Self-supervised recalibration network for person re-identification

The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.

Hepatitis B Surface Antigen and Hepatitis C Virus Antibodies among Drug Users in Burkina Faso

Introduction: The epidemiology of both hepatitis B virus (HBV) and hepatitis C virus (HCV) infections among drug users (DUs) is little known in West Africa. The study aimed to assess the prevalence of hepatitis B and C viruses among drug users in Burkina Faso. Methodology: This was a cross-sectional biological and behavioral survey conducted between June and August 2022, among drug users in Ouagadougou and Bobo Dioulasso, the two main cities of Burkina Faso. A respondent-driven sampling (RDS) was used to recruit drug users. Hepatitis B surface antigen was determined using lateral flow rapid test kits and antibodies to hepatitis C virus in serum determined using an Enzyme-Linked Immunosorbent Assay. Data were entered and analyzed using Stata 17 software. Weighted binary logistic regression was used to identify the associated factors of hepatitis B and C infections and a p-value Results: A total of 323 drug users were recruited with 97.5% males. The mean age was 32.7 years old. The inhaled or smoked mode was the most used by drug users. The adjusted hepatitis B and hepatitis C prevalence among study participants were 11.1% and 2.3% respectively. The marital status (p = 0.001), and the nationality (p = 0.011) were significantly associated with hepatitis B infection. The type of drug used was not significantly associated with hepatitis B infection or hepatitis C infection. Conclusion: The prevalence of HBsAg and anti-HCV antibodies among DUs are comparable to those reported in the general population in Burkina Faso. This result suggests that the main routes of contamination by HBV and HCV among DUs are similar to those in the population, and could be explained by the low use of the injectable route by DUs in Burkina Faso.

Enhancing Unsupervised Domain Adaptation for Person Re-Identification with the Minimal Transfer Cost Framework

In Unsupervised Domain Adaptation(UDA)for person re-identification(re-ID),the primary challenge is reducing the distribution discrepancy between the source and target domains.This can be achieved by implicitly or explicitly constructing an appropriate intermediate domain to enhance recognition capability on the target domain.Implicit construction is difficult due to the absence of intermediate state supervision,making smooth knowledge transfer from the source to the target domain a challenge.To explicitly construct the most suitable intermediate domain for the model to gradually adapt to the feature distribution changes from the source to the target domain,we propose the Minimal Transfer Cost Framework(MTCF).MTCF considers all scenarios of the intermediate domain during the transfer process,ensuring smoother and more efficient domain alignment.Our framework mainly includes threemodules:Intermediate Domain Generator(IDG),Cross-domain Feature Constraint Module(CFCM),and Residual Channel Space Module(RCSM).First,the IDG Module is introduced to generate all possible intermediate domains,ensuring a smooth transition of knowledge fromthe source to the target domain.To reduce the cross-domain feature distribution discrepancy,we propose the CFCM Module,which quantifies the difficulty of knowledge transfer and ensures the diversity of intermediate domain features and their semantic relevance,achieving alignment between the source and target domains by incorporating mutual information and maximum mean discrepancy.We also design the RCSM,which utilizes attention mechanism to enhance the model’s focus on personnel features in low-resolution images,improving the accuracy and efficiency of person re-ID.Our proposed method outperforms existing technologies in all common UDA re-ID tasks and improves the Mean Average Precision(mAP)by 2.3%in the Market to Duke task compared to the state-of-the-art(SOTA)methods.

Evaluation of an Autonomous Vehicle User Interface for Sensory Impaired Users

Autonomous vehicles (AVs) hold immense promises in revolutionizing transportation, and their potential benefits extend to individuals with impairments, particularly those with vision and hearing impairments. However, the accommodation of these individuals in AVs requires developing advanced user interfaces. This paper describes an explorative study of a multimodal user interface for autonomous vehicles, specifically developed for passengers with sensory (vision and/or hearing) impairments. In a driving simulator, 32 volunteers with simulated sensory impairments, were exposed to multiple drives in an autonomous vehicle while freely interacting with standard and inclusive variants of the infotainment and navigation system interface. The two user interfaces differed in graphical layout and voice messages, which adopted inclusive design principles for the inclusive variant. Questionnaires and structured interviews were conducted to collect participants’ impressions. The data analysis reports positive user experiences, but also identifies technical challenges. Verified guidelines are provided for further development of inclusive user interface solutions.

