Reviewerly/exHarmony huggingface dataset:
Students, Experience Using a Web-based Virtual Reality Tool

The Covid-19 social distancing policies forced educational institutions to make significant changes. Schools had to migrate to a non-face-to-face modality using technological tools. However, using these communication platforms had negative consequences for students, such as concentration difficulties and feelings of isolation and anxiety. One alternative disruptive technology used to reduce these problems is virtual reality. This research aimed to know the users' experience with a Virtual Campus based on WebVR activities at a higher education institution in Mexico. The methodology applied was a qualitative case study. The results indicated satisfactory student experience, engaged participant interaction, dynamic activities, and socialization in an environment similar to face-to-face education. The main difficulties related to specific technical problems using the tool. However, these did not affect the positive assessment of the participants since 92.7% of the respondents would recommend a friend to use it. Future studies should include the teachers' perspective, design strategies that promote activities organized by students, and inclusion of others unrelated to academia.
Programmatic Reward Design by Example

Reward design is a fundamental problem in reinforcement learning (RL). A misspecified or poorly designed reward can result in low sample efficiency and undesired behaviors. In this paper, we propose the idea of programmatic reward design, i.e. using programs to specify the reward functions in RL environments. Programs allow human engineers to express sub-goals and complex task scenarios in a structured and interpretable way. The challenge of programmatic reward design, however, is that while humans can provide the high-level structures, properly setting the low-level details, such as the right amount of reward for a specific sub-task, remains difficult. A major contribution of this paper is a probabilistic framework that can infer the best candidate programmatic reward function from expert demonstrations. Inspired by recent generative-adversarial approaches, our framework searches for themost likely programmatic reward function under whichthe optimally generated trajectories cannot be differen-tiated from the demonstrated trajectories. Experimental results show that programmatic reward functions learned using this framework can significantly outperform those learned using existing reward learning algorithms, and enable RL agents to achieve state-of-the-art performance on highly complex tasks.
Voronoi Progressive Widening: Efficient Online Solvers for Continuous State, Action, and Observation POMDPs

This paper introduces Voronoi Progressive Widening (VPW), a generalization of Voronoi optimistic optimization (VOO) and action progressive widening to partially observable Markov decision processes (POMDPs). Tree search algorithms can use VPW to effectively handle continuous or hybrid action spaces by efficiently balancing local and global action searching. This paper proposes two VPW-based algorithms and analyzes them from theoretical and simulation perspectives. Voronoi Optimistic Weighted Sparse Sampling (VOWSS) is a theoretical tool that justifies VPW-based online solvers, and it is the first algorithm with global convergence guarantees for continuous state, action, and observation POMDPs. Voronoi Optimistic Monte Carlo Planning with Observation Weighting (VOMCPOW) is a versatile and efficient algorithm that consistently outperforms state-of-the-art POMDP algorithms in several simulation experiments.
Transcoded Video Restoration by Temporal Spatial Auxiliary Network

In most video platforms, such as Youtube, Kwai, and TikTok, the played videos usually have undergone multiple video encodings such as hardware encoding by recording devices, software encoding by video editing apps, and single/multiple video transcoding by video application servers. Previous works in compressed video restoration typically assume the compression artifacts are caused by one-time encoding. Thus, the derived solution usually does not work very well in practice. In this paper, we propose a new method, temporal spatial auxiliary network (TSAN), for transcoded video restoration. Our method considers the unique traits between video encoding and transcoding, and we consider the initial shallow encoded videos as the intermediate labels to assist the network to conduct self-supervised attention training. In addition, we employ adjacent multi-frame information and propose the temporal deformable alignment and pyramidal spatial fusion for transcoded video restoration. The experimental results demonstrate that the performance of the proposed method is superior to that of the previous techniques. The code is available at https://github.com/icecherylXuli/TSAN.
High accuracy single-layer free-space diffractive neuromorphic classifiers for spatially incoherent light

Free-space all-optical diffractive systems have shown promise for neuromorphic classification of objects without converting light to the electronic domain. While the factors that govern these systems have been studied for coherent light, the fundamental properties for incoherent light have not been addressed, despite the importance for many applications. Here we use a co-design approach to show that optimized systems for spatially incoherent light can achieve performance on par with the best linear electronic classifiers even with a single layer containing few diffractive features. This performance is limited by the inherent linear nature of incoherent optical detection. We circumvent this limit by using a differential detection scheme that achieves greater than 94% classification accuracy on the MNIST dataset and greater than 85% classification accuracy for Fashion-MNIST, using a single layer metamaterial.
Eye tracking applied to tobacco smoking: current directions and future perspectives

