embedded knowledge
Recently Published Documents


TOTAL DOCUMENTS

111
(FIVE YEARS 35)

H-INDEX

11
(FIVE YEARS 3)

2022 ◽  

The book “Applied social sciences: concepts and perspectives vol.01, edited and published by South Florida Publishing, gathers ten chapters that approach themes of relevance in the context of education and are available in Spanish. The book will feature, a study on establishing the development of literary transcendentalism and how it manifests itself between the islands of Cuba, the Dominican Republic and Puerto Rico from the years 1927 to the mid-1950s. Literary transcendentalism was a manifestation that contemplated various ideologies and positions among our Caribbean islands. Another study that will be discussed is the explanation of basic personality traits in a case of homicide perpetrated by a subject who exercised professional activity in the elite military field (he was a sniper specialized in special missions abroad), what are the repercussions or consequences juridical-juridical that led to the crime of (civil) homicide perpetrated by him and sentence handed down to that effect. The third chapter presents a search for a model for the assessment of competencies in basic education through a case study at the Los Pinos de Algeciras school. We are in the middle of the infant school. A survey will also be presented in a global company, located in Brazil, on how it is facing knowledge management and its dissemination, through corporate tools and by managers. It also aims to research market tools that can improve this management and make companies move towards a future within the plan, without significant loss of their intellectual capital and embedded knowledge, among other works. Thus, we thank all authors for their commitment and dedication to their work and we hope to be able to contribute to the scientific community, in the dissemination of knowledge and in the advancement of science.


2021 ◽  
Author(s):  
Wei Huang ◽  
Xingyu Zhao ◽  
Xiaowei Huang

AbstractThe embedding and extraction of knowledge is a recent trend in machine learning applications, e.g., to supplement training datasets that are small. Whilst, as the increasing use of machine learning models in security-critical applications, the embedding and extraction of malicious knowledge are equivalent to the notorious backdoor attack and defence, respectively. This paper studies the embedding and extraction of knowledge in tree ensemble classifiers, and focuses on knowledge expressible with a generic form of Boolean formulas, e.g., point-wise robustness and backdoor attacks. For the embedding, it is required to be preservative (the original performance of the classifier is preserved), verifiable (the knowledge can be attested), and stealthy (the embedding cannot be easily detected). To facilitate this, we propose two novel, and effective embedding algorithms, one of which is for black-box settings and the other for white-box settings. The embedding can be done in PTIME. Beyond the embedding, we develop an algorithm to extract the embedded knowledge, by reducing the problem to be solvable with an SMT (satisfiability modulo theories) solver. While this novel algorithm can successfully extract knowledge, the reduction leads to an NP computation. Therefore, if applying embedding as backdoor attacks and extraction as defence, our results suggest a complexity gap (P vs. NP) between the attack and defence when working with tree ensemble classifiers. We apply our algorithms to a diverse set of datasets to validate our conclusion extensively.


Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7665
Author(s):  
Cristina Luna-Jiménez ◽  
David Griol ◽  
Zoraida Callejas ◽  
Ricardo Kleinlein ◽  
Juan M. Montero ◽  
...  

Emotion Recognition is attracting the attention of the research community due to the multiple areas where it can be applied, such as in healthcare or in road safety systems. In this paper, we propose a multimodal emotion recognition system that relies on speech and facial information. For the speech-based modality, we evaluated several transfer-learning techniques, more specifically, embedding extraction and Fine-Tuning. The best accuracy results were achieved when we fine-tuned the CNN-14 of the PANNs framework, confirming that the training was more robust when it did not start from scratch and the tasks were similar. Regarding the facial emotion recognizers, we propose a framework that consists of a pre-trained Spatial Transformer Network on saliency maps and facial images followed by a bi-LSTM with an attention mechanism. The error analysis reported that the frame-based systems could present some problems when they were used directly to solve a video-based task despite the domain adaptation, which opens a new line of research to discover new ways to correct this mismatch and take advantage of the embedded knowledge of these pre-trained models. Finally, from the combination of these two modalities with a late fusion strategy, we achieved 80.08% accuracy on the RAVDESS dataset on a subject-wise 5-CV evaluation, classifying eight emotions. The results revealed that these modalities carry relevant information to detect users’ emotional state and their combination enables improvement of system performance.


Author(s):  
Alexander Pastukhov ◽  
Lisa Koßmann ◽  
Claus-Christian Carbon

AbstractWhen several multistable displays are viewed simultaneously, their perception is synchronized, as they tend to be in the same perceptual state. Here, we investigated the possibility that perception may reflect embedded statistical knowledge of physical interaction between objects for specific combinations of displays and layouts. We used a novel display with two ambiguously rotating gears and an ambiguous walker-on-a-ball display. Both stimuli produce a physically congruent perception when an interaction is possible (i.e., gears counterrotate, and the ball rolls under the walker’s feet). Next, we gradually manipulated the stimuli to either introduce abrupt changes to the potential physical interaction between objects or keep it constant despite changes in the visual stimulus. We characterized the data using four different models that assumed (1) independence of perception of the stimulus, (2) dependence on the stimulus’s properties, (3) dependence on physical configuration alone, and (4) an interaction between stimulus properties and a physical configuration. We observed that for the ambiguous gears, the perception was correlated with the stimulus changes rather than with the possibility of physical interaction. The perception of walker-on-a-ball was independent of the stimulus but depended instead on whether participants responded about a relative motion of two objects (perception was biased towards physically congruent motion) or the absolute motion of the walker alone (perception was independent of the rotation of the ball). None of the two experiments supported the idea of embedded knowledge of physical interaction.


