Reviewerly/exHarmony huggingface dataset:
Day-to-night image translation via transfer learning to keep semantic information for driving simulator

Recently, autonomous driving technologies require robust perception performance through deep learning with huge data and annotations. To guarantee performance, perception accuracy should be robust even in nighttime. However, lots of perception technologies perform poorly with nighttime data. It is because most current datasets with annotation are composed of daytime scenes and there are few datasets for adverse conditions especially in nighttime. A massive cost of human resources and time is required to collect large amounts of data with annotation. To deal with the upper problem, many image translation methods by Generative Adversarial Networks (GANs) are proposed to generate realistic synthetic data. However, there is a significant limitation in traditional image translation methods. It is that generated images are inconsistent on semantic information to their original images. To handle this limitation, we propose an image translation with applying transfer learning to keep semantic information. There are two steps to train the proposed network. First, the segmentation network is trained on the source domain, i.e., daytime. After that, we transfer the pretrained segmentation weights to the encoder of generator and retrain only the decoder of GANs for day-to-night image translation. Experimental results show that the proposed method can generate more semantic consistent nighttime images than traditional studies.
Direction constraints adaptive extended bidirectional A* algorithm based on random two-dimensional map environments

This paper focuses on the mobile robot path planning problem of optimizing the performance metrics of bidirectional A* algorithm in randomized two-dimensional map environments. An algorithm called direction constraints adaptive extended bidirectional A* (DCAE-BA*), which is an improvement of the traditional target dynamic bidirectional A* algorithm (TTD-BA*), is proposed to improve the performance metrics of the algorithm. Regarding the improvement, we propose the adaptive extension method and the direction-constrained optimal node extension method (DCONE). Simulation experiments were conducted for DCAE-BA*, TTD-BA* and traditional A* algorithm (A*) in a large number of random two-dimensional map environments. The simulation experimental scenarios consider four types of start and end point relative directions and three obstacle proportions to objectively and comprehensively evaluate the performance of the proposed algorithms. The results show that different scenarios have a significant impact on the algorithm performance metrics. Finally, the overall performance of the proposed algorithm is evaluated with a large number of experiments in “random” scenarios, and the results show that DCAE-BA* obtains significantly better search time for all three obstacle proportions, and better path length and number of expanded nodes for 10% and 25% obstacle proportions. The effectiveness of the proposed DCAE-BA* algorithm is demonstrated, which provides an essential reference for the path planning of mobile robots in a random 2D map environment.
Effectiveness of digital resources for cloned Linear Algebra courses at the undergraduate level

The changes forced by the COVID-19 pandemic, have made educational institutions adopt new practices in the use of VLEs platforms and, one of these is to homogenize virtual classrooms, for which this study aims to diagnose how effective are the digital resources for cloned courses, taking as a pilot the subject of Linear Algebra. The development of this research is longitudinal, empirical-analytical, and quantitative. The study is carried out in two periods, from October 2021 to September 2022, at the Salesian Polytechnic University in the city of Guayaquil, Ecuador, with a total of 944 first year students of engineering careers. As a result, fewer courses for academic risk monitoring were obtained, as well as a higher satisfaction among students and professors involved. It is concluded that the cloned classrooms are a factor of improvement in the learning results to be achieved.
Multi-Semantic Fusion Generative Adversarial Network for Text-to-Image Generation

Text-to-image synthesis always faces two challenges: image quality and image-text alignment. Existing methods mainly use a single sentence to synthesize images, which are challenging to extract adequate semantic features, resulting in the generated images being far apart from ground-truth images. In this paper, we propose a novel Multi-Semantic Fusion Generative Adversarial Network. Our model can fuse the same semantics from different sentences and preserve their unique semantics to generate accurate images. In addition, we have designed a multi-sentence joint discriminator to ensure that the generated images match all sentences. Experiments on CUB and MSCOCO datasets demonstrate that our model has significant advantages.
Impact of homogeneous use of virtual classrooms based on a management model for teaching-learning processes

The objective of this research is to determine the impact of the homogeneous use of virtual classrooms based on a management model for teaching-learning processes in common courses of engineering careers at Universidad Politécnica Salesiana, Guayaquil- Ecuador. Students of Linear Algebra and Programming of the first year participate in the study. An empirical-analytical research methodology is developed with a longitudinal approach. The management model proposes the assignment of teachers, construction of renewable learning resources and follow-up to students in massive courses as a first pilot to reduce the number of students failing. Survey technique used for the two clusters of students in September 2022, with a simple random sampling of 14 Linear Algebra courses and 9 Programming courses to know their level of satisfaction. The results show a performance above 70% in course approval. A level of satisfaction higher than 90%.
SVScanner: Detecting smart contract vulnerabilities via deep semantic extraction

