Reviewerly/exHarmony huggingface dataset:
Transforming Design Education іn Ukraine: Insights from Global Best Practices

The article reveals some aspects of the problem of transforming the Ukrainian design education system through the prism of international experience. This problem is caused by the long-term neglect of the importance of the humanitarian segment of education in Ukraine, which, in turn, has become a determinant of the accumulation of numerous problems. In order to achieve the goal and objectives, it was important, firstly, to refer to the relevant source base, and secondly, to analyse certain aspects of the educational activities of higher education institutions abroad, in particular Seian University of Art and Design (Otsu, Shiga, Japan), Royal Collage of Art (London, Great Britain), KEDGE Design School (Marseille, France), Istituto Pantheon Design &amp; Technology (Rome, Italy). The analytical and synthetic activities carried out allowed us to identify the best international practices in this area and propose vectors for the transformation of domestic design education. These include: multidirectional international cooperation; introduction of a practical component of student training on the basis of business institutions and enterprises; focus on the educational needs of students; active use of digital innovative technologies, etc. We see the prospects for further research in this direction in the practical application of the proposed steps to modernise design education.
PersonaGen: A Tool for Generating Personas from User Feedback

Personas are crucial in software development processes, particularly in agile settings. However, no effective tools are available for generating personas from user feedback in agile software development processes. To fill this gap, we propose a novel tool that uses the GPT-4 model and knowledge graph to generate persona templates from well-processed user feedback, facilitating requirement analysis in agile software development processes. We developed a tool called PersonaGen. We evaluated PersonaGen using qualitative feedback from a small-scale user study involving student software projects. The results were mixed, highlighting challenges in persona-based educational practice and addressing non-functional requirements.
Quadratic Phase Multiresolution Analysis and the Construction of Orthonormal Wavelets in L2(ℝ)

The multi-resolution analysis (MRA) associated with quadratic phase Fourier transform (QPFT) serves as a tool to construct orthogonal bases of the L2(R). Consequently, it assumes a pivotal role in facilitating potential applications of QPFT. Inspired by the sampling theorem applicable to band-limited signals in the QPFT domain, this paper formulates the development of the MRA linked with QPFT. Subsequently, we develop a method for constructing orthogonal bases for L2(R), followed by some examples.
Path Planning Using Adaptive RRT*-Connect Algorithm Base on NURBS Curve Smoothing

Optimal Path Planning for Robots refers to the autonomous search and planning of the shortest and smoothest path between the start and end points while avoiding obstacles in the space. This article presents an adaptive RRT*-Connect global path planning method based on NURBS curve smoothing (NARRT*-Connect). Building on the RRT*-Connect path planning, an adaptive step size adjustment strategy is introduced to accelerate the path search. In addition, NURBS curves are used for secondary path smoothing optimization, which does not require moving control points but only defines appropriate weighting criteria, ensuring that the path is smoothed while controlling local path changes within a safe threshold. Through simulation and experimentation, NARRT*-Connect is studied and compared with RRT*-Connect, RRT*, and other algorithms. The results indicate that the NARRT*-Connect algorithm can significantly improve iteration speed, generate shorter paths, and facilitate secondary safety smoothing of the paths.
A Biobjective Scheme for Kinematic Control of Mobile Robotic Arms With Manipulability Optimization

In combination with a robotic arm, a mobile platform can fulfill complicated functions but may encounter singularity problems. To solve them, this work investigates the kinematic control of a mobile robotic arm (MRA) that is based on a redundant arm mounted on a mobile robot. Specifically, a quadratic program-based biobjective (QPBO) scheme is proposed, which integrates manipulability optimization and the minimum velocity infinite-norm. Accordingly, a modified neural dynamics (MND) is founded with its stability substantiated by Lyapunov stability analyses theoretically. Furthermore, simulations, comparisons, and experiments are carried out on an MRA, from which the feasibility, effectiveness, and superiority of QPBO are validated. In conclusion, the proposed scheme is of great significance for enhancing the manipulability of MRA while reducing the maximum joint velocity.
T-VAKS: A Tutoring-Based Multimodal Dialog System via Knowledge Selection

Advancements in Conversational Natural Language Processing (NLP) have the potential to address critical social challenges, particularly in achieving the United Nations’ Sustainable Development Goal of quality education. However, the application of NLP in the educational domain, especially language learning, has been limited due to the inherent complexities of the field and the scarcity of available datasets. In this paper, we introduce T-VAKS (Tutoring Virtual Agent with Knowledge Selection), a novel language tutoring multimodal Virtual Agent (VA) designed to assist students in learning a new language, thereby promoting AI for Social Good. T-VAKS aims to bridge the gap between NLP and the educational domain, enabling more effective language tutoring through intelligent virtual agents. Our approach employs an information theory-based knowledge selection module built on top of a multimodal seq2seq generative model, facilitating the generation of appropriate, informative, and contextually relevant tutor responses. The knowledge selection module in turn consists of two sub-modules: (i) knowledge relevance estimation, and (ii) knowledge focusing framework. We evaluate the performance of our proposed end-to-end dialog system against various baseline models and the most recent state-of-the-art models, using multiple evaluation metrics. The results demonstrate that T-VAKS outperforms competing models, highlighting the potential of our approach in enhancing language learning through the use of conversational NLP and virtual agents, ultimately contributing to addressing social challenges and promoting well-being.
Diffusion Model for Camouflaged Object Detection

