Implementation Of Code Research Articles

Bank-affiliated directors' monitoring, earnings management, and financial reporting quality in emerging markets: Evidence from India

In this novel investigation of creditor governance in an emerging market, this paper examines the monitoring by Indian lenders of firms' earnings management and financial reporting quality through the appointment of a bank-affiliated director to the borrower's board. Specifically, we study the effect of having a bank-affiliated director on the board (a “BDB”), prior to the implementation of India's Insolvency and Bankruptcy Code (IBC). This setting represents a business environment characterized by underdeveloped financial institutions, a weak legal system, and inefficient bankruptcy resolutions. Unlike the U.S., where BDBs play a limited monitoring role, our evidence suggests bank-affiliated directors played an active role in firm governance and lowered earnings management as well as improving financial reporting quality in India during the period of our study. Specifically, we find lower discretionary accruals among firms with BDBs in cross-sectional, pre-post, performance matched, and propensity score matched analyses – signaling higher reporting quality and less earnings management. This finding holds after controlling for endogeneity and when measures of accounting conservatism or earnings persistence are utilized as additional measures of reporting quality. Further, the dampening effect of BDBs on discretionary accruals is more pronounced for companies subject to greater information asymmetry and those prone to agency problems. In addition, firms with BDBs on the board have less volatile ROAs and lower idiosyncratic risk, consistent with BDBs encouraging borrowers to pursue less risky investments to safeguard creditors' interests. As the first examination of the monitoring influence of BDBs on financial reporting quality – and earnings management in particular – in an emerging market, our results provide unique insights for policymakers and creditors seeking to enhance governance and reporting quality of firms in evolving business environments.

Benchmarking deep Facial Expression Recognition: An extensive protocol with balanced dataset in the wild

Facial expression recognition (FER) is crucial in enhancing human-computer interaction. While current FER methods, leveraging various open-source deep learning models and training techniques, have shown promising accuracy and generalizability, their efficacy often diminishes in real-world scenarios that are not extensively studied. Addressing this gap, we introduce a novel in-the-wild balanced testing facial expression dataset designed for cross-domain validation, called BTFER. We rigorously evaluated widely utilized networks and self-designed architectures, adhering to a standardized protocol. Additionally, we explored different configurations, including input resolutions, class balance management, and pre-trained strategies, to ascertain their impact on performance. Through comprehensive testing across three major FER datasets and our in-depth cross-validation, we have ranked these network architectures and formulated a series of practical guidelines for implementing deep learning-based FER solutions in real-life applications. This paper also delves into the ethical considerations, privacy concerns, and regulatory aspects relevant to the deployment of FER technologies in sectors such as marketing, education, entertainment, and healthcare, aiming to foster responsible and effective use. The BTFER dataset and the implementation code are available in Kaggle and Github, respectively.

A micro Reinforcement Learning architecture for Intrusion Detection Systems

This paper proposes an Intrusion Detection System (IDS) that utilizes Deep Reinforcement Learning (DRL) in a fine-grained manner to enhance the performance of binary and multiclass intrusion classification tasks. The proposed system, named Micro Reinforcement Learning Classifier (MRLC), is evaluated using three standard datasets. MRLC architecture utilizes a fine-grained learning approach to enhance IDS accuracy. Simulation studies demonstrate that MRLC has a high efficiency in discriminating different intrusion classes, outperforming state-of-the-art RL-based methods. The average accuracy of MRLC is 99.56%, 99.99%, 99.01% for NSL-KDD, CIC-IDS2018, and UNSW-NB15 datasets respectively. The implementation codes are available at https://github.com/boshradarabi/MICRO-RL-IDS.

