Reviewerly/exHarmony huggingface dataset:
Adversarial Robustness via Multi-experts Framework for SAR Recognition with Class Imbalanced

With the rapid development of deep learning technology, significant progress has been made in the field of synthetic aperture radar (SAR) target recognition algorithms. However, deep neural networks are vulnerable to adversarial attacks in practical applications, inducing learning models to make wrong predictions. Existing works on adversarial robustness always assumed that the datasets are balanced. While in real-world applications, SAR datasets always suffer serious imbalanced distributions, which brings challenges to target recognition tasks and also affects the adversarial defense of models. So far few works have been reported on adversarial robustness for imbalanced SAR target recognition. Besides, single model is easily limited to a specific adversarial sample distribution. Motivated by this, a multi-expert collaborative diagnosis framework based on Contrastive Self-Supervised Aggregation (CS2AME) is proposed. The framework trains multiple personalized expert models precisely for dealing with specific SAR targets by formulating three expert guidance schemes, to better deal with different adversarial samples. In addition, a contrastive self-supervised aggregation strategy is designed to adaptively aggregate the professional expertise of each expert model. Extensive adversarial robustness recognition experiments on three publicly available imbalanced SAR datasets have demonstrated that the proposed CS2AME outperforms existing works in terms of standard performance and robust performance.
DFAformer: A Dual Filtering Auxiliary Transformer for Efficient Online Action Detection in Streaming Videos

Online action detection (OAD) aims to identify the specific type of ongoing action frame by frame without future information. The full exploration of historical memory with limited yet redundant information constraints for potential patterns thus becomes an important yet challenging problem. We propose a novel transformer-based framework called Dual Filtering Auxiliary Transformer (DFAformer) to achieve this goal. In DFAformer, a two-stage filtering mechanism filters impurities related to background and uninterested actions in the historical memory at the frame and element levels. To make the model concentrate on the ongoing action, we elaborate an auxiliary task, Jaccard Summary Unit, explicitly correlates the past with the future. This auxiliary task guide the learning of model weights without extra computational costs during inference. Experiments on three real-world benchmark datasets demonstrate the superiority of the proposed method.
Bayesian network analysis of BIN1 risk allele and other risk factors and biomarkers of Alzheimer’s disease

Abstract Background The Bridging Integrator I (BIN1) is a significant genetic risk gene, second only to the established risk gene apolipoprotein E (APOE4), for late‐onset Alzheimer’s disease (LOAD, AD) (Carrasquillo, 2011). BIN1 rs744733 risk allele was shown to affect tau pathology and cognitive decline (Franzmeier, 2021). In this study, we compared BIN1 risk allele rs6733839 with APOE4 on interdependency among AD risk factors and biomarkers by applying Bayesian Network (BN) modeling (Scutari, 2017) to data from Lilly AD clinical trials. Method Data from A05C (n = 142, NCT01565382), A05E (n = 217, NCT02016560), and untreated arms of AZES (n = 206, NCT02245737), LLCF (n = 229, NCT02791191), and LZAX (n = 194, NCT01900665) trial participants were used for the analysis (table 1). We constructed graphs representing the dependencies among the variables, including demographics, genetic risk genes, brain amyloid and tau measured by 18F‐Florbetapir or 18F‐Flortaucipir positron emission tomography (PET) imaging quantified globally and regionally as a standardized uptake value ratio. Longitudinal tau changes from baseline were computed for a sub cohort (n = 234). Conditional Gaussian Bayesian Network (CGBN) was used to model discrete (gender, genotype) and continuous (age, PET quantifications) variables. Directed Acyclic Graphs (DAG) structure was learned using constraint‐based Incremental Association (iamb) algorithm and score‐based Hill‐Climbing (hc) algorithm with one‐sided type I error rate at 10%. The connection strength was estimated by bootstrap resampling and computing the probability of any variable influencing another variable. Model parameters were estimated using the maximum likelihood estimation method. Result The result shows that age influences baseline amyloid (Strength s = 1.0) and parietal tau (s = 0.83). APOE4 influences baseline amyloid (s = 0.75) and tau at four lopes with strength ranging from 0.84 to 0.31 in the order of frontal, parietal, occipital, and temporal lobes. BIN1 influences baseline tau at the parietal lobe (s = 0.42) and other lobes with strength below 0.3. We have not observed consistent dependency between variables and change in tau, likely due to the small sample size (n = 234). Conclusion Our study result is consistent with current knowledge of APOE4 influencing amyloid and tau. The finding that BIN1 influences tau supports the observation of BIN1 rs744373, in linkage disequilibrium with rs6733839, in ADNI and BioFINDER studies (Franzmeier, 2021).
Innovatsionnye modeli formirovaniia edinogo prostranstva podgotovki, otsenki kachestva i povysheniia kvalifikatsii pedagogicheskikh kadrov v RF: po materialam effektivnykh praktik raboty FIP

