Reviewerly/exHarmony huggingface dataset:
<i>X</i>-Secure <i>T</i>-Private Federated Submodel Learning With Elastic Dropout Resilience

Motivated by recent interest in federated submodel learning, this work explores the fundamental problem of privately reading from and writing to a database comprised of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> files (submodels) that are stored across <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> distributed servers according to an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> -secure threshold secret sharing scheme. One after another, various users wish to retrieve their desired file, locally process the information and then update the file in the distributed database while keeping the identity of their desired file private from any set of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> colluding servers. The availability of servers changes over time, so elastic dropout resilience is required. The main contribution of this work is an adaptive scheme, called ACSA-RW, that takes advantage of all currently available servers to reduce its communication costs, fully updates the database after each write operation even though the database is only partially accessible due to server dropouts, and ensures a memoryless operation of the network in the sense that the storage structure is preserved and future users may remain oblivious of the past history of server dropouts. The ACSA-RW construction builds upon cross-subspace alignment (CSA) codes that were originally introduced for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> -secure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> -private information retrieval and have been shown to be natural solutions for secure distributed batch matrix multiplication problems. ACSA-RW achieves the desired private read and write functionality with elastic dropout resilience, matches the best results for private-read from PIR literature, improves significantly upon available baselines for private-write, reveals a striking symmetry between upload and download costs, and exploits storage redundancy to accommodate arbitrary read and write dropout servers up to certain threshold values. It also answers in the affirmative an open question by Kairouz et al. for the case of partially colluding servers (i.e., tolerating collusion up to a threshold) by exploiting synergistic gains from the joint design of private read and write operations.
Optimizing in-memory database engine for AI-powered on-line decision augmentation using persistent memory

On-line decision augmentation (OLDA) has been considered as a promising paradigm for real-time decision making powered by Artificial Intelligence (AI). OLDA has been widely used in many applications such as real-time fraud detection, personalized recommendation, etc. On-line inference puts real-time features extracted from multiple time windows through a pre-trained model to evaluate new data to support decision making. Feature extraction is usually the most time-consuming operation in many OLDA data pipelines. In this work, we started by studying how existing in-memory databases can be leveraged to efficiently support such real-time feature extractions. However, we found that existing in-memory databases cost hundreds or even thousands of milliseconds. This is unacceptable for OLDA applications with strict real-time constraints. We therefore propose FEDB ( &lt;u&gt;F&lt;/u&gt; eature &lt;u&gt;E&lt;/u&gt; ngineering &lt;u&gt;D&lt;/u&gt; ata &lt;u&gt;b&lt;/u&gt; ase), a distributed in-memory database system designed to efficiently support on-line feature extraction. Our experimental results show that FEDB can be one to two orders of magnitude faster than the state-of-the-art in-memory databases on real-time feature extraction. Furthermore, we explore the use of the Intel Optane DC Persistent Memory Module (PMEM) to make FEDB more cost-effective. When comparing the proposed PMEM-optimized persistent skiplist to the FEDB using DRAM+SSD, PMEM-based FEDB can shorten the tail latency up to 19.7%, reduce the recovery time up to 99.7%, and save up to 58.4% total cost of a real OLDA pipeline.
CyberAid: Are your children safe from cyberbullying?

Researchers around the world have been implementing machine learning as a method to detect cyberbullying text. The machine is trained using features such as variations in texts, through social media context and interactions in a social network environment. The machine can also identify and profile users through gender or use of hate speech. In this study, we analysed different types of mobile applications that manage cyberbullying. This study proposes a mechanism, which combines the best cyberbullying detection features to fill the gaps and limitations of existing applications. The results of the study have shown that the proposed mobile application records a higher accuracy in detecting cyberbully than other available applications.
Research of modern opportunities for obtaining the grant support for innovative projects financing

Abstract This paper examines innovative, including social entrepreneurship and modern opportunities for obtaining grant support for financing innovative projects, including social ones. The possibility of receiving grants from various funds is being considered. Detailed attention is paid to the stages of project development for drawing up an application for grant funding. Attention is drawn to certain nuances in the writing and processing of grant applications. We also conduct research on current conditions and factors that influence the development of innovative entrepreneurship, and compare Russian indicators with foreign ones. Detailed attention is paid to economic and innovative conditions, educational factors, measures to promote competition and tax incentives.
Chi-MFlexDT:Chi-square-based multi flexible fuzzy decision tree for data stream classification

