Reviewerly/exHarmony huggingface dataset:
Building Hard Problems by Combining Easy Ones

In this work, we initiate a new conceptual line of attack on the fundamental question of how to generate hard problems. Motivated by the need for one-way functions in cryptography, we propose an information-theoretic framework to study the question of generating new provably hard one-way functions by composing functions that are easy to invert and evaluate, where each such easy function is modeled as a random oracles paired with another oracle that implements an inverse function.
Entropic and Operational Characterizations of Dynamic Quantum Resources

Dynamic quantum resource theories study the manipulation of quantum channels by means of a restricted set of free superoperations. In this paper, we formulate general dynamic resource theories using a "top-down" framework, and we provide systematic characterizations for closed and convex resource theories from both information-theoretic and operational perspectives. Our results are summarized as follows. First, we propose and investigate a branch of resource-induced measures of uncertainty, called the free conditional min-entropy (FCME), generalizing the conditional min-entropy and its dynamic extension to scenarios where information processing is subject to variable operational restriction. We provide a complete set of entropic conditions in terms of the FCME for characterizing channel convertibility via free superoperations in any closed and convex resource theory. We also find that the resource global robustness of channels can be equivalently cast as a mutual-information-like quantity derived from the FCME, thereby offering the resource global robustness an information-theoretic interpretation. Apart from the entropic approach, we also study closed and convex resource theories in the contexts of various operational tasks. These tasks are formulated such that each of them induces a complete set of operationally meaningful resource monotones, and therefore they can be used to faithfully test free convertibility between channels. We also systematically study the quantitative relations between the operational advantage of channels in these tasks and the resource robustness measures of channels. In particular, we prove that every well-defined robustness-based measure can be operationally interpreted as some kind of advantage in a task called semiquantum partial preprocessing. Ultimately, our results provide both entropic and operational characterizations for general dynamic quantum resources with a closed and convex structure.
On Correlation Detection of Gaussian Databases via Local Decision Making

In this work, we propose an efficient statistical test solving a problem of correlation detection between two Gaussian databases. Correlation detection is a hypothesis testing problem; under the null hypothesis, the databases are independent, and under the alternate hypothesis, they are correlated, under an unknown row permutation. We develop relatively tight bounds on the type-I and type-II error probabilities, and show that the analyzed detector performs better than a recently proposed one, at least for some specific parameter choices. Since the proposed detector relies on a statistic, which is a sum of dependent indicator random variables, then in order to bound the type-I probability of error, we develop a novel graph-theoretic technique for bounding the k-th order moments of such statistics.
An end to end workflow for synthetic data generation for robust object detection<sup>*</sup>

Object detection is a task in computer vision that involves detecting instances of visual objects of a particular class in digital images. Numerous computer vision tasks highly depend on object detection such as instance segmentation, image captioning and object tracking. A major purpose of object detection is to develop computational models that provide inputs crucial to computer vision applications. Convolutional Neural Networks (CNNs) have recently become popular due to their key roles in enabling object detection. However, the performance of CNNs is largely dependent upon the quality and quantity of training datasets, which are often difficult to obtain in real-world applications. In order to ensure the robustness of such models, it is vital that training instances are provided under various randomized conditions. These conditions are typically a combination of a variety of factors, including lighting conditions, object location, the presence of multiple objects in the scene, varieties of backgrounds, and the angle of the camera. In particular, companies, depending on their applications (such as fault detection, anomaly detection, condition monitoring, predictive quality and so on), require specialized models for their custom products and so always face difficulties in providing a large number of randomized conditioned instances of their objects. The primary reason is that the process of capturing randomized conditioned images of real objects is usually costly, time-consuming, and challenging in practice. Due to the efficiency gained so far via the use of synthetic data for training such systems, synthetic data has recently attracted considerable attention. This paper presents an end-to-end synthetic data generation method for building a robust object detection model for customized products using NVIDIA Omniverse and CNNs. In this paper, we demonstrate and evaluate our contribution to the modeling of chess pieces, where a total accuracy of 98.8 % was obtained.
RIPGeo: Robust Street-Level IP Geolocation

