Hasil untuk "Management information systems"

Sayeed Salih, Omayma Husain, Refan Mohamed Almohamedh et al.

Generative Artificial Intelligence (GAI) is revolutionizing digital marketing by auto-content creation, personalized customer experience, and data-driven decisions. This study conducts a systematic literature review and case study analysis to explore GAI applications, benefits, and challenges in modern digital marketing. Drawing on an extensive analysis of academic journals and industry publications, the current research examines leading GAI software such as ChatGPT, DALL-E, MidJourney, Jasper.ai, and Synthesia based on how they aid in content creation, visual design, and video production. The research also provides real-world case studies in multiple industries, such as retail and fashion, food and beverages, and travel and tourism. The case findings illustrated how GAI augments marketing automation, facilitates customer engagement, and amplifies brand engagement, resulting in greater customer satisfaction, higher conversion rates, and better campaign performance. Although it has several benefits, the adoption of GAI is hampered by several critical barriers, such as data privacy, ethical risks, worker resistance, quality control issues, and infrastructure constraints. This research pinpoints these essential challenges and offers practical solutions. It provides actionable insights for businesses seeking to leverage GAI for competitive advantage in the evolving digital landscape by bridging the gap between theory and practice. The findings contribute to the growing discourse on AI-driven marketing strategies and lay the foundation for future research on GAI's long-term impact on consumer engagement and brand loyalty.

Mingli Lin, Mingming Liu, Heidi Ma et al.

Estimating the population abundance of marine wildlife is fundamental for developing evidence-based management strategies. However, standard field-based methods for estimating marine animal abundance can be time-consuming and costly, and may not effectively monitor populations over long time frames or large spatial scales. In this study, we attempted to utilize local ecological knowledge (LEK) to estimate population abundance of a marine mammal by collecting interview-based sighting data from a large-scale fisher questionnaire survey in the mainland of China for the Indo-Pacific humpback dolphin (Sousa chinensis). To validate our findings, we compared this information with data obtained from standard field surveys published in the literature. Despite the inability to accurately predict absolute abundance, all four indices (encounter rate, encounter rate in the past decade, sighting frequency, and mean annual sightings in the past five years) derived from interview sighting history had a highly significant correlation with abundance rank from field survey. In addition, the severe population declines and identified causes (water pollution, habitat destruction, overfishing, and bycatch) reported by respondents also aligned closely with independent field survey data. This is the first time that interview data have been shown to provide accurate quantitative information on a marine species’ relative abundance. We therefore propose that interview-based surveys can serve as a valuable monitoring technique to assess the population status of cetaceans and other distinctive marine megafauna, particularly in systems where field survey programs are limited in scale and scope.

Yang Xiong, Zhuo-er Huang, Wei-lian Peng et al.

Abstract Background Improving nursing interns’ behavioral patterns toward the constructive conflict management style is critical for promoting effective nurse-patient conflict resolution. However, there is a dearth of research on the factors that influence constructive conflict management styles guided by theoretical principles. Aim To explore the relationships among nursing interns’ constructive conflict management style, career-related social support, emotional intelligence, and communication ability with angry patients using self-determination theory. Methods We conducted a cross-sectional study following STROBE guidelines and recruited 375 nursing interns from 31 universities at a comprehensive teaching hospital in Hunan Province. Data on general information, career-related social support, emotional intelligence, communication ability with angry patients, and constructive conflict management style was collected from nursing interns using a general information questionnaire and reliable scales. Structural equation modeling was used to model and test the hypothesis. Results The nursing interns’ career-related social support had a significant immediate impact on constructive conflict management style (β = 0.320, CI: 0.099–0.213). Career-related social support and constructive conflict management style were mediated by emotional intelligence (β = 0.088, CI: 0.010–0.081) and communication ability with angry patients (β = 0.098, CI: 0.023–0.078), respectively. While multiple mediating effects (β = 0.067, CI: 0.014–0.057) were found. Conclusion High levels of professional social support, emotional intelligence, and communication ability with angry patients positively influence constructive conflict management style, especially social support. In order to promote the constructive conflict management style of nursing interns, nursing educators and managers should emphasize the establishment and maintenance of career support systems, and use experiential teaching methods such as role playing, virtual reality scenario simulation, and peer mentoring.

