Reviewerly/exHarmony huggingface dataset:
Health Analysis of Footballer Using Big Data and Deep Learning

With the development of information technology, health management and big data have risen and developed in recent years. Big data need proper analysis and shape in order to extract meaningful information from it. This paper analyzes the application status and prospects of big data in the field of health management. The results show that the most widely used big data in health management are intelligent wearable devices. Big data applications in football players’ mental health monitoring systems and chronic disease health management systems also have a good prospect. The intelligent wearable device is applied to several aspects of sports work: teaching and sports training, real-time monitoring of football players’ physical exercise process, collecting football players’ heart rate, calorie consumption, exercise steps, and track, blood pressure, blood oxygen, and other physical exercise data; through monitoring the heart rate, we can get the intensity and duration of football players’ physical exercise in school; through the calculation, we can also get the football players’ time energy consumption and understand the overall situation of football players’ physical exercise in school; through step counting and track monitoring, we can master the number of steps and track of football players; by monitoring the changes of blood oxygen and blood pressure of football players, we need to build a third-party residents’ health information storage and analysis system and further realize the marketization of residents’ health big data. The experimental results of the proposed study show the effectiveness of the proposed work.
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization

Many weakly supervised classification methods employ a noise transition matrix to capture the class-conditional label corruption. To estimate the transition matrix from noisy data, existing methods often need to estimate the noisy class-posterior, which could be unreliable due to the overconfidence of neural networks. In this work, we propose a theoretically grounded method that can estimate the noise transition matrix and learn a classifier simultaneously, without relying on the error-prone noisy class-posterior estimation. Concretely, inspired by the characteristics of the stochastic label corruption process, we propose total variation regularization, which encourages the predicted probabilities to be more distinguishable from each other. Under mild assumptions, the proposed method yields a consistent estimator of the transition matrix. We show the effectiveness of the proposed method through experiments on benchmark and real-world datasets.
CircuitQ: an open-source toolbox for superconducting circuits

Abstract We introduce CircuitQ, an open-source toolbox for the analysis of superconducting circuits implemented in Python. It features the automated construction of a symbolic Hamiltonian of the input circuit and a dynamic numerical representation of the Hamiltonian with a variable basis choice. The software implementation is capable of choosing the basis in a fully automated fashion based on the potential energy landscape. Additional features include the estimation of the T 1 lifetimes of the circuit states under various noise mechanisms. We review previously established circuit quantization methods and formulate them in a way that facilitates the software implementation. The toolbox is then showcased by applying it to practically relevant qubit circuits and comparing it to specialized circuit solvers. Our circuit quantization is applicable to circuit inputs from a large design space, and the software is open-sourced. We thereby add an important resource for the design of new quantum circuits for quantum information processing applications.
Does Structure Matter? Encoding Documents for Machine Reading Comprehension

Machine reading comprehension is a challenging task especially for querying documents with deep and interconnected contexts.Transformer-based methods have shown advanced performances on this task; however, most of them still treat documents as a flat sequence of tokens.This work proposes a new Transformer-based method that reads a document as tree slices.It contains two modules for identifying more relevant text passage and the best answer span respectively, which are not only jointly trained but also jointly consulted at inference time.Our evaluation results show that our proposed method outperforms several competitive baseline approaches on two datasets from varied domains.How to appeal a TVB ticket You can appeal online if you have been convicted in a NYSTVB the TVB traffic ticket number full name, date of birth, and gender ...
SAFS: Object Tracking Algorithm Based on Self-Adaptive Feature Selection

Object tracking is one of the most challenging problems in the field of computer vision. In challenging object tracking scenarios such as illumination variation, occlusion, motion blur and fast motion, existing algorithms can present decreased performances. To make better use of the various features of the image, we propose an object tracking method based on the self-adaptive feature selection (SAFS) algorithm, which can select the most distinguishable feature sub-template to guide the tracking task. The similarity of each feature sub-template can be calculated by the histogram of the features. Then, the distinguishability of the feature sub-template can be measured by their similarity matrix based on the maximum a posteriori (MAP). The selection task of the feature sub-template is transformed into the classification task between feature vectors by the above process and adopt modified Jeffreys’ entropy as the discriminant metric for classification, which can complete the update of the sub-template. Experiments with the eight video sequences in the Visual Tracker Benchmark dataset evaluate the comprehensive performance of SAFS and compare them with five baselines. Experimental results demonstrate that SAFS can overcome the difficulties caused by scene changes and achieve robust object tracking.
OIL PRICES, CONTROL OF CORRUPTION, GOVERNANCE, AND ECONOMIC GROWTH NEXUS IN SAUDI ARABIA

