Reviewerly/exHarmony huggingface dataset:
GAZEL: Runtime Gaze Tracking for Smartphones

Although work has been conducted on smartphone gaze tracking, the existing techniques are not pervasively used because of their heavy weight and low accuracy. Our preliminary analysis shows that these techniques would work better if their models were trained with data from tablets which have large screens. In this paper, we propose GAZEL, a runtime smartphone gaze-tracking scheme that achieves high accuracy on real devices. The key idea of GAZEL, a tablet-to-smartphone transfer learning, is to train a CNN model with data collected from tablets and then transplant the model to a smartphone. To achieve the goal, we designed a new CNN-based model architecture that is head pose resilient and light enough to operate at runtime. We also exploit implicit calibration to alleviate errors caused by differences in users' visual and device characteristics. The experiment results with commercial smartphones show that GAZEL achieves 27.5% better accuracy on smartphones compared to the state-of-the-art techniques and provides gaze tracking at up to 18 fps which is practically usable at runtime.
CATE: Computation-aware Neural Architecture Encoding with Transformers

Recent works (White et al., 2020a; Yan et al., 2020) demonstrate the importance of architecture encodings in Neural Architecture Search (NAS). These encodings encode either structure or computation information of the neural architectures. Compared to structure-aware encodings, computation-aware encodings map architectures with similar accuracies to the same region, which improves the downstream architecture search performance (Zhang et al., 2019; White et al., 2020a). In this work, we introduce a Computation-Aware Transformer-based Encoding method called CATE. Different from existing computation-aware encodings based on fixed transformation (e.g. path encoding), CATE employs a pairwise pre-training scheme to learn computation-aware encodings using Transformers with cross-attention. Such learned encodings contain dense and contextualized computation information of neural architectures. We compare CATE with eleven encodings under three major encoding-dependent NAS subroutines in both small and large search spaces. Our experiments show that CATE is beneficial to the downstream search, especially in the large search space. Moreover, the outside search space experiment demonstrates its superior generalization ability beyond the search space on which it was trained. Our code is available at: this https URL.
Mapping of Research in Agricultural Scientist’s Citation Analysis in Google Scholar, Scopus and Publons

An innovative idea developed by the researchers should reach the end user groups. The output of any researcher can be judged in terms of patents or his publications in the respective fields. Quality and impact of research work can be based on the citation and h index. Nowadays, there are several parameters considered for evaluating the performance of the research. Citation and h index are the quality parameters used for rating the originality and use of research. They are more useful for accessing the potentials of research as well as for providing a platform for collaborative projects at national / International levels. This paper presents the comparative analysis of citation and h index for identified researcher through three popular databases viz., Google Scholar, Scopus, and Publons. Among the three databases, Google Scholar showed a higher number of citations of the selected author due to data collected from unauthenticated documents as well as well established research databases.
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness

Gaussian processes are ubiquitous in machine learning, statistics, and applied mathematics. They provide a flexible modelling framework for approximating functions, whilst simultaneously quantifying uncertainty. However, this is only true when the model is well-specified, which is often not the case in practice. In this paper, we study the properties of Gaussian process means when the smoothness of the model and the likelihood function are misspecified. In this setting, an important theoretical question of practial relevance is how accurate the Gaussian process approximations will be given the difficulty of the problem, our model and the extent of the misspecification. The answer to this problem is particularly useful since it can inform our choice of model and experimental design. In particular, we describe how the experimental design and choice of kernel and kernel hyperparameters can be adapted to alleviate model misspecification.
Angular offset distributions during fixation are, more often than not, multimodal

Typically, the position error of an eye-tracking device is measured as the distance of the eye-position from the target position in two-dimensional space (angular offset). Accuracy is the mean angular offset. The mean is a highly interpretable measure of central tendency if the underlying error distribution is unimodal and normal. However, in the context of an underlying multimodal distribution, the mean is less interpretable. We will present evidence that the majority of such distributions are multimodal. Only 14.7% of fixation angular offset distributions were unimodal, and of these, only 11.5% were normally distributed. (Of the entire dataset, 1.7% were unimodal and normal.) This multimodality is true even if there is only a single, continuous tracking fixation segment per trial. We present several approaches to measure accuracy in the face of multimodality. We also address the role of fixation drift in partially explaining multimodality.
Slowing quantum decoherence of oscillators by hybrid processing