Over the years the general awareness of the health costs associated with tobacco smoking has motivated scientists to apply the measurement of eye movements to this form of addiction. On one hand they have investigated whether smokers attend and look preferentially at smoking related scenes and objects. In parallel, on the other hand eye tracking has been used to test how smokers and nonsmokers interact with the different types of health warning that policymakers have mandated in tobacco advertisements and packages. Here we provide an overview of the main findings from the different lines of research, such as the evidence related to the attentional bias for smoking cues in smokers and the evidence that graphic warning labels and plain packages measurably increase the salience of the warning labels. We point to some open questions, such as the conditions that determine whether heavy smokers exhibit a tendency to actively avoid looking at graphic warning labels. Finally we argue that the research applied to gaze exploration of warning labels would benefit from a more widespread use of the more naturalistic testing conditions (e.g. mobile eye tracking or virtual reality) that have been introduced to study the smokers’ attentional bias for tobacco-related objects when freely exploring the surrounding environment.
CoCo DistillNet: a Cross-layer Correlation Distillation Network for Pathological Gastric Cancer Segmentation

In recent years, deep convolutional neural networks have made significant advances in pathology image segmentation. However, pathology image segmentation encounters with a dilemma in which the higher-performance networks generally require more computational resources and storage. This phenomenon limits the employment of high-accuracy networks in real scenes due to the inherent high-resolution of pathological images. To tackle this problem, we propose CoCo DistillNet, a novel Cross-layer Correlation (CoCo) knowledge distillation network for pathological gastric cancer segmentation. Knowledge distillation, a general technique which aims at improving the performance of a compact network through knowledge transfer from a cumbersome network. Concretely, our CoCo DistillNet models the correlations of channel-mixed spatial similarity between different layers and then transfers this knowledge from a pre-trained cumbersome teacher network to a non-trained compact student network. In addition, we also utilize the adversarial learning strategy to further prompt the distilling procedure which is called Adversarial Distillation (AD). Furthermore, to stabilize our training procedure, we make the use of the unsupervised Paraphraser Module (PM) to boost the knowledge paraphrase in the teacher network. As a result, extensive experiments conducted on the Gastric Cancer Segmentation Dataset demonstrate the prominent ability of CoCo DistillNet which achieves state-of-the-art performance.
SUBS: Subtree Substitution for Compositional Semantic Parsing

Although sequence-to-sequence models often achieve good performance in semantic parsing for i.i.d.data, their performance is still inferior in compositional generalization.Several data augmentation methods have been proposed to alleviate this problem.However, prior work only leveraged superficial grammar or rules for data augmentation, which resulted in limited improvement.We propose to use subtree substitution for compositional data augmentation, where we consider subtrees with similar semantic functions as exchangeable.Our experiments showed that such augmented data led to significantly better performance on SCAN and GEOQUERY, and reached new SOTA on compositional split of GEOQUERY.We have publicly released our code at https: //github.com/GT-SALT/SUBS .
Fuzzy-Based Spatiotemporal Hot Spot Intensity and Propagation—An Application in Crime Analysis

Cluster-based hot spot detection is applied in many disciplines to analyze the locations, concentrations, and evolution over time for a phenomenon occurring in an area of study. The hot spots consist of areas within which the phenomenon is most present; by detecting and monitoring the presence of hot spots in different time steps, it is possible to study their evolution over time. One of the most prominent problems in hot spot analysis occurs when measuring the intensity of a phenomenon in terms of the presence and impact on an area of study and evaluating its evolution over time. In this research, we propose a hot spot analysis method based on a fuzzy cluster hot spot detection algorithm, which allows us to measure the incidence of hot spots in the area of study. We analyze its variation over time, and in order to evaluate its reliability we use a well-known fuzzy entropy measure that was recently applied to measure the reliability of hot spots by executing fuzzy clustering algorithms. We apply this method in crime analysis of the urban area of the City of London, using a dataset of criminal events that have occurred since 2011, published by the City of London Police. The obtained results show a decrease in the frequency of all types of criminal events over the entire area of study in recent years.
Computer-Aided College English Teaching System Based on Virtual Reality and Artificial Intelligence

People are realizing that linguistic competence is a vital aspect of communicative competence as language study and instruction have progressed. Grammar instruction is not only important but also the primary means of achieving communication skills in foreign language classes. This article focuses on the computer vision and machine learning-based virtual reality technology college English immersive context teaching approach. The goal is to increase pupils’ capacity to learn English. The experimental groups used VR innovation immersive environment education from the standpoint of constructivist theory in a compared teaching experiment with 2 classifications of freshman at a university, while the control class used standard multimedia equipment and conventional teaching techniques. Students only passively acquire a lot of material from instructors in the class, they have limited opportunity to engage in the exchange of information and express thoughts in the chosen language, and they are “immersed” in the Chinese environment most of the time. With an average score of 2.8 points higher than the control group, the overall English level was likewise higher. English teaching system based on computer big data method, optimization of oral English teaching system based on computer-aided technology, and artificial intelligence are the existing methods used for comparison. This demonstrates that using constructivism theory and virtual reality technology in college English immersing contextual education may genuinely increase students’ English levels.
Unsupervised Domain Adaptation Learning for Hierarchical Infant Pose Recognition with Synthetic Data

