53 research outputs found
Context-aware Helpfulness Prediction for Online Product Reviews
Modeling and prediction of review helpfulness has become more predominant due
to proliferation of e-commerce websites and online shops. Since the
functionality of a product cannot be tested before buying, people often rely on
different kinds of user reviews to decide whether or not to buy a product.
However, quality reviews might be buried deep in the heap of a large amount of
reviews. Therefore, recommending reviews to customers based on the review
quality is of the essence. Since there is no direct indication of review
quality, most reviews use the information that ''X out of Y'' users found the
review helpful for obtaining the review quality. However, this approach
undermines helpfulness prediction because not all reviews have statistically
abundant votes. In this paper, we propose a neural deep learning model that
predicts the helpfulness score of a review. This model is based on
convolutional neural network (CNN) and a context-aware encoding mechanism which
can directly capture relationships between words irrespective of their distance
in a long sequence. We validated our model on human annotated dataset and the
result shows that our model significantly outperforms existing models for
helpfulness prediction.Comment: Published as a proceeding paper in AIRS 201
Characterization of industrial wastes as raw materials for emulsified modified bitumen (EMB) formulation
This study was conducted to characterize industrial wastes for the formulation of emulsified modified bitumen (EMB), in relation to their physical characteristics and elemental compositions. The aim was to determine which raw materials from industrial wastes could be used for EMB formulation. Bitumen is produced from crude oil extracted from the ground, which categorizes crude oil as one of the non-renewable fossil fuels. Various environmental issues that have risen in Malaysia are caused by the excessive manufacturing activities and the miss-management of industrial wastes. In an effort to mitigate these issues, industrial wastes are being used in various EMB formulations. Industrial wastes, such as polystyrene, polyethylene, and used automotive oil can be used as alternatives to formulate bitumen. Normally, a suitable emulsifier is needed to produce the final product, which is EMB. The emulsifier will yield a charge, depending on its properties, to bind the oily bitumen with water. In this current study, physical characteristic studies were performed using thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), flash point test, density test, and moisture content test. Fourier Transform Infrared Spectroscopy (FTIR) analysis was also performed to determine the material’s molecular composition and structure
Treatment of waste emulsion using coagulation method
Waste emulsion is one of the major toxic wastes that are generated from the metal processing industry. Emulsions are commonly used in the metal processing production as emulsified coolant and in power plant as lubricating oil. This paper presents a research to suggest the best coagulant in treating waste emulsion from manufacturing plant via coagulation method. Chitosan and bio-solvent will represent the natural coagulants, alum and polyaluminium chloride (PAC) will represent the industrial coagulant. Jar Test apparatus was used to treat the waste emulsion. It will be conducted in a batch test with a row of six beakers. One jar will be used as a control and the other five will be of different dosages of coagulant at 150 rpm of mixing rate, 30 minutes of mixing time and at room temperature. From the results, PAC showed to be the best coagulant with 82.29%, 95.45%, 99.95%, 96.4%, and 53% removal of COD, BOD O&G, TSS, and Turbidity, respectively, at a pH of 3.7 and minimum dosage of 2wt%. In conclusion, the PAC managed to break the oil and water bonding from the waste emulsion. Thus, it was suitable to treat the industrial waste emulsion
Can disordered mobile phone use be considered a behavioral addiction? An update on current evidence and a comprehensive model for future research
Despite the many positive outcomes, excessive mobile phone use is now often associated with potentially harmful and/or disturbing behaviors (e.g., symptoms of deregulated use, negative impact on various aspects of daily life such as relationship problems, and work intrusion). Problematic mobile phone use (PMPU) has generally been considered as a behavioral addiction that shares many features with more established drug addictions. In light of the most recent data, the current paper reviews the validity of the behavioral addiction model when applied to PMPU. On the whole, it is argued that the evidence supporting PMPU as an addictive behavior is scarce. In particular, it lacks studies that definitively show behavioral and neurobiological similarities between mobile phone addiction and other types of legitimate addictive behaviors. Given this context, an integrative pathway model is proposed that aims to provide a theoretical framework to guide future research in the field of PMPU. This model highlights that PMPU is a heterogeneous and multi-faceted condition
Performance Analysis Of Neural Network Model For Automated Visual Inspection With Robotic Arm Controller System
The concept of Automated Visual Inspection (AVI) have emerged as a powerful platform for industrial machine
vision where it used to inspect a large number of products rapidly.However,a major problem with this kind of application is the quality produced by the recognition process.In this paper,a system with the capability of identifying and categorizing a product based on image processing has been implemented.The image was processed by using Radial Basis Function (RBF) based on output center and spread values optimization.Robotic arm controller developed for pick and place the product based
on their categories.Two performance measures are used to
validate the model classification range and the spread values.The results of this project indicate that the model used able to identify the product and classify it according to their shape
An Adoption Framework for Mobile Augmented Reality Games: The Case of Pokémon Go
Pokémon Go was the first mobile augmented reality (AR) game to reach the top of the download charts of mobile applications. However, little is known about this new generation of mobile online AR games. Existing theories provide limited applicability for user understanding. Against this background, this research provides a comprehensive framework based on uses and gratification theory, technology risk research, and flow theory. The proposed framework aims to explain the drivers of attitudinal and intentional reactions, such as continuance in gaming or willingness to invest money in in-app purchases. A survey among 642 Pokémon Go players provides insights into the psychological drivers of mobile AR games. The results show that hedonic, emotional, and social benefits and social norms drive consumer reactions while physical risks (but not data privacy risks) hinder consumer reactions. However, the importance of these drivers differs depending on the form of user behavior
Uso Pessoal das Tecnologias no Trabalho: Motivadores e Efeitos à Distração Profissional
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