Reviewerly/exHarmony huggingface dataset:
MTDOT: A Multilingual Translation-Based Data Augmentation Technique for Offensive Content Identification in Tamil Text Data

The posting of offensive content in regional languages has increased as a result of the accessibility of low-cost internet and the widespread use of online social media. Despite the large number of comments available online, only a small percentage of them are offensive, resulting in an unequal distribution of offensive and non-offensive comments. Due to this class imbalance, classifiers may be biased toward the class with the most samples, i.e., the non-offensive class. To address class imbalance, a Multilingual Translation-based Data augmentation technique for Offensive content identification in Tamil text data (MTDOT) is proposed in this work. The proposed MTDOT method is applied to HASOC’21, which is the Tamil offensive content dataset. To obtain a balanced dataset, each offensive comment is augmented using multi-level back translation with English and Malayalam as intermediate languages. Another balanced dataset is generated by employing single-level back translation with Malayalam, Kannada, and Telugu as intermediate languages. While both approaches are equally effective, the proposed multi-level back-translation data augmentation approach produces more diverse data, which is evident from the BLEU score. The MTDOT technique proposed in this work achieved a promising improvement in F1-score over the widely used SMOTE class balancing method by 65%.
Extracting High-Precision Vehicle Motion Data from Unmanned Aerial Vehicle Video Captured under Various Weather Conditions

At present, there are many aerial-view datasets that contain motion data from vehicles in a variety of traffic scenarios. However, there are few datasets that have been collected under different weather conditions in an urban mixed-traffic scenario. In this study, we propose a framework for extracting vehicle motion data from UAV videos captured under various weather conditions. With this framework, we improve YOLOv5 (you only look once) with image-adaptive enhancement for detecting vehicles in different environments. In addition, a new vehicle-tracking algorithm called SORT++ is proposed to extract high-precision vehicle motion data from the detection results. Moreover, we present a new dataset that includes 7133 traffic images (1311 under sunny conditions, 961 under night, 3366 under rainy, and 1495 under snowy) of 106,995 vehicles. The images were captured by a UAV to evaluate the proposed method for vehicle orientation detection. In order to evaluate the accuracy of the extracted traffic data, we also present a new dataset of four UAV videos, each having 30,000+ frames, of approximately 3K vehicle trajectories collected under sunny, night, rainy, and snowy conditions, respectively. The experimental results show the high accuracy and stability of the proposed methods.
Uncalibrated image-based visual servoing approach for translational trajectory tracking with an uncertain robot manipulator

This work presents an adaptive image-based visual servoing approach for uncertain robot manipulators using an eye-to-hand camera configuration, which does not require any calibration procedure and image velocity measurement. A monocular camera provides visual feedback for the robot's joint control, allowing successful tracking of the translational trajectory, prescribed in image space, for a given target object attached to the robot end-effector. As our contribution, an indirect adaptive control approach is proposed to deal with the uncertain parameters of the camera–robot system and any camera misalignment concerning the robot frame. The indirect adaptive visual servoing approach is then combined with a direct adaptive motion controller using a cascade control framework to consider the uncertain robot dynamics in our stand-alone robot vision system. Lastly, an adaptive model-based state observer is designed to avoid the need for measuring the image feature velocity in the interconnected control loops. The Lyapunov stability theory and the passivity paradigm are synergized to demonstrate the stability properties of the overall closed-loop system. Experimental results, obtained with a 4-DoF robot manipulator carrying out translational trajectory tracking tasks using a commercial webcam, illustrate the performance and effectiveness of the proposed adaptive control methodology.
Augmented Reality to overcome Visual Management implementation barriers in construction: a MEP case study

Visual Management (VM) is an important Lean method to enhance information flow and reduce waste in construction. However, its adoption is hindered by several barriers. Scientific works mention that emerging technologies can support or replace conventional VM practices, but empirical evidence is missing. Based on an in-depth literature review, we derived the research questions (RQs), arguing if Augmented Reality (AR) could mitigate VM implementation barriers. Thus, a corresponding AR measurement model was developed. Through a case study of mechanical, electrical, and plumbing (MEP) installations in a multi-story apartment building, the RQs were answered using an AR head-mounted display (HMD). To gather the necessary empirical evidence, the data was collected through direct observations on-site and through semi-structured interviews. The study findings show that (1) AR provided time savings and generally satisfactory accuracy levels. (2) AR demonstrably reduced the training effort to better support MEP marking work. (3) The use of AR reduced the resistance to change to adopt VM practices, although concerns were raised about poor ergonomics and work safety risks. Future research activities should consist of investigating the potential of other emerging technologies to overcome the common Lean implementation barriers in construction.
An Open Dataset for Video Coding for Machines Standardization

