13 research outputs found
Study of e-book platforms' digital content library construction and marketing strategy
No full textLAUREA MAGISTRALEL’industria cinese di e-book è un settore in piena espansione che attira l'attenzione di una vasta gamma di aziende di diversi settori, tra cui produttori di hardware, operatori di telecomunicazione, rivenditori di Internet, letteratura e piattaforme Internet. Tutti i concorrenti adottano diversi modelli di business nel tentativo di raggiungere alte entrate, il mercato è nel caos. Il contesto economico turbolento e l’intensa concorrenza nel settore non solo è riuscito a far fallire alcuni operatori storici, ma anche a frenare il sano sviluppo del settore cinese di e-book. Per esempio la concorrenza feroce sui prezzi riduce il valore della proprietà intellettuale dello scrittore e lo scoraggiano dalla creazione.
Questo documento ha raccolto dati affidabili per analizzare due importanti operatori del settore e-book – le strategie di business e di marketing di Cloudary e Amazon, nonché persegue lo scopo di affrontare la questione su come guadagnare vantaggi competitivi attraverso costruzione della libreria di contenuti digitali e di come promuovere l'e-piattaforma libro così come il suo contenuto digitale, per fornire alcuni suggerimenti per le operazioni di business e-book.Chinese e-book industry is a booming industry that draws attention of a wide range of companies from different sectors, including hardware manufacturers, telecom operators, Internet retailers, and Internet literature platforms. With all competitors adopt various business models in attempts to achieve high revenue, the market is in chaos. The turbulent business environment and intensive competition within the industry not only failed some incumbents, but also prevent the healthy development of Chinese e-book industry, for instance, vicious competition on price reduces writer’s intellectual property income and discourage him from creation.
This paper collected reliable data to analyze two notable e-book industry participants – Cloudary’s and Amazon’s business strategies and marketing strategies as well, on the purpose to address the question of how to gain competitive advantages through digital content library construction and how to promote the e-book platform as well as its digital content, to provide some suggestions for e-book business operations
Design, fabrication, and application of stimuli-responsive hydrogel actuators
No full textIn this thesis, the topics around stimuli-responsive hydrogel actuators were discussed. In each project, a specially designed, stimuli-responsive, hydrogel was fabricated. By initiating a corresponding stimuli, swell-deswell changes were triggered within the hydrogels, and certain mechanical movements can be created and controlled by manipulating the patterns and structures of the hydrogels. With these projects, we are targeting improvement of the fabrication of biomimetic actuators and soft robotics. Among the 5 projects that will be discussed in this thesis, the first 3 systems are chemically initiated stimuli-responsive actuators (chemomechanical actuators), and the other two systems are non-chemically initiated.
In the chemomechanical systems, we explored relationships between different chemical systems and hydrogels, as well as the conversion of chemical energy into mechanical movements in a biomimetic fashion. For the first chemomechanical project, we described a new planar processing chemistry that allows the synthesis and patterning of dynamically self-actuating gels of diverse form. The chemical reaction covalently incorporates a methacrylate-modified ruthenium trisbipyridine (Ru(bpy)32+) monomer within a poly acrylamide (PAAm) gel, modified to provide a flexible chemistry for UV-curable, self-oscillating Belousov-Zhabotinsky (BZ) gels. Then, inspired by the photosensitizing capability of Ru(bpy)32+ from the first project, we successfully used visible light to trigger macroscopic movements of polyacrylic acid (PAA) based pH sensitive hydrogels through a photo catalytic water-splitting reaction system, which contains Ru(bpy)32+ as the photosensitizer and iridium dioxide (IrO2) nanoparticles as the catalyst. Finally, inspired by the pH sensitive PAA materials from the second project, we developed electrochemically-induced micro and macro scaled chemomechanical hydrogel actuators. The actuation from pH sensitive PAA hydrogel was achieved through the electrochemically-induced oxygen reduction reaction (ORR), during which protons are produced at the anode and consumed at the cathode, forming a pH gradient between the two electrodes.
