Deep Imbalanced Regression via Hierarchical Classification Adjustment

Regression tasks in computer vision, such as age estimation or counting, are often formulated into classification by quantizing the target space into classes. Yet real-world data is often imbalanced -- the majority of training samples lie in a head range of target values, while a minority of samples span a usually larger tail range. By selecting the class quantization, one can adjust imbalanced regression targets into balanced classification outputs, though there are trade-offs in balancing classification accuracy and quantization error. To improve regression performance over the entire range of data, we propose to construct hierarchical classifiers for solving imbalanced regression tasks. The fine-grained classifiers limit the quantization error while being modulated by the coarse predictions to ensure high accuracy. Standard hierarchical classification approaches, however, when applied to the regression problem, fail to ensure that predicted ranges remain consistent across the hierarchy. As such, we propose a range-preserving distillation process that can effectively learn a single classifier from the set of hierarchical classifiers. Our novel hierarchical classification adjustment (HCA) for imbalanced regression shows superior results on three diverse tasks: age estimation, crowd counting and depth estimation. We will release the source code upon acceptance.Comment: 14 pages, 5 figure

A Novel Kernel for Text Classification Based on Semantic and Statistical Information

In text categorization, a document is usually represented by a vector space model which can accomplish the classification task, but the model cannot deal with Chinese synonyms and polysemy phenomenon. This paper presents a novel approach which takes into account both the semantic and statistical information to improve the accuracy of text classification. The proposed approach computes semantic information based on HowNet and statistical information based on a kernel function with class-based weighting. According to our experimental results, the proposed approach could achieve state-of-the-art or competitive results as compared with traditional approaches such as the k-Nearest Neighbor (KNN), the Naive Bayes and deep learning models like convolutional networks

Design for Children’s Playful Learning with Robots

This article presents an investigation of the implications of designing for children’s playful learning with robots. This study was carried out by adopting a Research through Design approach that resulted in the development of a novel low-anthropomorphic robot called Shybo. The article reports the main phases of the project: the preliminary and exploratory research that was carried out to define a list of design requirements; the design of the robot and its supplementary materials for carrying out playful learning experiences; and the evaluation of the project that involved both parents and children. The robot, in fact, was finally tested as part of a two-hour experience that engaged children in activities related to the associations between sounds and colours. The article presents and discusses the results of this evaluation to point out positive aspects of the experience, emerging issues and hints for future works. These are documented to share lessons learned that might be supportive of the general development of children’s playful learning and cognitive experiences with robots

An Abnormal Network Traffic Detection Algorithm Based on Big Data Analysis

Anomaly network detection is a very important way to analyze and detect malicious behavior in network. How to effectively detect anomaly network flow under the pressure of big data is a very important area, which has attracted more and more researchers’ attention. In this paper, we propose a new model based on big data analysis, which can avoid the influence brought by adjustment of network traffic distribution, increase detection accuracy and reduce the false negative rate. Simulation results reveal that, compared with k-means, decision tree and random forest algorithms, the proposed model has a much better performance, which can achieve a detection rate of 95.4% on normal data, 98.6% on DoS attack, 93.9% on Probe attack, 56.1% on U2R attack, and 77.2% on R2L attack

Do Pre-trained Language Models Indeed Understand Software Engineering Tasks?

Artificial intelligence (AI) for software engineering (SE) tasks has recently achieved promising performance. In this paper, we investigate to what extent the pre-trained language model truly understands those SE tasks such as code search, code summarization, etc. We conduct a comprehensive empirical study on a board set of AI for SE (AI4SE) tasks by feeding them with variant inputs: 1) with various masking rates and 2) with sufficient input subset method. Then, the trained models are evaluated on different SE tasks, including code search, code summarization, and duplicate bug report detection. Our experimental results show that pre-trained language models are insensitive to the given input, thus they achieve similar performance in these three SE tasks. We refer to this phenomenon as overinterpretation, where a model confidently makes a decision without salient features, or where a model finds some irrelevant relationships between the final decision and the dataset. Our study investigates two approaches to mitigate the overinterpretation phenomenon: whole word mask strategy and ensembling. To the best of our knowledge, we are the first to reveal this overinterpretation phenomenon to the AI4SE community, which is an important reminder for researchers to design the input for the models and calls for necessary future work in understanding and implementing AI4SE tasks.Comment: arXiv admin note: text overlap with arXiv:2202.08005 by other author

Analysis of taxi travels during an epidemic period using system dynamics method

This paper explores the factors influencing taxi travel in the context of COVID-19 from both demand and supply sides and provides a quantitative comparison of taxi travel characteristics and taxi industry operations before and after the epidemic. A model was established using system dynamics to simulate a taxi travel system, which was used to analyze the changes in demand and supply of taxi travel under scenarios such as closedowns, travel restrictions, etc. The analysis is based on a typical middle-sized city in China, Ningbo in Zhejiang Province, revealing factors leading to the significant drop in the amount of taxi travel due to the epidemic. The study can provide insights into impacts of public (or similar anomalous or catastrophic) events on taxi travel systems and could be useful for urban transport planning and management.Postprint (published version

MTA3-SOX2 Module Regulates Cancer Stemness and Contributes to Clinical Outcomes of Tongue Carcinoma.

Cancer cell plasticity plays critical roles in both tumorigenesis and tumor progression. Metastasis-associated protein 3 (MTA3), a component of the nucleosome remodeling and histone deacetylase (NuRD) complex and multi-effect coregulator, can serve as a tumor suppressor in many cancer types. However, the role of MTA3 in tongue squamous cell cancer (TSCC) remains unclear although it is the most prevalent head and neck cancer and often with poor prognosis. By analyzing both published datasets and clinical specimens, we found that the level of MTA3 was lower in TSCC compared to normal tongue tissues. Data from gene set enrichment analysis (GSEA) also indicated that MTA3 was inversely correlated with cancer stemness. In addition, the levels of MTA3 in both samples from TSCC patients and TSCC cell lines were negatively correlated with SOX2, a key regulator of the plasticity of cancer stem cells (CSCs). We also found that SOX2 played an indispensable role in MTA3-mediated CSC repression. Using the mouse model mimicking human TSCC we demonstrated that the levels of MTA3 and SOX2 decreased and increased, respectively, during the process of tumorigenesis and progression. Finally, we showed that the patients in the MTA

MTA3-SOX2 Module Regulates Cancer Stemness and Contributes to Clinical Outcomes of Tongue Carcinoma

Cancer cell plasticity plays critical roles in both tumorigenesis and tumor progression. Metastasis-associated protein 3 (MTA3), a component of the nucleosome remodeling and histone deacetylase (NuRD) complex and multi-effect coregulator, can serve as a tumor suppressor in many cancer types. However, the role of MTA3 in tongue squamous cell cancer (TSCC) remains unclear although it is the most prevalent head and neck cancer and often with poor prognosis. By analyzing both published datasets and clinical specimens, we found that the level of MTA3 was lower in TSCC compared to normal tongue tissues. Data from gene set enrichment analysis (GSEA) also indicated that MTA3 was inversely correlated with cancer stemness. In addition, the levels of MTA3 in both samples from TSCC patients and TSCC cell lines were negatively correlated with SOX2, a key regulator of the plasticity of cancer stem cells (CSCs). We also found that SOX2 played an indispensable role in MTA3-mediated CSC repression. Using the mouse model mimicking human TSCC we demonstrated that the levels of MTA3 and SOX2 decreased and increased, respectively, during the process of tumorigenesis and progression. Finally, we showed that the patients in the MTA