bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal deployment of large-scale wireless sensor networks based on graph clustering and matrix factorization
Authors: Gao Hefei , Zhu Qianwen , Wang Wei 
Published: March 2023

AbstractIt is undeniable that there are a large number of redundant nodes in a wireless sensor network. These redundant nodes cause a colossal waste of resources and seriously threaten the life of the sensor network. In this paper, we provide a sensor nodes optimization selection algorithm based on a graph for a large-scale wireless sensor network. Firstly, we propose a representation-clustering joint algorithm based on Graph Neural Network to partition the large-scale graph into several subgraphs. Then, we use Singular-Value-QR Decomposition for the node selection of each subgraph and achieve the optimal deployment for a large-scale wireless sensor network. We conduct the experiments on the CIMIS dataset. The results show that the mean square error between the reconstructed network and the original network is as low as 0.02433. Meanwhile, we also compare our algorithm with the classical optimization algorithm. The results imply that the mean square error of the proposed algorithm is lower and the distribution is more uniform. Further, we verify the scalability of the algorithm for the optimal deployment of the large-scale wireless sensor network.
Learning spatial regularized correlation filters with response consistency and distractor repression for UAV tracking
Authors: Zhang Wei 
Published: March 2023

AbstractCorrelation filter-based trackers have made significant progress in visual object tracking for various types of unmanned aerial vehicle (UAV) applications due to their promising performance and efficiency. However, the boundary effect remains a challenging problem. Several methods enlarge search areas to handle this shortcoming but introduce more background noise, and the filter is prone to learn from distractors. To address this issue, we present spatial regularized correlation filters with response consistency and distractor repression. Specifically, a temporal constraint is introduced to reinforce the consistency across frames by minimizing the difference between consecutive correlation response maps. A dynamic spatial constraint is also integrated by exploiting the local maximum points of the correlation response produced during the detection phase to mitigate the interference from background distractions. The proposed appearance model can optimize the temporal and spatial constraints together with a spatial regularization weight simultaneously. Meanwhile, the proposed appearance model can be solved effectively based on the alternating direction method of multipliers algorithm. The spatial and temporal information concealed in the response maps is fully taken into consideration to boost overall tracking performance. Extensive experiments are conducted on a public UAV benchmark dataset with 123 challenging sequences. The experimental results and analysis demonstrate that the proposed method outperforms 12 state-of-the-art trackers in terms of both accuracy and robustness while efficiently operating in real time.
Decision-level fusion detection method of visible and infrared images under low light conditions
Authors: Hu Zuhui , Jing Yaguang , Wu Guoqing 
Published: March 2023

AbstractAiming at the problem of poor effect of object detection with visible images under low light conditions, the decision-level fusion detection method of visible and infrared images is studied. Taking YOLOX as the object detection network based on deep learning, a decision-level fusion detection algorithm of visible and infrared images based on light sensing is proposed. Experiments are carried out on LLVIP dataset, which is a visible-infrared paired dataset for low light vision. Through comparative analysis, it is found that the decision-level fusion algorithm based on Soft-NMS and light sensing obtained the optimal AP value of 69.0%, which is 11.4% higher than the object detection with visible images and 1.1% higher than the object detection with infrared images. The experimental results show that the decision-level fusion algorithm based on Soft-NMS and light sensing can effectively fuse the complementary information of visible and infrared images, and improve the object detection effect under low light conditions.
Gaussian class-conditional simplex loss for accurate, adversarially robust deep classifier training
Authors: Ali Arslan , Migliorati Andrea , Bianchi Tiziano , Magli Enrico 
Published: March 2023

AbstractIn this work, we present the Gaussian Class-Conditional Simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that improves over the state-of-the-art in terms of classification accuracy and adversarial robustness, with little extra cost for network training. The proposed method learns a mapping of the input classes onto Gaussian target distributions in a latent space such that a hyperplane can be used as the optimal decision surface. Instead of maximizing the likelihood of target labels for individual samples, our loss function pushes the network to produce feature distributions yielding high inter-class separation and low intra-class separation. The mean values of the learned distributions are centered on the vertices of a simplex such that each class is at the same distance from every other class. We show that the regularization of the latent space based on our approach yields excellent classification accuracy. Moreover, GCCS provides improved robustness against adversarial perturbations, outperforming models trained with conventional adversarial training (AT). In particular, our model learns a decision space that minimizes the presence of short paths toward neighboring decision regions. We provide a comprehensive empirical evaluation that shows how GCCS outperforms state-of-the-art approaches over challenging datasets for targeted and untargeted gradient-based, as well as gradient-free adversarial attacks, both in terms of classification accuracy and adversarial robustness.
A reinforcement learning-based sleep scheduling algorithm for compressive data gathering in wireless sensor networks
Authors: Wang Xun , Chen Hongbin , Li Shichao 
Published: March 2023

