Reviewerly/exHarmony huggingface dataset:
Self-conditioning via Intermediate Predictions for End-to-end Neural Speaker Diarization

This paper presents a speaker diarization model that incorporates label dependency via intermediate predictions. The proposed method is categorized as an end-to-end neural diarization (EEND), which has been a promising method for solving the speaker diarization problem with a multi-label classification neural network. While most EEND-based models assume conditional independence between frame-level speaker labels, the proposed method introduces the label dependency to the models by exploiting the self-conditioning mechanism, which has been originally applied to an automatic speech recognition model. With the self-conditioning mechanism, speaker labels are iteratively refined by taking the whole sequence of intermediate speaker labels as a reference. We demonstrate the effectiveness of self-conditioning in both Transformer-based and attractor-based EEND models. To efficiently train the attractor-based EEND model, we propose an improved attractor computation module named non-autoregressive attractor, which produces speaker-wise attractors simultaneously in a non-autoregressive manner. The experiments with the CALLHOME two-speaker dataset show that the proposed self-conditioning boosts the diarization performance and progressively reduces errors through successive intermediate predictions. In addition, the proposed non-autoregressive attractor improves training efficiency and provides a synergetic boost with self-conditioning, leading to superior performance compared with existing diarization models.
ps-CALR: Periodic-Shift Cosine Annealing Learning Rate for Deep Neural Networks

There are continued efforts to build on the performance of deep learning (DL) models in various fields of application. Developing new DL models continues to open unprecedented opportunities in diverse application areas despite the enormous resources required. Generally, the learning mechanism of DL models depends on the term "cost function" (CF) or "loss function" (LF), and DL models require varied hyperparameter settings and, precisely, parameters that can help the model to continually minimize the cost function until faster convergence, with better generalization over the data in the loss landscape, is assumed. The learning rate (LR) update seeks to find the optimal solution for DL models through relative cost function minimization. Therefore, selecting the appropriate LR is essential to the performance of DL models. The existing cosine annealing LR, despite its demonstration for fast model convergence, lacks complete loss landscape exploration of the flat minima, hence limiting its ability to model better generalization. To address this, the paper proposes a period-shift Cosine Annealing Learning Rate with warm-up epochs (ps-CALR) to perturb the LR update. Six different publicly available datasets were used to benchmark the proposed LR method by experimenting with custom DL (multilayer perceptron and convolutional neural networks) and pre-trained DL models. The proposed ps-CARL enhances model generalization and convergence, pushing the solution to notably better performance compared with fixed LR and the existing cosine annealing method.
UAV Control for Close Tracking of a Flying Object Using Search Region Focused Detector and Target-Visibility Enhancing Control

As drones become more prevalent, their malicious use threatens public safety. To reduce this threat, an unmanned aerial vehicle (UAV) that can chase unknown drones is required. However, visual detection for the chasing flight is challenging because the target objects can be small and fly at high speed. Additionally, if the background in the UAV vision is similar to the color of the flying object, detection becomes more difficult. Thus, this paper presents a new UAV system to effectively detect and chase randomly flying objects. First, the detecting performance of YOLOv8 was improved by introducing a search region (SR) technique, which can reduce the probability of detection failure and increase the inference speed. Second, the UAV is controlled to visually capture its target from a different field of view to improve target visibility. If the target object is less noticeable within the image background, the UAV moves to change its position with respect to the target considering the covariance value of the target and the background pixels. The performance of the proposed control system was validated with outdoor flight experiments, where the UAV successfully chased an object that was flying randomly at a speed of 5 m/s with an average distance error of 0.66 m.
Analyzing the Impact and Prospects of Metaverse in Learning Environments through Systematic and Case Study Research

Recent years have witnessed a remarkable technology shift with immersive technology applications like Metaverse across industries and sectors. As 3D-based virtual reality environments, these applications create digital pairs of the physical world and provide an immersive experience to users with technological competencies supporting daily and social activities by means of their virtual avatars and with real-virtual environments and human-machine interactions. Following the COVID-19 pandemic, educational systems are actively engaged in integrating modern and emerging technologies with traditional teaching methods. The pandemic has significantly altered the landscape of education by introducing a new era of virtual environments and interactions, which has placed new demands on learning settings. Immersive technologies have added new dimensions to the learning process. This current study delves into the feasibility of incorporating immersive technologies, such as the Metaverse, into education, and it assesses the overall academic experience for both educators and learners. The primary aim is to investigate whether immersive technologies are a suitable choice for contemporary and future digital learning environments. To achieve this, the study conducts: a) a thorough examination of existing knowledge through systematic literature research, b) analysis of case studies from selected educational institutions, and c) an exploration of the benefits, challenges, and potential of the Metaverse in various learning environments. The findings endorse the promising opportunities that the Metaverse and other immersive technologies offer for present and future educational innovations, as well as interdisciplinary research possibilities.
Research on the classification method of CTC alignment joint-test problems based on RoBERTa-wwm and deep learning integration

