Reviewerly/exHarmony huggingface dataset:
Real Time RF Based Gesture Controlled Robotic Vehicle

Robotics is one of the emerging fields nowadays. It can be defined as a design gadget which can assist humans in their day-to-day activities and help them by amalgamating electronics and mechanical engineering. Robots are assuming a significant number of jobs over sectors like construction, military, medical, etc. Various attempts have been implemented to make interfaces among users and PCs put together frameworks based on human gestures. These gesture based interfaces can substitute the regular interface gadgets. In the wake of making some essential robots like a line follower robot or a computer-controlled robot, human gestures can operate these types of robots and therefore an accelerometer based Hand Gesture Robot is on the rise. This technique will reduce the dichotomy between the digital and physical world. In this paper, we will see how a robot can move by using Hand Gestures.
Study Approach Based on Art Appreciation ˋˋShould'' be an Option in Art Learning During Online Study

Cultural arts education faces new challenges in the era of the COVID-19 pandemic, where all forms of offline learning in schools are eliminated. Cultural arts teachers are faced with a lot more problems because the curriculum 2013 still sticks to the competency of artwork skills. Also, the current cultural arts education curriculum explicitly no longer includes art appreciation and artistic expression in the achievement of competency in learning arts and culture. Art in education basically seeks to take advantage of unique artistic activities, which in education are used to shape children into full human beings who also develop emotional intelligence. Art appreciation activities teach empathy and foster a sense of emotion, helping to build new imaginations in the form of aesthetic and artistic experiences. Thus, art learning based on art appreciation is considered to be the most effective way to solve the problems of learning arts and culture online.&#x0D; Keywords: art appreciation, emotional intelligence, empathy, aesthetic creativity, artistic creativity
Image Captioning through Cognitive IOT and Machine-Learning Approaches

Text generation is the technique of producing a textual explanation of an image. Both natural-language processing (NLP) and cognitive science (CS) techniques are used to create the captions of images. Subtitles and descriptions are both texts shown on a video that delivers extra or interpretive details for observers who are deaf and hard of hearing or require extra clues than just the sound. Many times, the shown text comprises an interpretation or conversion of the pronounced language in the video. Other usages of text-generation have been originated based on the requirements of various spectators. For instance, the caption for the deaf and people who have severe hearing impairments contain an explanation of other acoustic details that audiences with hearing problems might miss, such as the description of the music, proof that the narrator is now offscreen, etc. Caption-generation is a very inspiring Cognitive Internet of things (CIoT) and artificial intelligence (AI) task where the description of text must be originated from a given image. It requires the approach of computer vision to recognize image details or content and a language model from the NLP region to translate the image interpretation into the words in the right order. In this survey paper, we describe the comprehensive overview of prevalent deep-learning based text generation methods. Besides this, we describe the various datasets and the famous evaluation metrics used in the deep-learning based automatic text generation.
Robotic Smart Prosthesis Arm with BCI and Kansei / Kawaii / Affective Engineering Approach. Pt I: Quantum Soft Computing Supremacy

A description of the design stage and results of the development of the conceptual structure of a robotic prosthesis arm is given. As a result, a prototype of manmade prosthesis on a 3D printer as well as a foundation for computational intelligence presented. The application of soft computing technology (the first step of IT) allows to extract knowledge directly from the physical signal of the electroencephalogram, as well as to form knowledge-based intelligent robust control of the lower performing level taking into account the assessment of the patient’s emotional state. The possibilities of applying quantum soft computing technologies (the second step of IT) in the processes of robust filtering of electroencephalogram signals for the formation of mental commands and quantum supremacy simulation of robotic prosthetic arm discussed.
Towards robust and domain agnostic reinforcement learning competitions.

Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field. Despite this, a majority of challenges suffer from the same fundamental problems: participant solutions to the posed challenge are usually domain-specific, biased to maximally exploit compute resources, and not guaranteed to be reproducible. In this paper, we present a new framework of competition design that promotes the development of algorithms that overcome these barriers. We propose four central mechanisms for achieving this end: submission retraining, domain randomization, desemantization through domain obfuscation, and the limitation of competition compute and environment-sample budget. To demonstrate the efficacy of this design, we proposed, organized, and ran the MineRL 2020 Competition on Sample-Efficient Reinforcement Learning. In this work, we describe the organizational outcomes of the competition and show that the resulting participant submissions are reproducible, non-specific to the competition environment, and sample/resource efficient, despite the difficult competition task.
Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking

Fact Extraction and VERification (FEVER) is a recently introduced task that consists of the following subtasks (i) document retrieval, (ii) sentence retrieval, and (iii) claim verification.In this work, we focus on the subtask of sentence retrieval.Specifically, we propose an evidence-aware transformer-based model that outperforms all other models in terms of FEVER score by using a subset of training instances.In addition, we conduct a large experimental study to get a better understanding of the problem, while we summarize our findings by presenting future research challenges 1 ModelsWe frame the sentence selection subtask, where the input is a claim sentence and a list of candidate evidence sentences (i.e., as retrieved from the document retrieval step, for that we used the same input as in the work of Liu et al. ( 2020)), as an NLI problem.Specifically, the claim is the "hypothesis"
Steganography in Frames of Graphical Animation

Information exchanged between two parties is often targeted by a third party. Steganography in images is often used for handling threats, such as, in this case, an attacker suspecting the existence of confidential information. But even in this case, if a pattern is used, e.g., hiding the information in successive or every third pixel, the attacker might discover the pattern, and the following revelation of the secret information will cause no difficulties. To avoid such vulnerabilities, the information is hidden in the frames of graphical animation. In the frames, the positions of the pixels selected to store confidential information in the given work are determined by the values of a mathematical function. These actions will ensure the high secrecy of information.
Learning Groupwise Explanations for Black-Box Models

We study two user demands that are important during the exploitation of explanations in practice: 1) understanding the overall model behavior faithfully with limited cognitive load and 2) predicting the model behavior accurately on unseen instances. We illustrate that the two user demands correspond to two major sub-processes in the human cognitive process and propose a unified framework to fulfill them simultaneously. Given a local explanation method, our framework jointly 1) learns a limited number of groupwise explanations that interpret the model behavior on most instances with high fidelity and 2) specifies the region where each explanation applies. Experiments on six datasets demonstrate the effectiveness of our method.
On the training of sparse and dense deep neural networks: less parameters, same performance.

Deep neural networks can be trained in reciprocal space, by acting on the eigenvalues and eigenvectors of suitable transfer operators in direct space. Adjusting the eigenvalues, while freezing the eigenvectors, yields a substantial compression of the parameter space. This latter scales by definition with the number of computing neurons. The classification scores, as measured by the displayed accuracy, are however inferior to those attained when the learning is carried in direct space, for an identical architecture and by employing the full set of trainable parameters (with a quadratic dependence on the size of neighbor layers). In this Letter, we propose a variant of the spectral learning method as appeared in Giambagli et al {Nat. Comm.} 2021, which leverages on two sets of eigenvalues, for each mapping between adjacent layers. The eigenvalues act as veritable knobs which can be freely tuned so as to (i) enhance, or alternatively silence, the contribution of the input nodes, (ii) modulate the excitability of the receiving nodes with a mechanism which we interpret as the artificial analogue of the homeostatic plasticity. The number of trainable parameters is still a linear function of the network size, but the performances of the trained device gets much closer to those obtained via conventional algorithms, these latter requiring however a considerably heavier computational cost. The residual gap between conventional and spectral trainings can be eventually filled by employing a suitable decomposition for the non trivial block of the eigenvectors matrix. Each spectral parameter reflects back on the whole set of inter-nodes weights, an attribute which we shall effectively exploit to yield sparse networks with stunning classification abilities, as compared to their homologues trained with conventional means.
A Survey on Key Management using Particle Swarm Optimization in MANET

Adequate assurance is the leading requirement in a mobile ad-hoc network (MANET). So, the increase in desire for a reliable and dynamic mode of communication we are compelled to find the fervour of the present cryptography techniques. This work presents the use of a heuristic(partial search algorithm) approach that may present us with a proper satisfactory result to an optimization dilemma of key selection, or generation. Today's epoch of computer s cryptography is a correlation of the conversion of plain text into ciphertext. A ciphertext is a text made that the receiver of the text can only probably decide it to plain text. The process of conversion of plaintext to ciphertext is known as encryption and it's vice-versa is known as decryption. Among the three types of cryptography techniques, viz Asymmetric key, Symmetric-key and Hash_ Functions; A s y m m e t r i c key cryptography(particularly RSA algorithm ) is formed for work. We will incite swarm intelligence, ciphering particularly called as Particle(s) Swarm Optimization(PSO) technique to diminish the key search space RSA algorithm. PSO is a technique of optimization and is used to attain the optimized value of the key. The key will not be easy to be formed computationally be the interlopers or depreciators. We will circumscribe a function, induce robustness function that is intended for the contemporaries of the key within the reduced search space. The foremost advantage of using PSO signifies the diminishing of the keys that are to be collected and disseminated. Furthermore, the time necessitated for the encryption is extremely more subordinate than its other metaheuristic counterpart which engages ACO for the corresponding problem. This paper concentrates on the regulation of PSO to find the key that is best in course of security and efficacy in a MANET.
Extending the known families of scalable Huffman sequences

