Reviewerly/exHarmony huggingface dataset:
Semi-Supervised Object Detection via Multi-instance Alignment with Global Class Prototypes

Semi-Supervised object detection (SSOD) aims to improve the generalization ability of object detectors with large-scale unlabeled images. Current pseudo-labeling-based SSOD methods individually learn from labeled data and unlabeled data, without considering the relation be-tween them. To make full use of labeled data, we pro-pose a Multi-instance Alignment model which enhances the prediction consistency based on Global Class Proto-types (MA-GCP). Specifically, we impose the consistency between pseudo ground-truths and their high-IoU candi-dates by minimizing the cross-entropy loss of their class distributions computed based on global class prototypes. These global class prototypes are estimated with the whole labeled dataset via the exponential moving average algorithm. To evaluate the proposed MA-GCP model, we inte-grate it into the state-of-the-art SSOD framework and ex-periments on two benchmark datasets demonstrate the ef-fectiveness of our MA-GCP approach.
High-Order Convolutional Attention Networks for Automatic Modulation Classification in Communication

Automatic modulation classification is a challenging and critical task in the field of communication. Deep convolutional networks (ConvNets) have been recently applied in cognitive radio and achieved remarkable performance. However, existing ConvNet-based methods mainly focus on the first-order architecture design, while fail to explore feature correlations of radio signal, which are particularly significant for useful information extraction in low signal-to-noise ratios (SNRs). In this paper, we propose high-order convolutional attention networks (HoCANs) for radio signal expression and feature correlation learning, based on a novel high-order attention mechanism to rescale the convolutional features along channel and sequence dimensions. High-order convolutional layer and covariance matrix after nonlinear transformation are led for tenser filtering with more discriminative representations of radio signals. Experiments have been conducted to validate the superiority of HoCANs which achieve state-of-the-art accuracy for automatic modulation classification on RADIOML 2018.01A dataset.
NDF: Neural Deformable Fields for Dynamic Human Modelling

We propose Neural Deformable Fields (NDF), a new representation for dynamic human digitization from a multi-view video. Recent works proposed to represent a dynamic human body with shared canonical neural radiance fields which links to the observation space with deformation fields estimations. However, the learned canonical representation is static and the current design of the deformation fields is not able to represent large movements or detailed geometry changes. In this paper, we propose to learn a neural deformable field wrapped around a fitted parametric body model to represent the dynamic human. The NDF is spatially aligned by the underlying reference surface. A neural network is then learned to map pose to the dynamics of NDF. The proposed NDF representation can synthesize the digitized performer with novel views and novel poses with a detailed and reasonable dynamic appearance. Experiments show that our method significantly outperforms recent human synthesis methods.
Matrix Syncer - A Multi-chain Data Aggregator for Supporting Blockchain-Based Metaverses

Due to the rising complexity of the metaverse's business logic and the low-latency nature of the metaverse, developers typically encounter the challenge of effectively reading, writing, and retrieving historical on-chain data in order to facilitate their functional implementations at scale. While it is true that accessing blockchain states is simple, more advanced real-world operations such as search, aggregation, and conditional filtering are not available when interacting directly with blockchain networks, particularly when dealing with requirements for on-chain event reflection. We offer Matrix Syncer, the ultimate middleware that bridges the data access gap between blockchains and end-user applications. Matrix Syncer is designed to facilitate the consolidation of on-chain information into a distributed data warehouse while also enabling customized on-chain state transformation for a scalable storage, access, and retrieval. It offers a unified layer for both on- and off-chain state, as well as a fast and flexible atomic query. Matrix Syncer is easily incorporated into any infrastructure to aggregate data from various blockchains concurrently, such as Ethereum and Flow. The system has been deployed to support several metaverse projects with a total value of more than $15 million USD.
Supporting Developers in Addressing Human-Centric Issues in Mobile Apps

Failure to consider the characteristics, limitations, and abilities of diverse end-users during mobile app development may lead to problems for end-users, such as accessibility and usability issues. We refer to this class of problems as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">human-centric issues</i> . Despite their importance, there is a limited understanding of the types of human-centric issues that are encountered by end-users and taken into account by the developers of mobile apps. In this paper, we examine what human-centric issues end-users report through Google App Store reviews, what human-centric issues are a topic of discussion for developers on GitHub, and whether end-users and developers discuss the same human-centric issues. We then investigate whether an automated tool might help detect such human-centric issues and whether developers would find such a tool useful. To do this, we conducted an empirical study by extracting and manually analysing a random sample of 1,200 app reviews and 1,200 issue comments from 12 diverse projects that exist on both Google App Store and GitHub. Our analysis led to a taxonomy of human-centric issues that characterises human-centric issues into three-high level categories: App Usage, Inclusiveness, and User Reaction. We then developed machine learning and deep learning models that are promising in automatically identifying and classifying human-centric issues from app reviews and developer discussions. A survey of mobile app developers shows that the automated detection of human-centric issues has practical applications. Guided by our findings, we highlight some implications and possible future work to further understand and better incorporate addressing human-centric issues into mobile app development.
3D Morphology Measurement for Blastocyst Evaluation from “All Angles”