A text classification method based on RoBERTa-wwm and deep learning integration is proposed for fast classification of the massive unstructured problematic text data generated during the alignment joint-test of CTC (Centralized Traffic Control). Firstly, 10 common types of problems were summarized based on the statistical results of the problems from the alignment joint-test of CTC generated between 2011 and 2021; Secondly, in the text classification model, the pre-trained model RoBERTa-wwm is used to capture the semantic features of words in the problem text; Building an integrated model for deep learning based on BiLSTM-BiGRU and CNN to fully learn the deep hidden information in texts; Combining the principles of BiLSTM and BiGRU based on combined weight calculation to maximize performance; Normalization by Softmax function yields classification results for the CTC JCT problem. Finally, experimental validation is performed using data from CTC alignment joint-test problems generated during the last decade, The experimental results show that compared with several existing typical pre-trained models and classifier combinations, the proposed method in this paper achieves better results in terms of accuracy, precision, recall and F1 values, reaching 0.9317, 0.9322, 0.9317 and 0.9318, respectively, and the final Loss is only 0.24.
Radio Frequency Public Key Generator for Digital Cryptographic Application

In Internet of Things (IoT) environments, effective public key management is crucial for managing numerous devices. RF features, primarily considered analog features within physical layer authentication by RF Fingerprinting (RFF) processes, present a novel approach to key management. In this research, we introduce a novel RF-based Public Key Generator (RF-PubKG) model that maps RF features into cryptographic sequences by incorporating a Key Generation (KeyGen) layer into the RFF model. The RF-PubKG demonstrates superior performance, achieving 97.2% accuracy at a 20dB SNR and further improving to 99.6% in noise-free conditions with a Frame Error Rate (FER) below 1%. The generated public key sets exhibit negligible correlation, with intra-key-set correlations not exceeding 0.24 and inter-model correlations falling below 0.04, highlighting the reliability of the RF-PubKG model. The integration with the Rivest–Shamir–Adleman (RSA) algorithm provides proof-of-concept for the RF-PubKG-based digital signature scheme, effectively simplifying the Certificate Authorities (CAs) management and, consequently, reducing Public Key Infrastructure (PKI) complexity. This simplification promises effective public key management within the Public Key Cryptography (PKC), thereby enhancing the efficiency of digital signature verification processes.
Combining social relations and interaction data in Recommender System with Graph Convolution Collaborative Filtering

A recommender system is an important subject in the field of data mining, where the item rating information from users is exploited and processed to make suitable recommendations with all other users. The recommender system creates convenience for e-commerce users and stimulates the consumption of items that are suitable for users. In addition to e-commerce, a recommender system is also used to provide recommendations on books to read, movies to watch, courses to take or websites to visit. Similarity between users is an important impact for recommendation, which could be calculated from the data of past user ratings of the item by methods of collaborative filtering, matrix factorization or singular vector decomposition. In the development of graph data mining techniques, the relationships between users and items can be represented by matrices from which collaborative filtering could be done with the larger database, more accurate and faster in calculation. All of these data can be represented graphically and mined by today’s highly developed graph neural network models. On the other hand, users’ social friendship data also influence consumption habits because recommendations from friends will be considered more carefully than information sources. However, combining a user’s friend influence and the similarity between users whose similar shopping habits is challenging. Because the information is noisy and its affects each particular data set in different ways. In this study, we present the input data processing method to remove outliers which are single reviews or users with little interaction with the items; the next proposed model will combine the social relationship data and the similarity in the rating history of users to improve the accuracy and recall of the recommender system. We perform a comparative assessment of the influence of each data set and calculation method on the final recommendation. We also propose and implement a model and compared it with base line models which include NGCF, LightGCN, WiGCN, SocialLGN and SEPT.
A New Method for Short Text Compression

In this study, a method calledWord-based Static Dictionary Compression (WSDC), which can compress short texts at a high ratio, and a model that uses iterative clustering to create static dictionaries used in this method are proposed. The number of static dictionaries to be created can vary by running the k-Means clustering algorithm iteratively according to some rules.Wikipedia article abstracts consisting of 5 different text size groups and 6 different languages were used as dataset. The developed WSDC method is compared with both general-purpose compression methods (Gzip, Bzip2, PPMd, Brotli and Zstd) and special methods used for compression of short texts (shoco, b64pack and smaz). According to the test results, it has been seen that WSDC gives better compression ratios than other compression methods, especially for very short texts.
NS-IL: Neuro-Symbolic Visual Question Answering using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes.