AbstractCompressive data gathering (CDG) is an adequate method to reduce the amount of data transmission, thereby decreasing energy expenditure for wireless sensor networks (WSNs). Sleep scheduling integrated with CDG can further promote energy efficiency. Most of existing sleep scheduling methods for CDG were formulated as centralized optimization problems which introduced many extra control message exchanges. Meanwhile, a few distributed methods usually adopted stochastic decision which could not adapt to variance in residual energy of nodes. A part of nodes were prone to prematurely run out of energy. In this paper, a reinforcement learning-based sleep scheduling algorithm for CDG (RLSSA-CDG) is proposed. Active nodes selection is modeled as a finite Markov decision process. The mode-free Q learning algorithm is used to search optimal decision strategies. Residual energy of nodes and sampling uniformity are considered into the reward function of the Q learning algorithm for load balance of energy consumption and accurate data reconstruction. It is a distributed algorithm that avoids large amounts of control message exchanges. Each node takes part in one step of the decision process. Thus, computation overhead for sensor nodes is affordable. Simulation experiments are carried out on the MATLAB platform to validate the effectiveness of the proposed RLSSA-CDG against the distributed random sleep scheduling algorithm for CDG (DSSA-CDG) and the original sparse-CDG algorithm without sleep scheduling. The simulation results indicate that the proposed RLSSA-CDG outperforms the two contrast algorithms in terms of energy consumption, network lifetime, and data recovery accuracy. The proposed RLSSA-CDG reduces energy consumption by 4.64% and 42.42%, respectively, compared to the DSSA-CDG and the original sparse-CDG, prolongs life span by 57.3%, and promotes data recovery accuracy by 84.7% compared to the DSSA-CDG.
Keloid treatments: an evidence-based systematic review of recent advances
Authors: Walsh Laura A. , Wu Ellen , Pontes David , Kwan Kevin R. , Poondru Sneha , Miller Corinne H. , Kundu Roopal V. 
Published: March 2023

Abstract
Background
Keloids are pathologic scars that pose a significant functional and cosmetic burden. They are challenging to treat, despite the multitude of treatment modalities currently available.

Objective
The aim of this study was to conduct an evidence-based review of all prospective data regarding keloid treatments published between 2010 and 2020.

Methods
A systematic literature search of PubMed (National Library of Medicine), Embase (Elsevier), and Cochrane Library (Wiley) was performed in November of 2020. Search strategies with the keywords “keloid” and “treatment” were performed by a medical librarian. The search was limited to prospective studies that were peer-reviewed, reported on clinical outcomes of keloid therapies, and were published in the English language between January 1, 2010, and November 24, 2020.

Results
A total of 3462 unique citations were identified, of which 108 studies met inclusion criteria. Current literature supports silicone gel or sheeting with corticosteroid injections as first-line therapy for keloids. Adjuvant intralesional 5-fluorouracil (5-FU), bleomycin, or verapamil can be considered, although mixed results have been reported with each. Laser therapy can be used in combination with intralesional corticosteroids or topical steroids with occlusion to improve drug penetration. Excision of keloids with immediate post-excision radiation therapy is an effective option for recalcitrant lesions. Finally, silicone sheeting and pressure therapy have evidence for reducing keloid recurrence.

Conclusions
This review was limited by heterogeneity of subject characteristics and study outcome measures, small sample sizes, and inconsistent study designs. Larger and more robust controlled studies are necessary to further understand the variety of existing and emerging keloid treatments, including corticosteroids, cryotherapy, intralesional injections, lasers, photodynamic therapy, excision and radiation, pressure dressings, and others.
The move towards living systematic reviews and living guidelines in healthcare: consideration of the possibilities and challenges for living qualitative evidence syntheses
Authors: Carmona Chris , Carroll Christopher , Baxter Susan 
Published: March 2023

