Reviewerly/exHarmony huggingface dataset:
Unsupervised Domain Adaptive Semantic Segmentation Based on Improved DAFormer

To overcome the intensive of manual labeling tasks at the pixel level required for semantic segmentation under traditional supervised learning, an Unsupervised Domain Adaptive for Semantic Segmentation (UDASS) method based on DAFormer improved model is proposed. This model adapted the Max Mean Discrepancy (MMD) method in the regenerated Hilbert space to help the alignment of the feature distribution, the soft paste strategy to retain the partially covered image blocks to help the model to accelerate convergence, the non-convex consistency regularization at the output level to enhance the robustness of the network, and the spatial pyramid pooling framework and the decoder with large window attention collaboration to improve its consistency. The proposed method was evaluated on the public dataset, and obtained the of 2.4% mIoU improvement in GTA5-to-Cityscapes and 1.1% mIoU in SYSTHIA-to-Cityscapes, respectively, which proved that this method was effective for DAFormer improvement.
A Study on the Development Direction of AI Attack Drone and Its Implication

The role of attack drones in the ongoing war between Ukraine and Russia is drawing worldwide attention. In order to prepare for changes in the defense security environment and rapid decline in military resources, the Ministry of National Defense announced the National Defense Innovation 4.0 Basic Plan in March 2023, and established the concept of joint operation by reflecting the operation of AI-based manned and unmanned complex systems and new concept weapon systems. did Drones are the most important force in building manned and unmanned complex systems, and the dronebot combat system will be the basis for promoting Defense Innovation 4.0. Drones are a system that can be operated for multiple purposes that change their use according to mission equipment, and it is key to have advanced technology that is suitable for the future battlefield environment and efficient. If the existing attack drone is an operating system in which combatants remotely control the ground control device while watching the ground control device, the future direction of the development of attack drones for the Korean military to apply to dronebots is an AI attack drone in which the drone detects the target by itself and directly strikes and self-destructs. will develop into In order to develop AI attack drones, not only the performance of the drone system, but also AI-based cutting-edge technology for object tracking technology, object identification technology and shape recognition technology of reconnaissance equipment (EO/IR), which is mission equipment, as well as attack ammunition or Small, high-performance warhead design technology, ultra-small fuze design technology, shock absorption and launch control device technology, etc. must be developed to mount the projectile. In addition, as the amount of image information acquired through mission equipment increases, high-speed data transmission technology development to improve the transmission rate between the aircraft and the ground control equipment in real time must be simultaneously developed. It is necessary to quickly overcome drone and AI technologies, which are currently very poor compared to advanced countries, and take the lead in developing intelligent AI attack drones with excellent autonomy based on Korea's excellent ICT technology. Therefore, this study aims to find out the technological trends of domestic and foreign attack drones and to consider the development plans and tasks of AI attack drones.
Analysis and effectiveness of deeper levels of SVD on performance of hybrid DWT and SVD watermarking

Abstract In this paper, an analysis on hybrid Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) for image watermarking is carried out to investigate the effect of a deeper level of the SVD on imperceptibility and robustness to resist common signal processing and geometric attacks. For this purpose, we have designed two hybrid watermarking schemes, the first one with DWT and first level of SVD, whereas, in the second scheme, the same design is employed with a second level of SVD. In this experiment, a comprehensive analysis is performed on the two designed schemes and the effect of robustness and imperceptibility is compared in the first and second levels of SVD in each DWT sub-band. Having analyzed more than 100 medical and non-medical images in standard datasets and real medical samples of patients, the experimental outcomes show a remarkable increase in both imperceptibility and robustness in the second level of SVD, in comparison to the first level. In addition, the achieved result shows that the SVD2 scheme offers the highest imperceptibility in the LL sub-band (more than 60 dB on average PSNR), with satisfactory robustness against noise attacks, but less persistence in some geometric attacks such as cropping. For the HH sub-band, strong robustness against all types of tested of attacks is obtained, though its imperceptibility is slightly lower than the achieved PSNR in the LL sub-band. In HH sub-band, an average growth of 5 dB in PSNR and 2% in NC can be observed from the second level of SVD in comparison to the first level. These results make SVD2 a good candidate for content protection, especially for medical images.
Linear Optical Logical Bell State Measurements with Optimal Loss-Tolerance Threshold

