Reviewerly/exHarmony huggingface dataset:
Copy Motion From One to Another: Fake Motion Video Generation

One compelling application of artificial intelligence is to generate a video of a target person performing arbitrary desired motion (from a source person). While the state-of-the-art methods are able to synthesize a video demonstrating similar broad stroke motion details, they are generally lacking in texture details. A pertinent manifestation appears as distorted face, feet, and hands, and such flaws are very sensitively perceived by human observers. Furthermore, current methods typically employ GANs with a L2 loss to assess the authenticity of the generated videos, inherently requiring a large amount of training samples to learn the texture details for adequate video generation. In this work, we tackle these challenges from three aspects: 1) We disentangle each video frame into foreground (the person) and background, focusing on generating the foreground to reduce the underlying dimension of the network output. 2) We propose a theoretically motivated Gromov-Wasserstein loss that facilitates learning the mapping from a pose to a foreground image. 3) To enhance texture details, we encode facial features with geometric guidance and employ local GANs to refine the face, feet, and hands. Extensive experiments show that our method is able to generate realistic target person videos, faithfully copying complex motions from a source person. Our code and datasets are released at https://github.com/Sifann/FakeMotion.
Surrogate-assisted neuroevolution

Though Neuroevolution (NE) and Neural Architecture Search (NAS) have emerged as techniques for automating the design of neural networks, they are expensive and time consuming: they require training many neural networks and have largely resisted the benefits of surrogate-based optimization approaches, as it is difficult to model the performance of variable network architectures. We propose a novel and general framework for surrogate-assisted search of neural architectures consisting of two components: (1) an algorithm which leverages grammars to generate tensor representations of variable neural network topologies; and an evolutionary algorithm which employs a surrogate model to expedite architecture search using active learning. We demonstrate that our model can produce accurate performance predictions for unseen architectures, realizing a 5x reduction in the total compute required for search while improving asymptotic performance. We also illustrate that the surrogate models are transferable to new domains via a real-world transfer learning case study using industrial time series data.
Homomorphic Encryption Privacy Preserving Amicable E-voting System

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
An Improved Object Detection of Image Based on Multi-task Learning

Object detection has a wide range of applications in the real world. For example, accurate identification of object in autonomous driving is an important prerequisite for realizing safe autonomous driving. The current object detection models can't utilize more detailed semantic information and solve the problems of different objects in the image. This paper improves YOLOV5 by introducing semantic segmentation to structure a multi task framework. Convolutional Neural Network (CNN) is used to extract features from input image in this paper. The feature maps are shared by detection thread and segmentation thread to complete object detection and semantic segmentation. Experimental results on the Cityscapes dataset show that the improved model achieves better performance compared with some other models.
Security Issues and Challenges in Internet of Things (IOT) System

In terms of privacy and security, the Internet of Things has considerable limitations. Some IoT issues include insufficient device upgrades, a lack of effective and strong security measures, operator ignorance, and untrustworthy dynamic device monitoring. Researchers are investigating the history of internet-of-things systems and security measures in this study, as well as recognizing (a) various security and confidentiality concerns, (b) methods for securing the apparatuses of IoT-enabled settings and structures, (c) currently available security resolutions, and (d) the most appropriate privacy representations for the various levels of IoT-powered apps. Researchers suggested a new layered paradigm in general and extended with privacy and security apparatuses and layer documentation. Implementation and evaluation of the suggested cloud/edge-based IoT system. The lowest layer is made up of Internet of Things (IoT) nodes that were constructed as Virtual Machines with Amazon Web Services (AWS). Researchers leveraged AWS's cloud-enabled IoT ecosystem to create the topcoat (the cloud). Researchers developed security certificates to facilitate data transmission across the projected cloud/edge permitted IoT representation's tiers.
Privacy-Preserving Synthetic Educational Data Generation

Institutions collect massive learning traces but they may not disclose it for privacy issues. Synthetic data generation opens new opportunities for research in education. In this paper we present a generative model for educational data that can preserve the privacy of participants, and an evaluation framework for comparing synthetic data generators. We show how naive pseudonymization can lead to re-identification threats and suggest techniques to guarantee privacy. We evaluate our method on existing massive educational open datasets.
Comparisons of Filter, Wrapper and Embedded-Based Feature Selection Techniques for Consistency of Software Metrics Analysis

