Reviewerly/exHarmony huggingface dataset:
Exploring routinization and reflexivity in change and reproduction of consumption towards lower climate impact

A change to less carbon-intensive everyday practices is needed to address climate change. Based on existing literature, we discuss which relations between dynamics of routinization and reflexivity that potentially constitute reproduction and change of practices related to food, mobility and housing. Looking across different consumption fields can help clarify more general insights on the importance of routinization and reflexivity in reproduction and change of consumption. Our analysis is informed by practice theories, which emphasize how reproduction and change are intermingled in the performance of practices. We suggest three analytical themes for exploring the relations between routinization and reflexivity in consumption: Variation in performances of practices, visibility of practice performances and resource use, and social interaction involved in performances of practices. We explore the usefulness of variation, visibility and social interaction by applying them in a systematic discussion of food, housing and mobility consumption in order to shed light upon the relations between routinization and reflexivity and the implications for reproduction and change towards lower carbon intensity.
IMSCSO: An Intensified Sand Cat Swarm Optimization With Multi-Strategy for Solving Global and Engineering Optimization Problems

Optimization challenges are becoming more complex as the world advances. Since deterministic and heuristic approaches are no longer sufficient to deal with such complex problems, metaheuristics have recently emerged as a viable option to address optimization difficulties. Since Sand Cat Swarm Optimization (SCSO) is a famous meta-heuristic algorithm, SCSO has a weak ability to balance search between exploration and exploitation and slow convergence, so it may not be effective in finding the global optima, particularly for complex problems. Hence, this paper proposes an intensified SCSO with multiple strategies (IMSCSO). The performance of the IMSCSO algorithm was evaluated on 23 standard test functions and test suites of CEC 2017, CEC 2019, and CEC 2020. Experimental results show that the IMSCSO algorithm performs significantly better than or is on par with other state-of-the-art optimizers. The statistical results obtained from the Wilcoxon signed-rank test and the Friedman test also indicate that the IMSCSO algorithm has a high ability to significantly outperform and rank first among all methods. Moreover, seven typical engineering issues were employed to estimate the efficacy of IMSCSO in optimizing constrained problems. The experimental findings show that the suggested IMSCSO method can efficiently handle real-world application issues.
Comparison of Tumor Segmentation Techniques from Medical Images

A Brain Tumor (BT) is one of the leading causes of mortality worldwide. Because of this, early diagnosis is crucial. BT localization and segmentation from magnetic resonance imaging (MRI) is a challenging but crucial task with many medical analytic applications. Many methods have been presented for tumor segmentation. However, they each have their issues, such as reliance on manual intervention from experts and uncertainty over which feature extractor to use. To overcome these obstacles, a novel, hybrid strategy for tumor region segmentation based on a convolutional neural network is suggested. Kaggle’s Brain MRI data was used for training and testing the proposed approach. First, a pre-processing strategy is used to operate on a selected portion of the image instead of the entire image to obtain a robust and adaptable BT segmentation system. Reduced processing time and no overfitting issues are the results of this strategy. Second, the Res-SegNet hybrid model was employed, now the model works only with a smaller portion of the MRI Accuracy, loss, Intersection of Union (IoU), and Dice coefficient are some of the metrics that were used to gauge how well the proposed method performed. When applied to tumor segmentation, the model achieved an accuracy of 0.99, an IoU of 0.745, a Dice of 0.831, and a loss of 0.176.
Enhanced Privacy and Security Maintenance to Preserve BigData using Cipher Policy

As the use of big data in cloud computing continues to increase at an exponential rate, privacy concerns have emerged as a major concern. Every person and business should make data protection a top priority. Security risks may arise during transmission from client to cloud server. Cloud computing technology makes it possible for mobile clients to access the large data stores. With the use of a unified parallel and distributed encryption and decryption system, mobile clients and cloud computing may be made more secure. The security of older systems, such as those that rely on plaintext on the cloud, is questionable at best. Dynamic Data Encryption Strategy (DDES) is the suggested approach that aims to improve the effectiveness of privacy safeguards. The Dynamic Encryption Determination (DED) method was designed to dynamically alternate data packages for encryptions under varying time restrictions, and it was the primary algorithm underpinning the DDES paradigm. Our suggested solution utilizes privacy categorization techniques to selectively encrypt data while working within time limitations. Utilizing a selective encryption strategy while still meeting the necessary execution time constraints, this method aims to increase the privacy protection scope. Our experimental results assess DDES's performance and demonstrate the privacy boost.
Quantization for Bayesian Deep Learning: Low-Precision Characterization and Robustness

