Reviewerly/exHarmony huggingface dataset:
Width-resolution multiplier lightweight network for person re-identification

Person re-identification is a key sub-problem in image retrieval. It is a technique to judge whether there is a specific pedestrian in an image or video by using computer vision technology, that is, a monitoring pedestrian image is given to retrieve a cross-device pedestrian image. With the development of technology, many excellent algorithms have successfully achieved excellent result in video surveillance and security fields. However, which many ReID algorithm rely on large backbone network, such as ResNet, which are designed for image classification instead of ReID. This paper presents a combined depth space (CDS) network algorithm based on width and resolution. The algorithm is mainly designed by efficient calculation for pedestrian re recognition in the current backbone network, which focus on the combined pattern information in the pedestrian image by reproducing a pedestrian re recognition network based on the combined depth space architecture search through CDs. The low-cost search strategy of Top-k sample search strategy is used to make full use of the search space and avoid falling into local optimization. Furthermore, an effective Fine-grained Balance Neck (FBLNect) is presented to balance the effects of triplet loss and SoftMax loss during the training process. Finally, the experiments on three different data sets: Market1501, DukeMTMC and MSMT17, the proposed algorithm achieves better performance when the parameters are much smaller than those of other mainstream networks.
Research on face recognition based on fusion detection and pose estimation

This paper discusses the research of face recognition based on fusion detection and pose estimation. The auxiliary network is added in face detection, and the SSD based on face pose prediction method is used in pose estimation to improve the accuracy and speed of face recognition. The generated lightweight model is deployed to the embedded system. The experimental results show that it is feasible.
An 𝓵p-nonconvex regularization method for image smoothing

Image smoothing techniques are widely used in computer vision and graphics applications, such as detail enhancement, artifact removal, image denoising and high dynamic range (HDR) tone mapping. In this paper, an 𝓵<i><sub>p</sub></i>-nonconvex minimization model is presented to achieve diverse smoothness of edges. To induce sparsity more strongly than the 𝓵<i><sub>1</sub></i> norm regularization, we take the nonconvex arctangent penalty function of the image gradient as the regularization term. To make the model more flexible and effective, we use the 𝓵<i><sub>p</sub></i> norm function as the fidelity term. The majorization-minimization (MM) algorithm is employed for the proposed nonconvex optimization model. We discuss the convergence of the resulting MM algorithm. Comprehensive experiments and comparisons show that the proposed method is effective in a variety of image processing tasks.
Exploring the Security Challenges in the Cloud enabled IoMT Sector and Promising Solutions Ahead

Abstract: The advent of IoT devices has ushered in a new era of connectivity, permeating various facets of contemporary life. This pervasive integration of technology has engendered a future teeming with possibilities, promising manifold benefits for individuals, information management, and operational processes. As individuals find themselves equipped with an increasing amount of leisure time, they are afforded the opportunity to orchestrate comprehensive life cycles that encompass both personal and professional spheres of influence. Notably, the Internet of Medical Things (IoMT) has emerged as a thriving domain, providing hospitals and clinicians with access to sensitive information pertaining to human lives. However, this proliferation of interconnectedness also introduces a concomitant risk—a fertile ground for malicious actors seeking to exploit vulnerabilities inherent in the IoMT infrastructure
Research on personalized recommendation from the perspective of staff-position matching

In the context of the COVID-19 epidemic, the mutual selection between job seekers and enterprises mostly relies on online publishing of recruitment information and the delivering resumes online. How can job seekers obtain suitable employment information quickly and effectively, and how companies can accurately recruit people who are suitable for their positions, these are very challenging problems. In this paper, taking personnel-job matching as a theoretical guide, based on the latent semantic model, the correlation model between the job seeker and the applicant's recruitment requirements was built, after comparing the similarity values of similar companies and job seekers. Realized the purpose of recommending data information with a high degree of matching to job seekers and enterprises.
RVSRT: real-time video super resolution transformer

Video super-resolution is the task of converting low-resolution video to high-resolution video. Existing methods with better intuitive effects are mainly based on convolutional neural networks (CNNs), but the architecture is heavy, resulting in a slow inference structure. Aiming at this problem, this paper proposes a real-time video super-resolution. Real-time video super resolution transformer (RVSRT) can quickly complete the super-resolution task while considering the visual fluency of video frame switching. Unlike traditional methods based on CNNs, this paper does not process video frames separately with different network modules in the temporal domain, but batches adjacent frames through a single UNet-style structure end-to-end Transformer network architecture. Moreover, this paper creatively sets up two-stage interpolation sampling before and after the end-to-end network to maximize the performance of the traditional CV algorithm. The experimental results show that compared with SOTA TMNet, RVSRT has only 50% of the network size (6.1M vs 12.3M, parameters) while ensuring comparable performance, and the speed is increased by 80% (26.2 fps vs 14.3 fps, frame size is 720*576).
Toward a holistic approach to EdTech effectiveness: Lessons from Covid-19 research in Bangladesh, Ghana, Kenya, Pakistan, and Sierra Leone

