Reviewerly/exHarmony huggingface dataset:
COVID Precaution System Design Using IoT and AI for Facemask Detection

All over the world the spread of coronavirus (COVID) caused huge impact in all sectors like health, financial, industrial, etc. Nowadays, huge research has been done to avoid the spread of coronavirus (COVID). As of now we are in this pandemic situation, we have to take precautionary measures to end the chain. In this regard, wearing the face mask is made compulsory by the government when we step out to shops, colleges, public places, and transportation. In this paper, we proposed a system that detects and monitors the people are not wearing face mask with the help of images captured by the camera. Automatic recording of attendance without human invention, like signing and biometric, is collected along with the temperature monitoring system. If the employee or public who are not wearing mask will be give an warning sound and the database will be stored for further process. Artificial intelligence is incorporated to maintain the attendance and also collects the check in and check out time of the employers as well as students. Artificial intelligence is trained with face of the employee and allows them to enter into the premises after identification from the directory. Machine learning system uses an algorithm to train the datasets with and without mask. The system stores over 1,00,000 images without any delay. The trained architecture achieves 99% accuracy in monitoring people. With the above system, an IR temperature monitoring system is embedded to monitor the body temperature of each individual person using the IR sensor.
An Efficient Video Streaming Architecture with QoS Control for Virtual Desktop Infrastructure in Cloud Computing

In virtual desktop infrastructure (VDI) environments, the remote display protocol has a big responsibility to transmit video data from a data center-hosted desktop to the endpoint. The protocol must ensure a high level of client perceived end-to-end quality of service (QoS) under heavy work load conditions. Each remote display protocol works differently depending on the network and which applications are being delivered. In healthcare applications, doctors and nurses can use mobile devices directly to monitor patients. Moreover, the ability to implement tasks requiring high consumption of CPU and other resources is applicable to a variety of applications including research and cloud gaming. Such computer games and complex processes will run on powerful cloud servers and the screen contents will be transmitted to the client. TO enable such applications, remote display technology requires further enhancements to meet more stringent requirements on bandwidth and QoS, an to allow realtime operation. In this paper, we present an architecture including flexible QoS control to improve the user quality of experience (QoE). The QoS control is developed based on linear regression modeling using historical network data. Additionally, the architecture includes a novel compression algorithm of 2D images, designed to guarantee the best image quality and to reduce video delay; this algorithm is based on k-means clustering and can satisfy the requirements of realtime onboard processing. Through simulations with a real work dataset collected by the MIT Computer Science and Artificial Lab, we present experimental as well as explain the performance of the QoS system.
Digital Environments for Biology Learning in High School

Currently, due to the Covid-19 pandemic, education has had to change the way of imparting knowledge to students, since the use of digital environments has been promoted, supported by digital tools that allow reaching meaningful learning. Therefore, the present investigation is carried out to know if using digital environments in Biology learning favors the teaching-learning process of First High School students, through the application of a quasi-experimental design in which two groups intervened: experimental and control. At the beginning of the intervention, a pre-test was carried out on the two groups, where it was found that there were no significant differences with respect to the marks obtained. Next, the digital environments were applied on the Gnomio platform supported by several digital tools such as: PowToon, Genially, Edpuzzle, Forums, where innovative methodologies were applied, the same that allowed promoting different learning styles in students. At the end of the course, the post-test was performed on both groups under study, identifying that there is a significant difference of 6.85 points between them, for which the alternative hypothesis was accepted, demonstrating that digital environments fostered the academic development of the participants. favoring their concentration and learning, which is reflected in the results of the evaluations.
Precision cosmology from large-scale structure of the Universe

Large scale structure of the Universe becomes a leading source of precision cosmological information. We present two particular tools that can be used in cosmological analyses of the redshift space galaxy clustering data: a new open-source code CLASS-PT and the theoretical error approach. CLASS-PT computes one-loop power auto- and cross-power spectra for matter fields and biased tracers in real and redshift spaces. We show that the code meets the precision standards set by the upcoming high-precision large-scale structure surveys. The theoretical error likelihood approach allows one to analyze galaxy clustering data without having to measure the scale cut $k_{\rm max}$. This approach takes into account that theoretical uncertainties affect parameter estimation gradually, which helps optimize data analysis and ensures that all available cosmological information is extracted.
Design for implementation of discrete-time quantum walk with circulant matrix on graph by optical polarizing elements