Measuring the 3D morphology of spherical cell aggregates is required in both biology and medicine. Traditional methods either use fluorescent labeling, which cause cell toxicity and are unsuitable for clinical treatment, or use 2D images to roughly estimate 3D morphology. To overcome these limitations, this paper presents a quantitative label-free 3D morphology measurement technique using multi-view images. This technique, for the first time, enables the morphological evaluation of a blastocyst (Day 5 embryo) from "all angles" for IVF treatment. In this technique, a spherical rotation scale invariant feature transform (SR-SIFT) is proposed to address feature distortions for the rotation matrix calculation of the multi-view images. U-Net with generalized Dice loss is used to segment individual trophectoderm (TE) cells and the inner cell mass (ICM) of the blastocyst. Based on the rotation matrices and the segmentation results, the 3D morphological parameters of the entire blastocyst were quantified. Experimental results showed that the error of rotation angle was less than 1 °, the Dice was 95.6% for TE segmentation and 92.3% for ICM segmentation, and the overall measurement error of clinically defined blastocyst parameters was less than 6.7%.
An efficient anonymous authentication and supervision system based on blockchain

The rapid development of the Internet and people’s attention to personal privacy and security have put forward higher requirements for the identity authentication mechanism. The existing controllable anonymous authentication schemes can realize anonymous authentication and supervision, but they have low efficiency. The one problem is the large computational overhead in presenting and verifying the certificate. The other problem is that a single certification authority (CA) cannot respond in time when there are many requests for certificates and tracking malicious users in a short time. This paper proposes an efficient anonymous authentication and supervision system based on blockchain (EAAS) to address these issues. Compared with existing solutions, our EAAS system employs a double-layer CA architecture to solve the problem that a single CA cannot respond to many requests in a short time. It also reduces the computational overhead to improve the efficiency of certificate presentation and verification.
Triple Pendulum Based Nonlinear Chaos Generator and its Applications in Cryptography

A nonlinear chaos generator scheme derived from a mechanical triple pendulum physical system is proposed here.The chaotic behavior of the proposed generator is validated against various standardized tests, such as the Lyapunov exponents test, bifurcation diagrams, sensitivity to parametric and to initial values, ergodicity, key space and sensitivity, histogram, correlation, NPCR and UACI, collision test, etc. and compared with existing contemporary methods.The generated chaotic map is utilized to develop various cryptography applications, such as PRNG and symmetric key encryption schemes, which are realized on an FPGA and an ASIC.Chaos-based PRNG is validated successfully using N IST -SP 800 benchmarks.The proposed encryption scheme illustrates its usage in low power, high throughput applications, where the power consumption, resource utilization, and throughput are 1.785×, 1.825×, and 2.396× better than other known contemporary chaos-based encryption methods.The average power and area of its ASIC implementation at 180-nm technology are 61.8836mW and 0.20374 mm 2 at 250 MHz.
FairStyle: Debiasing StyleGAN2 with Style Channel Manipulations

Recent advances in generative adversarial networks have shown that it is possible to generate high-resolution and hyperrealistic images. However, the images produced by GANs are only as fair and representative as the datasets on which they are trained. In this paper, we propose a method for directly modifying a pre-trained StyleGAN2 model that can be used to generate a balanced set of images with respect to one (e.g., eyeglasses) or more attributes (e.g., gender and eyeglasses). Our method takes advantage of the style space of the StyleGAN2 model to perform disentangled control of the target attributes to be debiased. Our method does not require training additional models and directly debiases the GAN model, paving the way for its use in various downstream applications. Our experiments show that our method successfully debiases the GAN model within a few minutes without compromising the quality of the generated images. To promote fair generative models, we share the code and debiased models at http://catlab-team.github.io/fairstyle .
Analyzing the Impact of Components of Yelp.com on Recommender System Performance: Case of Austin

