Reviewerly/exHarmony huggingface dataset:
Human Motion Retargeting to Pepper Humanoid Robot from Uncalibrated Videos Using Human Pose Estimation

Human motion retargeting to humanoid robots (i.e., transferring motion data to robots for human imitation) is a challenging process with many potential real-work applications. However, current state-of-the-art frameworks present practical limitations, such as the requirement of camera calibration and the implementation of expensive equipment for motion capture. Therefore, we propose a novel framework for motion retargeting based on a single-view camera and human pose estimation. Unlike previous works, our framework is cost and computationally efficient, and it is applicable both on prerecorded uncalibrated videos and web-camera live streams. The framework is composed of three modules: 1) 2D coordinate extraction from the integrated Google BlazePose, 2) 3D modeling by depth estimation using a geometrical algorithm, and 3) human joint angles computation and input process to Pepper robot. Pepper’s imitation accuracy is evaluated qualitatively by direct motion similarity observation and quantitatively by comparison between output and input motion data to observe the effect of Pepper’s physical limitations. Results suggest that our proposed framework is able to reproduce human-like motion sequences, however with some limitations due to the hardware.
EQUAL: Efficient QUasi Adiabatic Logic for Enhanced Side-Channel Resistance

Adiabatic circuits have the potential to achieve ultra-low power consumption while also exhibiting inherent resistance against power side-channel attacks. A novel adiabatic logic family is proposed in this work with application to lightweight devices where both energy efficiency and hardware security are of primary concern. The side-channel security characteristics of the proposed adiabatic logic are evaluated by quantifying normalized energy deviation (NED) and the normalized standard deviation (NSD). These metrics are compared with the existing secure adiabatic logic families. The simulations are performed at an RFID frequency of 13.56 MHz using a 65 nm technology node. The average energy per transition consumed by the NAND/AND and NOR/OR gates in the proposed logic family is up to 34.5% lower at the expense of 3% increase in the NED and 1.5% increase in the NSD. The proposed approach also reduces the number of transistors by 40%. Furthermore, the proposed adiabatic logic family does not require any external four-phase input signals to achieve input-independent power consumption, thereby significantly reducing the overall overhead.
Faking Signals to Fool Deep Neural Networks in AMC via Few Data Points

The recent years has witnessed a rapid development of Deep Learning (DL) based Automation Modulation Classification (AMC) methods, which has proved to outperform traditional classification approaches. In order to disturb the deep neural networks for AMC, in this paper, we propose an adversarial attack method to generate fake signals for fooling DL-based classifiers. Firstly, some constraints on visual difference and recoverability of fake signals are defined. Next, a Few Data Point Attacker (FDPA) is proposed to generate fake signals with few perturbed data points via differential evolution algorithm. Some experiments are taken on a public dataset, RML 2016.10a, and the results show that fake signals generated by the FDPA can remarkably reduce the accuracies of three types of DL-based AMC classifiers, a Convolutional Neural Network (CNN) based classifier, a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) based classifier, and a classifier combined with CNN and LSTM-RNN. The code will be available.
Comparing Online Robot Joint Space Trajectory Optimization for Task Space Applications

Online trajectory planning allows robotic manipulators to adapt to changing environments and dynamic tasks, which becomes increasingly relevant through human-robot interaction. optimizing motion objectives is a common way of planning but is usually conducted in joint space, whereas many planning objectives are rather defined in task space. Joint Space Planning (JSP) first transforms target poses into joint space and then performs conventional optimization. Implicit Task Space Planning (ITSP) uses a special objective function to directly consider task quantities and is similar to economic model predictive control. Remarkably, comparative studies of both approaches with respect to their performance are scarce. This contribution aims at filling this gap by providing a systematic analysis of both methods using a 6-DoF collaborative robot in practical experiments. Results show that the first method is less sensitive to local minima and profits from active reconfiguration abilities while it generates less straight-lined motions and is not able to account for target manifolds in the task space. The second method can easily consider target manifolds and performs more straight-lined motions while it is more sensitive to local minima and provides active reconfiguration only partially. Although the cost function of the second method is significantly more complex, the computational effort relativizes during planning.
Blended learning: the amount of requisite professional competencies in faculty members of Al Ain University from viewpoint of students

