Hasil untuk "Computer Science"

D. Lazer, M. Baum, Y. Benkler et al.

Addressing fake news requires a multidisciplinary effort The rise of fake news highlights the erosion of long-standing institutional bulwarks against misinformation in the internet age. Concern over the problem is global. However, much remains unknown regarding the vulnerabilities of individuals, institutions, and society to manipulations by malicious actors. A new system of safeguards is needed. Below, we discuss extant social and computer science research regarding belief in fake news and the mechanisms by which it spreads. Fake news has a long history, but we focus on unanswered scientific questions raised by the proliferation of its most recent, politically oriented incarnation. Beyond selected references in the text, suggested further reading can be found in the supplementary materials.

S. Cheryan, V. Plaut, Paul G. Davies et al.

J. Flum, Martin Grohe

Arnold O. Allen

Shuchi Grover, R. Pea, S. Cooper

Brian H. W. Guo, Y. Zou, Yihai Fang et al.

Abstract Recent years have seen growing interests in developing and applying computer vision technologies to solve safety problems in the construction industry. Despite the technological advancements, there is no research that exams the theoretical links between computer vision technology and safety science and management. Thus, the objectives of this paper are to: (1) investigate the current status of applying computer vision technology to construction safety, (2) examine the links between computer vision applications and key research themes of construction safety, (3) discuss the theoretical challenges of applying computer vision to construction safety, and (4) recommend future research directions. A five-step review approach was adopted to search and analyze peer-reviewed academic journal articles. A three-level computer vision development framework was proposed to categorized computer vision applications in the construction industry. The links between computer vision and three main safety research traditions: safety management system, behavior-based safety program, and safety culture, were discussed. The results suggest that the majority of past efforts were focused on object recognition, object tracking, and action recognition, with limited research focused on recognizing unsafe behavior. There are even fewer studies aimed at developing vision-based safety assessment and prediction systems. Based on the review findings, four future research directions are suggested: (1) develop and test a behavioral-cues-based safety climate measure, (2) develop safety behavior datasets, (3) develop a formal hazard identification and assessment model, and (4) develop criteria to evaluate the real impacts of vision-based technologies on safety performance.

Marcos Gabriel Mendes Lauande, Geraldo Braz Júnior, João Dallyson Sousa de Almeida et al.

Penile cancer has an incidence strongly linked to sociocultural factors, being more common in underdeveloped countries like Brazil, where it represents approximately 2% of cancers affecting men. This dataset was created to address the scarcity of publicly available resources for classifying histopathological images in penile cancer research. The images were collected in 2021 from tissue samples obtained through biopsies of patients undergoing treatment for penile cancer. After staining with Hematoxylin and Eosin (H&E), the tissue samples were photographed using a Leica ICC50 HD camera attached to a bright-field microscope (Leica DM500). The dataset comprises 194 high-resolution images (2048 × 1536 pixels), categorized by magnification (40X and 100X) and pathological classification (Tumor or Non-Tumor). Metadata includes additional information such as histological grade and, for some images, HPV status. Although previous works have focused primarily on binary classification tasks, the dataset includes additional labels, such as histological grade and HPV (Human Papilloma Virus) presence, which provide opportunities for multi-label classification or other types of predictive modelling. These extended labels enhance the dataset’s versatility for more complex tasks in medical image analysis. The dataset holds significant reuse potential for machine learning tasks beyond binary classification, allowing researchers to explore additional layers of analysis, such as HPV detection and histological grading. It can also be used for model benchmarking and comparative studies in cancer research, contributing to developing new diagnostic tools. The dataset and metadata are available for further research and model development.

Rahman Shafique, Khadija Kanwal, Venkata Chunduri et al.

Abstract Regular inspection of the health of railway tracks is crucial to maintaining reliable and safe train operations. Some factors including cracks, rail discontinuity, ballast issues, burn wheels, super-elevation, loose nuts and bolts, and misalignment developed on the railways due to pre-emptive investigations, non-maintenance, and delay in detection pose grave threats and danger to the safe operation of railway transportation. In the past, manual inspection was performed for the rail track by a rail cart which is both prone to error and inefficient due to human biases and error. Several train accidents are reported in Pakistan; it is important to automate these techniques to avoid such train accidents for the safety of countless lives. This study aims to enhance railway track fault detection using an automatic rail track fault detection technique with acoustic analysis. Moreover, the proposed method contributes to making the dataset large by using the CTGAN technique. Results show that acoustic data may help to determine the railway track faults effectively and logistic regression is used to perform the classification for railway track faults with an accuracy of 100%.

