Reviewerly/exHarmony huggingface dataset:
Nonlinear control for the extended model of the load-suspended UAV based on the experiments

Abstract In recent years, to solve the problem of shortage of delivery staff caused by high demand in the delivery industry, it is expected to carry packages while a UAV tows them. However, when towing, the control performance of the aircraft deteriorates due to the influence of the weight and shaking of the towed object, so it is necessary to control the aircraft in consideration of the behavior of the towed object. To address this issue, an input design with hierarchical linearization has been proposed for the UAV that tows an object, but it has not been experimentally verified. In this paper, we conducted experiments and confirmed the existence of the modeling error. Thus, we built a model reflecting the actual structure of towing of UAV. We applied the redesigned controller to the simulation model and verified its performance.
Determining the pointer positions of aircraft analog indicators using deep learning

Purpose The purpose of this paper is to monitor the backup indicators in case of indicator failure and to minimize the situations when the pilot may be unable to monitor the indicator effectively in emergency situations. Design/methodology/approach In this study, the pointer positions of different indicators were determined with a deep learning-based algorithm. Within the scope of the study, the pointer on the analog indicators obtained from aircraft cockpits was detected with the YOLOv4 object detector. Then, segmentation was made with the GrabCut algorithm to detect the pointer in the detected region more precisely. Finally, a line including the segmented pointer was found using the least-squares method, and the exact direction of the pointer was determined and the angle value of the pointer was obtained by using the inverse tangent function. In addition, to detect the pointer of the YOLOv4 object detection method and to test the designed method, a data set consisting of videos taken from aircraft cockpits was created and labeled. Findings The analog indicator pointers were detected with great accuracy by the YOLOv4 and YOLOv4-Tiny detectors. The experimental results show that the proposed method estimated the angle of the pointer with a high degree of accuracy. The developed method can reduce the workloads of both pilots and flight engineers. Similarly, the performance of pilots can be evaluated with this method. Originality/value The authors propose a novel real-time method which consists of detection, segmentation and line regression modules for mapping the angle of the pointers on analog indicators. A data set that includes analog indicators taken from aircraft cockpits was collected and labeled to train and test the proposed method.
Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Models

This work explores the capacities of characterbased Neural Machine Translation to translate noisy User-Generated Content (UGC) with a strong focus on exploring the limits of such approaches to handle productive UGC phenomena, which almost by definition, cannot be seen at training time.Within a strict zeroshot scenario, we first study the detrimental impact on translation performance of various user-generated content phenomena on a small annotated dataset we developed, and then show that such models are indeed incapable of handling unknown letters, which leads to catastrophic translation failure once such characters are encountered.We further confirm this behavior with a simple, yet insightful, copy task experiment and highlight the importance of reducing the vocabulary size hyper-parameter to increase the robustness of character-based models for machine translation.
Comprehensive Review on Video Watermarking Security Threats, Challenges, and Its Applications

Data is a crucial resource for every business, and it must be protected both during storage and transmission. One efficient way of securing data and transferring it is through digital watermarking, where data is hidden inside a medium like text, audio, or video. Video watermarking is visible or invisible embedded data on a video in a logo, text, or video copyright disclaimer. In this proposed paper, the goal is to analyze the characteristics of video watermarking algorithms and the different metrics used for them. It deals with the extent to which the different requirements can be fulfilled, taking into consideration the conflicts between them and the practical challenges of video watermarking in terms of attacks like geometric attacks and non-geometric attacks. It also focuses on the process of watermarking a video. Recent advances in data security indicate that employing a video watermarking technology to transmit private data will be an effective method of transmitting sensitive data.
MILA: Multi-Task Learning from Videos via Efficient Inter-Frame Attention

Prior work in multi-task learning has mainly focused on predictions on a single image. In this work, we present a new approach for multi-task learning from videos via efficient inter-frame local attention (MILA). Our approach contains a novel inter-frame attention module which allows learning of task-specific attention across frames. We embed the attention module in a "slow-fast" architecture, where the slow network runs on sparsely sampled keyframes and the fast shallow network runs on non-keyframes at a high frame rate. We also propose an effective adversarial learning strategy to encourage the slow and fast net-work to learn similar features to well align keyframes and non-keyframes. Our approach ensures low-latency multi-task learning while maintaining high quality predictions. MILA obatins competitive accuracy compared to state-of-the-art on two multi-task learning benchmarks while reducing the number of floating point operations (FLOPs) by up to 70%. In addition, our attention based feature propagation method (ILA) outperforms prior work in terms of task accuracy while also reducing up to 90% of FLOPs.
Investigating the Feasibility of Deep Learning Methods for Urdu Word Sense Disambiguation

