Reviewerly/exHarmony huggingface dataset:
Ontologias, dos sistemas aos modelos: uma abordagem introdutória no contexto dos sistemas de organização do conhecimento

An approach to ontologies as representation artifacts should bear in mind the different understandings associated with them. We aim to contribute to the clarification of this complex topic in the context of knowledge organization systems. The role of the ontological analysis process and the distinction between ontological system and model are analyzed. We conclude that in an ontological system the process of analyzing the types of existing entities, which can be objectively categorized, needs to follow strict ontological principles and an adaptation to the reality outside the system itself, so that it can be understood as an ontological model. These models need a rigorous formal application, of a philosophical nature, in their development. Different approaches to the ontological analysis process result in different products. Recognizing these distinctions is essential for the disambiguation of the term ontology.
Best Mid Century Computer Desks

If you are interested in the perfect balance of vintage aesthetics and uncompromising work-centric productivity, it’s time to consider mid-century ...
It's not just cookies and tea

Three of the major research themes over my career have been concurrency, integration and provenance. In this talk, I will explain why these themes are not only important in database research, but how they have played a role in my personal success. I will also discuss how we as a community can use some of these ideas to encourage diversity in our field.
Library Guides: Creating a LibGuide: How Do I?

This guide will help you design an accessible, user-friendly LibGuide. Provides instructions on different ways to add content to your guide.
Comparing the Effects of Varying the Concentration of Chemical Treatment on Chitin Yield from Mud Crabs (Scylla serrata).

This study aims to provide an optimal method that can yield the largest amount of chitin. Specifically, this study aims to compare the amount of chitin extracted from mud crabs (Scylla serrata) between different samples varying in the concentrations of the acid treatments during the demineralization and deproteinization processes. Chitin was extracted from ground Scylla serrata shells. During the demineralization process, samples were soaked in different HCl solutions: 1% concentration, 5% concentration, and 8% concentration. Likewise, during the demineralization process, samples were soaked in different NaOH solutions: 1% concentration, 5% concentration, and 8% concentration. The samples were tested for chitin through FTIR analysis, while chitin yield was determined by ANOVA testing. The results showed the optimal chitin yield can be extracted from Scylla serrata shells by soaking the samples in the lower HCl concentrations during demineralization and higher concentrations during deproteinization, specifically 5% HCl and 8% NaOH respectively. Furthermore, soaking the samples in 1% HCl does not successfully extract the calcium carbonate, thus unable extract chitin. The results of this study can be used to further extend current knowledge of chitin extraction from mud crabs (Scylla serrata) to obtain high-yielding results.
Fighting the Gender Gap in ICT

Girls and women are still under-represented in ICT, both in education and the tech industry. In this paper, we investigate how to design games for teenager girls to address the identified gender gap. Based on existing knowledge about this societal challenge, we identify four learning goals that games can address: promote self-confidence; fight gender stereotypes; boost subject knowledge; and provide role models. In addition, based on a systematic literature review, we identify game elements that are reported in previous research as having a positive impact on girls’ game experience. The learning goals and game elements are summarized in a set of 16 design guidelines. The design guidelines are intended for game designers and developers. In addition, they might be used by educators to reflect on the games to introduce in their classes.
Multi-Scale Deep Residual Learning-based Single Image Haze Removal

Images captured from outside visible devices are generally degraded by way of turbid media, including haze, fog, rain, and snow. Haze adversely degrades the quality of an image. Because of the weather conditions, haze is the most typical one in outdoor scenes. Due to the ill-posed nature of single images, dehazing them might be difficult. In this, a deep                  learning-based architecture that is denoted by MSRL Dehaze Net for single image haze removal is counting on multi-scale residual learning (MSRL). Haze removal in base can be achieved by multi-scale residual learning and simplified U-Net learning for mapping between hazy and haze-free components. The resulted dehazed image is obtained by the haze-removed base and also enhanced detail image components. This dehazed result passes through the clahe method for enhancement and obtained better final dehazed results. Experimental results have demonstrated and compared with other approaches such as Dark Channel Prior and Dehazing using GAN.
Hardness of Detecting Abelian and Additive Square Factors in Strings

