Reviewerly/exHarmony huggingface dataset:
Streaming Speaker-Attributed ASR with Token-Level Speaker Embeddings

This paper presents a streaming speaker-attributed automatic speech recognition (SA-ASR) model that can recognize "who spoke what" with low latency even when multiple people are speaking simultaneously. Our model is based on token-level serialized output training (t-SOT) which was recently proposed to transcribe multi-talker speech in a streaming fashion. To further recognize speaker identities, we propose an encoder-decoder based speaker embedding extractor that can estimate a speaker representation for each recognized token not only from non-overlapping speech but also from overlapping speech. The proposed speaker embedding, named t-vector, is extracted synchronously with the t-SOT ASR model, enabling joint execution of speaker identification (SID) or speaker diarization (SD) with the multi-talker transcription with low latency. We evaluate the proposed model for a joint task of ASR and SID/SD by using LibriSpeechMix and LibriCSS corpora. The proposed model achieves substantially better accuracy than a prior streaming model and shows comparable or sometimes even superior results to the state-of-the-art offline SA-ASR model.
YOEO–You Only Encode Once: A CNN for Embedded Object Detection and Semantic Segmentation

Fast and accurate visual perception utilizing a robot’s limited hardware resources is necessary for many mobile robot applications. We are presenting YOEO, a novel hybrid CNN which unifies previous object detection and semantic segmentation approaches using one shared encoder backbone to increase performance and accuracy. We show that it outperforms previous approaches on the TORSO-21 and Cityscapes datasets.
Online Monitoring for Non-Stationary Operation via a Collaborative Neural Network

Online monitoring has become a challenging issue because of the changing knowledge in non-stationary operation. It requires an appropriate monitoring algorithm to timely self-adjust based on a finite number of online data. This study proposes a collaborative neural network (CoNN) for detecting physical intrusions in perimeter security monitoring through real-time adaptation. The proposed CoNN method consists of two half-shared neural networks for online learning with finite measurements through preserving previous knowledge and absorbing the new knowledge. Specifically, the shared feature layers can learn latent domain-invariant representations through weight sharing and adapt CoNN to prior knowledge changes. On this basis, the classification layers use concept-exclusive parameters to model the changes in posterior knowledge. Furthermore, the objective of CoNN is formulated as a linearly constrained non-convex minimization problem with coupled functions, and an alternating direction method is used to solve it with theoretically guaranteed convergence. The feasibility and effectiveness of the proposed method are validated through a real-field experiment of a perimeter security system.
Gamifikasi Bahasa Arab dengan Model Blended Learning

Arabic learning in Indonesia is entering a new phase during the pandemic. Students are allowed to join limited face-to-face meetings with reduced class hours. In response to this, a blended learning model is applied by combining face-to-face learning models in class and at other times conducting online learning outside the classroom or at home. To help students achieve the objectives of learning Arabic, gamification of learning is needed by utilizing online or android-based games. This study aims to describe how educators apply gamification in blended learning. The method used in writing this article is descriptive analysis or literature review which produces descriptive data from various research results on teacher experiences of gamification in blended learning. The results of this study indicate that students can practice with the educational games provided. This provides easy access and efficiency and enhances student interest.
Construction of Deep ReLU Nets for Spatially Sparse Learning

Training an interpretable deep net to embody its theoretical advantages is difficult but extremely important in the community of machine learning. In this article, noticing the importance of spatial sparseness in signal and image processing, we develop a constructive approach to generate a deep net to capture the spatial sparseness feature. We conduct both theoretical analysis and numerical verifications to show the power of the constructive approach. Theoretically, we prove that the constructive approach can yield a deep net estimate that achieves the optimal generalization error bounds in the framework of learning theory. Numerically, we show that the constructive approach is essentially better than shallow learning in the sense that it provides better prediction accuracy with less training time.
Deep Learning-Based Human Posture Recognition

Human posture recognition using real-life images is a challenging task. In order to devise effective spatial-temporal representations, we propose a hybrid model by integrating object detection, segmentation and classification methods for the recognition of three human postures, i.e. jumping, sitting and standing, from challenging real-world images. Specifically, a well-known deep learning model, i.e. Mask R-CNN, is first employed to detect and segment each human subject in an image. The extracted regional features from each segmented region are then passed to a revised Inception-ResNet-v2 model for posture recognition. In particular, the revised Inception-ResNet-v2 model shows great efficiency in deep feature extraction. Evaluated using human action data sets, the proposed hybrid model outperforms two other deep learning methods for posture classification. This two-stage process provides a fundamental step for future research, such as human recognition within a commercial environment.
Lyme Disease Detection Using Progressive Resizing and Self-Supervised Learning Algorithmslyme Disease Detection Using Progressive Resizing and Self-Supervised Learning Algorithms

