Reviewerly/exHarmony huggingface dataset:
Striving for Diagnostic Excellence in Emergencies by Using Principles of Personalized Prevention and Medical Cybernetics

This article presents a comprehensive approach imbedded into the evolving Digital Health infrastructure and how diagnostic excellence in emergencies and primary prevention of the most significant pathologies regarding morbidity and mortality could be pursued. Tools of Medical Cybernetics are introduced here which could enable: 1. Collection of large amounts of data from wearable sensors. 2. Fitting dynamic process models to the data which have their power to predict the next measurement. 3. Creation of automated risk profile with indicators of morbidity and mortality of diseases which carry the highest risk and feedback of information to the user. 4. Creation of a network between users, providers, and other health services. We propose a Personal Health Data Organizer App Service working in unison with a Cyber Physical System and its extension the Integrated Cyber Physical System cloud-based computer platform realizing long-term observation and continuous risk assessment for measuring quality and effectiveness of the diagnostic process and supporting diagnostic excellence. A list of principles of primary prevention is compiled which is fully supported by the same system. Example is shown how primary prevention of atherosclerotic heart diseases could be implemented. The significance of the system is to recognize early developing risks at subclinical stages of the highest-risk conditions that result in the most harm and potential lawsuits if the correct diagnosis is missed or delayed.
Cloud infrastructure architecture and the zero trust model as a cybersecurity strategy

Since the emergence of the internet, the vertiginous growth in the use of electronic media has grown in the same proportion as cyber crimes, applied in an increasingly sophisticated way. In addition to these two factors that impact the use of the internet, the speed with which the resources and tools inherent in the area of Information and Communication Technology (ICTs) evolve, which enhance the need to continuously develop and improve the means for the protection of sensitive data of people and public and private organizations. In this perspective, Cloud Computing emerges, which allows the storage of data, networks and applications, and other resources through integrated environments through the internet, from collective providers, as opposed to the on-premise system, which is based on the custody and access through local servers, including mainframes, which are still maintained in most large organizations, such as the banking system, for example. Added to Cloud Computing are Zero Trust practices, whose main innovation is the adoption of several layers of access verification. This article was prepared using bibliographical research as a methodology. The question that arises on the subject is: how does Zero Trust provide greater security to network users? The objective is to demonstrate the advantages of security provided by Zero Trust, combined with Cloud Computing. In view of the analyzed literature, it was possible to conclude the existence of two main aspects brought by Zero Trust: internet user access only from 7 layers of verification, and the mitigation of vulnerabilities, including the idea of responsible browsing by different users.
Virtual Reality Special Effects with Houdini SideFX Grid

Most of the special effects in Houdini sideFXare created by using one simple planar node.Some of the planar nodes are grid, circle, and heightField.These nodes have interesting properties similar and unlike in terms of geometry and special effects.We explore the hidden gems to show how Houdini SideFX can leverage them to develop "grid" based animations.We describe the progression of systematic development using elementary Houdini grid node and explore how this node can lead to complex multifaceted scene views in virtual reality.With the advent of COVID-19, Virtual Reality has revolutionized academic world whether it is in-class teaching, online teaching, participating in the conferences in-person, or remotely, individual zoom meetings and all that.In addition, it leads to innovation and development of new courses eBooks replacing printed books.Virtual reality was mainly in the realm of film and entertainment industry.It is changing the education culture altogether, andis becoming the mainstay in every aspect of visualization.With this paper we show how immersion results in innovation of new applications and how complex scenes can be created easily.
Navigation as Attackers Wish? Towards Building Byzantine-Robust Embodied Agents under Federated Learning

Federated embodied agent learning protects the data privacy of individual visual environments by keeping data locally at each client (the individual environment) during training. However, since the local data is inaccessible to the server under federated learning, attackers may easily poison the training data of the local client to build a backdoor in the agent without notice. Deploying such an agent raises the risk of potential harm to humans, as the attackers may easily navigate and control the agent as they wish via the backdoor. Towards Byzantine-robust federated embodied agent learning, in this paper, we study the attack and defense for the task of vision-and-language navigation (VLN), where the agent is required to follow natural language instructions to navigate indoor environments. First, we introduce a simple but effective attack strategy, Navigation as Wish (NAW), in which the malicious client manipulates local trajectory data to implant a backdoor into the global model. Results on two VLN datasets (R2R and RxR) show that NAW can easily navigate the deployed VLN agent regardless of the language instruction, without affecting its performance on normal test sets. Then, we propose a new Prompt-Based Aggregation (PBA) to defend against the NAW attack in federated VLN, which provides the server with a ''prompt'' of the vision-and-language alignment variance between the benign and malicious clients so that they can be distinguished during training. We validate the effectiveness of the PBA method on protecting the global model from the NAW attack, which outperforms other state-of-the-art defense methods by a large margin in the defense metrics on R2R and RxR.
Electric strings in non-Abelian theories

