Software defect prediction based on association rule classification.

In software defect prediction, predictive models are estimated based on various code attributes to assess the likelihood of software modules containing errors. Many classification methods have been suggested to accomplish this task. However, association based classification methods have not been investigated so far in this context. This paper assesses the use of such a classification method, CBA2, and compares it to other rule based classification methods. Furthermore, we investigate whether rule sets generated on data from one software project can be used to predict defective software modules in other, similar software projects. It is found that applying the CBA2 algorithm results in both accurate and comprehensible rule sets.Software defect prediction; Association rule classification; CBA2; AUC;

Examining the Causal Relationship between Screen Size and Cellular Data Consumption

This study utilizes a terabyte dataset from a telecommunications company to examine the relationship between screen size and cellular data consumption for a large number of phone and tablet users. We find the relationship exhibits a different pattern within the two device categories of phones and tablets. For phone users, there is an overall positive and significant relationship over the range of screen size from 1 inch to below 7 inches, which is, however, mainly driven by the dramatic decrease in usage on traditional phones with screens less than 3 inches. Particularly for smartphones with screens 3.5 inches or higher, we do not find a significant relationship between screen size and cellular data consumption measured by either the time spent on the mobile network or the amount of data transmitted. For tablet users, we find evidence that suggests that people spend less time on tablets with bigger screens, which could potentially be due to the reduced portability of large tablets. Our findings can provide important implications for mobile network operators in promoting data plans to users with different devices

Understanding the Role of Bounty Awards in Improving Content Contribution: Bounty Amount and Temporal Scarcity

The bounty award system has been implemented on UGC platforms to address specific issues and improve content contributions. This study aims to assess its effectiveness by examining the bounty amount and temporal scarcity. Based on the optimistic bias theory, we posit that the competition for bounty awards among users can have a positive effect, as users may overestimate their chances of winning and persist in their efforts. Additionally, we hypothesize that the amount of bounty award does not have a linear effect on the quantity and quality of user-generated content, but instead follows an inverted U-shaped relationship. Furthermore, drawing on the stuck-in-the-middle (STIM) effect, we hypothesize that temporal scarcity influences contributors\u27 effort allocation in a U-shaped relationship. By exploring these hypotheses, we aim to advance the understanding of the underlying mechanisms of bounty awards and contribute to the development of effective peer incentive strategies

UNDERSTANDING THE MASSIVE ONLINE REVIEWS: A NOVEL REPRESENTATIVE SUBSET EXTRACTION METHOD

Online review hasalready been recognized as an important sales assistant for consumers to make their purchase decision. However, with the rapid development of electronic commerce,overwhelming informationoverloads and review manipulation make consumers lost in ocean of reviews and face huge cognitive stress. To address this issue, different types of online review have been developed by online marketplaces. Especially, except traditional types of online reviews (positive, neutral and negative), several new types of online review (review with picture and additional review) do not only contain plain text, but also pictures. Consumers could attach additional reviews to the original reviews to further share their experience sometimes later. Few studies have focused on which types of online reviews are able to influence consumers’ decisions more efficiently. Especially, research on new types of reviews is still unanswered.Using data from Taobao.com, the biggest electronic marketplace in China,this study conducts an empirical investigation to bridge the gap. Weinvestigatethat whether and howtraditional text reviewsand new types of reviews influence consumers’ purchase decision making. The results show that under the context of information overload and review manipulation, traditional reviewsare still influential, but less effective than new types of reviews. Although review with picture and additional review don’t show valence directly, they present more reliable references towards product quality and attract consumers’ attention more efficiently.And it is more interesting that new types of online review provide an effective channel for consumers to alleviate their dissatisfaction to effect potential consumers purchase decision making. The findings of this study can provide useful implications for researchers by highlighting the roles of different types of online review in consumers’ decision making. Also, the empirical investigation in this paper will remind business vendors to focus on online reviews especially new types of online reviews and conduct targeted marketing strategies to increase competitive advantage and improve their sales performance

Utilizing Geospatial Information in Cellular Data Usage for Key Location Prediction

Previous research on the identification of key locations (e.g., home and workplace) for a user largely relies on call detail records (CDRs). Recently, cellular data usage (i.e., mobile internet) is growing rapidly and offers fine-grained insights into various human behavior patterns. In this study, we introduce a novel dataset containing both voice and mobile data usage records of mobile users. We then construct a new feature based on the geospatial distribution of cell towers connected by mobile users and employ bivariate kernel density estimation to help predict users’ key locations. The evaluation results suggest that augmented features based on both voice and mobile data usage improve the prediction precision and recall

