Social Media in Quality Management: An Empirical Statistical Research on Hotel Online Review

Hotel Online review is becoming a more and more popular topic in the hotel industry nowadays. Lots of research has been done and many interesting implications have been investigated. But very little research has been conducted from the different customer group perspective. In my thesis, I conducted a comprehensive statistical analysis mainly from the different customer group perspective and found out some very meaningful implications for the hotel industry. Some key contributions have been summarized as below: First, there exist significant mean differences in terms of six individual ratings and overall rating among different customer groups (Family, Business, Friend, Solo, and Couple). Second, the six different individual review items account for different weights in the overall rating scale. Third, there is a significant positive relationship between six individual review items and overall rating. Fourth, independent hotels are making better performance than chain hotels except for some certain customer group in terms of rooms and sleep quality rating. Also, among the five different customer groups, the ratings of individual and overall given by business customer group are the lowest compared with the other groups. These implications will help hotels allocate their resources more flexible and efficient rather than focus on every single aspect. Especially for those small and medium sized hotels, they may be able to run better business since they now learn where to allocate more resources according to the rank of the importance

Reference Governor Design in the Presence of Uncertain Polynomial Constraints

Reference governors are add-on schemes that are used to modify trajectories to prevent controlled dynamical systems from violating constraints and so are playing an increasingly important role in aerospace, robotic, and other engineering applications. Here we present a novel reference governor design for systems whose polynomial constraints depend on unknown bounded parameters. This is a significant departure from earlier treatments of reference governors, where the constraints were linear or known, because here we transfer the uncertainties into the constraints instead of having them in the closed loop dynamics, which greatly simplifies the task of determining future evolution of the constraints. Unlike our earlier treatment of reference governors with polynomial constraints, which transformed the constraints into linear ones that depend on an augmented state of the system, here we transform the constraints into linear ones that depend on both the system's state and uncertain parameters. Convexity allows us to compute the maximal output admissible set for an uncertain pre-stabilized linear system. We show that it is sufficient to only consider the extreme values of the uncertain parameters when computing and propagating the polynomial constraints. We illustrate our method using an uncertain longitudinal dynamics for civilian aircraft, which is controlled using a disturbance compensation method and needs to satisfy input and state constraints, and where our reference governor method ensures that safety constraints are always satisfied

Fast gradient method for Low-Rank Matrix Estimation

Projected gradient descent and its Riemannian variant belong to a typical class of methods for low-rank matrix estimation. This paper proposes a new Nesterov's Accelerated Riemannian Gradient algorithm by efficient orthographic retraction and tangent space projection. The subspace relationship between iterative and extrapolated sequences on the low-rank matrix manifold provides a computational convenience. With perturbation analysis of truncated singular value decomposition and two retractions, we systematically analyze the local convergence of gradient algorithms and Nesterov's variants in the Euclidean and Riemannian settings. Theoretically, we estimate the exact rate of local linear convergence under different parameters using the spectral radius in a closed form and give the optimal convergence rate and the corresponding momentum parameter. When the parameter is unknown, the adaptive restart scheme can avoid the oscillation problem caused by high momentum, thus approaching the optimal convergence rate. Extensive numerical experiments confirm the estimations of convergence rate and demonstrate that the proposed algorithm is competitive with first-order methods for matrix completion and matrix sensing.Comment: Accepted for publication in Journal of Scientific Computin

Lokalno diskriminantna projekcija difuzije i njena primjena za prepoznavanje emocionalnog stanja iz govornog signala

The existing Diffusion Maps method brings diffusion to data samples by Markov random walk. In this paper, to provide a general solution form of Diffusion Maps, first, we propose the generalized single-graph-diffusion embedding framework on the basis of graph embedding framework. Second, by designing the embedding graph of the framework, an algorithm, namely Locally Discriminant Diffusion Projection (LDDP), is proposed for speech emotion recognition. This algorithm is the projection form of the improved Diffusion Maps, which includes both discriminant information and local information. The linear or kernelized form of LDDP (i.e., LLDDP or KLDDP) is used to achieve the dimensionality reduction of original speech emotion features. We validate the proposed algorithm on two widely used speech emotion databases, EMO-DB and eNTERFACE\u2705. The experimental results show that the proposed LDDP methods, including LLDDP and KLDDP, outperform some other state-of-the-art dimensionality reduction methods which are based on graph embedding or discriminant analysis.Postojeće metode mapiranja difuzije u uzorke podataka primjenjuju Markovljevu slučajnu šetnju. U ovom radu, kako bismo pružili općenito rješenje za mapiranje difuzije, prvo predlažemo generalizirano okruženje za difuziju jednog grafa, zasnovano na okruženju za primjenu grafova. Drugo, konstruirajući ugrađeni graf, predlažemo algoritam lokalno diskriminantne projekcije difuzije (LDDP) za prepoznavanje emocionalnog stanja iz govornog signala. Ovaj algoritam je projekcija poboljšane difuzijske mape koja uključuje diskriminantnu i lokalnu informaciju. Linearna ili jezgrovita formulacija LDDP-a (i.e., LLDDP ili KLDDP) koristi se u svrhu redukcije dimenzionalnosti originalnog skupa značajki za prepoznavanje emocionalnog stanja iz govornog signala. Predloženi algoritam testiran je nad dvama široko korištenim bazama podataka za prepoznavanje emocionalnog stanja iz govornog signala, EMO-DB i eNTERFACE\u2705. Eksperimentalni rezultati pokazuju kako predložena LDDP metoda, uključujući LLDDP i KLDDP, pokazuje bolje ponašanje od nekih drugih najsuvremenijih metoda redukcije dimenzionalnosti, zasnovanim na ugrađenim grafovima ili analizi diskriminantnosti

Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration

Underwater images are subject to intricate and diverse degradation, inevitably affecting the effectiveness of underwater visual tasks. However, most approaches primarily operate in the raw pixel space of images, which limits the exploration of the frequency characteristics of underwater images, leading to an inadequate utilization of deep models' representational capabilities in producing high-quality images. In this paper, we introduce a novel Underwater Image Enhancement (UIE) framework, named WF-Diff, designed to fully leverage the characteristics of frequency domain information and diffusion models. WF-Diff consists of two detachable networks: Wavelet-based Fourier information interaction network (WFI2-net) and Frequency Residual Diffusion Adjustment Module (FRDAM). With our full exploration of the frequency domain information, WFI2-net aims to achieve preliminary enhancement of frequency information in the wavelet space. Our proposed FRDAM can further refine the high- and low-frequency information of the initial enhanced images, which can be viewed as a plug-and-play universal module to adjust the detail of the underwater images. With the above techniques, our algorithm can show SOTA performance on real-world underwater image datasets, and achieves competitive performance in visual quality

Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?

Prompt tuning (PT) which only tunes the embeddings of an additional sequence of tokens per task, keeping the pre-trained language model (PLM) frozen, has shown remarkable performance in few-shot learning. Despite this, PT has been shown to rely heavily on good initialization of the prompt embeddings. In this work, we study meta prompt tuning (MPT) to systematically explore how meta-learning can help improve (if it can) cross-task generalization in PT through learning to initialize the prompt embeddings from other relevant tasks. We empirically analyze a representative set of meta learning algorithms in a wide range of adaptation settings with different source/target task configurations on a large set of few-shot tasks. With extensive experiments and analysis, we demonstrate the effectiveness of MPT. We find the improvement to be significant particularly on classification tasks. For other kinds of tasks such as question answering, we observe that while MPT can outperform PT in most cases, it does not always outperform multi-task learning. We further provide an in-depth analysis from the perspective of task similarity

Clean and Sustainable Hydrogen-Electric Propulsion

For future hypersonic and supersonic flight, clean, sustainable and energy-efficient propulsion should be addressed in the general background of the sensational clean electric transition of aircraft. This chapter is to draw the attention of the research communities on the possible feasibilities and challenges of hydrogen-electric propulsion in hypersonic and supersonic flight. This chapter is structured with the following aspects, (1) general design and hybridisation concepts of hydrogen-electric propulsion for general aircraft and their hypersonic and supersonic considerations; (2) merits of hydrogen-electric propulsion on thermofluids process integrations; (3) potential merits of hydrogen-electric propulsion projected through thermofluids structural engineering and re-engineering; (4) storage options and their challenges in design and operation; and (5) reliability considerations

Lifelong Event Detection with Embedding Space Separation and Compaction

To mitigate forgetting, existing lifelong event detection methods typically maintain a memory module and replay the stored memory data during the learning of a new task. However, the simple combination of memory data and new-task samples can still result in substantial forgetting of previously acquired knowledge, which may occur due to the potential overlap between the feature distribution of new data and the previously learned embedding space. Moreover, the model suffers from overfitting on the few memory samples rather than effectively remembering learned patterns. To address the challenges of forgetting and overfitting, we propose a novel method based on embedding space separation and compaction. Our method alleviates forgetting of previously learned tasks by forcing the feature distribution of new data away from the previous embedding space. It also mitigates overfitting by a memory calibration mechanism that encourages memory data to be close to its prototype to enhance intra-class compactness. In addition, the learnable parameters of the new task are initialized by drawing upon acquired knowledge from the previously learned task to facilitate forward knowledge transfer. With extensive experiments, we demonstrate that our method can significantly outperform previous state-of-the-art approaches.Comment: NAACL 2024 main conferenc