Reviewerly/exHarmony huggingface dataset:
Duygulara ve Yaşam Tarzına Dayalı Tipleme (Personas) Çalışması

Personas is the virtual characters inspired by the real people who represent a particular audience. It brings out the profile based on the human characteristics of the users. This study is user ethnographic research conducted at one of Turkey's most popular sports center chain. Previously, the management conducted surveys. Although the surveys indicate that the customers are satisfied from sport center, those who start sports usually come for a short period, then stop coming to the hall and management cannot understand why the customers left. Application software is planned to increase participation in sports while determining personas. In the beginning, while only two groups are considered for the personas (those who come to body and to loose the weight), there are more than two groups as a result of the ethnographic work. There are very different expectations, goals, challenges and breakpoints in contrast to the previous satisfaction notes of the surveys.  Four personas exist: those who work out for a healthy lifestyle, those who come to health problems, those who do sports to look good, and those who want to have an occupation in their lives. The most important point of the article is that from the beginning of an innovative project, ethnographic interview based personas is essential in understanding the user's inner world to act from the user's point of view.
A multi-platform crowd-mapping application for urban object mapping using street-level imagery

Crowd-mapping is a relatively new field of research, which involves the collection of geographic data by a crowd of workers. The collection of said data is of great importance for organizations like municipalities, where it is used for applications such as maintaining streets and greenery. The benefit of crowd-mapping over traditional mapping methods, where workers physically observe the area, is that it has the potential to be far more cost-effective and time-efficient. As this should not come at the cost of losing accuracy, research needs to be done on how to effectively map objects in a city. Although previous work has focused on mapping urban objects using street-level imagery, they are all specifically aimed at a single type of object. Furthermore, they do not offer a general method for geo-location estimation and do not estimate the height of the objects. All of the systems designed in previous work only support task execution using a web platform. As crowd-mapping is nothing without the crowd, it is important to keep the workers engaged. No research had been done on how the task execution platform and type of task could affect the worker engagement and satisfaction. In this thesis we will design a system for crowd-mapping urban objects using street-level imagery. We will propose novel methods for geo-location and object height estimation. Experimentation showed that the proposed geo-location method was able to deliver an accuracy with up to 83% of the estimations being within 2.5 meters of the ground truth with a mean distance of 1.85 meters. The height estimation showed up to 85% of the estimations being within 30 centimeters from the ground truth with a mean difference of 15 centimeters. Furthermore, the system supports task execution on three platforms; web, mobile and mobile virtual reality. We demonstrated the feasibility of executing mapping-, data-enrichment- and verification tasks on each of these platforms. Experimentation with the different platforms showed that the type of task and execution platform affects user engagement, cognitive load, satisfaction and execution time.
LibGuides: LEP Orientation Guide: 24-Hour Item Pickup

This guide is created for participants of the Leaders in Education Programme. It gives an introduction to the relevant library resources, facilities and services.
Improvement of Particle Swarm Optimization in Multi-Robot Trajectory Motion Coordination

In swarm robotics, motion coordination is being viewed as a whole system consists of multiple robots each with different velocity and path being optimized. This research focus on improve an optimization process called Particle Swarm Optimization (PSO) by altering the fitness function from static to dynamic. PSO has tendency to being trapped in local optima. By employing dynamic acceleration coefficient, the position of particles will be less dependent on the global best position if the global best fitness is found to be saturated in the region and hence release the swarm to another region for searching in other unexplored region. Nominal, dynamic and extreme scenarios were employed to test the performance between PSO and dynamic coefficient PSO. The different on expected optimization time between DCPSO and PSO for nominal condition is DCPSO lead only 0.26 second. While in dynamic condition the different is DCPSO lead 1.09 second. For extreme condition, the different become significant, which DCPSO lead 3.58 second.
Matrix-Based Neural Networks

This chapter revises the implementation of our neural network. In this revision, our network will use matrices to compute the forward and backward propagation algorithms. Overall, our matrix-based implementation is composed of two classes, NMLayer and NMNetwork. Since most of the computation is delegated to the matrix library we defined in the previous chapter, our new version of the neural network is rather light in terms of code.
LibGuides: New York Public Library : NYPL - SimplyE App

A 92 Library Branch system that services Manhattan, the Bronx and Staten Island, plus anyone who lives, works or goes to school in New York State. Library App
Learning Resources: Online Safety & Security: RADICALISATION AND EXTREMISM

In this section we will look at Online Security, Online Safety, Radicalisation and Extremism
Application of Static Forensics Method for Extracting Steganographic Files on Digital Evidence Using the DFRWS Framework