Purpose The purpose of the present research is to examine the role and influence of modern method of blended learning in the formation and development of professional competencies on the example of the faculty members of Al Ain University from the viewpoint of its students and determine the relevance of the educational method of blended learning in modern society and science according to world situation. Design/methodology/approach The study sample consisted of 980 male and female students in the second semester of 2019–2020. The comparative analysis of the influence of blended learning on the knowledge level and improvement of professional competencies among students of two faculties was taken into account. Findings The results of the survey according to chosen criteria showed that the professional competencies required for the faculty members were moderate. According to the results of the survey, most of respondents (75%) are sure that the level of blended learning at their educational institution is high. 35% of respondents think that blended learning helps to develop technological competency as well as self-development (25%), motivation (18%), critical thinking (12%), self-discipline and time management (10%). Originality/value The relevance of problematic issue of blended learning is determined by the new conditions of educational process connected with pandemic. The prospects for further research can be based on the results of this study especially the relevance is connected with the study of the experience of using modern method of blended learning in different foreign countries on the basis of surveys and developed programs.
Security Issues and Application of Blockchain

Blockchain is the most important IT invention in current technology. Blockchain is much secured, easily shared and works with various distributed ledgers. Blockchain is changing the working environment from centralized to decentralize. Blockchain has removed the problem of single point of failure (SPOF). In the blockchain, you can easily track all previous transactions and can validate future transactions. Due to various benefits in blockchain, the number of organizations works on blockchain technology for providing solutions and many others are working on security issues in blockchain like 51% vulnerability, private key security, double spending, criminal activities, transaction leakage, smart contracts and many others.
Self-Supervised Depth Completion via Adaptive Sampling and Relative Consistency

Depth sensing is crucial for many computer vision applications. Commodity-level RGB-D cameras are often unable to sense depth in distant, reflective and transparent regions, resulting in large missing areas. As the acquisition of depth annotations in missing areas is tedious, we propose a self-supervised method for the task of completing depth values of missing areas. Specifically, we sample the incomplete raw depth map via an adaptive sampling strategy to generate a more incomplete depth map as the input and use the raw depth map as the training label. To enable the network to propagate long-range depth information to fill large invalid areas, we further propose a relative consistency loss during training. Experiments validate the effectiveness of our self-supervised method, which outperforms previous unsupervised methods and even can compete with some supervised methods. Our code is available at https://github.com/caozidong/DepthCompletion.
Natural Evolution Strategy for Unconstrained and Implicitly Constrained Problems with Ridge Structure

In this paper, we propose a new natural evolution strategy for unconstrained black-box function optimization (BBFO) problems and implicitly constrained BBFO problems. BBFO problems are known to be difficult because explicit representations of objective functions are not available. Implicit constraints make the problems more difficult because whether or not a solution is feasible is revealed when the solution is evaluated with the objective function. DX-NES-IC is one of the promising methods for implicitly constrained BBFO problems. DX-NES-IC has shown better performance than conventional methods on implicitly constrained benchmark problems. However, DX-NES-IC has a problem in that the moving speed of the probability distribution is slow on ridge structure. To address the problem, we propose the Fast Moving Natural Evolution Strategy (FM-NES) that accelerates the movement of the probability distribution on ridge structure by introducing the rank-one update into DX-NES-IC. The rank-one update is utilized in CMA-ES. Since naively introducing the rank-one update makes the search performance deteriorate on implicitly constrained problems, we propose a condition of performing the rank-one update. We also propose to reset the shape of the probability distribution when an infeasible solution is sampled at the first time. In numerical experiments using unconstrained and implicitly constrained benchmark problems, FM-NES showed better performance than DX-NES-IC on problems with ridge structure and almost the same performance as DX-NES-IC on the others. Furthermore, FM-NES outperformed xNES, CMA-ES, xNES with the resampling technique, and CMA-ES with the resampling technique.
Simulating language knowledge across the EU: language regimes, language learning and consequences for linguistic disenfranchisement

The objective of this paper is to develop an simulation model able to test different language education orientations and their consequences for the EU population in terms of linguistic disenfranchisement, that is, the inability of citizens to understand EU documents and parliamentary discussions should their native language(s) no longer be official. I will focus on the impact of linguistic distance and language learning. Ideally, this model would be a tool to help EU policy makers make informed decisions about language practices and education policies, taking into account their consequences in terms of diversity and linguistic disenfranchisement. The model can be used to force agents to make certain choices in terms of language skills acquisition. The user can then go on to compare different scenarios in which language skills are acquired according to different rationales. The idea is that, by forcing agents to adopt certain language learning strategies, the model user can simulate policies promoting the acquisition of language skills and get an idea of their impact. In this way, the model allows not only to sketch various scenarios of the evolution of language skills among EU citizens, but also to estimate the level of disenfranchisement in each of these scenarios.
Sentiment, Stance, and Intent Detection in Turkish Tweets