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A Survey on Video Anomaly Detection

The number of surveillance cameras has increased considerably over the last decade, and so is the research in order to reduce the human intervention in surveillance in order to automate the process. Because human-based monitoring is time-consuming and difficult, there is an increasing demand for autonomous systems to detect abnormalities in crowded locations. Deep learning has shown to be a game-changing computing technology in the realm of computer vision. As a result, it's routinely utilised to perform complex cognitive tasks like spotting abnormalities in surveillance footage. Deep learning anomaly detection technologies beat conventional machine learning systems. Our research seeks to provide a complete analysis of video anomaly detection systems that employ deep learning that have been published since 2019. In this paper, we examined the learning methods as well as the dataset used for training and testing. Along with it, the model's accuracy is described. In addition, this work gives a brief review of video datasets for anomaly detection. Furthermore, present and emerging trends are emphasised.
Abuse, power and discourse in the public trial of Timothy Omotoso

Exposure of gender-based violence (GBV) has recently received attention from both scholars and the public. However, GBV within Christian discourse, and specifically as it occurs among pastors of the new Prophetic Pentecostal Churches (PPC), is yet to be explored in detail. This article begins to address this research gap by highlighting the difficulty of proving sexual and spiritual abuse in a secular court of law, as shown in the public trial of Timothy Omotoso. The study uses critical discourse analysis (CDA) as the methodology to examine aspects of the trial and the social media discourse commenting on the trial. In doing so, the article highlights three different discourses surrounding the Zondi trial, namely the discourse of GBV in Pentecostalism; the secular legal discourse and how it reshapes faith, spirituality and the abuse of believers; and the public trial victims endure on social media if they chose to come forward to testify against a church leader. By examining these discourses, the study shows how each of these need to be taken seriously and should inform pastoral care given to victims who experienced GBV by Christian leaders. Using the findings from Zondi’s testimony, this article proposes a framework of pastoral care that can support people who experience GBV within a Christian context and consider bringing their abusers to trial. Interdisciplinary and/or interdisciplinary implications: This article is an interface between GBV and Pentecostalism in a public trial of Timothy Omotoso through CDA. The article proposes a pastoral care as a remedy for those undergoing trauma of abuse.
Redis automatic performance tuning based on eBPF

In Internet applications, caching has become a key component of a high-concurrency architecture. As more and more companies use Redis as a caching database, the performance of Redis has become a particularly important part of the production environment. Performance tuning has always been a challenging field. Most engineers use the experience to determine the bottleneck of the system and then achieve performance tuning. This type of method is not only extremely time-consuming but also does not have the reusability of system parameters. In response to these problems, this article establishes an automatic tuning model that uses the eBPF program to identify different Redis working scenarios and uses the random forest method to sort the memory parameters in advance in different working scenarios to filter out the high-efficiency parameters. And then realize Redis automatic performance tuning by changing the parameter values. Different from the optimization at the application level, the operating system directly manages the resources of the hardware, so that not only the performance of the server can be maximized, but also the service scenario can be more suitable.
Data Plane Technology-agnostic Control and Orchestration Architecture for Optical Disaggregated Data Centers

We propose a novel SDN control and orchestration architecture to provide composed IaaS over optical disaggregated data centers. We experimentally validate intent-based mechanisms that make the architecture independent from the underlying physical infrastructure technology.
Image quality evaluation of video conferencing solutions with realistic laboratory scenes

Video conferencing has become extremely relevant in the world in the latest years.Traditional image and video quality evaluation techniques prove insufficient to properly assess the quality of these systems, since they often include special processing pipelines, for example, to improve face rendering.Our team proposes a suite of equipment, laboratory scenes and measurements that include realistic mannequins to simulate a more true-to-life scene, while still being able to reliably measure image quality in terms of exposure, dynamic range, color and skin tone rendering, focus, texture, and noise.These metrics are used to evaluate and compare three categories of cameras for video conference that are available on the market: external webcams, laptop integrated webcams and selfie cameras of mobile devices.Our results showed that external webcams provide a real image quality advantage over most built-in webcams in laptops but cannot match the superior image quality of tablets and smartphones selfie cameras.Our results are consistent with perceptual evaluation and allow for an objective comparison of very different systems.
Photonic Interferometric Imager with monolithic silicon CMOS photonic integrated circuits