Maqsood Ahmad, Qiang Wu

Abstract Purpose When considering the influence of recognition-based heuristics on entrepreneurs’ strategic decision-making (ESDM), especially in emerging markets, conventional theories and literature on entrepreneurs’ management approach are notably sparse. This study investigates how recognition-based heuristics influence ESDM, particularly in an emerging Asian economy. Design/methodology/approach Data was collected through a survey completed by 237 owners and senior managers of small and medium-sized enterprises (SMEs) in the service, trade, and manufacturing sectors located in the Pakistani cities of Rawalpindi and Islamabad (twin cities). Data was collected using a convenient purposive sampling technique and snowball sampling method. A structural equation modeling-artificial neural network (SEM-ANN) based approach was applied to evaluate the role of recognition-based heuristic predictors. The results were authenticated using regression analysis. Findings The results indicate that recognition-based heuristics—such as alphabetical order, name fluency, and name memorability—have a positive impact on ESDM. This means that recognition-based heuristics are useful tools for entrepreneurs in strategic decision-making. Entrepreneurs who use recognition-based heuristics are more likely to make effective strategic decisions. The ANN results reveal that name memorability has the highest predictive power in positively influencing ESDM, suggesting that memorability plays a crucial role in facilitating more efficient and informed strategic choices. Originality/value This study pioneers research examining the connection between recognition-based heuristics—alphabetical order, name fluency and name memorability—and ESDM in an emerging Asian market. This study contributes to the entrepreneurial management field, particularly regarding the role of recognition-based heuristics in strategic decision-making. This research area is still in its early stages, even in developed economies, and very little work has been conducted in emerging economies. This study makes a significant contribution to the literature in this field. We employed a novel SEM-ANN based evaluation approach that combines the strengths of SEM and ANN. This integration allows for a comprehensive analysis of both linear and nonlinear relationships between variables, providing a nuanced understanding of the complex dynamics involved in ESDM, and differentiating this study from other studies in the field.

Hindun Hindun, Titik Haryati, Ghufron Abdullah

The objectives of this research are to determine the effect of management information systems, transformational leadership, and school-based management on the quality of educational services in elementary schools in Boja District. This research uses a quantitative approach of the ex-post facto type. The population in this study was all elementary school teachers in the Boja District, Kendal Regency, totaling 138 people. The sampling technique used was proportionate random sampling, resulting in a sample of 103 teachers. Data collection was conducted using a questionnaire. Data sources consist of primary data (sourced from questionnaires) and secondary data (sourced from books, journals, and elementary school archives in the Boja District). Data analysis techniques include Partial Correlation Analysis, Multiple Regression Analysis, Hypothesis Testing, Determination Coefficient Test (R2), Structural Test, Effective Contribution, Relative Contribution, and Relative Contribution (SR) and Effective Contribution (SE). The research results show (1) There is an influence of management information systems on the quality of elementary school education services in Boja District of 76.4% with the highest contribution from the management information system dimension of 0.830 (83.0%), while the lowest dimension is system quality of 0.517 (51.7%), (2) There is an influence of transformational leadership on the quality of elementary school education services in Boja District of 76% with the highest contribution from the dimension that influences the highest transformational leadership from the intellectual stimulation dimension, namely 0.718 (71.8%). While the lowest dimension is Individualized influence of 0.474 (47.4%), (3) There is an influence of school-based management on the quality of elementary school education services in Boja District. of 79.3% with the highest contribution from the dimension that influences school-based management is the process dimension, namely 0.820 (82.0%). While the lowest dimension is educational input of 0.539 (53.9%). 4) There is an influence of management information systems, transformational leadership, and school-based management on the quality of elementary school education services in the Boja District of 84.4% with the highest contribution from the school-based management dimension of 79.3% and the lowest from the transformational leadership dimension of 76%.

Fatoumata Binetou Diongue, Adama Faye, Cheikh Loucoubar et al.

