Reviewerly/exHarmony huggingface dataset:
Watch and Act: Learning Robotic Manipulation From Visual Demonstration

Learning from demonstration holds the promise of enabling robots to learn diverse actions from expert experience. In contrast to learning from observation-action pairs, humans learn to imitate in a more flexible and efficient manner: learning behaviors by simply “watching.” In this article, we propose a “watch-and-act” imitation learning pipeline that endows a robot with the ability of learning diverse manipulations from visual demonstrations. Specifically, we address this problem by intuitively casting it as two subtasks: 1) understanding the demonstration video and 2) learning the demonstrated manipulations. First, a captioning module based on visual change is presented to understand the demonstration by translating the demonstration video into a command sentence. Then, to execute the captioning command, a manipulation module that learns the demonstrated manipulations is built upon an instance segmentation model and a manipulation affordance prediction model. We validate the superiority of the two modules over existing methods separately via extensive experiments and demonstrate the whole robotic imitation system developed based on the two modules in diverse scenarios using a real robotic arm. Supplementary video is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://vsislab.github.io/watch-and-act/</uri> .
Feature Map Compression for Video Coding for Machines Based on Receptive Block Based Principal Component Analysis

This paper presents a method to effectively compress the intermediate layer feature map of a convolutional neural network for the potential structures of Video Coding for Machines, which is an emerging technology for future machine consumption applications. Notably, most extant studies compress a single feature map and hence cannot entirely consider both global and local information within the feature map. This limits performance maintenance during machine consumption tasks that analyze objects with various sizes in images/videos. To address this problem, a multiscale feature map compression method is proposed that consists of two major processes: receptive block based principal component analysis (RPCA) and uniform integer quantization. The RPCA derives the complete basis kernels of a feature map by selecting a set of major basis kernels that can represent a sufficient percentage of global or local information according to the variable-size receptive blocks of each feature map. After transforming each feature map using the set of major basis kernels, a uniform integer quantizer converts the 32-bit floating-point values of the set of major basis kernels, corresponding RPCA coefficients, and a mean vector to five-bit integer representation values. Experiment results reveal that the proposed method reduces the amount of feature maps by 99.30% with a loss of 8.30% in the average precision ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AP</i> ) on the OpenImageV6 dataset and 0.77% in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AP<sub>M</sub></i> and 0.47% in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AP<sub>L</sub></i> on the MS COCO 2017 validation set while outperforming previous PCA-based feature map compression methods even at higher compression rates.
Behavioral Simulation of Blockchain-Enabled Market for Supplier Capacity Trading among Retailers

We study a supply chain distribution system (comprising a supplier and multiple retailers) and investigate experimentally how the trading by retailers of digital claims (tokens) on the supplier's capacity affects supply-demand mismatches. Creating markets for such tokens relies on advances in blockchain technology. Subjects play the role of retailers, who have heterogeneous valuations of goods, face random demands, and buy tokens on the supplier's capacity. Following demand realization, retailers trade these tokens with each other. We conduct six behavioral experiments (featuring two wholesale prices and three market sizes) and find that markets reduce excess inventory and shortages. Market-clearing prices are anchored to wholesale prices and do not signal the value of goods in large markets. Players deploy novel ordering and trading strategies that differ from transshipper strategies, as studied in the literature. Applying unsupervised machine learning algorithms, we classify these strategies. In one strategy, players, whom we call spot buyers, buy a few claims initially, and after demand realization, use the market to satisfy it. Other players, whom we call spot sellers, buy more claims initially than the maximum demand, and once demand is known, sell their excess on the market. Both strategies reduce costs from demand uncertainty. Despite this benefit, the transshipper strategy is more profitable, due to lower exposure to liquidity shocks and trading mistakes. With markets, the initial orders to the supplier exhibit the pull-to-the-mean effect, and at the same time, have greater variability because of spot buyers and sellers. Retailers' average profit is higher with markets, but suppliers with low wholesale prices suffer from lower revenues because of the pull-to-the-mean effect. Our results provide guidance for the introduction of blockchain-enabled markets in practice.
An Efficient Trajectory Planning Algorithm for High-Speed Quadrotor in Large-Scale and Cluttered Environments

