Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Collaborative Learning with Linear Communication Complexity

Collaborative machine learning enables privacy-preserving training of machine learning models without collecting sensitive client data. Despite recent breakthroughs, communication bottleneck is still a major challenge against its scalability to larger networks. To address this challenge, in this work we propose PICO, the first collaborative learning framework with linear communication complexity, significantly improving over the quadratic state-of-the-art, under formal information-theoretic privacy guarantees. Theoretical analysis demonstrates that PICO slashes the communication cost while achieving equal computational complexity, adversary resilience, robustness to client dropouts, and model accuracy to the state-of-the-art. Extensive experiments demonstrate up to 91× reduction in the communication overhead, and up to 8× speed-up in the wall-clock training time compared to the state-of-the-art. As such, PICO addresses a key technical challenge in multi-party collaborative learning, paving the way for future large-scale privacy-preserving learning frameworks.
U-Net##: A Powerful Novel Architecture for Medical Image Segmentation

As medical image segmentation has been one of the most widely implemented tasks in deep learning, there have been various solutions proposed for its applications to achieve better results. The encoder-decoder based U-Net architecture and its variants have shown outstanding performance in this field. However, most of these solutions have limited capacity to extract sufficient features from the input images. In this paper, we propose a powerful novel architecture named U-Net##, which consists of multiple overlapping U-Net pathways and has the strategies of sharing feature maps between parallel neural networks, using auxiliary convolutional blocks for additional feature extractions and deep supervision, so that it performs as a boosted U-Net model for medical image segmentation. Our architecture is essentially a combination of encoder-decoder based multiple U-Net pathways which have different depth levels, and all their overlapping feature maps on the same sampling steps share their own feature data with the others by following a specific addition rule. Each network pathway has its own concatenated long skip connections from their encoder to decoder sections, and the final output is obtained with deep supervision method. All these strategies help the model explore much more features effectively and achieve higher accuracy. PyTorch implementation of the U-Net## with step-by-step coding is available here: https://github.com/firatkorkmaz/unetsharpsharp
Forming of Validation Dataset for Deep Learning Based Model of Medical Image Grouping

This paper presents a human-machine method for creating multimodal, hierarchically organised medical image validation datasets for the purposes of an AI image annotation algorithm. The multimodal image datasets provided here are independent of the training and testing datasets, as they are obtained in different ways from publicly available medical repositories. Objectivity in the formation of the datasets is maintained based solely on DICOM metadata. These are mainly provided by the medical instruments themselves and by computer-assisted parsing of the original metadata. To overcome the inconsistency of metadata from repository to repository, the algorithm incorporates human observation and monitoring that can guarantee higher confidence in morphological uniformity. The presented methodology provides and discusses the use of image parameters for broader and quantity-based selection and grouping of medical images. The rendered groups contain images with the obtained information leading to the general source of the data for easier use and understanding.
A Change Detection with Machine Learning Approach for Medical Image Analysis

The research activity focuses on evaluating data from medical images by applying clustering techniques on extracted components for an ex-ante/ex-post evaluation, commonly identified as “change detection”, with respect to evolution times and/or comparison with other analyzed subjects. The methodological approach examines an unsupervised automatic method through the implementation of an algorithm for the extraction of meaningful features related to the properties of the medical image data analyzed. It then goes on to normalize the data contained in the matrix to evaluate through multivariate analysis the notion that similar objects produce similar responses without knowing their entity, type, and class descriptions, which are inferred by making observations on the clusters. The main specificity of this algorithm is that classes are identified from compact, well-distinguishable clusters without knowing the extent of their nature; the entire feature space is divided into classes using proximity or similarity criteria. After finishing the process of class identification, properties will be associated with them in relation to the known descriptions. At the end of the procedure, factorial analysis using the principal component method is applied. The clusters extracted from the data are described by their properties, which make it possible to identify, on each of the new factorial axes, homogeneous classes of clusters characterized predominantly by the only variables that have a high correlation value between variable and factor. The automatic identification of classes or “phenomena” that exhibit very different mean and/or variance allows easy reading of the results for domain experts.
2.5D Lightweight Network Integrating Multi-scale Semantic Features for Liver Tumor Segmentation