This study examines the impact of control of corruption and the effectiveness of governance on the economic growth in Saudi Arabia using the latest cointegration approach of Pesaran et al. (2001) and Kripfganz and Schneider (2019). We utilize unit root and cointegration tests using a maximum available sample from 1996-2019. The effect of control of corruption is found insignificant on economic growth, and a positive effect of governance is found in the long-run. The study is corroborated that good governance is an appropriate tool for economic growth in the long-run. Moreover, oil prices show a positive effect on economic growth both in the long and short run. It corroborates that the economic growth of Saudi Arabia is oil price dependent. The short-run effects of governance and control of corruption are found insignificant. Hence, a long period with good governance is required to support the economic growth of the Kingdom. So, it is recommended that the quality of governance should be enhanced to support economic growth.Keywords: Control of corruption, governance, economic growth, oil prices JEL Classifications: P16, O43, O13DOI: https://doi.org/10.32479/ijeep.11181
Associative Blockchain for Decentralized PKI Transparency

The conventional public key infrastructure (PKI) model, which powers most of the Internet, suffers from an excess of trust into certificate authorities (CAs), compounded by a lack of transparency which makes it vulnerable to hard-to-detect targeted stealth impersonation attacks. Existing approaches to make certificate issuance more transparent, including ones based on blockchains, are still somewhat centralized. We present decentralized PKI transparency (DPKIT): a decentralized client-based approach to enforcing transparency in certificate issuance and revocation while eliminating single points of failure. DPKIT efficiently leverages an existing blockchain to realize an append-only, distributed associative array, which allows anyone (or their browser) to audit and update the history of all publicly issued certificates and revocations for any domain. Our technical contributions include definitions for append-only associative ledgers, a security model for certificate transparency, and a formal analysis of our DPKIT construction with respect to the same. Intended as a client-side browser extension, DPKIT will be effective at fraud detection and prosecution, even under fledgling user adoption, and with better coverage and privacy than federated observatories, such as Google’s or the Electronic Frontier Foundation’s.
Attention Head Masking for Inference Time Content Selection in Abstractive Summarization

How can we effectively inform content selection in Transformer-based abstractive summarization models?In this work, we present a simple-yet-effective attention head masking technique, which is applied on encoderdecoder attentions to pinpoint salient content at inference time.Using attention head masking, we are able to reveal the relation between encoder-decoder attentions and content selection behaviors of summarization models.We then demonstrate its effectiveness on three document summarization datasets based on both in-domain and cross-domain settings.Importantly, our models outperform prior state-ofthe-art models on CNN/Daily Mail and New York Times datasets.Moreover, our inferencetime masking technique is also data-efficient, requiring less than 20% of the training samples to outperform BART fine-tuned on the full CNN/DailyMail dataset.
Design Of 4DOF 3D Robotic Arm to Separate the Objects Using a Camera

Nowadays robotic arm is widely used in various industries, especially those engaged in manufacturing. Robotic arms are usually used to perform jobs such as picking up and moving goods from their place of origin to the location desired by the operator. In this study, a 3d 4 DOF (Degree of Freedom) robotic arm. The prototype was made to move goods with random coordinates to places or boxes whose coordinates were determined in advance. The robot can know the coordinates of the object to be taken or moved. The arm robot prototype design is completed with a camera connected to a computer, where the camera is installed statically (fixed position) above the robot's work area. The camera functions like image processing to detect the object's position by taking the coordinates of the object. Then the object coordinates will be input into inverse kinematics that will produce an angle in every point of the servo arm so that the position of the end effector on the robot arm can be founded and reach the intended object. From the results of testing and analysis, it was found that the error in the webcam test to detect object coordinates was 2.58%, the error in the servo motion test was 12.68%, and the error in the inverse kinematics test was 7.85% on the x-axis, the error was 6.31% on the y-axis and an error of 12.77% on the z-axis. The reliability of the whole system is 66.66%.
DeepReDuce: ReLU Reduction for Fast Private Inference