The chapter highlights innovative models for the formation of a unified training, quality assessment and professional development of pedagogical personnel in the higher education system of the Russian Federation, based on the analysis of the best practices of federal innovation platforms. The work was carried out within the framework of the Research Agreement on &amp;quot;Identification of best practices based on the results of expert evaluation of applications and reports of federal innovation platforms&amp;quot; with Financial Expert Systems LLC. Moscow, the operator of the organization of the procedure for evaluating the activities of FIP subjects within the framework of the execution of the State Contract dated April 25, 2022 No.05.2022.244.02.003.021 for the performance of works (provision of services) under the project &amp;quot;Information, analytical and technological support for the formation and functioning of a network of federal innovation platforms in the advanced educational ecosystem of the Russian Federation&amp;quot;. The authors consider projects of federal innovation platforms of higher education institutions implemented in the field of &amp;quot;pedagogical education&amp;quot;, as well as in other interdisciplinary areas related to the professional development of teaching staff.
Developing an immersive gamification technology system for the management of patients with behavioral and psychological symptoms of dementia (Phase 1 trial)

Abstract Background Virtual reality (VR) technology has been increasingly used in the management of people living with dementia, serving as a novel and important non‐pharmacologic approach to address the complex behavioral and psychological symptoms. The objective of this phase 1 study was to develop an immersive gamification technology system (ImGTS), specifically, a VR application, and describe its acceptability, usability, and safety in a cohort of healthy adults. Method The development of ImGTS included software and hardware designs appropriate for the local context and tested using two distinct set‐ups: head‐mounted display (HMD) and the Semi‐Cave automatic virtual environment (Semi‐CAVE). Qualitative and quantitative data questionnaires, with responses analyzed using thematic analysis and descriptive statistics were used to measure the sense of place, usability and safety of the two set‐ups. Result Participants who used the HMD (n = 15) and the Semi‐CAVE (n = 15) had positive impressions and clear recollections of the VR experience. Incidence of VR sickness symptoms was low, and the mean total VR sickness scores decreased across sessions. Participants in both groups were able to perform key tasks in the VR experience, and the majority performed these actions with ease by the final testing session (n = 15 for the HMD, n = 13 for the Semi‐CAVE). The three main activities were given a ‘neutral to positive’ description in terms of satisfaction. Conclusion The technology we developed had a considerable level of impact in eliciting important aspects of cognition and emotion. Through self‐reported measures and researcher observations, we found that the HMD and Semi‐CAVE VR setups were safe, acceptable and usable by healthy adult participants.
Aspects of creating a corporate question-and-answer system using generative pre-trained language models

The article describes various ways to use generative pre-trained language models to build a corporate question-and-answer system. A significant limitation of the current generative pre-trained language models is the limit on the number of input tokens, which does not allow them to work "out of the box" with a large number of documents or with a large document. To overcome this limitation, the paper considers the indexing of documents with subsequent search query and response generation based on two of the most popular open source solutions at the moment – the Haystack and LlamaIndex frameworks. It has been shown that using the open source Haystack framework with the best settings allows you to get more accurate answers when building a corporate question-and-answer system compared to the open source LlamaIndex framework, however, requires the use of an average of several more tokens. The article used a comparative analysis to evaluate the effectiveness of using generative pre-trained language models in corporate question-and-answer systems using the Haystack and Llamaindex frameworks. The evaluation of the obtained results was carried out using the EM (exact match) metric. The main conclusions of the conducted research on the creation of question-answer systems using generative pre-trained language models are: 1. Using hierarchical indexing is currently extremely expensive in terms of the number of tokens used (about 160,000 tokens for hierarchical indexing versus 30,000 tokens on average for sequential indexing), since the response is generated by sequentially processing parent and child nodes. 2. Processing information using the Haystack framework with the best settings allows you to get somewhat more accurate answers than using the LlamaIndex framework (0.7 vs. 0.67 with the best settings). 3. Using the Haystack framework is more invariant with respect to the accuracy of responses in terms of the number of tokens in the chunk. 4. On average, using the Haystack framework is more expensive in terms of the number of tokens (about 4 times) than the LlamaIndex framework. 5. The "create and refine" and "tree summarize" response generation modes for the LlamaIndex framework are approximately the same in terms of the accuracy of the responses received, however, more tokens are required for the "tree summarize" mode.
Revisiting Knowledge-Based Inference of Python Runtime Environments: A Realistic and Adaptive Approach