Author(s):  
Louis Celliers ◽  
Dianne Scott ◽  
Mvuselelo Ngcoya ◽  
Susan Taljaard

AbstractHybrid science-society approaches for knowledge production are often framed by a transdisciplinary approach. Most forms of “linear” progression of science informing policy or the “production” of knowledge as a one-way process are increasingly being challenged. This is also true for coastal and marine sciences informing decision-making to support sustainable development of coastal areas. From the early 2010s, South Africa had one of the most progressive and well-structured frameworks for the establishment of integrated coastal management (ICM) in order to achieve societal objectives for its valuable coastal area. Even so, the implementation of the legislation, policies and guidelines remain a challenge, especially at the local level in municipalities. This paper reports on a social experiment that was intended to examine the possibility for a new knowledge negotiation process to unsettle the highly structured, nested and regular policy process, which forms the basis of ICM in South Africa. This paper reflects on an experimental application of a participatory methodology known as a “competency group” to co-produce knowledge for coastal and marine management. The group members, a combination of codified, tacit and embedded knowledge holders, agreed to serve on a competency group and met on six occasions over a 12-month period in 2013. This group “negotiated” amongst themselves to achieve a common understanding of knowledge useful for the management of beach water quality on the Golden Mile, the prime beachfront of Durban, a South African city. The paper provides a novel lens into a potentially distinctive, challenging and imminently useful approach of co-producing knowledge for coastal governance, especially in a middle-income country where the social and political context is complex.


2021 ◽  
pp. 026638212110328
Author(s):  
Carol Giles ◽  
Alison Day

Health Education England advocates for the introduction of more embedded knowledge specialist roles within healthcare organisations to save healthcare staff time ensuring that evidence from research, examples of good practice and learning from staff know-how and organisational knowledge are used to inform policy and practice. In 2020, a pilot was established to explore how a knowledge specialist role could bring benefits to the specialist business of an Academic Health Science Network in the South West of England. Using the structure of a retrospective learning after technique, this paper explores the rationale for the pilot, considers what was actually achieved despite the COVID-19 pandemic and examines reasons for successes and disappointments. Four key lessons are gathered that can be applied by anyone introducing a new knowledge management or embedded knowledge specialist service. The study concludes by outlining the unique contribution that an embedded knowledge specialist brings to the team to produce a tailored knowledge management service that saves staff time, keeps staff informed and connected while evolving to meet the ever changing priorities of complex healthcare organisations.


2021 ◽  
Vol 2021 (6) ◽  
Author(s):  
Sang Eon Park ◽  
Dylan Rankin ◽  
Silviu-Marian Udrescu ◽  
Mikaeel Yunus ◽  
Philip Harris

Abstract Discoveries of new phenomena often involve a dedicated search for a hypothetical physics signature. Recently, novel deep learning techniques have emerged for anomaly detection in the absence of a signal prior. However, by ignoring signal priors, the sensitivity of these approaches is significantly reduced. We present a new strategy dubbed Quasi Anomalous Knowledge (QUAK), whereby we introduce alternative signal priors that capture some of the salient features of new physics signatures, allowing for the recovery of sensitivity even when the alternative signal is incorrect. This approach can be applied to a broad range of physics models and neural network architectures. In this paper, we apply QUAK to anomaly detection of new physics events at the CERN Large Hadron Collider utilizing variational autoencoders with normalizing flow.


2021 ◽  
Vol 23 (06) ◽  
pp. 69-76
Author(s):  
Shubham Kokane ◽  
◽  
Shreeyash Khalate ◽  
Shreya Newale ◽  
Sakshi Dubewar ◽  
...  

An emotionally intelligent chatbot system aims to make an effective conversation between humans and machines in as natural and interactive a manner as possible. The chatbot agent has the pre-embedded knowledge base to identify the sentences, intents, entities, and context of the input query to be precise for making a valid, predictable decision itself as a self-generated response to answer the query. The present technical project consists of developing an intelligent system for college enquiry purposes using a web-based chatbot agent, through machine learning, query processing, and sentiment and emotion classification system to analyze the sentiment of the visitor towards the college. Emotionally Intelligent College Enquiry Chatbot System is nothing but a chatbot to understand the user queries and respond to them during a conversation. A chatbot can actively help humans to involve in a digital automated conversation with a machine or a system effective. In the following proposed system, feature extraction and data cleaning techniques are applied to the dataset, and classifiers such as multinomial naive Bayes, logistic regression and k nearest neighbours are used to train the model. The classifier with the highest accuracy is further used for the emotion classification of users.


Author(s):  
Simon Penny ◽  
Tom Fisher

This paper seeks to understand the skills of operating automated manufacturing machines of the C19th as craft practices, employing externally powered and automated tools around which new cultures of practice emerged. We draw upon situated/embodied/enactive/extended/distributed (SEEED) approaches to cognition to explicate the sensibilities of these practices, as well as the history of science and technology, Anthropology, STS and related fields. Our case study is a body of work focused on embodied/ embedded knowledge in the textile industry – specifically in the making of machine lace. We conclude with a proposal for multi-modal museum exhibits that provide an understanding of know-how, kinesthetic/proprioceptive skills and procedures. The authors are both long term practitioners of crafts, both traditional and industrial (see bios). This experience informs the research at every step.


Sign in / Sign up

Export Citation Format

Share Document