Reviewerly/exHarmony huggingface dataset:
Cross-Domain Indoor Visual Place Recognition for Mobile Robot via Generalization Using Style Augmentation

The article presents an algorithm for the multi-domain visual recognition of an indoor place. It is based on a convolutional neural network and style randomization. The authors proposed a scene classification mechanism and improved the performance of the models based on synthetic and real data from various domains. In the proposed dataset, a domain change was defined as a camera model change. A dataset of images collected from several rooms was used to show different scenarios, human actions, equipment changes, and lighting conditions. The proposed method was tested in a scene classification problem where multi-domain data were used. The basis was a transfer learning approach with an extension style applied to various combinations of source and target data. The focus was on improving the unknown domain score and multi-domain support. The results of the experiments were analyzed in the context of data collected on a humanoid robot. The article shows that the average score was the highest for the use of multi-domain data and data style enhancement. The method of obtaining average results for the proposed method reached the level of 92.08%. The result obtained by another research team was corrected.
FOLIO: The Future of Library is Open

Ranganathan's 5th law of library science says that libraries never become stagnant. While financial constraints are a common challenge for libraries, user expectations are continuously growing. To meet and exceed user expectations, libraries must embrace new technologies and software solutions. Open-source options have proven to be particularly beneficial for nonprofit organizations like libraries. Looking to the future, as libraries continue their digital transformation, many library automation software, especially traditional library management systems (ILS), are becoming outdated. In response to the diverse requirements of libraries, the architecture of Library Services Platforms (LSP) offers enhanced flexibility and functionality within a unified platform. Among the various LSPs that have emerged over the past seven years, FOLIO has demonstrated rapid development and progress. Upon exploration, it becomes evident that FOLIO's developers have prioritized a micro-services architecture, emphasizing apps over modules. This approach provides a comprehensive multitasking app environment, a user-friendly Dashboard for quick access, efficient data management, and much more. The user-friendly nature of FOLIO sets it apart from traditional Integrated Library Systems (ILS), which may lack such ease of use. Despite its abundant features, FOLIO is still relatively under-recognized in India. This paper aims to discuss the distinctive features of FOLIO, it’s potential as a comprehensive solution for libraries, and whether it can effectively replace traditional ILS systems.
Threshold Lattice-Based Signature Scheme for Authentication by Wearable Devices

This paper presents a new threshold signature scheme based on Damgaard’s work. The proposed scheme allows for changing the message signature threshold, thereby improving the flexibility of the original Damgaard scheme. This scheme can be applied as a user authentication system using wearable devices. Based on the hardness of lattice problems, this scheme is resistant to attacks on a quantum computer, which is an advantage over the currently used multi-factor authentication schemes. The scheme’s security relies on the computational complexity of the Module-LWE and Module-SIS problems, as well as the Shamir secret sharing scheme’s security.
Unsupervised blind image quality assessment via joint spatial and transform features

A novel unsupervised blind image quality assessment (BIQA) method, which requires no mean opinion scores for model training is presented in this paper. The method employs joint spatial and transform features as quality degradation metrics, specifically, phase congruency, gradient magnitude (GM), and GM and Laplacian of Gaussian response and local normalized coefficient are extracted as spatial features, and Karhunen-Loéve transform coefficient and discrete cosine transform coefficient are modeled as transform features. Both spatial and transform features are well analyzed to remove the redundancy, and then fitted to the multivariate Gaussian model for no-reference image quality assessment. Extensive experiments conducted on seven IQA databases demonstrate the superiority of the proposed method over the state-of-the-art both supervised and unsupervised BIQA methods.
Arabic Sentiment Analysis of YouTube Comments: NLP-Based Machine Learning Approaches for Content Evaluation

YouTube is a popular video-sharing platform that offers a diverse range of content. Assessing the quality of a video without watching it poses a significant challenge, especially considering the recent removal of the dislike count feature on YouTube. Although comments have the potential to provide insights into video content quality, navigating through the comments section can be time-consuming and overwhelming work for both content creators and viewers. This paper proposes an NLP-based model to classify Arabic comments as positive or negative. It was trained on a novel dataset of 4212 labeled comments, with a Kappa score of 0.818. The model uses six classifiers: SVM, Naïve Bayes, Logistic Regression, KNN, Decision Tree, and Random Forest. It achieved 94.62% accuracy and an MCC score of 91.46% with NB. Precision, Recall, and F1-measure for NB were 94.64%, 94.64%, and 94.62%, respectively. The Decision Tree had a suboptimal performance with 84.10% accuracy and an MCC score of 69.64% without TF-IDF. This study provides valuable insights for content creators to improve their content and audience engagement by analyzing viewers’ sentiments toward the videos. Furthermore, it bridges a literature gap by offering a comprehensive approach to Arabic sentiment analysis, which is currently limited in the field.
SIRN: An iterative reasoning network for transmission lines based on scene prior knowledge

