Reviewerly/exHarmony huggingface dataset:
An Optimized DNN Model for Real-Time Inferencing on an Embedded Device

For many automotive functionalities in Advanced Driver Assist Systems (ADAS) and Autonomous Driving (AD), target objects are detected using state-of-the-art Deep Neural Network (DNN) technologies. However, the main challenge of recent DNN-based object detection is that it requires high computational costs. This requirement makes it challenging to deploy the DNN-based system on a vehicle for real-time inferencing. The low response time and high accuracy of automotive applications are critical factors when the system is deployed in real time. In this paper, the authors focus on deploying the computer-vision-based object detection system on the real-time service for automotive applications. First, five different vehicle detection systems are developed using transfer learning technology, which utilizes the pre-trained DNN model. The best performing DNN model showed improvements of 7.1% in Precision, 10.8% in Recall, and 8.93% in F1 score compared to the original YOLOv3 model. The developed DNN model was optimized by fusing layers horizontally and vertically to deploy it in the in-vehicle computing device. Finally, the optimized DNN model is deployed on the embedded in-vehicle computing device to run the program in real-time. Through optimization, the optimized DNN model can run 35.082 fps (frames per second) on the NVIDIA Jetson AGA, 19.385 times faster than the unoptimized DNN model. The experimental results demonstrate that the optimized transferred DNN model achieved higher accuracy and faster processing time for vehicle detection, which is vital for deploying the ADAS system.
Residual dense network with non-residual guidance for blind image denoising

Residual learning is one of the most effective components in blind image denoising. It learns to estimate the noise instead of the clean image itself. A shortcoming of residual learning is that it cannot capture hierarchical features efficiently because there are no connections between layers to extract multi-level features. Therefore, residual dense network (RDN) adds dense connections among layers in the residual block to facilitate extraction of hierarchical features. Although RDN is a superior non-blind denoiser, it is only better than state-of-the-art methods in blind denoising when noise level is high. In this paper, we employ the concept of Wiener filters and add a non-residual network as a guidance for RDN. The non-residual network predicts the signal instead of the noise to assist RDN in achieving satisfactory performance across all noise levels. Then, a guidance network combines the outputs from both residual and non-residual networks to generate the final denoised output. Experimental results show that the new architecture performs better than state-of-the-art blind denoisers quantitatively and image quality of the new method is also better than existing methods.
Future libraries’ blockchain opportunities and challenges: a systematic literature review and research agenda

Purpose The purpose of this study is to look into the concept of blockchain technology which stores data in a distributed, tamper-resistant setting and how this technology may affect the future library and information (LI) centers’ operations and policy. It explores the potential benefits and limitations of this technology, for LI centers are the core objectives of this paper. Design/methodology/approach A systematic review of both academic and practitioner literature was conducted. It followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (2020) standards. Findings The review shows that the decentralized nature of blockchain will transform the work in libraries and improve the inter-communication among communities. It can provide better privacy of user data thus increasing collaboration. However, there might be technological, financial, legal and social challenges that may act as hurdles to the realization of these goals in the true sense. The overall result shows that by keeping data updated, libraries can evaluate blockchain opportunities and make the best use of this technology. Practical implications Given the huge investments by industry, academic research that explores potential ramifications and supports libraries is required. The adoption of blockchain was investigated in this study from a variety of perspectives for LI center’s while also providing directions for future research. Originality/value It is evident that globally there is progress in blockchain technology development in terms of research and adoption in many industries, but the intention to adopt blockchain by libraries is still a fantasy. The lack of empirical evidence in the existing literature justifies the gap that needs to be filled. This systematic review attempts to fill the gaps in the existing knowledge which may serve as a reference for future initiatives in this field.
A 3DCNN-Based Knowledge Distillation Framework for Human Activity Recognition