In recent years, a number of classification algorithms using fuzzy decision trees have been proposed. A key issue encountered in all of these algorithms is how to select the division feature. In traditional algorithms, it is common to use the information gain criterion. There is a claim that the use of information-based criteria leads to an unfair selection of features that have large number of values while not being suitable for division. This problem causes to decrease accuracy of classification in stream data. However, other algorithms for classifying stream data and extracting fuzzy rules have been proposed but the problem of unfairly selecting features with large amount of values still persists. This paper, present a new algorithm to solve problems of selecting splitting feature in fuzzy decision tree for classification of stream data. In proposed algorithm, we extend multi flexible fuzzy decision tree (MFlexDT) with chi-square based fuzzy partitioning of values. In this paper, we also extend traditional chi-square statistical to fuzzy chi-square statistical data distribution. In evaluating the proposed algorithm, tree depth and accuracy are two factors that affect performance. Experimental results show that the new algorithm has been able to improve the performance of other algorithms.
End-to-End Ship Detection in SAR Images for Complex Scenes Based on Deep CNNs

Ship detection on synthetic aperture radar (SAR) imagery has many valuable applications for both civil and military fields and has received extraordinary attention in recent years. The traditional detection methods are insensitive to multiscale ships and usually time-consuming, results in low detection accuracy and limitation for real-time processing. To balance the accuracy and speed, an end-to-end ship detection method for complex inshore and offshore scenes based on deep convolutional neural networks (CNNs) is proposed in this paper. First, the SAR images are divided into different grids, and the anchor boxes are predefined based on the responsible grids for dense ship prediction. Then, Darknet-53 with residual units is adopted as a backbone to extract features, and a top-down pyramid structure is added for multiscale feature fusion with concatenation. By this means, abundant hierarchical features containing both spatial and semantic information are extracted. Meanwhile, the strategies such as soft non-maximum suppression (Soft-NMS), mix-up and mosaic data augmentation, multiscale training, and hybrid optimization are used for performance enhancement. Besides, the model is trained from scratch to avoid learning objective bias of pretraining. The proposed one-stage method adopts end-to-end inference by a single network, so the detection speed can be guaranteed due to the concise paradigm. Extensive experiments are performed on the public SAR ship detection dataset (SSDD), and the results show that the method can detect both inshore and offshore ships with higher accuracy than other mainstream methods, yielding the accuracy with an average of 95.52%, and the detection speed is quite fast with about 72 frames per second (FPS). The actual Sentinel-1 and Gaofen-3 data are utilized for verification, and the detection results also show the effectiveness and robustness of the method.
On the Generalizability of Programs Synthesized by Grammar-Guided Genetic Programming

Grammar-guided Genetic Programming is a common approach for program synthesis where the user's intent is given by a set of input/output examples. For use in real-world software development, the generated programs must work on previously unseen test cases too. Therefore, we study in this work the generalizability of programs synthesized by grammar-guided GP with lexicase selection. As benchmark, we analyze proportionate and tournament selection too. We find that especially for program synthesis problems with a low output cardinality (e.g., a Boolean output) lexicase selection overfits the training cases and does not generalize well to unseen test cases. An analysis using common software metrics shows for such a problem that lexicase selection generates more complex programs with many code lines and a heavier use of control structures compared to the other studied selection methods. Nevertheless, the generalizability can be improved when we do not stop a GP run as usual after a first program is found that solves all training cases correctly, but give GP more time to find further solution candidates (also solving correctly all training cases) and select the smallest program (measured with different software metrics) out of these.
An electronic environment of higher education institution (on the example of Zhytomyr Polytechnic State University)

