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The Final Volume of the Groundbreaking Trilogy on Agent-Based Modeling In this pioneering synthesis, Joshua Epstein introduces a new theoretical entity: Agent_Zero. This software individual, or \"agent,\" is endowed with distinct emotional/affective, cognitive/deliberative, and social modules. Grounded in contemporary neuroscience, these internal components interact to generate observed, often far-from-rational, individual behavior. When multiple agents of this new type move and interact spatially, they collectively generate an astonishing range of dynamics spanning the fields of social conflict, psychology, public health, law, network science, and economics. Epstein weaves a computational tapestry with threads from Plato, Hume, Darwin, Pavlov, Smith, Tolstoy, Marx, James, and Dostoevsky, among others. This transformative synthesis of social philosophy, cognitive neuroscience, and agent-based modeling will fascinate scholars and students of every stripe. Epstein's computer programs are provided in the book or on its Princeton University Press website, along with movies of his \"computational parables.? Agent_Zero is a signal departure in what it includes (e.g., a new synthesis of neurally grounded internal modules), what it eschews (e.g., standard behavioral imitation), the phenomena it generates (from genocide to financial panic), and the modeling arsenal it offers the scientific community. For generative social science, Agent_Zero presents a groundbreaking vision and the tools to realize it.

This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks. Representing much of the foundational research in this field, the book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding Web-scale data. Sep Kamvar focuses on eigenvector-based techniques in Web search, introducing a personalized variant of Google's PageRank algorithm, and he outlines algorithms--such as the now-famous quadratic extrapolation technique--that speed up computation, making personalized PageRank feasible. Kamvar suggests that Power Method-related techniques ultimately should be the basis for improving the PageRank algorithm, and he presents algorithms that exploit the convergence behavior of individual components of the PageRank vector. Kamvar then extends the ideas of reputation management and personalized search to distributed networks like peer-to-peer and social networks. He highlights locality and computational considerations related to the structure of the network, and considers such unique issues as malicious peers. He describes the EigenTrust algorithm and applies various PageRank concepts to P2P settings. Discussion chapters summarizing results conclude the book's two main sections. Clear and thorough, this book provides an authoritative look at central innovations in search for all of those interested in the subject.

According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents’ physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.

In recent years, there has been a remarkable surge in the adoption of open-source software (OSS). However, with the growing usage of OSS components in both free and proprietary software, vulnerabilities that are present within them can be spread to a vast array of underlying applications. Even worse, a myriad of vulnerabilities are fixed secretly via patch commits, which causes other software re-using the vulnerable code snippets to be left in the dark. Thus, source code patch commit mining toward vulnerability discovery is receiving immense attention, and a variety of approaches are proposed. Despite that, there is no comprehensive survey summarizing and discussing the current progress within this field. To fill this gap, we survey, evaluate, and systematize a list of literature and provide the community with our insights on both successes and remaining issues in this space. Special attention is paid on the work toward vulnerability discovery. In this paper, we also provide an introductory panorama with our replicable hands-on experience, which can help readers quickly understand and step into the pertinent field. Our empirical study reveals noteworthy challenges which need to be highlighted and addressed in this field. We also discuss potential directions for the future work. To the best of knowledge, we provide the first literature review to study source code patch commit mining in the vulnerability discovery context. The systematic framework, hands-on practices, and list of potential challenges provide new knowledge for mining source code patch commit toward a more robust software eco-system. The research gaps found in this literature review show the need for future research, such as the concern on data quality, high false alarms, and the significance of textual information.

Facial expression recognition has made a significant progress as a result of the advent of more and more convolutional neural networks (CNN). However, with the improvement of CNN, the models continues to get deeper and larger so as to a greater focus on the high-level features of the image and the low-level features tend to be lost. Because of the reason above, the dependence of low-level features between different areas of the face often cannot be summarized. In response to this problem, we propose a novel network based on the CNN model. To extract long-range dependencies of low-level features, multiple attention mechanisms has been introduced into the network. In this paper, the patch attention mechanism is designed to obtain the dependence between low-level features of facial expressions firstly. After fusion, the feature maps are input to the backbone network incorporating convolutional block attention module (CBAM) to enhance the feature extraction ability and improve the accuracy of facial expression recognition, and achieve competitive results on three datasets CK+ (98.10%), JAFFE (95.12%) and FER2013 (73.50%). Further, according to the PA Net designed in this paper, a hardware friendly implementation scheme is designed based on memristor crossbars, which is expected to provide a software and hardware co-design scheme for edge computing of personal and wearable electronic products.

In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server. A machine learning (CatBoost)-based ECG classification method was proposed to detect AF in the cloud server. In case of detected AF, the cloud server pushed the ECG data and classification results to the web browser of a doctor. Finally, the Android APP displayed the doctor’s diagnosis for the ECG signals. Experimental results showed the proposed CatBoost classifier trained with 17 selected features achieved an overall F1 score of 0.92 on the test set (n = 7270). The proposed wearable ECG monitoring system may potentially be useful for long-term ECG telemonitoring for AF detection.