In this paper, we introduce a quantum walk whose local scattering at each vertex is denoted by a unitary circulant matrix; namely the circulant quantum walk. We also introduce another quantum walk induced by the circulant quantum walk; namely the optical quantum walk, whose underlying graph is a $2$-regular directed graph and obtained by blowing up the original graph in some way. We propose a design of an optical circuit which implements the stationary state of the optical quantum walk. We show that if the induced optical quantum walk does not have $+1$ eigenvalue, then the stationary state of the optical quantum walk gives that of the original circulant quantum walk. From this result, we give a useful condition for the setting of the circulant quantum walks which can be implemented by this optical circuit.
A Deep Learning Approach To Estimation Using Measurements Received Over a Network

We propose a novel deep neural network (DNN) based approximation architecture to learn estimates of measurements. We detail an algorithm that enables training of the DNN. The DNN estimator only uses measurements, if and when they are received over a communication network. The measurements are communicated over a network as packets, at a rate unknown to the estimator. Packets may suffer drops and need retransmission. They may suffer waiting delays as they traverse a network path. Works on estimation often assume knowledge of the dynamic model of the measured system, which may not be available in practice. The DNN estimator doesn't assume knowledge of the dynamic system model or the communication network. It doesn't require a history of measurements, often used by other works. The DNN estimator results in significantly smaller average estimation error than the commonly used Time-varying Kalman Filter and the Unscented Kalman Filter, in simulations of linear and nonlinear dynamic systems. The DNN need not be trained separately for different communications network settings. It is robust to errors in estimation of network delays that occur due to imperfect time synchronization between the measurement source and the estimator. Last but not the least, our simulations shed light on the rate of updates that result in low estimation error.
Usability and Aesthetics: Better Together for Automated Repair of Web Pages

With the recent explosive growth of mobile devices such as smartphones or tablets, guaranteeing consistent web appearance across all environments has become a significant problem. This happens simply because it is hard to keep track of the web appearance on different sizes and types of devices that render the web pages. Therefore, fixing the inconsistent appearance of web pages can be difficult, and the cost incurred can be huge, e.g., poor user experience and financial loss due to it. Recently, automated web repair techniques have been proposed to automatically resolve inconsistent web page appearance, focusing on improving usability. However, generated patches tend to disrupt the webpage's layout, rendering the repaired webpage aesthetically unpleasing, e.g., distorted images or misalignment of components. In this paper, we propose an automated repair approach for web pages based on meta-heuristic algorithms that can assure both usability and aesthetics. The key novelty that empowers our approach is a novel fitness function that allows us to optimistically evolve buggy web pages to find the best solution that optimizes both usability and aesthetics at the same time. Empirical evaluations show that our approach is able to successfully resolve mobile-friendly problems in 94% of the evaluation subjects, significantly outperforming state-of-the-art baseline techniques in terms of both usability and aesthetics.
K-sparse Pure State Tomography with Phase Estimation

Quantum state tomography (QST) for reconstructing pure states requires exponentially increasing resources and measurements with the number of qubits by using state-of-the-art quantum compressive sensing (CS) methods. In this article, QST reconstruction for any pure state composed of the superposition of $K$ different computational basis states of $n$ qubits in a specific measurement set-up, i.e., denoted as $K$-sparse, is achieved without any initial knowledge and with quantum polynomial-time complexity of resources based on the assumption of the existence of polynomial size quantum circuits for implementing exponentially large powers of a specially designed unitary operator. The algorithm includes $\mathcal{O}(2 \, / \, \vert c_{k}\vert^2)$ repetitions of conventional phase estimation algorithm depending on the probability $\vert c_{k}\vert^2$ of the least possible basis state in the superposition and $\mathcal{O}(d \, K \,(log K)^c)$ measurement settings with conventional quantum CS algorithms independent from the number of qubits while dependent on $K$ for constant $c$ and $d$. Quantum phase estimation algorithm is exploited based on the favorable eigenstructure of the designed operator to represent any pure state as a superposition of eigenvectors. Linear optical set-up is presented for realizing the special unitary operator which includes beam splitters and phase shifters where propagation paths of single photon are tracked with which-path-detectors. Quantum circuit implementation is provided by using only CNOT, phase shifter and $- \pi \, / \, 2$ rotation gates around X-axis in Bloch sphere, i.e., $R_{X}(- \pi \, / \, 2)$, allowing to be realized in NISQ devices. Open problems are discussed regarding the existence of the unitary operator and its practical circuit implementation.
Experimental demonstration of composite pulses on IBM's quantum computer

