Search Results (93)

Rural public cultural spaces serve as vital venues for information exchange, interpersonal interaction, and cultural and leisure activities in rural communities. Since the Rural Revitalization Strategy was proposed in 2017, the planning and provision of rural public cultural spaces have attracted increasing attention in China. However, many such spaces remain underutilized, accompanied by low levels of user satisfaction among villagers. A key reason for this is the mismatch between standardized spatial configurations and villagers’ dynamic functional needs. Drawing on Hertzberger’s theory of spatial polyvalence, this study proposes a framework to evaluate spatial flexibility in rural public cultural spaces. The framework introduces quantitative indicators and computational methods across two dimensions: “competence”, referring to a space’s potential to accommodate multiple functions, and “performance”, reflecting the efficiency of functional transformation during actual use. Employing the proposed method, this study conducts a case analysis of the Xiangyang Village Neighborhood Center in Shanghai to evaluate its spatial characteristics and actual usage. The evaluation reveals two key issues at the overall level: (1) many residual spaces remain undesigned and lack strategies to support spontaneous use; (2) the spatial layout shows rigid public–private divisions, with little adaptability. At the room level, spaces such as the elevator, hairdressing room, party secretary’s office, and health center are functionally rigid and underutilized. Drawing on exemplary cases, this study proposes several key strategies such as (1) optimizing and innovatively activating residual spaces, (2) integrating multifunctional programs, and (3) improving spatial inclusiveness. Full article

The protection of personally identifiable information (PII) is being increasingly demanded by customers and governments via data protection regulations. Private and public organizations store and exchange through the Internet a large amount of data that include the personal information of users, employees, and customers. While discovering PII from a large unstructured text corpus is still challenging, a lot of research work has focused on identifying methods and tools for the detection of PII in real-time scenarios and the ability to discover data exfiltration attacks. In those research attempts, natural language processing (NLP)-based schemas are widely adopted. Our work combines NLP with deep learning to identify PII in unstructured texts. NLP is used to extract semantic information and the syntactic structure of the text. This information is then processed by a pre-trained Bidirectional Encoder Representations from Transformers (BERT) algorithm. We achieved high performance in detecting PII, reaching an accuracy of 99.558%. This represents an improvement of 7.47 percentage points over the current state-of-the-art model that we analyzed. However, the experimental results show that there is still room for improvement to obtain better accuracy in detecting PII, including working on a new, balanced, and higher-quality training dataset for pre-trained models. Our study contributions encourage researchers to enhance NLP-based PII detection models and practitioners to transform those models into privacy detection tools to be deployed in security operation centers. Full article

Corporate taxation assumes a pivotal role in all economies, as it constitutes a substantial source of revenue for governmental agencies tasked with fulfilling social obligations. Nonetheless, modifications in financial policies and the unpredictability of macroeconomic factors result in a significant decline in this vital revenue source for the government. This study examines the financial determinants influencing corporate tax revenue in Nigeria from 1990 to 2022. In this analysis, the broad money supply, access to credit by the private sector, borrowing costs, and exchange rates are utilized as independent variables, while corporate tax revenue serves as the dependent variable. Data pertinent to this investigation on corporate income tax are sourced from the Federal Inland Revenue Service, whereas information regarding the broad money supply and credit extended to the private sector is acquired from the Central Bank of Nigeria. Additionally, statistical data on interest and exchange rates are gathered from the World Bank. This investigation applies autoregressive distributed lag and error correction models, acknowledging the existence of a long-term relationship within the series. The significant findings indicate that the broad money supply positively and significantly affects corporate income tax in the short run, but this effect diminishes to a positively insignificant level in the long run. Additionally, the interest rate is shown to have a significant harmful effect on corporate tax income in the short run, while it becomes negatively insignificant over the long term. Other financial policy factors do not significantly account for changes in corporate income tax. This study suggests the formulation of financial policies that are advantageous to corporate organizations, particularly through the reduction in borrowing costs, to facilitate business growth and enhance the government’s ability to collect substantial corporate tax revenue. The originality of this research is apparent in its utilization of financial policy instruments to illustrate the effectiveness of financial guidelines on corporate tax receipts and to argue for particular amendments that are essential when these guidelines prove detrimental to business activities. Full article

