Reviewerly/exHarmony huggingface dataset:
An Automated Segmentation of Leukocytes Using Modified Watershed Algorithm on Peripheral Blood Smear Images

Leukemia can be detected by an abnormal rise in the number of immature lymphocytes and by a decrease in the number of other blood cells. To diagnose leukemia, image processing techniques are utilized to examine microscopic peripheral blood smear (PBS) images automatically and swiftly. To the best of our knowledge, the initial step in subsequent processing is a robust segmentation technique for identifying leukocytes from their surroundings. The paper presents the segmentation of leukocytes in which three color spaces are considered in this study for image enhancement. The proposed algorithm uses a marker-based watershed algorithm and peak local maxima. The algorithm was used on three different datasets with various color tones, image resolutions, and magnifications. The average precision for all three-color spaces was the same, i.e. 94% but the Structural Similarity Index Metric (SSIM) and recall of HSV were better than other two. The results of this study will aid experts in narrowing down their options for segmenting leukemia. Based on the comparison, it was concluded that when the colour space correction technique is used, the accuracy of the proposed methodology improves.
MoLE : Mixture Of Language Experts For Multi-Lingual Automatic Speech Recognition

Multi-lingual speech recognition aims to distinguish linguistic expressions in different languages and integrate acoustic processing simultaneously. In contrast, current multilingual speech recognition research follows a language-aware paradigm, mainly targeted to improve recognition performance rather than discriminate language characteristics. In this paper, we present a multi-lingual speech recognition network named Mixture-of-Language-Experts (MoLE), which digests speech in a variety of languages. Specifically, MoLE analyzes linguistic expression from input speech in arbitrary languages, activating a language-specific expert with a lightweight language gating network. The gating network not only activates experts, but also estimates the reliability of the activation. Based on the reliability, the activated expert and the language-agnostic expert are aggregated to represent language-conditioned embedding for efficient speech recognition. Our proposed model is evaluated in 5 languages scenario, and the experimental results show that our structure is advantageous on multi-lingual recognition, especially for speech in low-resource language.
Quantum Variational Bayes on Manifolds

Variational Bayes (VB) has become a widely-used tool for Bayesian inference in statistics and machine learning. VB algorithms are generally restricted to the case where the variational parameter space is Euclidean. Recently, the VB framework has been extended to the case where the variational parameter space is a Riemannian manifold. Such a manifold-based algorithm, by utilizing the natural gradient, can exploit the geometric structures to derive an efficient VB procedure. However, natural gradient in high dimension can be prohibitively computationally expensive to compute. This work proposes using a quantum natural gradient within the manifold VB framework. We demonstrate that the quantum natural gradient does not suffer from the well-known quantum-classical readout computational bottleneck, and hence delivers a highly computational efficiency. The resulting quantum manifold VB algorithm is provably convergent.
MHSCNET: A Multimodal Hierarchical Shot-Aware Convolutional Network for Video Summarization

Video summarization is an essential problem in signal processing, which intends to produce a concise summary of the original video. Existing video summarization approaches regard the task as a keyframe selection problem and generally construct the frame-wise representation by combining the long-range temporal dependency with either unimodal or bimodal information. The optimal keyframe should offer the semantic summarization of the whole content by exploiting the multimodal and shot-level hierarchical natures of videos, however, such natures are not fully exploited in existing methods. In this paper, we propose to construct a more powerful and robust frame-wise representation and predict the frame-level importance score in a fair and comprehensive manner. Specifically, we propose a multimodal hierarchical shot-aware convolutional network, denoted as MHSCNet, to enhance the frame-wise representation via combining the comprehensive available multimodal information. We further design a hierarchical ShotConv network to incorporate the adaptive shot-aware frame-level representation by considering the short-range and long-range temporal dependencies. Based on the learned shot-aware representations, MHSCNet can predict the frame-level importance score in the local and global view of the video. Extensive experiments on two standard video summarization datasets demonstrate that our proposed method consistently outperforms state-of-the-arts.
Continuous User Authentication on Multiple Smart Devices