Steganography is one of the anti-forensic techniques that allow criminals to hide information in other messages so that during the investigation, the investigator will experience problems and difficulty in getting evidence of original information on the crime. Therefore an investigator is required to have the ability to be able to find and extract (decoding) using the right tools when opening messages that have been inserted by steganography techniques. The purpose of this study is to analyze digital evidence using the static forensics method by applying the six stages to the Digital Forensics Research Workshop (DFRWS) framework and extracting steganography on files that have been compromised based on case scenarios involving digital crime. The tools used are FTK Imager, Autopsy, WinHex, Hiderman, and StegSpy. The results of extraction of 9 out of 10 files that were scanned by steganography files had 90% success and 10% of steganography files were not found, so it can be concluded that the extraction files in steganographic messages can be used as legal digital proofs according to law.&#x0D;
Особенности реализации модели «Университет 3.0» и развития инновационной инфраструктуры в БГУИР

The manuscript presents main directions of higher education institutions development using «University 3.0» experimental approach. The analysis of the model realization in higher education system and DSUIR experience are presented. The features of business-oriented training of students and increasing efficiency of research projects through development of information and organizing infrastructure are shown. Presented mechanism of educational process transformation is based on R&D potential and life-long distant learning approach.
Multi-task transfer learning for biomedical machine reading comprehension

Biomedical machine reading comprehension aims to extract the answer to the given question from complex biomedical passages, which requires the machine to have the ability to process strong comprehension on natural language. Recent progress has made on this task, but still severely restricted by the insufficient training data due to the domain-specific nature. To solve this problem, we propose a hierarchical question-aware context learning model trained by the multi-task transfer learning algorithm, which can capture the interaction between the question and the passage layer by layer, with multi-level embeddings to strengthen the ability of the language representation. The multi-task transfer learning algorithm leverages the advantages of different machine reading comprehension tasks to improve model generalisation and robustness, pre-training on multiple large-scale open-domain data sets and fine-tuning on the target-domain training set. Moreover, data augmentation is also adopted to create new training samples with various expressions. The public biomedical data set collected from PubMed provided by BioASQ is used to evaluate the model performance. The results show that our method is superior to the best recent solution and achieves a new state of the art.
Digital Hyperplane Fitting

This paper addresses the hyperplane fitting problem of discrete points in any dimension (i.e. in $$\mathbb {Z}^d$$ ). For that purpose, we consider a digital model of hyperplane, namely digital hyperplane, and present a combinatorial approach to find the optimal solution of the fitting problem. This method consists in computing all possible digital hyperplanes from a set $$\mathbf {S}$$ of n points, then an exhaustive search enables us to find the optimal hyperplane that best fits $$\mathbf {S}$$ . The method has, however, a high complexity of $$O(n^d)$$ , and thus can not be applied for big datasets. To overcome this limitation, we propose another method relying on the Delaunay triangulation of $$\mathbf {S}$$ . By not generating and verifying all possible digital hyperplanes but only those from the elements of the triangulation, this leads to a lower complexity of $$O(n^{\lceil \frac{d}{2} \rceil + 1})$$ . Experiments in 2D, 3D and 4D are shown to illustrate the efficiency of the proposed method.
Visualization of Musical Emotions by Colors of Images

Visualizing musical sound for content expression is very efficient application which allows presenting music in all its various facets. This article explores the significance of musical emotion anticipating image emotion features. This is a novel representation of the music data to show how the emotion features can add value to a set of existing sound aspects. The musical emotions were then represented by a filter with support of the Gaussian distribution to be used as a color balance filter diversified in term of musical features. With this filter, a color adjustment model can use RGB color system to modify color channels to produce color transform for image regions which are associated with the original musical emotions. As the transform filter is based on music emotions, the image has its color changed adaptively by the emotions. The visualizing solution is then performed in experiments with a music database and image dataset to evaluate the performance. The experiments show the productive visual effect of emotion taking place in the music database with a wide range of instruments and styles and should be of interest for applications of mapping the music and the visual data.
On the Explanation of Similarity for Developing and Deploying CBR Systems.

During the early stages of developing Case-Based Reasoning (CBR) systems the definition of similarity measures is challenging since this task requires transferring implicit knowledge of domain experts into knowledge representations. While an entire CBR system is very explanatory, the similarity measure determines the ranking but do not necessarily show which features contribute to high (or low) rankings. In this paper we present our work on opening the knowledge engineering process for similarity modelling. This work present is a result of an interdisciplinary research collaboration between AI and public health researchers developing e-Health applications. During this work explainability and transparency of the development process is crucial to allow in-depth quality assurance of the by the domain experts.
Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders.

