Reviewerly/exHarmony huggingface dataset:
Generalized Domain Adaptation Framework for Parametric Back-End in Speaker Recognition

State-of-the-art speaker recognition systems comprise a speaker embedding front-end followed by a probabilistic linear discriminant analysis (PLDA) back-end. The effectiveness of these components relies on the availability of a large amount of labeled training data. In practice, it is common for domains (e.g., language, channel, demographic) in which a system is deployed to differ from that in which a system has been trained. To close the resulting gap, domain adaptation is often essential for PLDA models. Among two of its variants are Heavy-tailed PLDA (HT-PLDA) and Gaussian PLDA (G-PLDA). Though the former better fits real feature spaces than does the latter, its popularity has been severely limited by its computational complexity and, especially, by the difficulty, it presents in domain adaptation, which results from its non-Gaussian property. Various domain adaptation methods have been proposed for G-PLDA. This paper proposes a generalized framework for domain adaptation that can be applied to both of the above variants of PLDA for speaker recognition. It not only includes several existing supervised and unsupervised domain adaptation methods but also makes possible more flexible usage of available data in different domains. In particular, we introduce here two new techniques: (1) correlation-alignment in the model level, and (2) covariance regularization. To the best of our knowledge, this is the first proposed application of such techniques for domain adaptation w.r.t. HT-PLDA. The efficacy of the proposed techniques has been experimentally validated on NIST 2016, 2018, and 2019 Speaker Recognition Evaluation (SRE’16, SRE’18 and SRE’19) datasets.
A Transformer-Based Approach to Authorship Attribution in Classical Arabic Texts

Authorship attribution (AA) is a field of natural language processing that aims to attribute text to its author. Although the literature includes several studies on Arabic AA in general, applying AA to classical Arabic texts has not gained similar attention. This study focuses on investigating recent Arabic pretrained transformer-based models in a rarely studied domain with limited research contributions: the domain of Islamic law. We adopt an experimental approach to investigate AA. Because no dataset has been designed specifically for this task, we design and build our own dataset using Islamic law digital resources. We conduct several experiments on fine-tuning four Arabic pretrained transformer-based models: AraBERT, AraELECTRA, ARBERT, and MARBERT. Results of the experiments indicate that for the task of attributing a given text to its author, ARBERT and AraELECTRA outperform the other models with an accuracy of 96%. We conclude that pretrained transformer models, specifically ARBERT and AraELECTRA, fine-tuned using the Islamic legal dataset, show significant results in applying AA to Islamic legal texts.
Synthesizing Electronic Health Records for Predictive Models in Low-Middle-Income Countries (LMICs)

The spread of machine learning models, coupled with by the growing adoption of electronic health records (EHRs), has opened the door for developing clinical decision support systems. However, despite the great promise of machine learning for healthcare in low-middle-income countries (LMICs), many data-specific limitations, such as the small size and irregular sampling, hinder the progress in such applications. Recently, deep generative models have been proposed to generate realistic-looking synthetic data, including EHRs, by learning the underlying data distribution without compromising patient privacy. In this study, we first use a deep generative model to generate synthetic data based on a small dataset (364 patients) from a LMIC setting. Next, we use synthetic data to build models that predict the onset of hospital-acquired infections based on minimal information collected at patient ICU admission. The performance of the diagnostic model trained on the synthetic data outperformed models trained on the original and oversampled data using techniques such as SMOTE. We also experiment with varying the size of the synthetic data and observe the impact on the performance and interpretability of the models. Our results show the promise of using deep generative models in enabling healthcare data owners to develop and validate models that serve their needs and applications, despite limitations in dataset size.
The ins and outs of decentralized autonomous organizations (DAOs) unraveling the definitions, characteristics, and emerging developments of DAOs

Despite the increase in the number of blockchain-based Decentralized Autonomous Organizations (DAOs), there is no consensus on what constitutes a DAO. This paper provides an in-depth study of DAOs by analyzing their definitions, characteristics, and emerging developments. Existing definitions in the literature hardly recognize common functionalities and intermingle coded DAOs, DAO deployment platforms, and blockchain DAOs. We developed a comprehensive DAO definition by reviewing the literature and empirically analyzing 1,859 DAOs. The findings show that many DAOs were inactive and that a threshold of 20 tokenholders is a tipping point for DAOs to survive over time and maintain sustained levels of activity. Finally, based on an empirical analysis of 9,845 perceived DAOs, we identified the emerging development of off-chain voting. This emerging development challenges the autonomous nature of DAOs. We recommend further research to investigate the effect of governance structures on their long-term sustainability and viability for both on-chain and off-chain DAOs.
Research on Object Detection and Recognition Method for UAV Aerial Images Based on Improved YOLOv5

