Reviewerly/exHarmony huggingface dataset:
Scene graph descriptors for visual place classification from noisy scene data

In visual robot place recognition (VPR), a scene graph is a rich scene model that can describe the complex contexts in a scene such as the relationships between various types of visual contents including appearance, space, and semantics. However, training an efficient scene graph classifier is not straightforward. Existing approaches typically rely on exhaustive matching between query and database graphs and are not scalable to large-size VPR problems. Our research is motivated by a recent development of the graph convolutional neural network (GCN) as an efficient and discriminative classifier for graph data, and it aims to explore the potential of the GCN as a scene graph classifier. However, unlike several existing GCN applications, no valid scene graph descriptor for a GCN classifier on noisy scene data exists. To address this issue, herein, we propose to train the GCN model in a teacher-to-student knowledge transfer scheme by employing an existing state-of-the-art single-view VPR system as the teacher model. The proposed approach is implemented within a practical VPR framework by combining the best of the following three independent fields: multimodal information retrieval, rank matching, and similarity-based pattern recognition. Experiments using the public NCLT dataset validate the effectiveness of the proposed approach.
Automatic identification of storytelling responses to past‐behavior interview questions via machine learning

Abstract Structured interviews often feature past‐behavior questions, where applicants are asked to tell a story about past work experience. Applicants often experience difficulties producing such stories. Automatic analyses of applicant behavior in responding to past‐behavior questions may constitute a basis for delivering feedback and thus helping them improve their performance. We used machine learning algorithms to predict storytelling in transcribed speech of participants responding to past‐behavior questions in a simulated selection interview. Responses were coded as to whether they featured a story or not. For each story, utterances were also manually coded as to whether they described the situation, the task/action performed, or results obtained. The algorithms predicted whether a response features a story or not (best accuracy: 78%), as well as the count of situation, task/action, and response utterances. These findings contribute to better automatic identification of verbal responses to past‐behavior questions and may support automatic provision of feedback to applicants about their interview performance.
A fault-tolerant automatic mission planner for a fleet of aerial vehicles

Unmanned Aerial Vehicles are versatile tools for inspection tasks, for example of the built environment. Due to their limited flight time, it is sensible to employ multiple units to decrease the total mission time and avoid additional trips to change batteries. The use of more units makes the mission planning more complex; moreover, a fault tolerant plan that is resilient at least to a single failure is desirable. To solve these problems, a new algorithm is proposed, which, via hierarchical decomposition and numerical optimization, effectively deals with: (1) the efficient generation of a suitable path for each drone, and (2) guaranteeing mission robustness against a single fault. The automated generation and clustering of points of interest to be visited is addressed, too, as part of the whole procedure. Using accurate models of two real buildings, it is shown that the approach delivers close-to-optimal solutions with small computational time, thus being compatible with real-world operation.
Mixture of calibrated networks for domain generalization in brain tumor segmentation

Recent advances in deep learning for brain tumor segmentation demonstrate good performance when the training data and test data share the same distribution. However, medical images are often faced with distribution shifts due to different grades of cancers, various imaging qualities, and data from different medical institutions. Model calibration is highly related to domain generalization ability, and well-calibrated deep models show good domain generalization ability. Besides, ensemble learning is an implicit method for model calibration where domain shift problems can be alleviated by combining multiple models. In this paper, we aim to improve the generalization ability by explicitly calibrating the model in an ensemble. We proposed Mixture of Calibrated Networks (MCN) where multiple networks are jointly learned on the source and the augmented domain. We introduce a temperature scale to each network in an ensemble, which can be seen as the uncertainty of each domain to calibrate the predicted probabilities. These temperature scales form an uncertainty-aware loss to adaptively weigh the losses of multiple networks. The expectation–maximization (EM) algorithm is used to learn the parameters and explicitly model the relationship between key parameters, achieving better interpretability and robust parameter estimation. Moreover, we introduce an orthogonal constraint between convolutional kernels from the corresponding layer of multiple sub-networks, which keeps the diversity of sub-networks. We conduct extensive experiments on the brain tumor segmentation dataset from BRATS 2018, BRATS 2019, BRATS 2020, and FeTS 2021. Our proposed approach consistently improves the generalization performance and shows better calibration over inter-domain methods and intra-domain methods.
timeseriesflattener: A Python package for summarizing features from (medical) time series

