Reviewerly/exHarmony huggingface dataset:
Doubly Efficient Interactive Proofs over Infinite and Non-commutative Rings

We introduce the first proof system for layered arithmetic circuits over an arbitrary ring R that is (possibly) non-commutative and (possibly) infinite, while only requiring black-box access to its arithmetic and a subset $$A \subseteq R$$ . Our construction only requires limited commutativity and regularity properties from A, similar to recent work on efficient information theoretic multi-party computation over non-commutative rings by Escudero and Soria-Vazquez (CRYPTO 2021), but furthermore covering infinite rings. We achieve our results through a generalization of GKR-style interactive proofs (Goldwasser, Kalai and Rothblum, Journal of the ACM, 2015). When A is a subset of the center of R, generalizations of the sum-check protocol and other building blocks are not too problematic. The case when the elements of A only commute with each other, on the other hand, introduces a series of challenges. In order to overcome those, we need to introduce a new definition of polynomial ring over a non-commutative ring, the notion of left (and right) multi-linear extensions, modify the layer consistency equation and adapt the sum-check protocol. Despite these changes, our results are compatible with recent developments such as linear time provers. Moreover, for certain rings our construction achieves provers that run in sublinear time in the circuit size. We obtain such result both for known cases, such as matrix and polynomial rings, as well as new ones, such as for some rings resulting from Clifford algebras. Besides efficiency improvements in computation and/or round complexity for several instantiations, the core conclusion of our results is that state of the art doubly efficient interactive proofs do not require much algebraic structure. This enables exact rather than approximate computation over infinite rings as well as “agile” proof systems, where the black-box choice of the underlying ring can be easily switched through the software life cycle.
Emotion-aware Multi-view Contrastive Learning for Facial Emotion Recognition

When a person recognizes another’s emotion, he or she recognizes the (facial) features associated with emotional expression. So, for a machine to recognize facial emotion(s), the features related to emotional expression must be represented and described properly. However, prior arts based on label supervision not only failed to explicitly capture features related to emotional expression, but also were not interested in learning emotional representations. This paper proposes a novel approach to generate features related to emotional expression through feature transformation and to use them for emotional representation learning. Specifically, the contrast between the generated features and overall facial features is quantified through contrastive representation learning, and then facial emotions are recognized based on understanding of angle and intensity that describe the emotional representation in the polar coordinate, i.e., the Arousal-Valence space. Experimental results show that the proposed method improves the PCC/CCC performance by more than 10% compared to the runner-up method in the wild datasets and is also qualitatively better in terms of neural activation map. Code is available at https://github.com/kdhht2334/AVCE_FER .
Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints

Federated learning systems facilitate training of global models in settings where potentially heterogeneous data is distributed across a large number of clients. Such systems operate in settings with intermittent client availability and/or time-varying communication constraints. As a result, the global models trained by federated learning systems may be biased towards clients with higher availability. We propose F3AST, an unbiased algorithm that dynamically learns an availability-dependent client selection strategy which asymptotically minimizes the impact of client-sampling variance on the global model convergence, enhancing performance of federated learning. The proposed algorithm is tested in a variety of settings for intermittently available clients under communication constraints, and its efficacy demonstrated on synthetic data and realistically federated benchmarking experiments using CIFAR100 and Shakespeare datasets. We show up to 186% and 8% accuracy improvements over FedAvg, and 8% and 7% over FedAdam on CIFAR100 and Shakespeare, respectively.
An Automated Approach to Identifying Corporate Editing

OpenStreetMap (OSM) is the world’s largest peer-produced geospatial project. As a freely-editable open map of the world to which anyone may contribute or make use of, the dynamics and motivations of its contributors have been the object of significant scholarship. A growing phenomena in the OSM community is the increasing contributions of paid editing teams hired by tech corporations, such as, Microsoft, Apple, and Facebook. Though corporations have long supported OSM in various ways, the recent growth of teams of paid editors raises challenges to the community’s norms and policies, which are historically oriented around contributions by individual volunteer, making it hard to track the contribution of paid editors. This research addresses a fundamental problem in approaching these concerns: understanding the scale and character of corporate editing in OSM. We use machine-learning to improve upon prior approaches to estimating this phenomena, contributing both a novel methodology as well a more robust understanding of the latest corporate editing behavior in OSM.
Adaptive Hierarchical Representation Learning for Long-Tailed Object Detection