In this paper, an object detection and recognition method based on improved YOLOv5 is proposed for application on unmanned aerial vehicle (UAV) aerial images. Firstly, we improved the traditional Gabor function to obtain Gabor convolutional kernels with better edge enhancement properties. We used eight Gabor convolutional kernels to enhance the object edges from eight directions, and the enhanced image has obvious edge features, thus providing the best object area for subsequent deep feature extraction work. Secondly, we added a coordinate attention (CA) mechanism to the backbone of YOLOv5. The plug-and-play lightweight CA mechanism considers information of both the spatial location and channel of features and can accurately capture the long-range dependencies of positions. CA is like the eyes of YOLOv5, making it easier for the network to find the region of interest (ROI). Once again, we replaced the Path Aggregation Network (PANet) with a Bidirectional Feature Pyramid Network (BiFPN) at the neck of YOLOv5. BiFPN performs weighting operations on different input feature layers, which helps to balance the contribution of each layer. In addition, BiFPN adds horizontally connected feature branches across nodes on a bidirectional feature fusion structure to fuse more in-depth feature information. Finally, we trained the overall improved YOLOv5 model on our integrated dataset LSDUVD and compared it with other models on multiple datasets. The results show that our method has the best convergence effect and mAP value, which demonstrates that our method has unique advantages in processing detection tasks of UAV aerial images.
Radiometric Dating Applied to Ore Deposits: Theory and Methods

Abstract Metallic ore deposits have contributed to the development of the human society since pre-historic times and nowadays are one of the pillars of unprecedented technological developments. In order to understand how metallic ore deposits form and thus construct genetic models that may serve as exploration guides, determining the age of an ore deposit is one of the most important pieces of information needed. More recently it has also become evident that determining the temporal duration of mineralizing events can offer valuable information on how certain deposits form and thus improve genetic models. Radiometric dating of ore minerals or of other minerals that are demonstrably associated in space and time with mineralization is the most accurate and precise tool to date an ore deposit. This Introductory Chapter summarizes basic concepts on why ore deposit dating is important and how this can be achieved through different methods. It illustrates basic differences among different methods and serves as an introduction to the more detailed descriptions of specific dating methods presented in the following Chapters.
Echo State Networks: Novel reservoir selection and hyperparameter optimization model for time series forecasting

The use of computational intelligence models for multi-step time series forecasting tasks has presented satisfactory results in such a way that they are considered models with an excellent future for this type of problem. From the point of view of computational cost, the current alternatives combined with classical models are generating hybrid models that present even better results. Within the AutoML category, the optimization of hyperparameters and the selection of network topologies has become a challenge. Reservoir Computing, which is within the area of ​​Recurrent Neural Networks (RNN), proposes a particular model called Echo State Networks. which has been tested in different applications with excellent results; however, the difficulty in specifying the hyperparameters has been the subject of continuous study given the random nature of the set of neurons called Reservoir. Based on the Separation Ratio Graph (SRG) model for performance analysis, this paper proposes a new model, called Echo State Network - Genetic Algorithm - Separation Ratio Graph (ESN-GA-SRG), which optimizes network hyperparameters and at the same time selects the best topology for the Reservoir using the SRG coefficient, to find the reservoir that offers the most suitable dynamic behavior. The performance of this new model is evaluated on forecasting two sets of time series benchmarks with different characteristics of sampling periodicity, skewness, and stationarity. The results obtained show that the ESN-GA-SRG model was superior in predicting these time series in most cases, with statistical significance, when compared to other models that have been presented for this type of problem in the literature.
Hashing for secure optical information compression in a heterogeneous convolutional neural network

