Characteristics Description of Potential User Segments on the E-Commerce Website oriented to Precision Marketing

In the increasingly competitive environment between e-commerce companies, for more accurate implementation of marketing strategies, e-commerce websites often choose to subdivide the consumer market of the enterprise to identify site users’ characteristics to find their needs. In this paper, we subdivide consumer market from the four dimensions of behavior, geography, demography and psychology and propose a model to describe the characteristics of potential user market segments. Based on the web log data and user transaction data, a classification algorithm is used to analyze user behavior data in Web log to find the potential user segments and the user\u27s descriptive characteristics in user transaction data are clustered to obtain the distribution of consumer characteristics under various product categories, then we use the product categories in e-commerce website as an intermediary to give every single potential user in potential user market segments the descriptive characteristics, which can provide data support for the realization of precision marketing. The proposed model is applied to the actual data of a certain insurance e-commerce platform, and based on the results, we gain some implications for marketing of the e-commerce website

Measuring Immediate Effect and Carry-over Effect of Multi-channel Online Ads

Faced with various online ads, firms are hard to choose the most appropriate advertising channels which have best advertising effects. Online advertising has immediate and carry-over effects. We constructed a comprehensive evaluation model of multi-channel online advertising effects which can evaluate not only immediate effect but also carry-over effect based on lag effect factors. Then, we conducted a restricted grid search and multiple linear regressions to estimate the immediate effect and carry-over effect of paid search ads, mobile phone message ads and e-mail ads based on user behavior data and transaction data of an e-commerce website. The results show that the immediate effect intensity of paid-search ads is the highest, the carry-over effect duration of e-mail ads is the longest, and the cumulative carry-over effect intensity of e-mail ads is the highest. This study puts forward suggestions on how to evaluate the effects of multi-channel online ads more accurately, which can guide this e-commerce website to make better advertising strategy for online marketing

Real-Time Scheduling for Time-Sensitive Networking: A Systematic Review and Experimental Study

Time-Sensitive Networking (TSN) has been recognized as one of the key enabling technologies for Industry 4.0 and has been deployed in many time- and mission-critical industrial applications, e.g., automotive and aerospace systems. Given the stringent real-time communication requirements raised by these applications, the Time-Aware Shaper (TAS) draws special attention among the many traffic shapers developed for TSN, due to its ability to achieve deterministic latency guarantees. Extensive efforts on the designs of scheduling methods for TAS shapers have been reported in recent years to improve the system schedulability, each with their own distinct focuses and concerns. However, these scheduling methods have yet to be thoroughly evaluated, especially through experimental comparisons, to provide a systematical understanding on their performance using different evaluation metrics in various application scenarios. In this paper, we fill this gap by presenting a comprehensive experimental study on the existing TAS-based scheduling methods for TSN. We first categorize the system models employed in these work along with their formulated problems, and outline the fundamental considerations in the designs of TAS-based scheduling methods. We then perform extensive evaluation on 16 representative solutions and compare their performance under both synthetic scenarios and real-life industrial use cases. Through these experimental studies, we identify the limitations of individual scheduling methods and highlight several important findings. This work will provide foundational knowledge for the future studies on TSN real-time scheduling problems, and serve as the performance benchmarking for scheduling method development in TSN.Comment: 22 pages, ac

Learning Meta Model for Zero- and Few-shot Face Anti-spoofing

Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks, which need large scale training data to cover as many attacks as possible. However, the trained model is easy to overfit several common attacks and is still vulnerable to unseen attacks. To overcome this challenge, the detector should: 1) learn discriminative features that can generalize to unseen spoofing types from predefined presentation attacks; 2) quickly adapt to new spoofing types by learning from both the predefined attacks and a few examples of the new spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot learning problem. In this paper, we propose a novel Adaptive Inner-update Meta Face Anti-Spoofing (AIM-FAS) method to tackle this problem through meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task of detecting unseen spoofing types by learning from predefined living and spoofing faces and a few examples of new attacks. To assess the proposed approach, we propose several benchmarks for zero- and few-shot FAS. Experiments show its superior performances on the presented benchmarks to existing methods in existing zero-shot FAS protocols.Comment: Accepted by AAAI202

DECODE: DilatEd COnvolutional neural network for Detecting Extreme-mass-ratio inspirals

The detection of Extreme Mass Ratio Inspirals (EMRIs) is intricate due to their complex waveforms, extended duration, and low signal-to-noise ratio (SNR), making them more challenging to be identified compared to compact binary coalescences. While matched filtering-based techniques are known for their computational demands, existing deep learning-based methods primarily handle time-domain data and are often constrained by data duration and SNR. In addition, most existing work ignores time-delay interferometry (TDI) and applies the long-wavelength approximation in detector response calculations, thus limiting their ability to handle laser frequency noise. In this study, we introduce DECODE, an end-to-end model focusing on EMRI signal detection by sequence modeling in the frequency domain. Centered around a dilated causal convolutional neural network, trained on synthetic data considering TDI-1.5 detector response, DECODE can efficiently process a year's worth of multichannel TDI data with an SNR of around 50. We evaluate our model on 1-year data with accumulated SNR ranging from 50 to 120 and achieve a true positive rate of 96.3% at a false positive rate of 1%, keeping an inference time of less than 0.01 seconds. With the visualization of three showcased EMRI signals for interpretability and generalization, DECODE exhibits strong potential for future space-based gravitational wave data analyses.Comment: 13 pages, 5 figures, and 2 table

