Hasil untuk "Instruments and machines"

T. Baumeister, L. S. Marks, E. Avallone

Elena Rovenskaya, Alexey Ivanov, Sarah Hathiari et al.

Abstract Economic and social interactions are shifting to digital platforms which grow into vast ecosystems where user engagement creates value for members while ecosystem orchestrators harvest massive revenue. The digital ecosystem business model succeeds by adeptly navigating fast-changing environments, including new technologies and volatile demands, through dynamic innovation in a decentralized decision-making setting. This renders digital platform ecosystems complex adaptive systems. Recognizing that natural ecosystems are a prime example of complex adaptive systems, we propose a systematic hierarchical framework for describing and understanding digital ecosystems, rooted in ecology and evolution. Our framework compares digital ecosystems hosted by societies to natural ecosystems embedded in biomes, products to species, and technologies and elements of business strategy to the genetic makeup of a species. As digital platforms face heightened scrutiny about their socio-economic power and societal value, our approach contributes to the development of deeper understanding and sustainable governance of the digital economy.

Jiayin Wang, Weidong Zhao

Objective This study aims to develop and evaluate an autonomous surgical system based on the Toumai laparoscopic surgical robot, focusing on improving the precision and reliability of automated cutting and suturing operations. Methods The proposed system integrates several key components: (1) Robotic arms and associated control systems. (2) An endoscopic system supporting advanced visual image algorithms. (3) Specialized surgical instruments for cutting and suturing. A binocular stereo matching algorithm is employed to obtain depth information from the field of binocular camera. The DarkPose image key point localization algorithm and the Yolov5 image detection algorithm are utilized to accurately determine the positions of surgical instruments, suture needles, and target points. Additionally, an image classification discriminator is introduced to assess the success of the surgical tasks. A finite state machine model is used to guide the robotic arm's end-effector through real-time trajectory planning and execution, ensuring precise completion of surgical tasks. Results Experimental evaluation demonstrated that the autonomous system achieves high precision and reliability in both cutting and suturing tasks. Quantitative analysis shows that the system maintains an 85% success rate in automatic cutting, with a mean time of 5.10 s per cutting action. The automatic suturing task achieves a 92% accuracy rate in instrument positioning and a 90% success rate in needle grasping. Conclusion The developed system shows significant promise in automating key laparoscopic surgical tasks, with the potential to enhance surgical efficiency and improve outcomes in clinical practice. Further development and validation of this system could lead to its broader adoption in the field of autonomous surgery.

Md Javeed Khan, Mohammed Raahil Ahmed, Mohammed Abdul Aziz Taha et al.

Nanhao Liang, Xiaoyuan Yang, Yingwei Xia et al.

Abstract Panoptic Scene Graph Generation (PSG) aims to segment objects and predict the relation triplets <subject, relation, object> within an image. Despite the impressive achievements in PSG, current methods still struggle to capture fine-grained visual context, eschewing spatial and situational information in favor of visual features related to object identity. This limitation naturally impedes the model’s ability to distinguish subtle visual differences between relation triplets, such as “cat-on-person” and “cat-lying on-person”. To address this challenge, we propose CVCPSG, a novel DETR-based method that uncovers composite visual clues for PSG. Specifically, drawing inspiration from how humans capture visual context using diverse visual clues, we first construct a composite visual clues bank based on three key aspects: object, spatial, and situational. Then, we introduce a multi-level visual extractor to align visual features from objects, interactions, and image levels with the composite visual clues bank. Additionally, we incorporate a cross-modal learning module with a multitower architecture to seamlessly integrate visual clues into the relation decoder, thereby improving PSG detection. Extensive experiments on two PSG benchmarks confirm the effectiveness and interpretability of CVCPSG.