Reform of the Irrigation Sector and Creation of Functional and Sustainable Irrigation Water Users Associations (AUEI) in Niger: Capitalization of the Experience of the Konni AHA

During the 1980s, as part of a policy of liberalization, following budgetary cuts linked to the implementation of structural adjustment programs, management responsibilities for AHAs were transferred from ONAHA to cooperatives concerned. Due to lack of financial resources, but also because of poor management, everywhere in Niger we are witnessing an accelerated deterioration of the irrigation infrastructure of hydro-agricultural developments. Institutional studies carried out on this situation led the State of Niger to initiate a reform of the governance of hydro-agricultural developments, by streng-thening the status of ONAHA, by creating an Association of Irrigation Water Users (AUEI) and by restructuring the old cooperatives. Indeed, this research aims to analyze the creation of functional and sustainable Irrigation Water User Associations (AUEI) in Niger in a context of reform of the irrigation sector, and based on the experience of the Konni AHA. It is based on a methodological approach which takes into account documentary research and the collection of data from 115 farmers, selected by reasoned choice and directly concerned by the management of the irrigated area. The data collected was analyzed and the results were analyzed using the systemic approach and the diagnostic process. The results show that the main mission of the AUEI is to ensure better management of water, hydraulic equipment and infrastructure on the hydro-agricultural developments of Konni. The creation of the Konni AUEI was possible thanks to massive support from the populations and authorities in the implementation process. After its establishment, the AUEI experienced a certain lethargy for some time due to the rehabilitation work of the AHA but currently it is functional and operational in terms of associative life and governance. Thus, the constraints linked to the legal system, the delay in the completion of the work, the uncertainties of access to irrigation water but also the problems linked to the change in mentality of certain ONAHA agents constitute the challenges that must be resolved in the short term for the operationalization of the Konni AUEI.

Design Method for Optimizing the Interactive Interface of Live Broadcasting Platform for the Elderly Users

In the era of network live broadcasting for everyone,the development of live broadcasting platforms is also more intelligent and diversified.However,in the face of a large group of elderly users,the interface interaction design mode used is still mainly based on the interaction mode for young groups,and is not designed for elderly users.Therefore,a design method for optimizing the interaction interface of live broadcasting platform for elderly users was proposed in this study.Firstly,the case study method and Delphi expert survey method were used to determine the design needs of elderly users and the design mode was analysed.Secondly,the orthogonal design principle was used to design a test sample of the interactive interface of live broadcasting platform applicable for the elderly users,and then a user evaluation system was established to calculate the weights of the design elements using hierarchical analysis,and then the predictive relationship between the design mode of the interactive interface of live broadcasting platform and the elderly users was established by Quantitative Theory I.Finally,Genetic Algorithm was applied to generate the optimized design scheme.The results showed that the design method based on the Genetic Algorithm and the combination of Quantitative Theory can scientifically and effectively optimize the design of the interactive interface of the live broadcasting platform for the elderly users,and improve the experience of the elderly users.

The Intersection of Privacy by Design and Behavioral Economics: Nudging Users towards Privacy-Friendly Choices

This paper conducts a comprehensive review of existing research on Privacy by Design (PbD) and behavioral economics, explores the intersection of Privacy by Design (PbD) and behavioral economics, and how designers can leverage “nudges” to encourage users towards privacy-friendly choices. We analyze the limitations of rational choice in the context of privacy decision-making and identify key opportunities for integrating behavioral economics into PbD. We propose a user-centered design framework for integrating behavioral economics into PbD, which includes strategies for simplifying complex choices, making privacy visible, providing feedback and control, and testing and iterating. Our analysis highlights the need for a more nuanced understanding of user behavior and decision-making in the context of privacy, and demonstrates the potential of behavioral economics to inform the design of more effective PbD solutions.