Effect of The Board of Directors Size, Independence, and Frequency of Board Meetings on Audit Fees: Evidence From GCC Market

Purpose: This study aims to examine the potential impact of board size, board independence, and meeting frequency on audit fees for companies listed on the financial markets of the United Arab Emirates (UAE). Design/Methodology/Approach: The Abu Dhabi Securities Exchange (ADX) and the Dubai Financial Market (DFM) are the two financial markets in the UAE. The data utilised in the investigation were obtained from the ADX and the DFM. The data was gathered over six years, from 2011 to 2016. The final sample comprises 312 observations. The study employs descriptive and correlation tests, in addition to multiple regressions, as statistical methods. Findings: The analysis revealed a strong correlation between audit fees and both the independence and frequency of board meetings. The findings demonstrated a positive correlation between stringent corporate governance procedures and the requirement for superior audit quality, resulting in higher audit costs. The results also indicated a significant correlation between business size, profitability, leverage, and audit fees. Originality/Value: Due to the recent implementation of the corporate governance code in the UAE and its financial markets, including the ADX and DFM, the efficacy of the code remains uncertain and, therefore, subject to scrutiny. Inadequate governance and audit standards may adversely impact the integrity of financial reporting. Nevertheless, there is limited empirical research investigating the elements influencing the quality of financial reporting in the UAE. This study contributes to the existing literature by enhancing comprehension of the impact of the board of directors on audit quality, utilising data from a GCC country.

Implementation of Meat Inspection Code (Republic Act No. 10536) and Challenges Encountered by Butchers and Meat Inspectors in Calamba City

Implementation of the Meat Inspection Code and Compliance with Food Safety are two important factors required to meet the quality of fresh and safe meat produce and satisfy the consumers' desire to serve this fresh meat at their dining table. This has been a long-time struggle for the City Veterinary-Slaughterhouse Management Division since no system has been implemented. The authority whose function is to safeguard the cleanliness and safeness of these produce and maintain the quality of food animal that consumers expect every day for their families. This discussed the problems and concerns that could potentially obstruct achieving the goal of client satisfaction and retention, as well as the relationships between the level of implementation of the meat inspection code and the degree of seriousness of the challenges faced by the butchers and meat inspectors. A researcher-made questionnaire was utilized in this study. This was used to randomly gather surveys from 64 respondents of butchers and meat inspectors. The Mean, Pearson Product-Moment Correlation, and Four-Point Likert Scale were used to analyze and assess the model. Based on the findings, there was a slight but statistically significant positive correlation between the degree of implementation and the severity of the difficulties faced. Additionally, the study suggested a course of action to create and enhance new methods to support the current program and modernize the system, which will help enhance the quality and condition of meat produced at a slaughterhouse that has received accreditation.

Learning experimental data to predict fluid-elastic instability and optimize configuration of tube arrays

Fluid-elastic instability (FEI) poses a particularly significant challenge to the safety of steam generators, given its potential to cause substantial damage in short periods. The tube bundle system model is inherently complex, resulting in expensive costs for experiments and numerical simulations during the design process. In this study, a substantial volume of FEI experimental data, conducted by various scholars, was compiled to create FEI surrogate model. The accuracy and feasibility of this surrogate model were demonstrated and compared to experimental results. This work represents the pioneering reference on the optimization of tube array configurations, achieved by combining FEI deep neural network (DNN) surrogate model with genetic algorithm (GA). The design variables for optimization encompassed tube array configurations and structural parameters. The optimization objective is to achieve a tubular array structure exhibiting a higher critical velocity, Ur, enabling safe operation of the heat exchanger tube bundle across a wider range of flow rates without any FEI accident. The developed approach holds promise for data-driven FEI analysis and optimized tube bundle design, resulting in substantial time and cost savings. We publicly share all code implementations, and we believe that our efforts open a door for the surrogate-model-assisted structural optimization of tube arrays.