Word Sense Disambiguation (WSD), the process of automatically identifying the correct meaning of a word used in a given context, is a significant challenge in Natural Language Processing. A range of approaches to the problem has been explored by the research community. The majority of these efforts has focused on a relatively small set of languages, particularly English. Research on WSD for South Asian languages, particularly Urdu, is still in its infancy. In recent years, deep learning methods have proved to be extremely successful for a range of Natural Language Processing tasks. The main aim of this study is to apply, evaluate, and compare a range of deep learning methods approaches to Urdu WSD (both Lexical Sample and All-Words) including Simple Recurrent Neural Networks, Long-Short Term Memory, Gated Recurrent Units, Bidirectional Long-Short Term Memory, and Ensemble Learning. The evaluation was carried out on two benchmark corpora: (1) the ULS-WSD-18 corpus and (2) the UAW-WSD-18 corpus. Results (Accuracy = 63.25% and F1-Measure = 0.49) show that a deep learning approach outperforms previously reported results for the Urdu All-Words WSD task, whereas performance using deep learning approaches (Accuracy = 72.63% and F1-Measure = 0.60) are low in comparison to previously reported for the Urdu Lexical Sample task.
URLB: Unsupervised Reinforcement Learning Benchmark

Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range of complex yet specific control tasks. Yet training generalist agents that can quickly adapt to new tasks remains an outstanding challenge. Recent advances in unsupervised RL have shown that pre-training RL agents with self-supervised intrinsic rewards can result in efficient adaptation. However, these algorithms have been hard to compare and develop due to the lack of a unified benchmark. To this end, we introduce the Unsupervised Reinforcement Learning Benchmark (URLB). URLB consists of two phases: reward-free pre-training and downstream task adaptation with extrinsic rewards. Building on the DeepMind Control Suite, we provide twelve continuous control tasks from three domains for evaluation and open-source code for eight leading unsupervised RL methods. We find that the implemented baselines make progress but are not able to solve URLB and propose directions for future research.
Noise estimation in single coil MR images

Magnetic resonance (MR) signal is degraded by acquisition noise. Estimation and removal of noise from MR images is essential for analysis and further pre-processing. The work proposes a new method for noise variance estimation in single coil magnitude MR images. The proposed noise estimator is derived using the Maximum Á Posterior (MAP) estimator. Noise follows a Gaussian distribution at high Signal to Noise (SNR) ratio and Rician distribution at low SNR. In the MAP framework, Gaussian noise model and local statistics of the noisy image are instrumental in estimation of noise at high SNR. The proposed work also exploits the local second order moments in the MAP framework for noise estimation at low SNR. Convergence of the proposed MAP framework further highlights the optimal performance of the noise estimator. Noise estimation experiments conducted on Brainweb database exhibit encouraging performance at high as well as low SNR.
Encryption of Data over HTTP (Hypertext Transfer Protocol)/HTTPS (Hypertext Transfer Protocol Secure) Requests for Secure Data transfers over the Internet

With the increasing use of the internet for transferring data, the security of this data has been a serious concern since the very beginning. There has been an ever-increasing number of cyber-attacks happening all over the internet. Hackers, after getting access to the end user's personal computer, have complete control over all the data flowing in and out of the computer. In this case, if any sensitive data gets in the hands of the hacker, it might create a great catastrophe for that person and the party he wants to communicate with. Hence, there is a need for creating an encryption system for data transfer that is extremely sensitive such as Criminal Data, Banking data and it can extend to a person's private details such as banking details and account passwords. For any such sensitive data transfer, we need a very strong encryption system, which ensures that the data being transferred is safe and is only accessible to the person who is authenticated to view that data. This paper discusses the various methodologies, algorithms, and proposes a solution to securely transfer sensitive data over the internet.
A Creative Approach to Creating a Livable Urban Environment

Every year, the participation of citizens in the creation of urban projects is becoming more and more. It is especially popular to involve citizens in creative projects to create popular public spaces on the site of industrial zones, abandoned buildings, and cultural heritage sites. The relevance of the study is due to the popularity of the creation and renovation of creative spaces in urban practices that identify the needs of the population. The aim of the work is to analyze the development of creative industries in the Sverdlovsk region (Russia). The use of modern tools and algorithms that consider what the city wants in socially significant projects for all segments of the population is possible today. An example of citizens’ participation in the formation of a livable urban environment is considered in detail by the example of adapting a cultural heritage object for modern use. The results of the study show that the joint work of citizens, business and administration allows you to generate ideas and implement social creative projects.
Spatial and time variations of seismicity before strong earthquakes in the southern part of the Balkans

