Reviewerly/exHarmony huggingface dataset:
Security Operations &amp;amp; Incident Management: Case Study Analysis

As a professional independent digital intruder consultant, we would undertake this investigation under full credibility and responsibility of the procedure; minimizing the cost involved, providing clear-cut instructions and guidance required for the process of investigation. So, the main purpose conducting this digital intruder investigation, on your company is to carefully analyse the evidence from the crime scene and develop proper in detailed report of the digital investigation procedure, objectives, findings that could help in the forensic investigation and provide your company a clear insight on what should be done. The main objective is to find out the criminal and the motive of the breach and perhaps secure your company from possible threats and intruders to make the investigation successful. We have identified that Dash LLC is an international retail company which has a management system for its managers used to view and manage the employees’ information, web browsing capabilities for its employees and guests which are accessible only from the London branch, also an e-commerce website which could be accessed from anywhere for the customers from the new branch in London. This branch of Dash LLC in London has been attacked by a DDOS (Distributed Denial of Service) Attack which disrupted its service for many hours. Later Dash LLC’s IT department has identified that the attack was caused by the Mirai malware infection [r] . This Mirai malware attack is launched from botnets from Internet enabled devices and other devices like CCTVs connected to the network of the London branch which would create a DDOS attack by remotely controlling these devices and connecting them together to flood the servers of the London branch. (Dobbins, 2016)
SYNTHESIS OF A RADAR RECOGNITION ALGORITHM WITH ABILITY TO MEET RELIABILITY OF DECISIONS

This paper is devoted to the variant of synthesis of a radar recognition algorithm with the ability to meet reliability of decisions. The algorithm is based on the theory of sequential analysis in combination with flexible change in the level of the classification detail when the observation time cannot be increased. Compared with one-step algorithms, the proposed algorithm allows guaranteeing “the posteriori probability of decisions is not smaller than the set value”. The proposed algorithm can be used in radar target recognition systems.
НЕОЕТИКА, ЛІБЕРАЛІЗМ ЧИ НЕОРОМАНТИКА В РЕЖИСУРІ

The article continues a series of the author's scientific works devoted to the consistent formation and development of a new direction of modern knowledge - the theory of directing a stage spectacle, as a separate type of research work. The theory of dire
Learning Acceptance Regions for Many Classes with Anomaly Detection

Set-valued classification, a new classification paradigm that aims to identify all the plausible classes that an observation belongs to, can be obtained by learning the acceptance regions for all classes. Many existing set-valued classification methods do not consider the possibility that a new class that never appeared in the training data appears in the test data. Moreover, they are computationally expensive when the number of classes is large. We propose a Generalized Prediction Set (GPS) approach to estimate the acceptance regions while considering the possibility of a new class in the test data. The proposed classifier minimizes the expected size of the prediction set while guaranteeing that the class-specific accuracy is at least a pre-specified value. Unlike previous methods, the proposed method achieves a good balance between accuracy, efficiency, and anomaly detection rate. Moreover, our method can be applied in parallel to all the classes to alleviate the computational burden. Both theoretical analysis and numerical experiments are conducted to illustrate the effectiveness of the proposed method.
FoVolNet: Fast Volume Rendering using Foveated Deep Neural Networks

Volume data is found in many important scientific and engineering applications. Rendering this data for visualization at high quality and interactive rates for demanding applications such as virtual reality is still not easily achievable even using professional-grade hardware. We introduce FoVolNet -- a method to significantly increase the performance of volume data visualization. We develop a cost-effective foveated rendering pipeline that sparsely samples a volume around a focal point and reconstructs the full-frame using a deep neural network. Foveated rendering is a technique that prioritizes rendering computations around the user's focal point. This approach leverages properties of the human visual system, thereby saving computational resources when rendering data in the periphery of the user's field of vision. Our reconstruction network combines direct and kernel prediction methods to produce fast, stable, and perceptually convincing output. With a slim design and the use of quantization, our method outperforms state-of-the-art neural reconstruction techniques in both end-to-end frame times and visual quality. We conduct extensive evaluations of the system's rendering performance, inference speed, and perceptual properties, and we provide comparisons to competing neural image reconstruction techniques. Our test results show that FoVolNet consistently achieves significant time saving over conventional rendering while preserving perceptual quality.
Fast-Image2Point: Towards Real-Time Point Cloud Reconstruction of a Single Image using 3D Supervision

