Box Operators Research Articles

Modified M‐RCNN approach for abandoned object detection in public places

AbstractDetection of abandoned and stationary objects like luggage, boxes, machinery, and so forth, in public places is one of the challenging and critical tasks in the video surveillance system. These objects may contain weapons, bombs, or other explosive materials that threaten the public. Though various applications have been developed to detect stationary objects, different challenges, like occlusions, changes in geometrical features of things, and so forth, are still to be addressed. Considering the complexity of scenarios in public places and the variety of objects, a context‐aware model is developed based on mask region‐based convolution network (M‐RCNN) for detecting abandoned objects. A modified convolution operation is implemented in the Backbone network to understand features from geometric variations near objects. These modified operation layers can be adapted based on geometric interpretations to extract required features. Finally, a bounding box operation is performed to locate the abandoned object and mask the particular thing. Experiments have been performed on the benchmark dataset like ABODA and our dataset, which shows that an mAP of 0. 0.699 is achieved for model 1, 0.675 is achieved for model 2, and 0.734 mAP is completed for model 3. An ablation analysis has also been performed and compared with other state‐of‐the‐art methods. Based on the results, the proposed model better detects abandoned objects than existing state‐of‐the‐art methods.

Modal algebra of multirelations

Abstract We formalize the modal operators from the concurrent dynamic logics of Peleg, Nerode and Wijesekera in a multirelational algebraic language based on relation algebras and power allegories, using relational approximation operators on multirelations developed in a companion article. We relate Nerode and Wijesekera’s box operator with a relational approximation operator for multirelations and two related operators that approximate multirelations by different kinds of deterministic multirelations. We provide an algebraic soundness proof of Goldblatt’s axioms for concurrent dynamic logic and one for a multirelational Hoare logic based on Nerode and Wijesekera’s box as applications.

South Korean Christian Communities Supporting Women in Need

Christianity does not have as long of a history as other monotheistic religions and traditional ideologies in Korea, but—especially from the end of the 19th century—its new concepts have had a huge impact on the basic thoughts of Korean society. This paper focuses on the effects of Christianity and the activity of Christian communities on women’s lives. According to my preliminary findings, in the late 1800s, the Christian missionaries and their newly formed communities offered opportunities for girls and women to get education, a profession, access to better health care, and learn self-care. After the complicated decades between 1910 and 1950, South Korea was experiencing remarkable changes, and Christians were an active part of this rebuilding, helping the lives of those who were struggling with poverty, lack of daily necessities, education, and health care. This paper aims to examine the thoughts and actions of Korean Christian communities towards decisions on childbirth, children out of wedlock, adoption, and single motherhood. This study investigates the approaches of South Korean Christian communities towards women related to the above-mentioned circumstances, focusing on the early examples and the last five to six decades. It is assumed that even though South Korea is now considered a modernized country, the government sometimes fails to cope with current problems, and traditional notions are still strong in familial matters. Therefore, not obeying conventional forms may cause social conflicts, or the decisions are hidden because of taboos and stigmas. This research is based on the publications of Christian communities and involves documentaries and case studies, including the baby box operation and adoption. This paper contributes to the expanding studies on Korean Christian communities, highlighting the social norms and their changes generated by new religious thoughts, while giving an insight into the daily struggles of Korean women’s lives when it comes to decision-making about their motherhood.

Revealing the Denoising Principle of Zero-Shot N2N-Based Algorithm from 1D Spectrum to 2D Image.