AbstractOver the past decade qualitative evidence synthesis (QES), a range of methods for synthesising qualitative research evidence, has become a valued form of evidence for guideline producers who wish to understand more about patient preference and acceptability of treatments. The surge in interest in living systematic reviews and the appearance of living guidelines as a response to the COVID-19 pandemic potentially weaken the value and usability of QES.There are currently no published methods for producing living QES, and if QES are to remain of worth to guideline producers then methods for the rapid, frequent updating of them will need to be developed. We discuss some of the similarities and differences between qualitative and quantitative evidence syntheses and highlight areas where development is needed if reviewers are to progress with living approaches to QES.
The nature of properly human mathematics
Authors: Ruelle David 
Published: March 2023

AbstractWe claim that human mathematics is only a limited part of the consequences of the chosen basic axioms. Properly human mathematics varies with time but appears to have universal features which we try to analyse. In particular, the functioning of the human brain privileges concept naming and short formulations. This leads to organising mathematical knowledge structurally. We consider briefly the problem of non-mathematical sciences.
Cost-based hierarchy genetic algorithm for service scheduling in robot cloud platform
Authors: Yin Lei , Liu Jin , Zhou Fengyu , Gao Ming , Li Ming 
Published: March 2023

AbstractService robot cloud platform is effective method to improve the intelligence of robots. An efficient cloud service scheduling algorithm is the basis of ensuring service quality and platform concurrency. In this paper, Hierarchy Genetic Algorithm of robot service(RHGA) is presented to solve the scheduling problem with the crucial constraints. Firstly, the limitations and attributes of the cloud service robots and cloud services are presented and boiled down to an important optimization goal. Secondly, three factors (i.e. evolutionary factor, hunting factor and parent similarity) are integrated with RHGA to promote the efficiency of small-scale service invocations and improve the performance of large-scale service invocations on the platform. Finally, a series of experiments are conducted on several service scheduling algorithms, including four traditional efficient algorithms and two state-of-art algorithms. The experimental results demonstrate that the RHGA can enhance the performance on small-scale service scheduling and ensure its excellent ability in large-scale service scheduling. Moreover, the empirical studies also prove that our proposal has a better performance in service scheduling completion time and cost-savings with comparison to other methods.
An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems
Authors: Selvarajan Shitharth , Srivastava Gautam , Khadidos Alaa O. , Khadidos Adil O. , Baza Mohamed , Alshehri Ali , Lin Jerry Chun-Wei 
Published: March 2023

AbstractThe Industrial Internet of Things (IIoT) promises to deliver innovative business models across multiple domains by providing ubiquitous connectivity, intelligent data, predictive analytics, and decision-making systems for improved market performance. However, traditional IIoT architectures are highly susceptible to many security vulnerabilities and network intrusions, which bring challenges such as lack of privacy, integrity, trust, and centralization. This research aims to implement an Artificial Intelligence-based Lightweight Blockchain Security Model (AILBSM) to ensure privacy and security of IIoT systems. This novel model is meant to address issues that can occur with security and privacy when dealing with Cloud-based IIoT systems that handle data in the Cloud or on the Edge of Networks (on-device). The novel contribution of this paper is that it combines the advantages of both lightweight blockchain and Convivial Optimized Sprinter Neural Network (COSNN) based AI mechanisms with simplified and improved security operations. Here, the significant impact of attacks is reduced by transforming features into encoded data using an Authentic Intrinsic Analysis (AIA) model. Extensive experiments are conducted to validate this system using various attack datasets. In addition, the results of privacy protection and AI mechanisms are evaluated separately and compared using various indicators. By using the proposed AILBSM framework, the execution time is minimized to 0.6 seconds, the overall classification accuracy is improved to 99.8%, and detection performance is increased to 99.7%. Due to the inclusion of auto-encoder based transformation and blockchain authentication, the anomaly detection performance of the proposed model is highly improved, when compared to other techniques.
Enhancement of an IoT hybrid intrusion detection system based on fog-to-cloud computing
Authors: Mohamed Doaa , Ismael Osama 
Published: March 2023

