tens of megawatt-hour of battery capacity. The techno-economic aspects considered are the cost effectiveness of the investment and the reduction of power injections into the grid.
A BERT-Based Approach to Intent Recognition

Conversational agents are increasingly present in the context of Industry 4.0, in particular for customer care applications. To be really useful, these agents must correctly recognize the users' intents in order to provide them with adequate and satisfactory answers. In this paper, we introduce an intent recognition approach that leverages Google's Bidirectional Encoder Representations from Transformers (BERT), a language representation model based on deep neural networks. The results of a comparative analysis performed on three publicly available datasets, including Kaggle, Alexa, and Converse datasets, show that the proposed approach can outperform other state-of-the-art models based on different techniques.
Towards a Lightweight 2D U-Net for Accurate Semantic Segmentation of Kidney Tumors in Abdominal CT Images

Accurate segmentation of the kidney anatomy is crucial in the diagnosis and treatment of various kidney diseases. However, 3D U-Net-based Neural Networks entail significant computational requirements, complex architectures, and a fully annotated volumetric dataset. To address these challenges, our study designs and implement a custom image preprocessing workflow that suppresses fat and uninformative structures and compares the performances of 2D U-Net-based Neural Networks for semantic segmentation of kidneys and tumors from abdominal CT images. We found the ResU-Net model to achieve an accuracy of 89.17% for kidney segmentation, outperforming other models, while the Vanilla U-Net during the renal tumor segmentation task, with up to 11.7% higher DSC scores. Moreover, all the investigated methods do not require 3D CNNs, thus reducing computational costs. This comparison could be potentially useful to make a step forward in identifying the most accurate and lightweight technology to aid physicians in diagnosing kidney diseases while improving patient outcomes.
Educational Certification on Ethereum blockchain: Analysis on How BCert and UZHBC differ

Counterfeit certificates and diplomas are a common issue. Blockchain technology offers a potential solution through its decentralized ledger system, which uses cryptographic techniques and incentives to verify and store transactions in blocks that are added to the public blockchain. This secure and transparent method of storing data makes it ideal for Industry 4.0 and beyond as it is difficult for information to be stolen or for corrupt institutions to sell unauthenticated certificates. In this research paper, we compare and analyze existing two different methods of certification issuance and management which is BCert, and University of Zurich Blockchain. We also discuss the differences in algorithms for hashing and how we can improve the existing code for each method. Our aim is to thoroughly examine these approaches and contribute to the field of certification through our analysis and conclusions.
Stain Transfer using CycleGAN for Histopathological Images

Histopathology refers to the observation of tissues to identify the manifestation of diseases, e.g., cancer. Tiny tissue samples are taken from the patient and studied through a microscope; the analysis of the different cells, particularly their nuclei and other structures, allows for disease detection. The biological specimens need some preparation, namely Hematoxylin and Eosin (H&E) staining is often used to highlight nuclei and cytoplasm. Although staining is fundamental, given that cells are transparent when imaged, it is still highly affected by casual errors: colors change when a small preparation step is slightly different and even when a different microscope is used. This factor leads to Computer Aided Detection (CAD) systems losing performance. Therefore, to solve this problem and allow for the integration of multiple low-dimensional datasets, we propose a CycleGAN-based architecture exploiting PatchGAN and U-Net backbones as discriminators and generators, respectively, demonstrating an improvement in mean Structural Similarity Index Measure (SSIM) over the one computed on the original datasets of around 1.8%.
Co-designing an FPGA-Accelerated Encryption Library With PYNQ: The Pynqrypt Case Study

The number of low-power Internet-connected de-vices has significantly increased in the last decade, mainly due to the proliferation of smartphones and IoT devices. However, strict power constraints and high computational demands in conjunction with the end of fundamental computer science laws have introduced new hardware and software design challenges. Domain-specialized FPGA-based hardware accelerators are a promising approach to overcoming these challenges, providing an excellent tradeoff between performance and flexibility in accelerating complex tasks. However, the required competencies to design hardware accelerators strongly limit their adoption. This paper presents a co-design approach describing the general steps to take while developing a hardware-accelerated kernel using the PYNQ platform for software implementation. We applied the proposed approach using the AES-CTR cipher as a case study, one of the most common algorithms for secure communications and data encryption, showing the benefits that hardware acceleration can bring in constrained scenarios. The reported execution results of our approach demonstrate that exploiting hardware acceleration brings a remarkable execution speed-up while keeping the design complexity low.
Medical Named Entity Recognition (MedNER): A Deep Learning Model for Recognizing Medical Entities (Drug, Disease) from Scientific Texts