Abstract Quantum information encoded into the superposition of coherent states is an illustrative representation of practical applications of macroscopic quantum coherence possessing. However, these states are very sensitive to energy loss, losing their non-classical aspects of coherence very rapidly. An available deterministic strategy to slow down this decoherence process is to apply a Gaussian squeezing transformation prior to the loss as a protective step. Here, we propose a deterministic hybrid protection scheme utilizing strong but feasible interactions with two-level ancillas immune to spontaneous emission. We verify the robustness of the scheme against the dephasing of qubit ancilla. Our scheme is applicable to complex superpositions of coherent states in many oscillators, and remarkably, the robustness to loss is enhanced with the amplitude of the coherent states. This scheme can be realized in experiments with atoms, solid-state systems, and superconducting circuits.
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data

Making predictions and quantifying their uncertainty when the input data is sequential is a fundamental learning challenge, recently attracting increasing attention. We develop SigGPDE, a new scalable sparse variational inference framework for Gaussian Processes (GPs) on sequential data. Our contribution is twofold. First, we construct inducing variables underpinning the sparse approximation so that the resulting evidence lower bound (ELBO) does not require any matrix inversion. Second, we show that the gradients of the GP signature kernel are solutions of a hyperbolic partial differential equation (PDE). This theoretical insight allows us to build an efficient back-propagation algorithm to optimize the ELBO. We showcase the significant computational gains of SigGPDE compared to existing methods, while achieving state-of-the-art performance for classification tasks on large datasets of up to 1 million multivariate time series.
Masked Face Recognition

The World is facing a huge health crisis due to the rapid transmission of coronavirus (COVID-19). In order to effectively prevent the spread of COVID-19 virus, almost everyone have to wear a mask as its one of the most important element to prevent from this virus as per World Health Organization (WHO). It makes conventional facial recognition technology almost ineffective in several cases, such as community access control, face access control, facial attendance, facial security checks at airports, etc. Thus, there's an immediate requirement to improve the recognition performance of the existing technology on the masked faces. The current advanced face recognition approaches are architected based on deep learning, which depend on or requires a large number of face samples. With no publicly accessible datasets or database of face samples available, a dataset needs to be created for the recognition system.
Solving Common-Payoff Games with Approximate Policy Iteration

For artificially intelligent learning systems to have widespread applicability in real-world settings, it is important that they be able to operate decentrally.Unfortunately, decentralized control is difficult---computing even an epsilon-optimal joint policy is a NEXP complete problem.Nevertheless, a recently rediscovered insight---that a team of agents can coordinate via common knowledge---has given rise to algorithms capable of finding optimal joint policies in small common-payoff games.The Bayesian action decoder (BAD) leverages this insight and deep reinforcement learning to scale to games as large as two-player Hanabi.However, the approximations it uses to do so prevent it from discovering optimal joint policies even in games small enough to brute force optimal solutions.This work proposes CAPI, a novel algorithm which, like BAD, combines common knowledge with deep reinforcement learning.However, unlike BAD, CAPI prioritizes the propensity to discover optimal joint policies over scalability.While this choice precludes CAPI from scaling to games as large as Hanabi, empirical results demonstrate that, on the games to which CAPI does scale, it is capable of discovering optimal joint policies even when other modern multi-agent reinforcement learning algorithms are unable to do so.
Deep Radial-Basis Value Functions for Continuous Control

A core operation in reinforcement learning (RL) is finding an action that is optimal with respect to a learned value function. This operation is often challenging when the learned value function takes continuous actions as input. We introduce deep radial-basis value functions (RBVFs): value functions learned using a deep network with a radial-basis function (RBF) output layer. We show that the maximum action-value with respect to a deep RBVF can be approximated easily and accurately. Moreover, deep RBVFs can represent any true value function owing to their support for universal function approximation. We extend the standard DQN algorithm to continuous control by endowing the agent with a deep RBVF. We show that the resultant agent, called RBF-DQN, significantly outperforms value-function-only baselines, and is competitive with state-of-the-art actor-critic algorithms.
Evaluation and empirical analysis of the influence of disciplinary competition on innovation practice ability