AbstractNowadays, with the proliferation of internet of things-connected devices, the scope of cyber-attacks on the internet of things has grown exponentially. So, it makes it a necessity to develop an efficient and accurate intrusion detection system that should be fast, dynamic, and scalable in an internet of things environment. On the other hand, Fog computing is a decentralized platform that extends Cloud computing to deal with the inherent issues of the Cloud computing. As well, maintaining a high level of security is critical in order to ensure secure and reliable communication between Fog nodes and internet of things devices. To address this issue, we present an intrusion detection method based on artificial neural networks and genetic algorithms to efficiently detect various types of network intrusions on local Fog nodes. Through this approach, we applied genetic algorithms to optimize the interconnecting weights of the network and the biases associated with each neuron. Therefore, it can quickly and effectively establish a back-propagation neural network model. Moreover, the distributed architecture of fog computing enables the distribution of the intrusion detection system over local Fog nodes with a centralized Cloud, which achieves faster attack detection than the Cloud intrusion detection mechanism. A set of experiments were conducted on the Raspberry Pi4 as a Fog node, based on the UNSW-NB15 and ToN_IoT data sets for binary-class classification, which showed that the optimized weights and biases achieved better performance than those who used the neural network without optimization. The optimized model showed interoperability, flexibility, and scalability. Furthermore, achieving a higher intrusion detection rate through decreasing the neural network error rate and increasing the true positive rate is also possible. According to the experiments, the suggested approach produces better outcomes in terms of detection accuracy and processing time. In this case, the proposed approach achieved an 16.35% and 37.07% reduction in execution time for both data sets, respectively, compared to other state-of-the-art methods, which enhanced the acceleration of the convergence process and saved processing power.
Food insecurity status and its contributing factors in slums’ dwellers of southwest Iran, 2021: a cross-sectional study
Authors: Joulaei Hassan , Keshani Parisa , Foroozanfar Zohre , Afrashteh Sima , Hosseinkhani Zahra , Mohsenpour Mohammad Ali , Moghimi Ghasem , Homayouni Meymandi Arash 
Published: March 2023

Abstract
Background
One major factor causing food insecurity is believed to be poverty. Approximately 20 million Iranians live in slums with a vulnerable socioeconomic context. The outbreak of COVID-19, on top of the economic sanctions against Iran, has increased this vulnerability and made its inhabitants prone to food insecurity. The current study investigates food insecurity and its associated socioeconomic factors among slum residents of Shiraz, southwest Iran.

Methods
Random cluster sampling was used to select the participants in this cross-sectional study. The heads of the households completed the validated Household Food Insecurity Access Scale questionnaire to assess food insecurity. Univariate analysis was utilized to calculate the unadjusted associations between the study variables. Moreover, a multiple logistic regression model was employed to determine the adjusted association of each independent variable with the food insecurity risk.

Results
Among the 1227 households, the prevalence of food insecurity was 87.20%, with 53.87% experiencing moderate and 33.33% experiencing severe food insecurity. A significant relationship was observed between socioeconomic status and food insecurity, indicating that people with low socioeconomic status are more prone to food insecurity (P < 0.001).

Conclusions
The current study revealed that food insecurity is highly prevalent in slum areas of southwest Iran. The socioeconomic status of households was the most important determinant of food insecurity among them. Noticeably, the coincidence of the COVID-19 pandemic with the economic crisis in Iran has amplified the poverty and food insecurity cycle. Hence, the government should consider equity-based interventions to reduce poverty and its related outcomes on food security. Furthermore, NGOs, charities, and governmental organizations should focus on local community-oriented programs to make basic food baskets available for the most vulnerable households.
Challenges connected with the energy choice and transition in bakeries of Abidjan, Côte d'Ivoire
Authors: Berte Salimata , Adou Djané Dit Fatogoma 
Published: March 2023

Abstract
Background
The great dependence on fuelwood due to demographic factors, logging, agricultural expansion and the repercussions of political instability, is presented for Côte d'Ivoire, as a contributor to forest degradation and deforestation. Faced with this situation, the country has been trying for many years, through the promotion of alternative energy sources to firewood, to put in place a policy to protect the environment. However, the findings from the urban bakery industry question the social logic behind the choice of energy used. The bakery industry is considered as one of the main contributors to the erosion of the country's main forest due to its propensity to use firewood for cooking. Despite the recommendation of the Ivorian government to use butane and the promotion of “clean” energies, firewood is still the most used energy source in Abidjan. Against this background, the social determinants of the continued use of firewood in those bakeries should be critically assessed.

Methods
The study relied on a qualitative approach using semi-structured interviews and direct observation for data collection. Interviews were conducted with state and non-state actors selected using a "snowball" sampling method. Data collected were subject to thematic content analysis.

Results
The energy transition in the bakery sub-sector in Abidjan is hampered by the plurality of actors and the lack of clarity in energy management. It has led to a higher consumption of firewood and other sources of energy in the production of bread.

Conclusion
Three elements shape the adoption of clean energies in bakeries in Abidjan. These are the politics of the government, its bilateral and multilateral partners, the immediate social pressure linked to wood resources and the availability of infrastructure.