Reviewerly/exHarmony huggingface dataset:
Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Models

Although over 300M around the world speak Bangla, scant work has been done in improving Bangla voice-to-text transcription due to Bangla being a low-resource language. However, with the introduction of the Bengali Common Voice 9.0 speech dataset, Automatic Speech Recognition (ASR) models can now be significantly improved. With 399hrs of speech recordings, Bengali Common Voice is the largest and most diversified open-source Bengali speech corpus in the world. In this paper, we outperform the State-of-the-Art (SOTA) pretrained Bengali ASR models by finetuning a pretrained wav2vec2 model on the common voice dataset. We also demonstrate how to significantly improve the performance of an ASR model by adding an n-gram language model as a post-processor. Finally, we do some experiments and hyperparameter tuning to generate a robust Bangla ASR model that is better than the existing ASR models.
Comparison of Distance Metrics for Generating Cluster-based Ensemble Learning

The basis of ensemble learning is using multiple learning algorithms to improve predictive performance compared to individual learners. Behind the various advantages of ensemble learning, there are several issues that need attention, one of which is related to finding a set of diverse base learners. Recently, clustering has been used to generate diverse base learners as opposed to bagging. The main advantages of cluster-based ensemble learners are their robustness and versatility. The key parameters for implementing a clustering algorithm are the cluster size and distance metrics. The contribution of this study is to compare four distance metrics, including the Euclidean, Manhattan, Chebyshev, and Canberra distances, in the clustering method for ensemble generation and evaluate them based on accuracy, purity, and diversity. The methodology is tested on 10 benchmark UCI datasets. The results show that the use of the Chebyshev and Canberra distances achieved superior accuracy to both the Euclidean and Manhattan distances, while the purity and diversity values of the use of the Chebyshev distance outperformed the other three.
The Hull of Two Classical Propagation Rules and Their Applications

In this work, we study and determine the dimensions of Euclidean and Hermitian hulls of two classical propagation rules, namely, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(u,u+v)$ </tex-math></inline-formula> -construction and the direct sum construction. Some new criteria for the resulting codes derived from these two propagation rules being self-dual, self-orthogonal, or linear complementary dual (LCD) codes are given. As applications, we employ the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(u,u+v)$ </tex-math></inline-formula> -construction to obtain (almost) self-orthogonal codes; employ the direct sum construction to provide lower bounds on the minimum distance of FSD (LCD) codes; and employ both these two constructions to derive linear codes with prescribed hull dimensions. Many (almost) optimal codes are presented. In particular, a family of binary almost Euclidean self-orthogonal Griesmer codes is constructed. We also obtain many binary, ternary Euclidean and quaternary Hermitian FSD LCD codes of larger lengths and improve some lower bounds on the minimum distance of known ternary Euclidean LCD codes.
Segment Anything Model for Semi-Supervised Medical Image Segmentation via Selecting Reliable Pseudo-Labels

Download This Paper Open PDF in Browser Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
New Approach for Sine and Cosine in Secure Fixed-Point Arithmetic

In this paper we present a new class of protocols for the secure computation of the sine and cosine functions. The precision for the underlying secure fixed-point arithmetic is parametrized by the number of fractional bits f and can be set to any desired value. We perform a rigorous error analysis to provide an exact bound for the absolute error of $$2^{-f}$$ in the worst case. Existing methods rely on polynomial approximations of the sine and cosine, whereas our approach relies on the random self-reducibility of the problem, using efficiently generated solved instances for uniformly random angles. As a consequence, most of the $$O(f^2)$$ secure multiplications can be done in preprocessing, leaving only O(f) work for the online part. The overall round complexity can be limited to O(1) using standard techniques. We have integrated our solution in MPyC.
A <scp>blockchain‐enabled</scp> solution to improve <scp>intra‐inter</scp> organizational innovation processes in software small medium enterprises

Abstract Besides the substantial advantages of adopting information and communication technologies (ICTs), software small medium enterprises (SSMEs) face major challenges largely connected to knowledge sharing, security, and management of data and information. The objective of this paper is to propose a platform backed with the advantages of blockchain technologies, allowing intra‐ and inter‐organizational knowledge and information sharing in SSMEs without losing control and ownership. Blockchain technology has the potential to bring about significant improvements in enhancing business models and processes wherein the authenticity of data is provided by smart contracts at every access point in a distributed information cycle in a transparent manner. The findings will support businesses in enhancing information flow, innovation, and competitive advantage in the current dynamic market. Unlike existing studies, which merely discuss the broader use of blockchain technology in the SSME domain, this paper provides a fresh promising direction in transforming SSMEs digitally.
Security and application of semi-quantum key distribution protocol for users with different quantum capabilities

