HyperSoRec: Exploiting Hyperbolic User and Item Representations with Multiple Aspects for Social-aware Recommendation

Social recommendation has achieved great success in many domains including e-commerce and location-based social networks. Existing methods usually explore the user-item interactions or user-user connections to predict users’ preference behaviors. However, they usually learn both user and item representations in Euclidean space, which has large limitations for exploring the latent hierarchical property in the data. In this article, we study a novel problem of hyperbolic social recommendation, where we aim to learn the compact but strong representations for both users and items. Meanwhile, this work also addresses two critical domain-issues, which are under-explored. First, users often make trade-offs with multiple underlying aspect factors to make decisions during their interactions with items. Second, users generally build connections with others in terms of different aspects, which produces different influences with aspects in social network. To this end, we propose a novel graph neural network (GNN) framework with multiple aspect learning, namely, HyperSoRec. Specifically, we first embed all users, items, and aspects into hyperbolic space with superior representations to ensure their hierarchical properties. Then, we adapt a GNN with novel multi-aspect message-passing-receiving mechanism to capture different influences among users. Next, to characterize the multi-aspect interactions of users on items, we propose an adaptive hyperbolic metric learning method by introducing learnable interactive relations among different aspects. Finally, we utilize the hyperbolic translational distance to measure the plausibility in each user-item pair for recommendation. Experimental results on two public datasets clearly demonstrate that our HyperSoRec not only achieves significant improvement for recommendation performance but also shows better representation ability in hyperbolic space with strong robustness and reliability.

Constraints and strategies for municipal Sukuk issuance in Indonesia

Purpose This study aims to examine the constraints in the issuance of municipal Sukuk in Indonesia and proposed potential solutions and strategies to support its implementation. Design/methodology/approach This study used the analytic network process (ANP) method with four groups of relevant respondents that comprised representatives from the local government, universities (academicians), experts (practitioners) and regulators. Findings Four elements support municipal Sukuk issuance: issuer (regional government or municipality), investor, market and infrastructure. The element with the highest score was the lack of commitment from the regional government in issuing municipal sukuk due to leadership change that occurs every five years. Thus, specific regulations that strengthen and regulate municipal Sukuk issuance and the support from the central government are the priority solutions and strategies that should be undertaken to encourage regional governments in issuing municipal Sukuk. Practical implications The establishment of specific regulations by the central government that support and strengthen municipal Sukuk issuance is necessary to promote successful implementation. Supports from the central government include tax incentives, accessible licensing processes and bureaucracy aids. Originality/value This study extended the literature by examining problems, solutions and strategies for municipal Sukuk issuance in terms of four aspects: the issuer (municipal government), investors, markets and infrastructure. The multiple aspect analyses provided a broader understanding of the lack of municipal Sukuk issuance in Indonesia.

MSegnet, a Practical Network for Building Detection from High Spatial Resolution Images

Building detection in big earth data by remote sensing is crucial for urban development. However, improving its accuracy remains challenging due to complicated background objects and different viewing angles from various remotely sensed images. The hereto proposed methods predominantly focus on multi-scale feature learning, which omits features in multiple aspect ratios. Moreover, postprocessing is required to refine the segmentation performance. We propose modified semantic segmentation (MSegnet), a single-shot semantic segmentation model based on a matrix of convolution layers to extract features in multiple scales and aspect ratios. MSegnet consists of two modules: backbone feature learning and matrix convolution to conduct vertical and horizontal learning. The matrix convolution comprises a set of convolution operations with different aspect ratios. MSegnet is applied to a public building data set that is widely used for evaluation and shown to achieve satisfactory accuracy, compared with the published single-shot methods.

Multi-Level and Multiple Aspect Semantic Trajectory Model: Application to the Tourism Domain

Here we design a semantic trajectory model responding to specific needs expressed by tourism analyst experts. Thus, this model takes into account: (i) the description of sequences of imbricated semantic segments, (ii) the definition of enrichment data integrating spatial, temporal and thematic dimensions and (iii) the association of such data with positions or with trajectory segments. Each of these features is necessary for the processing and analysis of tourist mobility data, which we will detail. For validation purposes, we experiment our model on two outdoor mobility track scenarios computed in a processing chain. We also show that our model is generic and extensible thanks to two other scenarios on different datasets.

A STUDY ON ASSAULT CASES ADMITTED TO COLOMBO SOUTH TEACHING HOSPITAL-SRI LANKA

Assault cases are commonly encountered in the day to day life of a Judicial Medical Officer or an Accident Service Surgeon. Most of the admissions to the Accident Service Unit of any major hospital in almost anywhere in the world are due to accidents and assaults. Accidents are preventable disasters and there are major organizations whose main role is to draw strategies and plans to prevent accidents. On the other hand, assaults are always man-made and the underlying factors are very different from those of accidents. Assaults are a serious health and economic burden in a country. Additionally, there are socio-economic, religious, ethnic, cultural and legal issues intermingled with the broad picture of ‘assaults’. Very few studies have been done in depth about assaults in the recent past which cover the multiple aspect of the issue. Even the conclusions drawn from the most extensive study on this subject may become obsolete and dilapidated after few years, as the patterns of wounding and the methods and trends are changing constantly across the globe. This justifies the necessity of continuous research by different authorities to this ever-changing subject. This study discusses certain demographic characters of victims, assailants, contributory factors and legal implications of assaults.