Sentiment analysis, stance detection, and intent detection on social media texts are all significant research problems with several application opportunities. In this chapter, the authors explore the possible contribution of sentiment and intent information to machine learning-based stance detection on tweets. They first annotate a Turkish tweet dataset with sentiment and proprietary intent labels, where the dataset was already annotated with stance labels. Next, they perform stance detection experiments on the dataset using sentiment and intent labels as additional features. The experiments with SVM classifiers show that using sentiment and intent labels as additional features improves stance detection performance considerably. The final form of the dataset is made publicly available for research purposes. The findings reveal the contribution of sentiment and intent information to the solution of stance detection task on the Turkish tweet dataset employed. Yet, further studies on other datasets are needed to confirm that our findings are generalizable to other languages and on other topics.
Transcribed Digits and Apparel Classification using Convolutional Neural Network

Image classification is one of the most trending technologies that have lot many applications. It would not be wrong if we say that it has become a necessity for research community due to the explosion of image data, we deal in our daily life. This paper illustrates and exemplifies the influence of Convolutional Neural Networks (CNN) in the challenging sophisticated image classification riddle. CNN are composed of manifold processing layers with numerous abstract levels. Millions of parameters to tune the model, multiple layers and handcrafted hyperparameters make them unique and significantly important in image processing. In this research paper, our focus has been to explore the structure of CNN along with its various parameters and formulating the problem on the benchmark datasets - MNIST & Fashion-MNIST (FMNIST).
Deep Active Learning For Human Pose Estimation Via Consistency Weighted Core-Set Approach

Aiming to develop an annotation-efficient algorithm by selecting the most informative samples to be labeled for human pose estimation, we propose a novel active learning approach that generalized the standard core-set approach by dynamically incorporating the uncertainty and representativeness cues. Based on our designed assignment cost that consists of fast spatial consistency and domain-shift scaled distance, our method proposed an adaptive information measurement as data selection criterion. Extensive experiments conducted on two network architectures and various datasets demonstrate the effectiveness and superiority of our proposed approach in comparison with other state-of-the-art methods.
Effect of Label Noise on Robustness of Deep Neural Network Object Detectors

Label noise is a primary point of interest for safety concerns in previous works as it affects the robustness of a machine learning system by a considerable amount. This paper studies the sensitivity of object detection loss functions to label noise in bounding box detection tasks. Although label noise has been widely studied in the classification context, less attention is paid to its effect on object detection. We characterize different types of label noise and concentrate on the most common type of annotation error, which is missing labels. We simulate missing labels by deliberately removing bounding boxes at training time and study its effect on different deep learning object detection architectures and their loss functions. Our primary focus is on comparing two particular loss functions: cross-entropy loss and focal loss. We also experiment on the effect of different focal loss hyperparameter values with varying amounts of noise in the datasets and discover that even up to 50% missing labels can be tolerated with an appropriate selection of hyperparameters. The results suggest that focal loss is more sensitive to label noise, but increasing the gamma value can boost its robustness.
Optimization of the forearm angle for arm wrestling using multi-camera stereo digital image correlation: A preliminary study

This study analyzes the function of different muscles during arm wrestling and proposes a method to analyze the optimal forearm angle for professional arm wrestlers. We built a professional arm-wrestling platform to measure the shape and deformation of the skin at the biceps brachii of a volunteer in vivo during arm wrestling. We observed the banding phenomenon of arm skin strain during muscle contraction and developed a model to evaluate the moment provided by the biceps brachii. According to this model, the strain field of the area of interest on the skin was measured, and the forearm angles most favorable and unfavorable to the work of the biceps brachii were analyzed. This study demonstrates the considerable potential of applying DIC and its extension method to the in vivo measurement of human skin and facilitates the use of the in vivo measurement of skin deformation in various sports in the future.
Recent trends towards cognitive science: from robots to humanoids

Each upsurge of new computational creativity has been cultivated to guide new kinds of systems, new ways of creating tools, a new form of data, and so on, which have often overturned their predecessors. In this regard, cognitive computing (CC) is described as the merger of artificial intelligence and signal processing disciplines. The objective of cognitive processing is to replicate the human thought processes in a computerized model. Employing self-learning algorithms that use data mining, pattern recognition, and natural language processing concerning this specific subject in which the computer can mimic the human brain function. CC makes a new class of glitches computable. The problem can address the complex situations characterized by vagueness as well as doubt. The diverse, ample-data, and frequently changing circumstances, data manage to shift continuously, moreover it is contradictory also. Another objective of CC is to evaluate users’ needs through learning and reframing their goals and objectives. Following the diversity of user’s interpretation of problems, the CC system presents the reorganization of types of data and sways meaning and analysis. When arriving at a conclusion the CC system usually relies on considering contradictory proof and proposing a solution which is “best” more than precisely “right.” The systems with CC ability create quantifiable situations. Machines with cognitive systems recognize as well as excerpt factor characteristics for example location, time, history, work, or description to portray the data set suitable for a person or for a relying implementation involved in a particular method at an exact schedule and location. These systems reframe the scenery involving the association of individuals and their progressively all-encompassing digital environment. This has been shown to play a larger role as the mentor or assistant for the user, similarly they may behave digitally independent for resolving various problems.
Modelling other agents through evolutionary behaviours