Denoising is a necessary step in image analysis to extract weak signals, especially those hardly identified by the naked eye. Unlike the data-driven deep-learning denoising algorithms relying on a clean image as the reference, Noise2Noise (N2N) was able to denoise the noise image, providing sufficiently noise images with the same subject but randomly distributed noise. Further, by introducing data augmentation to create a big data set and regularization to prevent model overfitting, zero-shot N2N-based denoising was proposed in which only a single noisy image was needed. Although various N2N-based denoising algorithms have been developed with high performance, their complicated black box operation prevented the lightweight. Therefore, to reveal the working function of the zero-shot N2N-based algorithm, we proposed a lightweight Peak2Peak algorithm (P2P) and qualitatively and quantitatively analyzed its denoising behavior on the 1D spectrum and 2D image. We found that the high-performance denoising originates from the trade-off balance between the loss function and regularization in the denoising module, where regularization is the switch of denoising. Meanwhile, the signal extraction is mainly from the self-supervised characteristic learning in the data augmentation module. Further, the lightweight P2P improved the denoising speed by at least ten times but with little performance loss, compared with that of the current N2N-based algorithms. In general, the visualization of P2P provides a reference for revealing the working function of zero-shot N2N-based algorithms, which would pave the way for the application of these algorithms toward real-time (in situ, in vivo, and operando) research improving both temporal and spatial resolutions. The P2P is open-source at https://github.com/3331822w/Peak2Peakand will be accessible online access at https://ramancloud.xmu.edu.cn/tutorial.

Realizing impossibilities

It is common in epistemic modal logic to model the epistemic states of agents via box operators in the normal logic S5. However, this approach treats agents as logically omniscient by requiring their knowledge to be closed under classical logical consequence. A promising way of avoiding logical omniscience consists in extending epistemic models with impossible states, that is states, where complex formulas are not evaluated recursively. However, this approach faces the dual problem of logical ignorance by modeling agents as not even minimally logically competent. In this paper I will outline an epistemic logic that combines impossible states with dynamic realization modalities akin to the dynamic announcement operators from public announcement logic (PAL). I will show that this epistemic logic avoids both the problem of logical omniscience and the problem of logical ignorance. Furthermore, I prove that so-called successful updates in my logic can be characterized in the same way as in PAL and that a similar logic due to Johan van Benthem can be simulated in my logic. Finally, I will compare my epistemic logic with a similar one, which has recently been advanced by JC Bjerring and Mattias Skipper.

Interpretable machine learning-based analysis of hydration and carbonation of carbonated reactive magnesia cement mixes

This study explored the influence of different input variables on the hydration and carbonation degree of carbonated reactive magnesia cement (RMC) system by employing six machine learning algorithms. These included support vector machine (SVM), particle swarm optimization-based SVM (PSO-SVM), extreme learning machine (ELM), grey wolf optimizer-based SVM (GWO-SVM), kernel extreme learning machine (KELM), and extreme gradient boosting (XGBoost). The followed approach enabled the deep learning of the relevant database to achieve parameter prediction. Two feature analysis methodologies, i.e. partial dependence plot (PDP) and SHapley Additive exPlanations (SHAP), were applied to uncover the operating laws underpinning the black box operation characteristics of machine learning models. Results revealed that GWO-SVM and XGBoost outperformed all other models in predicting the hydration and carbonation degree of the complete database set (R2 of the total database set was 0.9470/0.9775 and 0.9663/0.9727 for hydration and carbonation degree, respectively). Factors such as carbonation duration, CO2 concentration, pre-curing temperature, and w/b directly influenced the degree of hydration and carbonation. Among them, carbonation duration and CO2 concentration were two of the most influential factors, resulting in the promotion of brucite consumption and accelerating the formation of carbonation products.

OCTess: AN OPTICAL CHARACTER RECOGNITION ALGORITHM FOR AUTOMATED DATA EXTRACTION OF SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY REPORTS.

