Reviewerly/exHarmony huggingface dataset:
RLSegNet: An Medical Image Segmentation Network Based on Reinforcement Learning

In the area of medical image segmentation, the spatial information can be further used to enhance the image segmentation performance. And the 3D convolution is mainly used to better utilize the spatial information. However, how to better utilize the spatial information in the 2D convolution is still a challenging task. In this paper, we propose an image segmentation network based on reinforcement learning (RLSegNet), which can translate the image segmentation process into a serial of decision-making problem. The proposed RLSegNet is a U-shaped network, which is composed of three components: the feature extraction network, the Mask Prediction Network (MPNet), and the up-sampling network with the cascade attention module. The deep semantic feature in the image is first extracted by adopting the feature extraction network. Then, the Mask Prediction Network (MPNet) is proposed to generate the prediction mask for the current frame based on the prior knowledge (segmentation result). And the proposed cascade attention module is mainly used to generate the weighted feature mask so that the up-sampling network pays more attention to the interesting region. Specifically, the state, action and reward used in the reinforcement learning are redesigned in the proposed RLSegNet to translate the segmentation process as the decision-making process, which performs as the reinforcement learning to realize the brain tumor segmentation. Extensive experiments are conducted on the BRATS 2015 dataset to evaluate the proposed RLSegNet. The experimental results demonstrate that the proposed method can achieve a better segmentation performance, in comparison with other state-of-the-art methods.
Question Answering with BERT: designing a 3D virtual avatar for Cultural Heritage exploration

Recent technological developments are changing how people experience physical and virtual environments. Technologies like Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), today, are impacting daily life and are being used in various domains including Cultural Heritage. Applying intelligence to applications using these technologies, through AI and Deep Learning can provide a more immersive user experience. In this paper, we propose the design of a system that realize an avatar with question-answering capabilities, for the specific purpose of giving help in immersive navigation of cultural heritage sites. It is based on a BERT model tailored to the Italian language and will use the Unity platform for the implementation. The system in its current state works as a prototype and will be used soon inside virtual tours of Sicilian Cultural Heritage sites.
Low-Dose CT Image Denoising Based on Improved DD-Net and Local Filtered Mechanism

Low-dose CT (LDCT) images can reduce the radiation damage to the patients; however, the unavoidable information loss will influence the clinical diagnosis under low-dose conditions, such as noise, streak artifacts, and smooth details. LDCT image denoising is a significant topic in medical image processing to overcome the above deficits. This work proposes an improved DD-Net (DenseNet and deconvolution-based network) joint local filtered mechanism, the DD-Net is enhanced by introducing improved residual dense block to strengthen the feature representation ability, and the local filtered mechanism and gradient loss are also employed to effectively restore the subtle structures. First, the LDCT image is inputted into the network to obtain the denoised image. The original loss between the denoised image and normal-dose CT (NDCT) image is calculated, and the difference image between the NDCT image and the denoised image is obtained. Second, a mask image is generated by taking a threshold operation to the difference image, and the filtered LDCT and NDCT images are obtained by conducting an elementwise multiplication operation with LDCT and NDCT images using the mask image. Third, the filtered image is inputted into the network to obtain the filtered denoised image, and the correction loss is calculated. At last, the sum of original loss and correction loss of the improved DD-Net is used to optimize the network. Considering that it is insufficient to generate the edge information using the combination of mean square error (MSE) and multiscale structural similarity (MS-SSIM), we introduce the gradient loss that can calculate the loss of the high-frequency portion. The experimental results show that the proposed method can achieve better performance than conventional schemes and most neural networks. Our source code is made available at https://github.com/LHE-IT/Low-dose-CT-Image-Denoising/tree/main/Local Filtered Mechanism.
Blockchain and Double Auction-Based Trustful EVs Energy Trading Scheme for Optimum Pricing

Electric vehicles (EVs) have gained prominence in smart transportation due to their unparalleled benefits of reduced carbon footprints, improved performance, and intelligent energy trading mechanisms. These potential benefits have increased EV adoption at massive scales, but energy management in EVs is a critical study problem. The problem is further intensified due to the scarcity of charging stations (CSs) in near EV proximity. Moreover, as energy transactions occur over open channels, it presents critical security, privacy, and trust issues among decentralized channels. To address the open limitations of trusted energy management and optimize the pricing control among EV entities (i.e., prosumers and consumers), the paper proposes a scheme that integrates blockchain and a truthful double auction strategy for trustful EV trading. To address the transaction scalability, we integrate an Interplanetary File System (IPFS) with a double auction mechanism handled through the Remix Smart Contract environment. The double auction leverages an optimal payoff condition between peer EVs. To address the communication latency, we present the scheme at the backdrop of Fifth Generation (5G) networks that minimizes the optimal payoff response time. The scheme is simulated against parameters such as convergence, profit for consumers, computation time, and blockchain analysis regarding node commit latency, collusion attacks, and EV energy consumption. The results indicate the scheme’s viability against traditional (non-blockchain) approaches with high reliability, scalability, and improved cost-efficiency.
Chatbot with Dialogflow for FAQ Services in Matana University Library