Abstract The article considers the expediency of developing and using the electronic environment of a higher education institution. It was found that the existence of such an electronic environment of a higher education institution would allow us to effectively use the available resources of higher education. A model of the electronic environment of a higher education institution is proposed, which consists of 4 components: educational, scientific, organizational, and managerial. The structural elements of each of the components are described. An example of the implementation of such an electronic environment on the example of the Zhytomyr Polytechnic State University is considered. The personal offices of the student and the teacher, which are realized according to the given model at the Zhytomyr Polytechnic State University, are considered in detail. It is established that the indisputable advantage is the complete identification of the person who went to different parts of such an environment, another advantage is the integrated use of logins and passwords to all these components.
How to Make Augmented Reality a Tool for Railway Maintenance Operations: Operator 4.0 Perspective

In the last few decades, several initiatives and approaches are set up to support maintenance procedures for the railway industry in adopting the principles of Industry 4.0. Contextualized maintenance technologies such as Augmented Reality (AR) overlay can integrate virtual information on physical objects to improve decision-making and action-taking processes. Operators work in a dynamic working environment requiring both high adaptive capabilities and expert knowledge. There is a need to support the operators with tailor-based information that is customized and contextualized to their expertise and experience. It calls for AR tools and approaches that combine complex methodologies with high usability requirements. The development of these AR tools could benefit from a structured approach. Therefore, the objective of this paper is to propose an adaptive architectural framework aimed at shaping and structuring the process that provides operators with tailored support when using an AR tool. Case study research is applied within a revelatory railway industry setting. It was found that the framework ensures that self-explanatory AR systems can capture the knowledge of the operator, support the operator during maintenance activities, conduct failure analysis, provide problem-solving strategies, and improve learning capabilities. This study contributes to the necessity of having a human-centered approach for the successful adaption of AR technology tools for the railway industry.
An adaptive nonlinear filter for integrated navigation systems using deep neural networks

This paper presents a novel nonlinear adaptive sensor fusion method for integrated navigation systems with varying noise parameters. The innovation is utilizing deep neural networks to mine the noise-related patterns of specific sensors and combining it with conventional nonlinear filters. This hybrid approach improves the feasibility and robustness of adaptive filtering by achieving an effective estimation of the originally weakly observable noise parameters. The specific sensors are defined as α-type sensors whose errors are entirely generated by themselves. The mathematical model for analyzing α-type sensors output sequence and the deep neural network for mining the patterns of interest are established. All adaptive filtering systems using α-type sensors can benefit from this paper. Specifically, it is applied to inertial and satellite integrated navigation system. The numerical experiments indicate that the proposed filter achieves promising accuracy and robustness improvement as compared to conventional nonlinear filters. And the comparisons between different nonlinear approximation algorithms indicate that the first-order approximation is accurate enough for our application.
A novel vehicle tracking and speed estimation with varying UAV altitude and video resolution

In this paper, a novel vehicle tracking and speed estimation method based on aerial videos is developed. The main objective of this research is to investigate how depth features of a vehicle extracted from aerial videos by a wide residual network could be used to realize accurate tracking for estimating the speed of the vehicle. The salient characteristics of the proposed method are: (1) Traffic analysis and robust vehicle detection are possible by virtue of the use of deep neural networks. (2) Employing the state-of-the-art object detector, namely the You Only Look Once (YOLO) network, a tracking-by-detection method is then designed to detect and track vehicles. (3) The error rate of data association is reduced by associating the tracking process with a vehicle re-identification algorithm whereby the motion features are combined with depth features of the vehicle. (4) Considering the varying Unmanned Aerial Vehicle (UAV) altitude and video resolution, a novel exponential mapping model of the vehicle speed from the pixel space to the real world is presented. The accuracy of speed estimation is effectively improved by employing the least squares algorithm to fit the measurement data. Simulation results and comparative studies with the state-of-the-art methods demonstrate superior performance of the proposed approach for vehicle tracking and speed estimation.
From convolutional neural networks to models of higher‐level cognition (and back again)

The remarkable successes of convolutional neural networks (CNNs) in modern computer vision are by now well known, and they are increasingly being explored as computational models of the human visual system. In this paper, we ask whether CNNs might also provide a basis for modeling higher-level cognition, focusing on the core phenomena of similarity and categorization. The most important advance comes from the ability of CNNs to learn high-dimensional representations of complex naturalistic images, substantially extending the scope of traditional cognitive models that were previously only evaluated with simple artificial stimuli. In all cases, the most successful combinations arise when CNN representations are used with cognitive models that have the capacity to transform them to better fit human behavior. One consequence of these insights is a toolkit for the integration of cognitively motivated constraints back into CNN training paradigms in computer vision and machine learning, and we review cases where this leads to improved performance. A second consequence is a roadmap for how CNNs and cognitive models can be more fully integrated in the future, allowing for flexible end-to-end algorithms that can learn representations from data while still retaining the structured behavior characteristic of human cognition.
Color Channel Perturbation Attacks for Fooling Convolutional Neural Networks and A Defense Against Such Attacks