Bayesian Deep Learning is an emerging field for building robust and trustworthy AI systems due to its ability to estimate reliable uncertainty in neural networks. The need for modeling distribution over parameters and multiple Monte Carlo forward runs in Bayesian neural networks leads to larger model size and significant increase in inference latency compared to deterministic models, which poses challenges for practical deployment. Quantization is a technique that can reduce the model size and also speed up the inference through low-precision computation. In this work, we propose and evaluate a quantization framework and workflow for Bayesian deep learning workloads, which leverages 8-bit integer (INT8) operations to accelerate inference on the 4th Gen Intel Xeon scalable processor (formerly codenamed Sapphire Rapids). We demonstrate that our quantization workflow achieves 6.9x inference throughput speedup on the ImageNet benchmark without sacrificing the model accuracy and quality of uncertainty. Furthermore, we evaluate the effects of quantization on Bayesian neural networks w.r.t. generalizability, robustness against data drift, and its capability in uncertainty estimation on large-scale datasets including a real-world safety-critical application. Our code has been integrated into an open-source project and made available on GitHub at the following URL: https://github.com/IntelLabs/bayesian-torch.
A Cascade of Learners for Firemen’ Emergency Events Classification

The main objective of firefighters is to optimise readiness in response to hazardous events and to minimise their collateral effects. In this context, few but growing research is investigating machine learning algorithms to support firefighters’ work. Hence, this paper presents a decision support system to promptly identify relevant interventions, which are those events for which the national control room needs to alert the competent authorities because they could be dangerous for the community. The aim is to provide firefighters useful information for the management of such interventions and of the available resources. We define a set of new hand-crafted features specifically designed for the task, which catch both static and dynamic characteristics of the events. Furthermore, we design a learning approach based on a cascade of binary classifiers, which exploits the ability of most of the available classification algorithms to learn binary functions and it takes advantage of some characteristic of the dataset. The experiments were performed in leave-one-out on a real-world data set provided by the Italian National Fire Corps, analysing how the system works to distinguish among relevant and not-relevant interventions, both at the time of the call and during the events updates. The results show that machine learning-based decision support system significantly outperform the human operator.
Generic Skeleton Object Detection Framework with Gradient Maps

In real-world applications, we find a special type of object with poor detection results, which we call skeleton objects. They have a relatively small percentage of truly meaningful pixels in the bounding box. The hollows of skeleton objects contain a lot of cluttered background information. Through observation and experience from previous practice, we try to use gradient map to improve the detection results of skeleton objects. Because the gradient map is equivalent to sharpening the foreground information with regular texture and smoothing the background information with clutter, which meets our requirements. So we propose the GAM (gradient attention module) to let the gradient map guide the learning of semantic information of the original image by the network. We also construct a dataset for skeleton object detection, containing 3131 images with 7 categories. We conduct experiments in several state-of-the-art object detection frameworks such as Faster R-CNN, RetinaNet, and YOLOv5, and our method is obviously superior to the corresponding baseline in almost all categories. Meanwhile the method we proposed can be easily generalized to various detection frameworks. `1`
Enhancing Credit Card Fraud Prediction using Decision Trees, SMOTE, and Hyper-Tuned Random Forests: A Comprehensive Approach

In the realm of financial security, this research work delves into a comprehensive investigation of credit card fraud analysis and prediction. The journey begins with meticulous data cleaning and preparation, followed by an exploratory data analysis (EDA) to uncover underlying patterns. Employing a decision tree algorithm, the study advances by harnessing predictive insights to classify fraudulent transactions. To elevate the accuracy and robustness of predictions, a Synthetic Minority Oversampling Technique (SMOTE) is integrated, augmenting the dataset. Further enhancing predictive capabilities, a Random Forest regression model is intricately fine-tuned through hyperparameter optimization. The culmination of these methodological strides reveals a substantial enhancement in the F1 score, signifying an augmented ability to detect and predict credit card fraud. This research presents a cohesive approach that amalgamates diverse techniques, offering a comprehensive strategy for bolstering credit card fraud analysis and prediction.
A High Precison Phase Extraction Algorithm from a Single Shot Interferogram

Abstract. Phase shifting interferometry is an optical metrology with a high precision, but it usually requires the expensive high precision phase shifter. Therefore, low cost methods of extracting phase from a single shot interferogram were very valuable, but available algorithms are usually effective only in a limited conditions. To solve these problems, a novel method is presented in this work. Based on this method, the interference fringe is retrieved to a wrapped phase and divided into different regions and the index of the pixel is calculated. The pixels in the same region have the same parity and the PSF’s gravity center of part wavefront so as to solve the sign ambiguity. The theoretical simulation results indicate that the PV of wavefront error is 0.00054λ and the rms is 0.000125λ,which is much better than the results from the Fast Fourier Transformation method. The experimentally measured interferogram is also used to validate the method. It has the advantages of simplicity, high precision and effective for both open and closed interferometer fringes.
MixerSR: A New Feature Extraction Paradigm for Single Image Super-Resolution

