Reviewerly/exHarmony huggingface dataset:
A robust illumination-invariant face recognition based on fusion of thermal IR, maximum filter and visible image

Face recognition has many challenges especially in real life detection, whereby to maintain consistency in getting an accurate recognition is almost impossible. Even for well-established state-of-the-art algorithms or methods will produce low accuracy in recognition if it was conducted under poor or bad lighting. To create a more robust face recognition with illumination invariant, this paper proposed an algorithm using a triple fusion approach. We are also implementing a hybrid method that combines the active approach by implementing thermal infrared imaging and also the passive approach of Maximum Filter and visual image. These approaches allow us to improve the image pre-processing as well as feature extraction and face detection, even if we capture a person’s face image in total darkness. In our experiment, Extended Yale B database are tested with Maximum Filter and compared against other state-of-the-filters. We have conduct-ed several experiments on mid-wave and long-wave thermal Infrared performance during pre-processing and saw that it is capable to improve recognition beyond what meets the eye. In our experiment, we found out that PCA eigenface cannot be produced in a poor or bad illumination. Mid-wave thermal creates the heat signature in the body and the Maximum Filter maintains the fine edges that are easily used by any classifiers such as SVM, OpenCV or even kNN together with Euclidian distance to perform face recognition. These configurations have been assembled for a face recognition portable robust system and the result showed that creating fusion between these processed image illumination invariants during preprocessing show far better results than just using visible image, thermal image or maximum filtered image separately.
Compositional Abstraction Error and a Category of Causal Models

Interventional causal models describe several joint distributions over some variables used to describe a system, one for each intervention setting. They provide a formal recipe for how to move between the different joint distributions and make predictions about the variables upon intervening on the system. Yet, it is difficult to formalise how we may change the underlying variables used to describe the system, say moving from fine-grained to coarse-grained variables. Here, we argue that compositionality is a desideratum for such model transformations and the associated errors: When abstracting a reference model M iteratively, first obtaining M' and then further simplifying that to obtain M'', we expect the composite transformation from M to M'' to exist and its error to be bounded by the errors incurred by each individual transformation step. Category theory, the study of mathematical objects via compositional transformations between them, offers a natural language to develop our framework for model transformations and abstractions. We introduce a category of finite interventional causal models and, leveraging theory of enriched categories, prove the desired compositionality properties for our framework.
A multi-party attentive listening robot which stimulates involvement from side participants

We demonstrate the moderating abilities of a multi-party attentive listening robot system when multiple people are speaking in turns. Our conventional one-on-one attentive listening system generates listener responses such as backchannels, repeats, elaborating questions, and assessments. In this paper, additional robot responses that stimulate a listening user (side participant) to become more involved in the dialogue are proposed. The additional responses elicit assessments and questions from the side participant, making the dialogue more empathetic and lively.
Proyecto pedagógico mediatizado para la formación en tecnologías de la información y la comunicación a docentes de la Institución Educativa Combia sede “El Placer” , de la ciudad de Pereira - Risaralda desde el área de ciencias sociales

This mediated project aimed to train teachers in the design and implementation of new educational strategies, through digital technologies that are currently essential for the information and knowledge society. It is of great importance to propose this type of training in the educational field and to gradually incorporate information and communication technologies (ICT) in the teaching and learning processes, so that the institutions achieve progress with respect to the new demands of society , forming capable students who are committed to the task of generating knowledge and innovating and learning throughout their lives, by their own means, promoting autonomous learning, through the use of tools that allow meaningful learning.
Quantitative Association Rules Mining

Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining presents novel algorithms for academic search, recommendation and association rule mining that have been developed and optimized for different commercial as well as academic purpose systems. Along with the design and implementation of algorithms, a major part of the work presented in the book involves the development of new systems both for commercial as well as for academic use. In the first part of the book the author introduces a novel hierarchical heuristic scheme for re-ranking academic publications retrieved from standard digital libraries. The scheme is based on the hierarchical combination of a custom implementation of the term frequency heuristic, a time-depreciated citation score and a graph-theoretic computed score that relates the paper's index terms with each other. In order to evaluate the performance of the introduced algorithms, a meta-search engine has been designed and developed that submits user queries to standard digital repositories of academic publications and re-ranks the top-n results using the introduced hierarchical heuristic scheme. In the second part of the book the design of novel recommendation algorithms with application in different types of e-commerce systems are described. The newly introduced algorithms are a part of a developed Movie Recommendation system, the first such system to be commercially deployed in Greece by a major Triple Play services provider. The initial version of the system uses a novel hybrid recommender (user, item and content based) and provides daily recommendations to all active subscribers of the provider (currently more than 30,000). The recommenders that we are presenting are hybrid by nature, using an ensemble configuration of different content, user as well as item-based recommenders in order to provide more accurate recommendation results. The final part of the book presents the design of a quantitative association rule mining algorithm. Quantitative association rules refer to a special type of association rules of the form that antecedent implies consequent consisting of a set of numerical or quantitative attributes. The introduced mining algorithm processes a specific number of user histories in order to generate a set of association rules with a minimally required support and confidence value. The generated rules show strong relationships that exist between the consequent and the antecedent of each rule, representing different items that have been consumed at specific price levels. This research book will be of appeal to researchers, graduate students, professionals, engineers and computer programmers.
Measuring Data Leakage in Machine-Learning Models with Fisher Information

