Embedding Mechanism Research Articles

The role of leaders in building research cultures in sub-Saharan African universities: A six-nation study

ABSTRACT Existing research attributes the problem of weak research productivity of academics in African universities primarily to institutional resource poverty and inadequate research skills. However, there has been little attention to research cultures and the role of leaders in fostering productive ones. Drawing from the literature on organizational culture, this study examines the role of university leaders in developing research cultures. The study explores how institution leaders do this within the higher education contexts in their countries. The empirical work is based on qualitative interviews with senior and mid-level university leaders in six countries in sub-Saharan Africa. While all of the leaders espoused clear views about the elements of a productive research culture, results indicate a significant gap remains between espoused values for research and the actual research culture. Theoretically, the research extends the concept of research cultures by demonstrating the complex dynamics between research cultures, culture embedding mechanisms, and leader behavior within contextual constraints.

Learning Multi-Scale Heterogeneous Representations and Global Topology for Drug-Target Interaction Prediction

Identification of interactions between drugs and target proteins plays a critical role not only in drug discovery but also in drug repositioning. Deep integration of inter-connections and intra-similarities between heterogeneous multi-source data about drugs and targets, however, is a challenging issue. We propose a drug-target interaction (DTI) prediction model by learning from drug and protein related multi-scale attributes and global topology formed by heterogeneous connections. A drug-protein-disease heterogeneous network (RPD-Net) is firstly constructed to associate diverse similarities, interactions and associations across nodes. Secondly, we propose a multi-scale pairwise deep representation learning module consisting of a new embedding strategy to integrate diverse inter-relations and intra-relations, and dilation convolutions for multi-scale deep representation extraction. A global topology learning module is proposed which is composed of strategy based on non-negative matrix factorization (NMF) to extract topology from RPD-Net, and a new relational-level attention mechanism for discriminative topology embedding. Experimental results using public dataset demonstrate improved performance over state-of-the-art methods and contributions of our major innovations. Evaluation results by top k recall rates and case studies on five drugs further show the effectiveness of our method in retrieving potential target candidates for drugs.

Protective, controlled-release and embedding mechanism of porcine plasma protein cold-set gel on quercetin: An effective carrier of hydrophobic compounds

Protection and embedding of hydrophobic bioactive compounds using protein hydrogels are emerging focus during the latest decade. In present study, we fabricated the porcine plasma protein (PPP) cold-set gel by microbial transglutaminase (MTGase) and glucono-δ-lactone (GDL) as coupling precursors. As a result, the embedding, protection and controlled-release effect of the gel on vulnerable hydrophobic bioactive components (quercetin (Que) as representative) with proposed molecular mechanisms were investigated in detail. The results showed that high concentration of Que (5 mmol/L) could be loaded with PPP cold-set gel and embedding efficiency (EE) was over 98%. Compared with free Que, the embedded one exhibited significantly higher thermostability, photochemical stability and storage stability (P < 0.05). In addition, the gel loaded with 5 mmol L−1 of Que had higher swelling potency under gastric (low pH) media and controlled release performance following simulated intestinal digestive pathway. Water holding capacity (WHC) implied that free water molecules played less role in the retention ability of Que in fabricated gel network. Spectral-assisted structural characterization proved that Que was efficiently embedded mainly by generating PPP-Que complex through hydrogen bond and van der Waals force, and the binding site was mainly near Trp residue. This work gave novel insight into the potential use of PPP cold-set gel as an excellent carrier towards protection and selective delivery of vulnerable small hydrophobic nutraceutical compounds.

Multipurpose medical image watermarking for effective security solutions.