Recent developments in the mobile and intelligence industry have led to an explosion in the use of multiple smart devices such as smartphones, tablets, smart bracelets, etc. To achieve lasting security after initial authentication, many studies have been conducted to apply user authentication through behavioral biometrics. However, few of them consider continuous user authentication on multiple smart devices. In this paper, we investigate user authentication from a new perspective—continuous authentication on multi-devices, that is, continuously authenticating users after both initial access to one device and transfer to other devices. In contrast to previous studies, we propose a continuous user authentication method that exploits behavioral biometric identification on multiple smart devices. In this study, we consider the sensor data captured by accelerometer and gyroscope sensors on both smartphones and tablets. Furthermore, multi-device behavioral biometric data are utilized as the input of our optimized neural network model, which combines a convolutional neural network (CNN) and a long short-term memory (LSTM) network. In particular, we construct two-dimensional domain images to characterize the underlying features of sensor signals between different devices and then input them into our network for classification. In order to strengthen the effectiveness and efficiency of authentication on multiple devices, we introduce an adaptive confidence-based strategy by taking historical user authentication results into account. This paper evaluates the performance of our multi-device continuous user authentication mechanism under different scenarios, and extensive empirical results demonstrate its feasibility and efficiency. Using the mechanism, we achieved mean accuracies of 99.8% and 99.2% for smartphones and tablets, respectively, in approximately 2.3 s, which shows that it authenticates users accurately and quickly.
Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology

Uncertainty estimation is crucial for understanding the reliability of deep learning (DL) predictions, and critical for deploying DL in the clinic. Differences between training and production datasets can lead to incorrect predictions with underestimated uncertainty. To investigate this pitfall, we benchmarked one pointwise and three approximate Bayesian DL models for predicting cancer of unknown primary, using three RNA-seq datasets with 10,968 samples across 57 cancer types. Our results highlight that simple and scalable Bayesian DL significantly improves the generalisation of uncertainty estimation. Moreover, we designed a prototypical metric-the area between development and production curve (ADP), which evaluates the accuracy loss when deploying models from development to production. Using ADP, we demonstrate that Bayesian DL improves accuracy under data distributional shifts when utilising 'uncertainty thresholding'. In summary, Bayesian DL is a promising approach for generalising uncertainty, improving performance, transparency, and safety of DL models for deployment in the real world.
Association Strategies of Speech Behavior of Communicators in Coding Discourse: An Interdisciplinary Approach Towards Understanding the Role of Cognitive and Linguistic Processes in Communication

In modern media, the names of fairy-tale and mythological images are used to convey certain emotions and connotations. The aim of the study is to analyze the characteristic associative strategies presented with the mythological images of a dragon, a paper tiger and a chimera in news texts of European and Chinese mass media. In this article, the method of text analysis was used to identify patterns and the most possible interpretations of lexical units. 100 articles from Chinese and European publications were selected for the analysis (About People's Daily Online, China News Service, Guardian and France 24). The required lexemes were most widely used in articles on political topics. The most used was the image of a paper tiger (4001 and 3587 units). This is due to its well-known metaphorical meaning in both cultures, while dragon differs in Chinese and European ones. Further research may focus on the search and analysis of other fairy tale and mythological images in mass media. The present study results may also be applied for further research in the field of linguistics and journalism.
USWAVG-BS: Under-Sampled Weighted AVeraGed BorderlineSMOTE to handle data intrinsic difficulties

In two-class classification problems, learning from imbalanced data is a challenging task due to the bias of machine learning algorithms towards the majority class. It has been shown in some studies that the imbalance problem is not the only reason for this difficulty, and other issues related to the nature of the data, such as small disjunct problems, borderline and rare examples, overlap between classes, and data shift, also contribute. To address this issue, an Under-Sampled Weighted AVeraGed BorderlineSMOTE (USWAVG-BS) is proposed in this paper, which consists of three phases: determining the types of examples, under-sampling, and over-sampling. In the first phase, the feature space is transformed into the Heterogeneous Value Distance Metric (HVDM) space, and a new formula based on the imbalance ratio is defined to identify noise examples from the majority class. In the second phase, noise examples from the majority class are either converted to the minority class or removed until a specific threshold between the two classes is reached. In the last phase, new examples are generated using a similar way to Safe-level-SMOTE and LN-SMOTE to further boost the minority class by emphasizing minority class regions. The proposed approach’s performance is evaluated using recall, precision, and f1-score as performance criteria on 10 real-world data sets using the JRip algorithm. Moreover, USWAVG-BS is compared with other sampling methods including 3 under-sampling, 7 over-sampling, and 4 hybrid ones using f1-score metric. The obtained results indicate that the proposed approach outperforms other methods significantly using the Wilcoxon signed-rank test. It is worth mentioning that RandomUnderSampler, despite its simplicity, performs better than two other under-sampling methods on average and SMOTE performs better than its extensions such as BorderlineSMOTE and ASN-SMOTE on the examined data sets.
Local context is calculated domain by domain, for each maximal projection