IP geolocation refers to the process of determining the geographic locations of Internet Protocol (IP) addresses, which is important for mobile computing and spatial data management. Despite extensive research efforts, a client-independent geolocation service with high accuracy and reliability has not yet been developed. This paper presents a graph neural network (GNN) model, dubbed RIPGeo, for robust street-level IP geolocation. Three factors that affect data quality are identified, and the importance of considering data quality in algorithm development is emphasized. Two novel self-supervised perturbational training strategies are proposed to enhance the generalization and robustness of the model. A multi-task learning framework is introduced to solve the homogenized representation problem caused by perturbational training, demonstrating much more efficiency than prevailing solutions. Theoretical analysis and experimental results demonstrate the superiority of our framework in significantly improving the accuracy and stability of street-level IP geolocation.
Collaborative Static and Dynamic Vision-Language Streams for Spatio-Temporal Video Grounding

Spatio-Temporal Video Grounding (STVG) aims to localize the target object spatially and temporally according to the given language query. It is a challenging task in which the model should well understand dynamic visual cues (e.g., motions) and static visual cues (e.g., object appearances) in the language description, which requires effective joint modeling of spatiotemporal visuallinguistic dependencies. In this work, we propose a novel framework in which a static vision-language stream and a dynamic vision-language stream are developed to collaboratively reason the target tube. The static stream performs cross-modal understanding in a single frame and learns to attend to the target object spatially according to intraframe visual cues like object appearances. The dynamic stream models visual-linguistic dependencies across multiple consecutive frames to capture dynamic cues like motions. We further design a novel cross-stream collaborative block between the two streams, which enables the static and dynamic streams to transfer useful and complementary information from each other to achieve collaborative reasoning. Experimental results show the effectiveness of the collaboration of the two streams and our overall frame-work achieves new state-of-the-art performance on both HCSTVG and VidSTG datasets.
The Privacy-Utility Tradeoff in Rank-Preserving Dataset Obfuscation

Dataset obfuscation refers to techniques in which random noise is added to the entries of a given dataset, prior to its public release, to protect against leakage of private information. In this work, dataset obfuscation under two objectives is considered: i) rank-preservation: to preserve the row ordering in the obfuscated dataset induced by a given rank function, and ii) anonymity: to protect user anonymity under fingerprinting attacks. The first objective, rank-preservation, is of interest in applications such as the design of search engines and recommendation systems, feature matching, and social network analysis. Fingerprinting attacks, considered in evaluating the anonymity objective, are privacy attacks where an attacker constructs a fingerprint of a victim based on its observed activities, such as online web activities, and compares this fingerprint with information extracted from a publicly released obfuscated dataset to identify the victim. By evaluating the performance limits of a class of obfuscation mechanisms over asymptotically large datasets, a fundamental trade-off is quantified between rank-preservation and user anonymity. Single-letter obfuscation mechanisms are considered, where each entry in the dataset is perturbed by independent noise, and their fundamental performance limits are characterized by leveraging large deviation techniques. The optimal obfuscating test-channel, optimizing the privacy-utility tradeoff, is characterized in the form of a convex optimization problem which can be solved efficiently. Numerical simulations of various scenarios are provided to verify the theoretical derivations.
DINN360: Deformable Invertible Neural Network for Latitude-aware 360° Image Rescaling

With the rapid development of virtual reality, 360° images have gained increasing popularity. Their wide field of view necessitates high resolution to ensure image quality. This, however, makes it harder to acquire, store and even process such 360° images. To alleviate this issue, we propose the first attempt at 360° image rescaling, which refers to downscaling a 360° image to a visually valid lowresolution (LR) counterpart and then upscaling to a highresolution (HR) 360° image given the LR variant. Specifically, we first analyze two 360° image datasets and observe several findings that characterize how 360° images typically change along their latitudes. Inspired by these findings, we propose a novel deformable invertible neural network (INN), named DINN360, for latitude-aware 360° image rescaling. In DINN360, a deformable INN is designed to downscale the LR image, and project the high-frequency (HF) component to the latent space by adaptively handling various deformations occurring at different latitude regions. Given the downscaled LR image, the high-quality HR image is then reconstructed in a conditional latitude-aware manner by recovering the structure-related HF component from the latent space. Extensive experiments over four public datasets show that our DINN360 method performs considerably better than other state-of-the-art methods for 2 x, 4 x and 8 x 360° image rescaling.
Power Bundle Adjustment for Large-Scale 3D Reconstruction