Identifying and selecting the most consistent subset of metrics which improves the performance of software defect prediction model is paramount but challenging problem as it receives little attention in literature. The current research aimed at investigating the consistency of subsets of metrics that are produced by embedded feature selection techniques. Ten (10) feature selection techniques used from the families of filter and wrapper-based feature selection techniques commonly used in the defect prediction domain. Ten (10) publicly available defect datasets were studied which span both proprietary and open source domains. SVM-RFE-RF presented 42-93% consistent metrics across datasets. While the prior study on non-Embedded produced 56.5% consistent metrics at median. SVM-RFE-LF approach of Embedded Feature Selection Technique produced 54-80% consistent metrics across datasets and 42.5% at median. To state the purpose of tittle has been achieved Embedded based Feature Selection Techniques produced most efficient consistent subset selection across the entire datasets and amongst the feature selection techniques as compared with counterpart filter and wrapper-based feature selection techniques
A Moderated Mediation Model of Self-Leadership and Innovative Work Behaviors

By infusing social cognitive theory, the present study examines the moderated mediation model of self-leadership on innovative work behaviors. This study investigates the mediating effect of creative self-efficacy and the moderating influence of knowledge sharing in the proposed model. To assess this phenomenon, the data were collected at two different times from 283 voluntary participants working in different software houses in major cities of Punjab, Pakistan. The findings indicated that self-leadership is essential for promoting innovative work behaviors. The results also support the mediating role of creative self-efficacy in the association between self-leadership and innovative work behaviors. Moreover, the moderating impact of knowledge sharing indicated that a higher level of knowledge sharing strengthens the relationship between self-leadership and innovative work behaviors. This study recommends ways for organizations to develop innovative work behavior through self-leadership attributes. Keywords Self-Leadership; Creative Self-Efficacy; Knowledge Sharing; Innovative Work Behaviors
Image Recognition Method based on Artificial Intelligence Technology

Nowadays, with the rapid development of computer technology, image recognition technology is gradually applied to all aspects of life. Image transmission technology and storage technology in image technology have been very mature, but the key technology in image technology, image recognition technology is still In the process of development, it has gradually become a major research hotspot. Due to the limitations of traditional image recognition methods, there are still many problems in the application process. On this basis, image recognition combined with artificial intelligence has gradually become a research hotspot. The purpose of this article is to study image recognition methods based on artificial intelligence technology, and analyze the existing commonly used image recognition methods, and then use several algorithms to conduct experiments on the basis of artificial intelligence technology and perform the recognition value and image matching rate. The purpose of comparison is to find the optimal image recognition algorithm. Experiments show that the weighted average filter algorithm proposed in this paper based on the average filter algorithm has the highest performance index of 32.68 in the filter algorithm experiment. The image matching rate in the image matching rate experiment above 94%, it meets the expectations of the experiment and has certain practical significance for practical applications.
Multimodal Intent Discovery from Livestream Videos

Individuals, educational institutions, and businesses are prolific at generating instructional video content such as "how-to" and tutorial guides.While significant progress has been made in basic video understanding tasks, identifying procedural intent within these instructional videos is a challenging and important task that remains unexplored but essential to video summarization, search, and recommendations.This paper introduces the problem of instructional intent identification and extraction from software instructional livestreams.We construct and present a new multimodal dataset consisting of software instructional livestreams and containing manual annotations for both detailed and abstract procedural intent that enable training and evaluation of joint video and text understanding models.We then introduce a multimodal cascaded cross-attention model to efficiently combine the weaker and noisier video signal with the more discriminative text signal.Our experiments show that our proposed model brings significant gains compared to strong baselines, including large-scale pretrained multimodal models.Our analysis further identifies that the task benefits from spatial as well as motion features extracted from videos, and provides insight on how the video signal is preferentially used for intent discovery.We also show that current models struggle to comprehend the nature of abstract intents, revealing important gaps in multimodal understanding and paving the way for future work. 1
HUE: Pretrained Model and Dataset for Understanding Hanja Documents of Ancient Korea