For the non-chemically triggered actuator systems, we exploited the fast-responsive and vast volume change of the hydrogel due to thermal change or solubility change and aimed to explore the hierarchical programmability of matter through strain in a 3D fashion. In the fourth project, we show the controlled nonuniform swelling and de-swelling of one 3D hydrogel based on integrating electronic meshes, utilized as a local heating agent, into thermally responsive poly N-isopropyl acrylamide (PNIPAAm). Such hydrogel yields from embedding electronic meshes with functionalities and controlling abilities to locally program the shape of the hydrogel. In the last project, we used light to “program” the folding mechanics of a flat, two-dimensional material. Mixtures of polydimethylsiloxane (PDMS) and SU-8 photoresist are exposed to different photomasks, creating a disparity in cross-linked SU-8 density between the exposed and unexposed portions. Upon immersion in nonpolar organic solvent, strain gradients are formed into the folding configuration, due to the swelling difference of PDMS and SU-8. The photomasks in the fabrication process can be varied to tailor the strain and direct folding into different 3D configurations upon immersion into nonpolar solvent. By changing the exposure pattern, different folding configurations can be generated from the same two-dimensional precursor.Item withdrawn by Mark Zulauf ([email protected]) on 2013-09-25T20:58:39Z
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Joint Learning of Semantic and Latent Attributes
No full textAs mid-level semantic properties shared across object categories, attributes have been studied extensively. Recent approaches have attempted joint modelling of multiple attributes together with class labels so as to exploit their correlations for better attribute prediction and object recognition. However, they often ignore the fact that there exist some shared properties other than nameable/semantic attributes, which we call latent attributes. Basically, they can be further divided into discriminative and non-discriminative parts depending on whether they can contribute to an object recognition task. We argue that learning the latent attributes jointly with user-defined semantic attributes not only leads to better representation for object recognition but also helps with semantic attribute prediction. A novel dictionary learning model is proposed which decomposes the dictionary space into three parts corresponding to semantic, latent discriminative and latent background attributes respectively. An efficient algorithm is then formulated to solve the resultant optimization problem. Extensive experiments show that the proposed attribute learning method produces state-of-the-art results on both attribute prediction and attribute-based person re-identification.CPCI-S(ISTP)[email protected]; [email protected]; [email protected]; [email protected]; [email protected]
Multi-camera Pedestrian Detection with a Multi-view Bayesian Network Model
No full textIn this paper, we propose a novel method with the multi-view Bayesian network (MBN) model to detect pedestrians from multi-camera surveillance videos. In our method, the ground plane is discretized in a predefined set of locations and our aim is to estimate the occupancy probability of each location that can be then used to predict the occurrence of pedestrians. To reduce the possible phantoms, we use MBN to model the potential occlusion relationship of all locations in all views, and the "subjective supposing" node states (SSNS) as a set of Boolean parameters of MBN to denote whether a pedestrian occurs at the corresponding location. Thus a learning algorithm is proposed to estimate the SSNS parameters, by finding such a configuration that the final occupancy possibility can best explain the image observations (i.e., foreground masks) from different views. The experimental results on the APIDIS and PETS09 S2L1 benchmark datasets show that our method can obtain at least 10% performance gain compared with several state-of-the-art algorithms.Computer Science, Artificial IntelligenceEICPCI-S(ISTP)
Annotation Efficient Person Re-Identification with Diverse Cluster-Based Pair Selection
No full textPerson Re-identification (Re-ID) has attracted great attention due to its
promising real-world applications. However, in practice, it is always costly to
annotate the training data to train a Re-ID model, and it still remains
challenging to reduce the annotation cost while maintaining the performance for
the Re-ID task. To solve this problem, we propose the Annotation Efficient
Person Re-Identification method to select image pairs from an alternative pair
set according to the fallibility and diversity of pairs, and train the Re-ID
model based on the annotation. Specifically, we design an annotation and
training framework to firstly reduce the size of the alternative pair set by
clustering all images considering the locality of features, secondly select
images pairs from intra-/inter-cluster samples for human to annotate, thirdly
re-assign clusters according to the annotation, and finally train the model