One critical research area in the development of a computer-aided diagnosis system for liver cancer is efficient and automatic segmentation of lesion from CT scans. To overcome this issue, we investigated a 2.5D lightweight liver tumor segmentation by fusing the multi-scale semantic features, named MAA-Net. Our framework enhanced the information interaction between the input 2.5D stacked slice via introducing parallel convolution and increasing the knowledge weight of the lesion channel in different receptive fields. To ease the shortage of missed detection of tumors, MAA-Net fused the hierarchical semantic information extracted from the encoder. Moreover, we evaluated our MAA-Net on LiTS2017 and 3DIRCADb datasets. Extensive experiments shows the proposed method outperforms the others on both accuracy and total number of calculation. Specifically, our approach can improve liver tumor segmentation tasks by 2.4%, while reducing amount of parameters by 57.5%. Both quantitative and qualitative results illustrated the MAA-Net can effectively address with the limitation of small tumors, and some tumors are on the edge.
Community Detection in Medical Image Datasets: Using Wavelets and Spectral Methods

Medical image datasets may contain a large number of images representing patients with different health conditions. When dealing with raw unlabeled datasets, the large number of samples often makes it hard for experts and non-experts to understand the variety of images present in a dataset. Here, we propose an algorithm to facilitate the automatic identification of communities in medical image datasets. We further demonstrate that such analysis can be insightful in a supervised setting when the images are already labeled. Such insights are useful because health and disease severity can be considered a continuous spectrum. In our approach, we use wavelet decomposition of images in tandem with spectral methods. We show that the eigenvalues of a graph Laplacian can reveal the number of notable communities in an image dataset. Moreover, analyzing the similarities may be used to infer a spectrum representing the severity of the disease (code is available at https://github.com/roozbeh-yz/community_medical_images ).
Towards Convention-Based Game Strategies

To effectively develop cooperative multiagent systems, we introduce an architecture that facilitates the agents’ dynamic adoption of conventions. It expands an existing agent model’s action selection architecture with a component that uses Natural Language Processing techniques. This component embeds conventions into agent interaction strategies to improve the predictability of other agents’ actions if all agents adopt the same conventions in their strategies.
Adding Preferences and Moral Values in an Agent-Based Simulation Framework for High-Performance Computing

Abstract Agent-Based Simulation is a suitable approach used now-a-days to simulate and analyze complex societal environments and scenarios. Current Agent-Based Simulation frameworks either scale quite well in computation but implement very simple reasoning mechanisms, or employ complex reasoning systems at the expense of scalability. In this paper we present our work to extend an agent-based HPC platform, enabling goal-driven agents with HTN planning capabilities to scale and run parallelly. Our extension includes preferences over their objectives, preferences over their plans, actions, and moral values. We show the expresiveness of the extended platform with a sample scenario.
Developing a Transaction System in Blockchain

Blockchain technology is advancing quickly and does not appear to be slowing down. Many things that looked inconceivable in the last few decades—like excessive transaction costs, double spending, net fraud, recovering lost data, etc.—turned out to be true. But due to blockchain technology, all of this may now be avoided. Every block on the blockchain includes some information and the cryptographic hash of the previous block. This system is reliable. At present, various types of transactions are done using various types of systems. This system can be replaced by such. Current online transaction gateways are vulnerable to hacking, allowing attackers to manipulate the network and cause financial loss. As transactions have to pass through multiple transaction systems, which takes time and carries the risk of transaction failure. Based on our research, it is possible to conclude that blockchain is the best technology for cryptocurrencies in commercial transactions, as it enables cryptocurrencies to operate decentralized. This can reduce risk as well as transaction costs. So, our system would change the security change the current banking system is some criticism that there are uses that are not legal and also some community affects the research circle has curiosity in it.
Unsupervised Global and Local CNN Feature Extraction Using Multi-Scale Attention Aggregation for Fine-Grained Image Retrieval

In this paper, we will propose an unsupervised fine-grained image retrieval (FGIR) approach using multi-scale attention aggregation for global and local CNN feature extraction. First, the query image is fed into a pre-trained CNN to get activation maps of different scales in different CNN layers. A global attention map is then generated in an unsupervised manner by aggregating the multi-scale activation maps. The global attention map is then used to localize the global object region and some local part regions, in which global (object) feature and local (part) features will be extracted for image similarity computation. Unsupervised image retrieval is realized since no manual labeling or re-training/fine-tuning CNN models is needed. The experiments were conducted on six datasets to evaluate the performance of the proposed unsupervised FGIR approach, including the CUB-200-2011, Stanford Dogs, Oxford Flowers 102, Stanford Aircrafts, Oxford-IIIT Pets, and Stanford Cars. Experiment results have shown the effectiveness of the proposed approach.
THE INFLUENCE OF GLOBAL TRENDS ON THE FORMATION OF EDUCATIONAL TRENDS

Digital transformation is the process of implementing digital technologies and changing business models to optimize business processes, improve efficiency and create new opportunities for innovation. It includes the use of technologies such as artificial intelligence, data analytics, the Internet of Things, blockchain, and others in order to transform traditional business processes.
Testamento blockchainizado, bienes digitales extrapatrimoniales y herencia de activos digitales (NFT y criptomonedas) [Blockchain Wills, Digital Goods and Inheritance of Digital Assets (NFTs and Cryptocurrencies)].