In recent years, video has dominated internet traffic and has become one of the major media formats. Besides its consumption by humans, videos are now often consumed by machines for analysis tasks such as object detection, segmentation, tracking, etc. Thus, efficient video coding for machines (VCM) becomes an important topic in academia and industry. Datasets are essential in the evaluation of a variety of coding tools for VCM. However, most of the publicly available datasets are only for academic usage, which prohibits their usage for many participants in the field of VCM study. In this paper, an open dataset with permissive license terms will be introduced. This dataset has been accepted by MPEG-VCM group as a test dataset. It is based on a larger video dataset, of which annotations for a subset of images are used to evaluate the performance of object detection and instance segmentation tasks. In addition, annotations for object tracking for a subset of videos are provided. The characteristics of the dataset and details of the annotations will be described. Evaluation results for multiple machine vision tasks will be presented to demonstrate the usage of the dataset.
Multi-Granularity Aggregation Transformer for Light Field Image Super-Resolution

Many light field image super-resolution methods aim to improve the quality of light-field image super-resolution reconstruction by exploiting the complementary information between sub-aperture images. Although these methods have achieved good results, the mutual information learning strategies between sub-aperture images are mostly handcrafted, limiting the super-resolution method to deal with the reconstruction of different scenes. We design a Transformer-based network named Multi-granularity Aggregation Transformer (MAT) to dynamically learn the complementary information between sub-aperture images in this paper. MAT is mainly implemented with the proposed multi-granularity aggregation blocks, which process sub-aperture images with three different granularity aggregation approaches and generate comprehensive spatial-angular representations for light field image super-resolution reconstruction. Extensive experiments are carried out on the mainstream light field image super-resolution datasets. MAT achieves new state-of-the-art results compared with other light field image super-resolution methods.
Face Reconstruction from Deep Facial Embeddings using a Convolutional Neural Network

State-of-the-art (SOTA) face recognition systems generally use deep convolutional neural networks (CNNs) to extract deep features, called embeddings, from face images. The face embeddings are stored in the system’s database and are used for recognition of the enrolled system users. Hence, these features convey important information about the user’s identity, and therefore any attack using the face embeddings jeopardizes the user’s security and privacy. In this paper, we propose a CNN-based structure to reconstruct face images from face embeddings and we train our network with a multi-term loss function. In our experiments, our network is trained to reconstruct face images from SOTA face recognition models (ArcFace and ElasticFace) and we evaluate our face reconstruction network on the MOBIO and LFW datasets. The source code of all the experiments presented in this paper is publicly available so our work can be fully reproduced.
Surveillance Video Quality Assessment Based on Quality Related Retraining

Surveillance videos have been widely used in many vision-based systems, supporting intelligent tasks such as object detection and tracking. However, the quality of surveillance videos suffers from poor weather conditions and inevitable compression error, which may have a negative influence on the performance of such tasks. Therefore, accurately distinguishing distortions and predicting severity levels are crucial. In this paper, we propose a quality related retraining framework as well as a no-reference (NR) multi-task video quality assessment (VQA) model to tackle the challenge of surveillance videos quality assessment. The quality related retraining framework operates on a synthetic VQA database. The proposed NR VQA method utilizes both spatial and temporal information by using ResNet50 and SlowFast. Then multiple distortion detection heads are applied to predict the severity levels for corresponding distortions. The experimental results show that the proposed method gains competitive performance on the Video Surveillance Quality Assessment Dataset (VSQuAD). The ablation study further confirms the contributions of the quality related retraining framework, spatial information, and temporal information.
Enhancing Deformable Convolution Based Video Frame Interpolation with Coarse-To-Fine 3d Cnn

This paper presents a new deformable convolution-based video frame interpolation (VFI) method, using a coarse to fine 3D CNN to enhance the multi-flow prediction. This model first extracts spatio-temporal features at multiple scales using a 3D CNN, and estimates multi-flows using these features in a coarse-to-fine manner. The estimated multi-flows are then used to warp the original input frames as well as context maps, and the warped results are fused by a synthesis network to produce the final output. This VFI approach has been fully evaluated against 12 state-of-the-art VFI methods on three commonly used test databases. The results evidently show the effectiveness of the proposed method, which offers superior interpolation performance over other state of the art algorithms, with PSNR gains up to 0.19dB.
Machine Vision-Based Fall Detection System using MediaPipe Pose with IoT Monitoring and Alarm

