Hasil untuk "Information technology"

G. Kurizki, P. Bertet, Y. Kubo et al.

An extensively pursued current direction of research in physics aims at the development of practical technologies that exploit the effects of quantum mechanics. As part of this ongoing effort, devices for quantum information processing, secure communication, and high-precision sensing are being implemented with diverse systems, ranging from photons, atoms, and spins to mesoscopic superconducting and nanomechanical structures. Their physical properties make some of these systems better suited than others for specific tasks; thus, photons are well suited for transmitting quantum information, weakly interacting spins can serve as long-lived quantum memories, and superconducting elements can rapidly process information encoded in their quantum states. A central goal of the envisaged quantum technologies is to develop devices that can simultaneously perform several of these tasks, namely, reliably store, process, and transmit quantum information. Hybrid quantum systems composed of different physical components with complementary functionalities may provide precisely such multitasking capabilities. This article reviews some of the driving theoretical ideas and first experimental realizations of hybrid quantum systems and the opportunities and challenges they present and offers a glance at the near- and long-term perspectives of this fascinating and rapidly expanding field.

Peter Brusilovsky, A. Kobsa, W. Nejdl

Maged N. Kamel Boulos, S. Wheeler

B. Brumitt, B. Meyers, J. Krumm et al.

Ibrar Yaqoob, Ibrahim Abaker, Targio Hashem et al.

J. Geng

E. Hirsch, R. Silverstone

R. Angeles

Mei Kobayashi, Koichi Takeda

M. Siponen

R. Silverstone, L. Haddon

Роман Штонда, Світлана Паламарчук, Олена Бокій et al.

У сучасних умовах інтенсивного розвитку інформаційно-комунікаційних технологій та стрімкого зростання кількості кіберзагроз, захист кінцевих пристроїв та інформаційно-комунікаційних систем організацій набуває критичного значення. У зв’язку з цим антивірусні програмні засоби залишаються ключовим інструментом у забезпеченні кіберзахисту від шкідливого програмного забезпечення та сценаріїв цілеспрямованих атак. Однак, для вибору оптимального антивірусного програмного засобу важливо мати об’єктивний і комплексний підхід до оцінки їхніх функціональних можливостей. Метою цієї статті є розробка Комплексної методики оцінювання функціональних можливостей антивірусного програмного забезпечення. Запропонована методика враховує широкий спектр тестів, що моделюють типові та нетипові вектори проникнення шкідливого програмного забезпечення: від заражених ZIP-архівів, фішингових листів, змін системних файлів (hosts, реєстру) до виявлення Beacon-активності, автозавантажуваних скриптів, обфускованих PowerShell-команд, макросів Office-документів тощо. У дослідженні оцінюються чотири популярні антивірусні програмні засоби: ESET Endpoint Security, Avast Business Antivirus, Zillya та Windows Defender. У межах експерименту дослідницька група провела оцінювання функцій кожного антивірусного програмного засобу за 21 критерієм. Оцінювання здійснювалось у балах (0–2) із відповідною вагою критичності (1 – критична, 0.8 – висока, 0.5 – середня). Методика дозволяє визначити загальний рівень функціональності та ефективність у відсотковому значенні. Це дозволяє об’єктивно підходити до вибору антивірусного програмного засобу залежно від характеру інформаційної інфраструктури та рівня ризику. Запропонований підхід є універсальним і придатним до адаптації під інші платформи та умови, а також може бути розширений для взаємодії з системами класу Endpoint Detection and Response (Extended Detection and Response). Результати дослідження підтверджують важливість комплексного підходу до кіберзахисту, з урахуванням особливостей сучасних кібератак.

