Reviewerly/exHarmony huggingface dataset:
Improved Resume Parsing based on Contextual Meaning Extraction using BERT

With the advent of technological advancement recruitment process is almost online. Most companies will first identify resumes from the received resumes online based on the required qualifications. This, in turn, requires resume culling based on the word-matching technique. It is hard for recruiters to identify the perfect resume, as the existing techniques utilized for classification are purely based on the words written in the resume. Hence, it is necessary to understand the given document contextually. This study exploits this concept of understanding the document contextually. Hence, it is proposed to use Bidirectional Encoder Representations from Transformer (BERT) vectorization to identify the text contextually. Results show that the proposed algorithm parses the resume better than the existing techniques.
Subtype and Stage Inference with Timescales

Neurodegenerative conditions typically have highly heterogeneous trajectories, with variability in both the spatial and temporal progression of neurological changes. Disentangling the variability in spatiotemporal progression patterns offers major benefits for patient stratification and disease understanding but is a complex methodological challenge. Here we present Temporal Subtype and Stage Inference (T-SuStaIn), a technique that uniquely integrates distinct ideas from unsupervised learning: disease progression modelling, clustering, and hidden Markov modelling. We formulate T-SuStaIn mathematically and devise an algorithm for inferring the model parameters and uncertainty. We demonstrate that the combination of disease progression modelling, clustering, and hidden Markov modelling uniquely enables the discovery of subtypes distinguished not just by ordering of abnormality accumulation, but also timescale. We apply T-SuStaIn to longitudinal volumetric imaging data from the Alzheimer's Disease Neuroimaging Initiative, deriving spatiotemporal Alzheimer's disease subtypes together with their timelines of evolution and associated uncertainty. T-SuStaIn has broad utility across a range of longitudinal clustering problems, both in neurodegenerative conditions and more widely in progressive diseases.
Feature selection-based decision model for UAV path planning on rough terrains

Path planning and obstacle avoidance in 3D terrain have been identified as a monumental challenge for a UAV in a variety of autonomous missions, such as disaster management, and search and rescue operations. In large terrain areas, it is a key problem for traditional approaches to search within the point-cloud maps to find a global path for a UAV considering the flight safety, maneuverability, weather constraints, and fuel cost. Hence, this paper proposes a trajectory planning technique for global and local path planning of a fixed-wing UAV above 3D terrain under static and dynamic constraints. For global path generation, a novel feature selection-based decision model has been proposed to select the features of a point-cloud map and transform them into the feature set. The feature set is utilized by an A* multi-directional planner with an extensive search area to deliver an optimal global path. The global path is assumed as the UAV's reference waypoints. The motion of the UAV on reference waypoints is simplified with two coordinates (R,d), where R is the cumulative distance covered by the UAV along the reference waypoints and d is its offset distance from the reference line segment in time t. For the local path planning, offset trajectories are generated along with reference waypoints to avoid collisions. Cost functions have been added so that the best global and local path can be chosen, taking into account altitude, weather, and fuel constraints. The simulation results and comparison show that the proposed approach outperforms various other 3D UAV path planning techniques in complex terrain.
IoMT-Assisted Medical Vehicle Routing Based on UAV-Borne Human Crowd Sensing and Deep Learning in Smart Cities

An emergency medical vehicle can save the patient’s life if it arrives at his location as quickly as possible. Unmanned aerial vehicles (UAVs) offer wide visibility and mobility, making them a viable choice for smart cities and intelligent transportation systems (ITSs) as edge devices for the Internet of Things (IoT). Based on population behavior and overcrowding, video surveillance through the Internet of multimedia things (IoMT) and public safety in smart cities can help determine the most efficient routes for emergency medical vehicles. This study investigates UAV overcrowding and abnormal population activity patterns, which affect the flow of emergency medical vehicles and traffic flow. Moreover, the purpose of this article is to analyze received video frames from UAVs in order to identify the most efficient route for emergency medical vehicles in smart cities to transfer patients in the event of abnormalities or overcrowding. In order to detect overcrowding on the streets, a hybrid Cascade-ResNet is utilized, which detects congestion based on many data points. Based on our proposed approach, we achieve a 2.5% improvement over similar methods because it is effective, flexible, and accurate. UAV video frames can be used to communicate with emergency response vehicles, to monitor traffic congestion, and to monitor other aspects of smart city life.
Sign Language Recognition using LSTM and Media Pipe