We find electric string solutions in Yang-Mills-Higgs theory.
КОМУНІКАЦІЙНА ВЗАЄМОДІЯ УНІВЕРСИТЕТСЬКОЇ БІБЛІОТЕКИ ТА СТУДЕНТІВ

&lt;p&gt;&lt;strong&gt;&lt;em&gt;The article aims&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; to establish the level of communication interaction between the university library and students using social media.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;The research methodology&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; includes the survey, analysis, synthesis, statistical, comparison, grouping, generalization, descriptive, and visualization methods. The purpose of an online survey was to obtain data on the peculiarities of using social media in the university library by students of Lviv Polytechnic. The statistical method was used to evaluate and generalize the survey data. Compared with other specialties, the comparison method was used to determine the peculiarities of the communication interaction of students of the specialty "Information, library and archival affairs" and the Sci entific Library of the Lviv Polytechnic. The method of generalization and grouping was used to develop an array of respondents' recommendations regarding the desired content of library pages on social media. The descriptive and visualization methods were used to present and visualize the obtained research results.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Results.&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; The analysis of an online survey of 245 students of the Lviv Polytechnic of 53 specialties made it possible to establish the basic mechanisms of communication interaction with the university library using its social media. A comparison of the mechanisms of communication interaction of students majoring in IBAS and other majors did not reveal any significant differences but showed a low level of involvement of students in the information space of the university library.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;The novelty &lt;/em&gt;&lt;/strong&gt;&lt;em&gt;of the obtained research results lies in determining the features of the communication interaction between the university library and students of the IBAS specialty compared to students of other specialties.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Practical meaning.&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; The obtained results can be used to adjust the media strategy of academic libraries and to establish communication interaction between the library and graduate departments.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Key words:&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; social media, university library, target audience, students, content.&lt;/em&gt;&lt;/p&gt;
Diffusion Generative Models in Infinite Dimensions

Diffusion generative models have recently been applied to domains where the available data can be seen as a discretization of an underlying function, such as audio signals or time series. However, these models operate directly on the discretized data, and there are no semantics in the modeling process that relate the observed data to the underlying functional forms. We generalize diffusion models to operate directly in function space by developing the foundational theory for such models in terms of Gaussian measures on Hilbert spaces. A significant benefit of our function space point of view is that it allows us to explicitly specify the space of functions we are working in, leading us to develop methods for diffusion generative modeling in Sobolev spaces. Our approach allows us to perform both unconditional and conditional generation of function-valued data. We demonstrate our methods on several synthetic and real-world benchmarks.
Trie-Compressed Intersectable Sets

We introduce space- and time-efficient algorithms and data structures for the offline set intersection problem. We show that a sorted integer set $S \subseteq [0{..}u)$ of $n$ elements can be represented using compressed space while supporting $k$-way intersections in adaptive $O(k\delta\lg{\!(u/\delta)})$ time, $\delta$ being the alternation measure introduced by Barbay and Kenyon. Our experimental results suggest that our approaches are competitive in practice, outperforming the most efficient alternatives (Partitioned Elias-Fano indexes, Roaring Bitmaps, and Recursive Universe Partitioning (RUP)) in several scenarios, offering in general relevant space-time trade-offs.
Self-supervised On-device Federated Learning from Unlabeled Streams

The ubiquity of edge devices has led to a growing amount of unlabeled data produced at the edge. Deep learning models deployed on edge devices are required to learn from these unlabeled data to continuously improve accuracy. Self-supervised representation learning has achieved promising performances using centralized unlabeled data. However, the increasing awareness of privacy protection limits centralizing the distributed unlabeled image data on edge devices. While federated learning has been widely adopted to enable distributed machine learning with privacy preservation, without a data selection method to efficiently select streaming data, the traditional federated learning framework fails to handle these huge amounts of decentralized unlabeled data with limited storage resources on edge. To address these challenges, we propose a Self-supervised On-device Federated learning framework with coreset selection, which we call SOFed, to automatically select a coreset that consists of the most representative samples into the replay buffer on each device. It preserves data privacy as each client does not share raw data while learning good visual representations. Experiments demonstrate the effectiveness and significance of the proposed method in visual representation learning.
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning

Recent advances in Federated Learning (FL) have paved the way towards the design of novel strategies for solving multiple learning tasks simultaneously, by leveraging cooperation among networked devices. Multi-Task Learning (MTL) exploits relevant commonalities across tasks to improve efficiency compared with traditional transfer learning approaches. By learning multiple tasks jointly, significant reduction in terms of energy footprints can be obtained. This article provides a first look into the energy costs of MTL processes driven by the Model-Agnostic Meta-Learning (MAML) paradigm and implemented in distributed wireless networks. The paper targets a clustered multi-task network setup where autonomous agents learn different but related tasks. The MTL process is carried out in two stages: the optimization of a meta-model that can be quickly adapted to learn new tasks, and a task-specific model adaptation stage where the learned meta-model is transferred to agents and tailored for a specific task. This work analyzes the main factors that influence the MTL energy balance by considering a multi-task Reinforcement Learning (RL) setup in a robotized environment. Results show that the MAML method can reduce the energy bill by at least 2 times compared with traditional approaches without inductive transfer. Moreover, it is shown that the optimal energy balance in wireless networks depends on uplink/downlink and sidelink communication efficiencies.
Membership Inference Attacks Against Semantic Segmentation Models

Membership inference attacks aim to infer whether a data record has been used to train a target model by observing its predictions. In sensitive domains such as healthcare, this can constitute a severe privacy violation. In this work we attempt to address the existing knowledge gap by conducting an exhaustive study of membership inference attacks and defences in the domain of semantic image segmentation. Our findings indicate that for certain threat models, these learning settings can be considerably more vulnerable than the previously considered classification settings. We additionally investigate a threat model where a dishonest adversary can perform model poisoning to aid their inference and evaluate the effects that these adaptations have on the success of membership inference attacks. We quantitatively evaluate the attacks on a number of popular model architectures across a variety of semantic segmentation tasks, demonstrating that membership inference attacks in this domain can achieve a high success rate and defending against them may result in unfavourable privacy-utility trade-offs or increased computational costs.
Assessing Anonymized System Logs Usefulness for Behavioral Analysis in RNN Models

System logs are a common source of monitoring data for analyzing computing systems' behavior. Due to the complexity of modern computing systems and the large size of collected monitoring data, automated analysis mechanisms are required. Numerous machine learning and deep learning methods are proposed to address this challenge. However, due to the existence of sensitive data in system logs their analysis and storage raise serious privacy concerns. Anonymization methods could be used to clean the monitoring data before analysis. However, anonymized system logs, in general, do not provide adequate usefulness for the majority of behavioral analysis. Content-aware anonymization mechanisms such as PaRS preserve the correlation of system logs even after anonymization. This work evaluates the usefulness of anonymized system logs taken from the Taurus HPC cluster anonymized using PaRS, for behavioral analysis via recurrent neural network models.
Disparity estimation for fisheye images with an application to intermediate view synthesis

To obtain depth information from a stereo camera setup, a common way is to conduct disparity estimation between the two views; the disparity map thus generated may then also be used to synthesize arbitrary intermediate views. A straightforward approach to disparity estimation is block matching, which performs well with perspective data. When dealing with non-perspective imagery such as obtained from ultra wide-angle fisheye cameras, however, block matching meets its limits. In this paper, an adapted disparity estimation approach for fisheye images is introduced. The proposed method exploits knowledge about the fisheye projection function to transform the fisheye coordinate grid to a corresponding perspective mesh. Offsets between views can thus be determined more accurately, resulting in more reliable disparity maps. By re-projecting the perspective mesh to the fisheye domain, the original fisheye field of view is retained. The benefit of the proposed method is demonstrated in the context of intermediate view synthesis, for which both objectively evaluated as well as visually convincing results are provided.
Policy iteration using Q-functions: Linear dynamics with multiplicative noise

This paper presents a novel model-free and fully data-driven policy iteration scheme for quadratic regulation of linear dynamics with state- and input-multiplicative noise. The implementation is similar to the least-squares temporal difference scheme for Markov decision processes, estimating Q-functions by solving a least-squares problem with instrumental variables. The scheme is compared with a model-based system identification scheme and natural policy gradient through numerical experiments.
BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks

Bird's-Eye-View (BEV) 3D Object Detection is a crucial multi-view technique for autonomous driving systems. Recently, plenty of works are proposed, following a similar paradigm consisting of three essential components, i.e., camera feature extraction, BEV feature construction, and task heads. Among the three components, BEV feature construction is BEV-specific compared with 2D tasks. Existing methods aggregate the multi-view camera features to the flattened grid in order to construct the BEV feature. However, flattening the BEV space along the height dimension fails to emphasize the informative features of different heights. For example, the barrier is located at a low height while the truck is located at a high height. In this paper, we propose a novel method named BEV Slice Attention Network (BEV-SAN) for exploiting the intrinsic characteristics of different heights. Instead of flattening the BEV space, we first sample along the height dimension to build the global and local BEV slices. Then, the features of BEV slices are aggregated from the camera features and merged by the attention mechanism. Finally, we fuse the merged local and global BEV features by a transformer to generate the final feature map for task heads. The purpose of local BEV slices is to emphasize informative heights. In order to find them, we further propose a LiDAR-guided sampling strategy to leverage the statistical distribution of LiDAR to determine the heights of local slices. Compared with uniform sampling, LiDAR-guided sampling can determine more informative heights. We conduct detailed experiments to demonstrate the effectiveness of BEV-SAN. Code will be released.
Software Requirements Engineering Healthcare Implementation Maturity Model (SRE-HIMM) for Global Health-Care Information System

The fundamental objective of this research work is to develop a Software Requirement Engineering Healthcare Implementation Maturity Model (SRE-HIMM) that will assist healthcare organizations to effectively evaluate and implement HIS development process. The model was developed based on the systematic literature review (SLR) approach and Empirical results. The 53 primary studies were extracted using the SLR approach and CSFs, CBs, and best practices were identified form the extracted primary studies. The identified success factors and barriers were further ranked using the analytical hierarchy process (AHP) approach. Furthermore, I have adopted the critical success factors (CSFs) and critical barriers (CBs) instead of PAs and available Maturity models i.e., CMMI for the development of (SRE-HIMM). The identified CSFs and CBs were classified into five maturity levels based on the CMMI, IMM, and SOVRM. The empirical investigation was conducted HIS experts to evaluate the findings of SLR. Further, a case study was conducted with the company to evaluate the effectiveness of SRE-HIMM which shows satisfactory results.
CC-3DT: Panoramic 3D Object Tracking via Cross-Camera Fusion

To track the 3D locations and trajectories of the other traffic participants at any given time, modern autonomous vehicles are equipped with multiple cameras that cover the vehicle's full surroundings. Yet, camera-based 3D object tracking methods prioritize optimizing the single-camera setup and resort to post-hoc fusion in a multi-camera setup. In this paper, we propose a method for panoramic 3D object tracking, called CC-3DT, that associates and models object trajectories both temporally and across views, and improves the overall tracking consistency. In particular, our method fuses 3D detections from multiple cameras before association, reducing identity switches significantly and improving motion modeling. Our experiments on large-scale driving datasets show that fusion before association leads to a large margin of improvement over post-hoc fusion. We set a new state-of-the-art with 12.6% improvement in average multi-object tracking accuracy (AMOTA) among all camera-based methods on the competitive NuScenes 3D tracking benchmark, outperforming previously published methods by 6.5% in AMOTA with the same 3D detector.
Generative Reasoning Integrated Label Noise Robust Deep Image Representation Learning

The development of deep learning based image representation learning (IRL) methods has attracted great attention for various image understanding problems. Most of these methods require the availability of a high quantity and quality of annotated training images, which can be time-consuming and costly to gather. To reduce labeling costs, crowdsourced data, automatic labeling procedures or citizen science projects can be considered. However, such approaches increase the risk of including label noise in training data. It may result in overfitting on noisy labels when discriminative reasoning is employed. This leads to sub-optimal learning procedures, and thus inaccurate characterization of images. To address this, we introduce a generative reasoning integrated label noise robust deep representation learning (GRID) approach. Our approach aims to model the complementary characteristics of discriminative and generative reasoning for IRL under noisy labels. To this end, we first integrate generative reasoning into discriminative reasoning through a supervised variational autoencoder. This allows GRID to automatically detect training samples with noisy labels. Then, through our label noise robust hybrid representation learning strategy, GRID adjusts the whole learning procedure for IRL of these samples through generative reasoning and that of other samples through discriminative reasoning. Our approach learns discriminative image representations while preventing interference of noisy labels independently from the IRL method being selected. Thus, unlike the existing methods, GRID does not depend on the type of annotation, neural network architecture, loss function or learning task, and thus can be directly utilized for various problems. Experimental results show its effectiveness compared to state-of-the-art methods. The code of GRID is publicly available at https://github.com/gencersumbul/GRID.