General object detectors are always evaluated on hand-designed datasets, e.g., MS COCO and Pascal VOC, which tend to maintain balanced data distribution over different classes. However, it goes against the practical applications in the real world which suffer from a heavy class imbalance problem, known as the long-tailed object detection. In this paper, we propose a novel method, named Adaptive Hierarchical Representation Learning (AHRL), from a metric learning perspective to address long-tailed object detection. We visualize each learned class representation in the feature space, and observe that some classes, especially under-represented scarce classes, are prone to cluster with analogous ones due to the lack of discriminative representation. Inspired by this, we propose to split the whole feature space into a hierarchical structure and eliminate the problem in a coarse-to-fine way. AHRL contains a two-stage training paradigm. First, we train a normal baseline model and construct the hierarchical structure under the unsupervised clustering method. Then, we design an AHR loss that consists of two optimization objectives. On the one hand, AHR loss retains the hierarchical structure and keeps representation clusters away from each other. On the other hand, AHR loss adopts adaptive margins according to specific class pairs in the same cluster to further optimize locally. We conduct extensive experiments on the challenging LVIS dataset and AHRL outperforms all the existing state-of-the-art methods, with 29.1% segmentation AP and 29.3% box AP on LVIS v0.5 and 27.6% segmentation AP and 28.7% box AP on LVIS v1.0 based on ResNet-101. We hope our simple yet effective approach will serve as a solid baseline to help stimulate future research in long-tailed object detection. Code will be released soon
TwinsReID: Person re-identification based on twins transformer's multi-level features

&lt;abstract&gt; &lt;p&gt;In the traditional person re-identification model, the CNN network is usually used for feature extraction. When converting the feature map into a feature vector, a large number of convolution operations are used to reduce the size of the feature map. In CNN, since the receptive field of the latter layer is obtained by convolution operation on the feature map of the previous layer, the size of this local receptive field is limited, and the computational cost is large. For these problems, combined with the self-attention characteristics of Transformer, an end-to-end person re-identification model (twinsReID) is designed that integrates feature information between levels in this article. For Transformer, the output of each layer is the correlation between its previous layer and other elements. This operation is equivalent to the global receptive field because each element needs to calculate the correlation with other elements, and the calculation is simple, so its cost is small. From these perspectives, Transformer has certain advantages over CNN's convolution operation. This paper uses Twins-SVT Transformer to replace the CNN network, combines the features extracted from the two different stages and divides them into two branches. First, convolve the feature map to obtain a fine-grained feature map, perform global adaptive average pooling on the second branch to obtain the feature vector. Then divide the feature map level into two sections, perform global adaptive average pooling on each. These three feature vectors are obtained and sent to the Triplet Loss respectively. After sending the feature vectors to the fully connected layer, the output is input to the Cross-Entropy Loss and Center-Loss. The model is verified On the Market-1501 dataset in the experiments. The mAP/rank1 index reaches 85.4%/93.7%, and reaches 93.6%/94.9% after reranking. The statistics of the parameters show that the parameters of the model are less than those of the traditional CNN model.&lt;/p&gt; &lt;/abstract&gt;
An Eye Tracking and Brain–Computer Interface-Based Human–Environment Interactive System for Amyotrophic Lateral Sclerosis Patients

Amyotrophic lateral sclerosis (ALS) patients suffer a great inconvenience in their daily lives due to the gradual loss of their motion abilities. In order to help ALS patients regain their self-care abilities at a certain level, an eye-tracking and brain–computer interface (BCI)-based human–environment interactive (HEI) system is proposed. Through a single-channel EEG recorder and a pair of eye-tracking glasses, the user’s attention levels and gaze points are detected. Based on the two types of intention information, the target objects are recognized by a You Only Look Once v5 (YOLOv5) model. Then, according to voice instructions, users can control the household electric appliances. The system performed a high success rate of 89.3% experimentally, providing a reliable and capable assistive solution for ALS patients.
Dominance of National Systems of Higher Education

The theory of economic dominance in a multilevel economy, developed by Blokhin A.A. and Lomakin-Rumyantsev I.V., has been actively tested by researches on different markets since 2019 — the distribution of players in the markets allows to formulate different strategies for their development. This article is a development of studies devoted to the study of competition in the education market and presents the result of economic dominance of national higher education systems in the international market. This research aims to analyze the subject areas of social sciences and management, for further identifying dominance in the QS ranking. The sample consists of 58 QS subject rankings for 2018–2021. The main instrument is the SV-matrix (strength/variety). The analysis outlines areas of subject ratings where there is dominance of national educational systems and characterizes its level. In addition, an interpretation of the situation when constructing the SV matrix, where Lind index shows boundary values when determining dominant group size. The SV matrix gives valid results and allows both qualitatively and quantitatively assessing the positions of countries in educational rankings. Analysis using SV-matrix made it possible to identify 8 out of 15 subject areas, in which there is clear dominance and determined positions of Russian universities relative to the dominant group. The hypothesis of United States presence in the dominant group in most areas of social sciences and management was confirmed, however, several subjects were defined, where the higher education system of anther countries were in the lead. The scientific novelty of this study lies in the proposal and testing of a method for assessing the level of dominance of national educational systems in the international education market. The SV matrix allows to analyze the positions of different countries in the international educational space, both in dynamics and in statics (to assess the level of competitiveness of national educational systems); to understand theoretically the level of differentiation of national educational systems in certain subject ratings; to assess the concentration (barriers to enter the rating) and the strength of competition (how strongly the players differ) of subject areas.
An Analysis of the Impact of Social Media Addiction on Academic Engagement of Students

