Large Crowd Count Based on Improved SURF Algorithm

This paper uses an analysis of Speeded up Robust Feature (SURF), based on the method of Linear Interpolation for camera distortion calibration, for high-density crowd counting. The eigenvalues are built on the Gray Level Co-occurrence Matrix (GLCM) features and the SURF features. Though the method of linear interpolation, weight values are interpolated to reduce the error, which is caused by camera distortion calibration. The optimized crowd’s feature vector can be got then. Through the method of support vector regression, the crowd’s number can be forecast by training model. The experiment result shows that the method of this paper has a higher accuracy than the previous methods

Detecting Incentivized Review Groups With Co-Review Graph

Online reviews play a crucial role in the ecosystem of nowadays business (especially e-commerce platforms), and have become the primary source of consumer opinions. To manipulate consumers’ opinions, some sellers of e-commerce platforms outsource opinion spamming with incentives (e.g., free products) in exchange for incentivized reviews. As incentives, by nature, are likely to drive more biased reviews or even fake reviews. Despite e-commerce platforms such as Amazon have taken initiatives to squash the incentivized review practice, sellers turn to various social networking platforms (e.g., Facebook) to outsource the incentivized reviews. The aggregation of sellers who request incentivized reviews and reviewers who seek incentives forms incentivized review groups. In this paper, we focus on the incentivized review groups in e-commerce platforms. We perform the data collections from various social networking platforms, including Facebook, WeChat, and Douban. A measurement study of incentivized review groups is conducted with regards to group members, group activities, and products. To identify the incentivized review groups, we propose a new detection approach based on co-review graphs. Specifically, we employ the community detection method to find the suspicious communities from co-review graphs. We also build a “gold standard” dataset from the data we collected, which contains the information of reviewers who belong to incentivized review groups. We utilize the “gold standard” dataset to evaluate the effectiveness of our detection approach

LOST: An Open-Source Suite of Star Tracking Software

We present LOST: Open-source Star Tracker (LOST), a suite of star tracking software particularly suitable for small satellite missions with limited computing resources and low-cost cameras. LOST contains implementations of a number of previously-proposed star tracking algorithms and a flexible framework for running and evaluating these algorithms. Our evaluation finds that LOST\u27s algorithms are simultaneously able to maintain a strong combination of accuracy, runtime, and memory usage. In scenarios representative of a low-cost star tracker, LOST correctly identifies over 95% of images, and importantly, performs the entire star tracking pipeline in less than 35 milliseconds on a Raspberry Pi while using less than 1 MiB of memory, backed by a \u3c 350 KiB database. These results indicate that LOST could be ported to an embedded or radiation-hardened CPU and still perform well enough to meet the accuracy requirements of many missions

The Impact of Deep Learning on Organizational Agility

Artificial intelligence advances business model, strategizes competitive resources, and impacts on organizational agility. Deep learning as a subset of AI brings changes in different aspects that substantially influences organizational capabilities. We argue that deep learning enables new conceptualization of organizational agility. We will conduct a case study in a leading Chinese FinTech company to inductively ground these impacts

A sample-decimation based fast preamble detection algorithm

Random access is a commonly used multiple access scheme that allows multiple users to share the same resource in a distributed fashion. In a Universal Mobile Telecommunication System (UMTS), the preamble of a random access channel (RACH) message is used by a mobile user to signal the base station for requesting network access or short data packets transportation. The base station is responsible in a timely fashion for detecting the preambles and informing the user whether the request has been granted or denied through the acquisition indication channel (AICH). Preamble detection is one of the most computationally intensive functional units of a base station. It has attracted many research attentions and investments in the past a few decades. The drawback of the existing preamble detection (PD) algorithms for UMTS base-station is that either their computational complexity is high or the detection accuracy is low. The conventional full search PD algorithm gives the best result in terms of the detection probability, but its complexity is high. On the hand, the parallel-serial code phase detector PD algorithm provides a reduced computational complexity, but the detection accuracy becomes low. In this thesis, a sample-decimation based preamble detection technique is proposed in order to substantially reduce the computational complexity and at the same time retain a high detection accuracy. The proposed algorithm comprises two stages. Delay hypotheses or delay offsets which are unlikely to have a strong correlation power between the antenna samples and the locally generated preamble replica are identified and discarded in the first stage. The second stage operates on the remaining offsets and employs all the antenna samples within the preamble signal. Extensive computer simulations are conducted under different levels of additive white Gaussian noise interferences. The results show that the proposed algorithm has a detection performance very close to that of the conventional full search PD algorithm, while at the same time it reduces the computational complexity by more than sixty percen