Reviewerly/exHarmony huggingface dataset:
SliceSamp: A Promising Downsampling Alternative for Retaining Information in a Neural Network

Downsampling, which aims to improve computational efficiency by reducing the spatial resolution of feature maps, is a critical operation in neural networks. Many downsampling methods have been proposed to address the challenge of retaining feature map information. However, some detailed information is still lost, even though these methods can extract features with stronger semantics. In this paper, we propose a novel downsampling method which combines feature slicing and depthwise separable convolution for information-retaining downsampling. It slices the input feature map into multiple non-overlapping sub-feature maps by using indexes with a stride of two in the spatial dimension and applies depthwise separable convolution on each slice to extract feature information. To demonstrate the effectiveness of SliceSamp, we compare it with classical downsampling methods on image classification, object detection, and semantic segmentation tasks using several benchmark datasets, including ImageNet-1K, COCO, VOC, and ADE20K. Extensive experiments demonstrate that SliceSamp outperforms classical downsampling methods with consistent improvements in various computer vision tasks. The proposed SliceSamp shows advanced model performance with lower computational costs and memory requirements. By replacing the downsampling layers in different network architectures (including ResNet (Residual Network), YOLOv5, and Swin Transformer), SliceSamp brings different degrees of performance gains (+0.54~3.64%) compared to these baseline models. Additionally, SliceUpsamp enables high-resolution feature reconstruction and alignment during upsampling. SliceSamp and SliceUpsamp can be plug-and-play-integrated into existing neural network architectures. As a promising downsampling alternative to traditional methods, SliceSamp can also provide a reference for designing lightweight and high-performance model architectures in the future.
Institutional Repositories of Russian Organisations: a Comparative Analysis

In order to study the current state of domestic institutional repositories (IR), the official websites of 106 Russian universities — participants of the programme “Priority-2030” and 454 scientific research institutes (SRI) of three categories approved by the Federal Agency of Scientific Organisations according to the Decree of the Government of the Russian Federation from April 8, 2009 No. 312 “On the assessment and monitoring of the effectivness of scientific organisations performing research, development and technological works of civilian designation” were monitored. A comparative analysis of IR was carried out. In the course of the study the following data were analyzed: availability of the IR on the site, navigation on the site for the convenience of using the IR, content, functionality, visibility, accessibility of materials, etc. Despite the fact that the problems of creating and developing repositories are actively studied both in Russia and abroad, the results of the study showed that the practice of maintaining IRs in Russian universities and scientific organisations is unevenly developed. In scientific institutions repositories are created much less frequently than in educational organisations. However, a half of higher education institutionsʼ IRs are closed to remote users. Access is granted only to teachers and students of educational institutions. In some repositories it is not always possible to successfully use scientific information, as not all repository objects contain the full text of the document, references to external sources are not always correct. The resource base of repositories of higher education institutions is richer than that of research institutes, because in addition to the works of their staff, it includes educational collections and student works. Mostly IRs provide text documents, very rarely other data (audio, video materials, data sets). Repositories of educational and scientific institutions are limited by chronological frameworks, including mainly documents of 1990—2022. Materials from earlier years of publication are presented in small quantities or include only metadata. The IRs of higher education institutions much more often provide wide search possibilities: choice of simple or advanced search options, use of logical operators, faceted navigation. Except for isolated cases, the IRs of research institutes have one search window with the possibility to search by keywords. Providing open access to information in repositories increases the visibility and accessibility of scientific research results, citability of publications, and promotes scientific communication.
3D-printed design iteration of a low-tech positive obstacle pushing/gliding wheelchair accessory

Steering a wheelchair while navigating through manual doors or against obstacles is challenging for some users. Previously, a low-cost, low-tech accessory made using off-the-shelf components, conventional manufacturing, and 3D-printed fasteners demonstrated the proof-of-concept for uncrossable positive obstacle pushing or gliding. Current work presents the fabrication and testing of an entirely 3D-printed prototype of the accessory.The accessory was 3D-printed using ABS (10% fill density) in sections. A finite element stress analysis simulation was performed for the entire accessory. Prototype tests were done with the accessory installed on an unoccupied powered wheelchair against a door and an obstacle with ∼25 N and ∼50 N resistance forces, respectively.The maximum stresses in none of the crucial components exceeded the break strength of ABS. Test results demonstrate the ability and mechanical robustness of the fully 3D-printed accessory to push open manual doors, allowing easy navigation through doors, and to push or glide against obstacles. The current prototype improves over the previous prototype in terms of manufacturability, weight, design, and safety.To the best of our knowledge, this is the first demonstration of an entirely 3D-printed wheelchair accessory that pushes or glides against uncrossable positive obstacles. Future studies would involve end-user satisfaction assessment and functionality evaluation in different scenarios under clinical supervision. The pushing or gliding ability of the accessory could be beneficial to wheelchair users with neuromuscular disorders or paraplegia.Low-tech 3D-printed accessory allows wheelchair users to push or glide against doors and other obstacles.Protective nature of the accessory could prove highly beneficial to some wheelchair users (those with diabetic feet).Pushing or gliding ability of the accessory could be beneficial to wheelchair users with neuromuscular disorders or paraplegia.Accessory could reduce wheelchair accidents and falls due to uncrossable positive obstacles.3D printing allows easy prototyping of wheelchair-related assistive technology solutions.
Natural Language Processing: Concepts and Applications using Chatbot