Hengjin Ke, Cang Cai, Jia Wu et al.

Biprateep Dey, David Zhao, Brett H Andrews et al.

Key science questions, such as galaxy distance estimation and weather forecasting, often require knowing the full predictive distribution of a target variable Y given complex inputs X . Despite recent advances in machine learning and physics-based models, it remains challenging to assess whether an initial model is calibrated for all x , and when needed, to reshape the densities of y toward ‘instance-wise’ calibration. This paper introduces the local amortized diagnostics and reshaping of conditional densities (LADaR) framework and proposes a new computationally efficient algorithm ( Cal-PIT ) that produces interpretable local diagnostics and provides a mechanism for adjusting conditional density estimates (CDEs). Cal-PIT learns a single interpretable local probability–probability map from calibration data that identifies where and how the initial model is miscalibrated across feature space, which can be used to morph CDEs such that they are well-calibrated. We illustrate the LADaR framework on synthetic examples, including probabilistic forecasting from image sequences, akin to predicting storm wind speed from satellite imagery. Our main science application involves estimating the probability density functions of galaxy distances given photometric data, where Cal-PIT achieves better instance-wise calibration than all 11 other literature methods in a benchmark data challenge, demonstrating its utility for next-generation cosmological analyzes ^9 .

Yu Wang, Shu Xu, Zenghui Ding et al.

Background/Objectives: Knowledge Graphs (KGs) are often incomplete, which can significantly impact the performance of downstream applications. Manual completion of KGs is time-consuming and costly, emphasizing the importance of developing automated methods for KGC. Link prediction serves as a fundamental task in this domain. The semantic correlation among entity features plays a crucial role in determining the effectiveness of link-prediction models. Notably, the human brain can often infer information using a limited set of salient features. Methods: Inspired by this cognitive principle, this paper proposes a lightweight Bi-level routing attention mechanism specifically designed for link-prediction tasks. This proposed module explores a theoretically grounded and lightweight structural design aimed at enhancing the semantic recognition capability of language models without altering their core parameters. The proposed module enhances the model’s ability to attend to feature regions with high semantic relevance. With only a marginal increase of approximately one million parameters, the mechanism effectively captures the most semantically informative features. Result: It replaces the original feature-extraction module within the KGML framework and is evaluated on the publicly available WN18RR and FB15K-237 dataset. Conclusions: Experimental results demonstrate consistent improvements in standard evaluation metrics, including Mean Rank (MR), Mean Reciprocal Rank (MRR), and Hits@10, thereby confirming the effectiveness of the proposed approach.

Somporn Sahachaiseree, Takashi Oguchi

ABSTRACT Reinforcement learning (RL) is a promising machine‐learning solution to traffic signal control problems, which have been extensively studied. However, variants of non‐linear, deep artificial neural network (ANN) function approximators (FAs) have been predominantly employed in previous studies proposing RL‐based controllers, leaving a significant interpretability issue due to their black‐box nature. In this work, the use of the linear FA for a value‐based RL agent in traffic signal control problems is investigated along with the least‐squares Q‐learning method, abbreviated as LSTDQ. The interpretable linear FA was found to be adequate for the RL agent to learn an optimal policy. This leads to the proposal to replace a non‐linear ANN FA with the linear FA counterpart, resolving the interpretability issue. Moreover, the LSTDQ learning method shows superior behaviour convergence compared to a gradient descent method. In a low‐intensity arrival pattern scenario, the control by the RL agent cuts about half of the average delay resulting from the pretimed control. Owing to the conciseness of the linear FA, a direct interpretation analysis of the converged linear‐FA parameters is presented. Lastly, two online relearning tests of the agents under non‐stationary arrivals are conducted to demonstrate the online performance of LSTDQ. In conclusion, the linear‐FA specification and the LSTDQ method are together proposed to be used for its control algorithm interpretability property, superior convergence quality, and lack of hyperparameters.

S. Beyer

Al Amin Biswas

Nowadays, artificial intelligence (AI) has been utilized in several domains of the healthcare sector. Despite its effectiveness in healthcare settings, its massive adoption remains limited due to the transparency issue, which is considered a significant obstacle. To achieve the trust of end users, it is necessary to explain the AI models' output. Therefore, explainable AI (XAI) has become apparent as a potential solution by providing transparent explanations of the AI models' output. In this review paper, the primary aim is to review articles that are mainly related to machine learning (ML) or deep learning (DL) based human disease diagnoses, and the model's decision-making process is explained by XAI techniques. To do that, two journal databases (Scopus and the IEEE Xplore Digital Library) were thoroughly searched using a few predetermined relevant keywords. The PRISMA guidelines have been followed to determine the papers for the final analysis, where studies that did not meet the requirements were eliminated. Finally, 90 Q1 journal articles are selected for in-depth analysis, covering several XAI techniques. Then, the summarization of the several findings has been presented, and appropriate responses to the proposed research questions have been outlined. In addition, several challenges related to XAI in the case of human disease diagnosis and future research directions in this sector are presented.