Variational Auto-encoders (VAEs) are deep generative latent variable models consisting of two components: a generative model that captures a data distribution p(x) by transforming a distribution p(z) over latent space, and an inference model that infers likely latent codes for each data point (Kingma and Welling, 2013). Recent work shows that traditional training methods tend to yield solutions that violate modeling desiderata: (1) the learned generative model captures the observed data distribution but does so while ignoring the latent codes, resulting in codes that do not represent the data (e.g. van den Oord et al. (2017); Kim et al. (2018)); (2) the aggregate of the learned latent codes does not match the prior p(z). This mismatch means that the learned generative model will be unable to generate realistic data with samples from p(z)(e.g. Makhzani et al. (2015); Tomczak and Welling (2017)). In this paper, we demonstrate that both issues stem from the fact that the global optima of the VAE training objective often correspond to undesirable solutions. Our analysis builds on two observations: (1) the generative model is unidentifiable - there exist many generative models that explain the data equally well, each with different (and potentially unwanted) properties and (2) bias in the VAE objective - the VAE objective may prefer generative models that explain the data poorly but have posteriors that are easy to approximate. We present a novel inference method, LiBI, mitigating the problems identified in our analysis. On synthetic datasets, we show that LiBI can learn generative models that capture the data distribution and inference models that better satisfy modeling assumptions when traditional methods struggle to do so.
LibGuides: Mathematics and Computer Science Resource Guide: Home

A guide from Funderburg Library on how to use library resources to do research for your mathematics, data science and computer science classes.
NASA, Partner Space Agencies Amass Global View of COVID-19 Impacts

In response to the global coronavirus (COVID-19) pandemic, NASA, ESA (European Space Agency), and JAXA (Japan Aerospace Exploration Agency) have joined forces to use the collective scientific power of their Earth-observing satellite data to document planet-wide changes in the environment and human society.
Towards Multi-Robot Coordination under Temporal Uncertainty

When robots act in an environment, there will be temporal uncertainty over the execution of their actions, i.e. the duration of an action and the time it takes place will be stochastic. The presence of multiple robots in the environment contributes towards this uncertainty. Temporal uncertainty is often disregarded in multi-robot coordination, and so we aim to develop planning solutions that explicitly model this uncertainty to generate effective plans.
Darboux calculus

We introduce a formalism to analyze partially defined functions between ordered sets. We show that our construction provides a uniform and conceptual approach to all the main definitions encountered in elementary real analysis including Dedekind cuts, limits and continuity.
Asymmetric Quantum Codes

To make reliable the transmission or storage of quantum information against noise caused by the environment, there exist many works available in the literature dealing with constructions of efficient quantum error-correcting codes (QECCs) over unbiased quantum channels [11, 12, 25, 55, 80, 89, 92, 133, 148].
Primary percutaneous coronary intervention during the initial period of the COVID-19 pandemic in a public hospital in Triângulo Mineiro region

The severe acute respiratory syndrome coronavirus (SARS-CoV-2), identified in December 2019, in Wuhan, China, is an enveloped, single-stranded RNA genome betacoronavirus that causes pneumonia and severe respiratory failure, which characterize the coronavirus disease 2019 (COVID-19). – This disease was considered a pandemic by the World Health Organization (WHO) and, currently, more than 7.5 million people in the world have already been affected. Due to the high transmission rate of the disease, which requires healthcare assistance and hospitalization, with the possibility [...]
Digitala verktyg i svenskundervisningen : En kvalitativ studie om lärares arbete med och förhållningssätt till digitala verktyg i svenskundervisningen

This study presents, among other things, which working methods apply to digital tools in Swedish teaching that are most prevalent, what teachers think of that the curriculum was revised and if teac ...
FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback

Reinforcement learning has been successful in training autonomous agents to accomplish goals in complex environments. Although this has been adapted to multiple settings, including robotics and computer games, human players often find it easier to obtain higher rewards in some environments than reinforcement learning algorithms. This is especially true of high-dimensional state spaces where the reward obtained by the agent is sparse or extremely delayed. In this paper, we seek to effectively integrate feedback signals supplied by a human operator with deep reinforcement learning algorithms in high-dimensional state spaces. We call this FRESH (Feedback-based REward SHaping). During training, a human operator is presented with trajectories from a replay buffer and then provides feedback on states and actions in the trajectory. In or- der to generalize feedback signals provided by the human operator to previously unseen states and actions at test-time, we use a feed-back neural network. We use an ensemble of neural networks with a shared network architecture to represent model uncertainty and the confidence of the neural network in its output. The output of the feedback neural network is converted to a shaping reward that is augmented to the reward provided by the environment. We evaluate our approach on the Bowling and Skiing Atari games in the arcade learning environment. Although human experts have achieved high scores in these environments, state-of-the-art deep learning algorithms perform poorly. We observe that FRESH achieves much higher scores than state-of-the-art deep learning algorithms in both environments. FRESH also achieves a 21.4% higher score than a human expert in Bowling and does as well as an expert in Skiing.
Induced Inflection-Set Keyword Search in Speech