A chatbot is a paradigm of human-computer interaction (HCI) and artificial intelligence software. A chatbot is described as "computer software designed to simulate human-to-human interaction, particularly over the Internet." Chatbot is a popular computer programme that is used to promote human user interaction and communication, particularly via the internet. As a computer programme application, it is becoming more popular. Previously, a user would ask a query, and a computer programme would respond. By entering in the question, the user can answer it. If a user asks a question that isn't answered in the database, the chat bot conducts a Google search and responds with pertinent information. When a user enters a query, the bot compares and correlates the query terms with words in the database using pattern matching. User's input will be used to analyses the terms in the phrase for similarity. As a result, the highest score is printed. NLP (Natural Language Processing) is a technique for helping computers understand human language. This document also offers a summary of the metrics used to evaluate bot performance. This contributes in the development of more effective bots.
Leveraging Deep Learning and Knowledge Distillation for Enhanced Traffic Anomaly Detection in Transportation Systems

This paper introduces an innovative approach to enhance traffic anomaly detection in transportation systems using deep learning and knowledge distillation. We create a robust dataset from 427 videos containing 1,415 accident-related events, spanning various anomalies like accidents, car crashes, and pedestrian violations. To address real-time anomaly detection challenges, we propose a novel lightweight neural network architecture inspired by EfficientNet-B0, designed for efficient video anomaly detection. Through knowledge distillation, a student model learns from a teacher model’s predictions, resulting in heightened anomaly detection accuracy. Experimental results highlight the approach’s efficacy, with the knowledge-distilled student model consistently outperforming the standalone lightweight network, achieving an accuracy of 94.83% compared to 94.16%. This research offers a practical solution for real-time traffic anomaly detection, which is especially valuable in resource-constrained environments. Fusing a unique dataset, EfficientNet-B0-like structure, lightweight architecture, and knowledge distillation holds significant potential for fostering safer and more efficient transportation systems.
Scene Parsing Using Fully Convolutional Network for Semantic Segmentation

When it comes to computer vision, scene parsing is a crucial part of semantic segmentation. It has a wide range of applications, including autonomous driving, robotics, gaming, natural language processing, object detection, and image and video editing. Semantic segmentation works by classifying each pixel of an image according to the object it belongs to, and scene parsing provides contextual information to improve the accuracy and robustness of deep learning models used for this purpose. In this study, we used the Fully Convolutional Network (FCN-8) architecture, a popular deep learning-based technique that achieves higher accuracy than traditional and state-of-the-art methods. This is achieved by creating hierarchies of distinctive features in an image. The FCN-8 is used to perform semantic segmentation efficiently, taking an image of any size as input and producing correspondingly sized output with effective inference and learning. To fine-tune the FCN-8 for the MIT Scene Parsing Challenge Dataset, we employed a transfer learning approach. Our results showed that our proposed approach achieved an accuracy of 72% on the dataset. This is significant given the relatively small number of samples and the 150 classes of objects. Our work demonstrates a successful pilot study for deploying transfer learning and the FCN-8 architecture for scene parsing and semantic segmentation.
Dataset Announcement: MS-BioGraphs, Trillion-Scale Public Real-World Sequence Similarity Graphs

Progress in High-Performance Computing in general, and High-Performance Graph Processing in particular, is highly dependent on the availability of publicly-accessible, relevant, and realistic data sets.In this paper, we announce publication of MS-BioGraphs, a new family of publicly-available real-world edge-weighted graph datasets with up to 2.5 trillion edges, that is, 6.6 times greater than the largest graph published recently.We briefly review the two main challenges we faced in generating large graph datasets and our solutions, that are, (i) optimizing data structures and algorithms for this multi-step process and (ii) WebGraph parallel compression technique. We also study some characteristics of MS-BioGraphs.The datasets and the complete version of this paper are available on https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs.
American Sign Language Character Recognition using Convolutional Neural Networks