We perform comprehensive experimental tests of various composite pulse sequences using one of open-access IBM's quantum processors, based on superconducting transmon qubits. We implement explicit pulse control of the qubit by making use of the opportunity of low-level access to the backend, provided by IBM Quantum. We obtain the excitation profiles for a huge variety of broadband, narrowband, and passband composite pulses, producing any pre-chosen target probabilities, ranging from zero to one. We also test universal composite pulses which compensate errors in any experimental parameter. In all experiments, we find excellent agreement between theoretical and experimental excitation profiles. This proves both the composite pulses as a very efficient and flexible quantum control tool and the high quality of the IBM quantum processor. As an extreme example, we test and observe a pronounced narrowband excitation profile for a composite sequence of as many as 1001 pulses.
Level set learning with pseudo-reversible neural networks for nonlinear  dimension reduction in function approximation

Due to the curse of dimensionality and the limitation on training data, approximating high-dimensional functions is a very challenging task even for powerful deep neural networks. Inspired by the Nonlinear Level set Learning (NLL) method that uses the reversible residual network (RevNet), in this paper we propose a new method of Dimension Reduction via Learning Level Sets (DRiLLS) for function approximation. Our method contains two major components: one is the pseudo-reversible neural network (PRNN) module that effectively transforms high-dimensional input variables to low-dimensional active variables, and the other is the synthesized regression module for approximating function values based on the transformed data in the low-dimensional space. The PRNN not only relaxes the invertibility constraint of the nonlinear transformation present in the NLL method due to the use of RevNet, but also adaptively weights the influence of each sample and controls the sensitivity of the function to the learned active variables. The synthesized regression uses Euclidean distance in the input space to select neighboring samples, whose projections on the space of active variables are used to perform local least-squares polynomial fitting. This helps to resolve numerical oscillation issues present in traditional local and global regressions. Extensive experimental results demonstrate that our DRiLLS method outperforms both the NLL and Active Subspace methods, especially when the target function possesses critical points in the interior of its input domain.
Quantum Capacities of Transducers

High-performance quantum transducers, which can faithfully convert quantum information between disparate physical carriers, are essential elements in quantum science and technology. To assess their ability to coherently transfer quantum information, quantum transducers are typically characterized by different figures of merit including conversion efficiency, bandwidth, and added noise. Here we utilize the concept of quantum capacity, the highest achievable qubit communication rate through a channel, to quantify the performance of a transducer. By evaluating the continuous-time quantum capacity across the conversion band, quantum capacity can serve as a single metric that unifies various desirable criteria of a transducer -- high efficiency, large bandwidth, and low noise. Moreover, using the quantum capacities of bosonic pure-loss channels as benchmarks, we investigate the optimal designs of generic quantum transduction schemes implemented by transmitting external signals through a coupled bosonic chain. Under the physical constraint of a bounded maximal coupling rate $g_{\rm max}$, the highest continuous-time quantum capacity $Q^{\rm max} \approx 31.4 g_{\rm max}$ is achieved by transducers with a maximally flat conversion frequency response, analogous to Butterworth electric filters. We further extend our method to include thermal noise by considering upper and lower bounds on the quantum capacities of transducers and characterize the performance of maximally flat transducers under the effect of thermal loss.
Action Density Based Frame Sampling for Human Action Recognition in Videos

Action recognition has been widely used to identify and monitor special activities in videos, and a proper frame sampling method cannot only reduce redundant video information, but also improve the accuracy of action recognition. In this paper, an action density based non-isometric frame sampling method, namely NFS, is proposed to discard the redundant video information and select the rational frames for neural networks to achieve high accuracy on human action recognition in videos. In particular, action density is introduced in our method to indicate the intensity of actions in videos, and a reinforcement learning based frame selection mechanism with considering the action density as the reward is proposed to select frames with the best action features. Then, a non-isometric frame sampling (NFS) method is proposed for neural network to simultaneously select a series of the rational frames (i.e., achieve the frame sampling in videos) based on the RLFS mechanism for action recognition. Finally, the extension of NFS method on various neural networks is discussed. Via the evaluations with various neural networks and datasets, our results not only show the effectiveness of using action density as a metric in frame selection, but also show that the proposed NFS method can achieve great effectiveness and rationality in frame sampling and can assist in achieving better accuracy on action recognition, in comparison with existing methods.
Autoregressive Quantile Flows for Predictive Uncertainty Estimation

Numerous applications of machine learning involve predicting flexible probability distributions over model outputs. We propose Autoregressive Quantile Flows, a flexible class of probabilistic models over high-dimensional variables that can be used to accurately capture predictive aleatoric uncertainties. These models are instances of autoregressive flows trained using a novel objective based on proper scoring rules, which simplifies the calculation of computationally expensive determinants of Jacobians during training and supports new types of neural architectures. We demonstrate that these models can be used to parameterize predictive conditional distributions and improve the quality of probabilistic predictions on time series forecasting and object detection.
Do Prompts Solve NLP Tasks Using Natural Language?