Abstract Semi-quantum protocols serve as a bridge between quantum users and “classical” users with limited quantum capabilities, providing support for application scenarios that cannot afford the excessively high cost of quantum resources. In this paper, we present a semi-quantum key distribution (SQKD) protocol based on Bell states and single particles, which is designed for key distribution between different types of users. The protocol enables simultaneous key distribution between quantum and classical users, as well as key establishment between two classical users. The security analysis demonstrates that the protocol can reach the same level of security as the full quantum protocol. Furthermore, we extrapolate the proposed protocol to other semi-quantum protocols, such as semi-quantum key agreement and semi-quantum private comparison protocols. Compared with previous similar ones, our SQKD protocol and its extended versions can fulfill the requirements of their respective counterparts individually. Therefore, our SQKD protocol has the potential for broader applications in practical scenarios.
An improved dynamic programming tracking-before-detection algorithm based on LSTM network value function

The detection and tracking of small and weak maneuvering radar targets in complex electromagnetic environments is still a difficult problem to effectively solve. To address this problem, this paper proposes a dynamic programming tracking-before-detection method based on long short-term memory (LSTM) network value function(VL-DP-TBD). With the help of the estimated posterior probability provided by the designed LSTM network, the calculation of the posterior value function of the traditional DP-TBD algorithm can be more accurate, and the detection and tracking effect achieved for maneuvering small and weak targets is improved. Utilizing the LSTM network to model the posterior probability estimation of the target motion state, the posterior probability moving features of the maneuvering target can be learned from the noisy input data. By incorporating these posterior probability estimation values into the traditional DP-TBD algorithm, the accuracy and robustness of the calculation of the posterior value function can be enhanced, so that the improved architecture is capable of effectively recursively accumulating the movement trend of the target. Simulation results show that the improved architecture is able to effectively reduce the aggregation effect of a posterior value function and improve the detection and tracking ability for non-cooperative nonlinear maneuvering dim small target.AbbreviationsLSTM: Long short-term memory; DP-TBD: Dynamic programming-based tracking before detection; DBT: Detection before tracking; TBD: Tracking before detection; HT-TBD: Tracking-before-detection algorithm based on the Hough transform; PF-TBD: Tracking-before-detection algorithm based on particle filtering; RFS-TBD: Tracking-before-detection algorithm based on random finite sets; SNR: Signal-to-noise ratio; DP: Dynamic programming; EVT: Extreme value theory; EVT: Generalized extreme value theory; GLRT: Generalized likelihood ratio detection; KT: Keystone transformation; PGA: Phase gradient autofocusing; CFAR: Constant false-alarm rate; J-CA-CFAR: Joint intensity-spatial CFAR; MF: Merit function; CP-DP-TBD: Candidate plot-based DP-TBD; CIT: Coherent integration time; RNN: Recurrent neural network; CS: Current statistical; Pd: Detection probability; Pt: Tracking probability.
Multi-objective tracking for smart substation onsite surveillance based on YOLO Approach and AKCF

The onsite surveillance plays an important role in the smart substation since the smart substation is unattended. All the sites and operation staff should be supervised throughout the process since a series of risks exist on the working sites. KCF (Kernel Correlation Filter) is an effective method to track a moving object for safety surveillance. However, the occlusion and shape changes worsen the performance of KCF, especially on the occasion of multi-objective detection. This paper proposes a comprehensive method for improving the precision and robustness of detection. Firstly, all the moving objects are detected by the YOLO method. In the tracking part, an AKCF (Augmented Kernel Correlation Filter) is proposed for the heavily occluded object, and the Kalman Filter (KF) serves as a supplementary output. Moreover, in the target association section, based on priority matching and rematching based on motion estimation, a two-stage target association method is proposed. Test outcomes indicate that the proposed algorithm is accurate and robust for tracking workers’ trajectories and conducting surveillance.
No Adversaries to Zero-Shot Learning: Distilling an Ensemble of Gaussian Feature Generators

In zero-shot learning (ZSL), the task of recognizing unseen categories when no data for training is available, state-of-the-art methods generate visual features from semantic auxiliary information (e.g., attributes). In this work, we propose a valid alternative (simpler, yet better scoring) to fulfill the very same task. We observe that, if first- and second-order statistics of the classes to be recognized were known, sampling from Gaussian distributions would synthesize visual features that are almost identical to the real ones as per classification purposes. We propose a novel mathematical framework to estimate first- and second-order statistics, even for unseen classes: our framework builds upon prior compatibility functions for ZSL and does not require additional training. Endowed with such statistics, we take advantage of a pool of class-specific Gaussian distributions to solve the feature generation stage through sampling. We exploit an ensemble mechanism to aggregate a pool of softmax classifiers, each trained in a one-seen-class-out fashion to better balance the performance over seen and unseen classes. Neural distillation is finally applied to fuse the ensemble into a single architecture which can perform inference through one forward pass only. Our method, termed Distilled Ensemble of Gaussian Generators, scores favorably with respect to state-of-the-art works.
A Secure Data-Sharing Scheme for Privacy-Preserving Supporting Node–Edge–Cloud Collaborative Computation