A key question in the problem of 3D reconstruction is how to train a machine or a robot to model 3D objects. Many tasks like navigation in real-time systems such as autonomous vehicles directly depend on this problem. These systems usually have limited computational power. Despite considerable progress in 3D reconstruction systems in recent years, applying them to real-time systems such as navigation systems in autonomous vehicles is still challenging due to the high complexity and computational demand of the existing methods. This study addresses current problems in reconstructing objects displayed in a single-view image in a faster (real-time) fashion. To this end, a simple yet powerful deep neural framework is developed. The proposed framework consists of two components: the feature extractor module and the 3D generator module. We use point cloud representation for the output of our reconstruction module. The ShapeNet dataset is utilized to compare the method with the existing results in terms of computation time and accuracy. Simulations demonstrate the superior performance of the proposed method. Index Terms-Real-time 3D reconstruction, single-view reconstruction, supervised learning, deep neural network
Information-theoretic Abstraction of Semantic Octree Models for Integrated Perception and Planning

In this paper, we develop an approach that enables autonomous robots to build and compress semantic environment representations from point-cloud data. Our approach builds a three-dimensional, semantic tree representation of the environment from sensor data which is then compressed by a novel information-theoretic tree-pruning approach. The proposed approach is probabilistic and incorporates the uncertainty in semantic classification inherent in real-world environments. Moreover, our approach allows robots to prioritize individual semantic classes when generating the compressed trees, so as to design multi-resolution representations that retain the relevant semantic information while simultaneously discarding unwanted semantic categories. We demonstrate the approach by compressing semantic octree models of a large outdoor, semantically rich, real-world environment. In addition, we show how the octree abstractions can be used to create semantically-informed graphs for motion planning, and provide a comparison of our approach with uninformed graph construction methods such as Halton sequences.
Dynamic Relevance Graph Network for Knowledge-Aware Question Answering

This work investigates the challenge of learning and reasoning for Commonsense Question Answering given an external source of knowledge in the form of a knowledge graph (KG). We propose a novel graph neural network architecture, called Dynamic Relevance Graph Network (DRGN). DRGN operates on a given KG subgraph based on the question and answers entities and uses the relevance scores between the nodes to establish new edges dynamically for learning node representations in the graph network. This explicit usage of relevance as graph edges has the following advantages, a) the model can exploit the existing relationships, re-scale the node weights, and influence the way the neighborhood nodes' representations are aggregated in the KG subgraph, b) It potentially recovers the missing edges in KG that are needed for reasoning. Moreover, as a byproduct, our model improves handling the negative questions due to considering the relevance between the question node and the graph entities. Our proposed approach shows competitive performance on two QA benchmarks, CommonsenseQA and OpenbookQA, compared to the state-of-the-art published results.
Improved noise resistance analysis for path-entangled subspace quantum key distribution

We revisit the practical implementation of high-dimensional quantum key distribution protocol using path entanglement reported in [Phys.Rev.Lett.~127,~110505,~2021]. Here we refine the noise robustness analysis performed there by simulating the effect of different types of channel noise on the noiseless experimental data. Further, we extend the analysis to two kinds of asymmetric scenarios. In the first scenario the entanglement source is placed in Alice's lab and thus Only one noisy channel has to be considered. In the second scenario, Bob's detectors are placed at different physical locations and therefore such scenario implements a simple communication network. Our analysis can also be extended to other experimental platforms and therefore provides a novel framework for fair noise robustness comparison of high-dimensional quantum key distribution protocols.
A Fast Algorithm for Implementation of Koul's Minimum Distance Estimators and Their Application to Image Segmentation