In recent years, heterogeneous machine learning accelerators have become of significant interest to science, engineering, and industry. At the same time, the looming post-quantum encryption era instigates the demand for increased data security. From a hardware processing point of view, electronic computing hardware is challenged by electronic capacitive interconnect delay and associated energy consumption. In heterogeneous systems, such as electronic–photonic accelerators, parasitic domain crossings limit throughput and speed. With analog optical accelerators exhibiting a strong potential for high throughput (up to petaoperations per second) and operation efficiency, their ability to perform machine learning classification tasks on encrypted data has not been broadly recognized. This work is a significant step in that direction. Here, we present an optical hashing and compression scheme that is inspired by SWIFFT, a post-quantum hashing family of algorithms. High degree optical hardware-to-algorithm homomorphism allows one to optimally harvest the potential of free-space data processing: innate parallelism, low latency tensor by-element multiplication, and zero-energy Fourier transformation operations. The algorithm can provide several orders of magnitude increase in processing speed as compared to optical machine learning accelerators with non-compressed input. This is achieved by replacing slow, high-resolution CMOS cameras with ultra-fast and signal-triggered CMOS detector arrays. Additionally, information acquired in this way will require much lower transmission throughput, less in silico processing power, storage, and will be pre-hashed, facilitating optical information security. This concept has the potential to allow heterogeneous convolutional Fourier classifiers to approach the performance of their fully electronic counterparts and enables data classification on hashed data.
Remote sensing image and multi-type image joint encryption based on NCCS

In this paper, an encryption algorithm for remote sensing image based on a new type of Novel Chebyshev chaotic system (NCCS) and a combined encryption algorithm for remote sensing image, gray image and color image are proposed. Aiming at the problem of large amount of remote sensing image data, this paper proposes NCCS algorithm, which effectively reduces the time complexity of the algorithm, and the generated pseudo-random sequence is more uniform, and the performance is better. On this basis, the remote sensing image encryption, first of all, each band of remote sensing image in a different channel, to obtain a three-dimensional matrix, using three-dimensional spiral curve to read each section of the three-dimensional matrix, a two-dimensional matrix composed of several one-dimensional sequences is obtained. This method makes each channel produce some coupling and reduces the dimension of the matrix, thus effectively improving the scrambling effect. Chaotic maps scramble one-dimensional sequences, then scramble one-dimensional sequences, and diffuse them by cyclic left shift based on additive modules. Because this method is suitable for multi-channel image encryption, it can be used not only for remote sensing image encryption, but also for remote sensing image, gray image, and color image encryption. Simulation results and performance analysis show that the method has good security. Compared with some existing encryption schemes, this method has a wider application range.
Hierarchical Graph Convolutional Networks for Action Quality Assessment

Action quality assessment (AQA) automatically evaluates how well humans perform actions in a given video, a technique widely used in fields such as rehabilitation medicine, athletic competitions, and specific skills assessment. However, existing works that uniformly divide the video sequence into small clips of equal length suffer from intra-clip confusion and inter-clip incoherence, hindering the further development of AQA. To address this issue, we propose a hierarchical graph convolutional network (GCN). First, semantic information confusion is corrected through clip refinement, generating the ‘shot’ as the basic action unit. We then construct a scene graph by combining several consecutive shots into meaningful scenes to capture local dynamics. These scenes can be viewed as different procedures of a given action, providing valuable assessment cues. The video-level representation is finally extracted via sequential action aggregation among scenes to regress the predicted score distribution, enhancing discriminative features and improving assessment performance. Experiments on the AQA-7, MTL-AQA, and JIGSAWS datasets demonstrate the superiority of the proposed hierarchical GCN over state-of-the-art methods.
Spatial-Temporal Frequency Forgery Clue for Video Forgery Detection in VIS and NIR Scenario

In recent years, with the rapid development of face editing and generation, more and more fake videos are circulating on social media, which has caused extreme public concerns. Existing face forgery detection methods based on frequency domain find that the GAN forged images have obvious grid-like visual artifacts in the frequency spectrum. But for synthesized videos, these methods only confine to a single frame and pay little attention to the most discriminative part and temporal frequency clue among different frames. To take full advantage of the rich information in video sequences, this paper performs video forgery detection on both spatial and temporal frequency domains and proposes a Discrete Cosine Transform-based Forgery Clue Augmentation Network (FCAN-DCT) to achieve a more comprehensive spectrum spatial-temporal feature representation. FCAN-DCT totally consists of a backbone network and two branches: Compact Feature Extraction (CFE) module and Frequency Temporal Attention (FTA) module. We conduct thorough experimental assessments on three visible light (VIS) based datasets (i.e.,, FaceForensics++, Celeb-DF (v2), WildDeepfake), and our self-built video forgery dataset DeepfakeNIR, which is the first video forgery dataset on near-infrared (NIR) modality. The experimental results demonstrate the effectiveness and robustness of our method for detecting forgery videos in both VIS and NIR scenarios.DeepfakeNIR and code are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/AEP-WYK/DeepfakeNIR</uri> .
Preventing Unauthorized Users from Accessing Cloud Data