Spanish Abstract: Este trabajo propone la adaptación del tratamiento legal de la sucesión testamentaria mediante tecnología blockchain. Asimismo, el trabajo examina la sucesión en los activos digitales patrimoniales, a la luz del soft law del ELI y UNIDROIT y el «testamento digital» como modo testamentario a los herederos para los bienes digitales extrapatrimoniales.English Abstract: This paper proposes the adaptation of the legal treatment of testamentary succession using blockchain technology. The paper also examines the succession of digital assets in the light of the soft law of the ELI and UNIDROIT and the "digital will" as a testamentary mode of inheritance for non-proprietary digital assets.
Attendance Monitoring System using Face Recognition

The automatic face attendance monitoring system is a project that records the presence of students in an institute or a class. Within this system, we can offer an automatic way to count attendance management projects. Based on facial recognition and recognition algorithms, this system automatically recognizes a student via CCTV when they enter the institute or in class and signals their presence by recognizing them, as well as the techniques to be used to deal with pitfalls such as spoofing. If we compare traditional attendance checks with this system, we save time and also monitor the students. Maintaining attendance is very essential part of all institutions to monitor student performance. Each institution has its way of dealing with it. Some measure attendance manually using a register or paper method and some have implemented automated methods of attendance using biometrics. This system uses facial recognition to automatically detect the presence of students in the classroom without student involvement. Online Attendance Management System Benefits of Automated Student Attendance Management is software designed to manage a student's day-to-day attendance at the university. Here, the subject staff have one less task to mark student attendance and focus on teaching students until the end of class, as attendance is via CCTV by capturing photos of students and storing them in the database. Generated an automatic report with high fidelity. Our system also helps us to assess student attendance criteria. A detailed student attendance report is generated weekly and monthly.
Changes in Reading Skills After Learning Using Puzzle in the Kindergarten

The ability to read is an academic substance that is difficult for early childhood to learn. One of the effective ways that can be applied in the process of learning to read in early childhood is by combining learning with play such as using puzzle media. The purpose of this study was to determine changes in reading skills after being given learning using puzzle media in children at Kindergarten, Sooko Village, Mojokerto Regency. In this study, the research design used was pre-experimental with one group pretest-posttest design. The population in this study were all 45 children in Kindergarten, Sooko Village, Mojokerto Regency. The sample in this study were 40 people. The independent variable in this study is the puzzle media. The dependent variable in this study is reading ability. The instrument used in this study was an observation sheet. The data analysis technique used is the Wilcoxon signed rank test. The results of this study indicate that respondents who experienced changes in reading ability in the good category were 9 respondents (50%), the sufficient category was 4 respondents (22.2%), and the less category was 5 respondents (27.8%). The results of statistical tests using the Wilcoxon signed rank test statistic showed sig. 0.001 &lt; (0.05) then H0 is rejected and H1 is accepted, which means that there is a change in reading ability after being given learning to use reading puzzle media in children aged at Kindergarten, Sooko Village, Mojokerto Regency.
Navigating the Global Marketplace: the Linguistic Landscape of E-Commerce

This article delves into the intricate relationship between language choices on ecommerce websites and their impact on user experience, trust-building, and market penetration across diverse linguistic and cultural contexts. Through a comprehensive analysis of website language strategies, the article aims to uncover the nuances that contribute to the success of e-commerce platforms in global markets. By investigating real-world examples and employing theoretical frameworks, it seeks to provide insights into how linguistic considerations can be leveraged as a strategic tool for navigating the complexities of the global e-commerce landscape.
Pembuatan Media Infografis Melalui Pelatihan dan Pendampingan Desain Media Pembelajaran Berbasis Canva for Education Bagi Guru-guru SD di Kota Palembang

In the current digital era, easy and fast access to information positively influences students' ability to access helpful information in the learning process. This service aims to increase teachers' capacity to design and produce products in the form of infographic learning media. The technological innovation trained in this service is to realize the development in electronic and printed form through Canva for education. Participatory Action Research (PAR) is the method used in the implementation of service activities with stages: 1) giving an initial test, 2) implementing the service, 3) the final stage consisting of evaluation and reflection, and 4) giving the final test. The target participants of the service activities were elementary school teachers of science subjects in Palembang City. Based on the data obtained, there is an increase in test results with an N-gain of 0.81, which is included in the high category. Based on the above results, the training participants strongly agree that this service activity can increase their understanding and skills in creating infographic learning media through Canva for Education
A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19

