Reviewerly/exHarmony huggingface dataset:
Detection of Cyberbullying Patterns in Low Resource Colloquial Roman Urdu Microtext using Natural Language Processing, Machine Learning, and Ensemble Techniques

Social media platforms have become a substratum for people to enunciate their opinions and ideas across the globe. Due to anonymity preservation and freedom of expression, it is possible to humiliate individuals and groups, disregarding social etiquette online, inevitably proliferating and diversifying the incidents of cyberbullying and cyber hate speech. This intimidating problem has recently sought the attention of researchers and scholars worldwide. Still, the current practices to sift the online content and offset the hatred spread do not go far enough. One factor contributing to this is the recent prevalence of regional languages in social media, the dearth of language resources, and flexible detection approaches, specifically for low-resource languages. In this context, most existing studies are oriented towards traditional resource-rich languages and highlight a huge gap in recently embraced resource-poor languages. One such language currently adopted worldwide and more typically by South Asian users for textual communication on social networks is Roman Urdu. It is derived from Urdu and written using a Left-to-Right pattern and Roman scripting. This language elicits numerous computational challenges while performing natural language preprocessing tasks due to its inflections, derivations, lexical variations, and morphological richness. To alleviate this problem, this research proposes a cyberbullying detection approach for analyzing textual data in the Roman Urdu language based on advanced preprocessing methods, voting-based ensemble techniques, and machine learning algorithms. The study has extracted a vast number of features, including statistical features, word N-Grams, combined n-grams, and BOW model with TFIDF weighting in different experimental settings using GridSearchCV and cross-validation techniques. The detection approach has been designed to tackle users’ textual input by considering user-specific writing styles on social media in a colloquial and non-standard form. The experimental results show that SVM with embedded hybrid N-gram features produced the highest average accuracy of around 83%. Among the ensemble voting-based techniques, XGboost achieved the optimal accuracy of 79%. Both implicit and explicit Roman Urdu instances were evaluated, and the categorization of severity based on prediction probabilities was performed. Time complexity is also analyzed in terms of execution time, indicating that LR, using different parameters and feature combinations, is the fastest algorithm. The results are promising with respect to standard assessment metrics and indicate the feasibility of the proposed approach in cyberbullying detection for the Roman Urdu language.
LAKA-UAV: Lightweight authentication and key agreement scheme for cloud-assisted Unmanned Aerial Vehicle using blockchain in flying ad-hoc networks

Unmanned Aerial Vehicle (UAV) can be employed in various applications, including traffic control, and delivery application. However, these services are susceptible to various security attacks because sensitive data are exchanged through an open channel. Thus, a secure authentication scheme is essential for UAV. UAV has limited storage resources and computing capabilities. To overcome these problems, cloud computing is considered as a promising solution. Cloud computing provides various properties such as storage availability and scalability. Unfortunately, the cloud server's database can be a major target for an adversary because it is a centralized system. If an adversary intrudes on the cloud server's database, he/she may attempt to intercept or learn the stored data. To mitigate these issues, we design a secure and lightweight authentication and key agreement scheme for cloud-assisted UAV using blockchain in flying ad-hoc networks, called LAKA-UAV. LAKA-UAV utilizes blockchain technology to ensure access control and data integrity using log transactions and the cloud server securely manages collected data from UAV. We prove the security of LAKA-UAV based on informal security analysis and formal security verification implementation. Based on testbed experiments using MIRACL, LAKA-UAV provides about 2.13 times more efficient performance on average compared with related schemes in terms of computation. We present the blockchain implementation using Hyperledger Sawtooth platform.
Asymmetric Loss Functions for Noise-Tolerant Learning: Theory and Applications

Supervised deep learning has achieved tremendous success in many computer vision tasks, which however is prone to overfit noisy labels. To mitigate the undesirable influence of noisy labels, robust loss functions offer a feasible approach to achieve noise-tolerant learning. In this work, we systematically study the problem of noise-tolerant learning with respect to both classification and regression. Specifically, we propose a new class of loss function, namely asymmetric loss functions (ALFs), which are tailored to satisfy the Bayes-optimal condition and thus are robust to noisy labels. For classification, we investigate general theoretical properties of ALFs on categorical noisy labels, and introduce the asymmetry ratio to measure the asymmetry of a loss function. We extend several commonly-used loss functions, and establish the necessary and sufficient conditions to make them asymmetric and thus noise-tolerant. For regression, we extend the concept of noise-tolerant learning for image restoration with continuous noisy labels. We theoretically prove that lp loss ( ) is noise-tolerant for targets with the additive white Gaussian noise. For targets with general noise, we introduce two losses as surrogates of l0 loss that seeks the mode when clean pixels keep dominant. Experimental results demonstrate that ALFs can achieve better or comparative performance compared with the state-of-the-arts. The source code of our method is available at: https://github.com/hitcszx/ALFs.
Joint Inversion of Receiver Functions and Apparent Incidence Angles to Determine the Crustal Structure of Mars

