Reviewerly/exHarmony huggingface dataset:
Synthesizing Realistic Snow Effects in Driving Images Using GANs and Real Data with Semantic Guidance*

Intelligent vehicle perception algorithms often have difficulty accurately analyzing and interpreting images in adverse weather conditions. Snow is a corner case that not only reduces visibility and contrast but also affects the stability of the road environment. While it is possible to train deep learning models on real-world driving datasets in snow weather, obtaining such data can be challenging. Synthesizing snow effects on existing driving datasets is a viable alternative. In this work, we propose a method based on Cycle Consistent Generative Adversarial Networks (CycleGANs) that utilizes additional semantic information to generate snow effects. We apply deep supervision by using intermediate outputs from the last two convolutional layers in the generator as multi-scale supervision signals for training. We collect a small set of driving image data captured under heavy snow as the translation source. We compare the generated images with those produced by various network architectures and evaluate the results qualitatively and quantitatively on the Cityscapes and EuroCity Persons datasets. Experiment results indicate that our model can synthesize realistic snow effects in driving images.
Enhancing Robustness of LiDAR-Based Perception in Adverse Weather using Point Cloud Augmentations

LiDAR-based perception systems have become widely adopted in autonomous vehicles. However, their performance can be severely degraded in adverse weather conditions, such as rain, snow or fog. To address this challenge, we propose a method for improving the robustness of LiDAR-based perception in adverse weather, using data augmentation techniques on point clouds. We use novel as well as established data augmentation techniques, such as realistic weather simulations, to provide a wide variety of training data for LiDAR-based object detectors. The performance of the state-of-the-art detector Voxel R-CNN using the proposed augmentation techniques is evaluated on a data set of real-world point clouds collected in adverse weather conditions. The achieved improvements in average precision (AP) are 4.00 p.p. in fog, 3.35 p.p. in snow, and 4.87 p.p. in rain at moderate difficulty. Our results suggest that data augmentations on point clouds are an effective way to improve the robustness of LiDAR-based object detection in adverse weather.
Low-Complexity Deep HDR Fusion And Tone Mapping for Urban Traffic Scenes

In this paper we propose a computationally efficient neural network for high dynamic range fusion and tone mapping, for application in perception systems of autonomous vehicles. The proposed approach fuses two consecutive, differently exposed images into a single output with good exposure in all regions, in a standard dynamic range. Motion is compensated based on fast optical flow estimation, and subsequently by including an error mask as an input to the network to indicate the remaining artifact-prone regions. This is an efficient way for the network to learn to reduce the ghosting artifacts without increasing computational complexity. Unlike the conventional approach, we train the network on versatile traffic data, and evaluate the performance based on object detection quality metrics, rather than for visual quality. The performance was compared to a similarly complex representative method from literature. We achieved improved performance in challenging light conditions due to the robustness of our method in variable traffic conditions.
Cloud Cost Optimization: A Comprehensive Review of Strategies and Case Studies

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Header Recognition Utilizing an All-Optical Reservoir with Delay- Bandwidth-Product-Maximized Double-Ring Resonators

The delay-bandwidth product in double-ring resonators (DRRs) is optimized using reinforcement learning. Then, the optimized DRRs are used to build an all-optical reservoir for optical packet header recognition, enabling a word-error rate as low as 9×10 -4 .
A Photonic Deep Neural Network Processor on a Single Chip with Optically Accelerated Training

We report an end-to-end photonic deep neural network processor, fabricated in a commercial CMOS process, that integrates optical matrix algebra and nonlinear function units onto a single chip and implements all-optical DNN inference and training.
BSVM: A BERT-Based Support Vector Machine for Hindi Hostile Content Detection

Social media platforms are increasingly providing hostile content. This has caused a requirement for accurate hostile post detection so that appropriate countermove can be made. Increasingly hostile content in various electronic media has created new obstacles for language comprehension. Regional languages make it more challenging. Despite being a good number of studies have been done in the English language, there has not been much progress in Regional languages because the appropriate datasets and tools are not yet available. Hindi is the native language of 615M persons. This research offers a Bidirectional Encoder Representations from Transformers (BERT)-based contextual embedding approach with a combination of Support Vector Machine (SVM) in order to categorize social media posts in Hindi Devanagari script as hostile or non-hostile using the Constraint 2021 Hindi Dataset. Offensive, fake, defamatory, and hateful posts are further evaluated to determine their status. In this research work, several SOTA BERT-based techniques are also subjected to comparative analysis. Our proposed model is found to perform better than the baseline model for all the hostile subclasses (defamation, fake, hate, and offensive).
Implementation Solutions for FAIR Clinical Research Data Management