Machine-learning models contain information about the data they were trained on. This information leaks either through the model itself or through predictions made by the model. Consequently, when the training data contains sensitive attributes, assessing the amount of information leakage is paramount. We propose a method to quantify this leakage using the Fisher information of the model about the data. Unlike the worst-case a priori guarantees of differential privacy, Fisher information loss measures leakage with respect to specific examples, attributes, or sub-populations within the dataset. We motivate Fisher information loss through the Cramer-Rao bound and delineate the implied threat model. We provide efficient methods to compute Fisher information loss for output perturbed generalized linear models. Finally, we empirically validate Fisher information loss as a useful measure of information leakage.
DialogStitch: Synthetic Deeper and Multi-Context Task-Oriented Dialogs

Real-world conversational agents must effectively handle long conversations that span multiple contexts. Such context can be interspersed with chitchat (dialog turns not directly related to the task at hand), and potentially grounded in a multimodal setting. While prior work focused on the above aspects in isolation, there is a lack of a unified framework that studies them together. To overcome this, we propose DialogStitch, a novel framework to seamlessly ‘stitch’ multiple conversations and highlight these desirable traits in a taskoriented dialog. After stitching, our dialogs are provably deeper, contain longer-term dependencies, and span multiple contexts, when compared with the source dialogs—all free of cost without any additional annotations! Though our framework generalizes to a variety of combinations, we demonstrate its benefits in two settings: (a) multimodal, imagegrounded conversations, and, (b) task-oriented dialogs fused with chit-chat conversations. We benchmark state-of-the-art dialog models on our datasets and find accuracy drops of (a) 12% and (b) 45% respectively, indicating the additional challenges in the stitched dialogs. Our code and data are publicly available.
Deep Learning in 3D Hand Pose and Mesh Estimation

Author(s): Chen, Liangjian | Advisor(s): Xie, Xiaohui | Abstract: 3D Hand pose estimation is an important problem because of its wide range of potential applications, such as sign language translation, robotics, movement disorder detection and monitoring, and human-computer interaction (HCI). However, despite of the previous progress, it remains a challenge problem in the field of computer vision due to the difficulty to acquire high quality hand pose annotation. In this dissertation, we develop various of approaches to address this problem aiming for achieving a better estimation accuracy or provide easier training environment. First, to bridge the image quality gap between the synthetic dataset and real world dataset, we propose TAGAN(Tonality-Aligned Generative Adversarial Networks) to produce more realistic hand poses image. Second, to loose the requirement of paired RGB and Depth image requirement for most state-of-the-art $3$D hand pose estimator, we propose DGGAN(Depth-image Guided Generative Adversarial Networks) to let those hand pose estimator could be trained on RGB image only dataset. Third, since the accurate 3D hand pose estimation is very difficult to acquired, we propose the TASSN(Temporal-Aware Self-Supervised Network) with temporal consistency constraints which learns 3D hand poses and meshes from videos with only 2D keypoint position annotations. Last but not the least, since 3D hand pose from single image is intrinsically ill-posed. We want to build a multi-view hand mesh benchmark to tackle this problem from multi-view perspective. we design a spin match algorithm that enables a rigid mesh model matching with any target mesh ground truth. Based on the match algorithm, we propose an efficient pipeline to generate a large-scale multi-view hand mesh (MVHM) dataset with accurate 3D hand mesh and joint labels.
Low Level Radioactivity with Electromagnetic data for Prospecting of Shallow Sub-surface Uranium Mineralization

Abstract Summary Low Level Radioactivity with Electromagnetic data for Prospecting of Shallow Sub-surface Uranium Mineralization.
ANALISIS KERJASAMA GURU INTI DAN GURU PENDAMPING DALAM PENYSUSUNAN PERANGKAT PEMBELAJARAN DI TK SAVE THE KIDS BANDA ACEH