Abstract Mathematical modelling is an embodiment of the application of mathematics in various fields. Exploring the effect of mathematical modeling on the innovative practical ability of college students is conducive to college students’ understanding of mathematical modeling competitions and colleges’ education on mathematical modeling courses. It can also stimulate the confidence of students majoring in mathematics in their majors. On the basis of clarifying the theoretical basis and basic principles of the evaluation of innovative practical ability, this paper combines the characteristics of current college students to construct a college student’s innovation that includes 5 first-level indicators and 15 second-level indicators. Practice system. By the Delphi method and improved analytic hierarchy process we can determine the index weights and the weighted average model of fuzzy mathematics to analyze the collected evaluation membership matrix. We also obtain the membership matrix of the impact of mathematical modeling on the innovative practical ability of college students. Using the principle of maximum membership to get the degree of influence <?CDATA ${V}_{3}^{* }=0.3941$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msubsup> <mml:mi>V</mml:mi> <mml:mn>3</mml:mn> <mml:mo>*</mml:mo> </mml:msubsup> <mml:mo>=</mml:mo> <mml:mn>0.3941</mml:mn> </mml:mrow> </mml:math> , the research results show that the mathematical modeling competition can improve the innovative and practical ability of college students.
Classical Planning in Deep Latent Space

Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We evaluate Latplan using image-based versions of 6 planning domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of LightsOut.
Flexible Non-Autoregressive Extractive Summarization with Threshold: How to Extract a Non-Fixed Number of Summary Sentences

Sentence-level extractive summarization is a fundamental yet challenging task, and recent powerful approaches prefer to pick sentences sorted by the predicted probabilities until the length limit is reached, a.k.a. ``Top-K Strategy''. This length limit is fixed based on the validation set, resulting in the lack of flexibility. In this work, we propose a more flexible and accurate non-autoregressive method for single document extractive summarization, extracting a non-fixed number of summary sentences without the sorting step. We call our approach ThresSum as it picks sentences simultaneously and individually from the source document when the predicted probabilities exceed a threshold. During training, the model enhances sentence representation through iterative refinement and the intermediate latent variables receive some weak supervision with soft labels, which are generated progressively by adjusting the temperature with a knowledge distillation algorithm. Specifically, the temperature is initialized with high value and drops along with the iteration until a temperature of 1. Experimental results on CNN/DM and NYT datasets have demonstrated the effectiveness of ThresSum, which significantly outperforms BERTSUMEXT with a substantial improvement of 0.74 ROUGE-1 score on CNN/DM. Our source code will be available on Github.
Sentiment Analysis on Overseas Tweets on the Impact of COVID-19 in Indonesia

This study aims to conduct analysis to determine the trend of sentiment on tweets about Covid-19 in Indonesia from the Twitter accounts overseas on big data perspective. The data was obtained from Twitter in the period of April 2020, with the word query "Indonesian Corona Virus" from foreign user accounts in English. The process of retrieving data comes from Twitter tweets by crawling the text using Twitter's API (Application Programming Interface) by employing Python programming language. Twitter was chosen because it is very fast and easy to spread through status updates from and among the user accounts. The number of tweets obtained was 8,740 in text format, with a total engagement of 217,316. The data was sorted from the tweets with the largest to smallest engagement, then cleaned from unnecessary fonts and symbols as well as typo words and abbreviations. The sentiment classification was carried out by analytical tools, extracting information with text mining, into positive, negative, and neutral polarity. To sharpen the analysis, the cleaned data was selected only with the largest engagement until those with 100 engagements; then was grouped into 30 sub-topics to be analyzed. The interesting facts are found that most tweets and sub-topics were dominated by the negative sentiment; and some unthinkable sub-topics were talked by many users.
A New Upper Bound on Extremal Number of Even Cycles

In this paper, we prove $\mathrm{ex}(n, C_{2k})\le (16\sqrt{5}\sqrt{k\log k} + o(1))\cdot n^{1+1/k}$. This improves on a result of Bukh and Jiang from 2017, thereby reducing the best known upper bound by a factor of $\sqrt{5\log k}$.
Language Type Frequency and Learnability. A Connectionist Appraisal.

In this paper, I present experimental data bearing on the controversial issue of the possible relationship between the frequency of language types and how easily they can be learnt. Using simple, artificial languages which only differ with respect to the properties we are interested in, I show that there does appear to be a relationship of some kind, although not as strong as one might have hoped. In particular, if a language type can be learnt relatively easily, then the models fail to predict its actual frequency in the real world. On the other hand, the connectionist models provide evidence that the language types which are unattested or highly infrequent are also impossible or hard to learn.
Human rights dimensions of COVID-19 responses in Bangladesh: challenges and recommendations

