Reviewerly/exHarmony huggingface dataset:
Reducing Geographic Disparities in Automatic Speech Recognition via Elastic Weight Consolidation

We present an approach to reduce the performance disparity between geographic regions without degrading performance on the overall user population for ASR. A popular approach is to fine-tune the model with data from regions where the ASR model has a higher word error rate (WER). However, when the ASR model is adapted to get better performance on these high-WER regions, its parameters wander from the previous optimal values, which can lead to worse performance in other regions. In our proposed method, we utilize the elastic weight consolidation (EWC) regularization loss to identify directions in parameters space along which the ASR weights can vary to improve for high-error regions, while still maintaining performance on the speaker population overall. Our results demonstrate that EWC can reduce the word error rate (WER) in the region with highest WER by 3.2% relative while reducing the overall WER by 1.3% relative. We also evaluate the role of language and acoustic models in ASR fairness and propose a clustering algorithm to identify WER disparities based on geographic region.
Application of online and offline mixed teaching mode in the teaching of Chinese modern and Contemporary Literature

The online teaching mode is becoming more and more mature with the development of Internet technology, but the online teaching mode also has some defects.Some experts put forward the online and offline mixed teaching mode.Therefore, this paper puts forward the application of online and offline mixed teaching mode in the teaching of Chinese modern and contemporary literature, puts forward the necessity of online and offline mixed teaching mode of Chinese modern and contemporary literature in the information-based education era, and expounds the application mode of online and offline mixed teaching mode in Chinese modern and contemporary literature, Design the online teaching link of Chinese modern and contemporary literature and the offline teaching link of Chinese modern and contemporary literature, optimize the assessment method of mixed teaching mode, change the traditional idea of respecting scores, and improve the teaching efficiency of Chinese modern and contemporary literature.
Design considerations for digital learning during the COVID-19 pandemic

The COVID-19 pandemic has highlighted the value of digital technology – in preserving work, play and learning – at a time when physical interactions are constrained. Given the challenges involved in the sudden conscription towards digital teaching and learning, the reception of both teachers and students towards the adoption of digital technologies for home-based teaching and learning has been mixed. While appreciating the affordances of digital technologies in supporting the continuity of teaching during the lockdowns, concerns have been raised by teachers on the effectiveness of digital learning. Our chapter discusses the teacher’s use of semiotic technologies to design learning experiences for students. From the perspective of design considerations, we explore the different ways of meaning making in digital learning – in particular, how knowledge can be represented, pedagogic relations expressed and learning organised through the affordances of semiotic technologies. We do this by studying three types of semiotic technologies, namely, the ubiquitous video lectures, digital games for learning and social media, specifically in its appropriation as a learning platform. Our chapter concludes by reflecting on losses and gains in digital learning and the need for teachers to expand their pedagogical repertoire in the post-pandemic education normal.
Board Game “Making Financial Decisions” in the System of Teaching the Mathematical Foundations of Financial Literacy

The aim of this article is to present the theoretical model for the construction of tasks in improving financial literacy in algebra course at school and to submit the economic and mathematical game based on the developed model. At the state level, a number of important documents have been adopted on the formation of financial literacy of schoolchildren, various measures are being implemented to solve this problem. Despite this, today the tasks presented in the school mathematics course do not fully solve the issue of increasing the level of financial literacy. In the course of work on the creation of a theoretical model for Teaching mathematical foundations of financial literacy, an analysis of Russian textbooks on mathematics was carried out, federal regulations and international standards were studied. Our analysis allows us to assert that there are not enough tasks for the formation of financial literacy skills in the school course of mathematics. In addition, these tasks form initial knowledge in the financial sector. Also, the tasks are of the same type and often affect only two areas of handling finance. These are purchases and loans. Also, the tasks of the school mathematics course correspond only to the first level of the PISA standard. Thus, it became obvious that it was necessary to develop a theoretical model for the construction of tasks in improving financial literacy in algebra course at school according with the System (frame) financial capability for school age pupils and PISA diagnostic model.
Leveraging Fully-decoupled Radio Access Network for Wireless Federated Learning Acceleration