The incidence of falls is more commonly experienced by the ageing population globally, due to their increasing frailty. Fall detection systems are being developed combined with machine learning approaches that include those wearable devices, ambience-based systems, and vision-based systems. Wearable systems are omnipresent but intrusive while ambience-based systems are temperature-dependent that require a dedicated GPU. Challenges on vision-based fall detection systems include large computational cost, use of specialized camera, and system integration into smartphones. This research aims to propose a vision-based fall detection system ported on a smartphone application, that utilizes deep learning trained and tested from multiple RGB camera setups. In an event of a fall, the system is designed to provide an on-premises auditory alarm, an IoT notification, and a real-time video feed via the smartphone application. Using the pretrained MobileNetv2 CNN-based MediaPipe Pose and Random Forest Classifier for fall detection, experimental results show high-performance evaluation metrics.
Locally alignment based manifold learning for simultaneous feature selection and extraction in classification problems

Dimensionality reduction is an important step in increasing the performance of machine learning algorithms while decreasing the processing time. From feature reduction approaches, feature extraction is aimed to achieve better data representation while feature selection is meant to discard redundant features. The main contribution of this manuscript is to propose a hybrid feature selection and extraction approach which performs these tasks in a unified framework, simultaneously. Also, the proposed approach aims to maintain the manifold of data using locally alignment constrains. The main goal of these suggestions is to discriminate different classes while reducing the redundancy of the samples specially for the imbalanced data classification problems. The proposed approach optimizes a hybrid objective function that tries to both increase the between class discrimination and decrease the within class distribution while minimizing the information loss. Also, we have embedded a weighting factor into the objective function to achieve a basis for feature importance measurement and feature selection. To evaluate the proposed approach, a multiclass linear SVM classifier is applied on the reduced data in a k-fold cross-validation scheme, and the accuracy, as well as F-score, is used as the performance measure. Comparisons of the proposed method with some recent approaches on 12 datasets of UCI, show the superiority of the proposed method over previously proposed approaches. Also, Friedman and Nemenyi tests are applied which show significant improvement in the proposed approach.
Bayesian networks in project management: A scoping review

To support project management (PM) activities, many methods and techniques have been proposed. However, little information exists on how these methods and techniques are used. This is particularly the case with the technique of Bayesian networks (BNs). The purpose of this scoping review is to categorize and to describe how BNs are applied in PM. To this, 102 journal articles published between 2004 and 2021 were analyzed. First, we describe the nature and evolution over time of the articles. Second, based on a specifically developed framework, we classified the articles by project type (Construction & Infrastructure; Software & IT; Engineering & Manufacturing; and Others), project aspect (Challenges & Risks; Context & Process; and Outcomes), reasons for using BNs (Description; Prediction; and Prescription) and types of BNs (Basic BNs; Combined BNs; and Extended BNs). In general, the findings highlight continued evolution of the literature on the subject, mainly in the last five years (2017–2021). Furthermore, thanks to the classification framework, we have suggested to researchers avenues for future research. Practitioners could also use it as a basic tool to implement a PM program.
A vector-encirclement-model-based sparrow search algorithm for engineering optimization and numerical optimization problems

Sparrow search algorithm (SSA) is a new global optimization tool with high performance. In order to further improve its global and local search abilities, in this paper, an improved SSA (ISSA) is put forward where the main novelties lie in the producer centralization strategy, the vector encirclement model and the direction selection strategy. The producer centralization strategy is designed to update the position of producer with the hope of improving the global search ability of the producer, while the vector encirclement model and the direction selection strategy are proposed to update the position of scrounger for the purpose of improving the local search ability of scrounger. For the comparison purpose, the performances of the proposed ISSA and the existing excellent algorithms are tested in CEC2017 benchmark functions and 30 real-world constrained optimization problems. The experimental results demonstrate that the proposed ISSA substantially improves the convergence rate, optimization accuracy, stability of the SSA and can solve a wide range of real-world constrained optimization problems successfully with satisfactory performances. Finally, the proposed ISSA is successfully applied in the trajectory optimization of mechanical arms.
Experimental performance analysis of cloud resource allocation framework using spider monkey optimization algorithm