Computational subphenotyping, a data-driven approach to understanding disease subtypes, is a prominent topic in medical research. Numerous ongoing studies are dedicated to developing advanced computational subphenotyping methods for cross-sectional data. However, the potential of time-series data has been underexplored until now. Here, we propose a Multivariate Levenshtein Distance (MLD) that can account for address correlation in multiple discrete features over time-series data. Our algorithm has two distinct components: it integrates an optimal threshold score to enhance the sensitivity in discriminating between pairs of instances, and the MLD itself. We have applied the proposed distance metrics on the k-means clustering algorithm to derive temporal subphenotypes from time-series data of biomarkers and treatment administrations from 1039 critically ill patients with COVID-19 and compare its effectiveness to standard methods. In conclusion, the Multivariate Levenshtein Distance metric is a novel method to quantify the distance from multiple discrete features over time-series data and demonstrates superior clustering performance among competing time-series distance metrics.
Deep-PDANet: Camera-Radar Fusion for Depth Estimation in Autonomous Driving Scenarios

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The results of monocular depth estimation are no satisfactory in the automatic driving scenario. The combination of radar and camera for depth estimation is a feasible solution to the problem of depth estimation in similar scenes. The radar-camera pixel depth association model establishes a reliable correlation between radar depth and camera pixel. In this paper, a new depth estimation model named Deep-PDANet based on RC-PDA is proposed, which increases the depth and width of the network and alleviates the problem of network degradation through residual structure. Convolution kernels of different sizes are selected in the basic units to further improve the ability to extract global information while taking into account the extraction of information from a single pixel. The convergence speed and learning ability of the network are improved by the training strategy of multi-weight loss function in stages. In this paper, comparison experiments and ablation study were performed on the NuScenes dataset, and the accuracy of the multidimensional model was improved over the baseline model, which exceeded the existing excellent algorithms.&lt;/div&gt;&lt;/div&gt;
DEVELOPING INVERSE MOTION PLANNING TECHNIQUE FOR AUTONOMOUS VEHICLES USING INTEGRAL NONLINEAR CONSTRAINTS

The study considers issues of elaborating and validating a technique of autonomous vehicle (AV) motion planning based on sequential trajectory and speed optimization. This method includes components such as representing sought-for functions by finite elements (FE), vehicle kinematic model, sequential quadratic programming (SQP) for nonlinear constrained optimization, and Gaussian N-point quadrature integration. The primary novelty consists of using the inverse approach for obtaining vehicle trajectory and speed. The curvature and speed are represented by integrated polynomials to reduce the number of unknowns. For this, piecewise functions with two and three degrees of freedom (DOF) are implemented through FE nodal parameters. The technique ensures higher differentiability compared to the needed in the geometric and kinematic equations. Thus, the generated reference curves are characterized by simple and unambiguous forms. The latter fits best the control accuracy and efficiency during the motion tracking phase. Another advantage is replacing the nodal linear equality constraints with integral nonlinear ones. This ensures the non-violation of boundary limits within each FE and not only in nodes. The optimization technique implies that the spatial and time variables must be found separately and staged. The trajectory search is accomplished in the restricted allowable zone composed by superposing an area inside the external and internal boundaries, based on keeping safe distances, excluding areas for moving obstacles. Thus, this study compares two models that use two and three nodal DOF on optimization quality, stability, and rapidity in real-time applications. The simulation example shows numerous graph results of geometric and kinematic parameters with smoothed curves up to the highest derivatives. Finally, the conclusions are made on the efficiency and quality of prognosis, outlining the similarities and differences between the two applied models.
Evaluating the difficulty for novice engineers in learning and using Transition Systems for modeling software systems

Modern software products are complex systems and are better comprehended when engineers can think of the software as a system. Systems Science suggests that learning about a complex system is aided by modeling. It stands to reason that if we can help novice engineers model the software products as systems, it should improve their comprehension.
Automaton Model Updating Based on the L* Algorithm

When a system is upgraded, it is necessary to update its formal model accordingly. This study proposes an automaton model updating method based on the L* algorithm. Firstly, the observation table with respect to the original model is modified by performing membership queries with the upgraded system. Secondly, the redundancy information in the observation table is deleted. After these operations, the values of the observation table are consistent with the behavior of the new system. Then the observation table is extended in the framework of the L* algorithm. Finally, an automaton model describing the behavior of the new system is obtained. The effectiveness of the proposed algorithm and the impact of system upgrade rate on modeling are studied quantitatively by experiments.