Time series from e.g.electronic health records often have a large number of variables that are sampled at irregular and differing intervals.Before this type of data can be used for prediction modelling with machine learning methods such as logistic regression or XGBoost (Chen & Guestrin, 2016), the data needs to be reshaped.In essence, the time series need to be flattened so that each prediction time is represented by a vector of predefined length.This vector should hold the set of predictor values and an outcome value.These predictor values can be constructed by aggregating the preceding values in the time series within a certain time window.This process of flattening the data lays the foundation for further analyses and requires handling a number of tasks such as 1) how to deal with missing values, 2) which value to use if none fall within the prediction window, 3) how to handle variables measured multiple times within the chosen time window, and 4) how to handle predictors that attempt to look further back than the start of the dataset.
Improving Chinese Named Entity Recognition by Interactive Fusion of Contextual Representation and Glyph Representation

Named entity recognition (NER) is a fundamental task in natural language processing. In Chinese NER, additional resources such as lexicons, syntactic features and knowledge graphs are usually introduced to improve the recognition performance of the model. However, Chinese characters evolved from pictographs, and their glyphs contain rich semantic information, which is often ignored. Therefore, in order to make full use of the semantic information contained in Chinese character glyphs, we propose a Chinese NER model that combines character contextual representation and glyph representation, named CGR-NER (Character–Glyph Representation for NER). First, CGR-NER uses the large-scale pre-trained language model to dynamically generate contextual semantic representations of characters. Secondly, a hybrid neural network combining a three-dimensional convolutional neural network (3DCNN) and bi-directional long short-term memory network (BiLSTM) is designed to extract the semantic information contained in a Chinese character glyph, the potential word formation knowledge between adjacent glyphs and the contextual semantic and global dependency features of the glyph sequence. Thirdly, an interactive fusion method with a crossmodal attention and gate mechanism is proposed to fuse the contextual representation and glyph representation from different models dynamically. The experimental results show that our proposed model achieves 82.97% and 70.70% F1 scores on the OntoNotes 4 and Weibo datasets. Multiple ablation studies also verify the advantages and effectiveness of our proposed model.
Blockchain-driven integration technology for the AEC industry

In the architecture, engineering, and construction (AEC) industry, blockchain-driven integration technology (BDIT) has witnessed rapid development. A critical literature review of BDIT can contribute toward innovation for the AEC industry. In this study, a quantitative mapping of 247 BDIT literatures from 2017 to 2022 was conducted. Following the clue of quantitative work, two critical levels of technological development and application for BDIT were analyzed. The findings suggest: (1) the technological developments of BDIT may involve technological integration (i.e., integration of blockchain and internet of thing/building information modeling/edge computing) and knowledge framework; and (2) the technological applications of BDIT may involve information automation management and building information management. To further explore the trends of BDIT, some future evolutions (i.e., integration of blockchain and federated learning/digital twin/cloud-edge-end), application scenarios and challenges of BDIT were discussed. This study provides valuable theoretical and practical references for future research on BDIT in the AEC industry.
Multiple Object Tracking Using Re-Identification Model with Attention Module

Multi-object tracking (MOT) has gained significant attention in computer vision due to its wide range of applications. Specifically, detection-based trackers have shown high performance in MOT, but they tend to fail in occlusive scenarios such as the moment when objects overlap or separate. In this paper, we propose a triplet-based MOT network that integrates the tracking information and the visual features of the object. Using a triplet-based image feature, the network can differentiate similar-looking objects, reducing the number of identity switches over a long period. Furthermore, an attention-based re-identification model that focuses on the appearance of objects was introduced to extract the feature vectors from the images to effectively associate the objects. The extensive experimental results demonstrated that the proposed method outperforms existing methods on the ID switch metric and improves the detection performance of the tracking system.
Effects of Mobile Learning in English Language Learning: A Meta-Analysis and Research Synthesis

English has become the most important language for communication worldwide, but learning it as a second language presents multiple challenges. Given its multimedia nature, mobile learning is an ally in learning this language. However, although the use of mobile devices in English education has been broadly documented, there is little evidence of its effect on students’ learning. This article presents a meta-analysis of 62 studies to assess the effects of mobile learning on students’ learning. Moreover, the study considered the moderating effect of education level, pedagogical approach, learning environment, mobile device, and control treatment. The results show that mobile learning has a large effect (g=0.89) on students’ learning. Regarding education level, the best results were found at the Bachelor’s level. Similarly, collaborative learning provided the best results among the pedagogical approaches. As for the learning environment, semi-formal settings, such as field trips and outdoor activities, performed better than formal settings within classrooms or laboratories. Furthermore, smartphones yielded better results than any other mobile device. Finally, the results indicated that mobile learning produces better results than traditional lectures, traditional pedagogical tools, or other multimedia resources. Therefore, it should be promoted as a pedagogical alternative to foster quality education for all.
Hyperspectral image denoising based on the similar spectra approaching