A retrospective analysis of the spatial and time variations of three main statistical parameters of the seismicity before recent 4 stronger earthquakes (2015 – 2020) in the southern Balkans is presented. The modern extended software package ZMAP with various advanced seismological functions for earthquake catalog analysis is used for estimating the spatial-time variations in a- value (seismic activity), b-value (slope of the recurrence graph) and z-value (parameter of the relative seismic quiescence). The catalog data from constantly updated catalog of the University of Athens for the period 1964-2020 and spatial window 32° - 44° N and 10° – 30° E are used for the various statistical interpretations. The main result of the whole analysis is that the abnormally low b-values and high z-values, defining the zones of relatively seismic quiescence, may be an indicator of imminent release of more significant stress in areas adjacent to the zones of relatively high a-values. Thus, the result of the proposed joint interpretation of the spatial-time variations of these three statistical parameters of seismicity could be considered as a kind of predictor of the stronger recent seismic events in the southern part of Balkans.
Chapter 1. Research in data-driven learning

Data-driven learning (DDL) typically involves language learners consulting corpus data, either directly or via prepared materials, to answer questions about language. The approach has been mooted since the beginning of the modern era of corpus linguistics and has come to be associated with work by Tim Johns who coined the term in print in 1990. Since then, hundreds of studies have attempted to evaluate some aspect of DDL, giving rise to several reviews and syntheses. This paper introduces DDL and discusses the syntheses to date, before analysing a rigorous collection of 351 studies published up to and including 2018. While previous syntheses have evaluated the field, the objective here is to provide an overview of how researchers see DDL across the board, to identify more clearly what DDL actually looks like today, how it has evolved from its early beginnings in the 1980s, and to suggest avenues for future research in underexplored areas.
Continuous vs. Discrete Optimization of Deep Neural Networks

Existing analyses of optimization in deep learning are either continuous, focusing on (variants of) gradient flow, or discrete, directly treating (variants of) gradient descent. Gradient flow is amenable to theoretical analysis, but is stylized and disregards computational efficiency. The extent to which it represents gradient descent is an open question in deep learning theory. The current paper studies this question. Viewing gradient descent as an approximate numerical solution to the initial value problem of gradient flow, we find that the degree of approximation depends on the curvature along the latter's trajectory. We then show that over deep neural networks with homogeneous activations, gradient flow trajectories enjoy favorable curvature, suggesting they are well approximated by gradient descent. This finding allows us to translate an analysis of gradient flow over deep linear neural networks into a guarantee that gradient descent efficiently converges to global minimum almost surely under random initialization. Experiments suggest that over simple deep neural networks, gradient descent with conventional step size is indeed close to the continuous limit. We hypothesize that the theory of gradient flows will be central to unraveling mysteries behind deep learning.
Data Augmentation Can Improve Robustness

Adversarial training suffers from robust overfitting, a phenomenon where the robust test accuracy starts to decrease during training. In this paper, we focus on reducing robust overfitting by using common data augmentation schemes. We demonstrate that, contrary to previous findings, when combined with model weight averaging, data augmentation can significantly boost robust accuracy. Furthermore, we compare various augmentations techniques and observe that spatial composition techniques work the best for adversarial training. Finally, we evaluate our approach on CIFAR-10 against $\ell_\infty$ and $\ell_2$ norm-bounded perturbations of size $\epsilon = 8/255$ and $\epsilon = 128/255$, respectively. We show large absolute improvements of +2.93% and +2.16% in robust accuracy compared to previous state-of-the-art methods. In particular, against $\ell_\infty$ norm-bounded perturbations of size $\epsilon = 8/255$, our model reaches 60.07% robust accuracy without using any external data. We also achieve a significant performance boost with this approach while using other architectures and datasets such as CIFAR-100, SVHN and TinyImageNet.
Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering

In this paper, we aim at synthesizing a free-viewpoint video of an arbitrary human performance using sparse multi-view cameras. Recently, several works have addressed this problem by learning person-specific neural radiance fields (NeRF) to capture the appearance of a particular human. In parallel, some work proposed to use pixel-aligned features to generalize radiance fields to arbitrary new scenes and objects. Adopting such generalization approaches to humans, however, is highly challenging due to the heavy occlusions and dynamic articulations of body parts. To tackle this, we propose Neural Human Performer, a novel approach that learns generalizable neural radiance fields based on a parametric human body model for robust performance capture. Specifically, we first introduce a temporal transformer that aggregates tracked visual features based on the skeletal body motion over time. Moreover, a multi-view transformer is proposed to perform cross-attention between the temporally-fused features and the pixel-aligned features at each time step to integrate observations on the fly from multiple views. Experiments on the ZJU-MoCap and AIST datasets show that our method significantly outperforms recent generalizable NeRF methods on unseen identities and poses. The video results and code are available at this https URL.
Improving Robustness using Generated Data