Camouflaged object detection is a challenging task that aims to identify objects that are highly similar to their background. Due to the powerful noise-to-image denoising capability of denoising diffusion models, in this paper, we propose a diffusion-based framework for camouflaged object detection, termed diffCOD, a new framework that considers the camouflaged object segmentation task as a denoising diffusion process from noisy masks to object masks. Specifically, the object mask diffuses from the ground-truth masks to a random distribution, and the designed model learns to reverse this noising process. To strengthen the denoising learning, the input image prior is encoded and integrated into the denoising diffusion model to guide the diffusion process. Furthermore, we design an injection attention module (IAM) to interact conditional semantic features extracted from the image with the diffusion noise embedding via the cross-attention mechanism to enhance denoising learning. Extensive experiments on four widely used COD benchmark datasets demonstrate that the proposed method achieves favorable performance compared to the existing 11 state-of-the-art methods, especially in the detailed texture segmentation of camouflaged objects. Our code will be made publicly available at: https://github.com/ZNan-Chen/diffCOD.
Invisible Backdoor Attacks Using Data Poisoning in Frequency Domain

Backdoor attacks have become a significant threat to deep neural networks (DNNs), whereby poisoned models perform well on benign samples but produce incorrect outputs when given specific inputs with a trigger. These attacks are usually implemented through data poisoning by injecting poisoned samples (samples patched with a trigger and mislabelled to the target label) into the dataset, and the models trained with that dataset will be infected with the backdoor. However, most current backdoor attacks lack stealthiness and robustness because of the fixed trigger patterns and mislabelling, which humans or some backdoor defense approach can easily detect. To address this issue, we propose a frequency-domain-based backdoor attack method that implements backdoor implantation without mislabeling the poisoned samples or accessing the training process. We evaluated our approach on four benchmark datasets and two popular scenarios: no-label self-supervised and clean-label supervised learning. The experimental results demonstrate that our approach achieved a high attack success rate (above 90%) on all tasks without significant performance degradation on main tasks and robust against mainstream defense approaches.
Human emotion recognition with a microcomb-enabled integrated optical neural network

Abstract State-of-the-art deep learning models can converse and interact with humans by understanding their emotions, but the exponential increase in model parameters has triggered an unprecedented demand for fast and low-power computing. Here, we propose a microcomb-enabled integrated optical neural network (MIONN) to perform the intelligent task of human emotion recognition at the speed of light and with low power consumption. Large-scale tensor data can be independently encoded in dozens of frequency channels generated by the on-chip microcomb and computed in parallel when flowing through the microring weight bank. To validate the proposed MIONN, we fabricated proof-of-concept chips and a prototype photonic-electronic artificial intelligence (AI) computing engine with a potential throughput up to 51.2 TOPS (tera-operations per second). We developed automatic feedback control procedures to ensure the stability and 8 bits weighting precision of the MIONN. The MIONN has successfully recognized six basic human emotions, and achieved 78.5 % accuracy on the blind test set. The proposed MIONN provides a high-speed and energy-efficient neuromorphic computing hardware for deep learning models with emotional interaction capabilities.
SimPLe: Similarity-Aware Propagation Learning for Weakly-Supervised Breast Cancer Segmentation in DCE-MRI

Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in the screening and prognosis assessment of high-risk breast cancer. The segmentation of cancerous regions is essential useful for the subsequent analysis of breast MRI. To alleviate the annotation effort to train the segmentation networks, we propose a weakly-supervised strategy using extreme points as annotations for breast cancer segmentation. Without using any bells and whistles, our strategy focuses on fully exploiting the learning capability of the routine training procedure, i.e., the train - fine-tune - retrain process. The network first utilizes the pseudo-masks generated using the extreme points to train itself, by minimizing a contrastive loss, which encourages the network to learn more representative features for cancerous voxels. Then the trained network fine-tunes itself by using a similarity-aware propagation learning (SimPLe) strategy, which leverages feature similarity between unlabeled and positive voxels to propagate labels. Finally the network retrains itself by employing the pseudo-masks generated using previous fine-tuned network. The proposed method is evaluated on our collected DCE-MRI dataset containing 206 patients with biopsy-proven breast cancers. Experimental results demonstrate our method effectively fine-tunes the network by using the SimPLe strategy, and achieves a mean Dice value of 81%. Our code is publicly available at https://github.com/Abner228/SmileCode .
Medical Phrase Grounding with Region-Phrase Context Contrastive Alignment

