Reviewerly/exHarmony huggingface dataset:
Contrast Limited Adaptive Histogram Equalization for Recognizing Road Marking at Night Based on Yolo Models

In recent years, artificial intelligence has led to rapid development and application across various industries, which has prompted this. One of the significant developments is the improvement of transportation methods. Accidents involving vehicles frequently result in a high number of fatalities as well as economic damage. Road detection is one of the applications that can be used by self-driving cars. Traffic accidents happen, but artificial intelligence is used in many nations to construct smart cities and apps for self-driving cars. Since public road sign datasets have been used in significant research for road sign identification and analysis, these datasets are particularly significant for training autonomous vehicles. This study records the roads of various cities in Taiwan through road driving. It manually collects traffic signs in Taiwan to create a data set of road signs in Taiwan in daytime environments as well as nighttime environments. Since there is currently no data set of road signs in Taiwan, this study is necessary to create a data set of road signs in Taiwan. The YOLO model is utilized in this work to design road signs in Taiwan for mark detection. The techniques of Contrast Stretching (CS), Histogram Equalization (HE), and Contrast Limited Adaptive Histogram Equalization (CLAHE) are evaluated in a nighttime setting and compared to the original image captured at night. The experimental results show that the best model during the day is YOLO V4 (no flip), the test set mAP is 86.77%, the Precision is 82%, the Recall is 87%, the F1-score is 84%, and the IoU is 63.92%. At night, the CLAHE image method works best in the YOLOv5x model, with a mAP of 86.40%. And YOLOv5 can be used in mobile devices or embedded devices, so this study recommends using CLAHE’s YOLOv5x as the best model at night and used to improve the effect of road sign detection at night.
Cross-Camera Inference on the Constrained Edge

The proliferation of edge devices has pushed computing from the cloud to the data sources, and video analytics is among the most promising applications of edge computing. Running video analytics is compute- and latency-sensitive, as video frames are analyzed by complex deep neural networks (DNNs) which put severe pressure on resource-constrained edge devices. To resolve the tension between inference latency and resource cost, we present Polly, a cross-camera inference system that enables co-located cameras with different but overlapping fields of views (FoVs) to share inference results between one another, thus eliminating the redundant inference work for objects in the same physical area. Polly’s design solves two basic challenges of cross-camera inference: how to identify overlapping FoVs automatically, and how to share inference results accurately across cameras. Evaluation on NVIDIA Jetson Nano with a real-world traffic surveillance dataset shows that Polly reduces the inference latency by up to 71.4% while achieving almost the same detection accuracy with state-of-the-art systems.
Traffic Sign Detection Based on the Improved YOLOv5

With the advancement of intelligent driving technology, researchers are paying more and more attention to the identification of traffic signs. Although a detection method of traffic signs based on color or shape can achieve recognition of large categories of signs such as prohibitions and warnings, the recognition categories are few, and the accuracy is not high. A traffic sign detection algorithm based on color or shape is small in computation and good in real-time, but the color features are greatly affected by light and weather. For the questions raised above, this paper puts forward an improved YOLOv5 method. The method uses the SIoU loss function to take the place of the loss function in the YOLOv5 model, which optimizes the training model, and the convolutional block attention model (CBAM) is fused with the CSP1_3 model in YOLOv5 to form a new CSP1_3CBAM model, which enhances YOLOv5’s feature extraction ability and improves the accuracy regarding traffic signs. In addition, the ACONC is introduced as the activation function of YOLOv5, which promotes YOLOv5’s generalization ability through adaptive selection of activation by linear–nonlinear switching factors. The research results on the TT100k dataset show that the improved YOLOv5 precision rate increased from 73.2% to 81.9%, an increase of 8.7%; the recall rate increased from 74.2% to 77.2%, an increase of 3.0%; and the mAP increased from 75.7% to 81.9%, an increase of 6.2%. The FPS also increased from 26.88 to 30.42 frames per second. The same training was carried out on the GTSDB traffic sign dataset, and the mAP increased from 90.2% to 92.5%, which indicates that the algorithm has good generalization ability.
ChatGPT Perpetuates Gender Bias in Machine Translation and Ignores Non-Gendered Pronouns: Findings across Bengali and Five other Low-Resource Languages