Sensory Manipulation as a Countermeasure to Robot Teleoperation Delays: System and Evidence

In the field of robotics, robot teleoperation for remote or hazardous environments has become increasingly vital. A major challenge is the lag between command and action, negatively affecting operator awareness, performance, and mental strain. Even with advanced technology, mitigating these delays, especially in long-distance operations, remains challenging. Current solutions largely focus on machine-based adjustments. Yet, there's a gap in using human perceptions to improve the teleoperation experience. This paper presents a unique method of sensory manipulation to help humans adapt to such delays. Drawing from motor learning principles, it suggests that modifying sensory stimuli can lessen the perception of these delays. Instead of introducing new skills, the approach uses existing motor coordination knowledge. The aim is to minimize the need for extensive training or complex automation. A study with 41 participants explored the effects of altered haptic cues in delayed teleoperations. These cues were sourced from advanced physics engines and robot sensors. Results highlighted benefits like reduced task time and improved perceptions of visual delays. Real-time haptic feedback significantly contributed to reduced mental strain and increased confidence. This research emphasizes human adaptation as a key element in robot teleoperation, advocating for improved teleoperation efficiency via swift human adaptation, rather than solely optimizing robots for delay adjustment.Comment: Submitted to Scientific Report

Chiral Anomaly Beyond Fermionic Paradigm

Two-dimensional magnets have manifested themselves as promising candidates for quantum devices. We here report that the edge and strain effects during the device fabrication with two-dimensional honeycomb ferromagnets such as CrX$_3$ (X=Cl, I, Br) and CrXTe$_3$ (X=Si, Ge) can be characterized by a (1+1)-dimensional magnon chiral anomaly beyond the fermionic paradigm. In the presence of zigzag edges, a pair of chiral bulk-edge magnon bands appear and cause an imbalance of left- and right-chirality magnons when subjected to nonuniform temperature or magnetic fields. In the presence of a uniaxial strain, the bulk Dirac magnons are broken into chiral magnon pseudo-Landau levels, resulting in a magnon chiral anomaly observable through a negative strain-resistivity of the magnetic dipole and heat. Our work demonstrates a chiral anomaly with (quasi)particles obeying non-fermionic statistics and will be instructive in understanding anomalous magnon transport.Comment: 4.5 pages, 4 figure

Brain Functional Connectivity under Teleoperation Latency: a fNIRS Study

Objective: This study aims to understand the cognitive impact of latency in teleoperation and the related mitigation methods, using functional Near-Infrared Spectroscopy (fNIRS) to analyze functional connectivity. Background: Latency between command, execution, and feedback in teleoperation can impair performance and affect operators mental state. The neural underpinnings of these effects are not well understood. Method: A human subject experiment (n = 41) of a simulated remote robot manipulation task was performed. Three conditions were tested: no latency, with visual and haptic latency, with visual latency and no haptic latency. fNIRS and performance data were recorded and analyzed. Results: The presence of latency in teleoperation significantly increased functional connectivity within and between prefrontal and motor cortexes. Maintaining visual latency while providing real-time haptic feedback reduced the average functional connectivity in all cortical networks and showed a significantly different connectivity ratio within prefrontal and motor cortical networks. The performance results showed the worst performance in the all-delayed condition and best performance in no latency condition, which echoes the neural activity patterns. Conclusion: The study provides neurological evidence that latency in teleoperation increases cognitive load, anxiety, and challenges in motion planning and control. Real-time haptic feedback, however, positively influences neural pathways related to cognition, decision-making, and sensorimotor processes. Application: This research can inform the design of ergonomic teleoperation systems that mitigate the effects of latency.Comment: Submitted to Human Factor

Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer

Space-based gravitational wave detection is one of the most anticipated gravitational wave (GW) detection projects in the next decade, which will detect abundant compact binary systems. However, the precise prediction of space GW waveforms remains unexplored. To solve the data processing difficulty in the increasing waveform complexity caused by detectors' response and second-generation time-delay interferometry (TDI 2.0), an interpretable pre-trained large model named CBS-GPT (Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer) is proposed. For compact binary system waveforms, three models were trained to predict the waveforms of massive black hole binary (MBHB), extreme mass-ratio inspirals (EMRIs), and galactic binary (GB), achieving prediction accuracies of 98%, 91%, and 99%, respectively. The CBS-GPT model exhibits notable interpretability, with its hidden parameters effectively capturing the intricate information of waveforms, even with complex instrument response and a wide parameter range. Our research demonstrates the potential of large pre-trained models in gravitational wave data processing, opening up new opportunities for future tasks such as gap completion, GW signal detection, and signal noise reduction