Blockchain is a significant advancement in technology recently, transforming the traditional centralized system into a decentralized one. Smart contracts, as one of the best applications of blockchain, show great potential in various fields, such as finance, supply chain, and the Internet of Things (IoT). As the world's first blockchain platform to support turing complete smart contracts, Ethereum has become the most critical infrastructure for the digital world. However, with the vigorous development of smart contracts, malicious attacks against smart contracts have frequently occurred in recent years. The issue of smart contract security has attracted widespread attention due to the huge financial losses caused by smart contract vulnerabilities. Although researchers have made some progress in detecting smart contract vulnerabilities through symbolic execution and fuzzing-based methods, existing methods mainly rely on expert knowledge and hand-crafted features, leading to many detection errors. Even worse, existing methods take tens of seconds or even minutes to detect each smart contract on average, which is extremely time-consuming. In this work, we present SVScanner, the new method combining two features of heterogeneous patterns to detect smart contract vulnerabilities in the blockchain. Specifically, we first extract global semantic features from the sequence of contract code tokens. Then we further use the attention mechanism to capture deep structural semantics from the Abstract Syntax Tree (AST) of smart contracts. Finally, we combine these two features from different patterns and use a text convolutional neural network (TextCNN) to detect contract bugs. Experimental results show that SVScanner has the ability to detect vulnerabilities effectively in real-world smart contract datasets. SVScanner achieves a 7.33% improvement in accuracy compared with other traditional methods. Moreover, our method requires significantly less detection time.
A deep learning approach for automatic delineation of clinical target volume in stereotactic partial breast irradiation (S-PBI)

Accurate and efficient delineation of the clinical target volume (CTV) is of utmost significance in post-operative breast cancer radiotherapy. However, CTV delineation is challenging as the exact extent of microscopic disease encompassed by CTV is not visualizable in radiological images and remains uncertain. We proposed to mimic physicians' contouring practice for CTV segmentation in stereotactic partial breast irradiation (S-PBI) where CTV is derived from tumor bed volume (TBV) via a margin expansion followed by correcting the extensions for anatomical barriers of tumor invasion (e.g. skin, chest wall). We proposed a deep-learning model, where CT images and the corresponding TBV masks formed a multi-channel input for a 3D U-Net based architecture. The design guided the model to encode the location-related image features and directed the network to focus on TBV to initiate CTV segmentation. Gradient weighted class activation map (Grad-CAM) visualizations of the model predictions revealed that the extension rules and geometric/anatomical boundaries were learnt during model training to assist the network to limit the expansion to a certain distance from the chest wall and the skin. We retrospectively collected 175 prone CT images from 35 post-operative breast cancer patients who received 5-fraction partial breast irradiation regimen on GammaPod. The 35 patients were randomly split into training (25), validation (5) and test (5) sets. Our model achieved mean (standard deviation) of 0.94 (±0.02), 2.46 (±0.5) mm, and 0.53 (±0.14) mm for Dice similarity coefficient, 95th percentile Hausdorff distance, and average symmetric surface distance respectively on the test set. The results are promising for improving the efficiency and accuracy of CTV delineation during on-line treatment planning procedure.
Which method to choose for measurement of oranic and inorganic carbon content in carbonate-rich soils? Advantages and disadvantages of dry and wet chemistry

The pedosphere is an essential reservoir of carbon represented by organic (SOC) and inorganic (SIC) forms. Various methods are used worldwide to measure SOC and SIC. The trend towards globalization of data on soils requires having the accumulated information harmonized. Global soil databases are crucial for inventory and mapping, modeling and forecasting, monitoring and rational use of soil resources. This study compared the six most common methods for measuring carbon in soils on calcareous rocks. Standard soil samples and soils of the Polar Urals with a variation in CaCO3 (0–100%) and SOC (0–40%) were selected as research targets. The loss-on-ignition (LOI) method (T = 550 °C) overestimates SOC. It is explained by the fact that the SOC to SOM conversion factor of 1.724 seems inadequate. The soils under study demand a coefficient of 2.326. The second reason is the presence of thermolabile mineral components in soils, as well as adsorbed and chemically bound water. For soils with a carbonate content of 0–100% and SOC < 8.7%, harmonized results of SOC measurements may be obtained on the analyzer and by the dichromatometric method (Walkley-Black) only with conversion factors applied (1.3). At a higher SOC (8.7–40%) content, soils in the area under study require a reduced coefficient of 1.18. The measurement results of SIC within the range of 3.2–12% (CaCO3 27–100%) obtained by LOI methods (T = 800 °C) and with a calcimeter are identical with SOC < 16%. The presence of CaCO3 does not inhibit the use of the dichromatometric method (Walkley-Black, Tyurin) to measure SOC. The reliability of carbon content measurement methods for soils was determined both by the specific composition of the soil and by the accuracy of the method itself. To facilitate future application of the methodology, guidelines were proposed to use methods for quantitative assessment of SOC and SIC.
Credit Risk Models for Financial Fraud Detection