In this multicultural age, language translation is one of the most performed tasks, and it is becoming increasingly AI-moderated and automated. As a novel AI system, ChatGPT claims to be proficient in machine translation tasks and in this paper, we put that claim to the test. Specifically, we examine ChatGPT's accuracy in translating between English and languages that exclusively use gender-neutral pronouns. We center this study around Bengali, the 7th most spoken language globally, but also generalize our findings across five other languages: Farsi, Malay, Tagalog, Thai, and Turkish. We find that ChatGPT perpetuates gender defaults and stereotypes assigned to certain occupations (e.g., man = doctor, woman = nurse) or actions (e.g., woman = cook, man = go to work), as it converts gender-neutral pronouns in languages to 'he' or 'she'. We also observe ChatGPT completely failing to translate the English gender-neutral singular pronoun 'they' into equivalent gender-neutral pronouns in other languages, as it produces translations that are incoherent and incorrect. While it does respect and provide appropriately gender-marked versions of Bengali words when prompted with gender information in English, ChatGPT appears to confer a higher respect to men than to women in the same occupation. We conclude that ChatGPT exhibits the same gender biases which have been demonstrated for tools like Google Translate or MS Translator, as we provide recommendations for a human centered approach for future designers of AI systems that perform machine translation to better accommodate such low-resource languages.
Assessing Breast Cancer Risk by Combining AI for Lesion Detection and Mammographic Texture

Background Recent mammography-based risk models can estimate short-term or long-term breast cancer risk, but whether risk assessment may improve by combining these models has not been evaluated. Purpose To determine whether breast cancer risk assessment improves when combining a diagnostic artificial intelligence (AI) system for lesion detection and a mammographic texture model. Materials and Methods This retrospective study included Danish women consecutively screened for breast cancer at mammography from November 2012 to December 2015 who had at least 5 years of follow-up data. Examinations were evaluated for short-term risk using a commercially available diagnostic AI system for lesion detection, which produced a score to indicate the probability of cancer. A mammographic texture model, trained on a separate data set, assessed textures associated with long-term cancer risk. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate both the individual and combined performance of the AI and texture models for the prediction of future cancers in women with a negative screening mammogram, including those with interval cancers diagnosed within 2 years of screening and long-term cancers diagnosed 2 years or more after screening. AUCs were compared using the DeLong test. Results The Danish screening cohort included 119 650 women (median age, 59 years [IQR, 53-64 years]), of whom 320 developed interval cancers and 1401 developed long-term cancers. The combination model achieved a higher AUC for interval and long-term cancers grouped together than either the diagnostic AI (AUC, 0.73 vs 0.70; P < .001) or the texture risk (AUC, 0.73 vs 0.66; P < .001) models. The 10% of women with the highest combined risk identified by the combination model accounted for 44.1% (141 of 320) of interval cancers and 33.7% (472 of 1401) of long-term cancers. Conclusion Combining a diagnostic AI system and mammographic texture model resulted in improved risk assessment for interval cancers and long-term cancers and enabled identification of women at high risk. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Poynton and Slanetz in this issue.
Graph embedding deep broad learning system for data imbalance fault diagnosis of rotating machinery