The Alberta Infant Motor Scale (AIMS) is a well-known assessment scheme that evaluates the gross motor development of infants by recording the number of specific poses achieved. With the aid of the image-based pose recognition model, the AIMS evaluation procedure can be shortened and automated, providing early diagnosis or indicator of potential developmental disorder. Due to limited public infant-related datasets, many works use the SMIL-based method to generate synthetic infant images for training. However, this domain mismatch between real and synthetic training samples often leads to performance degradation during inference. In this paper, we present a CNN-based model which takes any infant image as input and predicts the coarse and fine-level pose labels. The model consists of an image branch and a pose branch, which respectively generates the coarse-level logits facilitated by the unsupervised domain adaptation and the 3D keypoints using the HRNet with SMPLify optimization. Then the outputs of these branches will be sent into the hierarchical pose recognition module to estimate the fine-level pose labels. We also collect and label a new AIMS dataset, which co—tains 750 real and 4000 synthetic infants images with AIMS pose labels. Our experimental results show that the proposed method can significantly align the distribution of synthetic and real-world datasets, thus achieving accurate performance on fine-grained infant pose recognition.
Improved Algorithms for Population Recovery from the Deletion Channel

The population recovery problem asks one to recover an unknown distribution over n-bit strings given access to independent noisy samples of strings drawn from the distribution. Recently, Ban et al. [BCF+ 19] studied the problem where the noise is induced through the deletion channel. This problem generalizes the famous trace reconstruction problem, where one wishes to learn a single string under the deletion channel.Ban et al. showed how to learn ℓ-sparse distributions over strings using exp (n1/2 · (log n)O(ℓ)) samples. In this work, we learn the distribution using only exp (Õ(n1/3) · ℓ2) samples, by developing a higher-moment analog of the algorithms of [DOS17a, NP17], which solve trace reconstruction in exp (Õ(n1/3)) samples. We also give the first algorithm with a runtime subexponential in n, solving population recovery in exp (Õ(n1/3) · ℓ3) samples and time.Notably, our dependence on n nearly matches the upper bound of [DOS17a, NP17] when ℓ = O(1), and we reduce the dependence on ℓ from doubly to singly exponential. Therefore, we are able to learn large mixtures of strings: while Ban et al.'s algorithm can only learn a mixture of O(log n/ log log n) strings with a subexponential number of samples, we are able to learn a mixture of no(1) strings in exp (n1/3+o(1)) samples and time.
Differential privacy

Statistical databases store the data of a large number of individuals, and data analysts are allowed to pose statistical queries about those data. Typical queries include average values, total counting, or the percentage of the entries that satisfy a given property.
Exploring Multitask Learning for Low-Resource Abstractive Summarization

This paper explores the effect of using multitask learning for abstractive summarization in the context of small training corpora.In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target task of abstractive summarization via multitask learning.We show that for many task combinations, a model trained in a multitask setting outperforms a model trained only for abstractive summarization, with no additional summarization data introduced.Additionally, we do a comprehensive search and find that certain tasks (e.g.paraphrase detection) consistently benefit abstractive summarization, not only when combined with other tasks but also when using different architectures and training corpora.
Era of Quantum Computing- An Intelligent and Evaluation based on Quantum Computers

In today's world, the necessity for prime speed computing is extremely high that the classic computers area unit undoubtedly not sufficient. Because of the limitation of Newtonian mechanics, quantum technicalities are taking the position of game-changer in competition of calculation. Quantum computing is the study of quantum pc that works underneath the laws of quantum physics like tunneling, annealing, web, and superposition to complete tasks that take an enormous quantity of your time. During this paper we'll concisely see however quantum computers work and the way it will be employed in decrypting personal keys that the classic computers cannot reach during a short span of your time. One in all the most blessings of victimization quantum computers area unit that the work with efficiency and area unit 1000X times quicker than our classic computers.
Multiple share creation scheme with optimal key generation for secure medical image transmission in the internet of things environment

