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Based on the view that community participation can be defined as the continuous trans-local mobility and localisation of not only organisations and individuals but also various nonhuman elements such as information and events, this paper explores how the community participation of Hong Kong in the conservation of Pokfulam Village is understood as socially extrospective and introspective processes. This paper is aimed to reveal how the knowledge of Treasure Hill in Taipei has been adapted (or not) to the community participation of Hong Kong's Pokfulam Village's heritage conservation. This provides insights into how the global flow of people, information, discourse, and technology has influenced the community participation in heritage conservation, especially across the Pacific Rim cities. Finally, the research discusses how local community can play a role in the dynamic local social structure of conserving their heritage, as well as 'whose heritage' in the mobility of global knowledge. [ABSTRACT FROM AUTHOR]

Abstract The drug discovery process is demanding and time-consuming, and machine learning-based research is increasingly proposed to enhance efficiency. A significant challenge in this field is predicting whether a drug molecule’s structure will interact with a target protein. A recent study attempted to address this challenge by utilizing an encoder that leverages prior knowledge of molecular and protein structures, resulting in notable improvements in the prediction performance of the drug-target interactions task. Nonetheless, the target encoders employed in previous studies exhibit computational complexity that increases quadratically with the input length, thereby limiting their practical utility. To overcome this challenge, we adopt a hint-based learning strategy to develop a compact and efficient target encoder. With the adaptation parameter, our model can blend general knowledge and target-oriented knowledge to build features of the protein sequences. This approach yielded considerable performance enhancements and improved learning efficiency on three benchmark datasets: BIOSNAP, DAVIS, and Binding DB. Furthermore, our methodology boasts the merit of necessitating only a minimal Video RAM (VRAM) allocation, specifically 7.7GB, during the training phase (16.24% of the previous state-of-the-art model). This ensures the feasibility of training and inference even with constrained computational resources.

The drug discovery process is demanding and time-consuming, and machine learning-based research is increasingly proposed to enhance efficiency. A significant challenge in this field is predicting whether a drug molecule's structure will interact with a target protein. A recent study attempted to address this challenge by utilizing an encoder that leverages prior knowledge of molecular and protein structures, resulting in notable improvements in the prediction performance of the drug-target interactions task. Nonetheless, the target encoders employed in previous studies exhibit computational complexity that increases quadratically with the input length, thereby limiting their practical utility. To overcome this challenge, we adopt a hint-based learning strategy to develop a compact and efficient target encoder. With the adaptation parameter, our model can blend general knowledge and target-oriented knowledge to build features of the protein sequences. This approach yielded considerable performance enhancements and improved learning efficiency on three benchmark datasets: BIOSNAP, DAVIS, and Binding DB. Furthermore, our methodology boasts the merit of necessitating only a minimal Video RAM (VRAM) allocation, specifically 7.7GB, during the training phase (16.24% of the previous state-of-the-art model). This ensures the feasibility of training and inference even with constrained computational resources. [ABSTRACT FROM AUTHOR]

Copyright of Mexican Journal of Behavioral Analysis / Revista Mexicana de Análisis de la Conducta is the property of Sociedad Mexicana de Analisis de la Conducta and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, we show the success of unsupervised transfer learning between Electroencephalographic (brainwave) classification and Electromyographic (muscular wave) domains with both MLP and CNN methods. To achieve this, signals are measured from both the brain and forearm muscles and EMG data is gathered from a 4-class gesture classification experiment via the Myo Armband, and a 3-class mental state EEG dataset is acquired via the Muse EEG Headband. A hyperheuristic multi-objective evolutionary search method is used to find the best network hyperparameters. We then use this optimised topology of deep neural network to classify both EMG and EEG signals, attaining results of 84.76% and 62.37% accuracy, respectively. Next, when pre-trained weights from the EMG classification model are used for initial distribution rather than random weight initialisation for EEG classification, 93.82%(+29.95) accuracy is reached. When EEG pre-trained weights are used for initial weight distribution for EMG, 85.12% (+0.36) accuracy is achieved. When the EMG network attempts to classify EEG, it outperforms the EEG network even without any training (+30.25% to 82.39% at epoch 0), and similarly the EEG network attempting to classify EMG data outperforms the EMG network (+2.38% at epoch 0). All transfer networks achieve higher pre-training abilities, curves, and asymptotes, indicating that knowledge transfer is possible between the two signal domains. In a second experiment with CNN transfer learning, the same datasets are projected as 2D images and the same learning process is carried out. In the CNN experiment, EMG to EEG transfer learning is found to be successful but not vice-versa, although EEG to EMG transfer learning did exhibit a higher starting classification accuracy. The significance of this work is due to the successful transfer of ability between models trained on two different biological signal domains, reducing the need for building more computationally complex models in future research.

