Reviewerly/exHarmony huggingface dataset:
ClothFormer: Taming Video Virtual Try-on in All Module

The task of video virtual try-on aims to fit the target clothes to a person in the video with spatio-temporal consistency. Despite tremendous progress of image virtual tryon, they lead to inconsistency between frames when applied to videos. Limited work also explored the task of video-based virtual try-on but failed to produce visually pleasing and temporally coherent results. Moreover, there are two other key challenges: 1) how to generate accurate warping when occlusions appear in the clothing region; 2) how to generate clothes and non-target body parts (e.g. arms, neck) in harmony with the complicated background; To address them, we propose a novel video virtual try-on framework, ClothFormer, which successfully synthesizes realistic, harmonious, and spatio-temporal consistent results in complicated environment. In particular, ClothFormer involves three major modules. First, a two-stage anti-occlusion warping module that predicts an accurate dense flow mapping between the body regions and the clothing regions. Second, an appearance-flow tracking module utilizes ridge regression and optical flow correction to smooth the dense flow sequence and generate a temporally smooth warped clothing sequence. Third, a dual-stream transformer ex-tracts and fuses clothing textures, person features, and en-vironment information to generate realistic try-on videos. Through rigorous experiments, we demonstrate that our method highly surpasses the baselines in terms of synthesized video quality both qualitatively and quantitatively <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> The code and all demos are available at https://github.com/luxiangju-PersonAI/ClothFormer.
Multi-signatures for ECDSA and Its Applications in Blockchain

Multi-signatures enable a group of t signers to sign a message jointly and obtain a single signature. Multi-signatures help validating blockchain transactions, such as transactions with multiple inputs or transactions from multisig addresses. However, multi-signatures schemes are always realised naively in most blockchain systems by directly concatenating t ECDSA signatures. In this paper, we give the first multi-signature scheme for ECDSA. Technically, we design a new ephemeral group public key for the set of signers and introduce an interactive signing protocol to output a single ECDSA signature. The signature can be validated by the ephemeral group public key. Then, we instantiate the ECDSA multi-signature scheme with class group, for which we design a secret exchanging mechanism that ensures the hiding content is well-constructed. Moreover, our scheme is able to identify the malicious party in the signing phase and help to minimize unnecessary resource consumption. This ECDSA multi-signatures can be used in blockchain to reduce the transaction cost and provide accountability for signers and backward compatibility with existing ECDSA addresses.
Dilithium for Memory Constrained Devices

We investigate the use of the Dilithium post-quantum digital signature scheme on memory-constrained systems. Reference and optimized implementations of Dilithium in the benchmarking framework pqm4 (Cortex-M4) require 50–100 KiB of memory, demonstrating the significant challenge to use Dilithium on small Internet-of-Things platforms. We show that compressing polynomials, using an alternative number theoretic transform, and falling back to the schoolbook method for certain multiplications reduces the memory footprint significantly. This results in the first implementation of Dilithium for which the recommended parameter set requires less than 7 KiB of memory for key and signature generation and less than 3 KiB of memory for signature verification. We also provide benchmark details of a portable implementation in order to estimate the performance impact when using these memory reduction methods.
Efficient Time Series Classification using Spiking Reservoir

In recent times, in many industrial domains, the need for in-situ processing of time series data from sensors have grown extensively, to ensure low-latency real-time responses, making embedded edge AI an important area of work. Most current techniques, being computationally intensive, are not suited for edge implementation; and in real world scenarios, connectivity to the cloud for processing sensed data leads to higher latency and often less reliability. Neuromorphic systems, coupled with spiking neural networks (SNN) offer a solution to such problems. This paper explores this evolving paradigm to address the need for low-footprint efficient time series classifiers implementable on edge, and targeted for predictive maintenance scenarios. A reservoir-based SNN architecture is designed and tried for classification of different vibration time series datasets. While the system is found to obtain at par classification accuracy for each of the datasets compared to prior arts, it is also observed to be more efficient in terms of synaptic operations per timestep (13% to 38%) using Gaussian temporal spike encoding scheme compared to Poisson rate encoding. Moreover, the system is found to be robust with respect to learning with reduction in training data (upto 20%).
About one approach to create personally centered educational resources based on a question-problem knowledge tree