Quantum threshold theorems impose hard limits on the hardware capabilities to process quantum information. We derive tight and fundamental upper bounds to loss-tolerance thresholds in different linear-optical quantum information processing settings through an adversarial framework, taking into account the intrinsically probabilistic nature of linear optical Bell measurements. For logical Bell state measurements—ubiquitous operations in photonic quantum information—we demonstrate analytically that linear optics can achieve the fundamental loss threshold imposed by the no-cloning theorem even though, following the work of Lee et al. [Phys. Rev. A 100, 052303 (2019)] the constraint was widely assumed to be stricter. We spotlight the assumptions of the latter publication and find their bound holds for a logical Bell measurement built from adaptive physical linear-optical Bell measurements. We also give an explicit even stricter bound for nonadaptive Bell measurements.7 MoreReceived 21 February 2023Revised 22 May 2023Accepted 22 September 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040322Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasIntegrated opticsQuantum communicationQuantum error correctionQuantum foundationsQuantum information theoryQuantum Information, Science & TechnologyAtomic, Molecular & Optical
Self-Assessment Chatbot for COVID-19 prognosis using Deep Learning-based Natural Language Processing (NLP)

Chatbots are computer programs that communicate with humans using natural language processing. Conversational programming is the most crucial part of the development of a chatbot. Existing chatbots are based on simple rule-based techniques. They are therefore limited in terms of functionality, context, and coherence, with narrow domains. Some of these systems also lack flexibility and do not emulate actual human conversation. The available chatbots use machine learning algorithms to answer questions and have structured conversations. However, they lack flexibility and do not emulate actual human conversation. The study proposes a chatbot that will help users self-diagnose symptoms related to COVID-19. Even today, the creation of a good chatbot model remains a significant challenge in Natural Language Processing (NLP) using Machine Learning (ML). This study suggests a newly developed method of creating a chatbot by using deep learning: The Recurrent Neural Network (RNN) will be used to model sequence data since it is modeled after the functionality of the human brain. Based on the classification of the available response classes, this study outputs both the chatbot response and the probability value. The system achieves a response time of 0.3 seconds and an accuracy of 95.35%. The study suggests a newly developed method of creating a chatbot by using deep learning with multiple processing layers, the deep learning approach provides better learning for chatbots to make intelligent decisions. Deep learning-based NLP chatbots result in better text classification accuracy with the ability to understand sarcasm, humor, and conversations better.
ANALYSIS OF THE SUBJECT AREAS OF PROJECT MANAGEMENT. CONTENT MANAGEMENT

The article discusses an important area of project management – content management. The relevance of the topic is due to the importance of working out the content for the further progress of the project. Correctly compiled content allows you to save the other two components of the triangle of the project – time and money.
Efficient Mixed Transformer for Single Image Super-Resolution

Recently, Transformer-based methods have achieved impressive results in single image super-resolution (SISR). However, the lack of locality mechanism and high complexity limit their application in the field of super-resolution (SR). To solve these problems, we propose a new method, Efficient Mixed Transformer (EMT) in this study. Specifically, we propose the Mixed Transformer Block (MTB), consisting of multiple consecutive transformer layers, in some of which the Pixel Mixer (PM) is used to replace the Self-Attention (SA). PM can enhance the local knowledge aggregation with pixel shifting operations. At the same time, no additional complexity is introduced as PM has no parameters and floating-point operations. Moreover, we employ striped window for SA (SWSA) to gain an efficient global dependency modelling by utilizing image anisotropy. Experimental results show that EMT outperforms the existing methods on benchmark dataset and achieved state-of-the-art performance. The Code is available at https://github.com/Fried-Rice-Lab/FriedRiceLab.
Analysis of Research Trends on Family Centers: 2004∼2022

The purposes of this research were to analyze research trends on family centers by year,research type, data collection method, research subjects, and research topics, to improvein-depth understanding of the centers, and to suggest future developmental directions for thecenters. For this, 175 research papers on family centers from 2004 to 2022, were analyzed. Theanalysis results are as follows. First, research papers on family centers have been performedcontinuously. Second, academic journals were published more than theses. Third, quantitativeresearches were performed more than qualitative researches. Fourth, the subjects were mainlycenter employees and participants. Fifth, major research topics were various projects. Based onthese results, this research suggests future directions of researches on family centers.
A Security-Enhanced Certificateless Conditional Privacy-Preserving Authentication Scheme for Vehicular Ad Hoc Networks

By adopting advanced Internet of Things (IoT) technology to sense and collect traffic-related information to improve traffic safety and efficiency, the vehicular ad hoc network (VANET) is becoming a prominent application that changes human driving experiences in the current era. Because frequent data exchange occurs in open environments, VANETs are inherently vulnerable to security and privacy attacks. In history, many certificateless aggregate signature (CLAS) schemes with conditional privacy-preserving (CPP) have been proposed to ensure the authenticity and integrity of the exchanged data and protect users’ privacy. However, we reveal that the state-of-the-art schemes cannot be deployed in practical VANET applications by proposing concrete signature forgery attacks. To this end, we propose a new CLAS-based authentication scheme with CPP for VANETs. The rigorous security proofs based on the standard cryptographic assumption show that the scheme has enhanced security. Moreover, theoretical analysis and experimental evaluation illustrate the practicality of our design.
Local Optimization Often is Ill-conditioned in Genetic Programming for Symbolic Regression

