Reviewerly/exHarmony huggingface dataset:
Domain Agnostic Few-shot Learning for Speaker Verification

Deep learning models for verification systems often fail to generalize to new users and new environments, even though they learn highly discriminative features. To address this problem, we propose a few-shot domain generalization framework that learns to tackle distribution shift for new users and new domains. Our framework consists of domain-specific and domain-aggregation networks, which are the experts on specific and combined domains, respectively. By using these networks, we generate episodes that mimic the presence of both novel users and novel domains in the training phase to eventually produce better generalization. To save memory, we reduce the number of domain-specific networks by clustering similar domains together. Upon extensive evaluation on artificially generated noise domains, we can explicitly show generalization ability of our framework. In addition, we apply our proposed methods to the existing competitive architecture on the standard benchmark, which shows further performance improvements.
The effectiveness of distance and traditional teaching natural sciences: A comparative analysis

Introduction. The article focuses on the problem of the effectiveness of distance learning. The most recent pandemic has led to the widespread use of distance learning as an alternative to the traditional classroom instruction. However, there is no consensus on whether distance education is superior to the traditional teaching in terms of learning outcomes and perception. The purpose of this study is to compare the effectiveness of teaching natural sciences in distance and traditional modes. Materials and Methods. The data were collected via the following general research methods: synthesis and generalization of scholarly literature on the problem, empirical methods including surveys and interviews, and comparative analysis of research findings. A comparative analysis of the effectiveness of distance learning and traditional classroom instruction was conducted relying on the results of teaching natural science disciplines at Gorno-Altaisk State University in the period of 2020-2022, synthesis and analysis of international and Russian studies on the problem. Results. The authors identified the disadvantages and advantages of distance learning with the main focus on natural science disciplines. It has been found that laboratory assignments performed in a distance mode changed the student’s role from being a researcher to the role of an observer. Moreover, there was a decrease in the development of practical skills. Within the framework of traditional learning, the teacher acts as a mentor responsible for enhancing students’ knowledge, for nurturing the culture of behavior while the teacher’s role in distance education is that of a mediator facilitating students’ independent learning. It should also be noted that within distance learning environment teachers cannot perform a range of professional functions beyond educational. As far as final assessment is concerned, it must be emphasized that it can be extremely difficult to conduct objective assessment of students’ knowledge and competencies in a distance learning environment. On the other hand, the advantages of assessment via electronic and online media include flexibility and convenience. Conclusions. The authors conclude that nowadays the effectiveness of teaching natural sciences in the distance learning environment is lower than in the traditional one. In order to increase its efficiency, it is necessary to improve digital technologies and develop new teaching methods suitable for the distance learning environment. The limitations of distance learning include decrease in students’ learning outcomes, objectivity of final assessment, and partial implementation of moral education activities.
Local linear model and restoration method of underwater images

When light transports in water, it will be scattered and absorbed by the water body and water particles, resulting in blurred images and color distortion. In order to improve the quality of underwater imaging, the local linear model and restoration method of underwater images are proposed in this paper. Based on the distance-invariant feature in the local region, the local linear model is established, and the slope and intercept of the model represent the transmission rate and the backscattered light of the local region of the image, respectively. Utilizing this model, the problem of underwater image restoration has been transformed into the problem of solving the slope and intercept of linear equations. To solve the linear imaging model, the concept of local special-value is defined in this paper, and several fitting points can be obtained through the special-value. Then the linear model is solved by the fitting method, and the restoration of underwater images is completed. The restoration results of different underwater scene images verify that the linear model has a good effect in improving the image clarity and removing the color distortion.
USE OF AN E-TEXTBOOK FOR PRE-SERVICE TEACHERS IN AUTONOMOUS LEARNING OF ENGLISH FOR SPECIFIC PURPOSES