The recent rise of privacy concerns has led researchers to devise methods for private neural inference -- where inferences are made directly on encrypted data, never seeing inputs. The primary challenge facing private inference is that computing on encrypted data levies an impractically-high latency penalty, stemming mostly from non-linear operators like ReLU. Enabling practical and private inference requires new optimization methods that minimize network ReLU counts while preserving accuracy. This paper proposes DeepReDuce: a set of optimizations for the judicious removal of ReLUs to reduce private inference latency. The key insight is that not all ReLUs contribute equally to accuracy. We leverage this insight to drop, or remove, ReLUs from classic networks to significantly reduce inference latency and maintain high accuracy. Given a target network, DeepReDuce outputs a Pareto frontier of networks that tradeoff the number of ReLUs and accuracy. Compared to the state-of-the-art for private inference DeepReDuce improves accuracy and reduces ReLU count by up to 3.5% (iso-ReLU count) and 3.5$\times$ (iso-accuracy), respectively.
OCID-Ref: A 3D Robotic Dataset With Embodied Language For Clutter Scene Grounding

To effectively apply robots in working environments and assist humans, it is essential to develop and evaluate how visual grounding (VG) can affect machine performance on occluded objects.However, current VG works are limited in working environments, such as offices and warehouses, where objects are usually occluded due to space utilization issues.In our work, we propose a novel OCID-Ref dataset featuring a referring expression segmentation task with referring expressions of occluded objects.OCID-Ref consists of 305,694 referring expressions from 2,300 scenes with providing RGB image and point cloud inputs.To resolve challenging occlusion issues, we argue that it's crucial to take advantage of both 2D and 3D signals to resolve challenging occlusion issues.Our experimental results demonstrate the effectiveness of aggregating 2D and 3D signals but referring to occluded objects still remains challenging for the modern visual grounding systems.
Granulated Rest Frames Outperform Field of View Restrictors on Visual Search Performance

Dynamic field of view (FOV) restrictors have been successfully used to reduce visually induced motion sickness (VIMS) during continuous viewpoint motion control (virtual travel) in virtual reality (VR). This benefit, however, comes at the cost of losing peripheral awareness during provocative motion. Likewise, the use of visual references that are stable in relation to the physical environment, called rest frames (RFs), has also been shown to reduce discomfort during virtual travel tasks in VR. We propose a new RF-based design called Granulated Rest Frames (GRFs) with a soft-edged circular cutout in the center that leverages the rest frames’ benefits without completely blocking the user’s peripheral view. The GRF design is application-agnostic and does not rely on context-specific RFs, such as commonly used cockpits. We report on a within-subjects experiment with 20 participants. The results suggest that, by strategically applying GRFs during a visual search session in VR, we can achieve better item searching efficiency as compared to restricted FOV. The effect of GRFs on reducing VIMS remains to be determined by future work.
Emojis as Contextual Indicants in Location-Based Social Media Posts

The presented study aims to investigate the relationship between the use of emojis in location-based social media and the location of the corresponding post in terms of perceived objects and conducted activities connected to this place. The basis for this is not a purely frequency-based assessment, but a specifically introduced measure called typicality. To evaluate the typicality measure and examine the assumption that emojis are contextual indicants, a dataset of worldwide geotagged posts from Instagram relating to sunset and sunrise events is used, converted to a privacy-aware version based on a Hyperloglog approach. Results suggest that emojis can often provide more nuanced information about user activities and the surrounding environment than is possible with hashtags. Thus, emojis may be suitable for identifying less obvious characteristics and the sense of a place. Emojis are already explored in research, but mainly for sentiment analysis, for semantic studies or as part of emoji prediction. In contrast, this work provides novel insights into the user’s spatial or activity context by applying the typicality measure and therefore considers emojis contextual indicants.
Facilitating reuse of planetary spatial research data – Conceptualizing an open map repository as part of a Planetary Research Data Infrastructure