Federated learning (FL) has emerged as an innovative machine learning (ML) paradigm that can utilize the computation capability of both the cloud and the end-users to support data-intensive tasks in the next-generation mobile communication networks. However, the collaboration between the cloud and the end-users is usually constrained by the worst wireless link quality during the uplink and downlink transmission. In this paper, targeting at reducing the FL training latency over the wireless networks, we leverage the uplink and downlink fully-decoupled radio access network (FD-RAN) architecture through the coordinated multiple base stations (BSs) access mechanism. First, based on a proven data rate lower bound, we exploit the integer variable relaxation and the successive convex approximation (SCA) algorithms to transform the original computationally prohibitive non-convex problem into a solvable convex form. Subsequently, the Lagrange dual decomposition method is used to obtain an optimal BS serving cluster (OSC) for the uplink and downlink transmission respectively. Extensive simulations are conducted to verify the effectiveness of the proposed coordinated multiple BSs access solution for realizing a faster FL training task.
Boundary-Based Fake Face Anomaly Detection in Videos Using Recurrent Neural Networks

With the surge in the videos over the internet and the spread of digital communications, the attackers are greatly benefited by creating and publishing the fake distorted videos and images known as Deepfakes. The recent trends, Machine Learning powered applications are highly implied to create deepfakes that leads to fallacious arguments. To detect such an instance, there are several existing techniques that uses convolutional neural networks and machine learning methods which are limited to identify the larger details of the deepfake created. We propose a novel anomaly detection technique for face areas in videos using recurrent neural networks based on boundary marking and hashing technique which is efficient in detecting the deepfakes over a smaller face boundary regions. The proposed method is evaluated, and the results show promising performance for deepfake detection in the face area based on the boundaries. Our proposed method revealed good accuracy in detecting the smaller frame 8 $$\times $$ 8 raster based anomalies in videos.
Preparation and Analysis of Two-Dimensional Four-Qubit Entangled States with Photon Polarization and Spatial Path

Entanglement states serve as the central resource for a number of important applications in quantum information science, including quantum key distribution, quantum precision measurement, and quantum computing. In pursuit of more promising applications, efforts have been made to generate entangled states with more qubits. However, the efficient creation of a high-fidelity multiparticle entanglement remains an outstanding challenge due to the difficulty that increases exponentially with the number of particles. We design an interferometer that is capable of coupling the polarization and spatial paths of photons and prepare 2-D four-qubit GHZ entanglement states. Using quantum state tomography, entanglement witness, and the violation of Ardehali inequality against local realism, the properties of the prepared 2-D four-qubit entangled state are analyzed. The experimental results show that the prepared four-photon system is an entangled state with high fidelity.
Deep Named Entity Recognition in Hindi Using Neural Networks

In Natural Language Processing, named entity recognition (NER) is a task of (NLP) that tries to automatically identify and annotate Named Entities in text, such as people, places, and organisations. We employ a deep learning-based architecture in this work to solve the problem of recognising named entities in a Hindi text phrase. In the literature, approaches based on bidirectional long short-term memory (BiLSTM) have been utilized for the NER task. We performed recursive BiLSTM in this study, which includes a de-noising autoencoder with conditioning logic. Experiments were undertaken to examine the behaviour of individual word embeddings and batch sizes, which is vital for training deep models. The findings of the suggested system architecture, as well as a comparison of performance characteristics with existing systems, are presented in this study.
Forward projection multilayer refraction model for underwater camera calibration

Unlike imaging in air, underwater imaging is affected by two distinct properties of water: the absorption and scattering effect of water and the refraction effect of water, resulting in the conventional pinhole model no longer being suitable for underwater imaging. Therefore, without prior calibration in air to calculate the camera intrinsic parameters and distortions for subsequent underwater calibration, an underwater multilayer refraction model based on forward projection method is represented. Along the direction of light propagation, the multilayer refraction model presents a unified scale factor instead of focal length during the perspective projection transformation from the camera coordinate system to the image coordinate system. Simulations regarding the analysis of different medium thicknesses, different medium refractive indices, and different point pixels are performed to explore the influence of the above three parameters on the refraction model. Through the underwater parameter identification and verification, the proposed multilayer refraction model has high measurement accuracy, excellent solution efficiency, stable identification results, more identification parameters, and wide application fields, which are suitable for both single-time refraction and double-time refraction.
Anomaly Detection for Monte-Carlo Simulation Data of Missile Control System

In this paper, we propose an anomaly detection method for missile simulation data utilizing a clustering-based algorithm. In the proposed method, to assess the performance of the control system, the parameters related to the missile’s guidance and control are selected from simulation data for the automated detection of anomalous cases. Then, the selected data is sampled at fixed intervals to match the analysis scope and reject unnecessary information. The feature points for each parameter are chosen in a way that emphasizes abnormal patterns in time series data. Finally, the feature vector of each data sample is formed by combining all feature points. In data analysis, DBSCAN, an unsupervised clustering algorithm, is applied to detect abnormal simulation data in the feature space. In this work, the proposed method was tested with a 6DOF surface-to-surface guided-missile simulation dataset. The results indicate that the proposed method can be used to automatically identify simulation data that reflects abnormal behavior in the control history, which implies the method’s potential to assist the process of finding critical simulation data without predefined criteria or training data.
Defect corrections for coherent optical information processing of grayscale images in a DMD-based 4f-system using a collimated light source

