Hasil untuk "Information technology"

Dongli Deng, Sen Zhao, Meixia Miao

Local Differential Privacy (LDP) and its personalized variants (PLDP) have been widely used for privacy-preserving data analytics. However, existing schemes often enforce a uniform indistinguishability level among users, failing to accommodate the nuanced privacy needs of diverse individuals. To address this, we propose User-Distinguished Local Differential Privacy (UDPLDP), a novel framework that formalizes user-level distinguishability to support more flexible, non-uniform privacy budgets. Under this framework, we tackle the fundamental task of frequency range queries, namely UDPLDP-Tree, which overcomes the challenge due to limited user-level distinguishability, insufficient robustness in estimation under complex data distributions, and the assumption of uniform privacy requirements across different attributes in existing multi-dimensional schemes. To demonstrate the effectiveness, we conduct extensive experiments and the results show that UDPLDP-Tree reduces the mean squared error (MSE) by about 30–50% compared with a recent state-of-the-art baseline.

Mariana Aristizábal

The COVID-19 pandemic shocked the world in 2020, altering almost every aspect of daily life. One of the areas that suffered the most during the pandemic was the education system. As urban areas transitioned to online platforms and software, rural towns lacked the technological resources to handle internet connectivity challenges that deepened the crisis. One such example is the city of Leticia, the capital of the Amazonas Department in Colombia. Located in the southern part of the country, Leticia can only be accessed by flight or boat. In 2020, Leticia was already facing significant educational inequalities, and teachers and students alike struggled with remote learning due to the limited access to technology and internet connectivity. Established offline teaching practices were barely modified for remote learning and the crisis was aggravated when the rapid spread of COVID-19 impacted entire families and communities. Unable to work and already facing financial issues that hindered access to food and services, residents witnessed the death of loved ones and community leaders before the arrival of vaccinations. Now that in-person classes have resumed, it is worthwhile to analyze the implications of the last year´s gap in students’ learning process, the role of administrators through government initiatives, and the current challenges that teachers and students face in their new classroom reality. This article provides valuable information to understand the urgent needs of the educational community in Leticia in the Post-COVID scenario.

S. Vijayan, Chiranji Lal Chowdhary

Abstract The agricultural industry significantly relies on autonomous systems for detecting and analyzing rice diseases to minimize financial and resource losses, reduce yield reductions, improve processing efficiency, and ensure healthy crop production. Advances in deep learning have greatly enhanced disease diagnostic techniques in agriculture. Accurate identification of rice plant diseases is crucial to preventing the severe consequences these diseases can have on crop yield. Current methods often struggle with reliably diagnosing conditions and detecting issues in leaf images. Previously, leaf segmentation posed challenges, and while analyzing complex disease stages can be effective, it is computationally intensive. Therefore, segmentation methods need to be more accurate, cost-effective, and reliable. To address these challenges, we propose a hybrid bio-inspired algorithm, named the Hybrid WOA_APSO algorithm, which merges Adaptive Particle Swarm Optimization (APSO) with the Whale Optimization Algorithm (WOA). For disease classification in rice crops, we utilize a Convolutional Neural Network (CNN). Multiple experiments are conducted to evaluate the performance of the proposed model using benchmark datasets (Plantvillage), with a focus on feature extraction, segmentation, and preprocessing. Optimizing feature selection is a critical factor in enhancing the classification algorithm’s accuracy. We compare the accuracy, sensitivity, and specificity of our model against industry-standard techniques such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and conventional CNN models. The experimental results indicate that the proposed hybrid approach achieves an impressive accuracy of 97.5% (Refer Table 8), which could inspire further research in this field.

Qiguang Li, Xiru Zheng, Yu He et al.

Aiming at the existing high-temperature liquid metal flow online accurate measurement by the metal melt characteristics, installation space, and high-temperature environment adaptability limitations, this paper innovatively puts forward a soft measurement method based on the impact force generated in the fluid flow process as an observational variable series. Fluid mechanics theory and simulation software are used to analyze and verify the feasibility of the impact force as an observable variable to measure the flow rate, followed by the construction of the CNN-LSTM-CNN-Double (CLCD) flow measurement model of impact force and flow rate based on the parameters of the learning rate and the number of training times, and finally the construction of a test platform for the flow measurement, and the validity of the method is verified through actual operation.