Minimum distance estimation methodology based on an empirical distribution function has been popular due to its desirable properties including robustness. Even though the statistical literature is awash with the research on the minimum distance estimation, the most of it is confined to the theoretical findings: only few statisticians conducted research on the application of the method to real world problems. Through this paper, we extend the domain of application of this methodology to various applied fields by providing a solution to a rather challenging and complicated computational problem. The problem this paper tackles is an image segmentation which has been used in various fields. We propose a novel method based on the classical minimum distance estimation theory to solve the image segmentation problem. The performance of the proposed method is then further elevated by integrating it with the ``segmenting-together" strategy. We demonstrate that the proposed method combined with the segmenting-together strategy successfully completes the segmentation problem when it is applied to the complex, real images such as magnetic resonance images.
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to learn collaboratively without sharing their private data. However, excessive computation and communication demands pose challenges to current FL frameworks, especially when training large-scale models. To prevent these issues from hindering the deployment of FL systems, we propose a lightweight framework where clients jointly learn to fuse the representations generated by multiple fixed pre-trained models rather than training a large-scale model from scratch. This leads us to a more practical FL problem by considering how to capture more client-specific and class-relevant information from the pre-trained models and jointly improve each client's ability to exploit those off-the-shelf models. In this work, we design a Federated Prototype-wise Contrastive Learning (FedPCL) approach which shares knowledge across clients through their class prototypes and builds client-specific representations in a prototype-wise contrastive manner. Sharing prototypes rather than learnable model parameters allows each client to fuse the representations in a personalized way while keeping the shared knowledge in a compact form for efficient communication. We perform a thorough evaluation of the proposed FedPCL in the lightweight framework, measuring and visualizing its ability to fuse various pre-trained models on popular FL datasets.
Progressive with Purpose: Guiding Progressive Inpainting DNNs through Context and Structure

The advent of deep learning in the past decade has significantly helped advance image inpainting. Although achieving promising performance, deep learning-based inpainting algorithms still struggle from the distortion caused by the fusion of structural and contextual features, which are commonly obtained from, respectively, deep and shallow layers of a convolutional encoder. Motivated by this observation, we propose a novel progressive inpainting network that maintains the structural and contextual integrity of a processed image. More specifically, inspired by the Gaussian and Laplacian pyramids, the core of the proposed network is a feature extraction module named GLE. Stacking GLE modules enables the network to extract image features from different image frequency components. This ability is important to maintain structural and contextual integrity, for high frequency components correspond to structural information while low frequency components correspond to contextual information. The proposed network utilizes the GLE features to progressively fill in missing regions in a corrupted image in an iterative manner. Our benchmarking experiments demonstrate that the proposed method achieves clear improvement in performance over many state-of-the-art inpainting algorithms.
Extreme Multi-Domain, Multi-Task Learning With Unified Text-to-Text Transfer Transformers

Text-to-text transformers have shown remarkable success in the task of multi-task transfer learning, especially in natural language processing (NLP). However, while there have been several attempts to train transformers on different domains, there is usually a clear relationship between these domains, e.g.,, code summarization, where the natural language summary describes the code. There have been very few attempts to study how multi-task transfer learning works on tasks in significantly different domains. In this project, we investigated the behavior of multi-domain, multi-task learning using multi-domain text-to-text transfer transformers (MD-T5) on four tasks across two domains - Python Code and Chess. We carried out extensive experiments using three popular training strategies: Bert-style joint pretraining + successive finetuning, GPT-style joint pretraining + successive finetuning, and GPT-style joint pretraining + joint finetuning. Also, we evaluate the model on four metrics - Play Score, Eval Score, BLEU Score, and Multi-Domain Learning Score (MDLS). These metrics measure performance across the various tasks and multi-domain learning. We show that while negative knowledge transfer and catastrophic forgetting are still considerable challenges for all the models, the GPT-style joint pretraining + joint finetuning strategy showed the most promise in multi-domain, multi-task learning as it performs well across all four tasks while still keeping its multi-domain knowledge.
FT-HID: A Large Scale RGB-D Dataset for First and Third Person Human Interaction Analysis

Analysis of human interaction is one important research topic of human motion analysis. It has been studied either using first person vision (FPV) or third person vision (TPV). However, the joint learning of both types of vision has so far attracted little attention. One of the reasons is the lack of suitable datasets that cover both FPV and TPV. In addition, existing benchmark datasets of either FPV or TPV have several limitations, including the limited number of samples, participant subjects, interaction categories, and modalities. In this work, we contribute a large-scale human interaction dataset, namely, FT-HID dataset. FT-HID contains pair-aligned samples of first person and third person visions. The dataset was collected from 109 distinct subjects and has more than 90K samples for three modalities. The dataset has been validated by using several existing action recognition methods. In addition, we introduce a novel multi-view interaction mechanism for skeleton sequences, and a joint learning multi-stream framework for first person and third person visions. Both methods yield promising results on the FT-HID dataset. It is expected that the introduction of this vision-aligned large-scale dataset will promote the development of both FPV and TPV, and their joint learning techniques for human action analysis. The dataset and code are available at \href{https://github.com/ENDLICHERE/FT-HID}{here}.
NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation

Optical computing is an emerging technology for next-generation efficient artificial intelligence (AI) due to its ultra-high speed and efficiency. Electromagnetic field simulation is critical to the design, optimization, and validation of photonic devices and circuits. However, costly numerical simulation significantly hinders the scalability and turn-around time in the photonic circuit design loop. Recently, physics-informed neural networks have been proposed to predict the optical field solution of a single instance of a partial differential equation (PDE) with predefined parameters. Their complicated PDE formulation and lack of efficient parametrization mechanisms limit their flexibility and generalization in practical simulation scenarios. In this work, for the first time, a physics-agnostic neural operator-based framework, dubbed NeurOLight, is proposed to learn a family of frequency-domain Maxwell PDEs for ultra-fast parametric photonic device simulation. We balance the efficiency and generalization of NeurOLight via several novel techniques. Specifically, we discretize different devices into a unified domain, represent parametric PDEs with a compact wave prior, and encode the incident light via masked source modeling. We design our model with parameter-efficient cross-shaped NeurOLight blocks and adopt superposition-based augmentation for data-efficient learning. With these synergistic approaches, NeurOLight generalizes to a large space of unseen simulation settings, demonstrates 2-orders-of-magnitude faster simulation speed than numerical solvers, and outperforms prior neural network models by ~54% lower prediction error with ~44% fewer parameters. Our code is available at https://github.com/JeremieMelo/NeurOLight.
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition

Skeleton-based action recognition receives increasing attention because the skeleton representations reduce the amount of training data by eliminating visual information irrelevant to actions. To further improve the sample efficiency, meta-learning-based one-shot learning solutions were developed for skeleton-based action recognition. These methods find the nearest neighbor according to the similarity between instance-level global average embedding. However, such measurement holds unstable representativity due to inadequate generalized learning on local invariant and noisy features, while intuitively, more fine-grained recognition usually relies on determining key local body movements. To address this limitation, we present the Adaptive Local-Component-aware Graph Convolutional Network, which replaces the comparison metric with a focused sum of similarity measurements on aligned local embedding of action-critical spatial/temporal segments. Comprehensive one-shot experiments on the public benchmark of NTU-RGB+D 120 indicate that our method provides a stronger representation than the global embedding and helps our model reach state-of-the-art.
Creating quantum-resistant classical-classical OWFs from quantum-classical OWFs

One-way functions (OWF) are one of the most essential cryptographic primitives, the existence of which results in wide-ranging ramifications such as private-key encryption and proving $P \neq NP$. These OWFs are often thought of as having classical input and output (i.e. binary strings), however, recent work proposes OWF constructions where the input and/or the output can be quantum. In this paper, we demonstrate that quantum-classical (i.e. quantum input, classical output) OWFs can be used to produce classical-classical (i.e. classical input, classical output) OWFs that retain the one-wayness property against any quantum polynomial adversary (i.e. quantum-resistant). We demonstrate this in two ways. Firstly, we propose a definition of quantum-classical OWFs and show that the existence of such a quantum-classical OWF would imply the existence of a classical-classical OWF. Secondly, we take a proposed quantum-classical OWF and demonstrate how to turn it into a classical-classical OWF. In summary, this paper showcases another possible route into proving the existence of classical-classical OWFs (assuming intermediate quantum computations are allowed) using a "domain-shifting" technique between classical and quantum information, with the added bonus that such OWFs are also going to be quantum-resistant.
A Data-Centric Methodology and Task Typology for Time-Stamped Event Sequences

Task abstractions and taxonomic structures for tasks are useful for designers of interactive data analysis approaches, serving as design targets and evaluation criteria alike. For individual data types, dataset-specific taxonomic structures capture unique data characteristics, while being generalizable across application domains. The creation of dataset-centric but domain-agnostic taxonomic structures is difficult, especially if best practices for a focused data type are still missing, observing experts is not feasible, and means for reflection and generalization are scarce. We discovered this need for methodological support when working with time-stamped event sequences, a datatype that has not yet been fully systematically studied in visualization research. To address this shortcoming, we present a methodology that enables researchers to abstract tasks and build dataset-centric taxonomic structures in five phases (data collection, coding, task categorization, task synthesis, and action-target(criterion) crosscut). We validate the methodology by applying it to time-stamped event sequences and present a task typology that uses triples as a novel language of description for tasks: (1) action, (2) data target, and (3) data criterion. We further evaluate the descriptive power of the typology with a real-world case on cybersecurity.