Summary Introduction In Senegal, the Routine Health Information System (RHIS) captures the majority of data from the Ministry of Health and Social Action (MHSA) public structures and very little health data from the private sector and other ministerial departments. Quality data strengthens the validity and reliability of research results. Common areas of data quality include accuracy, completeness, consistency, credibility, and timeliness. The work aims to assess the quality of routine maternal, child, and adolescent health data in Senegal. Materials and methods A mixed quantitative and qualitative design was chosen in four health districts, including Thiès, Mbour, Kédougou, and Saraya. The study included functional health structures that produce maternal, child, and adolescent health data. For the quantitative part, a descriptive and analytical study was carried out. Lot Quality Assurance Sampling (LQAS) was used as the sampling method. Data were collected using Performance of Routine Information Systems Management (PRISM) data collection tools and the ODK application and analyzed (univariate and bivariate) using R and Stata with an alpha risk of 5%. The following data quality indicators (accuracy, completeness, and promptness) were estimated. An exploratory case study and purposive sampling supported the qualitative part by implementing individual interviews. Results The study showed an accuracy ratio of 1 in the intervention districts, a difference in the control districts, and a disparity in the transmission of guidelines between districts (inter- and intra-region). The average level of completeness was 0.64 (+/- 0.44) for all regions combined, with no significant difference between districts. The promptness rate for Kédougou, Saraya, Thiès, and Mbour districts was 81%, 75.9%, 72.2%, and 86.7%, respectively. Between 40% and 60% of facilities in each district carried out self-assessments. Data collection tools were considered to be numerous. A large number of tools were easy to use. The recording space was appreciated. On the other hand, the length of the forms was little or not appreciated by the providers. Few of the providers in the 4 districts had been trained to record data in DHIS2. Conclusion Assessment of data quality in the districts studied shows shortcomings in terms of completeness and timeliness. Many factors influence the SMEA data quality situation, including knowledge or application of RHIS policies, standards, and protocols, perception of the importance of RHIS, ease of use of data collection tools, training of providers, and diversity of data production sources.

Asnake Enawgaw Amognehegn, Asmare Belay Nigussie, Wondye Admasu Molla

Abstract This study identifies groundwater potential zones (GWPZs) in the Borkena watershed, Ethiopia, using Geographic Information Systems (GIS), remote sensing, and the Analytical Hierarchy Process (AHP). Ten critical parameters elevation, slope, drainage density, land use/land cover (LULC), rainfall, soil type, aspect, curvature, lineament density, and geology were integrated to produce a GWPZ map. The results show that areas with slopes between 0° and 6.3°, elevations below 1500 m, and specific soil and lithological characteristics exhibit high groundwater potential, covering approximately 340 km2. Additionally, 453 km2 and 867 km2 were identified as moderate and medium potential zones, respectively. Most existing studies focus on the technical and spatial aspects of delineating potential zones without explicitly addressing how these findings contribute to broader sustainability goals, particularly in the context of long-term water security, climate resilience, and socioeconomic development. This gap is significant given that effective groundwater management directly supports multiple Sustainable Development Goals (SDGs), especially SDG 6 (Clean Water and Sanitation), SDG 2 (Zero Hunger), and SDG 13 (Climate Action) But, This study underscores the effectiveness of multi-criteria decision-making (MCDM) and geospatial technologies in groundwater resource assessment and supports Sustainable Development Goals (SDGs), particularly SDG 6 (Clean Water and Sanitation), SDG 2 (Zero Hunger), and SDG 13 (Climate Action), by informing sustainable water resource planning and management.

Maria Ioana Telecan, Petru Lucian Curseu, Claudia Lenuta Rus

Purpose – We grounded this study in the Too-Much-of-a-Good-Thing (TMGT) meta-theoretical framework to disentangle the costs and benefits associated with workplace friendship in a military setting. Design/methodology/approach – We collected data cross-sectionally through self-reports from 287 employees from the Romanian Air Force. Findings – The number of friends had an inverted U-shaped association with perceived social support. Our results show that as the number of friends increases from 9 to 10, so does the social support. However, as the number of friends further increases above 10, social support tends to decrease rather than increase. Furthermore, we found that social support and all dimensions of mental well-being (emotional, social and psychological well-being) were positively associated. Moreover, social support mediated the relationship between the number of friends and the three dimensions of mental well-being. Research limitations/implications – Our findings can help human resources policies in military organizations foster an organizational climate that cultivates friendship ties between employees, which is crucial for their social support and overall mental well-being. Originality/value – This work provides additional information about the specific mechanisms through which the effects of workplace friendships on mental well-being occur.