We demonstrate, for the first time to our knowledge, a monolithically-integrated photonic interferometric imager circuit with on-chip detectors, CMOS trans-impedance-amplifiers, and associated photonic imager components. A proof-of-principle demonstration of interferogram fringe generation will be discussed.
Fast Computation of Generalized Eigenvectors for Manifold Graph Embedding

Our goal is to efficiently compute low-dimensional latent coordinates for nodes in an input graph—known as graph embedding—for subsequent data processing such as clustering. Focusing on finite graphs that are interpreted as uniform samples on continuous manifolds (called manifold graphs), we leverage existing fast extreme eigenvector computation algorithms for speedy execution. We first pose a generalized eigenvalue problem for sparse matrix pair (A, B), where A = L – μQ + ∊I is a sum of graph Laplacian L and disconnected two-hop difference matrix Q. Eigenvector v minimizing Rayleigh quotient $\tfrac{\mathbf{v}^ \top \mathbf{Av}}{\mathbf{v}^ \top \mathbf{v}}$ thus minimizes 1-hop neighbor distances while maximizing distances between disconnected 2-hop neighbors, preserving graph structure. Matrix B = diag({b <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> }) that defines eigenvector orthogonality is then chosen so that boundary/interior nodes in the sampling domain have the same generalized degrees. K-dimensional latent vectors for the N graph nodes are the first K generalized eigenvectors for (A, B), computed in $\mathcal{O}(N)$ using LOBPCG, where K ≪ N. Experiments show that our embedding is among the fastest in the literature, while producing the best clustering performance for manifold graphs.
Hessenberg factorization and firework algorithms for optimized data hiding in digital images

Abstract Data hiding and watermarking are considered one of the most important topics in cyber security. This article proposes an optimized method for embedding a watermark image in a cover medium (color image). First, the color of the image is separated into three components (RGB). Consequently, the discrete wavelet transform is applied to each component to obtain four bands (high–high, high–low, low–high, and low–low), resulting in 12 bands in total. By omitting the low–low band from each component, a new square matrix is formed from the rest bands to be used for the hiding process after adding keys to it. These keys are generated using a hybrid approach, combining two chaotic functions, namely Gaussian and exponential maps. The embedding matrix is divided into square blocks with a specific length, each of which is converted using Hessenberg transform into two matrices, P and H . For each block, a certain location within the H -matrix is used for embedding a secret value; the updated blocks are assembled, and the reverse process is performed. An optimization method is applied, through the application of the firework algorithm, on the set of the initial values that generate keys. Using an optimization procedure to obtain keys requires performing lowest possible change rate in an image and maintain the quality of the image. To analyze and test the efficiency of the proposed method, mean-square error (MSE) and peak signal-to-noise ratio (PSNR) measurements are calculated. Furthermore, the robustness of the watermark is computed by applying several attacks. The experimental results show that the value of the MSE is reduced by about 0.01 while the value of the PSNR is increased by about 1.25 on average. Moreover, the proposed method achieved a high-retrieval rate in comparison with the non-optimization approach.
Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

&lt;span lang="EN-US"&gt;Load balancing is crucial to ensure scalability, reliability, minimize response time, and processing time and maximize resource utilization in cloud computing. However, the load fluctuation accompanied with the distribution of a huge number of requests among a set of virtual machines (VMs) is challenging and needs effective and practical load balancers. In this work, a two listed throttled load balancer (TLT-LB) algorithm is proposed and further simulated using the CloudAnalyst simulator. The TLT-LB algorithm is based on the modification of the conventional TLB algorithm to improve the distribution of the tasks between different VMs. The performance of the TLT-LB algorithm compared to the TLB, round robin (RR), and active monitoring load balancer (AMLB) algorithms has been evaluated using two different configurations. Interestingly, the TLT-LB significantly balances the load between the VMs by reducing the loading gap between the heaviest loaded and the lightest loaded VMs to be 6.45% compared to 68.55% for the TLB and AMLB algorithms. Furthermore, the TLT-LB algorithm considerably reduces the average response time and processing time compared to the TLB, RR, and AMLB algorithms.&lt;/span&gt;
Valid Virtual Reality Applications for Commercial Kitchen Safety Training