Cooperation between core teachers and companion teachers in the preparation of learning learning devices. This research was done to know the cooperation between core teachers and accompanying teachers in the preparation of learning devices. This research is a method of research that is used to examine the scientific conditions that result in more emphasis on generalization. The subject of research is THE principal and 4 Guu class Save The Kids Banda Aceh. Data collection techniques are interviews, observations and documentation. Penlitian result is designing early childhood development activities based on curriculum (annual Program, Semester, weekly learning Plan and daily Learning execution plan) designed together with different forms of cooperation. Communicate effectively, emphatically and politely with fellow educators, education personnel, parents and community (respecting the opinions of others and the communication with the...
Telemedicina TELEMEDICINA MAPEAMENTO DE PROJETOS COM A FINALIDADE DE LEVAR O AUXÍLIO A SAÚDE ÀS POPULAÇÕES ISOLADAS

The first half of the year 2020 was marked by the pandemic of the COVID-19 virus, the high spread of the virus led people to become isolated, giving them as an option, to seek remote assistance for medical care, for studies, and among others. As for medical care, telemedicine has been widely used and is shown to be important for communication between doctors and between doctors and patients. In order to ensure access to health and find solutions for medical care in remote areas, the objective of this work is to carry out a qualitative analysis structuring 12 articles with projects published in the years 2000 to 2020 in the telemedicine study area, such articles were selected from the initial reading of their respective abstracts, which describe technological applications that facilitate remote medical care, providing assistance to isolated populations. These 12 selected articles present ways to improve and streamline communication between doctors of different specialties, improve the diagnosis of X-ray exams, and among others. The software presented in this work aims to help doctors and patients, and show positive results, however, there are needs such as access to a quality internet network, and still, there is a need for monitors that allow good visualization of images. The great advantage of telemedicine is the elimination of the distance factor. However, face-to-face contact with the doctor is extremely important, however, in selected cases, such as isolated populations, telemedicine is the only solution.
Blockchain in the supply chain: A cross-case analysis based on 21 case studies

This paper contains a cross-case analysis of 21 feasibility studies on the application of blockchain in 16 companies, performed at two Universities of Applied Sciences as a part of the research SIA-Raak project ‘From hype to reality’ between 2018 and 2020 and the affiliated BlockStart project. The paper analyses the effect of blockchain on the KPIs of the companies, by inventorying and classifying the KPIs and connecting them with the logistics processes. The research findings show that the main benefits of blockchain for these companies are transparency and traceability, especially in food chain companies. The advice for these companies is to invest in blockchain. Reasons for companies why they do not invest in blockchain are related to preferences for proven technologies. In these situations, it shows that a better business case can be exploited, or companies already trust each other and do not feel the need to secure trust even more. This cross-case analysis is a first attempt to understand the main drivers for blockchain implementation at companies. For future analysis on blockchain feasibility studies, we advise to use a more standardized set of KPIs and the application of a methodology developed during this project to connect the use of blockchain to the companies’ strategies.
VUT Libraries: Health Sciences: Database of the month - ProQuest E-Book Central

Welcome to the one-stop library guide destination for Health Sciences students and lecturers. The ProQuest E-book Central – platform combines e-book content from several other ProQuest e-book platforms, into a single interface.
Metadata Requirements for 3D Data

Chapter in forthcoming: Moore, Jennifer, Adam Rountrey, and Hannah Scates Kettler. 2022. Data Creation to Curation: Community Standards for 3D Data Preservation. Association of College & Research Libraries.
근현대 한국 과학기술사에서 인물 연구

This study aims to review the current state and characteristics of scientists and engineers within the history of modern science and technology in Korea. It is peculiar that individual scientists and engineers have not been adequately represented in the historical narrative of Korean science and technology. Research in modern scientists and engineers gained momentum with the building of “The Hall of Fame of Scientists and Engineers” in 2003. Studies on the history of modern science and technology in Korea often focus on the contents of achievements and related policies; yet figures remain as peripheral characters. It is partially owing to the tendencies in the past that research in the history of science and technology has sought to find remarkable scientific achievements buried in history. Research in scientists and engineers was considered ineffective for the pursuit of such a purpose. Historical inquiry on scientific achievements, however, is not far from the historical figures who made them. Simply, the focus should be adjusted to relevant groups as much as distinguished individuals for understanding the advancement of science and technology. Furthermore, research in scientists and engineers can reveal science and technology more microscopically and specific relations with society. Unlike before, as studies in scientists and engineers expand horizons from famous figures to ordinary people, from achievements to whole activities, and from individuals to groups, it can trigger a shift in research in Korean history of science and technology. The prosopography is one of the research approaches worthy of attention. It should also be noted that archives equipped with rich and diverse resources are essential to research scientists and engineers accordingly.
Modern tendencies of full life learning and “intellectual competence”