BOMS: blockchain-enabled organ matching system

This work proposes a Blockchain-enabled Organ Matching System (BOMS) designed to manage the process of matching, storing, and sharing information. Biological factors are incorporated into matching and the cross-matching process is implemented into the smart contracts. Privacy is guaranteed by using patient-associated blockchain addresses, without transmitting or using patient personal records in the matching process. The matching algorithm implemented as a smart contract is verifiable by any party. Clinical records, process updates, and matching results are also stored on the blockchain, providing tamper-resistance of recipient’s records and the recipients’ waiting queue. The system also is capable of handling cases in which there is a donor without an immediate compatible recipient. The system is implemented on the Ethereum blockchain and several scenarios were tested. The performance of the proposed system is compared to other existing organ donation systems, and ours outperformed any existing organ matching system built on blockchain. BOMS is tested to ascertain its compatibility with public, private, and consortium blockchain networks, checks for security vulnerabilities and cross-matching efficiency. The implementation codes are available online.

CUSTOMER SEGMENTATION USING RFM ANALYSIS

This research presents a comprehensive approach to customer segmentation using Recency, Frequency, and Monetary (RFM) analysis, combining statistical insights, data visualization, and machine learning techniques. The study utilizes a real-world dataset obtained from a retail environment, aiming to categorize customers based on their recent purchasing behavior, visit frequency, and monetary contributions to the store. The code begins with data preparation and exploration, ensuring data integrity by addressing issues such as negative quantities and missing customer identifiers. Following this, the Recency, Frequency, and Monetary metrics are computed, providing a holistic view of customer engagement and spending patterns. Visualizations, including violin plots, histograms, and box plots, are employed to intuitively convey the distribution of these metrics. The research then delves into the quantile-based segmentation of customers, allowing for a more granular classification. Quantiles are calculated to divide customers into four segments for each RFM metric. The resulting quantile labels are applied to the dataset, enabling the creation of a compound RFM quantile that combines recency, frequency, and monetary information. This combined quantile facilitates the definition of distinct customer segments. To further enhance the interpretability of customer segments, the study introduces a set of rules for labeling customers based on their RFM quantiles. These rules yield segments such as "Best Customer," "Loyal Customer," "Big Spender," "Dead Beats," and "Lost Customer." The resulting customer segmentation is presented visually through histograms and a pie chart, providing a clear and concise representation of the distribution of customers across different segments. Moreover, the research integrates machine learning models, including XGBClassifier, and CatBoostClassifier, to explore the potential of automating the segmentation process and predicting customer segments based on historical data. However, the machine learning aspect is introduced with commented-out sections, leaving room for further exploration and experimentation. In conclusion, this research contributes a comprehensive and detailed code implementation for RFM-based customer segmentation. The integration of visualization techniques aids in the interpretation of customer behavior, while the inclusion of machine learning models opens avenues for predictive analytics in customer segmentation. The presented approach provides valuable insights for businesses seeking to tailor marketing and customer relationship strategies based on individualized customer segments. KEYWORDS— Customer Segmentation, Frequency, Monetary Value, Recency, RFM Analysis.

Stereo-RIVO: Stereo-Robust Indirect Visual Odometry

Mobile robots and autonomous systems rely on advanced guidance modules which often incorporate cameras to enable key functionalities. These modules are equipped with visual odometry (VO) and visual simultaneous localization and mapping (VSLAM) algorithms that work by analyzing changes between successive frames captured by cameras. VO/VSLAM-based systems are critical backbones for autonomous vehicles, virtual reality, structure from motion, and other robotic operations. VO/VSLAM systems encounter difficulties when implementing real-time applications in outdoor environments with restricted hardware and software platforms. While many VO systems target achieving high accuracy and speed, they often exhibit high degree of complexity and limited robustness. To overcome these challenges, this paper aims to propose a new VO system called Stereo-RIVO that balances accuracy, speed, and computational cost. Furthermore, this algorithm is based on a new data association module which consists of two primary components: a scene-matching process that achieves exceptional precision without feature extraction and a key-frame detection technique based on a model of scene movement. The performance of this proposed VO system has been tested extensively for all sequences of KITTI and UTIAS datasets for analyzing efficiency for outdoor dynamic and indoor static environments, respectively. The results of these tests indicate that the proposed Stereo-RIVO outperforms other state-of-the-art methods in terms of robustness, accuracy, and speed. Our implementation code of stereo-RIVO is available at: https://github.com/salehierfan/Stereo-RIVO.