The COVID-19 pandemic and its global actions have emphasized the importance of more detailed clinical data for studies of diseases and their effects. However, collecting and processing these data and publishing them according to rules defined by the responsible institutions is challenging. This paper addresses a modular, scalable, distributed, and flexible platform to promote such processing and publication in a FAIR manner. In addition, we describe approaches adopted to automate the FAIRification phase activities. A proposal for a FAIR Data Point on the cloud is also presented, aiming to improve the scalability of the metadata repository.
Multi-Scale Cyclic Image Deblurring Based on PVC-Resnet

Aiming at the non-uniform blurring of image caused by optical system defects or external interference factors, such as camera shake, out-of-focus, and fast movement of object, a multi-scale cyclic image deblurring model based on a parallel void convolution-Resnet (PVC-Resnet) is proposed in this paper, in which a multi-scale recurrent network architecture and a coarse-to-fine strategy are used to restore blurred images. The backbone network is built based on Unet codec architecture, where a PVC-Resnet module designed by combinations of parallel dilated convolution and residual network is constructed in the encoder of the backbone network. The convolution receptive field is expanded with parallel dilated convolution to extract richer global features. Besides, a multi-scale feature extraction module is designed to extract the shallow features of different scale targets in blurred images, and then the extracted features are sent to the backbone network for feature refinement. The SSIM loss function and the L1 loss function are combined to construct the SSIM-L1 joint loss function for the optimization of the overall network to ensure that the image restoration at different stages can be optimized. The experimental results show that the average peak signal-to-noise ratio (PSNR) of the proposed model on different data sets is as high as 32.84 dB, and the structural similarity (SSIM) reaches 0.9235. and statistical structural similarity (Stat-SSIM) of 0.9249 on different datasets. Compared with other methods, the deblurred images generated by this method are superior to the methods proposed by Nah et al., Kupyn et al. and Cho S J et al., especially on the calibration board data set. The model proposed in this paper applies parallel dilated convolution and SSIM-L1 joint loss function to improve the performance of the network so that the edge and texture details of the restored image are clearer.
A Novel Approach for Fraud Detection in Blockchain-Based Healthcare Networks Using Machine Learning

Recently, the advent of blockchain (BC) has sparked a digital revolution in different fields, such as finance, healthcare, and supply chain. It is used by smart healthcare systems to provide transparency and control for personal medical records. However, BC and healthcare integration still face many challenges, such as storing patient data and privacy and security issues. In the context of security, new attacks target different parts of the BC network, such as nodes, consensus algorithms, Smart Contracts (SC), and wallets. Fraudulent data insertion can have serious consequences on the integrity and reliability of the BC, as it can compromise the trustworthiness of the information stored on it and lead to incorrect or misleading transactions. Detecting and preventing fraudulent data insertion is crucial for maintaining the credibility of the BC as a secure and transparent system for recording and verifying transactions. SCs control the transfer of assets, which is why they may be subject to several adverbial attacks. Therefore, many efforts have been proposed to detect vulnerabilities and attacks in the SCs, such as utilizing programming tools. However, their proposals are inadequate against the newly emerging vulnerabilities and attacks. Artificial Intelligence technology is robust in analyzing and detecting new attacks in every part of the BC network. Therefore, this article proposes a system architecture for detecting fraudulent transactions and attacks in the BC network based on Machine Learning (ML). It is composed of two stages: (1) Using ML to check medical data from sensors and block abnormal data from entering the blockchain network. (2) Using the same ML to check transactions in the blockchain, storing normal transactions, and marking abnormal ones as novel attacks in the attacks database. To build our system, we utilized two datasets and six machine learning algorithms (Logistic Regression, Decision Tree, KNN, Naive Bayes, SVM, and Random Forest). The results demonstrate that the Random Forest algorithm outperformed others by achieving the highest accuracy, execution time, and scalability. Thereby, it was considered the best solution among the rest of the algorithms for tackling the research problem. Moreover, the security analysis of the proposed system proves its robustness against several attacks which threaten the functioning of the blockchain-based healthcare application.
An Examination of Studies on the Use of Chatbot Technology in the Field of Education