This article emphasizes the need for the formation of intellectual competence of students in lifelong education, lifelong learning, the essence of the content of intellectual competence, the need for the conscious acquisition of skills to find content as a competitive specialist at different stages of life. The formation of special competencies in the learning process is the orientation of an individual to a particular field, and special competencies are formed by the student in the process of performing a certain system of actions and activities. Building a business strategy, developing intellectual competence to move to a higher level, and consciously acquiring the skills to find content as a competitive professional at different stages of life.
LibGuides: Science - Year 8 - First Hand Investigation: College Library Resources

A selection of resources to support Year 8 Science students to complete their assessment task.
Safety concerns of lead chromate in enamel paints: A study based on the Sri Lankan enamel paints industry after the lead paint regulatory enforcement

The Journal of the National Science Foundation of Sri Lanka publishes the results of research in all aspects of Science and Technology. The journal also has a website at http://www.nsf.gov.lk/. 2020 Impact Factor: 0.515The JNSF provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
Movie Recommendation Using Conversational Mechanism and Knowledge Based Filtering

Conversational recommender system created for helping users in searching information in a domain by using conversational mechanism. These systems help user to get recommendation by selecting items that most suitable to user’s preference by asking user needed. The recommendations generated by eliciting user’s experience e.g. his favourite movies, actor and director and then gives the item that match their interest. There are many methods to get the suitable recommendation that match the user’s preference. In this paper, we use ontology which represents knowledge to get result of recommendation that fit to user preference by using knowledge-based filtering to determine the user’s need. Our system has been implemented for movie domain. We test our system performance by studying user's perception.
Statistical Data Analysis (ANOVA) of Circumonchobothrium shindei collected from Indapur, Palasdev and Bhigwan backwater sites of Ujani Reservoir, Maharashtra State, India.

In the present study the Statistical Analysis of Circumonchobothrium shindei collected from freshwater fish Mastacembelus armatus (Lacepede) from Indapur, Palasdev and Bhigwan backwater sites of Ujani Reservoir, Maharashtra State, India was done. This work is an attempt to bring out the different aspect of anatomy of infection of freshwater fish Mastacembelus armatus (Lacepede) by the adult cestode Circumoncobothrium shindei. The Analysis is used to examine whether there are any statistically notable variations between the averages of three or more independent groups.
Robust multi-agent Q-learning in cooperative games with adversaries

We present RoM-Q 1, a new Q-learning-like algorithm for finding policies robust to attacks in multi-agent systems (MAS). We consider a novel type of attack, where a team of adversaries, aware of the optimal multi-agent Q-value function, performs a worst-case selection of both the agents to attack and the actions to perform. Our motivation lies in real-world MAS where vulnerabilities of particular agents emerge due to their characteristics and robust policies need to be learned without requiring the simulation of attacks during training. In our simulations, where we train policies using RoMQ, Q-learning and minimax-Q and derive corresponding adversarial attacks, we observe that policies learned using RoM-Q are more robust, as they accrue the highest rewards against all considered adversarial attacks.
HIDING CRITICAL INFORMATION WHEN TRAINING LANGUAGE MODELS

Machine learning language models are combinations of algorithms and neural networks designed for text processing composed in natural language (Natural Language Processing, NLP). &#x0D; In 2020, the largest language model from the artificial intelligence research company OpenAI, GPT-3, was released, the maximum number of parameters of which reaches 175 billion. The parameterization of the model increased by more than 100 times made it possible to improve the quality of generated texts to a level that is hard to distinguish from human-written texts. It is noteworthy that this model was trained on a training dataset mainly collected from open sources on the Internet, the volume of which is estimated at 570 GB. &#x0D; This article discusses the problem of memorizing critical information, in particular, personal data of individual, at the stage of training large language models (GPT-2/3 and derivatives), and also describes an algorithmic approach to solving this problem, which consists in additional preprocessing training dataset and refinement of the model inference in the context of generating pseudo-personal data and embedding into the results of work on the tasks of summarization, text generation, formation of answers to questions and others from the field of seq2seq.