High-accuracy positioning and identification of transmission line components is the premise for their status detection and fault diagnosis. However, due to the limitations of imbalance object scale and distribution in aerial images, the problem of detecting dense-tiny objects still needs to be solved. Considering the prior knowledge of fixed connection scenes, we proposed a novel method named scene iterative reasoning network (SIRN) based on self-adaptive clustering and local scene knowledge. It consists of the coarse-grained detector (CGD), scene self-adaptive clustering (SSAC) subnetwork, and scene structure iterative reasoning (SSIR) subnetwork. The CGD was proposed to obtain global coarse detection results. Then, the SSAC subnetwork utilized unsupervised self-adaptive clustering to obtain accurate dense regions based on the CGD results. Finally, the SSIR subnetwork was designed to extract and fuse the scene semantic structure information of local regions for realizing accurate dense-tiny object detection. In summary, the SIRN model utilizes an iterative inference strategy to detect dense regions accurately. By leveraging scene structure information, the model effectively transforms the challenge of detecting dense objects into an advantage, resulting in precise detection. The SIRN model is compatible with single-stage and two-stage object detection models, achieving a notable mean average precision (mAP) improvement ranging from 4.4% to 12.2% compared to the baseline models. Compared to other state-of-the-art object detection models, SIRN demonstrates significant advantages in accuracy and error rate metrics. In addition, qualitative and quantitative experiments show that the SIRN model considerably enhanced the detection of dense-tiny objects. The code is available at https://github.com/CharmingWang/SIRN.
MPP-net: Multi-perspective perception network for dense video captioning

Applying deformable transformer for dense video captioning has achieved great success recently. However, deformable transformer only explores local-perspective perception by attending to a small set of key sampling points, which will make the decoder short-sighted and generate semantically incoherent and contradictory dense captions for a long video. In this paper, we propose a novel Multi-Perspective Perception Network to improve this problem. We first introduce a hierarchical temporal-spatial summary method to generate global-perspective summary context for each decoder layer and avoid redundant information. Then our new designed multi-perspective attention encourages the model to selectively incorporate the multi-perspective perception feature. Finally, we propose a novel multi-perspective generator to perform both multi-perspective feature fusion and caption generation. Experiments show that our proposed model outperforms previously published methods and achieves a competitive performance on ActivityNet Captions and YouCook2. The design of our model also shows the universality of other visual tasks that we obtain comparable results by applying our model for Object Detection and Paragraph Video Captioning.
Performance Evaluation of Container Networking Technology

Hybrid cloud and heterogeneous cloud have become industry hotspots in recent years, and are also one of the main growth points of cloud computing market in the coming years. We hope to investigate container virtual network solutions and virtual machine container fusion solutions to manage hybrid and heterogeneous cloud. In this paper, a performance comparison of different container networking solutions, including Flannel, Calico, Weave and SR-IOV+DPDK, are presented. We aim to provide a fair comparison of these solutions for management problems of hybrid cloud and heterogeneous cloud. Results show compared with other solutions at the same packet size, SRIOV has a much better performance even when the packet size is 256 bytes, it can reach linear speed. Then, we put forward some advice on application based on the results.
Chaotic dung beetle optimization algorithm based on adaptive t-Distribution

The Dung Beetle optimizer (DBO) is a metaheuristic algorithm that creates a set of random individuals within a given search space and combines, moves, and evolves these individuals during the iterative process. Although the DBO improves the convergence speed and accuracy in both global and local search compared to other intelligent optimization algorithms, the use of a random distribution in the initialization process leads to an uneven distribution of individuals in the initial population, and the rigid structure of the dung beetle cannot be adaptively adjusted, resulting in invalid iterations in the later stages. To address these issues, this study proposes an Improved Dung Beetle optimizer (IDBO) based on an adaptive t-distribution strategy. First, the population is initialized using a logistic map to achieve a chaotic state, and then an adaptive t-distribution is introduced to update the positions of the dung beetles. The effectiveness of the IDBO is validated by conducting experiments on 23 standard benchmark functions, and the results show a significant improvement in optimization performance and search accuracy compared to traditional DBO and other improved algorithms. This demonstrates the effectiveness of the IDBO algorithm.
Computational Time Complexity for k-Sum Problem Amalgamated with Quantum Search