Recent work argues that robust training requires substantially larger datasets than those required for standard classification. On CIFAR-10 and CIFAR-100, this translates into a sizable robust-accuracy gap between models trained solely on data from the original training set and those trained with additional data extracted from the 80 Million Tiny Images dataset (TI-80M). In this paper, we explore how generative models trained solely on the original training set can be leveraged to artificially increase the size of the original training set and improve adversarial robustness to $\ell_p$ norm-bounded perturbations. We identify the sufficient conditions under which incorporating additional generated data can improve robustness, and demonstrate that it is possible to significantly reduce the robust-accuracy gap to models trained with additional real data. Surprisingly, we even show that even the addition of non-realistic random data (generated by Gaussian sampling) can improve robustness. We evaluate our approach on CIFAR-10, CIFAR-100, SVHN and TinyImageNet against $\ell_\infty$ and $\ell_2$ norm-bounded perturbations of size $\epsilon = 8/255$ and $\epsilon = 128/255$, respectively. We show large absolute improvements in robust accuracy compared to previous state-of-the-art methods. Against $\ell_\infty$ norm-bounded perturbations of size $\epsilon = 8/255$, our models achieve 66.10% and 33.49% robust accuracy on CIFAR-10 and CIFAR-100, respectively (improving upon the state-of-the-art by +8.96% and +3.29%). Against $\ell_2$ norm-bounded perturbations of size $\epsilon = 128/255$, our model achieves 78.31% on CIFAR-10 (+3.81%). These results beat most prior works that use external data.
Restricted Randomness DBSCAN : A faster DBSCAN Algorithm

Data Mining is the process of extracting useful and accurate information or patterns from large databases using different algorithms and methods of machine learning. To analyze the data, Clustering is one of the methods in which similar data is grouped together and DBSCAN clustering algorithm is the one, which is broadly used in numerous practical applications. This paper presents a more efficient density based clustering algorithm, which has the ability to discover cluster faster than the existing DBSCAN algorithm. The efficiency is achieved by restricting the randomness of choosing points from the dataset. Our proposed algorithm named Restricted Randomness DBSCAN (RR DBSCAN) is compared with conventional DBSCAN algorithm over 9 datasets on the basis of Silhouette Coefficient, Time taken in formation of clusters and accuracy. The results show that RR DBSCAN performs better than traditional DBSCAN in terms of accuracy and time taken to form clusters.
Multi-Degree-of-Freedom Manipulator Joint Trajectory Tracking Control Method Based on Decision Tree

Abstract For industrial-grade manipulators, the study of trajectory tracking control issues provides an important guarantee for accurate and safe work. Therefore, the trajectory control input driving torque can meet the requirements of the robot arm to accurately track a given target trajectory, and the process of building a decision tree is a process of dividing the feature space. For a given training data set, a set of if-then is summarized the rule of. Based on this, this paper launches the research of multi-degree-of-freedom manipulator joint trajectory tracking control method based on decision tree. Based on the established kinematics and dynamics model of the manipulator, this paper uses a proportional-integral-derivative (PID) sliding mode controller based on the sliding mode surface of the manipulator to perform the trajectory tracking control of the end of the manipulator, and the simulation results of the improved sliding mode control are compared with the simulation results of the improved sliding mode control. The simulation results of the PID controller and the traditional sliding mode controller are compared. This paper finally verifies the effectiveness of the proposed new sliding mode controller based on the expanded state observer through the experimental platform. The speed and chattering problems of the trajectory tracking at the end of the manipulator are better than those of the controller on the experimental platform. Finally, this paper adopts the sliding mode variable structure control strategy combining the double-power reaching law and the improved terminal sliding mode surface to study the trajectory tracking control of the planar two-degree-of-freedom manipulator.
Person Re-ID Testbed with Multi-Modal Sensors

Person Re-ID is a challenging problem and is gaining more attention due to demands in security, intelligent system and other applications. Most person Re-ID works are vision-based, such as image, video, or broadly speaking, face recognition-based techniques. Recently, several multi-modal person Re-ID datasets were released, including RGB+IR, RGB+text, RGB+WiFi, which shows the potential of the multi-modal sensor-based person Re-ID approach. However, there are several common issues in public datasets, such as short time duration, lack of appearance change, and limited activities, resulting in un-robust models. For example, vision-based Re-ID models are sensitive to appearance change. In this work, a person Re-ID testbed with multi-modal sensors is created, allowing the collection of sensing modalities including RGB, IR, depth, WiFi, radar, and audio. This novel dataset will cover normal daily office activities with large time span over multi-seasons. Initial analytic results are obtained for evaluating different person Re-ID models, based on small datasets collected in this testbed.