ACADEMIC HOT-SPOT ANALYSIS ON INFORMATION SYSTEM BASED ON THE CO-TERM NETWORK

The amount of research literature is increasing so fast that the scholars are hard to clearly know the state of art about a certain research field. For IS scholars, understanding research hot-spots among numerous academic papers on IS field is always a significant and key task. In this paper, taking Information System field as example, an academic hot-spot analysis method is proposed to automatically find the research hot topic. Firstly, based on the key words of literatures, a co-term network is build, then fast greedy clustering method is used to find research topic, and hot degree of research topics are computed. After downloading the literature information about IS field from WEB OF SCIENCE, research hot topic during latest five years is identified. The result show that three general topics, respectively as GIS, Health-Care IS, Management & Internet, are important research direction. Then, the hot-spots analysis method, which decomposes the Management & Internet topic into 10 topic communities, generates and discussed the IS trends on top 5 academic topics of each year from 2009 to 2013 and the heat map of the IS hot-spots in 2013

Practical Batch Bayesian Sampling Algorithms for Online Adaptive Traffic Experimentation

Online controlled experiments have emerged as industry gold standard for assessing new web features. As new web algorithms proliferate, experimentation platform faces an increasing demand on the velocity of online experiments, which encourages adaptive traffic testing methods to speed up identifying best variant by efficiently allocating traffic. This paper proposed four Bayesian batch bandit algorithms (NB-TS, WB-TS, NB-TTTS, WB-TTTS) for eBay's experimentation platform, using summary batch statistics of a goal metric without incurring new engineering technical debts. The novel WB-TTTS, in particular, demonstrates as an efficient, trustworthy and robust alternative to fixed horizon A/B testing. Another novel contribution is to bring trustworthiness of best arm identification algorithms into evaluation criterion and highlight the existence of severe false positive inflation with equivalent best arms. To gain the trust of experimenters, experimentation platform must consider both efficiency and trustworthiness; However, to the best of authors' knowledge, trustworthiness as an important topic is rarely discussed. This paper shows that Bayesian bandits without neutral posterior reshaping, particularly naive Thompson sampling (NB-TS), are untrustworthy because they can always identify an arm as the best from equivalent best arms. To restore trustworthiness, a novel finding uncovers connections between convergence distribution of posterior optimal probabilities of equivalent best arms and neutral posterior reshaping, which controls false positives. Lastly, this paper presents lessons learned from eBay's experience, as well as thorough evaluations. We hope this work is useful to other industrial practitioners and inspires academic researchers interested in the trustworthiness of adaptive traffic experimentation

An Ensemble Score Filter for Tracking High-Dimensional Nonlinear Dynamical Systems

We propose an ensemble score filter (EnSF) for solving high-dimensional nonlinear filtering problems with superior accuracy. A major drawback of existing filtering methods, e.g., particle filters or ensemble Kalman filters, is the low accuracy in handling high-dimensional and highly nonlinear problems. EnSF attacks this challenge by exploiting the score-based diffusion model, defined in a pseudo-temporal domain, to characterizing the evolution of the filtering density. EnSF stores the information of the recursively updated filtering density function in the score function, in stead of storing the information in a set of finite Monte Carlo samples (used in particle filters and ensemble Kalman filters). Unlike existing diffusion models that train neural networks to approximate the score function, we develop a training-free score estimation that uses mini-batch-based Monte Carlo estimator to directly approximate the score function at any pseudo-spatial-temporal location, which provides sufficient accuracy in solving high-dimensional nonlinear problems as well as saves tremendous amount of time spent on training neural networks. Another essential aspect of EnSF is its analytical update step, gradually incorporating data information into the score function, which is crucial in mitigating the degeneracy issue faced when dealing with very high-dimensional nonlinear filtering problems. High-dimensional Lorenz systems are used to demonstrate the performance of our method. EnSF provides surprisingly impressive performance in reliably tracking extremely high-dimensional Lorenz systems (up to 1,000,000 dimension) with highly nonlinear observation processes, which is a well-known challenging problem for existing filtering methods.Comment: arXiv admin note: text overlap with arXiv:2306.0928

Improving the Expressive Power of Deep Neural Networks through Integral Activation Transform

The impressive expressive power of deep neural networks (DNNs) underlies their widespread applicability. However, while the theoretical capacity of deep architectures is high, the practical expressive power achieved through successful training often falls short. Building on the insights gained from Neural ODEs, which explore the depth of DNNs as a continuous variable, in this work, we generalize the traditional fully connected DNN through the concept of continuous width. In the Generalized Deep Neural Network (GDNN), the traditional notion of neurons in each layer is replaced by a continuous state function. Using the finite rank parameterization of the weight integral kernel, we establish that GDNN can be obtained by employing the Integral Activation Transform (IAT) as activation layers within the traditional DNN framework. The IAT maps the input vector to a function space using some basis functions, followed by nonlinear activation in the function space, and then extracts information through the integration with another collection of basis functions. A specific variant, IAT-ReLU, featuring the ReLU nonlinearity, serves as a smooth generalization of the scalar ReLU activation. Notably, IAT-ReLU exhibits a continuous activation pattern when continuous basis functions are employed, making it smooth and enhancing the trainability of the DNN. Our numerical experiments demonstrate that IAT-ReLU outperforms regular ReLU in terms of trainability and better smoothness.Comment: 26 pages, 6 figure

Moving Metric Detection and Alerting System at eBay

At eBay, there are thousands of product health metrics for different domain teams to monitor. We built a two-phase alerting system to notify users with actionable alerts based on anomaly detection and alert retrieval. In the first phase, we developed an efficient anomaly detection algorithm, called Moving Metric Detector (MMD), to identify potential alerts among metrics with distribution agnostic criteria. In the second alert retrieval phase, we built additional logic with feedbacks to select valid actionable alerts with point-wise ranking model and business rules. Compared with other trend and seasonality decomposition methods, our decomposer is faster and better to detect anomalies in unsupervised cases. Our two-phase approach dramatically improves alert precision and avoids alert spamming in eBay production.Comment: The work is oral presented on the AAAI-20 Workshop on Cloud Intelligence, 202

The impact of transformational leadership style on the development of human resources management

This study proposes a model of the impact of transformational leadership style on the development of human resources management (HRM). The model collected and analysed questionnaire data of 280 employees from four construction enterprises in China. The results show that transformational leadership style has an important influence on the development of HRM. From the four dimensions (commitment, flexibility, quality and integration) measuring the development of HRM, transformational leadership style has the greatest impact on organisational commitment. The paper also verifies the positive influence of transformational leadership style on organisational integration and fills the gaps in the early literature. Finally, we discuss practical and theoretical implications. Keywords: Transformational leadership; Development of HRM; Chin