We prove 3SUM-hardness (no strongly subquadratic-time algorithm, assuming the 3SUM conjecture) of several problems related to finding Abelian square and additive square factors in a string. In particular, we conclude conditional optimality of the state-of-the-art algorithms for finding such factors. Overall, we show 3SUM-hardness of (a) detecting an Abelian square factor of an odd half-length, (b) computing centers of all Abelian square factors, (c) detecting an additive square factor in a length-n string of integers of magnitude n^{𝒪(1)}, and (d) a problem of computing a double 3-term arithmetic progression (i.e., finding indices i ≠ j such that (x_i+x_j)/2 = x_{(i+j)/2}) in a sequence of integers x₁,… ,x_n of magnitude n^{𝒪(1)}.Problem (d) is essentially a convolution version of the AVERAGE problem that was proposed in a manuscript of Erickson. We obtain a conditional lower bound for it with the aid of techniques recently developed by Dudek et al. [STOC 2020]. Problem (d) immediately reduces to problem (c) and is a step in reductions to problems (a) and (b). In conditional lower bounds for problems (a) and (b) we apply an encoding of Amir et al. [ICALP 2014] and extend it using several string gadgets that include arbitrarily long Abelian-square-free strings.Our reductions also imply conditional lower bounds for detecting Abelian squares in strings over a constant-sized alphabet. We also show a subquadratic upper bound in this case, applying a result of Chan and Lewenstein [STOC 2015].
An algorithm combining coordinate descent and split Bregman iterative for fractional image denoising model

The split Bregman algorithm and the coordinate descent method are efficient tools for solving optimization problems, which have been proven to be effective for the total variation model. We propose an algorithm for fractional total variation model in this paper, and employ the coordinate descent method to decompose the fractional-order minimization problem into scalar sub-problems, then solve the sub-problem by using split Bregman algorithm. Numerical results are presented in the end to demonstrate the superiority of the proposed algorithm.
On using focal loss for neural network calibration

Miscalibration – a mismatch between a model’s confidence and its correctness – of Deep NeuralNetworks (DNNs) makes their predictions hard to rely on. Ideally, we want networks to be accurate and calibrated. In this work, we study focal loss as an alternative to the conventional crossentropy loss and show that, focal loss allows us to learn models that are comparitively well calibrated while preserving accuracy. We provide a thorough analysis of the factors causing miscalibration, and use the insights we glean from this to justify the superior performance of focal loss. Finally, we perform extensive experiments on a variety of datasets, and with a wide variety of network architectures, and show that focal loss indeed achieves excellent calibration without compromising on accuracy in almost all cases.
ANALYSIS OF SERVICE QUALITY AT FERTILIZER DISTRIBUTION USING SERVQUAL METHOD AND KANO MODEL TO CUSTOMER SATISFACTION INCREASING

PT. Cheil Jedang Indonesia is a company engaged in the bioprocess sector. The by-product is fertilizer, including liquid fertilizer under the Bagitani brand. There are still several customer complaints about PT. Cheil Jedang Indonesia sued PT. Cheil Jedang Indonesia, to look again at the level of service provided to customers and needs to know its position in the eyes of customers in terms of service quality and customer satisfaction with the services they have provided.
Improving the Anonymity of the Lightning Network using Sub-Optimal Routes

The Lightning Network is a second layer payment protocol built on top of Bitcoin, which is scalable and has reduced transaction fees. It does so by eliminating the need to broadcast every transaction to the whole network. When one user wants to send a payment to another, the routing protocol generates a path between them that is always fast and cost efficient. The low degree of randomness in the existing routing protocols during path selection allows an adversary to compromise the anonymity of the sender and recipient. In this work, we propose a new routing algorithm that is less predictable when creating a transaction path. We show that this increases the anonymity of the users of the Lightning Network by creating an attack on the new routing protocol. The attacker tries to identify the potential source and recipient of a transaction. Our results suggest that there is a trade-off between the offered anonymity and transaction fees; it is not possible to get higher anonymity at no cost by designing a non-deterministic routing algorithm.
DIALOGUE OF CULTURES AS A TOOL OF THE MODERN PROFESSIONAL OF PEDAGOGICAL PROFILE

The article discusses the most important issue of personality-oriented general cultural basisof the future professional, its components and the main focus of the educational process to this result. Theauthors substantiate the model of culture of the spiritualized and the position that on this wayone can only expect the development of society without global catastrophes. In the process of a person'smovement to a profession in a certain educational space in pedagogy and psychology, its main vectors(directions) are distinguished, which allow us to structure and clarify our ideas about the possible trajectoriesof a student's movement in this space. Traditional education was actually determined by the sole focus on theacquisition by students of well-known, well-established information about the main objects of the relevantsciences, professional skills and practical skills. Despite the introduction of competence and competenciesinto everyday life, the focus on zuna still remains the basis of the set standards, educational programs, nomatter how they are labeled differently, and in most cases it is it that is implemented in practice, no matterwhat teaching technologies are used by school teachers or university professors. A deeper penetration into thepatterns of becoming a professional allows us to understand that a person becomes a professional as a resultof a dialectical combination of two processes - mastering knowledge from the relevant professional field.This is the essence of professionalism. So, in the professional educational space, two relatively independentvectors can be distinguished, conventionally called the vertical and horizontal of the educational process.At the same time, in the scientific literature, there are different interpretations of these vectors: in one ofthe models, the formation of an individual style in professional activity and in the line of enriching one'sprofessional awareness.
IMAGE SEO FOR VISUAL SEARCH