Today, online education resources are gaining more and more popularity, providing unlimited access to sources of knowledge for all social groups. However, modern educational resources do not fully provide learners with content that is adjusted to their level of subject training and cognitive requirements. Taking into account the peculiarities of the modern “digital” generation, which impose high requirements on visualization, structuring of educational material that gives answers to their questions and requests, it seems necessary to search for new ways of presenting educational material, built along an individual trajectory. In this regard, it is of interest to model adaptive mechanisms for the presentation of educational content in an inverted form, ensuring the maximum consideration of the preferences and abilities of modern youth. The purpose of the work is to substantiate an approach to the creation of personally-centered digital educational resources based on a question-problem tree of knowledge of the subject area, which provides self-construction of educational content by students. The analysis of the methodological, psychological, pedagogical and scientific literature devoted to the research problem, carried out by the authors, made it possible to describe the method of the inverted presentation of educational content in the format of a question-problem tree of knowledge of the subject area from the perspective of personally-centered education. The structure of the topological representation of the knowledge tree allows the student to personally select the questions and tasks of interest to him, and, therefore, to receive informational educational answers to these declared preferences, as well as to construct a holistic educational material presented in a format corresponding to the psychotype of the student's perception. Approbation of the proposed approach in real educational practice of a higher educational institution and school has shown great efficiency and interest of the students.
Automating Adaptive Execution Behaviors for Robot Manipulation

Robotic manipulation in semi-structured and changing environments requires systems with: a) perception and reasoning capabilities able to capture and understand the state of the environment; b) planning and replanning capabilities at both symbolic and geometric levels; c) automatic and robust execution capabilities.To cope with these issues, this paper presents a framework with the following features.First, it uses perception and ontology-based reasoning procedures to obtain the Planning Description Domain Language files that describe the manipulation problem at task level.This is used in the planning stage as well as during task execution in order to adapt to new situations, if required.Second, the proposed framework is able to plan at both task and motion levels, intertwining them by incorporating geometric reasoning modules to determine some of the symbolic predicates needed to describe the states.Finally, the framework automatically generates the behavior trees required to execute the task.The proposal takes advantage of the ability of behavior trees to be edited during run time, allowing adaptation of the action plan or of the trajectories according to changes in the state of the environment.The approach allows for robot manipulation tasks to be automatically planned and robustly executed, contributing to achieve fully functional service robots.
A variational Bayesian method for similarity learning in non-rigid image registration

We propose a novel variational Bayesian formulation for diffeomorphic non-rigid registration of medical images, which learns in an unsupervised way a data-specific similarity metric. The proposed framework is general and may be used together with many existing image registration models. We evaluate it on brain MRI scans from the UK Biobank and show that use of the learnt similarity metric, which is parametrised as a neural network, leads to more accurate results than use of traditional functions, e.g. SSD and LCC, to which we initialise the model, without a negative impact on image registration speed or transformation smoothness. In addition, the method estimates the uncertainty associated with the transformation. The code and the trained models are available in a public repository: https://github.com/dgrzech/learnsim.
Layout Aware Inpainting for Automated Furniture Removal in Indoor Scenes

We address the problem of detecting and erasing furniture from a wide angle photograph of a room. Inpainting large regions of an in-door scene often results in geometric inconsistencies of background elements within the inpaint mask. To address this problem, we uti-lize perceptual information (e.g. instance segmentation, and room layout) to produce a geometrically consistent empty version of a room. We share important details to make this system viable, such as per-plane inpainting, automatic rectification, and texture refinement. We provide detailed ablation along with qualitative examples, justifying our design choices. We show an application of our system by removing real furniture from a room and redecorating it with virtual furniture.
Multiple Colour Detection of RGB Images Using Machine Learning Algorithm

Colour detection is the act of identifying the name of any colour. Color detection is required for object recognition, and it is also utilized in a variety of picture editing and sketching programs. Machine Learning (ML) has been proof useful in this area, and lot of researches have been done. This has been utilized in fields of neural networks and digital image processing recently. RGB images multiple colour recognition are powerful tools for various images for images and sketches. There have been several suggested regression models that use crop image characteristics and image indices, however, they have not been properly tested for accuracy and adaptation effectiveness for multiple colors. Therefore, this paper proposes K- Nearest Neighbour (KNN) classifier for efficient colour detection of RGB images. The KNN algorithm is a prominent ML technique and neural network classification technique. The KNN classifier is utilized to segregate distinct colors in the RGB images. The paper utilized colour histogram for feature extraction to find the features that most relevant pattern that define certain colours. The feature extraction further improved the efficacy and accuracy of KNN classifier’s in the classification of RGB images.
Risk-aware Motion Planning for Collision-tolerant Aerial Robots subject to Localization Uncertainty