Topology Optimization of Anisotropic Materials with Smooth Fiber Orientation

In the concurrent optimization of topology and fiber orientation, the design of smooth fiber helps to maintain the stability of numerical calculation and the compatibility of the manufacturing process. However, the improvement of fiber continuity is often accompanied by a significant decrease in the overall structural stiffness. Aiming at this problem, this paper proposes a topology optimization method for anisotropic materials with smooth fiber orientation. This method improves the smoothness of fiber orientation and reduces stiffness loss by introducing a fiber angle constraint strategy and adaptive filtering technology. The fiber angle constraint strategy integrates the created angle constraint function into the Method of Moving Asymptotes (MMA) to complete the strong constraint of the angle. This strategy quantifies the continuity of the fiber and effectively improves the continuity of the fiber. At the same time, the application of adaptive filtering technology can adjust a reasonable fiber angle distribution on the basis of smoothing fibers, thereby enhancing the stiffness of the overall structure. In addition, this paper shows the complete optimization process and MATLAB code implementation and verifies the effectiveness of the method through a series of numerical examples, that is, on the basis of improving fiber continuity, the stiffness of the whole structure is guaranteed, and then the effective balance between the two is realized.

Extending the Finite Area Method for enhanced simulation of deformable membranes and its application to extrusion blow moulding

This paper investigates the mechanics of the Extrusion Blow Moulding (EBM) process, focusing on the clamp and inflation phases. The simulation framework employed in this study has been developed using OpenFOAM ® and extends the available Finite Area Method with a vertex-centred approach. This cutting-edge developments integrated a corotational formulation with a quasi-static approximation. The computational code and algorithm are verified and validated through comparisons between analytical solutions, experimental measurements and simulation results, ensuring the accuracy and reliability of the proposed approach. The implemented code is verified by checking fundamental principles of hyperelastic materials behaviour, demonstrated through equibiaxial deformation of spherical balloons. The use of neo-Hookean and Mooney–Rivlin material models provides crucial insights into material responses under different inflation conditions. The studies begin employing a conceptual mould within the EBM process that allows examining the effects of mould clamping on parison deformation. Then the study extends to analysing a real bottle manufactured via EBM, employing Stereolithography (STL) digitization and thickness mapping to quantify the thickness distribution. The simulation results closely replicates experimental observations, capturing the inherent non-linearity of these process stages, including large deformations and displacements, hyperelasticity and parison-mould contacts. This interdisciplinary research enhances understanding of the EBM process phases and analogous processes, providing valuable insights for plastic manufacturing. It improves the design process for meeting specific requirements and offers guidelines to avoid potential failures. The findings underscore the importance of such studies in advancing sustainable and efficient plastic manufacturing practices, with applications ranging from inflatable structures to EBM processes.

A FAC potential for autostructure

We describe the implementation of the current Flexible Atomic Code (FAC) unique electron central potential within the autostructure code. We show that the two codes then give the same atomic data for all practical application purposes. However, autostructure has more flexible potential options which can lead to a more accurate description of atomic processes, especially in low-charged ions.Graphic

Data-driven definition and modelling of plant growth

Many horticultural tasks require a definition of and means to measure ‘plant state’, a quantity that is used to represent and compare plants. The plant state's evolution through time (plant growth) can then be tracked. This is often performed informally by humans based on heuristic features for particular plant types. It can be used for example to recommend interventions to catch up on expected growth, or to measure the effects of experimental interventions on growth. This work provides a purely data-driven definition, that is easy to train, easy to non-destructively acquire training data, does not require expert annotations, and is easy to compute for any new plant type and growing conditions. The presented method is applied to a dataset of lettuce plants where it exceeds the performance of a hard baseline. This work also demonstrates that the presented method retains the intuitive properties expected for a plant growth model. Open source code implementation and data is provided.