The research work proposed for the X-band microstrip line-based magnetic resonance imaging (MRI) coils has been accomplished with coplanar waveguide feeding and ha highlighted the design parameters to be employed in the internet of medical things (IoMT) features. The proposed research has focused on the wireless body area networks (WBAN) phenomenon in simulated human organs. It has been employed to study the electro-magnetic (EM) parameters of the simulated human organ and the functioning of wearable MRI coils on the human body. Therefore, these coils have been configured in triangle-shaped hierarchical structures, and each layer has been printed on both sides of the conductive strips. These proposed coils utilize a Bakelite substrate with a 1.6-mm thickness and an equivalent dielectric strength of 1.2. It has 69.9 × 85.2 × 1.6 mm3 dimensions and was fabricated using microwave integrated circuits (MIC). These coils have been generated at 8 GHz and this spectrum has been justified with the microwave X band (8–12 GHz) using the standard measured results. Hence, these coils have demonstrated 45.81-dB signal attenuation with a 1-dB standing wave ratio (SWR). Therefore, this research has extended to the different kinds of virtual simulation scenarios in diagnosis applications. Additionally, the research delves into the electromagnetic characteristics, encompassing electric and magnetic fields, the specific absorption ratio (SAR), and temperature. These characteristics are thoroughly analyzed using MRI phantom models within virtual environments. As a result of this comprehensive analysis, the suitability and efficacy of these MRI coils have met rigorous standards. These coils are highly demanded by complicated systems functioning in these bands for IoMT and MRI diagnosis applications.

Background As one of the major complications of diabetes, diabetic retinopathy (DR) is a leading cause of visual impairment and blindness due to delayed diagnosis and intervention. Microaneurysms appear as the earliest symptom of DR. Accurate and reliable detection of microaneurysms in color fundus images has great importance for DR screening. Methods A microaneurysms' detection method using machine learning based on directional local contrast (DLC) is proposed for the early diagnosis of DR. First, blood vessels were enhanced and segmented using improved enhancement function based on analyzing eigenvalues of Hessian matrix. Next, with blood vessels excluded, microaneurysm candidate regions were obtained using shape characteristics and connected components analysis. After image segmented to patches, the features of each microaneurysm candidate patch were extracted, and each candidate patch was classified into microaneurysm or non-microaneurysm. The main contributions of our study are (1) making use of directional local contrast in microaneurysms' detection for the first time, which does make sense for better microaneurysms' classification. (2) Applying three different machine learning techniques for classification and comparing their performance for microaneurysms' detection. The proposed algorithm was trained and tested on e-ophtha MA database, and further tested on another independent DIARETDB1 database. Results of microaneurysms' detection on the two databases were evaluated on lesion level and compared with existing algorithms. Results The proposed method has achieved better performance compared with existing algorithms on accuracy and computation time. On e-ophtha MA and DIARETDB1 databases, the area under curve (AUC) of receiver operating characteristic (ROC) curve was 0.87 and 0.86, respectively. The free-response ROC (FROC) score on the two databases was 0.374 and 0.210, respectively. The computation time per image with resolution of 2544×1969, 1400×960 and 1500×1152 is 29 s, 3 s and 2.6 s, respectively. Conclusions The proposed method using machine learning based on directional local contrast of image patches can effectively detect microaneurysms in color fundus images and provide an effective scientific basis for early clinical DR diagnosis.

Target-aware Transformer (TaT) knowledge distillation effectively extracts information from intermediate layers but faces high computational costs for large feature maps. While the non-overlapping Patch-group distillation in TaT reduces complexity, it loses boundary information, affecting accuracy. We propose an improved Shifted Windows Target-aware Transformer (Swin TaT) knowledge distillation method, utilizing a hierarchical shift window strategy to preserve boundary information and balance computational efficiency. Our multi-scale approach optimizes Patch-group distillation with dynamic adjustment, ensuring effective local and global feature transfer. This flexible and efficient design enhances distillation performance, addressing previous limitations. The proposed Swin TaT method demonstrates exceptional performance across various architectures, with ResNet18 as the student network. It achieves 73.03% Top-1 accuracy on ImageNet1K, surpassing the SOTA by 1.06% while reducing parameters to approximately 46% less, and improves mIoU by 2.13% on COCOStuff10k.

Voltage sensors regulate the conformations of voltage-dependent ion channels and enzymes. Their nearly switchlike response as a function of membrane voltage comes from the movement of positively charged amino acids, arginine or lysine, across the membrane field. We used mutations with natural and unnatural amino acids, electrophysiological recordings, and x-ray crystallography to identify a charge transfer center in voltage sensors that facilitates this movement. This center consists of a rigid cyclic \"cap\" and two negatively charged amino acids to interact with a positive charge. Specific mutations induce a preference for lysine relative to arginine. By placing lysine at specific locations, the voltage sensor can be stabilized in different conformations, which enables a dissection of voltage sensor movements and their relation to ion channel opening.