Aseel Alsaedi, Abeer Alhuzali, Omaimah Bamasag

Kostiantyn Fuks

The purpose of this article is to provide a comprehensive examination of the informational and organisational capabilities of marketing activities in the market for digital products and services. It highlights the importance of data analysis, web analytics and technology partnerships for success in the digital marketplace. It also examines modern organisational strategies to help IT companies effectively implement marketing initiatives and adapt quickly to changing business landscapes. Methodology. This article is based on a theoretical and methodological review of the existing scientific literature on digital technologies, the marketing of digital products and services, and an overview of current technological and organisational solutions in the digital field. In addition, it includes a survey of marketing managers from renowned IT companies with the aim of delineating the typology of organisational structures within marketing departments. Results. Information delivery, data analytics, monitoring tools and web analytics are critical to digital marketing in IT organisations, facilitating the collection and analysis of data from multiple sources such as websites, social media and CRM systems. By leveraging big data and machine learning algorithms, it is possible to identify complex dependencies and predict consumer behaviour. Technological partnerships and collaborations with startups are becoming increasingly important for IT companies' marketing efforts, providing access to fresh ideas, technologies and a competitive edge. Organisational structures in the marketing departments of IT companies emphasise agility and cross-functional teamwork, often using agile methodologies. This promotes adaptability to market changes. Marketing structures typically include inbound approaches, flexible growth-oriented setups, and streamlined hierarchies. Practical implications. These marketing tools and organisational methods are recommended for implementation in the marketing departments of IT companies. The correlation between informational and organisational capabilities contributes to the achievement of marketing goals and the competitive advantage of IT companies in the marketplace. Scrum and Kanban, widely used agile frameworks, are not limited to technology companies but are also common in financial services and retail. Value / Оriginality. In the context of the ongoing military conflict, successful operation of Ukrainian IT companies in the modern world requires not only technological superiority, but also effective marketing and a well-organised internal structure. To accelerate the recovery of the Ukrainian IT sector and improve existing practices, the following recommendations have been made.

Mario Hidalgo

The technological revolution experienced in today’s society has led educational organisations to assume the responsibility of training skills and abilities for this new context. Despite the high degree of complexity within technology-mediated ecosystems, the scientific literature seems to agree that the development of teachers' digital competence is identified to be a key variable of success. The definition of digital competence is however not clearly delineated. Moreover, direct and indirect interactions within this technology-mediated teaching and learning process are not clearly identified. The objective of this study is to define the concept of Digital Teaching Competence through a Systematic Literature Review published between 2017 and 2022, using the Scopus and Dialnet databases. A total of 316 references in English and Spanish were identified, of which 32 were selected for the final analysis, following the PRISMA protocol guidelines. The results confirm the existence of a high degree of conceptual fragmentation, as well as the lack of agreement on the terminology to be used. There is a need to reach a consensus on a taxonomy that facilitates, on one hand, the analysis of the elements that make up Digital Teaching Competence, and on the other hand, the improvement of the capacity to analyse specific contextual variables that contribute to enhancing technology-mediated teaching and learning processes.

Liaqat Ali, Wiqar H. Shah, Akhtar Ali et al.

Pellets of Pure and Fe-doped dilute magnetic semiconducting (DMS) samples are studied for bulk magneto-resistance (BMR) at room temperature and at low-temperatures ∼100K. Raman-, photoluminescence- and X-ray photoelectron-spectroscopic techniques are used to determine chemical and electronic structures of the samples. A broadband intense yellow-green-orange luminescence is observed in Fe-doped ZnO samples and emission red-shifts are investigated. Electrical transport is studied with and without applied magnetic field up to 9T and thermal activation and hopping modes of conduction is discussed in light of nature of Fe substitution in the host lattice. Several decremental- to incremental-BMR crossovers are obtained corresponding to experimental variables of Fe concentration 0.025 ≤ x ≤ 0.1, temperature and applied magnetic field. Several possible modes of magneto-transport are discussed to further elucidate the origin of the as-found BMR crossovers in our samples. Positive BMR in pure- and highly doped (x ≥ 0.1) ZnO is found to originate from F-centers and thermal fluctuations, respectively. However, modestly doped (x ≤ 0.05) ZnO exhibit thermally activated conduction and magnetic poloron mediated negative BMR.

Zijian Wang, Haimei Lu, Haixin Yan et al.

Abstract Diabetic Retinopathy (DR) is a major cause of blindness worldwide. Early detection and treatment are crucial to prevent vision loss, making accurate and timely diagnosis critical. Deep learning technology has shown promise in the automated diagnosis of DR, and in particular, multi-lesion segmentation tasks. In this paper, we propose a novel Transformer-based model for DR segmentation that incorporates hyperbolic embeddings and a spatial prior module. The proposed model is primarily built on a traditional Vision Transformer encoder and further enhanced by incorporating a spatial prior module for image convolution and feature continuity, followed by feature interaction processing using the spatial feature injector and extractor. Hyperbolic embeddings are used to classify feature matrices from the model at the pixel level. We evaluated the proposed model’s performance on the publicly available datasets and compared it with other widely used DR segmentation models. The results show that our model outperforms these widely used DR segmentation models. The incorporation of hyperbolic embeddings and a spatial prior module into the Vision Transformer-based model significantly improves the accuracy of DR segmentation. The hyperbolic embeddings enable us to better capture the underlying geometric structure of the feature matrices, which is important for accurate segmentation. The spatial prior module improves the continuity of the features and helps to better distinguish between lesions and normal tissues. Overall, our proposed model has potential for clinical use in automated DR diagnosis, improving accuracy and speed of diagnosis. Our study shows that the integration of hyperbolic embeddings and a spatial prior module with a Vision Transformer-based model improves the performance of DR segmentation models. Future research can explore the application of our model to other medical imaging tasks, as well as further optimization and validation in real-world clinical settings.