The study's goal is to comprehend how internet addiction affects students' academic performance.However, very few research has been able to explain how excessive internet use causes students to lose interest in their academic work.Many studies have examined the detrimental association between addictions and academic performance.This research consists of two factors: internet addiction (emotional and cognitive preoccupation with internet and loss of control and interference with daily life) and academic engagement (enthusiasm and commitment).Through questionnaires, data was gathered from 186 students at a higher institution in Nigeria.Both correlation and regression were used to evaluate the data.The results of the investigation demonstrated that internet addiction significantly and unfavorably affects enthusiasm and commitment.It's interesting to note that internet obsession on an emotional or cognitive level was not shown to be a reliable indicator of internet addiction or loss of control.
Hyperchaotic Image Encryption Algorithm Based on BD-Zigzag Transformation and DNA Coding

For the features of Zigzag transformation, a Bidirectional Deformed Zigzag (BD-Zigzag) transformation is proposed in this paper, which starts from both sides of the diagonal and crosses in both directions. This method is more complex than the traditional Zigzag transformation method and has better dislocation effect. Then, a Hyperchaotic image encryption algorithm based on BD-Zigzag transformation and DNA coding is proposed. First, the images are processed with multiple rounds of permutations using the BD-Zigzag transformation. Second, a 4D-hyperchaotic Lorenz system is used to produce random numbers to permute the images at the bit level. Finally, DNA encoding is used to diffuse the pixels in the image. The experimental simulations and performance analysis data can prove that the encryption algorithm has high-level security capability.
A Federated Learning-Based Patient Monitoring System in Internet of Medical Things

Patient activities’ monitoring is a promising application of the Internet of Medical Things (IoMT), revolutionizing clinical diagnosis. An IoMT uses sensory data collected from smart devices to train a model on the server. The trained model recognizes the patient activities on smart devices. However, training the model on the server has raised privacy concerns and security threats. The sensitive medical data transferred from the smart devices to the Cloud poses different cybersecurity challenges, such as distributed denial of service (DDoS), phishing, network penetration, and side-channel. This article proposes a secure patient monitoring system using federated learning (FL). The system performs training on local devices and sends only weight matrices to the server for aggregation; thus, it preserves data privacy and security compromises. The system intelligently divides the participants into clusters based on the available resources, trains suitable models on each cluster, and enhances the performance via knowledge distillation (KD). The model of high-performing clusters distills knowledge to the model of small-size clusters to improve their performance. The experimental results illustrate that the proposed system successfully work in the presence of unequal resources.
Sentiment Analysis of Latin Poetry: First Experiments on the Odes of Horace

In this paper we present a set of annotated data and the results of a number of unsupervised experiments for the analysis of sentiment in Latin poetry. More specifically, we describe a small gold standard made of eight poems by Horace, in which each sentence is labeled manually for the sentiment using a four-value classification (positive, negative, neutral and mixed). Then, we report on how this gold standard has been used to evaluate two automatic approaches for sentiment classification: one is lexicon-based and the other adopts a zero-shot transfer approach.1
A Dynamic Mode Decomposition Approach for Decentralized Spectral Clustering of Graphs

We propose a novel robust decentralized graph clustering algorithm that is provably equivalent to the popular spectral clustering approach. Our proposed method uses the existing wave equation clustering algorithm that is based on propagating waves through the graph [1], [2]. However, instead of using a fast Fourier transform (FFT) computation at every node, our proposed approach exploits the Koopman operator framework. Specifically, we show that propagating waves in the graph followed by a local dynamic mode decomposition (DMD) computation at every node is capable of retrieving the eigenvalues and the local eigenvector components of the graph Laplacian, thereby providing local cluster assignments for all nodes. We demonstrate that the DMD computation is more robust than the existing FFT based approach and requires 20 times fewer steps of the wave equation to accurately recover the clustering information and reduces the relative error by orders of magnitude. We demonstrate the decentralized approach on a range of graph clustering problems.
Fun physics learning using augmented reality