Manual extraction of spectral domain optical coherence tomography (SD-OCT) reports is time and resource intensive. This study aimed to develop an optical character recognition (OCR) algorithm for automated data extraction from Cirrus SD-OCT macular cube reports. SD-OCT monocular macular cube reports (n = 675) were randomly selected from a single-center database of patients from 2020 to 2023. Image processing and bounding box operations were performed, and Tesseract (an OCR library) was used to develop the algorithm, OCTess. The algorithm was validated using a separate test data set. The long short-term memory deep learning version of Tesseract achieved the best performance. After reverifying all discrepancies between human and algorithmic data extractions, OCTess achieved accuracies of 100.00% and 99.98% in the training (n = 125) and testing (n = 550) datasets, while the human error rate was 1.11% (98.89% accuracy) and 0.49% (99.51% accuracy) in each, respectively. OCTess extracted data in 3.1 seconds, compared with 94.3 seconds per report for human evaluators. We developed an OCR and machine learning algorithm that extracted SD-OCT data with near-perfect accuracy, outperforming humans in both accuracy and efficiency. This algorithm can be used for efficient construction of large-scale SD-OCT data sets for researchers and clinicians.

EXPLAINING AI: UNDERSTANDING DEEP LEARNING MODELS FOR HERITAGE POINT CLOUDS

Abstract. Deep Learning has been pivotal in many real-world applications (e.g., autonomous driving, medicine and retail). With the wide availability of consumer-grade depth sensors, acquiring 3D data has become more affordable and effective, and many 3D datasets are currently publicly available. 3D data provides a great opportunity for a better comprehension of the surrounding environment for machines. There is a growing need for innovative methods for the treatment and analysis of point clouds and for their classification. The complex hidden layers, which are at the basis of deep neural networks (DNNs), make it difficult to interpret these models, that up to a few years ago DNNs were considered and treated as black box operators. Still, with their increasing popularity, making them explainable and interpretable has become mandatory. A lot of efforts were devoted to developing an Explainable Artificial Intelligence (XAI) framework for explaining DNNs decisions with 2D data, while only a few studies have attempted to investigate the explainability of 3D DNNs and, even more, heritage scenarios. To overcome these limitations, it was proposed the BubblEX framework: a novel multimodal fusion framework to learn the 3D point features. In our work, BubblEX has been exploited to understand the decisions taken by DNNs for heritage point clouds. The approach has been applied to a Digital Cultural Heritage Dataset, which is publicly available: the ArCH (Architectural Cultural Heritage) Dataset.

Corrigendum: “Black box operation” in carbon reduction: based on empirical evidence of carbon transfer by subsidiaries of Chinese enterprises

CORRECTION article Front. Environ. Sci., 15 May 2023Sec. Environmental Economics and Management Volume 11 - 2023 | https://doi.org/10.3389/fenvs.2023.1204768

“Black box operation” in carbon reduction: Based on empirical evidence of carbon transfer by subsidiaries of Chinese enterprises

Avoiding the transfer of “carbon” and encouraging the digestion of “carbon” are essential to promote the green and low-carbon transformation of China’s economy. In accordance with the standpoint of off-site subsidiaries, this paper examines the transfer of “carbon” from high-carbon enterprises using the data of A-share listed companies from 2009 to 2018 using a DID approach and the 2013 China carbon emissions trading pilot as a quasi-natural experiment. As demonstrated by the reach findings: (1) Part of the effect of corporate “carbon reduction” is achieved by shifting high-carbon sectors. (2) As demonstrated in mechanism analysis, when high-carbon companies face the dual cost pressure of R&D expenditure and purchasing carbon trading rights, they will establish subsidiaries to avoid the parent company’s pressure to lessen emissions. As revealed in heterogeneity analysis. (3) companies with stronger R&D capabilities and higher success rates are more willing to respond to the impact of carbon trading policies with technological upgrades. Companies with weaker R&D capabilities and higher failure rates are more likely to choose to transfer “carbon” to avoid the “dual cost” of R&D failures. (4) Owing to the constraint of the migration threshold, the trajectory of “carbon” transfer is primarily domestic interregional transfer supplemented by cross-country transfer. (5) Larger enterprises emitting more “carbon”, are not only more likely to pay more “carbon” reduction costs in the face of carbon policy shocks, are but also more likely to shift “carbon”. This study not only provides a new perspective to explain the “carbon” transfer phenomenon in China, but also provides crucial policy implications for further strengthening environmental governance as well as regional joint prevention and control in China.

