Reviewerly/exHarmony huggingface dataset:
The Use of Military Profiles in Romance Fraud Schemes

Romance fraud occurs when offenders use the guise of a genuine relationship to defraud a victim, typically via online communications. Research has focused on the general structure of romance frauds, but few scholars have considered the potential link between victimization and specific narratives employed by offenders. This article explores the reported romance fraud victimizations of a group of Australians and explores similarities and differences in how a military narrative is involved compared to other genres. Limited differences were observed between those frauds using military narratives, highlighting the need for broader consideration of fraud prevention and disruption measures.
Relevance vector machine with tuning based on self-adaptive differential evolution approach for predictive modelling of a chemical process

In the past decade, relevance vector machines have gained the attention of many researchers, and this machine learning technique is a Bayesian sparse kernel method, both for classification and regression problems. In general, the choice of appropriate learning hyperparameters is a crucial step in obtaining a well-tuned model. To overcome this issue, we apply a self-adaptive differential evolution algorithm. In this paper, we propose a relevance vector machine for regression combined with a novel self-adaptive differential evolution approach for predictive modelling of phosphorus concentration levels in a steelmaking process with real data. We compared the performance of proposed relevance vector machine (RVM) with other machine learning techniques, such as random forest (RF), artificial neural network (ANN), K-nearest neighbors (K-NN), and also with statistical learning techniques as, Beta regression model and multiple linear regression model. The RVM has performance better than RF, ANN, K-NN, and statistical techniques used. Our study indicates that RVM models are an adequate tool for the prediction of the phosphorus concentration levels in the steelmaking process.
Cross-Modal Semantic Matching Generative Adversarial Networks for Text-to-Image Synthesis

Synthesizing photo-realistic images based on text descriptions is a challenging image generation problem. Although many recent approaches have significantly advanced the performance of text-to-image generation, to guarantee semantic matchings between the text description and synthesized image remains very challenging. In this paper, we propose a new model, Cross-modal Semantic Matching Generative Adversarial Networks (CSM-GAN), to improve the semantic consistency between text description and synthesized image for a fine-grained text-to-image generation. Two new modules are proposed in CSM-GAN: Text Encoder Module (TEM) and Textual-Visual Semantic Matching Module (TVSMM). TVSMM is aimed at making the distance of the pairs of synthesized image and its corresponding text description closer, in global semantic embedding space, than those of mismatched pairs. This improves the semantic consistency and consequently, the generalizability of CSM-GAN. In TEM, we introduce Text Convolutional Neural Networks (Text_CNNs) to capture and highlight local visual features in textual descriptions. Thorough experiments on two public benchmark datasets demonstrated the superiority of CSM-GAN over other representative state-of-the-art methods.
Student Self‐Initiated Use of Smartphones in Multilingual Writing Classrooms: Making Learner Agency and Multiple Involvements Visible

Abstract This study examines students’ self‐initiated use of smartphones in multilingual writing classrooms in English‐language programs at a U.S. university. Responding to calls for empirical research on materials use in second‐language (L2) classrooms, researchers have focused on the roles of language learning and teaching materials in various L2 classroom contexts. Despite widespread access to smartphones among university students, how these students self‐initiate use of smartphones for learning in L2 classrooms in noninterventional settings (cf. mobile‐assisted language learning, which is often implemented for pedagogical purposes) has been under‐researched. Employing linguistic ethnography along with a multimodal approach, this study examines moments (a) when L2 learners select smartphones as a relevant resource for learning and (b) when learner agency (e.g., Larsen–Freeman, 2019) and multiple involvements (i.e., simultaneously engaging in multiple activities and responsibilities as interlocutors; see Greer, 2016) become visible while students self‐initiate smartphone use. It highlights how smartphones enable L2 learners to access diverse semiotic resources, maintain long turns by gazing at the devices, gain epistemic authority or primacy with those resources, resolve communicative problems, and change interactional dynamics.
Multi-layer segmentation framework for cell nuclei using improved GVF Snake model, Watershed, and ellipse fitting