The local-area-based methods that utilize the weighted average of the neighbor pixels to replace the central noisy pixel, are widely used for hyperspectral image (HSI) denoising due to their good spectrum preservation performance. However, most existing local-area-based denoising methods are originally designed for the RGB image without considering the special characteristic of HSI, and they usually require a parameter to obtain the weights of the neighbor pixels. In this paper, based on the linear mixed model of HSI, we derive a significant property of the noise-free HSI that each pixel can be linearly represented by its neighbor pixels. Inspired by this property, we develop a simple but effective local-area-based denoising method, in which the weights are obtained without any parameters. Moreover, our method can automatically select those neighbor pixels which are similar to the central pixel, so the overall shape of the spectrum to be denoised can be effectively maintained. Experiments on synthetic data and real data demonstrate that the proposed denoising method is superior to the state-of-the-art methods.
Reduced-Parameter YOLO-like Object Detector Oriented to Resource-Constrained Platform

Deep learning-based target detectors are in demand for a wide range of applications, often in areas such as robotics and the automotive industry. The high computational requirements of deep learning severely limit its ability to be deployed on resource-constrained and energy-first devices. To address this problem, we propose a class YOLO target detection algorithm and deploy it to an FPGA platform. Based on the FPGA platform, we can make full use of its computational features of parallel computing, and the computational units such as convolution, pooling and Concat layers in the model can be accelerated for inference.To enable our algorithm to run efficiently on FPGAs, we quantized the model and wrote the corresponding hardware operators based on the model units. The proposed object detection accelerator has been implemented and verified on the Xilinx ZYNQ platform. Experimental results show that the detection accuracy of the algorithm model is comparable to that of common algorithms, and the power consumption is much lower than that of the CPU and GPU. After deployment, the accelerator has a fast inference speed and is suitable for deployment on mobile devices to detect the surrounding environment.
Digital distribution processes and “new” research tools in SM-CDS

The present chapter aspires to clarify the critical role of different distribution processes in conceptual organisation for Social Media CDS (SM-CDS), an emerging theory and method wherein the specific nature and context of digitally mediated meaning-making is structurally integrated into CDS research (KhosraviNik 2020; KhosraviNik and Esposito 2018). In order to map key distribution processes and critical caveats around them in the digital sphere, we discuss some specific theoretical views around the notion of the distribution process in CDS, introduce analytical tools and techniques for the visualisation and analysis of social media data, and show how they can work as complementary techniques for SM-CDS studies. While rooted in core critical discursive principles, in fact, SM-CDS aspires to selectively and systematically incorporate digital theory approaches such as data science and analytics, visualization methods and videography, among others. In particular, we present three different methods, namely Social Network Analysis, Sentiment Analysis, and Digital Ethnography, as useful approaches to explore complex aspects of discursive distribution, affectivity, social and power relations in the cybersphere.
The Constitution of Dubai’s Mobile-App-Mediated Spatiotemporal Glocalization: Postphenomenology and Postdigitality in Dialogue

The present study proposes the notion-complex of “spatiotemporal glocalization” as a potentially postdigital phenomenon inhering in the touristscape representations mediated by mobile apps, with an analytic focus on the Dubai Travel mobile app. An integrative tripartite approach has been adopted with three theoretical models at work: (1) Don Ihde’s postphenomenological perspective as reconcilable with Roudometof’s model of refracted glocalities; (2) Vásquez and Cooren’s theoretical model of Communicative Constitution of Organizing (CCO); and (3) Jürgen Habermas’s pragmatic model of speech acts. Three findings have resulted from the approach-informed analysis. First, there existed various postdigital refracted touristic glocalities about Dubai with globally flowing cultural landscapes: ethnoscapes, mediascapes, financescapes, ideoscapes, and technoscapes. Second, it was demonstrated that the app-mediated glocalities have been organized through three spacing practices: (1) presentifying the materiality of glocalized hybrid interactions of techno-human actors, (2) ordering the glocal scripted trajectories of Dubai touristscape by creating more space and time across a 5-day regular framework of intervals, and (3) accounting as associated with the glocal narrative of Dubai touristscape through specifying its scripted trajectory at traditional and modern levels. Third, speech act theory has been instrumental in defining the “discourse worlds” in which human–nonhuman interactions communicatively unfolded, namely, mobile-interface technological actors and app users.
GMFIM: A generative mask-guided facial image manipulation model for privacy preservation