The node–edge–cloud collaborative computation paradigm has introduced new security challenges to data sharing. Existing data-sharing schemes suffer from limitations such as low efficiency and inflexibility and are not easily integrated with the node–edge–cloud environment. Additionally, they do not provide hierarchical access control or dynamic changes to access policies for data privacy preservation, leading to a poor user experience and lower security. To address these issues, we propose a data-sharing scheme using attribute-based encryption (ABE) that supports node–edge–cloud collaborative computation (DS-ABE-CC). Our scheme incorporates access policies into ciphertext, achieving fine-grained access control and data privacy preservation. Firstly, considering node–edge–cloud collaborative computation, it outsources the significant computational overhead of data sharing from the owner and user to the edge nodes and the cloud. Secondly, integrating deeply with the “node–edge–cloud” scenario, the key distribution and agreement between all entities embedded in the encryption and decryption process, with a data privacy-preserving mechanism, improve the efficiency and security. Finally, our scheme supports flexible and dynamic access control policies and realizes hierarchical access control, thereby enhancing the user experience of data sharing. The theoretical analysis confirmed the security of our scheme, while the comparison experiments with other schemes demonstrated the practical feasibility and efficiency of our approach in node–edge–cloud collaborative computation.
Video Surveillance Object Forgery Detection using PDCL Network with Residual-based Steganalysis Feature

Video surveillance has various applications in various fields and industries. However, the rapid development of video processing technology has made video surveillance information susceptible to multiple malicious attacks. At present, the state-of-the-art methods, including the latest deep learning techniques, cannot get satisfactory results when addressing video surveillance object forgery detection (VSOFD) due to the following limitations: (i) lack of VSOFD-specific features for effective processing and (ii) lack of effective deep network architecture designed explicitly for VSOFD. This paper proposes a new detection scheme to alleviate these limitations. The proposed approach first extracted VSOFD-specific features via residual-based steganalysis feature (RSF) from the spatial-temporal-frequent domain. Key clues of video frames can be more effectively learned from RSF, instead of raw frame images. Then, the RSF feature is used to form the residual-based steganography feature vector group (RSFVG), which serves as the input of our following network. Finally, a new VSOFD-specific deep network architecture called parallel-DenseNet-concatenated-LSTM (PDCL) network is designed, which includes two improved CNN and RNN modules. The improved CNN module fuses and processes the coarse-to-fine feature extraction and simultaneously preserves the frame independence in video frames. The improved RNN module learns the correlation features between the adjacent frames to identify forgery frames. Experimental results show that the proposed scheme using the PDCL network with RSF can achieve high performance in test error, precision, recall, and F1 scores in our newly constructed dataset (SYSU-OBJFORG + newly generated forgery video clips). Compared to existing SOTA methods, our framework achieves the best F1 score of 90.33%, which is greatly improved by nearly 8%.
EnsemDeepCADx: Empowering Colorectal Cancer Diagnosis with Mixed-Dataset Features and Ensemble Fusion CNNs on Evidence-Based CKHK-22 Dataset

Colorectal cancer is associated with a high mortality rate and significant patient risk. Images obtained during a colonoscopy are used to make a diagnosis, highlighting the importance of timely diagnosis and treatment. Using techniques of deep learning could enhance the diagnostic accuracy of existing systems. Using the most advanced deep learning techniques, a brand-new EnsemDeepCADx system for accurate colorectal cancer diagnosis has been developed. The optimal accuracy is achieved by combining Convolutional Neural Networks (CNNs) with transfer learning via bidirectional long short-term memory (BILSTM) and support vector machines (SVM). Four pre-trained CNN models comprise the ADaDR-22, ADaR-22, and DaRD-22 ensemble CNNs: AlexNet, DarkNet-19, DenseNet-201, and ResNet-50. In each of its stages, the CADx system is thoroughly evaluated. From the CKHK-22 mixed dataset, colour, greyscale, and local binary pattern (LBP) image datasets and features are utilised. In the second stage, the returned features are compared to a new feature fusion dataset using three distinct CNN ensembles. Next, they incorporate ensemble CNNs with SVM-based transfer learning by comparing raw features to feature fusion datasets. In the final stage of transfer learning, BILSTM and SVM are combined with a CNN ensemble. The testing accuracy for the ensemble fusion CNN DarD-22 using BILSTM and SVM on the original, grey, LBP, and feature fusion datasets was optimal (95.96%, 88.79%, 73.54%, and 97.89%). Comparing the outputs of all four feature datasets with those of the three ensemble CNNs at each stage enables the EnsemDeepCADx system to attain its highest level of accuracy.
Learning State Transition Rules from High-Dimensional Time Series Data with Recurrent Temporal Gaussian-Bernoulli Restricted Boltzmann Machines