Abstract Recent estimates of the crustal thickness of Mars show a bimodal result of either ∼20 or ∼40 km beneath the InSight lander. We propose an approach based on random matrix theory applied to receiver functions (RFs) to further constrain the subsurface structure. Assuming a spiked covariance model for our data, we first use the phase transition properties of the singular value spectrum of random matrices to detect coherent arrivals in the waveforms. Examples from terrestrial data show how the method works in different scenarios. We identify three previously undetected converted arrivals in the InSight data, including the first multiple from a deeper third interface. We then use this information to jointly invert RFs with the absolute S‐wave velocity information in the polarization of body waves. Results show a crustal thickness of 43 ± 5 km beneath the lander with two mid‐crustal interfaces at depths of 8 ± 1 and 21 ± 3 km.
Graph neural networks for representing multivariate resource usage: A multiplayer mobile gaming case-study

The emergence of Multiplayer Mobile Gaming (MMG) applications is intertwined with a plethora of Quality of Service and Quality of Experience requirements. Resource usage prediction can provide valuable insights into the corresponding orchestration and management process in the form of several proactive functionalities in resource scaling, service migration, task offloading and scheduling. These processes are crucial in the Cloud and Edge environments exploited by MMG applications. Thus, producing accurate resource usage predictions concerning these types of applications is of paramount importance. To that end, we propose a resource usage representation paradigm based on Graph Neural Networks (GNNs). The novelty of this approach is based on the process of leveraging the dependencies that exist among the various types of computational resources. Furthermore, we expand upon this representation approach to develop a GNN-based Encoder-Decoder model that caters to the complexities of resource usage and can provide multi-step resource usage predictions. This model is compared against numerous well-established Encoder-Decoder and Deep Learning prediction models to assess its efficiency. Finally, the proposed model is incorporated in a proactive Horizontal Autoscaling solution that manages to outperform a standard reactive Horizontal Autoscaling approach in the context of a large-scale simulation, in terms of various performance metrics, while keeping the volume of the required computational resources to a minimum. The findings of this work showcase the importance of developing novel approaches in order to represent resource usage and the numerous benefits in the context of application performance and resource consumption that may derive from such scientific endeavors.
Context understanding in computer vision: A survey

Contextual information plays an important role in many computer vision tasks, such as object detection, video action detection, image classification, etc. Recognizing a single object or action out of context could be sometimes very challenging, and context information may help improve the understanding of a scene or an event greatly. Appearance context information, e.g., colors or shapes of the background of an object can improve the recognition accuracy of the object in the scene. Semantic context (e.g. a keyboard on an empty desk vs. a keyboard next to a desktop computer ) will improve accuracy and exclude unrelated events. Context information that are not in the image itself, such as the time or location of an images captured, can also help to decide whether certain event or action should occur. Other types of context (e.g. 3D structure of a building) will also provide additional information to improve the accuracy. In this survey, different context information that has been used in computer vision tasks is reviewed. We categorize context into different types and different levels. We also review available machine learning models and image/video datasets that can employ context information. Furthermore, we compare context based integration and context-free integration in mainly two classes of tasks: image-based and video-based. Finally, this survey is concluded by a set of promising future directions in context learning and utilization.
MUNet: Motion uncertainty-aware semi-supervised video object segmentation

The task of semi-supervised video object segmentation (VOS) has been greatly advanced and state-of-the-art performance has been made by dense matching-based methods. The recent methods leverage space-time memory (STM) networks and learn to retrieve relevant information from all available sources, where the past frames with object masks form an external memory and the current frame as the query is segmented using the mask information in the memory. However, when forming the memory and performing matching, these methods only exploit the appearance information while ignoring the motion information. In this paper, we advocate for the return of the motion information and propose a motion uncertainty-aware framework (MUNet) for semi-supervised VOS. First, we propose an implicit method to learn the spatial correspondences between neighboring frames, building upon a correlation cost volume. To handle the challenging cases of occlusion and textureless regions during constructing dense correspondences, we incorporate the uncertainty in dense matching and achieve motion uncertainty-aware feature representation. Second, we introduce a motion-aware spatial attention module to effectively fuse the motion features with the semantic features. Comprehensive experiments on challenging benchmarks show that using a small amount of data and combining it with powerful motion information can bring a significant performance boost. We achieve 76.5% J&F only using DAVIS17 for training2, which significantly outperforms the SOTA methods under the low-data protocol. The code and supplementary materials will be available at https://npucvr.github.io/MUNet.
Online Meta-Learning for Hybrid Model-Based Deep Receivers