Industry 4.0 and situation of Corona pandemic encourage the development of chatbots. The Matana university library in Indonesia is facing protocol implementation due to the pandemic, library staff vacancies and the need to improve 24x7 online services. Therefore, a solution is needed to resolve these kinds of problems. The success of chatbots in the business world and public services is the reason for the adoption of chatbots to overcome these problems. The first phase of chatbot development focuses on Frequently Asked Questions (FAQ). The development model for chatbot applications development uses a prototyping model by utilizing Dialogflow, namely the natural language understanding (NLU) platform. Agents, intents, entities, contexts, events, fulfilment, and integration need to be considered before designing a chatbot conversation. The test results are the chatbot is feasible to be accepted and operated. The variety of phrases entered into the Intent will improve accuracy. However, the accuracy of this chatbot will become lower if the language conveyed uses a lot of abbreviations or local / foreign languages. In further development, it is necessary to apply the system implementation and system maintenance phases, make chatbot responses not rigid like robots, add other library services such as new member registration, information on book availability, user satisfaction and so on.
Quantum Secure Communication Between Service Provider and Sim

In resource-constrained devices such as Subscriber Identity Module (SIM), the possibility of using authentication and key exchange methods, which include the mutual verification of two parties to ensure the security of communication, provides a significant advantage. As the cost efficient computations may not be efficiently performed on resource-constrained devices, security is the main problem for SIMs. With the advancing technology and the use of quantum computers, it is predicted that this may lead to threats. It is a well-known fact that security on traditional public key cryptosystems will become vulnerable by using quantum computers due to Shor&#x2019;s algorithm. In this paper, two different resistant to quantum attacks structures are proposed to ensure secure communication between SIM and service providers. In the proposed methods, Advanced Encryption Standard (AES-256) is used for communication with resource-constrained devices, and N-th degree Truncated polynomial Ring Units (NTRU) encryption system is used for communication with servers. Two methods are proposed, one with the private key and one without the private key in the production phase. This protocol provides authentication, data privacy and integrity for post-quantum SIM cards. The proposed method is inspired by the FLAT (Federated Lightweight Authentication) protocol. However, the main difference from the FLAT protocol is that it has more nodes and is resistant to quantum attacks.
MPoR: A Modified Consensus for Blockchain-Based Internet of Vehicles

Ride-sharing services, such as ride-hailing and carpooling, have become attractive travel patterns for worldwide users. Due to the high dynamic topology, heterogeneous wireless communication mode, and centralization, the Internet of Vehicles (IoV) is much more vulnerable to security issues such as privacy theft, single point of failure, data island, and unauthorized access, resulting in great security risks, while ride-sharing services provide convenience. Blockchain technology used to solve the security problems of the IoV has become a current research hotspot, including authentication and privacy protection. Nevertheless, the existing algorithms still face challenges such as large amount of computation, low throughput, low scalability, consensus, and node security. Achieving an efficient, lightweight, and scalable secure blockchain–based IoV system still needs to be solved urgently. In this paper, we propose an effective consensus algorithm called Modified Proof of Reputation (MPoR). Firstly, by using the average network access time of the whole network nodes as the filtering threshold, the number of consensus nodes can be controlled adaptively. Then, a new multiweight reputation algorithm is proposed to quantify the reputation value of nodes, so as to detect and eliminate malicious nodes in the consensus node pool. Theoretical analysis and extensive simulation experiments reflect that under the IoV scenario, MPoR can adaptively select the number of consensus nodes, to effectively improve the consensus efficiency. When malicious nodes are less than 1/3 of the total nodes in the network, MPoR can effectively resist latent attack and collusive attack and has strong robustness.
Imbalanced Disk Failure Data Processing Method Based on CTGAN

Most research on disk failure prediction is based on Self-Monitoring Analysis and Reporting Technology (SMART) data. And the data of the failed disk is extremely small, so the characteristics of the imbalanced data set have created obstacles to the classification problem, especially tabular data. For the study of the imbalanced dataset, this paper proposes to use Conditional Tabular GANs (CTGAN) to augment disk failure data. CTGAN generates some fake data that matches the distribution of real data features. Use the classic models (BP-ANN, SVM, decision tree, random forest) as a reference to verify the effectiveness of CTGAN. The experiment results show that CTGAN achieves great results on the augmentation of imbalanced datasets.
A Secure Architecture to Protect the Network from Replay Attacks during Client-to-Client Data Transmission