The k - Sum Problem, which is a generic member of the family of which 2 - Sum and 3 - Sum problems are the youngest siblings is one of the most interesting problems in the domain of Optimization Techniques. Many researchers have shown that the k - Sum problem can be solved in no less than the order of $n_{k-1}$. On the other side, many researchers have tried and have successfully minimized its Computational Complexity, though quite negligibly. But since any subtle method doesn’t exist to minimize its Computational Complexity by a major pie, the Query - “Can k - Sum problem be solved in $O(n_{k-1-\epsilon})$ for some $\epsilon \gt 0$ ” have been added in the list of UPCS (Unsolved Problems in Computer Science). In this article, we will effort to analyse the Complexity of Computing the $k - Sum$ problem, by exemplifying minimal bounds of Quantum Search, $\Omega\left(\frac{\sqrt[2]{\log _2 n}}{\log _2\left(\log _2 n\right)}\right)$ as stated by Buhrman. Now, one assumption that this minimal bound holds is that the element to be searched will be composed in some ordered manner. To extrude that, we will extend our work by making use of Grover’s Search, with Computational Complexity of the order, $O(\sqrt[2]{n})$, which is not known to make use of any prerequisite.
Augmentation of FTIR spectral datasets using Wasserstein generative adversarial networks for cancer liquid biopsies

Over recent years, deep learning (DL) has become more widely used within the field of cancer diagnostics. However, DL often requires large training datasets to prevent overfitting, which can be difficult and expensive to acquire. Data augmentation is a method that can be used to generate new data points to train DL models. In this study, we use attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectra of patient dried serum samples and compare non-generative data augmentation methods to Wasserstein generative adversarial networks (WGANs) in their ability to improve the performance of a convolutional neural network (CNN) to differentiate between pancreatic cancer and non-cancer samples in a total cohort of 625 patients. The results show that WGAN augmented spectra improve CNN performance more than non-generative augmented spectra. When compared with a model that utilised no augmented spectra, adding WGAN augmented spectra to a CNN with the same architecture and same parameters, increased the area under the receiver operating characteristic curve (AUC) from 0.661 to 0.757, presenting a 15% increase in diagnostic performance. In a separate test on a colorectal cancer dataset, data augmentation using a WGAN led to an increase in AUC from 0.905 to 0.955. This demonstrates the impact data augmentation can have on DL performance for cancer diagnosis when the amount of real data available for model training is limited.
PEFNet: Position Enhancement Faster Network for Object Detection in Roadside Perception System

Roadside perception is a challenging research area that presents even greater difficulties than vehicle perception. Due to the different locations and angles of cameras, roadside objects exhibit violent multiscale variations, while the vast sensing field introduces more small-scale targets and complex backgrounds, making target recognition more challenging. To address these problems, we focus on position information encoding to achieve accurate roadside object detection by proposing the position enhancement faster network (PEFNet). Based on YOLOv6, the FasterNet Block is introduced into Backbone and Neck networks to provide efficient feature extraction while achieving model lightweight transformation. To improve small target detection performance, a position-aware feature pyramid network (PA-PAN) is proposed to enhance position information encoding, and the SPD-Conv is applied in the PA-PAN to further enhance effective feature extraction. Finally, the TSCODE is integrated into the detection head to achieve accurate target recognition and suppress background noise interference. Experiments on the Rope3D and UA-DETRAC datasets show that our model outperforms advanced YOLOv6, YOLOX, and FCOS in roadside object detection. Compared with YOLOv6, our method improves the mAP0.50 on the Rope3D dataset from 78.18% to 82.39%, with the AP of small objects such as pedestrians increasing by 7.01%. Furthermore, PEFNet reduces the weight of the network by 43.1% while maintaining detection speed at 75fps and achieving higher accuracy than previous algorithms for the same number of frames.
Study on multi-period palaeotectonic stress fields simulation and fractures distribution prediction in Lannigou gold mine, Guizhou