We introduce Power Bundle Adjustment as an expansion type algorithm for solving large-scale bundle adjustment problems. It is based on the power series expansion of the inverse Schur complement and constitutes a new family of solvers that we call inverse expansion methods. We theoretically justify the use of power series and we prove the convergence of our approach. Using the real-world BAL dataset we show that the proposed solver challenges the state-of-the-art iterative methods and significantly accelerates the solution of the normal equation, even for reaching a very high accuracy. This easy-to-implement solver can also complement a recently presented distributed bundle adjustment framework. We demonstrate that employing the proposed Power Bundle Adjustment as a subproblem solver significantly improves speed and accuracy of the distributed optimization.
Sustainable Educational Metaverse Content and System Based on Deep Learning for Enhancing Learner Immersion

Social distancing has encouraged the use of various non-face-to-face services utilizing information and communication technology, especially in the education sector. Educators and learners are increasingly utilizing online technology to conduct non-face-to-face classes, which has resulted in an increased use of EduTech. Virtual education is expected to expand continuously. However, students involved in virtual education find it difficult to focus and participate in the classes. Hence, we propose a system that can improve learners’ focus and immersion in metaverse-based education. In this paper, we propose a sustainable educational metaverse content and system based on deep learning that can enhance learners’ immersion. We built an AI-based simulation that judges learning activities based on the learning behavior rather than on the learner’s device and program events and allows the user to proceed to the next level of education. In the simulation implemented in this study, virtual reality educational contents were created for 12 educational activities, and the effectiveness of four learning models in assessing the learning effectiveness of learners was evaluated. From the four models, an ensemble model with boosting was adopted considering its accuracy, complexity, and efficiency. The F1-score and specificity of the adopted learning model were confirmed. This model was applied to the system in a simulation.
Public education about ShakeAlert<sup>®</sup> earthquake early warning: evaluation of an animated video in English and Spanish

Publicly available technologies are only truly ‘public’ if people are aware of them and know how to use them. In the US, significant resources have been devoted to public education around the recently released ShakeAlert® earthquake early warning (EEW) system operated by the U.S. Geological Survey; however, those efforts have been inconsistently evaluated, leaving educators and communicators with questions about their effectiveness, particularly for engaging broad, diverse, and multilingual publics. This mixed methods, dual-language (English/Spanish) study addresses this gap by examining an animated video designed to increase awareness, understanding, and action related to ShakeAlert EEW. Results indicate that the video was enjoyable, easily understandable, and positively impacted viewers’ intentions to ‘Drop, Cover, and Hold On’ (DCHO) following a ShakeAlert-powered alert. Viewers also expressed a desire for more information about how to receive alerts and how to protect themselves in situations where DCHO is impractical. This study suggests that animated video, developed and disseminated in multiple languages, can be an effective tool for impacting public understanding and behavior related to geohazards. Additionally, the process and findings of this study suggest that outcomes may be improved by engaging multilingual and multicultural audiences earlier and throughout the video development process.
Intelligent Automation, Service Robots, and AI – the Service Revolution is in Full Swing

The world economyservice revolution received a significant boost during the late eighteenth century with the advent of the Industrial Revolution, which introduced automated manufacturing processes.
Quantum canonical correlation analysis algorithm

Abstract Canonical correlation analysis (CCA) is a fundamental technique used to analyze data correlation in various fields, including video and medical data analysis. In this paper, we propose a quantum canonical correlation analysis (QCCA) algorithm. First, we introduce a combined density matrix representation method that transforms CCA into generalized eigenvalue decomposition. Moreover, to address the challenge of performing generalized eigenvalue decomposition in high-dimensional scenarios, we propose a quantum method for extracting the canonical principal axes. In this method, two sets of variables are transformed into a reduced density matrix, so that the product of variable matrices can be accelerated by phase estimation and controlled rotation. Complexity analysis shows that the QCCA algorithm achieves exponential acceleration in variable dimensions n , p and variable size m compared to classical algorithms. The QCCA algorithm serves as a foundation for the subsequent development of quantum algorithms for classification, regression, and other machine learning tasks.
Cryptocurrency Portfolio Management Using Reinforcement Learning