We investigate the problem of searching for a lexeme-set in speech by searching for its inflectional variants. Experimental results indicate how lexeme-set search performance changes with the number of hypothesized inflections, while ablation experiments highlight the relative importance of different components in the lexeme-set search pipeline and the value of using curated inflectional paradigms. We provide a recipe and evaluation set for the community to use as an extrinsic measure of the performance of inflection generation approaches.
Collective Curation: An update from the Data Curation Network

February 2020 poster presented at the International Digital Curation Conference in Dublin, Ireland.
The Art of Character Recognition Using Artificial Intelligence

This research paper investigates the uses of AI in the problem that occurred during defining the machine-printed characters. It uses backpropagation net which uses 84 attribute font styles and examined over two different printing font styles. These results which are retrieved from the test are compared with the results got on the identical data by a more used and suitable path.
Variance Reduction for Evolution Strategies via Structured Control Variates.

Evolution Strategies (ES) are a powerful class of blackbox optimization techniques that recently became a competitive alternative to state-of-the-art policy gradient (PG) algorithms for reinforcement learning (RL). We propose a new method for improving accuracy of the ES algorithms, that as opposed to recent approaches utilizing only Monte Carlo structure of the gradient estimator, takes advantage of the underlying MDP structure to reduce the variance. We observe that the gradient estimator of the ES objective can be alternatively computed using reparametrization and PG estimators, which leads to new control variate techniques for gradient estimation in ES optimization. We provide theoretical insights and show through extensive experiments that this RL-specific variance reduction approach outperforms general purpose variance reduction methods.
Isolating the Effects of Modeling Recursive Structures: A Case Study in Pronunciation Prediction of Chinese Characters

Finding that explicitly modeling structures leads to better generalization, we consider the task of predicting Cantonese pronunciations of logographs (Chinese characters) using logographs’ recursive structures. This task is a suitable case study for two reasons. First, logographs’ pronunciations depend on structures (i.e. the hierarchies of sub-units in logographs) Second, the quality of logographic structures is consistent since the structures are constructed automatically using a set of rules. Thus, this task is less affected by confounds such as varying quality between annotators. Empirical results show that modeling structures explicitly using treeLSTM outperforms LSTM baseline, reducing prediction error by 6.0% relative.
Weak Disambiguation for Partial Structured Output Learning

Existing disambiguation strategies for partial structured output learning just cannot generalize well to solve the problem that there are some candidates that can be false positive or similar to the ground-truth label. In this article, we propose a novel weak disambiguation for partial structured output learning (WD-PSL). First, a piecewise large margin formulation is generalized to partial structured output learning, which effectively avoids handling a large number of candidate-structured outputs for complex structures. Second, in the proposed weak disambiguation strategy, each candidate label is assigned with a confidence value indicating how likely it is the true label, which aims to reduce the negative effects of wrong ground-truth label assignment in the learning process. Then, two large margins are formulated to combine two types of constraints which are the disambiguation between candidates and noncandidates, and the weak disambiguation for candidates. In the framework of alternating optimization, a new 2n -slack variables cutting plane algorithm is developed to accelerate each iteration of optimization. The experimental results on several sequence labeling tasks of natural language processing show the effectiveness of the proposed model.
Online course: Shared service provision

This course will give you a greater understanding of what a shared service is and what conditions are likely to ensure a successful shared service implementation.
SECURITY ANALYSIS OF VARIOUS HASH ALGORITHMS FOR AUTHENTICATION UNDER HARDWARE CONSTRAINED ENVIRONMENT

Protecting passwords is now a big challenge because users want to do all types of work online via user-friendly devices such as mobile, tablets etc. Now, It is difficult to implement the secure heavy weight algorithms such as AES, RSA etc. in hardware constrained devices. It has been observed that users want all types of security services in an online public environment. Authentication is the first and foremost step to enhance security. Various applications are available for real time authentications such as keyless car entry and opening home-doors through security algorithms under remote keyless entry System (RKES). Now, it is the demand of the time to implement the lightweight security algorithms without compromising the security. In order to fulfill this challenge, this paper proposed a strong model for enhancing authentication security. In this work, strong authentication techniques are implemented with the light weight algorithms. This model received good comparison results.