Trajectory planning is the core of autonomous flight, however how to get a trajectory that satisfies safety and higher-order dynamics constraints in real time has been a challenge. In this paper, a front-end plus back-end hierarchical trajectory planning algorithm is proposed to solve this challenging problem. A novel motion primitive generation method is proposed for efficient dynamic search, which makes front-end can obtain a trajectory that satisfies higher-order dynamic constraints in a very short time. At the back-end, a trajectory optimization algorithm based on the convex hull property of parametric curves is proposed to improve the smoothness of the trajectory by constructing a quadratic programming problem to optimize the trajectory in a continuous space. Extensive Monte Carlo simulation experimental results show that the proposed algorithm is more efficient compared to other algorithms.
The Tracking Estimation of Maneuvering UAVs Based on Enhanced Convergence IMM-EKF

Efficient and accurate state estimation of maneuvering targets is a key step in radar surveillance systems. Especially in the unmanned aerial vehicles (UAVs) tracking scenes, the estimation of UAV’s position and velocity is not enough, and more details, such as heading angle and turning rate, are required in the estimated state space. In this paper, our effort is to establish a novel estimator that incorporates an enhanced extended Kalman filter (EKF) into the interactive multiple model (IMM) to improve the effectiveness of maneuvering UAV tracking. More specifically, a time-varying scaling process noise is introduced into the EKF to increase the filers’ convergence rate without losing its persistent estimation performance. And the nearly constant speed horizontal turn (NCSHT) model, as a special maneuvering model, is introduced to ensure the tracking performance during maneuver. Applied into the ground-to-air UAVs’ tracking experiment, the real-time results demonstrate the effectiveness of the proposed algorithm in convergence and accuracy.
Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military and civilian fields.With the continuous enrichment and extensive expansion of application scenarios, the safety of UAVs is constantly being challenged.To address this challenge, we propose algorithms to detect anomalous data collected from drones to improve drone safety.We deployed a one-class kernel extreme learning machine (OCKELM) to detect anomalies in drone data.By default, OCKELM uses the radial basis (RBF) kernel function as the kernel function of the model.To improve the performance of OCKELM, we choose a Triangular Global Alignment Kernel (TGAK) instead of an RBF Kernel and introduce the Fast Independent Component Analysis (FastICA) algorithm to reconstruct UAV data.Based on the above improvements, we create a novel anomaly detection strategy FastICA-TGAK-OCELM.The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies (ALFA) dataset.The experimental results show that compared with other methods, the accuracy of this method is improved by more than 30%, and point anomalies are effectively detected.
Multi-agent Reinforcement Learning Algorithm Based on Local Information

In recent years unmanned systems cluster collaborative applications have put forward higher requirements on reinforcement learning techniques. The increasing number of agents in multi-agent reinforcement learning leads to the effect of multi-agent reinforcement learning algorithms represented by Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is not satisfactory, and the algorithm time consumption is difficult to predicate and control. To solve this problem, this paper proposes a hypothesis and verifies the possibility of state behavior value estimation by replacing global information with local information in two scenarios: predator-prey scenario and dual identity, and obtains better training results than MADDPG. Based on this, we further analyze the effectiveness of local information selection by distance selection method, type selection method and correlation class proximity method in food chain scenarios. The experiments show that using correlation class proximity selection method has significant improvement over MADDPG algorithm in terms of training effect and training time consumption. The method in this paper provides support for multi-agent reinforcement learning applied to large-scale unmanned system clustering problems.
Many-Objective Optimization-Based Task Scheduling in Hybrid Cloud Environments

Due to the security and scalability features of hybrid cloud architecture, it can better meet the diverse requirements of users for cloud services.And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud.However, most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling, even ignoring the conflicts between its security privacy features and other requirements.Based on the above problems, a many-objective hybrid cloud task scheduling optimization model (HCTSO) is constructed combining risk rate, resource utilization, total cost, and task completion time.Meanwhile, an opposition-based learning knee point-driven many-objective evolutionary algorithm (OBL-KnEA) is proposed to improve the performance of model solving.The algorithm uses opposition-based learning to generate initial populations for faster convergence.Furthermore, a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range.By comparing the experiments with other excellent algorithms on HCTSO, OBL-KnEA achieves excellent results in terms of evaluation metrics, initial populations, and model optimization effects.
Firefly Geisha &amp; Cypris Butterflies: Lessons for metaverses

Online educators looking for platforms more dynamic than conferencing software see the current enthusiasm for investment and development of a new Web 3.0 metaverse platform on the horizon as a hopeful alternative. However, what this new metaverse platform should offer users is still unclear. One indicator of what to expect might be Linden Lab’s Second Life (SL), a virtual world platform released in 2003 and still online today. In this paper, the author summarizes his ethnographic research in two SL learning communities in order to pinpoint the appeal and learning affordances of the platform. Through discussion of the strengths and weaknesses of the SL platform in regards to education, the author provides advice to prospective metaverse instructors on what to expect in this developing milieu. He also outlines the challenges that metaverse developers will likely encounter as they attempt to overcome the problems that have led to SL’s slow decline.
Information Sustainability in Rural Bangladesh: The Use of Analogue and Digital Backups

