Existing recommender algorithms mainly focused on recommending individual items by utilizing user-item interactions. However, little attention has been paid to recommend user generated lists (e.g., playlists and booklists). On one hand, contain rich signal about item co-occurrence, as within a list are usually gathered based specific theme. the other user's preference over also indicate her list. We believe that 1) if relevance can be properly leveraged, an enhanced recommendation for...

Metro systems play a key role in meeting urban transport demands large cities. The close relationship between historical weather conditions and the corresponding passenger flow has been widely analyzed by researchers. However, few studies have explored issue of how to use data make forecasting more accurate. To this end, an hourly metro model using deep long short-term memory neural network (LSTM_NN) was developed. optimized traditional input variables, including different temporal data,...

With the rapid development of social economy, our lives are flooded with all kinds counterfeit products. The public's attitude greedy for petty and cheap has encouraged unscrupulous manufacturers to take advantage opportunity provide low-cost products, suppress profits legitimate manufacturers, also make public lose confidence in quality At present, most widely used anti-counterfeiting system based on QR codes market. However, existing traceability systems still mostly built a centralized...

With the popularity of internet 5G network, network constructions hospitals have also rapidly developed. Operations management in healthcare system is becoming paperless, for example, via a shared electronic medical record (EMR) system. A plays an important role reducing diagnosis costs and improving diagnostic accuracy. In traditional system, centralized database storage typically used. Once there problem with data storage, it could cause privacy disclosure security risks. Blockchain...

This paper presents an improved semi-supervised learning approach for defect prediction involving class imbalanced and limited labeled data problem. employs random under-sampling technique to resample the original training set updat

An transfer learning method, called Kernel Canonical Correlation Analysis plus (KCCA+), is proposed for heterogeneous Cross-company defect prediction. Combining the kernel method and techniques, this improves performance of predictor with more adaptive ability in nonlinearly separable scenarios. Experiments validate its effectiveness.

Past efforts on the automated processing medical infrared images has typically focused specialized applications like detection of breast cancer. We propose application content-based image retrieval (CBIR) to thermal images. CBIR allows similar based features directly extracted from data. Hence, for a that shows symptoms certain disease will provide visually cases which usually also represent similarities in terms. The we this purpose are set moment invariants grayscale

Software defect prediction studies usually build models without analyzing the data used in procedure. As a result, same approach has different performances on sets. In this paper, we introduce discrimination analysis for providing good method to give insight into inherent property of software data. Based analysis, find that sets field have nonlinearly separable and class-imbalanced problems. Unlike prior works, try exploit kernel map high-dimensional feature space. By combating these two...

Abstract This study investigated the differences between different large colour‐difference (LCD) data sets (with a mean Δ E value about 10). Six were studied. For each set, various CIELAB based colour difference models derived to fit data. These compared shed light on sets. It was found that all have very similar characteristics except for Munsell Detailed investigation showed discrepancy is mainly due balance lightness and chromatic used previously set. one unit of Value appears be three...

Abstract Accurately predicting traffic flow is crucial for intelligent transportation systems (ITS). In recent years, many deep learning‐based prediction models have been widely applied in prediction, and various spatio‐temporal networks proposed. However, most of the existing follow a general technical route to extract features, which lack capacity extracting important historical information with high spatial temporal correlations dynamically deeply. How develop well‐performance model...

An experiment was carried out using CRT colors. The stimuli were selected along 24 vectors in CIELAB color space. data used to test various difference formulae and uniform spaces. results show that there are some discrepencies between space experimental data. also suggest three types of models according the develop these models: small color-difference, large color-difference Munsell

Semantic segmentation technology based on deep learning has played an important role for doctors in identifying brain tumor regions and formulating treatment plans. Popular automated methods tumors include 2D 3D convolution networks. The networks give better results but lead to a significant increase parameters computational cost. In this paper, we propose lightweight network composed of inverted residual modules, which can significantly reduce the complexity models. Based depthwise...

Binary Code Similarity Detection is a method that involves comparing two or more binary code segments to identify their similarities and differences. This technique plays crucial role in areas such as software security, vulnerability detection, composition analysis. With the extensive use of development system optimization, similarity detection has become an important area research. Traditional methods source face challenges when dealing with unreadable complex nature code, necessitating...

The security issues brought by third-party components in software are becoming increasingly prominent. However, current component analysis tools only analyze the existence of vulnerabilities introduced components, resulting much false positives. In this paper, we present a vulnerability accessibility framework to whether these would be actually called project.

Current optimization methods of learned spatial indexes are often based on data distribution. However, when in coldstart scenarios whose query distributions unknown, these not suitable. In this paper, a sophisticated index, K-Index, is designed for such cold-start environments. It employs clustering-based partitioning strategy, utilizing the K-means algorithm to cluster dataset. Then, cube-shaped partitions formed with clusters. For sake that should be stored certain order speed up...