The rise in the number of workers in gastronomy tourism has coincided with an increase in workplace accidents. New workers lack hazard identification abilities and fail to recognize dangerous events in the workplace. Thus, a two-fold goal has been established: (1) design and develop VR applications for spotting risks in the kitchen workplace, and (2) validate the VR application's effectiveness. The invention involved the 360-degree camera and VR software, and it was validated with multiple experts who had long working experience. The VR technology was found noteworthy as it received positive acceptance in the hospitality business. Keywords: Virtual Reality; Workplace Safety; Food Service Safety; Employee Safety eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7i19.3207
Content-Based Image Retrieval Using Gamma Distribution and Mixture Model

Since the last decade, the complexity of multimedia data, specifically images, is emerging exponentially as millions of images are uploaded by users on daily basis. Searching for a relevant image from such a substantial amount of data is very hectic and resource-demanding. To cope with this issue, researchers are working on content-based image retrieval (CBIR) approaches. This article proposes an efficient and novel probabilistic technique as a solution for content-based image retrieval. The patterns formed by the glyph structure of an image are excavated to yield content representations. These representations are accumulatively used to form a distribution, whereas the characteristics of this distribution represent the semantic structure of the image. In the end, the mixture model for gamma distribution is applied and parameters are refined through maximum likelihood. Furthermore, a mechanism is devised to retrieve matching images having comparable distribution patterns. Experiments show not only that the proposed technique yields a comparable precision to other competitive techniques but it also demonstrates that it is sufficiently efficient with high performance compared as compared to the others and requires unsupervised training.
Multifractal properties of oil and gas indices in the Songnan Low Uplift of the Qiongdongnan Basin deepwater area

Recently, a high-quality reservoir was discovered in the Songnan Low Uplift in the eastern deepwater area of the Qiongdongnan Basin, northern South China Sea. To establish whether this area has good exploration prospects, this paper uses the multifractal moment method to analyse 2689 data points acquired from the basin in the SE of the southern low bulge. It is pointed out that the fractal behaviours of different types of geochemical indices are often distinguished by multifractal spectra of different shapes, therefore multifractal spectrum function analysis is used in this paper. These data were considered within the context of 13 oil and gas indices describing the multifractal spectrum function morphological characteristics. The results show that these indices can be divided into three types according to the multifractal spectrum. Indices with a strong multifractal spectrum function in the Songnan Low Uplift have the characteristics of a wide and continuous multifractal spectrum function, forming a right-skewed arc shape, while the other indices show weak or single-fractal characteristics. The results are in good agreement with principal component analysis results, where the spectral function of each principal component shows that the gas source of the C1, C2, C3, nC4, nC5, and heavy hydrocarbon indices in the Songnan Low Bulge has multifractal characteristics. The weight of these indices with strong multifractal characteristics shows that the principal components C1, C2 and C3, and heavy hydrocarbons, are important indices for delineating hydrocarbon prospects in the Songnan Low Uplift. Multifractal and spatial analysis techniques provide new ideas for index selection and comprehensive information extraction for oil and gas exploration. Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis
Classification of Phishing Email Using Word Embedding and Machine Learning Techniques

Email phishing is a cyber-attack, bringing substantial financial damage to corporate and commercial organizations. A phishing email is a special type of spamming, used to trick the user to disclose personal information to access his digital assets. Phishing attack is generally triggered by emailing links to spoofed websites that collect sensitive information. The APWG survey suggests that the existing countermeasures remain ineffective and insufficient for detecting phishing attacks. Hence there is a need for an efficient mechanism to detect phishing emails to provide better security against such attacks to the common user. The existing open-source data sets are limited in diversity, hence they do not capture the real picture of the attack. Hence there is a need for real-time input data set to design accurate email anti-phishing solutions. In the current work, it has been created a real-time in-house corpus of phishing and legitimate emails and proposed efficient techniques to detect phishing emails using a word embedding and machine learning algorithms. The proposed system uses only four email header-based heuristics for the classification of emails. The proposed word embedding cum machine learning framework comprises six word embedding techniques with five machine learning classifiers to evaluate the best performing combination. Among all six combinations, Random Forest consistently performed the best with FastText (CBOW) by achieving an accuracy of 99.50% with a false positive rate of 0.053%, TF-IDF achieved an accuracy of 99.39% with a false positive rate of 0.4% and Count Vectorizer achieved an accuracy of 99.18% with a false positive rate of 0.98% respectively for three datasets used.