The research reveals the advantages of using an e-textbook in autonomous learning of the discipline “Foreign Language for Professional Communication (English)” by pre-service teachers of mathematics. The article presents the method of application of the ESP (English for specific purposes) e-textbook “Mathematics”, its structure, and functions. The ESP e-textbook includes learning material of three different levels of language proficiency. Each part contains professional vocabulary and speech situations aimed at forming the necessary skills and abilities in the mathematical field. The authors describe the stages of work with the learning material within a unit of the ESP e-textbook. The necessity of introduction of ESP e-textbooks in the process of learning a foreign language by pre-service teachers of mathematics is justified. The benefits of using the ESP e-textbook by students for autonomous learning are elucidated, and new opportunities for an ESP teacher to structure foreign language classes are outlined in the research. Experimental training with the use of the ESP e-textbook is conducted among pre-service teachers of mathematics at Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University (Ukraine), where the ESP e-textbook is considered as an element of the learning environment for professional creative activity and accumulation of knowledge in the conditions of autonomous learning. The study presents and analyzes the results of students’ English proficiency tests, and the survey on the students’ attitude to the use of ESP e-textbooks in the process of autonomous learning of ESP. The analysis of students’ surveys shows the effectiveness and expediency of using the e-textbook by pre-service teachers of mathematics in autonomous learning of English for professional communication. In the paper, some samples of educational activities from the ESP e-textbook “Mathematics” are presented.
The Practice and Application of AR Games to Assist Children’s English Pronunciation Teaching

This paper conducts an in-depth study and analysis of the practice and application of teaching English pronunciation to children using AR (augmented reality) game assistance. The article analyzes the design of AR educational games based on the concept of learning behavior input, clarifies and determines the factors influencing students’ learning behavior input in AR gamified teaching environment, and proposes the design principles of AR educational games based on learning behavior input. By analyzing the characteristics of experiential learning and AR, the model of AR-based English experiential learning activities for elementary school is established. Based on certain demand analysis, the AR software that can be used for teaching pronunciation in elementary school English classrooms was designed and developed using Unity3D and 3ds Max software. The experimental method was used to compare and apply the software in the target school, and the students of elementary school X were selected as the experimental subjects. The data were analyzed with the help of SPSS and NVivo software, and the questionnaire was used to measure the “ease of use,” “usefulness,” and “satisfaction with the software.” The questionnaire was used to measure students’ acceptance of AR software in three dimensions: “ease of use,” “usefulness,” and “satisfaction with the software.”
The THUEE System Description for the IARPA OpenASR21 Challenge

This paper describes the THUEE team's speech recognition system for the IARPA Open Automatic Speech Recognition Challenge (OpenASR21), with further experiment explorations. We achieve outstanding results under both the Constrained and Constrained-plus training conditions. For the Constrained training condition, we construct our basic ASR system based on the standard hybrid architecture. To alleviate the Out-Of-Vocabulary (OOV) problem, we extend the pronunciation lexicon using Grapheme-to-Phoneme (G2P) techniques for both OOV and potential new words. Standard acoustic model structures such as CNN-TDNN-F and CNN-TDNN-F-A are adopted. In addition, multiple data augmentation techniques are applied. For the Constrained-plus training condition, we use the self-supervised learning framework wav2vec2.0. We experiment with various fine-tuning techniques with the Connectionist Temporal Classification (CTC) criterion on top of the publicly available pre-trained model XLSR-53. We find that the frontend feature extractor plays an important role when applying the wav2vec2.0 pre-trained model to the encoder-decoder based CTC/Attention ASR architecture. Extra improvements can be achieved by using the CTC model finetuned in the target language as the frontend feature extractor.
Rancang Bangun Website Company Profile Berbasis Framework Boostrap dan Framework Codeigniter Pada Yayasan Khalifah Cendekia Mandiri

The Khalifah Cendekia Mandiri Foundation is located in Moncongloe District, Maros Regency, South Sulawesi Province. Having problems in terms of delivering information that still uses conventional methods, including the delivery of information by placing banners and through bulletin boards and brochures which are felt to have many shortcomings, the reach of information dissemination is not wide enough, the information content is limited, the process of updating information is not profitable either regarding cost and time. The research method used in this research is the RAD (Rapid Application Development) method. From the research conducted, namely designing a Company Profile Website based on the Boostrap Framework and the CodeIgniter Framework using the CSS Bootstrap Framework interface design, it has produced a website application that can facilitate data and information management as well as a form of convenience for the public to access information.
Legalization of live game streaming through statutory licence in China

Live game streaming depends on the use of audiovisual images of the games play, which may infringe the copyright of game developers. In recent years, there has been a surge in copyright litigation initiated by game developers against players/streamers in China. The courts needed to resolve several fundamental copyright issues in these cases, such as those pertaining to copyright subject matter, economic right, and copyright limitation. This article provides an in-depth exploration on copyright doctrines relevant to live game streaming industry, following by a proposal of new statutory licensing mechanism specifically for live game streaming. We argue that the proposal can properly balance various interests of different stakeholders in the game industry, such as incentive and proper reasonable compensation for developers, platforms’ dissemination of entertaining information to the audiences, and gamers’ development of professional skills. Under this mechanism, streamers would be allowed to stream games without permission and streaming platform operators would be automatically licensed for game streaming and obliged to remunerate game developers. Compared to the traditional “one-to-one” licensing, the proposed licensing scheme would effectively reduce transaction costs and improve licensing efficiency.
Morphology Within the Parallel Architecture Framework