A canonical Huffman sequence is characterized by a zero inner-product between itself and each of its shifted copies, except at their largest relative shifts: their aperiodic auto-correlation then becomes delta-like, a single central peak surrounded by zeros, with one non-zero entry at each end. Prior work showed that the few known families of Huffman sequences (of length $N = 4n-1$, for integers $n &gt; 1$, with continuously scalable elements) are based upon Fibonacci polynomials. Related multi-dimensional ($nD$) Huffman arrays were designed, as well as non-canonical quasi-Huffman arrays that also possess delta-like auto-correlations. We examined links between these discrete sequences and delta-correlated functions defined on the continuum, and provided simple non-iterative approaches to successfully deconvolve $nD$ data blurred by diffuse Huffman arrays. Here we describe new constructions for canonical Huffman sequences. Examples of length $N = 4n+1$, $N = 2n$ and families of arbitrary length are given, including scaled forms, as well as for Fibonacci-based arrays with perfect periodic auto-correlations, that are zero for all non-zero cyclic shifts. A generalization to include canonical sequences with complex scale factors invokes an equally useful dual form of delta-correlation. We also present $1D$ arrays with a much smaller dynamic range than those where the elements are built using Fibonacci recursion. When Huffman arrays (that are comprised of inherently signed values) are employed as diffuse probe beams for image acquisition, a new two-mask de-correlating step is described here that significantly reduces the total incident radiation dose compared to a prior method that added a positive pedestal-offset.
The Category Of “Continuity”: General And Special In Its Study And Understanding

The article presents a study aimed at implementing the principle of continuity in the system of developing continuous education of the individual.The article substantiates that the complex, unified (cosmobiopsychosocial and cultural) nature of man itself requires a reasonable combination of the ideas of complementarity and continuity in solving the problems of the formation of "the man of the future."This discussion gives the reader incomplete, but necessary information for the successful implementation of the original multifunctional programmed exercise for the study, multilevel understanding and diagnosis of assimilation of the category of "continuity".The main guideline in the disclosure of the category of "continuity", the authors use their generalized plans for cognition of knowledge of the essence of the subject (phenomenon).An original and multifunctional "Programmed exercise for the study, development and diagnosis of the concept of" continuity "in the development of the individual is presented.This exercise not only "teaching develops and educates," but also, in the general case, allows you to evaluate the style of thinking, worldview, their intuitive and rational components, reflective skills, include assessment and selfesteem, i.e. to carry out control, adjustment and self-control of mastering the concept.The conclusions of this research are presented, the main guidelines and directions for further study and solution of the problem of continuity in the system of developing continuing education of the individual are shown.
Knowledge-Rich BERT Embeddings for Readability Assessment.

Automatic readability assessment (ARA) is the task of evaluating the level of ease or difficulty of text documents for a target audience. For researchers, one of the many open problems in the field is to make such models trained for the task show efficacy even for low-resource languages. In this study, we propose an alternative way of utilizing the information-rich embeddings of BERT models through a joint-learning method combined with handcrafted linguistic features for readability assessment. Results show that the proposed method outperforms classical approaches in readability assessment using English and Filipino datasets, and obtaining as high as 12.4% increase in F1 performance. We also show that the knowledge encoded in BERT embeddings can be used as a substitute feature set for low-resource languages like Filipino with limited semantic and syntactic NLP tools to explicitly extract feature values for the task.
Adversarial Motion Modelling helps Semi-supervised Hand Pose Estimation