Digital micromirror device (DMD)-based 4f-systems, a type of coherent optical information processing system, have become a powerful tool for optical convolutional neural networks taking advantage of their fast modulation speed and high-resolution capability. However, proper high bit-depth image information processing remains challenging due to the optical diffractions that arise from the binary nature of DMD operation. In this paper, we first characterize the diffraction phenomena that cause irradiance defects, namely the nonlinear grayscale and unintended dark lines. Then to resolve the issues, we propose a DMD operation method and a modified structure of the 4f-system based on blazed diffraction grating theory and numerical calculation of the Rayleigh–Sommerfeld propagation model. As a demonstration, we implement high bit-depth image information processing with an optimized optical 4f-system using DMDs and a collimated coherent light source.
The structure and algorithm of the intelligent decision support system for the identification of cultural values

The article discusses the algorithm of the work of an intelligent decision support system for identifying cultural values and the user with it, the issues of determining the main scenarios of work and restrictions. The author provides the expediency of dividing the software implementation of the system into separate modules, which will make the process of scaling the system less labor-intensive and more efficient. A set of tools for solving the problem is presented, where a decision is made as such. Moreover, for the identification and analysis of different types of cultural property objects, different types of neural networks can be provided. It also notes the complexity and complexity of the approach to solving the problem, certain prospects and ways of further research of this subject area. The use of the system specified in the article, built on the basis of neural networks and modern software and hardware, will allow the user, in conditions of uncertainty and in conditions of limited time, to receive additional help in making decisions in identification and CC, the use of a system of this type is relevant and promising. The approach proposed by the author to the creation of this system is convenient and effective, has a powerful potential for further development and improvement of the system. The combination of databases and knowledge in one system, together with a powerful mathematical and analytical apparatus, will allow reaching a higher level of identification and analysis of objects of cultural property in comparison with conventional databases and catalogs. The conclusions set tasks for solving organizational, legal and technical issues related to the implementation and operation of the system.
SELF-SHRINKING CHAOS BASED PSEUDO-RANDOM ALGORITHM

We propose a novel self-shrinking chaos based pseudo-random number output algorithm. The result of the analysis shows that the presented generator ensures a secure way for sending electronic information with critical applications in data encryption.
A Dynamic Grid-based Q-learning for Noise Covariance Adaptation in EKF and its Application in Navigation

The process and measurement noise covariance matrices significantly impact the Extended Kalman Filter (EKF) performance and are often hand-tuned in practice, which usually entails a tedious task. Q-learning, a well-known method in reinforcement learning, has been applied recently to better adapt the noise covariance matrices for the EKF, thanks to its simplicity and capability in handling uncertain environments. Typically, some heuristics are involved in designing the Q-learning-based EKF (QLEKF), such as tuning grid size and covariance matrices values of each state, which inevitably degrades the estimation performance when the heuristics are not suitable. We propose a dynamic grid-based Q-learning EKF (DG-QLEKF) to overcome that drawback, which brings two novelties, an updated ϵ-greedy algorithm and a dynamic grid strategy. The proposed algorithm and strategy can thoroughly exploit arbitrary search scope and find appropriate values of noise covariance matrices. The effectiveness of DGQLEKF, applied in navigation for attitude and bias estimation, is validated through the Monte Carlo method and real flight data from an unmanned aerial vehicle. The DG-QLEKF leads to much more improved state estimation than the QLEKF and traditional EKF.
Extensible Conflict-Free Replicated Datatypes for Real-time Collaborative Software Engineering

Real-time collaboration has become a prominent feature of nowadays' software engineering practices.Conflictfree replicated data types (CRDT) offer efficient mechanisms for implementing real-time collaborative environments.However, the lack of extensibility of CRDT limits their applicability.This is a particularly important problem in settings relying on complex, non-linear data types.In this paper, we report our results in augmenting primitive CRDT with extension mechanisms.We demonstrate our technique through an example from the realm of model-driven engineering, where graph types are prevalent.
Twitter Sentiment Analysis Using Machine Learning and Deep Learning

