Sentiment analysis on Chinese web forums using elastic nets: Features, classification and interpretation: Working paper series--11-11

Consumer opinion has always been of great concern for businesses and others in the commercial sector. Among all social media which contain opinion-rich content, Web forums have become influential due to the large volume of discussions and high levels of interactivity. The Chinese market has now emerged as one of the largest ones over the world, therefore understanding the opinions and sentiments expressed by Chinese consumers has become increasingly important. In this study, we proposed a generic framework to analyze sentiment in Chinese Web forums. To detect online sentiment, we developed a classification method using Elastic Nets with rich feature representation. The proposed sentiment analysis framework was evaluated on two of the most famous Chinese forums with topics on Chinese stock market and laptop. Findings about interesting features were discussed

What E-gov Systems should African Countries Invest in? A Panel Data Analysis

an applying e-gov increase African countries’ FDI inflows? While e-gov has been proved to improve various aspects of governance, the link between e-gov and FDI inflows seems to be ignored by previous literature. At the same time, as an emerging economy, China’s outward FDI has increased steadily in recent years. However, China’s OFDI flows to Africa is relatively low compared with the flows to Asia and other industrialised economies. Leveraging the current literature, this study used institutional factors by Kaufmann and indices of ease of doing business to explore how each institutional factor influenced by e-gov affect FDI inflows from China. This research argues that to attract more China’s FDI, African countries should invest in those factors which can influence FDI inflows, such as staring a new business and paying taxes. This study contributes to the literature by revealing the most influence factors for African countries to invest e-gov in to attract China’s OFDI

Chern Number Tunable Quantum Anomalous Hall Effect in Monolayer Transitional Metal Oxides via Manipulating Magnetization Orientation

Although much effort has been made to explore quantum anomalous Hall effect (QAHE) in both theory and experiment, the QAHE systems with tunable Chern numbers are yet limited. Here, we theoretically propose that NiAsO$_3$ and PdSbO$_3$, monolayer transitional metal oxides, can realize QAHE with tunable Chern numbers via manipulating their magnetization orientations. When the magnetization lies in the \textit{x-y} plane and all mirror symmetries are broken, the low-Chern-number (i.e., $\mathcal{C}=\pm1$) phase emerges. When the magnetization exhibits non-zero \textit{z}-direction component, the system enters the high-Chern-number (i.e., $\mathcal{C}=\pm3$) phase, even in the presence of canted magnetization. The global band gap can approach the room-temperature energy scale in monolayer PdSbO$_3$ (23.4 meV), when the magnetization is aligned to \textit{z}-direction. By using Wannier-based tight-binding model, we establish the phase diagram of magnetization induced topological phase transition. Our work provides a high-temperature QAHE system with tunable Chern number for the practical electronic application

Using social media big data for tourist demand forecasting: A new machine learning analytical approach

This study explores the possibility of using a machine learning approach to analysing social media big data for tourism demand forecasting. We demonstrate how to extract the main topics discussed on Twitter and calculate the mean sentiment score for each topic as the proxy of the general attitudes towards those topics, which are then used for predicting tourist arrivals. We choose Sydney, Australia as the case for testing the performance and validity of our proposed forecasting framework. The study reveals key topics discussed in social media that can be used to predict tourist arrivals in Sydney. The study has both theoretical implications for tourist behavioural research and practical implications for destination marketing

What Factors Influence Customers’ Purchase Intentions in Travel-Related Social Commerce?

Social commerce significantly impacts the tourism and hospitality industry. Nonetheless, further empirical research investigating the factors that impact the purchase intentions of those who engage with travel-related social commerce. Combining the Uses and Gratification Theory (UGT) and TAM, the present study will investigate the relationships between purchases intentions and the following factors: perceived usefulness, perceived ease-of-use, entertainment, interaction and information seeking. During the research, four different models will be compared. The Ridge Model will be used to explain the effects of the aforementioned factors. The findings indicate that customers’ social commerce purchase intentions are positively impacted by all five factors