This paper is motivated by the challenge of providing accurate and contextually relevant answers to natural language questions about visual scenes, particularly in support of individuals with visual impairments. We present a system that is capable of incrementally learning both visual concepts and symbolic facts to answer natural language questions about visual scenes via rich concepts. Deep neural networks are used to learn a feature space from which visual classes are learned as independent probability distributions, allowing new classes to be added arbitrarily with small sample sizes and without the risk of catastrophic forgetting associated with traditional neural networks. Visual classes are not limited to object labels, but also include visual attributes. A knowledge graph is used to represent facts about objects, such as their actions, locations and the relationships between different objects. This allows facts to be stored explicitly and added incrementally. A large language model is used to translate between natural language questions and knowledge graph traversal queries, providing a natural visual question answering process.
Analyzing Component Composability of Cloud Security Configurations

Security is a major concern when building large-scale computer systems. Cloud services have made it easier to provision large-scale systems on demand over the Internet. While the cloud service providers provide the required building blocks or components such as compute units, database servers, and storage, customers are still responsible for securely combining these systems to satisfy their organization’s security policy. The secure development and operation of such large-scale systems present technical challenges. Composing a larger system using components with known security properties that satisfy a given security policy without re-analyzing the individual components is a difficult problem. In this study, we attempted to analyze the composability of components from a security perspective using first-order predicate logic. We posit that if we build a system using individual components that satisfy a security policy, the composed system will be sound with regard to that policy. Additionally, the methodology can be used to identify drifts or violations during future changes in the system by running checks during the system release cycles for continuous verification.
Self-organizing Multiple Readouts for Reservoir Computing

With advancements in deep learning (DL), artificial intelligence (AI) technology has become an indispensable tool. However, the application of DL incurs significant computational costs, making it less viable for edge AI scenarios. Consequently, the demand for cost-effective AI solutions, other than DL-based approaches, is increasing. Reservoir computing (RC) has attracted interest owing to its ability to provide low-cost training alternatives, holding great promise for edge AI applications. However, the training capability of RC is constrained by its reliance on a single linear layer, while weight connections in the remaining layers remain static during training. Moreover, accomplishing continuous learning tasks is difficult owing to the catastrophic forgetting in the linear layer. Therefore, we propose the integration of self-organizing multiple readouts to enhance RC’s training capability. Our method distributes training data across multiple readouts, which prevents catastrophic forgetting of readouts and empowers each readout to adeptly assimilate new data, thereby elevating the overall training performance. The self-organizing function, which assigns similar data to the same readout, optimizes the memory utilization of these multiple readouts. Experimental results show that an RC equipped with the proposed multiple readouts successfully solved a continuous learning task by mitigating catastrophic forgetting because of the data distribution to the multiple readouts. Additionally, the RC achieved higher accuracy in a sound recognition task compared with the existing RC paradigm because of ensemble learning in the multiple readouts. Multiple readouts are effective in enhancing the training capability of RC and can contribute to the realization of RC applications.
Extracting fallen objects on the road from accident reports using a natural language processing model-based approach

Keyword extraction is an effective way to quickly identify key elements in text. It can accelerate the identification of key factors that play a role in accidents when applied to incident report analysis. Our research presents an innovative process for extracting keywords from incident reports with the pre-trained natural language processing models. We utilized fine-tuning techniques to integrate a BiLSTM-CRF with a fully-connected layer and pre-trained natural language models. The process of extracting keyphrases is approached as a task of labeling sequences. To analyze incident reports from Korea, we employ pre-trained models customized for the Korean context, such as KoBERT and KoELECTRA. Our approach is assessed using a range of metrics, including accuracy, area under the curve (AUC), F1-score, slot error rate (SER), and simple matching coefficient (SMC). In contrast to traditional approaches which mainly concentrate on document summarization, our research provides a distinct method tailored to identifying falling objects as the main cause of accidents. Our findings demonstrate that the ELECTRA-based model with a BiLSTM-CRF outperforms other models, achieving an accuracy of 0.943, an AUC of 0.991, and a low SER of 0.075. The F1-score and SMC closely resemble the BERT-based model with a BiLSTM-CRF, with no significant differences observed within the 95% confidence interval. These results underscore the potential of fine-tuning pre-trained models for post-hoc traffic accident analysis. This method offers a swift preliminary step to identify the key factors before human analysis, presenting a multifaceted strategy to enhance road safety and prevent accidents.
Particle Filtering On Lie Group for Mobile Robot Localization With Range-Bearing Measurements