The distribution of monitored data during the service life of machinery equipment is imbalanced, especially there is more monitoring data for health conditions than for failure conditions. Unfortunately, most existing intelligent fault diagnosis methods are built on the assumption of data balance and cannot effectively handle unbalanced data. Therefore, to solve the above problem, a graph embedding based deep broad learning system (GEDBLS) for data imbalance fault diagnosis of rotating machinery is proposed in this paper. Different from the traditional broad learning system (BLS), the designed GEDBLS not only utilizes the category and structure information of the data in the reconstruction process, but also allows learning the high-level abstract features of the vibration signal through a progressive encoding and decoding mechanism. In addition, GEDBLS considers category weights and intra-class tightness in the classification loss function for imbalanced data category classification. The effectiveness of the presented approach is verified via monitoring data from key equipment of rotating machinery. Experimental results indicate that compared with other methods, the proposed method has stronger ability of feature representation and imbalanced data processing.
Insulator and Defect Detection Model Based on Improved Yolo-S

A large number of insulators play an important role in insulating and supporting complex power grids, and they are constantly exposed to challenges such as lightning strikes and contamination from the external environment. Only by accurately detecting insulator damage can potential safety hazards and equipment damage due to damaged insulators be avoided in time. Aiming at the problems of low accuracy and large model computation of existing insulator and defect detection algorithms, this paper proposes the insulator and defect detection model YOLO-S (YOLO-Small). First, the PAN structure in Neck uses lightweight convolutional GSConv instead of the standard convolutional Conv, introduces GSbottleneck on the basis of GSConv, and uses the method of OSA to design the cross-level partial network GSCSP module (VoV-GSCSP). The use of VoV-GSCSP instead of the C3 module in Neck reduces the computational effort and maintains the accuracy. Secondly, a new attention module, MaECA (MainECA), is designed based on the ECA attention mechanism to enhance target perception. After that, the SILU function in the SPPF in YOLOv5s is replaced with the Mish function, HardSwish function, and ReLU function, respectively, and the results show that the replacement of the Mish function (MishSPPF) is more effective in preventing the distortion of the image caused by image cropping and scaling, and thus improving the accuracy. Finally, the SIoU loss function is used to replace the original loss function CIoU, which improves the number of transmitted frames per second and the detection accuracy of the pictures.The mAP of YOLO-S is 4.2% higher than that of YOLOv5s, the detection accuracy is improved by 2.1%, and the model parameter computation is reduced by 6.0%, which still gives YOLO-S a higher recognition accuracy when compared with existing detection algorithms under the same conditions.
MCNN-LSTM: Combining CNN and LSTM to Classify Multi-Class Text in Imbalanced News Data

Searching, retrieving, and arranging text in ever-larger document collections necessitate more efficient information processing algorithms. Document categorization is a crucial component of various information processing systems for supervised learning. As the quantity of documents grows, the performance of classic supervised classifiers has deteriorated because of the number of document categories. Assigning documents to a predetermined set of classes is called text classification. It is utilized extensively in a wide range of data-intensive applications. However, the fact that real-world implementations of these models are plagued with shortcomings begs for more investigation. Imbalanced datasets hinder the most prevalent high-performance algorithms. In this paper, we propose an approach name multi-class Convolutional Neural Network (MCNN)-Long Short-Time Memory (LSTM), which combines two deep learning techniques, Convolutional Neural Network (CNN) and Long Short-Time Memory, for text classification in news data. CNN’s are used as feature extractors for the LSTMs on text input data and have the spatial structure of words in a sentence, paragraph, or document. The dataset is also imbalanced, and we use the Tomek-Link algorithm to balance the dataset and then apply our model, which shows better performance in terms of F1-score (98%) and Accuracy (99.71%) than the existing works. The combination of deep learning techniques used in our approach is ideal for the classification of imbalanced datasets with underrepresented categories. Hence, our method outperformed other machine learning algorithms in text classification by a large margin. We also compare our results with traditional machine learning algorithms in terms of imbalanced and balanced datasets.
BAOA: Binary Arithmetic Optimization Algorithm With K-Nearest Neighbor Classifier for Feature Selection