There are learning aids available for those who are deaf or have trouble speaking or hearing, but they are rarely used. Live sign motions would be handled via image processing in the suggested system, which would operate in real-time. Classifiers would then be employed to distinguish between distinct signs, and the translated output would show text. On the set of data, machine learning algorithms will be trained. With the use of effective algorithms, top-notch data sets, and improved sensors, the system aims to enhance the performance of the current system in this area in terms of response time and accuracy. Due to the fact that they solely employ image processing, the current systems can identify movements with considerable latency. In this project, our research aims to create a cognitive system that is sensitive and reliable so that persons with hearing and speech impairments may utilize it in day-to-day applications
Secure Federated Learning for Intelligent Industry 4.0 IoT Enabled Self Skin Care Application System

Secure Federated Learning is a cutting-edge technology for training machine learning models in a decentralized manner, while preserving the privacy of sensitive personal data. In the context of Intelligent Industry 4.0 IoT enabled self-skin care application systems, secure federated learning enables the aggregation of data from multiple devices, allowing for the development of more accurate and personalized skin care recommendations, while maintaining the security of personal health data. Despite the challenges associated with secure federated learning, such as data heterogeneity, network latency, and privacy protection, the future of this technology in self-skin care systems is promising, with potential for increased accuracy, wider adoption, and integration with other technologies.
An intelligent impact load identification and localization method based on autonomic feature extraction and anomaly detection

The main problems of impact load identification are the complexity of dynamic data and the establishment of theoretical model. This paper proposed an intelligent impact load identification and localization method based on autonomic feature extraction and anomaly detection. The method firstly extracts features automatically from dynamic response through principal component analysis and trains ensemble learning model to identify and locate impact loads. Then the method sifts abnormal impact samples through mathematical statistics and studies the causes of the impact anomaly to improve the accuracy of identification. An anomaly detection model is also trained. The effectiveness and efficiency of the method are verified by identifying the force peak and location of impact loads acting on a thin-walled cylinder structure. The final error of the proposed method is less than 6.62% and the accuracy of anomaly detection is more than 91%. The result proves that the automatic feature extraction and anomaly detection make the proposed method more intelligent and superior to the existing methods. The method can be applied to various engineering structures and exhibits potential for intelligent structural design and structural health monitoring.
Differential Evolution with wavelet basis function based parameter control and dimensional interchange for diversity enhancement

Differential Evolution (DE) is a potent population-based global optimization algorithm which has already proven efficient for optimization demands in engineering applications. However, even the state-of-the-art DE variants still suffer premature convergence and lack of diversity. In order to overcome the above mentioned weaknesses, this paper proposes a brand-new DE variant, namely zDE algorithm, for single-objective numerical optimization. The main contributions can be summarized as follows: First, an improved wavelet basis function is incorporated into the generation of the scale factor F and a Minkowski Distance based adaptation scheme is proposed for the adaptation of it. Second, a new trial vector generation strategy is first proposed as a supplementary of the existing strategies, and t-distribution is incorporated into this strategy. Third, a novel diversity enhancement technique is firstly proposed by changing the dimensional parameters of the individuals in the population. The zDE algorithm is validated under 88 benchmarks from the CEC2013, CEC2014, and CEC2017 test suites for real-parameter single-objective optimization, and the results show the superiority of our algorithm in comparison with the recent state-of-the-art DE variants.
On the adaptation of recurrent neural networks for system identification

This paper presents a transfer learning approach which enables fast and efficient adaptation of Recurrent Neural Network (RNN) models of dynamical systems. A nominal RNN model is first identified using available measurements. The system dynamics are then assumed to change, leading to an unacceptable degradation of the nominal model performance on the perturbed system. To cope with the mismatch, the model is augmented with an additive correction term trained on fresh data from the new dynamic regime. The correction term is learned through a Jacobian Feature Regression (JFR) method defined in terms of the features spanned by the model's Jacobian with respect to its nominal parameters. A non-parametric view of the approach is also proposed, which extends recent work on Gaussian Process (GP) with Neural Tangent Kernel (NTK-GP) to the RNN case (RNTK-GP). This can be more efficient for very large networks or when only few data points are available. Implementation aspects for fast and efficient computation of the correction term, as well as the initial state estimation for the RNN model are described. Numerical examples show the effectiveness of the proposed methodology in presence of significant system variations.
EBSE-YOLO: High Precision Recognition Algorithm for Small Target Foreign Object Detection

For the complex background along the electrified railroad, there are problems such as missed detection and false detection of invasive small target foreign objects.A high-precision recognition algorithm for small target foreign object detection is proposed.A channel attention mechanism ECA-Net is added to the backbone network of YOLOv5s so that the network can focus more on small target objects; borrowing from the network structure of weighted bidirectional feature pyramid network (BiFPN), the backbone network is fused with Neck by adding an edge to achieve cross-level feature fusion; the SPD-Conv module is used to replace the seventh layer convolutional kernel to increase the detail information extraction; using EIOU loss function to shorten the actual difference in width and height between the a priori frame and the real frame, The experimental results show that the improved algorithm increases the mAP value by 3.35 percentage points with little growth in computational volume, and the highest mAP value reaches 97.96%.The detection accuracy is greatly improved with a small loss of time cost, and the demand for small target foreign body detection in complex environments is achieved.
LightGCAN: A lightweight graph convolutional attention network for user preference modeling and personalized recommendation

Graph Neural Network (GNN) is a promising technique in representation learning on graph data. Graph Convolution Network (GCN) and Graph Attention Network (GAT) are two main representative techniques in GNN, which can learn node embedding on the graph by aggregating the embedding of the neighborhoods. However, the complex computation process of existing GCN and GAT makes them less effective for tasks with complex graph structures. Moreover, traditional GCN and GAT ignore the time factor in user representation learning, so the obtained user preference model is static and cannot reflect the dynamics of user preferences. In this work, we propose a time-aware Lightweight Graph Convolutional Attention Network (LightGCAN), which can capture static and dynamic user preferences efficiently by using different GNN strategies. Specifically, static user preferences are modeled by a GCN with only neighborhood aggregation, and dynamic user preferences are extracted by a time-aware GAT based on recently interacted items. Static and dynamic user preferences are then combined and fed into a dual-channel Deep Neural Network (DNN) model for feature interaction learning and matching score prediction. Extensive experiments on different datasets show that LightGCAN is superior to the SOTA recommendation method.
Foreign Objects Identification of Transmission Line Based on Improved YOLOv7

As the grid coverage rises, foreign objects invade more and more frequently, causing grid failures to rise every year.To address this issue, this paper proposes a deep learning-based transmission line unmanned inspection of foreign objects recognition algorithm.The algorithm is based on YOLOv7 (You Only Look Once) algorithm, combining with hyperparameter optimization based on genetic algorithm (GA) and space-to-depth (SPD) convolution to complete the foreign object recognition of transmission line Unmanned Aerial Vehicle (UAV) images.The proposed method can promptly determine and locate these targets' presence in aerial images.Finally, this paper compares the improved YOLOv7 algorithm with other YOLO series algorithms (Faster-rcnn, Centernet, and other target detection models).The comparison results show that the method has the highest Mean Average Precision (mAP) of 92.2% and the Frames Per Second (FPS) of 19 is second only to Centernet.Compared with the unimproved YOLOv7, the average accuracy in the recognition of tower cranes has increased by 11.9%, which is the most obvious improvement in accuracy compared with other detection targets.Meanwhile, the hyperparameter optimization based on genetic algorithm speeds up the convergence of the model.
Correlated Reference-Assisted Variational Quantum Eigensolver

We propose an active-space approximation to reduce the quantum resources required for variational quantum eigensolver (VQE). Starting from the double exponential unitary coupled-cluster ansatz and employing the downfolding technique, we arrive at an effective Hamiltonian for active space composed of the bare Hamiltonian and a correlated potential caused by the internal-external interaction. The correlated potential is obtained from the one-body second-order Møller-Plesset perturbation theory (OBMP2), which is derived using the canonical transformation and cumulant approximation. Considering different systems with singlet and doublet ground states, we examine the accuracy in predicting both energy and density matrix (by evaluating dipole moment). We show that our approach can dramatically outperform the active-space VQE with an uncorrelated Hartree-Fock reference.
A Semisupervised Arbitrary-Oriented SAR Ship Detection Network Based on Interference Consistency Learning and Pseudolabel Calibration

The rapid development of deep learning cannot be achieved without the support of abundant labeled data. However, obtaining such a large amount of annotated data needs the support of professionals in the field of synthetic aperture radar (SAR) image understanding, which leads to the scarcity of SAR datasets with annotations. The scarcity of annotations poses a bottleneck in the performance of SAR ship detectors based on deep learning. Recently, semi-supervised learning has become a hot paradigm, which can mine effective information from unlabeled data to further improve the performance of SAR ship detectors. However, existing semi-supervised SAR ship detection studies all adopted multi-stage semi-supervised frameworks, which are complex and inefficient. In this article, we first design an end-to-end semi-supervised framework for SAR ship detection. To overcome the strong interferences resulting from the imaging or quantization processes in SAR, we Introduce the interference consistency learning mechanism to enhance the model's robustness. To solve the complex background in the inshore scenario, a pseudo-label calibration network is designed to calibrate the pseudo-label according to the context knowledge around the ships. Based on the HRSID and the other four datasets, the superiority of the proposed approach over several state-of-the-art semi-supervised frameworks has been evaluated under various labeling ratios, i.e., 1%, 5%, 10%, and 100%.
A novel combined model based on echo state network optimized by whale optimization algorithm for blast furnace gas prediction

The accurate prediction of blast furnace gas (BFG) holder level is crucial for reasonable gas dispatch. Therefore, we propose an improved echo-state network (ESN) model, based on variational modal decomposition (VMD) and whale optimization algorithm (WOA), for the BFG system. This model extracts inherent characteristic components of the sequence using VMD for prediction and determines model inputs via partial autocorrelation functions (PACF). ESN effectively handles the shortcomings of other classical neural networks with many training parameters and slow calculation speed. The important parameters are optimized through WOA to improve the prediction accuracy of the proposed model. Based on our proposed model, the average MAPE of three datasets is 0.1805% for 1-Step prediction, 0.1829% for 3-Step prediction, and 0.1886% for PACF prediction. With high prediction accuracy for BFG holder level, our model takes less time to establish and calculate, meeting requirements for short-term level prediction and more reasonable BFG scheduling decisions. The experiments and discussions support the correctness, effectiveness, and superiority of our model.
How to Optimise Interaction with Chatbots? Key Parameters Emerging from Actual Application

AbstractAbstractConsumers’ resistance towards chatbot agency questions the quality of human-computer interaction, and thus, invites further investigation on factors determining chatbot efficiency. Current study addresses the above challenges by providing understanding on functionality and enjoyment, emerging as crucial drivers of satisfaction. Consumers who have used chatbots at least once in their daily life were invited to complete a survey, evaluating the interaction with the bot. The results are clear in showing that information quality, accuracy and competence are pivotal for chatbot functionality. Personal care and social presence enhanced interactivity perception and the enjoyment of the chatbot use. These factors might however turn into barriers when not met, as reported by consumers in low satisfaction group. Current findings provide valuable insights for developing and implementing chatbot applications to satisfy the user demand for enjoyable and functional interaction, through enhanced information quality, competence, personal care and social presence.Keywords: AIChatbotsenjoymentfunctionalityinteractivitysatisfaction Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsSvetlana BialkovaSvetlana Bialkova is a global professor, internationally recognised expert, author, speaker. She worked on numerous projects in top labs in The Netherlands, Belgium, Denmark, Germany, Switzerland, UK, Japan. Her recent projects explore the joint interaction between human, media, and technology, and how this influences multisensory experiences.
Can a Virtual Reality Training Scenario Elicit Similar Stress Response as a Realistic Scenario-Based Training Scenario?

One key feature for scenario-based training is introducing stressful, realistic scenarios for trainees. Some law enforcement agencies have begun using virtual reality systems to provide scenario training for their officers. Therefore, the current study was conducted to assess if a virtual reality training scenario can elicit a similar stress response as a realistic scenario-based training scenario. The independent groups quasi-experiment collected salivary markers of acute stress (α-amylase and secretory immunoglobulin A) prior to, and immediately following, either an in-person scenario-based training exercise (n=31) or a virtual reality scenario (n=27) based on the in-person exercise. Difference-in-difference and two-way ANOVA tests were performed. Overall, participants exposed to the virtual reality scenario experienced a similar stress response to the realistic in-person scenario-based training exercise. Implications for law enforcement agencies are discussed to hopefully move police training forward.
The role of multimedia tools in Hashemite Kingdom of Jordan education classroom teaching in the digital era

The article aims to reveal the role of education as a key component in the growth of nations, and it is essential for the construction of a more promising future for the nation’s youth, both as future professionals and as citizens, so that they may demonstrate their capacity to acquire the skills necessary for successful jobs. Technology development has led to the creation of many multimedia tools, which have enhanced the educational system. To boost one’s capacity for memory or understanding, one may use multimedia by combining more than one material with technology. Students, instructors, and educators need to have a solid understanding of how to use multimedia technologies that provide considerable educational advantages. This article presents an overview of numerous various kinds of multimedia tools that are used in learning and teaching. These technologies are beneficial in increasing educational quality as well as student performance in academics. Because the current generation in the coming decades is already acquainted with gadgets and other technical equipment, the educational system needs to make the most of this potential by adding multimedia tools into classroom settings.