George Kopsiaftis, Maria Kaselimi, Eftychios Protopapadakis et al.

In this work we investigate the performance of various lower-fidelity models of seawater intrusion in coastal aquifer management problems. The variable density model is considered as the high-fidelity model and a pumping optimization framework is applied on a hypothetical coastal aquifer system in order to calculate the optimal pumping rates which are used as a benchmark for the lower-fidelity approaches. The examined lower-fidelity models could be classified in two categories: (1) physics-based models, which include several widely used variations of the sharp-interface approximation and (2) machine learning assisted models, which aim to improve the efficiency of the SI approach. The Random Forest method was utilized to create a spatially adaptive correction factor for the original sharp-interface model, which improves its accuracy without compromising its efficiency as a lower-fidelity model. Both the original sharp-interface and Machine Learning assisted model are then tested in a single-fidelity optimization method. The optimal pumping rated which were calculated using the Machine Learning based SI model sufficiently approximate the solution from the variable density model. The Machine Learning assisted approximation seems to be a promising surrogate for the high-fidelity, variable density model and could be utilized in multi-fidelity groundwater management frameworks.

Indrė Čergelytė-Podgrušienė, Vida Gudžinskienė

Diabetes mellitus is a disease during which certain processes in the body which maintain a normal blood glucose concentration become imbalanced. With diabetes, the level of blood glucose increases, affecting the entire metabolism. Diabetes mellitus is becoming a leading disease in paediatric endocrinology, and causes health problems and complications that can shorten life expectancy. In Lithuania, cases of type 1 diabetes mellitus in children have been registered since 1983. More than 995 children and young people (up to 19 years of age) were registered in 2019. On average, more than 80 children are diagnosed with diabetes in the country per year. After 10–20 years, poorly controlled diabetes can cause damage not only to the endocrine system, but also to other bodily systems: it can cause the appearance of diabetic retinopathy, diabetic neuropathy, chronic kidney disease, cardiovascular diseases (stroke, ischemic heart disease, peripheral vascular diseases), infertility problems, and foot complications. Moreover, in order to keep glucose levels as optimal as possible, children with diabetes need daily insulin injections, as without them they are not able to survive. Diabetes mellitus becomes a challenge for the whole family, as the rhythm of family life changes and additional responsibilities to maintain the stability of the child’s health are assumed. In order to control the disease, children and their parents need information, skills and values that can be acquired in various educational environments. Research conducted in Lithuania and abroad is focused on the treatment of patients with diabetes, disease management, and the psychological problems experienced by parents who have learned about their child’s illness. Nonetheless, this topic has not been extensively studied from an educational perspective, and there is a lack of research that analyses the diversity of environments in which the health competences of children with diabetes mellitus can be developed. Researchers note that the involvement of children with diabetes in the process of health competence development as well as their acquisition of knowledge and skills depend on their educational environments. By providing educational functions, the shapers of educational environments convey knowledge regarding health competence, teach, offer advice and demonstrate the necessary skills, as well as form value attitudes that help children and their parents to achieve better control of the disease, which is the goal of secondary prevention. Thus, the diversity of environments for the development of health competences helps parents and their children to learn as much as possible about this chronic disease and acquire skills that enable them to properly manage its consequences. However, not all environments for the development of health competences encourage their development. Accordingly, this article aims to reveal the development of the health competences of children with diabetes mellitus in various environments. The research object is the development of the health competences of children with diabetes mellitus in various environments. The aim of the article is to reveal the development of the health competences of children with diabetes mellitus in various environments. Tasks: 1. Highlight the importance of the educational environment for education. 2) Identify environments for the development of children’s health competences. 3) Reveal how and in which environments children with diabetes mellitus develop health competences. Research questions: 1. What environments exist for the development of the health competences of children with diabetes mellitus? 2. What health competence aspects do children with diabetes mellitus develop in various environments? 3. Which educational environments are the most acceptable for children and why? Research methods. Theoretical – the analysis, summarization and systematization of scientific literature methods were used; empirical – the semi-structured interview method was used for data collection; the content analysis method was applied for the analysis of research data. Research context and participants. Semi-structured interviews with children with diabetes mellitus were conducted in the period from 5 February 2019 to 1 September 2021. Children were chosen because their health states depended on their health competences (knowledge, skills and value attitudes). In total, 7 children (4 girls and 3 boys) aged from 12 to 16 with diabetes mellitus agreed to participate in the qualitative research. The children had been diagnosed from 1 to 7 years ago and were selected according to the following criteria: 1) children with diabetes mellitus; 2) children with diabetes mellitus aged from 7 to 18 years. The analysis of the experiences of children with diabetes mellitus who participated in the research allowed six environments for the development of children’s health competences to be distinguished: medical institutions; family environments; summer/health camps; self-directed learning environments; social media; and environments involving other people with similar issues. However, it is not only the diversity of environments for the development of health competence that is important, but also how different environments encourage children with diabetes mellitus to get involved and actively develop their health competences. The analysis of research data on the importance of environments for the development of health competences in children with diabetes mellitus allowed four factors to be distinguished. The research results show that it is important for children that their educational environment: is safe and cosy; provides them with the opportunity to reveal their personalities, be themselves and express their thoughts; enables them to develop through experiences; and is organised in a manner that motivates children and includes interesting activities and creative methods. Conclusions: 1. Children with diabetes mellitus find the diversity and availability of educational environments important, since diabetes is a chronic disease and needs to be extensively controlled to avoid possible complications in the future. It is easier for children with diabetes to get involved in the process of health competence development when interesting and relevant topics are discussed, when there is mutual encouragement and interaction between the participants of the educational process, and when the child can actively engage and learn. 2. Empirical research established that children with diabetes mellitus can acquire health competences in the following environments: medical institutions; family environments; summer/health camps; self-directed learning environments; social media; and environments involving other people with similar issues. In these environments, children receive knowledge regarding type 1 diabetes mellitus, develop skills that help them to control this chronic disease, and form value attitudes and understand that health is the most important thing. 3. The research identified that it is important for children with diabetes mellitus to create various educational environments where they can fully understand their disease.