Multi-level Attack with Dynamic S-box Variable key Pattern Generation for Key Cohort Using AES

In recent times, data transmission in electronic medium is found to be more susceptible to several attacks. The study aims to control the multi-level attacks in encryption and decryption process by using Advanced Encryption Standard (AES) algorithm based S box operations. In AES based variable key generation pattern, every round generates the new key. The generation of multiple keys strengthen the operation of AES-dynamic S box. The AES algorithm performs operation on a 128 bit plain text and utilizes identical key for decryption and encryption process. The proposed algorithm shows significant improvements in the quality of encryption and decryption. The performance of the proposed system has been analysed in accordance with delay, power consumption and number of slices. Further the efficiency of the proposed system has been compared with other existing methods such as Positive Polarity Reed Muller (PPRM), Modified Positive Polarity Reed Muller (MPPRM) Twisted Binary Decision Diagram (TBDD) and Composite Field (CF) architecture. The results exposed that the proposed system outperforms with superior performance.

Study on dynamic modeling of composite thin cylindrical shells subjected to low-velocity impact based on an analytical method

<p indent="0mm">Aiming at the problems of traditional finite element modeling with many black box operations, high computational costs, and no independent intellectual property rights, this paper establishes a fiber-reinforced composite under low-speed impact excitation based on the first-order shear deformation theory, the Von Kamen large deformation theory and the law of conservation of energy. Analytical model of impact characteristics of cylindrical shells under the fixed-supported boundary at both ends. By considering the impact delamination damage and the Hoffman failure criterion, the expression of impact contact force when each failure event occurs is deduced. Therefore, the impact contact force and load-displacement curves are successfully solved, with a feasible analysis process of low-velocity impact property being summarized. Finally, the proposed model are validated against the Abaqus finite element model with different laminate configuration and impact velocities being considered. It can provide a new idea and approach for predicting and evaluating the impact dynamics of other complex cylindrical shells with anisotropic characteristics.

Continuance intention to use self-delivery boxes: An empirical study in Tianjin, China

Self-delivery boxes reduce delivery and pick-up costs significantly, and are helpful for alleviating the “last mile delivery” problem. The goal of the current research is to examine continuance intention to use self-delivery boxes from three aspects (e.g. consumer, courier, and self-delivery box). Based on survey data from 279 Tianjin residents, in this study, we found that time pressure, perceived behavioral control, and self-delivery box reliability significantly affected continuance intention regarding self-delivery boxes. Also, we explored the effect of consumer group on continuance intention to use self-delivery boxes and found that younger consumers had a higher continuance intention to use them than older consumers. This study provides practical implications for self-delivery box operators.

Human Operating Risk Assessment for Outdoor Terminal Box of Electric Power

The outdoor terminal box is the basic node of the power Internet of Things as an intermediate link between outdoor electrical equipment and indoor equipment, like measurement and control, protection, communication and other equipment. Terminal boxes and terminals need to be regularly checked on site, loop tests, transformations, and equipment replacement. Operator errors have become one of the main factors in outdoor terminal box accidents. In terms of human risk assessment of outdoor terminal boxes, few papers are involved. In order to correctly evaluate the influence of human factors on the failure of outdoor terminal boxes, the paper first analyzes the human behavior factors of outdoor terminal box operators, the common performance conditions of the CREAM (Cognitive Reliability and Error Analysis Method) model is used to analyze the human behavior mechanism and behavior reliability factors during the operation of the terminal box. Then, the SLIM (Success Likelihood Index Method) model is used to calculate the probability of human error. The proportional failure model is used to calculate the failure rate of the outdoor terminal box itself. Finally, taking a circuit breaker terminal box as an example for simulation, the probability of human error is 1.56%, the equipment failure rate is 0.84%, the risk value of the system is 10.7%, and the risk level of the system is 3. From the probabilistic perspective, it shows that human factors have a greater influence on the causes of accidents.