Today, it is observed that technologies are rapidly advancing and being utilized in educational environments. Among these technologies, chatbots, which have gained significant popularity with ChatGPT, play a crucial role in enhancing learning experiences. Chatbots offer numerous opportunities to support learning performance, increase engagement, and provide extracurricular feedback and assistance to students. These chatbots are capable of interacting with users in a natural language format, enabling seamless communication. They are designed to follow predefined patterns, answer questions, and provide guidance within their defined scope. This article aims to examine the existing research on the utilization of chatbots in educational environments to shed light on the significance of their implementation. To achieve this objective, 19 articles and papers related to the field were analyzed using Google Scholar, incorporating keywords such as 'chatbot+education' and 'chatbot in education'. The articles and papers were subjected to a descriptive analysis, covering sections such as the subject, purpose, methodology, results, discussion, and recommendations. Based on the review, it was observed that many studies focused on the usability and product design of chatbots in education. The samples used in these studies predominantly comprised university-level participants, with limited applications targeting larger student populations. It is anticipated that the data and results presented in this study will serve as a guide to identify existing applications and research gaps in the utilization of chatbots in education.
Verification-Free Approaches to Efficient Locally Densest Subgraph Discovery

Finding dense subgraphs from a large graph is a fundamental graph mining task with many applications. The notion is recently formulated of locally densest subgraph (LDS) is recently formulated to identify multiple dense subgraphs that cover different regions of a large graph. Informally, an LDS is a subgraph with the highest density in its local region. The state-of-the-art algorithm for computing top-k LDSes with the highest densities is LDS. It iteratively computes the densest subgraph and removes it from the graph, where all the computed densest subgraphs form the candidates of LDSes. Then, each candidate is verified through a costly maximum flow computation. Although advanced pruning techniques are proposed in LDS, the verification step is still time consuming especially for not-so-small k values. In this paper, we aim to improve the efficiency of finding top-k LDSes by designing verification-free approaches. Our algorithms are based on our observation that the set of maximal λ-compact subgraphs for all possible λ values form a hierarchical structure, and LDSes are simply leaves in the hierarchical structure. Thus, we propose a divide-and-conquer algorithm LDS-DC as well as an optimized algorithm LDS-Opt to efficiently identify top-k LDSes without constructing the entire hierarchical structure. Both of our algorithms have lower time complexities than LDS. Extensive empirical studies on real graphs show that our optimized algorithm LDS-Opt outperforms LDS for all k values, and the improvement is up-to several orders of magnitude.
Open datasets in perioperative medicine: a narrative review

With the growing interest of researchers in machine learning and artificial intelligence (AI) based on large data, their roles in medical research have become increasingly prominent. Despite the proliferation of predictive models in perioperative medicine, external validation is lacking. Open datasets, defined as publicly available datasets for research, play a crucial role by providing high-quality data, facilitating collaboration, and allowing an objective evaluation of the developed models. Among the available datasets for surgical patients, VitalDB has been the most widely used, with the Medical Informatics Operating Room Vitals and Events Repository recently launched and the Informative Surgical Patient dataset for Innovative Research Environment expected to be released soon. For critically ill patients, the available resources include the Medical Information Mart for Intensive Care, the eICU Collaborative Research Database, the Amsterdam University Medical Centers Database, and the High time Resolution ICU Dataset, with the anticipated release of the Intensive Care Network with Million Patients' information for the AI Clinical decision support system Technology dataset. This review presents a detailed comparison of each to enrich our understanding of these open datasets for data science and AI research in perioperative medicine.
A study on learning analytics of using mobile augmented reality application to enhance cultural competence for design cultural creation in higher education

Abstract Background Taiwan's higher education institutions prioritize interdisciplinary knowledge and cultural competence in cultural design, emphasizing the value of immersion in the local environment to develop cultural competence. However, challenges arise from the disappearance of traditional local lifestyles and limitations of traditional outdoor lectures such as group size and learning environment. Mobile augmented reality technology serves as a personal learning tool to overcome these challenges, enabling learners to uncover hidden knowledge in scenic locations and improving learning efficiency. Objective The study utilized learning analytics to investigate the impact of using mobile augmented reality on enhancing learners' cultural competence. Methods The study used both qualitative and quasi‐experimental quantitative research to evaluate the effects of mobile augmented reality on cultural competence. A total of 78 third‐year university students participated in the study, with 37 of them using mobile augmented reality to learn about culture. The researchers utilized both the Mann–Whitney U test and interviews to compare the effectiveness of mobile augmented reality with that of a ‘real’ tour guide in field courses. Results The experimental results indicate that learners were able to continuously seek and find information about local culture through field exploration with mobile augmented reality, resulting in similar effects to those of attending a live lecture. Conclusions It showed that incorporating mobile AR into field visits can create gamified and active learning scenarios that enhance immersion and drive active learning in local culture. The results in greater cultural knowledge and understanding, leading to the development of cultural competence in students.
Social Networks during Software Ecosystems’ Death