Purpose The purpose of this paper is to scrutinize the human rights dimensions of COVID-19 responses in Bangladesh through a viewpoint methodology in four critical areas: freedom of opinion and expression; access to information; protection of health-care workers; and marginalized populations’ access to health care. However, these responses remain non-aligned with the international human rights law obligations of Bangladesh, which undermines the human rights and dignity of its population. Based on the responses shaping and aggravating the situation, this paper concludes with some recommendations, which can be helpful for Bangladesh for better human rights responses in these areas, should a parallel situation emerge in the future. Design/methodology/approach This paper scrutinizes the human rights dimensions of COVID-19 responses in Bangladesh through a viewpoint methodology. Findings The responses of Bangladesh remain non-aligned with international human rights law obligations of Bangladesh, which undermines the human rights and dignity of its population. Originality/value This paper concludes with some recommendations, which can be helpful for Bangladesh for better human rights responses in these areas, should a parallel situation emerge in the future.
Paragraph-level Commonsense Transformers with Recurrent Memory

Human understanding of narrative texts requires making commonsense inferences beyond what is stated in the text explicitly. A recent model, COMET, can generate such inferences along several dimensions such as pre- and post-conditions, motivations, and mental states of the participants. However, COMET was trained on short phrases, and is therefore discourse-agnostic. When presented with each sentence of a multi-sentence narrative, it might generate inferences that are inconsistent with the rest of the narrative. We present the task of discourse-aware commonsense inference. Given a sentence within a narrative, the goal is to generate commonsense inferences along predefined dimensions, while maintaining coherence with the rest of the narrative. Such large-scale paragraph-level annotation is hard to get and costly, so we use available sentence-level annotations to efficiently and automatically construct a distantly supervised corpus. Using this corpus, we train PARA-COMET, a discourse-aware model that incorporates paragraph-level information to generate coherent commonsense inferences from narratives. PARA-COMET captures both semantic knowledge pertaining to prior world knowledge, and episodic knowledge involving how current events relate to prior and future events in a narrative. Our results confirm that PARA-COMET outperforms the sentence-level baselines, particularly in generating inferences that are both coherent and novel.
How Do We Optimize Risk in Enterprise Architecture when Deploying Emerging Technologies?

Emerging Technologies which merge cyber-physical systems continue to transform businesses and digital agility in transformative ways. Importantly, most investigations around focus on either cyber risk or the risk around physical systems but it does not encompass both. However, the immediate challenge is new opportunities occurring with emerging technologies. Examples include automobiles, the Internet of Things (IoT), medical devices, and building controls. In this study we will focus identifying risk as an optimization not a minimization problem and how to develop a practical approach for executives and boards to use in the oversight of cyber physical systems. Based on interviews with executive leadership teams and boards of directors we explored the over-arching research question: How can we apply a risk-based approach to cyber-physical security and what questions should business leaders be asking? The research methodology used a survey instrument and multiple qualitative methods involving business leaders from 60 companies and 80 business leaders from September 2018 – September 2019. Based on this analysis, we developed an extended framework for executives, as well as questions and process for boards to consider as part of their oversight. The Extended Risk-Based Approach equips boards and executives as they begin to develop their thinking around enterprise cyber physical risk.
AI - Robotic Applications in Logistics Industry and Savings Calculation

In this study, we present the development of using artificial intelligence and robotic technologies in the logistics sector and differences of the change created by such technologies. We identified the most correct predictions pertaining to the future of logistics by comparing the results of studies with our findings. To analyze this change, the developments observed in the logistics perspective synthesized by doing research through the conceptual approach and literature review model and then, t
Optical Tracking System for Multi-UAV Clustering

This paper proposes an optical tracking system to realize the clustering of multiple unmanned aerial vehicles (UAVs). The system uses a laser spot as a visual recognition target to realize cluster flight operations in a complex environment. To reduce the interference of sunlight in the optical visual navigation and obtain the optimal combination of laser receiving devices, we selected lasers in a variety of spectrum bands and filter films with different light transmittances to conduct a comprehensive test. By performing a fluid simulation of the UAVs with the related modules mounted in different positions, the optimal installation position was selected to enhance the effect of the UAV wind field. An online learning tracking framework based on the multi-feature extraction of convolutional networks was developed to realize robust long-term visual tracking. In the tracking process, the tracking algorithm could train the detector online according to the status of the target and adaptively initialize the target. Finally, to verify the practical application performance of the clustering system, a group of UAVs was tested in flight. We verified that the proposed method and device exhibit a high feasibility and reliability.