Physics Physics learning media is currently starting to increase according to the times. Android-based learning applications are currently starting to develop towards augmented reality (AR) technology. This study aims to develop a product in FULAN (Fun physics learning using augmented reality) learning media as a learning medium on magnetic electricity material. This type of research is development research. The subjects involved in this study were a learning media expert and physics education expert, and physics teacher candidate students at Sultan Ageng Tirtayasa University. The data processed in this study were collected through expert validation sheets, student response questionnaires with a Likert scale. The collected data were analyzed descriptively. The model used in this study is the ADDIE model, which consists of five stages, namely assessment analysis, design, development, implementation, and evaluation. The results of this study are: the learning media made obtained a score of 81% from instructional media experts, 85% from physics education experts, and 88% from student responses. FULAN learning media using Augmented Reality technology as a creative, innovative teaching aid can increase student enthusiasm in studying magnetic electricity material. Based on these results, it can be concluded that the FULAN learning media is suitable for use in learning physics.
AI in Emergency Remote Learning Environments

This chapter is devoted to examining and reflecting on the importance of artificial intelligence techniques and technologies applied to the construction of intelligent learning environments (ILE), specially, for remote teaching and learning purpose. Their potential for educational purpose goes hand in hand with the vision of the education as a pilar for a sustainable future (e.g. UNESCO, 2014), which aims at designing cost-effective and at the same time quality teaching and learning opportunities for all students. Intelligent tutoring systems (ITS) cases will receive a special focus as their potential for personalization, adaptation to the student, and automatic recommendations based on the student's profile, comprising important features for fostering adaptive and autonomous learning paths. All those principles combined with AI contributions for ITS design will be pointed out. Finally, future research directions in the field of intelligent learning environments are figured out.
Implementation of Closing Eyes Detection with Ear Sensor of Muse EEG Headband using Support Vector Machine Learning

Epilepsy patients might need continuous electroencephalography (EEG) monitoring to help them understand their own condition's improvements or their doctors to determine the frequency of the seizures.In this study, we demonstrated how a low-cost, portable EEG headband could be used to detect absence seizures in epilepsy patients.The method used is Support Vector Machine (SVM) to separate the initial limit and Machine Learning with Tensorflow to predict its confidence level.Then, we tried to test our method on 2 other patients to see its measurement divergence.Furthermore, the Tensorflow library has been applied and developed to train and classify the data, which is also one of the novelty approaches in this study.From the result, closed eyes can be detected from open-eye conditions with more than 96 % accuracy and a loss of only 2.35%.The findings demonstrate the feasibility of detecting absence seizures using only two electrodes which were TP9 and TP10 of Muse headband which is also positioned in the ear like an ear-EEG.Overall, the study successfully developed a novel method utilizing a low-cost headband to provide affordable health system access.
Satellite Altimetry and Gravimetry Data for Mapping Marine Geodetic and Geophysical Setting of the Seychelles and the Somali Sea, Indian Ocean

Abstract Evaluation of the representative cartographic techniques demonstrated that there are still considerable challenges facing the methods of marine geodetic, geophysical and bathymetric data visualisation. In an oceanic seafloor formation, the interaction between the geological structural elements and topographical relief can be analysed by advanced mapping. In present study, a correlation between geodesy, geophysics and topography has been examined including the following variables: geological structure, coastal topography and bathymetry, geophysical fields, free-air gravity anomalies and geoid undulation, sediment thickness, bathymetric patterns, and extension of the transform faults. The variables were visualised on the high-resolution raster grids using Generic Mapping Tools (GMT) scripting toolset. The study area is located in the Seychelles and the Somali Sea segment of the Indian Ocean. The data incorporates satellite-derived gravity grid, EGM-2008, geological structures, topography from GEBCO grid and GlobSed sediment thickness, processed by GMT scripts. The results demonstrated that western continental slope of Somalia is wide, gently declining to the seafloor at depths exceeding -5000 m. Kenya and Tanzania present a wide continental foot with depths ranging from -3500 to -5000 m. The Somali Sea basin shows low sedimentation lower than 500 m, while ridges and island chains have higher sediment influx (1,000-2,000 m). The Mozambique Channel has dominating values at 2,500-3,500 m. Higher values are noted near the Reunion and Mauritius islands until the Seychelles via the Mascarene Plateau (500-1,000 m) against the &lt;500 m in the areas of the Mid-Indian Ridge, Carlsberg Ridge and open water.