Portfolio management is the science of choosing the best investment policies and strategies with the aim of getting maximum returns. Simply, it means managing the assets/stocks of a company, organization, or individual and taking into account the risks, and increasing the profit. This paper proposes portfolio management using a bot leveraging a reinforcement learning environment specifically for cryptocurrencies which are a hot topic in the current world of technology. The reinforcement Learning Environment gives the reward/penalty to the agent, which helps it train itself during the training process and make decisions based on the trial-and-error method. Dense and CNN networks are used for training the agent to taking the decision to either buy, hold or sell the coin. Various technical indicators, like MACD, SMA, etc., are also included in the dataset while making the decisions. The bot is trained on 3-year hourly data of Bitcoin, and results demonstrate that the Dense and CNN network models show a good amount of profit against a starting balance of 1,000, indicating that reinforcement learning environments can be efficacious and can be incorporated into the trading environments.
Deep SLRT: The Development of Deep Learning based Multilingual and Multimodal Sign Language Recognition and Translation Framework

Developing deep neural models for continuous recognition of sign gestures and generation of sign videos from spoken sentences is still challenging and requires much investigation in earlier studies. Although the recent approaches provide plausible solutions for these tasks, they still fail to perform well in handling continuous sentences and visual quality aspects. The recent advancements in deep learning techniques envisioned new milestones in handling such complex tasks and producing impressive results. This paper proposes novel approaches to develop a deep neural framework for recognizing multilingual sign datasets and multimodal sign gestures. In addition to that, the proposed model generates sign gesture videos from spoken sentences. In the first fold, it deals with the sign gesture recognition tasks using a hybrid CNN-LSTM algorithm. The second fold uses the hybrid NMT-GAN techniques to produce high quality sign gesture videos. The proposed model has been evaluated using different quality metrics. We also compared the proposed model performance qualitatively using different benchmark sign language datasets. The proposed model achieves 98% classification accuracy and improved video quality in sign language recognition and video generation tasks.
Hybrid Convolutional Recurrent Neural Network for Isolated Indian Sign Language Recognition

Even though the hearing and vocally impaired populace rely entirely on Sign Language (SL) as a way of communication, the majority of the worldwide people are unable to interpret it. This creates a significant language barrier between these two categories. The need for developing Sign Language Recognition (SLR) systems has arisen as a result of the communication breakdown between the deaf-mute and the general populace. This paper proposes a Hybrid Convolutional Recurrent Neural Network-based (H-CRNN) framework for Isolated Indian Sign Language recognition. The proposed framework is divided into two modules: the Feature Extraction module and the Sign Model Recognition module. The Feature Extraction module exploits the Convolutional Neural Network-based framework, and the Model recognition exploits the LSTM/GRU-based framework for Indian sign representation of English Alphabets and numbers. The proposed models are evaluated using a newly created Isolated Sign dataset called ISLAN, the first multi-signer Indian Sign Language representation for English Alphabets and Numbers. The performance evaluation with the other state-o- -the-art neural network models have shown that the proposed H-CRNN model has better accuracy.
An Ensemble Learning Approach Based on TabNet and Machine Learning Models for Cheating Detection in Educational Tests

The pervasive issue of cheating in educational tests has emerged as a paramount concern within the realm of education, prompting scholars to explore diverse methodologies for identifying potential transgressors. While machine learning models have been extensively investigated for this purpose, the untapped potential of TabNet, an intricate deep neural network model, remains uncharted territory. Within this study, a comprehensive evaluation and comparison of 12 base models (naive Bayes, linear discriminant analysis, Gaussian process, support vector machine, decision tree, random forest, Extreme Gradient Boosting (XGBoost), AdaBoost, logistic regression, k-nearest neighbors, multilayer perceptron, and TabNet) was undertaken to scrutinize their predictive capabilities. The area under the receiver operating characteristic curve (AUC) was employed as the performance metric for evaluation. Impressively, the findings underscored the supremacy of TabNet (AUC = 0.85) over its counterparts, signifying the profound aptitude of deep neural network models in tackling tabular tasks, such as the detection of academic dishonesty. Encouraged by these outcomes, we proceeded to synergistically amalgamate the two most efficacious models, TabNet (AUC = 0.85) and AdaBoost (AUC = 0.81), resulting in the creation of an ensemble model christened TabNet-AdaBoost (AUC = 0.92). The emergence of this novel hybrid approach exhibited considerable potential in research endeavors within this domain. Importantly, our investigation has unveiled fresh insights into the utilization of deep neural network models for the purpose of identifying cheating in educational tests.