Access to information plays an important role in supporting sustainable development goals. In marginalised communities where people have limited access to information, preservation of information previously accessed can play a crucial role in supporting economic, social, and personal activities and fostering sustainable development. However, little is known about how marginalised rural communities in developing countries preserve information and how digital technologies have impacted on their practices. In order to investigate how individuals in remote rural communities access and preserve the information that they need to support their work and daily activities, the author organised two focus group discussions and ten individual interviews with village men and women in the district of Satkhira in Bangladesh. The findings from the research show that villagers, who have limited access to smartphones and to the internet, are conscious of the fragility of digital technologies and digital data and use paper notebooks as a form of backup for information found on the internet, and that some of them also use digital backups to preserve important information. These findings highlight the importance of ensuring the sustainability of information and of encouraging the use of methods to access and preserve information that are appropriate to the cultural and technological contexts and that meet the needs of the marginalised communities.
Deep Video Matting with Temporal Consistency

Temporal consistency is a significant issue for video matting. In this study, we propose a temporal feature enhancement network for video matting. To enhance temporal feature propagation and alignment between video frames, recurrent pipelines are designed as our network architecture. In addition, an attention module is add to correct the temporal information of misaligned video frames. Finally revert the output video to the input resolution. We do the experiments on the benchmark which contains of VideoMatte240K (VM), Distinctions-646 (D646) and Adobe Image Matting (AIM) datasets. The experiment result shows that our method outperforms the state-of-the-art methods and prove that temporal consistency enhancement can make the video matting algorithm robust.
Noise Removal Filtering Methods for Mammogram Breast Images

Breast cancer detection in the early stage is an important factor to reduce the mortality rate. Mammogram examination is one of the best optimistic from various approaches used in the early detection of breast cancer at a different stage of cancer and the raw mammogram images are required to pre-process for better radiologist perception and to obtain an enhanced and clear image. It also helps to extract the Region of Interest from the processed image by using statistical feature methods to find the size and shape of the tumor. This paper is on an experimental study performed on sample mammogram images and applies different noise smoothing methods. Methods used to remove noise from the images by applying filtering methods like Gaussian Filter, Tri-State Filter, Mean Filter, Mean-Median Filter, Threshold Filter, Bilateral Filter, Wiener Filter, and Adaptive filter. The processed and obtained quality image will help doctors and radiologists to give an accurate impression on a patient case study. Results: quality of the image obtained on sample mammogram images of CBIS-DDSM dataset achieved min 80% of quality PSNR values.
Trustworthy Digital Repository Certification: A Longitudinal Study

Increasingly, government policies are directing federal agencies to make the results of federally funded scientific research publicly available in data repositories. Additionally, academic journal policies are progressively recommending that researchers deposit the data upon which they base their articles in repositories to ensure their long-term preservation and access. Unfortunately, having the necessary technical, legal, financial, and organizational resources for digital preservation is a significant challenge for some repositories. Repositories that become certified as Trustworthy Digital Repositories (TDRs) demonstrate to their stakeholders (e.g., users, funders) that an authoritative third party has evaluated them and verified their trustworthiness. To understand the impact of certification on repositories’ infrastructure, processes, and services, we analyzed a sample of publicly available TDR audit reports (n = 175) from the Data Seal of Approval (DSA) and Core Trust Seal (CTS) certification programs. This first longitudinal study of TDR certification over a ten-year period (from 2010 to 2020) found that many repositories either maintain a relatively high standard of trustworthiness in terms of their compliance with guidelines in DSA or CTS standards or improve their trustworthiness by raising their compliance levels with these guidelines each time they get recertified. Although preparing for audit and certification adds to repository staff’s dockets of responsibilities, our study suggests that certification can be beneficial. Therefore, we advocate for more specific policies that encourage certification and the use of TDRs.
Consistency of mixture models with a prior on the number of components