The Parallel Architecture (PA) framework (Jackendoff 2002, 2007, Culicover &amp; Jackendoff 2005) is one of the most complete constraint-based linguistic theories that encompasses phonology, syntax and semantics. However, it lacks a fully developed model of word formation. More recently, a theory called Relational Morphology (RM) (Jackendoff &amp; Audring 2020) has been developed, that integrates into the PA. The current study shows how the Slot Structure model (Benavides 2003, 2009, 2010), which is compatible with the PA and is based on the dual-route model and percolation of features (Pinker 1999, 2006; Huang &amp; Pinker 2010), can provide a better account of morphology than RM, and can also be incorporated into the PA, thus contributing to make this a more explanatory framework. Spanish data are used as the basis to demonstrate the implementation of the SSM. The current paper demonstrates two key problems for RM: inconsistent and confusing coindexation, and a proliferation of schemas, and shows that these issues do not arise in the Slot Structure model. Overall, the paper points out significant drawbacks in the RM framework, while at the same time showing how the PA’s morphological component can be enriched with the Slot Structure model.
Task-Level Self-Supervision for Cross-Domain Few-Shot Learning

Learning with limited labeled data is a long-standing problem. Among various solutions, episodic training progres-sively classifies a series of few-shot tasks and thereby is as-sumed to be beneficial for improving the model’s generalization ability. However, recent studies show that it is eveninferior to the baseline model when facing domain shift between base and novel classes. To tackle this problem, we pro-pose a domain-independent task-level self-supervised (TL-SS) method for cross-domain few-shot learning.TL-SS strategy promotes the general idea of label-based instance-levelsupervision to task-level self-supervision by augmenting mul-tiple views of tasks. Two regularizations on task consistencyand correlation metric are introduced to remarkably stabi-lize the training process and endow the generalization ability into the prediction model. We also propose a high-order associated encoder (HAE) being adaptive to various tasks.By utilizing 3D convolution module, HAE is able to generate proper parameters and enables the encoder to flexibly toany unseen tasks. Two modules complement each other andshow great promotion against state-of-the-art methods experimentally. Finally, we design a generalized task-agnostic test,where our intriguing findings highlight the need to re-think the generalization ability of existing few-shot approaches.
ReX: An Efficient Approach to Reducing Memory Cost in Image Classification

Exiting simple samples in adaptive multi-exit networks through early modules is an effective way to achieve high computational efficiency. One can observe that deployments of multi-exit architectures on resource-constrained devices are easily limited by high memory footprint of early modules. In this paper, we propose a novel approach named recurrent aggregation operator (ReX), which uses recurrent neural networks (RNNs) to effectively aggregate intra-patch features within a large receptive field to get delicate local representations, while bypassing large early activations. The resulting model, named ReXNet, can be easily extended to dynamic inference by introducing a novel consistency-based early exit criteria, which is based on the consistency of classification decisions over several modules, rather than the entropy of the prediction distribution. Extensive experiments on two benchmark datasets, i.e., Visual Wake Words, ImageNet-1k, demonstrate that our method consistently reduces the peak RAM and average latency of a wide variety of adaptive models on low-power devices.
Unsupervised Identification of Abnormal Nodes and Edges in Graphs

Much of today’s data are represented as graphs, ranging from social networks to bibliographic citations. Nodes in such graphs correspond to records that generally represent entities, while edges represent relationships between these entities. Both nodes and edges in a graph can have attributes that characterize the entities and their relationships. Relationships are either explicitly known (like friends in a social network), or they are inferred using link prediction (such as two babies are siblings because they have the same mother). Any graph representing real-world data likely contains nodes and edges that are abnormal, and identifying these can be important for outlier detection in applications ranging from crime and fraud detection to viral marketing. We propose a novel approach to the unsupervised detection of abnormal nodes and edges in graphs. We first characterize nodes and edges using a set of features, and then employ a one-class classifier to identify abnormal nodes and edges. We extract patterns of features from these abnormal nodes and edges, and apply clustering to identify groups of patterns with similar characteristics. We finally visualize these abnormal patterns to show co-occurrences of features and relationships between those features that mostly influence the abnormality of nodes and edges. We evaluate our approach on datasets from diverse domains, including historical birth certificates, COVID patient records, e-mails, books, and movies. This evaluation demonstrates that our approach is well suited to identify both abnormal nodes and edges in graphs in an unsupervised way, and it can outperform several baseline anomaly detection techniques.
Quantum routing of few photons using a nonlinear cavity coupled to two chiral waveguides