Federated learning (FL) is a machine learning technique where clients exchange only local model updates with a central server that combines them to create a global model after local training. While FL offers privacy benefits through local training, privacy-preserving strategies are needed since model updates can leak training data information due to various attacks. To enhance privacy and attack robustness, techniques like homomorphic encryption (HE), Secure Multi-Party Computation (SMPC), and the Private Aggregation of Teacher Ensembles (PATE) can be combined with FL. Currently, no study has combined more than two privacy-preserving techniques with FL or comparatively analyzed their combinations. We conducted a comparative study of privacy-preserving techniques in FL, analyzing performance and security. We implemented FL using an artificial neural network (ANN) with a Malware Dataset from Kaggle for malware detection. To enhance privacy, we proposed models combining FL with the PATE, SMPC, and HE. All models were evaluated against poisoning attacks (targeted and untargeted), a backdoor attack, a model inversion attack, and a man in the middle attack. The combined models maintained performance while improving attack robustness. FL_SMPC, FL_CKKS, and FL_CKKS_SMPC improved both their performance and attack resistance. All the combined models outperformed the base FL model against the evaluated attacks. FL_PATE_CKKS_SMPC achieved the lowest backdoor attack success rate (0.0920). FL_CKKS_SMPC best resisted untargeted poisoning attacks (0.0010 success rate). FL_CKKS and FL_CKKS_SMPC best defended against targeted poisoning attacks (0.0020 success rate). FL_PATE_SMPC best resisted model inversion attacks (19.267 MSE). FL_PATE_CKKS_SMPC best defended against man in the middle attacks with the lowest degradation in accuracy (1.68%), precision (1.94%), recall (1.68%), and the F1-score (1.64%). Full article

In smart grids, power monitoring equipment produces large volumes of data that are exchanged between microgrids and the main grid. This data exchange can potentially expose users’ private information, including their living habits and economic status. Therefore, implementing secure and effective data access control mechanisms is crucial. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a widely used encryption scheme in distributed systems, offering fine-grained access control. However, in CP-ABE systems, malicious users might leak decryption keys to third parties, creating a significant security threat. Thus, there is an urgent need for tracing mechanisms to identify and track these malicious users. Moreover, tracing and user revocation are complementary processes. Although using a binary tree for user revocation is efficient, it limits the number of users. This paper suggests an access control scheme that combines CP-ABE with blockchain to overcome these limitations, leveraging blockchain’s tamper-resistant features. This scheme enables user revocation, tracing, partial policy hiding, and ciphertext searchability, and it has been proven secure. Simulation results show that our approach reduces time overhead by 24% to 68%, compared to other solutions. While some solutions are similar in efficiency to ours, our approach offers more comprehensive functionality and better meets the security requirements of smart grids. Full article

The pore structure plays a crucial role in soil systems. It affects a range of processes essential for soil ecological functions, such as the transport and retention of water and nutrients, as well as gas exchanges. The mechanical and hydrological characteristics of soil are predominantly determined by the three-dimensional pore pore-space structure. A precise analysis of pore structure can help specialists understand how these shapes impact plant root activity, leading to better cultivation practices. X-ray computed tomography provides detailed information without destroying the sample. However, manually delineating pore structure and estimating porosity are challenging tasks. This work proposes an automated method for 3D pore segmentation and characterization using convolutional neural networks with attention mechanisms. The method introduces a novel approach that combines attention at both channel and spatial levels, enhancing the segmentation and property estimation, providing valuable insights for a more detailed study of soil conditions. In experiments conducted with a private dataset, the segmentation results achieved mean Dice values of 99.10% ± 0.0004 and mean IoU values of 98.23% ± 0.0008. Additionally, in tests with Phaeozem Albic, the automatic method provided porosity estimates comparable to those obtained by a method based on integral geometry and morphology. Full article

Forest fires, i.e., wildfires, often cause an inevitable strain on society and human living conditions. Incident Commanders (IC) at the Fire and Rescue Services (FRS) are challenged to handle forest fires and at the same time address the forest owners’ needs; this stipulates a need for collaboration, information, and communication. Hence, the aim of this study was to explore and describe the ICs’ experiences and actions in their interactions with forest owners during forest fires on private property. Interviews were conducted and analyzed using Flanagan’s Critical Incident Technique (CIT) to describe the experiences and actions of 22 ICs. The results showed that a firefighting operation needs clarity in information exchange with the forest owner as a stakeholder, not a victim. The trust between forest owner and IC accelerated the operational phase. The ICs demonstrate more care than the law stipulates, and they worry about the forest owners. Therefore, the FRS needs to form a strategic partnership with forest owners and their network on a local level. Also, future forest fire drills should not only include emergency stakeholders (i.e., police, ambulance, etc.) but also forest owners and local volunteer organizations. For a resilient community, FRS and forest owner collaboration is vital. Full article