This study presents a convolutional neural network (CNN) architecture developed using the TensorFlow framework to accurately recognize individual letters of American Sign Language (ASL). The CNN architecture consists of various layers including two-dimensional convolutional layers, max-pooling layers, batch normalization layers, dropout layers, and fully connected layers. The model achieved a mean validation accuracy of 95.48% and a test accuracy of 99.77% in identifying ASL characters. However, the live visual depictions revealed certain difficulties encountered by the model in identifying some ASL letters, highlighting the need for further improvement of the model’s framework and dataset curation. This research contributes to the scholarly discussion on the use of machine learning approaches in identifying sign language alphabets and provides insights into the feasibility and effectiveness of utilizing these techniques in ASL recognition tasks.
Upsampling of Unmanned Aerial Vehicle Images of Sugarcane Crop Lines with a REAL-ESRGAN

Sugarcane is one of the main economic crops in the world, moving the financial market with the sale of its products, resulting from this cultivation. The amount of images that can be extracted from this crop have increased exponentially due to the advance of remote sensing technologies like Unmanned Aerial Vehicle (UAV). When processed and analyzed, the images can provide valuable information about productivity, diseases, water stress, among others. However, the collected images have low resolution, given the flight altitude of the UAVs. Therefore, our goal in this work is to improve the resolution of the images of the sugarcane crop, applying deep learning techniques. In order to improve further processing by algorithms for extracting data of interest, we experimented with a REAL-ORGAN, which is a variation of ESRGAN (Enhanced super-resolution generative adversarial networks). It is important to note that, although that model was originally designed to work with images of landscapes, people, cars, and even anime, our initial experiments with agricultural images are quite promising, with superior results by 141,37% when compared to classic algorithms used for upsampling. in images. Our proposal managed to visually improve the images significantly, proving to be an attractive alternative for extracting information about the culture. As future work, it is intended to improve the accuracy of the proposal and extend the comparison with other algorithms.
Continuous People Crowd Monitoring defined as a Regression Problem using Radar Networks

Radar-based human activity recognition in crowded environments using regression approaches is addressed. Whereas previous research has focused on single activities and subjects, the problem of continuous activity recognition involving up to five individuals moving in arbitrary directions in an indoor area is introduced. To treat the problem, a regression-based approach is used, which offers innovative insights into creating robust and accurate systems for monitoring human activities.Novel approaches utilizing LSTM or CNN regression techniques with Linear Regression and Support Vector Machine regressor are compared on extracted features from radar data through the Histogram of Oriented Gradients and Principal Component Analysis. These approaches are rigorously evaluated by a Leave-One-Group-Out method, with performance assessed using common regression metrics such as the RMSE. The most promising outcomes were observed for crowds of three and five individuals, with respective RMSE of approximately 0.4 and 0.6. These results were primarily achieved by utilizing the micro-Doppler (µD) Spectrogram or range-Doppler data domain.
Improved snow ablation optimizer with heat transfer and condensation strategy for global optimization problem

Abstract The snow ablation optimizer (SAO) is a new metaheuristic algorithm proposed in April 2023. It simulates the phenomenon of snow sublimation and melting in nature and has a good optimization effect. The SAO proposes a new two-population mechanism. By introducing Brownian motion to simulate the random motion of gas molecules in space. However, as the temperature factor changes, most water molecules are converted into water vapor, which breaks the balance between exploration and exploitation, and reduces the optimization ability of the algorithm in the later stage. Especially in the face of high-dimensional problems, it is easy to fall into local optimal. In order to improve the efficiency of the algorithm, this paper proposes an improved snow ablation optimizer with heat transfer and condensation strategy (SAOHTC). Firstly, this article proposes a heat transfer strategy, which utilizes gas molecules to transfer heat from high to low temperatures and move their positions from low to high temperatures, causing individuals with lower fitness in the population to move towards individuals with higher fitness, thereby improving the optimization efficiency of the original algorithm. Secondly, a condensation strategy is proposed, which can transform water vapor into water by simulating condensation in nature, improve the deficiency of the original two-population mechanism, and improve the convergence speed. Finally, to verify the performance of SAOHTC, in this paper, two benchmark experiments of IEEE CEC2014 and IEEE CEC2017 and five engineering problems are used to test the superior performance of SAOHTC.
Simple Sentiment Analysis Ansatz for Sentiment Classification in Quantum Natural Language Processing