Screening of diseases including cervical cancer, breast cancer and colorectal cancer using cell images from pap smears have been widely applied in recent years. Accurate segmentation of cells including nuclei are important steps of the diagnosis and analysis. Separation of touching nuclei in microscopy images has become a challenge in biological and medical fields. Aiming at the specific screening scenes with limited dataset and labels, a novel multi-layer segmentation framework is proposed, by which the nuclei are segmented efficiently and accurately. First the Watershed and the improved GVF (gradient vector flow) Snake model (optimized by the rectified gradient) are combined for rough segmentation, which preliminarily separates the nuclei from background. Then the outputs from rough segmentation are used for the initialization of refined segmentation, overlapped nuclei is addressed by the convex hull detection, concave point detection and ellipse fitting. Two criteria for ellipse verification are presented to ensure the uniqueness of the fitted border. Better accuracy, robustness and lighter structure of nuclei splitting are gained. Two private (BJTU & BIT) and two publicly available dataset (U20S & NIH3T3) are used for the performance evaluation. Experimental results showed the nuclei are segmented by the proposed framework with effective results (sensitivity: 0.812, precision: 0.913, false positive: 1.8, false negative: 1.7). Proposed method outperforms deep learning models with lighter weight and less computation complexity, it can effectively and automatically segment nuclei or cells from smear images, especially for scenes without sufficient dataset and labels.
Usability of various dwell times for eye-gaze-based object selection with eye tracking

This study investigates the usability of various "dwell times" for selecting visual objects with eye-gaze-based input by means of eye tracking. Two experiments are described in which participants used eye-gaze-based input to select visual objects consisting of alphanumeric characters, dots, or visual icons. First, a preliminary experiment was designed to identify the range of dwell time durations suitable for eye-gaze-based object selection. Twelve participants were asked to evaluate, on a 7-point rating scale, how easily they could perform an object-selection task with a dwell time of 250, 500, 1000, or 2000 ms per object. The evaluations showed that a dwell time of 250 ms to around 1000 ms was rated as potentially useful for object selection with eye-gaze-based input. In the following main experiment, therefore, 30 participants used eye tracking to select object sequences from a display with a dwell time of 200, 400, 800, 1000 or 1200 ms per object. Object selection time, object selection success rate, the number of object selection corrections, and dwell time evaluations were obtained. The results showed that the total time necessary to select visual objects (object selection time) increased when dwell time increased, but longer dwell times resulted in a higher object-selection success rate and fewer object selection corrections. Furthermore, regardless of object type, eye-gaze-based object selection with dwell times of 200–800 ms was significantly slower for participants with glasses than for those without glasses. Most importantly, participant evaluations showed that a dwell time of 600 ms per object was easiest to use for eye-gaze-based selection of all three types of visual objects.
Deep neural networks-based real-time optimal navigation for an automatic guided vehicle with static and dynamic obstacles

In this paper, a hybrid-intelligent real-time optimal control approach based on deep neural networks (DNNs) is proposed to improve the autonomy and intelligence of automatic guided vehicles (AGVs) navigation control. We first formulate the motion planning problem of an AGV with static and dynamic obstacles as a nonlinear optimal control problem (OCP). Subsequently, a direct method incorporating a smooth transformation technique is utilized to obtain the high-fidelity optimal solutions off-line. The optimal state-action samples are then extracted from the optimal trajectories generated by the OCP from perturbed initial states and stored as a large optimal trajectory data set. Thereafter, the DNNs architecture is designed and trained off-line through the collected data set to learn the optimal state-action relationship. As a result, the well-trained DNNs are used to produce the corresponding to optimal feedback actions on-board. The main advantage of our approach is that the time-consuming computation process is only carried out off-line and thus the traditional repeated off-line planning for the AGV due to changes such as the perturbed initial conditions in the system can be effectively avoided. Numerical experimental results are carried out to demonstrate our designed DNNs-based optimal control approach can generate the optimal control instructions on-board to steer the AGV to the desired location with high robustness to initial conditions as well as satisfying different obstacle constraints.
Radio Frequency Fingerprinting on the Edge

Deep learning methods have been very successful at radio frequency fingerprinting tasks, predicting the identity of transmitting devices with high accuracy. We study radio frequency fingerprinting deployments at resource-constrained edge devices. We use structured pruning to jointly train and sparsify neural networks tailored to edge hardware implementations. We compress convolutional layers by a <inline-formula><tex-math notation="LaTeX">$27.2\times$</tex-math></inline-formula> factor while incurring a negligible prediction accuracy decrease (less than 1 percent). We demonstrate the efficacy of our approach over multiple edge hardware platforms, including a Samsung Gallaxy S10 phone and a Xilinx-ZCU104 FPGA. Our method yields significant inference speedups, <inline-formula><tex-math notation="LaTeX">$11.5\times$</tex-math></inline-formula> on the FPGA and <inline-formula><tex-math notation="LaTeX">$3\times$</tex-math></inline-formula> on the smartphone, as well as high efficiency: the FPGA processing time is <inline-formula><tex-math notation="LaTeX">$17\times$</tex-math></inline-formula> smaller than in a V100 GPU. To the best of our knowledge, we are the first to explore the possibility of compressing networks for radio frequency fingerprinting; as such, our experiments can be seen as a means of characterizing the informational capacity associated with this specific learning task.
Entity Slot Filling for Visual Captioning

