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Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems

作者： Andrew Bae Susu Xu Stony Brook University

ISBN: (纸本)9781450398862

Pedestrian trajectory prediction is an important module in autonomous vehicles (AVs) to ensure safe and effective motion planning. Recently, many deep learning algorithms that achieve near real-time trajectory predictions have been developed. However, people in the artificial intelligence (AI) ethics community have raised critical concerns about the bias and fairness of many general deep learning algorithms. For example, most pedestrian trajectory data is collected from majority populations, and models learned from this data may not generalize well to the heterogeneous needs and behavior patterns of different pedestrian groups, especially for vulnerable pedestrians like the disabled, the elderly, and children. Biases present in trajectory prediction algorithms could mean that pedestrians from certain vulnerable demographics are more likely to be involved in vehicle crashes. In this work, we test two state-of-the-art pedestrian trajectory prediction models for age and gender biases across three different datasets. We design and utilize novel evaluation metrics for comparing model performance. We find that both models perform worse on children and the elderly compared to adults. However, their performance is similar between men and women. We identify potential sources of these biases, as well as discuss several limitations of our study. Our future work will consist of testing more models, refining our evaluation metrics, further differentiating the dataset bias from the algorithmic bias, and mitigating the algorithmic biases.

关键词： fairness bias algorithm evaluation trajectory prediction

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Designing Reinforcement Learning algorithms for Digital Interventions: Pre-Implementation Guidelines

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algorithmS 2022年第8期15卷 255-255页

作者： Trella, Anna L. Zhang, Kelly W. Nahum-Shani, Inbal Shetty, Vivek Doshi-Velez, Finale Murphy, Susan A. Harvard Univ Sch Engn & Appl Sci Cambridge MA 02420 USA Univ Michigan Inst Social Res Ann Arbor MI 48109 USA Univ Calif Los Angeles Sch Dent Los Angeles CA 90095 USA Univ Calif Los Angeles Sch Engn Los Angeles CA 90095 USA

Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g., a lack of accurate mechanistic models for the user dynamics. To guide how one can tackle these challenges, we extend the PCS (predictability, computability, stability) framework, a data science framework that incorporates best practices from machine learning and statistics in supervised learning to the design of RL algorithms for the digital interventions setting. Furthermore, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the PCS framework. We show how we used the PCS framework to design an RL algorithm for Oralytics, a mobile health study aiming to improve users' tooth-brushing behaviors through the personalized delivery of intervention messages. Oralytics will go into the field in late 2022.

关键词： reinforcement learning (RL) online learning mobile health algorithm design algorithm evaluation

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A recognition method of multispectral images of soybean canopies based on neural network

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ECOLOGICAL INFORMATICS 2022年 68卷 101538-101538页

作者： Gao, Shijiao Guan, Haiou Ma, Xiaodan Heilongjiang Bayi Agr Univ Coll Informat & Elect Engn Da Qing 163319 Peoples R China

Multispectral images of soybean canopies can reflect plant physiological information and growth status effectively, which is of great significance for soybean high-quality breeding, scientific cultivation, and fine management. At present, it is uneven of the gray level difference of the soybean multispectral images occurred in the leaf edge, and is also small of the gray level difference between the target and the background, resulting in inaccurate recognition of the soybean canopies from the multispectral images. Thus, a multispectral images' recognition method of soybean canopies was proposed based on the neural network. First, the method of Gaussian smoothing filter was used to preprocess the raw soybean multispectral images (green light, near infrared, red light, red edge, and visible light), which maintained the leaf edge details of the soybean multi spectral image. Second, the feedforward neural network model was established to extract the canopy region in the soybean multispectral images. In addition, image morphology operation was used to improve the recognition effects of the soybean canopy. Finally, four quantitative indexes were used to evaluate the experimental results. The results showed that the average effective segmentation rate of the proposed method was 91.69%, the under segmentation rate was reduced by 33.34%, and the over-segmentation rate was reduced by 48.43%. The difference between the pixel average entropy of the proposed method and the standard canopy image was only 0.2295. The research results can provide not only reliable data for further analysis of physiological and ecological indexes of the soybean canopy, but also technical support for multispectral image recognition of other crop canopies.