Fenglei Li, Qiaoyu Hu, Xianglei Zhang et al.

The rational design of PROTACs is difficult due to their obscure structure-activity relationship. Here the authors present a deep neural network model - DeepPROTACs - for predicting the degradation capacity of a proposed PROTAC molecule.

Pedro Teixidó, Juan Antonio Gómez-Galán, Rafael Caballero et al.

Perimeter detection systems detect intruders penetrating protected areas, but modern solutions require the combination of smart detectors, information networks and controlling software to reduce false alarms and extend detection range. The current solutions available to secure a perimeter (infrared and motion sensors, fiber optics, cameras, radar, among others) have several problems, such as sensitivity to weather conditions or the high failure alarm rate that forces the need for human supervision. The system exposed in this paper overcomes these problems by combining a perimeter security system based on CEMF (control of electromagnetic fields) sensing technology, a set of video cameras that remain powered off except when an event has been detected. An autonomous drone is also informed where the event has been initially detected. Then, it flies through computer vision to follow the intruder for as long as they remain within the perimeter. This paper covers a detailed view of how all three components cooperate in harmony to protect a perimeter effectively, without having to worry about false alarms, blinding due to weather conditions, clearance areas, or privacy issues. The system also provides extra information of where the intruder is or has been, at all times, no matter whether they have become mixed up with more people or not during the attack.

Hiroyuki Tomita, Kunio Kutsuwada, Masahisa Kubota et al.

The reliability of surface net heat flux data obtained from the latest satellite-based estimation [the third-generation Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations (J-OFURO3, V1.1)] was investigated. Three metrics were utilized: (1) the global long-term (30 years) mean for 1988–2017, (2) the local accuracy evaluation based on comparison with observations recorded at buoys located at 11 global oceanic points with varying climatological characteristics, and (3) the physical consistency with the freshwater balance related to the global water cycle. The globally averaged value of the surface net heat flux of J-OFURO3 was −22.2 W m−2, which is largely imbalanced to heat the ocean surface. This imbalance was due to the turbulent heat flux being smaller than the net downward surface radiation. On the other hand, compared with the local buoy observations, the average difference was −5.8 W m−2, indicating good agreement. These results indicate a paradox of the global surface net heat flux. In relation to the global water cycle, the balance between surface latent heat flux (ocean evaporation) and precipitation was estimated to be almost 0 when river runoff from the land was taken into consideration. The reliability of the estimation of the latent heat flux was reconciled by two different methods. Systematic ocean-heating biases by surface sensible heat flux (SHF) and long wave radiation were identified. The bias in the SHF was globally persistent and especially large in the mid- and high latitudes. The correction of the bias has an impact on improving the global mean net heat flux by +5.5 W m−2. Furthermore, since J-OFURO3 SHF has low data coverage in high-latitudes areas containing sea ice, its impact on global net heat flux was assessed using the latest atmospheric reanalysis product. When including the sea ice region, the globally averaged value of SHF was approximately 1.4 times larger. In addition to the bias correction mentioned above, when assuming that the global ocean average of J3 SHF is 1.4 times larger, the net heat flux value changes to the improved value (−11.3 W m−2), which is approximately half the original value (−22.2 W m−2).

Zhuoyun Jiang, Yu Lei, Liqiong Zhang et al.

Microvascular imaging based on indocyanine green is an important tool for surgeons who carry out extracranial–intracranial arterial bypass surgery. In terms of blood perfusion, indocyanine green images contain abundant information, which cannot be effectively interpreted by humans or currently available commercial software. In this paper, an automatic processing framework for perfusion assessments based on indocyanine green videos is proposed and consists of three stages, namely, vessel segmentation based on the UNet deep neural network, preoperative and postoperative image registrations based on scale-invariant transform features, and blood flow evaluation based on the Horn–Schunck optical flow method. This automatic processing flow can reveal the blood flow direction and intensity curve of any vessel, as well as the blood perfusion changes before and after an operation. Commercial software embedded in a microscope is used as a reference to evaluate the effectiveness of the algorithm in this study. A total of 120 patients from multiple centers were sampled for the study. For blood vessel segmentation, a Dice coefficient of 0.80 and a Jaccard coefficient of 0.73 were obtained. For image registration, the success rate was 81%. In preoperative and postoperative video processing, the coincidence rates between the automatic processing method and commercial software were 89 and 87%, respectively. The proposed framework not only achieves blood perfusion analysis similar to that of commercial software but also automatically detects and matches blood vessels before and after an operation, thus quantifying the flow direction and enabling surgeons to intuitively evaluate the perfusion changes caused by bypass surgery.