Digital medical images contain important information regarding patient’s health and very useful for diagnosis. Even a small change in medical images (especially in the region of interest (ROI)) can mislead the doctors/practitioners for deciding further treatment. Therefore, the protection of the images against intentional/unintentional tampering, forgery, filtering, compression and other common signal processing attacks are mandatory. This manuscript presents a multipurpose medical image watermarking scheme to offer copyright/ownership protection, tamper detection/localization (for ROI (region of interest) and different segments of RONI (region of non-interest)), and self-recovery of the ROI with 100% reversibility. Initially, the recovery information of the host image’s ROI is compressed using LZW (Lempel-Ziv-Welch) algorithm. Afterwards, the robust watermark is embedded into the host image using a transform domain based embedding mechanism. Further, the 256-bit hash keys are generated using SHA-256 algorithm for the ROI and eight RONI regions (i.e. RONI-1 to RONI-8) of the robust watermarked image. The compressed recovery data and hash keys are combined and then embedded into the segmented RONI region of the robust watermarked image using an LSB replacement based fragile watermarking approach. Experimental results show high imperceptibility, high robustness, perfect tamper detection, significant tamper localization, and perfect recovery of the ROI (100% reversibility). The scheme doesn’t need original host or watermark information for the extraction process due to the blind nature. The relative analysis demonstrates the superiority of the proposed scheme over existing schemes.

Towards the Categorization of Brazilian Financial Market Headlines

Financial market news portals are valuable sources of information as they hold great power over investors' decision-making processes. Due to the vast amount of text data produced by news portals, several studies have been conducted to comprehend the behavioral variations of texts and automate the categorization of short texts. However, extracting useful information that influences investors' decision-making process is not a trivial task, given that news portals use a heterogeneous and specific language for each content produced, making it challenging to generate a standard document format. This work proposes GOOSE, a solution for the cateGOrizatiOn of Short texts derived from multiple sources of information, to portray the financial market's current situation. To this end, GOOSE is based on Bidirectional Long Short-Term Memory (Bi-LSTM) and GloVe Embeddings to increase reliability in the short texts classification process. That way, GOOSE obtains data from news portals, which, once combined with a word embedding mechanism, are used as input for the Bi-LSTM to classify financial market news texts. The results obtained showed that GOOSE's efficiency in categorizing texts had an accuracy of 84% but also demonstrated the feasibility of its use in the extraction of information from financial market news portals.

A relation aware embedding mechanism for relation extraction

A relation aware embedding mechanism for relation extraction

CGENet: A Deep Graph Model for COVID-19 Detection Based on Chest CT.

Simple SummaryThis study proposes a new COVID-19 detection system called CGENet, based on computer vision and chest computed tomography images. First, an optimal backbone selection algorithm was proposed to determine the best backbone network for the CGENet adaptively. Then, we introduced a novel graph embedding mechanism to fuse the spatial relationship into the feature vectors. Finally, we chose the extreme learning machine as the classifier of the proposed CGENet to boost the classification performance. The proposed CGENet was evaluated on a public dataset using 5-fold cross-validation and compared with other algorithms. The results revealed that the proposed model achieved state-of-the-art classification performance. In all, the CGENet can be an effective and efficient tool that can assist COVID-19 diagnosis.Accurate and timely diagnosis of COVID-19 is indispensable to control its spread. This study proposes a novel explainable COVID-19 diagnosis system called CGENet based on graph embedding and an extreme learning machine for chest CT images. We put forward an optimal backbone selection algorithm to select the best backbone for the CGENet based on transfer learning. Then, we introduced graph theory into the ResNet-18 based on the k-nearest neighbors. Finally, an extreme learning machine was trained as the classifier of the CGENet. The proposed CGENet was evaluated on a large publicly-available COVID-19 dataset and produced an average accuracy of 97.78% based on 5-fold cross-validation. In addition, we utilized the Grad-CAM maps to present a visual explanation of the CGENet based on COVID-19 samples. In all, the proposed CGENet can be an effective and efficient tool to assist COVID-19 diagnosis.