Single-image super-resolution (SR) tasks have achieved fancy success in recent years by leveraging deep convolution neural network (CNN). Although CNNs obtain powerful representation capabilities of reconstructing a high-resolution (HR) image from its corresponding low-resolution (LR) observation, it is hard to apply these accomplishments to real world for its large number of parameters that bring about huge computing resource cost. To solve this problem, many researchers turn their directions to lightening the network by delicate designs while keeping the performance in the high level. In this paper, we propose a new method to further reduce the parameters by using the new feature extraction paradigm called Mixer which only contains Multi-Layer Perceptron (MLP). Compared to convolutional operations, Mixer operation has the same representation capabilities with fewer parameters. In this paper, three typical lightweight networks, where we replace the convolutional operation with Mixer, are used to show the excellent ability. Our experimental results demonstrate that Mixer can help these lightweight networks further reduce the number of parameters by up to 38% while keep their performance in the same level.
Software Defect Prediction using ANN Algorithm

The software industry is rapidly evolving due to increasing demand and technology. Software created by humans can contain a number of errors which are known as defects. The primary responsibility of a tester is to identify these errors. Because defects can manifest at any stage during software development, it can be difficult to begin testing at an early stage. When the manager misinterprets customer needs, it can result in a defect during the system design phase. Quality assurance is essential for software, and as the demand for software applications increases, the complexity of software designs also rises, leading to a greater number of bugs. This can lead to manual detection of the errors, which takes more time to complete. The only way to improve software quality is to fix the defects. By implementing a time-saving method of software defect prediction through an enhanced Machine Learning and Artificial Neural Network techniques, this paper increase the quality of the software.
Research on Natural Image Stitching based on Extended Point-line Feature

Generating high-quality stitched images is a very challenging task in computer vision. In this paper, we propose a single-perspective natural image stitching method based on extended point-line features, which preserves the matching line features of image overlapping areas to the greatest extent and preserves image edge information as much as possible through the method of regional line segment connection. The matched line features are then extended to generate complementary matched point features, which optimizes image pre-alignment and constraints image geometry features. Finally, the image stitching is realized by parametric warping and grid deformation, and the point-line features of the stitching image are quantitatively evaluated by the root mean squared error (RMSE) and root mean squared error distance (RMSEdis) respectively. Experiments show that our method has obvious advantages for complex image stitching with obvious geometric shapes. Compared with the existing methods, RMSE has a maximum increase of 28.8%, and RMSEdis has a maximum increase of 63.2%.
ML-VehicleDet: A Unified Multi-label Vehicle Detection Framework

Vehicle detection based on deep learning has been developed rapidly and basically formed a certain pattern. Almost all works in vehicle detection are concentrated on single-label object detection. However, in the real world, a vehicle has multiple attributes from the perspective of a human being. When we observe a car, we tend to perceive its type, color, orientation, and other attributes. The categories of these labels are neither relevant nor hierarchical. For a neural network, this means that it needs to output multiple labels of a vehicle while regressing its bounding box. For traffic supervisors, a multi-label vehicle detection system could help them find out the targeted vehicle efficiently. Moreover, there has been no research on multi-label vehicle detection so far. Therefore, we design and develop a unified multi-label vehicle detection framework called ML-VehicleDet, which can detect the location (bounding box), type, color and orientation of the vehicle at the same time. In ML-VehicleDet, firstly, we design a hybrid one-stage object detection NN with ViT-encoder and CNN-decoder called Swin Only Look Once, which abbreviates SOLO. Such a SOLO is an anchor-free detector. Secondly, we design a practical loss function framework called MLC Loss, which includes two loss functions namely MLC-OM and MLC-OO for two different annotations of multi-label detection, specialized for alleviating the mutual inhibition problem in multi-label classification. Thirdly, we design a low-cost NMS algorithm called ML-NMS, specialized to merge bounding boxes with multiple labels for one vehicle. Furthermore, we reconstruct UA-DETRAC as a multi-label vehicle detection dataset (benchmark), called UA-DETRAC-ML. To the best of our knowledge, UA-DETRAC-ML is the first unified multi-label vehicle detection dataset. On UA-DETRAC-ML, ML-VehicleDet achieves 70.23% mAP, outperforming YOLOv5-M and YOLOX-M. To promote the development of the community, we release UA-DETRAC-ML at https://github.com/GuanRunwei/UA-DETRAC-ML.
Unpaired Thermal Infrared Image Colorization with CNN and Transformer

