Hasil untuk "Management information systems"

B. Morrow

Jae-Nam Lee, Young-Gul Kim

D. Bowersox, D. Closs

M. Bradford, J. Florin

Jesus Silva-Rodriguez, Xingpeng Li

The alternating direction method of multipliers (ADMM) is a powerful algorithm for solving decentralized optimization problems including networked microgrid energy management (NetMEM). However, its performance is highly sensitive to the selection of its penalty parameter \r{ho}, which can lead to slow convergence, suboptimal solutions, or even algorithm divergence. This paper evaluates and compares three district ADMM formulations to solve the NetMEM problem, which explore different methods to determine appropriate stopping points, aiming to yield high-quality solutions. Furthermore, an adaptive penalty heuristic is also incorporated into each method to analyze its potential impact on ADMM performance. Different case studies on networks of varying sizes demonstrate that an objective-based ADMM approach, denominated as OB-ADMM, is significantly more robust to the choice of \r{ho}, consistently yielding solutions closer to the centralized optimal benchmark by preventing premature algorithm stopping.

Antonio Brogi, Stefano Forti, Carlos Guerrero et al.

Orchestrating next gen applications over hterogeneous resources along the Cloud-IoT continuum calls for new strategies and tools to enable scalable and application-specific managements. Inspired by the self-organisation capabilities of bacteria colonies, we propose a declarative, fully decentralised application management solution, targeting pervasive opportunistic Cloud-IoT infrastructures. We present acustomisable declarative implementation of the approach and validate its scalability through simulation over motivating scenarios, also considering end-user's mobility and the possibility to enforce application-specific management policies for different classes of applications.

Nisa Adilla Rahmatika, Fitri Arnia, Maulisa Oktiana

Facial recognition is a critical biometric identification method in modern security systems, yet it faces significant challenges under varying lighting conditions, particularly when dealing with near-infrared (NIR) images, which exhibit reduced illumination compared to visible light (VIS) images. This study aims to evaluate the performance of Convolutional Neural Networks (CNNs) in addressing the Cross-Spectral Cross-Distance (CSCD) challenge, which involves face identification across different spectra (NIR and VIS) and varying distances. Three CNN models—VGG16, ResNet50, and EfficientNetB0—were assessed using a dataset comprising 800 facial images from 100 individuals, captured at four different distances (1m, 60m, 100m, and 150m) and across two wavelengths (NIR and VIS). The Multi-task Cascaded Convolutional Networks (MTCNN) algorithm was employed for face detection, followed by image preprocessing steps including resizing to 224x224 pixels, normalization, and homomorphic filtering. Two distinct data augmentation strategies were applied: one utilizing 10 different augmentation techniques and the other with 4 techniques, trained with a batch size of 32 over 100 epochs. Among the tested models, VGG16 demonstrated superior performance, achieving 100% accuracy in both training and validation phases, with a training loss of 0.55 and a validation loss of 0.612. These findings underscore the robustness of VGG16 in effectively adapting to the CSCD setting and managing variations in both lighting and distance.

Megha Varshney, Pravesh Kumar, Musrrat Ali et al.

One of the most important tasks in handling real-world global optimization problems is to achieve a balance between exploration and exploitation in any nature-inspired optimization method. As a result, the search agents of an algorithm constantly strive to investigate the unexplored regions of a search space. Aquila Optimizer (AO) is a recent addition to the field of metaheuristics that finds the solution to an optimization problem using the hunting behavior of Aquila. However, in some cases, AO skips the true solutions and is trapped at sub-optimal solutions. These problems lead to premature convergence (stagnation), which is harmful in determining the global optima. Therefore, to solve the above-mentioned problem, the present study aims to establish comparatively better synergy between exploration and exploitation and to escape from local stagnation in AO. In this direction, firstly, the exploration ability of AO is improved by integrating Dynamic Random Walk (DRW), and, secondly, the balance between exploration and exploitation is maintained through Dynamic Oppositional Learning (DOL). Due to its dynamic search space and low complexity, the DOL-inspired DRW technique is more computationally efficient and has higher exploration potential for convergence to the best optimum. This allows the algorithm to be improved even further and prevents premature convergence. The proposed algorithm is named DAO. A well-known set of CEC2017 and CEC2019 benchmark functions as well as three engineering problems are used for the performance evaluation. The superior ability of the proposed DAO is demonstrated by the examination of the numerical data produced and its comparison with existing metaheuristic algorithms.