Wettability Alteration of Sandstone by Chemical Treatments

Liquid condensation in the reservoir near a wellbore may kill gas production in gas-condensate reservoirs when pressure drops lower than the dew point. It is clear from investigations reported in the literature that gas production could be improved by altering the rock wettability from liquid-wetness to gas-wetness. In this paper, three different fluorosurfactants FG1105, FC911, and FG40 were evaluated for altering the wettability of sandstone rocks from liquid-wetting to gas-wetting using contact angle measurement. The results showed that FG40 provided the best wettability alteration effect with a concentration of 0.3% and FC911 at the concentration of 0.3%

PUBLIC OPINION ANALYSIS BASED ON PROBABILISTIC TOPIC MODELING AND DEEP LEARNING

With the rapid development of Internet, especially the social media technologies, the public have gradually published their perception of social events online through social media. In Web2.0 era, with the concept of extensive participation of public in social-event-related information sharing, the effective content analysis and better results presentation for these media published online thus possesses significant importance for public opinion analysis and monitoring. In view of this, this paper proposes a novel method for public opinion analysis on social media website. First, the probabilistic topic model of Latent Dirichlet Allocation (LDA) is adopted to extract the public ideas about the distinct topics of certain event, and then the deep learning model named word2vec is used to calculate the emotional intensity for each text. Next, the underlying themes in the whole as well as the events of emotional intensity are investigated, and the variation trend of public’s emotion intensities is tracked based on time series analysis. Finally, the rationality and effectiveness of the method are verified with the analysis of a real case

Age-related changes in energy metabolism and skeletal muscle function of Sprague-Dawley rats

As the aging population grows worldwide,the importance of research on aging and agerelated changes is emerging. Aging, as an inevitable process, could lead to physiological functional declines in metabolic, respiratory, and exercise capacity, and could be associated with many diseases. In this study,we examined age-related changes,including O2 consumption volume (VO2), spontaneous locomotor activity,tissue weight, blood biochemical index, and gene expression in skeletal muscle of Sprague-Dawley (SD) rats,aged 32 to 92 weeks. Our findings suggest that the aging process might contribute to a decline in the VO2,spontaneous locomotor activity, and glucose oxidation of rats aged 90 weeks compared to that of rats aged32 weeks. With advancing age, skeletal muscle mass decreased significantly in rats aged 85 weeks (soleus muscle: 0.47 g/kg, gastrocnemius (Gas) muscle: 5.00 g/kg), and 92 weeks (soleus muscle: 0.50 g/kg, Gas muscle: 5.62 g/kg) compared to that in rats aged 32 weeks (soleus muscle: 0.70 g/kg, Gas muscle: 8.89 g/kg).With aging, the levels of genes Myh7, pargc1α, Cycs, and sdha, and mitochondrial DNA, which are related to skeletal muscle function and muscle oxidative capacity, decreased significantly in the soleus muscle of rats aged 85 and 92 weeks compared to that of rats aged 32 weeks. In addition, lipid accumulation in skeletal muscle and weight of white adipose tissue (WAT) around kidney increased with age in rats. This study may clarify age-related changes in energy metabolism, skeletal muscle characteristics, and lipid accumulation in rats, and provide a potential perspective for anti-aging research

Pixel Adapter: A Graph-Based Post-Processing Approach for Scene Text Image Super-Resolution

Current Scene text image super-resolution approaches primarily focus on extracting robust features, acquiring text information, and complex training strategies to generate super-resolution images. However, the upsampling module, which is crucial in the process of converting low-resolution images to high-resolution ones, has received little attention in existing works. To address this issue, we propose the Pixel Adapter Module (PAM) based on graph attention to address pixel distortion caused by upsampling. The PAM effectively captures local structural information by allowing each pixel to interact with its neighbors and update features. Unlike previous graph attention mechanisms, our approach achieves 2-3 orders of magnitude improvement in efficiency and memory utilization by eliminating the dependency on sparse adjacency matrices and introducing a sliding window approach for efficient parallel computation. Additionally, we introduce the MLP-based Sequential Residual Block (MSRB) for robust feature extraction from text images, and a Local Contour Awareness loss ($\mathcal{L}_{lca}$) to enhance the model's perception of details. Comprehensive experiments on TextZoom demonstrate that our proposed method generates high-quality super-resolution images, surpassing existing methods in recognition accuracy. For single-stage and multi-stage strategies, we achieved improvements of 0.7\% and 2.6\%, respectively, increasing the performance from 52.6\% and 53.7\% to 53.3\% and 56.3\%. The code is available at https://github.com/wenyu1009/RTSRN