Hand pose estimation is difficult due to different environmental conditions, object- and self-occlusion as well as diversity in hand shape and appearance. Exhaustively covering this wide range of factors in fully annotated datasets has remained impractical, posing significant challenges for generalization of supervised methods. Embracing this challenge, we propose to combine ideas from adversarial training and motion modelling to tap into unlabeled videos. To this end we propose what to the best of our knowledge is the first motion model for hands and show that an adversarial formulation leads to better generalization properties of the hand pose estimator via semi-supervised training on unlabeled video sequences. In this setting, the pose predictor must produce a valid sequence of hand poses, as determined by a discriminative adversary. This adversary reasons both on the structural as well as temporal domain, effectively exploiting the spatio-temporal structure in the task. The main advantage of our approach is that we can make use of unpaired videos and joint sequence data both of which are much easier to attain than paired training data. We perform extensive evaluation, investigating essential components needed for the proposed framework and empirically demonstrate in two challenging settings that the proposed approach leads to significant improvements in pose estimation accuracy. In the lowest label setting, we attain an improvement of $40\%$ in absolute mean joint error.
Mythological Metamorphosis And The Genesis Of Individuality In The Information-Digital Space

Digital space is a unique specific artificial reality that allows people to go beyond their capabilities and limitations.In relation to objective reality, this environment does not have an identical existence, but, for example, in terms of the individual's attitude to it within it, this environment is quite capable of replacing the objective reality.The influence of the objective world in digital space is minimized.The possibilities of the latter have potentially endless characteristics, which generates fertile ground for various mythmaking processes.The created artificial mythologemes operate not only within this space.They go beyond it, they emanate through an individual, who is the part of objective reality.Through an individual's consciousness, thinking and attitudes, such mythologemes are rooted as some objective and existing phenomena.Due to its specifics, digital reality is an effective surrogate that forms an individual, replacing, in particular, society, culture, morality, etc. in the aspect of an individual's formation.However, the appearance and formation of a new digital individual is subject to many trends and changes.The paradox of the digital space is that for all the individuality of the approach to everyone who exists in it, it also imposes certain requirements that the one who is present in it should meet.Many attitudes towards the formation of the information space identity contain trends of destructing an individual's direct relationship to many of its spheres, creating a kind of autonomous world, isolating the presence of objective reality from an individual.
Diverse Pretrained Context Encodings Improve Document Translation

We propose a new architecture for adapting a sentence-level sequence-to-sequence transformer by incorporating multiple pretrained document context signals and assess the impact on translation performance of (1) different pretraining approaches for generating these signals, (2) the quantity of parallel data for which document context is available, and (3) conditioning on source, target, or source and target contexts.Experiments on the NIST Chinese-English, and IWSLT and WMT English-German tasks support four general conclusions: that using pretrained context representations markedly improves sample efficiency, that adequate parallel data resources are crucial for learning to use document context, that jointly conditioning on multiple context representations outperforms any single representation, and that source context is more valuable for translation performance than target side context.Our best multicontext model consistently outperforms the best existing context-aware transformers.
Learning Intra-Batch Connections for Deep Metric Learning

The goal of metric learning is to learn a function that maps samples to a lower-dimensional space where similar samples lie closer than dissimilar ones. Particularly, deep metric learning utilizes neural networks to learn such a mapping. Most approaches rely on losses that only take the relations between pairs or triplets of samples into account, which either belong to the same class or two different classes. However, these methods do not explore the embedding space in its entirety. To this end, we propose an approach based on message passing networks that takes all the relations in a mini-batch into account. We refine embedding vectors by exchanging messages among all samples in a given batch allowing the training process to be aware of its overall structure. Since not all samples are equally important to predict a decision boundary, we use an attention mechanism during message passing to allow samples to weigh the importance of each neighbor accordingly. We achieve state-of-the-art results on clustering and image retrieval on the CUB-200-2011, Cars196, Stanford Online Products, and In-Shop Clothes datasets. To facilitate further research, we make available the code and the models at this https URL.
E2E-based Multi-task Learning Approach to Joint Speech and Accent Recognition

In this paper, we propose a single multi-task learning framework to perform End-to-End (E2E) speech recognition (ASR) and accent recognition (AR) simultaneously. The proposed framework is not only more compact but can also yield comparable or even better results than standalone systems. Specifically, we found that the overall performance is predominantly determined by the ASR task, and the E2E-based ASR pretraining is essential to achieve improved performance, particularly for the AR task. Additionally, we conduct several analyses of the proposed method. First, though the objective loss for the AR task is much smaller compared with its counterpart of ASR task, a smaller weighting factor with the AR task in the joint objective function is necessary to yield better results for each task. Second, we found that sharing only a few layers of the encoder yields better AR results than sharing the overall encoder. Experimentally, the proposed method produces WER results close to the best standalone E2E ASR ones, while it achieves 7.7% and 4.2% relative improvement over standalone and single-task-based joint recognition methods on test set for accent recognition respectively.