Chenxing Wang, Sayed Fayaz Ahmad, Ahmad Y.A. Bani Ahmad Ayassrah et al.

Artificial Intelligence (AI) has become essential to Electronic-Commerce technology over the past decades. Its fast growth has changed the way consumers do online shopping. Using the Technology Acceptance Model (TAM) as a theoretical framework, this research examines how AI can be made more effective and profitable in e-commerce and how entrepreneurs can make AI technology to assist in achieving their business goals. In this regard, an online survey was conducted from the online purchasers of e-commerce firms. The Partial Least Square (PLS) Smart was used to examine the data. The broadly used TAM was identified as an appropriate hypothetical model for studying the acceptance of AI technology in e-commerce. The findings of this study show that Subjective Norms positively impact Perceived Usefulness (PU) and Pursued Ease of Use (PEU), trust has a positive effect on PEU, and PEU positively impacts PU and attitudes toward use. Similarly, PU also has a positive effect on attitudes toward use and intention to use. Furthermore, the findings do not support the impact of Trust on PU and attitudes towards behavioural intention to use. Lastly, behavioural intention to use positively impacted the actual use of AI technology. This study adds theoretical and practical knowledge for adopting the TAM model in the E-commerce sector. It helps entrepreneurs to implement the TAM model in their business to use AI in a better and more appropriate way.

Aditya Pribadi Kalapaaking, Ibrahim Khalil, Mohammed Atiquzzaman

The widespread adoption of Internet of Things (IoT) devices in smart cities, intelligent healthcare systems, and various real-world applications have resulted in the generation of vast amounts of data, often analyzed using different Machine Learning (ML) models. Federated learning (FL) has been acknowledged as a privacy-preserving machine learning technology, where multiple parties cooperatively train ML models without exchanging raw data. However, the current FL architecture does not allow for an audit of the training process due to the various data-protection policies implemented by each FL participant. Furthermore, there is no global model verifiability available in the current architecture. This paper proposes a smart contract-based policy control for securing the Federated Learning (FL) management system. First, we develop and deploy a smart contract-based local training policy control on the FL participants' side. This policy control is used to verify the training process, ensuring that the evaluation process follows the same rules for all FL participants. We then enforce a smart contract-based aggregation policy to manage the global model aggregation process. Upon completion, the aggregated model and policy are stored on blockchain-based storage. Subsequently, we distribute the aggregated global model and the smart contract to all FL participants. Our proposed method uses smart policy control to manage access and verify the integrity of machine learning models. We conducted multiple experiments with various machine learning architectures and datasets to evaluate our proposed framework, such as MNIST and CIFAR-10.