Recent years have witnessed growing interest in the application of deep neural networks (DNNs) for receiver design, which can potentially be applied in complex environments without relying on knowledge of the channel model. However, the dynamic nature of communication channels often leads to rapid distribution shifts, which may require periodically retraining. This paper formulates a data-efficient two-stage training method that facilitates rapid online adaptation. Our training mechanism uses a predictive meta-learning scheme to train rapidly from data corresponding to both current and past channel realizations. Our method is applicable to any deep neural network (DNN)-based receiver, and does not require transmission of new pilot data for training. To illustrate the proposed approach, we study DNN-aided receivers that utilize an interpretable model-based architecture, and introduce a modular training strategy based on predictive meta-learning. We demonstrate our techniques in simulations on a synthetic linear channel, a synthetic non-linear channel, and a COST 2100 channel. Our results demonstrate that the proposed online training scheme allows receivers to outperform previous techniques based on self-supervision and joint-learning by a margin of up to 2.5 dB in coded bit error rate in rapidly-varying scenarios.
Electronic HR Practices as a Critical Factor of Employee Satisfaction in Private Hospitals in Jordan

The aim of the study was to examine the impact of e-human resource management on employee satisfaction. Therefore, it focused on the private hospitals in Jordan. Data were primarily gathered through self-reported questionnaires creating by Google Forms which were distributed to a purposive sample of (320) physicians. In total, (285) responses were received including (11) invalid to statistical analysis due to uncompleted or inaccurate. Hence, the final sample contained (274) responses. Structural equation modeling (SEM) was conducted to test hypotheses. The results showed that E-human resource practices had a positive impact on employee satisfaction except e-compensation. They also indicated that the highest impact was for e-development. According to the study's findings, managers and decision-makers should follow the contract's implementation of all employee rights, as well as the employee's commitment to carry out all of the duties entrusted to him, ensure the employee's job stability, and work to ensure that an organization offers advanced technologies based on employee performance evaluation.
ORSI Salient Object Detection via Bidimensional Attention and Full-Stage Semantic Guidance

The application of optical remote sensing images (ORSIs) is prevalent in many fields. Accordingly, ORSI-oriented salient object detection (SOD) has attracted more attention in recent years. However, yet many previously proposed methods present appealing performance in natural scene images (NSIs), they are difficult to be directly extended to remote sensing images due to the more complex scenes, such as blended backgrounds and diversiform topological shapes. Most specifically designed models often fail to achieve satisfactory results due to the weak usage of edge information and the ignorance of attention loss. Besides, computational inefficiency often causes poor applicability. To solve these problems, we propose a new model, namely, Bidimensional Attention and Full-stage Semantic Guidance Network (BAFS-Net), containing an edge guidance branch and a mainstream detection branch. Concretely, edge guidance generates boundary information, in which supervision with border labels is imposed to highlight the salient regions and plays a complementary role on the main branch. The mainstream detection branch involves two important components, i.e., bidimensional attention modules (BAMs) and semantic-guided fusion modules (SGFMs). Between these two, BAM uniformly assembles channel and spatial attention in an efficient and rational manner, addressing the open issue of dimensionwisely attention computation. SGFM hammers at the fusion of high-level features and low-level features. Moreover, the semantic maps are employed to interact with SGFM in full stages. Our approach surpasses most state-of-the-art RSI-SOD methods proposed in recent years, with respect to the accuracy, parameter size, computational cost, and floating point operations per second (FLOPS). The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ZhengJianwei2/BAFS-Net</uri> .
Art as therapy in virtual reality: A scoping review

This scoping review focuses on therapeutic interventions, which involve the creation of artworks in virtual reality. The purpose of this research is to survey possible directions that traditional practices of art therapy and therapeutic artmaking could take in the age of new media, with emphasis on fully immersive virtual reality. After the collection of papers from online databases, data from the included papers were extracted and analyzed using thematic analysis. The results reveal that virtual reality introduces novel opportunities for artistic expression, self-improvement, and motivation for psychotherapy and neurorehabilitation. Evidence that artmaking in virtual reality could be highly beneficial in therapeutic settings can be found in many aspects of virtual reality, such as its virtuality, ludicity, telepresence capacity, controlled environments, utility of user data, and popularity with digital natives. However, deficiencies in digital literacy, technical limitations of the current virtual reality devices, the lack of tactility in virtual environments, difficulties in the maintenance of the technology, interdisciplinary concerns, as well as aspects of inclusivity should be taken into consideration by therapy practitioners, researchers, and software developers alike. Finally, the reported results reveal implications for future practice.
The Effect Knowledge Creation Process on Organizational Innovation in Social Security Corporation in Jordan