As people's demand for eating out is steadily increasing, the number of restaurants is continuously increasing, and catering industry platforms such as Yelp, Open Table, and Zomato provide basic information and evaluation information of restaurants and restaurant recommendation services suitable for users.Existing research on recommending restaurants mainly uses only evaluation information to find neighbors, and the use of user and restaurant information is still in its infancy.In addition, there is little study on how various types of input information affect the performance of the recommender system.This study examines the influence of three component information provided by Yelp.com on the performance of the recommender system using various real restaurants, reviews, and users dataset provided by Yelp.com.For this purpose, Two Phase Experiment was designed, and restaurant data located in Austin, Texas, USA, which has the largest number of review data, was collected.As a result of the experiment, elite status, the cumulative number of reviews, price range and average rating of restaurants could improve the recommendation performance.
Energy-Efficient and QoE-Aware 360-Degree Video Streaming on Mobile Devices

Tile-based streaming has been widely used in 360° video streaming to adapt to varying network conditions. However, downloading and processing many small tiles consumes a large amount of energy on mobile devices. To address this issue, we propose techniques to encode video by considering the viewing popularity, where the tiles requested by users of similar interests are encoded as a large tile (called Ptile). When encoding Ptiles, we propose to further save energy by reducing the insignificant frames in each video segment, i.e., reducing the frame rate to save energy while satisfying some QoE constraint. Based on real video traces, we model the impact of video features (i.e., video bitrate, frame rate) and user behavior (i.e., view switching) on QoE, and model the impact of video features on power consumption. Based on the QoE model and the power model, we formulate the energy-efficient and QoE-aware 360° video streaming problem as an optimization problem, and propose a control theory based algorithm to solve it. Through extensive evaluations based on real traces, we demonstrate that the proposed algorithm can significantly reduce the energy consumption (49.7%) and improve the QoE (7.4%).
CleAR Sight: Exploring the Potential of Interacting with Transparent Tablets in Augmented Reality

In this paper, we examine the potential of incorporating transparent, handheld devices into head-mounted Augmented Reality (AR). Additional mobile devices have long been successfully used in head-mounted AR, but they obscure the visual context and real world objects during interaction. Transparent tangible displays can address this problem, using either transparent OLED screens or rendering by the head-mounted display itself. However, so far, there is no systematic analysis of the use of such transparent tablets in combination with AR head-mounted displays (HMDs), with respect to their benefits and arising challenges. We address this gap by introducing a research platform based on a touch-enabled, transparent interaction panel, for which we present our custom hardware design and software stack in detail. Furthermore, we developed a series of interaction concepts for this platform and demonstrate them in the context of three use case scenarios: the exploration of 3D volumetric data, collaborative visual data analysis, and the control of smart home appliances. We validate the feasibility of our concepts with interactive prototypes that we used to elicit feedback from HCI experts. As a result, we contribute to a better understanding of how transparent tablets can be integrated into future AR environments.
InAction: Interpretable Action Decision Making for Autonomous Driving

Autonomous driving has attracted interest for interpretable action decision models that mimic human cognition. Existing interpretable autonomous driving models explore static human explanations, which ignore the implicit visual semantics that are not explicitly annotated or even consistent across annotators. In this paper, we propose a novel Interpretable Action decision making (InAction) model to provide an enriched explanation from both explicit human annotation and implicit visual semantics. First, a proposed visual-semantic module captures the region-based action-inducing components from the visual inputs, which learns the implicit visual semantics to provide a human-understandable explanation in action decision making. Second, an explicit reasoning module is developed by incorporating global visual features and action-inducing visual semantics, which aims to jointly align the human-annotated explanation and action decision making. Experimental results on two autonomous driving benchmarks demonstrate the effectiveness of our InAction model for explaining both implicitly and explicitly by comparing it to existing interpretable autonomous driving models. The source code is available at https://github.com/scottjingtt/InAction.git .
High-Dynamic-Range 3-D Shape Measurement With Adaptive Speckle Projection Through Segmentation-Based Mapping

The 3-D shape measurement of high-dynamic-range (HDR) scenes based on structured light projection is challenging because the conventional projection without considering the reflection distribution of the tested surface usually causes over-exposure and over-dark areas simultaneously in the captured images, which will lead to the failure or low accuracy of 3-D reconstruction. In this article, we propose a novel HDR 3-D surface measurement method based on adaptive speckle projection with fully considering the reflection distribution. An efficient segmentation-based mapping strategy is established to speed up the generation of adaptive speckle patterns, which segments the estimated mask with optimal projection intensity into multiple subregions by the eight-connected standard to raise mapping accuracy and project gray-coded and sinusoidal fringe patterns to build the pixelwise mapping relationship between binocular cameras and the projector for each subregion, respectively. Then the pixelwise intensity of projected speckle patterns can be accurately adjusted according to optimal intensity. The generated adaptive speckle pattern is used to scan the tested object, and high signal-to-noise-ratio (SNR) speckle images can be obtained for accurate and complete 3-D measurement. Experimental results verify that the point cloud produced by our method has obvious advantages compared with the traditional methods in terms of measurement accuracy, whose coverage is as large as 92.26% and the average distance (AD) relative to ground truth (GT) is as low as 0.128 mm. Meanwhile, the number of projected patterns for mapping is decreased by a third compared with the widely used mapping strategy.
Comparison of BERT-Base and GPT-3 for Marathi Text Classification