Zijian Feng, Xin Chen, Zijian Lv et al.

Deep learning (DL) algorithms have been widely applied to short-term voltage stability (STVS) assessment in power systems. However, transferring the knowledge learned in one power grid to other power grids with topology changes is still a challenging task. This paper proposed a transferable DL-based model for STVS assessment by constructing the topology-aware voltage dynamic features from raw PMU data. Since the reactive power flow and grid topology are essential to voltage stability, the topology-aware and physics-informed voltage dynamic features are utilized to effectively represent the topological and temporal patterns from post-disturbance system dynamic trajectories. The proposed DL-based STVS assessment model is tested under random operating conditions on the New England 39-bus system. It has 99.99\% classification accuracy of the short-term voltage stability status using the topology-aware and physics-informed voltage dynamic features. In addition to high accuracy, the experiments show good adaptability to PMU errors. Moreover, The proposed STVS assessment method has outstanding performance on new grid topologies after fine-tuning. In particular, the highest accuracy reaches 99.68\% in evaluation, which demonstrates a good knowledge transfer ability of the proposed model for power grid topology change.

Yubo Cai, Gleb Pogudin

Quadratization refers to a transformation of an arbitrary system of polynomial ordinary differential equations to a system with at most quadratic right-hand side. Such a transformation unveils new variables and model structures that facilitate model analysis, simulation, and control and offers a convenient parameterization for data-driven approaches. Quadratization techniques have found applications in diverse fields, including systems theory, fluid mechanics, chemical reaction modeling, and mathematical analysis. In this study, we focus on quadratizations that preserve the stability properties of the original model, specifically dissipativity at given equilibria. This preservation is desirable in many applications of quadratization including reachability analysis and synthetic biology. We establish the existence of dissipativity-preserving quadratizations, develop an algorithm for their computation, and demonstrate it in several case studies.

Eddy Soeryanto Soegoto, Hayin Ananta, Ilham Zaki et al.

Implementation of a management information system using Machine Learning technology with the aim of solving the problems owned by the shop owner, namely the owner has a problem in terms of increasing sales. Observation method as data collection by making direct observations of the object under study with the relevant agencies to collect data and information related to existing problems. Research on the implementation of information systems has succeeded in increasing sales by 1.5% each month, using Machine Learning technology, a product sales recommendation system. Because based on the existing problems required an information system to assist in increasing sales. Machine Learning technology used in this research is a product recommendation system that can affect sales increase.

Zhan Tian, Xin-Yang Lyu, Huan Zou et al.

One of the key issues in climate risk management is to develop climate resilient infrastructure so as to ensure safety and sustainability of urban functioning systems as well as mitigate the adverse impacts associated with increasing climate hazards. However, conventional methods of assessing risks do not fully address the interaction of various subsystems within the city system and are unable to consolidate diverse opinions of various stakeholders on their assessments of sector-specific risks posed by climate change. To address this gap, this study advances an integrated-systems-analysis tool - Climate Risk Assessment of Infrastructure Tool (CRAIT), and applies it to analyze and compare the extent of risk factor exposure and vulnerability over time across five critical urban infrastructure sectors in Shanghai and Shenzhen, two cities that have distinctive geo-climate profiles and histories of infrastructure development. The results show significantly higher level of variation between the two cities in terms of vulnerability levels than that of exposure. More specifically, the sectors of critical buildings, water, energy, and information & communication in Shenzhen have significantly higher vulnerability levels than Shanghai in both the 2000s and the 2050s. We further discussed the vulnerability levels of subsystems in each sector and proposed twelve potential adaptation options for the roads system based on four sets of criteria: technical feasibility, flexibility, co-benefits, and policy compatibility. The application of CRAIT is bound to be a knowledge co-production process with the local experts and stakeholders. This knowledge co-production process highlights the importance of management advancements and nature-based green solutions in managing climate change risk in the future though differences are observed across the efficacy categories due to the geographical and meteorological conditions in the two cities. This study demonstrates that this knowledge co-creation process is valuable in facilitating policymakers' decision-making and their feedback to scientific understanding in climate risk assessment, and that this approach has general applicability for cities in other regions and countries.