The reuse and integration of existing code is a common practice for efficient software development. Constantly updated Python interpreters and third-party packages introduce many challenges to Python runtime environment inference. Existing knowledge-based approaches have achieved good performance but still suffer from several limitations in the real world, especially from incomplete domain knowledge. In this paper, we propose ReadPyE, a realistic and adaptive approach to Python runtime environment inference. To leverage the rich code information, we present an automated approach to the construction and maintenance of our designed Python ecosystem knowledge graph (KG). Moreover, we are the first to handle real-world challenges such as complex dependency specifications and incomplete domain knowledge. Specifically, we define a naming similarity measure to match candidate packages for unknown modules and set priorities for multiple candidate packages. ReadPyE solves the optimization problems of candidate package selection and generates compatible runtime environments step by step based on the current Python environment. The inferred environments are iteratively validated and adjusted by matched exception templates in the validation logs. The evaluation results on three real-world datasets show the superior effectiveness and good efficiency of our ReadPyE compared to the existing knowledge-based approaches. ReadPyE solves the environment-related exceptions for 79.75% single-file code snippets, 93% Python projects, and 63.34% program pairs for code integration. We believe ReadPyE can help programmers reduce the time spent on inferring Python runtime environments and facilitate automated software configuration management.
Secure Access Control for Electronic Health Records in Blockchain-Enabled Consumer Internet of Medical Things

The popularization of electronic health records (EHRs) effectively improves the efficiency of diagnosis and treatment of diseases, however, it also lays a hidden danger for the leakage of patients’ privacy, so it needs a stricter and more flexible access control mechanism. In addition, the medical ministry (MM) usually investigates illegal medical activities after they have already taken place and caused harm, resulting in a serious lag in regulation. To solve these problems, we propose a blockchain-based system that enables patient-leading fine-grained access control against EHRs. In contrast to the currently existing systems, this scheme uses blank EHRs as the medium, combining attribute-based encryption and blockchain to enable MM to engage in the regulation of medical activities before they taking place. In order to reduce the storage cost of the whole system, we apply the chameleon hash function to the process of calculating file storage addresses in the inter-planetary file system. Moreover, the introduction of single sign-on can improve the security and efficiency of patients’ vital signs transmission in telemedicine scenario, and the introduction of proxy re-encryption can improve the efficiency of authorization of EHRs. Theoretical analysis and experiments show that the scheme satisfies both the security and feasibility requirements.
Deep Self-supervised Diversity Promoting Learning on Hierarchical Hyperspheres for Regularization

In this paper, we propose a novel approach to enhance the generalization performance of deep neural networks. Our method employs a hierarchical hypersphere-based constraint that organizes weight vectors hierarchically based on observed data. By diversifying the parameter space of hyperplanes in the classification layer, we aim to foster discriminative generalization. We introduce a self-supervised grouping method designed to unveil hierarchical structures in scenarios with unknown hierarchy information. To maximize distances between weight vectors on multiple hyperspheres, we propose a novel metric that combines discrete and continuous measures. This regularization encourages diverse orientations, consequently leading to improved generalization. Extensive evaluations on datasets, including CUB200-2011, Stanford-Cars, CIFAR-100, and TinyImageNet, consistently demonstrate enhancements in classification performance compared to baseline settings.
Device Scheduling for Relay-Assisted Over-the-Air Aggregation in Federated Learning

Federated learning (FL) leverages data distributed at the edge of the network to enable intelligent applications. The efficiency of FL can be improved by using over-the-air computation (AirComp) technology in the process of gradient aggregation. In this paper, we propose a relay-assisted large-scale FL framework, and investigate the device scheduling problem in relay-assisted FL systems under the constraints of power consumption and mean squared error (MSE). we formulate a joint device scheduling, and power allocation problem to maximize the number of scheduled devices. We solve the resultant non-convex optimization problem by transforming the optimization problem into multiple sparse optimization problems. By the proposed device scheduling algorithm, these sparse subproblems are solved and the maximum number of federated learning edge devices is obtained. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes.
Bibliometric Analysis of E-Government and Trust: A Lesson for Indonesia

The development of digitalisation has spectacularly affected global society, including Indonesia. Many governments are aware of the opportunities and benefits of this development, so they carry out digital transformation and adaptation through e-government systems. However, public trust emerges as one of the determining factors in assessing the success of implementing e-government. This tendency prompted this study to search for available research documents to examine relevant topics regarding e-government and trust. The method used was a bibliometric analysis by maximising the Vosviewer analysis tool. The data was obtained from the Scopus database. This study reveals the need for further research on the relationship between e-government and trust in Indonesia. These subjects are relatively less explored despite gaining global recognition as vital areas for future research. This unpopularity has contributed to the need for more discussion about the quality of egovernment services regarding the level of trust. Trust is a fundamental aspect of the government system in many countries. The findings of this study encourage a logical consequence to consider factors of trust in implementing e-government in Indonesia in the future. This approach can encourage the government to improve the quality of services and adopt e-government use. Such improvements are expected to bolster trust in the government and sustainably stimulate public interest in its utilisation.
Multimodal Person Verification With Generative Thermal Data Augmentation