Human action recognition has been actively explored over the past two decades to further advancements in video analytics domain. Numerous research studies have been conducted to investigate the complex sequential patterns of human actions in video streams. In this paper, we propose a knowledge distillation framework, which distills spatio-temporal knowledge from a large teacher model to a lightweight student model using an offline knowledge distillation technique. The proposed offline knowledge distillation framework takes two models: a large pre-trained 3DCNN (three-dimensional convolutional neural network) teacher model and a lightweight 3DCNN student model (i.e., the teacher model is pre-trained on the same dataset on which the student model is to be trained on). During offline knowledge distillation training, the distillation algorithm trains only the student model to help enable the student model to achieve the same level of prediction accuracy as the teacher model. To evaluate the performance of the proposed method, we conduct extensive experiments on four benchmark human action datasets. The obtained quantitative results verify the efficiency and robustness of the proposed method over the state-of-the-art human action recognition methods by obtaining up to 35% improvement in accuracy over existing methods. Furthermore, we evaluate the inference time of the proposed method and compare the obtained results with the inference time of the state-of-the-art methods. Experimental results reveal that the proposed method attains an improvement of up to 50× in terms of frames per seconds (FPS) over the state-of-the-art methods. The short inference time and high accuracy make our proposed framework suitable for human activity recognition in real-time applications.
An Overview of the Vision-Based Human Action Recognition Field

Artificial intelligence’s rapid advancement has enabled various applications, including intelligent video surveillance systems, assisted living, and human–computer interaction. These applications often require one core task: video-based human action recognition. Research in human video-based human action recognition is vast and ongoing, making it difficult to assess the full scope of available methods and current trends. This survey concisely explores the vision-based human action recognition field and defines core concepts, including definitions and explanations of the common challenges and most used datasets. Additionally, we provide in an easy-to-understand manner the literature approaches and their evolution over time, emphasizing intuitive notions. Finally, we explore current research directions and potential future paths. The core goal of this work is to provide future works with a shared understanding of fundamental ideas and clear intuitions about current works and find new research opportunities.
Software medical device maintenance: DevOps based approach for problem and modification management

Abstract Software problem resolution and change control processes are crucial for the performance, safety, and regulatory clearance of a medical device. Software changes are frequent throughout the whole life cycle of a product; therefore, iterative analysis and risk assessment are necessary to determine the impact of changes on the whole system. IEC standard 62304 clarifies software medical device life cycle processes including problem and modification analysis and requirements. The aim of this work is to describe a cost‐effective and fully customizable solution for software medical devices problem and modification management implemented in our group. Key features of the problem and modification management related to the software life cycle have been identified and taken into account. A digital commercial platform for software development and maintenance has been identified and used to implement an architecture satisfying the standard. The architecture solution (SWMA, software maintenance architecture) has been successfully developed and a description of how it addresses IEC 62304 standard requirements both in terms of problem resolution and in terms of change control has been provided. The presented system can be useful for manufacturers/groups to establish software maintenance plans that include activities and tasks related to software problem resolution and change control processes. The approach can be adopted for analyzing and resolving problems arising before and after the release of a medical device.
BooLSPLG: A Library with Parallel Algorithms for Boolean Functions and S-Boxes for GPU

In this paper, we present a library with sequential and parallel functions for computing some of the most important cryptographic characteristics of Boolean and vectorial Boolean functions. The library implements algorithms to calculate the nonlinearity, algebraic degree, autocorrelation, differential uniformity and related tables of vectorial Boolean functions. For the sake of completeness, we provide the mathematical basis of these algorithms. Furthermore, we compare the performance of the parallel functions from the developed software with the corresponding sequential functions and with analogous functions from the well-known SageMath and SET packages. Functions from BooLSPLG can be used to develop efficient algorithms for constructing Boolean and vectorial Boolean functions with good cryptographic properties. The parallel part of the library is implemented using a CUDA parallel programming model for recent NVIDIA GPU architectures. BooLSPLG is an open-source software library written in CUDA C/C++ with explicit documentation, test examples, and detailed input and output descriptions of all functions, both sequential and parallel, and it is available online.
Duration Patterns of Chinese Syllables in Commercial and Social Oral Advertisements

Information structure of an utterance is one of the key factors determining phonological unit realization patterns including the duration which is manifested differently in males and females. This article examines the influence of information load and gender on the Chinese syllable duration in commercial and social advertising discourse. The material was taken from the texts of commercial and social oral advertising produced by 6 professional speakers (3 males, 3 females). The measurements were performed in Praat on the syllables with identical phonemics and the same lexical tones occurring both on informative (previously classified as such by a minimum of 6 out of 10 listeners) and uninformative (the remaining) utterance parts. The results show that duration is one of the most important markers of a syllable information load. In both commercial and social advertisements, in over 50 % of cases informative syllables’ duration was longer than that of uninformative ones. The difference was not large but rather consistent. It means that not only duration is involved in manifesting the utterance information structure but other prosodic parameters as well. Another finding was gender difference: 1) on the whole, syllables in female speech have longer duration than in male; 2) males use the duration technique more effectively than females: the contrast between loaded and unloaded syllables is higher
Role of Dynamic Response in Inclined Transverse Crack Inspection for 3D-Printed Polymeric Beam with Metal Stiffener