In recent decades, the research community has been dealing with a growing amount and variety of new research data and derived research information. While primary research data, as derived from instruments, are commonly well maintained, derived research data might not always share the same fate. Scientific studies, resulting in further derived data, what we will call here as research data, does not often share the same attention. Fortunately, in the planetary sciences, most primary research data are commonly freely accessible for researchers to use, while research results have commonly not been re-inserted into the research cycle and a discussion about the process has only recently been initiated but there are not concrete methods or efforts to maintain this derived research data. We here discuss the requirements and needs in the planetary sciences to develop and coordinate a platform for research data and develop this idea using planetary cartographic products as an example of a higher-level research product that undergoes various development stages across different organizational levels. We here will visit the current practice and provide a number of scenarios showing how such a research-data life-cycle could look like in the field of planetary research. In order to develop a conceptual framework, experience from established terrestrial research-data frameworks and spatial data infrastructures are integrated into the discussion.
Is Word-Level Recursion Actually Recursion?

There is a longstanding debate in the literature about if, and where, recursion occurs in prosodic structure. While there are clear cases of genuine recursion at the phrase level and above, there are very few convincing cases of word-level recursion. Most cases are—by definition—not recursive and instead best analyzed as different constituents (e.g., the Composite Group, Prosodic Word Group, etc.). We show that two convincing cases of prosodic word-level recursion can easily be reanalyzed without recursion if phonology and prosody are evaluated cyclically at syntactic phase boundaries. Our analysis combines phase-based spell-out and morpheme-specific subcategorization frames of Cophonologies by Phase with Tri-P Mapping prosodic structure building. We show that apparent word-level recursion is due to cyclic spell-out, and non-isomorphisms between syntactic and prosodic structure are due to morpheme-specific prosodic requirements.
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data

Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization challenge and may severely deteriorate the generalization performance. In this paper, we investigate and identify the limitation of several decentralized optimization algorithms for different degrees of data heterogeneity. We propose a novel momentum-based method to mitigate this decentralized training difficulty. We show in extensive empirical experiments on various CV/NLP datasets (CIFAR-10, ImageNet, and AG News) and several network topologies (Ring and Social Network) that our method is much more robust to the heterogeneity of clients' data than other existing methods, by a significant improvement in test performance ($1\% \!-\! 20\%$). Our code is publicly available.
TAMNet: two attention modules-based network on facial expression recognition under uncertainty

Facial expression recognition (FER) has applications in many scenarios, making it a valuable research direction. However, due to the uncertainty of real-world images, their recognition accuracy is not satisfying. To deal with this problem, we propose a two attention modules-based network that uses two different attention modules to extract features. The main classifier uses the self-attention module (SAM), and the auxiliary classifier uses the channel enhancement module. At the same time, the classification results of the two classifiers are constrained by variance regularization to suppress uncertainty. In addition, we use knowledge distillation to further optimize the predicted results using the mutual information between the teacher network and the student network. Our model achieves an optimal accuracy rate of 88.75% in the RAF-DB dataset and 80.59% in the FERPlus dataset, which is occluded with the lower half being visible.
Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish.

Negation is a linguistic universal that poses difficulties for cognitive and computational processing.Despite many advances in text analytics, negation resolution remains an acute and continuously researched question in Natural Language Processing.Reliable negation parsing affects results in biomedical text mining, sentiment analysis, machine translation, and many other fields.The availability of multilingual pre-trained general representation models makes it possible to experiment with negation detection in languages that lack annotated data.In this work we test the performance of two state-of-the-art contextual representation models, Multilingual BERT and XLM-RoBERTa.We resolve negation scope by conducting zero-shot transfer between English, Spanish, French, and Russian.Our best result amounts to a token-level F1-score of 86.86% from Spanish to Russian.We correlate these results with a linguistic negation typology and lexical capacity of the models.
Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data.

In recent years, methods were proposed for assigning feature importance scores to measure the contribution of individual features. While in some cases the goal is to understand a specific model, in many cases the goal is to understand the contribution of certain properties (features) to a real-world phenomenon. Thus, a distinction has been made between feature importance scores that explain a model and scores that explain the data. When explaining the data, machine learning models are used as proxies in settings where conducting many real-world experiments is expensive or prohibited. While existing feature importance scores show great success in explaining models, we demonstrate their limitations when explaining the data, especially in the presence of correlations between features. Therefore, we develop a set of axioms to capture properties expected from a feature importance score when explaining data and prove that there exists only one score that satisfies all of them, the Marginal Contribution Feature Importance (MCI). We analyze the theoretical properties of this score function and demonstrate its merits empirically.