Historical records in Korea before the 20 th century were primarily written in Hanja, an extinct language based on Chinese characters and not understood by modern Korean or Chinese speakers.Historians with expertise in this time period have been analyzing the documents, but that process is very difficult and time-consuming, and language models would significantly speed up the process.Toward building and evaluating language models for Hanja, we release the Hanja Understanding Evaluation dataset consisting of chronological attribution, topic classification, named entity recognition, and summary retrieval tasks.We also present BERT-based models continued pretraining on the two major corpora from the 14 th to the 19 th centuries: the Annals of the Joseon Dynasty and Diaries of the Royal Secretariats. 1 We compare the models with several baselines on all tasks and show there are significant improvements gained by training on the two corpora.Additionally, we run zeroshot experiments on the Daily Records of the Royal Court and Important Officials (DRRI).The DRRI dataset has not been studied much by the historians, and not at all by the NLP community.
CurriculumTutor: An Adaptive Algorithm for Mastering a Curriculum

An important problem in an intelligent tutoring system (ITS) is that of adaptive sequencing of learning activities in a personalised manner so as to improve learning gains. In this paper, we consider intelligent tutoring in the learning by doing (LbD) setting, wherein the concepts to be learned along with their inter-dependencies are available as a curriculum graph, and a given concept is learned by performing an activity related to that concept (such as solving/answering a problem/question). For this setting, recent works have proposed algorithms based on multi-armed bandits (MAB), where activities are adaptively sequenced using the student response to those activities as a direct feedback. In this paper, we propose CurriculumTutor, a novel technique that combines a MAB algorithm and a change point detection algorithm for the problem of adaptive activity sequencing. Our algorithm improves upon prior MAB algorithms for the LbD setting by (i) providing better learning gains, and (ii) reducing hyper-parameters thereby improving personalisation. We show that our tutoring algorithm significantly outperforms prior approaches in the benchmark domain of two operand addition up to a maximum of four digits .
Square-root measurements and degradation of the resource state in port-based teleportation scheme

Abstract Port-based teleportation (PBT) is a protocol of quantum teleportation in which a receiver does not have to apply correction to the transmitted state. In this protocol two spatially separated parties can teleport an unknown quantum state only by exploiting joint measurements on number of shared d -dimensional maximally entangled states (resource state) together with a state to be teleported and one way classical communication. In this paper we analyse for the first time the recycling protocol for the deterministic PBT beyond the qubit case. In the recycling protocol the main idea is to re-use the remaining resource state after one or many rounds of PBT for further processes of teleportation. The key property is to learn how much the underlying resource state degrades after every round of the teleportation process. We measure this by evaluating quantum fidelity between respective resource states. To do so we first present analysis of the square-root measurements used by the sender in PBT by exploiting the symmetries of the system. In particular, we show how to effectively evaluate their square-roots and composition. These findings allow us to present the explicit formula for the recycling fidelity involving only group-theoretic parameters describing irreducible representations in the Schur–Weyl duality. For the first time, we also analyse the degradation of the resource state for the optimal PBT scheme and show its degradation for all d ⩾ 2. In the both versions, the qubit case is discussed separately resulting in compact expression for fidelity, depending only on the number of shared entangled pairs.
Checking Sufficient Completeness by Inductive Theorem Proving

Sufficient completeness of an equational program ensures that each input can be fully evaluated to a data result. Checking this fundamental property for programs in expressive equational languages supporting conditional equations, types and subtypes, and rewriting modulo structural axioms is a challenging problem for which few methods currently exist. This work presents a new method that reduces sufficient completeness verification to a standard inductive theorem proving problem for a wide class of conditional equational programs in such languages.
Enhancing Recommendation with Automated Tag Taxonomy Construction in Hyperbolic Space

The sparse interactions between users and items on the web have aggravated the difficulty of their representations in recommender systems. Existing approaches leverage tags to alleviate the data sparsity problem, so as to enhance the performance and interpretability of recommendation. However, directly using flat item tags fails to fully exploit the hierarchical relations in data, but tag taxonomies are not always available. To this end, we propose TaxoRec to jointly construct a tag taxonomy automatically and perform recommendation accurately in hyperbolic space. Specifically, we first leverage hyperbolic space and enable the optimization of a discrete taxonomy structure via a representation-aware scoring function and an adaptive clustering algorithm, and preserve the hierarchical structure for interpretability. Then, we propose to capture the complex relations among users, items, and tags in a unified hyperbolic metric space, where a novel tag-enhanced aggregation mechanism and tag-enhanced metric learning algorithm for users and items are defined. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed TaxoRec framework, which constantly achieves an average of 7.76% improvement over the state-of-the-art baselines regarding both Recall and NDCG metrics. Insightful case studies also show that our automatically constructed tag taxonomies are highly accurate and interpretable.
Quantum Dynamics for Energetic Advantage in a Charge-Based Classical Full Adder