This paper aims to quantify the relationship between the dynamic response of 3D-printed polymeric beams with metal stiffeners and the severity of inclined transverse cracks under mechanical loading. Very few studies in the literature have focused on defects starting from bolt holes in light-weighted panels and considered the defect's orientation in an analysis. The research outcomes can be applied to vibration-based structure health monitoring (SHM). In this study, an acrylonitrile butadiene styrene (ABS) beam was manufactured through material extrusion and bolted to an aluminium 2014-T615 stiffener as the specimen. It simulated a typical aircraft stiffened panel geometry. The specimen had seeded and propagated inclined transverse cracks of different depths (1/1.4 mm) and orientations (0°/30°/45°). Then, their dynamic response was investigated numerically and experimentally. The fundamental frequencies were measured with an experimental modal analysis. The numerical simulation provided the modal strain energy damage index (MSE-DI) to quantify and localise the defects. Experimental results showed that the 45° cracked specimen presented the lowest fundamental frequency with a decreased magnitude drop rate during crack propagation. However, the 0° cracked specimen generated a more significant frequency drop rate with an increased crack depth ratio. On the other hand, several peaks were presented at various locations where no defect was present in the MSE-DI plots. This suggests that the MSE-DI approach for assessing damage is unsuitable for detecting cracks beneath stiffening elements due to the restriction of the unique mode shape at the crack's location.
Mapping Specific Constituents of an Ochre-Coloured Watercourse Based on In Situ and Airborne Hyperspectral Remote Sensing Data

It is a well-known fact that water bodies are crucial for human life, ecosystems and biodiversity. Therefore, they are subject to regulatory monitoring in terms of water quality. However, land-use intensification, such as open-cast mining activities, can have a direct impact on water quality. Unfortunately, in situ measurements of water quality parameters are spatially limited, costly and time-consuming, which is why we proposed a combination of hyperspectral data, in situ data and simple regression models in this study to estimate and thus monitor various water quality parameters. We focused on the variables of total iron, ferrous iron, ferric iron, sulphate and chlorophyll-a. Unlike other studies, we used a combination of airborne hyperspectral and RGB data to ensure a very high spatial resolution of the data. To investigate the potential of our approach, we conducted simultaneous in situ measurements and airborne hyperspectral/RGB aircraft campaigns at different sites of the Spree River in Germany to monitor the impact of pyrite weathering on water bodies after open-cast mining activities. Appropriate regression models were developed to estimate the five variables mentioned above. The model with the best performance for each variable gave a coefficient of determination R2 of 64% to 79%. This clearly shows the potential of airborne hyperspectral/RGB data for water quality monitoring. In further investigations, we focused on the use of machine learning techniques, as well as transferability to other water bodies. The approach presented here has great potential for the development of a monitoring method for the continuous monitoring of still waters and large watercourses, especially given the freely available space-based hyperspectral missions via EnMAP.
A Semi-Automatic Semantic-Model-Based Comparison Workflow for Archaeological Features on Roman Ceramics

In this paper, we introduce applications of Artificial Intelligence techniques, such as Decision Trees and Semantic Reasoning, for semi-automatic and semantic-model-based decision-making for archaeological feature comparisons. This paper uses the example of Roman African Red Slip Ware (ARS) and the collection of ARS at the LEIZA archaeological research institute. The main challenge is to create a Digital Twin of the ARS objects and artefacts using geometric capturing and semantic modelling of archaeological information. Moreover, the individualisation and comparison of features (appliqués), along with their visualisation, extraction, and rectification, results in a strategy and application for comparison of these features using both geometrical and archaeological aspects with a comprehensible rule set. This method of a semi-automatic semantic model-based comparison workflow for archaeological features on Roman ceramics is showcased, discussed, and concluded in three use cases: woman and boy, human–horse hybrid, and bears with local twists and shifts.
The effect of mobile‐assisted learning in real language attainment: A systematic review