During school closures prompted by the Covid-19 pandemic, educational technology (EdTech) was often used to continue educational provision. In this article, we consider EdTech effectiveness using a holistic framework, and synthesise findings from 10 primary research studies of EdTech interventions conducted in low- and middle-income countries during the pandemic. The framework includes five main lenses: learning outcomes, enhancing equity, implementation context, cost and affordability, and alignment and scale. While in-person schooling has largely resumed, there continues to be further integration of EdTech into education systems globally. This analysis provides evidence-based insights and highlights knowledge gaps to shape holistic analysis of both EdTech mainstreaming and future research into the effective use of EdTech to strengthen learning.
The structure of the concept of kærlighed ‘love’ in Danish

Abstract Based on 4 sets of linguistic data (dictionaries, corpus, texts, and surveys), the article explores the way in which the concept of KÆRLIGHED ‘love’ is organized in Danish. Thus, the key factors which play a role in the complex structure of the concept are: the language system, together with the emotion terms it makes available to its speakers; metaphoric models; models at the level of single subcategories; as well as individual experience. There also seem to occur prototype effects within the concept, both with regard to the single attributes of the concept as well as its subcategories. Additionally, the analysis has revealed the unique character of the Danish concept of KÆRLIGHED, which is inherently connected to other parts of the Danish worldview such as gender parity, sex, motherhood, God, the self or homeland, and which uses unique linguistic tools to convey the single conceptualizations.
A Joint De-Rain and De-Mist Network Based on the Atmospheric Scattering Model

Rain can have a detrimental effect on optical components, leading to the appearance of streaks and halos in images captured during rainy conditions. These visual distortions caused by rain and mist contribute significant noise information that can compromise image quality. In this paper, we propose a novel approach for simultaneously removing both streaks and halos from the image to produce clear results. First, based on the principle of atmospheric scattering, a rain and mist model is proposed to initially remove the streaks and halos from the image by reconstructing the image. The Deep Memory Block (DMB) selectively extracts the rain layer transfer spectrum and the mist layer transfer spectrum from the rainy image to separate these layers. Then, the Multi-scale Convolution Block (MCB) receives the reconstructed images and extracts both structural and detailed features to enhance the overall accuracy and robustness of the model. Ultimately, extensive results demonstrate that our proposed model JDDN (Joint De-rain and De-mist Network) outperforms current state-of-the-art deep learning methods on synthetic datasets as well as real-world datasets, with an average improvement of 0.29 dB on the heavy-rainy-image dataset.
Bengali Text Classification Based on Probability Measure

Text is divided into categories based on its relevance. Understanding the meaning of a text frequently requires a subjective component. Linguistic advances, on the other hand, can reveal vital details about what drives text categorization in general and topic identification in particular. Theoretical underpinnings from formal linguistics and systemic functional linguistics, in particular, may provide vital insights into how difficulties can be addressed. Classification of a text is the most popular topic in recent research on the English language. Several powerful machine learning techniques are used in the field of Natural Language Processing or Computational Linguistics to classify a text. This paper describes a system to classify one text based on probability measures. Data with four different types of news (political, sports, entertainment, and a mix of these three) has been collected from a popular Bengali newspaper Anandabazar Patrika. The model is tested using Bengali news but it can be used to classify a text of any language.
AGORA: An intelligent system for the anonymization, information extraction and automatic mapping of sensitive documents

Public institutions, such as law enforcement agencies or health centers, have a vast volume of unstructured text documents, e.g. police reports. Currently, before this data can be shared (e.g. with research institutions), it must go through a lengthy and costly human anonymization procedure. This paper addresses this issue by presenting AGORA, a cutting-edge tool that automatically identifies key entities and anonymizes sensitive data in text documents. AGORA has been developed in partnership with the Spanish National Office Against Hate Crimes and validated in the police and medical domains. This tool allows to export both anonymized texts and identified entities to structured files, thus, simplifying its exploitation for analysis purposes. Also, AGORA is capable of plotting the location entities identified in the documents, as well as obtaining and displaying relevant information from their geographical surroundings. Thus, it simplifies the task of generating comprehensive datasets for subsequent data analysis or predictive tasks. Its main goal is to foster cooperation between public institutions and research centers by easing document sharing as well as serving as a foundation for addressing succeeding phases in data science. The paper conducts a comprehensive assessment of the literature on Named Entity Recognition methodologies and technologies. Followed by extensive computational experiments to identify the best configuration for the NER models embedded in AGORA which include both successful state-of-the-art model setups and novelly proposed ones. Finally, the methodology, conclusions and software provided can be easily reused in similar application scenarios.
Exploration of artificial intelligence to enhance after-school services in the context of double reduction policy