Software Ecosystems (SECO) depend on platforms that serve as environments for developers’ interaction. The SECO may die when the organization that owns the platform does not support the synergy between organizational goals and developers’ expectations. The death results in the suspension of vital activities, such as code development and maintenance, impacting developers who lose work, learning, and experience gained. On the other hand, the responsible corporation loses resources invested in SECO. These signs indicate a SECO death is an important event for the community and should be analyzed. This paper reports a GitHub (GH) study focusing on three web SECOs: AngularJS, PhantomJS, and MomentJS. We analyze metrics based on developer community engagement and collaboration to understand what happens in these SECOs before, during, and after the platform’s death. From the search questions, we found some directions: Users’ recruitment and permanence: Without management and engagement, communities get out of SECOS a few years after your entry, so methods to control and organize the community are needed. Community relationships: The community needs engagement to strengthen and maintain your relationships. Some types of programs are useful methods to engage and encourage users to collaborate between them.
An Intelligent Resource Scheduling Method with Edge Channel Deployment for BPM

Edge computing is a novel computing paradigm that offers kinds of resources at the network edge. In edge computing, terminal users are connected to edge servers via the wireless network and there are various channels in each wireless link. These wireless channels are limited resource while different channel has different cost and service ability. The dynamic changes of users’ status make it harder to find an appropriate method to satisfy the BPM requirements of channel deployment. With this observation, it is a tricky challenge to make a trade-off between the system cost(rental price) and the service ability(number of users). In view of this challenge, an intelligent resource scheduling method, named EdgeIRS, is proposed in this paper. In the technical sense, the EdgeIRS method can accommodate most users at the edge with a minimum cost of deploying channel resources in an online way. Its performance is analyzed theoretically and the experiments verify the superiority of the method.
Adaptive-Mask Fusion Network for Segmentation of Drivable Road and Negative Obstacle With Untrustworthy Features

Segmentation of drivable roads and negative obstacles is critical to the safe driving of autonomous vehicles. Currently, many multi-modal fusion methods have been proposed to improve segmentation accuracy, such as fusing RGB and depth images. However, we find that when fusing two modals of data with untrustworthy features, the performance of multi-modal networks could be degraded, even lower than those using a single modality. In this paper, the untrustworthy features refer to those extracted from regions (e.g., far objects that are beyond the depth measurement range) with invalid depth data (i.e., 0 pixel value) in depth images. The untrustworthy features can confuse the segmentation results, and hence lead to inferior results. To provide a solution to this issue, we propose the adaptive-mask fusion Network (AMFNet) by introducing adaptive-weight masks in the fusion module to fuse features from RGB and depth images with inconsistency. In addition, we release a large-scale RGB-depth dataset with manually-labeled ground truth based on the NPO dataset for drivable roads and negative obstacles segmentation. Extensive experimental results demonstrate that our network achieves state-of-the-art performance compared with other networks. Our code and dataset are available at: https://github.com/lab-sun/AMFNet.
Real-time Semantic Segmentation in Traffic Scene based on Cross Stage Partial Block

Real-time semantic segmentation in traffic scenes takes an essential part in the fields of autonomous driving. The encoder-decoder-based network architecture can well combine the context information and detailed information required for the semantic segmentation task. Achieving the best balance between inference speed and accuracy is a crucial challenge, as considerable real-time semantic segmentation models process information in real-time at the expense of accuracy degradation. In this paper, we present a model with encoder-decoder architecture based on Cross Stage Partial block for real-time semantic segmentation in traffic scenes. The integration of the Cross Stage Partial block can not only lessen the computational overhead but also enhance the feature extraction ability of the network. In addition, we append the Fast Spatial Pyramid Pooling module to the backbone of the network, which can aggregate global information at a low computational cost. On NVIDIA RTX 3090, the middle and large models of our method can achieve a mIOU of 80.8% at 64.3 FPS and a mIOU of 81.5% at 48.4 FPS on the Cityscapes test set. Our source code is available on https://github.com/zhouliguo/cspsg.