Abstract Understanding the dynamics of a system is crucial in various scientific and engineering domains. Machine learning techniques have been employed to learn state transition rules from observed time-series data. However, these data often contain sequences of noisy and ambiguous continuous variables, while we typically seek simplified dynamics rules that capture essential variables. In this work, we propose a method to extract a small number of essential hidden variables from high-dimensional time-series data and learn state transition rules between hidden variables. Our approach is based on the Restricted Boltzmann Machine (RBM), which models observable data in the visible layer and latent features in the hidden layer. However, real-world data, such as video and audio, consist of both discrete and continuous variables with temporal relationships. To address this, we introduce the Recurrent Temporal Gaussian-Bernoulli Restricted Boltzmann Machine (RTGB-RBM), which combines the Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM) to handle continuous visible variables and the Recurrent Temporal Restricted Boltzmann Machine (RT-RBM) to capture time dependencies among discrete hidden variables. Additionally, we propose a rule-based method to extract essential information as hidden variables and represent state transition rules in an interpretable form. We evaluate our proposed method on the Bouncing Ball, Moving MNIST, and dSprite datasets. Experimental results demonstrate that our approach effectively learns the dynamics of these physical systems by extracting state transition rules between hidden variables. Moreover, our method can predict unobserved future states based on observed state transitions.
Lightweight Small Target Detection Algorithm with Multi-Feature Fusion

Unmanned aerial vehicles (UAVs) are a highly sought-after technology with numerous applications in both military and non-military uses. The identification of targets is a crucial aspect of UAV applications, but there are challenges associated with complex detection models and difficulty in detecting small targets. To address these issues, this study proposes the lightweight L-YOLO algorithm for target detection tasks from a UAV perspective. The L-YOLO algorithm improves on YOLOv5 by improving the model’s detection performance for small targets while reducing the number of parameters and computational effort. The GhostNet module replaces the relevant convolution in the YOLOv5 model to create a lightweight model. The EIoU loss is used as the loss function of the algorithm to accelerate convergence and improve regression accuracy. Furthermore, feature-level extensions based on YOLOv5 are implemented, and a new detection head is proposed to improve the model’s detection accuracy for small targets. The size of the anchor boxes is redesigned to suit the small targets using the K-means++ clustering algorithm. The experiments were conducted on the VisDrone-2022 dataset, and the L-YOLO algorithm demonstrated a reduction in computational effort by 42.42% and number of parameters by 48.6% compared to the original algorithm. Furthermore, recall and mAP@0.5 improved by 2.1% and 1.4%, respectively. These results demonstrate that the L-YOLO algorithm not only has better detection performance for small targets but is also a lighter model, indicating promising prospects for target detection from a UAV perspective.
Predicting the use of a COVID‐19 contact tracing application: A study across two points of measurements

Abstract Contact tracing mobile applications (apps) were important in combating the COVID‐19 pandemic. Most previous studies predicting contact tracing app use were cross‐sectional and not theory‐based. This study aimed at contributing to a better understanding of app use intentions and app use by applying an extended version of the protection motivation theory across two measurement points while accounting for the development of the pandemic. A total of N = 1525 participants from Switzerland ( M age = 53.70, SD = 18.73; 47% female; n = 270 completed both assessments) reported on risk perceptions, response efficacy, self‐efficacy, social norms, trust in government, trust in the healthcare system, active search of COVID‐19‐related information, intentions for and actual (self‐reported) app use. Analyses included country‐specific incidences and death toll. Increases in response‐efficacy, self‐efficacy, trust in government, and the active search of COVID‐19‐related information predicted increased app‐use intentions. Increases in self‐efficacy, intentions, and the active search of COVID‐19‐related information predicted increased self‐reported app use. Risk perceptions, incidence, and death toll were unrelated to both outcomes. Across an aggravation of the pandemic situation, intentions for and app use were primarily related to response‐efficacy, self‐efficacy, trust in government, and the active search of COVID‐19‐related information.