This paper contributes a novel strategy towards risk-aware motion planning for collision-tolerant aerial robots subject to localization uncertainty. Attuned to the fact that micro aerial vehicles are often tasked to navigate within GPS-denied, possibly unknown, confined and obstacle-filled environments the proposed method exploits collision-tolerance at the robot design level to mitigate the risks of collisions especially as their likelihood increases with growing uncertainty. Accounting for the maximum kinetic energy with which an impact is considered safe, alongside the robot dynamics, the planner builds a set of admissible uncertainty-aware and collision-inclusive paths over a horizon involving multiple motion steps. The first step of the best path is executed by the robot, while the procedure is then repeated in a receding horizon manner. Evaluated in extensive simulation studies and experimental results with a collision-tolerant flying robot, the planner successfully considers the interplay between uncertainty and the likelihood of a collision, balances the risks of possible impacts and enables to navigate safely within highly cluttered environments.
Performance and Cost-Aware Task Scheduling via Deep Reinforcement Learning in Cloud Environment

In the cloud computing environment, task scheduling with multiple objectives optimization becomes a highly challenging problem in such a dynamic and bursty environment. Previous studies have mostly emphasized assigning the incoming tasks in a specific scenario, with a weak generalization ability to various objectives automatically. Thus, they suffer the inefficient issue under large-scale and heterogeneous cloud workloads. To address this issue, we propose a deep reinforcement learning (DRL)-based intelligent cloud task scheduler, which makes the optimal scheduling decision only dependent on learning directly from its experience without any prior knowledge. We formulate task scheduling as a dynamical optimization problem with constraints and then adopt the deep deterministic policy gradients (DDPG) network to find the optimal task assignment solution while meeting the performance and cost constraints. We propose a correlation-aware state representation method to capture the inherent characteristics of demands, and a dual reward model is designed to learn the optimal task allocation strategy. Extensive experimental results on Alibaba cloud workloads show that compared with other existing solutions, our proposed DDPG-based task scheduler enjoy superiority and effectiveness in performance and cost optimization.
Hardware Acceleration of YOLO-based Convolutional Neural Network Detector by Deep Learning Processor Unit for Intelligent Autonomous Mover

For intelligent autonomous vehicles, they play an important role in user-friendly usage in crowded environments, so the recent focus has been on providing object recognition and collision avoidance functions. In this study, a Xilinx Deep Learning Processor Unit (DPU)-based Light Convolutional Neural Network (CNN) model was implemented for object detection and classification, and FPGA-based hardware acceleration was used to efficiently detect pedestrians and other objects. With the DPU-based implementation, the accuracy of the proposed object classification and face orientation detection model reaches up to 90% on image datasets collected in supermarkets. Compared to previous GPU-based software implementations, the proposed approach provides better frames-per-second (FPS) performance for real-time applications.
Construction of Smart Campus Cloud Service Platform Based on Big Data Computer System

At present, universities are facing a critical period of transition from digital campus to smart campus. Because cloud service data has the characteristics of diversity, in addition to structured data, there are also unstructured data, which play an important role in the study and life of the majority of students. Therefore, it is very important to improve the construction level of smart campus cloud service platform. At this stage, there are great differences between the service platform under the traditional model and the smart campus platform under the big data computer system. Under the traditional mode, the construction method of smart campus cloud service platform in the city has some shortcomings. Therefore, it is urgent to build a smart campus cloud service platform. Based on this, this paper compares the smart campus cloud service platform of the traditional service platform and the big data computer system. The results show that the latter has a timely response speed and can bring high-quality and efficient data services to users. At the same time, it also analyzes the ideas and goals of building an intelligent cloud service platform, and puts forward corresponding construction methods.
Task Scheduling and Load Balancing for Minimization of Response Time in IoT Assisted Cloud Environments

In the proposed research work, novel heuristic energy efficiency architecture model is designed to minimize the response time and environmental impact, by considering task scheduling and load balancing operations. Scalable framework is used to enhance the QoS with auto scaling techniques. In this technique cloud resources are allocated and booted quickly to meet response time requirements, which depends upon incoming load. To improve the utilization of resources for load balancing, resource consolidation considers with two energy aware algorithm which includes VM placement and Live VM migration. Then modified Best Fit Decreasing (BFD) algorithm is used for VMs placement by considering CPU utilization and VMs resource consumption rate. Live migration is processed with significant moving constraints such as CPU state, memory content, storage content and network connections. The main aim of energy efficient model is to keep the CPU utilization under specified threshold limit for improving resource efficiency and ensure QoS.
Inertial Measurement Unit based Human Action Recognition Dataset for Cyclic Overhead Car Assembly and Disassembly