The fusion of audio, visual, and thermal modalities has proven effective in developing reliable person verification systems. In this study, we enhanced multimodal person verification performance by augmenting training data using domain transfer methods. Specifically, we enriched the audio-visual-thermal SpeakingFaces dataset with a combination of real audio-visual data and synthetic thermal data from the VoxCeleb dataset. We adapted visual images in VoxCeleb to the thermal domain using CycleGAN, trained on SpeakingFaces. Our results demonstrate the positive impact of augmented training data on all unimodal and multimodal models. The score fusion of unimodal audio, unimodal visual, bimodal, and trimodal systems trained on the combined data achieved the best results on both datasets and exhibited robustness in low-illumination and noisy conditions. Our findings emphasize the importance of utilizing synthetic data, produced by generative methods, to improve deep learning model performance. To facilitate reproducibility and further research in multimodal person verification, we have made our code, pretrained models, and preprocessed dataset freely available in our GitHub repository.
FlowX: Towards Explainable Graph Neural Networks via Message Flows

We investigate the explainability of graph neural networks (GNNs) as a step toward elucidating their working mechanisms. While most current methods focus on explaining graph nodes, edges, or features, we argue that, as the inherent functional mechanism of GNNs, message flows are more natural for performing explainability. To this end, we propose a novel method here, known as FlowX, to explain GNNs by identifying important message flows. To quantify the importance of flows, we propose to follow the philosophy of Shapley values from cooperative game theory. To tackle the complexity of computing all coalitions' marginal contributions, we propose a flow sampling scheme to compute Shapley value approximations as initial assessments of further training. We then propose an information-controlled learning algorithm to train flow scores toward diverse explanation targets: necessary or sufficient explanations. Experimental studies on both synthetic and real-world datasets demonstrate that our proposed FlowX and its variants lead to improved explainability of GNNs.
Predicting User Interest Using Hierarchical-Based Clustering for Recommender Systems

In the realm of personalized recommendation systems, accurately predicting a user's interest in specific items is paramount. In this study, we aim to develop an estimation method that predicts such interests with sufficient accuracy. Drawing inspiration from human decision-making processes, our method considers two primary sources of information: a user's voting behavior and their similarity to other users. By initializing all unrated items based on average user ratings and employing hierarchical clustering to group users, we can anticipate a user's rating for uncommented items. We evaluated our approach using the MovieLens database, which, due to its vast number of unrated items, presents a significant challenge. The results confirm the efficacy of our method in accurately estimating user interests due to RMSE-percent = 51.012.
Application of Electrical Network Frequency as an Entropy Generator in Distributed Systems

The development of public information technology (IT) infrastructures using modern edge devices has revolutionized the smart city ecosystem. As the number of devices in the ecosystem exponentially increases, the power grid infrastructure has continued to function as the backbone of smart cities and support the growing requirements for electricity, such as multimedia services. Media authentication technology leveraging the power system frequency, the Electrical Network Frequency (ENF) signal, has gained traction as an environmental fingerprint to counteract visual layer attacks targeted toward multimedia-based edge devices. Due to the unique and similar fluctuation patterns throughout the grid, the ENF signal is reliable in applications like multimedia synchronization, authentication, and source identification. ENF -based systems are inevitably targeted by perpetrators to bypass their detection schemes; however, such attacks are successful only if the ENF fingerprint can be pre-dicted. In this work, we studied the randomization nature of ENF signals and the societal impacts on its fluctuation patterns due to power grid consumption. Leveraging the ENF signal fluctuations, we integrate ENF as a source of randomness for the Random Bit Generator (RBG) system in cryptographic applications. An RBG-based encrypted communication and committee election mechanism is proposed for an enhanced distribution multimedia system. The paper further discusses the potential applications of the ENF -based randomness generator.
Monocular 3D Vehicle Detection on Roadside based on Foreground Depth Fusion

With the rapid development of deep learning techniques, monocular 3D vehicle detection has become a hot research field. Compared to traditional 2D vehicle detection, 3D detection provides more detailed information about vehicles, such as their dimensions (length, width, height), orientation angles, and more. However, this also places higher demands on the algorithms used.We present a two-stage monocular 3D vehicle detection framework that integrates foreground depth fusion. In the initial stage, a 2D detector is employed to generate 2D bounding boxes from the input images, followed by hierarchical annotation of different detection targets. In the subsequent stage, we fuse the foreground depth and hierarchical information to derive accurate 3D information for the identified objects. Experimental evaluation on the DAIR-V2X dataset showcases superior performance of our network compared to other approaches in handling occlusion issues.