StratMed: Relevance Stratification for Low-resource Medication Recommendation

With the growing imbalance between limited medical resources and escalating demands, AI-based clinical tasks have become paramount. Medication recommendation, as a sub-domain, aims to amalgamate longitudinal patient history with medical knowledge, assisting physicians in prescribing safer and more accurate medication combinations. Existing methods overlook the inherent long-tail distribution in medical data, lacking balanced representation between head and tail data, which leads to sub-optimal model performance. To address this challenge, we introduce StratMed, a model that incorporates an innovative relevance stratification mechanism. It harmonizes discrepancies in data long-tail distribution and strikes a balance between the safety and accuracy of medication combinations. Specifically, we first construct a pre-training method using deep learning networks to obtain entity representation. After that, we design a pyramid-like data stratification method to obtain more generalized entity relationships by reinforcing the features of unpopular entities. Based on this relationship, we designed two graph structures to express medication precision and safety at the same level to obtain visit representations. Finally, the patient's historical clinical information is fitted to generate medication combinations for the current health condition. Experiments on the MIMIC-III dataset demonstrate that our method has outperformed current state-of-the-art methods in four evaluation metrics (including safety and accuracy)

Effect of Crystallization Time on Behaviors of Glass-ceramic Produced from Sludge Incineration Ash

AbstractIncineration has become a significant treatment method for municipal sewage sludge because of the rising difficulty to find suitable sites for traditional landfill. However, a large amount of sludge incineration ash containing high levels of heavy metals is remained. In order to achieve resource utilization, glass–ceramics have been produced using sludge incineration ash. The optimum heat treatment was identified as Tn = 837°C for 1.0 h and Tc = 977°C for 2.0 h, respectively. The major crystalline phase identified from X-ray diffraction (XRD) and scanning electron microscopy (SEM) was wollastonite (CaSiO3) and the products displayed good performances. The results indicated that it was a feasible attempt to produce glass-ceramics from sludge incineration ash as decorative materials

Bidirectional Self-Training with Multiple Anisotropic Prototypes for Domain Adaptive Semantic Segmentation

A thriving trend for domain adaptive segmentation endeavors to generate the high-quality pseudo labels for target domain and retrain the segmentor on them. Under this self-training paradigm, some competitive methods have sought to the latent-space information, which establishes the feature centroids (a.k.a prototypes) of the semantic classes and determines the pseudo label candidates by their distances from these centroids. In this paper, we argue that the latent space contains more information to be exploited thus taking one step further to capitalize on it. Firstly, instead of merely using the source-domain prototypes to determine the target pseudo labels as most of the traditional methods do, we bidirectionally produce the target-domain prototypes to degrade those source features which might be too hard or disturbed for the adaptation. Secondly, existing attempts simply model each category as a single and isotropic prototype while ignoring the variance of the feature distribution, which could lead to the confusion of similar categories. To cope with this issue, we propose to represent each category with multiple and anisotropic prototypes via Gaussian Mixture Model, in order to fit the de facto distribution of source domain and estimate the likelihood of target samples based on the probability density. We apply our method on GTA5->Cityscapes and Synthia->Cityscapes tasks and achieve 61.2 and 62.8 respectively in terms of mean IoU, substantially outperforming other competitive self-training methods. Noticeably, in some categories which severely suffer from the categorical confusion such as "truck" and "bus", our method achieves 56.4 and 68.8 respectively, which further demonstrates the effectiveness of our design

Experimenting adaptive services in sea-cloud innovation environment

Most of existing network testbeds can only support the experimentation of L2~L4 forwarding protocols, leaving the evaluation of L4~L7 applications still a tremendous challenge. This paper pioneers to present the design of sea-cloud innovation environment (SCIE) based on the software defined networking (SDN) and network functions virtualization (NFV) paradigms to support adaptive service-oriented experimentation, where the virtualized network functions (VNFs) can be implemented or deimplemented dynamically on network devices according to ondemand requirements. The experimentation is running to form an adaptive chain of network functions, which can be achieved by the protocol oblivious forwarding (POF) via user-defined fields and generic flow instruction set to forward the data to appropriate devices with VNFs. In SCIE, we demonstrate the experimentation of DPI service with on-demand requirement of security check

Molecular Simulation of Hyperbranched Polyester

A new types of hyperbranched polyester was synthesized by the 2,2-bis(hydroxymethyl) propionic acid as an AB2-type monomer and glycerol as the core moiety. Molecular weights were confirmed by Gel Permeation Chromatography. Acid values were titrated by KOH. The hydroxy value was obtained by titration. Furthermore, we calculate logarithmic value of acid value, hydroxy value, and molecular weight, respectively, and the simulation model curves were obtained. Based on the simulation model curves, we establish the empirical equation of the relationship of molecular weight, acid value and hydroxy value