Outlier detection is currently applied in many fields, where existing research focuses on improving imbalanced data or enhancing classification accuracy. In the financial area, financial fraud detection puts higher demands on real-time and interpretability. This paper attempts to develop a credit risk model for financial fraud detection based on an extreme gradient boosting tree (XGBoost). SMOTE is adopted to deal with imbalanced data. AUC is the assessment indicator, and the running time is taken as the reference to compare with other frequently used classification algorithms. The results indicate that the method proposed by this paper performs better than others. At the same time, XGBoost can obtain a ranking of important features that impact the classification results when performing classification tasks, making the evaluation results of the model interpretable. The above shows that the model proposed in the paper is more practical in solving credit risk assessment problems. It has faster response times, reduced costs, and better interpretability.
Preparing school leaders for the fourth industrial revolution: An assessment of their continuous professional development needs

The Fourth Industrial Revolution (4IR) radically altered career and institutional development. Hence, this study assessed the CPD needs of school leaders to prepare them for 4IR. The human capital development (HCD) theory was adopted. A survey approach was used. Random and snowball sampling approaches was used to select 284 school leaders. School leaders exhibited moderate proficiency in basic IT applications. The type of school only significantly affects information management and evaluation (IME) skills among school leaders. Primary and secondary schools have different CPD needs for school leaders regarding IME. The study concluded that school leaders should receive training on 4IR attributes, basic IT applications, and cutting-edge 4IR technologies.
Flare: A Fast, Secure, and Memory-Efficient Distributed Analytics Framework

As big data processing in the cloud becomes prevalent today, data privacy on such public platforms raises critical concerns. Hardware-based trusted execution environments (TEEs) provide promising and practical platforms for low-cost privacy-preserving data processing. However, using TEEs to enhance the security of data analytics frameworks like Apache Spark involves challenging issues when separating various framework components into trusted and untrusted domains, demanding meticulous considerations for programmability, performance, and security. Based on Intel SGX, we build Flare, a fast, secure, and memory-efficient data analytics framework with a familiar user programming interface and useful functionalities similar to Apache Spark. Flare ensures confidentiality and integrity by keeping sensitive data and computations encrypted and authenticated. It also supports oblivious processing to protect against access pattern side channels. The main innovations of Flare include a novel abstraction paradigm of shadow operators and shadow tasks to minimize trusted components and reduce domain switch overheads, memory-efficient data processing with proper granularities for different operators, and adaptive parallelization based on memory allocation intensity for better scalability. Flare outperforms the state-of-the-art secure framework by 3.0× to 176.1×, and is also 2.8× to 28.3× faster than a monolithic libOS-based integration approach.
Nonlocal Structured Sparsity Regularization Modeling for Hyperspectral Image Denoising

The non-local-based model for hyperspectral image (HSI) denoising first uses non-local self-similarity (NSS) prior to group similar full-band patches into three-dimensional non-local full-band groups (tensors) using a block matching (BM) operation, and then a low-rank (LR) penalty is typically applied to each non-local full-band group to reduce noise. While non-local-based methods have shown promising performance in HSI denoising, most existing methods have only considered the LR property of the non-local full-band group while ignoring the strong correlation between sparse coefficients. Moreover, such methods often result in unsatisfactory visual artifacts due to the noise sensitivity of BM operations, while requiring expensive computations. To address these limitations, this paper proposes a novel non-local structured sparsity regularization (NLSSR) approach for HSI denoising. First, to mitigate the noise sensitivity of the BM operation, we propose a graph-based domain distance scheme to index similar full-band patches to form the non-local full-band group. Second, we design an adaptive unidirectional low-rank (LR) dictionary with low complexity that takes into account the differences in intrinsic structure correlation among different modes of the non-local full-band tensor. Third, we utilize a global spectral LR prior to reduce spectral redundancy. Fourth, we develop a generalized soft-thresholding (GST) algorithm based on the alternating minimization framework to solve the NLSSR-based HSI denoising problem. We perform extensive experiments on both simulated and real data to show that the proposed NLSSR algorithm outperforms many popular or state-of-the-art HSI denoising methods in both quantitative and visual evaluations.
An Image–Text Dual-Channel Union Network for Person Re-Identification