Copyright of Arab Journal of Administrative Sciences is the property of Kuwait University, Academic Publication Council and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Highlights • A novel knowledge adaptation framework called Semantic Adaptation based on Vector of Locally Max Pooled deep learn Features (SA-VLMPF) is proposed for action recognition. • A Cascaded Convolution Fusion Strategy (CCFS) is proposed to integrate the spatial features and temporal features effectively. • A feature encoding method called Vector of Locally Max-Pooled deep learned Features (VLMPF) is introduced for long-range video representation. • Extensive experiments on several public benchmark datasets verify the effectiveness of the proposed framework. Abstract Action recognition is a challenging task in the field of computer vision. The deficiency in training samples is a bottleneck problem in the current action recognition research. With the explosive growth of Internet data, some researchers try to use prior knowledge learned from various video sources to assist in recognizing the action video of the target domain, which is called knowledge adaptation. Based on this idea, we propose a novel framework for action recognition, called Semantic Adaptation based on the Vector of Locally Max Pooled deep learned Features (SA-VLMPF). The proposed framework consists of three parts: Two-Stream Fusion Network (TSFN), Vector of Locally Max-Pooled deep learned Features (VLMPF) and Semantic Adaptation Model (SAM). TSFN adopts a cascaded convolution fusion strategy to combine the convolutional features extracted from two-stream network. VLMPF retains the long-term information in videos and removes the irrelevant information by capturing multiple local features and extracting the features with the highest response to action category. SAM first maps the data of the auxiliary domain and the target domain into the high-level semantic representation through the deep network. Then the obtained high-level semantic representations from auxiliary domain are adapted into target domain in order to optimize the target classifier. Compared with the existing methods, the proposed methods can utilize the advantages of deep learning methods in obtaining the high-level semantic information to improve the performance of knowledge adaptation. At the same time, SA-VLMPF can make full use of the auxiliary data to make up for the insufficiency of training samples. Multiple experiments are conducted on several couples of datasets to validate the effectiveness of the proposed framework. The results show that the proposed SA-VLMPF outperforms the state-of-the-art knowledge adaptation methods. [ABSTRACT FROM AUTHOR]

Multimedia event detection (MED) is an emerging area of research. Previous work mainly focuses on simple event detection in sports and news videos, or abnormality detection in surveillance videos. In contrast, we focus on detecting more complicated and generic events that gain more users’ interest, and we explore an effective solution for MED. Moreover, our solution only uses few positive examples since precisely labeled multimedia content is scarce in the real world. As the information from these few positive examples is limited, we propose using knowledge adaptation to facilitate event detection. Different from the state of the art, our algorithm is able to adapt knowledge from another source for MED even if the features of the source and the target are partially different, but overlapping. Avoiding the requirement that the two domains are consistent in feature types is desirable as data collection platforms change or augment their capabilities and we should be able to respond to this with little or no effort. We perform extensive experiments on real-world multimedia archives consisting of several challenging events. The results show that our approach outperforms several other state-of-the-art detection algorithms. [ABSTRACT FROM AUTHOR]

The article presents a transfer of innovation experience in the context generated by the Certified Business Process Manager - CertiBPM project. The main ideas discussed are: 1) the BPM innovative training programme design process together with a brief overview of the units content; 2) the transfer of innovation process described as a knowledge transfer, share and adaptation to Romanian market; 3) description of some methodological aspects regarding the examination and certification process associated with the designed training programme. [ABSTRACT FROM AUTHOR]

The pre-built knowledge of traditional expert systems is only capable of limited responses to changes in the operating environment. If the data input is imperfect, a traditional system may fail to reach any rational conclusions. In this paper, we introduce the concept of self-adaptability to the inference process of an expert system, and propose a model that is capable of handling unexpected user input effectively and efficiently. Such a system can formulate operational knowledge on the move for inference. With this self-adaptive capability, an expert system can reach useful conclusions, even when the input data is insufficient. The architecture of the proposed system encodes domain knowledge with semantic networks. It also defines four types of adaptation, namely, condition knowledge adaptation, operational knowledge adaptation, conclusion knowledge adaptation, and presentation adaptation, and focuses on how the first three contribute to the adaptive capability of the system. In addition, to enable a self-adaptive expert system to effectively produce better conclusions, two entropy-based measuring mechanisms are proposed: one minimizes the information loss during knowledge adaptation, while the other selects the best attribute relation during the generation of operational knowledge. We have proved that a self-adaptive expert system based on this architecture can always reach a regular conclusion or an abstract conclusion, which is a more meaningful conclusion by automatically modifying its operational knowledge in response to user feedback during the inference process, even in unexpected situations. [ABSTRACT FROM AUTHOR]

Fuzzy logic programming has been lately used as a general framework for representing and handling imprecise knowledge. In this paper, we define the syntax and the semantics of definite weighted fuzzy logic programs, which extend definite fuzzy logic programs by allowing the inclusion of different significance weights in the individual atoms that make up the antecedent of a fuzzy logic rule. The weights add expressiveness to a fuzzy logic program and allow the determination of the level up to which an atom in the antecedent of a rule may affect the truth value of its consequent. In describing the semantics of definite weighted fuzzy logic programs we introduce the notion of the generalized weighted fuzzy conjunction operator, which can be regarded as a weighted t-norm based aggregation. We determine the properties of generalized weighted fuzzy conjunction operators and provide several examples. A methodology for constructing generalized weighted fuzzy conjunction operators using generator functions of existing t-norms is also introduced. Finally, a method for setting up a parametric weighted fuzzy logic program and automatically adapting the weights of its rules using a numerical dataset is developed. [ABSTRACT FROM AUTHOR]

• Co-predict multimodal demand given heterogeneous modes. • Enhance station-sparse modes demand prediction with station-intensive modes. • Demonstrate the importance of knowledge adaptation for demand prediction. Travel demand forecasting is useful for both trip and service planning, and thus is of great importance. Most existing studies focus on demand forecasting for a single mode, while much less attention has been paid to multimodal demand forecasting. This paper develops a multimodal demand forecasting approach, which can learn and utilize information/knowledge from different public transit modes and thus improve the demand prediction of the travel mode with sparse observations (e.g., station-sparse mode). In particular, this study focuses on improving the passenger demand prediction accuracy of the station-sparse mode(s) with the help of the station-intensive mode (i.e., the mode with more sufficient knowledge and intensive station distribution over space). We propose a novel K nowledge A daptation with A ttentive M ulti-task M emory Network (KA2M2) in order to utilize closely-related demand patterns from the station-intensive mode for demand forecasting of the station-sparse mode(s). Specifically, we first design a memory-augmented recurrent network for enhancing the ability to capture the long-and-short term demand information and storing the extracted temporal knowledge of each transit mode. Then, we develop and integrate an attention-based knowledge adaptation module to adapt relevant information from the station-intensive source to the station-sparse source(s). The experimental results on a real-world dataset collected from the Greater Sydney area covering four public transport modes (bus, train, light rail, and ferry) demonstrate that the proposed approach consistently outperforms a number of baseline methods and state-of-the-art models. Our findings also illustrate that incorporating information/knowledge from multimodal trip records can enhance the demand forecasting accuracy for station-sparse modes. [ABSTRACT FROM AUTHOR]

Call centres, or their contemporary successors contact centres, are the preferred and prevalent way for many companies to communicate with their customers, but perhaps the greatest challenge of running a call centre, is to ensure that customers are provided with the right information in a timely fashion. Knowledge management has a number of practical tools and strategies to meet this challenge but to leverage the potential of these tools organisations must understand how to implement knowledge management especially in their call centre department. This research paper focuses on five main roles of knowledge management namely; knowledge acquisition, utilisation, adaptation, dissemination and generation, while knowledge management is achieved by identifying and managing these roles efficiently in an organisation. The primary data comprises interviews with managers of the call centre department of two largest car makers in Iran. Using case study approach, this research tries to describe and illustrate how participating companies are managing their organisational knowledge in their call centres, which items are supported under their supervision and which items have not been covered. [ABSTRACT FROM AUTHOR]