Summary The cloud services demand has increased exponentially in the last decade due to its plethora of services. It becomes a significant platform to compute large and diverse applications over the internet. On the contrary, on‐demand resource allocation to a variety of applications becomes a serious issue due to dynamic workload conditions and uncertainty in the cloud environment. Several existing state of art techniques often fails to allocate the optimal resources to forthcoming demands, leading to an imbalance workload over cloud platform, degrading the performance. This article introduces a secure and self‐adaptive resource allocation framework that addressed the mentioned issues and allocates the most suitable resources to users' applications while ensuring the deadline constraints. Further, the proposed framework is integrated with a metaheuristic algorithm named enhanced spider monkey optimization algorithm that is based on the intelligent foraging behavior of spider monkeys. The proposed algorithm finds an optimal resource for the user's application using the fission‐fusion approach and improves multiple influential parameters like time, cost, degree of load balancing, energy consumption, task rejection ratio and so on. The experimental CloudSim based results verified that the proposed framework performs superior to state of art approaches like PSO, GSA, ABC, and IMMLB.
Enhanced Performance of Reservoir Computing Using Multiple Self-Injection and Mutual Injection VCSELs

We propose and numerically demonstrate two reservoir computing (RC) schemes using multiple vertical-cavity surface-emitting lasers (VCSELs) with different external injections, i.e., self-injection and mutual injection. The virtual nodes are obtained from the orthogonal polarization modes of multiple VCSELs. We evaluate the performance of our proposed RC systems by using the chaotic time-series prediction and memory capacity (MC). Similar to edge-emitting lasers (EELs), the multiple VCSEL-based RC scheme exhibits better computing performance compared with a single VCSEL using several delay times. More interestingly, numerical results show that our proposed scheme using dual-mode is superior to that using single-mode under the scenario of the same number of virtual nodes, and the scheme using mutual injection VCSELs outperforms that using self-injection VCSELs, in which the coupling mechanism between both polarization modes may play an important role. Further, the effects of different polarized feedback/coupling configurations on both reservoir schemes are discussed in terms of the prediction performance and MC. Additionally, we demonstrate for the first time that the polarization mode competition significantly affects the parallel VCSEL-based RC performance through analyzing its dependence on the feedback phase.
A harmless webtoon for all: An automatic age-restriction prediction system for webtoon contents

‘Webtoon’ a digital version of comics, is one of the leading mainstream cultural contents accessed in South Korea. While the country has allowed a number of webtoon service providers to launch their services in the market, the need to establish new conditions and regulations on webtoon services is crucial for both users and providers. Similar to other cultural formats (e.g., movies), there is a huge amount of demand for content-related restrictions and regulations. Responding to these regulations and the rapidly increasing contents, we aim to propose an automated webtoon content age restriction model using both machine learning and traditional restriction approaches. From a dataset containing webtoon descriptions and thumbnails, we achieved 81.88% of the F1-score and 79.09% accuracy in predicting each webtoon’s age restrictions.
Comparative Performance Analysis of Vibration Prediction Using RNN Techniques

Drones are increasingly used in several industries, including rescue, firefighting, and agriculture. If the motor connected to a drone’s propeller is damaged, there is a risk of a drone crash. Therefore, to prevent such incidents, an accurate and quick prediction tool of the motor vibrations in drones is required. In this study, normal and abnormal vibration data were collected from the motor connected to the propeller of a drone. The period and amplitude of the vibrations are consistent in normal vibrations, whereas they are irregular in abnormal vibrations. The collected vibration data were used to train six recurrent neural network (RNN) techniques: long short-term memory (LSTM), attention-LSTM (Attn.-LSTM), bidirectional-LSTM (Bi-LSTM), gated recurrent unit (GRU), attention-GRU (Attn.-GRU), and bidirectional GRU (Bi-GRU). Then, the simulation runtime it took for each RNN technique to predict the vibrations and the accuracy of the predicted vibrations were analyzed to compare the performances of the RNN model. Based on the simulation results, the Attn.-LSTM and Attn.-GRU techniques, incorporating the attention mechanism, had the best efficiency compared to the conventional LSTM and GRU techniques, respectively. The attention mechanism calculates the similarity between the input value and the to-be-predicted value in advance and reflects the similarity in the prediction.