Unpaired thermal infrared image colorization without infrared image-visible image pairs allows to directly colorize infrared images. The network structure using only CNN fails to utilize the global semantic information and reduces the quality of colorization. However, the use of Transformer structure increases the computational complexity. To address the above problems, we propose the thermal infrared image colorization method using CNN and Transformer. First, we design Transformer Block in the network for the colorization task, which reduces the computational complexity and makes full use of global and local information to generate high-quality colorized images. And we design a novel loss function which can improve the colorization quality and improve on the detail recovery. Extensive experimental results demonstrate that our proposed method can accurately recover texture information of small objects while maintaining global semantic information, and has significant advantages in color fidelity and color accuracy. Meanwhile, through quantitative experimental results analysis, our proposed method has significantly improved PSNR, SSIM, LPIPS and NIQE on the KAIST dataset and the FLIR dataset compared with other advanced methods.
A Dataset for Radar-based Traffic Gesture Recognition with Out-of-Distribution Detection

Radar-based gesture recognition has received a lot of attention in recent years, with many applications and neural network architectures proposed. However, public datasets have to be available for the reliable design and validation of algorithms, as is the case, for example, in computer vision. Hence, this paper presents a comprehensive traffic gesture dataset with different radar data representations for eight traffic gestures, recorded for 35 participants. Beyond the traffic gestures, the dataset comprises sequences of gesturing and unknown motions for the evaluation of continuous classification and Out-of-Distribution (OOD) detection, respectively. Results are presented for classification accuracy and OOD detection, serving as a baseline for further research. The dataset and additional resources available for download at https://www.uni-ulm.de/en/in/mwt/research/online-database/traffic-gesture-dataset
Semi-supervised 3D Medical Image Segmentation Using Transformer and CNN

Due to the lack of labeled information in medical images, semi-supervised learning has been highly valued in the field of image segmentation. How to effectively use unlabeled images to guide image segmentation is regarded as a key issue to achieve accurate segmentation. In this paper, an uncertainty-aware based consistent segmentation method is proposed to fully utilize the power of Transformer and Convolution Neural Network (CNN) in semi- supervised image segmentation. Our proposed framework consists of a feature learning module which is enhanced by Transformer and CNN, and a feature guidance module based on consistency perception. In the feature learning module, Transformer is used as an encoder to extract multi-scale features of labeled images, and CNN is used as a decoder to restore image dimensions. The feature-guided module learns the features of unlabeled images after data perturbation, and develops a feature-guided model by averaging network weights. In this paper, medical image segmentation based on consensus perception is implemented in a semi-supervised manner, and the proposed method achieves good performance on public benchmark datasets.
Robust Stereo Matching Algorithm Based on Spatial Constraints under Rectification

Medical intelligent navigation systems have attracted increasing attention, and binocular optical tracking systems being an important component to ensure accurate tracking navigation. However, ghost markers may appear when there are multiple markers on the same plane, which can result in tracking errors and significantly impact the surgical process. In this paper, we propose a robust stereo matching algorithm based on spatial constraints under rectification. The algorithm is verified using a binocular vision system based on Xilinx Zynq-7020 platform of field-programmable gate array (FPGA), which meets the requirement of fast, real-time tracking of multiple instruments. The used matching algorithm has a tracking processing time of only 107 under four surgical instruments, which is 82.46% higher than the epipolar geometry method. Furthermore, compared with the traditional sequential matching method, this method improves the tracking failure rate by 6.46% for tracking four instruments, effectively avoiding false matching caused by the appearance of ghost markers.
Map Fusion and Heterogeneous Objects Tracking in Joint Sensing and Communication Networks

We consider a joint sensing and communication (JSC) network where a set of base stations (BSs) cooperates through a fusion center (FC). The network is capable of tracking a set of heterogeneous objects and simultaneously communicating with a set of users. In particular, each BS acts as a monostatic radar that can scan the surrounding area, producing range-angle maps that are exploited to infer the targets positions. Such maps are fused at the FC, and then tracking algorithms, i.e., particle filter (PF) and multi-hypothesis tracker (MHT), are applied to estimate the targets states. Numerical results based on 5G new radio (NR) signals show that BSs are capable of serving user equipments (UEs) with a communication capacity of 850 Mbit/s while their cooperation for the radar task allows to localize point-like and extended targets with a root mean squared error (RMSE) less than 15 cm and 80 cm, respectively.