Medical phrase grounding (MPG) aims to locate the most relevant region in a medical image, given a phrase query describing certain medical findings, which is an important task for medical image analysis and radiological diagnosis. However, existing visual grounding methods rely on general visual features for identifying objects in natural images and are not capable of capturing the subtle and specialized features of medical findings, leading to a sub-optimal performance in MPG. In this paper, we propose MedRPG, an end-to-end approach for MPG. MedRPG is built on a lightweight vision-language transformer encoder and directly predicts the box coordinates of mentioned medical findings, which can be trained with limited medical data, making it a valuable tool in medical image analysis. To enable MedRPG to locate nuanced medical findings with better region-phrase correspondences, we further propose Tri-attention Context contrastive alignment (TaCo). TaCo seeks context alignment to pull both the features and attention outputs of relevant region-phrase pairs close together while pushing those of irrelevant regions far away. This ensures that the final box prediction depends more on its finding-specific regions and phrases. Experimental results on three MPG datasets demonstrate that our MedRPG outperforms state-of-the-art visual grounding approaches by a large margin. Additionally, the proposed TaCo strategy is effective in enhancing finding localization ability and reducing spurious region-phrase correlations.
Call for Papers Special Issue for Educational Technology Research &amp;amp; Development (ETR&amp;amp;D)

Download the PDF of the CfP &lt;strong&gt;Special issue editors&lt;/strong&gt; Dr. Michael Kerres, University Duisburg-Essen, michael.kerres@uni-due.de, http://kerres.name Dr. Pavlo Antonenko, University of Florida, p.antonenko@coe.ufl.edu, https://education.ufl.edu/faculty/antonenko-pavlo-pasha/ Dr. Marco Kalz, University of Education, Heidelberg, kalz@ph-heidelberg.de, https://kalz.cc &lt;strong&gt;Important Dates&lt;/strong&gt; • March 15, 2022 —outlines of pr
xSPDE3: Extensible software for stochastic ordinary and partial differential equations

The xSPDE toolbox treats stochastic partial and ordinary differential equations, with applications in biology, chemistry, engineering, medicine, physics and quantum technologies. It computes statistical averages, including time-step and/or sampling error estimation. xSPDE can provide higher order convergence, Fourier spectra and probability densities. The toolbox has graphical output and \chi^{2} <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msup><mml:mi>χ</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:math> statistics, as well as weighted, projected, or forward-backward equations. It can generate input-output quantum spectra. All equations may have independent periodic, Dirichlet, and Neumann or Robin boundary conditions in any dimension, for any vector field component, and at either end of any interval.
Operational entanglement-based quantum key distribution over 50 km of field-deployed optical fibers

We present a field-deployed quantum key distribution (QKD) link based on energy-time entanglement. Three nodes are connected, bridging the cities of Nice and Sophia Antipolis, by means of commercial grade optical fibers, spanning over a total distance of 50 km. We have built a packaged and resilient source of energy-time entangled photon pairs and implemented remote interferometer stabilization. Moreover, the clocks of the two end users are synchronized exploiting photon-counting techniques only, therefore requiring no dedicated channel. The system runs over the ITU 100-GHz standard telecom grid, through which a secure key rate of 7.0 kbit/s is extracted for one pair of channels. A homemade software performs all the necessary postprocessing procedures enabling to establish secret keys in real time. We present the first fully operational entanglement-based metropolitan QKD system being totally self-sufficient, i.e., requiring no additional overheads to be operated on a conventional metropolitan fiber network.
Design, Development, Implementation, and Evaluation of a Mobile Application for Academic Library Services

Universities and scientific educational institutions today need targeted information services to ensure that their user communities have the information they need. This study aims to design, develop, implement, and evaluate a mobile application for academic library services at Tarbiat Modares University (Tehran, Iran). A four-stage process was utilized to accomplish this aim. In the first phase, relevant literature was reviewed to obtain appropriate data requirements for the app. A questionnaire was designed and administered to survey expert librarians on the most suitable data requirements. The second phase involved the design of the user interface and user experience with the assistance of experts, followed by the evaluation of the experience. The third phase involved the development of the app in the Android Studio environment using the Java programming language, based on the requirements identified in the first and second phases. The app was then made available to the user community. Finally, the app was evaluated in the fourth phase using a questionnaire tool. The researchers found this approach to application development to be both economical and effective in the context of a developing country.
Retracted: Analysis of the Application Effect of Multidisciplinary Team Cooperation Model in Chronic Heart Failure under WeChat Platform