ReLeDP: Reinforcement-Learning-Assisted Dynamic Pricing for Wireless Smart Grid

The smart grid must ensure that power providers can obtain substantial benefits by selling energy, while at the same time, they need to consider the cost of consumers. To realize this win-win situation, the smart grid relies on dynamic pricing mechanisms. However, most of the existing dynamic pricing schemes are based on artificial objective rules or conventional models, which cannot ensure the desired effectiveness. Thus, we apply reinforcement learning to model the supply-demand relationship between power providers and consumers in a smart grid. The dynamic pricing problem of the smart grid is modeled as a discrete Markov decision process, and the decision process is solved by Q-learning. Now, the success of any intelligent dynamic pricing scheme relies on timely data transmission. However, the scale and speed of data generation can create several network bottlenecks that can further reduce the performance of any dynamic pricing scheme. Hence, to overcome this challenge, we have proposed an artificial-intelligence-based adaptive network architecture that adopts software-defined networking. In this architecture, we have used a self-organized map-based traffic classification approach followed by a dynamic virtual network embedding mechanism. We demonstrate the effectiveness of the dynamic pricing strategy supported through adaptive network architecture based on various performance indicators. The outcomes suggest that the proposed strategy is of great significance to realize the sustainability of power energy in the future. Lastly, we discuss various implementation challenges and future directions before concluding the article.

Dynamical time series embeddings in recurrent neural networks

Time series forecasting has historically been a key research problem in science and engineering. In recent years, machine learning algorithms have proven to be a very successful data-driven approach in this area. In particular, Recurrent Neural Networks (RNNs) represent the state-of-the-art algorithms in many sequential tasks. In this paper we train Long Short Term Memory networks (LSTM), which are a type of RNNs, to make predictions in time series corresponding to the observation of a single variable of a chaotic system. We show that, under certain conditions, networks learn to generate an embedding of the data in their inner sate that is topologically equivalent to the original strange attractor. Remarkably, this resembles standard forecasting methods from nonlinear science in which the time series is embedded in a multi-valued space using Takens's delay embedding mechanism.

Analisis Fatwa DSN-MUI No. 25/DSN-MUI/2002 Tentang Pelelangan Barang di Pegadaian Syariah

In Islam, pawning is known as Ar-rahn, which is to make an item as collateral for debt, provided that if there is difficulty in payment, the debt can be paid from the sale of the goods used as collateral. In sharia pawnshops, the auction system applies to customers, if the customer is unable to redeem the pawned goods and pay their debts after maturity. An auction is an attempt to repay loans that cannot be repaid to a certain extent. Auctions according to sharia must also be accountable for Islamic law, which is free from elements of gharar, maisir, usury and falsehood. The auction mechanism in sharia pawnshops is regulated in the second part of the General Provisions for Fatwa DSN No.25 / DSN-MUI / 2002, point 5 concerning auction. The purpose of this research is to find out whether the sharia auction mechanism is in accordance with the fatwa or not. The method used in this writing is qualitative descriptive. The result of this research is that the sharia embedding mechanism is in accordance with the DSN-MUI Fatwa No. 25 / DSN-MUI / 2002 concerning Auctions.

Mechanical performance of inclined Dougong bracket sets under vertical load: Experimental tests and finite element calculation

This paper presents an investigation on the mechanical performance of inclined Dougong bracket sets under the vertical load. Initial inclination angles of Dougongs are determined based on material test results and embedment mechanism theories. Four full-scale Dougongs with different initial inclination angles are tested under the vertical load. The failure patterns, load-displacement curves, load distribution and displacement analyses are disclosed. Moreover, finite element calculation is conducted, model validation, stress and strain analyses are illustrated. Parametric analyses considering initial inclination angles of Dougongs, compressive strength and elastic moduli of wood, and friction coefficients between embedded surfaces are also carried out. The results indicate that inclined Dougongs under the vertical load behave similarly to a timber block under compression in the perpendicular-to-grain direction. Increasing the initial inclination angles can enhance vertical stiffness to some extent, but the bearing capacities of the inclined Dougongs are not promoted. For inclined Dougongs, part of the load transferred in the width direction is gradually carried by other components in the overhanging direction. The inclined Dougong rotates under the vertical load, and as the load increases, the overall rotation becomes more obvious. Parametric studies show that as initial inclination angles increases, plastic stiffness decreases to some extent. With the increment of compressive strength, elastic moduli, and friction coefficients, ultimate displacement decreases but plastic stiffness obviously increases.