A proposal for quantum-based classical computation shows how approaches from quantum computing may enable energy-efficient classical information processing and highlights energy costs that currently limit this technology.
Three-dimensional stitching of stereo-deflectometry based on marker points

As a non-contact and three-dimensional (3D) mirror surface measurement method, stereo-deflectometry has been developing rapidly in recent years. For solving the problem of the measurement of large surface inclination with traditional stereo-deflectometry, 3D stitching of stereo-deflectometry based on marker points is proposed. In this method, the 3D points in the subregions under different perspectives were measured. Meanwhile, the 3D coordinates of the marker points on the sample table, which were calculated by binocular stereo vision, were used for coarse stitching, and the ICP algorithm was used for fine stitching. In order to verify the 3D stitching algorithm, we built a measurement system for the freeform surface of an ultra-short lens with a diameter of about 100 mm and a steepness of 52.6°. The spherical fitting error of the reflective bowl after stitching is within 60 mm. The experimental results verify the feasibility of the method, leading to potential mass application of stereo-deflectometry in 3D measurement of complex optical surfaces with a large aperture and high steepness.
Semi-supervised network regression with Gaussian process

In recent years, there has been a rapid growth in interest of using network-based machine learning. They offer the capacity to handle data that exist on irregular and complex structures with interactions between data points. In this paper, we present a semi-supervised regression model utilizing network-based Gaussian process. The proposed method constructs a Gaussian process prior using information from a given network. However, it incurs high computational costs from the required inversions to produce the predictive output and model selection. To overcome the difficulty, we further propose an approximated version that avoids matrix inversion. The proposed method was applied to several regression problems to validate the empirical performance and effectiveness in situations with limited amount of labeled data.
Improved weighting in particle filters applied to precise state estimation in GNSS

In the last decades, the increasing complexity of the fusion of proprioceptive and exteroceptive sensors with Global Navigation Satellite System (GNSS) has motivated the exploration of Artificial Intelligence related strategies for the implementation of the navigation filters. In order to meet the strict requirements of accuracy and precision for Intelligent Transportation Systems (ITS) and Robotics, Bayesian inference algorithms are at the basis of current Positioning, Navigation, and Timing (PNT). Some scientific and technical contributions resort to Sequential Importance Resampling (SIR) Particle Filters (PF) to overcome the theoretical weaknesses of the more popular and efficient Kalman Filters (KFs) when the application relies on non-linear measurements models and non-Gaussian measurements errors. However, due to its higher computational burden, SIR PF is generally discarded. This paper presents a methodology named Multiple Weighting (MW) that reduces the computational burden of PF by considering the mutual information provided by the input measurements about the unknown state. An assessment of the proposed scheme is shown through an application to standalone GNSS estimation as a baseline of more complex multi-sensors, integrated solutions. By relying on the a-priori knowledge of the relationship between states and measurements, a change in the conventional PF routine allows performing a more efficient sampling of the posterior distribution. Results show that the proposed strategy can achieve any desired accuracy with a considerable reduction in the number of particles. Given a fixed and reasonable available computational effort, the proposed scheme allows for an accuracy improvement of the state estimate in the range of 20-40%.
Sharp feature consolidation from raw 3D point clouds via displacement learning

Detecting sharp features in raw 3D point clouds is an essential step for designing efficient priors in several 3D Vision applications. This paper presents a deep learning-based approach that learns to detect and consolidate sharp feature points on raw 3D point clouds. We devise a multi-task neural network architecture that identifies points near sharp features and predicts displacement vectors toward the local sharp features. The so-detected points are thus consolidated via relocation. Our approach is robust against noise by utilizing a dynamic labeling oracle during the training phase. The approach is also flexible and can be combined with several popular point-based network architectures. Our experiments demonstrate that our approach outperforms the previous work in terms of detection accuracy measured on the popular ABC dataset. We show the efficacy of the proposed approach by applying it to several 3D vision tasks.