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats. Full article

We present an active attack that targets Ateniese et al.’s authenticated group key agreement, which, as a particular case, includes the well-known multiparty key exchange protocol CLIQUES that allows a group of users to build a common secret using some private values in a collaborative and distributed way, naturally extending the foundational key exchange introduced by Diffie and Hellman between two communicating parties that motivated the birth of public key cryptography. Ateniese et al.’s protocol adds some authentication information, allowing the parties to trust the exchanged information, but we show that it is possible to surpass this as well. The attack allows a malicious party to agree on a secret with the rest of the legal members of the group without their knowledge, so all the distributed information can be accessed using this secret. In addition, this is shown under a well-known cryptographic model that, in principle, requires absolute control of group communications, but, in fact, it only requires malicious control of the communications of a single arbitrary user and only for the duration of the key exchange. This means that after the attack, the malicious party does not have to take any other actions that could reveal a clue that an attack occurred and that the distributed information is being illegally accessed, contrary to a typical man-in-the-middle attack where the attacker has to continue the activity, meaning this could be detected at some point. Full article

Lahpet-Ye-Hsain, commonly referred to as “tea shops” or teahouses, are versatile establishments that serve as both dining places and social gathering spots. These micro-institutions are fundamental to Myanmar society at the local level, playing a significant role in daily social interactions, economic activities, and political discourse. Operated as private businesses and informal meeting spots, tea shops are primarily staffed by women who serve tea and food to a predominantly male clientele at various times of the day. The size of tea shops can vary, from small snack shops to larger establishments offering a wide range of menu options. In a typical small village tea shop, customers sit on footstools around small square tables, creating a space for sharing tea and food, and exchanging ideas, opinions, problems, hopes, and aspirations. Tea shops attract a diverse range of individuals, as they are known for their inclusive nature that welcomes people from all backgrounds and walks of life, although women are often on the periphery. Historically dominated by men, we suggest that empowering women’s involvement in tea shops could positively impact their agency in other aspects of life, especially during political change. These micro-institutions serve as the initial connection point for residents in the village or neighborhood, traditionally catering to men but now increasingly broadened to women, newcomers, and young individuals exploring new opportunities. Full article

This paper addresses a void in the research on auditing and initial public offering (IPO) failure by investigating the impact of the Big 4 auditing firms on the likelihood of an IPO failure. This research is the first comprehensive analysis of more than 33,000 global IPOs that either failed or were successful between 1995 and 2019 across a wide range of nations with vastly different regulatory, cultural, and economic settings. A cross-sectional probit regression model is utilized to investigate the influence of hiring the Big 4 auditing firms on IPO failure, building upon prior studies on IPO failure. We found strong evidence that IPO failure rates were diminished by up to 67% when one of the Big 4 auditing firms was involved in auditing the IPO prospectus. For IPO founders, hiring Big 4 auditors before an IPO is a quality signaling strategy that minimizes the risk of a failed IPO by reducing information asymmetry among IPO participants. Our findings provide useful policy implications. Hiring one of the Big 4 auditing firms before an IPO is a reassuring signaling strategy for founders, since it decreases information asymmetry among IPO investors and so lowers the risk of the IPO failing. Primary market investors now have access to credible evidence indicating that backing IPOs from companies that use the Big 4 auditing firms increases the likelihood of such IPOs being listed on stock exchanges and yields positive returns. This is the first time, as far as the academicians are aware, that conclusive evidence has been found of a strong inverse association between the presence of Big 4 audits and failure risk for IPO firms. Our research could be helpful to primary market regulators since it shows how crucial it is to encourage Big 4 audits in IPO companies. The quality work of the Big 4 auditors does lower the risk of failure in the IPO market, which might help owners of small private equities to list their firms on the IPO market, boosting economic growth. Full article