To explore the specific visual aspects and the language consistency at the same time, this paper introduces a new image captioning task, dubbed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">entity slot filling captioning (ESFCap)</i> . It is similar to the masked entity completion tasks in NLP, which are widely used to study language context and has been successfully employed to improve language understanding. Specifically, given a sentence with blank for describing an image, the ESFCap task aims to fill the blank with proper text content according to the visual information. The filled text should be grounded to correct visual entities and also in concordance with the sentence structure. To support the ESFCap research, we collect and release an entity slot filling captioning dataset, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Flickr30k-EnFi</i> , based on Flickr30k-Entities. The Flickr30k-EnFi dataset consists of 31,783 images and 565,750 masked sentences, as well as the text snippets for the masked slot. For tackling the ESFCap task, we propose a multi-modal fusion model equipped with a novel adaptive dynamic attention module, termed AdaMFN. The AdaMFN model effectively leverages both global and local information from vision and language. It is also able to adaptively focus on the key linguistic knowledge and visual regions to generate correct filling results. The experimental results and analysis demonstrate the effectiveness of our proposed model.
Deep Neural Network–Based Double-Check Method for Fall Detection Using IMU-L Sensor and RGB Camera Data

Existing methods for fall detection may not detect a fall when it occurs or may generate a false alarm when a fall does not occur. In order to overcome these limitations and detect falls with 100% accuracy, a double-check method for fall detection in elderly people via an inertial measurement unit-location (IMU-L) sensor and a red-green-blue (RGB) camera is proposed. The IMU-L sensor is a combination of an IMU sensor (accelerometer and gyroscope) and an ultrawideband signal-based location sensor; the RGB sensor is mounted on a robot. The proposed method involves detecting and confirming the fall of an elderly individual via the IMU-L sensor and an RGB image, respectively. The IMU-L sensor is worn on the body to detect falls. When a potential fall occurs, the individual's location information is synchronized with the motion data. During detection, because of the sequential nature of IMU data, a deep learning technique called a recurrent neural network (RNN) is trained to classify falls. When the IMU indicates a suspected fall situation, the robot moves to the corresponding location and confirms whether a fall has occurred. During the confirmation stage, a convolutional neural network-based technique is applied to the RGB image data to recognize and confirm the fall. Repeated confirmed fall detections using this method classified falls more accurately than existing methods that use only an IMU sensor. We conducted a real-time experiment to validate our method using a dataset developed in a laboratory and achieved 100% accuracy in our experimental environment.
Deep learning in denoising of micro-computed tomography images of rock samples

Nowadays, the advantages of Digital Rock Physics (DRP) are well known and widely applied in comprehensive core analysis. It is also known that the quality of the 3D pore scale model drastically influences the results of rock properties simulation, which makes the preprocessing stage of DRP very important. In this work, we consider the application of Deep Convolutional Neural Networks (CNNs) for the preprocessing of CT images, specifically for denoising, in two setups - conventional fully-supervised learning and the self-supervised learning, when the only available data is the noisy images. To train CNNs in a supervised setup, we use images processed by a combination of bilateral and bandpass filters. We trained CNNs of the same architecture with different loss functions to find out how the choice of a loss function influences the model's performance. Some of the obtained CNNs yielded the highest quality in terms of full-reference and no-reference metrics and significant histogram effect (bimodal intensity distribution). Images denoised with these models were qualitatively and quantitatively better than the reference "ground truth" images used for training. We use the Deep Image Prior algorithm to train denoising models in a self-supervised setup. The obtained models are much better than ones obtained in fully-supervised setup, but are too slow, as they are optimization-based rather than feed-forward. Such an algorithm can be used in the dataset generation for feed-forward meta-models. These results could help to develop an AI-based instrument to build high-quality 3D segmented models of rocks for DRP applications.
Blockchain in supply chain management

In this introductory chapter, we explain many challenges in modern supply chains (SCs) that have not been addressed yet and highlight blockchain’s potential to address them. Most firms currently lack visibility beyond their direct or tier 1 suppliers. It gives an overview of the general features of blockchain that make this a promising tool for SC management. It discusses the current status of blockchain deployment in SCs. It also touches on the diverse value proposition of blockchain across various industries. Definitions and explanations of the key terms used in this book are provided. Key emphases are on different types of blockchain, smart contract, tokenization, and traceability. Also, the roadmap of this book will be discussed.
Comparing perceptions of a dimmable LED lighting system between a real space and a virtual reality display