Medical Named Entity Recognition (MedNER) is an indispensable task in biomedical text mining. NER aims to recognize and categorize named entities in scientific literature, such as genes, proteins, diseases, and medications. This work is difficult due to the complexity of scientific language and the abundance of available material in the biomedical sector. Using domain-specific embedding and Bi-LSTM, we propose a novel NER model that employs deep learning approaches to improve the performance of NER on scientific publications. Our model gets 98% F1-score on a curated data-set of Covid-related scientific publications published in multiple web of science and pubmed indexed journals, significantly outperforming previous approaches deployed on the same data-set. Our findings illustrate the efficacy of our approach in reliably recognizing and classifying named entities (drug and disease) in scientific literature, opening the way for future developments in biomedical text mining.
Ablation Studies in Activation Maps for Explainable Semantic Segmentation in Industry 4.0

In recent years, much attention has been given to various eXplainable Artificial Intelligence (XAI) and interpretability methods. Their extension to dense prediction tasks, however, has been underexplored. Gradient-based saliency maps, highlighting feature importance in terms of input pixels, have been frequently used as fast and simple visual explanation techniques. Nonetheless, they face several problems, and the exploration of different types of attribution methods is warranted. In this paper, we investigate gradient-free semantic segmentation explanations that are based on ablating activation maps. We explore their potential for industrial applications, specifically for fruit pitting machines. We also extend the application of Ablation-CAM, a gradient-free ablation-based interpretability technique, to semantic segmentation. Finally, we discuss the sensitivity of activation maps to partial occlusions of either the foreground or the background class regions.
On Using Meta-Learning to Overcome Challenges in Speaker Localization

Speaker localization machine learning models can suffer from poor performance if the training dataset samples are very few and not sufficient. Moreover, these models require that the deployment environments be very similar to those used to train them; otherwise, they will experience a noticeable degradation in their performance. In this paper, we address this issue and try to solve it using meta-learning approach. In addition, we introduce a performance comparison between machine-learning and non-machine-learning-based methods used to localize speakers in different environments and scenarios. Our method was put to the test in a real-life scenario for a typical intensive care unit (ICU) room.
New Approach in the Creation of Region of Support of an Advanced Optimal Space/Spatial-Frequency Filter

An approach in the creation of the region of support of the optimal (Wiener) filter used in the estimation of two-dimensional (2D) nonstationary signals is proposed. The approach is based on the symmetrical spreading of the filter's region of support around the detected local frequency (LF) of the estimated 2D signal and on simultaneous inclusion (in the region of support creation) of points having equal distance from the detected LF. The proposed filter provides signal reconstruction free of distortion of the original (noiseless) signal.
A Reinforcement Learning based controller for optimal speed control of a DC motor using deep Q-network algorithm

Reinforcement Learning (RL) has been gaining significant attention in recent years as a powerful tool for solving complex control problems. This paper presents a possible application of an RL algorithm called deep Q-network (DQN) to the speed-tracking control of a DC motor. The proposed approach opens up new possibilities for the application of RL in the control of DC motors and other dynamic systems since it proposes a direct RL controller able to drive the motor in a realistic and fully randomized scenario.
Exploring the suitability of a Digital twin- and eXtended Reality-based telepresence platform for a collaborative robotics training scenario over next-generation mobile networks

This paper explores the application of Digital Twins (DTs) and eXtended Reality (XR) in the context of Industry 4.0, and investigates new ways in which these technologies can be used in remote assistance/training scenarios involving Collaborative Robots (CRs). The study builds upon a previous work that examined the suitability of a novel DT/XR-based telepresence platform for CR programming in terms of network capabilities. The present work addresses some of the limitations of the previous study in the context of a new use case, integrating human pose estimation and object tracking to enhance the DT functionalities of the platform, and testing it in a riveting task scenario. The results in terms of bandwidth and latencies obtained in a laboratory setup emulating 6G performance show the potential of DTs and XR in supporting the collaboration of distant human operators and CRs over future mobile networks, paving the way for the development of new services for next-generation industry.
Smart Contract-Based Checkpoint for Initial PoW Network Security

Proof of Work (PoW) is the most widely adopted consensus mechanism on public blockchains. The PoW blockchain network achieves consensus by solving computational problems. If a network participant owns more than 50% of the total computational power in the network, they can forge the blockchain. Hence, initial PoW blockchain networks with low computing power are vulnerable to block forgery attacks. We propose a smart contract-based checkpoint method to improve security vulnerabilities in the initial blockchain networks. In our method, participants periodically record block headers in an Ethereum smart contract. The recorded checkpoint block header validates the blockchain. Participants reject blocks with blocks that differ from the recorded checkpoints. Our method ensures the integrity of blocks until the height of the most recently created checkpoint, reducing the risk of double-spending. We optimize checkpoint costs by overlapping multiple checkpoint processes in a single transaction. The interval of our checkpoint method grows with the growth of the network, making the network less dependent on checkpoints. We analyze the performance of checkpoints in mitigating attacks and demonstrate that they significantly decrease the success probability of attacks in the network.