Abstract Modelling other agents is a challenging topic in artificial intelligence research particularly when a subject agent needs to optimise its own decisions by predicting their behaviours under uncertainty. Existing research often leads to a monotonic set of behaviours for other agents so that a subject agent can not cope with unexpected decisions from the other agents. It requires creative ideas about developing diversity of behaviours so as to improve the subject agent’s decision quality. In this paper, we resort to evolutionary computation approaches to generate a new set of behaviours for other agents and solve the complicated agents’ behaviour search and evaluation issues. The new approach starts with the initial behaviours that are ascribed to the other agents and expands the behaviours by using a number of genetic operators in the behaviour evolution. This is the first time that evolutionary techniques are used to modelling other agents in a general multiagent decision framework. We examine the new methods in two well-studied problem domains and provide experimental results in support.
4D Epanechnikov Mixture Regression in LF Image Compression

With the emergence of light field imaging in recent years, the compression of its elementary image array (EIA) has become a significant problem. Our coding framework includes modeling and reconstruction. For the modeling, the covariance-matrix form of the 4-D Epanechnikov kernel (4-D EK) and its correlated statistics were deduced to obtain the 4-D Epanechnikov mixture models (4-D EMMs). A 4-D Epanechnikov mixture regression (4-D EMR) was proposed based on this 4-D EK, and a 4-D adaptive model selection (4-D AMLS) algorithm was designed to realize the optimal modeling for a pseudo video sequence (PVS) of the extracted key-EIA. A linear function based reconstruction (LFBR) was proposed based on the correlation between adjacent elementary images (EIs). The decoded images realized a clear outline reconstruction and superior coding efficiency compared to high-efficiency video coding (HEVC) and JPEG 2000 below approximately 0.05 bpp. This work realized an unprecedented theoretical application by (1) proposing the 4-D Epanechnikov kernel theory, (2) exploiting the 4-D Epanechnikov mixture regression and its application in the modeling of the pseudo video sequence of light field images, (3) using 4-D adaptive model selection for the optimal number of models, and (4) employing a linear function-based reconstruction according to the content similarity.
Seeing Farther Than Supervision: Self-supervised Depth Completion in Challenging Environments

This paper tackles the problem of learning a depth completion network from a series of RGB images and short-range depth measurements as a new setting for depth completion. Commodity RGB-D sensors used in indoor environments can provide dense depth measurements; however, their acquisition distance is limited. Recent depth completion methods train CNNs to estimate dense depth maps in a supervised/self-supervised manner while utilizing sparse depth measurements. For self-supervised learning, indoor environments are challenging due to many non-textured regions, leading to the problem of inconsistency. To overcome this problem, we propose a self-supervised depth completion method that utilizes optical flow from two RGB-D images. Because optical flow provides accurate and robust correspondences, the ego-motion can be estimated stably, which can reduce the difficulty of depth completion learning in indoor environments. Experimental results show that the proposed method outperforms the previous self-supervised method in the new depth completion setting and produces qualitatively adequate estimates.
An Attention-Based Hypocenter Estimator for Earthquake Localization

The accuracy of earthquake localization is of great importance for earthquake monitoring systems. Traditionally, numerical optimization methods are used to estimate the hypocenter location and the origin time of an earthquake in an iterative manner. The traditional methods usually depend on certain theoretical models, but the geological conditions in practice can be quite different from the presumed models. In this study, an attention-based hypocenter estimation (AHE) model was proposed to locate the hypocenter and origin time of the earthquake. Rather than using the raw waveforms, the phase picking times and the positions of the triggered stations are used as the input. The attention mechanism is adapted to reveal the correlations among the input sequence. An experimental model was trained using data collected from earthquakes in Taiwan in 2016 and 2017 and tested on data in 2018. From the results, AHE is capable of locating the hypocenter with a high degree of accuracy in terms of the distance, depth, and origin time of earthquakes.