Compact and Versatile QEPAS-Based Sensor Box for Simultaneous Detection of Methane and Infrared Absorber Gas Molecules in Ambient Air

In this work we report on an innovative sensor box employing two acoustic detection modules connected in series for quartz-enhanced photoacoustic multi-gas detection. One detection module is coupled with an internal distributed-feedback quantum cascade laser (DFB-QCL) emitting at ∼7.719 µm for methane (CH4) sensing, while the second module has been designed to be coupled with an external laser source targeting the absorption features of a specific gas molecule Mx in the infrared spectral range. The sensor box can thus be employed for any application, depending on the CH4/Mx gas combination to be detected. The ∼7.719 µm DFB-QCL also allowed water vapor monitoring. To demonstrate the sensor versatility, we report on the QEPAS-box environmental monitoring application by simultaneously detecting in air methane, which is a greenhouse gas, nitric oxide (NO), an ozone depleting substance, and water vapor. Sensitivity levels of 4.30 mV ppm−1 and 17.51 mV ppm−1 and minimum detection limits of 48 ppb and 11 ppb for methane and nitric oxide detection were achieved, respectively. The sensor box operation was tested by analysing ambient air. Average concentrations of ∼1.73 ppm of CH4, ∼0.134 ppm of NO and 1.8% of H2O were measured.

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

White blood cells (WBCs) are blood cells that fight infections and diseases as a part of the immune system. They are also known as “defender cells.” But the imbalance in the number of WBCs in the blood can be hazardous. Leukemia is the most common blood cancer caused by an overabundance of WBCs in the immune system. Acute lymphocytic leukemia (ALL) usually occurs when the bone marrow creates many immature WBCs that destroy healthy cells. People of all ages, including children and adolescents, can be affected by ALL. The rapid proliferation of atypical lymphocyte cells can cause a reduction in new blood cells and increase the chances of death in patients. Therefore, early and precise cancer detection can help with better therapy and a higher survival probability in the case of leukemia. However, diagnosing ALL is time-consuming and complicated, and manual analysis is expensive, with subjective and error-prone outcomes. Thus, detecting normal and malignant cells reliably and accurately is crucial. For this reason, automatic detection using computer-aided diagnostic models can help doctors effectively detect early leukemia. The entire approach may be automated using image processing techniques, reducing physicians' workload and increasing diagnosis accuracy. The impact of deep learning (DL) on medical research has recently proven quite beneficial, offering new avenues and possibilities in the healthcare domain for diagnostic techniques. However, to make that happen soon in DL, the entire community must overcome the explainability limit. Because of the black box operation's shortcomings in artificial intelligence (AI) models' decisions, there is a lack of liability and trust in the outcomes. But explainable artificial intelligence (XAI) can solve this problem by interpreting the predictions of AI systems. This study emphasizes leukemia, specifically ALL. The proposed strategy recognizes acute lymphoblastic leukemia as an automated procedure that applies different transfer learning models to classify ALL. Hence, using local interpretable model-agnostic explanations (LIME) to assure validity and reliability, this method also explains the cause of a specific classification. The proposed method achieved 98.38% accuracy with the InceptionV3 model. Experimental results were found between different transfer learning methods, including ResNet101V2, VGG19, and InceptionResNetV2, later verified with the LIME algorithm for XAI, where the proposed method performed the best. The obtained results and their reliability demonstrate that it can be preferred in identifying ALL, which will assist medical examiners.