Over the last several decades, designers have used digital screens to view images of real and simulated spaces and make critical design decisions. Screen technology has improved during this time, as technologies like OLED have replaced legacy displays (CRT, plasma, and LCD). These new screens provide a higher pixel resolution, luminous output and contrast ratio. Immersive head-mounted displays now allow designers to view immersive images, and recent developments in real-time rendering have encouraged the uptake of virtual reality (VR) head-mounted displays in mainstream practice and design education. This paper presents an experiment on lighting perception using a series of LED lighting conditions in a real space and a virtual representation of those conditions captured using a 360° high-dynamic-range camera and presented on an HTC Vive Pro HMD. Fifty-three participants were asked to rate each lighting condition by viewing it in a real space (n = 30) or via immersive HDR photographs displayed in a VR HMD (n = 23). The results show that ratings of visual comfort, pleasantness, evenness, contrast and glare are similar between the HTC Vive Pro HMD and our real space when evaluating well-lit scenes, but significant differences emerge in dim and highly contrasted scenes for a number of rating scales.
Efficient Jamming Identification in Wireless Communication: Using Small Sample Data Driven Naive Bayes Classifier

The identification of malicious jamming pattern is significant for wireless communication system to adopt targeted anti-jamming approaches. Efficient identification of malicious jamming patterns requires sufficient sample data and computing resources for the complex electromagnetic environment. However, with the use of portable communication equipment and emergency communications, it is difficult to ensure adequate sample data and computing resources. Against this background, this letter focuses on the jamming identification based on a small sample data-driven Naive Bayes classifier. The main idea is to obtain approximate conditional feature probability via data augment and kernel density estimation (KDE) from a small sample labelled data and construct Naive Bayes classifier, so as to form a fast jamming recognition method. Simulation results show that compared with the methods using decision tree or simple neutral network, the proposed scheme achieves a better average accuracy between six jamming patterns, as well as low-complexity.
Template-based Question Answering using Recursive Neural Networks

Most question answering (QA) systems over Linked Data, i.e. Knowledge Graphs, approach the question answering task as a conversion from a natural language question to its corresponding SPARQL query. A common approach is to use query templates to generate SPARQL queries with slots that need to be filled. Using templates instead of running an extensive NLP pipeline or end-to-end model shifts the QA problem into a classification task, where the system needs to match the input question to the appropriate template. This paper presents an approach to automatically learn and classify natural language questions into corresponding templates using recursive neural networks. Our model was trained on 5000 questions and their respective SPARQL queries from the preexisting LC-QuAD dataset grounded in DBpedia, spanning 5042 entities and 615 predicates. The resulting model was evaluated using the FAIR GERBIL QA framework resulting in 0.419 macro f-measure on LC-QuAD and 0.417 macro f-measure on QALD-7.
NFMF: neural fusion matrix factorisation for QoS prediction in service selection

Selecting suitable web services based on the quality-of-service (QoS) is essential for developing high-quality service-oriented applications. A critical step in this direction is acquiring accurate, personalised QoS values of web services. As the number of web services is enormous and the QoS data are highly sparse, improving the accuracy of QoS prediction has become a challenging issue recently. In this study, we propose a novel QoS prediction model, called neural fusion matrix factorisation, wherein we combine neural networks and matrix factorisation to perform non-linear collaborative filtering for latent feature vectors of users and services. Moreover, we consider context bias and employ multi-task learning to reduce prediction error and improve the predicted performance. Furthermore, we conducted extensive experiments in a large-scale real-world QoS dataset, and the experimental results verify the effectiveness of our proposed method.
Simple and Effective Graph Autoencoders with One-Hop Linear Models

Over the last few years, graph autoencoders (AE) and variational autoencoders (VAE) emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering. Graph AE, VAE and most of their extensions rely on multi-layer graph convolutional networks (GCN) encoders to learn vector space representations of nodes. In this paper, we show that GCN encoders are actually unnecessarily complex for many applications. We propose to replace them by significantly simpler and more interpretable linear models w.r.t. the direct neighborhood (one-hop) adjacency matrix of the graph, involving fewer operations, fewer parameters and no activation function. For the two aforementioned tasks, we show that this simpler approach consistently reaches competitive performances w.r.t. GCN-based graph AE and VAE for numerous real-world graphs, including all benchmark datasets commonly used to evaluate graph AE and VAE. Based on these results, we also question the relevance of repeatedly using these datasets to compare complex graph AE and VAE.