Multi-category Comparative Analysis of Factors Affecting E-commerce Sales

With the continuous development of e-commerce, more and more types of goods are sold online, so merchants should develop different sales strategies for different types of goods. This paper firstly selects 15 variables to build a stepwise regression model. In the analysis of influencing factors on sales of products in different categories, we find that there are significant differences in the impact of the number of appended reviews and pictures reviews on the sales of utilitarian and hedonic products. In the analysis of influencing factors on sales of products in the same category, we find that the factors influencing the sales of different clothing products are also different to some extent. At last, we put forward some suggestions on adjusting price and title length, and writing product details. This paper is more detailed in variable selection and product classification than some previous studies. It is meaningful for merchants to optimize sales plans and improve product sales

Deep Learning Based Parking Vacancy Detection for Smart Cities

Parking shortage is a major problem in modern cities. Drivers cruising in search of a parking space directly translate into frustration, traffic congestion, and excessive carbon emission. We introduce a simple and effective deep learning-based parking space notification (PSN) system to inform drivers of new parking availabilities and re-occupancy of the freed spaces. Our system is particularly designed to target areas with severe parking shortages (i.e., nearly all parking spaces are occupied), a situation that allows us to convert the problem of detecting parking vacancies into recognizing vehicles leaving from their stationary positions. Our PSN system capitalizes on a calibrated Mask R-CNN model and a unique adaptation of the IoU concept to track the changes of vehicle positions in a video stream. We evaluated PSN using videos from a CCTV camera installed at a private parking lot and publicly available YouTube videos. The PSN system successfully captured all new parking vacancies arising from leaving vehicles with no false positive detections. Prompt notification messages were sent to users via cloud messaging services

The Role of Internet Search Index for Tourist Volume Prediction Based on GDFM Model

Tourist volume is increasing with the expansion of the scale of tourism, and improving the prediction of tourist volume is helpful for tourism managers to make decisions. Internet search index can be applied to predict the behavior of users, which is widely used in the study of tourist volume prediction and infectious disease prediction. However, the high dimension and correlation of Internet search index tends to reduce the accuracy of the models, which increases the average prediction error of common time-series models. The dynamic factor model (DFM) proposed in our study can be used to solve the problem. This study selects 23 variables and introduces the generalized dynamic factor model (GDFM) to predict tourist volume. The model cannot only reduce the dimensionality of high-dimensional Internet search index data, but also reflects the dynamic correlation between Internet search index data. The results show that the prediction accuracy is improved in our method, and the prediction accuracy of tourist volume is improved by over 10%, with an average error of only 4.3% when compared with the neural network (NN) model. Our study not only provides implications for decision-makers to predict tourist volume timely and accurately, but also helps companies understand tourist’ behavior and make the best strategic decisions

Pulsar discovery prospect of FASTA

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) has discovered more than 650 new pulsars, which account for 20% of our known Galactic pulsar population. In this paper, we estimate the prospect of a pulsar survey with a radio telescope array to be planned -- the FAST Array (FASTA), consists of six "FAST-type" telescopes. Such a sensitive radio telescope array would be a powerful instrument in probing the pulsar population deep into our Galaxy as well as in nearby galaxies. We simulate the FASTA pulsar discovery prospects with different Galactic pulsar population models and instrumental parameter combinations. We find that FASTA could detect tens of thousands of canonical pulsars and well-over thousands of millisecond pulsars. We also estimate the potential yield if the FASTA is used to search for pulsars from the nearby spiral galaxy M31, and find that it would probably discover around a hundred new radio pulsars

Improving Anchoring Vignette Methodology in Health Surveys with Image Vignettes

The anchoring vignette method is designed to improve comparisons across population groups and adjust for differential item functioning (DIF). Vignette questions are brief de­scriptions of hypothetical persons for respondents to rate. Although this method has been adopted widely in health surveys, there remain challenges. In particular, vignettes are com­plex, increasing survey time and respondent burden. Further, the assumptions underlying this method are often violated. To overcome such challenges, this paper introduces an inno­vative technique, namely image anchoring vignettes, conveying vignette information with varying health levels in images. We conducted a cross-cultural experimental study to ex­amine the performance of image and standard text vignettes in terms of response time, how well they satisfy the assumptions, and their DIF-adjusting quality using a confirmatory factor analysis. The study revealed that respondents can better differentiate the intensity levels of the three vignettes in the image vignette condition, compared to text vignettes. Response consistency assumption appears to be better satisfied for image vignettes than text vignettes. Using well-designed image vignettes greatly reduces survey time without losing the DIF-adjustment quality, indicating the potential of image vignettes to improve overall efficiencies of the anchoring vignette method. Improving vignette equivalence (i.e., minimizing different interpretations of vignettes by different groups), remains a challenge for both text and image vignettes. This study generates new insights into the design and use of image anchoring vignettes

Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference

Executing deep neural networks for inference on the server-class or cloud backend based on data generated at the edge of Internet of Things is desirable due primarily to the limited compute power of edge devices and the need to protect the confidentiality of the inference neural networks. However, such a remote inference scheme incurs concerns regarding the privacy of the inference data transmitted by the edge devices to the curious backend. This paper presents a lightweight and unobtrusive approach to obfuscate the inference data at the edge devices. It is lightweight in that the edge device only needs to execute a small-scale neural network; it is unobtrusive in that the edge device does not need to indicate whether obfuscation is applied. Extensive evaluation by three case studies of free spoken digit recognition, handwritten digit recognition, and American sign language recognition shows that our approach effectively protects the confidentiality of the raw forms of the inference data while effectively preserving the backend's inference accuracy.Comment: This paper has been accepted by IEEE Internet of Things Journal, Special Issue on Artificial Intelligence Powered Edge Computing for Internet of Thing

Telluride nanocrystals with adjustable amorphous shell thickness and core-shell structure modulation by aqueous cation-exchange

Engineering the structure of core-shell colloidal semiconductor nanoparticles (CSNPs) is attractive due to the potential to enhance photo-induced charge transfer (PICT) and induce favourable optical and electronic properties. Nonetheless, the sensitivity of telluride CSNPs to high temperatures makes it challenging to precisely modulate their surface crystallinity. Herein, we have developed an efficient strategy for synthesising telluride CSNPs with thin amorphous shells using aqueous cation exchange (ACE). By changing the synthesis temperature in the range 40 to 110C, the crystallinity of the CdTe nanoparticles was controllable from perfect crystals with no detectable amorphous shell (c-CdTe) to a core-shell structure with a crystalline CdTe NP core covered by an amorphous shell of tunable thickness up to 7-8nm (c@a-CdTe) . A second ACE step transformed the c@a-CdTe to crystalline CdTe@HgTe core-shell NPs. The c@a-CdTe nanoparticles synthesized at 60C and having a 4-5 nm thick amorphous shell, exhibited the highest surface-enhanced Raman scattering activity with a high enhancement factor around 8.82x10^5, attributed to the coupling between the amorphous shell and the crystalline core.Comment: 15 pages, 5 figures, plus supplementary informatio