We investigate few-photon scattering properties in two one-dimensional waveguides chirally coupled to a nonlinear cavity. The quantum states of scattered few photons are solved analytically via a real-space approach, and the solution indicates the few-photon reflection and transmission properties. When inputting two photons of equal energy to resonate with the cavity, the propagation characteristics of the two photons will be interesting, which is different from the previous anti-bunching effects with a quantum emitter. More importantly, when the total energy of the two incident photons equals the energy of a nonlinear cavity accommodating two photons, influence of the bound state will become larger to result in disappearance of antibunching effect. However, the bound state has no effect on probability of routing to another waveguide.
Capacitive eye tracker made of fractured carbon nanotube-paper composites for wearable applications

The uniqueness of eyes, facial geometry, and gaze direction makes eye tracking a very challenging technological pursuit. Although camera-based eye-tracking systems are popular, the obtrusiveness of their bulky equipment along with their high computational cost and power consumption is considered problematic for wearable applications. Noncontact gaze monitoring using capacitive sensing technique has been attempted but failed due to low sensitivity and parasitic capacitance. Here, we study the interaction between a novel capacitive sensor and eye movement for wearable eye-tracking. The capacitive sensors are made of a pair of asymmetric electrodes; one comprising carbon nanotube-paper composite fibers (CPC) and the other being a rectangular metal electrode. The interaction between the asymmetric sensor and a spherical object mimicking an eyeball is analyzed numerically. Using a face simulator, both single- and differential capacitive measurements are characterized with respect to proximity, geometry, and human body charge. Using a prototype eye tracker, multiple sensor locations are studied to determine the optimal configurations. The capacitive responses to vertical and horizontal gaze directions are analyzed in comparison to those of a commercial eye tracking system. The performance is demonstrated for sensitive eye-movement tracking, closed-eye monitoring, and human-machine interface. This research has important implications for the development of capacitive, wearable eye trackers, which can facilitate fields of human-machine interface, cognitive monitoring, neuroscience research, and rehabilitation.
A Hierarchical Framework for Drone Identification based on Radio Frequency Machine Learning

In recent years, drones have been widely used in many fields, but it also brings security threats. The detection and identification of drones, especially non-cooperative drones, is becoming a hot research topic. In this paper, we propose a hierarchical drone identification framework based on radio frequency machine learning. Our framework consists of two hierarchical stages: drone type classification and individual drone identification of the same type. In the stage of type classification, a specially designed convolutional neural network is trained to classify the signals from different types of drones. In the stage of individual identification, signals from the same type of drones are identified by estimating the pulse structure, reducing dimension through calculating fractal dimensions, and training the support vector machine classifier. In order to validate our approach, we collect and construct a dataset containing the real signals of seven DJI drones. The proposed framework achieves a classification accuracy of over 99% in both stages and performs well even at low signal-noise ratio levels.
Self-Guided Hard Negative Generation for Unsupervised Person Re-Identification

Recent unsupervised person re-identification (reID) methods mostly apply pseudo labels from clustering algorithms as supervision signals. Despite great success, this fashion is very likely to aggregate different identities with similar appearances into the same cluster. In result, the hard negative samples, playing important role in training reID models, are significantly reduced. To alleviate this problem, we propose a self-guided hard negative generation method for unsupervised person re-ID. Specifically, a joint framework is developed which incorporates a hard negative generation network (HNGN) and a re-ID network. To continuously generate harder negative samples to provide effective supervisions in the contrastive learning, the two networks are alternately trained in an adversarial manner to improve each other, where the reID network guides HNGN to generate challenging data and HNGN enforces the re-ID network to enhance discrimination ability. During inference, the performance of re-ID network is improved without introducing any extra parameters. Extensive experiments demonstrate that the proposed method significantly outperforms a strong baseline and also achieves better results than state-of-the-art methods.