Parsing-based theories of local contexts that either rely on strict linear order (Schlenker 2009) or c-command (Ingason 2016; Romoli &amp;amp; Mandelkern 2017) face a number of challenges, most notably in accounting for nominal modification (Schlenker 2020) and partitives (Anvari &amp;amp; Blumberg 2021) but also, depending on the theory, belief reports and coordination in head-final languages. To resolve such issues, I propose a hybrid theory which takes into account both linear order and syntactic structure: local context is calculated domain by domain, and for each maximal projection.
Secret Management in Managed Kubernetes Services

Purpose: Security and Privacy risks are increasing in the industry. Managed Kubernetes services help organizations to deploy micro service in the cloud. Securing the Secrets used by micro service applications in the cloud is a crucial topic. This paper studies how managed Kubernetes service industry secures the application Secrets in the cloud. Design/Methodology/Approach: This study includes various Kubernetes service providers in the industry. The documentation available in the provider website and the published whitepapers are used to understand the Secret management technology. Findings/Result: Software applications and their data are protected in managed cloud services using Secret keys. Across service providers, there is a trend and a common approach in managing the Secrets. External or remote services integration is an improvement area for the providers. Operators have the responsibility of protecting Secrets and ensuring it does not leak out. Originality/Value: This paper studies the efforts of different vendors to protect the micro service Secrets. This ensures that the organization's critical assets are protected as per regulations and do not lead to financial losses. Cloud misconfiguration is one of the most common mistakes leading to security attacks, the knowledge of vendor's features helps to configure the cloud services securely. Paper Type: Case study
3D Semantic Segmentation of Aerial Photogrammetry Models Based on Orthographic Projection

Semantic segmentation of 3D scenes is one of the most important tasks in the field of computer vision and has attracted much attention. In this paper, we propose a novel framework for 3D semantic segmentation of aerial photogrammetry models, which uses orthographic projection to improve efficiency while still ensuring high precision, and can also be applied to multiple types of models (i.e., textured mesh or colored point cloud). In our pipeline, we first obtain RGB images and elevation images from the 3D scene through orthographic projection, then use the image semantic segmentation network to segment these images to obtain pixel-wise semantic predictions, and finally back-project the segmentation results to the 3D model for fusion. Specifically, for the image semantic segmentation model, we design a cross-modality feature aggregation module and a context guidance module based on category features, which assist the network in learning more discriminative features between different objects. For the 2D-3D semantic fusion, we combine the segmentation results of the 2D images with the geometric consistency of the 3D models for joint optimization, which further improves the accuracy of the 3D semantic segmentation. Extensive experiments on two large-scale urban scenes demonstrate the efficiency and feasibility of our algorithm and surpass the current mainstream 3D deep learning methods.
Recognition and Estimation for Frequency-Modulated Continuous-Wave Radars in Unknown and Complex Spectrum Environments

The recognition and estimation of frequency-modulated continuous-wave (FMCW) radar signals is critical for both military electronic countermeasures (ECM) and civilian autonomous driving. However, the increasingly complex radio environment poses two new challenges for detection equipment. First, multiple FMCW radars can share the same frequency band, resulting in overlap of received FMCW signals in the time-frequency domain. Second, unexpected signals in unknown spectrum environments can affect the cognitive performance of FMCW signals. This paper proposes a semantic-based learning network (SLN) that simultaneously learns modulation classification and parameter regression of FMCW signals. By integrating recognition and estimation into a single network, the system can be optimized end-to-end as a whole. Additionally, instance-level semantic learning facilitates the parallel analysis of multiple components in overlapping signals. Finally, contrastive clustering in SLN achieves suppression of unexpected signals. Numerous comparative experimental results demonstrate that SLN has the desirable ability to simultaneously recognize and estimate FMCW signals in real-time, even in unknown spectrum environments.
Depth-First Neural Architecture With Attentive Feature Fusion for Efficient Speaker Verification