The aim of this study is to identify the effect knowledge creation process on organizational innovation in Social Security Corporation in Jordan. To achieve the objectives of the study, a questionnaire was prepared consisting of (47) items. The study population consists of all (Managers of departments, Directors of directorates, Heads of departments and staff), who perform their duties in Social Security Corporation in Jordan. The number of employees was about (435) employees. Where the study followed the method of comprehensive survey to withdraw the sample study. The study reached there is an effect of the process of creating knowledge on organizational innovation in the Social Security Corporation in Jordan. The study recommended that knowledge management should be adopted as an input to improve the levels of individual and organizational innovation, through the establishment of an independent department in Social Security Corporation in.
Learning machine learning with young children: exploring informal settings in an African context

Background and context Researchers have been investigating ways to demystify machine learning for students from kindergarten to twelfth grade (K–12) levels. As little evidence can be found in the literature, there is a need for additional research to understand and facilitate the learning experience of children while also considering the African context.Objective The purpose of this study was to explore how young children teach and develop their understanding of machine learning based technologies in playful and informal settings.Method Using a qualitative methodological approach through fine-grained analysis of video recordings and interviews, we analysed how 18 children aged 3–13 years constructed their interactions with a machine-based technology (Google’s Teachable Machine).Findings This study provides empirical support for the claim that Google’s Teachable Machine contributes to the development of data literacy and conceptual understanding across K–12 irrespective of the learners’ backgrounds. The results also confirmed children’s ability to infer the relationship between their own expressions and the output of the machine learning-based tool, thus, identifying the input-output relationships in machine learning. In addition, this study opens a discussion around differentials in emerging technology use across different contexts through participatory learning.Implications The results provide a baseline for future research on the topic and preliminary evidence to discern how children learn about machine learning in the African K–12 context.
A USV-UAV Cooperative Trajectory Planning Algorithm with Hull Dynamic Constraints

Efficient trajectory generation in complex dynamic environments remains an open problem in the operation of an unmanned surface vehicle (USV). The perception of a USV is usually interfered by the swing of the hull and the ambient weather, making it challenging to plan optimal USV trajectories. In this paper, a cooperative trajectory planning algorithm for a coupled USV-UAV system is proposed to ensure that a USV can execute a safe and smooth path as it autonomously advances through multi-obstacle maps. Specifically, the unmanned aerial vehicle (UAV) plays the role of a flight sensor, providing real-time global map and obstacle information with a lightweight semantic segmentation network and 3D projection transformation. An initial obstacle avoidance trajectory is generated by a graph-based search method. Concerning the unique under-actuated kinematic characteristics of the USV, a numerical optimization method based on hull dynamic constraints is introduced to make the trajectory easier to be tracked for motion control. Finally, a motion control method based on NMPC with the lowest energy consumption constraint during execution is proposed. Experimental results verify the effectiveness of the whole system, and the generated trajectory is locally optimal for USV with considerable tracking accuracy.
Personalization of CTC Speech Recognition Models

End-to-end speech recognition models trained using joint Connectionist Temporal Classification (CTC)-Attention loss have gained popularity recently. In these models, a non-autoregressive CTC decoder is often used at inference time due to its speed and simplicity. However, such models are hard to personalize because of their conditional independence assumption that prevents output tokens from previous time steps to influence future predictions. To tackle this, we propose a novel two-way approach that first biases the encoder with attention over a predefined list of rare long-tail and out-of-vocabulary (OOV) words and then uses dynamic boosting and phone alignment network during decoding to further bias the subword pre-dictions. We evaluate our approach on open-source VoxPopuli and in-house medical datasets to showcase a 60% improvement in F1 score on domain-specific rare words over a strong CTC baseline.
JOIST: A Joint Speech and Text Streaming Model for ASR

We present JOIST, an algorithm to train a streaming, cascaded, encoder end-to-end (E2E) model with both speech-text paired inputs, and text-only unpaired inputs. Unlike previous works, we explore joint training with both modalities, rather than pre-training and fine-tuning. In addition, we explore JOIST using a streaming E2E model with an order of magnitude more data, which are also novelties compared to previous works. Through a series of ablation studies, we explore different types of text modeling, including how to model the length of the text sequence and the appropriate text subword unit representation. We find that best text representation for JOIST improves WER across a variety of search and rare-word test sets by 4-14% relative, compared to a model not trained with text. In addition, we quantitatively show that JOIST maintains streaming capabilities, which is important for good user-level experience.