: A benchmark of strikingness models performance with an artificial image dataset is provided. Model performance is evaluated through strikingness metrics still because the influence of model inspiration and consistency with human experimental psychology. The structure is within the sort of a second composition that encodes the position and therefore the class of the objects. The transformation of the position and therefore the class of theobjects results in a continuous-valued relationship between visual compositions, that carries extremely helpful data, though not leveraged by previous techniques. Business isn'tany exception thereto. additional and additional firms and people area unit conveyance their business on-line. currently a days videos area unit used as a tool to advertise and promote the business. Enterprises transfer relevant videos on such promotional sites in order that folks will extract the foremost relevant video content. Users tend to settle on the highest hierarchic and therefore the most viewed videos no matter their relevancy. So,the key to produce appropriate content is Video computer programme improvement (VSEO). This analysis paper proposes a way to optimize the video rank by exploiting video search engine’s looking out strategy which will eventually cause increase in variety of views of videos with higher ranks and so promoting corresponding web site for each visit. For experimental functions, youtube.com has been thought-about.
Visualização de Informação como Ferramenta de Apoio à Curadoria de Dados em Coleções Biológicas

Biodiversity scientific collections are committed to being a permanent record of natural heritage, being managed by institutions, such as museums, herbariums, which are faithful custodians of collections. Biodiversity collections consist of specimens collected in the work field, plus information related to their collection site, such as geographic coordinates, date of collection and collector, and are given a registration number, which allows it to be incorporated into the collection. Ensuring the quality of this information is a complex and fundamentally important task. The present work proposes to apply principles and techniques of information visualization to the context of the databases of biodiversity scientific collections, by providing a set of visual representations whose primary purpose is to facilitate the verification of the quality of the records by an expert. Such techniques can be used a posteriori for the purpose of displaying and disseminating the collection data.
LibGuides: Library Services Platform (LSP): LSP Governance Committee and Work Groups Overview

Website for the systemwide Library Services Platform Descriptions and information on committees and work groups
EXPLORING THE SKETCHES OF HULU KERIS (DAGGER HILT) OF MASTER CRAFTSMAN NORHAIZA NOORDIN

Keris or dagger is one of the Malay traditional weapons that represent various cultural symbols, such as pride and power. In Malaysia, one of the famouswoodcarvers for keris is Norhaiza Noordin, who had received recognition as Adiguru kraf (Master Craftsman). He has carved different forms of keris hilts, and with various motifs. The abstract motifs of flora, fauna and geometric patterns have become the central features for the hilt. This research aims to explore the sketches of hulu keris made by him in the past few years, which have been developed to get insights of the keris hiltâ€™s forms. A total of 14 sketches of the hilts were analysed. The methodology is to identify their visual attributes, such as types of motifs and forms of carving. The results show that motifs of leaves and flowers are the prevailing features that dominate the hilt designs.
Exploring Data Aggregation and Transformations to Generalize across Visual Domains.

Computer vision has flourished in recent years thanks to Deep Learning advancements, fast and scalable hardware solutions and large availability of structured image data. Convolutional Neural Networks trained on supervised tasks with backpropagation learn to extract meaningful representations from raw pixels automatically, and surpass shallow methods in image understanding. Though convenient, data-driven feature learning is prone to dataset bias: a network learns its parameters from training signals alone, and will usually perform poorly if train and test distribution differ. To alleviate this problem, research on Domain Generalization (DG), Domain Adaptation (DA) and their variations is increasing. This thesis contributes to these research topics by presenting novel and effective ways to solve the dataset bias problem in its various settings. We propose new frameworks for Domain Generalization and Domain Adaptation which make use of feature aggregation strategies and visual transformations via data-augmentation and multi-task integration of self-supervision. We also design an algorithm that adapts an object detection model to any out of distribution sample at test time. With through experimentation, we show how our proposed solutions outperform competitive state-of-the-art approaches in established DG and DA benchmarks.
IbM ENHANCING ABILITIES OF TEACHERS IN ACCOUNTING INFORMATION TECHNOLOGY AT SMK NEGERI 1 BARRU

IbM ENHANCING ABILITIES OF TEACHERS IN ACCOUNTING INFORMATION TECHNOLOGY AT SMK NEGERI 1 BARRU
Quote Prediction with LSTM &amp; Greedy Search Decoder

Today, human beings want to express themselves smarter, better, faster and without mistakes. Text predictions increase the speed of typing significantly and also decrease the chance of typos. Auto-text completion property becomes a smart companion for users that helps them to respond to emails quickly, to think ahead for expressing thoughts and to compile documents faster. In this paper, the aim is to generate quotes using mood personalization which involves generating positive, negative or neutral codes on the basis of the input given by the user. The proposed approach deals with the generation of Quotes based on some training on Quotes given by some famous personalities using Long Short-Term Memories and the greedy search decoder approach. A spell checker library is used to replace meaningless words and to find closest in-vocabulary meaningful words depending on Levenshtein distance.
EEyePaint: A Gaze Estimation-Based System for Hands-Free Painting