Onur Günlü, Rafael F. Schaefer, Holger Boche et al.

The problem of secure source coding with multiple terminals is extended by considering a remote source whose noisy measurements are the correlated random variables used for secure source reconstruction. The main additions to the problem include 1) all terminals noncausally observe a noisy measurement of the remote source; 2) a private key is available to all legitimate terminals; 3) the public communication link between the encoder and decoder is rate-limited; and 4) the secrecy leakage to the eavesdropper is measured with respect to the encoder input, whereas the privacy leakage is measured with respect to the remote source. Exact rate regions are characterized for a lossy source coding problem with a private key, remote source, and decoder side information under security, privacy, communication, and distortion constraints. By replacing the distortion constraint with a reliability constraint, we obtain the exact rate region also for the lossless case. Furthermore, the lossy rate region for scalar discrete-time Gaussian sources and measurement channels is established.

Bingzhuo Zhong, Majid Zamani, Marco Caccamo

Current cyber-physical systems (CPS) are expected to accomplish complex tasks. To achieve this goal, high performance, but unverified controllers (e.g. deep neural network, black-box controllers from third parties) are applied, which makes it very challenging to keep the overall CPS safe. By sandboxing these controllers, we are not only able to use them but also to enforce safety properties over the controlled physical systems at the same time. However, current available solutions for sandboxing controllers are just applicable to deterministic (a.k.a. non-stochastic) systems, possibly affected by bounded disturbances. In this paper, for the first time we propose a novel solution for sandboxing unverified complex controllers for CPS operating in noisy environments (a.k.a. stochastic CPS). Moreover, we also provide probabilistic guarantees on their safety. Here, the unverified control input is observed at each time instant and checked whether it violates the maximal tolerable probability of reaching the unsafe set. If this probability exceeds a given threshold, the unverified control input will be rejected, and the advisory input provided by the optimal safety controller will be used to maintain the probabilistic safety guarantee. The proposed approach is illustrated empirically and the results indicate that the expected safety probability is guaranteed.

Masaki Waga, Étienne André, Ichiro Hasuo

Monitoring of hybrid systems attracts both scientific and practical attention. However, monitoring algorithms suffer from the methodological difficulty of only observing sampled discrete-time signals, while real behaviors are continuous-time signals. To mitigate this problem of sampling uncertainties, we introduce a model-bounded monitoring scheme, where we use prior knowledge about the target system to prune interpolation candidates. Technically, we express such prior knowledge by linear hybrid automata (LHAs) -- the LHAs are called bounding models. We introduce a novel notion of monitored language of LHAs, and we reduce the monitoring problem to the membership problem of the monitored language. We present two partial algorithms -- one is via reduction to reachability in LHAs and the other is a direct one using polyhedra -- and show that these methods, and thus the proposed model-bounded monitoring scheme, are efficient and practically relevant.

Levgeniia Kuzminykh, Bogdan Ghita, Stavros Shiaeles

Managing cryptographic keys can be a complex task for an enterprise and particularly difficult to scale when an increasing number of users and applications need to be managed. In order to address scalability issues, typical IT infrastructures employ key management systems that are able to handle a large number of encryption keys and associate them with the authorized requests. Given their necessity, recent years have witnessed a variety of key management systems, aligned with the features, quality, price and security needs of specific organisations. While the spectrum of such solutions is welcome and demonstrates the expanding nature of the market, it also makes it time consuming for IT managers to identify the appropriate system for their respective company needs. This paper provides a list of key management tools which include a minimum set of features, such as availability of secure database for managing keys, an authentication, authorization, and access control model for restricting and managing access to keys, effective logging of actions with keys, and the presence of an API for accessing functions directly from the application code. Five systems were comprehensively compared by evaluating the attributes related to complexity of the implementation, its popularity, linked vulnerabilities and technical performance in terms of response time and network usage. These were Pinterest Knox, Hashicorp Vault, Square Keywhiz, OpenStack Barbican, and Cyberark Conjur. Out of these five, Hachicorp Vault was determined to be the most suitable system for small businesses.