Enable Excel-Based Basic Cybersecurity Features for End Users by Using Python-Excel Integration

In the digital age, the global character of the Internet has significantly improved our daily lives by providing access to large amounts of knowledge and allowing for seamless connections. However, this enormously interconnected world is not without its risks. Malicious URLs are a powerful menace, masquerading as legitimate links while holding the intent to hack computer systems or steal sensitive personal information. As the sophistication and frequency of cyberattacks increase, identifying bad URLs has emerged as a critical aspect of cybersecurity. This study presents a new approach that enables the average end-user to check URL safety using Microsoft Excel. Using the powerful VirusTotal API for URL inspections, this study creates an Excel add-in that integrates Python and Excel to deliver a seamless, user-friendly interface. Furthermore, the study improves Excel’s capabilities by allowing users to encrypt and decrypt text communications directly in the spreadsheet. Users may easily encrypt their conversations by simply typing a key and the required text into predefined cells, enhancing their personal cybersecurity with a layer of cryptographic secrecy. This strategy democratizes access to advanced cybersecurity solutions, making attentive digital integrity a feature rather than a daunting burden.

Augmented Deep Multi-Granularity Pose-Aware Feature Fusion Network for Visible-Infrared Person Re-Identification

Visible-infrared Cross-modality Person Re-identification(VI-ReID)is a critical technology in smart public facilities such as cities,campuses and libraries.It aims to match pedestrians in visible light and infrared images for video surveillance,which poses a challenge in exploring cross-modal shared information accurately and efficiently.Therefore,multi-granularity feature learning methods have been applied in VI-ReID to extract potential multi-granularity semantic information related to pedestrian body structure attributes.However,existing research mainly uses traditional dual-stream fusion networks and overlooks the core of cross-modal learning networks,the fusion module.This paper introduces a novel network called the Augmented Deep Multi-Granularity Pose-Aware Feature Fusion Network(ADMPFF-Net),incorporating the Multi-Granularity Pose-Aware Feature Fusion(MPFF)module to generate discriminative representations.MPFF efficiently explores and learns global and local features with multi-level semantic information by inserting disentangling and duplicating blocks into the fusion module of the backbone network.ADMPFF-Net also provides a new perspective for designing multi-granularity learning networks.By incorporating the multi-granularity feature disentanglement(mGFD)and posture information segmentation(pIS)strategies,it extracts more representative features concerning body structure information.The Local Information Enhancement(LIE)module augments high-performance features in VI-ReID,and the multi-granularity joint loss supervises model training for objective feature learning.Experimental results on two public datasets show that ADMPFF-Net efficiently constructs pedestrian feature representations and enhances the accuracy of VI-ReID.

Person Re-Identification with Model-Contrastive Federated Learning in Edge-Cloud Environment

Person re-identification(ReID)aims to recognize the same person in multiple images from different camera views.Training person ReID models are time-consuming and resource-intensive;thus,cloud computing is an appropriate model training solution.However,the required massive personal data for training contain private information with a significant risk of data leakage in cloud environments,leading to significant communication overheads.This paper proposes a federated person ReID method with model-contrastive learning(MOON)in an edge-cloud environment,named FRM.Specifically,based on federated partial averaging,MOON warmup is added to correct the local training of individual edge servers and improve the model’s effectiveness by calculating and back-propagating a model-contrastive loss,which represents the similarity between local and global models.In addition,we propose a lightweight person ReID network,named multi-branch combined depth space network(MB-CDNet),to reduce the computing resource usage of the edge device when training and testing the person ReID model.MB-CDNet is a multi-branch version of combined depth space network(CDNet).We add a part branch and a global branch on the basis of CDNet and introduce an attention pyramid to improve the performance of the model.The experimental results on open-access person ReID datasets demonstrate that FRM achieves better performance than existing baseline.

Dual-stream coupling network with wavelet transform for cross-resolution person re-identification

Person re-identification is a prevalent technology deployed on intelligent surveillance.There have been remarkable achievements in person re-identification methods based on the assumption that all person images have a sufficiently high resolution,yet such models are not applicable to the open world.In real world,the changing distance between pedestrians and the camera renders the resolution of pedestrians captured by the camera inconsistent.When low-resolution(LR)images in the query set are matched with high-resolution(HR)images in the gallery set,it degrades the performance of the pedestrian matching task due to the absent pedestrian critical information in LR images.To address the above issues,we present a dualstream coupling network with wavelet transform(DSCWT)for the cross-resolution person re-identification task.Firstly,we use the multi-resolution analysis principle of wavelet transform to separately process the low-frequency and high-frequency regions of LR images,which is applied to restore the lost detail information of LR images.Then,we devise a residual knowledge constrained loss function that transfers knowledge between the two streams of LR images and HR images for accessing pedestrian invariant features at various resolutions.Extensive qualitative and quantitative experiments across four benchmark datasets verify the superiority of the proposed approach.

Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.

Pure Detail Feature Extraction Network for Visible-Infrared Re-Identification

Cross-modality pedestrian re-identification has important appli-cations in the field of surveillance.Due to variations in posture,camera per-spective,and camera modality,some salient pedestrian features are difficult to provide effective retrieval cues.Therefore,it becomes a challenge to design an effective strategy to extract more discriminative pedestrian detail.Although many effective methods for detailed feature extraction are proposed,there are still some shortcomings in filtering background and modality noise.To further purify the features,a pure detail feature extraction network(PDFENet)is proposed for VI-ReID.PDFENet includes three modules,adaptive detail mask generation module(ADMG),inter-detail interaction module(IDI)and cross-modality cross-entropy(CMCE).ADMG and IDI use human joints and their semantic associations to suppress background noise in features.CMCE guides the model to ignore modality noise by generating modality-shared feature labels.Specifically,ADMG generates masks for pedestrian details based on pose estimation.Masks are used to suppress background information and enhance pedestrian detail information.Besides,IDI mines the semantic relations among details to further refine the features.Finally,CMCE cross-combines classifiers and features to generate modality-shared feature labels to guide model training.Extensive ablation experiments as well as visualization results have demonstrated the effectiveness of PDFENet in eliminating background and modality noise.In addition,comparison experi-ments in two publicly available datasets also show the competitiveness of our approach.

Leveraging User-Generated Comments and Fused BiLSTM Models to Detect and Predict Issues with Mobile Apps

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user comments.However, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.

Orthogonal Frequency Division Multiplexing Adaptive Technology for Multinode Users of Seawater Channel Based on Inductively Coupled Mooring Chain

As an important part of buoy-type ocean monitoring systems,the inductively coupled mooring chain solves the problem of data cotransmission through the multinode sensors that it carries,which is significant for the rapid acquisition of fish,hydrology,and other information.This paper is based on a seawater channel transmission model with a depth of 300 m and a bandwidth of 2 MHz.An orthogonal frequency division multiplexing(OFDM)technology is used to overcome the multipath effect of signal transmission on a seawater medium.The adaptive technology is integrated into the OFDM,and an improved joint subcarrier and bit power allocation algorithm is proposed.This algorithm solves the problem of dynamic subcarrier allocation during the cotransmission of underwater multinode user data in seawater channels.The results show that the algorithm complexity can be reduced by 0.18126×10^(-2)s during one complete OFDM system data transmission by the improved greedy algorithm,and a total of 216 bits are transmitted by the OFDM.The normalized channel capacity can be improved by 0.012 bit s^(-1)Hz^(-1).At the bit error ratio(BER)of 10^(-3),the BER performance can be improved by approximately 6 d B.When the numbers of users are 4 and 8,the improved algorithm increases the channel capacity,and the higher the number of users,the more evident the channel capacity improvement effect is.The results of this paper have an important reference value for enhancing the transmission performance of inductively coupled mooring chain underwater multinode data.

Visible-infrared person re-identification using query related cluster

Visible-infrared person re-identification(VIPR), is a cross-modal retrieval task that searches a target from a gallery captured by cameras of different spectrums.The severe challenge for VIPR is the large intra-class variation caused by the modal discrepancy between visible and infrared images.For that, this paper proposes a query related cluster(QRC) method for VIPR.Firstly, this paper uses an attention mechanism to calculate the similarity relation between a visible query and infrared images with the same identity in the gallery.Secondly, those infrared images with the same query images are aggregated by using the similarity relation to form a dynamic clustering center corresponding to the query image.Thirdly, QRC loss function is designed to enlarge the similarity between the query image and its dynamic cluster center to achieve query related clustering, so as to compact the intra-class variations.Consequently, in the proposed QRC method, each query has its own dynamic clustering center, which can well characterize intra-class variations in VIPR.Experimental results demonstrate that the proposed QRC method is superior to many state-of-the-art approaches, acquiring a 90.77% rank-1 identification rate on the RegDB dataset.