Fouzia Atlas, Yuan Yitong, Kashif Ullah Khan

Abstract Burgeoning research in data sciences demonstrates that big data analytics capability (BDAC) transforms large amounts of data into valuable knowledge and information, enhancing decision processes and improving firm performance. Nevertheless, limited research has theoretically outlined and empirically established the frameworks and constructs through which BDAC impacts the performance of small and medium enterprises (SMEs). This study adds to existing research on the relationship between BDAC and SME performance. Drawing on the dynamic capability theory, it is essential to argue how BDAC influences marketing performance (MP) and financial performance (FP), which is dependent on the intervening role of knowledge management with big data analytics talent capability (BDATC). This study highlighted the mediating role of knowledge Management (KM) and the moderating effect of Big Data Analytics Talent Capabilities (BDATC) in relation to BDAC. Based on the Conceptual model, data was collected from 379 SMEs in China using a well-designed questionnaire. Structural Equation Modeling (SEM) was employed using AMOS and SPSS for data analysis. Findings show that BDAC positively influences the firm’s financial and marketing performance. Furthermore, results confirm that KM mediates the link between a firm’s BDAC and financial and marketing performance. The findings also confirm that BDATLC significantly moderates the relationship between BDAC and financial performance while negatively moderating the relationship between BDAC and marketing performance. This study contributes to understanding the important role of human talent capability in the era of technology and big data (BDATLC), particularly regarding the talent capability for Big Data Analytics (BDA). The findings highlight the strategic significance of nurturing and retaining BDA talent to enhance the performance of SMEs.

Wenhao Cui, Zhaoxin Wang, Lei Ma

Traditional emotion decoding methods typically rely on short sequences with limited context and coarse-grained emotion categories. To address these limitations, we proposed the Semantic and Emotion Decoding Generative Pre-trained Transformer (SED-GPT), a non-invasive method for long-sequence fine-grained semantics and emotions decoding on extended narrative stimuli. Using a publicly available fMRI dataset from 8 participants, this exploratory study investigates the feasibility of reconstructing complex semantic and emotional states from brain activity. SED-GPT achieves a BERTScore-F1 of 0.650 on semantic decoding and attains a cosine similarity (CS) of 0.504 and a Jensen–Shannon similarity (JSS) of 0.469 for emotion decoding (<i>p</i> < 0.05). Functional connectivity analyses reveal persistent coupling between the language network and the emotion network, which provides neural evidence for the language–emotion interaction mechanism in Chinese. These findings should be interpreted as pilot-level feasibility evidence.

F. Troiani, M. Affronte

WANG Weizheng, QIAO Hong, LI Xiaojun, WANG Jingjing

[Purpose/Significance] Generative artificial intelligence (AI) technology has been widely used in many fields, and the application of this technology has become popular among researchers. However, there are few studies on the willingness of researchers willingness to accept generative AI. This leads to an insufficient understanding of the psychological mechanism, cognitive process and behavioral pattern of users' acceptance of generative AI, which limits the ability of theoretical innovation and practical exploration in user information behavior. This study focuses on researchers acceptance of generative AI. By studying the evaluation process of ChatGPT by college students, it explores the acceptance behavior of generative AI. At the same time, it verifies the applicability of the AIDUA model in the new context, and introduces the new variable of school identity, which further extends the AIDUA model. [Method/Process] Based on the cognitive assessment theory and the AI acceptance framework (AIDUA), this paper constructs a theoretical model of the intention to use generative artificial intelligence, and develops and empirically tests the theoretical model of the intention to use generative AI. Taking college students as the main research object, based on the maturity scale in authoritative literature at home and abroad, 8 variables and 29 observation variables such as social influence, hedonic motivation and anthropomorphism were designed. College students with experience in using generative AI were invited to participate in the questionnaire survey. SPSS26.0 was used to analyze the data from 294 valid questionnaires collected. SmartPLS 3.2.9 was used to construct a structural equation model to test the hypothesis, and the JN method was used to detect the regulatory effect. [Results/Conclusions] The study found that users went through three stages of decision making before using generative AI. The PLS-SEM results show that: 1) Social influence, hedonic motivation and anthropomorphism significantly affect performance expectancy and effort expectancy, and anthropomorphism is the strongest variable affecting performance expectancy and effort expectancy. 2) Performance expectancy and effort expectancy are significantly negatively correlated with negative emotions, while hedonic motivation has no significant effect on negative emotions. 3) Negative emotions are significantly negatively correlated with users' intension to use. 4) School identity moderates the relationship between effort expectancy and negative emotions. This study combines anthropomorphic research on college students' acceptance of generative AI, and provides a framework for the acceptance of generative AI. Researchers can use this framework to better study the acceptance of AI. This study has some limitations. In the future, we will focus on the following three aspects: 1) to evaluate the users' acceptance of generative AI in different usage scenarios. 2) to use samples of other groups to test the applicability of the model, such as civil servants, librarians, researchers and other groups. 3) to incorporate variables from other technology acceptance models into the model, such as ease of use and practicality.

