A Broad Learning Approach for Context-Aware Mobile Application Recommendation

With the rapid development of mobile apps, the availability of a large number of mobile apps in application stores brings challenge to locate appropriate apps for users. Providing accurate mobile app recommendation for users becomes an imperative task. Conventional approaches mainly focus on learning users' preferences and app features to predict the user-app ratings. However, most of them did not consider the interactions among the context information of apps. To address this issue, we propose a broad learning approach for \textbf{C}ontext-\textbf{A}ware app recommendation with \textbf{T}ensor \textbf{A}nalysis (CATA). Specifically, we utilize a tensor-based framework to effectively integrate user's preference, app category information and multi-view features to facilitate the performance of app rating prediction. The multidimensional structure is employed to capture the hidden relationships between multiple app categories with multi-view features. We develop an efficient factorization method which applies Tucker decomposition to learn the full-order interactions within multiple categories and features. Furthermore, we employ a group $\ell_{1}-$norm regularization to learn the group-wise feature importance of each view with respect to each app category. Experiments on two real-world mobile app datasets demonstrate the effectiveness of the proposed method

Prediction of the C-13 NMR chemical shifts of organic species adsorbed on H-ZSM-5 zeolite by the ONIOM-GIAO method

The ONIOM-GIAO method has been used to accurately predict C-13 NMR chemical shifts for a series of organic species adsorbed on H-ZSM-5 zeolite. This is useful for the spectroscopic identification of complicated catalytic systems

Several inaccurate or erroneous conceptions and misleading propaganda about brain-computer interfaces

Brain-computer interface (BCI) is a revolutionizing human-computer interaction, which has potential applications for specific individuals or groups in specific scenarios. Extensive research has been conducted on the principles and implementation methods of BCI, and efforts are currently being made to bridge the gap from research to real-world applications. However, there are inaccurate or erroneous conceptions about BCI among some members of the public, and certain media outlets, as well as some BCI researchers, developers, manufacturers, and regulators, propagate misleading or overhyped claims about BCI technology. Therefore, this article summarizes the several misconceptions and misleading propaganda about BCI, including BCI being capable of “mind-controlled,” “controlling brain,” “mind reading,” and the ability to “download” or “upload” information from or to the brain using BCI, among others. Finally, the limitations (shortcomings) and limits (boundaries) of BCI, as well as the necessity of conducting research aimed at countering BCI systems are discussed, and several suggestions are offered to reduce misconceptions and misleading claims about BCI

Comprehensive evaluation methods for translating BCI into practical applications: usability, user satisfaction and usage of online BCI systems

Although brain-computer interface (BCI) is considered a revolutionary advancement in human-computer interaction and has achieved significant progress, a considerable gap remains between the current technological capabilities and their practical applications. To promote the translation of BCI into practical applications, the gold standard for online evaluation for classification algorithms of BCI has been proposed in some studies. However, few studies have proposed a more comprehensive evaluation method for the entire online BCI system, and it has not yet received sufficient attention from the BCI research and development community. Therefore, the qualitative leap from analyzing and modeling for offline BCI data to the construction of online BCI systems and optimizing their performance is elaborated, and then user-centred is emphasized, and then the comprehensive evaluation methods for translating BCI into practical applications are detailed and reviewed in the article, including the evaluation of the usability (including effectiveness and efficiency of systems), the evaluation of the user satisfaction (including BCI-related aspects, etc.), and the evaluation of the usage (including the match between the system and user, etc.) of online BCI systems. Finally, the challenges faced in the evaluation of the usability and user satisfaction of online BCI systems, the efficacy of online BCI systems, and the integration of BCI and artificial intelligence (AI) and/or virtual reality (VR) and other technologies to enhance the intelligence and user experience of the system are discussed. It is expected that the evaluation methods for online BCI systems elaborated in this review will promote the translation of BCI into practical applications

Considerations and discussions on the clear definition and definite scope of brain-computer interfaces

Brain-computer interface (BCI) is a revolutionizing human-computer interaction with potential applications in both medical and non-medical fields, emerging as a cutting-edge and trending research direction. Increasing numbers of groups are engaging in BCI research and development. However, in recent years, there has been some confusion regarding BCI, including misleading and hyped propaganda about BCI, and even non-BCI technologies being labeled as BCI. Therefore, a clear definition and a definite scope for BCI are thoroughly considered and discussed in the paper, based on the existing definitions of BCI, including the six key or essential components of BCI. In the review, different from previous definitions of BCI, BCI paradigms and neural coding are explicitly included in the clear definition of BCI provided, and the BCI user (the brain) is clearly identified as a key component of the BCI system. Different people may have different viewpoints on the definition and scope of BCI, as well as some related issues, which are discussed in the article. This review argues that a clear definition and definite scope of BCI will benefit future research and commercial applications. It is hoped that this review will reduce some of the confusion surrounding BCI and promote sustainable development in this field

Genomic selection analysis of morphological and adaptation traits in Chinese indigenous dog breeds

The significant morphological differences and abundant germplasm resources of Chinese indigenous dog breeds can be attributed to the diverse geographical environment, including plateaus, mountains, and a long history of raising dogs. The combination of both natural and artificial selection during the past several thousand years has led to hundreds of dog breeds with distinct morphological traits and environmental adaptations. China is one of the earliest countries to domesticate dogs and there are more than 50 ancient indigenous dog breeds. In this study, the run of homozygosity (ROH) and proportion of the autosomal genome covered by ROHs (FROH) were calculated for 10 dog breeds that are the most representative Chinese indigenous dogs based on 170K SNP microarray. The results of FROH showed that the Chuandong hound dogs (HCSSC) have the highest level of inbreeding among the tested breeds. The inbreeding in HCSSC occurred more recently than the Liangshan dogs (SCLSQ) dogs because of more numbers of long ROHs in HCSSC dogs, and the former also have higher inbreeding degree. In addition, there are significant differences in the inbreeding degree among different subpopulations of the same breed, such as the Thin dogs from Shaanxi and Shandong province. To explore genome-wide selection signatures among different breeds, including coat color, ear shape, and altitude adaptability, we performed genome selection analyses of FST and cross population extended haplotype homozygosity (XP-EHH). For the coat color, the FST analysis between Xiasi dogs (XSGZ) and HCSSC dogs was performed and identified multiple genes involved in coat color, hair follicle, and bone development, including MC1R, KITLG, SOX5, RSPO2, and TBX15. For the plateau adaptability, we performed FST and XP-EHH analyses between dogs from Tibet (Tibetan Mastiffs and Nyingchi dogs) and plain regions (Guangxi Biwei dogs GXBWQ and Guandong Sharpei dogs). The results showed the EPAS1 gene in dogs from Tibet undergo strong selection. Multiple genes identified for selection signals based on different usage of dogs. Furthermore, the results of ear shape analyses showed that MSRB3 was likely to be the main gene causing the drop ear of domestic dogs. Our study provides new insights into further understanding of Chinese indigenous dogs