This paper proposes a particle filtering method for mobile robot localization. The system model is created based on the robot motion and range-bearing measurements using the Lie group representation, and the perturbation system is obtained by introducing the random variable on the Lie group in order to solve the state estimation problem and reduce impacts from nonlinear approximations. The particle filter is derived to estimate the robot and landmark locations, where the posterior distribution of the robot pose perturbation is approximated by a mixture of Gaussian distributions to mitigate particle impoverishment and improve localization accuracy. The experiment is conducted to evaluate the performance of the proposed method.
Algorithmic Enforcement Tools: Governing Opacity with Due Process

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Challenges to Adopt Blockchain Technology in the Gulf Cooperation Council Countries

This study examines the challenges face blockchain technology adoption by companies operate in Gulf Cooperative Council Countries (GCC). The qualitative approach was utilized to understand the phenomenon. Accordingly, 38 interviews have been conducted with experts, businesses senior managers, professionals and analysts to deeply inquire into the reasons behind reluctance of GCC companies to adopt blockchain technology. The present study adds an original contribution to theory by concluding some very interesting information that facilitates the understanding of the challenges that prevent companies operate in GCC region from benefiting from blockchain technology. These basically come under three dimensions, namely cultural reasons, economic feasibility, and vagueness of the regulatory environment of blockchain technology and its future. This study provides some implications for industry and policymakers to help them to provide venue for proper implementation of this promising technology.
Handwritten Text Conversion By Using ANN Approach

In this paper, we used the artificial Intelligent based technique for handwritten. The hand written Handwritten to text conversion is a technique much required in the new era of technology for storing and processing handwritten text, documents, notes, etc. This initiates the opportunity for embossing of a variety of techniques and algorithms for implementing text conversion. To be as simplified as it could so as to be understood pictorially, the neural network techniques are the far most significant and vastly used techniques for the implementation of handwritten text conversion. This paper objective is to understand and implement artificial neural network and deep learning techniques on handwritten text to convert it to a machine text with a simpler understandable code. For processing the handwritten text the input is taken in the form of pictures of the written text and using python image processing with the famous and convenient convolutional neural networks. while implementing the ANN, processing can be easily broken down into easier steps.
Creating and Implementing a Teleneurology Simulation: Preparing Trainees for the Virtual World (1967)

Saturday, April 17April 13, 2021Free AccessCreating and Implementing a Teleneurology Simulation: Preparing Trainees for the Virtual World (1967)Rebecca Stainman, Steve Han, Sondra Zabar, Neil Busis, and Arielle KurzweilAuthors Info & AffiliationsApril 13, 2021 issue96 (15_supplement)https://doi.org/10.1212/WNL.96.15_supplement.1967 Letters to the Editor
WAS: improved white-box cryptographic algorithm over AS iteration

Abstract The attacker in white-box model has full access to software implementation of a cryptographic algorithm and full control over its execution environment. In order to solve the issues of high storage cost and inadequate security about most current white-box cryptographic schemes, WAS, an improved white-box cryptographic algorithm over AS iteration is proposed. This scheme utilizes the AS iterative structure to construct a lookup table with a five-layer ASASA structure, and the maximum distance separable matrix is used as a linear layer to achieve complete diffusion in a small number of rounds. Attackers can be prevented from recovering the key under black-box model. The length of nonlinear layer S and affine layer A in lookup table is 16 bits, which effectively avoids decomposition attack against the ASASA structure and makes the algorithm possess anti-key extraction security under the white-box model, while WAS possesses weak white-box (32 KB, 112)-space hardness to satisfy anti-code lifting security. WAS has provable security and better storage cost than existing schemes, with the same anti-key extraction security and anti-code lifting security, only 128 KB of memory space is required in WAS, which is only 14% of SPACE-16 algorithm and 33% of Yoroi-16 algorithm.