The response to COVID-19 and legal determinants

The panelist will focus on the distinction between ethics, law and on requirements towards building progressive alternatives from the legal perspective. It will also examine the impact legislative and regulatory framework and jurisprudence have on the enjoyment of the right of health during COVID-19. During the pandemic many national frameworks used emergency law in order to introduce containment and mitigation measures into society. Most of these were enacted as emergency legislation, and this poses certain additional challenges in terms of the rule of law, given the often limited impact assessment or the absence of judicial review. These key characteristics that carry an element which can only be deemed appropriate given temporal limitations. From divergent national responses to combat COVID-19 to broader multilateral agreements on global health security, inequities have come to the fore. Assessing the proportionality of measures adopted to achieve legal aims necessitates an interdisciplinary and cross-sectoral collaboration and examination, as well as embedding mechanisms to systematically monitor broader implications for global health, and societal cohesion and resilience. Law is a part of interdisciplinary collaboration needed to achieve efficient commitments in order to combat the COVID-19 pandemic effectively. Even though law itself is not a panacea, remedying broader societal inequities, it is an essential step needed towards implementation of effective health policies into society. Indicative examples on aspects of interest and steps taken by payers to ensure equitable access for all will be elaborated upon. An overview on the implications of dynamics between developed and developing countries will also be presented, with due emphasis on the future of both European and global health and partnerships for accessibility and accountability.

187Racial discrimination, life stress and allostatic load in Aboriginal and Torres Strait Islander adults

Abstract Background Increased allostatic load is linked with racial discrimination exposure, providing a mechanism for the biological embedding of racism as a psychosocial stressor. We undertook an examination of how racial discrimination interacts with socioecological, environmental and health conditions to affect multisystem dysregulation in a First Nations population. Methods We conducted latent class analysis (LCA) using indicators of life stress, socioeconomic background and physical and mental health from a nationally representative sample of Australian Aboriginal adults (N = 2 056). We used LCA with distal outcomes to estimate the effect of the latent class variable on our derived allostatic load index and conducted a stratified analysis to test whether allostatic load varied based on exposure to racial discrimination across latent classes. Results Our socioecological, environmental and health measures informed a four-class structure; ‘Low risk’ (30.8%), ‘Challenged but healthy’ (27.8%), ‘Mental health risk’ (24.0%) and ‘Multiple challenges’ (17.4%). Mean allostatic load was highest in ‘Multiple challenges’ compared to all other classes, both in those exposed (4.5; 95% CI: 3.9, 5.0) and not exposed (3.9; 95% CI: 3.7, 4.2) to racial discrimination. Allostatic load was significantly higher for those with exposure to racial discrimination in the ‘Multiple challenges’ class (t = 1.74, p=.04) and significantly lower in the ‘Mental health risk’ class (t=-1.67, p=.05). Conclusions Racial discrimination may not always modify physiological vulnerability to disease. Social and economic contexts must be considered when addressing the impact of racism, with a focus on individuals and sub-populations experiencing co-occurring life challenges. Key messages Racial discrimination can increase physiological dysregulation in Aboriginal and Torres Strait Islander adults with a profile of psychosocial stress and adverse health.

Energy-Aware Service Function Chain Embedding in Edge–Cloud Environments for IoT Applications

The implementation of Internet-of-Things (IoT) applications faces several challenges in practice, such as compliance with Quality-of-Service requirements, resource constraints, and energy consumption. In this context, the joint edge-cloud paradigm for IoT applications can resolve some of the issues arising in pure cloud computing scenarios, such as those related to latency, energy, or privacy. Therefore, an edge-cloud environment could be promising for resource and energy-efficient IoT applications that implement virtual network functions (VNFs) bound together into service function chains (SFCs). However, a resource and energy-efficient SFC placement requires smart SFC embedding mechanisms in the edge-cloud environment, as several challenges arise, such as IoT service chain modeling and evaluation, the tradeoff between resource allocation, energy efficiency and performance, and the resource dynamics. In this article, we address issues in modeling resource and energy utilization for IoT applications in edge-cloud environments. A smart traffic monitoring IP camera system is deployed as a use case for a realistic modeling of a service chain. The system is implemented in our testbed, which is designed and developed specifically to model and investigate the resource and energy utilization of SFC embedding strategies. A resource and energy-aware SFC strategy in the edge-cloud environment for IoT applications is then proposed. Our algorithm is able to cope with dynamic load and resource situations emerging from dynamic SFC requests. The strategy is evaluated systematically in terms of the acceptance ratio of SFC requests, resource efficiency and utilization, power consumption, and VNF migrations depending on the offered system load. Results show that our strategy outperforms some existing approaches in terms of resource and energy efficiency, thus it overcomes the relevant challenges from practice and meets the demands of IoT applications.