The Arithmetic Optimization Algorithm (AOA) is a recently proposed metaheuristic algorithm that has been shown to perform well in several benchmark tests. The AOA is a metaheuristic that uses the main arithmetic operators’ distribution behavior, such as multiplication, division, subtraction, and addition. This paper proposes a binary version of the Arithmetic Optimization Algorithm (BAOA) to tackle the feature selection problem in classification. The algorithm’s search space is converted from a continuous to a binary one using the sigmoid transfer function to meet the nature of the feature selection task. The classifier uses a method known as the wrapper-based approach K-Nearest Neighbors (KNN), to find the best possible solutions. This study uses 18 benchmark datasets from the University of California, Irvine (UCI) repository to evaluate the suggested binary algorithm’s performance. The results demonstrate that BAOA outperformed the Binary Dragonfly Algorithm (BDF), Binary Particle Swarm Optimization (BPSO), Binary Genetic Algorithm (BGA), and Binary Cat Swarm Optimization (BCAT) when various performance metrics were used, including classification accuracy, selected features as well as the best and worst optimum fitness values.
Deep Learning in Sign Language Recognition: A Hybrid Approach for the Recognition of Static and Dynamic Signs

A speech impairment limits a person’s capacity for oral and auditory communication. A great improvement in communication between the deaf and the general public would be represented by a real-time sign language detector. This work proposes a deep learning-based algorithm that can identify words from a person’s gestures and detect them. There have been many studies on this topic, but the development of static and dynamic sign language recognition models is still a challenging area of research. The difficulty is in obtaining an appropriate model that addresses the challenges of continuous signs that are independent of the signer. Different signers’ speeds, durations, and many other factors make it challenging to create a model with high accuracy and continuity. For the accurate and effective recognition of signs, this study uses two different deep learning-based approaches. We create a real-time American Sign Language detector using the skeleton model, which reliably categorizes continuous signs in sign language in most cases using a deep learning approach. In the second deep learning approach, we create a sign language detector for static signs using YOLOv6. This application is very helpful for sign language users and learners to practice sign language in real time. After training both algorithms separately for static and continuous signs, we create a single algorithm using a hybrid approach. The proposed model, consisting of LSTM with MediaPipe holistic landmarks, achieves around 92% accuracy for different continuous signs, and the YOLOv6 model achieves 96% accuracy over different static signs. Throughout this study, we determine which approach is best for sequential movement detection and for the classification of different signs according to sign language and shows remarkable accuracy in real time.
YOLO-Drone: An Optimized YOLOv8 Network for Tiny UAV Object Detection

With the widespread use of UAVs in commercial and industrial applications, UAV detection is receiving increasing attention in areas such as public safety. As a result, object detection techniques for UAVs are also developing rapidly. However, the small size of drones, complex airspace backgrounds, and changing light conditions still pose significant challenges for research in this area. Based on the above problems, this paper proposes a tiny UAV detection method based on the optimized YOLOv8. First, in the detection head component, a high-resolution detection head is added to improve the device’s detection capability for small targets, while the large target detection head and redundant network layers are cut off to effectively reduce the number of network parameters and improve the detection speed of UAV; second, in the feature extraction stage, SPD-Conv is used to extract multi-scale features instead of Conv to reduce the loss of fine-grained information and enhance the model’s feature extraction capability for small targets. Finally, the GAM attention mechanism is introduced in the neck to enhance the model’s fusion of target features and improve the model’s overall performance in detecting UAVs. Relative to the baseline model, our method improves performance by 11.9%, 15.2%, and 9% in terms of P (precision), R (recall), and mAP (mean average precision), respectively. Meanwhile, it reduces the number of parameters and model size by 59.9% and 57.9%, respectively. In addition, our method demonstrates clear advantages in comparison experiments and self-built dataset experiments and is more suitable for engineering deployment and the practical applications of UAV object detection systems.
Blockchain Network Analysis: A Comparative Study of Decentralized Banks