Liza Efriyanti, Ihwana As'ad

The design of curricula in Islamic universities frequently encounters difficulties in addressing the evolving needs of students, industry demands and the distinctive integration of Islamic values. Conventional methodologies are inadequate in their capacity to adapt to the evolving needs of the modern educational landscape. Furthermore, the integration of artificial intelligence (AI) in this domain remains underdeveloped, with many instances overlooking the crucial role of religious principles and institutional characteristics. This study addresses this gap by developing a Decision Support System (DSS) using Mamdani type 1 fuzzy logic, with the objective of assisting in determining an independent curriculum learning model tailored to Islamic higher education. The system incorporates a number of input variables, including student needs, industry requirements, institutional characteristics and data analysis. The output variables include an evaluation of the suitability of the learning model and a recommendation as to the most appropriate model. To illustrate, in situations where student needs are high, industry demands are moderate, institutional characteristics are high, and data analysis is moderate, the recommended model places an emphasis on balancing theoretical knowledge with practical application, while also aligning with Islamic values.  The validation of this AI-based model, utilizing 2023 historical data from five Islamic universities in West Sumatra, yielded an average Mean Absolute Error (MAE) of 0.64, thereby demonstrating good predictive accuracy. The integration of AI in this system facilitates data-driven decision-making, thereby enhancing the relevance and adaptability of the curriculum. It has the potential to improve the quality of education, support balanced student learning outcomes, and ensure alignment with Islamic principles, making it a transformative tool for curriculum development in Islamic higher education.

Jumiati Usman, Irwan Syarif, Wakhid Yunendar

Penelitian ini bertujuan merancang dan mengimplementasikan sistem informasi event dan pemesanan tiket. Fokus utama adalah memberikan informasi lengkap tentang event, memfasilitasi pemesanan tiket dengan cepat, serta meningkatkan pengalaman pengguna. Penelitian ini menggunakan metode analisis waterfall yang melibatkan pengumpulan data melalui observasi dan wawancara. Analisis kebutuhan dilakukan untuk memperoleh informasi mendalam tentang sistem informasi dan pemesanan tiket pada aplikasi ini berbasis Android. Hasil analisis ini menghasilkan lima jenis informasi penting, yaitu data lokasi, tanggal event, harga event, dan data formulir pendaftaran. Hasil penelitian adalah implementasi sukses sistem informasi event dan pemesanan tiket di aplikasi Android. Ini memberikan informasi lengkap tentang event, antarmuka pengguna yang intuitif, dan kemudahan pemesanan tiket online. Hasil penelitian menunjukkan bahwa aplikasi ini memudahkan pengguna dari segi antarmuka dan proses pemesanan tiket. Hal ini dibuktikan melalui hasil dari 21 responden, di mana 87% dari mereka menganggap aplikasi ini sangat memudahkan

R. C. Juvinall, K. M. Marshek

Zhenhai Qian, V. Belavin, V. Bokov et al.

The Jiangmen Underground Neutrino Observatory (JUNO) is an experiment designed to study neutrino oscillations. Determination of neutrino mass ordering and precise measurement of neutrino oscillation parameters $\sin^2 2\theta_{12}$, $\Delta m^2_{21}$ and $\Delta m^2_{32}$ are the main goals of the experiment. A rich physical program beyond the oscillation analysis is also foreseen. The ability to accurately reconstruct particle interaction events in JUNO is of great importance for the success of the experiment. In this work we present a few machine learning approaches applied to the vertex and the energy reconstruction. Multiple models and architectures were compared and studied, including Boosted Decision Trees (BDT), Deep Neural Networks (DNN), a few kinds of Convolution Neural Networks (CNN), based on ResNet and VGG, and a Graph Neural Network based on DeepSphere. Based on a study, carried out using the dataset, generated by the official JUNO software, we demonstrate that machine learning approaches achieve the necessary level of accuracy for reaching the physical goals of JUNO: $\sigma_E=3\%$ at $E_\text{vis}=1~\text{MeV}$ for the energy and $\sigma_{x,y,z}=10~\text{cm}$ at $E_\text{vis}=1~\text{MeV}$ for the position.

XIAORan(肖冉), ANXinlei(安新磊), QIHuimin(祁慧敏) et al.

详细分析了电场作用下四维Hindmarsh-Rose（HR）神经元模型的分岔模式及放电行为。通过数值仿真得到该神经元模型的多组双参数分岔图、最大Lyapunov指数图、峰峰间期分岔图等，发现该模型在双参数平面上存在倍周期分岔、加周期分岔等模式及“锯齿状”混沌结构。通过构建合适的目标函数，提出了自适应混合粒子群遗传算法，将神经元模型的参数辨识转化为最优化问题。数值仿真结果表明，算法对神经元模型的参数辨识效果较好，能更准确地辨识未知参数，具有一定优越性。