A data breach is the unauthorized disclosure of sensitive personal data, and it impacts millions of individuals annually in the United States, as reported by Privacy Rights Clearinghouse. These breaches jeopardize the physical safety of the individuals whose data are exposed and result in substantial economic losses for the affected companies. To diminish the frequency and severity of data breaches in the future, it is imperative to research their causes and explore preventive measures. In pursuit of this goal, this study considers a dataset of data breach incidents affecting companies listed on the New York Stock Exchange and NASDAQ. This dataset has been augmented with additional information regarding the targeted company. This paper employs statistical visualizations of the data to clarify these incidents and assess their consequences on the affected companies and individuals whose data were compromised. We then propose mitigation controls based on established frameworks such as the NIST Cybersecurity Framework. Additionally, this paper reviews the compliance scenario by examining the relevant laws and regulations applicable to each case, including SOX, HIPAA, GLBA, and PCI-DSS, and evaluates the impacts of data breaches on stock market prices. We also review guidelines for appropriately responding to data leaks in the U.S., for compliance achievement and cost reduction. By conducting this analysis, this work aims to contribute to a comprehensive understanding of data breaches and empower organizations to safeguard against them proactively, improving the technical quality of their basic services. To our knowledge, this is the first paper to address compliance with data protection regulations, security controls as countermeasures, financial impacts on stock prices, and incident response strategies. Although the discussion is focused on publicly traded companies in the United States, it may also apply to public and private companies worldwide. Full article

Logistics companies are increasingly adopting private blockchains for enhanced data management because of the trends in cooperation. Nevertheless, this practice poses new challenges concerning the security and sharing of data. The real-time nature and diversity of logistics data increase the difficulty of protecting the data. Additionally, when transportation information changes, downstream enterprises must promptly adjust their production plans to accommodate these alterations. The strict access controls of private blockchains can obstruct downstream enterprises from obtaining data, posing a challenge to the overall operational efficiency. In this paper, we propose an innovative logistics data protection scheme that employs private set intersection (PSI) and blockchain cross-chain technology to achieve data security. In our scheme, logistics companies within the logistics consortium are added as trusted agents to the public blockchain, enabling downstream enterprises to acquire logistics data integration from the public blockchain. Utilizing an RSA-based PSI protocol, our approach enhances exchange efficiency while protecting private data without transmitting additional information. We evaluate the performance of the proposed solution through a series of experiments, and the results demonstrate that our solution can achieve secure and efficient logistics data exchange. Full article

Cellular Vehicle-to-Everything (C-V2X) communications is a technology that enables intelligent vehicles to exchange information and thus coordinate with other vehicles, road users, and infrastructure. However, despite advancements in cellular technology for V2X applications, significant challenges remain regarding the ability of the system to meet stringent Quality-of-Service (QoS) requirements when deployed at scale. Thus, smaller-scale V2X use case deployments may embody a necessary stepping stone to address these challenges. This work assesses network architectures for an Intelligent Perception System (IPS) blind road junction or blind corner scenarios. Measurements were collected using a private 5G NR network with Sub-6GHz and mmWave connectivity, evaluating the feasibility and trade-offs of IPS network configurations. The results demonstrate the feasibility of the IPS as a V2X application, with implementation considerations based on deployment and maintenance costs. If computation resources are co-located with the sensors, sufficient performance is achieved. However, if the computational burden is instead placed upon the intelligent vehicle, it is questionable as to whether an IPS is achievable or not. Much depends on image quality, latency, and system performance requirements. Full article

China’s “carbon peak and neutrality” policy has thrust the convergence of corporate ecological conservation and economic progress to the forefront of sustainable development. This study, aiming to tackle the “sustainability challenge”, delves into the driving forces and operative mechanisms that intertwine corporate environmental performance with financial outcomes from 2015 to 2020. Focusing on A-share listed companies in heavily polluting sectors across Shanghai and Shenzhen stock exchanges, it categorizes formal institutional pressure into two types: command-oriented and market-driven, revealing a significantly stronger positive effect of market-based pressure compared to command-based pressure. Additionally, this research examines the distinct impacts of these institutional pressures under different conditions such as ownership structure, regional location, and executive education levels. The findings indicate that state-owned enterprises, eastern region firms, and those led by highly educated executives are more responsive to command-based pressure. Conversely, privately-owned businesses, entities in central–western regions, and those with lower executive education primarily respond to market-based pressure. Moreover, this study underscores the interplay between informal and formal institutions, observing that the influence of market-based pressure on corporate environmental–financial integration is notably amplified when public awareness of environmental protection increases, thereby highlighting social factors’ pivotal role in business decision-making. In essence, this paper accentuates the significance of aligning corporate environmental and financial goals for sustainable development, offering fresh insights to academia and fostering sustainable practices and research within the corporate realm. Full article