[This retracts the article DOI: 10.1155/2022/4051955.].
Retracted: On Improved 3D-CNN-Based Binary and Multiclass Classification of Alzheimer’s Disease Using Neuroimaging Modalities and Data Augmentation Methods

[This retracts the article DOI: 10.1155/2022/1302170.].
Retracted: Effectiveness of Artificial Intelligence (AI) in Improving Pupils’ Deep Learning in Primary School Mathematics Teaching in Fujian Province

[This retracts the article DOI: 10.1155/2022/1362996.].
A Practical Implementation of Medical Privacy-Preserving Federated Learning Using Multi-Key Homomorphic Encryption and Flower Framework

The digitization of healthcare data has presented a pressing need to address privacy concerns within the realm of machine learning for healthcare institutions. One promising solution is federated learning, which enables collaborative training of deep machine learning models among medical institutions by sharing model parameters instead of raw data. This study focuses on enhancing an existing privacy-preserving federated learning algorithm for medical data through the utilization of homomorphic encryption, building upon prior research. In contrast to the previous paper, this work is based upon Wibawa, using a single key for HE, our proposed solution is a practical implementation of a preprint with a proposed encryption scheme (xMK-CKKS) for implementing multi-key homomorphic encryption. For this, our work first involves modifying a simple “ring learning with error” RLWE scheme. We then fork a popular federated learning framework for Python where we integrate our own communication process with protocol buffers before we locate and modify the library’s existing training loop in order to further enhance the security of model updates with the multi-key homomorphic encryption scheme. Our experimental evaluations validate that, despite these modifications, our proposed framework maintains a robust model performance, as demonstrated by consistent metrics including validation accuracy, precision, f1-score, and recall.
Explainable Machine Learning Models of Consumer Credit Risk

In this work, the authors create machine learning (ML) models to forecast home equity credit risk for individuals using a real-world dataset and demonstrate methods to explain the output of these ML models to make them more accessible to the end user. They analyze the explainability for various stakeholders: loan companies, regulators, loan applicants, and data scientists, incorporating their different requirements with respect to explanations. For loan companies, they generate explanations for every model prediction of creditworthiness. For regulators, they perform a stress test for extreme scenarios. For loan applicants, they generate diverse counterfactuals to guide them with steps toward a favorable classification from the model. Finally, for data scientists, they generate simple rules that accurately explain 70%–72% of the dataset. Their study provides a synthesized ML explanation framework for all stakeholders and is intended to accelerate the adoption of ML techniques in domains that would benefit from explanations of their predictions.
How Good Is Your Location? Comparing and Understanding the Uncertainties in Location for the 1993 Rock Valley Sequence

Abstract Accurate event locations are important for many endeavors in seismology, and understanding the factors that contribute to uncertainties in those locations is complex. In this article, we present a case study that takes an in-depth look at the accuracy and precision possible for locating nine shallow earthquakes in the Rock Valley fault zone in southern Nevada. These events are targeted by the Rock Valley Direct Comparison phase of the Source Physics Experiment, as candidates for the colocation of a chemical explosion with an earthquake hypocenter to directly compare earthquake and explosion sources. For this comparison, it is necessary to determine earthquake hypocenters as accurately as possible so that different source types have nearly identical locations. Our investigations include uncertainty analysis from different sets of phase arrivals, stations, velocity models, and location algorithms. For a common set of phase arrivals and stations, we find that epicentral locations from different combinations of velocity models and algorithms are within 600 m of one another in most cases. Event depths exhibit greater uncertainties, but focusing on the S-P times at the nearest station allows for estimates within approximately 500 m.
Cloud Computing Technology in Development E-Business (Literature Review)

Good use of technology will be able to help various kinds of work activities including in the business world. High mobility requires a person or group to use information technology. One of the topics of technological development today is cloud computing. Cloud computing is a technology that uses the internet network to access resources in the process of managing data, processing data to its application. Cloud computing is widely used by people to start a business because it has many advantages in starting a business. One of the new concepts offered is e-business or electronic business. E-business is an expanded form of e-Commerce, where e-business is accompanied by customer service, collaboration with business partners with electronic support as a means of transaction or organization, but not only purchases, payments for goods, and services, but also Cloud computing services allow companies to determine the capacity to meet demand and move according to the speed of business growth of the company but not at a speed determined by enterprise infrastructure. This allows companies to generate better time to market, speed up application delivery and ensure management of capital or operational costs. Cloud computing implementation in business requires consideration of business and IT requirements as well as selecting available cloud computing to formulate the right cloud strategy. The goal after the implementation of cloud computing is that users can expand cloud computing capabilities more efficiently. In addition to having the advantages of cloud computing, it also has risks, namely only providers who know physically what is happening with user data, when disasters occur sometimes the ability of providers to recover data is still a concern for users, and provider compliance with regulations and when experiencing bankruptcy is still uncertain.