Gradient-based local optimization has been shown to improve results of genetic programming (GP) for symbolic regression. Several state-of-the-art GP implementations use iterative nonlinear least squares (NLS) algorithms such as the Levenberg-Marquardt algorithm for local optimization. The effectiveness of NLS algorithms depends on appropriate scaling and conditioning of the optimization problem. This has so far been ignored in symbolic regression and GP literature. In this study we use a singular value decomposition of NLS Jacobian matrices to determine the numeric rank and the condition number. We perform experiments with a GP implementation and six different benchmark datasets. Our results show that rank-deficient and ill-conditioned Jacobian matrices occur frequently and for all datasets. The issue is less extreme when restricting GP tree size and when using many non-linear functions in the function set.
Digital signature and hash algorithms used in Bitcoin and Ethereum

Digital signature and hash algorithms are essential components of the blockchain. Bitcoin and Ethereum use the same digital signature scheme Elliptic Curve Digital Signature Algorithm (ECDSA). However, they use the different hash algorithms. Bitcoin chooses to use Secure Hash Algorithm (SHA), and Ethereum uses Keccak-256. This paper studies the digital signature ECDSA by looking into its design, implementation and security. ECDSA is a variant of Digital Signature Algorithm (DSA). It requires a shorter key length than Rivest–Shamir–Adleman (RSA), so it was preferred to use in the blockchain. Furthermore, this paper will also explore the design and implementation of SHA-256 and Keccak- 256. Bitcoin chose to use SHA-256 since it came out earlier than Keccak-256 with adequate security. Conversely, Keccak-256 is preferred by Ethereum since it has better performance and security compared to SHA-256. The role of SHA-256 and Keccak-256 in Bitcoin and Ethereum are also explored. SHA-256 and Keccak-256 are used in the blockchains’ proof-of-work (or proof-of-stake) and merkle tree structure. The paper will also look into their security by analyzing the result of possible attacks against them. In addition, the paper will provide some thoughts on the security of ECDSA, SHA-256 and Keccak-256 by analyzing their designs and possible attacks.
IngridKG: A FAIR Knowledge Graph of Graffiti

Graffiti is an urban phenomenon that is increasingly attracting the interest of the sciences. To the best of our knowledge, no suitable data corpora are available for systematic research until now. The Information System Graffiti in Germany project (INGRID) closes this gap by dealing with graffiti image collections that have been made available to the project for public use. Within INGRID, the graffiti images are collected, digitized and annotated. With this work, we aim to support the rapid access to a comprehensive data source on INGRID targeted especially by researchers. In particular, we present INGRIDKG, an RDF knowledge graph of annotated graffiti, abides by the Linked Data and FAIR principles. We weekly update INGRIDKG by augmenting the new annotated graffiti to our knowledge graph. Our generation pipeline applies RDF data conversion, link discovery and data fusion approaches to the original data. The current version of INGRIDKG contains 460,640,154 triples and is linked to 3 other knowledge graphs by over 200,000 links. In our use case studies, we demonstrate the usefulness of our knowledge graph for different applications.
Multi-robot motion planning for unit discs with revolving areas

We study the problem of motion planning for a collection of n labeled unit disc robots in a polygonal environment. We assume that the robots have revolving areas around their start and final positions: that each start and each final is contained in a radius 2 disc lying in the free space, not necessarily concentric with the start or final position, which is free from other start or final positions. This assumption allows a weakly-monotone motion plan, in which robots move according to an ordering as follows: during the turn of a robot R in the ordering, it moves fully from its start to final position, while other robots do not leave their revolving areas. As R passes through a revolving area, a robot R′ that is inside this area may move within the revolving area to avoid a collision. Notwithstanding the existence of a motion plan, we show that minimizing the total traveled distance in this setting, specifically even when the motion plan is restricted to be weakly-monotone, is APX-hard, ruling out any polynomial-time (1+ε)-approximation algorithm. On the positive side, we present the first constant-factor approximation algorithm for computing a feasible weakly-monotone motion plan. The total distance traveled by the robots is within an O(1) factor of that of the optimal motion plan, which need not be weakly monotone. Our algorithm extends to an online setting in which the polygonal environment is fixed but the initial and final positions of robots are specified in an online manner. Finally, we observe that the overhead in the overall cost that we add while editing the paths to avoid robot-robot collision can vary significantly depending on the ordering we chose. Finding the best ordering in this respect is known to be NP-hard, and we provide a polynomial time O(log⁡nlog⁡log⁡n)-approximation algorithm for this problem.
Joint demosaicking and denoising benefits from a two-stage training strategy

Image demosaicking and denoising are the first two key steps of the color image production pipeline. The classical processing sequence has for a long time consisted of applying denoising first, and then demosaicking. Applying the operations in this order leads to oversmoothing and checkerboard effects. Yet, it was difficult to change this order, because once the image is demosaicked, the statistical properties of the noise are dramatically changed and hard to handle by traditional denoising models. In this paper, we address this problem by a hybrid machine learning method. We invert the traditional color filter array (CFA) processing pipeline by first demosaicking and then denoising. Our demosaicking algorithm, trained on noiseless images, combines a traditional method and a residual convolutional neural network (CNN). This first stage retains all known information, which is the key point to obtain faithful final results. The noisy demosaicked image is then passed through a second CNN restoring a noiseless full-color image. This pipeline order completely avoids checkerboard effects and restores fine image detail. Although CNNs can be trained to solve jointly demosaicking–denoising end-to-end, we find that this two-stage training performs better and is less prone to failure. It is shown experimentally to improve on the state of the art, both quantitatively and in terms of visual quality.
Research on Online Teaching Emotion Detection based on Federated Learning

With the integration of artificial intelligence and education, online education has received unprecedented attention. The new infrastructure system is improved, and artificial intelligence is widely used in teaching, management, resource construction and other fields. At present, the research on intelligent education is still focused on improving students' learning efficiency, intelligent education monitoring and other aspects, but there is no relevant research on the evaluation system of AI-assisted teachers' teaching level and effect. In fact, students' learning effect is not only related to their own efforts, advanced educational equipment or not, but also closely related to the education level and style of teachers. We propose a Federated-learning based online teaching emotion recognition detection for teachers. Through the recognition of teachers' teaching emotion, it can realize the auxiliary analysis of the teaching level of teachers, help to find the problems in the process of online teaching and improve the teaching level of teachers. At the same time, on the basis of protecting the privacy security and legal compliance of teachers' teaching data, the Federated learning realization realizes data sharing, joint modeling, and improves the recognition effect. The experimental results show that the modified model can realize the collaborative training of participants under the independent and unbalanced data, and realize the effective recognition of facial emotion in online education teaching of teachers.
Blockchain-based IoMT and secure EHR model: Case of Senegal

Interoperability between healthcare institutions, data security and privacy are the main challenges that blockchain is trying to address. The Internet of Things is a current reality that is justified by its many applications in the medical field. These connected devices perform tasks autonomously with minimal or no human interaction producing and exchanging a lot of data. However, these connected objects can fall victim to numerous attacks, often due to the resource constraints imposed on the sensor networks, preventing the implementation of very robust security mechanisms. This paper reviews blockchain technology and IoT devices in the medical domain, but also their impact on electronic medical records (EMR). Finally, we propose directions for future research on blockchain-based health data security and privacy.
Fuzzy Control Algorithm Applied on Constant Airflow Controlling of Fans

Keeping a certain constant airflow in a lot of applications is critical, such as certain airflow volumes in the cabin of an aircraft, submarines, tunnels, and buildings. All fans change their output airflow and air pressure by changing motor speed in real applications. In order to achieve the target airflow volume, normal operation detects the data first, then provides orders to engineers to adjust manually. Extra airflow volume sensors are equipped to measure the airflow so that people know what the current airflow is and how much needs to be adjusted. Based on normal technology, airflow volume sensors and motor speed inverters are necessary parts of a whole air supply system. This research tries to integrate a fuzzy control algorithm into a motor control technology to build a new kind of fan that can not only self-detect the airflow volume changes but also can self-adjust the airflow volumes automatically. This self-detecting and adjusting fan can lead air supply systems in devices and buildings to need no airflow volume sensors, make the air supply system building cost lower, and make the maintaining and operating cost lower.
Explainable AI (XAI): Explained

Artificial intelligence (AI) has become an integral part of our lives; from the recommendations we receive on social media to the diagnoses made by medical professionals. However, as AI continues to grow more complex, the “black box” nature of many AI models has become a cause for concern. The main objective of Explainable AI (XAI) research is to produce AI models that are easily interpretable and understandable by humans. In this view, this paper presents an overview of XAI and its techniques for creating interpretable models, specifically focusing on Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). Furthermore, this paper delves into the various applications of XAI in different domains, including healthcare, finance, and law. Additionally, the ethical and legal implications of using XAI are mentioned. Finally, the paper discusses various challenges and future research directions of XAI.