Crop disease identification and interpretation method based on multimodal deep learning

Identification methods of crop diseases based on image modality alone have achieved relative success under limited and restricted conditions. As a data-driven technology, its performance depends on a large amount of image labeling data. Many of the existing methods neglected the role and value of other modal data except images and only relies on low-level image features for disease recognition without utilizing high-level domain knowledge, leading to poor credibility and interpretability of identification results. This paper targets tomato and cucumber common invasive diseases recognition as the research object. First, for the problem of insufficient utilization of multimodal data in existing models, the semantic embedding methods for disease images and disease description texts were examined, and the correlation and complementarity between the two types of modal data was utilized to realize the joint representation learning of disease features. Second, in response to the requirements of reliable identification and interpretability of diseases, the knowledge representation and knowledge embedding mechanism in the field of disease identification was studied, and the high-level domain knowledge graph was used as the external guidance for image feature learning and disease identification. Lastly, a disease identification model based on “image-text” multimodal collaborative representation and knowledge assistance (ITK-Net) was constructed. The proposed model achieved an identification accuracy, precision, sensitivity and specificity of 99.63%, 99%, 99.07% and 99.78% respectively on a dataset composed of “image-text” pairs. Meanwhile, semantic interpretation was performed on the model inference process. The achievement of this paper can offer a new method for disease identification based on multimodal data and domain knowledge, which might help improve the intelligence level of crop disease identification.

Sentiment classification based on dependency-relationship embedding and attention mechanism

Aspect-based sentiment classification, a fine-grained sentiment analysis task, aims to predict the sentiment polarity for a specified aspect. However, the existing aspect-based sentiment classification approaches cannot fully model the dependency-relationship between words and are easily disturbed by irrelevant aspects. To address this problem, we propose a novel approach named Dependency-Relationship Embedding and Attention Mechanism-based LSTM. DA-LSTM first merges the word hidden vector output by LSTM with the dependency-relationship embedding to form a combined vector. This vector is then fed into the attention mechanism together with the aspect information which can avoid interference to calculate the final word representation for sentiment classification. Our extensive experiments on benchmark data sets clearly show the effectiveness of DA-LSTM.

An experimental investigation of the effect of long-term, time-dependent proppant embedment on fracture permeability and fracture aperture reduction

The inability to achieve satisfactory production targets from unconventional oil/gas deposits is a foremost concern for many hydraulic fracturing projects. One potential reason for recovery difficulties is the lack of a clear understanding of the severity of proppant damage mechanisms in fractures exposed to extreme reservoir environments. In this comprehensive laboratory-scale experimental analysis, we evaluate the significance of one such proppant damage mechanism: proppant embedment , which occurs in fractures over extended time frames, and its ultimate effect on fracture permeability and the reduction of fracture aperture . The results reveal that a significant reduction of fracture permeability occurred after only 36 h of exposure to confining stresses in uniformly (49%) and partially (29%) proppant-distributed fractures. Importantly, by 144 h, permeability reductions of 92.3% and 54.6% were observed for uniformly- and partially-distributed fractures. However, improved permeability results were obtained for multilayer proppant-distributed fractures. The effect of creep-induced proppant embedment was comprehensively studied. In uniformly and partially proppant-distributed specimens, both elastic and creep deformation dominate the embedment process. However, in multilayer proppant-distributed fractures, the embedment process is predominantly governed by the initial elastic embedment mechanism. CT scanning of uniformly and partially proppant-distributed fractures confirmed the substantial reduction in fracture aperture due to proppant embedment combined with fracture asperity damage. Nevertheless, CT scanning revealed only a minor degree of proppant embedment in multilayer proppant-distributed fractures, although significant proppant-re-arrangement occurs in multilayer proppant packs under high-stress conditions. Importantly, for both following scenarios: (1) high confining pressures (30 MPa) at lower testing times (12 h), and (2) low confining pressures (20 MPa) at longer testing times (144 h), the impact of the presence of either a uniform or partial proppant arrangement is minimal, as both proppant arrangements lead to significantly lower fracture permeability and aperture.