with the re-assigned clusters. During the pair selection, we seek for valuable
pairs according to pairs' fallibility and diversity, which includes an
intra-cluster criterion to construct image pairs with the most chaotic samples
and the representative samples within clusters, an inter-cluster criterion to
construct image pairs between clusters based on the second-order Wasserstein
distance, and a diversity criterion for clusterbased pair selection. Combining
all criteria above, a greedy strategy is developed to solve the pair selection
problem. Finally, the above
clustering-selecting-annotating-reassigning-training procedure will be repeated
until the annotation budget is reached. Extensive experiments on three widely
adopted Re-ID datasets show that we can greatly reduce the annotation cost
while achieving better performance compared with state-of-the-art works
Adaptive Discovering and Merging for Incremental Novel Class Discovery
No full textOne important desideratum of lifelong learning aims to discover novel classes
from unlabelled data in a continuous manner. The central challenge is twofold:
discovering and learning novel classes while mitigating the issue of
catastrophic forgetting of established knowledge. To this end, we introduce a
new paradigm called Adaptive Discovering and Merging (ADM) to discover novel
categories adaptively in the incremental stage and integrate novel knowledge
into the model without affecting the original knowledge. To discover novel
classes adaptively, we decouple representation learning and novel class
discovery, and use Triple Comparison (TC) and Probability Regularization (PR)
to constrain the probability discrepancy and diversity for adaptive category
assignment. To merge the learned novel knowledge adaptively, we propose a
hybrid structure with base and novel branches named Adaptive Model Merging
(AMM), which reduces the interference of the novel branch on the old classes to
preserve the previous knowledge, and merges the novel branch to the base model
without performance loss and parameter growth. Extensive experiments on several
datasets show that ADM significantly outperforms existing class-incremental
Novel Class Discovery (class-iNCD) approaches. Moreover, our AMM also benefits
the class-incremental Learning (class-IL) task by alleviating the catastrophic
forgetting problem.Comment: AAAI 2024. arXiv admin note: text overlap with arXiv:2207.08605 by
other author
A comparative analysis of hybrid RF models for efficient lithology prediction in hard rock tunneling using TBM working parameters
No full textWith the escalating demand for underground mining and infrastructure construction, the optimization of tunnel construction has emerged as a primary concern for researchers. The geological conditions encountered during the excavation of hard rock tunnels using tunnel boring machines (TBM) significantly impact construction efficiency and cost-effectiveness. The existing lithology testing methods need to be more efficient in aligning with TBM operational efficiency. In recent years, the rapid advancement of artificial intelligence has paved the way for its integration into numerous domains, including tunnel engineering. To address this issue, this study proposes three innovative hybrid RF-based intelligent models, namely PSO-RF, ALO-RF, and GWO-RF, for the precise prediction of lithology in hard rock tunnels using TBM working parameters. The TBM operating parameters of the Jilin Yinsong Water Supply Project serve as the basis for this investigation. Twelve distinct characteristic parameters relevant to the lithology of the tunnel working face were carefully selected as input parameters for lithology prediction. Comparative analysis of the three hybrid models reveals that GWO-RF demonstrates exceptional lithology prediction performance (ACC = 0.999924; PREA = 0.0.9999976; RECA = 0.999775; F1A = 0.999876; Kappa = 0.999911), whereas PSO-RF and ALO-RF exhibit slightly inferior performance. Nonetheless, all three hybrid models exhibit a significant improvement in prediction accuracy compared to the unoptimized RF model. The research findings presented herein facilitate the swift determination of TBM working surface lithology, enabling timely adjustment of TBM working parameters, reducing equipment wear and tear, and enhancing construction efficiency. © The Author(s), under exclusive licence to Institute of Geophysics of the Polish Academy of Sciences 2024
Robust multiple cameras pedestrian detection with multi-view Bayesian network