Abstract This article establishes general conditions for posterior consistency of Bayesian finite mixture models with a prior on the number of components. That is, we provide sufficient conditions under which the posterior concentrates on neighborhoods of the true parameter values when the data are generated from a finite mixture over the assumed family of component distributions. Specifically, we establish almost sure consistency for the number of components, the mixture weights, and the component parameters, up to a permutation of the component labels. The approach taken here is based on Doob’s theorem, which has the advantage of holding under extraordinarily general conditions, and the disadvantage of only guaranteeing consistency at a set of parameter values that has probability one under the prior. However, we show that in fact, for commonly used choices of prior, this yields consistency at Lebesgue-almost all parameter values, which is satisfactory for most practical purposes. We aim to formulate the results in a way that maximizes clarity, generality, and ease of use.
Challenges for XR in Games

Extended Reality as a consolidated game platform was always a dream for both final consumers and game producers. If for one side this technology had enchanted and called the attention due its possibilities, for other side many challenges and difficulties had delayed its proliferation and massification. This paper intends to rise and discuss aspects and considerations related to these challenges and solutions. We try to bring some of the most relevant research topics and try to guess how XR games should look in the near future. We divide the challenges into 7 topics, based on extensive literature reviews: Cybersickness, User Experience, Displays, Rendering, Movements, Body Tracking and External World Information. We believe that this topics are a Grand Challenge, since the next generation of entertainment depends on adequately solving them in the near future.
Exploring the Association Between Multiple Classifications and Journal Rankings

Journal classification systems use a variety of (partially) overlapping and non-exhaustive subject categories which results in many journals being classified into more than a single subject category. Given a subject category, respective journals are often ranked based on a common metric such as the Journal Impact Factor or SCImago Journal Rank. However, given a specific journal, it might be ranked very differently across its associated subject categories. In this study, we set to explore the possible association between the number of categories a journal is classified to and its associated rankings using the two most widely used indexing systems - Web Of Science and Scopus. Using known distance measures, our results show that a higher number of classified categories per journal is associated with an increased range and variance of the associated rankings within them. Findings and possible implications are discussed.
Efficient Image Watermarking Using Particle Swarm Optimization and Convolutional Neural Network

In today’s Internet environment, copyright protection of digital multimedia material is a major concern. Digital image watermarking provides the concept of secured copyright protection—the strength and quality of the watermark image loss due to geometrical attacks. However, minimization of the impact of geometrical attacks is a major challenge. This paper proposed a coefficient optimization-based watermarking algorithm. The particle swarm optimization applies to feature optimization of the source and symbol images. The processing of feature extraction of the source image and symbol image uses the wavelet transform function. The CNN algorithm follows the process of embedding with an optimal coefficient of the PSO algorithm. The proposed algorithm has been tested on MATLAB environments with reputed image datasets. The performance of the proposed algorithm is estimated as correlation coefficient and PSNR. The estimated results compare CNN algorithm and WCFOA. The improved outcome of the proposed algorithm against the existing algorithm was approx. 2%.
Calculating the Traffic Density of Real-Time Video Using Moving Object Detection

Moving object detection (MOD) is one of the more prominent fields of research now a day because the application areas of it increasing rapidly. Several applications of MOD are recognizing and tracing vehicles or animals or human beings, finding the speed of objects or vehicles etc. Traffic controlling is one of the prodigious issues facing by most of the great cities in the world especially in India all metropolitan cities suffering with this issue. Therefore, there is a huge demand to introduce an intelligent traffic controlling system which works by finding the live traffic and changing the traffic lights by its own intelligence. In this paper, proposing a paradigm to find the live traffic density using MOD algorithm which interns uses prominent background subtraction practice by giving a traffic video as input to the proposed model so that this can work efficiently with live traffic video.
Discrete Salp Swarm Algorithm for symmetric traveling salesman problem

In the Salp Swarm Algorithm (SSA), the update mechanism is inspired by the unique chain movement of the salp swarm. Numerous versions of SSA were already put forward to deal with various optimization problems, but there are very few discrete versions among them. d-opt is improved based on the 2-opt algorithm: a decreasing factor d is introduced to control the range of neighborhood search; TPALS are modified by Problem Aware Local Search (PALS) based on the characteristics of Travelling Salesman Problem (TSP); The second leader mechanism increases the randomness of the algorithm and avoids falling into the local optimal solution to a certain extent. We also select six classical crossover operators to experiment and select Subtour Exchange Crossover (SEC) and the above three mechanisms to integrate them into the SSA algorithm framework to form Discrete Salp Swarm Algorithm (DSSA). In addition, DSSA was tested on 23 known TSP instances to verify its performance. Comparative simulation studies with other advanced algorithms are conducted and from the results, it is observed that DSSA satisfactorily solves TSP.