Meltem Mutlutürk, Martin Wynn, Bilgin Metin

Academic research on the human element in phishing attacks is essential for developing effective prevention and detection strategies and guiding policymakers to protect individuals and organizations from cyber threats. This bibliometric study offers a comprehensive overview of international research on phishing and human factors from 2006 to 2024. Analysing 308 articles from the Web of Science database, a significant increase in publications since 2015 was identified, highlighting the growing importance of this field. The study revealed influential authors such as Vishwanath and Rao, leading journals like <i>Computers & Security</i>, and key contributing institutions including Carnegie Mellon University. The analysis uncovered strong collaborations between institutions and countries, with the USA being the most prolific and collaborative. Emerging research themes focus on psychological factors influencing phishing susceptibility, user-centric security measures, and the integration of technological solutions with human behaviour insights. The findings highlight the need for increased collaboration between academia and non-academic organizations and the exploration of industry-specific challenges. These insights offer valuable guidance for researchers, practitioners, and policymakers to advance their understanding of phishing attacks, human factors, and resource allocation in this critical aspect of digitalisation, which continues to have significant impacts across business and society at large.

Inzamam Mashood Nasir, Masad A. Alrasheedi, Nasser Aedh Alreshidi

Cancer-related diseases are some of the major health hazards affecting individuals globally, especially breast cancer. Cases of breast cancer among women persist, and the early indicators of the diseases go unnoticed in many cases. Breast cancer can therefore be treated effectively if the detection is correctly conducted, and the cancer is classified at the preliminary stages. Yet, direct mammogram and ultrasound image diagnosis is a very intricate, time-consuming process, which can be best accomplished with the help of a professional. Manual diagnosis based on mammogram images can be cumbersome, and this often requires the input of professionals. Despite various AI-based strategies in the literature, similarity in cancer and non-cancer regions, irrelevant feature extraction, and poorly trained models are persistent problems. This paper presents a new Multi-Feature Attention Network (MFAN) for breast cancer classification that works well for small lesions and similar contexts. MFAN has two important modules: the McSCAM and the GLAM for Feature Fusion. During channel fusion, McSCAM can preserve the spatial characteristics and extract high-order statistical information, while the GLAM helps reduce the scale differences among the fused features. The global and local attention branches also help the network to effectively identify small lesion regions by obtaining global and local information. Based on the experimental results, the proposed MFAN is a powerful classification model that can classify breast cancer subtypes while providing a solution to the current problems in breast cancer diagnosis on two public datasets.

QIAN Mujun, YU Shunchi, LIU Chen et al.

To solve the problem of high feedback overhead in a multi-user massive multiple-input multiple-output (MIMO) system assisted by a reconfigurable intelligent surface (RIS) in frequency-division duplexing (FDD) mode, a channel state information (CSI) feedback framework based on manifold learning was proposed. Firstly, the framework achieved initial feedback overhead reduction by simplifying the CSI feedback process. Then, the framework combined the manifold learning to train two set of dictionaries to achieve dimension reduction and reconstruction of incremental CSI. Finally, the original channel was restored at the base station. The simulation results show that the CSI feedback scheme proposed in this paper has lower overhead and complexity than the existing methods in the multi-user and limited scattering environment, and the reconstruction quality is significantly improved.

Alessandra Scaravilli, Mario Tranfa, Giuseppe Pontillo et al.

(1) Background: Although MRI is a well-established tool in Multiple Sclerosis (MS) diagnosis and management, neuroradiological reports often lack standardization and/or quantitative information, with possible consequences in clinical care. The aim of this study was to evaluate the impact of information provided by neuroradiological reports and different reporting systems on the clinical management of MS patients. (2) Methods: An online questionnaire was proposed to neurologists working in Italian tertiary care level MS centers. Questions assessed the impact of different MRI-derived biomarkers on clinical choices, the preferred way of receiving radiological information, and the neurologists’ opinions about different reporting systems and the use of automated software in clinical practice. (3) Results: The online survey was completed by 62 neurologists. New/enlarging (100%) lesions, the global T2w/FLAIR lesion load (96.8%), and contrast-enhancing (95.2%) lesions were considered the most important biomarkers for therapeutic decision, while new/enlarging lesions (98.4%), global T2w/FLAIR lesion load (96.8%), and cerebral atrophy (90.3%) were relevant to prognostic evaluations. Almost all participants (98.4%) considered software for medical imaging quantification helpful in clinical management, mostly in relation to prognostic evaluations. (4) Conclusions: These data highlight the impact of providing accurate and reliable data in neuroradiological reports. The use of software for medical imaging quantification in MS can be helpful to standardize radiological reports and to provide useful clinical information to neurologists.

Xiaokun Zhang, Bo Xu, Chenliang Li et al.

The session-based recommendation (SBR) garners increasing attention due to its ability to predict anonymous user intents within limited interactions. Emerging efforts incorporate various kinds of side information into their methods for enhancing task performance. In this survey, we thoroughly review the side information-driven session-based recommendation from a data-centric perspective. Our survey commences with an illustration of the motivation and necessity behind this research topic. This is followed by a detailed exploration of various benchmarks rich in side information, pivotal for advancing research in this field. Moreover, we delve into how these diverse types of side information enhance SBR, underscoring their characteristics and utility. A systematic review of research progress is then presented, offering an analysis of the most recent and representative developments within this topic. Finally, we present the future prospects of this vibrant topic.

Yang LIU, Anming DONG, Jiguo YU et al.

Human activity sensing based on Wi-Fi channel state information (CSI) has an important application prospect in future intelligent interaction scenarios such as virtual reality, intelligent games, and the metaverse.Accurate sensing of complex and continuous human activities is an important challenge for Wi-Fi sensing.Convolutional neural network (CNN) has the ability of spatial feature extraction but is poor at modeling the temporal features of the data.While long short-term memory (LSTM) network or gated recurrent unit (GRU) network, which are suitable for modeling time-series data, neglect learning spatial features of data.In order to solve this problem, an improved CNN that integrates bidirectional gated recurrent unit (BiGRU) network was proposed.The bi-directional feature extraction ability of BiGRU was used to capture the correlation and dependence of the front and back information in the time series data.The extraction of the spatiotemporal features of the time series CSI data was realized, and then the mapping relationship between the action and the CSI data was present.Thus the recognition accuracy of the complex continuous action was improved.The proposed network structure was tested with basketball actions.The results show that the recognition accuracy of this method is above 95% under various conditions.Compared with the traditional multi-layer perceptron (MLP), CNN, LSTM, GRU, and attention based bidirectional long short-term memory (ABLSTM) baseline methods, the recognition accuracy has been improved by 1%～20%.

Surwanti Arni, Purwaningsih Tutik

Persons with disabilities face a higher risk during disasters. Socioeconomic and physical vulnerabilities make them more vulnerable to disasters. Unfortunately, persons with disabilities tend to be overlooked in emergency preparedness systems. This fact causes persons with disabilities lack an understanding of disasters and how to overcome them. This community service program is conducted in Kepuharjo Village, a village prone to the “Merapi” volcano disaster. The programs implemented ensure that persons with disabilities are subjects in disaster preparedness. Activities are carried out by making innovations by realizing inclusive disaster risk reduction. The result of the innovation programs is the management information systems, disability data, and village disability groups. There is the increasing knowledge of village governments, disaster resilient village teams, persons with disabilities to obtain information and about disasters; there is standard operating inclusive evacuation and rescue procedure; there is advocacy for the availability of accessible refugee barracks. The village already had a policy and allocated a budget for providing protection and fulfilling the rights of persons with disabilities. The sister village also has an inclusive disaster preparedness program.

Kenji Kawaguchi, Zhun Deng, Xu Ji et al.

Numerous deep learning algorithms have been inspired by and understood via the notion of information bottleneck, where unnecessary information is (often implicitly) minimized while task-relevant information is maximized. However, a rigorous argument for justifying why it is desirable to control information bottlenecks has been elusive. In this paper, we provide the first rigorous learning theory for justifying the benefit of information bottleneck in deep learning by mathematically relating information bottleneck to generalization errors. Our theory proves that controlling information bottleneck is one way to control generalization errors in deep learning, although it is not the only or necessary way. We investigate the merit of our new mathematical findings with experiments across a range of architectures and learning settings. In many cases, generalization errors are shown to correlate with the degree of information bottleneck: i.e., the amount of the unnecessary information at hidden layers. This paper provides a theoretical foundation for current and future methods through the lens of information bottleneck. Our new generalization bounds scale with the degree of information bottleneck, unlike the previous bounds that scale with the number of parameters, VC dimension, Rademacher complexity, stability or robustness. Our code is publicly available at: https://github.com/xu-ji/information-bottleneck

R. Chenhall, D. Morris

ABSTRACT: This paper examines the effect of structural decentralization, perceived environmental uncertainty, and organizational interdependence on Management Accounting Systems [MAS] design. MAS design was defined in terms of the perceived usefulness of several information characteristics which may be associated with an MAS. These characteristics were scope, timeliness, level of aggregation, and information which assists integration. In addition to examining the direct effects of contextual variables, the study sought to determine how the independent variables interacted. Hypotheses were generated for both direct and indirect effects of contextual variables and were tested using data collected from 68 managers. The findings indicated that: 1) Decentralization was associated with a preference for aggregated and integrated information; perceived environmental uncertainty with broad scope and timely information; organizational interdependence with broad scope, aggregated, and integrated information. 2) The effects of perceived environmental uncertainty and organizational interdependence were, in part, indirect through their association with decentralization.

Tiara Lestari Subaran, Transmissia Semiawan, Nurjannah Syakrani

Background: A calorie estimation system based on food images uses computer vision technology to recognize and count calories. There are two key processes required in the system: detection and segmentation. Many algorithms can undertake both processes, each algorithm with different levels of accuracy. Objective: This study aims to improve the accuracy of calorie calculation and segmentation processes using a combination of Mask R-CNN and GrabCut algorithms. Methods: The segmentation mask generated from Mask R-CNN and GrabCut were combined to create a new mask, then used to calculate the calorie. By considering the image augmentation technique, the accuracy of the calorie calculation and segmentation processes were observed to evaluate the method’s performance. Results: The proposed method could achieve a satisfying result, with an average calculation error value of less than 10% and an F1 score above 90% in all scenarios. Conclusion: Compared to earlier studies, the combination of Mask R-CNN and GrabCut could obtain a more satisfying result in calculating food calories with different shapes. Keywords: Augmentation, Calorie Calculation, Detection

Abd Muhith, St. Mislikhah, Erma Fatmawati et al.

This paper describes total quality management and its impact on the effectiveness of the academic system at KH Achmad Siddiq State Islamic University Jember. This research uses a qualitative case study approach, using interviews, observation, and documentation as data collection techniques. Participants in it are leaders, lecturers, staff, and students with the purposive sampling technique. The data analysis uses Milles and Huberman's concepts, namely data reduction, data presentation, and conclusions. The results showed that the impact of total quality management and the effectiveness of the academic system was; 1) Using friendly, namely students and lecturers as users in this research, it becomes easier to use technology-based information systems, not complicated in using and getting services regarding academic matters; 2) Ease of access to information, namely making it easier for students to access information about academics; 3) Faster use, due to managerial processes that get more attention, students and lecturers feel faster in digging up information through accounts that have been provided with accurate results; 4) Public services faster, managerial processes that run more effectively and consistently as well as the presence of experts who manage them make students feel satisfied with college academic services.

Siw Lundqvist

Project management offices (PMOs) are frequently referred to as necessary, or even indispensable, for carrying out projects in multi-project settings, which often occur in public authorities’ Information Technology (IT) projects; particularly in times of today’s sweeping digitalization. Hence, this research studied Swedish public authorities and their IT departments’ use of PMOs; a survey was directed to IT project managers. Findings showed that even though PMOs are commonly described as significant, those that applied PMOs were fewer than those that did not. This research searched for correlations between the existence of PMOs and 88 variables that resulted in relatively few, mostly weak correlations. A hypothesis test did not show significant association between PMOs’ usage and project models’ usage. The research contributions are principally that PMOs do not appear to be that significant after all for Swedish public authorities, and to have reasonable expectations on PMOs. For practice, the implications foremost concern the importance of understanding conceivable pros and cons.