In order to better realize the double reduction, to open up new teaching ideas with artificial intelligence-enabled after-school services.Purpose-To introduce artificial intelligence into after-school services to revitalize all kinds of school resources and change stereotypes so that after-school services can really reduce students' burden and improve classroom efficiency while optimizing homework structure and enhancing students' overall literacy.Methods-Expand the book knowledge in the way of artificial intelligence, make the raw knowledge in the textbook more vivid and lively, and decompose the classroom teaching task into several artificial intelligence projects, so that students can accept the new knowledge more intuitively.Results-Teaching AI projects can focus students' attention well and maintain concentration for a long period of time, allowing students' learning efficiency to be further improved.Conclusion-Artificial intelligence applications have an important exploration significance in enhancing afterschool service teaching, and in the actual teaching process, make full use of its own advantages to help students learn better, so as to enhance their learning effect at the same time, to promote their overall development.
The Design and Application of Information System of Undergraduate Vocational Education Talent Training Mode in Java Language Environment

In order to solve the problems of single training mode of undergraduate vocational education, such as the separation between production and education, this paper takes the training mode of vocational education as the research object, and develops a set of information system of undergraduate vocational education training mode with the help of network information technology, database management technology and computer application technology.The system adopts B/S architecture, follows MVC design idea, uses Java language to write server and introduces SSH framework to complete the overall design of Web Server, and uses MySQL to realize data support.In order to build an "online+offline" talent training mode, modern information technology is used to promote the reform of talent training mode, and cultivate talents with solid knowledge and excellent skills who meet the needs of society.
Personalized Recommender System for House Selection

Housing is the key to improved health and welfare. Hence it is required to select the house wisely. In this paper, TOPSIS method is used for the ranking of various alternatives of houses as per the user’s personalized requirements and demand.
A Competitive Blended Learning Model of Computer Teaching

Universities are encouraged to popularize blended learning by digitalizing and reusing teaching resources.This paper proposed a competitive blended learning model of computer teaching.It is not only a creative learning model but also a major innovation in curriculum reform that changes the instructor-based stand-and-deliver teaching scheme into student-based proactive learning.A feasible teaching method is designed to lead the way for instructors.Competitive strategies are introduced to arouse students' desire to win and become competitive for instructors' recognition.A multifactor evaluation method is proposed to measure the effectiveness and availability of the proposed method.A comparative case study is carried out on computer students at Shantou Polytechnics.Instructors follow the teaching method to implement the proposed method.Students' performances are evaluated during the course of C programming.Compared with the blended learning model and traditional learning model, the proposed model shows significant improvement in teaching effectiveness and general availability in future.
Integrated Linear Regression and Random Forest Framework for E-Commerce Price Prediction of Pre-owned Vehicle

The E-Commerce industry has taken the world in its stride. It has been growing at an exponential rate. This paper presents a machine learning-based model to predict the price of a used vehicle for selling and buying purposes. The model proposes two algorithms: Linear Regression and Random Forest Regression and draws a comparison between the two on the basis of standard performance measures. The uniqueness of this work is that it is not only capable to predict the price of a used vehicle but the model can also be extended with minimal effort for any kind of product across various spheres of the E-commerce industry.
Photonic convolutional reservoir computing based on VCSEL with multiple optical injections

In this paper, a photonic convolutional time delay reservoir computing (C-TD-RC) system based on vertical cavity surface emitting laser (VCSEL) with multiple optical injections has been proposed. Here, the convolutional neural networks (CNNs) and the time delay reservoir computing (TD-RC) are combined in the system. Using TD-RC instead of the fully connected layer in the traditional CNNs can solve the problem of gradient vanishing and gradient explosion in CNNs. Two classification tasks including handwritten digits recognition and spoken digits classification are solved by the C-TD-RC system. In addition, in order to achieve better classification performance of RC, a new post-processing method combining ridge regression and winner take all strategy has been adopted. Numerical results demonstrate that the minimum Word Error Rate (WER) can reach 0.0135 for handwritten digits recognition and 0.0213 for spoken digits classification tasks by the C-TD-RC system. Furthermore, the effects of the virtual nodes, the injection paths and the number of convolution kernels applied in the first and second convolution layers on the C-TD-RC system are considered. The proposed C-TD-RC scheme is of great significance to the performance enhancement of RC in the future.
ANALYTICITY AND SYNTHETICITY IN TYPE THEORY REVISITED