In text summarization process, the text classification task plays an important role. So, to classify natural language text or text documents, various machine learning techniques are available. Within this paper, we have checked the performance of BERT and GPT-3 on the Marathi Polarity Labeled Corpora (MPLC) a tourism dataset. The MPLC dataset consists of 75 positive and the same number of negative reviews. The main purpose of this paper is to put GPT-3 into the limelight with its useful features. For this purpose, we compared GPT-3 with the best performing BERT model, and finally, in the race of evaluation, the GPT-3 model beats the BERT-base model.
HYFLEX ORGANIZATION OF FOREIGN LANGUAGE TEACHING: SPECIALTIES 013 «PRIMARY EDUCATION» AND 012 «PRESCHOOL EDUCATION»

The article is devoted to the theoretical aspects of the HyFlex organization of foreign language learning for students of specialties 013 «Primary Education» and 012 «Preschool Education». The essence of the basic concepts «HyFlex training» and «HyFlex organization of foreign language training» have been clarified. Based on the analysis of the scientific and pedagogical literature, the term «HyFlex learning» is defined as a form of learning in which one part of the students is present (physically) in the auditorium, while the other simultaneously participates in the learning process remotely (online). The concept «Hyflex organization of foreign language training for students of specialties 013 «Primary Education» and 012 «Preschool Education» is considered as a purposeful, structured set of actions of subjects of the educational process (lecturer and students) aimed at flexible provision and receipt of educational services with the purpose of achieving the results of foreign language training, which are determined by the educational and professional programs of the specified specialties. HyFlex foreign language training is aimed at expanding, improving and enriching the flexibility of education seekers in the content of their foreign language training. With the help of a complex of theoretical methods (analysis, synthesis, comparison, generalization, specification), the HyFlex technology of foreign language learning for students of specialties 013 «Primary Education» and 012 «Preschool Education» was determined. Its purpose is to create favorable conditions so that students could to master a foreign language effectively, taking into account the objective risks of nowadays (pandemic, war, etc.). The tasks and outcomes of the Hyflex technology of foreign language learning for students of specialties 013 «Primary Education» and 012 «Preschool Education» are correlated with the Educational and Professional Programs of the specified specialties. The structure and logic of the Hyflex organization of foreign language learning for students of specialties 013 «Primary Education» and 012 «Preschool Education» was determined, which includes the following stages (components): organizational-target, activity-procedural and evaluation-refective. The authors have characterized each structural component of the Hyflex foreign language learning technology in short.
Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification

Graph neural networks (GNNs) have attracted extensive interest in text classification tasks due to their expected superior performance in representation learning. However, most existing studies adopted the same semi-supervised learning setting as the vanilla Graph Convolution Network (GCN), which requires a large amount of labelled data during training and thus is less robust when dealing with large-scale graph data with fewer labels. Additionally, graph structure information is normally captured by direct information aggregation via network schema and is highly dependent on correct adjacency information. Therefore, any missing adjacency knowledge may hinder the performance. Addressing these problems, this paper thus proposes a novel method to learn a graph structure, NC-HGAT, by expanding a state-of-the-art self-supervised heterogeneous graph neural network model (HGAT) with simple neighbour contrastive learning. The new NC-HGAT considers the graph structure information from heterogeneous graphs with multilayer perceptrons (MLPs) and delivers consistent results, despite the corrupted neighbouring connections. Extensive experiments have been implemented on four benchmark short-text datasets. The results demonstrate that our proposed model NC-HGAT significantly outperforms state-of-the-art methods on three datasets and achieves competitive performance on the remaining dataset.
Structure-Preserving Linearly Homomorphic Signature with Designated Combiner for Subspace

Linearly homomorphic signature allows signature holders to perform arbitrary linear computation on signed vectors. The special "function" makes linearly homomorphic signature suitable for many applications. However, publicly combinable is not advisable in some specific scenarios. Although some schemes with designated combiners have been proposed, they break the homomorphism of the combined signature. The combined vectors cannot be combined again. In this paper, we put forth the notion of structure-preserving linearly homomorphic signatures with the designated combiner. The combined signature is indistinguishable from signatures generated by the signer. Only the signer and the designated entity can generate a valid signature for any combined vector. Finally, we prove our scheme is secure under the CDH problem assumption and show it is efficient.