Mahmut Nedim Alpdemir

Robust Autoencoders separate the input image into a Signal(L) and a Noise(S) part which, intuitively speaking, roughly corresponds to a more stable background scene (L) and an undesired anomaly (or defect) (S). This property of the method provides a convenient theoretical basis for divorcing intermittent anomalies that happen to clutter a relatively consistent background image. In this paper, we illustrate the use of Robust Deep Convolutional Autoencoders (RDCAE) for defect detection, via a pseudo-supervised training process. Our method introduces synthetic simulated defects (or structured noise) to the training process, that alleviates the scarcity of true (real-life) anomalous samples. As such, we offer a pseudo-supervised training process to devise a well-defined mechanism for deciding that the defect-normal discrimination capability of the autoencoders has reached to an acceptable point at training time. The experiment results illustrate that pseudo supervised Robust Deep Convolutional Autoencoders are very effective in identifying surface defects in an efficient way, compared to state of the art anomaly detection methods.

Marco Lippi, Stefano Mariani, Matteo Martinelli et al.

K. Kollias, Christine K. Syriopoulou-Delli, Panagiotis G. Sarigiannidis et al.

According to Diagnostic and Statistical Manual of Mental Disorders, Autism spectrum disorder (ASD) is a developmental disorder characterised by reduced social interaction and communication, and by restricted, repetitive, and stereotyped behaviour. An important characteristic of autism, referred in several diagnostic tests, is a deficit in eye gaze. The objective of this study is to review the literature concerning machine learning and eye-tracking in ASD studies conducted since 2015. Our search on PubMed identified 18 studies which used various eye-tracking instruments, applied machine learning in different ways, distributed several tasks and had a wide range of sample sizes, age groups and functional skills of participants. There were also studies that utilised other instruments, such as Electroencephalography (EEG) and movement measures. Taken together, the results of these studies show that the combination of machine learning, and eye-tracking technology can contribute to autism identification characteristics by detecting the visual atypicalities of ASD people. In conclusion, machine learning and eye-tracking ASD studies could be considered a promising tool in autism research and future studies could involve other technological approaches, such as Internet of Things (IoT), as well.

Sk Md Alfayeed, B. Saini

The gait analysis is interpreted to include an overwhelming number of interrelated parameters, which, due to the high volume of data and their relationships and is difficult to implement. The integration of machine learning with biomechanics is a promising approach to simplify the evaluation. The aim of this paper is to educate readers about the key directions to implement the gait analysis with machine learning techniques. The detailed survey is based on review and implementation articles performed by numerous research scholars to detect neurological effects in gait, gait asymmetry, gait disorders, gait events, and gait activities by using supervised machine learning algorithms. This study paper also reveals the effectiveness of ML approaches for condition identification, forecasting recovery time and monitoring for clinical diagnostic instruments.

Stanislav Abaimov, Giuseppe Bianchi

Code injection is one of the top cyber security attack vectors in the modern world. To overcome the limitations of conventional signature-based detection techniques, and to complement them when appropriate, multiple machine learning approaches have been proposed. While analysing these approaches, the surveys focus predominantly on the general intrusion detection, which can be further applied to specific vulnerabilities. In addition, among the machine learning steps, data preprocessing, being highly critical in the data analysis process, appears to be the least researched in the context of Network Intrusion Detection, namely in code injection. The goal of this survey is to fill in the gap through analysing and classifying the existing machine learning techniques applied to the code injection attack detection, with special attention to Deep Learning. Our analysis reveals that the way the input data is preprocessed considerably impacts the performance and attack detection rate. The proposed full preprocessing cycle demonstrates how various machine-learning-based approaches for detection of code injection attacks take advantage of different input data preprocessing techniques. The most used machine learning methods and preprocessing stages have been also identified.

Qun Zhao, Nisuo Du, Zhi Ouyang et al.

Abstract Person re‐identification (Re‐ID) is in significant demand for intelligent security and single or multiple‐target tracking. However, there are issues in the person Re‐ID tasks, such as sharp decline in cross‐data sets detection accuracy, poor generalization and cross‐domain ability of the model. This work mainly studies the generalization and adaptation of cross‐domain person Re‐ID models. Different from most existing methods for cross‐domain Re‐ID tasks, the authors use diversified spatial semantic feature in pixel‐level learning in the target domain to improve the generality and adaptability of the model. In the case that no information of the target domain is used during the model training, the trained model is directly tested on the data set of the target domain. It has proven effective to add the attention cascade module into the backbone network combining with the part‐level branch. The authors conducted extensive experiments based on the three data sets of Market‐1501, DukeMTMC‐ReID and MSMT17, resulting in both single‐domain and cross‐domain tests with an average improvement of Rank1 and mAP values of about 10% compared with Baseline through the authors' proposed method named Part‐Level Attention Network.