Multiple people can have similar appearances and portions of images can be occluded or have viewpoint changes in real scenarios, causing the increased difficulty of person reidentification (Re-ID).To address these problems, we propose a dual-channel person Re-ID algorithm that integrates person Re-ID image-text pairs into the classification network for end-toend learning.We construct an image channel and a text channel, and subsequently extract visual information and text information using a convolutional neural network and a simple recurrent units network, respectively.The text information is used to assist in the learning of visual information, consequently improving the robustness of the visual information.In addition, the visual features are divided into two branches to calculate the global and local features.Global features focus on the overall appearance of a person, whereas local features provide more fine-grained detail.Text information is more accurate and reliable, and it is thus more robust to occlusion and viewpoint changes.Visual information complemented by text information can describe a person more accurately and reliably.Extensive experiments demonstrate our method achieves state-of-the-art performance.
SDTAN: Scalable Deep Time-Aware Attention Network for Interpretable Hard Landing Prediction

Hard landing, as one of the most frequent flight safety incidents during the landing stage, is highly concerned by the aviation industry. Recently, the popularization of Quick Access Recorder (QAR), a modern flight data recording system, has made it possible to collect large volume of flight parameters and incorporate state-of-the-art AI technologies to improve flight safety. However, due to the complex, multivariate, and highly specialized nature of QAR data, most existing studies either suffer from information loss caused by rough feature extraction methods, or rely solely on black-box models with no interpretations, making themselves difficult to achieve satisfactory performance in terms of prediction and explainability. To address this issue, we propose a novel attention-driven model named SDTAN (Scalable Deep Time-Aware Attention Network), which can accurately predict hard landing events and provide interpretable insights to help reveal the possible reasons leading to the events. Specifically, SDTAN fully captures information to learn the local representations of parameters, and leverages the time-interval attention mechanism to focus on the entire temporal pattern of flight over the relevant time intervals. It further re-encodes the representations of parameters in a global view and learns the global effect of parameters on the predicted output to uncover the ones which strongly indicate the flight safety status, enabling both high prediction accuracy and qualitative interpretability. We conduct experiments on real-world QAR datasets of 37,920 Airbus A320 flight samples. Experimental results demonstrate that SDTAN outperforms other state-of-the-art baselines and provides effective interpretability by visualizing the importance of parameters.
Spam Consumer’s Reviews Detection for E-Commerce Website using Linguistic Approach in Deep Learning

Detecting spam reviews is an urgent need in today's society, so that consumers can avoid being duped by internet retailers. Many websites allow users to publish reviews, which opens the door to the publication of false or misleading information, such as reviews that have been paid for. To the general public, these fabricated evaluations can cause confusion as to whether or not they should believe the review. Spam review detection has been solved using prominent Machine and Deep learning techniques. Current research is mostly focused on approaches that require labelled data, which are insufficient for online review because they represent the majority of the currently conducted research on supervised learning. The goal of this research is to expose any misleading text reviews that may be out there. A number of deep learning algorithms for spam review identification have been developed, Convolutional Neural Networks (CNNs), Multi-Layer Perceptron’s (MLPs), and Long Short-Term Memory (LSTM), variant of Recurrent Neural Networks are all examples of these types of neural networks (RNN). Traditional machine learning classifiers such as Nave Bayes (NB), KNN, and SVM were also employed to detect spam reviews, and we compared the performance of traditional machine learning classifiers with that of deep learning classifiers.
Cross-Layer Triple-Branch Parallel Fusion Network for Small Object Detection in UAV Images

Detection of small objects in Unmanned Aerial Vehicle (UAV) images, which contain a large number of objects with extremely small pixels, has been a major challenge over the last few decades. Owing to the lack of sufficiently detailed representation of object features, conventional detectors based on deep learning are unsatisfactory when detecting small objects. Meanwhile, the flight altitude and the shooting angle of the drone always changing, which leads to an uneven dispersion of the multiple objects and a varying density of small objects. In response to the issues mentioned above, a novel lightweight Cross-layer Triple-branch Parallel Fusion Network (CTPFNet) is proposed to improve the real-time detection accuracy of small objects under UAV images. Firstly, a novel downsampling structure called the Inverse Residual Pooling Cascade (IRPC) module is proposed to obtain richer feature information about small objects. Secondly, we construct an improved global feature extraction structure Efficient Layer Aggregation Networks-Transformer (ELAN-Trans) to enhance the association between global features. Then, we design the Hybrid Dilated Depthwise Separable-Spatial Pyramid Pooling-Fast (HDDS-SPPF) to capture more contextual information by using the dilated convolution operations. To enhance the cross-scale transfer fusion of low-level original features containing more detailed localization information and deep-level deep features containing more semantic information, we propose a CTPF module embedded into the neck region for the secondary feature reuse of adjacent feature maps with varying resolutions. Extensive experiments on the public VisDrone2021-DET dataset show that the proposed model achieves significant performance gains with fewer parameters.