The adoption of cloud computing has brought numerous benefits, but it has also introduced concerns regarding the security of cloud data. Preventing unauthorized access to sensitive information stored in the cloud is a critical priority for organizations. This Article provides a concise overview of strategies and practices to mitigate unauthorized access risks in cloud environments. The Article emphasizes the importance of data classification and threat assessment in understanding cloud data security. Implementing access control mechanisms, such as strong authentication, role-based access control, and the principle of least privilege, is vital to restrict unauthorized access attempts. Data encryption, both during transit and at rest, adds an extra layer of protection to safeguard cloud data. Network security measures, such as secure network architectures and network segmentation, prevent unauthorized lateral movement within the cloud environment. Intrusion detection and prevention systems help detect and respond to potential intrusions, while monitoring and auditing mechanisms enable the tracking of user activities and the detection of anomalies.Regular security updates and patch management ensure that vulnerabilities are promptly addressed. Educating employees about cloud data security risks and best practices is crucial for creating a security-aware culture within the organization. By implementing these measures, organizations can strengthen the security of their cloud environments, safeguarding sensitive data from unauthorized access. Continuous monitoring, regular updates, and employee education are essential for maintaining a robust cloud data security posture in the face of evolving threats.
Longest Common Subsequence with Gap Constraints

We consider the longest common subsequence problem in the context of subsequences with gap constraints. In particular, following Day et al. 2022, we consider the setting when the distance (i. e., the gap) between two consecutive symbols of the subsequence has to be between a lower and an upper bound (which may depend on the position of those symbols in the subsequence or on the symbols bordering the gap) as well as the case where the entire subsequence is found in a bounded range (defined by a single upper bound), considered by Kosche et al. 2022. In all these cases, we present efficient algorithms for determining the length of the longest common constrained subsequence between two given strings.
White blood cell differentials performance of a new automated digital cell morphology analyzer: Mindray <scp>MC</scp>‐80

The manual differential count has been recognized for its disadvantages, including large interobserver variability and labor intensiveness. In this light, automated digital cell morphology analyzers have been increasingly adopted in hematology laboratories for their robustness and convenience. This study aims to evaluate the white blood cell differential performance of the Mindray MC-80, the new automated digital cell morphology analyzer.The cell identification performance of Mindray MC-80 was evaluated for sensitivity and specificity using pre-classification and post-classification of each cell class. The method comparison study used manual differentials as the gold standard for calculating Pearson correlation, Passing-Bablok regression, and Bland-Altman analysis. In addition, the precision study was performed and evaluated.The precision was within the acceptable limit for all cell classes. Overall, the specificity of cell identification was higher than 95% for all cell classes. The sensitivity was greater for 95% for most cell classes, except for myelocytes (94.9%), metamyelocytes (90.9%), reactive lymphocytes (89.7%), and plasma cells (60%). Pre-classification and post-classification results correlated well with the manual differential results for all the cell types investigated. The regression coefficients were greater than 0.9 for most cell classes except for promyelocytes, metamyelocytes, basophils, and reactive lymphocytes.The performance of Mindray MC-80 for white blood cell differentials is reliable and seems to be acceptable even in abnormal samples. However, the sensitivity is less than 95% for certain abnormal cell types, so the user should be aware of this limitation where such cells are suspected.
DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with the GPU

Recently Graph Neural Networks (GNNs) have achieved great success in many applications. The mini-batch training has become the de-facto way to train GNNs on giant graphs. However, the mini-batch generation task is extremely expensive which slows down the whole training process. Researchers have proposed several solutions to accelerate the mini-batch generation, however, they (1) fail to exploit the locality of the adjacency matrix, (2) cannot fully utilize the GPU memory, and (3) suffer from the poor adaptability to diverse workloads. In this work, we propose DUCATI, aDual-Cache system to overcome these drawbacks. In addition to the traditionalNfeat-Cache, DUCATI introduces a newAdj-Cache to further accelerate the mini-batch generation and better utilize GPU memory. DUCATI develops a workload-awareDual-Cache Allocator which adaptively finds the best cache allocation plan under different settings. We compare DUCATI with various GNN training systems on four billion-scale graphs under diverse workload settings. The experimental results show that in terms of training time, DUCATI can achieve up to 3.33 times speedup (2.07 times on average) compared to DGL and up to 1.54 times speedup (1.32 times on average) compared to the state-of-the-artSingle-Cache systems. We also analyze the time-accuracy trade-offs of DUCATI and four state-of-the-art GNN training systems. The analysis results offer users some guidelines on system selection regarding different input sizes and hardware resources.