No full textMulti-camera pedestrian detection is the challenging problem in the field of surveillance video analysis. However, existing approaches may produce "phantoms" (i.e., fake pedestrians) due to the heavy occlusions in real surveillance scenario, while calibration errors and the diverse heights of pedestrians may also heavily decrease the detection performance. To address these problems, this paper proposes a robust multiple cameras pedestrian detection approach with multi-view Bayesian network model (MvBN). Given the preliminary results obtained by any multi-view pedestrian detection method, which are actually comprised of both real pedestrians and phantoms, the MvBN is used to model both the occlusion relationship and the homography correspondence between them in all camera views. As such, the removal of phantoms can be formulated as an MvBN inference problem. Moreover, to reduce the influence of the calibration errors and keep robust to the diverse heights of pedestrians, a height-adaptive projection (HAP) method is proposed to further improve the detection performance by utilizing a local search process in a small neighborhood of heights and locations of the detected pedestrians. Experimental results on four public benchmarks show that our method outperforms several state-of-the-art algorithms remarkably and demonstrates high robustness in different surveillance scenes. (C) 2014 Elsevier Ltd. All rights reserved.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000349504700013&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Artificial IntelligenceEngineering, Electrical & ElectronicSCI(E)[email protected]
Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration
No full textAccurate prediction of ground vibration caused by blasting has always been a significant issue in the mining industry. Ground vibration caused by blasting is a harmful phenomenon to nearby buildings and should be prevented. In this regard, a new intelligent method for predicting peak particle velocity (PPV) induced by blasting had been developed. Accordingly, 150 sets of data composed of thirteen uncontrollable and controllable indicators are selected as input dependent variables, and the measured PPV is used as the output target for characterizing blast-induced ground vibration. Also, in order to enhance its predictive accuracy, the gray wolf optimization (GWO), whale optimization algorithm (WOA) and Bayesian optimization algorithm (BO) are applied to fine-tune the hyper-parameters of the extreme gradient boosting (XGBoost) model. According to the root mean squared error (RMSE), determination coefficient (R2), the variance accounted for (VAF), and mean absolute error (MAE), the hybrid models GWO-XGBoost, WOA-XGBoost, and BO-XGBoost were verified. Additionally, XGBoost, CatBoost (CatB), Random Forest, and gradient boosting regression (GBR) were also considered and used to compare the multiple hybrid-XGBoost models that have been developed. The values of RMSE, R2, VAF, and MAE obtained from WOA-XGBoost, GWO-XGBoost, and BO-XGBoost models were equal to (3.0538, 0.9757, 97.68, 2.5032), (3.0954, 0.9751, 97.62, 2.5189), and (3.2409, 0.9727, 97.65, 2.5867), respectively. Findings reveal that compared with other machine learning models, the proposed WOA-XGBoost became the most reliable model. These three optimized hybrid models are superior to the GBR model, CatB model, Random Forest model, and the XGBoost model, confirming the ability of the meta-heuristic algorithm to enhance the performance of the PPV model, which can be helpful for mine planners and engineers using advanced supervised machine learning with metaheuristic algorithms for predicting ground vibration caused by explosions. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature
A hybrid metaheuristic approach using random forest and particle swarm optimization to study and evaluate backbreak in open-pit blasting
No full textBackbreak is a rock fracture problem that exceeds the limits of the last row of holes in an explosion operation. Excessive backbreak increases operational costs and also poses a threat to mine safety. In this regard, a new hybrid intelligence approach based on random forest (RF) and particle swarm optimization (PSO) is proposed for predicting backbreak with high accuracy to reduce the unsolicited phenomenon induced by backbreak in open-pit blasting. A data set of 234 samples with six input parameters including special drilling (SD), spacing (S), burden (B), hole length (L), stemming (T) and powder factor (PF) and one output parameter backbreak (BB) is set up in this study. Seven input combinations (one with six parameters, six with five parameters) are built to generate the optimal prediction model. The PSO algorithm is integrated with the RF algorithm to find the optimal hyper-parameters of each model and the fitness function, which is the mean absolute error (MAE) of ten cross-validations. The performance capacities of the optimal models are assessed using MAE, root-mean-square error (RMSE), Pearson correlation coefficient (R2) and mean absolute percentage error (MAPE). Findings demonstrated that the PSO–RF model combining L–S–B–T–PF with MAE of 0.0132 and 0.0568, RMSE of 0.0811 and 0.1686, R2 of 0.9990 and 0.9961 and MAPE of 0.0027 and 0.0116 in training and testing phases, respectively, has optimal prediction performance. The optimal PSO–RF models were compared with the classical artificial neural network, RF, genetic programming, support vector machine and convolutional neural network models and show that the PSO–RF model has superiority in predicting backbreak. The Gini index of each input variable has also been calculated in the RF model, which was 31.2 (L), 23.1 (S), 27.4 (B), 36.6 (T), 23.4 (PF) and 16.9 (SD), respectively. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature
