Reviewerly/exHarmony huggingface dataset:
Snowmass White Paper: the Double Copy and its Applications

The double copy is, in essence, a map between scattering amplitudes in a broad variety of familiar field and string theories. In addition to the mathematically rich intrinsic structure, it underlies a multitude of active research directions and has a range of interesting applications in quantum, classical and effective field theories, including broad topics such as string theory, particle physics, astrophysics, and cosmology. This Snowmass white paper provides a brief introduction to the double copy, its applications, current research and future challenges.
Interpersonal Interface System of Multimedia Intelligent English Translation Based on Deep Learning

Artificial intelligence is a very challenging science, and people who are engaged in this work must understand computer knowledge, psychology, and philosophy. Artificial intelligence includes a wide range of sciences; it is composed of different fields, such as machine learning and computer vision. In recent years, with the rise and joint drive of technologies such as the Internet, big data, the Internet of Things, and voice recognition, the rapid development of artificial intelligence technology has presented new features such as deep learning, cross-border integration, and human-machine collaboration. Intelligent English translation is an innovation and experiment of the English language industry in the field of science and technology. Machine translation is the use of computer programmes to translate natural language into a specific natural language. This interdisciplinary subject includes three subjects: artificial intelligence, computational linguistics, and mathematical logic. Machine translation is becoming more and more popular with the public, and its advantages are mainly that machine translation is much cheaper than manual translation. From this study, it can be seen that the accuracy rate of the traditional ICTCLAS method is 76.40%, while the accuracy rate of the research method in this article is 94.58%, indicating that the research method used in this article is better. Machine translation greatly reduces the cost of translation because there are very few processes that require human participation, and the translation is basically done by computers. It is also an important research content in the field of English major research. Due to the wide range of language cultures and the influence of local culture, thought, and semantic environment, traditional human translation methods have many shortcomings. Nowadays, the demand for translation is unsatisfactory. Based on this, this paper analyzes the application status of intelligent translation, the prospect of intelligent translation, and realizes the optimization design of the human interface system for the intelligent translation of English. Through experiments, it is found that the multimedia intelligent English translation system based on deep learning not only improves the accuracy of English translation but also greatly improves the efficiency of people learning English.
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers

Scene understanding based on image segmentation is a crucial component of autonomous vehicles. Pixel-wise semantic segmentation of RGB images can be advanced by exploiting complementary features from the supplementary modality (X-modality). However, covering a wide variety of sensors with a modality-agnostic model remains an unresolved problem due to variations in sensor characteristics among different modalities. Unlike previous modality-specific methods, in this work, we propose a unified fusion framework, CMX, for RGB-X semantic segmentation. To generalize well across different modalities, that often include supplements as well as uncertainties, a unified cross-modal interaction is crucial for modality fusion. Specifically, we design a Cross-Modal Feature Rectification Module (CM-FRM) to calibrate bi-modal features by leveraging the features from one modality to rectify the features of the other modality. With rectified feature pairs, we deploy a Feature Fusion Module (FFM) to perform sufficient exchange of long-range contexts before mixing. To verify CMX, for the first time, we unify five modalities complementary to RGB, i.e., depth, thermal, polarization, event, and LiDAR. Extensive experiments show that CMX generalizes well to diverse multi-modal fusion, achieving state-of-the-art performances on five RGB-Depth benchmarks, as well as RGB-Thermal, RGB-Polarization, and RGB-LiDAR datasets. Besides, to investigate the generalizability to dense-sparse data fusion, we establish an RGB-Event semantic segmentation benchmark based on the EventScape dataset, on which CMX sets the new state-of-the-art. The source code of CMX is publicly available at https://github.com/huaaaliu/RGBX_Semantic_Segmentation.
SkiQL: A Unified Schema Query Language

Most NoSQL systems are schema-on-read: data can be stored without first having to declare a Schema that imposes a structure. This schemaless feature offers flexibility to evolve data-intensive applications when data frequently change. However, freeing from declaring schemas does not mean their absence, but rather that they are implicit in data and code. Therefore, diagramming tools similar to those available for relational systems are also needed to help developers and administrators understanding NoSQL schemas. Visualizing diagrams is not practical if schemas contain hundreds of database entities, and exploration or query facilities are then needed. In schemaless NoSQL stores, data of the same entity can be stored with different structure which can increase the difficulty of having readable diagrams. NoSQL schema management tools should therefore have three main components: schema extraction, schema visualization, and schema query. Since that there exist four main NoSQL data models, it is convenient that such tools can be built on a generic data model that provide platform-independence to query and visualize schemas. With the aim of favoring the creation of generic database tools, the authors of this paper defined the U-Schema unified data model that integrates the four main NoSQL data models and the relational model. This paper is focused on querying NoSQL and relational schemas which are represented as U-Schema models. We present the SkiQL language designed on U-Schema to achieve a platform-independent schema query service. SkiQL provides two constructs: schema-query and relationship-query. The former allows to obtain information of entity or relationship types, and the latter that of the aggregations or references (relations among types). We will show how SkiQL was evaluated by calculating well-known metrics for languages and using a survey.
Overparameterized Linear Regression under Adversarial Attacks

We study the error of linear regression in the face of adversarial attacks. In this framework, an adversary changes the input to the regression model in order to maximize the prediction error. We provide bounds on the prediction error in the presence of an adversary as a function of the parameter norm and the error in the absence of such an adversary. We show how these bounds make it possible to study the adversarial error using analysis from non-adversarial setups. The obtained results shed light on the robustness of overparameterized linear models to adversarial attacks. Adding features might be either a source of additional robustness or brittleness. On the one hand, we use asymptotic results to illustrate how double-descent curves can be obtained for the adversarial error. On the other hand, we derive conditions under which the adversarial error can grow to infinity as more features are added, while at the same time, the test error goes to zero. We show this behavior is caused by the fact that the norm of the parameter vector grows with the number of features. It is also established that $\ell_\infty$ and $\ell_2$-adversarial attacks might behave fundamentally differently due to how the $\ell_1$ and $\ell_2$-norms of random projections concentrate. We also show how our reformulation allows for solving adversarial training as a convex optimization problem. This fact is then exploited to establish similarities between adversarial training and parameter-shrinking methods and to study how the training might affect the robustness of the estimated models.
Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification

Prompt-based learning (i.e., prompting) is an emerging paradigm for exploiting knowledge learned by a pretrained language model. In this paper, we propose Automatic Multi-Label Prompting (AMuLaP), a simple yet effective method to automatically select label mappings for few-shot text classification with prompting. Our method exploits one-to-many label mappings and a statistics-based algorithm to select label mappings given a prompt template. Our experiments demonstrate that AMuLaP achieves competitive performance on the GLUE benchmark without human effort or external resources.
Classification of e-commerce financial transaction logs using machine learning approach

E-Commerce becomes inevitable in the current world. Especially in this pandemic period, almost activities have been carried out through digital mode to serve the customers with more safety. The financial transactions are much secured in the e-commerce payment. Still, many intruders are making these transactions into fail and hacking the customers’ information when the financial transactions are carried out. Along with, sometimes the transactions may get failed due to the network issues. Hence, the E-Commerce organizations are maintaining the transaction logs to check and take necessary action over the failed transactions. Out of huge transaction logs, identifying the suspicious failed transactions in manual method is not encouraged. Lot of technologies have come to support the detection of suspicious failed transactions such as Artificial Neural Network, Machine Learning, Deep Learning and other statistical methods. As the above methods are proved as more suitable for detecting and classifying the failed transaction logs, still the accuracy of classification has not been achieved much, which motivated to propose the machine learning based classification of E-Commerce Financial Transaction Logs.
Pyramid Grafting Network for One-Stage High Resolution Saliency Detection

Recent salient object detection (SOD) methods based on deep neural network have achieved remarkable performance. However, most of existing SOD models designed for low-resolution input perform poorly on high-resolution images due to the contradiction between the sampling depth and the receptive field size. Aiming at resolving this contradiction, we propose a novel one-stage framework called Pyramid Grafting Network (PGNet), using transformer and CNN backbone to extract features from different resolution images independently and then graft the features from transformer branch to CNN branch. An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process. Moreover, we design an Attention Guided Loss (AGL) to explicitly supervise the attention matrix generated by CMGM to help the network better interact with the attention from different models. We contribute a new Ultra-High-Resolution Saliency Detection dataset UHRSD, containing 5,920 images at 4K-8K resolutions. To our knowledge, it is the largest dataset in both quantity and resolution for high-resolution SOD task, which can be used for training and testing in future research. Sufficient experiments on UHRSD and widely-used SOD datasets demonstrate that our method achieves superior performance compared to the state-of-the-art methods.
Development of an Interactive Mobile Application for Learning Undergraduate Educational Technology Concepts

Today's schooling which is characterized by over-population, insufficient learning resources and incessant closure due to civic crises and pandemic demands more efficient learning models that allow the deployment of technology for students' active participation in their education. Hence, this study was conducted to develop an interactive mobile application for learning undergraduate educational technology concepts in Nigeria. The ADDIE instructional design model was adopted, and 15 well-structured topics with interactive learning contents and elements were embedded in the developed application built using JavaScript, Html, CSS, Cordova, Articulate 360, Moodle platform, and PHP programming language. The validation of the application was done in three stages and experts involved in the development and validation included educational technology specialists and computer programmers. The mobile application was tested on 30-randomly selected year three undergraduate educational technology students. Findings revealed that exposure to the application improved students' academic achievement (mean gain=27.87), and there was significant difference (df=28, t=19.97, p<0.05) between the academic achievement score of students at pre- and post-test in favor of the later. The application is therefore considered suitable for teaching and learning of undergraduate educational technology concepts.
The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot Transfer

Large multilingual language models such as mBERT or XLM-R enable zero-shot cross-lingual transfer in various IR and NLP tasks. Cao et al. (2020) proposed a data- and compute-efficient method for cross-lingual adjustment of mBERT that uses a small parallel corpus to make embeddings of related words across languages similar to each other. They showed it to be effective in NLI for five European languages. In contrast we experiment with a typologically diverse set of languages (Spanish, Russian, Vietnamese, and Hindi) and extend their original implementations to new tasks (XSR, NER, and QA) and an additional training regime (continual learning). Our study reproduced gains in NLI for four languages, showed improved NER, XSR, and cross-lingual QA results in three languages (though some cross-lingual QA gains were not statistically significant), while mono-lingual QA performance never improved and sometimes degraded. Analysis of distances between contextualized embeddings of related and unrelated words (across languages) showed that fine-tuning leads to "forgetting" some of the cross-lingual alignment information. Based on this observation, we further improved NLI performance using continual learning.
Modeling Indirect Illumination for Inverse Rendering

Recent advances in implicit neural representations and differentiable rendering make it possible to simultaneously recover the geometry and materials of an object from multi-view RGB images captured under unknown static illumination. Despite the promising results achieved, indirect illumination is rarely modeled in previous methods, as it requires expensive recursive path tracing which makes the inverse rendering computationally intractable. In this paper, we propose a novel approach to efficiently recovering spatially-varying indirect illumination. The key insight is that indirect illumination can be conveniently derived from the neural radiance field learned from input images instead of being estimated jointly with direct illumination and materials. By properly modeling the indirect illumination and visibility of direct illumination, interreflection- and shadow-free albedo can be recovered. The experiments on both synthetic and real data demonstrate the superior performance of our approach compared to previous work and its capability to synthesize realistic renderings under novel viewpoints and illumination. Our code and data are available at https://zju3dv.github.io/invrender/.
Hybrid Feature- and Similarity-Based Models for Joint Prediction and Interpretation

Electronic health records (EHRs) include simple features like patient age together with more complex data like care history that are informative but not easily represented as individual features. To better harness such data, we developed an interpretable hybrid feature- and similarity-based model for supervised learning that combines feature and kernel learning for prediction and for investigation of causal relationships. We fit our hybrid models by convex optimization with a sparsity-inducing penalty on the kernel. Depending on the desired model interpretation, the feature and kernel coefficients can be learned sequentially or simultaneously. The hybrid models showed comparable or better predictive performance than solely feature- or similarity-based approaches in a simulation study and in a case study to predict two-year risk of loneliness or social isolation with EHR data from a complex primary health care population. Using the case study we also present new kernels for high-dimensional indicator-coded EHR data that are based on deviations from population-level expectations, and we identify considerations for causal interpretations.
Research on the Application of Fall Detection Technology Based on the Security Field of the Smart Care for the Elderly