Chunmei Li, Jiuyun Sun, Xinnai Zhang et al.

Abstract Digital cameras are limited by a narrow field of view and a large photosensitive unit, resulting in images with a small frame size and low resolution. This reduces the acquisition range and measurement accuracy of stereo vision in close-range photogrammetry, making it difficult to meet the requirements for precise close-range photogrammetry in high-precision industrial engineering fields, and limiting the significant development of digital close-range photogrammetry. For this reason, based on the characteristics of ground close-range photogrammetry, this paper proposes a large-format image acquisition method for rotating cameras. By designing a simple and structurally relaxed rotating camera, a rigorous seamless stitching model for large-format images is constructed, forming a large-format equivalent central projection image acquisition mechanism that meets the requirements of precise close-range photogrammetry. Finally, the effectiveness of the proposed method is verified through experiments. The results show that the proposed method effectively increases the coverage of a single camera station. The large-format image obtained through three degrees of rotation increases the image size from 916 × 687 pixels in a single image to 4977 × 671 pixels in a large-format image. This method solves the problem of the small view field of digital cameras, complementing the theory of precision close-range photogrammetry and providing necessary theoretical support for technological development in the field of precision industrial engineering.

Lucia Trastulla, Georgii Dolgalev, Sylvain Moser et al.

Abstract Stratified medicine holds great promise to tailor treatment to the needs of individual patients. While genetics holds great potential to aid patient stratification, it remains a major challenge to operationalize complex genetic risk factor profiles to deconstruct clinical heterogeneity. Contemporary approaches to this problem rely on polygenic risk scores (PRS), which provide only limited clinical utility and lack a clear biological foundation. To overcome these limitations, we develop the CASTom-iGEx approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue specific gene expression levels. The paradigmatic application of this approach to coronary artery disease or schizophrenia patient cohorts identified diverse strata or biotypes. These biotypes are characterized by distinct endophenotype profiles as well as clinical parameters and are fundamentally distinct from PRS based groupings. In stark contrast to the latter, the CASTom-iGEx strategy discovers biologically meaningful and clinically actionable patient subgroups, where complex genetic liabilities are not randomly distributed across individuals but rather converge onto distinct disease relevant biological processes. These results support the notion of different patient biotypes characterized by partially distinct pathomechanisms. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine paradigms.

Özgün Akalın, Gülfem Isiklar Alptekin

Shangzhu Jin, Sheng Yu, Jun Peng et al.

Abstract In recent years, there have been several solutions to medical image segmentation, such as U-shaped structure, transformer-based network, and multi-scale feature learning method. However, their network parameters and real-time performance are often neglected and cannot segment boundary regions well. The main reason is that such networks have deep encoders, a large number of channels, and excessive attention to local information rather than global information, which is crucial to the accuracy of image segmentation. Therefore, we propose a novel multi-branch medical image segmentation network MBSNet. We first design two branches using a parallel residual mixer (PRM) module and dilate convolution block to capture the local and global information of the image. At the same time, a SE-Block and a new spatial attention module enhance the output features. Considering the different output features of the two branches, we adopt a cross-fusion method to effectively combine and complement the features between different layers. MBSNet was tested on five datasets ISIC2018, Kvasir, BUSI, COVID-19, and LGG. The combined results show that MBSNet is lighter, faster, and more accurate. Specifically, for a $$320 \times 320$$ 320 × 320 input, MBSNet’s FLOPs is 10.68G, with an F1-Score of $$85.29\%$$ 85.29 % on the Kvasir test dataset, well above $$78.73\%$$ 78.73 % for UNet++ with FLOPs of 216.55G. We also use the multi-criteria decision making method TOPSIS based on F1-Score, IOU and Geometric-Mean (G-mean) for overall analysis. The proposed MBSNet model performs better than other competitive methods. Code is available at https://github.com/YuLionel/MBSNet .