The convolutional neural networks (CNNs) have emerged as a very powerful data dependent hierarchical feature extraction method. It is widely used in several computer vision problems. The CNNs learn the important visual features from training samples automatically. It is observed that the network overfits the training samples very easily. Several regularization methods have been proposed to avoid the overfitting. In spite of this, the network is sensitive to the color distribution within the images which is ignored by the existing approaches. In this paper, we discover the color robustness problem of CNN by proposing a color channel perturbation (CCP) attack to fool the CNNs. In CCP attack new images are generated with new channels created by combining the original channels with the stochastic weights. Experiments were carried out over widely used CIFAR10, Caltech256 and TinyImageNet datasets in the image classification framework. The VGG, ResNet and DenseNet models are used to test the impact of the proposed attack. It is observed that the performance of the CNNs degrades drastically under the proposed CCP attack. Result show the effect of the proposed simple CCP attack over the robustness of the CNN trained model. The results are also compared with existing CNN fooling approaches to evaluate the accuracy drop. We also propose a primary defense mechanism to this problem by augmenting the training dataset with the proposed CCP attack. The state-of-the-art performance using the proposed solution in terms of the CNN robustness under CCP attack is observed in the experiments. The code is made publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jayendrakantipudi/Color-Channel-Perturbation-Attack</uri> .
Making sense of digitally remediated touch in virtual reality experiences

Touch, often called the 'first sense', is fundamental to how we experience and know ourselves, others and the world. Increasingly, touch is being brought into the digital landscape. This paper explores this shifting landscape to understand the ways in which touch is re-mediated in the context of virtual reality. With attention to the sensoriality and sociality of touch, it asks what 'counts' as touch in VR, how is touch experienced and how is it incorporated into meaning making. We present and discuss findings from a multimodal and multisensory study of 16 participants interacting in two VR experiences to describe: the participants' material encounters with the virtual through a focus on touch practices, expectations and norms; the ways in which participants made meaning of (and with) virtual touch through their dynamic selection and orchestration of the range of semiotic and experiential resources available; and how these virtual touch experiences translated into discourses of touch in VR to emphasize continuities and change between the past, present and futures. The paper comments on the methodological challenges of researching touch in the emergent landscape of VR and asks how multimodality might engage newly with touch, perhaps the most under-rated and neglected of modes and senses, and its digital remediation.
Bimodal behavior of microlasers investigated with a two-channel photon-number-resolving transition-edge sensor system

We explore the photon-number distribution of bimodal quantum-dot micropillar lasers with a two-channel transition-edge sensor (TES) detection system. The two channels of the photon-number-resolving TES system simultaneously detect light emission of two orthogonal components of the micropillar's fundamental emission mode. The applied experimental scheme provides unprecedented access to the joint photon-number distribution and enables a profound insight into the dynamics and photon statistics of the gain-coupled mode components. In particular, the two-channel TES measurements reveal an optical bistability of the correlated laser modes leading to temporal hopping between emission associated with Poissonian and thermal-like emission statistics. The experimental data and theoretical modeling based on Monte Carlo simulations are in good agreement and reveal the anticorrelated behavior of the mode hopping, which results in intensity fluctuations and superthermal values of the autocorrelation function. Our investigations clearly demonstrate the great benefit of using photon-number-resolving detectors in nanophotonics to explore the rich physics of multimode micro- and nanolasers.2 MoreReceived 29 September 2020Accepted 1 February 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.013263Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasCavity quantum electrodynamicsNanophotonicsPhoton statisticsPhotonicsSemiconductor microcavitiesSemiconductor quantum opticsTechniquesPhoton countingCondensed Matter, Materials & Applied PhysicsAtomic, Molecular & Optical