Motion datasets in industrial environments are essential for the research on human-robot interaction and new exoskeleton control. Currently, a lot of Activities of Daily Living (ADL) datasets are available for researchers, but only a few target an industrial context. This paper presents a dataset for a semi-industrial Overhead Car Assembly (OCA) task consisting of synchronized video and 9-Degrees of Freedom (DOF) Inertial Measurement Unit (IMU) data. The dataset was recorded with a soft-robotic exoskeleton equipped with 4 IMUs covering the upper body. It has a minimum sampling rate of 20 Hz, lasts approximately 360 minutes and comprises of 282 cycles of a realistic industrial assembly task. The annotations consist of 6 mid-level actions and an additional Null class. Five different test subjects performed the task without specific instructions on how to assemble the used car shielding. In this paper, we describe the dataset, set guidelines for using the data in supervised learning approaches, and analyze the labeling error caused by the labeler onto the dataset. We also compare different state-of-the-art neural networks to set the first benchmark and achieve a weighted F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score of 0.717.
Finite-Time Convergence Rates of Decentralized Stochastic Approximation With Applications in Multi-Agent and Multi-Task Learning

In this article, we study a decentralized variant of stochastic approximation (SA), a data-driven approach for finding the root of an operator under noisy measurements. A network of agents, each with its own operator and data observations, cooperatively find the fixed point of the aggregate operator over a decentralized communication graph. Our main contribution is to provide a finite-time analysis of this decentralized SA method when the data observed at each agent are sampled from a Markov process; this lack of independence makes the iterates biased and (potentially) unbounded. Under fairly standard assumptions, we show that the convergence rate of the proposed method is essentially the same as if the samples were independent, differing only by a log factor that accounts for the mixing time of the Markov processes. The key idea in our analysis is to introduce a novel Lyapunov–Razumikhin function, motivated by the one used in analyzing the stability of delayed ordinary differential equations. We also discuss applications of the proposed method on a number of interesting learning problems in multiagent systems.
ARSeek

It is not a trivial problem to collect API-relevant examples, usages, and mentions on venues such as Stack Overflow. It requires efforts to correctly recognize whether the discussion refers to the API method that developers/tools are searching for. The content of the Stack Overflow thread, which consists of both text paragraphs describing the involvement of the API method in the discussion and the code snippets containing the API invocation, may refer to the given API method. Leveraging this observation, we develop ARSeek, a context-specific algorithm to capture the semantic and syntactic information of the paragraphs and code snippets in a discussion. ARSeek combines a syntactic word-based score with a score from a predictive model fine-tuned from CodeBERT. In terms of F1-score, ARSeek achieves an average score of 0.8709 and beats the state-of-the-art approach by 14%.
A Comparison of Uncertainty Quantification Methods for Active Learning in Image Classification

The adoption of machine learning (ML) technology in real-world settings like medical imaging is currently hampered by a lack of trust in ML models and a lack of labeled data. These two issues are currently addressed in parallel by two subdisciplines of ML: uncertainty quantification (UQ) is concerned with obtaining reliable estimates of a model's confidence in its outputs, and active learning (AL) deals with efficiently training models in low-data regimes. To date, the usefulness of the new methods emerging from the field of UQ for AL remains under-explored. We here take a step to address this by comparing seven different UQ methods on three image classification data sets. Our experiments confirm previous indications in the AL literature that the ranking of sampling strategies can vary greatly across models and data sets. We find that Concrete Dropout, Least Confidence, Smallest Margin, and Entropy sampling consistently outperform Random sampling across data sets, whereas Ensembles, Monte-Carlo Dropout, and Bayes-by-Backprop do not. We also observe that AL training stability is sensitive to data quality.
A Knowledge Graph-Based Method for Automating Systematic Literature Reviews

Systematic Literature Reviews aim at investigating current approaches to conclude a research gap or determine a futuristic approach. They represent a significant part of a research activity, from which new concepts stem. However, with the massive availability of publications at a rapid growing rate, especially digitally, it becomes challenging to efficiently screen and assess relevant publications. Another challenge is the continuous assessment of related work over a long period of time and the consequent need for a continuous update, which can be a time-consuming task. Knowledge graphs model entities in a connected manner and enable new insights using different reasoning and analysis methods. The objective of this work is to present an approach to partially automate the conduction of a Systematic Literature Review as well as classify and visualize the results as a knowledge graph. The designed software prototype was used for the conduction of a review on context-awareness in automation systems with considerably accurate results compared to a manual conduction.