Sentiment classification is a valuable application of natural language processing that has seen wide usage in optimizing business processes. This paper explores a novel implementation of sentiment analysis using the Variational Quantum Algorithms (VQA) framework. As ansatz choice determines model performance in VQA, this paper proposes an alternative ansatz for the sentiment classification task in quantum representation. Specifically, it builds upon previous work in quantum sentiment classification by proposing an alternative ansatz to the Instantaneous Quantum Polytime ansatz, entitled Simple Sentiment Analysis (SimpleSA) ansatz. A key feature of the SimpleSA ansatz is the decision to neglect noun parameterization. The proposed SimpleSA has less complexity than the other ansätze in terms of number of parameters and number of gates. Moreover, experimental results show that the SimpleSA ansatz with H-CNOT-Rz-H compound block construction outperforms the Instantaneous Quantum Polytime (IQP) ansatz at 85.00% accuracy. Furthermore, SimpleSA optimization converges 20.89% faster than Instantaneous Quantum Polytime (IQP) for the Simultaneous Perturbation Stochastic Approximation (SPSA) method with 130 iterations. The proposed work is useful for applications of quantum computers for sentiment analyses and classifications.
Geometric Boundary Guided Feature Fusion and Spatial-Semantic Context Aggregation for Semantic Segmentation of Remote Sensing Images

Semantic segmentation of remote sensing images aims to achieve pixel-level semantic category assignment for input images. This task has achieved significant advances with the rapid development of deep neural network. Most current methods mainly focus on effectively fusing the low-level spatial details and high-level semantic cues. Other methods also propose to incorporate the boundary guidance to obtain boundary preserving segmentation. However, current methods treat the multi-level feature fusion and the boundary guidance as two separate tasks, resulting in sub-optimal solutions. Moreover, due to the large inter-class difference and small intra-class consistency within remote sensing images, current methods often fail to accurately aggregate the long-range contextual cues. These critical issues make current methods fail to achieve satisfactory segmentation predictions, which severely hinder downstream applications. To this end, we first propose a novel boundary guided multi-level feature fusion module to seamlessly incorporate the boundary guidance into the multi-level feature fusion operations. Meanwhile, in order to further enforce the boundary guidance effectively, we employ a geometric-similarity-based boundary loss function. In this way, under the explicit guidance of boundary constraint, the multi-level features are effectively combined. In addition, a channel-wise correlation guided spatial-semantic context aggregation module is presented to effectively aggregate the contextual cues. In this way, subtle but meaningful contextual cues about pixel-wise spatial context and channel-wise semantic correlation are effectively aggregated, leading to spatial-semantic context aggregation. Extensive qualitative and quantitative experimental results on ISPRS Vaihingen and GaoFen-2 datasets demonstrate the effectiveness of the proposed method.
Pyrrhic victory? Towards a decision support system for simulation projects in industrial practice based on Fuzzy cognitive mapping

Simulation has become a popular methodology to solve complex non-linear problems in research and practice. Yet, particularly in industrial practice, assessing the individual applicability of simulation projects remains an intricate, time-consuming and precarious challenge. This paper models simulation applicability by tapping into expert knowledge of simulation engineers with multiple years of simulation project experience. We decompose simulation applicability into a number of interconnected concept variables, which ultimately control the likelihood of project success and failure. The system is modeled in the form of a Fuzzy Cognitive Map, in which the nodes represent positive or negative project influencers in variables and link their interconnectedness. In doing so, this paper is the first to exceed the scope of simulation success factors or systemic risk assessment by addressing the need to provide holistic decision support on the context-dependent applicability of simulation projects. We contextualize organizational, environmental and methodology-related cause-effect relationships to develop a Fuzzy Decision Support System (FDSS) prototype that can guide scholars and practitioners when planning to adopt a simulation project for industrial purposes. The approach is illustrated using three applications, where the assessment has been conducted for (1) a large, (2) a medium-sized, as well as (3) a small organization, and evaluated through sensitivity analyses and a comparative survey. Ultimately, our study contributes to existing knowledge on success and risk factors for simulation projects, extends the realms of FCM to the case of simulation projects and sets a starting point for additional research on the context-specific applicability of the simulation methodology.
A game incentive mechanism for energy efficient federated learning in computing power networks

Computing Power Network (CPN) is emerging as one of the important research interests in beyond 5G (B5G) or 6G. This paper constructs a CPN based on Federated Learning (FL), where all Multi-access Edge Computing (MEC) servers are linked to a computing power center via wireless links. Through this FL procedure, each MEC server in CPN can independently train the learning models using localized data, thus preserving data privacy. However, it is challenging to motivate MEC servers to participate in the FL process in an efficient way and difficult to ensure energy efficiency for MEC servers. To address these issues, we first introduce an incentive mechanism using the Stackelberg game framework to motivate MEC servers. Afterwards, we formulate a comprehensive algorithm to jointly optimize the communication resource (wireless bandwidth and transmission power) allocations and the computation resource (computation capacity of MEC servers) allocations while ensuring the local accuracy of the training of each MEC server. The numerical data validates that the proposed incentive mechanism and joint optimization algorithm do improve the energy efficiency and performance of the considered CPN.