Abstract. The security of the elderly in an aging society has become a social problem attracting much attention, among which the problem of secondary injury caused by the fall has attracted wide concern of researchers. The application of fall detection combined with artificial intelligence technology has become an important application in the security field of smart care for the elderly. After analyzing and judging the human body features, this paper found out that the combination of human skeleton and fall detection technology could be used to identify the fall behavior and link it with the alarm system to solve the security problem of smart care for the elderly. The experimental results showed that the recognition rate of fall detection was increased by 2% after adding optimization of genetic algorithm factors and contrast calculation of prediction frame length in the process of human feature target detection.
Calibrating Class Weights with Multi-Modal Information for Partial Video Domain Adaptation

Assuming the source label space subsumes the target one, Partial Video Domain Adaptation (PVDA) is a more general and practical scenario for cross-domain video classification problems. The key challenge of PVDA is to mitigate the negative transfer caused by the source-only outlier classes. To tackle this challenge, a crucial step is to aggregate target predictions to assign class weights by up-weighing target classes and down-weighing outlier classes. However, the incorrect predictions of class weights can mislead the network and lead to negative transfer. Previous works improve the class weight accuracy by utilizing temporal features and attention mechanisms, but these methods may fall short when trying to generate accurate class weight when domain shifts are significant, as in most real-world scenarios. To deal with these challenges, we propose the Multi-modality Cluster-calibrated partial Adversarial Network (MCAN). MCAN enhances video feature extraction with multi-modal features from multiple temporal scales to form more robust overall features. It utilizes a novel class weight calibration method to alleviate the negative transfer caused by incorrect class weights. The calibration method tries to identify and weigh correct and incorrect predictions using distributional information implied by unsupervised clustering. Extensive experiments are conducted on prevailing PVDA benchmarks, and the proposed MCAN achieves significant improvements when compared to state-of-the-art PVDA methods.
DEVELOPMENT OF AN EDUCATIONAL PROGRAM FOR PREPARING A FUTURE PRIMARY SCHOOL TEACHER TO WORK WITH CHILDREN WITH LIMITED HEALTH OPPORTUNITIES IN THE CONDITIONS OF A SMALL RURAL SCHOOL

Дается обоснование необходимости разработки образовательной программы подготовки будущего учителя начальных классов к работе с детьми с ограниченными возможностями здоровья в условиях малочисленной сельской школы, описывается ее структура и содержание. Делается вывод о том, что процесс подготовки необходимо выстраивать с точки зрения вовлечения участников образовательного процесса в совместную деятельность, а также использовать образовательные технологии, позволяющие студентам быть субъектами собственного образования. The article substantiates the need to develop an educational program for preparing a future primary school teacher to work with children with disabilities in a small rural school, describes its structure and content. The author concludes that the preparation process must be built in terms of involving the participants in the educational process in joint activities, as well as using educational technologies that allow students to be subjects of their own education. The article emphasizes that in the context of this study, the term «education» is interpreted as the «involvement» of the subject of activity (student) in their development and education. When creating a program, the author notes that it is built on a modular basis and should be based on the concept of educational eventfulness, which considers pedagogical activity as a subject of personal attitude and action. Thus, the educational program in its content involves separate modules that are in a certain hierarchy: discovery, generation, creation of experience. The author comes to the conclusion that such a construction of the educational program will allow students to independently build the educational process, based on their own understanding of the meaning of professional activity in a small rural school with children with disabilities.
Systematic Analysis of Experiment Precision Measures and Methods for Experiments Comparison

The notion of experiment precision quantifies the variance of user ratings in a subjective experiment. Although there exist measures that assess subjective experiment precision, there are no systematic analyses of these measures available in the literature. To the best of our knowledge, there is also no systematic framework in the Multimedia Quality Assessment field for comparing subjective experiments in terms of their precision. Therefore, the main idea of this paper is to propose a framework for comparing subjective experiments in the field of MQA based on appropriate experiment precision measures. We present three experiment precision measures and three related experiment precision comparison methods. We systematically analyse the performance of the measures and methods proposed. We do so both through a simulation study (varying user rating variance and bias) and by using data from four real-world Quality of Experience (QoE) subjective experiments. In the simulation study we focus on crowdsourcing QoE experiments, since they are known to generate ratings with higher variance and bias, when compared to traditional subjective experiment methodologies. We conclude that our proposed measures and related comparison methods properly capture experiment precision (both when tested on simulated and real-world data). One of the measures also proves capable of dealing with even significantly biased responses. We believe our experiment precision assessment framework will help compare different subjective experiment methodologies. For example, it may help decide which methodology results in more precise user ratings. This may potentially inform future standardisation activities.