David John Lemay, Paul Bazelais, Tenzin Doleck

Background: With the new pandemic reality that has beset us, teaching and learning activities have been thrust online. While much research has explored student perceptions of online and distance learning, none has had a social laboratory to study the effects of an enforced transition on student perceptions of online learning. Purpose: We surveyed students about their perceptions of online learning before and after the transition to online learning. As student perceptions are influenced by a range of contextual and institutional factors beyond the classroom, we expected that students would be overall sanguine to the project given that access, technology integration, and family and government support during the pandemic shutdown would mitigate the negative consequences. Results: Students overall reported positive academic outcomes. However, students reported increased stress and anxiety and difficulties concentrating, suggesting that the obstacles to fully online learning were not only technological and instructional challenges but also social and affective challenges of isolation and social distancing. Conclusion: Our analysis shows that the specific context of the pandemic disrupted more than normal teaching and learning activities. Whereas students generally responded positively to the transition, their reluctance to continue learning online and the added stress and workload show the limits of this large scale social experiment. In addition to the technical and pedagogical dimensions, successfully supporting students in online learning environments will require that teachers and educational technologists attend to the social and affective dimensions of online learning as well.

Jorge Enrique Lana Cisneros, Carlos López Barrionuevo, Elsy Labrada González et al.

Currently, humanity has made significant progress in the development of telecommunications and the economic, social, and health sectors; probably, in the same way, a series of pathogenic organisms have evolved considerably, causing harm to humanity. That is why Health Sciences has resorted to the technological advances offered by the industrial and telecommunications era. Among the tools of great help to combat infectious agents are statistical tools, which contribute a decisive step in advancing scientific studies aimed at communities and society. The application of Statistics in Health Sciences is essential to apply its knowledge in preventive activities, health promotion, and clinical studies. This knowledge allows students to face more complex courses and content and formulate better scientific criteria for analyzing and developing healthcare and research activities. Although a level of evidence has been achieved in the recommendations for tracking the health problems faced by the communities and the possible treatments to be applied in patients, there are still certain levels of indeterminacy in the analyzed data that generate arbitrary or discretionary opinions outside the scope of the classical statistics which can be better covered if processed by neutrosophic statistics.

Long Zhang, Chuang Zhu, YueWei Wu et al.

Abstract Ischemic stroke is the most common stroke and the leading cause of disability and death in the world. Computed tomography (CT) is a popular and economical diagnostic device for the stroke, However the ischemic stroke lesions are not evident on CT images and the diagnostic result relies on the visual observation of neurologists, which may vary from doctor to doctor. To facilitate the treatment, a computer‐aided detection algorithm on CT images is proposed to help clinician for the ischemic stroke screening. In order to obtain accurate lesion annotation on CT images, novel automatic algorithms are developed to achieve image pairing, calibration, and registration. Then, a new framework with the symmetric feature extraction and comparison is proposed to identify and locate the ischemic stroke lesion. Experimental results show that this method achieves 75% of DICE in the detection of ischemic stroke lesions, which is higher than other methods by 4%. Its competitive results compared with seven latest methods is shown in terms of extensive qualitative and quantitative evaluation. This method can accurately detect the lesion in the CT images through the comparison of symmetric regional features, which has contributed to the clinical diagnosis of ischemic stroke.

Ziwei Ye, Yuanbo Guo, A. Ju et al.

Social engineering attacks are becoming serious threats to cloud service. Social engineering attackers could get Cloud service custom privacy information or attack virtual machine images directly. Existing security analysis instruments are difficult to quantify the social engineering attack risk, resulting in invalid defense guidance for social engineering attacks. In this article, a risk analysis framework for social engineering attack is proposed based on user profiling. The framework provides a pathway to quantitatively calculate the possibility of being compromised by social engineering attack and potential loss, so as to effectively complement current security assessment instruments. The frequency of related operations is used to profile and group users for respective risk calculation, and other features such as security awareness and capability of protection mechanism are also considered. Finally, examples are given to illustrate how to use the framework in actual scenario and apply it to security assessment.