Identify the HR factors that affect employee turnover in MasterHouse restaurant.

For organisations that seek reduction in employee turnover, it is crucial to identify which specific factors have the greatest impact on the staff turnover rate of the organisation. Similarly, after identifying influencing factors, it is also very important to formulate HR processes and policies based on the actual situation of the organisation. The aim of this research is to identify the HR factors that affect employee turnover rate in MasterHouse restaurant. High employee turnover rate will affect the performance of the organisation’s employees and the decline of daily functions, it will also increase the cost of organisation recruitment on-boarding and training. The purpose of this research is to identify the specific factors that affect employee turnover in MasterHouse and develop a practical plan for MasterHouse restaurant to improve employee loyalty and reduce employee turnover through the organisation’s human resources processes and policies. This research will lead to an understanding of the factors affecting the turnover rate of MasterHouse through secondary research, then researchers will collect data through quantitative research, and develop a strategic plan for MasterHouse to reduce employee turnover. The researcher will use questionnaires to investigate the HR strategies of MasterHouse and employees’ views on MasterHouse current HR process and policies. This research will involve five factors that affect employee turnover rate: Long-term relationships, benchmark, work-life balance, talent management, rewards and motivation. The researcher then connected and compared survey results with information in the literature and developed a practical plan for MasterHouse to reduce employee turnover rate

The Wave of Digital Revolution: New Trends in the Emergence, Participation, and Presentation of Metaverse Art

In early 2021, Roblox, known as the first stock in the metaverse, was officiallylisted on the New York Stock Exchange. In the same year, the famous American social media giant Facebook changed its name to Meta and focused on developing the meta-universe. At the same time, Microsoft, Nvidia, Qualcomm, Byte Jump, Baidu, Tencent and other technology giants laid out metaverse-related industries. Since then, the metaverse has become a new windfall. So what exactly is the metaverse? Meta-universe is a big concept, which is based on digital space to achieve a high degree of integration of the physical world, virtual world and human society, including all virtual worlds, augmented reality and the Internet, the core of which is a virtualization and digitization of the real world, the universe. As an excellent template for total art , metaverse undoubtedly cannot only rely on the technical support of Internet giants, but also needs to create a systematic and complete creator ecology, for this reason, metaverse and the art field naturally fuse with each other, and metaverse art sprouts and develops. For the metaverse, the intervention of artists and artworks injects new vitality into the metaverse. The artistic creation of artists can give the virtual world of the metaverse diversified and stylized forms of expression, preventing the metaverse from being reduced to a cold technical achievement or an empty virtual space. For art, the metaverseprovides a broader space for art display, participation and dissemination. The infinite virtual world of metaverse and its viewing method that breaks through the limit of screen border provide new ideas for the display of art works, and the art works with VR and AR technology as the core will be reborn in the metaverse. The combination of metaverse and art means mutual benefit and win-win. Art brings rich human connotation and emotion to metaverse technology, and art is like the flesh and blood of metaverse, which makes the originally cold technology have a temperature. Art is like the flesh and blood of the metaverse, giving the cold technology a warmth. The technology also provides a new space and platform for art, which is like a skeleton of the body supporting the outline of the whole body.

SCALABLE APPROXIMATION OF KERNEL FUZZY C-MEANS

Virtually every sector of business and industry that uses computing, including financial analysis, search engines, and electronic commerce, incorporate Big Data analysis into their business model. Sophisticated clustering algorithms are popular for deducing the nature of data by assigning labels to unlabeled data. We address two main challenges in Big Data. First, by definition, the volume of Big Data is too large to be loaded into a computer’s memory (this volume changes based on the computer used or available, but there is always a data set that is too large for any computer). Second, in real-time applications, the velocity of new incoming data prevents historical data from being stored and future data from being accessed. Therefore, we propose our Streaming Kernel Fuzzy c-Means (stKFCM) algorithm, which reduces both computational complexity and space complexity significantly. The proposed stKFCM only requires O(n2) memory where n is the (predetermined) size of a data subset (or data chunk) at each time step, which makes this algorithm truly scalable (as n can be chosen based on the available memory). Furthermore, only 2n2 elements of the full N × N (where N \u3e\u3e n) kernel matrix need to be calculated at each time-step, thus reducing both the computation time in producing the kernel elements and also the complexity of the FCM algorithm. Empirical results show that stKFCM, even with relatively very small n, can provide clustering performance as accurately as kernel fuzzy c-means run on the entire data set while achieving a significant speedup

Capacity Improvement in Wideband Reconfigurable Intelligent Surface-Aided Cell-Free Network

Thanks to the strong ability against the inter-cell interference, cell-free network has been considered as a promising technique to improve the network capacity of future wireless systems. However, for further capacity enhancement, it requires to deploy more base stations (BSs) with high cost and power consumption. To address the issue, inspired by the recently proposed technique called reconfigurable intelligent surface (RIS), we propose the concept of RIS-aided cell-free network to improve the network capacity with low cost and power consumption. Then, for the proposed RIS-aided cell-free network in the typical wideband scenario, we formulate the joint precoding design problem at the BSs and RISs to maximize the network capacity. Due to the non-convexity and high complexity of the formulated problem, we develop an alternating optimization algorithm to solve this challenging problem. Note that most of the considered scenarios in existing works are special cases of the general scenario in this paper, and the proposed joint precoding framework can also serve as a general solution to maximize the capacity in most of existing RIS-aided scenarios. Finally, simulation results verify that, compared with the conventional cell-free network, the network capacity of the proposed scheme can be improved significantly.Comment: 5 pages, 3 figures. Published in IEEE SPAWC'20, 27 May, 2020. This paper proposes a general joint precoding scheme for capacity improvement, which can be direcly applied to most of the RIS-aided communication systems. Simulation codes have been provided at: http://oa.ee.tsinghua.edu.cn/dailinglong/publications/publications.html. More insights can be found in the journal version of this paper: arXiv:2002.0374

XFlow: Benchmarking Flow Behaviors over Graphs

The occurrence of diffusion on a graph is a prevalent and significant phenomenon, as evidenced by the spread of rumors, influenza-like viruses, smart grid failures, and similar events. Comprehending the behaviors of flow is a formidable task, due to the intricate interplay between the distribution of seeds that initiate flow propagation, the propagation model, and the topology of the graph. The study of networks encompasses a diverse range of academic disciplines, including mathematics, physics, social science, and computer science. This interdisciplinary nature of network research is characterized by a high degree of specialization and compartmentalization, and the cooperation facilitated by them is inadequate. From a machine learning standpoint, there is a deficiency in a cohesive platform for assessing algorithms across various domains. One of the primary obstacles to current research in this field is the absence of a comprehensive curated benchmark suite to study the flow behaviors under network scenarios. To address this disparity, we propose the implementation of a novel benchmark suite that encompasses a variety of tasks, baseline models, graph datasets, and evaluation tools. In addition, we present a comprehensive analytical framework that offers a generalized approach to numerous flow-related tasks across diverse domains, serving as a blueprint and roadmap. Drawing upon the outcomes of our empirical investigation, we analyze the advantages and disadvantages of current foundational models, and we underscore potential avenues for further study. The datasets, code, and baseline models have been made available for the public at: https://github.com/XGraphing/XFlo