The use of social media has become so popular that people share photos every day on them. Automatic face recognition and tagging of people’s photos have caused privacy preservation issues and some methods have been proposed for hiding the identity of presented people in these images. Blurring and blacking the face area, adding physical adversarial patches to the face, and adding adversarial masks are some proposed methods for this purpose. However, these methods particularly suffer from dissimilarity of the input and output images and inadequate performance in identity concealment from automatic face recognition (AFR) systems. In this paper, we propose the Generative Mask-guided Face Image Manipulation (GMFIM) model based on Generative Adversarial Networks (GANs) to apply imperceptible edits to the input face image to preserve the identity of the person in the image. Our model consists of a face mask module, a GAN-based optimization module, and a merge module. Different criteria are considered in the objective function of the optimization step to produce high-quality images that are as similar as possible to the input image while they cannot be recognized by AFR systems. The results of the experiments on different datasets show that our model provides promising results in terms of the quality of the generated images and the identity concealment performance.
Machine Translation of Vedic Sanskrit using Deep Learning Algorithm

Machine Translation using deep learning is a big leap in the arena of automatic translations. In this paper we have proposed a neural translation method for end-to-end translation. The model is designed by using the 3 encoder and 3 decoder layers of gated Recurrent Neural Networks followed by the dense layer. We have also improved the translation by reversing the words from the source language using the Bi-Directional GRU. The output of translation from source language to target language achieves a significantly better BLUE and WRE score as compared to existing rule based translation.
Magnification independent fine-tuned transfer learning adaptation for multi-classification of breast cancer in histopathology images

Breast cancer is the prominent foremost disease causing death among the various cancers affecting female. Precise and initial diagnosis of diseases can often save lives. Histopathology microscopy imaging as a best practice used by pathologist to diagnose breast cancer. The procedure is labor intensive and time consuming. Hence, investigation of tissue samples in automated manner from histopathology images has constructive clinical implication in early diagnosis and treatment of disease. To capture discriminating traits from images in better way, images are acquired at various optical magnifications (x40, x100, x200 and x400). The disease can be treated well if the type of actual subclass of benign and malignant categories on a sample image is known early. The research gap observed in current literature isthat, limited study done on magnification independent multi- classification. With recent advances in Artificial Intelligence (AI)especially deep learning, the Convolutional Neural Networks (CNN) can be effectively utilized for automatic histopathology image analysis. In this work, multi-classification framework across different magnification factor using recent tax- onomy of BreakHis dataset is proposed. The proposed approach explores different transfer learning models with fine tuning adaptation. The experiments have shown impressive results on pre-trained models DenseNet121 as well as on DenseNet201. Promising results are observed towards multi-classification of breast cancer histopathology images as compared to other models.
Analysis and Comparison of DES, AES, RSA Encryption Algorithms

Cryptography is playing a fantastic role in shielding our records from steal by way of hackers. These days everywhere anybody is using internet for sending information,information,cryptography play a excellent function in protective those facts from frauders,hackers.Cryptography has distinctive algorithm for encrypting the information earlier than sending it to receiver. The Cryptography set of rules will continually remodel the plaintext into ciphertext which cannot recognize by way of unknown individual. So, sending a message to the receiver calls for encrypting that message with the sender’s public key. The textual content encrypted with a private key can handiest be decrypted with the equal public key. This paper represents the evaluation of DES,AES,RSA set of rules. these algorithms are one-of-a-kind from every other based totally on time taken to encrypt or decrypt the given information.
Cyber Security With Emerging Technologies &amp; Challenges

In order to save the online information systems of private, cooperative, and government organisations and prevent them from attacks that enters into organizations system must use flawless and smart cybersecurity technologies. Some industries technologies which are now emerging are machine learning, blockchain, forecast security, cloud computing and Artificial intelligence (AI). Likewise, security operations centers (SOCs) should increment the development of safety techniques to conquer defensive cases. For every user the advanced cyber security future protocols should be develop with the help of emerging security techniques.Cyber Security has a key aspect in the plot of IT. Data storage has been one of the biggest jobs of our time. Cyber crimes is the first thing which occurs in our mind when we start talking about cyber security that is increasing more & more with time. Different governments and organization took several steps to stop these cases. Exclusive of many covers cyber security is still a major challenge for many people. Challenges & difficulties faced by cyber security on the latest developed technologies and emerging cyber security techniques upgrading the world of cyber security will be discussed in this paper. Increasing of cybersecurity threat is overbearing and very unacceptable. A cyber-attack is visualized with the damage done by cyber-threats, but they are impossible to held – except for a digger situation between nations or states.Organizations which successfully created protective techniques should show them and provide it to the world. Fastly developing era increases the demand of safe and intelligent cyber defense techniques, including private cybersecurity groups, research centers of universities and government, corporations, public institutions, start-ups, etc.