In recent days, the latest developments in the internet of things (IoT) and cloud computing (CC) in the medical field pose a major issue related to security. This study introduces a new multiple share creation scheme (MSC) with optimal key generation (OKG) called MSC-OKG technique for secure medical transmission in the IoT environment. Primarily, the proposed method enables IoT devices to gather medical images. Next, the MSC process takes place for the generation of multiple shares for the input images and the shares are further encrypted with elliptic curve cryptography (ECC). The optimal key generation process is carried out by the manta ray foraging optimisation (MRFO) algorithm to encrypt and decrypt the shares. The experimental results indicated that the MSC-OKG technique has outperformed the compared methods in terms of mean square error (MSE), peak signal to noise ratio (PSNR), bit rate and correlation coefficient (CC).
Internet of Things Based on Smart Mirror to Improve Interactive Learning

Today, the technology of the Internet of Things (IoT) experiencing ongoing development can be said to be very rapid.Various office equipment and even city pages have been integrated with the internet.In addition to technological developments also occur in the mirror, smart mirror technology is one of the new things, especially in the field of learning, still in traditional technology where mirrors are only used for mirroring.The use of mirrors in education is not yet desirable, so there is a need for innovation in that field.Some studies have involved microcontrollers such as raspberry pi that have been applied to mirrors, where innovation through mirrors is becoming more and more.So that the innovations carried out on the Smart Mirror using mirrors of homes, offices and pages that have a scheduling automation system, turn on video, music, face recognition and control using Android.Students and lecturers need this technology to increase student involvement.The Smart Mirror in this study gave students and lecturers a variety of intelligent experiences.Students and Lecturers can innovate in teaching and learning activities by making changes to their control (Android).In this research has succeeded in creating a new smart mirror innovation with facial recognition needed for students and lecturers to be used for daily teaching and learning activities, especially in the university area in Bandung.
Using BERT for Multi-Label Multi-Language Web Page Classification

With a very large and constantly growing number of web pages available on the Internet and considering the diversity of the topics that the content of web pages develops, automatic web page classification is becoming increasingly important. The text represents one of the main components of the content of web pages. In the last years, researchers have achieved state-of-the-art results in various natural language processing tasks, including text classification. This was possible mainly due to the development of language models that are trained on large text corpora and afterwards fine-tuned for specific tasks in a transfer learning manner. Such a model is Bidirectional Encoder Representations from Transformers (BERT), which has proven to be very effective for text classification. In this work, we propose a series of experiments to evaluate the effectiveness of the pre-trained multilingual BERT on multi-label multi-language web page classification. Overall, our proposed web page classifier achieves competitive results and one may conclude that it can be part of an automatic web page classification system.
An inventive and innovative system to detect fall of old aged persons – a novel attempt with IoT, sensors and data analytics to prevent the post fall effects

According to the statistics of national council on aging (NCOA), these days rate of deaths in the old aged people has reached a critical state that for every 11 seconds an older adult is being treated for a fall and for every 19 minutes a death is taking place. Statistics by north eastern Ohio state reveals that, the rate of emergency room treatment of fall related injuries in persons aged 75 and older approached 80 per 1,000 per year in women and 60 per 1,000 per year in men. Falls in elders vary from that of the falls in kids, as the healing power in the old people slackens with their age. We present a frugal and affordable system that could monitor the motion of the old people and can detect their fall. We use IoT, sensors and data analytics to build this system. The system is tested for its functioning and is fool proof.
Color Space Geometry Uncovered with Magnetoencephalography

(Current Biology 31, 515–526.e1–e5; February 8, 2021) In the originally published version of this article, the luminance values in Figure 1 were mistakenly listed as 63 cd/m2, 50 cd/m2, and 37 cd/m2, instead of 42 cd/m2, 33.5 cd/m2, and 25 cd/m2, respectively. Additionally, the adapting gray of the stimuli was 33.5 cd/m2, not 50 cd/m2, as listed in STAR methods. These instances have been corrected and now appear in the corrected article online. The error has no impact on the results or conclusions of the study. The authors regret the error and any confusion that may have resulted.Figure 1. Specification of the Stimulus Colors and How They Are Named (Original)View Large Image Figure ViewerDownload Hi-res image Download (PPT) Color Space Geometry Uncovered with MagnetoencephalographyRosenthal et al.Current BiologyNovember 16, 2020In BriefRosenthal, Singh, Hermann, et al. use magnetoencephalography to decode color. By analyzing the similarity relationships among responses to colors varying in hue and luminance, the authors discover a dynamic geometry of the neural representation of color. The geometry predicts universal color-naming patterns and makes new predictions for perception. Full-Text PDF
Incident Response as a Lawyers’ Service

Thousands of incidents each year are managed by law firms. Victim firms call a hotline and delegate incident response to external counsel without a preexisting relationship. We examine how this model breaks from convention and outline questions for future research.