Identification of Influential Users in Online Social Network: A Brief Overview

Information networks where users join a network, publish their own content, and create links to other users are called Online Social Networks (OSNs). Nowadays, OSNs have become one of the major platforms to promote both new and viral applications as well as disseminate information. Social network analysis is the study of these information networks that leads to uncovering patterns of interaction among the entities. In this regard, finding influential users in OSNs is very important as they play a key role in the success above phenomena. Various approaches exist to detect influential users in OSNs, starting from simply counting the immediate neighbors to more complex machine-learning and message-passing techniques. In this paper, we review the recent existing research works that focused on identifying influential users in OSNs.

Effects of Emotion on Decision-Making ofMethamphetamine Users: Based on theEmotional Iowa Gambling Task

The relapse of methamphetamine (meth) is associated with decision-making dysfunction. The present study aims to investigate theimpact of different emotions on the decision-making behavior of meth users. We used 2 (gender: male, female) × 3 (emotion:positive, negative, neutral) × 5 (block: 1, 2, 3, 4, 5) mixed experiment design. The study involved 168 meth users who weredivided into three groups: positive emotion, negative emotion and neutral emotion group, and tested by the emotional IowaGambling Task (IGT). The IGT performance of male users exhibited a decreasing trend from Block 1 to Block 3. Female methusers in positive emotion had the best performance in IGT than females in the other two groups. In positive emotion, the IGTperformance of female meth users was significantly better than that of men. Female meth users in positive emotion had betterdecision-making than those in negative or neutral emotion. Female meth users in positive emotion had better decision-makingperformance than males in positive emotion. In negative and neutral emotions, there was no significant gender difference indecision-making.

Trip Purposes of Automobile Users Inference Using Multi-day Traffic Monitoring Data

Determining trip purpose is an important link to explore travel rules. In this paper,we takea utomobile users in urban areas as the research object,combine unsupervised learning and supervised learningm ethods to analyze their travel characteristics,and focus on the classification and prediction of automobileu sers’trip purposes. However,previous studies on trip purposes mainly focused on questionnaires and GPSd ata,which cannot well reflect the characteristics of automobile travel. In order to avoid the multi-dayb ehavior variability and unobservable heterogeneity of individual characteristics ignored in traditional traffic questionnaires,traffic monitoring data from the Northern District of Qingdao are used,and the K-meansc lustering method is applied to estimate the trip purposes of automobile users. Then,Adaptive Boosting(AdaBoost)and Random Forest(RF)methods are used to classify and predict trip purposes. Finally,ther esult shows:(1)the purpose of automobile users can be mainly divided into four clusters,which includeC ommuting trips,Flexible life demand travel in daytime,Evening entertainment and leisure shopping,andT axi-based trips for the first three types of purposes,respectively;(2)the Random Forest method performss ignificantly better than AdaBoost in trip purpose prediction for higher accuracy;(3)the average predictiona ccuracy of Random Forest under hyper-parameters optimization reaches96.25%,which proves the feasibilitya nd rationality of the above clustering results.

Reliable and Privacy-Preserving Federated Learning with Anomalous Users

Recently,various privacy-preserving schemes have been proposed to resolve privacy issues in federated learning(FL).However,most of them ignore the fact that anomalous users holding low-quality data may reduce the accuracy of trained models.Although some existing works manage to solve this problem,they either lack privacy protection for users’sensitive information or introduce a two-cloud model that is difficult to find in reality.A reliable and privacy-preserving FL scheme named reliable and privacy-preserving federated learning(RPPFL)based on a single-cloud model is proposed.Specifically,inspired by the truth discovery technique,we design an approach to identify the user’s reliability and thereby decrease the impact of anomalous users.In addition,an additively homomorphic cryptosystem is utilized to provide comprehensive privacy preservation(user’s local gradient privacy and reliability privacy).We give rigorous theoretical analysis to show the security of RPPFL.Based on open datasets,we conduct extensive experiments to demonstrate that RPPEL compares favorably with existing works in terms of efficiency and accuracy.