Abstract I discuss problems with Martin-Löf’s distinction between analytic and synthetic judgments in constructive type theory and propose a revision of his views. I maintain that a judgment is analytic when its correctness follows exclusively from the evaluation of the expressions occurring in it. I argue that Martin-Löf’s claim that all judgments of the forms $a : A$ and $a = b : A$ are analytic is unfounded. As I shall show, when A evaluates to a dependent function type $(x : B) \to C$ , all judgments of these forms fail to be analytic and therefore end up as synthetic. Going beyond the scope of Martin-Löf’s original distinction, I also argue that all hypothetical judgments are synthetic and show how the analytic–synthetic distinction reworked here is capable of accommodating judgments of the forms $A \&gt; \mathsf {type}$ and $A = B \&gt; \mathsf {type}$ as well. Finally, I consider and reject an alternative account of analyticity as decidability and assess Martin-Löf’s position on the analytic grounding of synthetic judgments.
SECURING DATA AND APPLICATIONS IN CLOUD. CASE STUDY

Security is a principal concern when entrusting an organization’s critical information to geographically dispersed cloud platforms. In addition to the conventional IT system security procedures, designing security into cloud during the software development life cycle can greatly reduce the cloud attack surface. The paper focuses on presenting IBM Cloud security policies and their architecture and conducts a study of the security implementation steps, beginning with user's authentication on the Cloud platform. Hereinafter, the developed experimental application - a monitoring environment system - retrieves temperature, humidity, and pressure values from dedicated sensors, thus using the facilities offered by IBM Cloud platform.
Hybrid Phishing Detection Based on Automated Feature Selection Using the Chaotic Dragonfly Algorithm

Due to the increased frequency of phishing attacks, network security has gained the attention of researchers. In addition to this, large volumes of data are created every day, and these data include inappropriate and unrelated features that influence the accuracy of machine learning. There is therefore a need for a robust method of detecting phishing threats and improving detection accuracy. In this study, three classifiers were applied to improve the accuracy of a detection algorithm: decision tree, k-nearest neighbors (KNN), and support vector machine (SVM). Selecting the relevant features improves the detection accuracy for a target class and determines the class label with the greatest probability. The proposed work clearly describes how feature selection using the Chaotic Dragonfly Algorithm provides more accurate results than all other baseline classifiers. It also indicates the appropriate classifier to be applied when detecting phishing websites. Three publicly available datasets were used to evaluate the method. They are reliable datasets for training the model and measuring prediction accuracy.
SGooTY: A Scheme Combining the GoogLeNet-Tiny and YOLOv5-CBAM Models for Nüshu Recognition

With the development of society, the intangible cultural heritage of Chinese Nüshu is in danger of extinction. To promote the research and popularization of traditional Chinese culture, we use deep learning to automatically detect and recognize handwritten Nüshu characters. To address difficulties such as the creation of a Nüshu character dataset, uneven samples, and difficulties in character recognition, we first build a large-scale handwritten Nüshu character dataset, HWNS2023, by using various data augmentation methods. This dataset contains 5500 Nüshu images and 1364 labeled character samples. Second, in this paper, we propose a two-stage scheme model combining GoogLeNet-tiny and YOLOv5-CBAM (SGooTY) for Nüshu recognition. In the first stage, five basic deep learning models including AlexNet, VGGNet16, GoogLeNet, MobileNetV3, and ResNet are trained and tested on the dataset, and the model structure is improved to enhance the accuracy of recognising handwritten Nüshu characters. In the second stage, we combine an object detection model to re-recognize misidentified handwritten Nüshu characters to ensure the accuracy of the overall system. Experimental results show that in the first stage, the improved model achieves the highest accuracy of 99.3% in recognising Nüshu characters, which significantly improves the recognition rate of handwritten Nüshu characters. After integrating the object recognition model, the overall recognition accuracy of the model reached 99.9%.
An Investigation on the Extractive Summarization of Kannada Text

Information content available for use in various real world scenarios and applications has greatly increased recently and continues to grow. Specifically, the huge growth of textual information available in the Internet and intranets has made finding relevant text information on time as a major technical challenge. Document summarization is a reductive transformation of input texts into a summary text and can help in comprehending the abundant textual information. The objective of this research work is to improve the effectiveness of the document summarization process to create high quality summaries of Kannada text. This work explores different preprocessing and feature extraction steps to identify relevant features of the document. Co-ranking and sentence ranking algorithms are used to summarize the text based on the relevance of the features. Obtained summary is subjected to analyse by both human expert and ROUGE score.