关键词： Soybean canopy Multispectral images Neural network Image recognition algorithm evaluation

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KMC3 and CHTKC: Best Scenarios, Deficiencies, and Challenges in High-Throughput Sequencing Data Analysis

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algorithmS 2022年第4期15卷 107-107页

作者： Tang, Deyou Tan, Daqiang Xiao, Weihao Lin, Jiabin Fu, Juan South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China South China Univ Technol Sch Med Guangzhou 510006 Peoples R China

Background: K-mer frequency counting is an upstream process of many bioinformatics data analysis workflows. KMC3 and CHTKC are the representative partition-based k-mer counting and non-partition-based k-mer counting algorithms, respectively. This paper evaluates the two algorithms and presents their best applicable scenarios and potential improvements using multiple hardware contexts and datasets. Results: KMC3 uses less memory and runs faster than CHTKC on a regular configuration server. CHTKC is efficient on high-performance computing platforms with high available memory, multi-thread, and low IO bandwidth. When tested with various datasets, KMC3 is less sensitive to the number of distinct k-mers and is more efficient for tasks with relatively low sequencing quality and long k-mer. CHTKC performs better than KMC3 in counting assignments with large-scale datasets, high sequencing quality, and short k-mer. Both algorithms are affected by IO bandwidth, and decreasing the influence of the IO bottleneck is critical as our tests show improvement by filtering and compressing consecutive first-occurring k-mers in KMC3. Conclusions: KMC3 is more competitive for running counter on ordinary hardware resources, and CHTKC is more competitive for counting k-mers in super-scale datasets on higher-performance computing platforms. Reducing the influence of the IO bottleneck is essential for optimizing the k-mer counting algorithm, and filtering and compressing low-frequency k-mers is critical in relieving IO impact.

关键词： k-mer KMC CHTKC next generation sequencing algorithm evaluation optimization

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Challenges in the Use of NLST Image Data for Quantitative algorithm Development

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JOURNAL OF THORACIC ONCOLOGY 2022年第9期17卷 S160-S161页

作者： Jirapatnakul, A. Avila, R. S. Yip, R. Gierada, D. Henschke, C. I. Yankelevitz, D. F. Icahn Sch Med Mt Sinai New York NY USA Accumetra LLC Clifton Pk NY USA Washington Univ Sch Med St Louis MO USA

EP01.01-00

关键词： NLST algorithm evaluation image databases

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Predicting the association between fine-grained attributes and black-boxed perceptual performance 35

Predicting the association between fine-grained attributes a...

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35th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Shang, Biyao Zhang, Chi Liu, Yuehu Wang, Le Li Li Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Shaanxi Key Lab Digital Technol & Intelligent Sys Xian Peoples R China Tsinghua Univ Dept Automat BNRist Beijing Peoples R China

ISBN: (纸本)9781728176840

Per-image performance of a certain visual perception algorithm is the combination of per-task performances in the image. Based on the black box test, we address to discover and explain the potential shortness of the evaluated intelligent algorithms/systems at the line-grained task-level by human knowledge. By assuming the domain knowledge in visual tasks could be represented by a latent vector which is a sparse embedding of the catenated object-level and image-level features, we propose a latent dictionary learning framework for joint latent knowledge representation and knowledge-output regression at task level. In this way, so we can use semantic concepts to explain the relationship between test cases and test results. The experiments validate the idea of task-level explainable AI evaluation initially as well as the effectiveness of proposed method.

关键词： algorithm evaluation interpretation visual perception algorithm task-level

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Prediction of Multi-site Protein Subcellular Localization

Prediction of Multi-site Protein Subcellular Localization

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IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)

作者： Zhao, Qing Li, Na Fang, Li Qilu Inst Technol Sch Computat Sci & Informat Engn Jinan 250000 Peoples R China

ISBN: (纸本)9781728181172

Multi-site protein sub cellular localization prediction has close relationship with protein function, metabolic and transduction between signals. It can play an important role in speculating the biological function of protein, clearing the gene function and clarifying the internal mechanism of diseases. As some proteins located in two or more subcellulars at the same time, it is necessary for us to do the research on multi-site protein subcellular localization prediction. This paper reviewed the problem from the following four aspects, dataset construction, protein features extraction, predicting algorithm and algorithm evaluation.