Ajay Dadhich, Jaideep Patel, Rovin Tiwari et al.

Mind-wandering (MW) is when an individual’s concentration drifts away from the task or activity. Researchers found a greater variability in electroencephalogram (EEG) signals due to MW. Collecting more nuanced information from raw EEG data to examine the harmful effects of MW is time-consuming. This study proposes a multi-resolution assessment of EEG signals using the flexible analytic wavelet transform (FAWT). The FAWT algorithm decomposes raw EEG data into more representative sub-bands (SBs). Several statistical characteristics are derived from the obtained SBs, and the effects of MW during meditation on the EEG signals are investigated. A set of significant characteristics is chosen and fed into the machine learning modules using a 10-fold validation approach to detect MW subjects automatically. Our proposed framework attained the highest classification accuracy of 92.41%, the highest sensitivity of 93.56%, and the highest specificity of 91.97%. The proposed framework can be used to design a suitable brain-computer interface (BCI) system to reduce MW and increase meditation depth for holistic and long-term health in society.

Tala Talaei Khoei, Naima Kaabouch

Intrusion Detection Systems are expected to detect and prevent malicious activities in a network, such as a smart grid. However, they are the main systems targeted by cyber-attacks. A number of approaches have been proposed to classify and detect these attacks, including supervised machine learning. However, these models require large labeled datasets for training and testing. Therefore, this paper compares the performance of supervised and unsupervised learning models in detecting cyber-attacks. The benchmark of CICDDOS 2019 was used to train, test, and validate the models. The supervised models are Gaussian Naïve Bayes, Classification and Regression Decision Tree, Logistic Regression, C-Support Vector Machine, Light Gradient Boosting, and Alex Neural Network. The unsupervised models are Principal Component Analysis, K-means, and Variational Autoencoder. The performance comparison is made in terms of accuracy, probability of detection, probability of misdetection, probability of false alarm, processing time, prediction time, training time per sample, and memory size. The results show that the Alex Neural Network model outperforms the other supervised models, while the Variational Autoencoder model has the best results compared to unsupervised models.

M. Zuckerberg

Y. Ong, Abhishek Gupta

SONG Wei, ZHAO Huifen, CAI Wenqin, ZHOU Wanqiang

The Virtual Traffic Light (VTL) in a Vehicle-to-Vehicle (V2V) environment can negotiate the right-of-way allocation through the information directly exchanged between vehicles.When the equipment obtains relevant information, the vehicle can strategically provide information to obtain the priority right of way.To apply to a scene where unmeasurable factors affect the right of way, a virtual traffic light with group strategy protection characteristics is proposed.By abstracting the real information provided by vehicles into a cost allocation and cooperative game, a group strategy protection auction mechanism is designed, and the Shapley value is used to calculate the cost allocation of each vehicle as the payment of vehicles.On this basis, the green light signal is established according to the real evaluation value in the auction results, and the green light signal generated by multiple auctions is integrated through the signal merging algorithm to produce a reasonable right-of-way allocation.The experimental results show that the virtual traffic light has the characteristics of group strategy protection, which can prevent vehicles from forming an alliance of false information to obtain benefits and can also prevent vehicles from obtaining the right-of-way priority through false information.Compared with the virtual traffic light with a fixed threshold of the number of green lights, the virtual traffic lights protected by the group strategy show some improvement in the overall average driving time and the average driving time of high-value vehicles.

Qianqian Qian, Ke Cheng, Wei Qian et al.

The gradient vector flow (GVF) model has been widely used in the field of computer image segmentation. In order to achieve better results in image processing, there are many research papers based on the GVF model. However, few models include image structure. In this paper, the smoothness constraint formula of the GVF model is re-expressed in matrix form, and the image knot represented by the Hessian matrix is included in the GVF model. Through the processing of this process, the relevant diffusion partial differential equation has anisotropy. The GVF model based on the Hessian matrix (HBGVF) has many advantages over other relevant GVF methods, such as accurate convergence to various concave surfaces, excellent weak edge retention ability, and so on. The following will prove the advantages of our proposed model through theoretical analysis and various comparative experiments.