Deep speaker embedding learning based on neural networks has become the predominant approach in speaker verification (SV) currently. In prior studies, researchers have investigated various network architectures. However, rare works pay attention to the question of how to design and scale up networks in a principled way to achieve a better trade-off on model performance and computational complexity. In this paper, we focus on efficient architecture design for speaker verification. Firstly, we systematically study the effect of the network depth and width on performance and empirically discover that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">depth is more important than the width of networks for speaker verification task</i> . Based on this observation, we propose a novel depth-first (DF) architecture design rule. By applying it to ResNet and ECAPA-TDNN, two new families of much deeper models, namely DF-ResNets and DF-ECAPAs, are constructed. In addition, to further boost the performance of small models in the low computation regime, a novel attentive feature fusion (AFF) scheme is proposed to replace the conventional feature fusion methods. Specifically, we design two different fusion strategies, including sequential AFF (S-AFF) and parallel AFF (P-AFF), which can dynamically fuse features in a learnable way. Experimental results on the VoxCeleb dataset show that the newly proposed DF-ResNets and DF-ECAPAs can achieve a much better trade-off on performance and complexity than the original ResNet and ECAPA-TDNN. Moreover, small models can further obtain up to 40% relative improvement in EER by adopting AFF scheme with negligible computational cost. Finally, a comprehensive comparison with various other published SV systems illustrates that our proposed models achieve the best trade-off on performance and complexity in both low and high computation scenarios.
FRNet: DCNN for Real-Time Distracted Driving Detection Toward Embedded Deployment

Real-time running deep convolutional neural networks on embedded electronics is one recent focus for distracted driving detection. In this work, we proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit {FRNet}$ </tex-math></inline-formula> , which is a unique, efficient, and real-time architecture. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit {FRNet}$ </tex-math></inline-formula> converts spatially distributed features into depth distribution by a feature reorganization block. This block compresses the volume of backbone, reduces the memory read/write volumes along with multiply-accumulate operations, and extracts key features faster. In addition, a ultra-lightweight backbone was designed, with an atypical reshape strategy. This atypical strategy designed based on pixel-level analysis, for compensating the accuracy decline along with feature reorganization. The proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit {FRNet}$ </tex-math></inline-formula> Â offered excellentÂ real-time performance on low power embedded platforms, and has a competitive accuracy with previous state-of-the-art models. It achieved 97.55% accuracy on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit {SFD+AUCDD-V1}$ </tex-math></inline-formula> , and 99.86% on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit {3MDAD}$ </tex-math></inline-formula> . On an automotive-grade embedded demo board, it costs 16.93 ms per frame and achieved 59 FPS. As of today, this is the fastest record for end-to-end distraction detection. Experiments revealedÂ the real-time and accuracy are best balanced with FRNet. The model is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/congduan-HNU/FRNet</uri> .
Cyber Mobility Mirror: A Deep Learning-Based Real-World Object Perception Platform Using Roadside LiDAR

Object perception plays a fundamental role in Cooperative Driving Automation (CDA) which is regarded as a revolutionary promoter for next-generation transportation systems. However, the vehicle-based perception may suffer from the limited sensing range and occlusion as well as low penetration rates in connectivity. In this paper, we propose Cyber Mobility Mirror (CMM), a next-generation real-world object perception system for 3D object detection, tracking, localization, and reconstruction, to explore the potential of roadside sensors for enabling CDA in the real world. The CMM system consists of six main components: i) the data pre-processor to retrieve and preprocess the raw data; ii) the roadside 3D object detector to generate 3D detection results; iii) the multi-object tracker to identify detected objects; iv) the global locator to generate geo-localization information; v) the mobile-edge-cloud-based communicator to transmit perception information to equipped vehicles, and vi) the onboard advisor to reconstruct and display the real-time traffic conditions. An automatic perception evaluation approach is proposed to support the assessment of data-driven models without human-labeling requirements and a CMM field-operational system is deployed at a real-world intersection to assess the performance of the CMM. Results from field tests demonstrate that our CMM prototype system can achieve 96.99% precision and 83.62% recall for detection and 73.55% ID-recall for tracking. High-fidelity real-time traffic conditions (at the object level) can be geo-localized with a root-mean-square error (RMSE) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.69m$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.33m$ </tex-math></inline-formula> for lateral and longitudinal direction, respectively, and displayed on the GUI of the equipped vehicle with a frequency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3-4 Hz$ </tex-math></inline-formula> .