Cloud computing is an online data storage system that can be easily accessed anywhere. Various algorithms and techniques have been developed to secure cloud data, which, if used correctly, can detect cloud attacks. These algorithms can only detect cloud data attacks and cannot prevent such attacks. If an attacker gains access to the data, he or she can tamper with the data in any way that could lead to misuse of the data. This paper will design a secure architecture for securing data with various algorithm implementations on the architecture so that cloud data can be saved from replay attacks. First, a text file will be taken, the text file will be encrypted using the encryption mechanism, and this file will be transmitted on the SaaS cloud server platform. Whenever a file is uploaded to a cloud server, it will be broadcast across the network, after which any user can access it, whether it is valid or invalid. Whenever a file is downloaded from a cloud server, it will be necessary to decrypt it to access it. If the file is successfully decrypted, then the file will be verified. A verification mechanism will be used to verify the data, which will be used to check whether a replay attack has been made on the data or not. If the data authentication is successful, this data will be called authentic data. The novelty of this paper is that an algorithm has been developed in this paper to prevent replay attacks so that if the data is retransmitted with slight changes, then the authenticity and inauthenticity of the data will be identified. Recent papers will be compared with the proposed paper in the conclusion, and it will be pointed out how much better the current technique is than the previous one.
Simdex

Self-adaptive systems comprise a complex domain of computing systems that are intensively studied but sparsely employed in real applications. Furthermore, recent trends in computer science are steering towards machine learning which has yet to fully penetrate this domain. We would like to present Simdex --- a realistic simulator of the self-adaptive backend that dispatches computing jobs among multiple workers. It is based on ReCodEx, a system for semi-automated evaluation of coding assignments that have been used for the past 5 years at our School of Computer Science. The simulator replays the workload logs recorded from ReCodEx over that period which provides a quite thorough evaluation and near-to-real feedback for the simulated scenarios. Furthermore, the design of the simulator is highly modular and allows the implementation of different self-adaptive controllers, including ones based on machine learning, as we demonstrate in our examples.
Uranium transfer in grasses grown on mining waste and natural soil

The transfer of radionuclides from soil to the food chain often begins with uptake by plant root system. The roots of most angiosperms showed symbiosis with arbuscular mycorrhizal fungi (AMF) and to understand the transfer process of these toxic elements it is important to consider different physical, chemical and biological factors in soils. In the present study, three grass species (Poaceae), Zea mays, Chrysopogon zizanioides and Aristida setifolia were cultivated with and without organic fertilization in experimental blocks on natural soils, at Fazenda Vargem Formosa (VF) with low uranium (U) contents in the soil, and in the leached ore deposit at the Uranium Concentrate Unit Mine (URA) in Caetité (Uraniferous Province of Lagoa Real - Brazil). In the present study, the biomass production of plants, their rate of root colonization by AMF, the levels of U in soils, roots and leaves, as well as different physico-chemical parameters related to soil fertility were evaluated. The data analysis was performed using Artificial Neural Networks (ANNs), specifically Self-Organizing Maps (SOMs). The levels of available uranium in the soil ranges from 0.33 to 1.11 mg kg-1 in VF and from 177.5 to 475.8 mg kg-1 in URA. The results revealed high percentage of root AMF colonization, even in soils with high U contents. There was an inverse relationship between soil U content and its transfer to the plant organs, with U transfer rates being influenced by plant species and not by soil parameters. C. zizanioides had the lowest transfer factor to the shoot and the highest mass productivity under conditions of high U content in soil. The results indicate that C. zizanioides is an important species for use in the recovery of U mining areas.
Exploring Bhutanese primary school teachers’ technological knowledge

The COVID-19 pandemic has disrupted the normal functions of schools globally. Online learning is a new concept in Bhutan. Nonetheless, classes have begun using various online learning platforms to continue their operations during the pandemic. This study examined primary school teachers’ perceived information technology knowledge and proficiency. The participants of this study consisted of 124 primary teachers from two western districts of Bhutan. Data were collected using an online survey. The study’s findings suggested that although the majority of participants (98.2%) had access to personal digital devices, slow internet connection and high internet data subscription charges (60.7%) were cited as significant challenges. Findings also revealed that a small percentage of the participants, less than 12 (9.7%) teachers in this study preferred to teach entirely in an online learning environment. The results of multiple linear regression suggested that only technological pedagogical knowledge (TPK) [ t = 2.68, p = .008, β = .236] and perceived information technology proficiency of teachers (PITP) [ t = 3.55, p = .001, β = .306] were statistically significant predictors of technological knowledge (TK).
Hybrid Navigation System Based Autonomous Positioning and Path Planning for Mobile Robots