Adaptively Isotropic Remeshing Based on Curvature Smoothed Field.

With the development of 3D digital geometry technology, 3D triangular meshes are becoming more useful and valuable in industrial manufacturing and digital entertainment. A high quality triangular mesh can be used to represent a real world object with geometric and physical characteristics. While anisotropic meshes have advantages of representing shapes with sharp features (such as trimmed surfaces) more efficiently and accurately, isotropic meshes allow more numerically stable computations. When there is no anisotropic mesh requirement, isotropic triangles are always a good choice. In this paper, we propose a remeshing method to convert an input mesh into an adaptively isotropic one based on a curvature smoothed field (CSF). With the help of the CSF, adaptively isotropic remeshing can retain the curvature sensitivity, which enables more geometric features to be kept, and avoid the occurrence of obtuse triangles in the remeshed model as much as possible. The remeshed triangles with locally isotropic property benefit various geometric processes such as neighbor-based feature extraction and analysis. The experimental results show that our method achieves better balance between geometric feature preservation and mesh quality improvement compared to peers. We provide the implementation codes of our resampling method at github.com/vvvwo/Adaptively-Isotropic-Remeshing.

Процессуальный код культуры в кабардинских пословицах и поговорках

The article examines the peculiarity of the implementation of the procedural code in Kabardian proverbs and sayings. The subject code is the most explored in linguistic study, allowing us to re-flect on numerous ideas that are important in people’s axiology. Due to the lack of research on the procedural codes of culture, the necessity of introducing these symbolic representations into the scientific field is justified. The aim of the study is to identify the semiotic elements of Kabardian paroemias that mark the value representations of native speakers. The scientific novelty consists in presenting the mechanisms of encoding the ethnomental paradigms of epistemology, in reflect-ing the deep structures of consciousness through words associated with the most important verbs of action. As a result of the study, the most frequent words projecting cultural dominants are iden-tified and their semantic classification is presented.

Implementation of Broadcast Code of Conduct and Broadcast Program Standards at Celebes TV Makassar

This paper discusses the Implementation of Broadcasting Code of Conduct and Broadcast Program Standards at Celebes TV Makassar. this research, the author uses Qualitative research (field research). Data collection is done by interview, observation and documentation. The results showed that The implementation of Broadcasting Code of Conduct and Broadcast Program Standard on Celebes TV Makassar broadcast program is by following the regulation set by Indonesian Broadcasting Commission (KPI) about Broadcasting Code of Conduct and Broadcast Program Standard (P3SPS) 2012. In disseminating information, it must be in accordance with the existing facts and before the information is disseminated to the audience or public, it is necessary to research the truth of the information so that there is no misunderstanding between the information giver and the information receiver.

Robust and effective ab initio molecular dynamics simulations on the GPU cloud infrastructure using the Schrödinger Materials Science Suite

Ab initio Born-Oppenheimer molecular dynamics (AIMD) is a valuable method for simulating physico-chemical processes of complex systems, including reactive systems, and for training machine learning models and force fields. Speed and stability issues on traditional hardware preclude routine AIMD simulations for larger systems and longer timescales. We postulate that any practically useful AIMD simulation must generate a trajectory of a minimum 1000 MD steps a day on a moderate cloud resource. In this work, we implement a computing workflow that enables routine calculations at this throughput and demonstrate results for several non-trivial atomistic dynamical systems. In particular, we have employed the GPU implementation of the Quantum ESPRESSO code which we will show increases AIMD productivity compared to the CPU version. In order to take advantage of transient servers (which are more cost and energy effective compared to the stable servers), we have implemented automatic restart/continuation of the AIMD runs within the Schrödinger Materials Science Suite. Finally, to reduce simulation size and thus reduce compute time when modeling surfaces, we have implemented a wall potential constraint. Our benchmarks using several reactive systems (lithium anode surface/solvent interface, hydrogen diffusion in an iron grain boundary) show a significant speed up when running on a GPU-enabled transient server using our updated implementation.

Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model.

High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. Simultaneously, chromatin structure analyses struggle with extremely sparse contact matrices. To address this problem, computational methods to enhance low-coverage contact matrices have been developed, but existing methods are largely based on resolution enhancement methods for natural images and hence often employ models that do not distinguish between biologically meaningful contacts, such as loops and other stochastic contacts. We present Capricorn, a machine learning model for Hi-C resolution enhancement that incorporates small-scale chromatin features as additional views of the input Hi-C contact matrix and leverages a diffusion probability model backbone to generate a high-coverage matrix. We show that Capricorn outperforms the state of the art in a cross-cell-line setting, improving on existing methods by 17% in mean squared error and 26% in F1 score for chromatin loop identification from the generated high-coverage data. We also demonstrate that Capricorn performs well in the cross-chromosome setting and cross-chromosome, cross-cell-line setting, improving the downstream loop F1 score by 14% relative to existing methods. We further show that our multiview idea can also be used to improve several existing methods, HiCARN and HiCNN, indicating the wide applicability of this approach. Finally, we use DNA sequence to validate discovered loops and find that the fraction of CTCF-supported loops from Capricorn is similar to those identified from the high-coverage data. Capricorn is a powerful Hi-C resolution enhancement method that enables scientists to find chromatin features that cannot be identified in the low-coverage contact matrix. Implementation of Capricorn and source code for reproducing all figures in this paper are available at https://github.com/CHNFTQ/Capricorn.

MolPLA: a molecular pretraining framework for learning cores, R-groups and their linker joints.

Molecular core structures and R-groups are essential concepts in drug development. Integration of these concepts with conventional graph pre-training approaches can promote deeper understanding in molecules. We propose MolPLA, a novel pre-training framework that employs masked graph contrastive learning in understanding the underlying decomposable parts in molecules that implicate their core structure and peripheral R-groups. Furthermore, we formulate an additional framework that grants MolPLA the ability to help chemists find replaceable R-groups in lead optimization scenarios. Experimental results on molecular property prediction show that MolPLA exhibits predictability comparable to current state-of-the-art models. Qualitative analysis implicate that MolPLA is capable of distinguishing core and R-group sub-structures, identifying decomposable regions in molecules and contributing to lead optimization scenarios by rationally suggesting R-group replacements given various query core templates. The code implementation for MolPLA and its pre-trained model checkpoint is available at https://github.com/dmis-lab/MolPLA.

Cyberscape: Designing an Escape Room Game with a Computer Network Security Theme to Encourage Understanding of Network Security Concepts Interactively

Network security is a crucial aspect in the IT world, especially the increasing activity of crecker in carrying out attacks without responsibility. cyberscape is an educational game that will be designed to provide a fundamental understanding of the importance of overcoming crecker attacks on the network. The method used in designing this game is the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) approach. In the analysis stage, needs collection and determination of learning objectives are carried out. The design stage includes storyboarding and game design. Characters and other visual elements are designed using pixil art techniques to provide a unique and attractive appearance. The development stage involves creating game content, initial testing, and code implementation using Unity Game Engine integrated with C# programming language. The implementation stage includes the deployment of the game to users. The results showed that "Cyberscape" was effective in improving players' understanding of basic network security concepts. Users reported a significant increase in their knowledge of how to identify and overcome cracker attacks after playing the game. Qualitative and quantitative evaluations showed a high level of satisfaction among users, as well as an increased awareness of the importance of network security. With this approach, it is hoped that "Cyberscape" can be an effective tool in network security education and help reduce vulnerability to cracker attacks.