关键词： multi-site subcellular localization protein features extraction predicting algorithm algorithm evaluation

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algorithms mention in full-text content of article from NLP domain:A comparative analysis between English and Chinese

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Data Science and Informetrics 2021年第2期1卷 19-33页

作者： Chengzhi Zhang Ruiyi Ding Yuzhuo Wang Department of Information Management Nanjing University of Science and TechnologyNanjingChina

algorithms play an increasingly important role in scientific work,especially in data-driven *** the mention of algorithms in full-text paper helps us understand the use and development of algorithms in a specific *** research on the mention of algorithms is limited to the academic papers in one language,which is hard to comprehensively investigate the use of *** example,in papers of Chinese conference,is the mention of algorithms consistent with it in English conference papers?In order to answer this question,this paper takes NLP as an example,and compares the mention frequency,mention location and mention time of the top10 data-mining algorithms between the papers of the famous international conference,Annual Meeting of the Association for Computational Linguistics(ACL),and the Chinese conference,China National Conference on Computational Linguistics(CCL).The results show that compared with ACL,the mention frequency of top10 data-mining algorithms in CCL is slightly lower and the mention time is slightly delayed,while the distribution of mention location is *** study can provide a reference for the research related to the mention,citation and evaluation of knowledge entities.

关键词： Knowledge entity Mention of algorithms Full-text analysis algorithm evaluation Scholarly big data

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Drug-pathway association prediction: from experimental results to computational models

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BRIEFINGS IN BIOINFORMATICS 2021年第3期22卷 000-000页

作者： Wang, Chun-Chun Zhao, Yan Chen, Xing China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Inst Bioinformat Xuzhou Jiangsu Peoples R China

Effective drugs are urgently needed to overcome human complex diseases. However, the research and development of novel drug would take long time and cost much money. Traditional drug discovery follows the rule of one drug-one target, while some studies have demonstrated that drugs generally perform their task by affecting related pathway rather than targeting single target. Thus, the new strategy of drug discovery, namely pathway-based drug discovery, have been proposed. Obviously, identifying associations between drugs and pathways plays a key role in the development of pathway-based drug discovery. Revealing the drug-pathway associations by experiment methods would take much time and cost. Therefore, some computational models were established to predict potential drug-pathway associations. In this review, we first introduced the background of drug and the concept of drug-pathway associations. Then, some publicly accessible databases and web servers about drug-pathway associations were listed. Next, we summarized some state-of-the-art computational methods in the past years for inferring drug-pathway associations and divided these methods into three classes, namely Bayesian spare factor-based, matrix decomposition-based and other machine learning methods. In addition, we introduced several evaluation strategies to estimate the predictive performance of various computational models. In the end, we discussed the advantages and limitations of existing computational methods and provided some suggestions about the future directions of the data collection and the calculation models development.

关键词： drug pathway drug-pathway association prediction computational models machine learning algorithm evaluation

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Challenging situations for background subtraction algorithms

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APPLIED INTELLIGENCE 2019年第5期49卷 1771-1784页

作者： Sanches, Silvio R. R. Oliveira, Claiton Sementille, Antonio C. Freire, Valdinei Univ Tecnol Fed Parana Cornelio Procopio PR Brazil Univ Estadual Paulista Bauru Brazil Univ Sao Paulo Sao Paulo Brazil

Background subtraction is the prerequisite for a wide range of applications including video surveillance, smart environments and content retrieval. Real environments present some challenging situations even for the most recent algorithms, such as shadows, illumination changes, dynamic background, among others. If a real environment is previously known and the challenging situations of this environment can be predicted, the choice of an appropriate algorithm to deal with such situations may be essential for obtaining better segmentation results. In our work, we identify the main situations that affect the performance of background subtraction algorithms and present a classification of these challenging situations. In addition, we present a solution that uses videos and ground-truths from existing datasets to evaluate the performance of segmentation algorithms when they need to deal with a specific challenging situation.

关键词： Background subtraction Foreground extraction algorithm evaluation Challenging situation

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