From spreading to embedding innovation in health care: Implications for theory and practice.

IssueIn broad terms, current thinking and literature on the spread of innovations in health care presents it as the study of two unconnected processes—diffusion across adopting organizations and implementation within adopting organizations. Evidence from the health care environment and beyond, however, shows the significance and systemic nature of postadoption challenges in sustainably implementing innovations at scale. There is often only partial diffusion of innovative practices, initial adoption that is followed by abandonment, incomplete or tokenistic implementation, and localized innovation modifications that do not provide feedback to inform global innovation designs.Critical Theoretical AnalysisSuch important barriers to realizing the benefits of innovation question the validity of treating diffusion and implementation as unconnected spheres of activity. We argue that theorizing the spread of innovations should be refocused toward what we call embedding innovation—the question of how innovations are successfully implemented at scale. This involves making the experience of implementation a central concern for the system-level spread of innovations rather than a localized concern of adopting organizations.Insight/AdvanceTo contribute to this shift in theoretical focus, we outline three mechanisms that connect the experience of implementing innovations locally to their diffusion globally within a health care system: learning, adapting, and institutionalizing. These mechanisms support the distribution of the embedding work for innovation across time and space.Practical ImplicationsApplying this focus enables us to identify the self-limiting tensions within existing top-down and bottom-up approaches to spreading innovation. Furthermore, we outline new approaches to spreading innovation, which better exploit these embedding mechanisms.

Dual-stage attention-based long-short-term memory neural networks for energy demand prediction

Forecasting energy demand of residential buildings plays an important role in the operation of smart cities, as it forms the basis for decision-making in the planning and operation of urban energy systems. Deep learning algorithms are commonly used to reliably predict potential energy usage since they can overcome the issue of dependency on long-distance data in energy forecasting relative to the standard regression model. However, there are still two problems to be solved for energy forecasting, including the encoding of categorical characteristics and adaptive extraction of the most relevant characteristics for the use in predictions. To address the problems, we proposed a sequential forecasting model for medium- and long-term energy demand forecasting based on an embedding mechanism and a two-stage attention-based long-term memory neural network. An empirical study was conducted on three years of daily electricity consumption data from the residential buildings of the Pudong district of Shanghai to evaluate the model. The results show that the model can effectively extract the key features that are highly correlated with energy consumption dynamics by employing long-term dependencies in time series. In addition, the hybrid model outperforms others in terms of long-term forecasting capability. This paper also discusses future research directions and the possibilities for applying deep learning techniques in the energy sector.

SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network

Point cloud semantic segmentation is a crucial task in 3D scene understanding. Existing methods mainly focus on employing a large number of annotated labels for supervised semantic segmentation. Nonetheless, manually labeling such large point clouds for the supervised segmentation task is time-consuming. In order to reduce the number of annotated labels, we propose a semi-supervised semantic point cloud segmentation network, named SSPC-Net, where we train the semantic segmentation network by inferring the labels of unlabeled points from the few annotated 3D points. In our method, we first partition the whole point cloud into superpoints and build superpoint graphs to mine the long-range dependencies in point clouds. Based on the constructed superpoint graph, we then develop a dynamic label propagation method to generate the pseudo labels for the unsupervised superpoints. Particularly, we adopt a superpoint dropout strategy to dynamically select the generated pseudo labels. In order to fully exploit the generated pseudo labels of the unsupervised superpoints, we furthermore propose a coupled attention mechanism for superpoint feature embedding. Finally, we employ the cross-entropy loss to train the semantic segmentation network with the labels of the supervised superpoints and the pseudo labels of the unsupervised superpoints. Experiments on various datasets demonstrate that our semisupervised segmentation method can achieve better performance than the current semi-supervised segmentation method with fewer annotated 3D points.