Thanks to the advanced improvement of large pre-trained language models, prompt-based fine-tuning is shown to be effective on a variety of downstream tasks. Though many prompting methods have been investigated, it remains unknown which type of prompts are the most effective among three types of prompts (i.e., human-designed prompts, schema prompts and null prompts). In this work, we empirically compare the three types of prompts under both few-shot and fully-supervised settings. Our experimental results show that schema prompts are the most effective in general. Besides, the performance gaps tend to diminish when the scale of training data grows large.
REPID: Regional Effect Plots with implicit Interaction Detection

Machine learning models can automatically learn complex relationships, such as non-linear and interaction effects. Interpretable machine learning methods such as partial dependence plots visualize marginal feature effects but may lead to misleading interpretations when feature interactions are present. Hence, employing additional methods that can detect and measure the strength of interactions is paramount to better understand the inner workings of machine learning models. We demonstrate several drawbacks of existing global interaction detection approaches, characterize them theoretically, and evaluate them empirically. Furthermore, we introduce regional effect plots with implicit interaction detection, a novel framework to detect interactions between a feature of interest and other features. The framework also quantifies the strength of interactions and provides interpretable and distinct regions in which feature effects can be interpreted more reliably, as they are less confounded by interactions. We prove the theoretical eligibility of our method and show its applicability on various simulation and real-world examples.
Non-Deterministic Face Mask Removal Based On 3D Priors

This paper presents a novel image inpainting framework for face mask removal. Although current methods have demonstrated their impressive ability in recovering damaged face images, they suffer from two main problems: the dependence on manually labeled missing regions and the deterministic result corresponding to each input. The proposed approach tackles these problems by integrating a multi-task 3D face reconstruction module with a face inpainting module. Given a masked face image, the former predicts a 3DMM-based reconstructed face together with a binary occlusion map, providing dense geometrical and textural priors that greatly facilitate the inpainting task of the latter. By gradually controlling the 3D shape parameters, our method generates high-quality dynamic inpainting results with different expressions and mouth movements. Qualitative and quantitative experiments verify the effectiveness of the proposed method.
Deep-Dive Analysis of Selfish and Stubborn Mining in Bitcoin and  Ethereum

Bitcoin and Ethereum are the top two blockchain-based cryptocurrencies whether from cryptocurrency market cap or popularity. However, they are vulnerable to selfish mining and stubborn mining due to that both of them adopt Proof-of-Work consensus mechanism. In this paper, we develop a novel Markov model, which can study selfish mining and seven kinds of stubborn mining in both Bitcoin and Ethereum. The formulas are derived to calculate several key metrics, including relative revenue of miners, blockchain performance in terms of stale block ratio and transactions per second, and blockchain security in terms of resistance against double-spending attacks. Numerical analysis is conducted to investigate the quantitative relationship between the relative-revenue-optimal mining strategy for malicious miners and two miner features in Bitcoin and Ethereum, respectively. The quantitative analysis results can assist honest miners in detecting whether there is any malicious miner in the system and setting the threshold of mining node's hash power in order to prevent malicious miners from making profit through selfish and stubborn mining.
A Case Study on Optimization of Platooning Coordination

In today's world, circumstances, processes, and requirements for software systems are becoming increasingly complex. In order to operate properly in such dynamic environments, software systems must adapt to these changes, which has led to the research area of Self-Adaptive Systems (SAS). Platooning is one example of adaptive systems in Intelligent Transportation Systems, which is the ability of vehicles to travel with close inter-vehicle distances. This technology leads to an increase in road throughput and safety, which directly addresses the increased infrastructure needs due to increased traffic on the roads. However, the No-Free-Lunch theorem states that the performance of one platooning coordination strategy is not necessarily transferable to other problems. Moreover, especially in the field of SAS, the selection of the most appropriate strategy depends on the current situation of the system. In this paper, we address the problem of self-aware optimization of adaptation planning strategies by designing a framework that includes situation detection, strategy selection, and parameter optimization of the selected strategies. We apply our approach on the case study platooning coordination and evaluate the performance of the proposed framework.
Recent, rapid advancement in visual question answering architecture: a review

Understanding visual question answering is going to be crucial for numerous human activities. However, it presents major challenges at the heart of the artificial intelligence endeavor. This paper presents an update on the rapid advancements in visual question answering using images that have occurred in the last couple of years. Tremendous growth in research on improving visual question answering system architecture has been published recently, showing the importance of multimodal architectures. Several points on the benefits of visual question answering are mentioned in the review paper by Manmadhan et al. (2020), on which the present article builds, including subsequent updates in the field.