Decentralized finance (DeFi) is known for its unique mechanism design, which applies smart contracts to facilitate peer-to-peer transactions. The decentralized bank is a typical DeFi application. Ideally, a decentralized bank should be decentralized in the transaction. However, many recent studies have found that decentralized banks have not achieved a significant degree of decentralization. This research conducts a comparative study among mainstream decentralized banks. We apply core-periphery network features analysis using the transaction data from four decentralized banks, Liquity, Aave, MakerDao, and Compound. We extract six features and compare the banks' levels of decentralization cross-sectionally. According to the analysis results, we find that: 1) MakerDao and Compound are more decentralized in the transactions than Aave and Liquity. 2) Although decentralized banking transactions are supposed to be decentralized, the data show that four banks have primary external transaction core addresses such as Huobi, Coinbase, and Binance, etc. We also discuss four design features that might affect network decentralization. Our research contributes to the literature at the interface of decentralized finance, financial technology (Fintech), and social network analysis and inspires future protocol designs to live up to the promise of decentralized finance for a truly peer-to-peer transaction network. We release our code on GitHub and data on Harvard Dataverse as open source for future research.
Predicting Good Quantum Circuit Compilation Options

Any potential application of quantum computing, once encoded as a quantum circuit, needs to be compiled in order to be executed on a quantum computer. Deciding which qubit technology, which device, which compiler, and which corresponding settings are best for the considered problem—according to a measure of goodness—requires expert knowledge and is overwhelming for end-users from different domains trying to use quantum computing to their advantage. In this work, we treat the problem as a statistical classification task and explore the utilization of supervised machine learning techniques to optimize the compilation of quantum circuits. Based on that, we propose a framework that, given a quantum circuit, predicts the best combination of these options and, therefore, automatically makes these decisions for end-users. Experimental evaluations show that, considering a prototypical setting with 3000 quantum circuits, the proposed framework yields promising results: for more than three quarters of all unseen test circuits, the best combination of compilation options is determined. Moreover, for more than 95% of the circuits, a combination of compilation options within the top-three is determined—while the median compilation time is reduced by more than one order of magnitude. Furthermore, the resulting methodology not only provides end-users with a prediction of the best compilation options, but also provides means to extract explicit knowledge from the machine learning technique. This knowledge helps in two ways: it lays the foundation for further applications of machine learning in this domain and, also, allows one to quickly verify whether a machine learning algorithm is reasonably trained. The corresponding framework and the pre-trained classifier are publicly available on GitHub (https://github.com/cda-tum/MQTPredictor) as part of the Munich Quantum Toolkit (MQT).
Constraint-weighted support vector ordinal regression to resist constraint noises

Ordinal regression (OR) is a crucial in machine learning. Usual assumption is that all training instances are perfectly denoted. However, when this assumption does not hold, the performance degrades significantly. As a widely used ordinal regression model, support vector ordinal regression (SVOR) identifies r-1 parallel hyperplanes to separate r ranks, where each instance is associated with r-1 constraints for r-1 parallel hyperplanes. Different from the traditional classification problem, an instance with incorrect label may have no influence on certain parallel hyperplanes during SVOR learning. If a constraint induces the deviation of parallel hyperplane(s), it is termed as constraint noise. To address constraint noises, this paper proposes constraint-weighted support vector ordinal regression (CWSVOR) by introducing a constraint weight vector whose length is r-1 to control the influence of r-1 constraints on parallel hyperplanes for each instance. When an instance is denoted as an incorrect rank, the elements of the weight vector for constraint noises are close to 0, while others remain close to 1. The proposed constraint-weighted strategy aims to mitigate the detrimental effects of constraint noises and simultaneously retain the useful constraints during SVOR learning. The experiments on several datasets demonstrate that CWSVOR outperforms KDLOR, ELMOP, NNOP, SVOR and NPSVOR when the training set is corrupted by noises and it shows comparable performance to pin-SVOR.