Abstract Positioning and navigation technology is a new trend of research in mobile robot area. Existing researches focus on the indoor industrial problems, while many application fields are in the outdoor environment, which put forward higher requirements for sensor selection and navigation scheme. In this paper, a complete hybrid navigation system for a class of mobile robots with load tasks and docking tasks is presented. The work can realize large-range autonomous positioning and path planning for mobile robots in unstructured scenarios. The autonomous positioning is achieved by adopting suitable guidance methods to meet different application requirements and accuracy requirements in conditions of different distances. Based on the Bezier curve, a path planning scheme is proposed and a motion controller is designed to make the mobile robot follow the target path. The Kalman filter is established to process the guidance signals and control outputs of the motion controller. Finally, the autonomous positioning and docking experiment are carried out. The results of the research verify the effectiveness of the hybrid navigation, which can be used in autonomous warehousing logistics and multi-mobile robot system.
Motion Trajectory Planning and Design of Material Spraying Service Robot

The application of robots reduces repetitive and dangerous tasks for humans, especially the spraying robot, because many spraying materials are corrosive, toxic, and harmful. This paper designs the motion trajectory planning of the material spraying service robot. With the increasing demand for technology, there are strict requirements for the uniformity and thickness of spraying. In view of this, this paper proposes an algorithm for modeling kinematics using a joint screw and optimizes the modeling algorithm using particle swarm optimization. This makes the industrial spraying robot more intelligent and more capable of completing high-standard tasks. The experimental results in this paper show that when the spraying radii on the large-curvature cone surface are 44.5 and 49.5 mm, respectively, the coating distribution in the intersection area of the curved surfaces can be well controlled, and the optimized algorithm can better plan the path of the spraying robot.
Do DEXs work? Using Uniswap V2 to explore the effectiveness of decentralized exchanges

This paper investigates the function of a blockchain-based decentralized exchange, specifically the effectiveness of the Ether–Tether liquidity pool on the Uniswap V2. We note that cointegration between the price set by the liquidity pool and its price elsewhere is a necessary condition of effectiveness. The trading price offered by the liquidity pool is determined by the token reserves ratio. We apply autoregressive distributed lag, vector autoregressive and vector error correction model methodologies to 154 days of hourly data for the Ether–Tether trading pair. We find that liquidity providers and arbitrageurs ensure that the ratio of reserves is cointegrated with the trading pair price elsewhere, and therefore that Uniswap can be an effective financial market. This raises the possibility that such decentralized exchanges could be used to improve the completeness of financial markets.
The politics and imaginary of ‘autonomous vehicles’: a participatory journey

The Connected and Automated Vehicles (CAVs) pilot project set out to explore the alternative mobility imaginaries of participants. These imaginaries challenged the automated vision of the future presented by vehicle and technology companies. This paper takes a post-normal science and digital anthropology approach to the question of automated technology and the role that citizens have in shaping mobility future(s). Through narrative analysis, interviews with stakeholders, and Futures Making Ateliers, this citizen engagement journey deconstructs the technological promises of CAVs, as well as their plausibility and desirability from the point of view of the participants of the participatory journey. Our findings suggest that the technology is solving a different problem than the mobility problem as articulated in policy documents. By investigating the matters of concern of participants, the problem of mobility was redefined in their own terms, and alternative futures were explored. We use the concept of MacGuffin as means to explore the wider relevance of CAVs in mobility futures.
Image Multi-Inpainting via Progressive Generative Adversarial Networks

Image inpainting aims to inpaint missing pixels of an image naturally and realistically. Previous deep learning approaches typically require specific design for different types of masks and cannot generalize well to multiple inpainting scenarios simultaneously. Thus on top of most common stroke-type mask approaches, we in this paper pro-pose a unified framework to handle multiple types of masks simultaneously (e.g. strokes, object shapes, extrapolation, dense and periodic grids et al). We address this problem by proposing a progressive learning scheme to an Semantic Aware Generative Adversarial Network (SA-PatchGAN). Specifically, the overall training proceeds in multiple stages with different type of mask inputs, so that the model can gradually generate an output image from coarse to fine with mask independent property. In our experiments, we show that this strategy yields a large performance gain compared to the single-scale learning methods. We also introduce additional semantic conditioning to the discriminator which encourage high quality local style statistics, and show that this approach is effective on a wider scenario/tasks and could better adapt to various types of mask. Our method produces promising results on various mask types using one single model.