This paper presents EyePaint, a system that enables hands-free painting on canvas via webcam-based gaze estimation software, a custom graphical user interface (GUI), and a computer numerical control (CNC) modified for painting. The proposed system’s primary use is to assist a user with a physical disability in painting on canvas. Gaze estimation is administered using only a webcam through a unique combination of object detection algorithms, like Haar cascades, to localize facial features and learned linear regression models to translate facial features to gaze location. The custom GUI allows a user to draw a line or a circle in four different paint colors on a virtual canvas before committing their shape to a physical canvas solely through using the low-accuracy webcam-based gaze estimation. The system and its prototype are found to have utility through basic acceptance and system testing, but the chosen gaze estimation solution is generally too low accuracy for a positive user experience.
LibGuides: Library Services Platform (LSP): ELUNA and NERS

Website for the systemwide Library Services Platform Learn about ELUNA membership and NERS voting for LSP members
Manage Software Development Project Using Virtual Teams During COVID-19

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Development of Augmented Reality-Based Animal Recognition Application Using Unity

Augmented Reality is a technology that combines two-dimensional and / or three-dimensional virtual objects into a real environment and then projects these virtual objects in reality in real time. The purpose of this study is to develop alternative instructional media using augmented reality technology. In this study, the instructional media is used to teach children about animals. By using augmented reality technology, the teaching and learning process will be more effective because augmented reality technology can display animals in 3D so that the children can see these animals as if they are real. The researchers conducted three stages that were pre-production, production, and post-production stage. The researchers created the animal; animal images in 3D by using the Blender application and also made a textbook containing the description of the animals and barcodes. Using an augmented reality application, the children can see the animals interactively in a real-world environment. The augmented reality application was developed by using a game engine, Unity. This study developed an augmented reality application and a textbook.
Prediction of visual attention in embodied real-world tasks

Understanding how we dynamically distribute our visual attention in real-world environments remains a hard computational problem, due to the complexity of the relationship between natural visual behaviour, task and motor actions. Shifting focus from classical static gaze behaviour experiments, where subjects perform reductionist, head-fixed screen-viewing tasks in front of monitors, to a more dynamic view of real-world tasks requires taking into account ecologically salient features like motion cues, optic flow, and gaze object semantics. To address this, healthy right-handed subjects (n=9, aged 22-28) were given a common neurorehabilitative rubric, and asked to perform a hierarchical, goal-directed task (cooking a predefined meal), whilst wearing synchronised wireless head-mounted eye-tracking glasses and a 66 degree-of-freedom full-body motion tracking suit. We developed three deep autoregressive models for modelling this gaze/body kinematics relationship in PyTorch, using 1) prior gaze alone to predict future gaze direction, 2) a model combining prior gaze and body kinematics as exogenous input to predict gaze, and 3) a linear regression model from body kinematics to gaze. Model training was validated in both open loop with known current gaze position, and closed loop using progressively more predicted values in a sliding window for multi-step-ahead prediction, with leave-one-section-out and leave-one-subject-out cross-validation for in- and cross-subject comparison. Predictive performance averaged 0.08 RMSE (open-loop), and 0.06 RMSE at ~2s (closed-loop). Through our model comparisons we found that incorporating body dynamics minimised gaze prediction error over longer rollahead timescales of 3s than using just prior gaze data alone. In real-world tasks we can utilise body kinematics to predict gaze on a second-by-second basis, without any visual context. This combination of bottom-up visually-derived saliency and top-down task-centric kinematics-derived saliency may improve further prediction performance and allow us to uncover the mechanisms of interaction.
Deep Similarity Preserving and Attention-based Hashing for Cross-Modal Retrieval

With the fast progress of deep neural networks and the quick search efficiency of hashing, deep cross-modal hashing (CMH) methods have attracted more and more attention.Generally speaking, the existing CMH methods simultaneously learn hash functions and hash codes in an end-to-end architecture.However, they primarily focus on the hash codes generation stage neglected the losing of rich semantic information in the hash representations learning stage.Besides, the single-label supervision information is leveraged, while most instances are labeled by multiple categories.Thus, we propose a novel Deep Semantic Preserving and Attention-based Hashing (DSPAH) for cross-modal retrieval.In the DSPAH, we first use a crosslevel attention block to emphasize significant parts of hash representations and oversee unnecessary ones.Moreover, a Fine-Grained Similarity Criterion (FGSC) is proposed to explore the multiple semantic of image or text instances, helping to learn robust and optimal hash codes.Extensive experiment results on two large-scale public datasets have shown the competition of our proposed DSPAH.