Innovative Technology of Accelerated Composting of Chicken Manure to Obtain an Organic Fertilizer with a High Content of Humic Acids

Abstract One of the most important fractions of soil organic matter which has significant environmental and agricultural importance is humus. The specific humus compounds of soil include the complex of amorphous organic humic substances which include humic acids, fulvic acids and humins. The effectiveness of the natural formation of active humic substances during composting processes depends on the chemical composition of organic residues and the environmental conditions influencing the development and activity of native microorganisms. The aim of the project was to build innovative composting boxes for chicken manure that allow to effectively obtain a fertilizer with the highest possible content of humic acids, by creating perfect and controlled conditions for the rapid development of indigenous thermophilic microorganisms and by combining it with the owned bio-acceleration technology. For perfect conditions (temperature, humidity, oxygen, pH) for the controlled growth of microorganisms, an algorithm of the dependence of the box operation parameters on the course of the biological process, was built. The product obtained after composting is an easily digestible complex organic fertilizer of brown-black colour, pH 7.5-8.0 and NPK 5:3:4 and a high content of native humic acids. Its use increases the growth of green mass of plants and improves soil fertility, which has been proved by the conducted pot research.

Interpretable machine learning with an eye for the physics: Hyperspectral Vis/NIR “video” of drying wood analyzed by hybrid subspace modeling

Chemometric multivariate analysis based on low-dimensional linear and bilinear data modelling is presented as a fast and interpretable alternative to more fancy “AI” for practical use of Big Data streams from hyperspectral “video” cameras. The purpose of the present illustration is to find, quantify and understand the various known and unknown factors affecting the process of drying moist wood. It involves an “interpretable machine learning” that analyses more than 350 million absorbance spectra, requiring 418 GB of data storage, without the use of black box operations. The 159-channel high-resolution hyperspectral wood “video” in the 500–1005 nm range was reduced to five known and four unknown variation components of physical and chemical nature, each with its spectral, spatial and temporal parameters quantified. Together, this 9-dimensional linear model explained more than 99.98% of the total input variance.

Causal G\xf6del-type metrics in non-local gravity theories

It is well known that non-local theories of gravity have been a flourish arena of studies for many reasons, for instance, the UV incompleteness of General Relativity (GR). In this paper we check the consistency of ST-homogeneous Gödel-type metrics within the non-local gravity framework. The non-local models considered here are ghost-free but not necessarily renormalizable since we focus on the classical solutions of the field equations. Furthermore, the non-locality is displayed in the action through transcendental entire functions of the d’Alembert operator Box that are mathematically represented by a power series of the Box operator. We find two exact solutions for the field equations correspondent to the degenerate (omega =0) and hyperbolic (m^{2}=4omega ^2) classes of ST-homogeneous Gödel-type metrics.

Hybrid dynamic logic institutions for event/data-based systems

We propose varepsilon^downarrow(mathcal{vec{D}})-logic as a formal foundation for the specification anddevelopment of event-based systems with data states. The framework is presentedas an institution in the sense of Goguen and Burstall and the logic itself isparametrised by an underlying institution mathcal{vec{D}} whose structures are used tomodel data states. varepsilon^downarrow(mathcal{vec{D}})-logic is intended to cover a broad range ofabstraction levels from abstract requirements specifications up to constructivespecifications. It uses modal diamond and box operators over complex actionsadopted from dynamic logic. Atomic actions are pairs where e is an event and psi a state transition predicate capturing the allowedreactions to the event. To write concrete specifications of recursive processstructures we integrate (control) state variables and binders of hybrid logic.The semantic interpretation relies on event/data transition systems. For thepresentation of constructive specifications we propose operational event/dataspecifications allowing for familiar, diagrammatic representations by statetransition graphs. We show that varepsilon^downarrow(mathcal{vec{D}})-logic is powerful enough tocharacterise the semantics of an operational specification by a singlevarepsilon^downarrow(mathcal{vec{D}})-sentence. Thus the whole (formal) development process forevent/data-based systems relies on varepsilon^downarrow(mathcal{vec{D}})-logic and its semantics as a commonbasis. It is supported by a variety of implementation constructors which canexpress, among others, event refinement and parallel composition. Due to thegenericity of the approach, it is also possible to change a data stateinstitution during system development when needed. All steps of our formaltreatment are illustrated by a running example.