Abstract Background As more people around the globe become immigrants or refugees, an increasingly popular subsection of research has aimed to investigate how these populations use mobile‐assisted language learning (MALL) to learn the language(s) of the host country. Objectives This study examines how the language learning, inclusion and self‐regulation are moderated by MALL different methods in immigrants and foreigners (with different backgrounds) regarding two settings (foreign language and second language). Materials and methods We desired to explore whether MALL results in better language learning for foreigners, refugees and immigrants compared to traditional methods (physical tools) and how various factors affect MALL, such as whether MALL is utilized in a formal versus informal context. Hence, this article conducts a systematic review through a meta‐analysis and thematic analysis of 17 total quantitative and qualitative studies regarding MALL research on immigrants and refugees. Results and Conclusions Our results suggested that the MALL method is in general beneficial for second or foreign language learning, but with limitations based on the individual learner (motivations, demographics, etc.) and sociocultural factors, especially within informal contexts (e.g. fear of being seen as an immigrant when using a mobile learning app). Considering the variables that affect MALL that we observed and discussed in our review, we suggest more attention to the specific needs and potential issues refugees and immigrants may encounter when using MALL in order to create mobile applications that best aid this population in learning the host country's language(s), thus making resettlement an easier process.
Predicting respiratory decompensation in mechanically ventilated adult ICU patients

Introduction: Mechanical ventilation is a life-saving treatment in the Intensive Care Unit (ICU), but often causes patients to be at risk of further respiratory complication. We created a statistical model utilizing electronic health record and physiologic vitals data to predict the Center for Disease Control and Prevention (CDC) defined Ventilator Associated Complications (VACs). Further, we evaluated the effect of data temporal resolution and feature generation method choice on the accuracy of such a constructed model. Methods: We constructed a random forest model to predict occurrence of VACs using health records and chart events from adult patients in the Medical Information Mart for Intensive Care III (MIMIC-III) database. We trained the machine learning models on two patient populations of 1921 and 464 based on low and high frequency data availability. Model features were generated using both basic statistical summaries and tsfresh, a python library that generates a large number of derived time-series features. Classification to determine whether a patient will experience VAC one hour after 35 h of ventilation was performed using a random forest classifier. Two different sample spaces conditioned on five varying feature extraction techniques were evaluated to identify the most optimal selection of features resulting in the best VAC discrimination. Each dataset was assessed using K-folds cross-validation (k = 10), giving average area under the receiver operating characteristic curves (AUROCs) and accuracies. Results: After feature selection, hyperparameter tuning, and feature extraction, the best performing model used automatically generated features on high frequency data and achieved an average AUROC of 0.83 ± 0.11 and an average accuracy of 0.69 ± 0.10. Discussion: Results show the potential viability of predicting VACs using machine learning, and indicate that higher-resolution data and the larger feature set generated by tsfresh yield better AUROCs compared to lower-resolution data and manual statistical features.
FontTransformer: Few-shot high-resolution Chinese glyph image synthesis via stacked transformers

Automatic generation of high-quality Chinese fonts from a few online training samples is a challenging task, especially when the amount of samples is very small. Existing few-shot font generation methods can only synthesize low-resolution glyph images that often possess incorrect topological structures or/and incomplete strokes. To address the problem, this paper proposes FontTransformer, a novel few-shot learning model, for high-resolution Chinese glyph image synthesis by using stacked Transformers. The key idea is to apply the parallel Transformer to avoid the accumulation of prediction errors and utilize the serial Transformer to enhance the quality of synthesized strokes. Meanwhile, we also design a novel encoding scheme to feed more glyph information and prior knowledge to our model, which further enables the generation of high-resolution and visually-pleasing glyph images. Both qualitative and quantitative experimental results demonstrate the superiority of our method compared to other existing approaches in few-shot Chinese font synthesis task.
EGRA-NeRF: Edge-Guided Ray Allocation for Neural Radiance Fields

Recently, Neural Radiance Fields (NeRF) has demonstrated great potential in synthesizing novel views for realistic video generation. However, renderings from NeRF appear excessively blurred and contain aliasing artifacts in some textures or edges. To alleviate this problem, Edge-Guided Ray Allocation (EGRA-NeRF) module is proposed in this paper. Such method proposes a novel ray allocation strategy to focus more rays on the textures and edges of the scene during the training stage. Specially, the Canny edge detector is introduced to generate the ray guidance map. This guidance map can lead the model to find areas with more textures. To focus more rays on edges and textures, a method is explored to re-adjust the ray guidance map and re-allocate rays dynamically. Compared with the ray allocation in most existing NeRF-based methods, EGRA-NeRF focuses rays more on textures and edges, which can achieve the refinement of edges and texture details. Experiments show that NeRF-based algorithms (NeRF and Instant-NGP) with our EGRA module can achieve more realistic video generation, while maintaining almost the same computation time.