Twitter is the widely used microblogging site, where millions of people share their feelings, views, or opinion regarding different things be it a product, service, or events. Huge volumes of data are being produced hourly because of the increase in the number of users. These data are unstructured in nature, and thus, it is a difficult task to analyze them and extract the meaning from it. This paper, however, will concentrate on sentiment analysis of Twitter data. We will perform text mining or opinion mining to obtain a better understanding of public sentiment. In this paper, Python is used to acquire, preprocess, and analyze tweets, after those three different machine learning algorithms and one deep learning algorithm are used for sentiment analysis and comparison is done among them to determine which approach or model gives the best accuracy.
Recursive comparison tests for dicot and dead-ending games under misère play

In partizan games, where players Left and Right may have different options, there is a partial order defined as preference by Left: G ⩾ H if Left wins G + X whenever she wins H + X for any game position X. In normal play, there is an easy test for comparison: G ⩾ H if and only if Left wins G−H playing second. In misère play, where the last player to move loses, the same test does not apply-for one thing, there are no additive inverses-and very few games are comparable. If we restrict the arbitrary game X to a subset of games u, then we may have G ⩾ H “modulo U”; but without the easy test from normal play, we must give a general argument about the outcomes of G + X and H + X for all X ∈ U. In this paper, we use the novel theory of absolute combinatorial games to develop recursive comparison tests for the well-studied universes of dicots and dead-ending games. This is the first constructive test for comparison of dead-ending games under misère play using a new family of end-games called perfect murders.
A Category Theoretic View of Contextual Types: From Simple Types to Dependent Types

We describe the categorical semantics for a simply typed variant and a simplified dependently typed variant of Cocon , a contextual modal type theory where the box modality mediates between the weak function space that is used to represent higher-order abstract syntax (HOAS) trees and the strong function space that describes (recursive) computations about them. What makes Cocon different from standard type theories is the presence of first-class contexts and contextual objects to describe syntax trees that are closed with respect to a given context of assumptions. Following M. Hofmann’s work, we use a presheaf model to characterise HOAS trees. Surprisingly, this model already provides the necessary structure to also model Cocon . In particular, we can capture the contextual objects of Cocon using a comonad ♭ that restricts presheaves to their closed elements. This gives a simple semantic characterisation of the invariants of contextual types (e.g. substitution invariance) and identifies Cocon as a type-theoretic syntax of presheaf models. We further extend this characterisation to dependent types using categories with families and show that we can model a fragment of Cocon without recursor in the Fitch-style dependent modal type theory presented by Birkedal et al.
DCAF-BERT: A Distilled Cachable Adaptable Factorized Model For Improved Ads CTR Prediction

In this paper we present a Click-through-rate (CTR) prediction model for product advertisement at Amazon. CTR prediction is challenging because the model needs to a) learn from text and numeric features, b) maintain low-latency at inference time, and c) adapt to a temporal advertisement distribution shift. Our proposed model is DCAF-BERT, a novel lightweight cache-friendly factorized model that consists of twin-structured BERT-like encoders for text with a mechanism for late fusion for tabular and numeric features. The factorization of the model allows for compartmentalised retraining which enables the model to easily adapt to distribution shifts. The twin encoders are carefully trained to leverage historical CTR data, using a large pre-trained language model and cross-architecture knowledge distillation (KD). We empirically find the right combination of pretraining, distillation and fine-tuning strategies for teacher and student which leads to a 1.7% ROC-AUC lift over the previous best model offline. In an online experiment we show that our compartmentalised refresh strategy boosts the CTR of DCAF-BERT by 3.6% on average over the baseline model consistently across a month.
Named entities detection by beam search algorithm

ABSTRACT Named entity recognition (NER) is a fundamental process in NLP and a requirement for most processes. This article aims to identify the named entities in the context of social networks. For this purpose, the idea of segmenting text into suitable and unsuitable expressions for the named entities has been used. So the contribution of this article is to process informal text in the Persian language by the Beam search algorithm to detect named entities. Due to the reproductive nature of language, new words and names are always produced, and available NER systems are inefficient in detecting new entities. The other contribution of this article is to make it possible to recognize the emerging named entity by applying dynamic external knowledge. According to a sense of the lack of datasets in low‐resource languages, N‐Gram and Wikipedia anchor datasets have been prepared for Persian and deployed as external knowledge. Also, a corpus of named entities in Persian from the telegram dataset has been generated. Three native experts have done labeling of this corpus. Evaluation of these three experts and the proposed method shows that the result of the proposed method is acceptable compared to the result of a human‐to‐human also to other methods.