Abstract A significant controlling factor for gold mineralisation is the tectonic stress field, and the fractures formed under its action are the migration channels and ore-holding spaces of ore-forming fluids, which often directly control the migration and accumulation of ore-forming fluids. Therefore, performing quantitative prediction research on the distribution of fractures in the Guizhou, Lannigou gold deposit in order to identify potential fluid flow pathways is of utmost importance for ore prospecting in practical. In this study, a 3D geological entity model was generated based on the GOCAD platform by analysing and processing the geological data of the studied area, as well achieved is the accurate characterisation of the study area’s geometric model. By integrating regional tectonic evolution history analysis, geological interpretation, rock mechanics experiments and acoustic emission testing, the finite element method was utilised to create a 3D geomechanical model of the research area, the paleotectonic stress field after the Indosinian and Yanshanian movements were superimposed was simulated, in associated with the rock failure criterion, the comprehensive fracture rate parameter ( I z ) is introduced to predict the fracture distribution. The results show that the research area’s maximum principal stress is primarily distributed between 153.85 and 189.53 MPa, and the maximum shear stress is between 83.53 and 98.42 MPa. The spatial distribution of faults influences the stress distribution characteristics significantly, and the stress level is relatively high at the intersection of the fault, the end of the fault and the vicinity of the fault zone, and the stress value between the faults is relatively low. The tectonic stress field primarily controls the distribution and development of fractures, which is usually consistent with the areas with high values of maximum principal stress and maximum shear stress. Using the combined modeling technique of GOCAD and midas GTS to realize the conversion from 3D geological model to geomechanical model, a set of comprehensive fracture distribution prediction technique for the superposition of multi-stage tectonic stress fields of mineral deposits in complex tectonic areas has been formed, and provide a reference for the prediction of fracture distribution in similar complex structural areas.
Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis

Recommendation systems (RSs) are widely used in e-commerce to improve conversion rates by aligning product offerings with customer preferences and interests. While traditional RSs rely solely on numerical ratings to generate recommendations, these ratings alone may not be sufficient to offer personalized and accurate suggestions. To overcome this limitation, additional sources of information, such as reviews, can be utilized. However, analyzing and understanding the information contained within reviews, which are often unstructured data, is a challenging task. To address this issue, sentiment analysis (SA) has attracted considerable attention as a tool to better comprehend a user’s opinions, emotions, and attitudes. In this study, we propose a novel RS that leverages ensemble learning by integrating sentiment analysis of textual data with collaborative filtering techniques to provide users with more precise and individualized recommendations. Our system was developed in three main steps. Firstly, we used unsupervised “GloVe” vectorization for better classification performance and built a sentiment model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Secondly, we developed a recommendation model based on collaborative filtering techniques. Lastly, we integrated our sentiment analysis model into the RS. Our proposed model of SA achieved an accuracy score of 93%, which is superior to other models. The results of our study indicate that our approach enhances the accuracy of the recommendation system. Overall, our proposed system offers customers a more reliable and personalized recommendation service in e-commerce.
Research on Hybrid Recommendation Model for Personalized Recommendation Scenarios

One of the five types of Internet information service recommendation technologies is the personalized recommendation algorithm, and knowledge graphs are frequently used in these algorithms. RippleNet is a personalized recommendation model based on knowledge graphs, but it is susceptible to localization issues in user portrait updating. In this study, we propose NRH (Node2vec-side and RippleNet Hybrid Model), a hybrid recommendation model based on RippleNet that uses Node2vec-side for item portrait modeling and explores potential association relationships of items; the user portrait is split into two parts, namely, a static history portrait and a dynamic preference portrait; the NRH model adopts a hybrid recommendation approach based on collaborative filtering and a knowledge graph to obtain the user’s preferences on three publicly accessible datasets; and comparison experiments with the mainstream model are lastly carried out. The AUC and ACC increased, respectively, by 0.9% to 29.5% and 1.6% to 31.4% in the MovieLens-1M dataset, by 1.5% to 17.1% and 4.4% to 18.7% in the Book-Crossing dataset, and by 0.8% to 27.9% and 2.9% to 24.1% in the Last.FM dataset. The RippleNet model was used for comparison experiments comparing suggestion diversity. According to the experimental findings, the NRH model performs better in accuracy and variety than the popular customized knowledge graph recommendation algorithms now in use.
Combining quantiles of calibrated solar forecasts from ensemble numerical weather prediction

This work is concerned with optimally combining quantiles of several post-processed versions of ensemble solar forecasts, which is new in this field. Numerical weather prediction (NWP) serves grid integration of solar energy by issuing dynamical ensemble irradiance forecasts. However, these ensemble members often suffer from under-dispersion, which motivates statistical calibration via quantile regression (QR) or ensemble model output statistics (EMOS). Given the numerous variants of QR and EMOS, it is generally unclear which variant offers the best performance under what situation, which further motivates combining quantile forecasts. A framework for combining solar forecasts in the form of quantiles is proposed, and a constrained quantile regression averaging scheme is used to exemplify the framework. Using the strictly proper pinball loss, ensemble irradiance forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System are first post-processed using five QR variants and five EMOS variants, and then combined through a linear program. It is found that combining quantiles is an effective strategy that can further improve the calibrated ECMWF forecasts across all locations herein considered.